diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..dd527c28b2a2a208c971889c29ec78a0f1277c9e 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +*.txt filter=lfs diff=lfs merge=lfs -text +*.jpg filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text diff --git a/INSTALL.md b/INSTALL.md new file mode 100644 index 0000000000000000000000000000000000000000..6a268df6947f41fdcd655b4aa086d8244396b692 --- /dev/null +++ b/INSTALL.md @@ -0,0 +1,34 @@ + +# Installation + +## Requirements +- Linux or macOS with Python ≥ 3.8 +- PyTorch ≥ 1.8 and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation. + Install them together at [pytorch.org](https://pytorch.org) to make sure of this. + Note, please check PyTorch version matches that is required by Detectron2. +- Detectron2: follow Detectron2 installation instructions. +- OpenCV ≥ 4.6 is needed by demo and visualization. + +## Example conda environment setup + +```bash +conda create --name cutler python=3.8 -y +conda activate cutler +conda install pytorch==1.8.1 torchvision==0.9.1 torchaudio==0.8.1 -c pytorch +pip install git+https://github.com/lucasb-eyer/pydensecrf.git + +# under your working directory +git clone git@github.com:facebookresearch/detectron2.git +cd detectron2 +pip install -e . +pip install git+https://github.com/cocodataset/panopticapi.git +pip install git+https://github.com/mcordts/cityscapesScripts.git + +cd .. +git clone --recursive git@github.com:facebookresearch/CutLER.git +cd CutLER +pip install -r requirements.txt +``` + +## datasets +If you want to train/evaluate on the datasets, please see [datasets/README.md](datasets/README.md) to see how we prepare datasets for this project. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1b7da88eaf0b6df93a18a9d44d361bfcd9fdbcd8 --- /dev/null +++ b/README.md @@ -0,0 +1,405 @@ +# Cut and Learn for Unsupervised Image & Video Object Detection and Instance Segmentation + +**Cut**-and-**LE**a**R**n (**CutLER**) is a simple approach for training object detection and instance segmentation models without human annotations. +It outperforms previous SOTA by **2.7 times** for AP50 and **2.6 times** for AR on **11 benchmarks**. + +

+ +> [**Cut and Learn for Unsupervised Object Detection and Instance Segmentation**](http://people.eecs.berkeley.edu/~xdwang/projects/CutLER/) +> [Xudong Wang](https://people.eecs.berkeley.edu/~xdwang/), [Rohit Girdhar](https://rohitgirdhar.github.io/), [Stella X. Yu](https://www1.icsi.berkeley.edu/~stellayu/), [Ishan Misra](https://imisra.github.io/) +> FAIR, Meta AI; UC Berkeley +> CVPR 2023 + +[[`project page`](http://people.eecs.berkeley.edu/~xdwang/projects/CutLER/)] [[`arxiv`](https://arxiv.org/abs/2301.11320)] [[`colab`](https://colab.research.google.com/drive/1NgEyFHvOfuA2MZZnfNPWg1w5gSr3HOBb?usp=sharing)] [[`bibtex`](#citation)] + +Unsupervised video instance segmentation (**VideoCutLER**) is also supported. ***We demonstrate that video instance segmentation models can be learned without using any human annotations, without relying on natural videos (ImageNet data alone is sufficient), and even without motion estimations!*** The code is available [here](videocutler). + +

+ +

+ +> [**VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation**](https://people.eecs.berkeley.edu/~xdwang/projects/VideoCutLER/videocutler.pdf) +> [Xudong Wang](https://people.eecs.berkeley.edu/~xdwang/), [Ishan Misra](https://imisra.github.io/), Ziyun Zeng, [Rohit Girdhar](https://rohitgirdhar.github.io/), [Trevor Darrell](https://people.eecs.berkeley.edu/~trevor/) +> UC Berkeley; FAIR, Meta AI +> CVPR 2024 + +[[`code`](videocutler/README.md)] [[`PDF`](https://people.eecs.berkeley.edu/~xdwang/projects/VideoCutLER/videocutler.pdf)] [[`arxiv`](https://arxiv.org/abs/2308.14710)] [[`bibtex`](#citation)] + +## Features +- We propose MaskCut approach to generate pseudo-masks for multiple objects in an image. +- CutLER can learn unsupervised object detectors and instance segmentors solely on ImageNet-1K. +- CutLER exhibits strong robustness to domain shifts when evaluated on 11 different benchmarks across domains like natural images, video frames, paintings, sketches, etc. +- CutLER can serve as a pretrained model for fully/semi-supervised detection and segmentation tasks. +- We also propose VideoCutLER, a surprisingly simple unsupervised video instance segmentation (UVIS) method without relying on optical flows. ImaegNet-1K is all we need for training a SOTA UVIS model! + +## Installation +See [installation instructions](INSTALL.md). + +## Dataset Preparation +See [Preparing Datasets for CutLER](datasets/README.md). + +## Method Overview +

+ +

+Cut-and-Learn has two stages: 1) generating pseudo-masks with MaskCut and 2) learning unsupervised detectors from pseudo-masks of unlabeled data. + +### 1. MaskCut + +MaskCut can be used to provide segmentation masks for multiple instances of each image. +

+ +

+ +### MaskCut Demo + +Try out the MaskCut demo using Colab (no GPU needed): [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1X05lKL_IBRvZB7q6n6pb4w00_tIYjGlf?usp=sharing) + +Try out the web demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/facebook/MaskCut) (thanks to [@hysts](https://github.com/hysts)!) + + + + +If you want to run MaskCut locally, we provide `demo.py` that is able to visualize the pseudo-masks produced by MaskCut. +Run it with: +``` +cd maskcut +python demo.py --img-path imgs/demo2.jpg \ + --N 3 --tau 0.15 --vit-arch base --patch-size 8 \ + [--other-options] +``` +We give a few demo images in maskcut/imgs/. If you want to run demo.py with cpu, simply add "--cpu" when running the demo script. +For imgs/demo4.jpg, you need to use "--N 6" to segment all six instances in the image. +Following, we give some visualizations of the pseudo-masks on the demo images. +

+ +

+ +### Generating Annotations for ImageNet-1K with MaskCut +To generate pseudo-masks for ImageNet-1K using MaskCut, first set up the ImageNet-1K dataset according to the instructions in [datasets/README.md](datasets/README.md), then execute the following command: +``` +cd maskcut +python maskcut.py \ +--vit-arch base --patch-size 8 \ +--tau 0.15 --fixed_size 480 --N 3 \ +--num-folder-per-job 1000 --job-index 0 \ +--dataset-path /path/to/dataset/traindir \ +--out-dir /path/to/save/annotations \ +``` +As the process of generating pseudo-masks for all 1.3 million images in 1,000 folders takes a significant amount of time, it is recommended to use multiple runs. Each run should process the pseudo-mask generation for a smaller number of image folders by setting "--num-folder-per-job" and "--job-index". Once all runs are completed, you can merge all the resulting json files by using the following command: +``` +python merge_jsons.py \ +--base-dir /path/to/save/annotations \ +--num-folder-per-job 2 --fixed-size 480 \ +--tau 0.15 --N 3 \ +--save-path imagenet_train_fixsize480_tau0.15_N3.json +``` +The "--num-folder-per-job", "--fixed-size", "--tau" and "--N" of merge_jsons.py should match the ones used to run maskcut.py. + +We also provide a submitit script to launch the pseudo-mask generation process with multiple nodes. +``` +cd maskcut +bash run_maskcut_with_submitit.sh +``` +After that, you can use "merge_jsons.py" to merge all these json files as described above. + +### 2. CutLER + +### Inference Demo for CutLER with Pre-trained Models +Try out the CutLER demo using Colab (no GPU needed): [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1NgEyFHvOfuA2MZZnfNPWg1w5gSr3HOBb?usp=sharing) + +Try out the web demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/facebook/CutLER) (thanks to [@hysts](https://github.com/hysts)!) + + +Try out Replicate demo and the API: [![Replicate](https://replicate.com/cjwbw/cutler/badge)](https://replicate.com/cjwbw/cutler) + + +If you want to run CutLER demos locally, +1. Pick a model and its config file from [model zoo](#model-zoo), + for example, `model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml`. +2. We provide `demo.py` that is able to demo builtin configs. Run it with: +``` +cd cutler +python demo/demo.py --config-file model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN_demo.yaml \ + --input demo/imgs/*.jpg \ + [--other-options] + --opts MODEL.WEIGHTS /path/to/cutler_w_cascade_checkpoint +``` +The configs are made for training, therefore we need to specify `MODEL.WEIGHTS` to a model from model zoo for evaluation. +This command will run the inference and show visualizations in an OpenCV window. + +* To run __on cpu__, add `MODEL.DEVICE cpu` after `--opts`. +* To save outputs to a directory (for images) or a file (for webcam or video), use `--output`. + +Following, we give some visualizations of the model predictions on the demo images. +

+ +

+ +### Unsupervised Model Learning +Before training the detector, it is necessary to use MaskCut to generate pseudo-masks for all ImageNet data. +You can either use the pre-generated json file directly by downloading it from [here](http://dl.fbaipublicfiles.com/cutler/maskcut/imagenet_train_fixsize480_tau0.15_N3.json) and placing it under "DETECTRON2_DATASETS/imagenet/annotations/", or generate your own pseudo-masks by following the instructions in [MaskCut](#1-maskcut). + +We provide a script `train_net.py`, that is made to train all the configs provided in CutLER. +To train a model with "train_net.py", first setup the ImageNet-1K dataset following [datasets/README.md](datasets/README.md), then run: +``` +cd cutler +export DETECTRON2_DATASETS=/path/to/DETECTRON2_DATASETS/ +python train_net.py --num-gpus 8 \ + --config-file model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml +``` + +If you want to train a model using multiple nodes, you may need to adjust [some model parameters](https://arxiv.org/abs/1706.02677) and some SBATCH command options in "tools/train-1node.sh" and "tools/single-node_run.sh", then run: +``` +cd cutler +sbatch tools/train-1node.sh \ + --config-file model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml \ + MODEL.WEIGHTS /path/to/dino/d2format/model \ + OUTPUT_DIR output/ +``` +You can also convert a pre-trained DINO model to detectron2's format by yourself following [this link](https://github.com/facebookresearch/moco/tree/main/detection). + +### Self-training +We further improve performance by self-training the model on its predictions. + +Firstly, we can get model predictions on ImageNet via running: +``` +python train_net.py --num-gpus 8 \ + --config-file model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml \ + --test-dataset imagenet_train \ + --eval-only TEST.DETECTIONS_PER_IMAGE 30 \ + MODEL.WEIGHTS output/model_final.pth \ # load previous stage/round checkpoints + OUTPUT_DIR output/ # path to save model predictions +``` +Secondly, we can run the following command to generate the json file for the first round of self-training: +``` +python tools/get_self_training_ann.py \ + --new-pred output/inference/coco_instances_results.json \ # load model predictions + --prev-ann DETECTRON2_DATASETS/imagenet/annotations/imagenet_train_fixsize480_tau0.15_N3.json \ # path to the old annotation file. + --save-path DETECTRON2_DATASETS/imagenet/annotations/cutler_imagenet1k_train_r1.json \ # path to save a new annotation file. + --threshold 0.7 +``` +Finally, place "cutler_imagenet1k_train_r1.json" under "DETECTRON2_DATASETS/imagenet/annotations/", then launch the self-training process: +``` +python train_net.py --num-gpus 8 \ + --config-file model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN_self_train.yaml \ + --train-dataset imagenet_train_r1 \ + MODEL.WEIGHTS output/model_final.pth \ # load previous stage/round checkpoints + OUTPUT_DIR output/self-train-r1/ # path to save checkpoints +``` + +You can repeat the steps above to perform multiple rounds of self-training and adjust some arguments as needed (e.g., "--threshold" for round 1 and 2 can be set to 0.7 and 0.65, respectively; "--train-dataset" for round 1 and 2 can be set to "imagenet_train_r1" and "imagenet_train_r2", respectively; MODEL.WEIGHTS for round 1 and 2 should point to the previous stage/round checkpoints). Ensure that all annotation files are placed under DETECTRON2_DATASETS/imagenet/annotations/. +Please ensure that "--train-dataset", json file names and locations match the ones specified in "cutler/data/datasets/builtin.py". +Please refer to this [instruction](https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html) for guidance on using custom datasets. + +You can also directly download the MODEL.WEIGHTS and annotations used for each round of self-training: + + + + + + + + + + + + + +
round 1cutler_cascade_r1.pthcutler_imagenet1k_train_r1.json
round 2cutler_cascade_r2.pthcutler_imagenet1k_train_r2.json
+ +### Unsupervised Zero-shot Evaluation +To evaluate a model's performance on 11 different datasets, please refer to [datasets/README.md](datasets/README.md) for instructions on preparing the datasets. Next, select a model from the model zoo, specify the "model_weights", "config_file" and the path to "DETECTRON2_DATASETS" in `tools/eval.sh`, then run the script. +``` +bash tools/eval.sh +``` + +### Model Zoo +We show zero-shot unsupervised object detection performance (AP50 | AR) on 11 different datasets spanning a variety of domains. ^: CutLER using Mask R-CNN as a detector; *: CutLER using Cascade Mask R-CNN as a detector. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
MethodsModelsCOCOCOCO20KVOCLVISUVOClipartComicWatercolorKITTIObjects365OpenImages
Prev. SOTA-9.6 | 12.69.7 | 12.615.9 | 21.33.8 | 6.410.0 | 14.27.9 | 15.19.9 | 16.36.7 | 16.27.7 | 7.18.1 | 10.29.9 | 14.9
CutLER^download21.1 | 29.621.6 | 30.036.6 | 41.07.7 | 18.729.8 | 38.420.9 | 38.531.2 | 37.137.3 | 39.915.3 | 25.419.5 | 30.017.1 | 26.4
CutLER*download21.9 | 32.722.4 | 33.136.9 | 44.38.4 | 21.831.7 | 42.821.1 | 41.330.4 | 38.637.5 | 44.618.4 | 27.521.6 | 34.217.3 | 29.6
+ +## Semi-supervised and Fully-supervised Learning +CutLER can also serve as a pretrained model for training fully supervised object detection and instance segmentation models and improves performance on COCO, including on few-shot benchmarks. + +### Training & Evaluation in Command Line +You can find all the semi-supervised and fully-supervised learning configs provided in CutLER under `model_zoo/configs/COCO-Semisupervised`. + +To train a model using K% labels with `train_net.py`, first set up the COCO dataset according to [datasets/README.md](datasets/README.md) and specify K value in the config file, then run: +``` +python train_net.py --num-gpus 8 \ + --config-file model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_{K}perc.yaml \ + MODEL.WEIGHTS /path/to/cutler_pretrained_model +``` + +You can find all config files used to train supervised models under `model_zoo/configs/COCO-Semisupervised`. +The configs are made for 8-GPU training. To train on 1 GPU, you may need to [change some parameters](https://arxiv.org/abs/1706.02677), e.g. number of GPUs (num-gpus your_num_gpus), learning rates (SOLVER.BASE_LR your_base_lr) and batch size (SOLVER.IMS_PER_BATCH your_batch_size). + +### Evaluation +To evaluate a model's performance, use +``` +python train_net.py \ + --config-file model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_{K}perc.yaml \ + --eval-only MODEL.WEIGHTS /path/to/checkpoint_file +``` +For more options, see `python train_net.py -h`. + +### Model Zoo +We fine-tune a Cascade R-CNN model initialized with CutLER or MoCo-v2 on varying amounts of labeled COCO data, and show results (Box | Mask AP) on the val2017 split below: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
% of labels1%2%5%10%20%30%40%50%60%80%100%
MoCo-v211.8 | 10.016.2 | 13.820.5 | 17.826.5 | 23.032.5 | 28.235.5 | 30.837.3 | 32.338.7 | 33.639.9 | 34.641.6 | 36.042.8 | 37.0
CutLER16.8 | 14.621.6 | 18.927.8 | 24.332.2 | 28.136.6 | 31.738.2 | 33.339.9 | 34.741.5 | 35.942.3 | 36.743.8 | 37.944.7 | 38.5
Downloadmodelmodelmodelmodelmodelmodelmodelmodelmodelmodelmodel
+ +Both MoCo-v2 and our CutLER are trained for the 1x schedule using Detectron2, except for extremely low-shot settings with 1% or 2% labels. When training with 1% or 2% labels, we train both MoCo-v2 and our model for 3,600 iterations with a batch size of 16. + +## License +The majority of CutLER, Detectron2 and DINO are licensed under the [CC-BY-NC license](LICENSE), however portions of the project are available under separate license terms: TokenCut, Bilateral Solver and CRF are licensed under the MIT license; If you later add other third party code, please keep this license info updated, and please let us know if that component is licensed under something other than CC-BY-NC, MIT, or CC0. + +## Ethical Considerations +CutLER's wide range of detection capabilities may introduce similar challenges to many other visual recognition methods. +As the image can contain arbitrary instances, it may impact the model output. + +## How to get support from us? +If you have any general questions, feel free to email us at [Xudong Wang](mailto:xdwang@eecs.berkeley.edu), [Ishan Misra](mailto:imisra@meta.com) and [Rohit Girdhar](mailto:rgirdhar@meta.com). If you have code or implementation-related questions, please feel free to send emails to us or open an issue in this codebase (We recommend that you open an issue in this codebase, because your questions may help others). + +## Citation +If you find our work inspiring or use our codebase in your research, please consider giving a star ⭐ and a citation. +``` +@inproceedings{wang2023cut, + title={Cut and learn for unsupervised object detection and instance segmentation}, + author={Wang, Xudong and Girdhar, Rohit and Yu, Stella X and Misra, Ishan}, + booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, + pages={3124--3134}, + year={2023} +} +``` + +``` +@article{wang2023videocutler, + title={VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation}, + author={Wang, Xudong and Misra, Ishan and Zeng, Ziyun and Girdhar, Rohit and Darrell, Trevor}, + journal={arXiv preprint arXiv:2308.14710}, + year={2023} +} +``` diff --git a/cog.yaml b/cog.yaml new file mode 100644 index 0000000000000000000000000000000000000000..185afeec9b7f23dcf15fc0f8299ace8f7085c1b0 --- /dev/null +++ b/cog.yaml @@ -0,0 +1,24 @@ +build: + gpu: true + cuda: "11.6" + python_version: "3.8" + python_packages: + - "torch==1.11.0" + - "torchvision==0.12.0" + - "faiss-gpu==1.7.2" + - "opencv-python==4.6.0.66" + - "scikit-image==0.19.2" + - "scikit-learn==1.1.1" + - "shapely==1.8.2" + - "timm==0.5.4" + - "pyyaml==6.0" + - "colored==1.4.4" + - "fvcore==0.1.5.post20220512" + - "gdown==4.5.4" + - "pycocotools==2.0.6" + - "numpy==1.20.0" + + run: + - pip install git+https://github.com/lucasb-eyer/pydensecrf.git + +predict: "maskcut/predict.py:Predictor" diff --git a/cutler/__init__.py b/cutler/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b420f7c6835f5a349dfee73390f28c9093ed60a6 --- /dev/null +++ b/cutler/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. + +import config +import engine +import modeling +import structures +import tools +import demo + +# dataset loading +from . import data # register all new datasets +from data import datasets # register all new datasets +from solver import * + +# from .data import register_all_imagenet \ No newline at end of file diff --git a/cutler/config/__init__.py b/cutler/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4c181f3dfa1414d9ad590a997bce46d5ec53baea --- /dev/null +++ b/cutler/config/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. + +from .cutler_config import add_cutler_config \ No newline at end of file diff --git a/cutler/config/__pycache__/__init__.cpython-312.pyc b/cutler/config/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eb5eac63b709aff871f4f590ee9d8dc825e3fc43 Binary files /dev/null and b/cutler/config/__pycache__/__init__.cpython-312.pyc differ diff --git a/cutler/config/__pycache__/cutler_config.cpython-312.pyc b/cutler/config/__pycache__/cutler_config.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..474d446997f2629eb6b7daf202db75a7f86d84ec Binary files /dev/null and b/cutler/config/__pycache__/cutler_config.cpython-312.pyc differ diff --git a/cutler/config/cutler_config.py b/cutler/config/cutler_config.py new file mode 100644 index 0000000000000000000000000000000000000000..344ea05de6d647661c63a4e7ecee4c677e1ce7b0 --- /dev/null +++ b/cutler/config/cutler_config.py @@ -0,0 +1,19 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. + +from detectron2.config import CfgNode as CN + +def add_cutler_config(cfg): + cfg.DATALOADER.COPY_PASTE = False + cfg.DATALOADER.COPY_PASTE_RATE = 0.0 + cfg.DATALOADER.COPY_PASTE_MIN_RATIO = 0.5 + cfg.DATALOADER.COPY_PASTE_MAX_RATIO = 1.0 + cfg.DATALOADER.COPY_PASTE_RANDOM_NUM = True + cfg.DATALOADER.VISUALIZE_COPY_PASTE = False + + cfg.MODEL.ROI_HEADS.USE_DROPLOSS = False + cfg.MODEL.ROI_HEADS.DROPLOSS_IOU_THRESH = 0.0 + + cfg.SOLVER.BASE_LR_MULTIPLIER = 1 + cfg.SOLVER.BASE_LR_MULTIPLIER_NAMES = [] + + cfg.TEST.NO_SEGM = False \ No newline at end of file diff --git a/cutler/data/__init__.py b/cutler/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..522de8b9f4473212eb8ba75a6781dc2c4400a8d4 --- /dev/null +++ b/cutler/data/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. + +from . import datasets # ensure the builtin datasets are registered +from .detection_utils import * # isort:skip +from .build import ( + build_batch_data_loader, + build_detection_train_loader, + build_detection_test_loader, + get_detection_dataset_dicts, + load_proposals_into_dataset, + print_instances_class_histogram, + ) +from detectron2.data.common import * + +__all__ = [k for k in globals().keys() if not k.startswith("_")] \ No newline at end of file diff --git a/cutler/data/__pycache__/__init__.cpython-312.pyc b/cutler/data/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b9b0c1566a6c67df06ddbbe924f37b298049680c Binary files /dev/null and b/cutler/data/__pycache__/__init__.cpython-312.pyc differ diff --git a/cutler/data/__pycache__/build.cpython-312.pyc b/cutler/data/__pycache__/build.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7df32558f70ef2e82dee6f3dc45b1e9177e804fe Binary files /dev/null and b/cutler/data/__pycache__/build.cpython-312.pyc differ diff --git a/cutler/data/__pycache__/dataset_mapper.cpython-312.pyc b/cutler/data/__pycache__/dataset_mapper.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2703738e4d045e83776eb998a624bdbf23dc2f7d Binary files /dev/null and b/cutler/data/__pycache__/dataset_mapper.cpython-312.pyc differ diff --git a/cutler/data/__pycache__/detection_utils.cpython-312.pyc b/cutler/data/__pycache__/detection_utils.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0cc737dfff58e9b5b7386845ce5f91023747b785 Binary files /dev/null and b/cutler/data/__pycache__/detection_utils.cpython-312.pyc differ diff --git a/cutler/data/build.py b/cutler/data/build.py new file mode 100644 index 0000000000000000000000000000000000000000..5867c8dd5e2111aa01ff8bcef4198f419687cd1a --- /dev/null +++ b/cutler/data/build.py @@ -0,0 +1,561 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/build.py + +import itertools +import logging +import numpy as np +import operator +import pickle +from typing import Any, Callable, Dict, List, Optional, Union +import torch +import torch.utils.data as torchdata +from tabulate import tabulate +from termcolor import colored + +from detectron2.config import configurable +from detectron2.structures import BoxMode +from detectron2.utils.comm import get_world_size +from detectron2.utils.env import seed_all_rng +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import _log_api_usage, log_first_n + +from detectron2.data.catalog import DatasetCatalog, MetadataCatalog +from detectron2.data.common import AspectRatioGroupedDataset, DatasetFromList, MapDataset, ToIterableDataset +from data.dataset_mapper import DatasetMapper +from data.detection_utils import check_metadata_consistency +from detectron2.data.samplers import ( + InferenceSampler, + RandomSubsetTrainingSampler, + RepeatFactorTrainingSampler, + TrainingSampler, +) + +""" +This file contains the default logic to build a dataloader for training or testing. +""" + +__all__ = [ + "build_batch_data_loader", + "build_detection_train_loader", + "build_detection_test_loader", + "get_detection_dataset_dicts", + "load_proposals_into_dataset", + "print_instances_class_histogram", +] + + +def filter_images_with_only_crowd_annotations(dataset_dicts): + """ + Filter out images with none annotations or only crowd annotations + (i.e., images without non-crowd annotations). + A common training-time preprocessing on COCO dataset. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 Dataset format. + + Returns: + list[dict]: the same format, but filtered. + """ + num_before = len(dataset_dicts) + + def valid(anns): + for ann in anns: + if ann.get("iscrowd", 0) == 0: + return True + return False + + dataset_dicts = [x for x in dataset_dicts if valid(x["annotations"])] + num_after = len(dataset_dicts) + logger = logging.getLogger(__name__) + logger.info( + "Removed {} images with no usable annotations. {} images left.".format( + num_before - num_after, num_after + ) + ) + print("Removed {} images with no usable annotations. {} images left.".format( + num_before - num_after, num_after + )) + return dataset_dicts + + +def filter_images_with_few_keypoints(dataset_dicts, min_keypoints_per_image): + """ + Filter out images with too few number of keypoints. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 Dataset format. + + Returns: + list[dict]: the same format as dataset_dicts, but filtered. + """ + num_before = len(dataset_dicts) + + def visible_keypoints_in_image(dic): + # Each keypoints field has the format [x1, y1, v1, ...], where v is visibility + annotations = dic["annotations"] + return sum( + (np.array(ann["keypoints"][2::3]) > 0).sum() + for ann in annotations + if "keypoints" in ann + ) + + dataset_dicts = [ + x for x in dataset_dicts if visible_keypoints_in_image(x) >= min_keypoints_per_image + ] + num_after = len(dataset_dicts) + logger = logging.getLogger(__name__) + logger.info( + "Removed {} images with fewer than {} keypoints.".format( + num_before - num_after, min_keypoints_per_image + ) + ) + return dataset_dicts + + +def load_proposals_into_dataset(dataset_dicts, proposal_file): + """ + Load precomputed object proposals into the dataset. + + The proposal file should be a pickled dict with the following keys: + + - "ids": list[int] or list[str], the image ids + - "boxes": list[np.ndarray], each is an Nx4 array of boxes corresponding to the image id + - "objectness_logits": list[np.ndarray], each is an N sized array of objectness scores + corresponding to the boxes. + - "bbox_mode": the BoxMode of the boxes array. Defaults to ``BoxMode.XYXY_ABS``. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 Dataset format. + proposal_file (str): file path of pre-computed proposals, in pkl format. + + Returns: + list[dict]: the same format as dataset_dicts, but added proposal field. + """ + logger = logging.getLogger(__name__) + logger.info("Loading proposals from: {}".format(proposal_file)) + + with PathManager.open(proposal_file, "rb") as f: + proposals = pickle.load(f, encoding="latin1") + + # Rename the key names in D1 proposal files + rename_keys = {"indexes": "ids", "scores": "objectness_logits"} + for key in rename_keys: + if key in proposals: + proposals[rename_keys[key]] = proposals.pop(key) + + # Fetch the indexes of all proposals that are in the dataset + # Convert image_id to str since they could be int. + img_ids = set({str(record["image_id"]) for record in dataset_dicts}) + id_to_index = {str(id): i for i, id in enumerate(proposals["ids"]) if str(id) in img_ids} + + # Assuming default bbox_mode of precomputed proposals are 'XYXY_ABS' + bbox_mode = BoxMode(proposals["bbox_mode"]) if "bbox_mode" in proposals else BoxMode.XYXY_ABS + + for record in dataset_dicts: + # Get the index of the proposal + i = id_to_index[str(record["image_id"])] + + boxes = proposals["boxes"][i] + objectness_logits = proposals["objectness_logits"][i] + # Sort the proposals in descending order of the scores + inds = objectness_logits.argsort()[::-1] + record["proposal_boxes"] = boxes[inds] + record["proposal_objectness_logits"] = objectness_logits[inds] + record["proposal_bbox_mode"] = bbox_mode + + return dataset_dicts + + +def print_instances_class_histogram(dataset_dicts, class_names): + """ + Args: + dataset_dicts (list[dict]): list of dataset dicts. + class_names (list[str]): list of class names (zero-indexed). + """ + num_classes = len(class_names) + hist_bins = np.arange(num_classes + 1) + histogram = np.zeros((num_classes,), dtype=np.int) + for entry in dataset_dicts: + annos = entry["annotations"] + classes = np.asarray( + [x["category_id"] for x in annos if not x.get("iscrowd", 0)], dtype=np.int + ) + if len(classes): + assert classes.min() >= 0, f"Got an invalid category_id={classes.min()}" + assert ( + classes.max() < num_classes + ), f"Got an invalid category_id={classes.max()} for a dataset of {num_classes} classes" + histogram += np.histogram(classes, bins=hist_bins)[0] + + N_COLS = min(6, len(class_names) * 2) + + def short_name(x): + # make long class names shorter. useful for lvis + if len(x) > 13: + return x[:11] + ".." + return x + + data = list( + itertools.chain(*[[short_name(class_names[i]), int(v)] for i, v in enumerate(histogram)]) + ) + total_num_instances = sum(data[1::2]) + data.extend([None] * (N_COLS - (len(data) % N_COLS))) + if num_classes > 1: + data.extend(["total", total_num_instances]) + data = itertools.zip_longest(*[data[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + data, + headers=["category", "#instances"] * (N_COLS // 2), + tablefmt="pipe", + numalign="left", + stralign="center", + ) + log_first_n( + logging.INFO, + "Distribution of instances among all {} categories:\n".format(num_classes) + + colored(table, "cyan"), + key="message", + ) + + +def get_detection_dataset_dicts( + names, + filter_empty=True, + min_keypoints=0, + proposal_files=None, + check_consistency=True, +): + """ + Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation. + + Args: + names (str or list[str]): a dataset name or a list of dataset names + filter_empty (bool): whether to filter out images without instance annotations + min_keypoints (int): filter out images with fewer keypoints than + `min_keypoints`. Set to 0 to do nothing. + proposal_files (list[str]): if given, a list of object proposal files + that match each dataset in `names`. + check_consistency (bool): whether to check if datasets have consistent metadata. + + Returns: + list[dict]: a list of dicts following the standard dataset dict format. + """ + if isinstance(names, str): + names = [names] + assert len(names), names + dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names] + + if isinstance(dataset_dicts[0], torchdata.Dataset): + if len(dataset_dicts) > 1: + # ConcatDataset does not work for iterable style dataset. + # We could support concat for iterable as well, but it's often + # not a good idea to concat iterables anyway. + return torchdata.ConcatDataset(dataset_dicts) + return dataset_dicts[0] + + for dataset_name, dicts in zip(names, dataset_dicts): + assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) + + if proposal_files is not None: + assert len(names) == len(proposal_files) + # load precomputed proposals from proposal files + dataset_dicts = [ + load_proposals_into_dataset(dataset_i_dicts, proposal_file) + for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files) + ] + + dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts)) + + has_instances = "annotations" in dataset_dicts[0] + if filter_empty and has_instances: + dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts) + if min_keypoints > 0 and has_instances: + dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints) + + if check_consistency and has_instances: + try: + class_names = MetadataCatalog.get(names[0]).thing_classes + check_metadata_consistency("thing_classes", names) + print_instances_class_histogram(dataset_dicts, class_names) + except AttributeError: # class names are not available for this dataset + pass + + assert len(dataset_dicts), "No valid data found in {}.".format(",".join(names)) + return dataset_dicts + + +def build_batch_data_loader( + dataset, + sampler, + total_batch_size, + *, + aspect_ratio_grouping=False, + num_workers=0, + collate_fn=None, +): + """ + Build a batched dataloader. The main differences from `torch.utils.data.DataLoader` are: + 1. support aspect ratio grouping options + 2. use no "batch collation", because this is common for detection training + + Args: + dataset (torch.utils.data.Dataset): a pytorch map-style or iterable dataset. + sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces indices. + Must be provided iff. ``dataset`` is a map-style dataset. + total_batch_size, aspect_ratio_grouping, num_workers, collate_fn: see + :func:`build_detection_train_loader`. + + Returns: + iterable[list]. Length of each list is the batch size of the current + GPU. Each element in the list comes from the dataset. + """ + world_size = get_world_size() + assert ( + total_batch_size > 0 and total_batch_size % world_size == 0 + ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format( + total_batch_size, world_size + ) + batch_size = total_batch_size // world_size + + if isinstance(dataset, torchdata.IterableDataset): + assert sampler is None, "sampler must be None if dataset is IterableDataset" + else: + dataset = ToIterableDataset(dataset, sampler) + + if aspect_ratio_grouping: + data_loader = torchdata.DataLoader( + dataset, + num_workers=num_workers, + collate_fn=operator.itemgetter(0), # don't batch, but yield individual elements + worker_init_fn=worker_init_reset_seed, + ) # yield individual mapped dict + data_loader = AspectRatioGroupedDataset(data_loader, batch_size) + if collate_fn is None: + return data_loader + return MapDataset(data_loader, collate_fn) + else: + return torchdata.DataLoader( + dataset, + batch_size=batch_size, + drop_last=True, + num_workers=num_workers, + collate_fn=trivial_batch_collator if collate_fn is None else collate_fn, + worker_init_fn=worker_init_reset_seed, + ) + + +def _train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None): + if dataset is None: + dataset = get_detection_dataset_dicts( + cfg.DATASETS.TRAIN, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE + if cfg.MODEL.KEYPOINT_ON + else 0, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + _log_api_usage("dataset." + cfg.DATASETS.TRAIN[0]) + + if mapper is None: + mapper = DatasetMapper(cfg, True) + + if sampler is None: + sampler_name = cfg.DATALOADER.SAMPLER_TRAIN + logger = logging.getLogger(__name__) + if isinstance(dataset, torchdata.IterableDataset): + logger.info("Not using any sampler since the dataset is IterableDataset.") + sampler = None + else: + logger.info("Using training sampler {}".format(sampler_name)) + if sampler_name == "TrainingSampler": + sampler = TrainingSampler(len(dataset)) + elif sampler_name == "RepeatFactorTrainingSampler": + repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency( + dataset, cfg.DATALOADER.REPEAT_THRESHOLD + ) + sampler = RepeatFactorTrainingSampler(repeat_factors) + elif sampler_name == "RandomSubsetTrainingSampler": + sampler = RandomSubsetTrainingSampler( + len(dataset), cfg.DATALOADER.RANDOM_SUBSET_RATIO + ) + else: + raise ValueError("Unknown training sampler: {}".format(sampler_name)) + + return { + "dataset": dataset, + "sampler": sampler, + "mapper": mapper, + "total_batch_size": cfg.SOLVER.IMS_PER_BATCH, + "aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING, + "num_workers": cfg.DATALOADER.NUM_WORKERS, + } + + +@configurable(from_config=_train_loader_from_config) +def build_detection_train_loader( + dataset, + *, + mapper, + sampler=None, + total_batch_size, + aspect_ratio_grouping=True, + num_workers=0, + collate_fn=None, +): + """ + Build a dataloader for object detection with some default features. + + Args: + dataset (list or torch.utils.data.Dataset): a list of dataset dicts, + or a pytorch dataset (either map-style or iterable). It can be obtained + by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper (callable): a callable which takes a sample (dict) from dataset and + returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=True)``. + sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces + indices to be applied on ``dataset``. + If ``dataset`` is map-style, the default sampler is a :class:`TrainingSampler`, + which coordinates an infinite random shuffle sequence across all workers. + Sampler must be None if ``dataset`` is iterable. + total_batch_size (int): total batch size across all workers. + aspect_ratio_grouping (bool): whether to group images with similar + aspect ratio for efficiency. When enabled, it requires each + element in dataset be a dict with keys "width" and "height". + num_workers (int): number of parallel data loading workers + collate_fn: a function that determines how to do batching, same as the argument of + `torch.utils.data.DataLoader`. Defaults to do no collation and return a list of + data. No collation is OK for small batch size and simple data structures. + If your batch size is large and each sample contains too many small tensors, + it's more efficient to collate them in data loader. + + Returns: + torch.utils.data.DataLoader: + a dataloader. Each output from it is a ``list[mapped_element]`` of length + ``total_batch_size / num_workers``, where ``mapped_element`` is produced + by the ``mapper``. + """ + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset(dataset, mapper) + + if isinstance(dataset, torchdata.IterableDataset): + assert sampler is None, "sampler must be None if dataset is IterableDataset" + else: + if sampler is None: + sampler = TrainingSampler(len(dataset)) + assert isinstance(sampler, torchdata.Sampler), f"Expect a Sampler but got {type(sampler)}" + return build_batch_data_loader( + dataset, + sampler, + total_batch_size, + aspect_ratio_grouping=aspect_ratio_grouping, + num_workers=num_workers, + collate_fn=collate_fn, + ) + + +def _test_loader_from_config(cfg, dataset_name, mapper=None): + """ + Uses the given `dataset_name` argument (instead of the names in cfg), because the + standard practice is to evaluate each test set individually (not combining them). + """ + if isinstance(dataset_name, str): + dataset_name = [dataset_name] + + dataset = get_detection_dataset_dicts( + dataset_name, + filter_empty=False, + proposal_files=[ + cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)] for x in dataset_name + ] + if cfg.MODEL.LOAD_PROPOSALS + else None, + ) + if mapper is None: + mapper = DatasetMapper(cfg, False) + return { + "dataset": dataset, + "mapper": mapper, + "num_workers": cfg.DATALOADER.NUM_WORKERS, + "sampler": InferenceSampler(len(dataset)) + if not isinstance(dataset, torchdata.IterableDataset) + else None, + } + + +@configurable(from_config=_test_loader_from_config) +def build_detection_test_loader( + dataset: Union[List[Any], torchdata.Dataset], + *, + mapper: Callable[[Dict[str, Any]], Any], + sampler: Optional[torchdata.Sampler] = None, + batch_size: int = 1, + num_workers: int = 0, + collate_fn: Optional[Callable[[List[Any]], Any]] = None, +) -> torchdata.DataLoader: + """ + Similar to `build_detection_train_loader`, with default batch size = 1, + and sampler = :class:`InferenceSampler`. This sampler coordinates all workers + to produce the exact set of all samples. + + Args: + dataset: a list of dataset dicts, + or a pytorch dataset (either map-style or iterable). They can be obtained + by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper: a callable which takes a sample (dict) from dataset + and returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``. + sampler: a sampler that produces + indices to be applied on ``dataset``. Default to :class:`InferenceSampler`, + which splits the dataset across all workers. Sampler must be None + if `dataset` is iterable. + batch_size: the batch size of the data loader to be created. + Default to 1 image per worker since this is the standard when reporting + inference time in papers. + num_workers: number of parallel data loading workers + collate_fn: same as the argument of `torch.utils.data.DataLoader`. + Defaults to do no collation and return a list of data. + + Returns: + DataLoader: a torch DataLoader, that loads the given detection + dataset, with test-time transformation and batching. + + Examples: + :: + data_loader = build_detection_test_loader( + DatasetRegistry.get("my_test"), + mapper=DatasetMapper(...)) + + # or, instantiate with a CfgNode: + data_loader = build_detection_test_loader(cfg, "my_test") + """ + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset(dataset, mapper) + if isinstance(dataset, torchdata.IterableDataset): + assert sampler is None, "sampler must be None if dataset is IterableDataset" + else: + if sampler is None: + sampler = InferenceSampler(len(dataset)) + return torchdata.DataLoader( + dataset, + batch_size=batch_size, + sampler=sampler, + drop_last=False, + num_workers=num_workers, + collate_fn=trivial_batch_collator if collate_fn is None else collate_fn, + ) + + +def trivial_batch_collator(batch): + """ + A batch collator that does nothing. + """ + return batch + + +def worker_init_reset_seed(worker_id): + initial_seed = torch.initial_seed() % 2**31 + seed_all_rng(initial_seed + worker_id) diff --git a/cutler/data/dataset_mapper.py b/cutler/data/dataset_mapper.py new file mode 100644 index 0000000000000000000000000000000000000000..02687c7c9491009159bfe6ab46307bb1b6e5d09c --- /dev/null +++ b/cutler/data/dataset_mapper.py @@ -0,0 +1,193 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/dataset_mapper.py + +import copy +import logging +import numpy as np +from typing import List, Optional, Union +import torch + +from detectron2.config import configurable + +import data.detection_utils as utils +import data.transforms as T + +""" +This file contains the default mapping that's applied to "dataset dicts". +""" + +__all__ = ["DatasetMapper"] + + +class DatasetMapper: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by the model. + + This is the default callable to be used to map your dataset dict into training data. + You may need to follow it to implement your own one for customized logic, + such as a different way to read or transform images. + See :doc:`/tutorials/data_loading` for details. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies cropping/geometric transforms to the image and annotations + 3. Prepare data and annotations to Tensor and :class:`Instances` + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + image_format: str, + use_instance_mask: bool = False, + use_keypoint: bool = False, + instance_mask_format: str = "polygon", + keypoint_hflip_indices: Optional[np.ndarray] = None, + precomputed_proposal_topk: Optional[int] = None, + recompute_boxes: bool = False, + ): + """ + NOTE: this interface is experimental. + + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + use_keypoint: whether to process keypoint annotations if available + instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation + masks into this format. + keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices` + precomputed_proposal_topk: if given, will load pre-computed + proposals from dataset_dict and keep the top k proposals for each image. + recompute_boxes: whether to overwrite bounding box annotations + by computing tight bounding boxes from instance mask annotations. + """ + if recompute_boxes: + assert use_instance_mask, "recompute_boxes requires instance masks" + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.instance_mask_format = instance_mask_format + self.use_keypoint = use_keypoint + self.keypoint_hflip_indices = keypoint_hflip_indices + self.proposal_topk = precomputed_proposal_topk + self.recompute_boxes = recompute_boxes + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + augs = utils.build_augmentation(cfg, is_train) + if cfg.INPUT.CROP.ENABLED and is_train: + augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE)) + recompute_boxes = cfg.MODEL.MASK_ON + else: + recompute_boxes = False + + ret = { + "is_train": is_train, + "augmentations": augs, + "image_format": cfg.INPUT.FORMAT, + "use_instance_mask": cfg.MODEL.MASK_ON, + "instance_mask_format": cfg.INPUT.MASK_FORMAT, + "use_keypoint": cfg.MODEL.KEYPOINT_ON, + "recompute_boxes": recompute_boxes, + } + + if cfg.MODEL.KEYPOINT_ON: + ret["keypoint_hflip_indices"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN) + + if cfg.MODEL.LOAD_PROPOSALS: + ret["precomputed_proposal_topk"] = ( + cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN + if is_train + else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST + ) + return ret + + def _transform_annotations(self, dataset_dict, transforms, image_shape): + # USER: Modify this if you want to keep them for some reason. + for anno in dataset_dict["annotations"]: + if not self.use_instance_mask: + anno.pop("segmentation", None) + if not self.use_keypoint: + anno.pop("keypoints", None) + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations( + obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices + ) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + instances = utils.annotations_to_instances( + annos, image_shape, mask_format=self.instance_mask_format + ) + + # After transforms such as cropping are applied, the bounding box may no longer + # tightly bound the object. As an example, imagine a triangle object + # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight + # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to + # the intersection of original bounding box and the cropping box. + if self.recompute_boxes: + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + dataset_dict["instances"] = utils.filter_empty_instances(instances) + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + # USER: Write your own image loading if it's not from a file + image = utils.read_image(dataset_dict["file_name"], format=self.image_format) + utils.check_image_size(dataset_dict, image) + + # USER: Remove if you don't do semantic/panoptic segmentation. + if "sem_seg_file_name" in dataset_dict: + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) + else: + sem_seg_gt = None + + aug_input = T.AugInput(image, sem_seg=sem_seg_gt) + transforms = self.augmentations(aug_input) + image, sem_seg_gt = aug_input.image, aug_input.sem_seg + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + if sem_seg_gt is not None: + dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) + + # USER: Remove if you don't use pre-computed proposals. + # Most users would not need this feature. + if self.proposal_topk is not None: + utils.transform_proposals( + dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk + ) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + dataset_dict.pop("sem_seg_file_name", None) + return dataset_dict + + if "annotations" in dataset_dict: + self._transform_annotations(dataset_dict, transforms, image_shape) + + return dataset_dict diff --git a/cutler/data/datasets/__init__.py b/cutler/data/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5f740d59c4cc48fd8663ba052cda92a2ba3e0727 --- /dev/null +++ b/cutler/data/datasets/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +from .coco import load_coco_json, load_sem_seg, register_coco_instances, convert_to_coco_json +from .builtin import ( + register_all_imagenet, + register_all_uvo, + register_all_coco_ca, + register_all_coco_semi, + register_all_lvis, + register_all_voc, + register_all_cross_domain, + register_all_kitti, + register_all_objects365, + register_all_openimages, + ) + +__all__ = [k for k in globals().keys() if not k.startswith("_")] \ No newline at end of file diff --git a/cutler/data/datasets/__pycache__/__init__.cpython-312.pyc b/cutler/data/datasets/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8ec7b4ef95c5e7388d3c36d7c9a7f7d48cbfdfc8 Binary files /dev/null and b/cutler/data/datasets/__pycache__/__init__.cpython-312.pyc differ diff --git a/cutler/data/datasets/__pycache__/builtin.cpython-312.pyc b/cutler/data/datasets/__pycache__/builtin.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..79d6027e2b0bde84c8d5640ed42fe8f003a2b446 Binary files /dev/null and b/cutler/data/datasets/__pycache__/builtin.cpython-312.pyc differ diff --git a/cutler/data/datasets/__pycache__/builtin_meta.cpython-312.pyc b/cutler/data/datasets/__pycache__/builtin_meta.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f58a46662c708d24fbede4c761cbef1fb07a7b2d Binary files /dev/null and b/cutler/data/datasets/__pycache__/builtin_meta.cpython-312.pyc differ diff --git a/cutler/data/datasets/__pycache__/coco.cpython-312.pyc b/cutler/data/datasets/__pycache__/coco.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..63a9a57cd209a32a4df28ef58f02196f2e5c373d Binary files /dev/null and b/cutler/data/datasets/__pycache__/coco.cpython-312.pyc differ diff --git a/cutler/data/datasets/builtin.py b/cutler/data/datasets/builtin.py new file mode 100644 index 0000000000000000000000000000000000000000..0757829f02c3b29fdfabf49abeb3b0d4e77db3a9 --- /dev/null +++ b/cutler/data/datasets/builtin.py @@ -0,0 +1,216 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/builtin.py + +""" +This file registers pre-defined datasets at hard-coded paths, and their metadata. + +We hard-code metadata for common datasets. This will enable: +1. Consistency check when loading the datasets +2. Use models on these standard datasets directly and run demos, + without having to download the dataset annotations + +We hard-code some paths to the dataset that's assumed to +exist in "./datasets/". + +Users SHOULD NOT use this file to create new dataset / metadata for new dataset. +To add new dataset, refer to the tutorial "docs/DATASETS.md". +""" + +import os + +from .builtin_meta import _get_builtin_metadata +from .coco import register_coco_instances + +# ==== Predefined datasets and splits for COCO ========== + +_PREDEFINED_SPLITS_COCO_SEMI = {} +_PREDEFINED_SPLITS_COCO_SEMI["coco_semi"] = { + # we use seed 42 to be consistent with previous works on SSL detection and segmentation + "coco_semi_1perc": ("coco/train2017", "coco/annotations/1perc_instances_train2017.json"), + "coco_semi_2perc": ("coco/train2017", "coco/annotations/2perc_instances_train2017.json"), + "coco_semi_5perc": ("coco/train2017", "coco/annotations/5perc_instances_train2017.json"), + "coco_semi_10perc": ("coco/train2017", "coco/annotations/10perc_instances_train2017.json"), + "coco_semi_20perc": ("coco/train2017", "coco/annotations/20perc_instances_train2017.json"), + "coco_semi_30perc": ("coco/train2017", "coco/annotations/30perc_instances_train2017.json"), + "coco_semi_40perc": ("coco/train2017", "coco/annotations/40perc_instances_train2017.json"), + "coco_semi_50perc": ("coco/train2017", "coco/annotations/50perc_instances_train2017.json"), + "coco_semi_60perc": ("coco/train2017", "coco/annotations/60perc_instances_train2017.json"), + "coco_semi_80perc": ("coco/train2017", "coco/annotations/80perc_instances_train2017.json"), +} + +_PREDEFINED_SPLITS_COCO_CA = {} +_PREDEFINED_SPLITS_COCO_CA["coco_cls_agnostic"] = { + "cls_agnostic_coco": ("coco/val2017", "coco/annotations/coco_cls_agnostic_instances_val2017.json"), + "cls_agnostic_coco20k": ("coco/train2014", "coco/annotations/coco20k_trainval_gt.json"), +} + +_PREDEFINED_SPLITS_IMAGENET = {} +_PREDEFINED_SPLITS_IMAGENET["imagenet"] = { + # maskcut annotations + "imagenet_train": ("imagenet/train", "imagenet/annotations/imagenet_train_fixsize480_tau0.15_N3.json"), + # self-training round 1 + "imagenet_train_r1": ("imagenet/train", "imagenet/annotations/cutler_imagenet1k_train_r1.json"), + # self-training round 2 + "imagenet_train_r2": ("imagenet/train", "imagenet/annotations/cutler_imagenet1k_train_r2.json"), + # self-training round 3 + "imagenet_train_r3": ("imagenet/train", "imagenet/annotations/cutler_imagenet1k_train_r3.json"), +} + +_PREDEFINED_SPLITS_VOC = {} +_PREDEFINED_SPLITS_VOC["voc"] = { + 'cls_agnostic_voc': ("voc/", "voc/annotations/trainvaltest_2007_cls_agnostic.json"), +} + +_PREDEFINED_SPLITS_CROSSDOMAIN = {} +_PREDEFINED_SPLITS_CROSSDOMAIN["cross_domain"] = { + 'cls_agnostic_clipart': ("clipart/", "clipart/annotations/traintest_cls_agnostic.json"), + 'cls_agnostic_watercolor': ("watercolor/", "watercolor/annotations/traintest_cls_agnostic.json"), + 'cls_agnostic_comic': ("comic/", "comic/annotations/traintest_cls_agnostic.json"), +} + +_PREDEFINED_SPLITS_KITTI = {} +_PREDEFINED_SPLITS_KITTI["kitti"] = { + 'cls_agnostic_kitti': ("kitti/", "kitti/annotations/trainval_cls_agnostic.json"), +} + +_PREDEFINED_SPLITS_LVIS = {} +_PREDEFINED_SPLITS_LVIS["lvis"] = { + "cls_agnostic_lvis": ("coco/", "coco/annotations/lvis1.0_cocofied_val_cls_agnostic.json"), +} + +_PREDEFINED_SPLITS_OBJECTS365 = {} +_PREDEFINED_SPLITS_OBJECTS365["objects365"] = { + 'cls_agnostic_objects365': ("objects365/val", "objects365/annotations/zhiyuan_objv2_val_cls_agnostic.json"), +} + +_PREDEFINED_SPLITS_OpenImages = {} +_PREDEFINED_SPLITS_OpenImages["openimages"] = { + 'cls_agnostic_openimages': ("openImages/validation", "openImages/annotations/openimages_val_cls_agnostic.json"), +} + +_PREDEFINED_SPLITS_UVO = {} +_PREDEFINED_SPLITS_UVO["uvo"] = { + "cls_agnostic_uvo": ("uvo/all_UVO_frames", "uvo/annotations/val_sparse_cleaned_cls_agnostic.json"), +} + +def register_all_imagenet(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_IMAGENET.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + +def register_all_voc(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_VOC.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + +def register_all_cross_domain(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_CROSSDOMAIN.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + +def register_all_kitti(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_KITTI.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + +def register_all_objects365(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_OBJECTS365.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + +def register_all_openimages(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_OpenImages.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + +def register_all_lvis(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_LVIS.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + +def register_all_uvo(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_UVO.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + +def register_all_coco_semi(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_COCO_SEMI.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + +def register_all_coco_ca(root): + for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_COCO_CA.items(): + for key, (image_root, json_file) in splits_per_dataset.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_builtin_metadata(dataset_name), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + +_root = os.path.expanduser(os.getenv("DETECTRON2_DATASETS", "datasets")) +register_all_coco_semi(_root) +register_all_coco_ca(_root) +register_all_imagenet(_root) +register_all_uvo(_root) +register_all_voc(_root) +register_all_cross_domain(_root) +register_all_kitti(_root) +register_all_openimages(_root) +register_all_objects365(_root) +register_all_lvis(_root) \ No newline at end of file diff --git a/cutler/data/datasets/builtin_meta.py b/cutler/data/datasets/builtin_meta.py new file mode 100644 index 0000000000000000000000000000000000000000..ec6c490b05d7c1a6a58d024700e0464389be117a --- /dev/null +++ b/cutler/data/datasets/builtin_meta.py @@ -0,0 +1,389 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/builtin_meta.py + +""" +Note: +For your custom dataset, there is no need to hard-code metadata anywhere in the code. +For example, for COCO-format dataset, metadata will be obtained automatically +when calling `load_coco_json`. For other dataset, metadata may also be obtained in other ways +during loading. + +However, we hard-coded metadata for a few common dataset here. +The only goal is to allow users who don't have these dataset to use pre-trained models. +Users don't have to download a COCO json (which contains metadata), in order to visualize a +COCO model (with correct class names and colors). +""" + + +# All coco categories, together with their nice-looking visualization colors +# It's from https://github.com/cocodataset/panopticapi/blob/master/panoptic_coco_categories.json +COCO_CATEGORIES = [ + {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"}, + {"color": [119, 11, 32], "isthing": 1, "id": 2, "name": "bicycle"}, + {"color": [0, 0, 142], "isthing": 1, "id": 3, "name": "car"}, + {"color": [0, 0, 230], "isthing": 1, "id": 4, "name": "motorcycle"}, + {"color": [106, 0, 228], "isthing": 1, "id": 5, "name": "airplane"}, + {"color": [0, 60, 100], "isthing": 1, "id": 6, "name": "bus"}, + {"color": [0, 80, 100], "isthing": 1, "id": 7, "name": "train"}, + {"color": [0, 0, 70], "isthing": 1, "id": 8, "name": "truck"}, + {"color": [0, 0, 192], "isthing": 1, "id": 9, "name": "boat"}, + {"color": [250, 170, 30], "isthing": 1, "id": 10, "name": "traffic light"}, + {"color": [100, 170, 30], "isthing": 1, "id": 11, "name": "fire hydrant"}, + {"color": [220, 220, 0], "isthing": 1, "id": 13, "name": "stop sign"}, + {"color": [175, 116, 175], "isthing": 1, "id": 14, "name": "parking meter"}, + {"color": [250, 0, 30], "isthing": 1, "id": 15, "name": "bench"}, + {"color": [165, 42, 42], "isthing": 1, "id": 16, "name": "bird"}, + {"color": [255, 77, 255], "isthing": 1, "id": 17, "name": "cat"}, + {"color": [0, 226, 252], "isthing": 1, "id": 18, "name": "dog"}, + {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"}, + {"color": [0, 82, 0], "isthing": 1, "id": 20, "name": "sheep"}, + {"color": [120, 166, 157], "isthing": 1, "id": 21, "name": "cow"}, + {"color": [110, 76, 0], "isthing": 1, "id": 22, "name": "elephant"}, + {"color": [174, 57, 255], "isthing": 1, "id": 23, "name": "bear"}, + {"color": [199, 100, 0], "isthing": 1, "id": 24, "name": "zebra"}, + {"color": [72, 0, 118], "isthing": 1, "id": 25, "name": "giraffe"}, + {"color": [255, 179, 240], "isthing": 1, "id": 27, "name": "backpack"}, + {"color": [0, 125, 92], "isthing": 1, "id": 28, "name": "umbrella"}, + {"color": [209, 0, 151], "isthing": 1, "id": 31, "name": "handbag"}, + {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "tie"}, + {"color": [0, 220, 176], "isthing": 1, "id": 33, "name": "suitcase"}, + {"color": [255, 99, 164], "isthing": 1, "id": 34, "name": "frisbee"}, + {"color": [92, 0, 73], "isthing": 1, "id": 35, "name": "skis"}, + {"color": [133, 129, 255], "isthing": 1, "id": 36, "name": "snowboard"}, + {"color": [78, 180, 255], "isthing": 1, "id": 37, "name": "sports ball"}, + {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "kite"}, + {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "baseball bat"}, + {"color": [45, 89, 255], "isthing": 1, "id": 40, "name": "baseball glove"}, + {"color": [134, 134, 103], "isthing": 1, "id": 41, "name": "skateboard"}, + {"color": [145, 148, 174], "isthing": 1, "id": 42, "name": "surfboard"}, + {"color": [255, 208, 186], "isthing": 1, "id": 43, "name": "tennis racket"}, + {"color": [197, 226, 255], "isthing": 1, "id": 44, "name": "bottle"}, + {"color": [171, 134, 1], "isthing": 1, "id": 46, "name": "wine glass"}, + {"color": [109, 63, 54], "isthing": 1, "id": 47, "name": "cup"}, + {"color": [207, 138, 255], "isthing": 1, "id": 48, "name": "fork"}, + {"color": [151, 0, 95], "isthing": 1, "id": 49, "name": "knife"}, + {"color": [9, 80, 61], "isthing": 1, "id": 50, "name": "spoon"}, + {"color": [84, 105, 51], "isthing": 1, "id": 51, "name": "bowl"}, + {"color": [74, 65, 105], "isthing": 1, "id": 52, "name": "banana"}, + {"color": [166, 196, 102], "isthing": 1, "id": 53, "name": "apple"}, + {"color": [208, 195, 210], "isthing": 1, "id": 54, "name": "sandwich"}, + {"color": [255, 109, 65], "isthing": 1, "id": 55, "name": "orange"}, + {"color": [0, 143, 149], "isthing": 1, "id": 56, "name": "broccoli"}, + {"color": [179, 0, 194], "isthing": 1, "id": 57, "name": "carrot"}, + {"color": [209, 99, 106], "isthing": 1, "id": 58, "name": "hot dog"}, + {"color": [5, 121, 0], "isthing": 1, "id": 59, "name": "pizza"}, + {"color": [227, 255, 205], "isthing": 1, "id": 60, "name": "donut"}, + {"color": [147, 186, 208], "isthing": 1, "id": 61, "name": "cake"}, + {"color": [153, 69, 1], "isthing": 1, "id": 62, "name": "chair"}, + {"color": [3, 95, 161], "isthing": 1, "id": 63, "name": "couch"}, + {"color": [163, 255, 0], "isthing": 1, "id": 64, "name": "potted plant"}, + {"color": [119, 0, 170], "isthing": 1, "id": 65, "name": "bed"}, + {"color": [0, 182, 199], "isthing": 1, "id": 67, "name": "dining table"}, + {"color": [0, 165, 120], "isthing": 1, "id": 70, "name": "toilet"}, + {"color": [183, 130, 88], "isthing": 1, "id": 72, "name": "tv"}, + {"color": [95, 32, 0], "isthing": 1, "id": 73, "name": "laptop"}, + {"color": [130, 114, 135], "isthing": 1, "id": 74, "name": "mouse"}, + {"color": [110, 129, 133], "isthing": 1, "id": 75, "name": "remote"}, + {"color": [166, 74, 118], "isthing": 1, "id": 76, "name": "keyboard"}, + {"color": [219, 142, 185], "isthing": 1, "id": 77, "name": "cell phone"}, + {"color": [79, 210, 114], "isthing": 1, "id": 78, "name": "microwave"}, + {"color": [178, 90, 62], "isthing": 1, "id": 79, "name": "oven"}, + {"color": [65, 70, 15], "isthing": 1, "id": 80, "name": "toaster"}, + {"color": [127, 167, 115], "isthing": 1, "id": 81, "name": "sink"}, + {"color": [59, 105, 106], "isthing": 1, "id": 82, "name": "refrigerator"}, + {"color": [142, 108, 45], "isthing": 1, "id": 84, "name": "book"}, + {"color": [196, 172, 0], "isthing": 1, "id": 85, "name": "clock"}, + {"color": [95, 54, 80], "isthing": 1, "id": 86, "name": "vase"}, + {"color": [128, 76, 255], "isthing": 1, "id": 87, "name": "scissors"}, + {"color": [201, 57, 1], "isthing": 1, "id": 88, "name": "teddy bear"}, + {"color": [246, 0, 122], "isthing": 1, "id": 89, "name": "hair drier"}, + {"color": [191, 162, 208], "isthing": 1, "id": 90, "name": "toothbrush"}, + {"color": [255, 255, 128], "isthing": 0, "id": 92, "name": "banner"}, + {"color": [147, 211, 203], "isthing": 0, "id": 93, "name": "blanket"}, + {"color": [150, 100, 100], "isthing": 0, "id": 95, "name": "bridge"}, + {"color": [168, 171, 172], "isthing": 0, "id": 100, "name": "cardboard"}, + {"color": [146, 112, 198], "isthing": 0, "id": 107, "name": "counter"}, + {"color": [210, 170, 100], "isthing": 0, "id": 109, "name": "curtain"}, + {"color": [92, 136, 89], "isthing": 0, "id": 112, "name": "door-stuff"}, + {"color": [218, 88, 184], "isthing": 0, "id": 118, "name": "floor-wood"}, + {"color": [241, 129, 0], "isthing": 0, "id": 119, "name": "flower"}, + {"color": [217, 17, 255], "isthing": 0, "id": 122, "name": "fruit"}, + {"color": [124, 74, 181], "isthing": 0, "id": 125, "name": "gravel"}, + {"color": [70, 70, 70], "isthing": 0, "id": 128, "name": "house"}, + {"color": [255, 228, 255], "isthing": 0, "id": 130, "name": "light"}, + {"color": [154, 208, 0], "isthing": 0, "id": 133, "name": "mirror-stuff"}, + {"color": [193, 0, 92], "isthing": 0, "id": 138, "name": "net"}, + {"color": [76, 91, 113], "isthing": 0, "id": 141, "name": "pillow"}, + {"color": [255, 180, 195], "isthing": 0, "id": 144, "name": "platform"}, + {"color": [106, 154, 176], "isthing": 0, "id": 145, "name": "playingfield"}, + {"color": [230, 150, 140], "isthing": 0, "id": 147, "name": "railroad"}, + {"color": [60, 143, 255], "isthing": 0, "id": 148, "name": "river"}, + {"color": [128, 64, 128], "isthing": 0, "id": 149, "name": "road"}, + {"color": [92, 82, 55], "isthing": 0, "id": 151, "name": "roof"}, + {"color": [254, 212, 124], "isthing": 0, "id": 154, "name": "sand"}, + {"color": [73, 77, 174], "isthing": 0, "id": 155, "name": "sea"}, + {"color": [255, 160, 98], "isthing": 0, "id": 156, "name": "shelf"}, + {"color": [255, 255, 255], "isthing": 0, "id": 159, "name": "snow"}, + {"color": [104, 84, 109], "isthing": 0, "id": 161, "name": "stairs"}, + {"color": [169, 164, 131], "isthing": 0, "id": 166, "name": "tent"}, + {"color": [225, 199, 255], "isthing": 0, "id": 168, "name": "towel"}, + {"color": [137, 54, 74], "isthing": 0, "id": 171, "name": "wall-brick"}, + {"color": [135, 158, 223], "isthing": 0, "id": 175, "name": "wall-stone"}, + {"color": [7, 246, 231], "isthing": 0, "id": 176, "name": "wall-tile"}, + {"color": [107, 255, 200], "isthing": 0, "id": 177, "name": "wall-wood"}, + {"color": [58, 41, 149], "isthing": 0, "id": 178, "name": "water-other"}, + {"color": [183, 121, 142], "isthing": 0, "id": 180, "name": "window-blind"}, + {"color": [255, 73, 97], "isthing": 0, "id": 181, "name": "window-other"}, + {"color": [107, 142, 35], "isthing": 0, "id": 184, "name": "tree-merged"}, + {"color": [190, 153, 153], "isthing": 0, "id": 185, "name": "fence-merged"}, + {"color": [146, 139, 141], "isthing": 0, "id": 186, "name": "ceiling-merged"}, + {"color": [70, 130, 180], "isthing": 0, "id": 187, "name": "sky-other-merged"}, + {"color": [134, 199, 156], "isthing": 0, "id": 188, "name": "cabinet-merged"}, + {"color": [209, 226, 140], "isthing": 0, "id": 189, "name": "table-merged"}, + {"color": [96, 36, 108], "isthing": 0, "id": 190, "name": "floor-other-merged"}, + {"color": [96, 96, 96], "isthing": 0, "id": 191, "name": "pavement-merged"}, + {"color": [64, 170, 64], "isthing": 0, "id": 192, "name": "mountain-merged"}, + {"color": [152, 251, 152], "isthing": 0, "id": 193, "name": "grass-merged"}, + {"color": [208, 229, 228], "isthing": 0, "id": 194, "name": "dirt-merged"}, + {"color": [206, 186, 171], "isthing": 0, "id": 195, "name": "paper-merged"}, + {"color": [152, 161, 64], "isthing": 0, "id": 196, "name": "food-other-merged"}, + {"color": [116, 112, 0], "isthing": 0, "id": 197, "name": "building-other-merged"}, + {"color": [0, 114, 143], "isthing": 0, "id": 198, "name": "rock-merged"}, + {"color": [102, 102, 156], "isthing": 0, "id": 199, "name": "wall-other-merged"}, + {"color": [250, 141, 255], "isthing": 0, "id": 200, "name": "rug-merged"}, +] + +IMAGENET_CATEGORIES = [ + {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "fg"}, +] + +UVO_CATEGORIES = [ + {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "object"}, +] + +# fmt: off +COCO_PERSON_KEYPOINT_NAMES = ( + "nose", + "left_eye", "right_eye", + "left_ear", "right_ear", + "left_shoulder", "right_shoulder", + "left_elbow", "right_elbow", + "left_wrist", "right_wrist", + "left_hip", "right_hip", + "left_knee", "right_knee", + "left_ankle", "right_ankle", +) +# fmt: on + +# Pairs of keypoints that should be exchanged under horizontal flipping +COCO_PERSON_KEYPOINT_FLIP_MAP = ( + ("left_eye", "right_eye"), + ("left_ear", "right_ear"), + ("left_shoulder", "right_shoulder"), + ("left_elbow", "right_elbow"), + ("left_wrist", "right_wrist"), + ("left_hip", "right_hip"), + ("left_knee", "right_knee"), + ("left_ankle", "right_ankle"), +) + +# rules for pairs of keypoints to draw a line between, and the line color to use. +KEYPOINT_CONNECTION_RULES = [ + # face + ("left_ear", "left_eye", (102, 204, 255)), + ("right_ear", "right_eye", (51, 153, 255)), + ("left_eye", "nose", (102, 0, 204)), + ("nose", "right_eye", (51, 102, 255)), + # upper-body + ("left_shoulder", "right_shoulder", (255, 128, 0)), + ("left_shoulder", "left_elbow", (153, 255, 204)), + ("right_shoulder", "right_elbow", (128, 229, 255)), + ("left_elbow", "left_wrist", (153, 255, 153)), + ("right_elbow", "right_wrist", (102, 255, 224)), + # lower-body + ("left_hip", "right_hip", (255, 102, 0)), + ("left_hip", "left_knee", (255, 255, 77)), + ("right_hip", "right_knee", (153, 255, 204)), + ("left_knee", "left_ankle", (191, 255, 128)), + ("right_knee", "right_ankle", (255, 195, 77)), +] + +# All Cityscapes categories, together with their nice-looking visualization colors +# It's from https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py # noqa +CITYSCAPES_CATEGORIES = [ + {"color": (128, 64, 128), "isthing": 0, "id": 7, "trainId": 0, "name": "road"}, + {"color": (244, 35, 232), "isthing": 0, "id": 8, "trainId": 1, "name": "sidewalk"}, + {"color": (70, 70, 70), "isthing": 0, "id": 11, "trainId": 2, "name": "building"}, + {"color": (102, 102, 156), "isthing": 0, "id": 12, "trainId": 3, "name": "wall"}, + {"color": (190, 153, 153), "isthing": 0, "id": 13, "trainId": 4, "name": "fence"}, + {"color": (153, 153, 153), "isthing": 0, "id": 17, "trainId": 5, "name": "pole"}, + {"color": (250, 170, 30), "isthing": 0, "id": 19, "trainId": 6, "name": "traffic light"}, + {"color": (220, 220, 0), "isthing": 0, "id": 20, "trainId": 7, "name": "traffic sign"}, + {"color": (107, 142, 35), "isthing": 0, "id": 21, "trainId": 8, "name": "vegetation"}, + {"color": (152, 251, 152), "isthing": 0, "id": 22, "trainId": 9, "name": "terrain"}, + {"color": (70, 130, 180), "isthing": 0, "id": 23, "trainId": 10, "name": "sky"}, + {"color": (220, 20, 60), "isthing": 1, "id": 24, "trainId": 11, "name": "person"}, + {"color": (255, 0, 0), "isthing": 1, "id": 25, "trainId": 12, "name": "rider"}, + {"color": (0, 0, 142), "isthing": 1, "id": 26, "trainId": 13, "name": "car"}, + {"color": (0, 0, 70), "isthing": 1, "id": 27, "trainId": 14, "name": "truck"}, + {"color": (0, 60, 100), "isthing": 1, "id": 28, "trainId": 15, "name": "bus"}, + {"color": (0, 80, 100), "isthing": 1, "id": 31, "trainId": 16, "name": "train"}, + {"color": (0, 0, 230), "isthing": 1, "id": 32, "trainId": 17, "name": "motorcycle"}, + {"color": (119, 11, 32), "isthing": 1, "id": 33, "trainId": 18, "name": "bicycle"}, +] + +# fmt: off +ADE20K_SEM_SEG_CATEGORIES = [ + "wall", "building", "sky", "floor", "tree", "ceiling", "road, route", "bed", "window ", "grass", "cabinet", "sidewalk, pavement", "person", "earth, ground", "door", "table", "mountain, mount", "plant", "curtain", "chair", "car", "water", "painting, picture", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock, stone", "wardrobe, closet, press", "lamp", "tub", "rail", "cushion", "base, pedestal, stand", "box", "column, pillar", "signboard, sign", "chest of drawers, chest, bureau, dresser", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator, icebox", "grandstand, covered stand", "path", "stairs", "runway", "case, display case, showcase, vitrine", "pool table, billiard table, snooker table", "pillow", "screen door, screen", "stairway, staircase", "river", "bridge, span", "bookcase", "blind, screen", "coffee table", "toilet, can, commode, crapper, pot, potty, stool, throne", "flower", "book", "hill", "bench", "countertop", "stove", "palm, palm tree", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel, hut, hutch, shack, shanty", "bus", "towel", "light", "truck", "tower", "chandelier", "awning, sunshade, sunblind", "street lamp", "booth", "tv", "plane", "dirt track", "clothes", "pole", "land, ground, soil", "bannister, banister, balustrade, balusters, handrail", "escalator, moving staircase, moving stairway", "ottoman, pouf, pouffe, puff, hassock", "bottle", "buffet, counter, sideboard", "poster, posting, placard, notice, bill, card", "stage", "van", "ship", "fountain", "conveyer belt, conveyor belt, conveyer, conveyor, transporter", "canopy", "washer, automatic washer, washing machine", "plaything, toy", "pool", "stool", "barrel, cask", "basket, handbasket", "falls", "tent", "bag", "minibike, motorbike", "cradle", "oven", "ball", "food, solid food", "step, stair", "tank, storage tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket, cover", "sculpture", "hood, exhaust hood", "sconce", "vase", "traffic light", "tray", "trash can", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass, drinking glass", "clock", "flag", # noqa +] +# After processed by `prepare_ade20k_sem_seg.py`, id 255 means ignore +# fmt: on + + +def _get_coco_instances_meta(): + thing_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 1] + thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1] + assert len(thing_ids) == 80, len(thing_ids) + # Mapping from the incontiguous COCO category id to an id in [0, 79] + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + "thing_colors": thing_colors, + } + return ret + +def _get_imagenet_instances_meta(): + thing_ids = [k["id"] for k in IMAGENET_CATEGORIES if k["isthing"] == 1] + thing_colors = [k["color"] for k in IMAGENET_CATEGORIES if k["isthing"] == 1] + assert len(thing_ids) == 1, len(thing_ids) + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in IMAGENET_CATEGORIES if k["isthing"] == 1] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + "thing_colors": thing_colors, + "class_image_count": [{'id': 1, 'image_count': 116986}] + } + return ret + +def _get_UVO_instances_meta(): + thing_ids = [k["id"] for k in UVO_CATEGORIES if k["isthing"] == 1] + thing_colors = [k["color"] for k in UVO_CATEGORIES if k["isthing"] == 1] + assert len(thing_ids) == 1, len(thing_ids) + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in UVO_CATEGORIES if k["isthing"] == 1] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + "thing_colors": thing_colors, + "class_image_count": [{'id': 1, 'image_count': 116986}] + } + return ret + +def _get_coco_panoptic_separated_meta(): + """ + Returns metadata for "separated" version of the panoptic segmentation dataset. + """ + stuff_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 0] + assert len(stuff_ids) == 53, len(stuff_ids) + + # For semantic segmentation, this mapping maps from contiguous stuff id + # (in [0, 53], used in models) to ids in the dataset (used for processing results) + # The id 0 is mapped to an extra category "thing". + stuff_dataset_id_to_contiguous_id = {k: i + 1 for i, k in enumerate(stuff_ids)} + # When converting COCO panoptic annotations to semantic annotations + # We label the "thing" category to 0 + stuff_dataset_id_to_contiguous_id[0] = 0 + + # 54 names for COCO stuff categories (including "things") + stuff_classes = ["things"] + [ + k["name"].replace("-other", "").replace("-merged", "") + for k in COCO_CATEGORIES + if k["isthing"] == 0 + ] + + # NOTE: I randomly picked a color for things + stuff_colors = [[82, 18, 128]] + [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 0] + ret = { + "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, + "stuff_classes": stuff_classes, + "stuff_colors": stuff_colors, + } + ret.update(_get_coco_instances_meta()) + return ret + + +def _get_builtin_metadata(dataset_name): + if dataset_name in ["coco", "coco_semi"]: + return _get_coco_instances_meta() + if dataset_name == "coco_panoptic_separated": + return _get_coco_panoptic_separated_meta() + elif dataset_name in ["imagenet", "kitti", "cross_domain", "lvis", "voc", "coco_cls_agnostic", "objects365", 'openimages']: + return _get_imagenet_instances_meta() + elif dataset_name == "uvo": + return _get_UVO_instances_meta() + elif dataset_name == "coco_panoptic_standard": + meta = {} + # The following metadata maps contiguous id from [0, #thing categories + + # #stuff categories) to their names and colors. We have to replica of the + # same name and color under "thing_*" and "stuff_*" because the current + # visualization function in D2 handles thing and class classes differently + # due to some heuristic used in Panoptic FPN. We keep the same naming to + # enable reusing existing visualization functions. + thing_classes = [k["name"] for k in COCO_CATEGORIES] + thing_colors = [k["color"] for k in COCO_CATEGORIES] + stuff_classes = [k["name"] for k in COCO_CATEGORIES] + stuff_colors = [k["color"] for k in COCO_CATEGORIES] + + meta["thing_classes"] = thing_classes + meta["thing_colors"] = thing_colors + meta["stuff_classes"] = stuff_classes + meta["stuff_colors"] = stuff_colors + + # Convert category id for training: + # category id: like semantic segmentation, it is the class id for each + # pixel. Since there are some classes not used in evaluation, the category + # id is not always contiguous and thus we have two set of category ids: + # - original category id: category id in the original dataset, mainly + # used for evaluation. + # - contiguous category id: [0, #classes), in order to train the linear + # softmax classifier. + thing_dataset_id_to_contiguous_id = {} + stuff_dataset_id_to_contiguous_id = {} + + for i, cat in enumerate(COCO_CATEGORIES): + if cat["isthing"]: + thing_dataset_id_to_contiguous_id[cat["id"]] = i + else: + stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id + meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id + + return meta + elif dataset_name == "coco_person": + return { + "thing_classes": ["person"], + "keypoint_names": COCO_PERSON_KEYPOINT_NAMES, + "keypoint_flip_map": COCO_PERSON_KEYPOINT_FLIP_MAP, + "keypoint_connection_rules": KEYPOINT_CONNECTION_RULES, + } + elif dataset_name == "cityscapes": + # fmt: off + CITYSCAPES_THING_CLASSES = [ + "person", "rider", "car", "truck", + "bus", "train", "motorcycle", "bicycle", + ] + CITYSCAPES_STUFF_CLASSES = [ + "road", "sidewalk", "building", "wall", "fence", "pole", "traffic light", + "traffic sign", "vegetation", "terrain", "sky", "person", "rider", "car", + "truck", "bus", "train", "motorcycle", "bicycle", + ] + # fmt: on + return { + "thing_classes": CITYSCAPES_THING_CLASSES, + "stuff_classes": CITYSCAPES_STUFF_CLASSES, + } + raise KeyError("No built-in metadata for dataset {}".format(dataset_name)) diff --git a/cutler/data/datasets/coco.py b/cutler/data/datasets/coco.py new file mode 100644 index 0000000000000000000000000000000000000000..472e15c2fc85928daadd211470fadab47728e91e --- /dev/null +++ b/cutler/data/datasets/coco.py @@ -0,0 +1,544 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/coco.py + +import contextlib +import datetime +import io +import json +import logging +import numpy as np +import os +import shutil +import pycocotools.mask as mask_util +from fvcore.common.timer import Timer +from iopath.common.file_io import file_lock +from PIL import Image + +from detectron2.structures import Boxes, BoxMode, PolygonMasks, RotatedBoxes +from detectron2.utils.file_io import PathManager + +from detectron2.data import DatasetCatalog, MetadataCatalog + +""" +This file contains functions to parse COCO-format annotations into dicts in "Detectron2 format". +""" + + +logger = logging.getLogger(__name__) + +__all__ = ["load_coco_json", "load_sem_seg", "convert_to_coco_json", "register_coco_instances"] + + +def load_coco_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): + """ + Load a json file with COCO's instances annotation format. + Currently supports instance detection, instance segmentation, + and person keypoints annotations. + + Args: + json_file (str): full path to the json file in COCO instances annotation format. + image_root (str or path-like): the directory where the images in this json file exists. + dataset_name (str or None): the name of the dataset (e.g., coco_2017_train). + When provided, this function will also do the following: + + * Put "thing_classes" into the metadata associated with this dataset. + * Map the category ids into a contiguous range (needed by standard dataset format), + and add "thing_dataset_id_to_contiguous_id" to the metadata associated + with this dataset. + + This option should usually be provided, unless users need to load + the original json content and apply more processing manually. + extra_annotation_keys (list[str]): list of per-annotation keys that should also be + loaded into the dataset dict (besides "iscrowd", "bbox", "keypoints", + "category_id", "segmentation"). The values for these keys will be returned as-is. + For example, the densepose annotations are loaded in this way. + + Returns: + list[dict]: a list of dicts in Detectron2 standard dataset dicts format (See + `Using Custom Datasets `_ ) when `dataset_name` is not None. + If `dataset_name` is None, the returned `category_ids` may be + incontiguous and may not conform to the Detectron2 standard format. + + Notes: + 1. This function does not read the image files. + The results do not have the "image" field. + """ + from pycocotools.coco import COCO + + timer = Timer() + json_file = PathManager.get_local_path(json_file) + with contextlib.redirect_stdout(io.StringIO()): + coco_api = COCO(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + + id_map = None + if dataset_name is not None: + meta = MetadataCatalog.get(dataset_name) + cat_ids = sorted(coco_api.getCatIds()) + cats = coco_api.loadCats(cat_ids) + # The categories in a custom json file may not be sorted. + thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])] + if "imagenet" not in dataset_name and "cls_agnostic" not in dataset_name: + meta.thing_classes = thing_classes + + # In COCO, certain category ids are artificially removed, + # and by convention they are always ignored. + # We deal with COCO's id issue and translate + # the category ids to contiguous ids in [0, 80). + + # It works by looking at the "categories" field in the json, therefore + # if users' own json also have incontiguous ids, we'll + # apply this mapping as well but print a warning. + if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): + if "coco" not in dataset_name: + logger.warning( + """ + Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. + """ + ) + id_map = {v: i for i, v in enumerate(cat_ids)} + meta.thing_dataset_id_to_contiguous_id = id_map + else: + id_map = meta.thing_dataset_id_to_contiguous_id + + # sort indices for reproducible results + img_ids = sorted(coco_api.imgs.keys()) + # imgs is a list of dicts, each looks something like: + # {'license': 4, + # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', + # 'file_name': 'COCO_val2014_000000001268.jpg', + # 'height': 427, + # 'width': 640, + # 'date_captured': '2013-11-17 05:57:24', + # 'id': 1268} + imgs = coco_api.loadImgs(img_ids) + # anns is a list[list[dict]], where each dict is an annotation + # record for an object. The inner list enumerates the objects in an image + # and the outer list enumerates over images. Example of anns[0]: + # [{'segmentation': [[192.81, + # 247.09, + # ... + # 219.03, + # 249.06]], + # 'area': 1035.749, + # 'iscrowd': 0, + # 'image_id': 1268, + # 'bbox': [192.81, 224.8, 74.73, 33.43], + # 'category_id': 16, + # 'id': 42986}, + # ...] + anns = [coco_api.imgToAnns[img_id] for img_id in img_ids] + total_num_valid_anns = sum([len(x) for x in anns]) + total_num_anns = len(coco_api.anns) + if total_num_valid_anns < total_num_anns: + logger.warning( + f"{json_file} contains {total_num_anns} annotations, but only " + f"{total_num_valid_anns} of them match to images in the file." + ) + + if "minival" not in json_file: + # The popular valminusminival & minival annotations for COCO2014 contain this bug. + # However the ratio of buggy annotations there is tiny and does not affect accuracy. + # Therefore we explicitly white-list them. + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format( + json_file + ) + + imgs_anns = list(zip(imgs, anns)) + logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file)) + + dataset_dicts = [] + + ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"] + (extra_annotation_keys or []) + + num_instances_without_valid_segmentation = 0 + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + record["file_name"] = os.path.join(image_root, img_dict["file_name"]) + record["height"] = img_dict["height"] + record["width"] = img_dict["width"] + image_id = record["image_id"] = img_dict["id"] + + objs = [] + for anno in anno_dict_list: + # Check that the image_id in this annotation is the same as + # the image_id we're looking at. + # This fails only when the data parsing logic or the annotation file is buggy. + + # The original COCO valminusminival2014 & minival2014 annotation files + # actually contains bugs that, together with certain ways of using COCO API, + # can trigger this assertion. + assert anno["image_id"] == image_id + + assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.' + + obj = {key: anno[key] for key in ann_keys if key in anno} + if "bbox" in obj and len(obj["bbox"]) == 0: + raise ValueError( + f"One annotation of image {image_id} contains empty 'bbox' value! " + "This json does not have valid COCO format." + ) + + segm = anno.get("segmentation", None) + if segm: # either list[list[float]] or dict(RLE) + if isinstance(segm, dict): + if isinstance(segm["counts"], list): + # convert to compressed RLE + segm = mask_util.frPyObjects(segm, *segm["size"]) + else: + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + num_instances_without_valid_segmentation += 1 + continue # ignore this instance + obj["segmentation"] = segm + + keypts = anno.get("keypoints", None) + if keypts: # list[int] + for idx, v in enumerate(keypts): + if idx % 3 != 2: + # COCO's segmentation coordinates are floating points in [0, H or W], + # but keypoint coordinates are integers in [0, H-1 or W-1] + # Therefore we assume the coordinates are "pixel indices" and + # add 0.5 to convert to floating point coordinates. + keypts[idx] = v + 0.5 + obj["keypoints"] = keypts + + obj["bbox_mode"] = BoxMode.XYWH_ABS + if id_map: + annotation_category_id = obj["category_id"] + try: + obj["category_id"] = id_map[annotation_category_id] + except KeyError as e: + raise KeyError( + f"Encountered category_id={annotation_category_id} " + "but this id does not exist in 'categories' of the json file." + ) from e + objs.append(obj) + record["annotations"] = objs + dataset_dicts.append(record) + + if num_instances_without_valid_segmentation > 0: + logger.warning( + "Filtered out {} instances without valid segmentation. ".format( + num_instances_without_valid_segmentation + ) + + "There might be issues in your dataset generation process. Please " + "check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully" + ) + return dataset_dicts + + +def load_sem_seg(gt_root, image_root, gt_ext="png", image_ext="jpg"): + """ + Load semantic segmentation datasets. All files under "gt_root" with "gt_ext" extension are + treated as ground truth annotations and all files under "image_root" with "image_ext" extension + as input images. Ground truth and input images are matched using file paths relative to + "gt_root" and "image_root" respectively without taking into account file extensions. + This works for COCO as well as some other datasets. + + Args: + gt_root (str): full path to ground truth semantic segmentation files. Semantic segmentation + annotations are stored as images with integer values in pixels that represent + corresponding semantic labels. + image_root (str): the directory where the input images are. + gt_ext (str): file extension for ground truth annotations. + image_ext (str): file extension for input images. + + Returns: + list[dict]: + a list of dicts in detectron2 standard format without instance-level + annotation. + + Notes: + 1. This function does not read the image and ground truth files. + The results do not have the "image" and "sem_seg" fields. + """ + + # We match input images with ground truth based on their relative filepaths (without file + # extensions) starting from 'image_root' and 'gt_root' respectively. + def file2id(folder_path, file_path): + # extract relative path starting from `folder_path` + image_id = os.path.normpath(os.path.relpath(file_path, start=folder_path)) + # remove file extension + image_id = os.path.splitext(image_id)[0] + return image_id + + input_files = sorted( + (os.path.join(image_root, f) for f in PathManager.ls(image_root) if f.endswith(image_ext)), + key=lambda file_path: file2id(image_root, file_path), + ) + gt_files = sorted( + (os.path.join(gt_root, f) for f in PathManager.ls(gt_root) if f.endswith(gt_ext)), + key=lambda file_path: file2id(gt_root, file_path), + ) + + assert len(gt_files) > 0, "No annotations found in {}.".format(gt_root) + + # Use the intersection, so that val2017_100 annotations can run smoothly with val2017 images + if len(input_files) != len(gt_files): + logger.warn( + "Directory {} and {} has {} and {} files, respectively.".format( + image_root, gt_root, len(input_files), len(gt_files) + ) + ) + input_basenames = [os.path.basename(f)[: -len(image_ext)] for f in input_files] + gt_basenames = [os.path.basename(f)[: -len(gt_ext)] for f in gt_files] + intersect = list(set(input_basenames) & set(gt_basenames)) + # sort, otherwise each worker may obtain a list[dict] in different order + intersect = sorted(intersect) + logger.warn("Will use their intersection of {} files.".format(len(intersect))) + input_files = [os.path.join(image_root, f + image_ext) for f in intersect] + gt_files = [os.path.join(gt_root, f + gt_ext) for f in intersect] + + logger.info( + "Loaded {} images with semantic segmentation from {}".format(len(input_files), image_root) + ) + + dataset_dicts = [] + for (img_path, gt_path) in zip(input_files, gt_files): + record = {} + record["file_name"] = img_path + record["sem_seg_file_name"] = gt_path + dataset_dicts.append(record) + + return dataset_dicts + + +def convert_to_coco_dict(dataset_name): + """ + Convert an instance detection/segmentation or keypoint detection dataset + in detectron2's standard format into COCO json format. + + Generic dataset description can be found here: + https://detectron2.readthedocs.io/tutorials/datasets.html#register-a-dataset + + COCO data format description can be found here: + http://cocodataset.org/#format-data + + Args: + dataset_name (str): + name of the source dataset + Must be registered in DatastCatalog and in detectron2's standard format. + Must have corresponding metadata "thing_classes" + Returns: + coco_dict: serializable dict in COCO json format + """ + + dataset_dicts = DatasetCatalog.get(dataset_name) + metadata = MetadataCatalog.get(dataset_name) + + # unmap the category mapping ids for COCO + if hasattr(metadata, "thing_dataset_id_to_contiguous_id"): + reverse_id_mapping = {v: k for k, v in metadata.thing_dataset_id_to_contiguous_id.items()} + reverse_id_mapper = lambda contiguous_id: reverse_id_mapping[contiguous_id] # noqa + else: + reverse_id_mapper = lambda contiguous_id: contiguous_id # noqa + + categories = [ + {"id": reverse_id_mapper(id), "name": name} + for id, name in enumerate(metadata.thing_classes) + ] + + logger.info("Converting dataset dicts into COCO format") + coco_images = [] + coco_annotations = [] + + for image_id, image_dict in enumerate(dataset_dicts): + coco_image = { + "id": image_dict.get("image_id", image_id), + "width": int(image_dict["width"]), + "height": int(image_dict["height"]), + "file_name": str(image_dict["file_name"]), + } + coco_images.append(coco_image) + + anns_per_image = image_dict.get("annotations", []) + for annotation in anns_per_image: + # create a new dict with only COCO fields + coco_annotation = {} + + # COCO requirement: XYWH box format for axis-align and XYWHA for rotated + bbox = annotation["bbox"] + if isinstance(bbox, np.ndarray): + if bbox.ndim != 1: + raise ValueError(f"bbox has to be 1-dimensional. Got shape={bbox.shape}.") + bbox = bbox.tolist() + if len(bbox) not in [4, 5]: + raise ValueError(f"bbox has to has length 4 or 5. Got {bbox}.") + from_bbox_mode = annotation["bbox_mode"] + to_bbox_mode = BoxMode.XYWH_ABS if len(bbox) == 4 else BoxMode.XYWHA_ABS + bbox = BoxMode.convert(bbox, from_bbox_mode, to_bbox_mode) + + # COCO requirement: instance area + if "segmentation" in annotation: + # Computing areas for instances by counting the pixels + segmentation = annotation["segmentation"] + # TODO: check segmentation type: RLE, BinaryMask or Polygon + if isinstance(segmentation, list): + polygons = PolygonMasks([segmentation]) + area = polygons.area()[0].item() + elif isinstance(segmentation, dict): # RLE + area = mask_util.area(segmentation).item() + else: + raise TypeError(f"Unknown segmentation type {type(segmentation)}!") + else: + # Computing areas using bounding boxes + if to_bbox_mode == BoxMode.XYWH_ABS: + bbox_xy = BoxMode.convert(bbox, to_bbox_mode, BoxMode.XYXY_ABS) + area = Boxes([bbox_xy]).area()[0].item() + else: + area = RotatedBoxes([bbox]).area()[0].item() + + if "keypoints" in annotation: + keypoints = annotation["keypoints"] # list[int] + for idx, v in enumerate(keypoints): + if idx % 3 != 2: + # COCO's segmentation coordinates are floating points in [0, H or W], + # but keypoint coordinates are integers in [0, H-1 or W-1] + # For COCO format consistency we substract 0.5 + # https://github.com/facebookresearch/detectron2/pull/175#issuecomment-551202163 + keypoints[idx] = v - 0.5 + if "num_keypoints" in annotation: + num_keypoints = annotation["num_keypoints"] + else: + num_keypoints = sum(kp > 0 for kp in keypoints[2::3]) + + # COCO requirement: + # linking annotations to images + # "id" field must start with 1 + coco_annotation["id"] = len(coco_annotations) + 1 + coco_annotation["image_id"] = coco_image["id"] + coco_annotation["bbox"] = [round(float(x), 3) for x in bbox] + coco_annotation["area"] = float(area) + coco_annotation["iscrowd"] = int(annotation.get("iscrowd", 0)) + coco_annotation["category_id"] = int(reverse_id_mapper(annotation["category_id"])) + + # Add optional fields + if "keypoints" in annotation: + coco_annotation["keypoints"] = keypoints + coco_annotation["num_keypoints"] = num_keypoints + + if "segmentation" in annotation: + seg = coco_annotation["segmentation"] = annotation["segmentation"] + if isinstance(seg, dict): # RLE + counts = seg["counts"] + if not isinstance(counts, str): + # make it json-serializable + seg["counts"] = counts.decode("ascii") + + coco_annotations.append(coco_annotation) + + logger.info( + "Conversion finished, " + f"#images: {len(coco_images)}, #annotations: {len(coco_annotations)}" + ) + + info = { + "date_created": str(datetime.datetime.now()), + "description": "Automatically generated COCO json file for Detectron2.", + } + coco_dict = {"info": info, "images": coco_images, "categories": categories, "licenses": None} + if len(coco_annotations) > 0: + coco_dict["annotations"] = coco_annotations + return coco_dict + + +def convert_to_coco_json(dataset_name, output_file, allow_cached=True): + """ + Converts dataset into COCO format and saves it to a json file. + dataset_name must be registered in DatasetCatalog and in detectron2's standard format. + + Args: + dataset_name: + reference from the config file to the catalogs + must be registered in DatasetCatalog and in detectron2's standard format + output_file: path of json file that will be saved to + allow_cached: if json file is already present then skip conversion + """ + + # TODO: The dataset or the conversion script *may* change, + # a checksum would be useful for validating the cached data + + PathManager.mkdirs(os.path.dirname(output_file)) + with file_lock(output_file): + if PathManager.exists(output_file) and allow_cached: + logger.warning( + f"Using previously cached COCO format annotations at '{output_file}'. " + "You need to clear the cache file if your dataset has been modified." + ) + else: + logger.info(f"Converting annotations of dataset '{dataset_name}' to COCO format ...)") + coco_dict = convert_to_coco_dict(dataset_name) + + logger.info(f"Caching COCO format annotations at '{output_file}' ...") + tmp_file = output_file + ".tmp" + with PathManager.open(tmp_file, "w") as f: + json.dump(coco_dict, f) + shutil.move(tmp_file, output_file) + + +def register_coco_instances(name, metadata, json_file, image_root): + """ + Register a dataset in COCO's json annotation format for + instance detection, instance segmentation and keypoint detection. + (i.e., Type 1 and 2 in http://cocodataset.org/#format-data. + `instances*.json` and `person_keypoints*.json` in the dataset). + + This is an example of how to register a new dataset. + You can do something similar to this function, to register new datasets. + + Args: + name (str): the name that identifies a dataset, e.g. "coco_2014_train". + metadata (dict): extra metadata associated with this dataset. You can + leave it as an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str or path-like): directory which contains all the images. + """ + assert isinstance(name, str), name + assert isinstance(json_file, (str, os.PathLike)), json_file + assert isinstance(image_root, (str, os.PathLike)), image_root + # 1. register a function which returns dicts + DatasetCatalog.register(name, lambda: load_coco_json(json_file, image_root, name)) + + # 2. Optionally, add metadata about this dataset, + # since they might be useful in evaluation, visualization or logging + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="coco", **metadata + ) + + +if __name__ == "__main__": + """ + Test the COCO json dataset loader. + + Usage: + python -m detectron2.data.datasets.coco \ + path/to/json path/to/image_root dataset_name + + "dataset_name" can be "coco_2014_minival_100", or other + pre-registered ones + """ + from detectron2.utils.logger import setup_logger + from detectron2.utils.visualizer import Visualizer + import detectron2.data.datasets # noqa # add pre-defined metadata + import sys + + logger = setup_logger(name=__name__) + assert sys.argv[3] in DatasetCatalog.list() + meta = MetadataCatalog.get(sys.argv[3]) + + dicts = load_coco_json(sys.argv[1], sys.argv[2], sys.argv[3]) + logger.info("Done loading {} samples.".format(len(dicts))) + + dirname = "coco-data-vis" + os.makedirs(dirname, exist_ok=True) + for d in dicts: + img = np.array(Image.open(d["file_name"])) + visualizer = Visualizer(img, metadata=meta) + vis = visualizer.draw_dataset_dict(d) + fpath = os.path.join(dirname, os.path.basename(d["file_name"])) + vis.save(fpath) diff --git a/cutler/data/detection_utils.py b/cutler/data/detection_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..19f4c8996514470b3af372a68c10f29da7c6d980 --- /dev/null +++ b/cutler/data/detection_utils.py @@ -0,0 +1,650 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/detection_utils.py + +""" +Common data processing utilities that are used in a +typical object detection data pipeline. +""" +import logging +import numpy as np +from typing import List, Union +import pycocotools.mask as mask_util +import torch +from PIL import Image + +from detectron2.structures import ( + Boxes, + BoxMode, + BitMasks, + Instances, + Keypoints, + PolygonMasks, + RotatedBoxes, + polygons_to_bitmask, +) + +from detectron2.utils.file_io import PathManager + +from data import transforms as T +from detectron2.data.catalog import MetadataCatalog + +__all__ = [ + "SizeMismatchError", + "convert_image_to_rgb", + "check_image_size", + "transform_proposals", + "transform_instance_annotations", + "annotations_to_instances", + "annotations_to_instances_rotated", + "build_augmentation", + "build_transform_gen", + "create_keypoint_hflip_indices", + "filter_empty_instances", + "read_image", +] + + +class SizeMismatchError(ValueError): + """ + When loaded image has difference width/height compared with annotation. + """ + + +# https://en.wikipedia.org/wiki/YUV#SDTV_with_BT.601 +_M_RGB2YUV = [[0.299, 0.587, 0.114], [-0.14713, -0.28886, 0.436], [0.615, -0.51499, -0.10001]] +_M_YUV2RGB = [[1.0, 0.0, 1.13983], [1.0, -0.39465, -0.58060], [1.0, 2.03211, 0.0]] + +# https://www.exiv2.org/tags.html +_EXIF_ORIENT = 274 # exif 'Orientation' tag + + +def convert_PIL_to_numpy(image, format): + """ + Convert PIL image to numpy array of target format. + + Args: + image (PIL.Image): a PIL image + format (str): the format of output image + + Returns: + (np.ndarray): also see `read_image` + """ + if format is not None: + # PIL only supports RGB, so convert to RGB and flip channels over below + conversion_format = format + if format in ["BGR", "YUV-BT.601"]: + conversion_format = "RGB" + image = image.convert(conversion_format) + image = np.asarray(image) + # PIL squeezes out the channel dimension for "L", so make it HWC + if format == "L": + image = np.expand_dims(image, -1) + + # handle formats not supported by PIL + elif format == "BGR": + # flip channels if needed + image = image[:, :, ::-1] + elif format == "YUV-BT.601": + image = image / 255.0 + image = np.dot(image, np.array(_M_RGB2YUV).T) + + return image + + +def convert_image_to_rgb(image, format): + """ + Convert an image from given format to RGB. + + Args: + image (np.ndarray or Tensor): an HWC image + format (str): the format of input image, also see `read_image` + + Returns: + (np.ndarray): (H,W,3) RGB image in 0-255 range, can be either float or uint8 + """ + if isinstance(image, torch.Tensor): + image = image.cpu().numpy() + if format == "BGR": + image = image[:, :, [2, 1, 0]] + elif format == "YUV-BT.601": + image = np.dot(image, np.array(_M_YUV2RGB).T) + image = image * 255.0 + else: + if format == "L": + image = image[:, :, 0] + image = image.astype(np.uint8) + image = np.asarray(Image.fromarray(image, mode=format).convert("RGB")) + return image + + +def _apply_exif_orientation(image): + """ + Applies the exif orientation correctly. + + This code exists per the bug: + https://github.com/python-pillow/Pillow/issues/3973 + with the function `ImageOps.exif_transpose`. The Pillow source raises errors with + various methods, especially `tobytes` + + Function based on: + https://github.com/wkentaro/labelme/blob/v4.5.4/labelme/utils/image.py#L59 + https://github.com/python-pillow/Pillow/blob/7.1.2/src/PIL/ImageOps.py#L527 + + Args: + image (PIL.Image): a PIL image + + Returns: + (PIL.Image): the PIL image with exif orientation applied, if applicable + """ + if not hasattr(image, "getexif"): + return image + + try: + exif = image.getexif() + except Exception: # https://github.com/facebookresearch/detectron2/issues/1885 + exif = None + + if exif is None: + return image + + orientation = exif.get(_EXIF_ORIENT) + + method = { + 2: Image.FLIP_LEFT_RIGHT, + 3: Image.ROTATE_180, + 4: Image.FLIP_TOP_BOTTOM, + 5: Image.TRANSPOSE, + 6: Image.ROTATE_270, + 7: Image.TRANSVERSE, + 8: Image.ROTATE_90, + }.get(orientation) + + if method is not None: + return image.transpose(method) + return image + + +def read_image(file_name, format=None): + """ + Read an image into the given format. + Will apply rotation and flipping if the image has such exif information. + + Args: + file_name (str): image file path + format (str): one of the supported image modes in PIL, or "BGR" or "YUV-BT.601". + + Returns: + image (np.ndarray): + an HWC image in the given format, which is 0-255, uint8 for + supported image modes in PIL or "BGR"; float (0-1 for Y) for YUV-BT.601. + """ + with PathManager.open(file_name, "rb") as f: + image = Image.open(f) + + # work around this bug: https://github.com/python-pillow/Pillow/issues/3973 + image = _apply_exif_orientation(image) + return convert_PIL_to_numpy(image, format) + + +def check_image_size(dataset_dict, image): + """ + Raise an error if the image does not match the size specified in the dict. + """ + if "width" in dataset_dict or "height" in dataset_dict: + image_wh = (image.shape[1], image.shape[0]) + expected_wh = (dataset_dict["width"], dataset_dict["height"]) + if not image_wh == expected_wh: + expected_wh = (dataset_dict["height"], dataset_dict["width"]) + dataset_dict["height"], dataset_dict["width"] = dataset_dict["width"], dataset_dict["height"] + if image_wh != expected_wh: + raise SizeMismatchError( + "Mismatched image shape{}, got {}, expect {}.".format( + " for image " + dataset_dict["file_name"] + if "file_name" in dataset_dict + else "", + image_wh, + expected_wh, + ) + + " Please check the width/height in your annotation." + ) + + # To ensure bbox always remap to original image size + if "width" not in dataset_dict: + dataset_dict["width"] = image.shape[1] + if "height" not in dataset_dict: + dataset_dict["height"] = image.shape[0] + + +def transform_proposals(dataset_dict, image_shape, transforms, *, proposal_topk, min_box_size=0): + """ + Apply transformations to the proposals in dataset_dict, if any. + + Args: + dataset_dict (dict): a dict read from the dataset, possibly + contains fields "proposal_boxes", "proposal_objectness_logits", "proposal_bbox_mode" + image_shape (tuple): height, width + transforms (TransformList): + proposal_topk (int): only keep top-K scoring proposals + min_box_size (int): proposals with either side smaller than this + threshold are removed + + The input dict is modified in-place, with abovementioned keys removed. A new + key "proposals" will be added. Its value is an `Instances` + object which contains the transformed proposals in its field + "proposal_boxes" and "objectness_logits". + """ + if "proposal_boxes" in dataset_dict: + # Transform proposal boxes + boxes = transforms.apply_box( + BoxMode.convert( + dataset_dict.pop("proposal_boxes"), + dataset_dict.pop("proposal_bbox_mode"), + BoxMode.XYXY_ABS, + ) + ) + boxes = Boxes(boxes) + objectness_logits = torch.as_tensor( + dataset_dict.pop("proposal_objectness_logits").astype("float32") + ) + + boxes.clip(image_shape) + keep = boxes.nonempty(threshold=min_box_size) + boxes = boxes[keep] + objectness_logits = objectness_logits[keep] + + proposals = Instances(image_shape) + proposals.proposal_boxes = boxes[:proposal_topk] + proposals.objectness_logits = objectness_logits[:proposal_topk] + dataset_dict["proposals"] = proposals + + +def transform_instance_annotations( + annotation, transforms, image_size, *, keypoint_hflip_indices=None +): + """ + Apply transforms to box, segmentation and keypoints annotations of a single instance. + + It will use `transforms.apply_box` for the box, and + `transforms.apply_coords` for segmentation polygons & keypoints. + If you need anything more specially designed for each data structure, + you'll need to implement your own version of this function or the transforms. + + Args: + annotation (dict): dict of instance annotations for a single instance. + It will be modified in-place. + transforms (TransformList or list[Transform]): + image_size (tuple): the height, width of the transformed image + keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`. + + Returns: + dict: + the same input dict with fields "bbox", "segmentation", "keypoints" + transformed according to `transforms`. + The "bbox_mode" field will be set to XYXY_ABS. + """ + if isinstance(transforms, (tuple, list)): + transforms = T.TransformList(transforms) + # bbox is 1d (per-instance bounding box) + bbox = BoxMode.convert(annotation["bbox"], annotation["bbox_mode"], BoxMode.XYXY_ABS) + # clip transformed bbox to image size + bbox = transforms.apply_box(np.array([bbox]))[0].clip(min=0) + annotation["bbox"] = np.minimum(bbox, list(image_size + image_size)[::-1]) + annotation["bbox_mode"] = BoxMode.XYXY_ABS + + if "segmentation" in annotation: + # each instance contains 1 or more polygons + segm = annotation["segmentation"] + if isinstance(segm, list): + # polygons + polygons = [np.asarray(p).reshape(-1, 2) for p in segm] + annotation["segmentation"] = [ + p.reshape(-1) for p in transforms.apply_polygons(polygons) + ] + elif isinstance(segm, dict): + # RLE + mask = mask_util.decode(segm) + mask = transforms.apply_segmentation(mask) + assert tuple(mask.shape[:2]) == image_size + annotation["segmentation"] = mask + else: + raise ValueError( + "Cannot transform segmentation of type '{}'!" + "Supported types are: polygons as list[list[float] or ndarray]," + " COCO-style RLE as a dict.".format(type(segm)) + ) + + if "keypoints" in annotation: + keypoints = transform_keypoint_annotations( + annotation["keypoints"], transforms, image_size, keypoint_hflip_indices + ) + annotation["keypoints"] = keypoints + + return annotation + + +def transform_keypoint_annotations(keypoints, transforms, image_size, keypoint_hflip_indices=None): + """ + Transform keypoint annotations of an image. + If a keypoint is transformed out of image boundary, it will be marked "unlabeled" (visibility=0) + + Args: + keypoints (list[float]): Nx3 float in Detectron2's Dataset format. + Each point is represented by (x, y, visibility). + transforms (TransformList): + image_size (tuple): the height, width of the transformed image + keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`. + When `transforms` includes horizontal flip, will use the index + mapping to flip keypoints. + """ + # (N*3,) -> (N, 3) + keypoints = np.asarray(keypoints, dtype="float64").reshape(-1, 3) + keypoints_xy = transforms.apply_coords(keypoints[:, :2]) + + # Set all out-of-boundary points to "unlabeled" + inside = (keypoints_xy >= np.array([0, 0])) & (keypoints_xy <= np.array(image_size[::-1])) + inside = inside.all(axis=1) + keypoints[:, :2] = keypoints_xy + keypoints[:, 2][~inside] = 0 + + # This assumes that HorizFlipTransform is the only one that does flip + do_hflip = sum(isinstance(t, T.HFlipTransform) for t in transforms.transforms) % 2 == 1 + + # Alternative way: check if probe points was horizontally flipped. + # probe = np.asarray([[0.0, 0.0], [image_width, 0.0]]) + # probe_aug = transforms.apply_coords(probe.copy()) + # do_hflip = np.sign(probe[1][0] - probe[0][0]) != np.sign(probe_aug[1][0] - probe_aug[0][0]) # noqa + + # If flipped, swap each keypoint with its opposite-handed equivalent + if do_hflip: + if keypoint_hflip_indices is None: + raise ValueError("Cannot flip keypoints without providing flip indices!") + if len(keypoints) != len(keypoint_hflip_indices): + raise ValueError( + "Keypoint data has {} points, but metadata " + "contains {} points!".format(len(keypoints), len(keypoint_hflip_indices)) + ) + keypoints = keypoints[np.asarray(keypoint_hflip_indices, dtype=np.int32), :] + + # Maintain COCO convention that if visibility == 0 (unlabeled), then x, y = 0 + keypoints[keypoints[:, 2] == 0] = 0 + return keypoints + + +def annotations_to_instances(annos, image_size, mask_format="polygon"): + """ + Create an :class:`Instances` object used by the models, + from instance annotations in the dataset dict. + + Args: + annos (list[dict]): a list of instance annotations in one image, each + element for one instance. + image_size (tuple): height, width + + Returns: + Instances: + It will contain fields "gt_boxes", "gt_classes", + "gt_masks", "gt_keypoints", if they can be obtained from `annos`. + This is the format that builtin models expect. + """ + boxes = ( + np.stack( + [BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS) for obj in annos] + ) + if len(annos) + else np.zeros((0, 4)) + ) + target = Instances(image_size) + target.gt_boxes = Boxes(boxes) + + classes = [int(obj["category_id"]) for obj in annos] + classes = torch.tensor(classes, dtype=torch.int64) + target.gt_classes = classes + + if len(annos) and "segmentation" in annos[0]: + segms = [obj["segmentation"] for obj in annos] + if mask_format == "polygon": + try: + masks = PolygonMasks(segms) + except ValueError as e: + raise ValueError( + "Failed to use mask_format=='polygon' from the given annotations!" + ) from e + else: + assert mask_format == "bitmask", mask_format + masks = [] + for segm in segms: + if isinstance(segm, list): + # polygon + masks.append(polygons_to_bitmask(segm, *image_size)) + elif isinstance(segm, dict): + # COCO RLE + masks.append(mask_util.decode(segm)) + elif isinstance(segm, np.ndarray): + assert segm.ndim == 2, "Expect segmentation of 2 dimensions, got {}.".format( + segm.ndim + ) + # mask array + masks.append(segm) + else: + raise ValueError( + "Cannot convert segmentation of type '{}' to BitMasks!" + "Supported types are: polygons as list[list[float] or ndarray]," + " COCO-style RLE as a dict, or a binary segmentation mask " + " in a 2D numpy array of shape HxW.".format(type(segm)) + ) + # torch.from_numpy does not support array with negative stride. + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x)) for x in masks]) + ) + target.gt_masks = masks + + if len(annos) and "keypoints" in annos[0]: + kpts = [obj.get("keypoints", []) for obj in annos] + target.gt_keypoints = Keypoints(kpts) + + return target + + +def annotations_to_instances_rotated(annos, image_size): + """ + Create an :class:`Instances` object used by the models, + from instance annotations in the dataset dict. + Compared to `annotations_to_instances`, this function is for rotated boxes only + + Args: + annos (list[dict]): a list of instance annotations in one image, each + element for one instance. + image_size (tuple): height, width + + Returns: + Instances: + Containing fields "gt_boxes", "gt_classes", + if they can be obtained from `annos`. + This is the format that builtin models expect. + """ + boxes = [obj["bbox"] for obj in annos] + target = Instances(image_size) + boxes = target.gt_boxes = RotatedBoxes(boxes) + boxes.clip(image_size) + + classes = [obj["category_id"] for obj in annos] + classes = torch.tensor(classes, dtype=torch.int64) + target.gt_classes = classes + + return target + + +def filter_empty_instances( + instances, by_box=True, by_mask=True, box_threshold=1e-5, return_mask=False +): + """ + Filter out empty instances in an `Instances` object. + + Args: + instances (Instances): + by_box (bool): whether to filter out instances with empty boxes + by_mask (bool): whether to filter out instances with empty masks + box_threshold (float): minimum width and height to be considered non-empty + return_mask (bool): whether to return boolean mask of filtered instances + + Returns: + Instances: the filtered instances. + tensor[bool], optional: boolean mask of filtered instances + """ + assert by_box or by_mask + r = [] + if by_box: + r.append(instances.gt_boxes.nonempty(threshold=box_threshold)) + if instances.has("gt_masks") and by_mask: + r.append(instances.gt_masks.nonempty()) + + # TODO: can also filter visible keypoints + + if not r: + return instances + m = r[0] + for x in r[1:]: + m = m & x + if return_mask: + return instances[m], m + return instances[m] + + +def create_keypoint_hflip_indices(dataset_names: Union[str, List[str]]) -> List[int]: + """ + Args: + dataset_names: list of dataset names + + Returns: + list[int]: a list of size=#keypoints, storing the + horizontally-flipped keypoint indices. + """ + if isinstance(dataset_names, str): + dataset_names = [dataset_names] + + check_metadata_consistency("keypoint_names", dataset_names) + check_metadata_consistency("keypoint_flip_map", dataset_names) + + meta = MetadataCatalog.get(dataset_names[0]) + names = meta.keypoint_names + # TODO flip -> hflip + flip_map = dict(meta.keypoint_flip_map) + flip_map.update({v: k for k, v in flip_map.items()}) + flipped_names = [i if i not in flip_map else flip_map[i] for i in names] + flip_indices = [names.index(i) for i in flipped_names] + return flip_indices + + +def get_fed_loss_cls_weights(dataset_names: Union[str, List[str]], freq_weight_power=1.0): + """ + Get frequency weight for each class sorted by class id. + We now calcualte freqency weight using image_count to the power freq_weight_power. + + Args: + dataset_names: list of dataset names + freq_weight_power: power value + """ + if isinstance(dataset_names, str): + dataset_names = [dataset_names] + + check_metadata_consistency("class_image_count", dataset_names) + + meta = MetadataCatalog.get(dataset_names[0]) + class_freq_meta = meta.class_image_count + class_freq = torch.tensor( + [c["image_count"] for c in sorted(class_freq_meta, key=lambda x: x["id"])] + ) + class_freq_weight = class_freq.float() ** freq_weight_power + return class_freq_weight + + +def gen_crop_transform_with_instance(crop_size, image_size, instance): + """ + Generate a CropTransform so that the cropping region contains + the center of the given instance. + + Args: + crop_size (tuple): h, w in pixels + image_size (tuple): h, w + instance (dict): an annotation dict of one instance, in Detectron2's + dataset format. + """ + crop_size = np.asarray(crop_size, dtype=np.int32) + bbox = BoxMode.convert(instance["bbox"], instance["bbox_mode"], BoxMode.XYXY_ABS) + center_yx = (bbox[1] + bbox[3]) * 0.5, (bbox[0] + bbox[2]) * 0.5 + assert ( + image_size[0] >= center_yx[0] and image_size[1] >= center_yx[1] + ), "The annotation bounding box is outside of the image!" + assert ( + image_size[0] >= crop_size[0] and image_size[1] >= crop_size[1] + ), "Crop size is larger than image size!" + + min_yx = np.maximum(np.floor(center_yx).astype(np.int32) - crop_size, 0) + max_yx = np.maximum(np.asarray(image_size, dtype=np.int32) - crop_size, 0) + max_yx = np.minimum(max_yx, np.ceil(center_yx).astype(np.int32)) + + y0 = np.random.randint(min_yx[0], max_yx[0] + 1) + x0 = np.random.randint(min_yx[1], max_yx[1] + 1) + return T.CropTransform(x0, y0, crop_size[1], crop_size[0]) + + +def check_metadata_consistency(key, dataset_names): + """ + Check that the datasets have consistent metadata. + + Args: + key (str): a metadata key + dataset_names (list[str]): a list of dataset names + + Raises: + AttributeError: if the key does not exist in the metadata + ValueError: if the given datasets do not have the same metadata values defined by key + """ + if len(dataset_names) == 0: + return + logger = logging.getLogger(__name__) + entries_per_dataset = [getattr(MetadataCatalog.get(d), key) for d in dataset_names] + for idx, entry in enumerate(entries_per_dataset): + if entry != entries_per_dataset[0]: + logger.error( + "Metadata '{}' for dataset '{}' is '{}'".format(key, dataset_names[idx], str(entry)) + ) + logger.error( + "Metadata '{}' for dataset '{}' is '{}'".format( + key, dataset_names[0], str(entries_per_dataset[0]) + ) + ) + raise ValueError("Datasets have different metadata '{}'!".format(key)) + + +def build_augmentation(cfg, is_train): + """ + Create a list of default :class:`Augmentation` from config. + Now it includes resizing and flipping. + + Returns: + list[Augmentation] + """ + if is_train: + min_size = cfg.INPUT.MIN_SIZE_TRAIN + max_size = cfg.INPUT.MAX_SIZE_TRAIN + sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING + else: + min_size = cfg.INPUT.MIN_SIZE_TEST + max_size = cfg.INPUT.MAX_SIZE_TEST + sample_style = "choice" + augmentation = [T.ResizeShortestEdge(min_size, max_size, sample_style)] + if is_train and cfg.INPUT.RANDOM_FLIP != "none": + augmentation.append( + T.RandomFlip( + horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal", + vertical=cfg.INPUT.RANDOM_FLIP == "vertical", + ) + ) + return augmentation + + +build_transform_gen = build_augmentation +""" +Alias for backward-compatibility. +""" diff --git a/cutler/data/transforms/__init__.py b/cutler/data/transforms/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..168241d5faa88ed97124a961b948f51e5be61ab8 --- /dev/null +++ b/cutler/data/transforms/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/transforms/__init__.py + +from fvcore.transforms.transform import * +from .transform import * +from detectron2.data.transforms.augmentation import * +from .augmentation_impl import * + +__all__ = [k for k in globals().keys() if not k.startswith("_")] + + +from detectron2.utils.env import fixup_module_metadata + +fixup_module_metadata(__name__, globals(), __all__) +del fixup_module_metadata \ No newline at end of file diff --git a/cutler/data/transforms/__pycache__/__init__.cpython-312.pyc b/cutler/data/transforms/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 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file mode 100644 index 0000000000000000000000000000000000000000..b62f918c510805902313e73828cbcb6dab84dbcd --- /dev/null +++ b/cutler/data/transforms/augmentation_impl.py @@ -0,0 +1,616 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/transforms/augmentation_impl.py + +""" +Implement many useful :class:`Augmentation`. +""" +import numpy as np +import sys +from typing import Tuple +import torch +from fvcore.transforms.transform import ( + BlendTransform, + CropTransform, + HFlipTransform, + NoOpTransform, + PadTransform, + Transform, + TransformList, + VFlipTransform, +) +from PIL import Image + +from detectron2.data.transforms.augmentation import Augmentation, _transform_to_aug +from .transform import ExtentTransform, ResizeTransform, RotationTransform + +__all__ = [ + "FixedSizeCrop", + "RandomApply", + "RandomBrightness", + "RandomContrast", + "RandomCrop", + "RandomExtent", + "RandomFlip", + "RandomSaturation", + "RandomLighting", + "RandomRotation", + "Resize", + "ResizeScale", + "ResizeShortestEdge", + "RandomCrop_CategoryAreaConstraint", +] + + +class RandomApply(Augmentation): + """ + Randomly apply an augmentation with a given probability. + """ + + def __init__(self, tfm_or_aug, prob=0.5): + """ + Args: + tfm_or_aug (Transform, Augmentation): the transform or augmentation + to be applied. It can either be a `Transform` or `Augmentation` + instance. + prob (float): probability between 0.0 and 1.0 that + the wrapper transformation is applied + """ + super().__init__() + self.aug = _transform_to_aug(tfm_or_aug) + assert 0.0 <= prob <= 1.0, f"Probablity must be between 0.0 and 1.0 (given: {prob})" + self.prob = prob + + def get_transform(self, *args): + do = self._rand_range() < self.prob + if do: + return self.aug.get_transform(*args) + else: + return NoOpTransform() + + def __call__(self, aug_input): + do = self._rand_range() < self.prob + if do: + return self.aug(aug_input) + else: + return NoOpTransform() + + +class RandomFlip(Augmentation): + """ + Flip the image horizontally or vertically with the given probability. + """ + + def __init__(self, prob=0.5, *, horizontal=True, vertical=False): + """ + Args: + prob (float): probability of flip. + horizontal (boolean): whether to apply horizontal flipping + vertical (boolean): whether to apply vertical flipping + """ + super().__init__() + + if horizontal and vertical: + raise ValueError("Cannot do both horiz and vert. Please use two Flip instead.") + if not horizontal and not vertical: + raise ValueError("At least one of horiz or vert has to be True!") + self._init(locals()) + + def get_transform(self, image): + h, w = image.shape[:2] + do = self._rand_range() < self.prob + if do: + if self.horizontal: + return HFlipTransform(w) + elif self.vertical: + return VFlipTransform(h) + else: + return NoOpTransform() + + +class Resize(Augmentation): + """Resize image to a fixed target size""" + + def __init__(self, shape, interp=Image.BILINEAR): + """ + Args: + shape: (h, w) tuple or a int + interp: PIL interpolation method + """ + if isinstance(shape, int): + shape = (shape, shape) + shape = tuple(shape) + self._init(locals()) + + def get_transform(self, image): + return ResizeTransform( + image.shape[0], image.shape[1], self.shape[0], self.shape[1], self.interp + ) + + +class ResizeShortestEdge(Augmentation): + """ + Resize the image while keeping the aspect ratio unchanged. + It attempts to scale the shorter edge to the given `short_edge_length`, + as long as the longer edge does not exceed `max_size`. + If `max_size` is reached, then downscale so that the longer edge does not exceed max_size. + """ + + @torch.jit.unused + def __init__( + self, short_edge_length, max_size=sys.maxsize, sample_style="range", interp=Image.BILINEAR + ): + """ + Args: + short_edge_length (list[int]): If ``sample_style=="range"``, + a [min, max] interval from which to sample the shortest edge length. + If ``sample_style=="choice"``, a list of shortest edge lengths to sample from. + max_size (int): maximum allowed longest edge length. + sample_style (str): either "range" or "choice". + """ + super().__init__() + assert sample_style in ["range", "choice"], sample_style + + self.is_range = sample_style == "range" + if isinstance(short_edge_length, int): + short_edge_length = (short_edge_length, short_edge_length) + if self.is_range: + assert len(short_edge_length) == 2, ( + "short_edge_length must be two values using 'range' sample style." + f" Got {short_edge_length}!" + ) + self._init(locals()) + + @torch.jit.unused + def get_transform(self, image): + h, w = image.shape[:2] + if self.is_range: + size = np.random.randint(self.short_edge_length[0], self.short_edge_length[1] + 1) + else: + size = np.random.choice(self.short_edge_length) + if size == 0: + return NoOpTransform() + + newh, neww = ResizeShortestEdge.get_output_shape(h, w, size, self.max_size) + return ResizeTransform(h, w, newh, neww, self.interp) + + @staticmethod + def get_output_shape( + oldh: int, oldw: int, short_edge_length: int, max_size: int + ) -> Tuple[int, int]: + """ + Compute the output size given input size and target short edge length. + """ + h, w = oldh, oldw + size = short_edge_length * 1.0 + scale = size / min(h, w) + if h < w: + newh, neww = size, scale * w + else: + newh, neww = scale * h, size + if max(newh, neww) > max_size: + scale = max_size * 1.0 / max(newh, neww) + newh = newh * scale + neww = neww * scale + neww = int(neww + 0.5) + newh = int(newh + 0.5) + return (newh, neww) + + +class ResizeScale(Augmentation): + """ + Takes target size as input and randomly scales the given target size between `min_scale` + and `max_scale`. It then scales the input image such that it fits inside the scaled target + box, keeping the aspect ratio constant. + This implements the resize part of the Google's 'resize_and_crop' data augmentation: + https://github.com/tensorflow/tpu/blob/master/models/official/detection/utils/input_utils.py#L127 + """ + + def __init__( + self, + min_scale: float, + max_scale: float, + target_height: int, + target_width: int, + interp: int = Image.BILINEAR, + ): + """ + Args: + min_scale: minimum image scale range. + max_scale: maximum image scale range. + target_height: target image height. + target_width: target image width. + interp: image interpolation method. + """ + super().__init__() + self._init(locals()) + + def _get_resize(self, image: np.ndarray, scale: float) -> Transform: + input_size = image.shape[:2] + + # Compute new target size given a scale. + target_size = (self.target_height, self.target_width) + target_scale_size = np.multiply(target_size, scale) + + # Compute actual rescaling applied to input image and output size. + output_scale = np.minimum( + target_scale_size[0] / input_size[0], target_scale_size[1] / input_size[1] + ) + output_size = np.round(np.multiply(input_size, output_scale)).astype(int) + + return ResizeTransform( + input_size[0], input_size[1], output_size[0], output_size[1], self.interp + ) + + def get_transform(self, image: np.ndarray) -> Transform: + random_scale = np.random.uniform(self.min_scale, self.max_scale) + return self._get_resize(image, random_scale) + + +class RandomRotation(Augmentation): + """ + This method returns a copy of this image, rotated the given + number of degrees counter clockwise around the given center. + """ + + def __init__(self, angle, expand=True, center=None, sample_style="range", interp=None): + """ + Args: + angle (list[float]): If ``sample_style=="range"``, + a [min, max] interval from which to sample the angle (in degrees). + If ``sample_style=="choice"``, a list of angles to sample from + expand (bool): choose if the image should be resized to fit the whole + rotated image (default), or simply cropped + center (list[[float, float]]): If ``sample_style=="range"``, + a [[minx, miny], [maxx, maxy]] relative interval from which to sample the center, + [0, 0] being the top left of the image and [1, 1] the bottom right. + If ``sample_style=="choice"``, a list of centers to sample from + Default: None, which means that the center of rotation is the center of the image + center has no effect if expand=True because it only affects shifting + """ + super().__init__() + assert sample_style in ["range", "choice"], sample_style + self.is_range = sample_style == "range" + if isinstance(angle, (float, int)): + angle = (angle, angle) + if center is not None and isinstance(center[0], (float, int)): + center = (center, center) + self._init(locals()) + + def get_transform(self, image): + h, w = image.shape[:2] + center = None + if self.is_range: + angle = np.random.uniform(self.angle[0], self.angle[1]) + if self.center is not None: + center = ( + np.random.uniform(self.center[0][0], self.center[1][0]), + np.random.uniform(self.center[0][1], self.center[1][1]), + ) + else: + angle = np.random.choice(self.angle) + if self.center is not None: + center = np.random.choice(self.center) + + if center is not None: + center = (w * center[0], h * center[1]) # Convert to absolute coordinates + + if angle % 360 == 0: + return NoOpTransform() + + return RotationTransform(h, w, angle, expand=self.expand, center=center, interp=self.interp) + + +class FixedSizeCrop(Augmentation): + """ + If `crop_size` is smaller than the input image size, then it uses a random crop of + the crop size. If `crop_size` is larger than the input image size, then it pads + the right and the bottom of the image to the crop size if `pad` is True, otherwise + it returns the smaller image. + """ + + def __init__(self, crop_size: Tuple[int], pad: bool = True, pad_value: float = 128.0): + """ + Args: + crop_size: target image (height, width). + pad: if True, will pad images smaller than `crop_size` up to `crop_size` + pad_value: the padding value. + """ + super().__init__() + self._init(locals()) + + def _get_crop(self, image: np.ndarray) -> Transform: + # Compute the image scale and scaled size. + input_size = image.shape[:2] + output_size = self.crop_size + + # Add random crop if the image is scaled up. + max_offset = np.subtract(input_size, output_size) + max_offset = np.maximum(max_offset, 0) + offset = np.multiply(max_offset, np.random.uniform(0.0, 1.0)) + offset = np.round(offset).astype(int) + return CropTransform( + offset[1], offset[0], output_size[1], output_size[0], input_size[1], input_size[0] + ) + + def _get_pad(self, image: np.ndarray) -> Transform: + # Compute the image scale and scaled size. + input_size = image.shape[:2] + output_size = self.crop_size + + # Add padding if the image is scaled down. + pad_size = np.subtract(output_size, input_size) + pad_size = np.maximum(pad_size, 0) + original_size = np.minimum(input_size, output_size) + return PadTransform( + 0, 0, pad_size[1], pad_size[0], original_size[1], original_size[0], self.pad_value + ) + + def get_transform(self, image: np.ndarray) -> TransformList: + transforms = [self._get_crop(image)] + if self.pad: + transforms.append(self._get_pad(image)) + return TransformList(transforms) + + +class RandomCrop(Augmentation): + """ + Randomly crop a rectangle region out of an image. + """ + + def __init__(self, crop_type: str, crop_size): + """ + Args: + crop_type (str): one of "relative_range", "relative", "absolute", "absolute_range". + crop_size (tuple[float, float]): two floats, explained below. + + - "relative": crop a (H * crop_size[0], W * crop_size[1]) region from an input image of + size (H, W). crop size should be in (0, 1] + - "relative_range": uniformly sample two values from [crop_size[0], 1] + and [crop_size[1]], 1], and use them as in "relative" crop type. + - "absolute" crop a (crop_size[0], crop_size[1]) region from input image. + crop_size must be smaller than the input image size. + - "absolute_range", for an input of size (H, W), uniformly sample H_crop in + [crop_size[0], min(H, crop_size[1])] and W_crop in [crop_size[0], min(W, crop_size[1])]. + Then crop a region (H_crop, W_crop). + """ + # TODO style of relative_range and absolute_range are not consistent: + # one takes (h, w) but another takes (min, max) + super().__init__() + assert crop_type in ["relative_range", "relative", "absolute", "absolute_range"] + self._init(locals()) + + def get_transform(self, image): + h, w = image.shape[:2] + croph, cropw = self.get_crop_size((h, w)) + assert h >= croph and w >= cropw, "Shape computation in {} has bugs.".format(self) + h0 = np.random.randint(h - croph + 1) + w0 = np.random.randint(w - cropw + 1) + return CropTransform(w0, h0, cropw, croph) + + def get_crop_size(self, image_size): + """ + Args: + image_size (tuple): height, width + + Returns: + crop_size (tuple): height, width in absolute pixels + """ + h, w = image_size + if self.crop_type == "relative": + ch, cw = self.crop_size + return int(h * ch + 0.5), int(w * cw + 0.5) + elif self.crop_type == "relative_range": + crop_size = np.asarray(self.crop_size, dtype=np.float32) + ch, cw = crop_size + np.random.rand(2) * (1 - crop_size) + return int(h * ch + 0.5), int(w * cw + 0.5) + elif self.crop_type == "absolute": + return (min(self.crop_size[0], h), min(self.crop_size[1], w)) + elif self.crop_type == "absolute_range": + assert self.crop_size[0] <= self.crop_size[1] + ch = np.random.randint(min(h, self.crop_size[0]), min(h, self.crop_size[1]) + 1) + cw = np.random.randint(min(w, self.crop_size[0]), min(w, self.crop_size[1]) + 1) + return ch, cw + else: + raise NotImplementedError("Unknown crop type {}".format(self.crop_type)) + + +class RandomCrop_CategoryAreaConstraint(Augmentation): + """ + Similar to :class:`RandomCrop`, but find a cropping window such that no single category + occupies a ratio of more than `single_category_max_area` in semantic segmentation ground + truth, which can cause unstability in training. The function attempts to find such a valid + cropping window for at most 10 times. + """ + + def __init__( + self, + crop_type: str, + crop_size, + single_category_max_area: float = 1.0, + ignored_category: int = None, + ): + """ + Args: + crop_type, crop_size: same as in :class:`RandomCrop` + single_category_max_area: the maximum allowed area ratio of a + category. Set to 1.0 to disable + ignored_category: allow this category in the semantic segmentation + ground truth to exceed the area ratio. Usually set to the category + that's ignored in training. + """ + self.crop_aug = RandomCrop(crop_type, crop_size) + self._init(locals()) + + def get_transform(self, image, sem_seg): + if self.single_category_max_area >= 1.0: + return self.crop_aug.get_transform(image) + else: + h, w = sem_seg.shape + for _ in range(10): + crop_size = self.crop_aug.get_crop_size((h, w)) + y0 = np.random.randint(h - crop_size[0] + 1) + x0 = np.random.randint(w - crop_size[1] + 1) + sem_seg_temp = sem_seg[y0 : y0 + crop_size[0], x0 : x0 + crop_size[1]] + labels, cnt = np.unique(sem_seg_temp, return_counts=True) + if self.ignored_category is not None: + cnt = cnt[labels != self.ignored_category] + if len(cnt) > 1 and np.max(cnt) < np.sum(cnt) * self.single_category_max_area: + break + crop_tfm = CropTransform(x0, y0, crop_size[1], crop_size[0]) + return crop_tfm + + +class RandomExtent(Augmentation): + """ + Outputs an image by cropping a random "subrect" of the source image. + + The subrect can be parameterized to include pixels outside the source image, + in which case they will be set to zeros (i.e. black). The size of the output + image will vary with the size of the random subrect. + """ + + def __init__(self, scale_range, shift_range): + """ + Args: + output_size (h, w): Dimensions of output image + scale_range (l, h): Range of input-to-output size scaling factor + shift_range (x, y): Range of shifts of the cropped subrect. The rect + is shifted by [w / 2 * Uniform(-x, x), h / 2 * Uniform(-y, y)], + where (w, h) is the (width, height) of the input image. Set each + component to zero to crop at the image's center. + """ + super().__init__() + self._init(locals()) + + def get_transform(self, image): + img_h, img_w = image.shape[:2] + + # Initialize src_rect to fit the input image. + src_rect = np.array([-0.5 * img_w, -0.5 * img_h, 0.5 * img_w, 0.5 * img_h]) + + # Apply a random scaling to the src_rect. + src_rect *= np.random.uniform(self.scale_range[0], self.scale_range[1]) + + # Apply a random shift to the coordinates origin. + src_rect[0::2] += self.shift_range[0] * img_w * (np.random.rand() - 0.5) + src_rect[1::2] += self.shift_range[1] * img_h * (np.random.rand() - 0.5) + + # Map src_rect coordinates into image coordinates (center at corner). + src_rect[0::2] += 0.5 * img_w + src_rect[1::2] += 0.5 * img_h + + return ExtentTransform( + src_rect=(src_rect[0], src_rect[1], src_rect[2], src_rect[3]), + output_size=(int(src_rect[3] - src_rect[1]), int(src_rect[2] - src_rect[0])), + ) + + +class RandomContrast(Augmentation): + """ + Randomly transforms image contrast. + + Contrast intensity is uniformly sampled in (intensity_min, intensity_max). + - intensity < 1 will reduce contrast + - intensity = 1 will preserve the input image + - intensity > 1 will increase contrast + + See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html + """ + + def __init__(self, intensity_min, intensity_max): + """ + Args: + intensity_min (float): Minimum augmentation + intensity_max (float): Maximum augmentation + """ + super().__init__() + self._init(locals()) + + def get_transform(self, image): + w = np.random.uniform(self.intensity_min, self.intensity_max) + return BlendTransform(src_image=image.mean(), src_weight=1 - w, dst_weight=w) + + +class RandomBrightness(Augmentation): + """ + Randomly transforms image brightness. + + Brightness intensity is uniformly sampled in (intensity_min, intensity_max). + - intensity < 1 will reduce brightness + - intensity = 1 will preserve the input image + - intensity > 1 will increase brightness + + See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html + """ + + def __init__(self, intensity_min, intensity_max): + """ + Args: + intensity_min (float): Minimum augmentation + intensity_max (float): Maximum augmentation + """ + super().__init__() + self._init(locals()) + + def get_transform(self, image): + w = np.random.uniform(self.intensity_min, self.intensity_max) + return BlendTransform(src_image=0, src_weight=1 - w, dst_weight=w) + + +class RandomSaturation(Augmentation): + """ + Randomly transforms saturation of an RGB image. + Input images are assumed to have 'RGB' channel order. + + Saturation intensity is uniformly sampled in (intensity_min, intensity_max). + - intensity < 1 will reduce saturation (make the image more grayscale) + - intensity = 1 will preserve the input image + - intensity > 1 will increase saturation + + See: https://pillow.readthedocs.io/en/3.0.x/reference/ImageEnhance.html + """ + + def __init__(self, intensity_min, intensity_max): + """ + Args: + intensity_min (float): Minimum augmentation (1 preserves input). + intensity_max (float): Maximum augmentation (1 preserves input). + """ + super().__init__() + self._init(locals()) + + def get_transform(self, image): + assert image.shape[-1] == 3, "RandomSaturation only works on RGB images" + w = np.random.uniform(self.intensity_min, self.intensity_max) + grayscale = image.dot([0.299, 0.587, 0.114])[:, :, np.newaxis] + return BlendTransform(src_image=grayscale, src_weight=1 - w, dst_weight=w) + + +class RandomLighting(Augmentation): + """ + The "lighting" augmentation described in AlexNet, using fixed PCA over ImageNet. + Input images are assumed to have 'RGB' channel order. + + The degree of color jittering is randomly sampled via a normal distribution, + with standard deviation given by the scale parameter. + """ + + def __init__(self, scale): + """ + Args: + scale (float): Standard deviation of principal component weighting. + """ + super().__init__() + self._init(locals()) + self.eigen_vecs = np.array( + [[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140], [-0.5836, -0.6948, 0.4203]] + ) + self.eigen_vals = np.array([0.2175, 0.0188, 0.0045]) + + def get_transform(self, image): + assert image.shape[-1] == 3, "RandomLighting only works on RGB images" + weights = np.random.normal(scale=self.scale, size=3) + return BlendTransform( + src_image=self.eigen_vecs.dot(weights * self.eigen_vals), src_weight=1.0, dst_weight=1.0 + ) diff --git a/cutler/data/transforms/transform.py b/cutler/data/transforms/transform.py new file mode 100644 index 0000000000000000000000000000000000000000..443a997d0648825c5722e9ddca4856c7ff8b1415 --- /dev/null +++ b/cutler/data/transforms/transform.py @@ -0,0 +1,355 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/transforms/transform.py + +""" +See "Data Augmentation" tutorial for an overview of the system: +https://detectron2.readthedocs.io/tutorials/augmentation.html +""" + +import numpy as np +import torch +import torch.nn.functional as F +from fvcore.transforms.transform import ( + CropTransform, + HFlipTransform, + NoOpTransform, + Transform, + TransformList, +) +from PIL import Image + +try: + import cv2 # noqa +except ImportError: + # OpenCV is an optional dependency at the moment + pass + +__all__ = [ + "ExtentTransform", + "ResizeTransform", + "RotationTransform", + "ColorTransform", + "PILColorTransform", +] + + +class ExtentTransform(Transform): + """ + Extracts a subregion from the source image and scales it to the output size. + + The fill color is used to map pixels from the source rect that fall outside + the source image. + + See: https://pillow.readthedocs.io/en/latest/PIL.html#PIL.ImageTransform.ExtentTransform + """ + + def __init__(self, src_rect, output_size, interp=Image.BILINEAR, fill=0): + """ + Args: + src_rect (x0, y0, x1, y1): src coordinates + output_size (h, w): dst image size + interp: PIL interpolation methods + fill: Fill color used when src_rect extends outside image + """ + super().__init__() + self._set_attributes(locals()) + + def apply_image(self, img, interp=None): + h, w = self.output_size + if len(img.shape) > 2 and img.shape[2] == 1: + pil_image = Image.fromarray(img[:, :, 0], mode="L") + else: + pil_image = Image.fromarray(img) + pil_image = pil_image.transform( + size=(w, h), + method=Image.EXTENT, + data=self.src_rect, + resample=interp if interp else self.interp, + fill=self.fill, + ) + ret = np.asarray(pil_image) + if len(img.shape) > 2 and img.shape[2] == 1: + ret = np.expand_dims(ret, -1) + return ret + + def apply_coords(self, coords): + # Transform image center from source coordinates into output coordinates + # and then map the new origin to the corner of the output image. + h, w = self.output_size + x0, y0, x1, y1 = self.src_rect + new_coords = coords.astype(np.float32) + new_coords[:, 0] -= 0.5 * (x0 + x1) + new_coords[:, 1] -= 0.5 * (y0 + y1) + new_coords[:, 0] *= w / (x1 - x0) + new_coords[:, 1] *= h / (y1 - y0) + new_coords[:, 0] += 0.5 * w + new_coords[:, 1] += 0.5 * h + return new_coords + + def apply_segmentation(self, segmentation): + segmentation = self.apply_image(segmentation, interp=Image.NEAREST) + return segmentation + + +class ResizeTransform(Transform): + """ + Resize the image to a target size. + """ + + def __init__(self, h, w, new_h, new_w, interp=None): + """ + Args: + h, w (int): original image size + new_h, new_w (int): new image size + interp: PIL interpolation methods, defaults to bilinear. + """ + # TODO decide on PIL vs opencv + super().__init__() + if interp is None: + interp = Image.BILINEAR + self._set_attributes(locals()) + + def apply_image(self, img, interp=None): + try: + img.shape[:2] == (self.h, self.w) + except: + (self.h, self.w) = (self.w, self.h) + assert img.shape[:2] == (self.h, self.w) + assert len(img.shape) <= 4 + interp_method = interp if interp is not None else self.interp + + if img.dtype == np.uint8: + if len(img.shape) > 2 and img.shape[2] == 1: + pil_image = Image.fromarray(img[:, :, 0], mode="L") + else: + pil_image = Image.fromarray(img) + pil_image = pil_image.resize((self.new_w, self.new_h), interp_method) + ret = np.asarray(pil_image) + if len(img.shape) > 2 and img.shape[2] == 1: + ret = np.expand_dims(ret, -1) + else: + # PIL only supports uint8 + if any(x < 0 for x in img.strides): + img = np.ascontiguousarray(img) + img = torch.from_numpy(img) + shape = list(img.shape) + shape_4d = shape[:2] + [1] * (4 - len(shape)) + shape[2:] + img = img.view(shape_4d).permute(2, 3, 0, 1) # hw(c) -> nchw + _PIL_RESIZE_TO_INTERPOLATE_MODE = { + Image.NEAREST: "nearest", + Image.BILINEAR: "bilinear", + Image.BICUBIC: "bicubic", + } + mode = _PIL_RESIZE_TO_INTERPOLATE_MODE[interp_method] + align_corners = None if mode == "nearest" else False + img = F.interpolate( + img, (self.new_h, self.new_w), mode=mode, align_corners=align_corners + ) + shape[:2] = (self.new_h, self.new_w) + ret = img.permute(2, 3, 0, 1).view(shape).numpy() # nchw -> hw(c) + + return ret + + def apply_coords(self, coords): + coords[:, 0] = coords[:, 0] * (self.new_w * 1.0 / self.w) + coords[:, 1] = coords[:, 1] * (self.new_h * 1.0 / self.h) + return coords + + def apply_segmentation(self, segmentation): + segmentation = self.apply_image(segmentation, interp=Image.NEAREST) + return segmentation + + def inverse(self): + return ResizeTransform(self.new_h, self.new_w, self.h, self.w, self.interp) + + +class RotationTransform(Transform): + """ + This method returns a copy of this image, rotated the given + number of degrees counter clockwise around its center. + """ + + def __init__(self, h, w, angle, expand=True, center=None, interp=None): + """ + Args: + h, w (int): original image size + angle (float): degrees for rotation + expand (bool): choose if the image should be resized to fit the whole + rotated image (default), or simply cropped + center (tuple (width, height)): coordinates of the rotation center + if left to None, the center will be fit to the center of each image + center has no effect if expand=True because it only affects shifting + interp: cv2 interpolation method, default cv2.INTER_LINEAR + """ + super().__init__() + image_center = np.array((w / 2, h / 2)) + if center is None: + center = image_center + if interp is None: + interp = cv2.INTER_LINEAR + abs_cos, abs_sin = (abs(np.cos(np.deg2rad(angle))), abs(np.sin(np.deg2rad(angle)))) + if expand: + # find the new width and height bounds + bound_w, bound_h = np.rint( + [h * abs_sin + w * abs_cos, h * abs_cos + w * abs_sin] + ).astype(int) + else: + bound_w, bound_h = w, h + + self._set_attributes(locals()) + self.rm_coords = self.create_rotation_matrix() + # Needed because of this problem https://github.com/opencv/opencv/issues/11784 + self.rm_image = self.create_rotation_matrix(offset=-0.5) + + def apply_image(self, img, interp=None): + """ + img should be a numpy array, formatted as Height * Width * Nchannels + """ + if len(img) == 0 or self.angle % 360 == 0: + return img + assert img.shape[:2] == (self.h, self.w) + interp = interp if interp is not None else self.interp + return cv2.warpAffine(img, self.rm_image, (self.bound_w, self.bound_h), flags=interp) + + def apply_coords(self, coords): + """ + coords should be a N * 2 array-like, containing N couples of (x, y) points + """ + coords = np.asarray(coords, dtype=float) + if len(coords) == 0 or self.angle % 360 == 0: + return coords + return cv2.transform(coords[:, np.newaxis, :], self.rm_coords)[:, 0, :] + + def apply_segmentation(self, segmentation): + segmentation = self.apply_image(segmentation, interp=cv2.INTER_NEAREST) + return segmentation + + def create_rotation_matrix(self, offset=0): + center = (self.center[0] + offset, self.center[1] + offset) + rm = cv2.getRotationMatrix2D(tuple(center), self.angle, 1) + if self.expand: + # Find the coordinates of the center of rotation in the new image + # The only point for which we know the future coordinates is the center of the image + rot_im_center = cv2.transform(self.image_center[None, None, :] + offset, rm)[0, 0, :] + new_center = np.array([self.bound_w / 2, self.bound_h / 2]) + offset - rot_im_center + # shift the rotation center to the new coordinates + rm[:, 2] += new_center + return rm + + def inverse(self): + """ + The inverse is to rotate it back with expand, and crop to get the original shape. + """ + if not self.expand: # Not possible to inverse if a part of the image is lost + raise NotImplementedError() + rotation = RotationTransform( + self.bound_h, self.bound_w, -self.angle, True, None, self.interp + ) + crop = CropTransform( + (rotation.bound_w - self.w) // 2, (rotation.bound_h - self.h) // 2, self.w, self.h + ) + return TransformList([rotation, crop]) + + +class ColorTransform(Transform): + """ + Generic wrapper for any photometric transforms. + These transformations should only affect the color space and + not the coordinate space of the image (e.g. annotation + coordinates such as bounding boxes should not be changed) + """ + + def __init__(self, op): + """ + Args: + op (Callable): operation to be applied to the image, + which takes in an ndarray and returns an ndarray. + """ + if not callable(op): + raise ValueError("op parameter should be callable") + super().__init__() + self._set_attributes(locals()) + + def apply_image(self, img): + return self.op(img) + + def apply_coords(self, coords): + return coords + + def inverse(self): + return NoOpTransform() + + def apply_segmentation(self, segmentation): + return segmentation + + +class PILColorTransform(ColorTransform): + """ + Generic wrapper for PIL Photometric image transforms, + which affect the color space and not the coordinate + space of the image + """ + + def __init__(self, op): + """ + Args: + op (Callable): operation to be applied to the image, + which takes in a PIL Image and returns a transformed + PIL Image. + For reference on possible operations see: + - https://pillow.readthedocs.io/en/stable/ + """ + if not callable(op): + raise ValueError("op parameter should be callable") + super().__init__(op) + + def apply_image(self, img): + img = Image.fromarray(img) + return np.asarray(super().apply_image(img)) + + +def HFlip_rotated_box(transform, rotated_boxes): + """ + Apply the horizontal flip transform on rotated boxes. + + Args: + rotated_boxes (ndarray): Nx5 floating point array of + (x_center, y_center, width, height, angle_degrees) format + in absolute coordinates. + """ + # Transform x_center + rotated_boxes[:, 0] = transform.width - rotated_boxes[:, 0] + # Transform angle + rotated_boxes[:, 4] = -rotated_boxes[:, 4] + return rotated_boxes + + +def Resize_rotated_box(transform, rotated_boxes): + """ + Apply the resizing transform on rotated boxes. For details of how these (approximation) + formulas are derived, please refer to :meth:`RotatedBoxes.scale`. + + Args: + rotated_boxes (ndarray): Nx5 floating point array of + (x_center, y_center, width, height, angle_degrees) format + in absolute coordinates. + """ + scale_factor_x = transform.new_w * 1.0 / transform.w + scale_factor_y = transform.new_h * 1.0 / transform.h + rotated_boxes[:, 0] *= scale_factor_x + rotated_boxes[:, 1] *= scale_factor_y + theta = rotated_boxes[:, 4] * np.pi / 180.0 + c = np.cos(theta) + s = np.sin(theta) + rotated_boxes[:, 2] *= np.sqrt(np.square(scale_factor_x * c) + np.square(scale_factor_y * s)) + rotated_boxes[:, 3] *= np.sqrt(np.square(scale_factor_x * s) + np.square(scale_factor_y * c)) + rotated_boxes[:, 4] = np.arctan2(scale_factor_x * s, scale_factor_y * c) * 180 / np.pi + + return rotated_boxes + + +HFlipTransform.register_type("rotated_box", HFlip_rotated_box) +ResizeTransform.register_type("rotated_box", Resize_rotated_box) + +# not necessary any more with latest fvcore +NoOpTransform.register_type("rotated_box", lambda t, x: x) diff --git a/cutler/demo/__init__.py b/cutler/demo/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2c79f1f75095f35ffb3f32eb96fbb105444ca27d --- /dev/null +++ b/cutler/demo/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +from demo import * +from predictor import * + +__all__ = [k for k in globals().keys() if not k.startswith("_")] \ No newline at end of file diff --git a/cutler/demo/__pycache__/predictor.cpython-312.pyc b/cutler/demo/__pycache__/predictor.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1702ea50bed8593d695ac9b92470a9a4a8ad72dc Binary files /dev/null and b/cutler/demo/__pycache__/predictor.cpython-312.pyc differ diff --git a/cutler/demo/cutler_cascade_final.pth b/cutler/demo/cutler_cascade_final.pth new file mode 100644 index 0000000000000000000000000000000000000000..bc523c53cccc2f5101945fe2a8ffed936fba0559 --- /dev/null +++ b/cutler/demo/cutler_cascade_final.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24fdc4544a348e5c27f6e334e4ff6557d8f2d50de94380a741bc91c2226b86bb +size 574672112 diff --git a/cutler/demo/demo.py b/cutler/demo/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..9e4017a7b4c08f034d529c40e384166becff24e1 --- /dev/null +++ b/cutler/demo/demo.py @@ -0,0 +1,197 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/demo/demo.py + +import argparse +import glob +import multiprocessing as mp +import numpy as np +import os +import tempfile +import time +import warnings +import cv2 +import tqdm + +from detectron2.config import get_cfg +from detectron2.data.detection_utils import read_image +from detectron2.utils.logger import setup_logger +import sys +sys.path.append('./') +sys.path.append('../') +from config import add_cutler_config + +from predictor import VisualizationDemo + +# constants +WINDOW_NAME = "CutLER detections" + + +def setup_cfg(args): + # load config from file and command-line arguments + cfg = get_cfg() + add_cutler_config(cfg) + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + # Disable the use of SyncBN normalization when running on a CPU + # SyncBN is not supported on CPU and can cause errors, so we switch to BN instead + if cfg.MODEL.DEVICE == 'cpu' and cfg.MODEL.RESNETS.NORM == 'SyncBN': + cfg.MODEL.RESNETS.NORM = "BN" + cfg.MODEL.FPN.NORM = "BN" + # Set score_threshold for builtin models + cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold + cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold + cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold + cfg.freeze() + return cfg + + +def get_parser(): + parser = argparse.ArgumentParser(description="Detectron2 demo for builtin configs") + parser.add_argument( + "--config-file", + default="model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml", + metavar="FILE", + help="path to config file", + ) + parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.") + parser.add_argument("--video-input", help="Path to video file.") + parser.add_argument( + "--input", + nargs="+", + help="A list of space separated input images; " + "or a single glob pattern such as 'directory/*.jpg'", + ) + parser.add_argument( + "--output", + help="A file or directory to save output visualizations. " + "If not given, will show output in an OpenCV window.", + ) + + parser.add_argument( + "--confidence-threshold", + type=float, + default=0.35, + help="Minimum score for instance predictions to be shown", + ) + parser.add_argument( + "--opts", + help="Modify config options using the command-line 'KEY VALUE' pairs", + default=[], + nargs=argparse.REMAINDER, + ) + return parser + + +def test_opencv_video_format(codec, file_ext): + with tempfile.TemporaryDirectory(prefix="video_format_test") as dir: + filename = os.path.join(dir, "test_file" + file_ext) + writer = cv2.VideoWriter( + filename=filename, + fourcc=cv2.VideoWriter_fourcc(*codec), + fps=float(30), + frameSize=(10, 10), + isColor=True, + ) + [writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)] + writer.release() + if os.path.isfile(filename): + return True + return False + + +if __name__ == "__main__": + mp.set_start_method("spawn", force=True) + args = get_parser().parse_args() + setup_logger(name="fvcore") + logger = setup_logger() + logger.info("Arguments: " + str(args)) + + cfg = setup_cfg(args) + + demo = VisualizationDemo(cfg) + + if args.input: + if len(args.input) == 1: + args.input = glob.glob(os.path.expanduser(args.input[0])) + assert args.input, "The input path(s) was not found" + for path in tqdm.tqdm(args.input, disable=not args.output): + # use PIL, to be consistent with evaluation + img = read_image(path, format="BGR") + start_time = time.time() + predictions, visualized_output = demo.run_on_image(img) + logger.info( + "{}: {} in {:.2f}s".format( + path, + "detected {} instances".format(len(predictions["instances"])) + if "instances" in predictions + else "finished", + time.time() - start_time, + ) + ) + + if args.output: + if os.path.isdir(args.output): + assert os.path.isdir(args.output), args.output + out_filename = os.path.join(args.output, os.path.basename(path)) + else: + assert len(args.input) == 1, "Please specify a directory with args.output" + out_filename = args.output + visualized_output.save(out_filename) + else: + cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) + cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1]) + if cv2.waitKey(0) == 27: + break # esc to quit + elif args.webcam: + assert args.input is None, "Cannot have both --input and --webcam!" + assert args.output is None, "output not yet supported with --webcam!" + cam = cv2.VideoCapture(0) + for vis in tqdm.tqdm(demo.run_on_video(cam)): + cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) + cv2.imshow(WINDOW_NAME, vis) + if cv2.waitKey(1) == 27: + break # esc to quit + cam.release() + cv2.destroyAllWindows() + elif args.video_input: + video = cv2.VideoCapture(args.video_input) + width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) + frames_per_second = video.get(cv2.CAP_PROP_FPS) + num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) + basename = os.path.basename(args.video_input) + codec, file_ext = ( + ("x264", ".mkv") if test_opencv_video_format("x264", ".mkv") else ("mp4v", ".mp4") + ) + if codec == ".mp4v": + warnings.warn("x264 codec not available, switching to mp4v") + if args.output: + if os.path.isdir(args.output): + output_fname = os.path.join(args.output, basename) + output_fname = os.path.splitext(output_fname)[0] + file_ext + else: + output_fname = args.output + assert not os.path.isfile(output_fname), output_fname + output_file = cv2.VideoWriter( + filename=output_fname, + # some installation of opencv may not support x264 (due to its license), + # you can try other format (e.g. MPEG) + fourcc=cv2.VideoWriter_fourcc(*codec), + fps=float(frames_per_second), + frameSize=(width, height), + isColor=True, + ) + assert os.path.isfile(args.video_input) + for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames): + if args.output: + output_file.write(vis_frame) + else: + cv2.namedWindow(basename, cv2.WINDOW_NORMAL) + cv2.imshow(basename, vis_frame) + if cv2.waitKey(1) == 27: + break # esc to quit + video.release() + if args.output: + output_file.release() + else: + cv2.destroyAllWindows() \ No newline at end of file diff --git a/cutler/demo/imgs/demo1.jpg b/cutler/demo/imgs/demo1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..25b5588c29783cb41f339871623d6192171297c2 --- /dev/null +++ b/cutler/demo/imgs/demo1.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6036356bf683ac920fd9eb048864c26b5fdbd546299228c2a4d638c7e649c59a +size 538500 diff --git a/cutler/demo/imgs/demo2.jpg b/cutler/demo/imgs/demo2.jpg new file mode 100644 index 0000000000000000000000000000000000000000..25b739e7b87d2e74bad9642b0a97012ffefdcd52 --- /dev/null +++ b/cutler/demo/imgs/demo2.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cc29e55f4d2de0a1e11440b4e406bb8b405c71a0276ea6844f109729c470e287 +size 382068 diff --git a/cutler/demo/imgs/demo3.jpg b/cutler/demo/imgs/demo3.jpg new file mode 100644 index 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0000000000000000000000000000000000000000..527d90c34d5bd8a5c40628278f755cfa8ebd7568 --- /dev/null +++ b/cutler/demo/imgs/demo8.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:54aad1e602f8c0a7e412e10a197c6d42540d6cef763eed13388b9f6b7958fda4 +size 47306 diff --git a/cutler/demo/predictor.py b/cutler/demo/predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..0920159d2de32428fbb25ecbf00839e9a56de044 --- /dev/null +++ b/cutler/demo/predictor.py @@ -0,0 +1,219 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +import atexit +import bisect +import multiprocessing as mp +from collections import deque +import cv2 +import torch + +from detectron2.data import MetadataCatalog +import sys +sys.path.append('./') +from engine.defaults import DefaultPredictor +from detectron2.utils.video_visualizer import VideoVisualizer +from detectron2.utils.visualizer import ColorMode, Visualizer + + +class VisualizationDemo(object): + def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False): + """ + Args: + cfg (CfgNode): + instance_mode (ColorMode): + parallel (bool): whether to run the model in different processes from visualization. + Useful since the visualization logic can be slow. + """ + self.metadata = MetadataCatalog.get( + cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else "__unused" + ) + self.cpu_device = torch.device("cpu") + self.instance_mode = instance_mode + + self.parallel = parallel + if parallel: + num_gpu = torch.cuda.device_count() + self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu) + else: + self.predictor = DefaultPredictor(cfg) + + def run_on_image(self, image): + """ + Args: + image (np.ndarray): an image of shape (H, W, C) (in BGR order). + This is the format used by OpenCV. + Returns: + predictions (dict): the output of the model. + vis_output (VisImage): the visualized image output. + """ + vis_output = None + predictions = self.predictor(image) + # Convert image from OpenCV BGR format to Matplotlib RGB format. + image = image[:, :, ::-1] + visualizer = Visualizer(image, self.metadata, instance_mode=self.instance_mode) + if "panoptic_seg" in predictions: + panoptic_seg, segments_info = predictions["panoptic_seg"] + vis_output = visualizer.draw_panoptic_seg_predictions( + panoptic_seg.to(self.cpu_device), segments_info + ) + else: + if "sem_seg" in predictions: + vis_output = visualizer.draw_sem_seg( + predictions["sem_seg"].argmax(dim=0).to(self.cpu_device) + ) + if "instances" in predictions: + instances = predictions["instances"].to(self.cpu_device) + vis_output = visualizer.draw_instance_predictions(predictions=instances) + + return predictions, vis_output + + def _frame_from_video(self, video): + while video.isOpened(): + success, frame = video.read() + if success: + yield frame + else: + break + + def run_on_video(self, video): + """ + Visualizes predictions on frames of the input video. + Args: + video (cv2.VideoCapture): a :class:`VideoCapture` object, whose source can be + either a webcam or a video file. + Yields: + ndarray: BGR visualizations of each video frame. + """ + video_visualizer = VideoVisualizer(self.metadata, self.instance_mode) + + def process_predictions(frame, predictions): + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + if "panoptic_seg" in predictions: + panoptic_seg, segments_info = predictions["panoptic_seg"] + vis_frame = video_visualizer.draw_panoptic_seg_predictions( + frame, panoptic_seg.to(self.cpu_device), segments_info + ) + elif "instances" in predictions: + predictions = predictions["instances"].to(self.cpu_device) + vis_frame = video_visualizer.draw_instance_predictions(frame, predictions) + elif "sem_seg" in predictions: + vis_frame = video_visualizer.draw_sem_seg( + frame, predictions["sem_seg"].argmax(dim=0).to(self.cpu_device) + ) + + # Converts Matplotlib RGB format to OpenCV BGR format + vis_frame = cv2.cvtColor(vis_frame.get_image(), cv2.COLOR_RGB2BGR) + return vis_frame + + frame_gen = self._frame_from_video(video) + if self.parallel: + buffer_size = self.predictor.default_buffer_size + + frame_data = deque() + + for cnt, frame in enumerate(frame_gen): + frame_data.append(frame) + self.predictor.put(frame) + + if cnt >= buffer_size: + frame = frame_data.popleft() + predictions = self.predictor.get() + yield process_predictions(frame, predictions) + + while len(frame_data): + frame = frame_data.popleft() + predictions = self.predictor.get() + yield process_predictions(frame, predictions) + else: + for frame in frame_gen: + yield process_predictions(frame, self.predictor(frame)) + + +class AsyncPredictor: + """ + A predictor that runs the model asynchronously, possibly on >1 GPUs. + Because rendering the visualization takes considerably amount of time, + this helps improve throughput a little bit when rendering videos. + """ + + class _StopToken: + pass + + class _PredictWorker(mp.Process): + def __init__(self, cfg, task_queue, result_queue): + self.cfg = cfg + self.task_queue = task_queue + self.result_queue = result_queue + super().__init__() + + def run(self): + predictor = DefaultPredictor(self.cfg) + + while True: + task = self.task_queue.get() + if isinstance(task, AsyncPredictor._StopToken): + break + idx, data = task + result = predictor(data) + self.result_queue.put((idx, result)) + + def __init__(self, cfg, num_gpus: int = 1): + """ + Args: + cfg (CfgNode): + num_gpus (int): if 0, will run on CPU + """ + num_workers = max(num_gpus, 1) + self.task_queue = mp.Queue(maxsize=num_workers * 3) + self.result_queue = mp.Queue(maxsize=num_workers * 3) + self.procs = [] + for gpuid in range(max(num_gpus, 1)): + cfg = cfg.clone() + cfg.defrost() + cfg.MODEL.DEVICE = "cuda:{}".format(gpuid) if num_gpus > 0 else "cpu" + self.procs.append( + AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue) + ) + + self.put_idx = 0 + self.get_idx = 0 + self.result_rank = [] + self.result_data = [] + + for p in self.procs: + p.start() + atexit.register(self.shutdown) + + def put(self, image): + self.put_idx += 1 + self.task_queue.put((self.put_idx, image)) + + def get(self): + self.get_idx += 1 # the index needed for this request + if len(self.result_rank) and self.result_rank[0] == self.get_idx: + res = self.result_data[0] + del self.result_data[0], self.result_rank[0] + return res + + while True: + # make sure the results are returned in the correct order + idx, res = self.result_queue.get() + if idx == self.get_idx: + return res + insert = bisect.bisect(self.result_rank, idx) + self.result_rank.insert(insert, idx) + self.result_data.insert(insert, res) + + def __len__(self): + return self.put_idx - self.get_idx + + def __call__(self, image): + self.put(image) + return self.get() + + def shutdown(self): + for _ in self.procs: + self.task_queue.put(AsyncPredictor._StopToken()) + + @property + def default_buffer_size(self): + return len(self.procs) * 5 \ No newline at end of file diff --git a/cutler/demo/wget-log 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cutler_casca 5%[> ] 32.52M --.-KB/s eta 45m 9s cutler_cascad 5%[> ] 32.52M --.-KB/s eta 45m 39s cutler_cascade 5%[> ] 32.52M --.-KB/s eta 45m 39s cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 46m 9s cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 46m 9s cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 46m 39s cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 46m 39s cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 47m 9s utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 47m 9s tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 47m 39s ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 47m 39s er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 48m 10s r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 48m 10s _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 48m 40s cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 48m 40s ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 49m 10s scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 49m 10s cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 49m 40s ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 49m 40s de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 50m 10s e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 50m 10s _final.pth.1 5%[> ] 32.52M --.-KB/s eta 50m 40s final.pth.1 5%[> ] 32.52M --.-KB/s eta 50m 40s inal.pth.1 5%[> ] 32.52M --.-KB/s eta 51m 10s nal.pth.1 5%[> ] 32.52M --.-KB/s eta 51m 10s al.pth.1 5%[> ] 32.52M --.-KB/s eta 51m 41s l.pth.1 5%[> ] 32.52M --.-KB/s eta 51m 41s .pth.1 5%[> ] 32.52M --.-KB/s eta 52m 11s pth.1 5%[> ] 32.52M --.-KB/s eta 52m 11s th.1 5%[> ] 32.52M --.-KB/s eta 52m 41s h.1 5%[> ] 32.52M --.-KB/s eta 52m 41s .1 5%[> ] 32.52M --.-KB/s eta 53m 11s 1 5%[> ] 32.52M --.-KB/s eta 53m 11s 5%[> ] 32.52M --.-KB/s eta 53m 41s c 5%[> ] 32.52M --.-KB/s eta 53m 41s cu 5%[> ] 32.52M --.-KB/s eta 54m 11s cut 5%[> ] 32.52M --.-KB/s eta 54m 11s cutl 5%[> ] 32.52M --.-KB/s eta 54m 41s cutle 5%[> ] 32.52M --.-KB/s eta 54m 41s cutler 5%[> ] 32.52M --.-KB/s eta 55m 12s cutler_ 5%[> ] 32.52M --.-KB/s eta 55m 12s cutler_c 5%[> ] 32.52M --.-KB/s eta 55m 42s cutler_ca 5%[> ] 32.52M --.-KB/s eta 55m 42s cutler_cas 5%[> ] 32.52M --.-KB/s eta 56m 12s cutler_casc 5%[> ] 32.52M --.-KB/s eta 56m 12s cutler_casca 5%[> ] 32.52M --.-KB/s eta 56m 42s cutler_cascad 5%[> ] 32.52M --.-KB/s eta 56m 42s cutler_cascade 5%[> ] 32.52M --.-KB/s eta 57m 12s cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 57m 12s cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 57m 42s cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 57m 42s cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 58m 12s cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 58m 12s utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 58m 43s tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 58m 43s ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 59m 13s er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 59m 13s r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 59m 43s _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 59m 43s cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 60m 13s ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 60m 13s scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 60m 43s cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 60m 43s ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 61m 13s de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 61m 13s e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 61m 44s _final.pth.1 5%[> ] 32.52M --.-KB/s eta 61m 44s final.pth.1 5%[> ] 32.52M --.-KB/s eta 62m 14s inal.pth.1 5%[> ] 32.52M --.-KB/s eta 62m 14s nal.pth.1 5%[> ] 32.52M --.-KB/s eta 62m 44s al.pth.1 5%[> ] 32.52M --.-KB/s eta 62m 44s l.pth.1 5%[> ] 32.52M --.-KB/s eta 63m 14s .pth.1 5%[> ] 32.52M --.-KB/s eta 63m 14s pth.1 5%[> ] 32.52M --.-KB/s eta 63m 44s th.1 5%[> ] 32.52M --.-KB/s eta 63m 44s h.1 5%[> ] 32.52M --.-KB/s eta 64m 14s .1 5%[> ] 32.52M --.-KB/s eta 64m 14s 1 5%[> ] 32.52M --.-KB/s eta 64m 44s 5%[> ] 32.52M --.-KB/s eta 64m 44s c 5%[> ] 32.52M --.-KB/s eta 65m 15s cu 5%[> ] 32.52M --.-KB/s eta 65m 15s cut 5%[> ] 32.52M --.-KB/s eta 65m 45s cutl 5%[> ] 32.52M --.-KB/s eta 65m 45s cutle 5%[> ] 32.52M --.-KB/s eta 66m 15s cutler 5%[> ] 32.52M --.-KB/s eta 66m 15s cutler_ 5%[> ] 32.52M --.-KB/s eta 66m 45s cutler_c 5%[> ] 32.52M --.-KB/s eta 66m 45s cutler_ca 5%[> ] 32.52M --.-KB/s eta 67m 15s cutler_cas 5%[> ] 32.52M --.-KB/s eta 67m 15s cutler_casc 5%[> ] 32.52M --.-KB/s eta 67m 45s cutler_casca 5%[> ] 32.52M --.-KB/s eta 67m 45s cutler_cascad 5%[> ] 32.52M --.-KB/s eta 68m 15s cutler_cascade 5%[> ] 32.52M --.-KB/s eta 68m 15s cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 68m 46s cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 68m 46s cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 69m 16s cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 69m 16s cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 69m 46s utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 69m 46s tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 70m 16s ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 70m 16s er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 70m 46s r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 70m 46s _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 71m 16s cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 71m 16s ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 71m 46s scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 71m 46s cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 72m 17s ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 72m 17s de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 72m 47s e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 72m 47s _final.pth.1 5%[> ] 32.52M --.-KB/s eta 73m 17s final.pth.1 5%[> ] 32.52M --.-KB/s eta 73m 17s inal.pth.1 5%[> ] 32.52M --.-KB/s eta 73m 47s nal.pth.1 5%[> ] 32.52M --.-KB/s eta 73m 47s al.pth.1 5%[> ] 32.52M --.-KB/s eta 74m 17s l.pth.1 5%[> ] 32.52M --.-KB/s eta 74m 17s .pth.1 5%[> ] 32.52M --.-KB/s eta 74m 47s pth.1 5%[> ] 32.52M --.-KB/s eta 74m 47s th.1 5%[> ] 32.52M --.-KB/s eta 75m 17s h.1 5%[> ] 32.52M --.-KB/s eta 75m 17s .1 5%[> ] 32.52M --.-KB/s eta 75m 48s 1 5%[> ] 32.52M --.-KB/s eta 75m 48s 5%[> ] 32.52M --.-KB/s eta 76m 18s c 5%[> ] 32.52M --.-KB/s eta 76m 18s cu 5%[> ] 32.52M --.-KB/s eta 76m 48s cut 5%[> ] 32.52M --.-KB/s eta 76m 48s cutl 5%[> ] 32.52M --.-KB/s eta 77m 18s cutle 5%[> ] 32.52M --.-KB/s eta 77m 18s cutler 5%[> ] 32.52M --.-KB/s eta 77m 48s cutler_ 5%[> ] 32.52M --.-KB/s eta 77m 48s cutler_c 5%[> ] 32.52M --.-KB/s eta 78m 18s cutler_ca 5%[> ] 32.52M --.-KB/s eta 78m 18s cutler_cas 5%[> ] 32.52M --.-KB/s eta 78m 49s cutler_casc 5%[> ] 32.52M --.-KB/s eta 78m 49s cutler_casca 5%[> ] 32.52M --.-KB/s eta 79m 19s cutler_cascad 5%[> ] 32.52M --.-KB/s eta 79m 19s cutler_cascade 5%[> ] 32.52M --.-KB/s eta 79m 49s cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 79m 49s cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 80m 19s cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 80m 19s cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 80m 49s cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 80m 49s utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 81m 19s tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 81m 19s ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 81m 49s er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 81m 49s r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 82m 20s _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 82m 20s cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 82m 50s ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 82m 50s scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 83m 20s cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 83m 20s ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 83m 50s de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 83m 50s e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 84m 20s _final.pth.1 5%[> ] 32.52M --.-KB/s eta 84m 20s final.pth.1 5%[> ] 32.52M --.-KB/s eta 84m 50s inal.pth.1 5%[> ] 32.52M --.-KB/s eta 84m 50s nal.pth.1 5%[> ] 32.52M --.-KB/s eta 85m 20s al.pth.1 5%[> ] 32.52M --.-KB/s eta 85m 20s l.pth.1 5%[> ] 32.52M --.-KB/s eta 85m 51s .pth.1 5%[> ] 32.52M --.-KB/s eta 85m 51s pth.1 5%[> ] 32.52M --.-KB/s eta 86m 21s th.1 5%[> ] 32.52M --.-KB/s eta 86m 21s h.1 5%[> ] 32.52M --.-KB/s eta 86m 51s .1 5%[> ] 32.52M --.-KB/s eta 86m 51s 1 5%[> ] 32.52M --.-KB/s eta 87m 21s 5%[> ] 32.52M --.-KB/s eta 87m 21s c 5%[> ] 32.52M --.-KB/s eta 87m 51s cu 5%[> ] 32.52M --.-KB/s eta 87m 51s cut 5%[> ] 32.52M --.-KB/s eta 88m 21s cutl 5%[> ] 32.52M --.-KB/s eta 88m 21s cutle 5%[> ] 32.52M --.-KB/s eta 88m 51s cutler 5%[> ] 32.52M --.-KB/s eta 88m 51s cutler_ 5%[> ] 32.52M --.-KB/s eta 89m 22s cutler_c 5%[> ] 32.52M --.-KB/s eta 89m 22s cutler_ca 5%[> ] 32.52M --.-KB/s eta 89m 52s cutler_cas 5%[> ] 32.52M --.-KB/s eta 89m 52s cutler_casc 5%[> ] 32.52M --.-KB/s eta 90m 22s cutler_casca 5%[> ] 32.52M --.-KB/s eta 90m 22s cutler_cascad 5%[> ] 32.52M --.-KB/s eta 90m 52s cutler_cascade 5%[> ] 32.52M --.-KB/s eta 90m 52s cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 91m 22s cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 91m 22s cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 91m 52s cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 91m 52s cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 92m 22s utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 92m 22s tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 92m 53s ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 92m 53s er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 93m 23s r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 93m 23s _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 93m 53s cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 93m 53s ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 94m 23s scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 94m 23s cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 94m 53s ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 94m 53s de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 95m 23s e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 95m 23s _final.pth.1 5%[> ] 32.52M --.-KB/s eta 95m 54s final.pth.1 5%[> ] 32.52M --.-KB/s eta 95m 54s inal.pth.1 5%[> ] 32.52M --.-KB/s eta 96m 24s nal.pth.1 5%[> ] 32.52M --.-KB/s eta 96m 24s al.pth.1 5%[> ] 32.52M --.-KB/s eta 96m 54s l.pth.1 5%[> ] 32.52M --.-KB/s eta 96m 54s .pth.1 5%[> ] 32.52M --.-KB/s eta 97m 24s pth.1 5%[> ] 32.52M --.-KB/s eta 97m 24s th.1 5%[> ] 32.52M --.-KB/s eta 97m 54s h.1 5%[> ] 32.52M --.-KB/s eta 97m 54s .1 5%[> ] 32.52M --.-KB/s eta 98m 24s 1 5%[> ] 32.52M --.-KB/s eta 98m 24s 5%[> ] 32.52M --.-KB/s eta 98m 54s c 5%[> ] 32.52M --.-KB/s eta 98m 54s cu 5%[> ] 32.52M --.-KB/s eta 99m 25s cut 5%[> ] 32.52M --.-KB/s eta 99m 25s cutl 5%[> ] 32.52M --.-KB/s eta 99m 55s cutle 5%[> ] 32.52M --.-KB/s eta 99m 55s cutler 5%[> ] 32.52M --.-KB/s eta 1h 40m cutler_ 5%[> ] 32.52M --.-KB/s eta 1h 40m cutler_c 5%[> ] 32.52M --.-KB/s eta 1h 40m cutler_ca 5%[> ] 32.52M --.-KB/s eta 1h 40m cutler_cas 5%[> ] 32.52M --.-KB/s eta 1h 41m cutler_casc 5%[> ] 32.52M --.-KB/s eta 1h 41m cutler_casca 5%[> ] 32.52M --.-KB/s eta 1h 41m cutler_cascad 5%[> ] 32.52M --.-KB/s eta 1h 41m cutler_cascade 5%[> ] 32.52M --.-KB/s eta 1h 42m cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 1h 42m cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 1h 42m cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 1h 42m cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 1h 43m cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 1h 43m utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 1h 43m tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 1h 43m ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 1h 44m er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 1h 44m r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 1h 44m _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 1h 44m cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 45m ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 45m scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 45m cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 45m ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 46m de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 46m e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 46m _final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 46m final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 47m inal.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 47m nal.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 47m al.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 47m l.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 48m .pth.1 5%[> ] 32.52M --.-KB/s eta 1h 48m pth.1 5%[> ] 32.52M --.-KB/s eta 1h 48m th.1 5%[> ] 32.52M --.-KB/s eta 1h 48m h.1 5%[> ] 32.52M --.-KB/s eta 1h 49m .1 5%[> ] 32.52M --.-KB/s eta 1h 49m 1 5%[> ] 32.52M --.-KB/s eta 1h 49m 5%[> ] 32.52M --.-KB/s eta 1h 49m c 5%[> ] 32.52M --.-KB/s eta 1h 50m cu 5%[> ] 32.52M --.-KB/s eta 1h 50m cut 5%[> ] 32.52M --.-KB/s eta 1h 50m cutl 5%[> ] 32.52M --.-KB/s eta 1h 50m cutle 5%[> ] 32.52M --.-KB/s eta 1h 51m cutler 5%[> ] 32.52M --.-KB/s eta 1h 51m cutler_ 5%[> ] 32.52M --.-KB/s eta 1h 51m cutler_c 5%[> ] 32.52M --.-KB/s eta 1h 51m cutler_ca 5%[> ] 32.52M --.-KB/s eta 1h 52m cutler_cas 5%[> ] 32.52M --.-KB/s eta 1h 52m cutler_casc 5%[> ] 32.52M --.-KB/s eta 1h 52m cutler_casca 5%[> ] 32.52M --.-KB/s eta 1h 52m cutler_cascad 5%[> ] 32.52M --.-KB/s eta 1h 53m cutler_cascade 5%[> ] 32.52M --.-KB/s eta 1h 53m cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 1h 53m cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 1h 53m cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 1h 54m cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 1h 54m cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 1h 54m utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 1h 54m tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 1h 55m ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 1h 55m er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 1h 55m r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 1h 55m _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 1h 56m cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 56m ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 57m scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 57m cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 57m ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 57m de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 58m e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 58m _final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 58m final.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 58m inal.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 59m nal.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 59m al.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 59m l.pth.1 5%[> ] 32.52M --.-KB/s eta 1h 59m .pth.1 5%[> ] 32.52M --.-KB/s eta 2h 0m pth.1 5%[> ] 32.52M --.-KB/s eta 2h 0m th.1 5%[> ] 32.52M --.-KB/s eta 2h 0m h.1 5%[> ] 32.52M --.-KB/s eta 2h 0m .1 5%[> ] 32.52M --.-KB/s eta 2h 1m 1 5%[> ] 32.52M --.-KB/s eta 2h 1m 5%[> ] 32.52M --.-KB/s eta 2h 1m c 5%[> ] 32.52M --.-KB/s eta 2h 1m cu 5%[> ] 32.52M --.-KB/s eta 2h 2m cut 5%[> ] 32.52M --.-KB/s eta 2h 2m cutl 5%[> ] 32.52M --.-KB/s eta 2h 2m cutle 5%[> ] 32.52M --.-KB/s eta 2h 2m cutler 5%[> ] 32.52M --.-KB/s eta 2h 3m cutler_ 5%[> ] 32.52M --.-KB/s eta 2h 3m cutler_c 5%[> ] 32.52M --.-KB/s eta 2h 3m cutler_ca 5%[> ] 32.52M --.-KB/s eta 2h 3m cutler_cas 5%[> ] 32.52M --.-KB/s eta 2h 4m cutler_casc 5%[> ] 32.52M --.-KB/s eta 2h 4m cutler_casca 5%[> ] 32.52M --.-KB/s eta 2h 4m cutler_cascad 5%[> ] 32.52M --.-KB/s eta 2h 4m cutler_cascade 5%[> ] 32.52M --.-KB/s eta 2h 5m cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 2h 5m cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 2h 5m cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 2h 5m cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 2h 6m cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 2h 6m utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 2h 6m tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 2h 6m ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 2h 7m er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 2h 7m r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 2h 7m _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 2h 7m cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 8m ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 8m scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 8m cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 8m ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 9m de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 9m e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 9m _final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 9m final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 10m inal.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 10m nal.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 10m al.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 10m l.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 11m .pth.1 5%[> ] 32.52M --.-KB/s eta 2h 11m pth.1 5%[> ] 32.52M --.-KB/s eta 2h 11m th.1 5%[> ] 32.52M --.-KB/s eta 2h 11m h.1 5%[> ] 32.52M --.-KB/s eta 2h 12m .1 5%[> ] 32.52M --.-KB/s eta 2h 12m 1 5%[> ] 32.52M --.-KB/s eta 2h 12m 5%[> ] 32.52M --.-KB/s eta 2h 12m c 5%[> ] 32.52M --.-KB/s eta 2h 13m cu 5%[> ] 32.52M --.-KB/s eta 2h 13m cut 5%[> ] 32.52M --.-KB/s eta 2h 13m cutl 5%[> ] 32.52M --.-KB/s eta 2h 13m cutle 5%[> ] 32.52M --.-KB/s eta 2h 14m cutler 5%[> ] 32.52M --.-KB/s eta 2h 14m cutler_ 5%[> ] 32.52M --.-KB/s eta 2h 14m cutler_c 5%[> ] 32.52M --.-KB/s eta 2h 14m cutler_ca 5%[> ] 32.52M --.-KB/s eta 2h 15m cutler_cas 5%[> ] 32.52M --.-KB/s eta 2h 15m cutler_casc 5%[> ] 32.52M --.-KB/s eta 2h 15m cutler_casca 5%[> ] 32.52M --.-KB/s eta 2h 15m cutler_cascad 5%[> ] 32.52M --.-KB/s eta 2h 16m cutler_cascade 5%[> ] 32.52M --.-KB/s eta 2h 16m cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 2h 16m cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 2h 16m cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 2h 17m cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 2h 17m cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 2h 17m utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 2h 17m tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 2h 18m ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 2h 18m er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 2h 18m r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 2h 18m _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 2h 19m cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 19m ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 19m scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 19m cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 20m ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 20m de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 20m e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 20m _final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 21m final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 21m inal.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 21m nal.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 21m al.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 22m l.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 22m .pth.1 5%[> ] 32.52M --.-KB/s eta 2h 22m pth.1 5%[> ] 32.52M --.-KB/s eta 2h 22m th.1 5%[> ] 32.52M --.-KB/s eta 2h 23m h.1 5%[> ] 32.52M --.-KB/s eta 2h 23m .1 5%[> ] 32.52M --.-KB/s eta 2h 23m 1 5%[> ] 32.52M --.-KB/s eta 2h 23m 5%[> ] 32.52M --.-KB/s eta 2h 24m c 5%[> ] 32.52M --.-KB/s eta 2h 24m cu 5%[> ] 32.52M --.-KB/s eta 2h 24m cut 5%[> ] 32.52M --.-KB/s eta 2h 24m cutl 5%[> ] 32.52M --.-KB/s eta 2h 25m cutle 5%[> ] 32.52M --.-KB/s eta 2h 25m cutler 5%[> ] 32.52M --.-KB/s eta 2h 25m cutler_ 5%[> ] 32.52M --.-KB/s eta 2h 25m cutler_c 5%[> ] 32.52M --.-KB/s eta 2h 26m cutler_ca 5%[> ] 32.52M --.-KB/s eta 2h 26m cutler_cas 5%[> ] 32.52M --.-KB/s eta 2h 26m cutler_casc 5%[> ] 32.52M --.-KB/s eta 2h 26m cutler_casca 5%[> ] 32.52M --.-KB/s eta 2h 27m cutler_cascad 5%[> ] 32.52M --.-KB/s eta 2h 27m cutler_cascade 5%[> ] 32.52M --.-KB/s eta 2h 27m cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 2h 27m cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 2h 28m cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 2h 28m cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 2h 28m cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 2h 28m utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 2h 29m tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 2h 29m ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 2h 29m er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 2h 29m r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 2h 30m _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 2h 30m cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 30m ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 30m scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 31m cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 31m ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 31m de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 31m e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 32m _final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 32m final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 32m inal.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 32m nal.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 33m al.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 33m l.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 33m .pth.1 5%[> ] 32.52M --.-KB/s eta 2h 33m pth.1 5%[> ] 32.52M --.-KB/s eta 2h 34m th.1 5%[> ] 32.52M --.-KB/s eta 2h 34m h.1 5%[> ] 32.52M --.-KB/s eta 2h 34m .1 5%[> ] 32.52M --.-KB/s eta 2h 34m 1 5%[> ] 32.52M --.-KB/s eta 2h 35m 5%[> ] 32.52M --.-KB/s eta 2h 35m c 5%[> ] 32.52M --.-KB/s eta 2h 35m cu 5%[> ] 32.52M --.-KB/s eta 2h 35m cut 5%[> ] 32.52M --.-KB/s eta 2h 36m cutl 5%[> ] 32.52M --.-KB/s eta 2h 36m cutle 5%[> ] 32.52M --.-KB/s eta 2h 36m cutler 5%[> ] 32.52M --.-KB/s eta 2h 36m cutler_ 5%[> ] 32.52M --.-KB/s eta 2h 37m cutler_c 5%[> ] 32.52M --.-KB/s eta 2h 37m cutler_ca 5%[> ] 32.52M --.-KB/s eta 2h 37m cutler_cas 5%[> ] 32.52M --.-KB/s eta 2h 37m cutler_casc 5%[> ] 32.52M --.-KB/s eta 2h 38m cutler_casca 5%[> ] 32.52M --.-KB/s eta 2h 38m cutler_cascad 5%[> ] 32.52M --.-KB/s eta 2h 38m cutler_cascade 5%[> ] 32.52M --.-KB/s eta 2h 38m cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 2h 39m cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 2h 39m cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 2h 39m cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 2h 39m cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 2h 40m utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 2h 40m tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 2h 40m ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 2h 40m er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 2h 41m r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 2h 41m _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 2h 41m cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 41m ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 42m scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 42m cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 42m ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 42m de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 43m e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 43m _final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 43m final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 43m inal.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 44m nal.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 44m al.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 44m l.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 44m .pth.1 5%[> ] 32.52M --.-KB/s eta 2h 45m pth.1 5%[> ] 32.52M --.-KB/s eta 2h 45m th.1 5%[> ] 32.52M --.-KB/s eta 2h 45m h.1 5%[> ] 32.52M --.-KB/s eta 2h 45m .1 5%[> ] 32.52M --.-KB/s eta 2h 46m 1 5%[> ] 32.52M --.-KB/s eta 2h 46m 5%[> ] 32.52M --.-KB/s eta 2h 46m c 5%[> ] 32.52M --.-KB/s eta 2h 46m cu 5%[> ] 32.52M --.-KB/s eta 2h 47m cut 5%[> ] 32.52M --.-KB/s eta 2h 47m cutl 5%[> ] 32.52M --.-KB/s eta 2h 47m cutle 5%[> ] 32.52M --.-KB/s eta 2h 47m cutler 5%[> ] 32.52M --.-KB/s eta 2h 48m cutler_ 5%[> ] 32.52M --.-KB/s eta 2h 48m cutler_c 5%[> ] 32.52M --.-KB/s eta 2h 48m cutler_ca 5%[> ] 32.52M --.-KB/s eta 2h 48m cutler_cas 5%[> ] 32.52M --.-KB/s eta 2h 49m cutler_casc 5%[> ] 32.52M --.-KB/s eta 2h 49m cutler_casca 5%[> ] 32.52M --.-KB/s eta 2h 49m cutler_cascad 5%[> ] 32.52M --.-KB/s eta 2h 49m cutler_cascade 5%[> ] 32.52M --.-KB/s eta 2h 50m cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 2h 50m cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 2h 50m cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 2h 50m cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 2h 51m cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 2h 51m utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 2h 51m tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 2h 51m ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 2h 52m er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 2h 52m r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 2h 52m _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 2h 52m cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 53m ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 53m scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 53m cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 53m ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 54m de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 54m e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 54m _final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 54m final.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 55m inal.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 55m nal.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 55m al.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 55m l.pth.1 5%[> ] 32.52M --.-KB/s eta 2h 56m .pth.1 5%[> ] 32.52M --.-KB/s eta 2h 56m pth.1 5%[> ] 32.52M --.-KB/s eta 2h 56m th.1 5%[> ] 32.52M --.-KB/s eta 2h 56m h.1 5%[> ] 32.52M --.-KB/s eta 2h 57m .1 5%[> ] 32.52M --.-KB/s eta 2h 57m 1 5%[> ] 32.52M --.-KB/s eta 2h 57m 5%[> ] 32.52M --.-KB/s eta 2h 57m c 5%[> ] 32.52M --.-KB/s eta 2h 58m cu 5%[> ] 32.52M --.-KB/s eta 2h 58m cut 5%[> ] 32.52M --.-KB/s eta 2h 58m cutl 5%[> ] 32.52M --.-KB/s eta 2h 58m cutle 5%[> ] 32.52M --.-KB/s eta 2h 59m cutler 5%[> ] 32.52M --.-KB/s eta 2h 59m cutler_ 5%[> ] 32.52M --.-KB/s eta 2h 59m cutler_c 5%[> ] 32.52M --.-KB/s eta 2h 59m cutler_ca 5%[> ] 32.52M --.-KB/s eta 3h 0m cutler_cas 5%[> ] 32.52M --.-KB/s eta 3h 0m cutler_casc 5%[> ] 32.52M --.-KB/s eta 3h 0m cutler_casca 5%[> ] 32.52M --.-KB/s eta 3h 0m cutler_cascad 5%[> ] 32.52M --.-KB/s eta 3h 1m cutler_cascade 5%[> ] 32.52M --.-KB/s eta 3h 1m cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 3h 1m cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 3h 1m cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 3h 2m cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 3h 2m cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 3h 2m utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 3h 2m tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 3h 3m ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 3h 3m er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 3h 3m r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 3h 3m _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 3h 4m cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 4m ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 4m scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 4m cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 5m ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 5m de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 5m e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 5m _final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 6m final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 6m inal.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 6m nal.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 6m al.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 7m l.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 7m .pth.1 5%[> ] 32.52M --.-KB/s eta 3h 7m pth.1 5%[> ] 32.52M --.-KB/s eta 3h 7m th.1 5%[> ] 32.52M --.-KB/s eta 3h 8m h.1 5%[> ] 32.52M --.-KB/s eta 3h 8m .1 5%[> ] 32.52M --.-KB/s eta 3h 8m 1 5%[> ] 32.52M --.-KB/s eta 3h 8m 5%[> ] 32.52M --.-KB/s eta 3h 9m c 5%[> ] 32.52M --.-KB/s eta 3h 9m cu 5%[> ] 32.52M --.-KB/s eta 3h 9m cut 5%[> ] 32.52M --.-KB/s eta 3h 9m cutl 5%[> ] 32.52M --.-KB/s eta 3h 10m cutle 5%[> ] 32.52M --.-KB/s eta 3h 10m cutler 5%[> ] 32.52M --.-KB/s eta 3h 10m cutler_ 5%[> ] 32.52M --.-KB/s eta 3h 10m cutler_c 5%[> ] 32.52M --.-KB/s eta 3h 11m cutler_ca 5%[> ] 32.52M --.-KB/s eta 3h 11m cutler_cas 5%[> ] 32.52M --.-KB/s eta 3h 11m cutler_casc 5%[> ] 32.52M --.-KB/s eta 3h 11m cutler_casca 5%[> ] 32.52M --.-KB/s eta 3h 12m cutler_cascad 5%[> ] 32.52M --.-KB/s eta 3h 12m cutler_cascade 5%[> ] 32.52M --.-KB/s eta 3h 12m cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 3h 12m cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 3h 13m cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 3h 13m cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 3h 13m cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 3h 13m utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 3h 14m tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 3h 14m ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 3h 14m er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 3h 14m r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 3h 15m _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 3h 15m cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 15m ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 15m scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 16m cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 16m ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 16m de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 16m e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 17m _final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 17m final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 17m inal.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 17m nal.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 18m al.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 18m l.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 18m .pth.1 5%[> ] 32.52M --.-KB/s eta 3h 18m pth.1 5%[> ] 32.52M --.-KB/s eta 3h 19m th.1 5%[> ] 32.52M --.-KB/s eta 3h 19m h.1 5%[> ] 32.52M --.-KB/s eta 3h 19m .1 5%[> ] 32.52M --.-KB/s eta 3h 19m 1 5%[> ] 32.52M --.-KB/s eta 3h 20m 5%[> ] 32.52M --.-KB/s eta 3h 20m c 5%[> ] 32.52M --.-KB/s eta 3h 20m cu 5%[> ] 32.52M --.-KB/s eta 3h 20m cut 5%[> ] 32.52M --.-KB/s eta 3h 21m cutl 5%[> ] 32.52M --.-KB/s eta 3h 21m cutle 5%[> ] 32.52M --.-KB/s eta 3h 21m cutler 5%[> ] 32.52M --.-KB/s eta 3h 21m cutler_ 5%[> ] 32.52M --.-KB/s eta 3h 22m cutler_c 5%[> ] 32.52M --.-KB/s eta 3h 22m cutler_ca 5%[> ] 32.52M --.-KB/s eta 3h 22m cutler_cas 5%[> ] 32.52M --.-KB/s eta 3h 22m cutler_casc 5%[> ] 32.52M --.-KB/s eta 3h 23m cutler_casca 5%[> ] 32.52M --.-KB/s eta 3h 23m cutler_cascad 5%[> ] 32.52M --.-KB/s eta 3h 23m cutler_cascade 5%[> ] 32.52M --.-KB/s eta 3h 23m cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 3h 24m cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 3h 24m cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 3h 24m cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 3h 24m cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 3h 25m utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 3h 25m tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 3h 25m ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 3h 25m er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 3h 26m r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 3h 26m _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 3h 26m cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 26m ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 27m scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 27m cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 27m ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 27m de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 28m e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 28m _final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 28m final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 28m inal.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 29m nal.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 29m al.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 29m l.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 29m .pth.1 5%[> ] 32.52M --.-KB/s eta 3h 30m pth.1 5%[> ] 32.52M --.-KB/s eta 3h 30m th.1 5%[> ] 32.52M --.-KB/s eta 3h 30m h.1 5%[> ] 32.52M --.-KB/s eta 3h 30m .1 5%[> ] 32.52M --.-KB/s eta 3h 31m 1 5%[> ] 32.52M --.-KB/s eta 3h 31m 5%[> ] 32.52M --.-KB/s eta 3h 31m c 5%[> ] 32.52M --.-KB/s eta 3h 31m cu 5%[> ] 32.52M --.-KB/s eta 3h 32m cut 5%[> ] 32.52M --.-KB/s eta 3h 32m cutl 5%[> ] 32.52M --.-KB/s eta 3h 32m cutle 5%[> ] 32.52M --.-KB/s eta 3h 32m cutler 5%[> ] 32.52M --.-KB/s eta 3h 33m cutler_ 5%[> ] 32.52M --.-KB/s eta 3h 33m cutler_c 5%[> ] 32.52M --.-KB/s eta 3h 33m cutler_ca 5%[> ] 32.52M --.-KB/s eta 3h 33m cutler_cas 5%[> ] 32.52M --.-KB/s eta 3h 34m cutler_casc 5%[> ] 32.52M --.-KB/s eta 3h 34m cutler_casca 5%[> ] 32.52M --.-KB/s eta 3h 34m cutler_cascad 5%[> ] 32.52M --.-KB/s eta 3h 34m cutler_cascade 5%[> ] 32.52M --.-KB/s eta 3h 35m cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 3h 35m cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 3h 35m cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 3h 35m cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 3h 36m cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 3h 36m utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 3h 36m tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 3h 36m ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 3h 37m er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 3h 37m r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 3h 37m _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 3h 37m cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 38m ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 38m scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 39m cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 39m ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 39m de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 39m e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 40m _final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 40m final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 40m inal.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 40m nal.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 41m al.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 41m l.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 41m .pth.1 5%[> ] 32.52M --.-KB/s eta 3h 41m pth.1 5%[> ] 32.52M --.-KB/s eta 3h 42m th.1 5%[> ] 32.52M --.-KB/s eta 3h 42m h.1 5%[> ] 32.52M --.-KB/s eta 3h 42m .1 5%[> ] 32.52M --.-KB/s eta 3h 42m 1 5%[> ] 32.52M --.-KB/s eta 3h 43m 5%[> ] 32.52M --.-KB/s eta 3h 43m c 5%[> ] 32.52M --.-KB/s eta 3h 43m cu 5%[> ] 32.52M --.-KB/s eta 3h 43m cut 5%[> ] 32.52M --.-KB/s eta 3h 44m cutl 5%[> ] 32.52M --.-KB/s eta 3h 44m cutle 5%[> ] 32.52M --.-KB/s eta 3h 44m cutler 5%[> ] 32.52M --.-KB/s eta 3h 44m cutler_ 5%[> ] 32.52M --.-KB/s eta 3h 45m cutler_c 5%[> ] 32.52M --.-KB/s eta 3h 45m cutler_ca 5%[> ] 32.52M --.-KB/s eta 3h 45m cutler_cas 5%[> ] 32.52M --.-KB/s eta 3h 45m cutler_casc 5%[> ] 32.52M --.-KB/s eta 3h 46m cutler_casca 5%[> ] 32.52M --.-KB/s eta 3h 46m cutler_cascad 5%[> ] 32.52M --.-KB/s eta 3h 46m cutler_cascade 5%[> ] 32.52M --.-KB/s eta 3h 46m cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 3h 47m cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 3h 47m cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 3h 47m cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 3h 47m cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 3h 48m utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 3h 48m tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 3h 48m ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 3h 48m er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 3h 49m r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 3h 49m _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 3h 49m cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 49m ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 50m scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 50m cade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 50m ade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 50m de_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 51m e_final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 51m _final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 51m final.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 51m inal.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 52m nal.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 52m al.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 52m l.pth.1 5%[> ] 32.52M --.-KB/s eta 3h 52m .pth.1 5%[> ] 32.52M --.-KB/s eta 3h 53m pth.1 5%[> ] 32.52M --.-KB/s eta 3h 53m th.1 5%[> ] 32.52M --.-KB/s eta 3h 53m h.1 5%[> ] 32.52M --.-KB/s eta 3h 53m .1 5%[> ] 32.52M --.-KB/s eta 3h 54m 1 5%[> ] 32.52M --.-KB/s eta 3h 54m 5%[> ] 32.52M --.-KB/s eta 3h 54m c 5%[> ] 32.52M --.-KB/s eta 3h 54m cu 5%[> ] 32.52M --.-KB/s eta 3h 55m cut 5%[> ] 32.52M --.-KB/s eta 3h 55m cutl 5%[> ] 32.52M --.-KB/s eta 3h 55m cutle 5%[> ] 32.52M --.-KB/s eta 3h 55m cutler 5%[> ] 32.52M --.-KB/s eta 3h 56m cutler_ 5%[> ] 32.52M --.-KB/s eta 3h 56m cutler_c 5%[> ] 32.52M --.-KB/s eta 3h 56m cutler_ca 5%[> ] 32.52M --.-KB/s eta 3h 56m cutler_cas 5%[> ] 32.52M --.-KB/s eta 3h 57m cutler_casc 5%[> ] 32.52M --.-KB/s eta 3h 57m cutler_casca 5%[> ] 32.52M --.-KB/s eta 3h 57m cutler_cascad 5%[> ] 32.52M --.-KB/s eta 3h 57m cutler_cascade 5%[> ] 32.52M --.-KB/s eta 3h 58m cutler_cascade_ 5%[> ] 32.52M --.-KB/s eta 3h 58m cutler_cascade_f 5%[> ] 32.52M --.-KB/s eta 3h 58m cutler_cascade_fi 5%[> ] 32.52M --.-KB/s eta 3h 58m cutler_cascade_fin 5%[> ] 32.52M --.-KB/s eta 3h 59m cutler_cascade_fina 5%[> ] 32.52M --.-KB/s eta 3h 59m utler_cascade_final 5%[> ] 32.52M --.-KB/s eta 3h 59m tler_cascade_final. 5%[> ] 32.52M --.-KB/s eta 3h 59m ler_cascade_final.p 5%[> ] 32.52M --.-KB/s eta 4h 0m er_cascade_final.pt 5%[> ] 32.52M --.-KB/s eta 4h 0m r_cascade_final.pth 5%[> ] 32.52M --.-KB/s eta 4h 0m _cascade_final.pth. 5%[> ] 32.52M --.-KB/s eta 4h 0m cascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 4h 1m ascade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 4h 1m scade_final.pth.1 5%[> ] 32.52M --.-KB/s eta 4h 1m cutler_cascade_fina 5%[> ] 32.52M --.-KB/s in 15m 14s + +2024-11-12 12:00:52 (36.4 KB/s) - Read error at byte 34104453/574672112 (Connection timed out). Retrying. + +--2024-11-12 12:00:53-- (try: 2) http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth +Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|2600:9000:253f:8400:13:6e38:acc0:93a1|:80... failed: Network is unreachable. +Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|2600:9000:253f:be00:13:6e38:acc0:93a1|:80... failed: Network is unreachable. +Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|2600:9000:253f:4200:13:6e38:acc0:93a1|:80... failed: Network is unreachable. +Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|2600:9000:253f:d000:13:6e38:acc0:93a1|:80... failed: Network is unreachable. +Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|2600:9000:253f:2800:13:6e38:acc0:93a1|:80... failed: Network is unreachable. +Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|2600:9000:253f:4600:13:6e38:acc0:93a1|:80... failed: Network is unreachable. +Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|2600:9000:253f:5400:13:6e38:acc0:93a1|:80... failed: Network is unreachable. +Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|2600:9000:253f:ce00:13:6e38:acc0:93a1|:80... failed: Network is unreachable. +Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|18.155.107.107|:80... connected. +HTTP request sent, awaiting response... 206 Partial Content +Length: 574672112 (548M), 540567659 (516M) remaining [binary/octet-stream] +Saving to: ‘cutler_cascade_final.pth.1’ + + [ skipping 33300K ] + 33300K ,,,,,..... .......... .......... .......... .......... 5% 2.07M 4m37s + 33350K .......... .......... .......... .......... .......... 5% 563K 10m25s + 33400K .......... .......... .......... .......... .......... 5% 1.47M 8m50s + 33450K .......... .......... .......... .......... .......... 5% 1.04M 8m41s + 33500K .......... .......... .......... .......... .......... 5% 264M 6m55s + 33550K .......... .......... .......... .......... .......... 5% 5.89M 6m0s + 33600K .......... .......... .......... .......... .......... 5% 1.22M 6m9s + 33650K .......... .......... .......... .......... .......... 6% 1.94M 5m56s + 33700K .......... .......... .......... .......... .......... 6% 93.1T 5m16s + 33750K .......... .......... .......... .......... .......... 6% 508M 4m44s + 33800K .......... .......... .......... .......... .......... 6% 1.22M 4m56s + 33850K .......... .......... .......... .......... .......... 6% 6.57M 4m38s + 33900K .......... .......... .......... .......... .......... 6% 323M 4m17s + 33950K .......... .......... .......... .......... .......... 6% 93.1T 3m58s + 34000K .......... .......... .......... .......... .......... 6% 735M 3m42s + 34050K .......... .......... .......... .......... .......... 6% 449M 3m28s + 34100K .......... .......... .......... .......... .......... 6% 2.91M 3m26s + 34150K .......... .......... .......... .......... .......... 6% 28.0M 3m16s + 34200K .......... .......... .......... 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.......... .......... .......... .......... .......... 8% 688M 74s + 47850K .......... .......... .......... .......... .......... 8% 2.41M 74s + 47900K .......... .......... .......... .......... .......... 8% 93.1T 74s + 47950K .......... .......... .......... .......... .......... 8% 5.06M 74s + 48000K .......... .......... .......... .......... .......... 8% 445M 74s + 48050K .......... .......... .......... .......... .......... 8% 93.1T 73s + 48100K .......... .......... .......... .......... .......... 8% 2.47M 74s + 48150K .......... .......... .......... .......... .......... 8% 489M 74s + 48200K .......... .......... .......... .......... .......... 8% 93.1T 73s + 48250K .......... .......... .......... .......... .......... 8% 587M 73s + 48300K .......... .......... .......... .......... .......... 8% 723M 73s + 48350K .......... .......... .......... .......... .......... 8% 820M 73s + 48400K .......... .......... .......... .......... .......... 8% 8.00M 72s + 48450K .......... .......... .......... .......... .......... 8% 435M 72s + 48500K .......... .......... .......... .......... .......... 8% 11.7M 72s + 48550K .......... .......... .......... .......... .......... 8% 2.94M 72s + 48600K .......... .......... .......... .......... .......... 8% 93.1T 72s + 48650K .......... .......... .......... .......... .......... 8% 29.1M 72s + 48700K .......... .......... .......... .......... .......... 8% 33.7M 72s + 48750K .......... .......... .......... .......... .......... 8% 5.47M 72s + 48800K .......... .......... .......... .......... .......... 8% 93.1T 72s + 48850K .......... .......... .......... .......... .......... 8% 13.5M 72s + 48900K .......... .......... .......... .......... .......... 8% 3.85M 72s + 48950K .......... .......... .......... .......... .......... 8% 11.5M 72s + 49000K .......... .......... .......... .......... .......... 8% 533M 71s + 49050K .......... .......... .......... .......... .......... 8% 3.59M 72s + 49100K .......... .......... .......... .......... .......... 8% 1.83M 72s + 49150K .......... .......... .......... .......... .......... 8% 93.1T 72s + 49200K .......... .......... .......... .......... .......... 8% 457M 72s + 49250K .......... .......... .......... .......... .......... 8% 743M 72s + 49300K .......... .......... .......... .......... .......... 8% 14.5M 71s + 49350K .......... .......... .......... .......... .......... 8% 3.10M 72s + 49400K .......... .......... .......... .......... .......... 8% 6.63M 72s + 49450K .......... .......... .......... .......... .......... 8% 432M 71s + 49500K .......... .......... .......... .......... .......... 8% 661M 71s + 49550K .......... .......... .......... .......... .......... 8% 5.88M 71s + 49600K .......... .......... .......... .......... .......... 8% 4.23M 71s + 49650K .......... .......... .......... .......... .......... 8% 1.46M 72s + 49700K .......... .......... .......... .......... .......... 8% 432M 72s + 49750K .......... .......... .......... .......... .......... 8% 632M 72s + 49800K .......... .......... .......... .......... .......... 8% 93.1T 72s + 49850K .......... .......... .......... .......... .......... 8% 607M 71s + 49900K .......... .......... .......... .......... .......... 8% 699M 71s + 49950K .......... .......... .......... .......... .......... 8% 779M 71s + 50000K .......... .......... .......... .......... .......... 8% 2.56M 71s + 50050K .......... .......... .......... .......... .......... 8% 493M 71s + 50100K .......... .......... .......... .......... .......... 8% 93.1T 71s + 50150K .......... .......... .......... .......... .......... 8% 532M 71s + 50200K .......... .......... .......... .......... .......... 8% 6.05M 71s + 50250K .......... .......... .......... .......... .......... 8% 17.3M 71s + 50300K .......... .......... .......... .......... .......... 8% 12.9M 70s + 50350K .......... .......... .......... .......... .......... 8% 6.93M 70s + 50400K .......... .......... .......... .......... .......... 8% 7.20M 70s + 50450K .......... .......... .......... .......... .......... 8% 5.62M 70s + 50500K .......... .......... .......... .......... .......... 9% 93.1T 70s + 50550K .......... .......... .......... .......... .......... 9% 5.49M 70s + 50600K .......... .......... .......... .......... .......... 9% 5.35M 70s + 50650K .......... .......... .......... .......... .......... 9% 2.32M 71s + 50700K .......... .......... .......... .......... .......... 9% 393M 71s + 50750K .......... .......... .......... .......... .......... 9% 721M 70s + 50800K .......... .......... .......... .......... .......... 9% 660M 70s + 50850K .......... .......... .......... .......... .......... 9% 93.1T 70s + 50900K .......... .......... .......... .......... .......... 9% 761M 70s + 50950K .......... .......... .......... .......... .......... 9% 3.76M 70s + 51000K .......... .......... .......... .......... .......... 9% 10.7M 70s + 51050K .......... .......... .......... .......... .......... 9% 2.68M 70s + 51100K .......... .......... .......... .......... .......... 9% 356M 70s + 51150K .......... .......... .......... .......... .......... 9% 645M 70s + 51200K .......... .......... .......... .......... .......... 9% 38.4M 70s + 51250K .......... .......... .......... .......... .......... 9% 4.73M 70s + 51300K .......... .......... .......... .......... .......... 9% 93.1T 69s + 51350K .......... .......... .......... .......... .......... 9% 444M 69s + 51400K .......... .......... .......... .......... .......... 9% 3.25M 69s + 51450K .......... .......... .......... .......... .......... 9% 573M 69s + 51500K .......... .......... .......... .......... .......... 9% 1.80M 70s + 51550K .......... .......... .......... .......... .......... 9% 505M 70s + 51600K .......... .......... .......... .......... .......... 9% 4.07M 70s + 51650K .......... .......... .......... .......... .......... 9% 3.58M 70s + 51700K .......... .......... .......... .......... .......... 9% 535M 70s + 51750K .......... .......... .......... .......... .......... 9% 8.64M 70s + 51800K .......... .......... .......... .......... .......... 9% 532M 70s + 51850K .......... .......... .......... .......... .......... 9% 93.1T 69s + 51900K .......... .......... .......... .......... .......... 9% 3.39M 70s + 51950K .......... .......... .......... .......... .......... 9% 2.83M 70s + 52000K .......... .......... .......... .......... .......... 9% 506M 70s + 52050K .......... .......... .......... .......... .......... 9% 93.1T 69s + 52100K .......... .......... .......... .......... .......... 9% 709M 69s + 52150K .......... .......... .......... .......... .......... 9% 58.1M 69s + 52200K .......... .......... .......... .......... .......... 9% 47.7M 69s + 52250K .......... .......... .......... .......... .......... 9% 6.33M 69s + 52300K .......... .......... .......... .......... .......... 9% 149M 69s + 52350K .......... .......... .......... .......... .......... 9% 604M 69s + 52400K .......... .......... .......... .......... .......... 9% 828M 68s + 52450K .......... .......... .......... .......... .......... 9% 7.32M 68s + 52500K .......... .......... .......... .......... .......... 9% 2.17M 69s + 52550K .......... .......... .......... .......... .......... 9% 12.9M 69s + 52600K .......... .......... .......... .......... .......... 9% 4.38M 69s + 52650K .......... .......... .......... .......... .......... 9% 2.42M 69s + 52700K .......... .......... .......... .......... .......... 9% 462M 69s + 52750K .......... .......... .......... .......... .......... 9% 93.1T 69s + 52800K .......... .......... .......... .......... .......... 9% 663M 69s + 52850K .......... .......... .......... .......... .......... 9% 732M 68s + 52900K .......... .......... .......... .......... .......... 9% 773M 68s + 52950K .......... .......... .......... .......... .......... 9% 93.1T 68s + 53000K .......... .......... .......... .......... .......... 9% 2.08M 69s + 53050K .......... .......... .......... .......... .......... 9% 4.87M 69s + 53100K .......... .......... .......... .......... .......... 9% 1.86M 69s + 53150K .......... .......... .......... .......... .......... 9% 419M 69s + 53200K .......... .......... .......... .......... .......... 9% 93.1T 69s + 53250K .......... .......... .......... .......... .......... 9% 721M 69s + 53300K .......... .......... .......... .......... .......... 9% 659M 68s + 53350K .......... .......... .......... .......... .......... 9% 3.61M 69s + 53400K .......... .......... .......... .......... .......... 9% 1.23M 69s + 53450K .......... .......... .......... .......... .......... 9% 141M 69s + 53500K .......... .......... .......... .......... .......... 9% 93.1T 69s + 53550K .......... .......... .......... .......... .......... 9% 237M 69s + 53600K .......... .......... .......... .......... .......... 9% 262M 69s + 53650K .......... .......... .......... .......... .......... 9% 127M 69s + 53700K .......... .......... .......... .......... .......... 9% 93.1T 68s + 53750K .......... .......... .......... .......... .......... 9% 211M 68s + 53800K .......... .......... .......... .......... .......... 9% 325M 68s + 53850K .......... .......... .......... .......... .......... 9% 17.0M 68s + 53900K .......... .......... .......... .......... .......... 9% 4.89M 68s + 53950K .......... .......... .......... .......... .......... 9% 28.9M 68s + 54000K .......... .......... .......... .......... .......... 9% 7.65M 68s + 54050K .......... .......... .......... .......... .......... 9% 7.44M 68s + 54100K .......... .......... .......... .......... .......... 9% 1.38M 69s + 54150K .......... .......... .......... .......... .......... 9% 378M 68s + 54200K .......... .......... .......... .......... .......... 9% 729M 68s + 54250K .......... .......... .......... .......... .......... 9% 93.1T 68s + 54300K .......... .......... .......... .......... .......... 9% 746M 68s + 54350K .......... .......... .......... .......... .......... 9% 2.09M 68s + 54400K .......... .......... .......... .......... .......... 9% 430M 68s + 54450K .......... .......... .......... .......... .......... 9% 596M 68s + 54500K .......... .......... .......... .......... .......... 9% 651M 68s + 54550K .......... .......... .......... .......... .......... 9% 93.1T 68s + 54600K .......... .......... .......... .......... .......... 9% 1016K 69s + 54650K .......... .......... .......... .......... .......... 9% 93.1T 68s + 54700K .......... .......... .......... .......... .......... 9% 443M 68s + 54750K .......... .......... .......... .......... .......... 9% 613M 68s + 54800K .......... .......... .......... .......... .......... 9% 565M 68s + 54850K .......... .......... .......... .......... .......... 9% 93.1T 68s + 54900K .......... .......... .......... .......... .......... 9% 46.7M 68s + 54950K .......... .......... .......... .......... .......... 9% 759M 67s + 55000K .......... .......... .......... .......... .......... 9% 947M 67s + 55050K .......... .......... .......... .......... .......... 9% 11.3M 67s + 55100K .......... .......... .......... .......... .......... 9% 8.77M 67s + 55150K .......... .......... .......... .......... .......... 9% 93.1T 67s + 55200K .......... .......... .......... .......... .......... 9% 3.24M 67s + 55250K .......... .......... .......... .......... .......... 9% 5.77M 67s + 55300K .......... .......... .......... .......... .......... 9% 15.8M 67s + 55350K .......... .......... .......... .......... .......... 9% 7.18M 67s + 55400K .......... .......... .......... .......... .......... 9% 1.32M 68s + 55450K .......... .......... .......... .......... .......... 9% 1.92M 68s + 55500K .......... .......... .......... .......... .......... 9% 432M 68s + 55550K .......... .......... .......... .......... .......... 9% 93.1T 68s + 55600K .......... .......... .......... .......... .......... 9% 587M 68s + 55650K .......... .......... .......... .......... .......... 9% 504M 68s + 55700K .......... .......... .......... .......... .......... 9% 18.3M 68s + 55750K .......... .......... .......... .......... .......... 9% 93.1T 67s + 55800K .......... .......... .......... .......... .......... 9% 3.15M 68s + 55850K .......... .......... .......... .......... .......... 9% 11.0M 68s + 55900K .......... .......... .......... .......... .......... 9% 603M 67s + 55950K .......... .......... .......... .......... .......... 9% 2.15M 68s + 56000K .......... .......... .......... .......... .......... 9% 93.1T 68s + 56050K .......... .......... .......... .......... .......... 9% 403M 67s + 56100K .......... .......... .......... .......... .......... 10% 653M 67s + 56150K .......... .......... .......... .......... .......... 10% 726M 67s + 56200K .......... .......... .......... .......... .......... 10% 31.0M 67s + 56250K .......... .......... .......... .......... .......... 10% 7.43M 67s + 56300K .......... .......... .......... .......... .......... 10% 7.19M 67s + 56350K .......... .......... .......... .......... .......... 10% 1.74M 67s + 56400K .......... .......... .......... .......... .......... 10% 93.1T 67s + 56450K .......... .......... .......... .......... .......... 10% 502M 67s + 56500K .......... .......... .......... .......... .......... 10% 691M 67s + 56550K .......... .......... .......... .......... .......... 10% 685M 67s + 56600K .......... .......... .......... .......... .......... 10% 7.60M 67s + 56650K .......... .......... .......... .......... .......... 10% 1.55M 67s + 56700K .......... .......... .......... .......... .......... 10% 443M 67s + 56750K .......... .......... .......... .......... .......... 10% 93.1T 67s + 56800K .......... .......... .......... .......... .......... 10% 517M 67s + 56850K .......... .......... .......... .......... .......... 10% 680M 67s + 56900K .......... .......... .......... .......... .......... 10% 1.23M 67s + 56950K .......... .......... .......... .......... .......... 10% 405M 67s + 57000K .......... .......... .......... .......... .......... 10% 617M 67s + 57050K .......... .......... .......... .......... .......... 10% 93.1T 67s + 57100K .......... .......... .......... .......... .......... 10% 585M 67s + 57150K .......... .......... .......... .......... .......... 10% 1.49M 67s + 57200K .......... .......... .......... .......... .......... 10% 460M 67s + 57250K .......... .......... .......... .......... .......... 10% 93.1T 67s + 57300K .......... .......... .......... .......... .......... 10% 4.30M 67s + 57350K .......... .......... .......... .......... .......... 10% 403M 67s + 57400K .......... .......... .......... .......... .......... 10% 554M 67s + 57450K .......... .......... .......... .......... .......... 10% 619M 67s + 57500K .......... .......... .......... .......... .......... 10% 93.1T 67s + 57550K .......... .......... .......... .......... .......... 10% 5.66M 67s + 57600K .......... .......... .......... .......... .......... 10% 17.7M 67s + 57650K .......... .......... .......... .......... .......... 10% 2.63M 67s + 57700K .......... .......... .......... .......... .......... 10% 428M 67s + 57750K .......... .......... .......... .......... .......... 10% 93.1T 67s + 57800K .......... .......... .......... .......... .......... 10% 655M 66s + 57850K .......... .......... .......... .......... .......... 10% 1.30M 67s + 57900K .......... .......... .......... .......... .......... 10% 1.45M 68s + 57950K .......... .......... .......... .......... .......... 10% 464M 67s + 58000K .......... .......... .......... .......... .......... 10% 93.1T 67s + 58050K .......... .......... .......... .......... .......... 10% 635M 67s + 58100K .......... .......... .......... .......... .......... 10% 5.11M 67s + 58150K .......... .......... .......... .......... .......... 10% 544M 67s + 58200K .......... .......... .......... .......... .......... 10% 3.12M 67s + 58250K .......... .......... .......... .......... .......... 10% 695M 67s + 58300K .......... .......... .......... .......... .......... 10% 809M 67s + 58350K .......... .......... .......... .......... .......... 10% 93.1T 67s + 58400K .......... .......... .......... .......... .......... 10% 402M 67s + 58450K .......... .......... .......... .......... .......... 10% 644M 67s + 58500K .......... .......... .......... .......... .......... 10% 1.33M 67s + 58550K .......... .......... .......... .......... .......... 10% 93.1T 67s + 58600K .......... .......... .......... .......... .......... 10% 490M 67s + 58650K .......... .......... .......... .......... .......... 10% 652M 67s + 58700K .......... .......... .......... .......... .......... 10% 709M 67s + 58750K .......... .......... .......... .......... .......... 10% 93.1T 66s + 58800K .......... .......... .......... .......... .......... 10% 2.38M 67s + 58850K .......... .......... .......... .......... .......... 10% 93.1T 67s + 58900K .......... .......... .......... .......... .......... 10% 536M 66s + 58950K .......... .......... .......... .......... .......... 10% 742M 66s + 59000K .......... .......... .......... .......... .......... 10% 661M 66s + 59050K .......... .......... .......... .......... .......... 10% 14.0M 66s + 59100K .......... .......... .......... .......... .......... 10% 93.1T 66s + 59150K .......... .......... .......... .......... .......... 10% 3.00M 66s + 59200K .......... .......... .......... .......... .......... 10% 15.3M 66s + 59250K .......... .......... .......... .......... .......... 10% 605M 66s + 59300K .......... .......... .......... .......... .......... 10% 11.3M 66s + 59350K .......... .......... .......... .......... .......... 10% 9.80M 66s + 59400K .......... .......... .......... .......... .......... 10% 7.43M 66s + 59450K .......... .......... .......... .......... .......... 10% 6.14M 66s + 59500K .......... .......... .......... .......... .......... 10% 37.8M 66s + 59550K .......... .......... .......... .......... .......... 10% 829K 67s + 59600K .......... .......... .......... .......... .......... 10% 93.1T 67s + 59650K .......... .......... .......... .......... .......... 10% 387M 67s + 59700K .......... .......... .......... .......... .......... 10% 658M 66s + 59750K .......... .......... .......... .......... .......... 10% 39.6M 66s + 59800K .......... .......... .......... .......... .......... 10% 781M 66s + 59850K .......... .......... .......... .......... .......... 10% 964M 66s + 59900K .......... .......... .......... .......... .......... 10% 1.30M 67s + 59950K .......... .......... .......... .......... .......... 10% 457M 66s + 60000K .......... .......... .......... .......... .......... 10% 3.70M 67s + 60050K .......... .......... .......... .......... .......... 10% 542M 66s + 60100K .......... .......... .......... .......... .......... 10% 93.1T 66s + 60150K .......... .......... .......... .......... .......... 10% 684M 66s + 60200K .......... .......... .......... .......... .......... 10% 680M 66s + 60250K .......... .......... .......... .......... .......... 10% 808M 66s + 60300K .......... .......... .......... .......... .......... 10% 4.23M 66s + 60350K .......... .......... .......... .......... .......... 10% 3.78M 66s + 60400K .......... .......... .......... .......... .......... 10% 93.1T 66s + 60450K .......... .......... .......... .......... .......... 10% 2.93M 66s + 60500K .......... .......... .......... .......... .......... 10% 449M 66s + 60550K .......... .......... .......... .......... .......... 10% 658M 66s + 60600K .......... .......... .......... .......... .......... 10% 93.1T 66s + 60650K .......... .......... .......... .......... .......... 10% 710M 66s + 60700K .......... .......... .......... .......... .......... 10% 2.58M 66s + 60750K .......... .......... .......... .......... .......... 10% 5.95M 66s + 60800K .......... .......... .......... .......... .......... 10% 1.54M 66s + 60850K .......... .......... .......... .......... .......... 10% 356M 66s + 60900K .......... .......... .......... .......... .......... 10% 93.1T 66s + 60950K .......... .......... .......... .......... .......... 10% 15.6M 66s + 61000K .......... .......... .......... .......... .......... 10% 476M 66s + 61050K .......... .......... .......... .......... .......... 10% 633M 66s + 61100K .......... .......... .......... .......... .......... 10% 93.1T 66s + 61150K .......... .......... .......... .......... .......... 10% 1.53M 66s + 61200K .......... .......... .......... .......... .......... 10% 363M 66s + 61250K .......... .......... .......... .......... .......... 10% 608M 66s + 61300K .......... .......... .......... .......... .......... 10% 93.1T 66s + 61350K .......... .......... .......... .......... .......... 10% 650M 66s + 61400K .......... .......... .......... .......... .......... 10% 1.36M 66s + 61450K .......... .......... .......... .......... .......... 10% 93.1T 66s + 61500K .......... .......... .......... .......... .......... 10% 406M 66s + 61550K .......... .......... .......... .......... .......... 10% 617M 66s + 61600K .......... .......... .......... .......... .......... 10% 697M 66s + 61650K .......... .......... .......... .......... .......... 10% 6.71M 66s + 61700K .......... .......... .......... .......... .......... 11% 1.38M 66s + 61750K .......... .......... .......... .......... .......... 11% 421M 66s + 61800K .......... .......... .......... .......... .......... 11% 2.71M 66s + 61850K .......... .......... .......... .......... .......... 11% 4.92M 66s + 61900K .......... .......... .......... .......... .......... 11% 464M 66s + 61950K .......... .......... .......... .......... .......... 11% 93.1T 66s + 62000K .......... .......... .......... .......... .......... 11% 613M 66s + 62050K .......... .......... .......... .......... .......... 11% 615M 66s + 62100K .......... .......... .......... .......... .......... 11% 1.78M 66s + 62150K .......... .......... .......... .......... .......... 11% 288M 66s + 62200K .......... .......... .......... .......... .......... 11% 435M 66s + 62250K .......... .......... .......... .......... .......... 11% 93.1T 66s + 62300K .......... .......... .......... .......... .......... 11% 2.81M 66s + 62350K .......... .......... .......... .......... .......... 11% 437M 66s + 62400K .......... .......... .......... .......... .......... 11% 660M 66s + 62450K .......... .......... .......... .......... .......... 11% 93.1T 66s + 62500K .......... .......... .......... .......... .......... 11% 644M 66s + 62550K .......... .......... .......... .......... .......... 11% 17.5M 65s + 62600K .......... .......... .......... .......... .......... 11% 93.1T 65s + 62650K .......... .......... .......... .......... .......... 11% 3.69M 65s + 62700K .......... .......... .......... .......... .......... 11% 1.28M 66s + 62750K .......... .......... .......... .......... .......... 11% 474M 66s + 62800K .......... .......... .......... .......... .......... 11% 818M 66s + 62850K .......... .......... .......... .......... .......... 11% 2.42M 66s + 62900K .......... .......... .......... .......... .......... 11% 533M 66s + 62950K .......... .......... .......... .......... .......... 11% 3.05M 66s + 63000K .......... .......... .......... .......... .......... 11% 93.1T 66s + 63050K .......... .......... .......... .......... .......... 11% 512M 66s + 63100K .......... .......... .......... .......... .......... 11% 614M 66s + 63150K .......... .......... .......... .......... .......... 11% 719M 66s + 63200K .......... .......... .......... .......... .......... 11% 26.4M 65s + 63250K .......... .......... .......... .......... .......... 11% 93.1T 65s + 63300K .......... .......... .......... .......... .......... 11% 458M 65s + 63350K .......... .......... .......... .......... .......... 11% 1.17M 66s + 63400K .......... .......... .......... .......... .......... 11% 93.1T 66s + 63450K .......... .......... .......... .......... .......... 11% 3.22M 66s + 63500K .......... .......... .......... .......... .......... 11% 353M 66s + 63550K .......... .......... .......... .......... .......... 11% 93.1T 66s + 63600K .......... .......... .......... .......... .......... 11% 2.05M 66s + 63650K .......... .......... .......... .......... .......... 11% 293M 66s + 63700K .......... .......... .......... .......... .......... 11% 646M 66s + 63750K .......... .......... .......... .......... .......... 11% 2.53M 66s + 63800K .......... .......... .......... .......... .......... 11% 1.60M 66s + 63850K .......... .......... .......... .......... .......... 11% 333M 66s + 63900K .......... .......... .......... .......... .......... 11% 93.1T 66s + 63950K .......... .......... .......... .......... .......... 11% 547M 66s + 64000K .......... .......... .......... .......... .......... 11% 592M 66s + 64050K .......... .......... .......... .......... .......... 11% 592M 66s + 64100K .......... .......... .......... .......... .......... 11% 93.1T 66s + 64150K .......... .......... .......... .......... .......... 11% 992K 66s + 64200K .......... .......... .......... .......... .......... 11% 342M 66s + 64250K .......... .......... .......... .......... .......... 11% 93.1T 66s + 64300K .......... .......... .......... .......... .......... 11% 509M 66s + 64350K .......... .......... .......... .......... .......... 11% 616M 66s + 64400K .......... .......... .......... .......... .......... 11% 660M 66s + 64450K .......... .......... .......... .......... .......... 11% 93.1T 66s + 64500K .......... .......... .......... .......... .......... 11% 683M 65s + 64550K .......... .......... .......... .......... .......... 11% 4.20M 66s + 64600K .......... .......... .......... .......... .......... 11% 558M 65s + 64650K .......... .......... .......... .......... .......... 11% 1.80M 66s + 64700K .......... .......... .......... .......... .......... 11% 353M 66s + 64750K .......... .......... .......... .......... .......... 11% 622M 66s + 64800K .......... .......... .......... .......... .......... 11% 791M 65s + 64850K .......... .......... .......... .......... .......... 11% 7.01M 65s + 64900K .......... .......... .......... .......... .......... 11% 1.29M 66s + 64950K .......... .......... .......... .......... .......... 11% 1.41M 66s + 65000K .......... .......... .......... .......... .......... 11% 396M 66s + 65050K .......... .......... .......... .......... .......... 11% 1.32M 67s + 65100K .......... .......... .......... .......... .......... 11% 390M 67s + 65150K .......... .......... .......... .......... .......... 11% 606M 66s + 65200K .......... .......... .......... .......... .......... 11% 93.1T 66s + 65250K .......... .......... .......... .......... .......... 11% 13.4M 66s + 65300K .......... .......... .......... .......... .......... 11% 428M 66s + 65350K .......... .......... .......... .......... .......... 11% 12.0M 66s + 65400K .......... .......... .......... .......... .......... 11% 548M 66s + 65450K .......... .......... .......... .......... .......... 11% 627M 66s + 65500K .......... .......... .......... .......... .......... 11% 941K 67s + 65550K .......... .......... .......... .......... .......... 11% 306M 67s + 65600K .......... .......... .......... .......... .......... 11% 717M 66s + 65650K .......... .......... .......... .......... .......... 11% 5.55M 66s + 65700K .......... .......... .......... .......... .......... 11% 93.1T 66s + 65750K .......... .......... .......... .......... .......... 11% 550M 66s + 65800K .......... .......... .......... .......... .......... 11% 746M 66s + 65850K .......... .......... .......... .......... .......... 11% 947M 66s + 65900K .......... .......... .......... .......... .......... 11% 787K 67s + 65950K .......... .......... .......... .......... .......... 11% 93.1T 67s + 66000K .......... .......... .......... .......... .......... 11% 389M 67s + 66050K .......... .......... .......... .......... .......... 11% 14.1M 67s + 66100K .......... .......... .......... .......... .......... 11% 560M 67s + 66150K .......... .......... .......... .......... .......... 11% 2.25M 67s + 66200K .......... .......... .......... .......... .......... 11% 5.30M 67s + 66250K .......... .......... .......... .......... .......... 11% 408M 67s + 66300K .......... .......... .......... .......... .......... 11% 3.21M 67s + 66350K .......... .......... .......... .......... .......... 11% 4.57M 67s + 66400K .......... .......... .......... .......... .......... 11% 5.80M 67s + 66450K .......... .......... .......... .......... .......... 11% 392M 67s + 66500K .......... .......... .......... .......... .......... 11% 2.94M 67s + 66550K .......... .......... .......... .......... .......... 11% 93.1T 67s + 66600K .......... .......... .......... .......... .......... 11% 220M 67s + 66650K .......... .......... .......... .......... .......... 11% 595M 67s + 66700K .......... .......... .......... .......... .......... 11% 4.32M 67s + 66750K .......... .......... .......... .......... .......... 11% 6.01M 67s + 66800K .......... .......... .......... .......... .......... 11% 22.6M 67s + 66850K .......... .......... .......... .......... .......... 11% 762M 66s + 66900K .......... .......... .......... .......... .......... 11% 3.29M 67s + 66950K .......... .......... .......... .......... .......... 11% 488M 66s + 67000K .......... .......... .......... .......... .......... 11% 3.36M 67s + 67050K .......... .......... .......... .......... .......... 11% 3.30M 67s + 67100K .......... .......... .......... .......... .......... 11% 2.77M 67s + 67150K .......... .......... .......... .......... .......... 11% 25.6M 67s + 67200K .......... .......... .......... .......... .......... 11% 738M 67s + 67250K .......... .......... .......... .......... .......... 11% 2.99M 67s + 67300K .......... .......... .......... .......... .......... 12% 2.99M 67s + 67350K .......... .......... .......... .......... .......... 12% 61.3M 67s + 67400K .......... .......... .......... .......... .......... 12% 8.06M 67s + 67450K .......... .......... .......... .......... .......... 12% 3.29M 67s + 67500K .......... .......... .......... .......... .......... 12% 4.55M 67s + 67550K .......... .......... .......... .......... .......... 12% 7.59M 67s + 67600K .......... .......... .......... .......... .......... 12% 93.1T 67s + 67650K .......... .......... .......... .......... .......... 12% 623M 67s + 67700K .......... .......... .......... .......... .......... 12% 3.14M 67s + 67750K .......... .......... .......... .......... .......... 12% 553M 67s + 67800K .......... .......... .......... .......... .......... 12% 789M 67s + 67850K .......... .......... .......... .......... .......... 12% 1.46M 67s + 67900K .......... .......... .......... .......... .......... 12% 1.79M 67s + 67950K .......... .......... .......... .......... .......... 12% 1.35M 68s + 68000K .......... .......... .......... .......... .......... 12% 268M 68s + 68050K .......... .......... .......... .......... .......... 12% 3.39M 68s + 68100K .......... .......... .......... .......... .......... 12% 493M 68s + 68150K .......... .......... .......... .......... .......... 12% 2.73M 68s + 68200K .......... .......... .......... .......... .......... 12% 19.5M 68s + 68250K .......... .......... .......... .......... .......... 12% 668M 68s + 68300K .......... .......... .......... .......... .......... 12% 8.67M 68s + 68350K .......... .......... .......... .......... .......... 12% 6.62M 68s + 68400K .......... .......... .......... .......... .......... 12% 5.71M 68s + 68450K .......... .......... .......... .......... .......... 12% 1.29M 68s + 68500K .......... .......... .......... .......... .......... 12% 93.1T 68s + 68550K .......... .......... .......... .......... .......... 12% 3.13M 68s + 68600K .......... .......... .......... .......... .......... 12% 3.37M 68s + 68650K .......... .......... .......... .......... .......... 12% 3.01M 68s + 68700K .......... .......... .......... .......... .......... 12% 93.1T 68s + 68750K .......... .......... .......... .......... .......... 12% 30.0M 68s + 68800K .......... .......... .......... .......... .......... 12% 2.04M 68s + 68850K .......... .......... .......... .......... .......... 12% 93.1T 68s + 68900K .......... .......... .......... .......... .......... 12% 264M 68s + 68950K .......... .......... .......... .......... .......... 12% 1.79M 68s + 69000K .......... .......... .......... .......... .......... 12% 387M 68s + 69050K .......... .......... .......... .......... .......... 12% 12.2M 68s + 69100K .......... .......... .......... .......... .......... 12% 3.07M 68s + 69150K .......... .......... .......... .......... .......... 12% 93.1T 68s + 69200K .......... .......... .......... .......... .......... 12% 7.10M 68s + 69250K .......... .......... .......... .......... .......... 12% 93.1T 68s + 69300K .......... .......... .......... .......... .......... 12% 3.90M 68s + 69350K .......... .......... .......... .......... .......... 12% 239M 68s + 69400K .......... .......... .......... .......... .......... 12% 93.1T 68s + 69450K .......... .......... .......... .......... .......... 12% 1.21M 68s + 69500K .......... .......... .......... .......... .......... 12% 93.1T 68s + 69550K .......... .......... .......... .......... .......... 12% 7.98M 68s + 69600K .......... .......... .......... .......... .......... 12% 1.92M 69s + 69650K .......... .......... .......... .......... .......... 12% 499M 68s + 69700K .......... .......... .......... .......... .......... 12% 93.1T 68s + 69750K .......... .......... .......... .......... .......... 12% 776M 68s + 69800K .......... .......... .......... .......... .......... 12% 7.04M 68s + 69850K .......... .......... .......... .......... .......... 12% 638M 68s + 69900K .......... .......... .......... .......... .......... 12% 690M 68s + 69950K .......... .......... .......... .......... .......... 12% 1.61M 68s + 70000K .......... .......... .......... .......... .......... 12% 435M 68s + 70050K .......... .......... .......... .......... .......... 12% 2.50M 68s + 70100K .......... .......... .......... .......... .......... 12% 630K 69s + 70150K .......... .......... .......... .......... .......... 12% 93.1T 69s + 70200K .......... .......... .......... .......... .......... 12% 430M 69s + 70250K .......... .......... .......... .......... .......... 12% 715M 69s + 70300K .......... .......... .......... .......... .......... 12% 769M 69s + 70350K .......... .......... .......... .......... .......... 12% 855M 69s + 70400K .......... .......... .......... .......... .......... 12% 3.08M 69s + 70450K .......... .......... .......... .......... .......... 12% 627M 69s + 70500K .......... .......... .......... .......... .......... 12% 37.4M 69s + 70550K .......... .......... .......... .......... .......... 12% 11.2M 69s + 70600K .......... .......... .......... .......... .......... 12% 14.7M 69s + 70650K .......... .......... .......... .......... .......... 12% 1.13M 69s + 70700K .......... .......... .......... .......... .......... 12% 427M 69s + 70750K .......... .......... .......... .......... .......... 12% 93.1T 69s + 70800K .......... .......... .......... .......... .......... 12% 577M 69s + 70850K .......... .......... .......... .......... .......... 12% 605M 69s + 70900K .......... .......... .......... .......... .......... 12% 575M 69s + 70950K .......... .......... .......... .......... .......... 12% 93.1T 69s + 71000K .......... .......... .......... .......... .......... 12% 1.41M 69s + 71050K .......... .......... .......... .......... .......... 12% 93.1T 69s + 71100K .......... .......... .......... .......... .......... 12% 439M 69s + 71150K .......... .......... .......... .......... .......... 12% 719M 69s + 71200K .......... .......... .......... .......... .......... 12% 76.4M 69s + 71250K .......... .......... .......... .......... .......... 12% 641K 69s + 71300K .......... .......... .......... .......... .......... 12% 93.1T 69s + 71350K .......... .......... .......... .......... .......... 12% 11.2M 69s + 71400K .......... .......... .......... .......... .......... 12% 9.90M 69s + 71450K .......... .......... .......... .......... .......... 12% 825M 69s + 71500K .......... .......... .......... .......... .......... 12% 546K 70s + 71550K .......... .......... .......... .......... .......... 12% 314M 70s + 71600K .......... .......... .......... .......... .......... 12% 93.1T 70s + 71650K .......... .......... .......... .......... .......... 12% 495M 70s + 71700K .......... .......... .......... .......... .......... 12% 510M 70s + 71750K .......... .......... .......... .......... .......... 12% 520M 70s + 71800K .......... .......... .......... .......... .......... 12% 476M 70s + 71850K .......... .......... .......... .......... .......... 12% 663K 71s + 71900K .......... .......... .......... .......... .......... 12% 93.1T 70s + 71950K .......... .......... .......... .......... .......... 12% 335M 70s + 72000K .......... .......... .......... .......... .......... 12% 674M 70s + 72050K .......... .......... .......... .......... .......... 12% 681M 70s + 72100K .......... .......... .......... .......... .......... 12% 714M 70s + 72150K .......... .......... .......... .......... .......... 12% 93.1T 70s + 72200K .......... .......... .......... .......... .......... 12% 686M 70s + 72250K .......... .......... .......... .......... .......... 12% 682M 70s + 72300K .......... .......... .......... .......... .......... 12% 735M 70s + 72350K .......... .......... .......... .......... .......... 12% 93.1T 70s + 72400K .......... .......... .......... .......... .......... 12% 640M 69s + 72450K .......... .......... .......... .......... .......... 12% 2.81M 70s + 72500K .......... .......... .......... .......... .......... 12% 30.5M 70s + 72550K .......... .......... .......... .......... .......... 12% 647K 70s + 72600K .......... .......... .......... .......... .......... 12% 93.1T 70s + 72650K .......... .......... .......... .......... .......... 12% 310M 70s + 72700K .......... .......... .......... .......... .......... 12% 471M 70s + 72750K .......... .......... .......... .......... .......... 12% 523M 70s + 72800K .......... .......... .......... .......... .......... 12% 372M 70s + 72850K .......... .......... .......... .......... .......... 12% 93.1T 70s + 72900K .......... .......... .......... .......... .......... 12% 602M 70s + 72950K .......... .......... .......... .......... .......... 13% 634M 70s + 73000K .......... .......... .......... .......... .......... 13% 571M 70s + 73050K .......... .......... .......... .......... .......... 13% 576M 69s + 73100K .......... .......... .......... .......... .......... 13% 93.1T 69s + 73150K .......... .......... .......... .......... .......... 13% 494M 69s + 73200K .......... .......... .......... .......... .......... 13% 727M 69s + 73250K .......... .......... .......... .......... .......... 13% 876M 69s + 73300K .......... .......... .......... .......... .......... 13% 4.27M 69s + 73350K .......... .......... .......... .......... .......... 13% 93.1T 69s + 73400K .......... .......... .......... .......... .......... 13% 653M 69s + 73450K .......... .......... .......... .......... .......... 13% 770M 69s + 73500K .......... .......... .......... .......... .......... 13% 969M 69s + 73550K .......... .......... .......... .......... .......... 13% 848M 69s + 73600K .......... .......... .......... .......... .......... 13% 3.24M 69s + 73650K .......... .......... .......... .......... .......... 13% 1.77M 69s + 73700K .......... .......... .......... .......... .......... 13% 1.43M 69s + 73750K .......... .......... .......... .......... .......... 13% 28.5M 69s + 73800K .......... .......... .......... .......... .......... 13% 10.0M 69s + 73850K .......... .......... .......... .......... .......... 13% 19.9M 69s + 73900K .......... .......... .......... .......... .......... 13% 555M 69s + 73950K .......... .......... .......... .......... .......... 13% 93.1T 69s + 74000K .......... .......... .......... .......... .......... 13% 580M 69s + 74050K .......... .......... .......... .......... .......... 13% 623M 69s + 74100K .......... .......... .......... .......... .......... 13% 709M 69s + 74150K .......... .......... .......... .......... .......... 13% 13.1M 69s + 74200K .......... .......... .......... .......... .......... 13% 10.6M 69s + 74250K .......... .......... .......... .......... .......... 13% 8.55M 69s + 74300K .......... .......... .......... .......... .......... 13% 9.84M 69s + 74350K .......... .......... .......... .......... .......... 13% 8.47M 69s + 74400K .......... .......... .......... .......... .......... 13% 1.32M 69s + 74450K .......... .......... .......... .......... .......... 13% 10.9M 69s + 74500K .......... .......... .......... .......... .......... 13% 417M 69s + 74550K .......... .......... .......... .......... .......... 13% 507M 69s + 74600K .......... .......... .......... .......... .......... 13% 93.1T 69s + 74650K .......... .......... .......... .......... .......... 13% 584M 68s + 74700K .......... .......... .......... .......... .......... 13% 679M 68s + 74750K .......... .......... .......... .......... .......... 13% 10.5M 68s + 74800K .......... .......... .......... .......... .......... 13% 10.6M 68s + 74850K .......... .......... .......... .......... .......... 13% 4.86M 68s + 74900K .......... .......... .......... .......... .......... 13% 1.07M 69s + 74950K .......... .......... .......... .......... .......... 13% 5.75M 69s + 75000K .......... .......... .......... .......... .......... 13% 881K 69s + 75050K .......... .......... .......... .......... .......... 13% 407M 69s + 75100K .......... .......... .......... .......... .......... 13% 93.1T 69s + 75150K .......... .......... .......... .......... .......... 13% 577M 69s + 75200K .......... .......... .......... .......... .......... 13% 2.33M 69s + 75250K .......... .......... .......... .......... .......... 13% 370M 69s + 75300K .......... .......... .......... .......... .......... 13% 584M 69s + 75350K .......... .......... .......... .......... .......... 13% 2.44M 69s + 75400K .......... .......... .......... .......... .......... 13% 93.1T 69s + 75450K .......... .......... .......... .......... .......... 13% 323M 69s + 75500K .......... .......... .......... .......... .......... 13% 479M 69s + 75550K .......... .......... .......... .......... .......... 13% 571M 69s + 75600K .......... .......... .......... .......... .......... 13% 93.1T 69s + 75650K .......... .......... .......... .......... .......... 13% 574M 69s + 75700K .......... .......... .......... .......... .......... 13% 626M 69s + 75750K .......... .......... .......... .......... .......... 13% 598M 69s + 75800K .......... .......... .......... .......... .......... 13% 645M 68s + 75850K .......... .......... .......... .......... .......... 13% 93.1T 68s + 75900K .......... .......... .......... .......... .......... 13% 578M 68s + 75950K .......... .......... .......... .......... .......... 13% 674M 68s + 76000K .......... .......... .......... .......... .......... 13% 4.68M 68s + 76050K .......... .......... .......... .......... .......... 13% 638M 68s + 76100K .......... .......... .......... .......... .......... 13% 680M 68s + 76150K .......... .......... .......... .......... .......... 13% 93.1T 68s + 76200K .......... .......... .......... .......... .......... 13% 686M 68s + 76250K .......... .......... .......... .......... .......... 13% 1.50M 68s + 76300K .......... .......... .......... .......... .......... 13% 93.1T 68s + 76350K .......... .......... .......... .......... .......... 13% 331M 68s + 76400K .......... .......... .......... .......... .......... 13% 624M 68s + 76450K .......... .......... .......... .......... .......... 13% 3.19M 68s + 76500K .......... .......... .......... .......... .......... 13% 93.1T 68s + 76550K .......... .......... .......... .......... .......... 13% 596M 68s + 76600K .......... .......... .......... .......... .......... 13% 861M 68s + 76650K .......... .......... .......... .......... .......... 13% 950M 68s + 76700K .......... .......... .......... .......... .......... 13% 1.17G 68s + 76750K .......... .......... .......... .......... .......... 13% 6.84M 68s + 76800K .......... .......... .......... .......... .......... 13% 1.42M 68s + 76850K .......... .......... .......... .......... .......... 13% 413M 68s + 76900K .......... .......... .......... .......... .......... 13% 93.1T 68s + 76950K .......... .......... .......... .......... .......... 13% 660M 68s + 77000K .......... .......... .......... .......... .......... 13% 756M 68s + 77050K .......... .......... .......... .......... .......... 13% 1.16M 68s + 77100K .......... .......... .......... .......... .......... 13% 580M 68s + 77150K .......... .......... .......... .......... .......... 13% 2.11M 68s + 77200K .......... .......... .......... .......... .......... 13% 5.06M 68s + 77250K .......... .......... .......... .......... .......... 13% 5.37M 68s + 77300K .......... .......... .......... .......... .......... 13% 534M 68s + 77350K .......... .......... .......... .......... .......... 13% 2.04M 68s + 77400K .......... .......... .......... .......... .......... 13% 4.79M 68s + 77450K .......... .......... .......... .......... .......... 13% 9.14M 68s + 77500K .......... .......... .......... .......... .......... 13% 19.5M 68s + 77550K .......... .......... .......... .......... .......... 13% 401M 68s + 77600K .......... .......... .......... .......... .......... 13% 11.6M 68s + 77650K .......... .......... .......... .......... .......... 13% 5.94M 68s + 77700K .......... .......... .......... .......... .......... 13% 2.21M 68s + 77750K .......... .......... .......... .......... .......... 13% 378M 68s + 77800K .......... .......... .......... .......... .......... 13% 666M 68s + 77850K .......... .......... .......... .......... .......... 13% 2.02M 68s + 77900K .......... .......... .......... .......... .......... 13% 93.1T 68s + 77950K .......... .......... .......... .......... .......... 13% 8.51M 68s + 78000K .......... .......... .......... .......... .......... 13% 93.1T 68s + 78050K .......... .......... .......... .......... .......... 13% 536M 68s + 78100K .......... .......... .......... .......... .......... 13% 653M 68s + 78150K .......... .......... .......... .......... .......... 13% 806M 68s + 78200K .......... .......... .......... .......... .......... 13% 3.10M 68s + 78250K .......... .......... .......... .......... .......... 13% 632M 68s + 78300K .......... .......... .......... .......... .......... 13% 20.4M 68s + 78350K .......... .......... .......... .......... .......... 13% 470M 68s + 78400K .......... .......... .......... .......... .......... 13% 708M 67s + 78450K .......... .......... .......... .......... .......... 13% 1.15M 68s + 78500K .......... .......... .......... .......... .......... 13% 93.1T 68s + 78550K .......... .......... .......... .......... .......... 14% 2.65M 68s + 78600K .......... .......... .......... .......... .......... 14% 93.1T 68s + 78650K .......... .......... .......... .......... .......... 14% 352M 68s + 78700K .......... .......... .......... .......... .......... 14% 621M 68s + 78750K .......... .......... .......... .......... .......... 14% 720M 68s + 78800K .......... .......... .......... .......... .......... 14% 1.65M 68s + 78850K .......... .......... .......... .......... .......... 14% 462M 68s + 78900K .......... .......... .......... .......... .......... 14% 617M 68s + 78950K .......... .......... .......... .......... .......... 14% 93.1T 68s + 79000K .......... .......... .......... .......... .......... 14% 737M 67s + 79050K .......... .......... .......... .......... .......... 14% 766M 67s + 79100K .......... .......... .......... .......... .......... 14% 1.15M 68s + 79150K .......... .......... .......... .......... .......... 14% 494M 68s + 79200K .......... .......... .......... .......... .......... 14% 15.3M 68s + 79250K .......... .......... .......... .......... .......... 14% 3.73M 68s + 79300K .......... .......... .......... .......... .......... 14% 5.48M 68s + 79350K .......... .......... .......... .......... .......... 14% 56.2M 68s + 79400K .......... 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.......... .......... .......... .......... .......... 14% 7.55M 67s + 80100K .......... .......... .......... .......... .......... 14% 10.1M 67s + 80150K .......... .......... .......... .......... .......... 14% 9.16M 67s + 80200K .......... .......... .......... .......... .......... 14% 8.38M 67s + 80250K .......... .......... .......... .......... .......... 14% 7.53M 67s + 80300K .......... .......... .......... .......... .......... 14% 4.43M 67s + 80350K .......... .......... .......... .......... .......... 14% 1.21M 67s + 80400K .......... .......... .......... .......... .......... 14% 2.02M 68s + 80450K .......... .......... .......... .......... .......... 14% 5.33M 68s + 80500K .......... .......... .......... .......... .......... 14% 1.20M 68s + 80550K .......... .......... .......... .......... .......... 14% 390M 68s + 80600K .......... .......... .......... .......... .......... 14% 561M 68s + 80650K .......... .......... .......... .......... .......... 14% 93.1T 68s + 80700K .......... .......... .......... .......... .......... 14% 2.85M 68s + 80750K .......... .......... .......... .......... .......... 14% 505M 68s + 80800K .......... .......... .......... .......... .......... 14% 1.84M 68s + 80850K .......... .......... .......... .......... .......... 14% 476M 68s + 80900K .......... .......... .......... .......... .......... 14% 451M 68s + 80950K .......... .......... .......... .......... .......... 14% 93.1T 68s + 81000K .......... .......... .......... .......... .......... 14% 599M 68s + 81050K .......... .......... .......... .......... .......... 14% 694M 68s + 81100K .......... .......... .......... .......... .......... 14% 717M 68s + 81150K .......... .......... .......... .......... .......... 14% 93.1T 67s + 81200K .......... .......... .......... .......... .......... 14% 1.80M 68s + 81250K .......... .......... .......... .......... .......... 14% 230M 68s + 81300K .......... .......... .......... .......... .......... 14% 313M 67s + 81350K .......... .......... .......... .......... .......... 14% 93.1T 67s + 81400K .......... .......... .......... .......... .......... 14% 2.64M 68s + 81450K .......... .......... .......... .......... .......... 14% 93.1T 67s + 81500K .......... .......... .......... .......... .......... 14% 493M 67s + 81550K .......... .......... .......... .......... .......... 14% 771M 67s + 81600K .......... .......... .......... .......... .......... 14% 6.17M 67s + 81650K .......... .......... .......... .......... .......... 14% 19.5M 67s + 81700K .......... .......... .......... .......... .......... 14% 4.26M 67s + 81750K .......... .......... .......... .......... .......... 14% 1.81M 67s + 81800K .......... .......... .......... .......... .......... 14% 386M 67s + 81850K .......... .......... .......... .......... .......... 14% 4.02M 67s + 81900K .......... .......... .......... .......... .......... 14% 458M 67s + 81950K .......... .......... .......... .......... .......... 14% 93.1T 67s + 82000K .......... .......... .......... .......... .......... 14% 1.72M 67s + 82050K .......... .......... .......... .......... .......... 14% 493M 67s + 82100K .......... .......... .......... .......... .......... 14% 93.1T 67s + 82150K .......... .......... .......... .......... .......... 14% 10.1M 67s + 82200K .......... .......... .......... .......... .......... 14% 2.06M 67s + 82250K .......... .......... .......... .......... .......... 14% 582M 67s + 82300K .......... .......... .......... .......... .......... 14% 93.1T 67s + 82350K .......... .......... .......... .......... .......... 14% 5.99M 67s + 82400K .......... .......... .......... .......... .......... 14% 2.48M 67s + 82450K .......... .......... .......... .......... .......... 14% 463M 67s + 82500K .......... .......... .......... .......... .......... 14% 728M 67s + 82550K .......... .......... .......... .......... .......... 14% 825M 67s + 82600K .......... .......... .......... 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.......... .......... .......... .......... 14% 351M 67s + 83300K .......... .......... .......... .......... .......... 14% 2.02M 67s + 83350K .......... .......... .......... .......... .......... 14% 528M 67s + 83400K .......... .......... .......... .......... .......... 14% 2.28M 68s + 83450K .......... .......... .......... .......... .......... 14% 93.1T 67s + 83500K .......... .......... .......... .......... .......... 14% 541M 67s + 83550K .......... .......... .......... .......... .......... 14% 15.2M 67s + 83600K .......... .......... .......... .......... .......... 14% 632M 67s + 83650K .......... .......... .......... .......... .......... 14% 1.74M 67s + 83700K .......... .......... .......... .......... .......... 14% 12.2M 67s + 83750K .......... .......... .......... .......... .......... 14% 480M 67s + 83800K .......... .......... .......... .......... .......... 14% 4.39M 67s + 83850K .......... .......... .......... .......... .......... 14% 93.1T 67s + 83900K .......... .......... .......... .......... .......... 14% 1.75M 68s + 83950K .......... .......... .......... .......... .......... 14% 93.1T 67s + 84000K .......... .......... .......... .......... .......... 14% 530M 67s + 84050K .......... .......... .......... .......... .......... 14% 6.40M 67s + 84100K .......... .......... .......... .......... .......... 14% 4.40M 67s + 84150K .......... .......... .......... .......... .......... 15% 1.84M 68s + 84200K .......... .......... .......... .......... .......... 15% 93.1T 67s + 84250K .......... .......... .......... .......... .......... 15% 374M 67s + 84300K .......... .......... .......... .......... .......... 15% 680M 67s + 84350K .......... .......... .......... .......... .......... 15% 6.92M 67s + 84400K .......... .......... .......... .......... .......... 15% 5.17M 67s + 84450K .......... .......... .......... .......... .......... 15% 20.2M 67s + 84500K .......... .......... .......... .......... .......... 15% 4.57M 67s + 84550K .......... .......... .......... .......... .......... 15% 29.7M 67s + 84600K .......... .......... .......... .......... .......... 15% 3.10M 67s + 84650K .......... .......... .......... .......... .......... 15% 93.1T 67s + 84700K .......... .......... .......... .......... .......... 15% 1.57M 67s + 84750K .......... .......... .......... .......... .......... 15% 307M 67s + 84800K .......... .......... .......... .......... .......... 15% 583M 67s + 84850K .......... .......... .......... .......... .......... 15% 2.08M 67s + 84900K .......... .......... .......... .......... .......... 15% 2.94M 68s + 84950K .......... .......... .......... .......... .......... 15% 457M 68s + 85000K .......... .......... .......... .......... .......... 15% 652M 67s + 85050K .......... .......... .......... .......... .......... 15% 4.65M 67s + 85100K .......... .......... .......... .......... .......... 15% 3.70M 68s + 85150K .......... .......... .......... .......... .......... 15% 1.34M 68s + 85200K .......... .......... .......... .......... .......... 15% 12.3M 68s + 85250K .......... .......... .......... .......... .......... 15% 565M 68s + 85300K .......... .......... .......... .......... .......... 15% 93.1T 68s + 85350K .......... .......... .......... .......... .......... 15% 1.48M 68s + 85400K .......... .......... .......... .......... .......... 15% 478M 68s + 85450K .......... .......... .......... .......... .......... 15% 6.81M 68s + 85500K .......... .......... .......... .......... .......... 15% 93.1T 68s + 85550K .......... .......... .......... .......... .......... 15% 529M 68s + 85600K .......... .......... .......... .......... .......... 15% 1.30M 68s + 85650K .......... .......... .......... .......... .......... 15% 515M 68s + 85700K .......... .......... .......... .......... .......... 15% 93.1T 68s + 85750K .......... .......... .......... .......... .......... 15% 577M 68s + 85800K .......... .......... .......... .......... .......... 15% 759M 68s + 85850K .......... .......... .......... .......... .......... 15% 12.6M 68s + 85900K .......... .......... .......... .......... .......... 15% 11.8M 68s + 85950K .......... .......... .......... .......... .......... 15% 2.84M 68s + 86000K .......... .......... .......... .......... .......... 15% 12.0M 68s + 86050K .......... .......... .......... .......... .......... 15% 3.71M 68s + 86100K .......... .......... .......... .......... .......... 15% 8.50M 68s + 86150K .......... .......... .......... .......... .......... 15% 5.96M 68s + 86200K .......... .......... .......... .......... .......... 15% 5.41M 68s + 86250K .......... .......... .......... .......... .......... 15% 8.60M 68s + 86300K .......... .......... .......... .......... .......... 15% 1.47M 68s + 86350K .......... .......... .......... .......... .......... 15% 93.1T 68s + 86400K .......... .......... .......... .......... .......... 15% 509M 68s + 86450K .......... .......... .......... .......... .......... 15% 2.87M 68s + 86500K .......... .......... .......... .......... .......... 15% 2.59M 68s + 86550K .......... .......... .......... .......... .......... 15% 8.49M 68s + 86600K .......... .......... .......... .......... .......... 15% 93.1T 68s + 86650K .......... .......... .......... .......... .......... 15% 1.87M 68s + 86700K .......... .......... .......... .......... .......... 15% 404M 68s + 86750K .......... .......... .......... .......... .......... 15% 1.45M 68s + 86800K .......... .......... .......... .......... .......... 15% 93.1T 68s + 86850K .......... .......... .......... .......... .......... 15% 362M 68s + 86900K .......... .......... .......... .......... .......... 15% 10.5M 68s + 86950K .......... .......... .......... .......... .......... 15% 678M 68s + 87000K .......... .......... .......... .......... .......... 15% 1.10M 68s + 87050K .......... .......... .......... .......... .......... 15% 93.1T 68s + 87100K .......... .......... .......... .......... .......... 15% 494M 68s + 87150K .......... .......... .......... .......... .......... 15% 3.12M 68s + 87200K .......... .......... .......... .......... .......... 15% 3.60M 68s + 87250K .......... .......... .......... .......... .......... 15% 93.1T 68s + 87300K .......... .......... .......... .......... .......... 15% 2.22M 68s + 87350K .......... .......... .......... .......... .......... 15% 533M 68s + 87400K .......... .......... .......... .......... .......... 15% 754M 68s + 87450K .......... .......... .......... .......... .......... 15% 93.1T 68s + 87500K .......... .......... .......... .......... .......... 15% 12.5M 68s + 87550K .......... .......... .......... .......... .......... 15% 3.50M 68s + 87600K .......... .......... .......... .......... .......... 15% 5.44M 68s + 87650K .......... .......... .......... .......... .......... 15% 5.92M 68s + 87700K .......... .......... .......... .......... .......... 15% 93.1T 68s + 87750K .......... .......... .......... .......... .......... 15% 1.49M 68s + 87800K .......... .......... .......... .......... .......... 15% 509M 68s + 87850K .......... .......... .......... .......... .......... 15% 4.63M 68s + 87900K .......... .......... .......... .......... .......... 15% 6.68M 68s + 87950K .......... .......... .......... .......... .......... 15% 695K 69s + 88000K .......... .......... .......... .......... .......... 15% 409M 69s + 88050K .......... .......... .......... .......... .......... 15% 750M 69s + 88100K .......... .......... .......... .......... .......... 15% 1.65M 69s + 88150K .......... .......... .......... .......... .......... 15% 468M 69s + 88200K .......... .......... .......... .......... .......... 15% 734M 69s + 88250K .......... .......... .......... .......... .......... 15% 93.1T 68s + 88300K .......... .......... .......... .......... .......... 15% 2.99M 69s + 88350K .......... .......... .......... .......... .......... 15% 551M 68s + 88400K .......... .......... .......... .......... .......... 15% 3.23M 69s + 88450K .......... .......... .......... .......... .......... 15% 93.1T 68s + 88500K .......... .......... .......... .......... .......... 15% 411M 68s + 88550K .......... .......... .......... .......... .......... 15% 2.69M 69s + 88600K .......... .......... .......... .......... .......... 15% 3.91M 69s + 88650K .......... .......... .......... .......... .......... 15% 588M 68s + 88700K .......... .......... .......... .......... .......... 15% 10.1M 68s + 88750K .......... .......... .......... .......... .......... 15% 2.89M 69s + 88800K .......... .......... .......... .......... .......... 15% 6.31M 69s + 88850K .......... .......... .......... .......... .......... 15% 11.1M 68s + 88900K .......... .......... .......... .......... .......... 15% 2.99M 69s + 88950K .......... .......... .......... .......... .......... 15% 2.40M 69s + 89000K .......... .......... .......... .......... .......... 15% 458M 69s + 89050K .......... .......... .......... .......... .......... 15% 93.1T 69s + 89100K .......... .......... .......... .......... .......... 15% 1.74M 69s + 89150K .......... .......... .......... .......... .......... 15% 561M 69s + 89200K .......... .......... .......... .......... .......... 15% 4.55M 69s + 89250K .......... .......... .......... .......... .......... 15% 555M 69s + 89300K .......... .......... .......... .......... .......... 15% 1.22M 69s + 89350K .......... .......... .......... .......... .......... 15% 541M 69s + 89400K .......... .......... .......... .......... .......... 15% 93.1T 69s + 89450K .......... .......... .......... .......... .......... 15% 2.86M 69s + 89500K .......... .......... .......... .......... .......... 15% 1.55M 69s + 89550K .......... .......... .......... .......... .......... 15% 481M 69s + 89600K .......... .......... .......... .......... .......... 15% 1.46M 69s + 89650K .......... .......... .......... .......... .......... 15% 93.1T 69s + 89700K .......... .......... .......... .......... .......... 15% 489M 69s + 89750K .......... .......... .......... .......... .......... 16% 812M 69s + 89800K .......... .......... .......... .......... .......... 16% 1.73M 69s + 89850K .......... .......... .......... .......... .......... 16% 1.51M 69s + 89900K .......... .......... .......... .......... .......... 16% 93.1T 69s + 89950K .......... .......... .......... .......... .......... 16% 505M 69s + 90000K .......... .......... .......... .......... .......... 16% 741M 69s + 90050K .......... .......... .......... .......... .......... 16% 3.99M 69s + 90100K .......... .......... .......... .......... .......... 16% 93.1T 69s + 90150K .......... .......... .......... .......... .......... 16% 482M 69s + 90200K .......... .......... .......... .......... .......... 16% 641M 69s + 90250K .......... .......... .......... .......... .......... 16% 805M 69s + 90300K .......... .......... .......... .......... .......... 16% 2.38M 69s + 90350K .......... .......... .......... .......... .......... 16% 606M 69s + 90400K .......... .......... .......... .......... .......... 16% 2.63M 69s + 90450K .......... .......... .......... .......... .......... 16% 20.2M 69s + 90500K .......... .......... .......... .......... .......... 16% 617M 69s + 90550K .......... .......... .......... .......... .......... 16% 730M 69s + 90600K .......... .......... .......... .......... .......... 16% 2.02M 69s + 90650K .......... .......... .......... .......... .......... 16% 8.32M 69s + 90700K .......... .......... .......... .......... .......... 16% 2.44M 69s + 90750K .......... .......... .......... .......... .......... 16% 93.1T 69s + 90800K .......... .......... .......... .......... .......... 16% 498M 69s + 90850K .......... .......... .......... .......... .......... 16% 6.03M 69s + 90900K .......... .......... .......... .......... .......... 16% 561M 69s + 90950K .......... .......... .......... .......... .......... 16% 846M 69s + 91000K .......... .......... .......... .......... .......... 16% 6.09M 69s + 91050K .......... .......... .......... .......... .......... 16% 1.78M 69s + 91100K .......... .......... .......... .......... .......... 16% 1.44M 69s + 91150K .......... .......... .......... .......... .......... 16% 93.1T 69s + 91200K .......... .......... .......... .......... .......... 16% 413M 69s + 91250K .......... .......... .......... .......... .......... 16% 1.12M 69s + 91300K .......... .......... .......... .......... .......... 16% 93.1T 69s + 91350K .......... .......... .......... .......... .......... 16% 478M 69s + 91400K .......... .......... .......... .......... .......... 16% 746M 69s + 91450K .......... .......... .......... .......... .......... 16% 858M 69s + 91500K .......... .......... .......... .......... .......... 16% 35.5M 69s + 91550K .......... .......... .......... .......... .......... 16% 4.04M 69s + 91600K .......... .......... .......... .......... .......... 16% 534M 69s + 91650K .......... .......... .......... .......... .......... 16% 32.3M 69s + 91700K .......... .......... .......... .......... .......... 16% 4.26M 69s + 91750K .......... .......... .......... .......... .......... 16% 3.16M 69s + 91800K .......... .......... .......... .......... .......... 16% 9.96M 69s + 91850K .......... .......... .......... .......... .......... 16% 93.1T 69s + 91900K .......... .......... .......... .......... .......... 16% 2.92M 69s + 91950K .......... .......... .......... .......... .......... 16% 697M 69s + 92000K .......... .......... .......... .......... .......... 16% 1.59M 69s + 92050K .......... .......... .......... .......... .......... 16% 511M 69s + 92100K .......... .......... .......... .......... .......... 16% 93.1T 69s + 92150K .......... .......... .......... .......... .......... 16% 762M 69s + 92200K .......... .......... .......... .......... .......... 16% 12.3M 69s + 92250K .......... .......... .......... .......... .......... 16% 2.50M 69s + 92300K .......... .......... .......... .......... .......... 16% 93.1T 69s + 92350K .......... .......... .......... .......... .......... 16% 6.49M 69s + 92400K .......... .......... .......... .......... .......... 16% 5.34M 69s + 92450K .......... .......... .......... .......... .......... 16% 4.45M 69s + 92500K .......... .......... .......... .......... .......... 16% 19.7M 69s + 92550K .......... .......... .......... .......... .......... 16% 5.54M 69s + 92600K .......... .......... .......... .......... .......... 16% 5.55M 69s + 92650K .......... .......... .......... .......... .......... 16% 13.1M 69s + 92700K .......... .......... .......... .......... .......... 16% 6.02M 69s + 92750K .......... .......... .......... .......... .......... 16% 512M 69s + 92800K .......... .......... .......... .......... .......... 16% 3.97M 69s + 92850K .......... .......... .......... .......... .......... 16% 93.1T 69s + 92900K .......... .......... .......... .......... .......... 16% 502M 69s + 92950K .......... .......... .......... .......... .......... 16% 340K 70s + 93000K .......... .......... .......... .......... .......... 16% 457K 70s + 93050K .......... .......... .......... .......... .......... 16% 309M 70s + 93100K .......... .......... .......... .......... .......... 16% 25.7M 70s + 93150K .......... .......... .......... .......... .......... 16% 2.90M 70s + 93200K .......... .......... .......... .......... .......... 16% 347M 70s + 93250K .......... .......... .......... .......... .......... 16% 511M 70s + 93300K .......... .......... .......... .......... .......... 16% 93.1T 70s + 93350K .......... .......... .......... .......... .......... 16% 105M 70s + 93400K .......... .......... .......... .......... .......... 16% 4.84M 70s + 93450K .......... .......... .......... .......... .......... 16% 416K 71s + 93500K .......... .......... .......... .......... .......... 16% 324M 71s + 93550K .......... .......... .......... .......... .......... 16% 25.9M 71s + 93600K .......... .......... .......... .......... .......... 16% 25.2M 71s + 93650K .......... .......... .......... .......... .......... 16% 2.30M 71s + 93700K .......... .......... .......... .......... .......... 16% 369M 71s + 93750K .......... .......... .......... .......... .......... 16% 598M 71s + 93800K .......... .......... .......... .......... .......... 16% 453M 71s + 93850K .......... .......... .......... .......... .......... 16% 15.1M 71s + 93900K .......... .......... .......... .......... .......... 16% 365M 71s + 93950K .......... .......... .......... .......... .......... 16% 93.1T 71s + 94000K .......... .......... .......... .......... .......... 16% 693M 71s + 94050K .......... .......... .......... .......... .......... 16% 449K 71s + 94100K .......... .......... .......... .......... .......... 16% 2.81M 71s + 94150K .......... .......... .......... .......... .......... 16% 14.3M 71s + 94200K .......... .......... .......... .......... .......... 16% 93.1T 71s + 94250K .......... .......... .......... .......... .......... 16% 394K 72s + 94300K .......... .......... .......... .......... .......... 16% 93.1T 72s + 94350K .......... .......... .......... .......... .......... 16% 2.84M 72s + 94400K .......... .......... .......... .......... .......... 16% 421M 72s + 94450K .......... .......... .......... .......... .......... 16% 93.1T 72s + 94500K .......... .......... .......... .......... .......... 16% 536M 72s + 94550K .......... .......... .......... .......... .......... 16% 710M 72s + 94600K .......... .......... .......... .......... .......... 16% 26.7M 72s + 94650K .......... .......... .......... .......... .......... 16% 390K 73s + 94700K .......... .......... .......... .......... .......... 16% 322M 73s + 94750K .......... .......... .......... .......... .......... 16% 680M 73s + 94800K .......... .......... .......... .......... .......... 16% 2.74M 73s + 94850K .......... .......... .......... .......... .......... 16% 146M 73s + 94900K .......... .......... .......... .......... .......... 16% 5.90M 73s + 94950K .......... .......... .......... .......... .......... 16% 465M 73s + 95000K .......... .......... .......... .......... .......... 16% 606M 73s + 95050K .......... .......... .......... .......... .......... 16% 93.1T 72s + 95100K .......... .......... .......... .......... .......... 16% 126M 72s + 95150K .......... .......... .......... .......... .......... 16% 544M 72s + 95200K .......... .......... .......... .......... .......... 16% 10.8M 72s + 95250K .......... .......... .......... .......... .......... 16% 467K 73s + 95300K .......... .......... .......... .......... .......... 16% 593M 73s + 95350K .......... .......... .......... .......... .......... 16% 2.12M 73s + 95400K .......... .......... .......... .......... .......... 17% 3.57M 73s + 95450K .......... .......... .......... .......... .......... 17% 118M 73s + 95500K .......... .......... .......... .......... .......... 17% 93.1T 73s + 95550K .......... .......... .......... .......... .......... 17% 256M 73s + 95600K .......... .......... .......... .......... .......... 17% 338M 73s + 95650K .......... .......... .......... .......... .......... 17% 906K 73s + 95700K .......... .......... .......... .......... .......... 17% 430M 73s + 95750K .......... .......... .......... .......... .......... 17% 93.1T 73s + 95800K .......... .......... .......... .......... .......... 17% 748M 73s + 95850K .......... .......... .......... .......... .......... 17% 747M 73s + 95900K .......... .......... .......... .......... .......... 17% 28.7M 73s + 95950K .......... .......... .......... .......... .......... 17% 698M 73s + 96000K .......... .......... .......... .......... .......... 17% 871M 73s + 96050K .......... .......... .......... .......... .......... 17% 72.8M 73s + 96100K .......... .......... .......... .......... .......... 17% 317K 74s + 96150K .......... .......... .......... .......... .......... 17% 93.1T 74s + 96200K .......... .......... .......... .......... .......... 17% 81.4M 74s + 96250K .......... .......... .......... .......... .......... 17% 225M 74s + 96300K .......... .......... .......... .......... .......... 17% 274M 74s + 96350K .......... .......... .......... .......... .......... 17% 343M 73s + 96400K .......... .......... .......... .......... .......... 17% 278K 75s + 96450K .......... .......... .......... .......... .......... 17% 93.1T 75s + 96500K .......... .......... .......... .......... .......... 17% 2.66M 75s + 96550K .......... .......... .......... .......... .......... 17% 171M 75s + 96600K .......... .......... .......... .......... .......... 17% 93.1T 75s + 96650K .......... .......... .......... .......... .......... 17% 344M 75s + 96700K .......... .......... .......... .......... .......... 17% 411M 74s + 96750K .......... .......... .......... .......... .......... 17% 5.01M 74s + 96800K .......... .......... .......... .......... .......... 17% 531M 74s + 96850K .......... .......... .......... .......... .......... 17% 405K 75s + 96900K .......... .......... .......... .......... .......... 17% 24.0M 75s + 96950K .......... .......... .......... .......... .......... 17% 680M 75s + 97000K .......... .......... .......... .......... .......... 17% 93.1T 75s + 97050K .......... .......... .......... .......... .......... 17% 765M 75s + 97100K .......... .......... .......... .......... .......... 17% 901M 75s + 97150K .......... .......... .......... .......... .......... 17% 1.80M 75s + 97200K .......... .......... .......... .......... .......... 17% 392M 75s + 97250K .......... .......... .......... .......... .......... 17% 677M 75s + 97300K .......... .......... .......... .......... .......... 17% 93.1T 75s + 97350K .......... .......... .......... .......... .......... 17% 6.45M 75s + 97400K .......... .......... .......... .......... .......... 17% 353K 76s + 97450K .......... .......... .......... .......... .......... 17% 330M 76s + 97500K .......... .......... .......... .......... .......... 17% 27.2M 76s + 97550K .......... .......... .......... .......... .......... 17% 1.78M 76s + 97600K .......... .......... .......... .......... .......... 17% 397M 76s + 97650K .......... .......... .......... .......... .......... 17% 93.1T 76s + 97700K .......... .......... .......... .......... .......... 17% 557M 76s + 97750K .......... .......... .......... .......... .......... 17% 658M 76s + 97800K .......... .......... .......... .......... .......... 17% 7.24M 76s + 97850K .......... .......... .......... .......... .......... 17% 429M 75s + 97900K .......... .......... .......... .......... .......... 17% 565M 75s + 97950K .......... .......... .......... .......... .......... 17% 93.1T 75s + 98000K .......... .......... .......... .......... .......... 17% 461M 75s + 98050K .......... .......... .......... .......... .......... 17% 182K 77s + 98100K .......... .......... .......... .......... .......... 17% 338M 77s + 98150K .......... .......... .......... .......... .......... 17% 620M 77s + 98200K .......... .......... .......... .......... .......... 17% 93.1T 77s + 98250K .......... .......... .......... .......... .......... 17% 646M 77s + 98300K .......... .......... .......... .......... .......... 17% 679M 77s + 98350K .......... .......... .......... .......... .......... 17% 641M 77s + 98400K .......... .......... .......... .......... .......... 17% 667M 77s + 98450K .......... .......... .......... .......... .......... 17% 93.1T 77s + 98500K .......... .......... .......... .......... .......... 17% 2.09M 77s + 98550K .......... .......... .......... .......... .......... 17% 351M 77s + 98600K .......... .......... .......... .......... .......... 17% 621M 77s + 98650K .......... .......... .......... .......... .......... 17% 693M 77s + 98700K .......... .......... .......... .......... .......... 17% 93.1T 76s + 98750K .......... .......... .......... .......... .......... 17% 704M 76s + 98800K .......... .......... .......... .......... .......... 17% 723M 76s + 98850K .......... .......... .......... .......... .......... 17% 568M 76s + 98900K .......... .......... .......... .......... .......... 17% 93.1T 76s + 98950K .......... .......... .......... .......... .......... 17% 659M 76s + 99000K .......... .......... .......... .......... .......... 17% 4.73M 76s + 99050K .......... .......... .......... .......... .......... 17% 313K 77s + 99100K .......... .......... .......... .......... .......... 17% 423K 78s + 99150K .......... .......... .......... .......... .......... 17% 316M 78s + 99200K .......... .......... .......... .......... .......... 17% 646M 78s + 99250K .......... .......... .......... .......... .......... 17% 93.1T 78s + 99300K .......... .......... .......... .......... .......... 17% 605M 78s + 99350K .......... .......... .......... .......... .......... 17% 649M 78s + 99400K .......... .......... .......... .......... .......... 17% 628M 78s + 99450K .......... .......... .......... .......... .......... 17% 567M 77s + 99500K .......... .......... .......... .......... .......... 17% 93.1T 77s + 99550K .......... .......... .......... .......... .......... 17% 636M 77s + 99600K .......... .......... .......... .......... .......... 17% 646M 77s + 99650K .......... .......... .......... .......... .......... 17% 668M 77s + 99700K .......... .......... .......... .......... .......... 17% 93.1T 77s + 99750K .......... .......... .......... .......... .......... 17% 691M 77s + 99800K .......... .......... .......... .......... .......... 17% 625M 77s + 99850K .......... .......... .......... .......... .......... 17% 590M 77s + 99900K .......... .......... .......... .......... .......... 17% 579M 77s + 99950K .......... .......... .......... .......... .......... 17% 93.1T 77s +100000K .......... .......... .......... .......... .......... 17% 686M 77s +100050K .......... .......... .......... .......... .......... 17% 494M 77s +100100K .......... .......... .......... .......... .......... 17% 655M 77s +100150K .......... .......... .......... .......... .......... 17% 93.1T 77s +100200K .......... .......... .......... .......... .......... 17% 835M 76s +100250K .......... .......... .......... .......... .......... 17% 1017M 76s +100300K .......... .......... .......... .......... .......... 17% 2.23M 77s +100350K .......... .......... .......... .......... .......... 17% 377M 76s +100400K .......... .......... .......... .......... .......... 17% 539M 76s +100450K .......... .......... .......... .......... .......... 17% 574M 76s +100500K .......... .......... .......... .......... .......... 17% 93.1T 76s +100550K .......... .......... .......... .......... .......... 17% 13.3M 76s +100600K .......... .......... .......... .......... .......... 17% 17.8M 76s +100650K .......... .......... .......... .......... .......... 17% 456M 76s +100700K .......... .......... .......... .......... .......... 17% 93.1T 76s +100750K .......... .......... .......... .......... .......... 17% 633M 76s +100800K .......... .......... .......... .......... .......... 17% 652M 76s +100850K .......... .......... .......... .......... .......... 17% 660M 76s +100900K .......... .......... .......... .......... .......... 17% 99.5M 76s +100950K .......... .......... .......... .......... .......... 17% 29.9M 76s +101000K .......... .......... .......... .......... .......... 18% 15.0M 76s +101050K .......... .......... .......... .......... .......... 18% 412K 76s +101100K .......... .......... .......... .......... .......... 18% 366M 76s +101150K .......... .......... .......... .......... .......... 18% 106K 79s +101200K .......... .......... .......... .......... .......... 18% 1.99M 80s +101250K .......... .......... .......... .......... .......... 18% 93.1T 80s +101300K .......... .......... .......... .......... .......... 18% 490M 79s +101350K .......... .......... .......... .......... .......... 18% 323M 79s +101400K .......... .......... .......... .......... .......... 18% 940M 79s +101450K .......... .......... .......... .......... .......... 18% 93.1T 79s +101500K .......... .......... .......... .......... .......... 18% 808M 79s +101550K .......... .......... .......... .......... .......... 18% 927M 79s +101600K .......... .......... .......... .......... .......... 18% 966M 79s +101650K .......... .......... .......... .......... .......... 18% 971M 79s +101700K .......... .......... .......... .......... .......... 18% 93.1T 79s +101750K .......... .......... .......... .......... .......... 18% 984M 79s +101800K .......... .......... .......... .......... .......... 18% 1.06G 79s +101850K .......... .......... .......... .......... .......... 18% 365K 80s +101900K .......... .......... .......... .......... .......... 18% 367M 80s +101950K .......... .......... .......... .......... .......... 18% 691M 80s +102000K .......... .......... .......... .......... .......... 18% 720M 79s +102050K .......... .......... .......... .......... .......... 18% 93.1T 79s +102100K .......... .......... .......... .......... .......... 18% 613M 79s +102150K .......... .......... .......... .......... .......... 18% 671M 79s +102200K .......... .......... .......... .......... .......... 18% 584M 79s +102250K .......... .......... .......... .......... .......... 18% 719M 79s +102300K .......... .......... .......... .......... .......... 18% 93.1T 79s +102350K .......... .......... .......... .......... .......... 18% 973M 79s +102400K .......... .......... .......... .......... .......... 18% 968M 79s +102450K .......... .......... .......... .......... .......... 18% 1000M 79s +102500K .......... .......... .......... .......... .......... 18% 93.1T 79s +102550K .......... .......... .......... .......... .......... 18% 981M 79s +102600K .......... .......... .......... .......... .......... 18% 836M 79s +102650K .......... .......... .......... .......... .......... 18% 1012M 79s +102700K .......... .......... .......... .......... .......... 18% 991M 79s +102750K .......... .......... .......... .......... .......... 18% 93.1T 78s +102800K .......... .......... .......... .......... .......... 18% 1.00G 78s +102850K .......... .......... .......... .......... .......... 18% 1.69M 79s +102900K .......... .......... .......... .......... .......... 18% 409K 79s +102950K .......... .......... .......... .......... .......... 18% 93.1T 79s +103000K .......... .......... .......... .......... .......... 18% 282M 79s +103050K .......... .......... .......... .......... .......... 18% 448M 79s +103100K .......... .......... .......... .......... .......... 18% 483M 79s +103150K .......... .......... .......... .......... .......... 18% 455M 79s +103200K .......... .......... .......... .......... .......... 18% 93.1T 79s +103250K .......... .......... .......... .......... .......... 18% 1.94M 79s +103300K .......... .......... .......... .......... .......... 18% 93.1T 79s +103350K .......... .......... .......... .......... .......... 18% 412M 79s +103400K .......... .......... .......... .......... .......... 18% 820M 79s +103450K .......... .......... .......... .......... .......... 18% 362K 80s +103500K .......... .......... .......... .......... .......... 18% 2.55M 80s +103550K .......... .......... .......... .......... .......... 18% 371K 80s +103600K .......... .......... .......... .......... .......... 18% 93.1T 80s +103650K .......... .......... .......... .......... .......... 18% 252M 80s +103700K .......... .......... .......... .......... .......... 18% 412M 80s +103750K .......... .......... .......... .......... .......... 18% 397M 80s +103800K .......... .......... .......... .......... .......... 18% 418M 80s +103850K .......... .......... .......... .......... .......... 18% 93.1T 80s +103900K .......... .......... .......... .......... .......... 18% 372M 80s +103950K .......... .......... .......... .......... .......... 18% 476M 80s +104000K .......... .......... .......... .......... .......... 18% 543M 80s +104050K .......... .......... .......... .......... .......... 18% 93.1T 80s +104100K .......... .......... .......... .......... .......... 18% 57.7M 80s +104150K .......... .......... .......... .......... .......... 18% 1.38M 80s +104200K .......... .......... .......... .......... .......... 18% 400M 80s +104250K .......... .......... .......... .......... .......... 18% 208K 81s +104300K .......... .......... .......... .......... .......... 18% 305M 81s +104350K .......... .......... .......... .......... .......... 18% 93.1T 81s +104400K .......... .......... .......... .......... .......... 18% 583M 81s +104450K .......... .......... .......... .......... .......... 18% 609M 81s +104500K .......... .......... .......... .......... .......... 18% 2.68M 81s +104550K .......... .......... .......... .......... .......... 18% 442M 81s +104600K .......... .......... .......... .......... .......... 18% 660M 81s +104650K .......... .......... .......... .......... .......... 18% 18.8M 81s +104700K .......... .......... .......... .......... .......... 18% 16.7M 81s +104750K .......... .......... .......... .......... .......... 18% 834M 81s +104800K .......... .......... .......... .......... .......... 18% 24.5M 81s +104850K .......... .......... .......... .......... .......... 18% 517M 81s +104900K .......... .......... .......... .......... .......... 18% 717M 81s +104950K .......... .......... .......... .......... .......... 18% 93.1T 81s +105000K .......... .......... .......... .......... .......... 18% 714M 81s +105050K .......... .......... .......... .......... .......... 18% 841M 80s +105100K .......... .......... .......... .......... .......... 18% 19.7M 80s +105150K .......... .......... .......... .......... .......... 18% 21.7M 80s +105200K .......... .......... .......... .......... .......... 18% 14.0M 80s +105250K .......... .......... .......... .......... .......... 18% 599M 80s +105300K .......... .......... .......... .......... .......... 18% 738M 80s +105350K .......... .......... .......... .......... .......... 18% 93.1T 80s +105400K .......... .......... .......... .......... .......... 18% 781M 80s +105450K .......... .......... .......... .......... .......... 18% 816M 80s +105500K .......... .......... .......... .......... .......... 18% 614M 80s +105550K .......... .......... .......... .......... .......... 18% 555M 80s +105600K .......... .......... .......... .......... .......... 18% 709K 80s +105650K .......... .......... .......... .......... .......... 18% 308M 80s +105700K .......... .......... .......... .......... .......... 18% 93.1T 80s +105750K .......... .......... .......... .......... .......... 18% 476M 80s +105800K .......... .......... .......... .......... .......... 18% 612M 80s +105850K .......... .......... .......... .......... .......... 18% 102K 83s +105900K .......... .......... .......... .......... .......... 18% 551M 83s +105950K .......... .......... .......... .......... .......... 18% 93.1T 83s +106000K .......... .......... .......... .......... .......... 18% 810M 83s +106050K .......... .......... .......... .......... .......... 18% 949M 83s +106100K .......... .......... .......... .......... .......... 18% 945M 83s +106150K .......... .......... .......... .......... .......... 18% 797M 83s +106200K .......... .......... .......... .......... .......... 18% 93.1T 83s +106250K .......... .......... .......... .......... .......... 18% 843M 83s +106300K .......... .......... .......... .......... .......... 18% 949M 82s +106350K .......... .......... .......... .......... .......... 18% 725M 82s +106400K .......... .......... .......... .......... .......... 18% 93.1T 82s +106450K .......... .......... .......... .......... .......... 18% 889M 82s +106500K .......... .......... .......... .......... .......... 18% 952M 82s +106550K .......... .......... .......... .......... .......... 18% 956M 82s +106600K .......... .......... .......... .......... .......... 19% 1.06G 82s +106650K .......... .......... .......... .......... .......... 19% 9.31M 82s +106700K .......... .......... .......... .......... .......... 19% 495K 83s +106750K .......... .......... .......... .......... .......... 19% 417M 83s +106800K .......... .......... .......... .......... .......... 19% 707M 82s +106850K .......... .......... .......... .......... .......... 19% 764M 82s +106900K .......... .......... .......... .......... .......... 19% 93.1T 82s +106950K .......... .......... .......... .......... .......... 19% 784M 82s +107000K .......... .......... .......... .......... .......... 19% 721M 82s +107050K .......... .......... .......... .......... .......... 19% 586M 82s +107100K .......... .......... .......... .......... .......... 19% 93.1T 82s +107150K .......... .......... .......... .......... .......... 19% 588M 82s +107200K .......... .......... .......... .......... .......... 19% 465M 82s +107250K .......... .......... .......... .......... .......... 19% 609M 82s +107300K .......... .......... .......... .......... .......... 19% 649M 82s +107350K .......... .......... .......... .......... .......... 19% 93.1T 82s +107400K .......... .......... .......... .......... .......... 19% 633M 82s +107450K .......... .......... .......... .......... .......... 19% 666M 82s +107500K .......... .......... .......... .......... .......... 19% 645M 82s +107550K .......... .......... .......... .......... .......... 19% 93.1T 81s +107600K .......... .......... .......... .......... .......... 19% 601M 81s +107650K .......... .......... .......... .......... .......... 19% 393M 81s +107700K .......... .......... .......... .......... .......... 19% 627M 81s +107750K .......... .......... .......... .......... .......... 19% 619M 81s +107800K .......... .......... .......... .......... .......... 19% 93.1T 81s +107850K .......... .......... .......... .......... .......... 19% 454M 81s +107900K .......... .......... .......... .......... .......... 19% 243M 81s +107950K .......... .......... .......... .......... .......... 19% 10.4M 81s +108000K .......... .......... .......... .......... .......... 19% 504M 81s +108050K .......... .......... .......... .......... .......... 19% 499M 81s +108100K .......... .......... .......... .......... .......... 19% 93.1T 81s +108150K .......... .......... .......... .......... .......... 19% 649M 81s +108200K .......... .......... .......... .......... .......... 19% 656M 81s +108250K .......... .......... .......... .......... .......... 19% 94.0M 81s +108300K .......... .......... .......... .......... .......... 19% 605M 81s +108350K .......... .......... .......... .......... .......... 19% 1.41M 81s +108400K .......... .......... .......... .......... .......... 19% 3.40M 81s +108450K .......... .......... .......... .......... .......... 19% 638M 81s +108500K .......... .......... .......... .......... .......... 19% 93.1T 81s +108550K .......... .......... .......... .......... .......... 19% 9.11M 81s +108600K .......... .......... .......... .......... .......... 19% 658M 81s +108650K .......... .......... .......... .......... .......... 19% 3.16M 81s +108700K .......... .......... .......... .......... .......... 19% 15.0M 81s +108750K .......... .......... .......... .......... .......... 19% 650M 80s +108800K .......... .......... .......... .......... .......... 19% 3.22M 80s +108850K .......... .......... .......... .......... .......... 19% 5.68M 80s +108900K .......... .......... .......... .......... .......... 19% 1.23M 81s +108950K .......... .......... .......... .......... .......... 19% 3.20M 81s +109000K .......... .......... .......... .......... .......... 19% 514M 81s +109050K .......... .......... .......... .......... .......... 19% 93.1T 81s +109100K .......... .......... .......... .......... .......... 19% 683M 81s +109150K .......... .......... .......... .......... .......... 19% 828M 80s +109200K .......... .......... .......... .......... .......... 19% 3.87M 80s +109250K .......... .......... .......... .......... .......... 19% 2.09M 81s +109300K .......... .......... .......... .......... .......... 19% 490M 80s +109350K .......... .......... .......... .......... .......... 19% 328M 80s +109400K .......... .......... .......... .......... .......... 19% 502M 80s +109450K .......... .......... .......... .......... .......... 19% 527M 80s +109500K .......... .......... .......... .......... .......... 19% 93.1T 80s +109550K .......... .......... .......... .......... .......... 19% 578M 80s +109600K .......... .......... .......... .......... .......... 19% 10.8M 80s +109650K .......... .......... .......... .......... .......... 19% 93.1T 80s +109700K .......... .......... .......... .......... .......... 19% 560M 80s +109750K .......... .......... .......... .......... .......... 19% 743M 80s +109800K .......... .......... .......... .......... .......... 19% 717M 80s +109850K .......... .......... .......... .......... .......... 19% 93.1T 80s +109900K .......... .......... .......... .......... .......... 19% 9.55M 80s +109950K .......... .......... .......... .......... .......... 19% 18.7M 80s +110000K .......... .......... .......... .......... .......... 19% 1.21M 80s +110050K .......... .......... .......... .......... .......... 19% 472M 80s +110100K .......... .......... .......... .......... .......... 19% 6.95M 80s +110150K .......... .......... .......... .......... .......... 19% 539M 80s +110200K .......... .......... .......... .......... .......... 19% 4.23M 80s +110250K .......... .......... .......... .......... .......... 19% 1.35M 80s +110300K .......... .......... .......... .......... .......... 19% 13.6M 80s +110350K .......... .......... .......... .......... .......... 19% 464M 80s +110400K .......... .......... .......... .......... .......... 19% 596M 80s +110450K .......... .......... .......... .......... .......... 19% 93.1T 80s +110500K .......... .......... .......... .......... .......... 19% 623M 80s +110550K .......... .......... .......... .......... .......... 19% 654M 80s +110600K .......... .......... .......... .......... .......... 19% 690M 80s +110650K .......... .......... .......... .......... .......... 19% 6.13M 80s +110700K .......... .......... .......... .......... .......... 19% 10.3M 79s +110750K .......... .......... .......... .......... .......... 19% 1.52M 80s +110800K .......... .......... .......... .......... .......... 19% 499M 80s +110850K .......... .......... .......... .......... .......... 19% 1.06G 80s +110900K .......... .......... .......... .......... .......... 19% 1.53M 80s +110950K .......... .......... .......... .......... .......... 19% 1.73M 80s +111000K .......... .......... .......... .......... .......... 19% 277M 80s +111050K .......... .......... .......... .......... .......... 19% 93.1T 80s +111100K .......... .......... .......... .......... .......... 19% 249M 80s +111150K .......... .......... .......... .......... .......... 19% 565M 79s +111200K .......... .......... .......... .......... .......... 19% 1.88M 80s +111250K .......... .......... .......... .......... .......... 19% 1.48M 80s +111300K .......... .......... .......... .......... .......... 19% 598M 80s +111350K .......... .......... .......... .......... .......... 19% 93.1T 80s +111400K .......... .......... .......... .......... .......... 19% 990M 80s +111450K .......... .......... .......... .......... .......... 19% 1.05G 79s +111500K .......... .......... .......... .......... .......... 19% 982M 79s +111550K .......... .......... .......... .......... .......... 19% 93.1T 79s +111600K .......... .......... .......... .......... .......... 19% 1.01G 79s +111650K .......... .......... .......... .......... .......... 19% 1.00G 79s +111700K .......... .......... .......... .......... .......... 19% 1.72M 79s +111750K .......... .......... .......... .......... .......... 19% 2.43M 79s +111800K .......... .......... .......... .......... .......... 19% 482M 79s +111850K .......... .......... .......... .......... .......... 19% 722M 79s +111900K .......... .......... .......... .......... .......... 19% 698M 79s +111950K .......... .......... .......... .......... .......... 19% 93.1T 79s +112000K .......... .......... .......... .......... .......... 19% 725M 79s +112050K .......... .......... .......... .......... .......... 19% 733M 79s +112100K .......... .......... .......... .......... .......... 19% 755M 79s +112150K .......... .......... .......... .......... .......... 19% 93.1T 79s +112200K .......... .......... .......... .......... .......... 20% 2.01M 79s +112250K .......... .......... .......... .......... .......... 20% 561M 79s +112300K .......... .......... .......... .......... .......... 20% 8.61M 79s +112350K .......... .......... .......... .......... .......... 20% 3.07M 79s +112400K .......... .......... .......... .......... .......... 20% 440M 79s +112450K .......... .......... .......... .......... .......... 20% 21.4M 79s +112500K .......... .......... .......... .......... .......... 20% 541M 79s +112550K .......... .......... .......... .......... .......... 20% 954M 79s +112600K .......... .......... .......... .......... .......... 20% 3.80M 79s +112650K .......... .......... .......... .......... .......... 20% 528M 79s +112700K .......... .......... .......... .......... .......... 20% 7.05M 79s +112750K .......... .......... .......... .......... .......... 20% 16.1M 79s +112800K .......... .......... .......... .......... .......... 20% 1.34M 79s +112850K .......... .......... .......... .......... .......... 20% 1018K 79s +112900K .......... .......... .......... .......... .......... 20% 374M 79s +112950K .......... .......... .......... .......... .......... 20% 93.1T 79s +113000K .......... .......... .......... .......... .......... 20% 572M 79s +113050K .......... .......... .......... .......... .......... 20% 5.08M 79s +113100K .......... .......... .......... .......... .......... 20% 20.8M 79s +113150K .......... .......... .......... .......... .......... 20% 638M 79s +113200K .......... .......... .......... .......... .......... 20% 1.46M 79s +113250K .......... .......... .......... .......... .......... 20% 93.1T 79s +113300K .......... .......... .......... .......... .......... 20% 323M 79s +113350K .......... .......... .......... .......... .......... 20% 293M 79s +113400K .......... .......... .......... .......... .......... 20% 285M 79s +113450K .......... .......... .......... .......... .......... 20% 382M 79s +113500K .......... .......... .......... .......... .......... 20% 2.18M 79s +113550K .......... .......... .......... .......... .......... 20% 264M 79s +113600K .......... .......... .......... .......... .......... 20% 3.44M 79s +113650K .......... .......... .......... .......... .......... 20% 1.64M 79s +113700K .......... .......... .......... .......... .......... 20% 857K 79s +113750K .......... .......... .......... .......... .......... 20% 93.1T 79s +113800K .......... .......... .......... .......... .......... 20% 459M 79s +113850K .......... .......... .......... .......... .......... 20% 748M 79s +113900K .......... .......... .......... .......... .......... 20% 837M 79s +113950K .......... .......... .......... .......... .......... 20% 93.1T 79s +114000K .......... .......... .......... .......... .......... 20% 3.01M 79s +114050K .......... .......... .......... .......... .......... 20% 93.1T 79s +114100K .......... .......... .......... .......... .......... 20% 381M 79s +114150K .......... .......... .......... .......... .......... 20% 429M 79s +114200K .......... .......... .......... .......... .......... 20% 410M 78s +114250K .......... .......... .......... .......... .......... 20% 93.1T 78s +114300K .......... .......... .......... .......... .......... 20% 1.48M 79s +114350K .......... .......... .......... .......... .......... 20% 417M 78s +114400K .......... .......... .......... .......... .......... 20% 730M 78s +114450K .......... .......... .......... .......... .......... 20% 593M 78s +114500K .......... .......... .......... .......... .......... 20% 1.46M 79s +114550K .......... .......... .......... .......... .......... 20% 13.6M 78s +114600K .......... .......... .......... .......... .......... 20% 93.1T 78s +114650K .......... .......... .......... .......... .......... 20% 521M 78s +114700K .......... .......... .......... .......... .......... 20% 631M 78s +114750K .......... .......... .......... .......... .......... 20% 833M 78s +114800K .......... .......... .......... .......... .......... 20% 15.2M 78s +114850K .......... .......... .......... .......... .......... 20% 556K 79s +114900K .......... .......... .......... .......... .......... 20% 459M 79s +114950K .......... .......... .......... .......... .......... 20% 93.1T 79s +115000K .......... .......... .......... .......... .......... 20% 679M 78s +115050K .......... .......... .......... .......... .......... 20% 729M 78s +115100K .......... .......... .......... .......... .......... 20% 709M 78s +115150K .......... .......... .......... .......... .......... 20% 736M 78s +115200K .......... .......... .......... .......... .......... 20% 93.1T 78s +115250K .......... .......... .......... .......... .......... 20% 1.35M 78s +115300K .......... .......... .......... .......... .......... 20% 93.1T 78s +115350K .......... .......... .......... .......... .......... 20% 351M 78s +115400K .......... .......... 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.......... .......... .......... .......... 20% 93.1T 78s +116100K .......... .......... .......... .......... .......... 20% 47.0M 78s +116150K .......... .......... .......... .......... .......... 20% 657M 78s +116200K .......... .......... .......... .......... .......... 20% 3.92M 78s +116250K .......... .......... .......... .......... .......... 20% 93.1T 78s +116300K .......... .......... .......... .......... .......... 20% 425M 78s +116350K .......... .......... .......... .......... .......... 20% 662M 78s +116400K .......... .......... .......... .......... .......... 20% 449M 78s +116450K .......... .......... .......... .......... .......... 20% 93.1T 78s +116500K .......... .......... .......... .......... .......... 20% 588M 78s +116550K .......... .......... .......... .......... .......... 20% 493M 78s +116600K .......... .......... .......... .......... .......... 20% 651M 78s +116650K .......... .......... .......... .......... .......... 20% 709M 78s +116700K .......... .......... .......... .......... .......... 20% 1.38M 78s +116750K .......... .......... .......... .......... .......... 20% 93.1T 78s +116800K .......... .......... .......... .......... .......... 20% 1.46M 78s +116850K .......... .......... .......... .......... .......... 20% 291M 78s +116900K .......... .......... .......... .......... .......... 20% 93.1T 78s +116950K .......... .......... .......... .......... .......... 20% 543M 78s +117000K .......... .......... .......... .......... .......... 20% 687M 78s +117050K .......... .......... .......... .......... .......... 20% 1.15M 78s +117100K .......... .......... .......... .......... .......... 20% 139M 78s +117150K .......... .......... .......... .......... .......... 20% 416M 78s +117200K .......... .......... .......... .......... .......... 20% 93.1T 78s +117250K .......... .......... .......... .......... .......... 20% 476M 77s +117300K .......... .......... .......... .......... .......... 20% 394K 78s +117350K .......... .......... .......... .......... .......... 20% 93.1T 78s +117400K .......... .......... .......... .......... .......... 20% 332M 78s +117450K .......... .......... .......... .......... .......... 20% 665M 78s +117500K .......... .......... .......... .......... .......... 20% 759M 78s +117550K .......... .......... .......... .......... .......... 20% 21.5M 78s +117600K .......... .......... .......... .......... .......... 20% 757M 78s +117650K .......... .......... .......... .......... .......... 20% 944M 78s +117700K .......... .......... .......... .......... .......... 20% 1.73M 78s +117750K .......... .......... .......... .......... .......... 20% 93.1T 78s +117800K .......... .......... .......... .......... .......... 20% 581M 78s +117850K .......... .......... .......... .......... .......... 21% 4.72M 78s +117900K .......... .......... .......... .......... .......... 21% 2.37M 78s +117950K .......... .......... .......... .......... .......... 21% 415M 78s +118000K .......... .......... .......... .......... .......... 21% 563M 78s +118050K .......... .......... .......... .......... .......... 21% 93.1T 78s +118100K .......... .......... .......... .......... .......... 21% 14.5M 78s +118150K .......... .......... .......... .......... .......... 21% 4.49M 78s +118200K .......... .......... .......... .......... .......... 21% 13.6M 78s +118250K .......... .......... .......... .......... .......... 21% 750M 77s +118300K .......... .......... .......... .......... .......... 21% 3.45M 77s +118350K .......... .......... .......... .......... .......... 21% 4.74M 77s +118400K .......... .......... .......... .......... .......... 21% 457M 77s +118450K .......... .......... .......... .......... .......... 21% 93.1T 77s +118500K .......... .......... .......... .......... .......... 21% 159M 77s +118550K .......... .......... .......... .......... .......... 21% 1.90M 77s +118600K .......... .......... .......... .......... .......... 21% 4.64M 77s +118650K .......... .......... .......... .......... .......... 21% 7.66M 77s +118700K .......... .......... .......... .......... .......... 21% 3.15M 77s +118750K .......... .......... .......... .......... .......... 21% 7.02M 77s +118800K .......... .......... .......... .......... .......... 21% 93.1T 77s +118850K .......... .......... .......... .......... .......... 21% 677K 78s +118900K .......... .......... .......... .......... .......... 21% 93.1T 78s +118950K .......... .......... .......... .......... .......... 21% 300M 78s +119000K .......... .......... .......... .......... .......... 21% 740M 78s +119050K .......... .......... .......... .......... .......... 21% 1.13M 78s +119100K .......... .......... .......... .......... .......... 21% 6.44M 78s +119150K .......... .......... .......... .......... .......... 21% 93.1T 78s +119200K .......... .......... .......... .......... .......... 21% 601M 78s +119250K .......... .......... .......... .......... .......... 21% 492M 78s +119300K .......... .......... .......... .......... .......... 21% 450M 77s +119350K .......... .......... .......... .......... .......... 21% 93.1T 77s +119400K .......... .......... .......... .......... .......... 21% 335M 77s +119450K .......... .......... .......... .......... .......... 21% 1.26M 77s +119500K .......... .......... .......... .......... .......... 21% 93.1T 77s +119550K .......... .......... .......... .......... .......... 21% 339M 77s +119600K .......... .......... .......... .......... .......... 21% 572M 77s +119650K .......... .......... .......... .......... .......... 21% 560M 77s +119700K .......... .......... .......... .......... .......... 21% 636M 77s +119750K .......... .......... .......... .......... .......... 21% 93.1T 77s +119800K .......... .......... .......... .......... .......... 21% 518M 77s +119850K .......... .......... .......... .......... .......... 21% 7.52M 77s +119900K .......... .......... 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+121850K .......... .......... .......... .......... .......... 21% 1.88M 77s +121900K .......... .......... .......... .......... .......... 21% 397M 77s +121950K .......... .......... .......... .......... .......... 21% 639M 76s +122000K .......... .......... .......... .......... .......... 21% 706M 76s +122050K .......... .......... .......... .......... .......... 21% 93.1T 76s +122100K .......... .......... .......... .......... .......... 21% 702M 76s +122150K .......... .......... .......... .......... .......... 21% 750M 76s +122200K .......... .......... .......... .......... .......... 21% 3.34M 76s +122250K .......... .......... .......... .......... .......... 21% 2.22M 76s +122300K .......... .......... .......... .......... .......... 21% 1.25M 77s +122350K .......... .......... .......... .......... .......... 21% 1.17M 77s +122400K .......... .......... .......... .......... .......... 21% 93.1T 77s +122450K .......... .......... .......... .......... .......... 21% 246M 77s +122500K .......... .......... .......... .......... .......... 21% 309M 77s +122550K .......... .......... .......... .......... .......... 21% 380M 76s +122600K .......... .......... .......... .......... .......... 21% 93.1T 76s +122650K .......... .......... .......... .......... .......... 21% 377M 76s +122700K .......... .......... .......... .......... .......... 21% 431M 76s +122750K .......... .......... .......... .......... .......... 21% 467M 76s +122800K .......... .......... .......... .......... .......... 21% 1.05M 76s +122850K .......... .......... .......... .......... .......... 21% 289M 76s +122900K .......... .......... .......... .......... .......... 21% 93.1T 76s +122950K .......... .......... .......... .......... .......... 21% 529M 76s +123000K .......... .......... .......... .......... .......... 21% 1.14M 76s +123050K .......... .......... .......... .......... .......... 21% 93.1T 76s +123100K .......... .......... .......... .......... 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.......... .......... .......... .......... .......... 22% 400M 76s +125750K .......... .......... .......... .......... .......... 22% 888M 76s +125800K .......... .......... .......... .......... .......... 22% 93.1T 76s +125850K .......... .......... .......... .......... .......... 22% 1021M 76s +125900K .......... .......... .......... .......... .......... 22% 1.04G 75s +125950K .......... .......... .......... .......... .......... 22% 2.71M 76s +126000K .......... .......... .......... .......... .......... 22% 544M 75s +126050K .......... .......... .......... .......... .......... 22% 42.4M 75s +126100K .......... .......... .......... .......... .......... 22% 1.74M 76s +126150K .......... .......... .......... .......... .......... 22% 9.46M 75s +126200K .......... .......... .......... .......... .......... 22% 93.1T 75s +126250K .......... .......... .......... .......... .......... 22% 616K 76s +126300K .......... .......... .......... .......... .......... 22% 93.1T 76s +126350K .......... .......... .......... .......... .......... 22% 300M 76s +126400K .......... .......... .......... .......... .......... 22% 429M 76s +126450K .......... .......... .......... .......... .......... 22% 605M 76s +126500K .......... .......... .......... .......... .......... 22% 559M 76s +126550K .......... .......... .......... .......... .......... 22% 93.1T 75s +126600K .......... .......... .......... .......... .......... 22% 594M 75s +126650K .......... .......... .......... .......... .......... 22% 448M 75s +126700K .......... .......... .......... .......... .......... 22% 1.09M 76s +126750K .......... .......... .......... .......... .......... 22% 93.1T 75s +126800K .......... .......... .......... .......... .......... 22% 327M 75s +126850K .......... .......... .......... .......... .......... 22% 1.15M 76s +126900K .......... .......... .......... .......... .......... 22% 93.1T 76s +126950K .......... .......... .......... .......... .......... 22% 331M 75s +127000K .......... .......... .......... .......... .......... 22% 1.69M 76s +127050K .......... .......... .......... .......... .......... 22% 93.1T 76s +127100K .......... .......... .......... .......... .......... 22% 399M 75s +127150K .......... .......... .......... .......... .......... 22% 663M 75s +127200K .......... .......... .......... .......... .......... 22% 807M 75s +127250K .......... .......... .......... .......... .......... 22% 2.27M 75s +127300K .......... .......... .......... .......... .......... 22% 93.1T 75s +127350K .......... .......... .......... .......... .......... 22% 217M 75s +127400K .......... .......... .......... .......... .......... 22% 396M 75s +127450K .......... .......... .......... .......... .......... 22% 733M 75s +127500K .......... .......... .......... .......... .......... 22% 719M 75s +127550K .......... .......... .......... .......... .......... 22% 93.1T 75s +127600K .......... .......... .......... .......... .......... 22% 815M 75s +127650K .......... .......... .......... .......... .......... 22% 2.46M 75s +127700K .......... .......... .......... .......... .......... 22% 2.72M 75s +127750K .......... .......... .......... .......... .......... 22% 319M 75s +127800K .......... .......... .......... .......... .......... 22% 93.1T 75s +127850K .......... .......... .......... .......... .......... 22% 669K 75s +127900K .......... .......... .......... .......... .......... 22% 370M 75s +127950K .......... .......... .......... .......... .......... 22% 610M 75s +128000K .......... .......... .......... .......... .......... 22% 93.1T 75s +128050K .......... .......... .......... .......... .......... 22% 600M 75s +128100K .......... .......... .......... .......... .......... 22% 671K 75s +128150K .......... .......... .......... .......... .......... 22% 293M 75s +128200K .......... .......... .......... .......... .......... 22% 561M 75s +128250K .......... .......... .......... .......... .......... 22% 543M 75s +128300K .......... .......... .......... .......... .......... 22% 93.1T 75s +128350K .......... .......... .......... .......... .......... 22% 624M 75s +128400K .......... .......... .......... .......... .......... 22% 611M 75s +128450K .......... .......... .......... .......... .......... 22% 514M 75s +128500K .......... .......... .......... .......... .......... 22% 93.1T 75s +128550K .......... .......... .......... .......... .......... 22% 510M 75s +128600K .......... .......... .......... .......... .......... 22% 1.07M 75s +128650K .......... .......... .......... .......... .......... 22% 299M 75s +128700K .......... .......... .......... .......... .......... 22% 502M 75s +128750K .......... .......... .......... .......... .......... 22% 533M 75s +128800K .......... .......... .......... .......... .......... 22% 93.1T 75s +128850K .......... .......... .......... .......... .......... 22% 1.53M 75s +128900K .......... .......... .......... .......... .......... 22% 347M 75s +128950K .......... .......... .......... .......... .......... 22% 801M 75s +129000K .......... .......... .......... .......... .......... 22% 835M 75s +129050K .......... .......... .......... .......... .......... 23% 93.1T 75s +129100K .......... .......... .......... .......... .......... 23% 2.97M 75s +129150K .......... .......... .......... .......... .......... 23% 5.27M 75s +129200K .......... .......... .......... .......... .......... 23% 6.57M 75s +129250K .......... .......... .......... .......... .......... 23% 4.84M 75s +129300K .......... .......... .......... .......... .......... 23% 4.41M 75s +129350K .......... .......... .......... .......... .......... 23% 376M 75s +129400K .......... .......... .......... .......... .......... 23% 3.45M 75s +129450K .......... .......... .......... .......... .......... 23% 306M 75s +129500K .......... .......... .......... .......... .......... 23% 93.1T 75s +129550K .......... .......... .......... .......... .......... 23% 497M 75s +129600K .......... .......... .......... .......... .......... 23% 1.97M 75s +129650K .......... .......... .......... .......... .......... 23% 1.58M 75s +129700K .......... .......... .......... .......... .......... 23% 1.32M 75s +129750K .......... .......... .......... .......... .......... 23% 93.1T 75s +129800K .......... .......... .......... .......... .......... 23% 362M 75s +129850K .......... .......... .......... .......... .......... 23% 638M 75s +129900K .......... .......... .......... .......... .......... 23% 613M 75s +129950K .......... .......... .......... .......... .......... 23% 93.1T 75s +130000K .......... .......... .......... .......... .......... 23% 639M 75s +130050K .......... .......... .......... .......... .......... 23% 647M 75s +130100K .......... .......... .......... .......... .......... 23% 2.54M 75s +130150K .......... .......... .......... .......... .......... 23% 10.1M 75s +130200K .......... .......... .......... .......... .......... 23% 898K 75s +130250K .......... .......... .......... .......... .......... 23% 93.1T 75s +130300K .......... .......... .......... .......... .......... 23% 333M 75s +130350K .......... .......... .......... .......... .......... 23% 9.15M 75s +130400K .......... .......... .......... .......... .......... 23% 509M 75s +130450K .......... .......... .......... .......... .......... 23% 93.1T 75s +130500K .......... .......... .......... .......... .......... 23% 560M 75s +130550K .......... .......... .......... .......... .......... 23% 19.0M 75s +130600K .......... .......... .......... .......... .......... 23% 93.1T 75s +130650K .......... .......... .......... .......... .......... 23% 2.13M 75s +130700K .......... .......... .......... .......... .......... 23% 1.08M 75s +130750K .......... .......... .......... .......... .......... 23% 6.71M 75s +130800K .......... .......... .......... .......... .......... 23% 584M 75s +130850K .......... .......... .......... .......... .......... 23% 326M 75s +130900K .......... .......... .......... .......... .......... 23% 5.88M 75s +130950K .......... .......... .......... .......... .......... 23% 532M 75s +131000K .......... .......... .......... .......... .......... 23% 1.81M 75s +131050K .......... .......... .......... .......... .......... 23% 322M 75s +131100K .......... .......... .......... .......... .......... 23% 93.1T 75s +131150K .......... .......... .......... .......... .......... 23% 517M 75s +131200K .......... .......... .......... .......... .......... 23% 593M 74s +131250K .......... .......... .......... .......... .......... 23% 519M 74s +131300K .......... .......... .......... .......... .......... 23% 628M 74s +131350K .......... .......... .......... .......... .......... 23% 93.1T 74s +131400K .......... .......... .......... .......... .......... 23% 5.86M 74s +131450K .......... .......... .......... .......... .......... 23% 4.27M 74s +131500K .......... .......... .......... .......... .......... 23% 316M 74s +131550K .......... .......... .......... .......... .......... 23% 1.01M 74s +131600K .......... .......... .......... .......... .......... 23% 93.1T 74s +131650K .......... .......... .......... .......... .......... 23% 379M 74s +131700K .......... .......... .......... .......... .......... 23% 621M 74s +131750K .......... .......... .......... .......... .......... 23% 698M 74s +131800K .......... .......... .......... .......... .......... 23% 93.1T 74s +131850K .......... .......... .......... .......... .......... 23% 9.10M 74s +131900K .......... .......... .......... .......... .......... 23% 7.71M 74s +131950K .......... .......... .......... .......... .......... 23% 3.65M 74s +132000K .......... .......... .......... .......... .......... 23% 331M 74s +132050K .......... .......... .......... .......... .......... 23% 917K 74s +132100K .......... .......... .......... .......... .......... 23% 93.1T 74s +132150K .......... .......... .......... .......... .......... 23% 166M 74s +132200K .......... .......... .......... .......... .......... 23% 474M 74s +132250K .......... .......... .......... .......... .......... 23% 445M 74s +132300K .......... .......... .......... .......... .......... 23% 93.1T 74s +132350K .......... .......... .......... .......... .......... 23% 461M 74s +132400K .......... .......... .......... .......... .......... 23% 1.84M 74s +132450K .......... .......... .......... .......... .......... 23% 583M 74s +132500K .......... .......... .......... .......... .......... 23% 2.19M 74s +132550K .......... .......... .......... .......... .......... 23% 399M 74s +132600K .......... .......... .......... .......... .......... 23% 8.09M 74s +132650K .......... .......... .......... .......... .......... 23% 93.1T 74s +132700K .......... .......... .......... .......... .......... 23% 2.90M 74s +132750K .......... .......... .......... .......... .......... 23% 319M 74s +132800K .......... .......... .......... .......... .......... 23% 672K 74s +132850K .......... .......... .......... .......... .......... 23% 93.1T 74s +132900K .......... .......... .......... .......... .......... 23% 404M 74s +132950K .......... .......... .......... .......... .......... 23% 714M 74s +133000K .......... .......... .......... .......... .......... 23% 593M 74s +133050K .......... .......... .......... .......... .......... 23% 93.1T 74s +133100K .......... .......... .......... .......... .......... 23% 729M 74s +133150K .......... .......... .......... .......... .......... 23% 817M 74s +133200K .......... .......... .......... .......... .......... 23% 875M 74s +133250K .......... .......... .......... .......... .......... 23% 876M 74s +133300K .......... .......... .......... .......... .......... 23% 93.1T 74s +133350K .......... .......... .......... .......... .......... 23% 855M 74s +133400K .......... .......... 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+135350K .......... .......... .......... .......... .......... 24% 466M 73s +135400K .......... .......... .......... .......... .......... 24% 93.1T 73s +135450K .......... .......... .......... .......... .......... 24% 560M 73s +135500K .......... .......... .......... .......... .......... 24% 638M 73s +135550K .......... .......... .......... .......... .......... 24% 484K 74s +135600K .......... .......... .......... .......... .......... 24% 93.1T 74s +135650K .......... .......... .......... .......... .......... 24% 3.19M 74s +135700K .......... .......... .......... .......... .......... 24% 272M 74s +135750K .......... .......... .......... .......... .......... 24% 451M 73s +135800K .......... .......... .......... .......... .......... 24% 93.1T 73s +135850K .......... .......... .......... .......... .......... 24% 928K 74s +135900K .......... .......... .......... .......... .......... 24% 315M 74s +135950K .......... .......... .......... .......... .......... 24% 885K 74s +136000K .......... .......... .......... .......... .......... 24% 266M 74s +136050K .......... .......... .......... .......... .......... 24% 93.1T 74s +136100K .......... .......... .......... .......... .......... 24% 408M 74s +136150K .......... .......... .......... .......... .......... 24% 400M 74s +136200K .......... .......... .......... .......... .......... 24% 552M 74s +136250K .......... .......... .......... .......... .......... 24% 692M 74s +136300K .......... .......... .......... .......... .......... 24% 93.1T 73s +136350K .......... .......... .......... .......... .......... 24% 523M 73s +136400K .......... .......... .......... .......... .......... 24% 711M 73s +136450K .......... .......... .......... .......... .......... 24% 698M 73s +136500K .......... .......... .......... .......... .......... 24% 1007M 73s +136550K .......... .......... .......... .......... .......... 24% 1.14M 73s +136600K .......... .......... .......... .......... .......... 24% 5.04M 73s +136650K .......... .......... .......... .......... .......... 24% 377M 73s +136700K .......... .......... .......... .......... .......... 24% 93.1T 73s +136750K .......... .......... .......... .......... .......... 24% 779K 74s +136800K .......... .......... .......... .......... .......... 24% 477M 74s +136850K .......... .......... .......... .......... .......... 24% 585M 73s +136900K .......... .......... .......... .......... .......... 24% 2.44M 73s +136950K .......... .......... .......... .......... .......... 24% 93.1T 73s +137000K .......... .......... .......... .......... .......... 24% 3.55M 73s +137050K .......... .......... .......... .......... .......... 24% 93.1T 73s +137100K .......... .......... .......... .......... .......... 24% 447M 73s +137150K .......... .......... .......... .......... .......... 24% 731M 73s +137200K .......... .......... .......... .......... .......... 24% 714M 73s +137250K .......... .......... .......... .......... 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.......... .......... .......... .......... .......... 24% 93.1T 73s +139900K .......... .......... .......... .......... .......... 24% 8.68M 73s +139950K .......... .......... .......... .......... .......... 24% 4.83M 73s +140000K .......... .......... .......... .......... .......... 24% 93.1T 73s +140050K .......... .......... .......... .......... .......... 24% 471M 72s +140100K .......... .......... .......... .......... .......... 24% 2.18M 73s +140150K .......... .......... .......... .......... .......... 24% 377M 72s +140200K .......... .......... .......... .......... .......... 24% 7.15M 72s +140250K .......... .......... .......... .......... .......... 24% 93.1T 72s +140300K .......... .......... .......... .......... .......... 25% 555M 72s +140350K .......... .......... .......... .......... .......... 25% 1.66M 72s +140400K .......... .......... .......... .......... .......... 25% 6.62M 72s +140450K .......... .......... .......... .......... .......... 25% 3.47M 72s +140500K .......... .......... .......... .......... .......... 25% 476M 72s +140550K .......... .......... .......... .......... .......... 25% 4.24M 72s +140600K .......... .......... .......... .......... .......... 25% 798K 73s +140650K .......... .......... .......... .......... .......... 25% 2.60M 73s +140700K .......... .......... .......... .......... .......... 25% 298M 73s +140750K .......... .......... .......... .......... .......... 25% 531M 73s +140800K .......... .......... .......... .......... .......... 25% 4.54M 73s +140850K .......... .......... .......... .......... .......... 25% 8.04M 73s +140900K .......... .......... .......... .......... .......... 25% 1.28M 73s +140950K .......... .......... .......... .......... .......... 25% 93.1T 73s +141000K .......... .......... .......... .......... .......... 25% 353M 73s +141050K .......... .......... .......... .......... .......... 25% 613M 73s +141100K .......... .......... .......... .......... .......... 25% 675M 72s +141150K .......... .......... .......... .......... .......... 25% 872K 73s +141200K .......... .......... .......... .......... .......... 25% 248M 73s +141250K .......... .......... .......... .......... .......... 25% 93.1T 73s +141300K .......... .......... .......... .......... .......... 25% 500M 73s +141350K .......... .......... .......... .......... .......... 25% 540M 72s +141400K .......... .......... .......... .......... .......... 25% 530M 72s +141450K .......... .......... .......... .......... .......... 25% 568M 72s +141500K .......... .......... .......... .......... .......... 25% 93.1T 72s +141550K .......... .......... .......... .......... .......... 25% 442M 72s +141600K .......... .......... .......... .......... .......... 25% 511M 72s +141650K .......... .......... .......... .......... .......... 25% 974K 72s +141700K .......... .......... .......... .......... .......... 25% 356M 72s +141750K .......... .......... .......... .......... .......... 25% 93.1T 72s +141800K .......... .......... .......... .......... .......... 25% 630M 72s +141850K .......... .......... .......... .......... .......... 25% 649M 72s +141900K .......... .......... .......... .......... .......... 25% 560M 72s +141950K .......... .......... .......... .......... .......... 25% 686M 72s +142000K .......... .......... .......... .......... .......... 25% 93.1T 72s +142050K .......... .......... .......... .......... .......... 25% 680K 72s +142100K .......... .......... .......... .......... .......... 25% 93.1T 72s +142150K .......... .......... .......... .......... .......... 25% 444M 72s +142200K .......... .......... .......... .......... .......... 25% 697M 72s +142250K .......... .......... .......... .......... .......... 25% 753M 72s +142300K .......... .......... .......... .......... .......... 25% 575M 72s +142350K .......... .......... .......... .......... .......... 25% 93.1T 72s +142400K .......... .......... .......... .......... .......... 25% 8.58M 72s +142450K .......... .......... .......... .......... .......... 25% 513M 72s +142500K .......... .......... .......... .......... .......... 25% 93.1T 72s +142550K .......... .......... .......... .......... .......... 25% 246M 72s +142600K .......... .......... .......... .......... .......... 25% 2.46M 72s +142650K .......... .......... .......... .......... .......... 25% 1.37M 72s +142700K .......... .......... .......... .......... .......... 25% 341M 72s +142750K .......... .......... .......... .......... .......... 25% 93.1T 72s +142800K .......... .......... .......... .......... .......... 25% 571M 72s +142850K .......... .......... .......... .......... .......... 25% 614M 72s +142900K .......... .......... .......... .......... .......... 25% 763M 72s +142950K .......... .......... .......... .......... .......... 25% 629M 72s +143000K .......... .......... .......... .......... .......... 25% 1.38M 72s +143050K .......... .......... .......... .......... .......... 25% 593M 72s +143100K .......... .......... .......... .......... .......... 25% 93.1T 72s +143150K .......... .......... .......... .......... .......... 25% 913M 72s +143200K .......... .......... .......... .......... .......... 25% 993M 72s +143250K .......... .......... .......... .......... .......... 25% 902M 72s +143300K .......... .......... .......... .......... .......... 25% 982M 72s +143350K .......... .......... .......... .......... .......... 25% 2.22M 72s +143400K .......... .......... .......... .......... .......... 25% 9.04M 72s +143450K .......... .......... .......... .......... .......... 25% 1.80M 72s +143500K .......... .......... .......... .......... .......... 25% 351M 72s +143550K .......... .......... .......... .......... .......... 25% 450M 72s +143600K .......... .......... .......... .......... .......... 25% 2.72M 72s +143650K .......... .......... .......... .......... .......... 25% 93.1T 72s +143700K .......... .......... .......... .......... .......... 25% 3.31M 72s +143750K .......... .......... .......... .......... .......... 25% 93.1T 72s +143800K .......... .......... .......... .......... .......... 25% 321M 72s +143850K .......... .......... .......... .......... .......... 25% 616M 72s +143900K .......... .......... .......... .......... .......... 25% 2.61M 72s +143950K .......... .......... .......... .......... .......... 25% 489M 72s +144000K .......... .......... .......... .......... .......... 25% 4.09M 72s +144050K .......... .......... .......... .......... .......... 25% 2.05M 72s +144100K .......... .......... .......... .......... .......... 25% 93.1T 72s +144150K .......... .......... .......... .......... .......... 25% 315M 72s +144200K .......... .......... .......... .......... .......... 25% 574M 72s +144250K .......... .......... .......... .......... .......... 25% 778M 71s +144300K .......... .......... .......... .......... .......... 25% 1.68M 72s +144350K .......... .......... .......... .......... .......... 25% 400M 72s +144400K .......... .......... .......... .......... .......... 25% 736M 71s +144450K .......... .......... .......... .......... .......... 25% 8.87M 71s +144500K .......... .......... .......... .......... .......... 25% 745K 72s +144550K .......... .......... .......... .......... .......... 25% 93.1T 72s +144600K .......... .......... .......... .......... .......... 25% 314M 72s +144650K .......... .......... .......... .......... .......... 25% 40.2M 72s +144700K .......... .......... .......... .......... .......... 25% 3.50M 72s +144750K .......... .......... .......... .......... .......... 25% 93.1T 72s +144800K .......... .......... .......... .......... .......... 25% 482M 71s +144850K .......... .......... .......... .......... .......... 25% 21.4M 71s +144900K .......... .......... .......... .......... .......... 25% 30.4M 71s +144950K .......... .......... .......... .......... .......... 25% 553M 71s +145000K .......... .......... .......... .......... .......... 25% 14.9M 71s +145050K .......... .......... .......... .......... .......... 25% 6.68M 71s +145100K .......... .......... .......... .......... .......... 25% 11.9M 71s +145150K .......... .......... .......... .......... .......... 25% 1.70M 71s +145200K .......... .......... .......... .......... .......... 25% 5.84M 71s +145250K .......... .......... .......... .......... .......... 25% 11.7M 71s +145300K .......... .......... .......... .......... .......... 25% 380M 71s +145350K .......... .......... .......... .......... .......... 25% 688M 71s +145400K .......... .......... .......... .......... .......... 25% 4.29M 71s +145450K .......... .......... .......... .......... .......... 25% 1.80M 71s +145500K .......... .......... .......... .......... .......... 25% 363M 71s +145550K .......... .......... .......... .......... .......... 25% 8.80M 71s +145600K .......... .......... .......... .......... .......... 25% 19.4M 71s +145650K .......... .......... .......... .......... .......... 25% 5.96M 71s +145700K .......... .......... .......... .......... .......... 25% 4.24M 71s +145750K .......... .......... .......... .......... .......... 25% 29.6M 71s +145800K .......... .......... .......... .......... .......... 25% 583M 71s +145850K .......... .......... .......... .......... .......... 25% 905M 71s +145900K .......... .......... .......... .......... .......... 26% 1.93M 71s +145950K .......... .......... .......... .......... .......... 26% 5.97M 71s +146000K .......... .......... .......... .......... .......... 26% 8.45M 71s +146050K .......... .......... .......... .......... .......... 26% 3.20M 71s +146100K .......... .......... .......... .......... .......... 26% 93.1T 71s +146150K .......... .......... .......... .......... .......... 26% 1.23M 71s +146200K .......... .......... .......... .......... .......... 26% 4.40M 71s +146250K .......... .......... .......... .......... .......... 26% 837M 71s +146300K .......... .......... .......... .......... .......... 26% 93.1T 71s +146350K .......... .......... .......... .......... .......... 26% 953M 71s +146400K .......... .......... .......... .......... .......... 26% 5.80M 71s +146450K .......... .......... .......... .......... .......... 26% 598M 71s +146500K .......... .......... .......... .......... .......... 26% 93.1T 71s +146550K .......... .......... .......... .......... .......... 26% 2.22M 71s +146600K .......... .......... .......... .......... .......... 26% 93.1T 71s +146650K .......... .......... .......... .......... .......... 26% 578M 71s +146700K .......... .......... .......... .......... .......... 26% 1.06G 71s +146750K .......... .......... .......... .......... .......... 26% 3.12M 71s +146800K .......... .......... .......... .......... .......... 26% 544M 71s +146850K .......... .......... .......... .......... .......... 26% 1.79M 71s +146900K .......... .......... .......... .......... .......... 26% 417M 71s +146950K .......... .......... .......... .......... .......... 26% 93.1T 71s +147000K .......... .......... .......... .......... .......... 26% 527M 71s +147050K .......... .......... .......... .......... .......... 26% 605M 71s +147100K .......... .......... .......... .......... .......... 26% 4.64M 71s +147150K .......... .......... .......... .......... .......... 26% 471M 71s +147200K .......... .......... .......... .......... .......... 26% 588M 71s +147250K .......... .......... .......... .......... .......... 26% 12.8M 71s +147300K .......... .......... .......... .......... .......... 26% 8.33M 71s +147350K .......... .......... .......... .......... .......... 26% 6.63M 71s +147400K .......... .......... .......... .......... .......... 26% 5.47M 71s +147450K .......... .......... .......... .......... .......... 26% 1.71M 71s +147500K .......... .......... .......... .......... .......... 26% 93.1T 71s +147550K .......... .......... .......... .......... .......... 26% 352M 71s +147600K .......... .......... .......... .......... .......... 26% 3.17M 71s +147650K .......... .......... .......... .......... .......... 26% 412M 71s +147700K .......... .......... .......... .......... .......... 26% 93.1T 71s +147750K .......... .......... .......... .......... .......... 26% 1.54M 71s +147800K .......... .......... .......... .......... .......... 26% 587M 71s +147850K .......... .......... .......... .......... .......... 26% 3.13M 71s +147900K .......... .......... .......... .......... .......... 26% 93.1T 71s +147950K .......... .......... .......... .......... .......... 26% 9.84M 71s +148000K .......... .......... .......... .......... .......... 26% 495M 71s +148050K .......... .......... .......... .......... .......... 26% 1.19M 71s +148100K .......... .......... .......... .......... .......... 26% 3.79M 71s +148150K .......... .......... .......... .......... .......... 26% 1.45M 71s +148200K .......... .......... .......... .......... .......... 26% 368M 71s +148250K .......... .......... .......... .......... .......... 26% 93.1T 71s +148300K .......... .......... .......... .......... .......... 26% 621M 71s +148350K .......... .......... .......... .......... .......... 26% 691M 71s +148400K .......... .......... .......... .......... .......... 26% 775M 71s +148450K .......... .......... .......... .......... .......... 26% 993M 71s +148500K .......... .......... .......... .......... .......... 26% 2.58M 71s +148550K .......... .......... .......... .......... .......... 26% 1.33M 71s +148600K .......... .......... .......... .......... .......... 26% 1.97M 71s +148650K .......... .......... .......... .......... .......... 26% 389M 71s +148700K .......... .......... .......... .......... .......... 26% 93.1T 71s +148750K .......... .......... .......... .......... .......... 26% 573M 71s +148800K .......... .......... .......... .......... .......... 26% 487M 71s +148850K .......... .......... .......... .......... .......... 26% 573M 70s +148900K .......... .......... .......... .......... .......... 26% 582M 70s +148950K .......... .......... .......... .......... .......... 26% 93.1T 70s +149000K .......... .......... .......... .......... .......... 26% 595M 70s +149050K .......... .......... .......... .......... .......... 26% 485M 70s +149100K .......... .......... .......... .......... .......... 26% 581M 70s +149150K .......... .......... .......... .......... .......... 26% 93.1T 70s +149200K .......... .......... .......... .......... .......... 26% 744M 70s +149250K .......... .......... .......... .......... .......... 26% 890M 70s +149300K .......... .......... .......... .......... .......... 26% 2.98M 70s +149350K .......... .......... .......... .......... .......... 26% 435M 70s +149400K .......... .......... .......... .......... .......... 26% 6.10M 70s +149450K .......... .......... .......... .......... .......... 26% 2.84M 70s +149500K .......... .......... .......... .......... .......... 26% 1.51M 70s +149550K .......... .......... .......... .......... .......... 26% 93.1T 70s +149600K .......... .......... .......... .......... .......... 26% 602M 70s +149650K .......... .......... .......... .......... .......... 26% 76.9M 70s +149700K .......... .......... .......... .......... .......... 26% 718M 70s +149750K .......... .......... .......... .......... .......... 26% 93.1T 70s +149800K .......... .......... .......... .......... .......... 26% 8.02M 70s +149850K .......... .......... .......... .......... .......... 26% 93.1T 70s +149900K .......... .......... .......... .......... .......... 26% 434M 70s +149950K .......... .......... .......... .......... .......... 26% 2.13M 70s +150000K .......... .......... .......... .......... .......... 26% 14.9M 70s +150050K .......... .......... .......... .......... .......... 26% 5.85M 70s +150100K .......... .......... .......... .......... .......... 26% 10.0M 70s +150150K .......... .......... .......... .......... .......... 26% 30.0M 70s +150200K .......... .......... .......... .......... .......... 26% 4.56M 70s +150250K .......... .......... .......... .......... .......... 26% 320M 70s +150300K .......... .......... .......... .......... .......... 26% 1.69M 70s +150350K .......... .......... .......... .......... .......... 26% 314M 70s +150400K .......... .......... .......... .......... .......... 26% 470M 70s +150450K .......... .......... .......... .......... .......... 26% 577M 70s +150500K .......... .......... .......... .......... .......... 26% 23.1M 70s +150550K .......... .......... .......... .......... .......... 26% 4.99M 70s +150600K .......... .......... .......... .......... .......... 26% 93.1T 70s +150650K .......... .......... .......... .......... .......... 26% 6.49M 70s +150700K .......... .......... .......... .......... .......... 26% 5.43M 70s +150750K .......... .......... .......... .......... .......... 26% 480M 70s +150800K .......... .......... .......... .......... .......... 26% 7.54M 70s +150850K .......... .......... .......... .......... .......... 26% 93.1T 70s +150900K .......... .......... .......... .......... .......... 26% 6.96M 70s +150950K .......... .......... .......... .......... .......... 26% 11.0M 70s +151000K .......... .......... .......... .......... .......... 26% 4.57M 70s +151050K .......... .......... .......... .......... .......... 26% 5.47M 70s +151100K .......... .......... .......... .......... .......... 26% 93.1T 70s +151150K .......... .......... .......... .......... .......... 26% 401M 70s +151200K .......... .......... .......... .......... .......... 26% 4.28M 70s +151250K .......... .......... .......... .......... .......... 26% 5.90M 70s +151300K .......... .......... .......... .......... .......... 26% 535M 69s +151350K .......... .......... .......... .......... .......... 26% 5.08M 69s +151400K .......... .......... .......... .......... .......... 26% 4.61M 69s +151450K .......... .......... .......... .......... .......... 26% 1.37M 70s +151500K .......... .......... .......... .......... .......... 27% 7.36M 70s +151550K .......... .......... .......... .......... .......... 27% 452M 69s +151600K .......... .......... .......... .......... .......... 27% 93.1T 69s +151650K .......... .......... .......... .......... .......... 27% 490M 69s +151700K .......... .......... .......... .......... .......... 27% 621M 69s +151750K .......... .......... .......... .......... .......... 27% 672M 69s +151800K .......... .......... .......... .......... .......... 27% 856M 69s +151850K .......... .......... .......... .......... .......... 27% 6.28M 69s +151900K .......... .......... .......... .......... .......... 27% 1.38M 69s +151950K .......... .......... .......... .......... .......... 27% 402M 69s +152000K .......... .......... .......... .......... .......... 27% 6.09M 69s +152050K .......... .......... .......... .......... .......... 27% 555M 69s +152100K .......... .......... .......... .......... .......... 27% 602M 69s +152150K .......... .......... .......... .......... .......... 27% 681M 69s +152200K .......... .......... .......... .......... .......... 27% 93.1T 69s +152250K .......... .......... .......... .......... .......... 27% 10.6M 69s +152300K .......... .......... .......... .......... .......... 27% 5.01M 69s +152350K .......... .......... .......... .......... .......... 27% 4.26M 69s +152400K .......... .......... .......... .......... .......... 27% 2.00M 69s +152450K .......... .......... .......... .......... .......... 27% 280M 69s +152500K .......... .......... .......... .......... .......... 27% 573M 69s +152550K .......... .......... .......... .......... .......... 27% 93.1T 69s +152600K .......... .......... .......... .......... .......... 27% 598M 69s +152650K .......... .......... .......... .......... .......... 27% 638M 69s +152700K .......... .......... .......... .......... .......... 27% 662M 69s +152750K .......... .......... .......... .......... .......... 27% 6.34M 69s +152800K .......... .......... .......... .......... .......... 27% 31.1M 69s +152850K .......... .......... .......... .......... .......... 27% 8.58M 69s +152900K .......... .......... .......... .......... .......... 27% 6.67M 69s +152950K .......... .......... .......... .......... .......... 27% 56.9M 69s +153000K .......... .......... .......... .......... .......... 27% 62.0M 69s +153050K .......... .......... .......... .......... .......... 27% 4.91M 69s +153100K .......... .......... .......... .......... .......... 27% 4.22M 69s +153150K .......... .......... .......... .......... .......... 27% 3.64M 69s +153200K .......... .......... .......... .......... .......... 27% 14.7M 69s +153250K .......... .......... .......... .......... .......... 27% 1.81M 69s +153300K .......... .......... .......... .......... .......... 27% 2.05M 69s +153350K .......... .......... .......... .......... .......... 27% 331M 69s +153400K .......... .......... .......... .......... .......... 27% 16.0M 69s +153450K .......... .......... .......... .......... .......... 27% 436M 69s +153500K .......... .......... .......... .......... .......... 27% 93.1T 69s +153550K .......... .......... .......... .......... .......... 27% 611M 69s +153600K .......... .......... .......... .......... .......... 27% 638M 69s +153650K .......... .......... .......... .......... .......... 27% 23.1M 69s +153700K .......... .......... .......... .......... .......... 27% 93.1T 69s +153750K .......... .......... .......... .......... .......... 27% 7.26M 69s +153800K .......... .......... .......... .......... .......... 27% 19.6M 69s +153850K .......... .......... .......... .......... .......... 27% 4.81M 69s +153900K .......... .......... .......... .......... .......... 27% 581M 69s +153950K .......... .......... .......... .......... .......... 27% 3.80M 69s +154000K .......... .......... .......... .......... .......... 27% 548M 69s +154050K .......... .......... .......... .......... .......... 27% 2.63M 69s +154100K .......... .......... .......... .......... .......... 27% 2.29M 69s +154150K .......... .......... .......... .......... .......... 27% 9.73M 69s +154200K .......... .......... .......... .......... .......... 27% 93.1T 69s +154250K .......... .......... .......... .......... .......... 27% 801K 69s +154300K .......... .......... .......... .......... .......... 27% 341M 69s +154350K .......... .......... .......... .......... .......... 27% 431M 69s +154400K .......... .......... .......... .......... .......... 27% 529M 69s +154450K .......... .......... .......... .......... .......... 27% 93.1T 69s +154500K .......... .......... .......... .......... .......... 27% 626M 69s +154550K .......... .......... .......... .......... .......... 27% 540M 68s +154600K .......... .......... .......... .......... .......... 27% 667M 68s +154650K .......... .......... .......... .......... .......... 27% 93.1T 68s +154700K .......... .......... .......... .......... .......... 27% 655M 68s +154750K .......... .......... .......... .......... .......... 27% 601M 68s +154800K .......... .......... .......... .......... .......... 27% 435M 68s +154850K .......... .......... .......... .......... .......... 27% 391M 68s +154900K .......... .......... .......... .......... .......... 27% 1.32M 68s +154950K .......... .......... .......... .......... .......... 27% 365M 68s +155000K .......... .......... .......... .......... .......... 27% 573M 68s +155050K .......... .......... .......... .......... .......... 27% 385M 68s +155100K .......... .......... .......... .......... .......... 27% 93.1T 68s +155150K .......... .......... .......... .......... .......... 27% 571M 68s +155200K .......... .......... .......... .......... .......... 27% 14.5M 68s +155250K .......... .......... .......... 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.......... .......... .......... .......... .......... 28% 716M 68s +157250K .......... .......... .......... .......... .......... 28% 17.9M 68s +157300K .......... .......... .......... .......... .......... 28% 7.34M 68s +157350K .......... .......... .......... .......... .......... 28% 1.57M 68s +157400K .......... .......... .......... .......... .......... 28% 93.1T 68s +157450K .......... .......... .......... .......... .......... 28% 355M 68s +157500K .......... .......... .......... .......... .......... 28% 746M 67s +157550K .......... .......... .......... .......... .......... 28% 859M 67s +157600K .......... .......... .......... .......... .......... 28% 93.1T 67s +157650K .......... .......... .......... .......... .......... 28% 2.59M 67s +157700K .......... .......... .......... .......... .......... 28% 11.8M 67s +157750K .......... .......... .......... .......... .......... 28% 675M 67s +157800K .......... .......... .......... .......... .......... 28% 1.33M 67s +157850K .......... .......... .......... .......... .......... 28% 418M 67s +157900K .......... .......... .......... .......... .......... 28% 820M 67s +157950K .......... .......... .......... .......... .......... 28% 93.1T 67s +158000K .......... .......... .......... .......... .......... 28% 784M 67s +158050K .......... .......... .......... .......... .......... 28% 2.51M 67s +158100K .......... .......... .......... .......... .......... 28% 401M 67s +158150K .......... .......... .......... .......... .......... 28% 93.1T 67s +158200K .......... .......... .......... .......... .......... 28% 715M 67s +158250K .......... .......... .......... .......... .......... 28% 1.85M 67s +158300K .......... .......... .......... .......... .......... 28% 93.1T 67s +158350K .......... .......... .......... .......... .......... 28% 398M 67s +158400K .......... .......... .......... .......... .......... 28% 623M 67s +158450K .......... .......... .......... .......... .......... 28% 596M 67s +158500K .......... .......... .......... .......... .......... 28% 93.1T 67s +158550K .......... .......... .......... .......... .......... 28% 7.76M 67s +158600K .......... .......... .......... .......... .......... 28% 2.06M 67s +158650K .......... .......... .......... .......... .......... 28% 398M 67s +158700K .......... .......... .......... .......... .......... 28% 624M 67s +158750K .......... .......... .......... .......... .......... 28% 93.1T 67s +158800K .......... .......... .......... .......... .......... 28% 76.0M 67s +158850K .......... .......... .......... .......... .......... 28% 740M 67s +158900K .......... .......... .......... .......... .......... 28% 883M 67s +158950K .......... .......... .......... .......... .......... 28% 3.00M 67s +159000K .......... .......... .......... .......... .......... 28% 396M 67s +159050K .......... .......... .......... .......... .......... 28% 594M 67s +159100K .......... .......... .......... .......... 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+162350K .......... .......... .......... .......... .......... 28% 359M 66s +162400K .......... .......... .......... .......... .......... 28% 1.09M 66s +162450K .......... .......... .......... .......... .......... 28% 648M 66s +162500K .......... .......... .......... .......... .......... 28% 93.1T 66s +162550K .......... .......... .......... .......... .......... 28% 7.30M 66s +162600K .......... .......... .......... .......... .......... 28% 3.78M 66s +162650K .......... .......... .......... .......... .......... 28% 333M 66s +162700K .......... .......... .......... .......... .......... 29% 160M 66s +162750K .......... .......... .......... .......... .......... 29% 93.1T 66s +162800K .......... .......... .......... .......... .......... 29% 390M 66s +162850K .......... .......... .......... .......... .......... 29% 2.71M 66s +162900K .......... .......... .......... .......... .......... 29% 315M 66s +162950K .......... .......... .......... .......... .......... 29% 93.1T 66s +163000K .......... .......... .......... .......... .......... 29% 532M 66s +163050K .......... .......... .......... .......... .......... 29% 535M 66s +163100K .......... .......... .......... .......... .......... 29% 561M 66s +163150K .......... .......... .......... .......... .......... 29% 93.1T 66s +163200K .......... .......... .......... .......... .......... 29% 11.0M 66s +163250K .......... .......... .......... .......... .......... 29% 2.29M 66s +163300K .......... .......... .......... .......... .......... 29% 66.3M 66s +163350K .......... .......... .......... .......... .......... 29% 387M 66s +163400K .......... .......... .......... .......... .......... 29% 374M 65s +163450K .......... .......... .......... .......... .......... 29% 93.1T 65s +163500K .......... .......... .......... .......... .......... 29% 15.7M 65s +163550K .......... .......... .......... .......... .......... 29% 93.1T 65s +163600K .......... .......... .......... .......... 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+166850K .......... .......... .......... .......... .......... 29% 93.1T 64s +166900K .......... .......... .......... .......... .......... 29% 3.73M 64s +166950K .......... .......... .......... .......... .......... 29% 4.39M 64s +167000K .......... .......... .......... .......... .......... 29% 1.11M 64s +167050K .......... .......... .......... .......... .......... 29% 93.1T 64s +167100K .......... .......... .......... .......... .......... 29% 318M 64s +167150K .......... .......... .......... .......... .......... 29% 520M 64s +167200K .......... .......... .......... .......... .......... 29% 620M 64s +167250K .......... .......... .......... .......... .......... 29% 749M 64s +167300K .......... .......... .......... .......... .......... 29% 93.1T 64s +167350K .......... .......... .......... .......... .......... 29% 8.56M 64s +167400K .......... .......... .......... .......... .......... 29% 19.9M 64s +167450K .......... .......... .......... .......... .......... 29% 3.08M 64s +167500K .......... .......... .......... .......... .......... 29% 465M 64s +167550K .......... .......... .......... .......... .......... 29% 8.86M 64s +167600K .......... .......... .......... .......... .......... 29% 14.8M 64s +167650K .......... .......... .......... .......... .......... 29% 7.48M 64s +167700K .......... .......... .......... .......... .......... 29% 9.72M 64s +167750K .......... .......... .......... .......... .......... 29% 5.63M 64s +167800K .......... .......... .......... .......... .......... 29% 3.46M 64s +167850K .......... .......... .......... .......... .......... 29% 6.88M 64s +167900K .......... .......... .......... .......... .......... 29% 14.8M 64s +167950K .......... .......... .......... .......... .......... 29% 11.9M 64s +168000K .......... .......... .......... .......... .......... 29% 7.36M 64s +168050K .......... .......... .......... .......... .......... 29% 4.64M 64s +168100K .......... .......... .......... .......... .......... 29% 93.1T 64s +168150K .......... .......... .......... .......... .......... 29% 4.75M 64s +168200K .......... .......... .......... .......... .......... 29% 338M 64s +168250K .......... .......... .......... .......... .......... 29% 5.06M 64s +168300K .......... .......... .......... .......... .......... 29% 12.0M 64s +168350K .......... .......... .......... .......... .......... 30% 7.42M 64s +168400K .......... .......... .......... .......... .......... 30% 1.33M 64s +168450K .......... .......... .......... .......... .......... 30% 93.1T 64s +168500K .......... .......... .......... .......... .......... 30% 491M 64s +168550K .......... .......... .......... .......... .......... 30% 779M 64s +168600K .......... .......... .......... .......... .......... 30% 761M 64s +168650K .......... .......... .......... .......... .......... 30% 1.38M 64s +168700K .......... .......... .......... .......... .......... 30% 408M 64s +168750K .......... .......... .......... 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.......... .......... .......... 30% 93.1T 64s +169450K .......... .......... .......... .......... .......... 30% 658M 64s +169500K .......... .......... .......... .......... .......... 30% 655M 64s +169550K .......... .......... .......... .......... .......... 30% 4.43M 64s +169600K .......... .......... .......... .......... .......... 30% 559M 64s +169650K .......... .......... .......... .......... .......... 30% 6.62M 64s +169700K .......... .......... .......... .......... .......... 30% 20.2M 64s +169750K .......... .......... .......... .......... .......... 30% 62.8M 64s +169800K .......... .......... .......... .......... .......... 30% 4.94M 64s +169850K .......... .......... .......... .......... .......... 30% 5.58M 64s +169900K .......... .......... .......... .......... .......... 30% 458M 64s +169950K .......... .......... .......... .......... .......... 30% 8.11M 64s +170000K .......... .......... .......... .......... .......... 30% 7.40M 64s +170050K .......... .......... .......... .......... .......... 30% 9.86M 64s +170100K .......... .......... .......... .......... .......... 30% 7.47M 64s +170150K .......... .......... .......... .......... .......... 30% 7.70M 64s +170200K .......... .......... .......... .......... .......... 30% 5.85M 63s +170250K .......... .......... .......... .......... .......... 30% 11.8M 63s +170300K .......... .......... .......... .......... .......... 30% 494M 63s +170350K .......... .......... .......... .......... .......... 30% 652M 63s +170400K .......... .......... .......... .......... .......... 30% 3.36M 63s +170450K .......... .......... .......... .......... .......... 30% 9.81M 63s +170500K .......... .......... .......... .......... .......... 30% 5.33M 63s +170550K .......... .......... .......... .......... .......... 30% 6.65M 63s +170600K .......... .......... .......... .......... .......... 30% 4.52M 63s +170650K .......... .......... .......... .......... .......... 30% 93.1T 63s +170700K .......... .......... .......... .......... .......... 30% 12.2M 63s +170750K .......... .......... .......... .......... .......... 30% 3.62M 63s +170800K .......... .......... .......... .......... .......... 30% 589M 63s +170850K .......... .......... .......... .......... .......... 30% 20.8M 63s +170900K .......... .......... .......... .......... .......... 30% 7.40M 63s +170950K .......... .......... .......... .......... .......... 30% 8.54M 63s +171000K .......... .......... .......... .......... .......... 30% 8.53M 63s +171050K .......... .......... .......... .......... .......... 30% 5.42M 63s +171100K .......... .......... .......... .......... .......... 30% 19.3M 63s +171150K .......... .......... .......... .......... .......... 30% 551M 63s +171200K .......... .......... .......... .......... .......... 30% 3.16M 63s +171250K .......... .......... .......... .......... .......... 30% 15.1M 63s +171300K .......... .......... .......... .......... .......... 30% 5.95M 63s +171350K .......... .......... .......... .......... .......... 30% 11.2M 63s +171400K .......... .......... .......... .......... .......... 30% 12.1M 63s +171450K .......... .......... .......... .......... .......... 30% 664M 63s +171500K .......... .......... .......... .......... .......... 30% 743M 63s +171550K .......... .......... .......... .......... .......... 30% 886M 63s +171600K .......... .......... .......... .......... .......... 30% 2.18M 63s +171650K .......... .......... .......... .......... .......... 30% 4.98M 63s +171700K .......... .......... .......... .......... .......... 30% 93.1T 63s +171750K .......... .......... .......... .......... .......... 30% 577M 63s +171800K .......... .......... .......... .......... .......... 30% 2.22M 63s +171850K .......... .......... .......... .......... .......... 30% 59.5M 63s +171900K .......... .......... .......... .......... .......... 30% 699M 63s +171950K .......... .......... .......... .......... .......... 30% 4.62M 63s +172000K .......... .......... .......... .......... .......... 30% 7.49M 63s +172050K .......... .......... .......... .......... .......... 30% 4.81M 63s +172100K .......... .......... .......... .......... .......... 30% 7.41M 63s +172150K .......... .......... .......... .......... .......... 30% 687M 63s +172200K .......... .......... .......... .......... .......... 30% 20.7M 63s +172250K .......... .......... .......... .......... .......... 30% 2.64M 63s +172300K .......... .......... .......... .......... .......... 30% 406M 63s +172350K .......... .......... .......... .......... .......... 30% 5.61M 63s +172400K .......... .......... .......... .......... .......... 30% 4.45M 63s +172450K .......... .......... .......... .......... .......... 30% 434M 63s +172500K .......... .......... .......... .......... .......... 30% 7.60M 63s +172550K .......... .......... .......... .......... .......... 30% 11.0M 63s +172600K .......... .......... .......... .......... .......... 30% 4.82M 63s +172650K .......... .......... .......... .......... .......... 30% 10.6M 63s +172700K .......... .......... .......... .......... .......... 30% 1.87M 63s +172750K .......... .......... .......... .......... .......... 30% 5.14M 63s +172800K .......... .......... .......... .......... .......... 30% 4.33M 63s +172850K .......... .......... .......... .......... .......... 30% 5.54M 63s +172900K .......... .......... .......... .......... .......... 30% 324M 63s +172950K .......... .......... .......... .......... .......... 30% 614M 63s +173000K .......... .......... .......... .......... .......... 30% 93.1T 63s +173050K .......... .......... .......... .......... .......... 30% 695M 63s +173100K .......... .......... .......... .......... .......... 30% 707M 63s +173150K .......... .......... .......... .......... .......... 30% 582K 63s +173200K .......... .......... .......... .......... .......... 30% 290M 63s +173250K .......... .......... .......... 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+175200K .......... .......... .......... .......... .......... 31% 7.33M 62s +175250K .......... .......... .......... .......... .......... 31% 9.69M 62s +175300K .......... .......... .......... .......... .......... 31% 2.18M 62s +175350K .......... .......... .......... .......... .......... 31% 93.1T 62s +175400K .......... .......... .......... .......... .......... 31% 318M 62s +175450K .......... .......... .......... .......... .......... 31% 2.20M 62s +175500K .......... .......... .......... .......... .......... 31% 448M 62s +175550K .......... .......... .......... .......... .......... 31% 739M 62s +175600K .......... .......... .......... .......... .......... 31% 21.8M 62s +175650K .......... .......... .......... .......... .......... 31% 93.1T 62s +175700K .......... .......... .......... .......... .......... 31% 8.60M 62s +175750K .......... .......... .......... .......... .......... 31% 4.54M 62s +175800K .......... .......... .......... .......... .......... 31% 93.1T 62s +175850K .......... .......... .......... .......... .......... 31% 9.98M 62s +175900K .......... .......... .......... .......... .......... 31% 3.25M 62s +175950K .......... .......... .......... .......... .......... 31% 20.0M 62s +176000K .......... .......... .......... .......... .......... 31% 9.78M 62s +176050K .......... .......... .......... .......... .......... 31% 5.93M 62s +176100K .......... .......... .......... .......... .......... 31% 9.84M 62s +176150K .......... .......... .......... .......... .......... 31% 93.1T 62s +176200K .......... .......... .......... .......... .......... 31% 5.73M 62s +176250K .......... .......... .......... .......... .......... 31% 6.21M 62s +176300K .......... .......... .......... .......... .......... 31% 7.60M 62s +176350K .......... .......... .......... .......... .......... 31% 11.6M 62s +176400K .......... .......... .......... .......... .......... 31% 2.29M 62s +176450K .......... .......... .......... .......... 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+179700K .......... .......... .......... .......... .......... 32% 93.1T 62s +179750K .......... .......... .......... .......... .......... 32% 1.91M 62s +179800K .......... .......... .......... .......... .......... 32% 93.1T 62s +179850K .......... .......... .......... .......... .......... 32% 432M 62s +179900K .......... .......... .......... .......... .......... 32% 757M 62s +179950K .......... .......... .......... .......... .......... 32% 1.35M 62s +180000K .......... .......... .......... .......... .......... 32% 342M 62s +180050K .......... .......... .......... .......... .......... 32% 679M 62s +180100K .......... .......... .......... .......... .......... 32% 93.1T 62s +180150K .......... .......... .......... .......... .......... 32% 746M 62s +180200K .......... .......... .......... .......... .......... 32% 807M 62s +180250K .......... .......... .......... .......... .......... 32% 188M 62s +180300K .......... .......... .......... .......... .......... 32% 625M 62s +180350K .......... .......... .......... .......... .......... 32% 3.08M 62s +180400K .......... .......... .......... .......... .......... 32% 421M 62s +180450K .......... .......... .......... .......... .......... 32% 3.74M 62s +180500K .......... .......... .......... .......... .......... 32% 5.55M 62s +180550K .......... .......... .......... .......... .......... 32% 31.5M 62s +180600K .......... .......... .......... .......... .......... 32% 28.5M 61s +180650K .......... .......... .......... .......... .......... 32% 2.89M 62s +180700K .......... .......... .......... .......... .......... 32% 378M 61s +180750K .......... .......... .......... .......... .......... 32% 5.92M 61s +180800K .......... .......... .......... .......... .......... 32% 6.93M 61s +180850K .......... .......... .......... .......... .......... 32% 588M 61s +180900K .......... .......... .......... .......... .......... 32% 4.62M 61s +180950K .......... .......... .......... .......... 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+184200K .......... .......... .......... .......... .......... 32% 2.76M 61s +184250K .......... .......... .......... .......... .......... 32% 93.1T 61s +184300K .......... .......... .......... .......... .......... 32% 1.60M 61s +184350K .......... .......... .......... .......... .......... 32% 258M 61s +184400K .......... .......... .......... .......... .......... 32% 607M 61s +184450K .......... .......... .......... .......... .......... 32% 93.1T 61s +184500K .......... .......... .......... .......... .......... 32% 2.24M 61s +184550K .......... .......... .......... .......... .......... 32% 208M 61s +184600K .......... .......... .......... .......... .......... 32% 423M 61s +184650K .......... .......... .......... .......... .......... 32% 2.85M 61s +184700K .......... .......... .......... .......... .......... 32% 334M 61s +184750K .......... .......... .......... .......... .......... 32% 626M 60s +184800K .......... .......... .......... .......... .......... 32% 668M 60s +184850K .......... .......... .......... .......... .......... 32% 11.9M 60s +184900K .......... .......... .......... .......... .......... 32% 3.76M 60s +184950K .......... .......... .......... .......... .......... 32% 93.1T 60s +185000K .......... .......... .......... .......... .......... 32% 683M 60s +185050K .......... .......... .......... .......... .......... 32% 11.9M 60s +185100K .......... .......... .......... .......... .......... 32% 634M 60s +185150K .......... .......... .......... .......... .......... 33% 15.0M 60s +185200K .......... .......... .......... .......... .......... 33% 539M 60s +185250K .......... .......... .......... .......... .......... 33% 1.93M 60s +185300K .......... .......... .......... .......... .......... 33% 8.70M 60s +185350K .......... .......... .......... .......... .......... 33% 13.8M 60s +185400K .......... .......... .......... .......... .......... 33% 6.10M 60s +185450K .......... .......... .......... .......... 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58s +195100K .......... .......... .......... .......... .......... 34% 52.0M 58s +195150K .......... .......... .......... .......... .......... 34% 2.94M 58s +195200K .......... .......... .......... .......... .......... 34% 10.1M 58s +195250K .......... .......... .......... .......... .......... 34% 13.3M 58s +195300K .......... .......... .......... .......... .......... 34% 2.57M 58s +195350K .......... .......... .......... .......... .......... 34% 302M 58s +195400K .......... .......... .......... .......... .......... 34% 7.78M 58s +195450K .......... .......... .......... .......... .......... 34% 3.77M 58s +195500K .......... .......... .......... .......... .......... 34% 20.0M 58s +195550K .......... .......... .......... .......... .......... 34% 668M 58s +195600K .......... .......... .......... .......... .......... 34% 4.03M 58s +195650K .......... .......... .......... .......... .......... 34% 93.1T 58s +195700K .......... .......... .......... .......... .......... 34% 31.0M 58s +195750K .......... .......... .......... .......... .......... 34% 1.91M 58s +195800K .......... .......... .......... .......... .......... 34% 93.1T 58s +195850K .......... .......... .......... .......... .......... 34% 413M 58s +195900K .......... .......... .......... .......... .......... 34% 682M 58s +195950K .......... .......... .......... .......... .......... 34% 784M 58s +196000K .......... .......... .......... .......... .......... 34% 6.16M 58s +196050K .......... .......... .......... .......... .......... 34% 32.1M 58s +196100K .......... .......... .......... .......... .......... 34% 9.79M 58s +196150K .......... .......... .......... .......... .......... 34% 11.1M 58s +196200K .......... .......... .......... .......... .......... 34% 6.80M 58s +196250K .......... .......... .......... .......... .......... 34% 366M 57s +196300K .......... .......... .......... .......... .......... 34% 3.54M 57s +196350K .......... .......... .......... .......... .......... 34% 11.1M 57s +196400K .......... .......... .......... .......... .......... 35% 7.51M 57s +196450K .......... .......... .......... .......... .......... 35% 93.1T 57s +196500K .......... .......... .......... .......... .......... 35% 3.62M 57s +196550K .......... .......... .......... .......... .......... 35% 5.11M 57s +196600K .......... .......... .......... .......... .......... 35% 19.7M 57s +196650K .......... .......... .......... .......... .......... 35% 3.16M 57s +196700K .......... .......... .......... .......... .......... 35% 93.1T 57s +196750K .......... .......... .......... .......... .......... 35% 1.98M 57s +196800K .......... .......... .......... .......... .......... 35% 93.1T 57s +196850K .......... .......... .......... .......... .......... 35% 437M 57s +196900K .......... .......... .......... .......... .......... 35% 774M 57s +196950K .......... .......... .......... .......... .......... 35% 796M 57s +197000K .......... .......... .......... .......... .......... 35% 7.32M 57s +197050K .......... .......... .......... .......... .......... 35% 10.5M 57s +197100K .......... .......... .......... .......... .......... 35% 1.27M 57s +197150K .......... .......... .......... .......... .......... 35% 93.1T 57s +197200K .......... .......... .......... .......... .......... 35% 499M 57s +197250K .......... .......... .......... .......... .......... 35% 830M 57s +197300K .......... .......... .......... .......... .......... 35% 775M 57s +197350K .......... .......... .......... .......... .......... 35% 2.54M 57s +197400K .......... .......... .......... .......... .......... 35% 4.33M 57s +197450K .......... .......... .......... .......... .......... 35% 2.10M 57s +197500K .......... .......... .......... .......... .......... 35% 438M 57s +197550K .......... .......... .......... .......... .......... 35% 591M 57s +197600K .......... .......... .......... .......... .......... 35% 93.1T 57s +197650K .......... .......... .......... .......... .......... 35% 688M 57s +197700K .......... .......... .......... .......... .......... 35% 739M 57s +197750K .......... .......... .......... .......... .......... 35% 883M 57s +197800K .......... .......... .......... .......... .......... 35% 4.96M 57s +197850K .......... .......... .......... .......... .......... 35% 4.45M 57s +197900K .......... .......... .......... .......... .......... 35% 1.57M 57s +197950K .......... .......... .......... .......... .......... 35% 93.1T 57s +198000K .......... .......... .......... .......... .......... 35% 369M 57s +198050K .......... .......... .......... .......... .......... 35% 672M 57s +198100K .......... .......... .......... .......... .......... 35% 590M 57s +198150K .......... .......... .......... .......... .......... 35% 1.56M 57s +198200K .......... .......... .......... .......... .......... 35% 1.56M 57s +198250K .......... .......... .......... .......... .......... 35% 1.30M 57s +198300K .......... .......... .......... .......... .......... 35% 516M 57s +198350K .......... .......... .......... .......... .......... 35% 3.02M 57s +198400K .......... .......... .......... .......... .......... 35% 1.23M 57s +198450K .......... .......... .......... .......... .......... 35% 93.1T 57s +198500K .......... .......... .......... .......... .......... 35% 349M 57s +198550K .......... .......... .......... .......... .......... 35% 591M 57s +198600K .......... .......... .......... .......... .......... 35% 509M 57s +198650K .......... .......... .......... .......... .......... 35% 93.1T 57s +198700K .......... .......... .......... .......... .......... 35% 556M 57s +198750K .......... .......... .......... .......... .......... 35% 583M 57s +198800K .......... .......... .......... .......... .......... 35% 644M 57s +198850K .......... .......... .......... .......... .......... 35% 685M 57s +198900K .......... .......... .......... .......... .......... 35% 1.63M 57s +198950K .......... .......... .......... .......... .......... 35% 453M 57s +199000K .......... .......... .......... .......... .......... 35% 93.1T 57s +199050K .......... .......... .......... .......... .......... 35% 669M 57s +199100K .......... .......... .......... .......... .......... 35% 711M 57s +199150K .......... .......... .......... .......... .......... 35% 611M 57s +199200K .......... .......... .......... .......... .......... 35% 93.1T 57s +199250K .......... .......... .......... .......... .......... 35% 713M 57s +199300K .......... .......... .......... .......... .......... 35% 744M 57s +199350K .......... .......... .......... .......... .......... 35% 763M 57s +199400K .......... .......... .......... .......... .......... 35% 768M 57s +199450K .......... .......... .......... .......... .......... 35% 93.1T 57s +199500K .......... .......... .......... .......... .......... 35% 753M 57s +199550K .......... .......... .......... .......... .......... 35% 780M 57s +199600K .......... .......... .......... .......... .......... 35% 702M 57s +199650K .......... .......... .......... .......... .......... 35% 579M 57s +199700K .......... .......... .......... .......... .......... 35% 93.1T 57s +199750K .......... .......... .......... .......... .......... 35% 736M 57s +199800K .......... .......... .......... .......... .......... 35% 4.38M 57s +199850K .......... .......... .......... .......... .......... 35% 545M 57s +199900K .......... .......... .......... .......... .......... 35% 93.1T 57s +199950K .......... .......... .......... .......... .......... 35% 773M 57s +200000K .......... .......... .......... .......... .......... 35% 6.14M 57s +200050K .......... .......... .......... .......... .......... 35% 621M 57s +200100K .......... .......... .......... .......... .......... 35% 4.48M 57s +200150K .......... .......... .......... .......... .......... 35% 93.1T 57s +200200K .......... .......... .......... .......... .......... 35% 917K 57s +200250K .......... .......... .......... .......... .......... 35% 216M 57s +200300K .......... .......... .......... .......... .......... 35% 463M 57s +200350K .......... .......... .......... .......... .......... 35% 483M 57s +200400K .......... .......... .......... .......... .......... 35% 93.1T 57s +200450K .......... .......... .......... .......... .......... 35% 436M 57s +200500K .......... .......... .......... .......... .......... 35% 641K 57s +200550K .......... .......... .......... .......... .......... 35% 242M 57s +200600K .......... .......... .......... .......... .......... 35% 93.1T 57s +200650K .......... .......... .......... .......... .......... 35% 577M 57s +200700K .......... .......... .......... .......... .......... 35% 521M 57s +200750K .......... .......... .......... .......... .......... 35% 502M 57s +200800K .......... .......... .......... .......... .......... 35% 623M 57s +200850K .......... .......... .......... .......... .......... 35% 93.1T 56s +200900K .......... .......... .......... .......... .......... 35% 579M 56s +200950K .......... .......... .......... .......... .......... 35% 323M 56s +201000K .......... .......... .......... .......... .......... 35% 614M 56s +201050K .......... .......... .......... .......... .......... 35% 93.1T 56s +201100K .......... .......... .......... .......... .......... 35% 685M 56s +201150K .......... .......... .......... .......... .......... 35% 2.08M 56s +201200K .......... .......... .......... .......... .......... 35% 8.56M 56s +201250K .......... .......... .......... .......... .......... 35% 453M 56s +201300K .......... .......... .......... .......... .......... 35% 6.02M 56s +201350K .......... .......... .......... .......... .......... 35% 523M 56s +201400K .......... .......... .......... .......... .......... 35% 93.1T 56s +201450K .......... .......... .......... .......... .......... 35% 826M 56s +201500K .......... .......... .......... .......... .......... 35% 2.30M 56s +201550K .......... .......... .......... .......... .......... 35% 93.1T 56s +201600K .......... .......... .......... .......... .......... 35% 507M 56s +201650K .......... .......... .......... .......... .......... 35% 2.36M 56s +201700K .......... .......... .......... .......... .......... 35% 93.1T 56s +201750K .......... .......... .......... .......... .......... 35% 5.37M 56s +201800K .......... .......... .......... .......... .......... 35% 93.1T 56s +201850K .......... .......... .......... .......... .......... 35% 1.41M 56s +201900K .......... .......... .......... .......... .......... 35% 14.9M 56s +201950K .......... .......... .......... .......... .......... 35% 429M 56s +202000K .......... .......... .......... .......... .......... 36% 3.20M 56s +202050K .......... .......... .......... .......... .......... 36% 359M 56s +202100K .......... .......... .......... .......... .......... 36% 489M 56s +202150K .......... .......... .......... .......... .......... 36% 1.99M 56s +202200K .......... .......... .......... .......... .......... 36% 93.1T 56s +202250K .......... .......... .......... .......... .......... 36% 2.91M 56s +202300K .......... .......... .......... .......... .......... 36% 2.30M 56s +202350K .......... .......... .......... .......... .......... 36% 369M 56s +202400K .......... .......... .......... .......... .......... 36% 655M 56s +202450K .......... .......... .......... .......... .......... 36% 93.1T 56s +202500K .......... .......... .......... .......... .......... 36% 837M 56s +202550K .......... .......... .......... .......... .......... 36% 1.68M 56s +202600K .......... .......... .......... .......... .......... 36% 402M 56s +202650K .......... .......... .......... .......... .......... 36% 15.8M 56s +202700K .......... .......... .......... .......... .......... 36% 1.74M 56s +202750K .......... .......... .......... .......... .......... 36% 259M 56s +202800K .......... .......... .......... .......... .......... 36% 543M 56s +202850K .......... .......... .......... .......... .......... 36% 93.1T 56s +202900K .......... .......... .......... .......... .......... 36% 589M 56s +202950K .......... .......... .......... .......... .......... 36% 601M 56s +203000K .......... .......... .......... .......... .......... 36% 2.59M 56s +203050K .......... .......... .......... .......... .......... 36% 312M 56s +203100K .......... .......... .......... .......... .......... 36% 537M 56s +203150K .......... .......... .......... .......... .......... 36% 93.1T 56s +203200K .......... .......... .......... .......... .......... 36% 600M 56s +203250K .......... .......... .......... .......... .......... 36% 644M 56s +203300K .......... .......... .......... .......... .......... 36% 598M 56s +203350K .......... .......... .......... .......... .......... 36% 648M 56s +203400K .......... .......... .......... .......... .......... 36% 750K 56s +203450K .......... .......... .......... .......... .......... 36% 93.1T 56s +203500K .......... .......... .......... .......... .......... 36% 374M 56s +203550K .......... .......... .......... .......... .......... 36% 12.4M 56s +203600K .......... .......... .......... .......... .......... 36% 93.1T 56s +203650K .......... .......... .......... .......... .......... 36% 483M 56s +203700K .......... .......... .......... .......... .......... 36% 594M 56s +203750K .......... .......... .......... .......... .......... 36% 487M 56s +203800K .......... .......... .......... .......... .......... 36% 93.1T 56s +203850K .......... .......... .......... .......... .......... 36% 1.55M 56s +203900K .......... .......... .......... .......... .......... 36% 93.1T 56s +203950K .......... .......... .......... .......... .......... 36% 12.2M 56s +204000K .......... .......... .......... .......... .......... 36% 397M 56s +204050K .......... .......... .......... .......... .......... 36% 1.25M 56s +204100K .......... .......... .......... .......... .......... 36% 93.1T 56s +204150K .......... .......... .......... .......... .......... 36% 338M 56s +204200K .......... .......... .......... .......... .......... 36% 462M 56s +204250K .......... .......... .......... .......... .......... 36% 1.47M 56s +204300K .......... .......... .......... .......... .......... 36% 93.1T 56s +204350K .......... .......... .......... .......... .......... 36% 340M 56s +204400K .......... .......... .......... .......... .......... 36% 2.96M 56s +204450K .......... .......... .......... .......... .......... 36% 342M 56s +204500K .......... .......... .......... .......... .......... 36% 746M 56s +204550K .......... .......... .......... .......... .......... 36% 10.2M 56s +204600K .......... .......... .......... .......... .......... 36% 93.1T 56s +204650K .......... .......... .......... .......... .......... 36% 480M 56s +204700K .......... .......... .......... .......... .......... 36% 79.2M 56s +204750K .......... .......... .......... .......... .......... 36% 93.1T 56s +204800K .......... .......... .......... .......... .......... 36% 613M 56s +204850K .......... .......... .......... .......... .......... 36% 702K 56s +204900K .......... .......... .......... .......... .......... 36% 405M 56s +204950K .......... .......... .......... .......... .......... 36% 742M 56s +205000K .......... .......... .......... .......... .......... 36% 788M 56s +205050K .......... .......... .......... .......... .......... 36% 93.1T 56s +205100K .......... .......... .......... .......... .......... 36% 750M 56s +205150K .......... .......... .......... .......... .......... 36% 810M 56s +205200K .......... .......... .......... .......... .......... 36% 823M 56s +205250K .......... .......... .......... .......... .......... 36% 93.1T 56s +205300K .......... .......... .......... .......... .......... 36% 779M 55s +205350K .......... .......... .......... .......... .......... 36% 4.90M 55s +205400K .......... .......... .......... .......... .......... 36% 236M 55s +205450K .......... .......... .......... .......... .......... 36% 93.1T 55s +205500K .......... .......... .......... .......... .......... 36% 681M 55s +205550K .......... .......... .......... .......... .......... 36% 2.15M 55s +205600K .......... .......... .......... .......... .......... 36% 93.1T 55s +205650K .......... .......... .......... .......... .......... 36% 598M 55s +205700K .......... .......... .......... .......... .......... 36% 791M 55s +205750K .......... .......... .......... .......... .......... 36% 2.88M 55s +205800K .......... .......... .......... .......... .......... 36% 1.14M 55s +205850K .......... .......... .......... .......... .......... 36% 365M 55s +205900K .......... .......... .......... .......... .......... 36% 925K 56s +205950K .......... .......... .......... .......... .......... 36% 355M 55s +206000K .......... .......... .......... .......... .......... 36% 93.1T 55s +206050K .......... .......... .......... .......... .......... 36% 619M 55s +206100K .......... .......... .......... .......... .......... 36% 523M 55s +206150K .......... .......... .......... .......... .......... 36% 532M 55s +206200K .......... .......... .......... .......... .......... 36% 622M 55s +206250K .......... .......... .......... .......... .......... 36% 93.1T 55s +206300K .......... .......... .......... .......... .......... 36% 620M 55s +206350K .......... .......... .......... .......... .......... 36% 7.58M 55s +206400K .......... .......... .......... .......... .......... 36% 532M 55s +206450K .......... .......... .......... .......... .......... 36% 93.1T 55s +206500K .......... .......... .......... .......... .......... 36% 2.47M 55s +206550K .......... .......... .......... .......... .......... 36% 93.1T 55s +206600K .......... .......... .......... .......... .......... 36% 1.49M 55s +206650K .......... .......... .......... .......... .......... 36% 93.1T 55s +206700K .......... .......... .......... .......... .......... 36% 212M 55s +206750K .......... .......... .......... .......... .......... 36% 1.71M 55s +206800K .......... .......... .......... .......... .......... 36% 12.0M 55s +206850K .......... .......... .......... .......... .......... 36% 4.05M 55s +206900K .......... .......... .......... .......... .......... 36% 3.72M 55s +206950K .......... .......... .......... .......... .......... 36% 93.1T 55s +207000K .......... .......... .......... .......... .......... 36% 277M 55s +207050K .......... .......... .......... .......... .......... 36% 4.85M 55s +207100K .......... .......... .......... .......... .......... 36% 349M 55s +207150K .......... .......... .......... .......... .......... 36% 610M 55s +207200K .......... .......... .......... .......... .......... 36% 93.1T 55s +207250K .......... .......... .......... .......... .......... 36% 635M 55s +207300K .......... .......... .......... .......... .......... 36% 703M 55s +207350K .......... .......... .......... .......... .......... 36% 575M 55s +207400K .......... .......... .......... .......... .......... 36% 6.02M 55s +207450K .......... .......... .......... .......... .......... 36% 2.06M 55s +207500K .......... .......... .......... .......... .......... 36% 374M 55s +207550K .......... .......... .......... .......... .......... 36% 93.1T 55s +207600K .......... .......... .......... .......... .......... 37% 489M 55s +207650K .......... .......... .......... .......... .......... 37% 538M 55s +207700K .......... .......... .......... .......... .......... 37% 593M 55s +207750K .......... .......... .......... .......... .......... 37% 523M 55s +207800K .......... .......... .......... .......... .......... 37% 93.1T 55s +207850K .......... .......... .......... .......... .......... 37% 9.42M 55s +207900K .......... .......... .......... .......... .......... 37% 93.1T 55s +207950K .......... .......... .......... .......... .......... 37% 5.81M 55s +208000K .......... .......... .......... .......... .......... 37% 65.3M 55s +208050K .......... .......... .......... .......... .......... 37% 93.1T 55s +208100K .......... .......... .......... .......... .......... 37% 11.0M 55s +208150K .......... .......... .......... .......... .......... 37% 3.66M 55s +208200K .......... .......... .......... .......... .......... 37% 876K 55s +208250K .......... .......... .......... .......... .......... 37% 4.23M 55s +208300K .......... .......... .......... .......... .......... 37% 537M 55s +208350K .......... .......... .......... .......... .......... 37% 1.19M 55s +208400K .......... .......... .......... .......... .......... 37% 93.1T 55s +208450K .......... .......... .......... .......... .......... 37% 311M 55s +208500K .......... .......... .......... .......... .......... 37% 12.0M 55s +208550K .......... .......... .......... .......... .......... 37% 552M 55s +208600K .......... .......... .......... .......... .......... 37% 1.64M 55s +208650K .......... .......... .......... .......... .......... 37% 309M 55s +208700K .......... .......... .......... .......... .......... 37% 93.1T 55s +208750K .......... .......... .......... .......... .......... 37% 574M 55s +208800K .......... .......... .......... .......... .......... 37% 580M 55s +208850K .......... .......... .......... .......... .......... 37% 656M 55s +208900K .......... .......... .......... .......... .......... 37% 93.1T 55s +208950K .......... .......... .......... .......... .......... 37% 730M 55s +209000K .......... .......... .......... .......... .......... 37% 1.26M 55s +209050K .......... .......... .......... .......... .......... 37% 374M 55s +209100K .......... .......... .......... .......... .......... 37% 760M 55s +209150K .......... .......... .......... .......... .......... 37% 6.55M 55s +209200K .......... .......... .......... .......... .......... 37% 391M 55s +209250K .......... .......... .......... .......... .......... 37% 684M 55s +209300K .......... .......... .......... .......... .......... 37% 93.1T 55s +209350K .......... .......... .......... .......... .......... 37% 641M 55s +209400K .......... .......... .......... .......... .......... 37% 1003K 55s +209450K .......... .......... .......... .......... .......... 37% 434M 55s +209500K .......... .......... .......... .......... .......... 37% 830M 55s +209550K .......... .......... .......... .......... .......... 37% 11.1M 55s +209600K .......... .......... .......... .......... .......... 37% 16.5M 55s +209650K .......... .......... .......... .......... .......... 37% 681M 55s +209700K .......... .......... .......... .......... .......... 37% 795M 55s +209750K .......... .......... .......... .......... .......... 37% 6.79M 55s +209800K .......... .......... .......... .......... .......... 37% 58.2M 55s +209850K .......... .......... .......... .......... .......... 37% 1.46M 55s +209900K .......... .......... .......... .......... .......... 37% 1.29M 55s +209950K .......... .......... .......... .......... .......... 37% 510M 55s +210000K .......... .......... .......... .......... .......... 37% 990M 55s +210050K .......... .......... .......... .......... .......... 37% 93.1T 55s +210100K .......... .......... .......... .......... .......... 37% 988M 55s +210150K .......... .......... .......... .......... .......... 37% 1.00G 55s +210200K .......... .......... .......... .......... .......... 37% 924M 55s +210250K .......... .......... .......... .......... .......... 37% 1003M 55s +210300K .......... .......... .......... .......... .......... 37% 93.1T 54s +210350K .......... .......... .......... .......... .......... 37% 1.04G 54s +210400K .......... .......... .......... .......... .......... 37% 1.04G 54s +210450K .......... .......... .......... .......... .......... 37% 873M 54s +210500K .......... .......... .......... .......... .......... 37% 93.1T 54s +210550K .......... .......... .......... .......... .......... 37% 1.01G 54s +210600K .......... .......... .......... .......... .......... 37% 1.04G 54s +210650K .......... .......... .......... .......... .......... 37% 1.29G 54s +210700K .......... .......... .......... .......... .......... 37% 847K 54s +210750K .......... .......... .......... .......... .......... 37% 229M 54s +210800K .......... .......... .......... .......... .......... 37% 593M 54s +210850K .......... .......... .......... .......... .......... 37% 617M 54s +210900K .......... .......... .......... .......... .......... 37% 93.1T 54s +210950K .......... .......... .......... .......... .......... 37% 652M 54s +211000K .......... .......... .......... .......... .......... 37% 2.87M 54s +211050K .......... .......... .......... .......... .......... 37% 323M 54s +211100K .......... .......... .......... .......... .......... 37% 608M 54s +211150K .......... .......... .......... .......... .......... 37% 2.62M 54s +211200K .......... .......... .......... .......... .......... 37% 379M 54s +211250K .......... .......... .......... .......... .......... 37% 5.51M 54s +211300K .......... .......... .......... .......... .......... 37% 351M 54s +211350K .......... .......... .......... .......... .......... 37% 1.65M 54s +211400K .......... .......... .......... .......... .......... 37% 93.1T 54s +211450K .......... .......... .......... .......... .......... 37% 310M 54s +211500K .......... .......... .......... .......... .......... 37% 484M 54s +211550K .......... .......... .......... .......... .......... 37% 1.16M 54s +211600K .......... .......... .......... .......... .......... 37% 3.19M 54s +211650K .......... .......... .......... .......... .......... 37% 93.1T 54s +211700K .......... .......... .......... .......... .......... 37% 368M 54s +211750K .......... .......... .......... .......... .......... 37% 578M 54s +211800K .......... .......... .......... .......... .......... 37% 591M 54s +211850K .......... .......... .......... .......... .......... 37% 585M 54s +211900K .......... .......... .......... .......... .......... 37% 93.1T 54s +211950K .......... .......... .......... .......... .......... 37% 6.22M 54s +212000K .......... .......... .......... .......... .......... 37% 7.28M 54s +212050K .......... .......... .......... .......... .......... 37% 348M 54s +212100K .......... .......... .......... .......... .......... 37% 13.0M 54s +212150K .......... .......... .......... .......... .......... 37% 1.09M 54s +212200K .......... .......... .......... .......... .......... 37% 414M 54s +212250K .......... .......... .......... .......... .......... 37% 93.1T 54s +212300K .......... .......... .......... .......... .......... 37% 584M 54s +212350K .......... .......... .......... .......... .......... 37% 724M 54s +212400K .......... .......... .......... .......... .......... 37% 4.04M 54s +212450K .......... .......... .......... .......... .......... 37% 93.1T 54s +212500K .......... .......... .......... .......... .......... 37% 2.45M 54s +212550K .......... .......... .......... .......... .......... 37% 805K 54s +212600K .......... .......... .......... .......... .......... 37% 273M 54s +212650K .......... .......... .......... .......... .......... 37% 93.1T 54s +212700K .......... .......... .......... .......... .......... 37% 335M 54s +212750K .......... .......... .......... .......... .......... 37% 2.31M 54s +212800K .......... .......... .......... .......... .......... 37% 408M 54s +212850K .......... .......... .......... .......... .......... 37% 636M 54s +212900K .......... .......... .......... .......... .......... 37% 2.83M 54s +212950K .......... .......... .......... .......... .......... 37% 469M 54s +213000K .......... .......... .......... .......... .......... 37% 2.51M 54s +213050K .......... .......... .......... .......... .......... 37% 430M 54s +213100K .......... .......... .......... .......... .......... 37% 9.89M 54s +213150K .......... .......... .......... .......... .......... 37% 761M 54s +213200K .......... .......... .......... .......... .......... 37% 726M 54s +213250K .......... .......... .......... .......... .......... 38% 718M 54s +213300K .......... .......... .......... .......... .......... 38% 21.5M 54s +213350K .......... .......... .......... .......... .......... 38% 93.1T 54s +213400K .......... .......... .......... .......... .......... 38% 8.55M 54s +213450K .......... .......... .......... .......... .......... 38% 19.3M 54s +213500K .......... .......... .......... .......... .......... 38% 11.3M 54s +213550K .......... .......... .......... .......... .......... 38% 93.1T 54s +213600K .......... .......... .......... .......... .......... 38% 12.8M 54s +213650K .......... .......... .......... .......... .......... 38% 5.93M 54s +213700K .......... .......... .......... .......... .......... 38% 8.48M 54s +213750K .......... .......... .......... .......... .......... 38% 956K 54s +213800K .......... .......... .......... .......... .......... 38% 245M 54s +213850K .......... .......... .......... .......... .......... 38% 93.1T 54s +213900K .......... .......... .......... .......... .......... 38% 596M 54s +213950K .......... .......... .......... .......... .......... 38% 5.20M 54s +214000K .......... .......... .......... .......... .......... 38% 93.1T 54s +214050K .......... .......... .......... .......... .......... 38% 2.81M 54s +214100K .......... .......... .......... .......... .......... 38% 399M 54s +214150K .......... .......... .......... .......... .......... 38% 1.65M 54s +214200K .......... .......... .......... .......... .......... 38% 93.1T 54s +214250K .......... .......... .......... .......... .......... 38% 19.4M 54s +214300K .......... .......... .......... .......... .......... 38% 473M 54s +214350K .......... .......... .......... .......... .......... 38% 1.91M 54s +214400K .......... .......... .......... .......... .......... 38% 93.1T 54s +214450K .......... .......... .......... .......... .......... 38% 336M 54s +214500K .......... .......... .......... .......... .......... 38% 6.45M 54s +214550K .......... .......... .......... .......... .......... 38% 93.1T 54s +214600K .......... .......... .......... .......... .......... 38% 814K 54s +214650K .......... .......... .......... .......... .......... 38% 333M 54s +214700K .......... .......... .......... .......... .......... 38% 816M 54s +214750K .......... .......... .......... .......... .......... 38% 738M 54s +214800K .......... .......... .......... .......... .......... 38% 93.1T 54s +214850K .......... .......... .......... .......... .......... 38% 706M 54s +214900K .......... .......... .......... .......... .......... 38% 742M 54s +214950K .......... .......... .......... .......... .......... 38% 740M 54s +215000K .......... .......... .......... .......... .......... 38% 4.61M 54s +215050K .......... .......... .......... .......... .......... 38% 5.19M 54s +215100K .......... .......... .......... .......... .......... 38% 757K 54s +215150K .......... .......... .......... .......... .......... 38% 442M 54s +215200K .......... .......... .......... .......... .......... 38% 648M 54s +215250K .......... .......... .......... .......... .......... 38% 93.1T 54s +215300K .......... .......... .......... .......... .......... 38% 790M 54s +215350K .......... .......... .......... .......... .......... 38% 475M 54s +215400K .......... .......... .......... .......... .......... 38% 392M 54s +215450K .......... .......... .......... .......... .......... 38% 726M 54s +215500K .......... .......... .......... .......... .......... 38% 93.1T 54s +215550K .......... .......... .......... .......... .......... 38% 547M 54s +215600K .......... .......... .......... .......... .......... 38% 629M 54s +215650K .......... .......... .......... .......... .......... 38% 2.34M 54s +215700K .......... .......... .......... .......... .......... 38% 438M 54s +215750K .......... .......... .......... .......... .......... 38% 93.1T 53s +215800K .......... .......... .......... .......... .......... 38% 2.54M 54s +215850K .......... .......... .......... .......... .......... 38% 19.7M 53s +215900K .......... .......... .......... .......... .......... 38% 507M 53s +215950K .......... .......... .......... .......... .......... 38% 700M 53s +216000K .......... .......... .......... .......... .......... 38% 741M 53s +216050K .......... .......... .......... .......... .......... 38% 93.1T 53s +216100K .......... .......... .......... .......... .......... 38% 771M 53s +216150K .......... .......... .......... .......... .......... 38% 884K 53s +216200K .......... .......... .......... .......... .......... 38% 4.48M 53s +216250K .......... .......... .......... .......... .......... 38% 338M 53s +216300K .......... .......... 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.......... .......... .......... .......... .......... 38% 93.1T 53s +217650K .......... .......... .......... .......... .......... 38% 713M 53s +217700K .......... .......... .......... .......... .......... 38% 716M 53s +217750K .......... .......... .......... .......... .......... 38% 4.12M 53s +217800K .......... .......... .......... .......... .......... 38% 14.9M 53s +217850K .......... .......... .......... .......... .......... 38% 93.1T 53s +217900K .......... .......... .......... .......... .......... 38% 503M 53s +217950K .......... .......... .......... .......... .......... 38% 4.30M 53s +218000K .......... .......... .......... .......... .......... 38% 9.79M 53s +218050K .......... .......... .......... .......... .......... 38% 3.62M 53s +218100K .......... .......... .......... .......... .......... 38% 93.1T 53s +218150K .......... .......... .......... .......... .......... 38% 2.35M 53s +218200K .......... .......... .......... .......... .......... 38% 364M 53s +218250K .......... .......... .......... .......... .......... 38% 1.99M 53s +218300K .......... .......... .......... .......... .......... 38% 8.52M 53s +218350K .......... .......... .......... .......... .......... 38% 719M 53s +218400K .......... .......... .......... .......... .......... 38% 20.5M 53s +218450K .......... .......... .......... .......... .......... 38% 2.36M 53s +218500K .......... .......... .......... .......... .......... 38% 402M 53s +218550K .......... .......... .......... .......... .......... 38% 683M 53s +218600K .......... .......... .......... .......... .......... 38% 20.0M 53s +218650K .......... .......... .......... .......... .......... 38% 3.94M 53s +218700K .......... .......... .......... .......... .......... 38% 6.57M 53s +218750K .......... .......... .......... .......... .......... 38% 11.8M 53s +218800K .......... .......... .......... .......... .......... 38% 2.15M 53s +218850K .......... .......... .......... .......... .......... 39% 1.06M 53s +218900K .......... .......... .......... .......... .......... 39% 5.30M 53s +218950K .......... .......... .......... .......... .......... 39% 517M 53s +219000K .......... .......... .......... .......... .......... 39% 958M 53s +219050K .......... .......... .......... .......... .......... 39% 93.1T 53s +219100K .......... .......... .......... .......... .......... 39% 938M 53s +219150K .......... .......... .......... .......... .......... 39% 1.01G 53s +219200K .......... .......... .......... .......... .......... 39% 17.2M 53s +219250K .......... .......... .......... .......... .......... 39% 626M 53s +219300K .......... .......... .......... .......... .......... 39% 3.77M 53s +219350K .......... .......... .......... .......... .......... 39% 93.1T 53s +219400K .......... .......... .......... .......... .......... 39% 508M 53s +219450K .......... .......... .......... .......... .......... 39% 4.40M 53s +219500K .......... .......... .......... .......... .......... 39% 93.1T 53s +219550K .......... .......... .......... .......... .......... 39% 7.39M 53s +219600K .......... .......... .......... .......... .......... 39% 4.11M 53s +219650K .......... .......... .......... .......... .......... 39% 509M 53s +219700K .......... .......... .......... .......... .......... 39% 823M 53s +219750K .......... .......... .......... .......... .......... 39% 3.47M 53s +219800K .......... .......... .......... .......... .......... 39% 6.47M 53s +219850K .......... .......... .......... .......... .......... 39% 93.1T 53s +219900K .......... .......... .......... .......... .......... 39% 469M 53s +219950K .......... .......... .......... .......... .......... 39% 865M 53s +220000K .......... .......... .......... .......... .......... 39% 1.06G 53s +220050K .......... .......... .......... .......... .......... 39% 6.50M 53s +220100K .......... .......... .......... .......... .......... 39% 6.92M 53s +220150K .......... .......... .......... 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.......... .......... .......... .......... .......... 39% 8.04M 52s +222150K .......... .......... .......... .......... .......... 39% 446M 52s +222200K .......... .......... .......... .......... .......... 39% 511K 52s +222250K .......... .......... .......... .......... .......... 39% 320M 52s +222300K .......... .......... .......... .......... .......... 39% 654M 52s +222350K .......... .......... .......... .......... .......... 39% 93.1T 52s +222400K .......... .......... .......... .......... .......... 39% 614M 52s +222450K .......... .......... .......... .......... .......... 39% 516M 52s +222500K .......... .......... .......... .......... .......... 39% 365M 52s +222550K .......... .......... .......... .......... .......... 39% 529M 52s +222600K .......... .......... .......... .......... .......... 39% 93.1T 52s +222650K .......... .......... .......... .......... .......... 39% 504M 52s +222700K .......... .......... .......... .......... .......... 39% 385M 52s +222750K .......... .......... .......... .......... .......... 39% 519M 52s +222800K .......... .......... .......... .......... .......... 39% 93.1T 52s +222850K .......... .......... .......... .......... .......... 39% 523M 52s +222900K .......... .......... .......... .......... .......... 39% 542M 52s +222950K .......... .......... .......... .......... .......... 39% 504M 52s +223000K .......... .......... .......... .......... .......... 39% 1.25M 52s +223050K .......... .......... .......... .......... .......... 39% 93.1T 52s +223100K .......... .......... .......... .......... .......... 39% 311M 52s +223150K .......... .......... .......... .......... .......... 39% 588M 52s +223200K .......... .......... .......... .......... .......... 39% 465M 52s +223250K .......... .......... .......... .......... .......... 39% 93.1T 52s +223300K .......... .......... .......... .......... .......... 39% 498M 52s +223350K .......... .......... .......... .......... .......... 39% 496M 52s +223400K .......... .......... .......... .......... .......... 39% 658M 52s +223450K .......... .......... .......... .......... .......... 39% 1.86M 52s +223500K .......... .......... .......... .......... .......... 39% 237M 52s +223550K .......... .......... .......... .......... .......... 39% 93.1T 52s +223600K .......... .......... .......... .......... .......... 39% 518M 52s +223650K .......... .......... .......... .......... .......... 39% 568M 52s +223700K .......... .......... .......... .......... .......... 39% 677M 52s +223750K .......... .......... .......... .......... .......... 39% 1.13M 52s +223800K .......... .......... .......... .......... .......... 39% 226M 52s +223850K .......... .......... .......... .......... .......... 39% 93.1T 52s +223900K .......... .......... .......... .......... .......... 39% 272M 52s +223950K .......... .......... .......... .......... .......... 39% 491M 52s +224000K .......... .......... .......... .......... .......... 39% 290M 52s +224050K .......... .......... .......... .......... .......... 39% 93.1T 52s +224100K .......... .......... .......... .......... .......... 39% 51.7M 52s +224150K .......... .......... .......... .......... .......... 39% 559M 52s +224200K .......... .......... .......... .......... .......... 39% 93.1T 52s +224250K .......... .......... .......... .......... .......... 39% 1.65M 52s +224300K .......... .......... .......... .......... .......... 39% 315M 52s +224350K .......... .......... .......... .......... .......... 39% 617M 52s +224400K .......... .......... .......... .......... .......... 39% 4.85M 52s +224450K .......... .......... .......... .......... .......... 40% 93.1T 52s +224500K .......... .......... .......... .......... .......... 40% 20.3M 52s +224550K .......... .......... .......... .......... .......... 40% 724M 52s +224600K .......... .......... .......... .......... .......... 40% 15.3M 52s +224650K .......... .......... .......... .......... .......... 40% 721M 52s +224700K .......... .......... .......... .......... .......... 40% 4.00M 52s +224750K .......... .......... .......... .......... .......... 40% 19.5M 52s +224800K .......... .......... .......... .......... .......... 40% 5.50M 52s +224850K .......... .......... .......... .......... .......... 40% 504M 52s +224900K .......... .......... .......... .......... .......... 40% 93.1T 52s +224950K .......... .......... .......... .......... .......... 40% 685M 52s +225000K .......... .......... .......... .......... .......... 40% 788M 52s +225050K .......... .......... .......... .......... .......... 40% 1.86M 52s +225100K .......... .......... .......... .......... .......... 40% 451M 52s +225150K .......... .......... .......... .......... .......... 40% 93.1T 52s +225200K .......... .......... .......... .......... .......... 40% 653M 52s +225250K .......... .......... .......... .......... .......... 40% 771M 52s +225300K .......... .......... .......... .......... .......... 40% 3.36M 52s +225350K .......... .......... .......... .......... .......... 40% 7.48M 52s +225400K .......... .......... .......... .......... .......... 40% 5.27M 52s +225450K .......... .......... .......... .......... .......... 40% 93.1T 52s +225500K .......... .......... .......... .......... .......... 40% 7.56M 52s +225550K .......... .......... .......... .......... .......... 40% 409M 51s +225600K .......... .......... .......... .......... .......... 40% 1.51M 52s +225650K .......... .......... .......... .......... .......... 40% 93.1T 52s +225700K .......... .......... .......... .......... .......... 40% 1.61M 52s +225750K .......... .......... .......... .......... .......... 40% 1.23M 52s +225800K .......... .......... .......... .......... .......... 40% 231M 52s +225850K .......... .......... .......... .......... .......... 40% 478M 52s +225900K .......... .......... .......... .......... .......... 40% 15.7M 52s +225950K .......... .......... .......... .......... .......... 40% 93.1T 52s +226000K .......... .......... .......... .......... .......... 40% 681M 51s +226050K .......... .......... .......... .......... .......... 40% 6.14M 51s +226100K .......... .......... .......... .......... .......... 40% 3.07M 51s +226150K .......... .......... .......... .......... .......... 40% 93.1T 51s +226200K .......... .......... .......... .......... .......... 40% 2.56M 51s +226250K .......... .......... .......... .......... .......... 40% 447M 51s +226300K .......... .......... .......... .......... .......... 40% 759M 51s +226350K .......... .......... .......... .......... .......... 40% 1.39M 51s +226400K .......... .......... .......... .......... .......... 40% 418M 51s +226450K .......... .......... .......... .......... .......... 40% 93.1T 51s +226500K .......... .......... .......... .......... .......... 40% 928K 52s +226550K .......... .......... .......... .......... .......... 40% 34.4M 51s +226600K .......... .......... .......... .......... .......... 40% 541M 51s +226650K .......... .......... .......... .......... .......... 40% 731M 51s +226700K .......... .......... .......... .......... .......... 40% 93.1T 51s +226750K .......... .......... .......... .......... .......... 40% 750M 51s +226800K .......... .......... .......... .......... .......... 40% 645M 51s +226850K .......... .......... .......... .......... .......... 40% 762M 51s +226900K .......... .......... .......... .......... .......... 40% 93.1T 51s +226950K .......... .......... .......... .......... .......... 40% 840M 51s +227000K .......... .......... .......... .......... .......... 40% 4.07M 51s +227050K .......... .......... .......... .......... .......... 40% 469M 51s +227100K .......... .......... .......... .......... .......... 40% 93.1T 51s +227150K .......... .......... .......... .......... .......... 40% 3.35M 51s +227200K .......... .......... .......... .......... .......... 40% 10.3M 51s +227250K .......... .......... .......... .......... .......... 40% 71.3M 51s +227300K .......... .......... .......... .......... .......... 40% 1.74M 51s +227350K .......... .......... .......... .......... .......... 40% 5.48M 51s +227400K .......... .......... .......... .......... .......... 40% 471M 51s +227450K .......... .......... .......... .......... .......... 40% 93.1T 51s +227500K .......... .......... .......... .......... .......... 40% 720M 51s +227550K .......... .......... .......... .......... .......... 40% 679M 51s +227600K .......... .......... .......... .......... .......... 40% 587M 51s +227650K .......... .......... .......... .......... .......... 40% 734M 51s +227700K .......... .......... .......... .......... .......... 40% 93.1T 51s +227750K .......... .......... .......... .......... .......... 40% 726M 51s +227800K .......... .......... .......... .......... .......... 40% 3.53M 51s +227850K .......... .......... .......... .......... .......... 40% 585M 51s +227900K .......... .......... .......... .......... .......... 40% 743M 51s +227950K .......... .......... .......... .......... .......... 40% 93.1T 51s +228000K .......... .......... .......... .......... .......... 40% 728M 51s +228050K .......... .......... .......... .......... .......... 40% 3.92M 51s +228100K .......... .......... .......... .......... .......... 40% 5.95M 51s +228150K .......... .......... .......... .......... .......... 40% 3.16M 51s +228200K .......... .......... .......... .......... .......... 40% 461M 51s +228250K .......... .......... .......... .......... .......... 40% 93.1T 51s +228300K .......... .......... .......... .......... .......... 40% 1.09M 51s +228350K .......... .......... .......... .......... .......... 40% 368M 51s +228400K .......... .......... .......... .......... .......... 40% 770M 51s +228450K .......... .......... .......... .......... .......... 40% 93.1T 51s +228500K .......... .......... .......... .......... .......... 40% 790M 51s +228550K .......... .......... .......... .......... .......... 40% 870M 51s +228600K .......... .......... .......... .......... .......... 40% 877M 51s +228650K .......... .......... .......... .......... .......... 40% 869M 51s +228700K .......... .......... .......... .......... .......... 40% 93.1T 51s +228750K .......... .......... .......... .......... .......... 40% 4.25M 51s +228800K .......... .......... .......... .......... .......... 40% 15.0M 51s +228850K .......... .......... .......... .......... .......... 40% 553M 51s +228900K .......... .......... .......... .......... .......... 40% 3.25M 51s +228950K .......... .......... .......... .......... .......... 40% 1.27M 51s +229000K .......... .......... .......... .......... .......... 40% 317M 51s +229050K .......... .......... .......... .......... .......... 40% 93.1T 51s +229100K .......... .......... .......... .......... .......... 40% 582M 51s +229150K .......... .......... .......... .......... .......... 40% 683M 51s +229200K .......... .......... .......... .......... .......... 40% 803M 51s +229250K .......... .......... .......... .......... .......... 40% 7.66M 51s +229300K .......... .......... .......... .......... .......... 40% 468M 51s +229350K .......... .......... .......... .......... .......... 40% 571M 51s +229400K .......... .......... .......... .......... .......... 40% 4.02M 51s +229450K .......... .......... .......... .......... .......... 40% 1.07M 51s +229500K .......... .......... .......... .......... .......... 40% 93.1T 51s +229550K .......... .......... .......... .......... .......... 40% 429M 51s +229600K .......... .......... .......... .......... .......... 40% 740M 51s +229650K .......... .......... .......... .......... .......... 40% 717M 51s +229700K .......... .......... .......... .......... .......... 40% 855M 51s +229750K .......... .......... .......... .......... .......... 40% 10.8M 51s +229800K .......... .......... .......... .......... .......... 40% 93.1T 51s +229850K .......... .......... .......... .......... .......... 40% 576M 51s +229900K .......... .......... .......... .......... .......... 40% 747M 51s +229950K .......... .......... .......... .......... .......... 40% 3.43M 51s +230000K .......... .......... .......... .......... .......... 40% 258M 51s +230050K .......... .......... .......... .......... .......... 41% 7.54M 51s +230100K .......... .......... .......... .......... .......... 41% 469M 51s +230150K .......... .......... .......... .......... .......... 41% 5.07M 51s +230200K .......... .......... .......... .......... .......... 41% 5.33M 51s +230250K .......... .......... .......... .......... .......... 41% 582M 51s +230300K .......... .......... .......... .......... .......... 41% 4.31M 51s +230350K .......... .......... .......... .......... .......... 41% 7.48M 51s +230400K .......... .......... .......... .......... .......... 41% 93.1T 51s +230450K .......... .......... .......... .......... .......... 41% 59.6M 50s +230500K .......... .......... .......... .......... .......... 41% 3.31M 50s +230550K .......... .......... .......... .......... .......... 41% 348M 50s +230600K .......... .......... .......... .......... .......... 41% 7.63M 50s +230650K .......... .......... .......... .......... .......... 41% 31.6M 50s +230700K .......... .......... .......... .......... .......... 41% 2.49M 50s +230750K .......... .......... .......... .......... .......... 41% 71.4M 50s +230800K .......... .......... .......... .......... .......... 41% 79.8M 50s +230850K .......... .......... .......... .......... .......... 41% 93.1T 50s +230900K .......... .......... .......... .......... .......... 41% 761K 50s +230950K .......... .......... .......... .......... .......... 41% 1.43M 51s +231000K .......... .......... .......... .......... .......... 41% 93.1T 51s +231050K .......... .......... .......... .......... .......... 41% 381M 50s +231100K .......... .......... .......... .......... .......... 41% 722M 50s +231150K .......... .......... .......... .......... .......... 41% 539M 50s +231200K .......... .......... .......... .......... .......... 41% 93.1T 50s +231250K .......... .......... .......... .......... .......... 41% 962K 50s +231300K .......... .......... .......... .......... .......... 41% 268M 50s +231350K .......... .......... .......... .......... .......... 41% 93.1T 50s +231400K .......... .......... .......... .......... .......... 41% 610M 50s +231450K .......... .......... .......... .......... .......... 41% 572M 50s +231500K .......... .......... .......... .......... .......... 41% 599M 50s +231550K .......... .......... .......... .......... .......... 41% 93.1T 50s +231600K .......... .......... .......... .......... .......... 41% 598M 50s +231650K .......... .......... .......... .......... .......... 41% 639M 50s +231700K .......... .......... .......... .......... .......... 41% 579M 50s +231750K .......... .......... .......... .......... .......... 41% 1.33M 50s +231800K .......... .......... .......... .......... .......... 41% 93.1T 50s +231850K .......... .......... .......... .......... .......... 41% 1.86M 50s +231900K .......... .......... .......... .......... .......... 41% 93.1T 50s +231950K .......... .......... .......... .......... .......... 41% 411M 50s +232000K .......... .......... .......... .......... .......... 41% 9.50M 50s +232050K .......... .......... .......... .......... .......... 41% 392M 50s +232100K .......... .......... .......... .......... .......... 41% 93.1T 50s +232150K .......... .......... .......... .......... .......... 41% 1.24M 50s +232200K .......... .......... .......... .......... .......... 41% 93.1T 50s +232250K .......... .......... .......... .......... .......... 41% 479M 50s +232300K .......... .......... .......... .......... .......... 41% 863M 50s +232350K .......... .......... .......... .......... .......... 41% 14.9M 50s +232400K .......... .......... .......... .......... .......... 41% 765M 50s +232450K .......... .......... .......... .......... .......... 41% 15.5M 50s +232500K .......... .......... .......... .......... .......... 41% 54.8M 50s +232550K .......... .......... .......... .......... .......... 41% 1.30M 50s +232600K .......... .......... .......... .......... .......... 41% 461M 50s +232650K .......... .......... .......... .......... .......... 41% 93.1T 50s +232700K .......... .......... .......... .......... .......... 41% 745M 50s +232750K .......... .......... .......... .......... .......... 41% 764M 50s +232800K .......... .......... .......... .......... .......... 41% 3.46M 50s +232850K .......... .......... .......... .......... .......... 41% 93.1T 50s +232900K .......... .......... .......... .......... .......... 41% 412M 50s +232950K .......... .......... .......... .......... .......... 41% 3.37M 50s +233000K .......... .......... .......... .......... .......... 41% 3.53M 50s +233050K .......... .......... .......... .......... .......... 41% 311M 50s +233100K .......... .......... .......... .......... .......... 41% 16.6M 50s +233150K .......... .......... .......... .......... .......... 41% 20.4M 50s +233200K .......... .......... .......... .......... .......... 41% 947K 50s +233250K .......... .......... .......... .......... .......... 41% 33.4M 50s +233300K .......... .......... .......... .......... .......... 41% 93.1T 50s +233350K .......... .......... .......... .......... .......... 41% 493M 50s +233400K .......... .......... .......... .......... .......... 41% 636M 50s +233450K .......... .......... .......... .......... .......... 41% 4.35M 50s +233500K .......... .......... .......... .......... .......... 41% 1.70M 50s +233550K .......... .......... .......... .......... .......... 41% 300M 50s +233600K .......... .......... .......... .......... .......... 41% 6.14M 50s +233650K .......... .......... .......... .......... .......... 41% 456M 50s +233700K .......... .......... .......... .......... .......... 41% 1.10M 50s +233750K .......... .......... .......... .......... .......... 41% 296M 50s +233800K .......... .......... .......... .......... .......... 41% 93.1T 50s +233850K .......... .......... .......... .......... .......... 41% 1.93M 50s +233900K .......... .......... .......... .......... .......... 41% 334M 50s +233950K .......... .......... .......... .......... .......... 41% 595M 50s +234000K .......... .......... .......... .......... .......... 41% 93.1T 50s +234050K .......... .......... .......... .......... .......... 41% 485M 50s +234100K .......... .......... .......... .......... .......... 41% 565M 50s +234150K .......... .......... .......... .......... .......... 41% 615M 50s +234200K .......... .......... .......... .......... .......... 41% 93.1T 50s +234250K .......... .......... .......... .......... .......... 41% 586M 50s +234300K .......... .......... .......... .......... .......... 41% 9.23M 50s +234350K .......... .......... .......... .......... .......... 41% 93.1T 50s +234400K .......... .......... .......... .......... .......... 41% 380M 50s +234450K .......... .......... .......... .......... .......... 41% 515M 50s +234500K .......... .......... .......... .......... .......... 41% 2.93M 50s +234550K .......... .......... .......... .......... .......... 41% 306M 50s +234600K .......... .......... .......... .......... .......... 41% 93.1T 50s +234650K .......... .......... .......... .......... .......... 41% 6.38M 50s +234700K .......... .......... .......... .......... .......... 41% 437M 50s +234750K .......... .......... .......... .......... .......... 41% 93.1T 50s +234800K .......... .......... .......... .......... .......... 41% 602M 50s +234850K .......... .......... .......... .......... .......... 41% 1.45M 50s +234900K .......... .......... .......... .......... .......... 41% 257M 50s +234950K .......... .......... 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.......... .......... .......... .......... 41% 487M 50s +235650K .......... .......... .......... .......... .......... 41% 755M 50s +235700K .......... .......... .......... .......... .......... 42% 93.1T 50s +235750K .......... .......... .......... .......... .......... 42% 2.77M 50s +235800K .......... .......... .......... .......... .......... 42% 17.3M 49s +235850K .......... .......... .......... .......... .......... 42% 1.26M 50s +235900K .......... .......... .......... .......... .......... 42% 352M 50s +235950K .......... .......... .......... .......... .......... 42% 93.1T 49s +236000K .......... .......... .......... .......... .......... 42% 642M 49s +236050K .......... .......... .......... .......... .......... 42% 520M 49s +236100K .......... .......... .......... .......... .......... 42% 187M 49s +236150K .......... .......... .......... .......... .......... 42% 592M 49s +236200K .......... .......... .......... .......... .......... 42% 54.7M 49s +236250K .......... .......... .......... .......... .......... 42% 4.96M 49s +236300K .......... .......... .......... .......... .......... 42% 93.1T 49s +236350K .......... .......... .......... .......... .......... 42% 5.41M 49s +236400K .......... .......... .......... .......... .......... 42% 1.42M 49s +236450K .......... .......... .......... .......... .......... 42% 454M 49s +236500K .......... .......... .......... .......... .......... 42% 2.75M 49s +236550K .......... .......... .......... .......... .......... 42% 230M 49s +236600K .......... .......... .......... .......... .......... 42% 539M 49s +236650K .......... .......... .......... .......... .......... 42% 93.1T 49s +236700K .......... .......... .......... .......... .......... 42% 601M 49s +236750K .......... .......... .......... .......... .......... 42% 687M 49s +236800K .......... .......... .......... .......... .......... 42% 11.1M 49s +236850K .......... .......... .......... .......... .......... 42% 8.26M 49s +236900K .......... .......... .......... .......... .......... 42% 665M 49s +236950K .......... .......... .......... .......... .......... 42% 3.78M 49s +237000K .......... .......... .......... .......... .......... 42% 1.38M 49s +237050K .......... .......... .......... .......... .......... 42% 9.78M 49s +237100K .......... .......... .......... .......... .......... 42% 431M 49s +237150K .......... .......... .......... .......... .......... 42% 93.1T 49s +237200K .......... .......... .......... .......... .......... 42% 549M 49s +237250K .......... .......... .......... .......... .......... 42% 684M 49s +237300K .......... .......... .......... .......... .......... 42% 806M 49s +237350K .......... .......... .......... .......... .......... 42% 41.4M 49s +237400K .......... .......... .......... .......... .......... 42% 4.22M 49s +237450K .......... .......... .......... .......... .......... 42% 561M 49s +237500K .......... .......... .......... .......... .......... 42% 3.47M 49s +237550K .......... .......... .......... .......... .......... 42% 633M 49s +237600K .......... .......... .......... .......... .......... 42% 3.51M 49s +237650K .......... .......... .......... .......... .......... 42% 14.6M 49s +237700K .......... .......... .......... .......... .......... 42% 12.1M 49s +237750K .......... .......... .......... .......... .......... 42% 5.93M 49s +237800K .......... .......... .......... .......... .......... 42% 9.91M 49s +237850K .......... .......... .......... .......... .......... 42% 669M 49s +237900K .......... .......... .......... .......... .......... 42% 3.71M 49s +237950K .......... .......... .......... .......... .......... 42% 5.14M 49s +238000K .......... .......... .......... .......... .......... 42% 65.5M 49s +238050K .......... .......... .......... .......... .......... 42% 2.09M 49s +238100K .......... .......... .......... .......... .......... 42% 1.83M 49s +238150K .......... .......... .......... .......... .......... 42% 283M 49s +238200K .......... .......... .......... .......... .......... 42% 93.1T 49s +238250K .......... .......... .......... .......... .......... 42% 633M 49s +238300K .......... .......... .......... .......... .......... 42% 673M 49s +238350K .......... .......... .......... .......... .......... 42% 588M 49s +238400K .......... .......... .......... .......... .......... 42% 1.14M 49s +238450K .......... .......... .......... .......... .......... 42% 374M 49s +238500K .......... .......... .......... .......... .......... 42% 8.64M 49s +238550K .......... .......... .......... .......... .......... 42% 550M 49s +238600K .......... .......... .......... .......... .......... 42% 93.1T 49s +238650K .......... .......... .......... .......... .......... 42% 584M 49s +238700K .......... .......... .......... .......... .......... 42% 626M 49s +238750K .......... .......... .......... .......... .......... 42% 696M 49s +238800K .......... .......... .......... 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+241400K .......... .......... .......... .......... .......... 43% 749M 48s +241450K .......... .......... .......... .......... .......... 43% 20.3M 48s +241500K .......... .......... .......... .......... .......... 43% 5.03M 48s +241550K .......... .......... .......... .......... .......... 43% 810K 48s +241600K .......... .......... .......... .......... .......... 43% 394M 48s +241650K .......... .......... .......... .......... .......... 43% 93.1T 48s +241700K .......... .......... .......... .......... .......... 43% 637M 48s +241750K .......... .......... .......... .......... .......... 43% 785M 48s +241800K .......... .......... .......... .......... .......... 43% 859M 48s +241850K .......... .......... .......... .......... .......... 43% 93.1T 48s +241900K .......... .......... .......... .......... .......... 43% 766M 48s +241950K .......... .......... .......... .......... .......... 43% 662M 48s +242000K .......... .......... .......... .......... .......... 43% 2.18M 48s +242050K .......... .......... .......... .......... .......... 43% 1.60M 48s +242100K .......... .......... .......... .......... .......... 43% 478M 48s +242150K .......... .......... .......... .......... .......... 43% 93.1T 48s +242200K .......... .......... .......... .......... .......... 43% 580M 48s +242250K .......... .......... .......... .......... .......... 43% 15.9M 48s +242300K .......... .......... .......... .......... .......... 43% 625M 48s +242350K .......... .......... .......... .......... .......... 43% 93.1T 48s +242400K .......... .......... .......... .......... .......... 43% 866M 48s +242450K .......... .......... .......... .......... .......... 43% 800M 48s +242500K .......... .......... .......... .......... .......... 43% 2.96M 48s +242550K .......... .......... .......... .......... .......... 43% 635M 48s +242600K .......... .......... .......... .......... .......... 43% 2.73M 48s +242650K .......... .......... .......... .......... .......... 43% 93.1T 48s +242700K .......... .......... .......... .......... .......... 43% 3.69M 48s +242750K .......... .......... .......... .......... .......... 43% 470M 48s +242800K .......... .......... .......... .......... .......... 43% 8.60M 48s +242850K .......... .......... .......... .......... .......... 43% 2.11M 48s +242900K .......... .......... .......... .......... .......... 43% 2.28M 48s +242950K .......... .......... .......... .......... .......... 43% 370M 48s +243000K .......... .......... .......... .......... .......... 43% 544M 48s +243050K .......... .......... .......... .......... .......... 43% 93.1T 48s +243100K .......... .......... .......... .......... .......... 43% 664M 48s +243150K .......... .......... .......... .......... .......... 43% 5.95M 48s +243200K .......... .......... .......... .......... .......... 43% 5.64M 48s +243250K .......... .......... .......... .......... .......... 43% 14.2M 48s +243300K .......... .......... .......... .......... .......... 43% 30.2M 48s +243350K .......... .......... .......... .......... .......... 43% 601M 48s +243400K .......... .......... .......... .......... .......... 43% 1.67M 48s +243450K .......... .......... .......... .......... .......... 43% 93.1T 48s +243500K .......... .......... .......... .......... .......... 43% 4.79M 48s +243550K .......... .......... .......... .......... .......... 43% 3.37M 48s +243600K .......... .......... .......... .......... .......... 43% 8.20M 48s +243650K .......... .......... .......... .......... .......... 43% 567M 48s +243700K .......... .......... .......... .......... .......... 43% 689M 48s +243750K .......... .......... .......... .......... .......... 43% 1.35M 48s +243800K .......... .......... .......... .......... .......... 43% 367M 48s +243850K .......... .......... .......... .......... .......... 43% 695M 48s +243900K .......... .......... .......... .......... .......... 43% 16.4M 48s +243950K .......... .......... 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+245250K .......... .......... .......... .......... .......... 43% 510M 48s +245300K .......... .......... .......... .......... .......... 43% 1.09M 48s +245350K .......... .......... .......... .......... .......... 43% 374M 48s +245400K .......... .......... .......... .......... .......... 43% 629M 48s +245450K .......... .......... .......... .......... .......... 43% 93.1T 48s +245500K .......... .......... .......... .......... .......... 43% 659M 48s +245550K .......... .......... .......... .......... .......... 43% 718M 48s +245600K .......... .......... .......... .......... .......... 43% 2.66M 48s +245650K .......... .......... .......... .......... .......... 43% 358M 48s +245700K .......... .......... .......... .......... .......... 43% 93.1T 48s +245750K .......... .......... .......... .......... .......... 43% 1.22M 48s +245800K .......... .......... .......... .......... .......... 43% 433M 48s +245850K .......... .......... .......... .......... .......... 43% 93.1T 48s +245900K .......... .......... .......... .......... .......... 43% 1.53M 48s +245950K .......... .......... .......... .......... .......... 43% 411M 48s +246000K .......... .......... .......... .......... .......... 43% 93.1T 48s +246050K .......... .......... .......... .......... .......... 43% 1.60M 48s +246100K .......... .......... .......... .......... .......... 43% 352M 48s +246150K .......... .......... .......... .......... .......... 43% 813M 48s +246200K .......... .......... .......... .......... .......... 43% 1.32M 48s +246250K .......... .......... .......... .......... .......... 43% 11.1M 48s +246300K .......... .......... .......... .......... .......... 43% 486M 48s +246350K .......... .......... .......... .......... .......... 43% 93.1T 48s +246400K .......... .......... .......... .......... .......... 43% 883M 48s +246450K .......... .......... .......... .......... .......... 43% 1.34M 48s +246500K .......... .......... .......... .......... 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.......... .......... .......... .......... .......... 44% 4.96M 47s +249150K .......... .......... .......... .......... .......... 44% 1.81M 48s +249200K .......... .......... .......... .......... .......... 44% 360M 47s +249250K .......... .......... .......... .......... .......... 44% 567M 47s +249300K .......... .......... .......... .......... .......... 44% 16.2M 47s +249350K .......... .......... .......... .......... .......... 44% 1.20M 47s +249400K .......... .......... .......... .......... .......... 44% 330M 47s +249450K .......... .......... .......... .......... .......... 44% 93.1T 47s +249500K .......... .......... .......... .......... .......... 44% 547M 47s +249550K .......... .......... .......... .......... .......... 44% 157M 47s +249600K .......... .......... .......... .......... .......... 44% 5.15M 47s +249650K .......... .......... .......... .......... .......... 44% 5.20M 47s +249700K .......... .......... .......... .......... .......... 44% 5.91M 47s +249750K .......... .......... .......... .......... .......... 44% 7.41M 47s +249800K .......... .......... .......... .......... .......... 44% 6.19M 47s +249850K .......... .......... .......... .......... .......... 44% 5.77M 47s +249900K .......... .......... .......... .......... .......... 44% 4.49M 47s +249950K .......... .......... .......... .......... .......... 44% 11.6M 47s +250000K .......... .......... .......... .......... .......... 44% 5.78M 47s +250050K .......... .......... .......... .......... .......... 44% 4.54M 47s +250100K .......... .......... .......... .......... .......... 44% 9.23M 47s +250150K .......... .......... .......... .......... .......... 44% 2.53M 47s +250200K .......... .......... .......... .......... .......... 44% 8.41M 47s +250250K .......... .......... .......... .......... .......... 44% 2.62M 47s +250300K .......... .......... .......... .......... .......... 44% 455M 47s +250350K .......... .......... .......... .......... .......... 44% 834M 47s +250400K .......... .......... .......... .......... .......... 44% 21.5M 47s +250450K .......... .......... .......... .......... .......... 44% 2.25M 47s +250500K .......... .......... .......... .......... .......... 44% 6.28M 47s +250550K .......... .......... .......... .......... .......... 44% 7.42M 47s +250600K .......... .......... .......... .......... .......... 44% 5.44M 47s +250650K .......... .......... .......... .......... .......... 44% 6.64M 47s +250700K .......... .......... .......... .......... .......... 44% 5.85M 47s +250750K .......... .......... .......... .......... .......... 44% 6.09M 47s +250800K .......... .......... .......... .......... .......... 44% 5.91M 47s +250850K .......... .......... .......... .......... .......... 44% 5.98M 47s +250900K .......... .......... .......... .......... .......... 44% 3.43M 47s +250950K .......... .......... .......... .......... .......... 44% 64.0M 47s +251000K .......... .......... .......... .......... .......... 44% 5.94M 47s +251050K .......... .......... .......... .......... .......... 44% 5.73M 47s +251100K .......... .......... .......... .......... .......... 44% 3.57M 47s +251150K .......... .......... .......... .......... .......... 44% 872K 47s +251200K .......... .......... .......... .......... .......... 44% 356M 47s +251250K .......... .......... .......... .......... .......... 44% 597M 47s +251300K .......... .......... .......... .......... .......... 44% 736M 47s +251350K .......... .......... .......... .......... .......... 44% 93.1T 47s +251400K .......... .......... .......... .......... .......... 44% 627M 47s +251450K .......... .......... .......... .......... .......... 44% 838M 47s +251500K .......... .......... .......... .......... .......... 44% 3.14M 47s +251550K .......... .......... .......... .......... .......... 44% 9.41M 47s +251600K .......... .......... .......... .......... .......... 44% 1.45M 47s +251650K .......... .......... .......... .......... .......... 44% 57.4M 47s +251700K .......... .......... .......... .......... .......... 44% 646M 47s +251750K .......... .......... .......... .......... .......... 44% 93.1T 47s +251800K .......... .......... .......... .......... .......... 44% 10.4M 47s +251850K .......... .......... .......... .......... .......... 44% 1.67M 47s +251900K .......... .......... .......... .......... .......... 44% 4.54M 47s +251950K .......... .......... .......... .......... .......... 44% 448M 47s +252000K .......... .......... .......... .......... .......... 44% 93.1T 47s +252050K .......... .......... .......... .......... .......... 44% 2.53M 47s +252100K .......... .......... .......... .......... .......... 44% 9.55M 47s +252150K .......... .......... .......... .......... .......... 44% 535M 47s +252200K .......... .......... .......... .......... .......... 44% 779M 47s +252250K .......... .......... .......... .......... .......... 44% 6.08M 47s +252300K .......... .......... .......... .......... .......... 44% 4.42M 47s +252350K .......... .......... .......... .......... .......... 44% 1.86M 47s +252400K .......... .......... .......... .......... .......... 44% 93.1T 47s +252450K .......... .......... .......... .......... .......... 44% 366M 47s +252500K .......... .......... .......... .......... .......... 45% 740M 47s +252550K .......... .......... .......... .......... .......... 45% 15.5M 47s +252600K .......... .......... .......... .......... .......... 45% 7.49M 47s +252650K .......... .......... .......... .......... .......... 45% 1.03M 47s +252700K .......... .......... .......... .......... .......... 45% 50.0M 47s +252750K .......... .......... .......... .......... .......... 45% 685M 47s +252800K .......... .......... .......... .......... .......... 45% 93.1T 47s +252850K .......... .......... .......... .......... .......... 45% 3.18M 47s +252900K .......... .......... .......... .......... .......... 45% 93.1T 47s +252950K .......... .......... .......... .......... .......... 45% 3.55M 47s +253000K .......... .......... .......... .......... .......... 45% 14.8M 47s +253050K .......... .......... .......... .......... .......... 45% 93.1T 47s +253100K .......... .......... .......... .......... .......... 45% 376M 47s +253150K .......... .......... .......... .......... .......... 45% 588M 47s +253200K .......... .......... .......... .......... .......... 45% 22.1M 47s +253250K .......... .......... .......... .......... .......... 45% 6.62M 47s +253300K .......... .......... .......... .......... .......... 45% 8.47M 47s +253350K .......... .......... .......... .......... .......... 45% 7.47M 47s +253400K .......... .......... .......... .......... .......... 45% 1.44M 47s +253450K .......... .......... .......... .......... .......... 45% 433M 47s +253500K .......... .......... .......... .......... .......... 45% 734M 47s +253550K .......... .......... .......... .......... .......... 45% 93.1T 47s +253600K .......... .......... .......... .......... .......... 45% 868M 47s +253650K .......... .......... .......... .......... .......... 45% 18.7M 47s +253700K .......... .......... .......... .......... .......... 45% 4.94M 47s +253750K .......... .......... .......... .......... .......... 45% 1.35M 47s +253800K .......... .......... .......... .......... .......... 45% 368M 47s +253850K .......... .......... .......... .......... .......... 45% 93.1T 47s +253900K .......... .......... .......... .......... .......... 45% 699M 47s +253950K .......... .......... .......... .......... .......... 45% 1.12M 47s +254000K .......... .......... .......... .......... .......... 45% 371M 47s +254050K .......... .......... .......... .......... .......... 45% 5.45M 47s +254100K .......... .......... .......... .......... .......... 45% 1.46M 47s +254150K .......... .......... .......... .......... .......... 45% 93.1T 47s +254200K .......... .......... .......... .......... .......... 45% 62.1M 47s +254250K .......... .......... .......... .......... .......... 45% 3.63M 47s +254300K .......... .......... .......... .......... .......... 45% 358M 47s +254350K .......... .......... .......... .......... .......... 45% 527M 47s +254400K .......... .......... .......... .......... .......... 45% 93.1T 47s +254450K .......... .......... .......... .......... .......... 45% 705M 47s +254500K .......... .......... .......... .......... .......... 45% 1.36M 47s +254550K .......... .......... .......... .......... .......... 45% 93.1T 47s +254600K .......... .......... .......... .......... .......... 45% 20.2M 47s +254650K .......... .......... .......... .......... .......... 45% 562M 47s +254700K .......... .......... .......... .......... .......... 45% 93.1T 47s +254750K .......... .......... .......... .......... .......... 45% 21.4M 47s +254800K .......... .......... .......... .......... .......... 45% 14.6M 47s +254850K .......... .......... .......... .......... .......... 45% 1.50M 47s +254900K .......... .......... .......... .......... .......... 45% 4.68M 47s +254950K .......... .......... .......... .......... .......... 45% 413M 47s +255000K .......... .......... .......... .......... .......... 45% 691M 47s +255050K .......... .......... .......... .......... .......... 45% 1.81M 47s +255100K .......... .......... .......... .......... .......... 45% 556M 47s +255150K .......... .......... .......... .......... .......... 45% 1.25M 47s +255200K .......... .......... .......... .......... .......... 45% 382M 47s +255250K .......... .......... .......... .......... .......... 45% 93.1T 47s +255300K .......... .......... .......... .......... .......... 45% 680M 47s +255350K .......... .......... .......... .......... .......... 45% 8.72M 47s +255400K .......... .......... .......... .......... .......... 45% 1.81M 47s +255450K .......... .......... .......... .......... .......... 45% 286M 47s +255500K .......... .......... .......... .......... .......... 45% 628M 47s +255550K .......... .......... .......... .......... .......... 45% 701M 47s +255600K .......... .......... .......... .......... .......... 45% 93.1T 47s +255650K .......... .......... .......... .......... .......... 45% 9.53M 47s +255700K .......... .......... .......... .......... .......... 45% 13.6M 47s +255750K .......... .......... .......... .......... .......... 45% 3.40M 47s +255800K .......... .......... .......... .......... .......... 45% 1.76M 47s +255850K .......... .......... .......... .......... .......... 45% 20.1M 47s +255900K .......... .......... .......... .......... .......... 45% 787M 47s +255950K .......... .......... .......... .......... .......... 45% 3.99M 47s +256000K .......... .......... .......... .......... .......... 45% 5.92M 47s +256050K .......... .......... .......... .......... .......... 45% 1.55M 47s +256100K .......... .......... .......... .......... .......... 45% 93.1T 47s +256150K .......... .......... .......... .......... .......... 45% 318M 47s +256200K .......... .......... .......... .......... .......... 45% 642M 47s +256250K .......... .......... .......... .......... .......... 45% 1.02M 47s +256300K .......... .......... .......... .......... .......... 45% 358M 47s +256350K .......... .......... .......... .......... .......... 45% 4.86M 47s +256400K .......... .......... .......... .......... .......... 45% 7.96M 47s +256450K .......... .......... .......... .......... .......... 45% 526M 47s +256500K .......... .......... .......... .......... .......... 45% 2.70M 47s +256550K .......... .......... .......... .......... .......... 45% 365M 46s +256600K .......... .......... .......... .......... .......... 45% 548M 46s +256650K .......... .......... .......... .......... .......... 45% 93.1T 46s +256700K .......... .......... .......... .......... .......... 45% 1.12M 47s +256750K .......... .......... .......... .......... .......... 45% 343M 46s +256800K .......... .......... .......... .......... .......... 45% 484M 46s +256850K .......... .......... .......... .......... .......... 45% 93.1T 46s +256900K .......... .......... .......... .......... .......... 45% 573M 46s +256950K .......... .......... .......... .......... .......... 45% 575M 46s +257000K .......... .......... .......... .......... .......... 45% 529M 46s +257050K .......... .......... .......... .......... .......... 45% 93.1T 46s +257100K .......... .......... .......... .......... .......... 45% 285M 46s +257150K .......... .......... .......... .......... .......... 45% 406M 46s +257200K .......... .......... .......... .......... .......... 45% 1.02M 46s +257250K .......... .......... .......... .......... .......... 45% 367M 46s +257300K .......... .......... .......... .......... .......... 45% 612M 46s +257350K .......... .......... .......... .......... .......... 45% 93.1T 46s +257400K .......... .......... .......... .......... .......... 45% 725M 46s +257450K .......... .......... .......... .......... .......... 45% 5.08M 46s +257500K .......... .......... .......... .......... .......... 45% 405M 46s +257550K .......... .......... .......... .......... .......... 45% 4.51M 46s +257600K .......... .......... .......... .......... .......... 45% 425M 46s +257650K .......... .......... .......... .......... .......... 45% 7.65M 46s +257700K .......... .......... .......... .......... .......... 45% 9.73M 46s +257750K .......... .......... .......... .......... .......... 45% 7.55M 46s +257800K .......... .......... .......... .......... .......... 45% 10.4M 46s +257850K .......... .......... .......... .......... .......... 45% 400M 46s +257900K .......... .......... .......... .......... .......... 45% 595M 46s +257950K .......... .......... .......... .......... .......... 45% 2.03M 46s +258000K .......... .......... .......... .......... .......... 45% 93.1T 46s +258050K .......... .......... .......... .......... .......... 45% 4.30M 46s +258100K .......... .......... .......... .......... .......... 45% 363M 46s +258150K .......... .......... .......... .......... .......... 46% 818M 46s +258200K .......... .......... .......... .......... .......... 46% 2.68M 46s +258250K .......... .......... .......... .......... .......... 46% 509M 46s +258300K .......... .......... .......... .......... .......... 46% 93.1T 46s +258350K .......... .......... .......... .......... .......... 46% 701M 46s +258400K .......... .......... .......... .......... .......... 46% 833M 46s +258450K .......... .......... .......... .......... .......... 46% 2.87M 46s +258500K .......... .......... .......... .......... .......... 46% 93.1T 46s +258550K .......... .......... .......... .......... .......... 46% 4.39M 46s +258600K .......... .......... .......... .......... .......... 46% 9.76M 46s +258650K .......... .......... .......... .......... .......... 46% 5.71M 46s +258700K .......... .......... .......... .......... .......... 46% 6.70M 46s +258750K .......... .......... .......... .......... .......... 46% 6.82M 46s +258800K .......... .......... .......... .......... .......... 46% 456M 46s +258850K .......... .......... .......... .......... .......... 46% 4.26M 46s +258900K .......... .......... .......... .......... .......... 46% 5.45M 46s +258950K .......... .......... .......... .......... .......... 46% 93.1T 46s +259000K .......... .......... .......... .......... .......... 46% 5.53M 46s +259050K .......... .......... .......... .......... .......... 46% 3.84M 46s +259100K .......... .......... .......... .......... .......... 46% 7.92M 46s +259150K .......... .......... .......... .......... .......... 46% 12.2M 46s +259200K .......... .......... .......... .......... .......... 46% 93.1T 46s +259250K .......... .......... .......... .......... .......... 46% 4.33M 46s +259300K .......... .......... .......... .......... .......... 46% 5.71M 46s +259350K .......... .......... .......... .......... .......... 46% 425M 46s +259400K .......... .......... .......... .......... .......... 46% 2.31M 46s +259450K .......... .......... .......... .......... .......... 46% 451M 46s +259500K .......... .......... .......... .......... .......... 46% 15.5M 46s +259550K .......... .......... .......... .......... .......... 46% 390M 46s +259600K .......... .......... .......... .......... .......... 46% 93.1T 46s +259650K .......... .......... .......... .......... .......... 46% 2.03M 46s +259700K .......... .......... .......... .......... .......... 46% 457M 46s +259750K .......... .......... .......... .......... .......... 46% 5.65M 46s +259800K .......... .......... .......... .......... .......... 46% 93.1T 46s +259850K .......... .......... .......... .......... .......... 46% 6.19M 46s +259900K .......... .......... .......... .......... .......... 46% 552M 46s +259950K .......... .......... .......... .......... .......... 46% 5.29M 46s +260000K .......... .......... .......... .......... .......... 46% 6.37M 46s +260050K .......... .......... .......... .......... .......... 46% 32.6M 46s +260100K .......... .......... .......... .......... .......... 46% 2.74M 46s +260150K .......... .......... .......... .......... .......... 46% 93.1T 46s +260200K .......... .......... .......... .......... .......... 46% 322M 46s +260250K .......... .......... .......... .......... .......... 46% 475M 46s +260300K .......... .......... .......... .......... .......... 46% 4.26M 46s +260350K .......... .......... .......... .......... .......... 46% 444M 46s +260400K .......... .......... .......... .......... .......... 46% 4.64M 46s +260450K .......... .......... .......... .......... .......... 46% 20.6M 46s +260500K .......... .......... .......... .......... .......... 46% 8.22M 46s +260550K .......... .......... .......... .......... .......... 46% 29.5M 46s +260600K .......... .......... .......... .......... .......... 46% 688M 46s +260650K .......... .......... .......... .......... .......... 46% 3.13M 46s +260700K .......... .......... .......... .......... .......... 46% 343M 46s +260750K .......... .......... .......... .......... .......... 46% 1.23M 46s +260800K .......... .......... .......... .......... .......... 46% 442M 46s +260850K .......... .......... .......... .......... .......... 46% 718M 46s +260900K .......... .......... .......... .......... .......... 46% 637M 46s +260950K .......... .......... .......... .......... .......... 46% 93.1T 46s +261000K .......... .......... .......... .......... .......... 46% 23.0M 46s +261050K .......... .......... .......... .......... .......... 46% 5.00M 46s +261100K .......... .......... .......... .......... .......... 46% 93.1T 46s +261150K .......... .......... .......... .......... .......... 46% 2.48M 46s +261200K .......... .......... .......... .......... .......... 46% 9.01M 46s +261250K .......... .......... .......... .......... .......... 46% 7.42M 46s +261300K .......... .......... .......... .......... .......... 46% 30.3M 46s +261350K .......... .......... .......... .......... .......... 46% 492M 46s +261400K .......... .......... .......... .......... .......... 46% 3.51M 46s +261450K .......... .......... .......... .......... .......... 46% 436M 46s +261500K .......... .......... .......... .......... .......... 46% 6.86M 46s +261550K .......... .......... .......... .......... .......... 46% 889K 46s +261600K .......... .......... .......... .......... .......... 46% 306M 46s +261650K .......... .......... .......... .......... .......... 46% 93.1T 46s +261700K .......... .......... .......... .......... .......... 46% 485M 46s +261750K .......... .......... .......... .......... .......... 46% 563M 45s +261800K .......... .......... .......... .......... .......... 46% 650M 45s +261850K .......... .......... .......... .......... .......... 46% 93.1T 45s +261900K .......... .......... .......... .......... .......... 46% 684M 45s +261950K .......... .......... .......... .......... .......... 46% 511M 45s +262000K .......... .......... .......... .......... .......... 46% 2.08M 45s +262050K .......... .......... .......... .......... .......... 46% 416M 45s +262100K .......... .......... .......... .......... .......... 46% 621M 45s +262150K .......... .......... .......... .......... .......... 46% 93.1T 45s +262200K .......... .......... .......... .......... .......... 46% 4.22M 45s +262250K .......... .......... .......... .......... .......... 46% 40.0M 45s +262300K .......... .......... .......... .......... .......... 46% 93.1T 45s +262350K .......... .......... .......... .......... .......... 46% 9.94M 45s +262400K .......... .......... .......... .......... .......... 46% 23.6M 45s +262450K .......... .......... .......... .......... .......... 46% 1.20M 45s +262500K .......... .......... .......... .......... .......... 46% 340M 45s +262550K .......... .......... .......... .......... .......... 46% 93.1T 45s +262600K .......... .......... .......... .......... .......... 46% 602M 45s +262650K .......... .......... .......... .......... .......... 46% 724M 45s +262700K .......... .......... .......... .......... .......... 46% 1.51M 45s +262750K .......... .......... .......... .......... .......... 46% 19.1M 45s +262800K .......... .......... .......... .......... .......... 46% 547M 45s +262850K .......... .......... .......... .......... .......... 46% 93.1T 45s +262900K .......... .......... .......... .......... .......... 46% 694M 45s +262950K .......... .......... .......... .......... .......... 46% 2.05M 45s +263000K .......... .......... .......... .......... .......... 46% 450M 45s +263050K .......... .......... .......... .......... .......... 46% 862M 45s +263100K .......... .......... .......... .......... .......... 46% 4.13M 45s +263150K .......... .......... .......... .......... .......... 46% 316M 45s +263200K .......... .......... .......... .......... .......... 46% 1.67M 45s +263250K .......... .......... .......... .......... .......... 46% 980K 45s +263300K .......... .......... .......... .......... .......... 46% 301M 45s +263350K .......... .......... .......... .......... .......... 46% 574M 45s +263400K .......... .......... .......... .......... .......... 46% 597M 45s +263450K .......... .......... .......... .......... .......... 46% 93.1T 45s +263500K .......... .......... .......... .......... .......... 46% 630M 45s +263550K .......... .......... .......... .......... .......... 46% 600M 45s +263600K .......... .......... .......... .......... .......... 46% 408M 45s +263650K .......... .......... .......... .......... .......... 46% 2.46M 45s +263700K .......... .......... .......... .......... .......... 46% 375M 45s +263750K .......... .......... .......... .......... .......... 47% 689M 45s +263800K .......... .......... .......... .......... .......... 47% 95.2M 45s +263850K .......... .......... .......... .......... .......... 47% 2.16M 45s +263900K .......... .......... .......... .......... .......... 47% 93.1T 45s +263950K .......... .......... .......... .......... .......... 47% 398M 45s +264000K .......... .......... .......... .......... .......... 47% 613M 45s +264050K .......... .......... .......... .......... .......... 47% 702M 45s +264100K .......... .......... .......... .......... .......... 47% 580M 45s +264150K .......... .......... .......... .......... .......... 47% 93.1T 45s +264200K .......... .......... .......... .......... .......... 47% 624M 45s +264250K .......... .......... .......... .......... .......... 47% 648M 45s +264300K .......... .......... .......... .......... .......... 47% 8.80M 45s +264350K .......... .......... .......... .......... .......... 47% 4.34M 45s +264400K .......... .......... .......... .......... .......... 47% 10.4M 45s +264450K .......... .......... .......... .......... .......... 47% 33.4M 45s +264500K .......... .......... .......... .......... .......... 47% 1.23M 45s +264550K .......... .......... .......... .......... .......... 47% 476M 45s +264600K .......... .......... .......... .......... .......... 47% 719M 45s +264650K .......... .......... .......... .......... .......... 47% 810M 45s +264700K .......... .......... .......... .......... .......... 47% 93.1T 45s +264750K .......... .......... .......... .......... .......... 47% 20.0M 45s +264800K .......... .......... .......... .......... .......... 47% 93.1T 45s +264850K .......... .......... .......... .......... .......... 47% 576M 45s +264900K .......... .......... .......... .......... .......... 47% 10.6M 45s +264950K .......... .......... .......... .......... .......... 47% 5.99M 45s +265000K .......... .......... .......... .......... .......... 47% 4.76M 45s +265050K .......... .......... .......... .......... .......... 47% 5.02M 45s +265100K .......... .......... .......... .......... .......... 47% 2.90M 45s +265150K .......... .......... .......... .......... .......... 47% 93.1T 45s +265200K .......... .......... .......... .......... .......... 47% 3.69M 45s +265250K .......... .......... .......... .......... .......... 47% 655K 45s +265300K .......... .......... .......... .......... .......... 47% 328M 45s +265350K .......... .......... .......... .......... .......... 47% 93.1T 45s +265400K .......... .......... .......... .......... .......... 47% 36.8M 45s +265450K .......... .......... .......... .......... .......... 47% 32.7M 45s +265500K .......... .......... .......... .......... .......... 47% 13.4M 45s +265550K .......... .......... .......... .......... .......... 47% 637M 45s +265600K .......... .......... .......... .......... .......... 47% 1.01M 45s +265650K .......... .......... .......... .......... .......... 47% 267M 45s +265700K .......... .......... .......... .......... .......... 47% 507M 45s +265750K .......... .......... .......... .......... .......... 47% 538M 45s +265800K .......... .......... .......... .......... .......... 47% 93.1T 45s +265850K .......... .......... .......... .......... .......... 47% 528M 45s +265900K .......... .......... .......... .......... .......... 47% 557M 45s +265950K .......... .......... .......... .......... .......... 47% 577M 45s +266000K .......... .......... .......... .......... .......... 47% 3.01M 45s +266050K .......... .......... .......... .......... .......... 47% 93.1T 45s +266100K .......... .......... .......... .......... .......... 47% 399M 45s +266150K .......... .......... .......... .......... .......... 47% 6.06M 45s +266200K .......... .......... .......... .......... .......... 47% 1.47M 45s +266250K .......... .......... .......... .......... .......... 47% 335M 45s +266300K .......... .......... .......... .......... .......... 47% 641M 45s +266350K .......... .......... .......... .......... .......... 47% 93.1T 45s +266400K .......... .......... .......... .......... .......... 47% 2.03M 45s +266450K .......... .......... .......... .......... .......... 47% 832K 45s +266500K .......... .......... .......... .......... .......... 47% 93.1T 45s +266550K .......... .......... .......... .......... .......... 47% 369M 45s +266600K .......... .......... .......... .......... .......... 47% 573M 45s +266650K .......... .......... .......... .......... .......... 47% 656M 45s +266700K .......... .......... .......... .......... .......... 47% 93.1T 45s +266750K .......... .......... .......... .......... .......... 47% 765M 45s +266800K .......... .......... .......... .......... .......... 47% 763M 45s +266850K .......... .......... .......... .......... .......... 47% 867M 45s +266900K .......... .......... .......... .......... .......... 47% 1.53M 45s +266950K .......... .......... .......... .......... .......... 47% 26.2M 45s +267000K .......... .......... .......... .......... .......... 47% 519M 45s +267050K .......... .......... .......... .......... .......... 47% 630M 45s +267100K .......... .......... .......... .......... .......... 47% 644M 45s +267150K .......... .......... .......... .......... .......... 47% 93.1T 45s +267200K .......... .......... .......... .......... .......... 47% 532M 45s +267250K .......... .......... .......... .......... .......... 47% 592M 45s +267300K .......... .......... .......... .......... .......... 47% 675M 44s +267350K .......... .......... .......... .......... .......... 47% 672M 44s +267400K .......... .......... .......... .......... .......... 47% 93.1T 44s +267450K .......... .......... .......... .......... .......... 47% 463M 44s +267500K .......... .......... .......... .......... .......... 47% 621M 44s +267550K .......... .......... .......... .......... .......... 47% 697M 44s +267600K .......... .......... .......... .......... .......... 47% 1.36M 44s +267650K .......... .......... .......... .......... .......... 47% 49.4M 44s +267700K .......... .......... .......... .......... .......... 47% 442M 44s +267750K .......... .......... .......... .......... .......... 47% 6.50M 44s +267800K .......... .......... .......... .......... .......... 47% 543M 44s +267850K .......... .......... .......... .......... .......... 47% 93.1T 44s +267900K .......... .......... .......... .......... .......... 47% 665M 44s +267950K .......... .......... .......... .......... .......... 47% 685M 44s +268000K .......... .......... .......... .......... .......... 47% 879M 44s +268050K .......... .......... .......... .......... .......... 47% 5.64M 44s +268100K .......... .......... .......... .......... .......... 47% 438M 44s +268150K .......... .......... .......... .......... .......... 47% 672M 44s +268200K .......... .......... .......... .......... .......... 47% 1.87M 44s +268250K .......... .......... .......... .......... .......... 47% 15.1M 44s +268300K .......... .......... .......... .......... .......... 47% 457M 44s +268350K .......... .......... .......... .......... .......... 47% 900K 44s +268400K .......... .......... .......... .......... .......... 47% 93.1T 44s +268450K .......... .......... .......... .......... .......... 47% 31.3M 44s +268500K .......... .......... .......... .......... .......... 47% 7.64M 44s +268550K .......... .......... .......... .......... .......... 47% 548M 44s +268600K .......... .......... .......... .......... .......... 47% 22.3M 44s +268650K .......... .......... .......... .......... .......... 47% 566M 44s +268700K .......... .......... .......... .......... .......... 47% 717M 44s +268750K .......... .......... .......... .......... .......... 47% 598M 44s +268800K .......... .......... .......... .......... .......... 47% 93.1T 44s +268850K .......... .......... .......... .......... .......... 47% 668M 44s +268900K .......... .......... .......... .......... .......... 47% 759M 44s +268950K .......... .......... .......... .......... .......... 47% 807M 44s +269000K .......... .......... .......... .......... .......... 47% 1.82M 44s +269050K .......... .......... .......... .......... .......... 47% 374M 44s +269100K .......... .......... .......... .......... .......... 47% 93.1T 44s +269150K .......... .......... .......... .......... .......... 47% 633M 44s +269200K .......... .......... .......... .......... .......... 47% 2.72M 44s +269250K .......... .......... .......... .......... .......... 47% 3.72M 44s +269300K .......... .......... .......... .......... .......... 47% 93.1T 44s +269350K .......... .......... .......... .......... .......... 48% 423M 44s +269400K .......... .......... .......... .......... .......... 48% 5.00M 44s +269450K .......... .......... .......... .......... .......... 48% 391M 44s +269500K .......... .......... .......... .......... .......... 48% 631M 44s +269550K .......... .......... .......... .......... .......... 48% 2.40M 44s +269600K .......... .......... .......... .......... .......... 48% 15.3M 44s +269650K .......... .......... .......... .......... .......... 48% 645M 44s +269700K .......... .......... .......... .......... .......... 48% 93.1T 44s +269750K .......... .......... .......... .......... .......... 48% 965K 44s +269800K .......... .......... .......... .......... .......... 48% 93.1T 44s +269850K .......... .......... .......... .......... .......... 48% 5.19M 44s +269900K .......... .......... .......... .......... .......... 48% 93.1T 44s +269950K .......... .......... .......... .......... .......... 48% 485M 44s +270000K .......... .......... .......... .......... .......... 48% 766M 44s +270050K .......... .......... .......... .......... .......... 48% 616M 44s +270100K .......... .......... .......... .......... .......... 48% 93.1T 44s +270150K .......... .......... .......... .......... .......... 48% 595M 44s +270200K .......... .......... .......... .......... .......... 48% 625M 44s +270250K .......... .......... .......... .......... .......... 48% 714M 44s +270300K .......... .......... .......... .......... .......... 48% 2.95M 44s +270350K .......... .......... .......... .......... .......... 48% 390M 44s +270400K .......... .......... .......... .......... .......... 48% 93.1T 44s +270450K .......... .......... .......... .......... .......... 48% 715M 44s +270500K .......... .......... .......... .......... .......... 48% 1.97M 44s +270550K .......... .......... .......... .......... .......... 48% 345M 44s +270600K .......... .......... .......... .......... .......... 48% 93.1T 44s +270650K .......... .......... .......... .......... .......... 48% 614M 44s +270700K .......... .......... .......... .......... .......... 48% 615M 44s +270750K .......... .......... .......... .......... .......... 48% 614M 44s +270800K .......... .......... .......... .......... .......... 48% 93.1T 44s +270850K .......... .......... .......... .......... .......... 48% 637M 44s +270900K .......... .......... .......... .......... .......... 48% 1.19M 44s +270950K .......... .......... .......... .......... .......... 48% 7.31M 44s +271000K .......... .......... .......... .......... .......... 48% 93.1T 44s +271050K .......... .......... .......... .......... .......... 48% 437M 44s +271100K .......... .......... .......... .......... .......... 48% 595M 44s +271150K .......... .......... .......... .......... .......... 48% 556M 44s +271200K .......... .......... .......... .......... .......... 48% 93.1T 44s +271250K .......... .......... .......... .......... .......... 48% 600M 44s +271300K .......... .......... .......... .......... .......... 48% 785K 44s +271350K .......... .......... .......... .......... .......... 48% 348M 44s +271400K .......... .......... .......... .......... .......... 48% 587M 44s +271450K .......... .......... .......... .......... .......... 48% 93.1T 44s +271500K .......... .......... .......... .......... .......... 48% 644M 44s +271550K .......... .......... .......... .......... .......... 48% 718M 44s +271600K .......... .......... .......... .......... .......... 48% 713M 44s +271650K .......... .......... .......... .......... .......... 48% 827M 44s +271700K .......... .......... .......... .......... .......... 48% 3.73M 44s +271750K .......... .......... .......... .......... .......... 48% 93.1T 44s +271800K .......... .......... .......... .......... .......... 48% 307M 44s +271850K .......... .......... .......... .......... .......... 48% 382M 44s +271900K .......... .......... .......... .......... .......... 48% 635M 44s +271950K .......... .......... .......... .......... .......... 48% 1.95M 44s +272000K .......... .......... .......... .......... .......... 48% 377M 44s +272050K .......... .......... .......... .......... .......... 48% 691M 43s +272100K .......... .......... .......... .......... .......... 48% 93.1T 43s +272150K .......... .......... .......... .......... .......... 48% 839M 43s +272200K .......... .......... .......... .......... .......... 48% 857M 43s +272250K .......... .......... .......... .......... .......... 48% 654M 43s +272300K .......... .......... .......... .......... .......... 48% 4.80M 43s +272350K .......... .......... .......... .......... .......... 48% 31.7M 43s +272400K .......... .......... .......... .......... .......... 48% 1.31M 43s +272450K .......... .......... .......... .......... .......... 48% 11.5M 43s +272500K .......... .......... .......... .......... .......... 48% 454M 43s +272550K .......... .......... .......... .......... .......... 48% 551M 43s +272600K .......... .......... .......... .......... .......... 48% 93.1T 43s +272650K .......... .......... .......... .......... .......... 48% 2.31M 43s +272700K .......... .......... .......... .......... .......... 48% 361M 43s +272750K .......... .......... .......... .......... .......... 48% 2.66M 43s +272800K .......... .......... .......... .......... .......... 48% 359M 43s +272850K .......... .......... .......... .......... .......... 48% 93.1T 43s +272900K .......... .......... .......... .......... .......... 48% 2.48M 43s +272950K .......... .......... .......... .......... .......... 48% 93.1T 43s +273000K .......... .......... .......... .......... .......... 48% 380M 43s +273050K .......... .......... .......... .......... .......... 48% 1.17M 43s +273100K .......... .......... .......... .......... .......... 48% 93.1T 43s +273150K .......... .......... .......... .......... .......... 48% 336M 43s +273200K .......... .......... .......... .......... .......... 48% 705M 43s +273250K .......... .......... .......... .......... .......... 48% 708M 43s +273300K .......... .......... .......... .......... .......... 48% 93.1T 43s +273350K .......... .......... .......... .......... .......... 48% 647M 43s +273400K .......... .......... .......... .......... .......... 48% 554M 43s +273450K .......... .......... .......... .......... .......... 48% 599M 43s +273500K .......... .......... .......... .......... .......... 48% 701M 43s +273550K .......... .......... .......... .......... .......... 48% 93.1T 43s +273600K .......... .......... .......... .......... .......... 48% 2.29M 43s +273650K .......... .......... .......... .......... .......... 48% 8.72M 43s +273700K .......... .......... .......... .......... .......... 48% 13.1M 43s +273750K .......... .......... .......... .......... .......... 48% 415M 43s +273800K .......... .......... .......... .......... .......... 48% 693M 43s +273850K .......... .......... .......... .......... .......... 48% 93.1T 43s +273900K .......... .......... .......... .......... .......... 48% 2.55M 43s +273950K .......... .......... .......... .......... .......... 48% 484M 43s +274000K .......... .......... .......... .......... .......... 48% 2.61M 43s +274050K .......... .......... .......... .......... .......... 48% 10.2M 43s +274100K .......... .......... .......... .......... .......... 48% 4.45M 43s +274150K .......... .......... .......... .......... .......... 48% 257M 43s +274200K .......... .......... .......... .......... .......... 48% 93.1T 43s +274250K .......... .......... .......... .......... .......... 48% 573M 43s +274300K .......... .......... .......... .......... .......... 48% 5.27M 43s +274350K .......... .......... .......... .......... .......... 48% 375M 43s +274400K .......... .......... .......... .......... .......... 48% 2.67M 43s +274450K .......... .......... .......... .......... .......... 48% 475M 43s +274500K .......... .......... .......... .......... .......... 48% 93.1T 43s +274550K .......... .......... .......... .......... .......... 48% 729M 43s +274600K .......... .......... .......... .......... .......... 48% 730M 43s +274650K .......... .......... .......... .......... .......... 48% 831M 43s +274700K .......... .......... .......... .......... .......... 48% 44.1M 43s +274750K .......... .......... .......... .......... .......... 48% 2.68M 43s +274800K .......... .......... 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.......... .......... .......... .......... .......... 49% 1.86M 43s +276150K .......... .......... .......... .......... .......... 49% 387M 43s +276200K .......... .......... .......... .......... .......... 49% 93.1T 43s +276250K .......... .......... .......... .......... .......... 49% 669M 43s +276300K .......... .......... .......... .......... .......... 49% 678M 43s +276350K .......... .......... .......... .......... .......... 49% 695M 43s +276400K .......... .......... .......... .......... .......... 49% 93.1T 43s +276450K .......... .......... .......... .......... .......... 49% 635M 43s +276500K .......... .......... .......... .......... .......... 49% 16.2M 43s +276550K .......... .......... .......... .......... .......... 49% 8.48M 43s +276600K .......... .......... .......... .......... .......... 49% 551M 43s +276650K .......... .......... .......... .......... .......... 49% 1.39M 43s +276700K .......... .......... .......... .......... .......... 49% 354M 43s +276750K .......... .......... .......... .......... .......... 49% 93.1T 43s +276800K .......... .......... .......... .......... .......... 49% 2.81M 43s +276850K .......... .......... .......... .......... .......... 49% 415M 43s +276900K .......... .......... .......... .......... .......... 49% 93.1T 43s +276950K .......... .......... .......... .......... .......... 49% 2.07M 43s +277000K .......... .......... .......... .......... .......... 49% 339M 43s +277050K .......... .......... .......... .......... .......... 49% 93.1T 43s +277100K .......... .......... .......... .......... .......... 49% 618M 43s +277150K .......... .......... .......... .......... .......... 49% 8.77M 42s +277200K .......... .......... .......... .......... .......... 49% 411M 42s +277250K .......... .......... .......... .......... .......... 49% 93.1T 42s +277300K .......... .......... .......... .......... .......... 49% 582M 42s +277350K .......... .......... .......... .......... .......... 49% 745M 42s +277400K .......... .......... .......... .......... .......... 49% 3.25M 42s +277450K .......... .......... .......... .......... .......... 49% 374M 42s +277500K .......... .......... .......... .......... .......... 49% 12.6M 42s +277550K .......... .......... .......... .......... .......... 49% 15.9M 42s +277600K .......... .......... .......... .......... .......... 49% 581M 42s +277650K .......... .......... .......... .......... .......... 49% 3.64M 42s +277700K .......... .......... .......... .......... .......... 49% 93.1T 42s +277750K .......... .......... .......... .......... .......... 49% 412M 42s +277800K .......... .......... .......... .......... .......... 49% 4.06M 42s +277850K .......... .......... .......... .......... .......... 49% 59.5M 42s +277900K .......... .......... .......... .......... .......... 49% 3.59M 42s +277950K .......... .......... .......... .......... .......... 49% 3.34M 42s +278000K .......... .......... .......... .......... .......... 49% 403M 42s +278050K .......... .......... .......... .......... .......... 49% 93.1T 42s +278100K .......... .......... .......... .......... .......... 49% 1.95M 42s +278150K .......... .......... .......... .......... .......... 49% 2.15M 42s +278200K .......... .......... .......... .......... .......... 49% 260M 42s +278250K .......... .......... .......... .......... .......... 49% 93.1T 42s +278300K .......... .......... .......... .......... .......... 49% 481M 42s +278350K .......... .......... .......... .......... .......... 49% 650M 42s +278400K .......... .......... .......... .......... .......... 49% 671M 42s +278450K .......... .......... .......... .......... .......... 49% 678M 42s +278500K .......... .......... .......... .......... .......... 49% 93.1T 42s +278550K .......... .......... .......... .......... .......... 49% 2.67M 42s +278600K .......... .......... .......... .......... .......... 49% 4.60M 42s +278650K .......... .......... .......... .......... .......... 49% 93.1T 42s +278700K .......... .......... .......... .......... .......... 49% 1.97M 42s +278750K .......... .......... .......... .......... .......... 49% 430M 42s +278800K .......... .......... .......... .......... .......... 49% 93.1T 42s +278850K .......... .......... .......... .......... .......... 49% 557M 42s +278900K .......... .......... .......... .......... .......... 49% 680M 42s +278950K .......... .......... .......... .......... .......... 49% 4.19M 42s +279000K .......... .......... .......... .......... .......... 49% 1.24M 42s +279050K .......... .......... .......... .......... .......... 49% 473M 42s +279100K .......... .......... .......... .......... .......... 49% 1.77M 42s +279150K .......... .......... .......... .......... .......... 49% 93.1T 42s +279200K .......... .......... .......... .......... .......... 49% 452M 42s +279250K .......... .......... .......... .......... .......... 49% 756M 42s +279300K .......... .......... .......... .......... .......... 49% 827M 42s +279350K .......... .......... .......... .......... .......... 49% 93.1T 42s +279400K .......... .......... .......... .......... .......... 49% 806M 42s +279450K .......... .......... .......... .......... .......... 49% 884M 42s +279500K .......... .......... .......... .......... .......... 49% 901M 42s +279550K .......... .......... .......... .......... .......... 49% 811M 42s +279600K .......... .......... .......... .......... .......... 49% 1.88M 42s +279650K .......... .......... .......... .......... .......... 49% 3.96M 42s +279700K .......... .......... .......... .......... .......... 49% 93.1T 42s +279750K .......... .......... .......... .......... .......... 49% 1.19M 42s +279800K .......... .......... .......... .......... .......... 49% 93.1T 42s +279850K .......... .......... .......... .......... .......... 49% 713K 42s +279900K .......... .......... .......... .......... .......... 49% 390M 42s +279950K .......... .......... .......... .......... .......... 49% 663K 42s +280000K .......... .......... .......... .......... .......... 49% 93.1T 42s +280050K .......... .......... .......... .......... .......... 49% 280M 42s +280100K .......... .......... .......... .......... .......... 49% 643M 42s +280150K .......... .......... .......... .......... .......... 49% 667M 42s +280200K .......... .......... .......... .......... .......... 49% 93.1T 42s +280250K .......... .......... .......... .......... .......... 49% 604M 42s +280300K .......... .......... .......... .......... .......... 49% 581M 42s +280350K .......... .......... .......... .......... .......... 49% 712M 42s +280400K .......... .......... .......... .......... .......... 49% 700M 42s +280450K .......... .......... .......... .......... .......... 49% 93.1T 42s +280500K .......... .......... .......... .......... .......... 49% 701M 42s +280550K .......... .......... .......... .......... .......... 49% 581M 42s +280600K .......... .......... .......... .......... .......... 50% 710M 42s +280650K .......... .......... .......... .......... .......... 50% 93.1T 42s +280700K .......... .......... .......... .......... .......... 50% 742M 42s +280750K .......... .......... .......... .......... .......... 50% 742M 42s +280800K .......... .......... .......... .......... .......... 50% 830M 42s +280850K .......... .......... .......... .......... .......... 50% 678M 42s +280900K .......... .......... .......... .......... .......... 50% 93.1T 42s +280950K .......... .......... .......... .......... .......... 50% 685M 42s +281000K .......... .......... .......... .......... .......... 50% 760M 42s +281050K .......... .......... .......... .......... .......... 50% 1.05G 42s +281100K .......... .......... .......... .......... .......... 50% 6.40M 42s +281150K .......... .......... .......... .......... .......... 50% 446M 42s +281200K .......... .......... .......... .......... .......... 50% 529M 42s +281250K .......... .......... .......... .......... .......... 50% 573M 42s +281300K .......... .......... .......... .......... .......... 50% 93.1T 42s +281350K .......... .......... .......... .......... .......... 50% 655M 42s +281400K .......... .......... .......... .......... .......... 50% 610M 42s +281450K .......... .......... .......... .......... .......... 50% 4.89M 42s +281500K .......... .......... .......... .......... .......... 50% 688M 42s +281550K .......... .......... .......... .......... .......... 50% 1.86M 42s +281600K .......... .......... .......... .......... .......... 50% 1.29M 42s +281650K .......... .......... .......... .......... .......... 50% 93.1T 42s +281700K .......... .......... .......... .......... .......... 50% 335M 42s +281750K .......... .......... .......... .......... .......... 50% 529M 42s +281800K .......... .......... .......... .......... .......... 50% 736M 42s +281850K .......... .......... .......... .......... .......... 50% 93.1T 42s +281900K .......... .......... .......... .......... .......... 50% 816M 42s +281950K .......... .......... .......... .......... .......... 50% 817M 42s +282000K .......... .......... .......... .......... .......... 50% 571M 42s +282050K .......... .......... .......... .......... .......... 50% 902M 42s +282100K .......... .......... .......... .......... .......... 50% 9.68M 42s +282150K .......... .......... .......... .......... .......... 50% 513M 42s +282200K .......... .......... .......... .......... .......... 50% 93.1T 42s +282250K .......... .......... .......... .......... .......... 50% 723M 42s +282300K .......... .......... .......... .......... .......... 50% 10.4M 42s +282350K .......... .......... .......... .......... .......... 50% 30.3M 41s +282400K .......... .......... .......... .......... .......... 50% 1.14M 42s +282450K .......... .......... .......... .......... .......... 50% 93.1T 42s +282500K .......... .......... .......... .......... .......... 50% 233M 41s +282550K .......... .......... .......... .......... .......... 50% 348M 41s +282600K .......... .......... .......... .......... .......... 50% 566M 41s +282650K .......... .......... .......... .......... .......... 50% 513M 41s +282700K .......... .......... .......... .......... .......... 50% 93.1T 41s +282750K .......... .......... .......... .......... .......... 50% 523M 41s +282800K .......... .......... .......... .......... .......... 50% 589M 41s +282850K .......... .......... .......... .......... .......... 50% 750M 41s +282900K .......... .......... .......... .......... .......... 50% 859K 41s +282950K .......... .......... .......... .......... .......... 50% 329M 41s +283000K .......... .......... .......... .......... .......... 50% 93.1T 41s +283050K .......... .......... .......... .......... .......... 50% 598M 41s +283100K .......... .......... .......... .......... .......... 50% 6.48M 41s +283150K .......... .......... .......... 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.......... .......... .......... .......... .......... 50% 1.37M 41s +285150K .......... .......... .......... .......... .......... 50% 93.1T 41s +285200K .......... .......... .......... .......... .......... 50% 6.67M 41s +285250K .......... .......... .......... .......... .......... 50% 93.1T 41s +285300K .......... .......... .......... .......... .......... 50% 385M 41s +285350K .......... .......... .......... .......... .......... 50% 1.97M 41s +285400K .......... .......... .......... .......... .......... 50% 93.1T 41s +285450K .......... .......... .......... .......... .......... 50% 362M 41s +285500K .......... .......... .......... .......... .......... 50% 5.30M 41s +285550K .......... .......... .......... .......... .......... 50% 9.21M 41s +285600K .......... .......... .......... .......... .......... 50% 460M 41s +285650K .......... .......... .......... .......... .......... 50% 2.45M 41s +285700K .......... .......... .......... .......... .......... 50% 493M 41s +285750K .......... .......... .......... .......... .......... 50% 5.00M 41s +285800K .......... .......... .......... .......... .......... 50% 93.1T 41s +285850K .......... .......... .......... .......... .......... 50% 449M 41s +285900K .......... .......... .......... .......... .......... 50% 772M 41s +285950K .......... .......... .......... .......... .......... 50% 858M 41s +286000K .......... .......... .......... .......... .......... 50% 1.55M 41s +286050K .......... .......... .......... .......... .......... 50% 340M 41s +286100K .......... .......... .......... .......... .......... 50% 10.5M 41s +286150K .......... .......... .......... .......... .......... 50% 6.68M 41s +286200K .......... .......... .......... .......... .......... 51% 3.58M 41s +286250K .......... .......... .......... .......... .......... 51% 494M 41s +286300K .......... .......... .......... .......... .......... 51% 1.25M 41s +286350K .......... .......... .......... .......... .......... 51% 372M 41s +286400K .......... .......... .......... .......... .......... 51% 706M 41s +286450K .......... .......... .......... .......... .......... 51% 93.1T 41s +286500K .......... .......... .......... .......... .......... 51% 536M 41s +286550K .......... .......... .......... .......... .......... 51% 7.20M 41s +286600K .......... .......... .......... .......... .......... 51% 5.81M 41s +286650K .......... .......... .......... .......... .......... 51% 13.7M 41s +286700K .......... .......... .......... .......... .......... 51% 8.65M 41s +286750K .......... .......... .......... .......... .......... 51% 8.43M 41s +286800K .......... .......... .......... .......... .......... 51% 368M 41s +286850K .......... .......... .......... .......... .......... 51% 93.1T 41s +286900K .......... .......... .......... .......... .......... 51% 5.32M 41s +286950K .......... .......... .......... .......... .......... 51% 415M 41s +287000K .......... .......... .......... .......... .......... 51% 4.51M 41s +287050K .......... .......... .......... .......... .......... 51% 4.71M 41s +287100K .......... .......... .......... .......... .......... 51% 8.18M 41s +287150K .......... .......... .......... .......... .......... 51% 11.8M 41s +287200K .......... .......... .......... .......... .......... 51% 456M 41s +287250K .......... .......... .......... .......... .......... 51% 93.1T 41s +287300K .......... .......... .......... .......... .......... 51% 719M 41s +287350K .......... .......... .......... .......... .......... 51% 1.48M 41s +287400K .......... .......... .......... .......... .......... 51% 550M 41s +287450K .......... .......... .......... .......... .......... 51% 762M 41s +287500K .......... .......... .......... .......... .......... 51% 93.1T 41s +287550K .......... .......... .......... .......... .......... 51% 1.90M 41s +287600K .......... .......... .......... .......... .......... 51% 93.1T 41s +287650K .......... .......... .......... .......... .......... 51% 464M 40s +287700K .......... .......... .......... .......... .......... 51% 9.84M 40s +287750K .......... .......... .......... .......... .......... 51% 439M 40s +287800K .......... .......... .......... .......... .......... 51% 709M 40s +287850K .......... .......... .......... .......... .......... 51% 798M 40s +287900K .......... .......... .......... .......... .......... 51% 93.1T 40s +287950K .......... .......... .......... .......... .......... 51% 8.97M 40s +288000K .......... .......... .......... .......... .......... 51% 3.55M 40s +288050K .......... .......... .......... .......... .......... 51% 9.47M 40s +288100K .......... .......... .......... .......... .......... 51% 1.44M 40s +288150K .......... .......... .......... .......... .......... 51% 7.03M 40s +288200K .......... .......... .......... .......... .......... 51% 93.1T 40s +288250K .......... .......... .......... .......... .......... 51% 5.22M 40s +288300K .......... .......... 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.......... .......... .......... .......... .......... 51% 486M 40s +289650K .......... .......... .......... .......... .......... 51% 558M 40s +289700K .......... .......... .......... .......... .......... 51% 93.1T 40s +289750K .......... .......... .......... .......... .......... 51% 725M 40s +289800K .......... .......... .......... .......... .......... 51% 119M 40s +289850K .......... .......... .......... .......... .......... 51% 1.22M 40s +289900K .......... .......... .......... .......... .......... 51% 433M 40s +289950K .......... .......... .......... .......... .......... 51% 6.22M 40s +290000K .......... .......... .......... .......... .......... 51% 12.9M 40s +290050K .......... .......... .......... .......... .......... 51% 6.64M 40s +290100K .......... .......... .......... .......... .......... 51% 2.66M 40s +290150K .......... .......... .......... .......... .......... 51% 93.1T 40s +290200K .......... .......... .......... .......... .......... 51% 410M 40s +290250K .......... .......... .......... .......... .......... 51% 759K 40s +290300K .......... .......... .......... .......... .......... 51% 93.1T 40s +290350K .......... .......... .......... .......... .......... 51% 2.41M 40s +290400K .......... .......... .......... .......... .......... 51% 311M 40s +290450K .......... .......... .......... .......... .......... 51% 2.18M 40s +290500K .......... .......... .......... .......... .......... 51% 70.7M 40s +290550K .......... .......... .......... .......... .......... 51% 770M 40s +290600K .......... .......... .......... .......... .......... 51% 2.06M 40s +290650K .......... .......... .......... .......... .......... 51% 307M 40s +290700K .......... .......... .......... .......... .......... 51% 93.1T 40s +290750K .......... .......... .......... .......... .......... 51% 447M 40s +290800K .......... .......... .......... .......... .......... 51% 468M 40s +290850K .......... .......... .......... .......... .......... 51% 432M 40s +290900K .......... .......... .......... .......... .......... 51% 447M 40s +290950K .......... .......... .......... .......... .......... 51% 93.1T 40s +291000K .......... .......... .......... .......... .......... 51% 365M 40s +291050K .......... .......... .......... .......... .......... 51% 460M 40s +291100K .......... .......... .......... .......... .......... 51% 579M 40s +291150K .......... .......... .......... .......... .......... 51% 701K 40s +291200K .......... .......... .......... .......... .......... 51% 481M 40s +291250K .......... .......... .......... .......... .......... 51% 1.60M 40s +291300K .......... .......... .......... .......... .......... 51% 93.1T 40s +291350K .......... .......... .......... .......... .......... 51% 582M 40s +291400K .......... .......... .......... .......... .......... 51% 1.00G 40s +291450K .......... .......... .......... .......... .......... 51% 1.30G 40s +291500K .......... .......... .......... .......... 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+294750K .......... .......... .......... .......... .......... 52% 5.63M 40s +294800K .......... .......... .......... .......... .......... 52% 408M 40s +294850K .......... .......... .......... .......... .......... 52% 2.63M 40s +294900K .......... .......... .......... .......... .......... 52% 389M 40s +294950K .......... .......... .......... .......... .......... 52% 2.60M 40s +295000K .......... .......... .......... .......... .......... 52% 390M 40s +295050K .......... .......... .......... .......... .......... 52% 93.1T 40s +295100K .......... .......... .......... .......... .......... 52% 3.13M 40s +295150K .......... .......... .......... .......... .......... 52% 477M 40s +295200K .......... .......... .......... .......... .......... 52% 93.1T 40s +295250K .......... .......... .......... .......... .......... 52% 700M 40s +295300K .......... .......... .......... .......... .......... 52% 793M 40s +295350K .......... .......... .......... .......... .......... 52% 5.38M 40s +295400K .......... .......... .......... .......... .......... 52% 15.0M 39s +295450K .......... .......... .......... .......... .......... 52% 586M 39s +295500K .......... .......... .......... .......... .......... 52% 1.11M 40s +295550K .......... .......... .......... .......... .......... 52% 4.28M 40s +295600K .......... .......... .......... .......... .......... 52% 453M 39s +295650K .......... .......... .......... .......... .......... 52% 774M 39s +295700K .......... .......... .......... .......... .......... 52% 16.3M 39s +295750K .......... .......... .......... .......... .......... 52% 596M 39s +295800K .......... .......... .......... .......... .......... 52% 785M 39s +295850K .......... .......... .......... .......... .......... 52% 3.61M 39s +295900K .......... .......... .......... .......... .......... 52% 8.02M 39s +295950K .......... .......... .......... .......... .......... 52% 337M 39s +296000K .......... .......... .......... .......... 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39s +298600K .......... .......... .......... .......... .......... 53% 382M 39s +298650K .......... .......... .......... .......... .......... 53% 93.1T 39s +298700K .......... .......... .......... .......... .......... 53% 631M 39s +298750K .......... .......... .......... .......... .......... 53% 616M 39s +298800K .......... .......... .......... .......... .......... 53% 636M 39s +298850K .......... .......... .......... .......... .......... 53% 659M 39s +298900K .......... .......... .......... .......... .......... 53% 93.1T 39s +298950K .......... .......... .......... .......... .......... 53% 708M 39s +299000K .......... .......... .......... .......... .......... 53% 643M 39s +299050K .......... .......... .......... .......... .......... 53% 1.02M 39s +299100K .......... .......... .......... .......... .......... 53% 309M 39s +299150K .......... .......... .......... .......... .......... 53% 93.1T 39s +299200K .......... .......... .......... .......... .......... 53% 444M 39s +299250K .......... .......... .......... .......... .......... 53% 598M 39s +299300K .......... .......... .......... .......... .......... 53% 26.6M 39s +299350K .......... .......... .......... .......... .......... 53% 2.02M 39s +299400K .......... .......... .......... .......... .......... 53% 419M 39s +299450K .......... .......... .......... .......... .......... 53% 651K 39s +299500K .......... .......... .......... .......... .......... 53% 273K 39s +299550K .......... .......... .......... .......... .......... 53% 471M 39s +299600K .......... .......... .......... .......... .......... 53% 93.1T 39s +299650K .......... .......... .......... .......... .......... 53% 1.19M 39s +299700K .......... .......... .......... .......... .......... 53% 392M 39s +299750K .......... .......... .......... .......... .......... 53% 635M 39s +299800K .......... .......... .......... .......... .......... 53% 93.1T 39s +299850K .......... .......... .......... .......... 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+303100K .......... .......... .......... .......... .......... 54% 4.93M 39s +303150K .......... .......... .......... .......... .......... 54% 464M 39s +303200K .......... .......... .......... .......... .......... 54% 2.85M 39s +303250K .......... .......... .......... .......... .......... 54% 443M 39s +303300K .......... .......... .......... .......... .......... 54% 1.63M 39s +303350K .......... .......... .......... .......... .......... 54% 93.1T 39s +303400K .......... .......... .......... .......... .......... 54% 1.55M 39s +303450K .......... .......... .......... .......... .......... 54% 282M 39s +303500K .......... .......... .......... .......... .......... 54% 396M 39s +303550K .......... .......... .......... .......... .......... 54% 404M 39s +303600K .......... .......... .......... .......... .......... 54% 93.1T 39s +303650K .......... .......... .......... .......... .......... 54% 415M 39s +303700K .......... .......... .......... .......... .......... 54% 448M 39s +303750K .......... .......... .......... .......... .......... 54% 493M 39s +303800K .......... .......... .......... .......... .......... 54% 93.1T 39s +303850K .......... .......... .......... .......... .......... 54% 555M 39s +303900K .......... .......... .......... .......... .......... 54% 13.6M 39s +303950K .......... .......... .......... .......... .......... 54% 8.67M 39s +304000K .......... .......... .......... .......... .......... 54% 5.01M 39s +304050K .......... .......... .......... .......... .......... 54% 1.40M 39s +304100K .......... .......... .......... .......... .......... 54% 397M 39s +304150K .......... .......... .......... .......... .......... 54% 631M 39s +304200K .......... .......... .......... .......... .......... 54% 93.1T 39s +304250K .......... .......... .......... .......... .......... 54% 1.82M 39s +304300K .......... .......... .......... .......... .......... 54% 20.7M 39s +304350K .......... .......... .......... .......... .......... 54% 2.19M 39s +304400K .......... .......... .......... .......... .......... 54% 392M 39s +304450K .......... .......... .......... .......... .......... 54% 28.4M 39s +304500K .......... .......... .......... .......... .......... 54% 637M 38s +304550K .......... .......... .......... .......... .......... 54% 2.76M 38s +304600K .......... .......... .......... .......... .......... 54% 3.40M 38s +304650K .......... .......... .......... .......... .......... 54% 398M 38s +304700K .......... .......... .......... .......... .......... 54% 690M 38s +304750K .......... .......... .......... .......... .......... 54% 5.41M 38s +304800K .......... .......... .......... .......... .......... 54% 340M 38s +304850K .......... .......... .......... .......... .......... 54% 5.24M 38s +304900K .......... .......... .......... .......... .......... 54% 9.74M 38s +304950K .......... .......... .......... .......... .......... 54% 339M 38s +305000K .......... .......... .......... .......... .......... 54% 2.19M 38s +305050K .......... .......... .......... .......... .......... 54% 398M 38s +305100K .......... .......... .......... .......... .......... 54% 93.1T 38s +305150K .......... .......... .......... .......... .......... 54% 2.78M 38s +305200K .......... .......... .......... .......... .......... 54% 8.89M 38s +305250K .......... .......... .......... .......... .......... 54% 3.00M 38s +305300K .......... .......... .......... .......... .......... 54% 306M 38s +305350K .......... .......... .......... .......... .......... 54% 93.1T 38s +305400K .......... .......... .......... .......... .......... 54% 5.71M 38s +305450K .......... .......... .......... .......... .......... 54% 10.1M 38s +305500K .......... .......... .......... .......... .......... 54% 444M 38s +305550K .......... .......... .......... .......... .......... 54% 3.68M 38s +305600K .......... .......... .......... .......... .......... 54% 454M 38s +305650K .......... .......... .......... .......... .......... 54% 2.68M 38s +305700K .......... .......... .......... .......... .......... 54% 603K 38s +305750K .......... .......... .......... .......... .......... 54% 93.1T 38s +305800K .......... .......... .......... .......... .......... 54% 749K 38s +305850K .......... .......... .......... .......... .......... 54% 366M 38s +305900K .......... .......... .......... .......... .......... 54% 64.7M 38s +305950K .......... .......... .......... .......... .......... 54% 493M 38s +306000K .......... .......... .......... .......... .......... 54% 93.1T 38s +306050K .......... .......... .......... .......... .......... 54% 571M 38s +306100K .......... .......... .......... .......... .......... 54% 544M 38s +306150K .......... .......... .......... .......... .......... 54% 592M 38s +306200K .......... .......... .......... .......... .......... 54% 618M 38s +306250K .......... .......... .......... .......... .......... 54% 93.1T 38s +306300K .......... .......... .......... .......... .......... 54% 603M 38s +306350K .......... .......... .......... .......... .......... 54% 525M 38s +306400K .......... .......... .......... .......... .......... 54% 582M 38s +306450K .......... .......... .......... .......... .......... 54% 93.1T 38s +306500K .......... .......... .......... .......... .......... 54% 1.19M 38s +306550K .......... .......... .......... .......... .......... 54% 393M 38s +306600K .......... .......... .......... .......... .......... 54% 93.1T 38s +306650K .......... .......... .......... .......... .......... 54% 539M 38s +306700K .......... .......... .......... .......... .......... 54% 650M 38s +306750K .......... .......... .......... .......... .......... 54% 677M 38s +306800K .......... .......... .......... .......... .......... 54% 93.1T 38s +306850K .......... .......... .......... .......... .......... 54% 713M 38s +306900K .......... .......... .......... .......... .......... 54% 701M 38s +306950K .......... .......... .......... .......... .......... 54% 716M 38s +307000K .......... .......... .......... .......... .......... 54% 657M 38s +307050K .......... .......... .......... .......... .......... 54% 93.1T 38s +307100K .......... .......... .......... .......... .......... 54% 734M 38s +307150K .......... .......... .......... .......... .......... 54% 774M 38s +307200K .......... .......... .......... .......... .......... 54% 758M 38s +307250K .......... .......... .......... .......... .......... 54% 1.91M 38s +307300K .......... .......... .......... .......... .......... 54% 1.39M 38s +307350K .......... .......... .......... .......... .......... 54% 93.1T 38s +307400K .......... .......... .......... .......... .......... 54% 357M 38s +307450K .......... .......... .......... .......... .......... 54% 689M 38s +307500K .......... .......... .......... .......... .......... 54% 673M 38s +307550K .......... .......... .......... .......... .......... 54% 708M 38s +307600K .......... .......... .......... .......... .......... 54% 93.1T 38s +307650K .......... .......... .......... .......... .......... 54% 2.29M 38s +307700K .......... .......... .......... .......... .......... 54% 93.1T 38s +307750K .......... .......... .......... .......... .......... 54% 11.4M 38s +307800K .......... .......... .......... .......... .......... 54% 93.1T 38s +307850K .......... .......... .......... .......... .......... 54% 618M 38s +307900K .......... .......... .......... .......... .......... 54% 886M 38s +307950K .......... .......... .......... .......... .......... 54% 3.32M 38s +308000K .......... .......... .......... .......... .......... 54% 523M 38s +308050K .......... .......... .......... .......... .......... 54% 93.1T 38s +308100K .......... .......... .......... .......... .......... 54% 5.44M 38s +308150K .......... .......... .......... .......... .......... 54% 579M 38s +308200K .......... .......... .......... .......... .......... 54% 3.09M 38s +308250K .......... .......... .......... .......... .......... 54% 409M 38s +308300K .......... .......... .......... .......... .......... 54% 11.4M 38s +308350K .......... .......... .......... .......... .......... 54% 540M 38s +308400K .......... .......... .......... .......... .......... 54% 20.7M 38s +308450K .......... .......... .......... .......... .......... 54% 93.1T 38s +308500K .......... .......... .......... .......... .......... 54% 4.28M 38s +308550K .......... .......... .......... .......... .......... 54% 20.5M 38s +308600K .......... .......... .......... .......... .......... 54% 4.44M 38s +308650K .......... .......... .......... .......... .......... 55% 59.4M 38s +308700K .......... .......... .......... .......... .......... 55% 11.8M 38s +308750K .......... .......... .......... .......... .......... 55% 413M 38s +308800K .......... .......... .......... .......... .......... 55% 680M 38s +308850K .......... .......... .......... .......... .......... 55% 1.90M 38s +308900K .......... .......... .......... .......... .......... 55% 3.53M 38s +308950K .......... .......... .......... .......... .......... 55% 9.36M 38s +309000K .......... .......... .......... .......... .......... 55% 353M 38s +309050K .......... .......... .......... .......... .......... 55% 750M 38s +309100K .......... .......... .......... .......... .......... 55% 4.10M 38s +309150K .......... .......... .......... .......... .......... 55% 15.2M 38s +309200K .......... .......... .......... .......... .......... 55% 62.6M 38s +309250K .......... .......... .......... .......... .......... 55% 2.09M 38s +309300K .......... .......... .......... .......... .......... 55% 20.9M 38s +309350K .......... .......... .......... .......... .......... 55% 747M 38s +309400K .......... .......... .......... .......... .......... 55% 843M 38s +309450K .......... .......... .......... .......... .......... 55% 93.1T 38s +309500K .......... .......... .......... .......... .......... 55% 4.29M 38s +309550K .......... .......... .......... .......... .......... 55% 495M 38s +309600K .......... .......... .......... .......... .......... 55% 93.1T 38s +309650K .......... .......... .......... .......... .......... 55% 2.33M 38s +309700K .......... .......... .......... .......... .......... 55% 476M 38s +309750K .......... .......... .......... .......... .......... 55% 1.40M 38s +309800K .......... .......... .......... .......... .......... 55% 321M 38s +309850K .......... .......... .......... .......... .......... 55% 588M 38s +309900K .......... .......... .......... .......... .......... 55% 93.1T 38s +309950K .......... .......... .......... .......... .......... 55% 1.74M 38s +310000K .......... .......... .......... .......... .......... 55% 9.83M 38s +310050K .......... .......... .......... .......... .......... 55% 3.17M 38s +310100K .......... .......... .......... .......... .......... 55% 409M 38s +310150K .......... .......... .......... .......... .......... 55% 93.1T 38s +310200K .......... .......... .......... .......... .......... 55% 651M 38s +310250K .......... .......... .......... .......... .......... 55% 886M 37s +310300K .......... .......... .......... .......... .......... 55% 949K 38s +310350K .......... .......... .......... .......... .......... 55% 430M 38s +310400K .......... .......... .......... .......... .......... 55% 528M 38s +310450K .......... .......... .......... .......... .......... 55% 93.1T 37s +310500K .......... .......... .......... .......... .......... 55% 645M 37s +310550K .......... .......... .......... .......... .......... 55% 734M 37s +310600K .......... .......... .......... .......... .......... 55% 959M 37s +310650K .......... .......... .......... .......... .......... 55% 93.1T 37s +310700K .......... .......... .......... .......... .......... 55% 5.18M 37s +310750K .......... .......... .......... .......... .......... 55% 508M 37s +310800K .......... 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.......... .......... .......... .......... .......... 55% 686M 37s +311500K .......... .......... .......... .......... .......... 55% 735M 37s +311550K .......... .......... .......... .......... .......... 55% 93.1T 37s +311600K .......... .......... .......... .......... .......... 55% 708M 37s +311650K .......... .......... .......... .......... .......... 55% 1.00M 37s +311700K .......... .......... .......... .......... .......... 55% 143M 37s +311750K .......... .......... .......... .......... .......... 55% 93.1T 37s +311800K .......... .......... .......... .......... .......... 55% 377M 37s +311850K .......... .......... .......... .......... .......... 55% 530M 37s +311900K .......... .......... .......... .......... .......... 55% 629M 37s +311950K .......... .......... .......... .......... .......... 55% 1.61M 37s +312000K .......... .......... .......... .......... .......... 55% 93.1T 37s +312050K .......... .......... .......... .......... .......... 55% 324M 37s +312100K .......... .......... .......... .......... .......... 55% 504M 37s +312150K .......... .......... .......... .......... .......... 55% 602M 37s +312200K .......... .......... .......... .......... .......... 55% 454M 37s +312250K .......... .......... .......... .......... .......... 55% 93.1T 37s +312300K .......... .......... .......... .......... .......... 55% 598M 37s +312350K .......... .......... .......... .......... .......... 55% 592M 37s +312400K .......... .......... .......... .......... .......... 55% 648M 37s +312450K .......... .......... .......... .......... .......... 55% 2.18M 37s +312500K .......... .......... .......... .......... .......... 55% 370M 37s +312550K .......... .......... .......... .......... .......... 55% 93.1T 37s +312600K .......... .......... .......... .......... .......... 55% 698M 37s +312650K .......... .......... .......... .......... .......... 55% 2.01M 37s +312700K .......... .......... .......... .......... .......... 55% 93.1T 37s +312750K .......... .......... .......... .......... .......... 55% 317M 37s +312800K .......... .......... .......... .......... .......... 55% 652M 37s +312850K .......... .......... .......... .......... .......... 55% 512M 37s +312900K .......... .......... .......... .......... .......... 55% 93.1T 37s +312950K .......... .......... .......... .......... .......... 55% 442M 37s +313000K .......... .......... .......... .......... .......... 55% 542M 37s +313050K .......... .......... .......... .......... .......... 55% 29.8M 37s +313100K .......... .......... .......... .......... .......... 55% 3.93M 37s +313150K .......... .......... .......... .......... .......... 55% 422M 37s +313200K .......... .......... .......... .......... .......... 55% 93.1T 37s +313250K .......... .......... .......... .......... .......... 55% 563M 37s +313300K .......... .......... .......... .......... .......... 55% 662M 37s +313350K .......... .......... .......... .......... .......... 55% 2.42M 37s +313400K .......... .......... .......... .......... .......... 55% 8.60M 37s +313450K .......... .......... .......... .......... .......... 55% 8.27M 37s +313500K .......... .......... .......... .......... .......... 55% 8.55M 37s +313550K .......... .......... .......... .......... .......... 55% 93.1T 37s +313600K .......... .......... .......... .......... .......... 55% 3.88M 37s +313650K .......... .......... .......... .......... .......... 55% 5.93M 37s +313700K .......... .......... .......... .......... .......... 55% 93.1T 37s +313750K .......... .......... .......... .......... .......... 55% 10.0M 37s +313800K .......... .......... .......... .......... .......... 55% 93.1T 37s +313850K .......... .......... .......... .......... .......... 55% 486M 37s +313900K .......... .......... .......... .......... .......... 55% 510M 37s +313950K .......... .......... .......... .......... .......... 55% 2.32M 37s +314000K .......... .......... .......... 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+316600K .......... .......... .......... .......... .......... 56% 549M 37s +316650K .......... .......... .......... .......... .......... 56% 93.1T 37s +316700K .......... .......... .......... .......... .......... 56% 561M 37s +316750K .......... .......... .......... .......... .......... 56% 581M 37s +316800K .......... .......... .......... .......... .......... 56% 477M 37s +316850K .......... .......... .......... .......... .......... 56% 596M 36s +316900K .......... .......... .......... .......... .......... 56% 93.1T 36s +316950K .......... .......... .......... .......... .......... 56% 2.33M 36s +317000K .......... .......... .......... .......... .......... 56% 455M 36s +317050K .......... .......... .......... .......... .......... 56% 4.98M 36s +317100K .......... .......... .......... .......... .......... 56% 435M 36s +317150K .......... .......... .......... .......... .......... 56% 583M 36s +317200K .......... .......... .......... .......... .......... 56% 93.1T 36s +317250K .......... .......... .......... .......... .......... 56% 619M 36s +317300K .......... .......... .......... .......... .......... 56% 650M 36s +317350K .......... .......... .......... .......... .......... 56% 650M 36s +317400K .......... .......... .......... .......... .......... 56% 93.1T 36s +317450K .......... .......... .......... .......... .......... 56% 636M 36s +317500K .......... .......... .......... .......... .......... 56% 525M 36s +317550K .......... .......... .......... .......... .......... 56% 647M 36s +317600K .......... .......... .......... .......... .......... 56% 674M 36s +317650K .......... .......... .......... .......... .......... 56% 93.1T 36s +317700K .......... .......... .......... .......... .......... 56% 635M 36s +317750K .......... .......... .......... .......... .......... 56% 588M 36s +317800K .......... .......... .......... .......... .......... 56% 578M 36s +317850K .......... .......... .......... .......... .......... 56% 93.1T 36s +317900K .......... .......... .......... .......... .......... 56% 563M 36s +317950K .......... .......... .......... .......... .......... 56% 580M 36s +318000K .......... .......... .......... .......... .......... 56% 564M 36s +318050K .......... .......... .......... .......... .......... 56% 605M 36s +318100K .......... .......... .......... .......... .......... 56% 93.1T 36s +318150K .......... .......... .......... .......... .......... 56% 1.29M 36s +318200K .......... .......... .......... .......... .......... 56% 93.1T 36s +318250K .......... .......... .......... .......... .......... 56% 433M 36s +318300K .......... .......... .......... .......... .......... 56% 621M 36s +318350K .......... .......... .......... .......... .......... 56% 2.36M 36s +318400K .......... .......... .......... .......... .......... 56% 93.1T 36s +318450K .......... .......... .......... .......... .......... 56% 466M 36s +318500K .......... .......... .......... .......... .......... 56% 758M 36s +318550K .......... .......... .......... .......... .......... 56% 15.1M 36s +318600K .......... .......... .......... .......... .......... 56% 29.8M 36s +318650K .......... .......... .......... .......... .......... 56% 3.87M 36s +318700K .......... .......... .......... .......... .......... 56% 59.2M 36s +318750K .......... .......... .......... .......... .......... 56% 1.07M 36s +318800K .......... .......... .......... .......... .......... 56% 4.88M 36s +318850K .......... .......... .......... .......... .......... 56% 93.1T 36s +318900K .......... .......... .......... .......... .......... 56% 248M 36s +318950K .......... .......... .......... .......... .......... 56% 453M 36s +319000K .......... .......... .......... .......... .......... 56% 471M 36s +319050K .......... .......... .......... .......... .......... 56% 93.1T 36s +319100K .......... .......... .......... .......... .......... 56% 445M 36s +319150K .......... .......... .......... 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+321750K .......... .......... .......... .......... .......... 57% 5.38M 36s +321800K .......... .......... .......... .......... .......... 57% 19.4M 36s +321850K .......... .......... .......... .......... .......... 57% 493M 36s +321900K .......... .......... .......... .......... .......... 57% 497M 36s +321950K .......... .......... .......... .......... .......... 57% 93.1T 36s +322000K .......... .......... .......... .......... .......... 57% 10.4M 36s +322050K .......... .......... .......... .......... .......... 57% 5.29M 36s +322100K .......... .......... .......... .......... .......... 57% 14.9M 36s +322150K .......... .......... .......... .......... .......... 57% 3.79M 36s +322200K .......... .......... .......... .......... .......... 57% 9.42M 36s +322250K .......... .......... .......... .......... .......... 57% 30.0M 36s +322300K .......... .......... .......... .......... .......... 57% 5.31M 36s +322350K .......... .......... .......... .......... .......... 57% 3.15M 36s +322400K .......... .......... .......... .......... .......... 57% 613M 36s +322450K .......... .......... .......... .......... .......... 57% 1.16M 36s +322500K .......... .......... .......... .......... .......... 57% 93.1T 36s +322550K .......... .......... .......... .......... .......... 57% 3.29M 36s +322600K .......... .......... .......... .......... .......... 57% 448M 36s +322650K .......... .......... .......... .......... .......... 57% 696M 36s +322700K .......... .......... .......... .......... .......... 57% 93.1T 36s +322750K .......... .......... .......... .......... .......... 57% 742M 36s +322800K .......... .......... .......... .......... .......... 57% 1.21M 36s +322850K .......... .......... .......... .......... .......... 57% 309M 36s +322900K .......... .......... .......... .......... .......... 57% 1.72M 36s +322950K .......... .......... .......... .......... .......... 57% 879K 36s +323000K .......... .......... .......... .......... 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+326250K .......... .......... .......... .......... .......... 58% 93.1T 35s +326300K .......... .......... .......... .......... .......... 58% 698M 35s +326350K .......... .......... .......... .......... .......... 58% 772M 35s +326400K .......... .......... .......... .......... .......... 58% 15.5M 35s +326450K .......... .......... .......... .......... .......... 58% 8.57M 35s +326500K .......... .......... .......... .......... .......... 58% 4.08M 35s +326550K .......... .......... .......... .......... .......... 58% 1.84M 35s +326600K .......... .......... .......... .......... .......... 58% 401M 35s +326650K .......... .......... .......... .......... .......... 58% 571M 35s +326700K .......... .......... .......... .......... .......... 58% 93.1T 35s +326750K .......... .......... .......... .......... .......... 58% 1.18M 35s +326800K .......... .......... .......... .......... .......... 58% 2.79M 35s +326850K .......... .......... .......... .......... .......... 58% 93.1T 35s +326900K .......... .......... .......... .......... .......... 58% 446M 35s +326950K .......... .......... .......... .......... .......... 58% 1.99M 35s +327000K .......... .......... .......... .......... .......... 58% 330M 35s +327050K .......... .......... .......... .......... .......... 58% 650M 35s +327100K .......... .......... .......... .......... .......... 58% 1.16M 35s +327150K .......... .......... .......... .......... .......... 58% 93.1T 35s +327200K .......... .......... .......... .......... .......... 58% 382M 35s +327250K .......... .......... .......... .......... .......... 58% 784M 35s +327300K .......... .......... .......... .......... .......... 58% 2.92M 35s +327350K .......... .......... .......... .......... .......... 58% 555M 35s +327400K .......... .......... .......... .......... .......... 58% 3.41M 35s +327450K .......... .......... .......... .......... .......... 58% 353M 35s +327500K .......... .......... .......... .......... 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35s +330750K .......... .......... .......... .......... .......... 58% 93.1T 35s +330800K .......... .......... .......... .......... .......... 58% 4.30M 35s +330850K .......... .......... .......... .......... .......... 58% 1.45M 35s +330900K .......... .......... .......... .......... .......... 58% 93.1T 35s +330950K .......... .......... .......... .......... .......... 58% 368M 34s +331000K .......... .......... .......... .......... .......... 58% 682M 34s +331050K .......... .......... .......... .......... .......... 58% 596M 34s +331100K .......... .......... .......... .......... .......... 59% 4.49M 34s +331150K .......... .......... .......... .......... .......... 59% 5.67M 34s +331200K .......... .......... .......... .......... .......... 59% 61.7M 34s +331250K .......... .......... .......... .......... .......... 59% 544M 34s +331300K .......... .......... .......... .......... .......... 59% 3.14M 34s +331350K .......... .......... .......... .......... .......... 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+357100K .......... .......... .......... .......... .......... 63% 2.21M 31s +357150K .......... .......... .......... .......... .......... 63% 93.1T 31s +357200K .......... .......... .......... .......... .......... 63% 338M 31s +357250K .......... .......... .......... .......... .......... 63% 5.11M 31s +357300K .......... .......... .......... .......... .......... 63% 4.60M 31s +357350K .......... .......... .......... .......... .......... 63% 440M 31s +357400K .......... .......... .......... .......... .......... 63% 93.1T 31s +357450K .......... .......... .......... .......... .......... 63% 1.66M 31s +357500K .......... .......... .......... .......... .......... 63% 93.1T 31s +357550K .......... .......... .......... .......... .......... 63% 304M 31s +357600K .......... .......... .......... .......... .......... 63% 560M 31s +357650K .......... .......... .......... .......... .......... 63% 659M 31s +357700K .......... .......... .......... .......... .......... 63% 449K 31s +357750K .......... .......... .......... .......... .......... 63% 287M 31s +357800K .......... .......... .......... .......... .......... 63% 562M 31s +357850K .......... .......... .......... .......... .......... 63% 601M 31s +357900K .......... .......... .......... .......... .......... 63% 93.1T 31s +357950K .......... .......... .......... .......... .......... 63% 577M 31s +358000K .......... .......... .......... .......... .......... 63% 591M 31s +358050K .......... .......... .......... .......... .......... 63% 576M 30s +358100K .......... .......... .......... .......... .......... 63% 93.1T 30s +358150K .......... .......... .......... .......... .......... 63% 345M 30s +358200K .......... .......... .......... .......... .......... 63% 549M 30s +358250K .......... .......... .......... .......... .......... 63% 555M 30s +358300K .......... .......... .......... .......... .......... 63% 971K 30s +358350K .......... .......... .......... .......... .......... 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.......... 63% 6.66M 30s +359050K .......... .......... .......... .......... .......... 63% 4.56M 30s +359100K .......... .......... .......... .......... .......... 63% 93.1T 30s +359150K .......... .......... .......... .......... .......... 64% 2.21M 30s +359200K .......... .......... .......... .......... .......... 64% 2.42M 30s +359250K .......... .......... .......... .......... .......... 64% 395M 30s +359300K .......... .......... .......... .......... .......... 64% 2.55M 30s +359350K .......... .......... .......... .......... .......... 64% 19.4M 30s +359400K .......... .......... .......... .......... .......... 64% 426M 30s +359450K .......... .......... .......... .......... .......... 64% 551M 30s +359500K .......... .......... .......... .......... .......... 64% 606M 30s +359550K .......... .......... .......... .......... .......... 64% 93.1T 30s +359600K .......... .......... .......... .......... .......... 64% 590M 30s +359650K .......... .......... .......... 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.......... .......... .......... .......... .......... 64% 7.97M 30s +361650K .......... .......... .......... .......... .......... 64% 4.57M 30s +361700K .......... .......... .......... .......... .......... 64% 413M 30s +361750K .......... .......... .......... .......... .......... 64% 2.77M 30s +361800K .......... .......... .......... .......... .......... 64% 655M 30s +361850K .......... .......... .......... .......... .......... 64% 1.66M 30s +361900K .......... .......... .......... .......... .......... 64% 2.14M 30s +361950K .......... .......... .......... .......... .......... 64% 292M 30s +362000K .......... .......... .......... .......... .......... 64% 409M 30s +362050K .......... .......... .......... .......... .......... 64% 93.1T 30s +362100K .......... .......... .......... .......... .......... 64% 540M 30s +362150K .......... .......... .......... .......... .......... 64% 5.91M 30s +362200K .......... .......... .......... .......... .......... 64% 8.45M 30s +362250K .......... .......... .......... .......... .......... 64% 93.1T 30s +362300K .......... .......... .......... .......... .......... 64% 398M 30s +362350K .......... .......... .......... .......... .......... 64% 2.86M 30s +362400K .......... .......... .......... .......... .......... 64% 9.99M 30s +362450K .......... .......... .......... .......... .......... 64% 15.2M 30s +362500K .......... .......... .......... .......... .......... 64% 528M 30s +362550K .......... .......... .......... .......... .......... 64% 93.1T 30s +362600K .......... .......... .......... .......... .......... 64% 1.76M 30s +362650K .......... .......... .......... .......... .......... 64% 30.3M 30s +362700K .......... .......... .......... .......... .......... 64% 5.92M 30s +362750K .......... .......... .......... .......... .......... 64% 5.91M 30s +362800K .......... .......... .......... .......... .......... 64% 505M 30s +362850K .......... .......... .......... .......... .......... 64% 2.70M 30s +362900K .......... .......... .......... .......... .......... 64% 364M 30s +362950K .......... .......... .......... .......... .......... 64% 93.1T 30s +363000K .......... .......... .......... .......... .......... 64% 408M 30s +363050K .......... .......... .......... .......... .......... 64% 512M 30s +363100K .......... .......... .......... .......... .......... 64% 9.49M 30s +363150K .......... .......... .......... .......... .......... 64% 20.0M 30s +363200K .......... .......... .......... .......... .......... 64% 1.48M 30s +363250K .......... .......... .......... .......... .......... 64% 328M 30s +363300K .......... .......... .......... .......... .......... 64% 93.1T 30s +363350K .......... .......... .......... .......... .......... 64% 5.91M 30s +363400K .......... .......... .......... .......... .......... 64% 4.41M 30s +363450K .......... .......... .......... .......... .......... 64% 93.1T 30s +363500K .......... .......... .......... .......... .......... 64% 3.37M 30s +363550K .......... .......... .......... .......... .......... 64% 408M 30s +363600K .......... .......... .......... .......... .......... 64% 12.6M 30s +363650K .......... .......... .......... .......... .......... 64% 4.12M 30s +363700K .......... .......... .......... .......... .......... 64% 305M 30s +363750K .......... .......... .......... .......... .......... 64% 13.3M 30s +363800K .......... .......... .......... .......... .......... 64% 4.30M 29s +363850K .......... .......... .......... .......... .......... 64% 2.58M 29s +363900K .......... .......... .......... .......... .......... 64% 8.69M 29s +363950K .......... .......... .......... .......... .......... 64% 1.86M 29s +364000K .......... .......... .......... .......... .......... 64% 93.1T 29s +364050K .......... .......... .......... .......... .......... 64% 352M 29s +364100K .......... .......... .......... .......... .......... 64% 755M 29s +364150K .......... .......... .......... 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29s +366100K .......... .......... .......... .......... .......... 65% 1.67M 29s +366150K .......... .......... .......... .......... .......... 65% 279M 29s +366200K .......... .......... .......... .......... .......... 65% 627M 29s +366250K .......... .......... .......... .......... .......... 65% 17.1M 29s +366300K .......... .......... .......... .......... .......... 65% 11.9M 29s +366350K .......... .......... .......... .......... .......... 65% 526M 29s +366400K .......... .......... .......... .......... .......... 65% 642M 29s +366450K .......... .......... .......... .......... .......... 65% 2.24M 29s +366500K .......... .......... .......... .......... .......... 65% 3.11M 29s +366550K .......... .......... .......... .......... .......... 65% 11.3M 29s +366600K .......... .......... .......... .......... .......... 65% 994K 29s +366650K .......... .......... .......... .......... .......... 65% 93.1T 29s +366700K .......... .......... .......... .......... .......... 65% 225M 29s +366750K .......... .......... .......... .......... .......... 65% 7.25M 29s +366800K .......... .......... .......... .......... .......... 65% 1.80M 29s +366850K .......... .......... .......... .......... .......... 65% 319M 29s +366900K .......... .......... .......... .......... .......... 65% 89.4M 29s +366950K .......... .......... .......... .......... .......... 65% 3.84M 29s +367000K .......... .......... .......... .......... .......... 65% 10.2M 29s +367050K .......... .......... .......... .......... .......... 65% 7.35M 29s +367100K .......... .......... .......... .......... .......... 65% 528M 29s +367150K .......... .......... .......... .......... .......... 65% 9.27M 29s +367200K .......... .......... .......... .......... .......... 65% 8.38M 29s +367250K .......... .......... .......... .......... .......... 65% 1.30M 29s +367300K .......... .......... .......... .......... .......... 65% 587M 29s +367350K .......... .......... .......... .......... 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+369950K .......... .......... .......... .......... .......... 65% 342M 29s +370000K .......... .......... .......... .......... .......... 65% 93.1T 29s +370050K .......... .......... .......... .......... .......... 65% 15.7M 29s +370100K .......... .......... .......... .......... .......... 65% 439M 29s +370150K .......... .......... .......... .......... .......... 65% 15.7M 29s +370200K .......... .......... .......... .......... .......... 65% 2.38M 29s +370250K .......... .......... .......... .......... .......... 65% 525M 29s +370300K .......... .......... .......... .......... .......... 65% 1.06M 29s +370350K .......... .......... .......... .......... .......... 66% 65.4M 29s +370400K .......... .......... .......... .......... .......... 66% 864M 29s +370450K .......... .......... .......... .......... .......... 66% 2.49M 29s +370500K .......... .......... .......... .......... .......... 66% 5.73M 29s +370550K .......... .......... .......... .......... .......... 66% 22.4M 29s +370600K .......... .......... .......... .......... .......... 66% 20.0M 29s +370650K .......... .......... .......... .......... .......... 66% 41.8M 29s +370700K .......... .......... .......... .......... .......... 66% 4.97M 29s +370750K .......... .......... .......... .......... .......... 66% 756M 28s +370800K .......... .......... .......... .......... .......... 66% 93.1T 28s +370850K .......... .......... .......... .......... .......... 66% 64.4M 28s +370900K .......... .......... .......... .......... .......... 66% 1.32M 28s +370950K .......... .......... .......... .......... .......... 66% 475M 28s +371000K .......... .......... .......... .......... .......... 66% 6.08M 28s +371050K .......... .......... .......... .......... .......... 66% 4.59M 28s +371100K .......... .......... .......... .......... .......... 66% 7.36M 28s +371150K .......... .......... .......... .......... .......... 66% 705M 28s +371200K .......... .......... .......... .......... .......... 66% 1.09G 28s +371250K .......... .......... .......... .......... .......... 66% 2.27M 28s +371300K .......... .......... .......... .......... .......... 66% 173M 28s +371350K .......... .......... .......... .......... .......... 66% 3.65M 28s +371400K .......... .......... .......... .......... .......... 66% 93.1T 28s +371450K .......... .......... .......... .......... .......... 66% 4.59M 28s +371500K .......... .......... .......... .......... .......... 66% 10.1M 28s +371550K .......... .......... .......... .......... .......... 66% 2.42M 28s +371600K .......... .......... .......... .......... .......... 66% 29.4M 28s +371650K .......... .......... .......... .......... .......... 66% 93.1T 28s +371700K .......... .......... .......... .......... .......... 66% 4.94M 28s +371750K .......... .......... .......... .......... .......... 66% 31.1M 28s +371800K .......... .......... .......... .......... .......... 66% 5.99M 28s +371850K .......... .......... 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.......... .......... .......... .......... 66% 14.8M 28s +372550K .......... .......... .......... .......... .......... 66% 476M 28s +372600K .......... .......... .......... .......... .......... 66% 790M 28s +372650K .......... .......... .......... .......... .......... 66% 1.09M 28s +372700K .......... .......... .......... .......... .......... 66% 93.1T 28s +372750K .......... .......... .......... .......... .......... 66% 3.54M 28s +372800K .......... .......... .......... .......... .......... 66% 117M 28s +372850K .......... .......... .......... .......... .......... 66% 523M 28s +372900K .......... .......... .......... .......... .......... 66% 93.1T 28s +372950K .......... .......... .......... .......... .......... 66% 644M 28s +373000K .......... .......... .......... .......... .......... 66% 4.89M 28s +373050K .......... .......... .......... .......... .......... 66% 18.7M 28s +373100K .......... .......... .......... .......... .......... 66% 6.82M 28s +373150K .......... .......... .......... .......... .......... 66% 9.76M 28s +373200K .......... .......... .......... .......... .......... 66% 9.89M 28s +373250K .......... .......... .......... .......... .......... 66% 541M 28s +373300K .......... .......... .......... .......... .......... 66% 2.87M 28s +373350K .......... .......... .......... .......... .......... 66% 41.6M 28s +373400K .......... .......... .......... .......... .......... 66% 7.92M 28s +373450K .......... .......... .......... .......... .......... 66% 9.71M 28s +373500K .......... .......... .......... .......... .......... 66% 5.45M 28s +373550K .......... .......... .......... .......... .......... 66% 1.70M 28s +373600K .......... .......... .......... .......... .......... 66% 352M 28s +373650K .......... .......... .......... .......... .......... 66% 2.72M 28s +373700K .......... .......... .......... .......... .......... 66% 5.44M 28s +373750K .......... .......... .......... .......... .......... 66% 451M 28s +373800K .......... .......... .......... .......... .......... 66% 93.1T 28s +373850K .......... .......... .......... .......... .......... 66% 772M 28s +373900K .......... .......... .......... .......... .......... 66% 877M 28s +373950K .......... .......... .......... .......... .......... 66% 21.7M 28s +374000K .......... .......... .......... .......... .......... 66% 5.95M 28s +374050K .......... .......... .......... .......... .......... 66% 30.4M 28s +374100K .......... .......... .......... .......... .......... 66% 13.3M 28s +374150K .......... .......... .......... .......... .......... 66% 3.57M 28s +374200K .......... .......... .......... .......... .......... 66% 5.44M 28s +374250K .......... .......... .......... .......... .......... 66% 29.1M 28s +374300K .......... .......... .......... .......... .......... 66% 1.15M 28s +374350K .......... .......... .......... .......... .......... 66% 93.1T 28s +374400K .......... .......... .......... .......... .......... 66% 429M 28s +374450K .......... .......... .......... .......... .......... 66% 659M 28s +374500K .......... .......... .......... .......... .......... 66% 570M 28s +374550K .......... .......... .......... .......... .......... 66% 93.1T 28s +374600K .......... .......... .......... .......... .......... 66% 1.70M 28s +374650K .......... .......... .......... .......... .......... 66% 4.87M 28s +374700K .......... .......... .......... .......... .......... 66% 464M 28s +374750K .......... .......... .......... .......... .......... 66% 15.9M 28s +374800K .......... .......... .......... .......... .......... 66% 3.31M 28s +374850K .......... .......... .......... .......... .......... 66% 8.32M 28s +374900K .......... .......... .......... .......... .......... 66% 349M 28s +374950K .......... .......... .......... .......... .......... 66% 93.1T 28s +375000K .......... .......... .......... .......... .......... 66% 686M 28s +375050K .......... .......... .......... 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.......... .......... .......... .......... .......... 67% 14.7M 28s +377050K .......... .......... .......... .......... .......... 67% 3.50M 28s +377100K .......... .......... .......... .......... .......... 67% 419M 28s +377150K .......... .......... .......... .......... .......... 67% 93.1T 27s +377200K .......... .......... .......... .......... .......... 67% 704M 27s +377250K .......... .......... .......... .......... .......... 67% 85.8M 27s +377300K .......... .......... .......... .......... .......... 67% 6.56M 27s +377350K .......... .......... .......... .......... .......... 67% 1.75M 27s +377400K .......... .......... .......... .......... .......... 67% 8.26M 27s +377450K .......... .......... .......... .......... .......... 67% 589M 27s +377500K .......... .......... .......... .......... .......... 67% 3.99M 27s +377550K .......... .......... .......... .......... .......... 67% 528M 27s +377600K .......... .......... .......... .......... .......... 67% 68.4M 27s +377650K .......... .......... .......... .......... .......... 67% 7.71M 27s +377700K .......... .......... .......... .......... .......... 67% 7.25M 27s +377750K .......... .......... .......... .......... .......... 67% 1.84M 27s +377800K .......... .......... .......... .......... .......... 67% 108M 27s +377850K .......... .......... .......... .......... .......... 67% 4.56M 27s +377900K .......... .......... .......... .......... .......... 67% 119M 27s +377950K .......... .......... .......... .......... .......... 67% 6.54M 27s +378000K .......... .......... .......... .......... .......... 67% 1.90M 27s +378050K .......... .......... .......... .......... .......... 67% 1.90M 27s +378100K .......... .......... .......... .......... .......... 67% 411M 27s +378150K .......... .......... .......... .......... .......... 67% 517M 27s +378200K .......... .......... .......... .......... .......... 67% 1.26M 27s +378250K .......... .......... .......... .......... .......... 67% 5.53M 27s +378300K .......... .......... .......... .......... .......... 67% 521M 27s +378350K .......... .......... .......... .......... .......... 67% 1.45M 27s +378400K .......... .......... .......... .......... .......... 67% 1.48M 27s +378450K .......... .......... .......... .......... .......... 67% 6.63M 27s +378500K .......... .......... .......... .......... .......... 67% 949K 27s +378550K .......... .......... .......... .......... .......... 67% 132M 27s +378600K .......... .......... .......... .......... .......... 67% 93.1T 27s +378650K .......... .......... .......... .......... .......... 67% 325M 27s +378700K .......... .......... .......... .......... .......... 67% 399M 27s +378750K .......... .......... .......... .......... .......... 67% 883M 27s +378800K .......... .......... .......... .......... .......... 67% 93.1T 27s +378850K .......... .......... .......... .......... .......... 67% 10.7M 27s +378900K .......... .......... .......... .......... 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+381500K .......... .......... .......... .......... .......... 67% 1.24M 27s +381550K .......... .......... .......... .......... .......... 67% 1.22M 27s +381600K .......... .......... .......... .......... .......... 68% 430M 27s +381650K .......... .......... .......... .......... .......... 68% 93.1T 27s +381700K .......... .......... .......... .......... .......... 68% 674M 27s +381750K .......... .......... .......... .......... .......... 68% 683M 27s +381800K .......... .......... .......... .......... .......... 68% 682M 27s +381850K .......... .......... .......... .......... .......... 68% 680M 27s +381900K .......... .......... .......... .......... .......... 68% 93.1T 27s +381950K .......... .......... .......... .......... .......... 68% 1.46M 27s +382000K .......... .......... .......... .......... .......... 68% 483M 27s +382050K .......... .......... .......... .......... .......... 68% 93.1T 27s +382100K .......... .......... .......... .......... .......... 68% 720M 27s +382150K .......... .......... .......... .......... .......... 68% 786M 27s +382200K .......... .......... .......... .......... .......... 68% 640M 27s +382250K .......... .......... .......... .......... .......... 68% 93.1T 27s +382300K .......... .......... .......... .......... .......... 68% 3.45M 27s +382350K .......... .......... .......... .......... .......... 68% 93.1T 27s +382400K .......... .......... .......... .......... .......... 68% 8.84M 27s +382450K .......... .......... .......... .......... .......... 68% 568M 27s +382500K .......... .......... .......... .......... .......... 68% 93.1T 27s +382550K .......... .......... .......... .......... .......... 68% 2.11M 27s +382600K .......... .......... .......... .......... .......... 68% 4.11M 27s +382650K .......... .......... .......... .......... .......... 68% 8.67M 27s +382700K .......... .......... .......... .......... .......... 68% 19.3M 27s +382750K .......... .......... .......... .......... 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.......... .......... .......... 68% 263M 27s +384100K .......... .......... .......... .......... .......... 68% 13.9M 27s +384150K .......... .......... .......... .......... .......... 68% 8.18M 27s +384200K .......... .......... .......... .......... .......... 68% 2.33M 27s +384250K .......... .......... .......... .......... .......... 68% 359M 27s +384300K .......... .......... .......... .......... .......... 68% 2.20M 27s +384350K .......... .......... .......... .......... .......... 68% 1.55M 27s +384400K .......... .......... .......... .......... .......... 68% 331M 27s +384450K .......... .......... .......... .......... .......... 68% 93.1T 26s +384500K .......... .......... .......... .......... .......... 68% 523M 26s +384550K .......... .......... .......... .......... .......... 68% 639M 26s +384600K .......... .......... .......... .......... .......... 68% 1.83M 26s +384650K .......... .......... .......... .......... .......... 68% 351M 26s +384700K .......... 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+385350K .......... .......... .......... .......... .......... 68% 381M 26s +385400K .......... .......... .......... .......... .......... 68% 2.05M 26s +385450K .......... .......... .......... .......... .......... 68% 4.20M 26s +385500K .......... .......... .......... .......... .......... 68% 93.1T 26s +385550K .......... .......... .......... .......... .......... 68% 12.0M 26s +385600K .......... .......... .......... .......... .......... 68% 1.28M 26s +385650K .......... .......... .......... .......... .......... 68% 317M 26s +385700K .......... .......... .......... .......... .......... 68% 666M 26s +385750K .......... .......... .......... .......... .......... 68% 93.1T 26s +385800K .......... .......... .......... .......... .......... 68% 4.72M 26s +385850K .......... .......... .......... .......... .......... 68% 2.32M 26s +385900K .......... .......... .......... .......... .......... 68% 348M 26s +385950K .......... .......... .......... .......... .......... 68% 93.1T 26s +386000K .......... .......... .......... .......... .......... 68% 567M 26s +386050K .......... .......... .......... .......... .......... 68% 1.28M 26s +386100K .......... .......... .......... .......... .......... 68% 359M 26s +386150K .......... .......... .......... .......... .......... 68% 93.1T 26s +386200K .......... .......... .......... .......... .......... 68% 399M 26s +386250K .......... .......... .......... .......... .......... 68% 20.7M 26s +386300K .......... .......... .......... .......... .......... 68% 1.37M 26s +386350K .......... .......... .......... .......... .......... 68% 1.50M 26s +386400K .......... .......... .......... .......... .......... 68% 19.5M 26s +386450K .......... .......... .......... .......... .......... 68% 515M 26s +386500K .......... .......... .......... .......... .......... 68% 769M 26s +386550K .......... .......... .......... .......... .......... 68% 840M 26s +386600K .......... .......... .......... .......... 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+389200K .......... .......... .......... .......... .......... 69% 722M 26s +389250K .......... .......... .......... .......... .......... 69% 93.1T 26s +389300K .......... .......... .......... .......... .......... 69% 679M 26s +389350K .......... .......... .......... .......... .......... 69% 43.6M 26s +389400K .......... .......... .......... .......... .......... 69% 598M 26s +389450K .......... .......... .......... .......... .......... 69% 5.96M 26s +389500K .......... .......... .......... .......... .......... 69% 4.21M 26s +389550K .......... .......... .......... .......... .......... 69% 71.9M 26s +389600K .......... .......... .......... .......... .......... 69% 930M 26s +389650K .......... .......... .......... .......... .......... 69% 2.76M 26s +389700K .......... .......... .......... .......... .......... 69% 15.2M 26s +389750K .......... .......... .......... .......... .......... 69% 60.9M 26s +389800K .......... .......... .......... .......... .......... 69% 12.9M 26s +389850K .......... .......... .......... .......... .......... 69% 608M 26s +389900K .......... .......... .......... .......... .......... 69% 5.61M 26s +389950K .......... .......... .......... .......... .......... 69% 8.21M 26s +390000K .......... .......... .......... .......... .......... 69% 6.76M 26s +390050K .......... .......... .......... .......... .......... 69% 4.94M 26s +390100K .......... .......... .......... .......... .......... 69% 462M 26s +390150K .......... .......... .......... .......... .......... 69% 4.32M 26s +390200K .......... .......... .......... .......... .......... 69% 4.31M 26s +390250K .......... .......... .......... .......... .......... 69% 2.92M 26s +390300K .......... .......... .......... .......... .......... 69% 3.60M 26s +390350K .......... .......... .......... .......... .......... 69% 1.23M 26s +390400K .......... .......... .......... .......... .......... 69% 310M 26s +390450K .......... .......... .......... .......... 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+398200K .......... .......... .......... .......... .......... 70% 634M 24s +398250K .......... .......... .......... .......... .......... 70% 93.1T 24s +398300K .......... .......... .......... .......... .......... 70% 600M 24s +398350K .......... .......... .......... .......... .......... 70% 1.86M 24s +398400K .......... .......... .......... .......... .......... 70% 6.06M 24s +398450K .......... .......... .......... .......... .......... 71% 8.64M 24s +398500K .......... .......... .......... .......... .......... 71% 20.6M 24s +398550K .......... .......... .......... .......... .......... 71% 3.96M 24s +398600K .......... .......... .......... .......... .......... 71% 1.50M 24s +398650K .......... .......... .......... .......... .......... 71% 384M 24s +398700K .......... .......... .......... .......... .......... 71% 93.1T 24s +398750K .......... .......... .......... .......... .......... 71% 618M 24s +398800K .......... .......... .......... .......... .......... 71% 89.3M 24s +398850K .......... .......... .......... .......... .......... 71% 1.32M 24s +398900K .......... .......... .......... .......... .......... 71% 370M 24s +398950K .......... .......... .......... .......... .......... 71% 571M 24s +399000K .......... .......... .......... .......... .......... 71% 93.1T 24s +399050K .......... .......... .......... .......... .......... 71% 575M 24s +399100K .......... .......... .......... .......... .......... 71% 1.33M 24s +399150K .......... .......... .......... .......... .......... 71% 93.1T 24s +399200K .......... .......... .......... .......... .......... 71% 7.36M 24s +399250K .......... .......... .......... .......... .......... 71% 492M 24s +399300K .......... .......... .......... .......... .......... 71% 633M 24s +399350K .......... .......... .......... .......... .......... 71% 93.1T 24s +399400K .......... .......... .......... .......... .......... 71% 613M 24s +399450K .......... .......... .......... .......... .......... 71% 2.15M 24s +399500K .......... .......... .......... .......... .......... 71% 545M 24s +399550K .......... .......... .......... .......... .......... 71% 668M 24s +399600K .......... .......... .......... .......... .......... 71% 711M 24s +399650K .......... .......... .......... .......... .......... 71% 93.1T 24s +399700K .......... .......... .......... .......... .......... 71% 834K 24s +399750K .......... .......... .......... .......... .......... 71% 581M 24s +399800K .......... .......... .......... .......... .......... 71% 93.1T 24s +399850K .......... .......... .......... .......... .......... 71% 2.55M 24s +399900K .......... .......... .......... .......... .......... 71% 405M 24s +399950K .......... .......... .......... .......... .......... 71% 532M 24s +400000K .......... .......... .......... .......... .......... 71% 93.1T 24s +400050K .......... .......... .......... .......... .......... 71% 591M 24s +400100K .......... .......... .......... .......... .......... 71% 498M 24s +400150K .......... .......... .......... .......... .......... 71% 679M 24s +400200K .......... .......... .......... .......... .......... 71% 73.1M 24s +400250K .......... .......... .......... .......... .......... 71% 7.02M 24s +400300K .......... .......... .......... .......... .......... 71% 877M 24s +400350K .......... .......... .......... .......... .......... 71% 988K 24s +400400K .......... .......... .......... .......... .......... 71% 340M 24s +400450K .......... .......... .......... .......... .......... 71% 93.1T 24s +400500K .......... .......... .......... .......... .......... 71% 782M 24s +400550K .......... .......... .......... .......... .......... 71% 993M 24s +400600K .......... .......... .......... .......... .......... 71% 870M 24s +400650K .......... .......... .......... .......... .......... 71% 2.44M 24s +400700K .......... .......... .......... .......... .......... 71% 374M 24s +400750K .......... .......... .......... .......... .......... 71% 93.1T 24s +400800K .......... .......... .......... .......... .......... 71% 572M 24s +400850K .......... .......... .......... .......... .......... 71% 10.9M 24s +400900K .......... .......... .......... .......... .......... 71% 14.2M 24s +400950K .......... .......... .......... .......... .......... 71% 4.59M 24s +401000K .......... .......... .......... .......... .......... 71% 7.39M 24s +401050K .......... .......... .......... .......... .......... 71% 568M 24s +401100K .......... .......... .......... .......... .......... 71% 15.2M 24s +401150K .......... .......... .......... .......... .......... 71% 3.45M 24s +401200K .......... .......... .......... .......... .......... 71% 12.7M 24s +401250K .......... .......... .......... .......... .......... 71% 486M 24s +401300K .......... .......... .......... .......... .......... 71% 2.77M 24s +401350K .......... .......... .......... .......... .......... 71% 469M 24s +401400K .......... .......... .......... .......... .......... 71% 4.39M 24s +401450K .......... .......... .......... .......... .......... 71% 1.14M 24s +401500K .......... .......... .......... .......... .......... 71% 380M 24s +401550K .......... .......... .......... .......... .......... 71% 93.1T 24s +401600K .......... .......... .......... .......... .......... 71% 637M 24s +401650K .......... .......... .......... .......... .......... 71% 682M 24s +401700K .......... .......... .......... .......... .......... 71% 647M 24s +401750K .......... .......... .......... .......... .......... 71% 789M 24s +401800K .......... .......... .......... .......... .......... 71% 2.26M 24s +401850K .......... .......... .......... .......... .......... 71% 435M 24s +401900K .......... .......... .......... .......... .......... 71% 7.70M 24s +401950K .......... .......... .......... .......... .......... 71% 960K 24s +402000K .......... .......... .......... .......... .......... 71% 93.1T 24s +402050K .......... .......... .......... .......... .......... 71% 358M 24s +402100K .......... .......... .......... .......... .......... 71% 644M 24s +402150K .......... .......... .......... .......... .......... 71% 3.29M 24s +402200K .......... .......... .......... .......... .......... 71% 405M 24s +402250K .......... .......... .......... .......... .......... 71% 93.1T 24s +402300K .......... .......... .......... .......... .......... 71% 803K 24s +402350K .......... .......... .......... .......... .......... 71% 363M 24s +402400K .......... .......... .......... .......... .......... 71% 93.1T 24s +402450K .......... .......... .......... .......... .......... 71% 663M 24s +402500K .......... .......... .......... .......... .......... 71% 608M 24s +402550K .......... .......... .......... .......... .......... 71% 682M 24s +402600K .......... .......... .......... .......... .......... 71% 679M 24s +402650K .......... .......... .......... .......... .......... 71% 93.1T 24s +402700K .......... .......... .......... .......... .......... 71% 531M 24s +402750K .......... .......... .......... .......... .......... 71% 643M 24s +402800K .......... .......... .......... .......... .......... 71% 729M 24s +402850K .......... .......... .......... .......... .......... 71% 1.74M 24s +402900K .......... .......... .......... .......... .......... 71% 413M 24s +402950K .......... .......... .......... .......... .......... 71% 93.1T 24s +403000K .......... .......... .......... .......... .......... 71% 629M 24s +403050K .......... .......... .......... .......... .......... 71% 294M 24s +403100K .......... .......... .......... .......... .......... 71% 691M 24s +403150K .......... .......... .......... .......... .......... 71% 93.1T 24s +403200K .......... .......... .......... .......... .......... 71% 737M 24s +403250K .......... .......... .......... .......... .......... 71% 745M 24s +403300K .......... .......... .......... .......... .......... 71% 859M 24s +403350K .......... .......... .......... .......... .......... 71% 2.53M 24s +403400K .......... .......... .......... .......... .......... 71% 495M 24s +403450K .......... .......... .......... .......... .......... 71% 6.71M 23s +403500K .......... .......... .......... .......... .......... 71% 480M 23s +403550K .......... .......... .......... .......... .......... 71% 698M 23s +403600K .......... .......... .......... .......... .......... 71% 93.1T 23s +403650K .......... .......... .......... .......... .......... 71% 6.28M 23s +403700K .......... .......... .......... .......... .......... 71% 5.97M 23s +403750K .......... .......... .......... .......... .......... 71% 1.49M 23s +403800K .......... .......... .......... .......... .......... 71% 29.8M 23s +403850K .......... .......... .......... .......... .......... 71% 8.50M 23s +403900K .......... .......... .......... .......... .......... 71% 562M 23s +403950K .......... .......... .......... .......... .......... 71% 2.26M 23s +404000K .......... .......... .......... .......... .......... 71% 10.7M 23s +404050K .......... .......... .......... .......... .......... 72% 14.8M 23s +404100K .......... .......... .......... .......... .......... 72% 634M 23s +404150K .......... .......... .......... .......... .......... 72% 1.11G 23s +404200K .......... .......... .......... .......... .......... 72% 2.01M 23s +404250K .......... .......... .......... .......... .......... 72% 315M 23s +404300K .......... .......... .......... .......... .......... 72% 638M 23s +404350K .......... .......... .......... .......... .......... 72% 3.40M 23s +404400K .......... .......... .......... .......... .......... 72% 423M 23s +404450K .......... .......... .......... .......... .......... 72% 6.15M 23s +404500K .......... .......... .......... .......... .......... 72% 631M 23s +404550K .......... .......... .......... .......... .......... 72% 93.1T 23s +404600K .......... .......... .......... .......... .......... 72% 564M 23s +404650K .......... .......... .......... .......... .......... 72% 8.72M 23s +404700K .......... .......... .......... .......... .......... 72% 5.39M 23s +404750K .......... .......... .......... .......... .......... 72% 7.50M 23s +404800K .......... .......... .......... .......... .......... 72% 6.62M 23s +404850K .......... .......... .......... .......... .......... 72% 1.80M 23s +404900K .......... .......... .......... .......... .......... 72% 418M 23s +404950K .......... .......... .......... .......... .......... 72% 93.1T 23s +405000K .......... .......... .......... .......... .......... 72% 3.34M 23s +405050K .......... .......... .......... .......... .......... 72% 408M 23s +405100K .......... .......... .......... .......... .......... 72% 664M 23s +405150K .......... .......... .......... .......... .......... 72% 8.71M 23s +405200K .......... .......... .......... .......... .......... 72% 7.57M 23s +405250K .......... .......... .......... .......... .......... 72% 6.09M 23s +405300K .......... .......... .......... .......... .......... 72% 7.67M 23s +405350K .......... .......... .......... .......... .......... 72% 11.9M 23s +405400K .......... .......... .......... .......... .......... 72% 7.39M 23s +405450K .......... .......... .......... .......... .......... 72% 7.42M 23s +405500K .......... .......... .......... .......... .......... 72% 1.45M 23s +405550K .......... .......... .......... .......... .......... 72% 93.1T 23s +405600K .......... .......... .......... .......... .......... 72% 498M 23s +405650K .......... .......... .......... .......... .......... 72% 903M 23s +405700K .......... .......... .......... .......... .......... 72% 1.04G 23s +405750K .......... .......... .......... .......... .......... 72% 5.14M 23s +405800K .......... .......... .......... .......... .......... 72% 1.68M 23s +405850K .......... .......... .......... .......... .......... 72% 10.2M 23s +405900K .......... .......... 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.......... .......... .......... .......... .......... 72% 327M 23s +407250K .......... .......... .......... .......... .......... 72% 2.31M 23s +407300K .......... .......... .......... .......... .......... 72% 93.1T 23s +407350K .......... .......... .......... .......... .......... 72% 2.36M 23s +407400K .......... .......... .......... .......... .......... 72% 404M 23s +407450K .......... .......... .......... .......... .......... 72% 40.1M 23s +407500K .......... .......... .......... .......... .......... 72% 1.63M 23s +407550K .......... .......... .......... .......... .......... 72% 3.72M 23s +407600K .......... .......... .......... .......... .......... 72% 292M 23s +407650K .......... .......... .......... .......... .......... 72% 93.1T 23s +407700K .......... .......... .......... .......... .......... 72% 389M 23s +407750K .......... .......... .......... .......... .......... 72% 450M 23s +407800K .......... .......... .......... .......... .......... 72% 541M 23s +407850K .......... .......... .......... .......... .......... 72% 1.32M 23s +407900K .......... .......... .......... .......... .......... 72% 93.1T 23s +407950K .......... .......... .......... .......... .......... 72% 450M 23s +408000K .......... .......... .......... .......... .......... 72% 15.5M 23s +408050K .......... .......... .......... .......... .......... 72% 365M 23s +408100K .......... .......... .......... .......... .......... 72% 540M 23s +408150K .......... .......... .......... .......... .......... 72% 1.79M 23s +408200K .......... .......... .......... .......... .......... 72% 348M 23s +408250K .......... .......... .......... .......... .......... 72% 612M 23s +408300K .......... .......... .......... .......... .......... 72% 2.18M 23s +408350K .......... .......... .......... .......... .......... 72% 372M 23s +408400K .......... .......... .......... .......... .......... 72% 93.1T 23s +408450K .......... .......... .......... .......... .......... 72% 593M 23s +408500K .......... .......... .......... .......... .......... 72% 678M 23s +408550K .......... .......... .......... .......... .......... 72% 1.91M 23s +408600K .......... .......... .......... .......... .......... 72% 352M 23s +408650K .......... .......... .......... .......... .......... 72% 3.98M 23s +408700K .......... .......... .......... .......... .......... 72% 1.41M 23s +408750K .......... .......... .......... .......... .......... 72% 304M 23s +408800K .......... .......... .......... .......... .......... 72% 93.1T 23s +408850K .......... .......... .......... .......... .......... 72% 473M 23s +408900K .......... .......... .......... .......... .......... 72% 592M 23s +408950K .......... .......... .......... .......... .......... 72% 652M 23s +409000K .......... .......... .......... .......... .......... 72% 11.8M 23s +409050K .......... .......... .......... .......... .......... 72% 632M 23s +409100K .......... .......... .......... .......... 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.......... .......... .......... .......... 73% 31.4M 22s +411100K .......... .......... .......... .......... .......... 73% 553M 22s +411150K .......... .......... .......... .......... .......... 73% 2.29M 22s +411200K .......... .......... .......... .......... .......... 73% 8.68M 22s +411250K .......... .......... .......... .......... .......... 73% 1.07M 22s +411300K .......... .......... .......... .......... .......... 73% 93.1T 22s +411350K .......... .......... .......... .......... .......... 73% 416M 22s +411400K .......... .......... .......... .......... .......... 73% 675M 22s +411450K .......... .......... .......... .......... .......... 73% 1.08M 22s +411500K .......... .......... .......... .......... .......... 73% 93.1T 22s +411550K .......... .......... .......... .......... .......... 73% 320M 22s +411600K .......... .......... .......... .......... .......... 73% 530M 22s +411650K .......... .......... .......... .......... .......... 73% 634M 22s +411700K .......... .......... .......... .......... .......... 73% 813M 22s +411750K .......... .......... .......... .......... .......... 73% 93.1T 22s +411800K .......... .......... .......... .......... .......... 73% 777M 22s +411850K .......... .......... .......... .......... .......... 73% 17.4M 22s +411900K .......... .......... .......... .......... .......... 73% 2.10M 22s +411950K .......... .......... .......... .......... .......... 73% 14.7M 22s +412000K .......... .......... .......... .......... .......... 73% 477M 22s +412050K .......... .......... .......... .......... .......... 73% 2.22M 22s +412100K .......... .......... .......... .......... .......... 73% 1.13M 22s +412150K .......... .......... .......... .......... .......... 73% 535M 22s +412200K .......... .......... .......... .......... .......... 73% 775M 22s +412250K .......... .......... .......... .......... .......... 73% 1.72M 22s +412300K .......... .......... .......... .......... .......... 73% 5.87M 22s +412350K .......... .......... .......... .......... .......... 73% 2.59M 22s +412400K .......... .......... .......... .......... .......... 73% 4.15M 22s +412450K .......... .......... .......... .......... .......... 73% 1.41M 22s +412500K .......... .......... .......... .......... .......... 73% 336M 22s +412550K .......... .......... .......... .......... .......... 73% 615M 22s +412600K .......... .......... .......... .......... .......... 73% 93.1T 22s +412650K .......... .......... .......... .......... .......... 73% 640M 22s +412700K .......... .......... .......... .......... .......... 73% 3.49M 22s +412750K .......... .......... .......... .......... .......... 73% 391M 22s +412800K .......... .......... .......... .......... .......... 73% 818M 22s +412850K .......... .......... .......... .......... .......... 73% 4.53M 22s +412900K .......... .......... .......... .......... .......... 73% 93.1T 22s +412950K .......... .......... .......... .......... .......... 73% 452M 22s +413000K .......... .......... .......... .......... .......... 73% 1.24M 22s +413050K .......... .......... .......... .......... .......... 73% 9.94M 22s +413100K .......... .......... .......... .......... .......... 73% 93.1T 22s +413150K .......... .......... .......... .......... .......... 73% 471M 22s +413200K .......... .......... .......... .......... .......... 73% 6.34M 22s +413250K .......... .......... .......... .......... .......... 73% 451M 22s +413300K .......... .......... .......... .......... .......... 73% 93.1T 22s +413350K .......... .......... .......... .......... .......... 73% 615M 22s +413400K .......... .......... .......... .......... .......... 73% 2.17M 22s +413450K .......... .......... .......... .......... .......... 73% 1.59M 22s +413500K .......... .......... .......... .......... .......... 73% 278M 22s +413550K .......... .......... .......... .......... .......... 73% 496M 22s +413600K .......... .......... .......... .......... .......... 73% 93.1T 22s +413650K .......... .......... .......... .......... .......... 73% 499M 22s +413700K .......... .......... .......... .......... .......... 73% 543M 22s +413750K .......... .......... .......... .......... .......... 73% 1.87M 22s +413800K .......... .......... .......... .......... .......... 73% 379M 22s +413850K .......... .......... .......... .......... .......... 73% 582K 22s +413900K .......... .......... .......... .......... .......... 73% 333M 22s +413950K .......... .......... .......... .......... .......... 73% 573M 22s +414000K .......... .......... .......... .......... .......... 73% 93.1T 22s +414050K .......... .......... .......... .......... .......... 73% 390M 22s +414100K .......... .......... .......... .......... .......... 73% 256M 22s +414150K .......... .......... .......... .......... .......... 73% 5.82M 22s +414200K .......... .......... .......... .......... .......... 73% 419M 22s +414250K .......... .......... .......... 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.......... .......... .......... 73% 93.1T 22s +414950K .......... .......... .......... .......... .......... 73% 318M 22s +415000K .......... .......... .......... .......... .......... 73% 570M 22s +415050K .......... .......... .......... .......... .......... 73% 576M 22s +415100K .......... .......... .......... .......... .......... 73% 1.78M 22s +415150K .......... .......... .......... .......... .......... 73% 30.3M 22s +415200K .......... .......... .......... .......... .......... 73% 93.1T 22s +415250K .......... .......... .......... .......... .......... 74% 459M 22s +415300K .......... .......... .......... .......... .......... 74% 739M 22s +415350K .......... .......... .......... .......... .......... 74% 6.79M 22s +415400K .......... .......... .......... .......... .......... 74% 93.1T 22s +415450K .......... .......... .......... .......... .......... 74% 20.2M 22s +415500K .......... .......... .......... .......... .......... 74% 8.48M 22s +415550K .......... .......... .......... .......... .......... 74% 1.54M 22s +415600K .......... .......... .......... .......... .......... 74% 391M 22s +415650K .......... .......... .......... .......... .......... 74% 746M 22s +415700K .......... .......... .......... .......... .......... 74% 10.2M 22s +415750K .......... .......... .......... .......... .......... 74% 15.0M 22s +415800K .......... .......... .......... .......... .......... 74% 2.96M 22s +415850K .......... .......... .......... .......... .......... 74% 93.1T 22s +415900K .......... .......... .......... .......... .......... 74% 754M 22s +415950K .......... .......... .......... .......... .......... 74% 2.20M 22s +416000K .......... .......... .......... .......... .......... 74% 12.0M 22s +416050K .......... .......... .......... .......... .......... 74% 470M 22s +416100K .......... .......... .......... .......... .......... 74% 2.64M 22s +416150K .......... .......... .......... .......... .......... 74% 349M 22s +416200K .......... .......... .......... .......... .......... 74% 8.68M 22s +416250K .......... .......... .......... .......... .......... 74% 21.0M 22s +416300K .......... .......... .......... .......... .......... 74% 93.1T 22s +416350K .......... .......... .......... .......... .......... 74% 3.20M 22s +416400K .......... .......... .......... .......... .......... 74% 93.1T 22s +416450K .......... .......... .......... .......... .......... 74% 11.6M 22s +416500K .......... .......... .......... .......... .......... 74% 296M 22s +416550K .......... .......... .......... .......... .......... 74% 571M 22s +416600K .......... .......... .......... .......... .......... 74% 2.37M 22s +416650K .......... .......... .......... .......... .......... 74% 3.15M 22s +416700K .......... .......... .......... .......... .......... 74% 11.7M 22s +416750K .......... .......... .......... .......... .......... 74% 12.2M 22s +416800K .......... .......... .......... .......... .......... 74% 1.89M 22s +416850K .......... .......... .......... .......... .......... 74% 5.45M 22s +416900K .......... .......... .......... .......... .......... 74% 2.38M 22s +416950K .......... .......... .......... .......... .......... 74% 164M 22s +417000K .......... .......... .......... .......... .......... 74% 4.02M 22s +417050K .......... .......... .......... .......... .......... 74% 455M 22s +417100K .......... .......... .......... .......... .......... 74% 5.46M 22s +417150K .......... .......... .......... .......... .......... 74% 1.21M 22s +417200K .......... .......... .......... .......... .......... 74% 1.81M 22s +417250K .......... .......... .......... .......... .......... 74% 296M 21s +417300K .......... .......... .......... .......... .......... 74% 454M 21s +417350K .......... .......... .......... .......... .......... 74% 5.10M 21s +417400K .......... .......... .......... .......... .......... 74% 276M 21s +417450K .......... .......... .......... .......... .......... 74% 665M 21s +417500K .......... .......... .......... .......... .......... 74% 93.1T 21s +417550K .......... .......... .......... .......... .......... 74% 742K 21s +417600K .......... .......... .......... .......... .......... 74% 338M 21s +417650K .......... .......... .......... .......... .......... 74% 517M 21s +417700K .......... .......... .......... .......... .......... 74% 93.1T 21s +417750K .......... .......... .......... .......... .......... 74% 524M 21s +417800K .......... .......... .......... .......... .......... 74% 551M 21s +417850K .......... .......... .......... .......... .......... 74% 499M 21s +417900K .......... .......... .......... .......... .......... 74% 93.1T 21s +417950K .......... .......... .......... .......... .......... 74% 472M 21s +418000K .......... .......... .......... .......... .......... 74% 576M 21s +418050K .......... .......... .......... .......... .......... 74% 474M 21s +418100K .......... .......... .......... .......... 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+421350K .......... .......... .......... .......... .......... 75% 513M 21s +421400K .......... .......... .......... .......... .......... 75% 677M 21s +421450K .......... .......... .......... .......... .......... 75% 1.27M 21s +421500K .......... .......... .......... .......... .......... 75% 3.17M 21s +421550K .......... .......... .......... .......... .......... 75% 374M 21s +421600K .......... .......... .......... .......... .......... 75% 93.1T 21s +421650K .......... .......... .......... .......... .......... 75% 8.23M 21s +421700K .......... .......... .......... .......... .......... 75% 4.45M 21s +421750K .......... .......... .......... .......... .......... 75% 7.39M 21s +421800K .......... .......... .......... .......... .......... 75% 11.9M 21s +421850K .......... .......... .......... .......... .......... 75% 492M 21s +421900K .......... .......... .......... .......... .......... 75% 93.1T 21s +421950K .......... .......... .......... .......... .......... 75% 474M 21s +422000K .......... .......... .......... .......... .......... 75% 1.67M 21s +422050K .......... .......... .......... .......... .......... 75% 22.5M 21s +422100K .......... .......... .......... .......... .......... 75% 1.47M 21s +422150K .......... .......... .......... .......... .......... 75% 309M 21s +422200K .......... .......... .......... .......... .......... 75% 422M 21s +422250K .......... .......... .......... .......... .......... 75% 524M 21s +422300K .......... .......... .......... .......... .......... 75% 93.1T 21s +422350K .......... .......... .......... .......... .......... 75% 8.19M 21s +422400K .......... .......... .......... .......... .......... 75% 3.44M 21s +422450K .......... .......... .......... .......... .......... 75% 336M 21s +422500K .......... .......... .......... .......... .......... 75% 567M 21s +422550K .......... .......... .......... .......... .......... 75% 4.59M 21s +422600K .......... .......... .......... .......... 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.......... .......... .......... .......... 75% 795M 20s +424600K .......... .......... .......... .......... .......... 75% 6.70M 20s +424650K .......... .......... .......... .......... .......... 75% 476M 20s +424700K .......... .......... .......... .......... .......... 75% 4.62M 20s +424750K .......... .......... .......... .......... .......... 75% 12.0M 20s +424800K .......... .......... .......... .......... .......... 75% 93.1T 20s +424850K .......... .......... .......... .......... .......... 75% 567M 20s +424900K .......... .......... .......... .......... .......... 75% 4.27M 20s +424950K .......... .......... .......... .......... .......... 75% 282M 20s +425000K .......... .......... .......... .......... .......... 75% 93.1T 20s +425050K .......... .......... .......... .......... .......... 75% 524M 20s +425100K .......... .......... .......... .......... .......... 75% 718M 20s +425150K .......... .......... .......... .......... .......... 75% 6.79M 20s +425200K .......... .......... .......... .......... .......... 75% 3.73M 20s +425250K .......... .......... .......... .......... .......... 75% 1.89M 20s +425300K .......... .......... .......... .......... .......... 75% 556M 20s +425350K .......... .......... .......... .......... .......... 75% 93.1T 20s +425400K .......... .......... .......... .......... .......... 75% 2.81M 20s +425450K .......... .......... .......... .......... .......... 75% 245M 20s +425500K .......... .......... .......... .......... .......... 75% 404M 20s +425550K .......... .......... .......... .......... .......... 75% 13.4M 20s +425600K .......... .......... .......... .......... .......... 75% 29.5M 20s +425650K .......... .......... .......... .......... .......... 75% 2.15M 20s +425700K .......... .......... .......... .......... .......... 75% 534M 20s +425750K .......... .......... .......... .......... .......... 75% 1.45M 20s +425800K .......... .......... .......... .......... .......... 75% 590M 20s +425850K .......... .......... .......... .......... .......... 75% 93.1T 20s +425900K .......... .......... .......... .......... .......... 75% 798M 20s +425950K .......... .......... .......... .......... .......... 75% 1.24M 20s +426000K .......... .......... .......... .......... .......... 75% 416M 20s +426050K .......... .......... .......... .......... .......... 75% 512M 20s +426100K .......... .......... .......... .......... .......... 75% 681M 20s +426150K .......... .......... .......... .......... .......... 75% 2.81M 20s +426200K .......... .......... .......... .......... .......... 75% 93.1T 20s +426250K .......... .......... .......... .......... .......... 75% 208M 20s +426300K .......... .......... .......... .......... .......... 75% 234M 20s +426350K .......... .......... .......... .......... .......... 75% 14.7M 20s +426400K .......... .......... .......... .......... .......... 75% 369K 20s +426450K .......... .......... .......... .......... .......... 75% 93.1T 20s +426500K .......... .......... .......... .......... .......... 76% 358M 20s +426550K .......... .......... .......... .......... .......... 76% 322M 20s +426600K .......... .......... .......... .......... .......... 76% 698M 20s +426650K .......... .......... .......... .......... .......... 76% 93.1T 20s +426700K .......... .......... .......... .......... .......... 76% 691M 20s +426750K .......... .......... .......... .......... .......... 76% 191M 20s +426800K .......... .......... .......... .......... .......... 76% 837M 20s +426850K .......... .......... .......... .......... .......... 76% 1.52M 20s +426900K .......... .......... .......... .......... .......... 76% 28.3M 20s +426950K .......... .......... .......... .......... .......... 76% 93.1T 20s +427000K .......... .......... .......... .......... .......... 76% 626M 20s +427050K .......... .......... .......... .......... .......... 76% 760K 20s +427100K .......... .......... .......... .......... .......... 76% 368M 20s +427150K .......... .......... .......... .......... .......... 76% 93.1T 20s +427200K .......... .......... .......... .......... .......... 76% 518M 20s +427250K .......... .......... .......... .......... .......... 76% 686M 20s +427300K .......... .......... .......... .......... .......... 76% 766M 20s +427350K .......... .......... .......... .......... .......... 76% 1.33M 20s +427400K .......... .......... .......... .......... .......... 76% 325M 20s +427450K .......... .......... .......... .......... .......... 76% 93.1T 20s +427500K .......... .......... .......... .......... .......... 76% 1.77M 20s +427550K .......... .......... .......... .......... .......... 76% 335M 20s +427600K .......... .......... .......... .......... .......... 76% 486M 20s +427650K .......... .......... .......... .......... .......... 76% 93.1T 20s +427700K .......... .......... .......... .......... .......... 76% 441M 20s +427750K .......... .......... .......... .......... .......... 76% 622M 20s +427800K .......... .......... .......... .......... .......... 76% 602M 20s +427850K .......... .......... .......... .......... .......... 76% 40.4M 20s +427900K .......... .......... .......... .......... .......... 76% 93.1T 20s +427950K .......... .......... .......... .......... .......... 76% 721M 20s +428000K .......... .......... .......... .......... .......... 76% 896M 20s +428050K .......... .......... .......... .......... .......... 76% 976M 20s +428100K .......... .......... .......... .......... .......... 76% 3.47M 20s +428150K .......... .......... .......... .......... .......... 76% 1.02M 20s +428200K .......... .......... .......... .......... .......... 76% 88.3M 20s +428250K .......... .......... .......... .......... .......... 76% 3.56M 20s +428300K .......... .......... .......... .......... .......... 76% 15.1M 20s +428350K .......... .......... .......... .......... .......... 76% 93.1T 20s +428400K .......... .......... .......... .......... .......... 76% 528M 20s +428450K .......... .......... .......... .......... .......... 76% 4.97M 20s +428500K .......... .......... .......... .......... .......... 76% 93.1T 20s +428550K .......... .......... .......... .......... .......... 76% 2.46M 20s +428600K .......... .......... .......... .......... .......... 76% 1.61M 20s +428650K .......... .......... .......... .......... .......... 76% 369M 20s +428700K .......... .......... .......... .......... .......... 76% 622M 20s +428750K .......... .......... .......... .......... .......... 76% 602M 20s +428800K .......... .......... .......... .......... .......... 76% 93.1T 20s +428850K .......... .......... .......... .......... .......... 76% 3.66M 20s +428900K .......... .......... .......... .......... .......... 76% 93.1T 20s +428950K .......... .......... .......... .......... .......... 76% 5.18M 20s +429000K .......... .......... .......... .......... .......... 76% 127M 20s +429050K .......... 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.......... .......... .......... .......... .......... 76% 7.89M 20s +429750K .......... .......... .......... .......... .......... 76% 7.80M 20s +429800K .......... .......... .......... .......... .......... 76% 11.9M 20s +429850K .......... .......... .......... .......... .......... 76% 459M 20s +429900K .......... .......... .......... .......... .......... 76% 1.96M 20s +429950K .......... .......... .......... .......... .......... 76% 359M 20s +430000K .......... .......... .......... .......... .......... 76% 93.1T 20s +430050K .......... .......... .......... .......... .......... 76% 652M 20s +430100K .......... .......... .......... .......... .......... 76% 650M 20s +430150K .......... .......... .......... .......... .......... 76% 6.09M 20s +430200K .......... .......... .......... .......... .......... 76% 93.1T 20s +430250K .......... .......... .......... .......... .......... 76% 5.67M 20s +430300K .......... .......... .......... .......... .......... 76% 5.22M 20s +430350K .......... .......... .......... .......... .......... 76% 467M 20s +430400K .......... .......... .......... .......... .......... 76% 8.61M 20s +430450K .......... .......... .......... .......... .......... 76% 93.1T 20s +430500K .......... .......... .......... .......... .......... 76% 3.62M 20s +430550K .......... .......... .......... .......... .......... 76% 1.40M 20s +430600K .......... .......... .......... .......... .......... 76% 1.15M 20s +430650K .......... .......... .......... .......... .......... 76% 1.53M 20s +430700K .......... .......... .......... .......... .......... 76% 366M 20s +430750K .......... .......... .......... .......... .......... 76% 749M 20s +430800K .......... .......... .......... .......... .......... 76% 580M 20s +430850K .......... .......... .......... .......... .......... 76% 93.1T 20s +430900K .......... .......... .......... .......... .......... 76% 724M 20s +430950K .......... .......... .......... .......... .......... 76% 606M 20s +431000K .......... .......... .......... .......... .......... 76% 749M 20s +431050K .......... .......... .......... .......... .......... 76% 122M 20s +431100K .......... .......... .......... .......... .......... 76% 993M 19s +431150K .......... .......... .......... .......... .......... 76% 1.78M 19s +431200K .......... .......... .......... .......... .......... 76% 1.46M 19s +431250K .......... .......... .......... .......... .......... 76% 341M 19s +431300K .......... .......... .......... .......... .......... 76% 545M 19s +431350K .......... .......... .......... .......... .......... 76% 93.1T 19s +431400K .......... .......... .......... .......... .......... 76% 547M 19s +431450K .......... .......... .......... .......... .......... 76% 13.0M 19s +431500K .......... .......... .......... .......... .......... 76% 482M 19s +431550K .......... .......... .......... .......... .......... 76% 497M 19s +431600K .......... .......... .......... .......... .......... 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.......... .......... .......... 78% 93.1T 18s +438100K .......... .......... .......... .......... .......... 78% 248M 18s +438150K .......... .......... .......... .......... .......... 78% 430M 18s +438200K .......... .......... .......... .......... .......... 78% 1.54M 18s +438250K .......... .......... .......... .......... .......... 78% 93.1T 18s +438300K .......... .......... .......... .......... .......... 78% 320M 18s +438350K .......... .......... .......... .......... .......... 78% 504M 18s +438400K .......... .......... .......... .......... .......... 78% 645M 18s +438450K .......... .......... .......... .......... .......... 78% 675M 18s +438500K .......... .......... .......... .......... .......... 78% 3.25M 18s +438550K .......... .......... .......... .......... .......... 78% 2.39M 18s +438600K .......... .......... .......... .......... .......... 78% 336M 18s +438650K .......... .......... .......... .......... .......... 78% 14.1M 18s +438700K .......... .......... .......... .......... .......... 78% 93.1T 18s +438750K .......... .......... .......... .......... .......... 78% 387M 18s +438800K .......... .......... .......... .......... .......... 78% 465M 18s +438850K .......... .......... .......... .......... .......... 78% 11.4M 18s +438900K .......... .......... .......... .......... .......... 78% 15.0M 18s +438950K .......... .......... .......... .......... .......... 78% 559K 18s +439000K .......... .......... .......... .......... .......... 78% 306M 18s +439050K .......... .......... .......... .......... .......... 78% 1.67M 18s +439100K .......... .......... .......... .......... .......... 78% 281M 18s +439150K .......... .......... .......... .......... .......... 78% 93.1T 18s +439200K .......... .......... .......... .......... .......... 78% 474M 18s +439250K .......... .......... .......... .......... .......... 78% 429M 18s +439300K .......... .......... .......... .......... .......... 78% 417M 18s +439350K .......... .......... .......... .......... .......... 78% 93.1T 18s +439400K .......... .......... .......... .......... .......... 78% 487M 18s +439450K .......... .......... .......... .......... .......... 78% 499M 18s +439500K .......... .......... .......... .......... .......... 78% 736M 18s +439550K .......... .......... .......... .......... .......... 78% 9.96M 18s +439600K .......... .......... .......... .......... .......... 78% 1.93M 18s +439650K .......... .......... .......... .......... .......... 78% 3.30M 18s +439700K .......... .......... .......... .......... .......... 78% 414M 18s +439750K .......... .......... .......... .......... .......... 78% 93.1T 18s +439800K .......... .......... .......... .......... .......... 78% 604K 18s +439850K .......... .......... .......... .......... .......... 78% 299M 18s +439900K .......... .......... .......... .......... .......... 78% 703M 18s +439950K .......... .......... .......... .......... .......... 78% 93.1T 18s +440000K .......... .......... .......... .......... .......... 78% 102M 18s +440050K .......... .......... .......... .......... .......... 78% 2.51M 18s +440100K .......... .......... .......... .......... .......... 78% 93.1T 18s +440150K .......... .......... .......... .......... .......... 78% 1.08M 18s +440200K .......... .......... .......... .......... .......... 78% 93.1T 18s +440250K .......... .......... .......... .......... .......... 78% 12.1M 18s +440300K .......... .......... .......... .......... .......... 78% 4.62M 18s +440350K .......... .......... .......... .......... .......... 78% 304M 18s +440400K .......... .......... .......... .......... .......... 78% 443M 18s +440450K .......... .......... .......... .......... .......... 78% 691M 18s +440500K .......... .......... .......... .......... .......... 78% 93.1T 18s +440550K .......... .......... .......... .......... .......... 78% 831K 18s +440600K .......... .......... .......... .......... .......... 78% 4.91M 18s +440650K .......... .......... .......... .......... .......... 78% 93.1T 18s +440700K .......... .......... .......... .......... .......... 78% 14.7M 18s +440750K .......... .......... .......... .......... .......... 78% 539M 18s +440800K .......... .......... .......... .......... .......... 78% 717M 18s +440850K .......... .......... .......... .......... .......... 78% 10.5M 18s +440900K .......... .......... .......... .......... .......... 78% 93.1T 18s +440950K .......... .......... .......... .......... .......... 78% 3.73M 18s +441000K .......... .......... .......... .......... .......... 78% 93.1T 18s +441050K .......... .......... .......... .......... .......... 78% 255M 18s +441100K .......... .......... .......... .......... .......... 78% 555M 18s +441150K .......... .......... .......... .......... .......... 78% 449K 18s +441200K .......... .......... .......... .......... .......... 78% 93.1T 18s +441250K .......... .......... .......... .......... .......... 78% 413M 18s +441300K .......... .......... .......... .......... .......... 78% 769M 18s +441350K .......... .......... .......... .......... .......... 78% 772M 18s +441400K .......... .......... .......... .......... .......... 78% 93.1T 18s +441450K .......... .......... .......... .......... .......... 78% 9.32M 18s +441500K .......... .......... .......... .......... .......... 78% 93.1T 18s +441550K .......... .......... .......... .......... .......... 78% 2.41M 18s +441600K .......... .......... .......... .......... .......... 78% 93.1T 18s +441650K .......... .......... .......... .......... .......... 78% 801K 18s +441700K .......... .......... .......... .......... .......... 78% 268M 18s +441750K .......... .......... .......... .......... .......... 78% 93.1T 18s +441800K .......... .......... .......... .......... .......... 78% 446M 18s +441850K .......... .......... .......... .......... .......... 78% 230M 18s +441900K .......... .......... .......... .......... .......... 78% 436M 18s +441950K .......... .......... .......... .......... .......... 78% 497M 18s +442000K .......... .......... .......... .......... .......... 78% 93.1T 18s +442050K .......... .......... .......... .......... .......... 78% 695M 18s +442100K .......... .......... .......... .......... .......... 78% 15.0M 18s +442150K .......... .......... .......... .......... .......... 78% 518M 18s +442200K .......... .......... .......... .......... .......... 78% 1.95M 18s +442250K .......... .......... .......... .......... .......... 78% 2.76M 18s +442300K .......... .......... .......... .......... .......... 78% 5.58M 18s +442350K .......... .......... .......... .......... .......... 78% 714M 18s +442400K .......... .......... .......... .......... .......... 78% 1.36M 18s +442450K .......... .......... .......... .......... .......... 78% 325M 18s +442500K .......... .......... .......... .......... .......... 78% 1.34M 18s +442550K .......... .......... .......... .......... .......... 78% 93.1T 18s +442600K .......... .......... .......... .......... .......... 78% 4.51M 18s +442650K .......... .......... .......... .......... .......... 78% 469M 18s +442700K .......... .......... .......... .......... .......... 78% 2.41M 18s +442750K .......... .......... .......... .......... .......... 78% 416M 18s +442800K .......... .......... .......... .......... .......... 78% 697M 18s +442850K .......... .......... .......... .......... .......... 78% 2.35M 18s +442900K .......... .......... .......... .......... .......... 78% 358M 18s +442950K .......... .......... .......... .......... .......... 78% 93.1T 18s +443000K .......... .......... .......... .......... .......... 78% 580M 18s +443050K .......... .......... .......... .......... .......... 78% 5.37M 18s +443100K .......... .......... .......... .......... .......... 78% 2.59M 18s +443150K .......... .......... .......... .......... .......... 78% 93.1T 18s +443200K .......... .......... .......... .......... .......... 78% 1.01M 18s +443250K .......... .......... .......... .......... .......... 78% 93.1T 18s +443300K .......... .......... .......... .......... .......... 78% 8.96M 18s +443350K .......... .......... .......... .......... .......... 79% 319M 18s +443400K .......... .......... .......... .......... .......... 79% 205M 18s +443450K .......... .......... .......... .......... .......... 79% 2.08M 18s +443500K .......... .......... .......... .......... .......... 79% 380M 18s +443550K .......... .......... .......... .......... .......... 79% 2.09M 18s +443600K .......... .......... .......... .......... .......... 79% 543M 18s +443650K .......... .......... .......... .......... .......... 79% 93.1T 18s +443700K .......... .......... .......... .......... .......... 79% 1.12M 18s +443750K .......... .......... .......... .......... .......... 79% 267M 18s +443800K .......... .......... .......... .......... .......... 79% 93.1T 18s +443850K .......... .......... .......... .......... .......... 79% 589M 18s +443900K .......... .......... .......... .......... .......... 79% 13.7M 18s +443950K .......... .......... .......... .......... .......... 79% 11.3M 18s +444000K .......... .......... .......... .......... .......... 79% 2.33M 18s +444050K .......... .......... .......... .......... .......... 79% 33.6M 18s +444100K .......... .......... .......... .......... .......... 79% 2.70M 18s +444150K .......... .......... .......... .......... .......... 79% 93.1T 18s +444200K .......... .......... .......... .......... .......... 79% 1.55M 18s +444250K .......... .......... .......... .......... .......... 79% 345M 18s +444300K .......... .......... .......... .......... .......... 79% 386M 18s +444350K .......... .......... .......... .......... .......... 79% 1.10G 18s +444400K .......... .......... .......... .......... .......... 79% 3.02M 18s +444450K .......... .......... .......... .......... .......... 79% 2.62M 18s +444500K .......... .......... .......... .......... .......... 79% 4.51M 18s +444550K .......... .......... .......... .......... .......... 79% 504M 18s +444600K .......... .......... .......... .......... .......... 79% 2.15M 18s +444650K .......... .......... .......... .......... .......... 79% 1.20M 18s +444700K .......... .......... .......... .......... .......... 79% 93.1T 18s +444750K .......... .......... .......... .......... .......... 79% 535M 18s +444800K .......... .......... .......... .......... .......... 79% 12.1M 18s +444850K .......... .......... .......... .......... .......... 79% 1.64M 18s +444900K .......... .......... .......... .......... .......... 79% 459M 17s +444950K .......... .......... .......... .......... .......... 79% 576M 17s +445000K .......... .......... .......... .......... .......... 79% 93.1T 17s +445050K .......... .......... .......... .......... .......... 79% 15.8M 17s +445100K .......... .......... .......... .......... .......... 79% 1.22M 17s +445150K .......... .......... .......... .......... .......... 79% 4.72M 17s +445200K .......... .......... .......... .......... .......... 79% 3.25M 17s +445250K .......... .......... .......... .......... .......... 79% 337M 17s +445300K .......... .......... .......... .......... .......... 79% 608M 17s +445350K .......... .......... .......... .......... .......... 79% 685M 17s +445400K .......... .......... .......... .......... .......... 79% 1.57M 17s +445450K .......... .......... .......... .......... .......... 79% 2.21M 17s +445500K .......... .......... .......... .......... .......... 79% 534M 17s +445550K .......... .......... .......... .......... .......... 79% 7.71M 17s +445600K .......... .......... .......... .......... .......... 79% 93.1T 17s +445650K .......... .......... .......... .......... .......... 79% 400M 17s +445700K .......... .......... .......... .......... .......... 79% 4.71M 17s +445750K .......... .......... .......... .......... .......... 79% 340M 17s +445800K .......... .......... .......... .......... .......... 79% 1.78M 17s +445850K .......... .......... .......... .......... .......... 79% 1.47M 17s +445900K .......... .......... .......... .......... .......... 79% 93.1T 17s +445950K .......... .......... .......... .......... .......... 79% 379M 17s +446000K .......... .......... .......... .......... .......... 79% 730M 17s +446050K .......... .......... .......... .......... .......... 79% 932K 17s +446100K .......... .......... .......... .......... .......... 79% 1.67M 17s +446150K .......... .......... .......... .......... .......... 79% 445M 17s +446200K .......... .......... .......... .......... .......... 79% 5.00M 17s +446250K .......... .......... .......... .......... .......... 79% 419M 17s +446300K .......... .......... .......... .......... .......... 79% 93.1T 17s +446350K .......... .......... .......... .......... .......... 79% 648M 17s +446400K .......... .......... .......... 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.......... .......... .......... .......... .......... 79% 567M 17s +448400K .......... .......... .......... .......... .......... 79% 93.1T 17s +448450K .......... .......... .......... .......... .......... 79% 3.09M 17s +448500K .......... .......... .......... .......... .......... 79% 3.37M 17s +448550K .......... .......... .......... .......... .......... 79% 1.54M 17s +448600K .......... .......... .......... .......... .......... 79% 93.1T 17s +448650K .......... .......... .......... .......... .......... 79% 426M 17s +448700K .......... .......... .......... .......... .......... 79% 1.29M 17s +448750K .......... .......... .......... .......... .......... 79% 406M 17s +448800K .......... .......... .......... .......... .......... 79% 6.05M 17s +448850K .......... .......... .......... .......... .......... 79% 161M 17s +448900K .......... .......... .......... .......... .......... 79% 532M 17s +448950K .......... .......... .......... .......... .......... 80% 720M 17s +449000K .......... .......... .......... .......... .......... 80% 9.49M 17s +449050K .......... .......... .......... .......... .......... 80% 7.00M 17s +449100K .......... .......... .......... .......... .......... 80% 16.4M 17s +449150K .......... .......... .......... .......... .......... 80% 2.96M 17s +449200K .......... .......... .......... .......... .......... 80% 93.1T 17s +449250K .......... .......... .......... .......... .......... 80% 470M 17s +449300K .......... .......... .......... .......... .......... 80% 583M 17s +449350K .......... .......... .......... .......... .......... 80% 16.4M 17s +449400K .......... .......... .......... .......... .......... 80% 6.50M 17s +449450K .......... .......... .......... .......... .......... 80% 1.47M 17s +449500K .......... .......... .......... .......... .......... 80% 4.17M 17s +449550K .......... .......... .......... .......... .......... 80% 15.4M 17s +449600K .......... .......... .......... .......... .......... 80% 2.11M 17s +449650K .......... .......... .......... .......... .......... 80% 93.1T 17s +449700K .......... .......... .......... .......... .......... 80% 627K 17s +449750K .......... .......... .......... .......... .......... 80% 93.1T 17s +449800K .......... .......... .......... .......... .......... 80% 31.0M 17s +449850K .......... .......... .......... .......... .......... 80% 93.1T 17s +449900K .......... .......... .......... .......... .......... 80% 401M 17s +449950K .......... .......... .......... .......... .......... 80% 530K 17s +450000K .......... .......... .......... .......... .......... 80% 340M 17s +450050K .......... .......... .......... .......... .......... 80% 93.1T 17s +450100K .......... .......... .......... .......... .......... 80% 320M 17s +450150K .......... .......... .......... .......... .......... 80% 617M 17s +450200K .......... .......... .......... .......... .......... 80% 555M 17s +450250K .......... .......... .......... .......... .......... 80% 93.1T 17s +450300K .......... .......... .......... .......... .......... 80% 613M 17s +450350K .......... .......... .......... .......... .......... 80% 2.65M 17s +450400K .......... .......... .......... .......... .......... 80% 444M 17s +450450K .......... .......... .......... .......... .......... 80% 771M 17s +450500K .......... .......... .......... .......... .......... 80% 773M 17s +450550K .......... .......... .......... .......... .......... 80% 93.1T 17s +450600K .......... .......... .......... .......... .......... 80% 649M 17s +450650K .......... .......... .......... .......... .......... 80% 505M 17s +450700K .......... .......... .......... .......... .......... 80% 779M 17s +450750K .......... .......... .......... .......... .......... 80% 93.1T 17s +450800K .......... .......... .......... .......... .......... 80% 787M 17s +450850K .......... .......... .......... .......... .......... 80% 6.90M 17s +450900K .......... .......... .......... .......... .......... 80% 93.1T 17s +450950K .......... .......... .......... .......... .......... 80% 516M 17s +451000K .......... .......... .......... .......... .......... 80% 760M 17s +451050K .......... .......... .......... .......... .......... 80% 1.43M 17s +451100K .......... .......... .......... .......... .......... 80% 93.1T 17s +451150K .......... .......... .......... .......... .......... 80% 6.94M 17s +451200K .......... .......... .......... .......... .......... 80% 154M 17s +451250K .......... .......... .......... .......... .......... 80% 6.14M 17s +451300K .......... .......... .......... .......... .......... 80% 18.1M 17s +451350K .......... .......... .......... .......... .......... 80% 552M 17s +451400K .......... .......... .......... .......... .......... 80% 93.1T 17s +451450K .......... .......... .......... .......... .......... 80% 7.22M 17s +451500K .......... .......... .......... .......... .......... 80% 3.26M 17s +451550K .......... .......... .......... .......... .......... 80% 2.39M 17s +451600K .......... .......... .......... .......... .......... 80% 262M 17s +451650K .......... .......... .......... .......... .......... 80% 5.13M 17s +451700K .......... .......... .......... .......... .......... 80% 350M 17s +451750K .......... .......... .......... .......... .......... 80% 93.1T 17s +451800K .......... .......... .......... .......... .......... 80% 561M 17s +451850K .......... .......... .......... .......... .......... 80% 2.34M 17s +451900K .......... .......... .......... .......... .......... 80% 485M 16s +451950K .......... .......... .......... .......... .......... 80% 700M 16s +452000K .......... .......... .......... .......... .......... 80% 93.1T 16s +452050K .......... .......... .......... .......... .......... 80% 719M 16s +452100K .......... .......... .......... .......... .......... 80% 13.4M 16s +452150K .......... .......... .......... .......... .......... 80% 716M 16s +452200K .......... .......... .......... .......... .......... 80% 2.80M 16s +452250K .......... .......... .......... .......... .......... 80% 364M 16s +452300K .......... .......... .......... .......... .......... 80% 2.91M 16s +452350K .......... .......... .......... .......... .......... 80% 998K 16s +452400K .......... .......... .......... .......... .......... 80% 93.1T 16s +452450K .......... .......... .......... .......... .......... 80% 406M 16s +452500K .......... .......... .......... .......... .......... 80% 717M 16s +452550K .......... .......... .......... .......... .......... 80% 417M 16s +452600K .......... .......... .......... .......... .......... 80% 93.1T 16s +452650K .......... .......... .......... .......... .......... 80% 863K 16s +452700K .......... .......... .......... .......... .......... 80% 352M 16s +452750K .......... .......... .......... .......... .......... 80% 93.1T 16s +452800K .......... .......... .......... .......... .......... 80% 831M 16s +452850K .......... .......... .......... .......... .......... 80% 853M 16s +452900K .......... .......... .......... .......... .......... 80% 801M 16s +452950K .......... .......... .......... .......... .......... 80% 887M 16s +453000K .......... .......... .......... .......... .......... 80% 2.11M 16s +453050K .......... .......... .......... .......... .......... 80% 503M 16s +453100K .......... .......... .......... .......... .......... 80% 93.1T 16s +453150K .......... .......... .......... .......... .......... 80% 646M 16s +453200K .......... .......... .......... .......... .......... 80% 2.88M 16s +453250K .......... .......... .......... .......... .......... 80% 306M 16s +453300K .......... .......... .......... .......... .......... 80% 619M 16s +453350K .......... .......... .......... .......... .......... 80% 2.24M 16s +453400K .......... .......... .......... .......... .......... 80% 1.19M 16s +453450K .......... .......... .......... .......... .......... 80% 286M 16s +453500K .......... .......... .......... .......... .......... 80% 532M 16s +453550K .......... .......... .......... .......... .......... 80% 1.27M 16s +453600K .......... .......... .......... .......... .......... 80% 389M 16s +453650K .......... .......... .......... .......... .......... 80% 93.1T 16s +453700K .......... .......... .......... .......... .......... 80% 639M 16s +453750K .......... .......... .......... .......... .......... 80% 682M 16s +453800K .......... .......... .......... .......... .......... 80% 715M 16s +453850K .......... .......... .......... .......... .......... 80% 93.1T 16s +453900K .......... .......... .......... .......... .......... 80% 708M 16s +453950K .......... .......... .......... .......... .......... 80% 4.67M 16s +454000K .......... .......... .......... .......... .......... 80% 563M 16s +454050K .......... .......... .......... .......... .......... 80% 93.1T 16s +454100K .......... .......... .......... .......... .......... 80% 2.57M 16s +454150K .......... .......... .......... .......... .......... 80% 12.9M 16s +454200K .......... .......... .......... .......... .......... 80% 879K 16s +454250K .......... .......... .......... .......... .......... 80% 155M 16s +454300K .......... .......... .......... .......... .......... 80% 479M 16s +454350K .......... .......... .......... .......... .......... 80% 93.1T 16s +454400K .......... .......... .......... .......... .......... 80% 474M 16s +454450K .......... .......... .......... .......... .......... 80% 630M 16s +454500K .......... .......... .......... .......... .......... 80% 851M 16s +454550K .......... .......... .......... .......... .......... 81% 798M 16s +454600K .......... .......... .......... .......... .......... 81% 93.1T 16s +454650K .......... .......... .......... .......... .......... 81% 1.34M 16s +454700K .......... .......... .......... .......... .......... 81% 192M 16s +454750K .......... .......... .......... .......... .......... 81% 93.1T 16s +454800K .......... .......... .......... .......... .......... 81% 2.20M 16s +454850K .......... .......... .......... .......... .......... 81% 365M 16s +454900K .......... .......... .......... .......... .......... 81% 93.1T 16s +454950K .......... .......... .......... .......... .......... 81% 62.9M 16s +455000K .......... .......... .......... .......... .......... 81% 777M 16s +455050K .......... .......... .......... .......... .......... 81% 382K 16s +455100K .......... .......... .......... .......... .......... 81% 309M 16s +455150K .......... .......... .......... .......... .......... 81% 329M 16s +455200K .......... .......... .......... .......... .......... 81% 1.77M 16s +455250K .......... .......... .......... .......... .......... 81% 304M 16s +455300K .......... .......... .......... .......... .......... 81% 605M 16s +455350K .......... .......... .......... .......... .......... 81% 369M 16s +455400K .......... .......... .......... 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.......... .......... .......... .......... .......... 81% 466M 16s +457400K .......... .......... .......... .......... .......... 81% 640M 16s +457450K .......... .......... .......... .......... .......... 81% 621M 16s +457500K .......... .......... .......... .......... .......... 81% 93.1T 16s +457550K .......... .......... .......... .......... .......... 81% 616M 16s +457600K .......... .......... .......... .......... .......... 81% 543M 16s +457650K .......... .......... .......... .......... .......... 81% 622M 16s +457700K .......... .......... .......... .......... .......... 81% 690M 16s +457750K .......... .......... .......... .......... .......... 81% 93.1T 16s +457800K .......... .......... .......... .......... .......... 81% 786M 16s +457850K .......... .......... .......... .......... .......... 81% 854M 16s +457900K .......... .......... .......... .......... .......... 81% 886M 16s +457950K .......... .......... .......... .......... .......... 81% 2.55M 16s +458000K .......... .......... .......... .......... .......... 81% 93.1T 16s +458050K .......... .......... .......... .......... .......... 81% 619K 16s +458100K .......... .......... .......... .......... .......... 81% 304M 16s +458150K .......... .......... .......... .......... .......... 81% 572M 16s +458200K .......... .......... .......... .......... .......... 81% 573M 16s +458250K .......... .......... .......... .......... .......... 81% 93.1T 16s +458300K .......... .......... .......... .......... .......... 81% 475M 16s +458350K .......... .......... .......... .......... .......... 81% 610M 16s +458400K .......... .......... .......... .......... .......... 81% 602M 16s +458450K .......... .......... .......... .......... .......... 81% 93.1T 16s +458500K .......... .......... .......... .......... .......... 81% 321K 16s +458550K .......... .......... .......... .......... .......... 81% 2.00M 16s +458600K .......... .......... .......... .......... .......... 81% 353M 16s +458650K .......... .......... .......... .......... .......... 81% 93.1T 16s +458700K .......... .......... .......... .......... .......... 81% 567M 16s +458750K .......... .......... .......... .......... .......... 81% 609M 16s +458800K .......... .......... .......... .......... .......... 81% 478M 16s +458850K .......... .......... .......... .......... .......... 81% 541M 16s +458900K .......... .......... .......... .......... .......... 81% 93.1T 16s +458950K .......... .......... .......... .......... .......... 81% 460M 16s +459000K .......... .......... .......... .......... .......... 81% 555M 16s +459050K .......... .......... .......... .......... .......... 81% 10.3M 16s +459100K .......... .......... .......... .......... .......... 81% 93.1T 16s +459150K .......... .......... .......... .......... .......... 81% 192K 16s +459200K .......... .......... .......... .......... .......... 81% 274M 16s +459250K .......... .......... .......... .......... .......... 81% 602M 16s +459300K .......... .......... .......... .......... .......... 81% 1.68M 16s +459350K .......... .......... .......... .......... .......... 81% 274M 16s +459400K .......... .......... .......... .......... .......... 81% 482M 16s +459450K .......... .......... .......... .......... .......... 81% 93.1T 16s +459500K .......... .......... .......... .......... .......... 81% 569M 16s +459550K .......... .......... .......... .......... .......... 81% 581M 16s +459600K .......... .......... .......... .......... .......... 81% 641M 16s +459650K .......... .......... .......... .......... .......... 81% 589M 16s +459700K .......... .......... .......... .......... .......... 81% 93.1T 16s +459750K .......... .......... .......... .......... .......... 81% 614M 16s +459800K .......... .......... .......... .......... .......... 81% 638M 15s +459850K .......... .......... .......... .......... .......... 81% 3.88M 15s +459900K .......... .......... .......... .......... .......... 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299M 15s +464450K .......... .......... .......... .......... .......... 82% 612M 15s +464500K .......... .......... .......... .......... .......... 82% 456M 15s +464550K .......... .......... .......... .......... .......... 82% 93.1T 15s +464600K .......... .......... .......... .......... .......... 82% 595M 15s +464650K .......... .......... .......... .......... .......... 82% 544M 15s +464700K .......... .......... .......... .......... .......... 82% 657M 15s +464750K .......... .......... .......... .......... .......... 82% 808M 15s +464800K .......... .......... .......... .......... .......... 82% 93.1T 15s +464850K .......... .......... .......... .......... .......... 82% 783M 15s +464900K .......... .......... .......... .......... .......... 82% 555M 15s +464950K .......... .......... .......... .......... .......... 82% 632M 15s +465000K .......... .......... .......... .......... .......... 82% 93.1T 15s +465050K .......... .......... .......... .......... .......... 82% 419K 15s +465100K .......... .......... .......... .......... .......... 82% 323M 15s +465150K .......... .......... .......... .......... .......... 82% 12.5M 15s +465200K .......... .......... .......... .......... .......... 82% 93.1T 15s +465250K .......... .......... .......... .......... .......... 82% 1.67M 15s +465300K .......... .......... .......... .......... .......... 82% 27.3M 15s +465350K .......... .......... .......... .......... .......... 82% 93.1T 15s +465400K .......... .......... .......... .......... .......... 82% 18.5M 15s +465450K .......... .......... .......... .......... .......... 82% 402M 15s +465500K .......... .......... .......... .......... .......... 82% 517M 15s +465550K .......... .......... .......... .......... .......... 82% 93.1T 15s +465600K .......... .......... .......... .......... .......... 82% 517M 15s +465650K .......... .......... .......... .......... .......... 82% 588M 15s +465700K .......... .......... .......... .......... 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.......... .......... 83% 4.53M 15s +466400K .......... .......... .......... .......... .......... 83% 353K 15s +466450K .......... .......... .......... .......... .......... 83% 93.1T 15s +466500K .......... .......... .......... .......... .......... 83% 339M 15s +466550K .......... .......... .......... .......... .......... 83% 622M 15s +466600K .......... .......... .......... .......... .......... 83% 657M 15s +466650K .......... .......... .......... .......... .......... 83% 93.1T 15s +466700K .......... .......... .......... .......... .......... 83% 611M 15s +466750K .......... .......... .......... .......... .......... 83% 728M 15s +466800K .......... .......... .......... .......... .......... 83% 730M 15s +466850K .......... .......... .......... .......... .......... 83% 16.9M 15s +466900K .......... .......... .......... .......... .......... 83% 367K 15s +466950K .......... .......... .......... .......... .......... 83% 342M 15s +467000K .......... .......... .......... .......... .......... 83% 25.9M 15s +467050K .......... .......... .......... .......... .......... 83% 1.19G 15s +467100K .......... .......... .......... .......... .......... 83% 24.4M 15s +467150K .......... .......... .......... .......... .......... 83% 363M 15s +467200K .......... .......... .......... .......... .......... 83% 28.9M 15s +467250K .......... .......... .......... .......... .......... 83% 916M 15s +467300K .......... .......... .......... .......... .......... 83% 2.76M 15s +467350K .......... .......... .......... .......... .......... 83% 373K 15s +467400K .......... .......... .......... .......... .......... 83% 329M 15s +467450K .......... .......... .......... .......... .......... 83% 572M 15s +467500K .......... .......... .......... .......... .......... 83% 737M 15s +467550K .......... .......... .......... .......... .......... 83% 93.1T 15s +467600K .......... .......... .......... .......... .......... 83% 676M 15s +467650K .......... .......... .......... .......... .......... 83% 596M 15s +467700K .......... .......... .......... .......... .......... 83% 778M 15s +467750K .......... .......... .......... .......... .......... 83% 7.00M 15s +467800K .......... .......... .......... .......... .......... 83% 227M 15s +467850K .......... .......... .......... .......... .......... 83% 25.3M 15s +467900K .......... .......... .......... .......... .......... 83% 416M 15s +467950K .......... .......... .......... .......... .......... 83% 7.24M 15s +468000K .......... .......... .......... .......... .......... 83% 2.84M 15s +468050K .......... .......... .......... .......... .......... 83% 1.25M 15s +468100K .......... .......... .......... .......... .......... 83% 314M 15s +468150K .......... .......... .......... .......... .......... 83% 619M 15s +468200K .......... .......... .......... .......... .......... 83% 93.1T 15s +468250K .......... .......... .......... .......... .......... 83% 7.26M 15s +468300K .......... .......... .......... .......... .......... 83% 3.03M 15s +468350K .......... .......... .......... .......... .......... 83% 4.15M 15s +468400K .......... .......... .......... .......... .......... 83% 4.66M 15s +468450K .......... .......... .......... .......... .......... 83% 4.77M 15s +468500K .......... .......... .......... .......... .......... 83% 4.85M 15s +468550K .......... .......... .......... .......... .......... 83% 4.79M 15s +468600K .......... .......... .......... .......... .......... 83% 1.28M 15s +468650K .......... .......... .......... .......... .......... 83% 539M 15s +468700K .......... .......... .......... .......... .......... 83% 1.07G 15s +468750K .......... .......... .......... .......... .......... 83% 4.80M 14s +468800K .......... .......... .......... .......... .......... 83% 2.15M 14s +468850K .......... .......... .......... .......... .......... 83% 20.3M 14s +468900K .......... .......... .......... .......... .......... 83% 4.06M 14s +468950K .......... .......... .......... .......... .......... 83% 3.05M 14s +469000K .......... .......... .......... .......... .......... 83% 3.51M 14s +469050K .......... .......... .......... .......... .......... 83% 12.4M 14s +469100K .......... .......... .......... .......... .......... 83% 3.59M 14s +469150K .......... .......... .......... .......... .......... 83% 6.00M 14s +469200K .......... .......... .......... .......... .......... 83% 3.70M 14s +469250K .......... .......... .......... .......... .......... 83% 4.12M 14s +469300K .......... .......... .......... .......... .......... 83% 2.61M 14s +469350K .......... .......... .......... .......... .......... 83% 5.79M 14s +469400K .......... .......... .......... .......... .......... 83% 4.32M 14s +469450K .......... .......... .......... .......... .......... 83% 775K 14s +469500K .......... .......... .......... .......... .......... 83% 356M 14s +469550K .......... .......... .......... .......... .......... 83% 93.1T 14s +469600K .......... .......... .......... .......... .......... 83% 5.75M 14s +469650K .......... .......... .......... .......... .......... 83% 447M 14s +469700K .......... .......... .......... .......... .......... 83% 93.1T 14s +469750K .......... .......... .......... .......... .......... 83% 4.19M 14s +469800K .......... .......... .......... .......... .......... 83% 4.30M 14s +469850K .......... .......... .......... .......... .......... 83% 3.36M 14s +469900K .......... .......... .......... .......... .......... 83% 4.66M 14s +469950K .......... .......... .......... .......... .......... 83% 6.50M 14s +470000K .......... .......... .......... .......... .......... 83% 4.16M 14s +470050K .......... .......... .......... .......... .......... 83% 4.20M 14s +470100K .......... .......... .......... .......... .......... 83% 1.32M 14s +470150K .......... .......... .......... .......... .......... 83% 423M 14s +470200K .......... .......... .......... 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.......... .......... .......... .......... .......... 84% 929K 14s +471550K .......... .......... .......... .......... .......... 84% 2.24M 14s +471600K .......... .......... .......... .......... .......... 84% 414M 14s +471650K .......... .......... .......... .......... .......... 84% 674M 14s +471700K .......... .......... .......... .......... .......... 84% 757M 14s +471750K .......... .......... .......... .......... .......... 84% 93.1T 14s +471800K .......... .......... .......... .......... .......... 84% 885K 14s +471850K .......... .......... .......... .......... .......... 84% 2.06M 14s +471900K .......... .......... .......... .......... .......... 84% 369M 14s +471950K .......... .......... .......... .......... .......... 84% 93.1T 14s +472000K .......... .......... .......... .......... .......... 84% 394M 14s +472050K .......... .......... .......... .......... .......... 84% 542M 14s +472100K .......... .......... .......... .......... .......... 84% 3.43M 14s +472150K .......... .......... .......... .......... .......... 84% 428K 14s +472200K .......... .......... .......... .......... .......... 84% 93.1T 14s +472250K .......... .......... .......... .......... .......... 84% 45.6M 14s +472300K .......... .......... .......... .......... .......... 84% 1.37M 14s +472350K .......... .......... .......... .......... .......... 84% 362M 14s +472400K .......... .......... .......... .......... .......... 84% 650M 14s +472450K .......... .......... .......... .......... .......... 84% 93.1T 14s +472500K .......... .......... .......... .......... .......... 84% 620M 14s +472550K .......... .......... .......... .......... .......... 84% 668M 14s +472600K .......... .......... .......... .......... .......... 84% 493M 14s +472650K .......... .......... .......... .......... .......... 84% 620M 14s +472700K .......... .......... .......... .......... .......... 84% 93.1T 14s +472750K .......... .......... .......... .......... .......... 84% 634M 14s +472800K .......... .......... .......... .......... .......... 84% 679M 14s +472850K .......... .......... .......... .......... .......... 84% 662M 14s +472900K .......... .......... .......... .......... .......... 84% 93.1T 14s +472950K .......... .......... .......... .......... .......... 84% 647M 14s +473000K .......... .......... .......... .......... .......... 84% 673M 14s +473050K .......... .......... .......... .......... .......... 84% 645M 14s +473100K .......... .......... .......... .......... .......... 84% 653M 14s +473150K .......... .......... .......... .......... .......... 84% 364 42s +473200K .......... .......... .......... .......... .......... 84% 166K 42s +473250K .......... .......... .......... .......... .......... 84% 532K 42s +473300K .......... .......... .......... .......... .......... 84% 263M 42s +473350K .......... .......... .......... .......... .......... 84% 93.1T 42s +473400K .......... .......... .......... .......... .......... 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.......... 84% 24.2M 42s +474100K .......... .......... .......... .......... .......... 84% 440K 42s +474150K .......... .......... .......... .......... .......... 84% 51.4K 42s +474200K .......... .......... .......... .......... .......... 84% 155M 42s +474250K .......... .......... .......... .......... .......... 84% 435K 42s +474300K .......... .......... .......... .......... .......... 84% 17.4K 42s +474350K .......... .......... .......... .......... .......... 84% 9.06K 43s +474400K .......... .......... .......... .......... .......... 84% 33.3K 44s +474450K .......... .......... .......... .......... .......... 84% 39.3K 44s +474500K .......... .......... .......... .......... .......... 84% 324M 44s +474550K .......... .......... .......... .......... .......... 84% 36.7K 44s +474600K .......... .......... .......... .......... .......... 84% 303M 44s +474650K .......... .......... .......... .......... .......... 84% 364K 44s +474700K .......... .......... .......... 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+477300K .......... .......... .......... .......... .......... 85% 93.1T 45s +477350K .......... .......... .......... .......... .......... 85% 327M 45s +477400K .......... .......... .......... .......... .......... 85% 581K 45s +477450K .......... .......... .......... .......... .......... 85% 848K 45s +477500K .......... .......... .......... .......... .......... 85% 524K 45s +477550K .......... .......... .......... .......... .......... 85% 326K 45s +477600K .......... .......... .......... .......... .......... 85% 1.42M 45s +477650K .......... .......... .......... .......... .......... 85% 522K 45s +477700K .......... .......... .......... .......... .......... 85% 117K 45s +477750K .......... .......... .......... .......... .......... 85% 269K 45s +477800K .......... .......... .......... .......... .......... 85% 118K 45s +477850K .......... .......... .......... .......... .......... 85% 304K 45s +477900K .......... .......... .......... .......... .......... 85% 349M 45s +477950K .......... .......... .......... .......... .......... 85% 33.2K 45s +478000K .......... .......... .......... .......... .......... 85% 93.1T 45s +478050K .......... .......... .......... .......... .......... 85% 320M 45s +478100K .......... .......... .......... .......... .......... 85% 11.5M 45s +478150K .......... .......... .......... .......... .......... 85% 900K 45s +478200K .......... .......... .......... .......... .......... 85% 93.1T 45s +478250K .......... .......... .......... .......... .......... 85% 526K 45s +478300K .......... .......... .......... .......... .......... 85% 122K 45s +478350K .......... .......... .......... .......... .......... 85% 807K 45s +478400K .......... .......... .......... .......... .......... 85% 459K 45s +478450K .......... .......... .......... .......... .......... 85% 474K 45s +478500K .......... .......... .......... .......... .......... 85% 500K 45s +478550K .......... .......... .......... .......... .......... 85% 436K 45s +478600K .......... .......... .......... .......... .......... 85% 558K 45s +478650K .......... .......... .......... .......... .......... 85% 351K 45s +478700K .......... .......... .......... .......... .......... 85% 582K 45s +478750K .......... .......... .......... .......... .......... 85% 2.66M 45s +478800K .......... .......... .......... .......... .......... 85% 335K 45s +478850K .......... .......... .......... .......... .......... 85% 1.92M 45s +478900K .......... .......... .......... .......... .......... 85% 448K 45s +478950K .......... .......... .......... .......... .......... 85% 3.22M 45s +479000K .......... .......... .......... .......... .......... 85% 36.6K 45s +479050K .......... .......... .......... .......... .......... 85% 196K 45s +479100K .......... .......... .......... .......... .......... 85% 41.0K 45s +479150K .......... .......... .......... .......... .......... 85% 93.1T 45s +479200K .......... .......... .......... .......... 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.......... .......... 85% 71.1K 47s +479900K .......... .......... .......... .......... .......... 85% 77.5K 47s +479950K .......... .......... .......... .......... .......... 85% 77.9K 47s +480000K .......... .......... .......... .......... .......... 85% 76.2K 47s +480050K .......... .......... .......... .......... .......... 85% 98.6K 47s +480100K .......... .......... .......... .......... .......... 85% 153K 47s +480150K .......... .......... .......... .......... .......... 85% 244K 47s +480200K .......... .......... .......... .......... .......... 85% 404K 47s +480250K .......... .......... .......... .......... .......... 85% 657K 47s +480300K .......... .......... .......... .......... .......... 85% 973K 47s +480350K .......... .......... .......... .......... .......... 85% 927K 47s +480400K .......... .......... .......... .......... .......... 85% 439K 47s +480450K .......... .......... .......... .......... .......... 85% 3.53M 47s +480500K .......... .......... .......... .......... .......... 85% 2.25M 47s +480550K .......... .......... .......... .......... .......... 85% 1.54M 47s +480600K .......... .......... .......... .......... .......... 85% 1.61M 47s +480650K .......... .......... .......... .......... .......... 85% 2.75M 47s +480700K .......... .......... .......... .......... .......... 85% 951K 47s +480750K .......... .......... .......... .......... .......... 85% 2.05M 47s +480800K .......... .......... .......... .......... .......... 85% 2.96M 47s +480850K .......... .......... .......... .......... .......... 85% 1.01M 46s +480900K .......... .......... .......... .......... .......... 85% 1.85M 46s +480950K .......... .......... .......... .......... .......... 85% 2.15M 46s +481000K .......... .......... .......... .......... .......... 85% 576M 46s +481050K .......... .......... .......... .......... .......... 85% 565K 46s +481100K .......... .......... .......... .......... .......... 85% 93.1T 46s +481150K .......... .......... .......... .......... .......... 85% 531M 46s +481200K .......... .......... .......... .......... .......... 85% 2.52M 46s +481250K .......... .......... .......... .......... .......... 85% 38.5M 46s +481300K .......... .......... .......... .......... .......... 85% 815K 46s +481350K .......... .......... .......... .......... .......... 85% 2.95M 46s +481400K .......... .......... .......... .......... .......... 85% 1.57M 46s +481450K .......... .......... .......... .......... .......... 85% 586M 46s +481500K .......... .......... .......... .......... .......... 85% 9.36M 46s +481550K .......... .......... .......... .......... .......... 85% 2.32M 46s +481600K .......... .......... .......... .......... .......... 85% 1.26M 46s +481650K .......... .......... .......... .......... .......... 85% 20.5M 46s +481700K .......... .......... .......... .......... .......... 85% 1.62M 46s +481750K .......... .......... .......... .......... .......... 85% 1.14M 46s +481800K .......... .......... .......... .......... .......... 85% 93.1T 46s +481850K .......... .......... .......... .......... .......... 85% 150M 46s +481900K .......... .......... .......... .......... .......... 85% 13.7M 46s +481950K .......... .......... .......... .......... .......... 85% 2.14M 46s +482000K .......... .......... .......... .......... .......... 85% 3.61M 46s +482050K .......... .......... .......... .......... .......... 85% 2.05M 46s +482100K .......... .......... .......... .......... .......... 85% 12.3M 46s +482150K .......... .......... .......... .......... .......... 85% 1.39M 46s +482200K .......... .......... .......... .......... .......... 85% 2.80M 46s +482250K .......... .......... .......... .......... .......... 85% 7.06M 46s +482300K .......... .......... .......... .......... .......... 85% 93.1T 46s +482350K .......... .......... .......... .......... .......... 85% 3.53M 46s +482400K .......... .......... .......... .......... .......... 85% 443M 46s +482450K .......... .......... .......... .......... .......... 85% 995K 46s +482500K .......... .......... .......... .......... .......... 85% 4.30M 45s +482550K .......... .......... .......... .......... .......... 85% 93.1T 45s +482600K .......... .......... .......... .......... .......... 86% 572M 45s +482650K .......... .......... .......... .......... .......... 86% 13.0M 45s +482700K .......... .......... .......... .......... .......... 86% 3.99M 45s +482750K .......... .......... .......... .......... .......... 86% 2.18M 45s +482800K .......... .......... .......... .......... .......... 86% 6.27M 45s +482850K .......... .......... .......... .......... .......... 86% 10.6M 45s +482900K .......... .......... .......... .......... .......... 86% 1.58M 45s +482950K .......... .......... .......... .......... .......... 86% 1.10M 45s +483000K .......... .......... .......... .......... .......... 86% 392M 45s +483050K .......... .......... .......... .......... .......... 86% 708M 45s +483100K .......... .......... .......... .......... .......... 86% 14.9M 45s +483150K .......... .......... .......... .......... .......... 86% 3.73M 45s +483200K .......... .......... .......... .......... .......... 86% 546M 45s +483250K .......... .......... .......... .......... .......... 86% 2.41M 45s +483300K .......... .......... .......... .......... .......... 86% 1.07M 45s +483350K .......... .......... .......... .......... .......... 86% 323M 45s +483400K .......... .......... .......... .......... .......... 86% 486M 45s +483450K .......... .......... .......... .......... .......... 86% 93.1T 45s +483500K .......... .......... .......... .......... .......... 86% 490M 45s +483550K .......... .......... .......... .......... .......... 86% 469M 45s +483600K .......... .......... .......... .......... .......... 86% 784K 45s +483650K .......... .......... .......... .......... .......... 86% 392M 45s +483700K .......... .......... .......... .......... 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+486950K .......... .......... .......... .......... .......... 86% 40.4M 43s +487000K .......... .......... .......... .......... .......... 86% 932K 43s +487050K .......... .......... .......... .......... .......... 86% 155M 43s +487100K .......... .......... .......... .......... .......... 86% 93.1T 43s +487150K .......... .......... .......... .......... .......... 86% 3.26M 42s +487200K .......... .......... .......... .......... .......... 86% 93.1T 42s +487250K .......... .......... .......... .......... .......... 86% 488M 42s +487300K .......... .......... .......... .......... .......... 86% 777M 42s +487350K .......... .......... .......... .......... .......... 86% 965K 42s +487400K .......... .......... .......... .......... .......... 86% 9.41M 42s +487450K .......... .......... .......... .......... .......... 86% 7.36M 42s +487500K .......... .......... .......... .......... .......... 86% 607M 42s +487550K .......... .......... .......... .......... .......... 86% 3.12M 42s +487600K .......... .......... .......... .......... .......... 86% 494M 42s +487650K .......... .......... .......... .......... .......... 86% 1.76M 42s +487700K .......... .......... .......... .......... .......... 86% 175M 42s +487750K .......... .......... .......... .......... .......... 86% 871K 42s +487800K .......... .......... .......... .......... .......... 86% 93.1T 42s +487850K .......... .......... .......... .......... .......... 86% 24.3M 42s +487900K .......... .......... .......... .......... .......... 86% 93.1T 42s +487950K .......... .......... .......... .......... .......... 86% 44.3M 42s +488000K .......... .......... .......... .......... .......... 86% 501M 42s +488050K .......... .......... .......... .......... .......... 86% 93.1T 42s +488100K .......... .......... .......... .......... .......... 86% 85.7M 42s +488150K .......... .......... .......... .......... .......... 86% 2.87M 42s +488200K .......... .......... .......... .......... 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+491450K .......... .......... .......... .......... .......... 87% 1.61M 40s +491500K .......... .......... .......... .......... .......... 87% 93.1T 40s +491550K .......... .......... .......... .......... .......... 87% 357M 40s +491600K .......... .......... .......... .......... .......... 87% 1.63M 40s +491650K .......... .......... .......... .......... .......... 87% 366M 40s +491700K .......... .......... .......... .......... .......... 87% 1.69M 40s +491750K .......... .......... .......... .......... .......... 87% 11.0M 40s +491800K .......... .......... .......... .......... .......... 87% 7.91M 40s +491850K .......... .......... .......... .......... .......... 87% 4.90M 40s +491900K .......... .......... .......... .......... .......... 87% 4.05M 39s +491950K .......... .......... .......... .......... .......... 87% 93.1T 39s +492000K .......... .......... .......... .......... .......... 87% 397M 39s +492050K .......... .......... .......... .......... .......... 87% 245M 39s +492100K .......... .......... .......... .......... .......... 87% 1.73M 39s +492150K .......... .......... .......... .......... .......... 87% 374M 39s +492200K .......... .......... .......... .......... .......... 87% 1.74M 39s +492250K .......... .......... .......... .......... .......... 87% 602M 39s +492300K .......... .......... .......... .......... .......... 87% 786M 39s +492350K .......... .......... .......... .......... .......... 87% 5.55M 39s +492400K .......... .......... .......... .......... .......... 87% 605M 39s +492450K .......... .......... .......... .......... .......... 87% 93.1T 39s +492500K .......... .......... .......... .......... .......... 87% 669M 39s +492550K .......... .......... .......... .......... .......... 87% 10.4M 39s +492600K .......... .......... .......... .......... .......... 87% 5.70M 39s +492650K .......... .......... .......... .......... .......... 87% 8.03M 39s +492700K .......... .......... .......... .......... 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.......... .......... .......... .......... .......... 88% 13.7M 37s +495350K .......... .......... .......... .......... .......... 88% 27.6M 37s +495400K .......... .......... .......... .......... .......... 88% 93.1T 37s +495450K .......... .......... .......... .......... .......... 88% 3.93M 37s +495500K .......... .......... .......... .......... .......... 88% 10.8M 37s +495550K .......... .......... .......... .......... .......... 88% 2.66M 37s +495600K .......... .......... .......... .......... .......... 88% 469M 37s +495650K .......... .......... .......... .......... .......... 88% 31.0M 37s +495700K .......... .......... .......... .......... .......... 88% 93.1T 37s +495750K .......... .......... .......... .......... .......... 88% 614M 37s +495800K .......... .......... .......... .......... .......... 88% 1.03M 37s +495850K .......... .......... .......... .......... .......... 88% 319M 37s +495900K .......... .......... .......... .......... .......... 88% 444M 37s +495950K .......... .......... .......... .......... .......... 88% 660M 37s +496000K .......... .......... .......... .......... .......... 88% 21.1M 37s +496050K .......... .......... .......... .......... .......... 88% 579M 37s +496100K .......... .......... .......... .......... .......... 88% 101M 37s +496150K .......... .......... .......... .......... .......... 88% 3.26M 37s +496200K .......... .......... .......... .......... .......... 88% 521M 37s +496250K .......... .......... .......... .......... .......... 88% 6.74M 37s +496300K .......... .......... .......... .......... .......... 88% 91.4M 37s +496350K .......... .......... .......... .......... .......... 88% 589M 37s +496400K .......... .......... .......... .......... .......... 88% 3.00M 37s +496450K .......... .......... .......... .......... .......... 88% 31.1M 37s +496500K .......... .......... .......... .......... .......... 88% 534M 37s +496550K .......... .......... .......... .......... .......... 88% 23.3M 37s +496600K .......... .......... .......... .......... .......... 88% 2.52M 37s +496650K .......... .......... .......... .......... .......... 88% 278M 36s +496700K .......... .......... .......... .......... .......... 88% 93.1T 36s +496750K .......... .......... .......... .......... .......... 88% 572M 36s +496800K .......... .......... .......... .......... .......... 88% 1.69M 36s +496850K .......... .......... .......... .......... .......... 88% 776M 36s +496900K .......... .......... .......... .......... .......... 88% 1.15G 36s +496950K .......... .......... .......... .......... .......... 88% 828K 36s +497000K .......... .......... .......... .......... .......... 88% 364M 36s +497050K .......... .......... .......... .......... .......... 88% 653M 36s +497100K .......... .......... .......... .......... .......... 88% 661M 36s +497150K .......... .......... .......... .......... .......... 88% 93.1T 36s +497200K .......... .......... .......... .......... 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.......... .......... .......... .......... .......... 89% 616M 35s +499850K .......... .......... .......... .......... .......... 89% 639M 34s +499900K .......... .......... .......... .......... .......... 89% 774M 34s +499950K .......... .......... .......... .......... .......... 89% 33.6M 34s +500000K .......... .......... .......... .......... .......... 89% 630M 34s +500050K .......... .......... .......... .......... .......... 89% 839M 34s +500100K .......... .......... .......... .......... .......... 89% 1.56M 34s +500150K .......... .......... .......... .......... .......... 89% 354M 34s +500200K .......... .......... .......... .......... .......... 89% 27.3M 34s +500250K .......... .......... .......... .......... .......... 89% 645M 34s +500300K .......... .......... .......... .......... .......... 89% 548M 34s +500350K .......... .......... .......... .......... .......... 89% 93.1T 34s +500400K .......... .......... .......... .......... .......... 89% 547M 34s +500450K .......... .......... .......... .......... .......... 89% 627M 34s +500500K .......... .......... .......... .......... .......... 89% 5.59M 34s +500550K .......... .......... .......... .......... .......... 89% 5.99M 34s +500600K .......... .......... .......... .......... .......... 89% 791K 34s +500650K .......... .......... .......... .......... .......... 89% 365M 34s +500700K .......... .......... .......... .......... .......... 89% 37.8M 34s +500750K .......... .......... .......... .......... .......... 89% 575M 34s +500800K .......... .......... .......... .......... .......... 89% 93.1T 34s +500850K .......... .......... .......... .......... .......... 89% 657M 34s +500900K .......... .......... .......... .......... .......... 89% 674M 34s +500950K .......... .......... .......... .......... .......... 89% 686M 34s +501000K .......... .......... .......... .......... .......... 89% 672M 34s +501050K .......... .......... .......... .......... .......... 89% 93.1T 34s +501100K .......... .......... .......... .......... .......... 89% 183M 34s +501150K .......... .......... .......... .......... .......... 89% 5.70M 34s +501200K .......... .......... .......... .......... .......... 89% 7.71M 34s +501250K .......... .......... .......... .......... .......... 89% 707K 34s +501300K .......... .......... .......... .......... .......... 89% 364M 34s +501350K .......... .......... .......... .......... .......... 89% 622M 34s +501400K .......... .......... .......... .......... .......... 89% 93.1T 34s +501450K .......... .......... .......... .......... .......... 89% 636M 34s +501500K .......... .......... .......... .......... .......... 89% 20.0M 33s +501550K .......... .......... .......... .......... .......... 89% 40.6M 33s +501600K .......... .......... .......... .......... .......... 89% 548M 33s +501650K .......... .......... .......... .......... .......... 89% 633M 33s +501700K .......... .......... .......... .......... 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+504950K .......... .......... .......... .......... .......... 89% 866M 31s +505000K .......... .......... .......... .......... .......... 89% 1.84M 31s +505050K .......... .......... .......... .......... .......... 90% 396M 31s +505100K .......... .......... .......... .......... .......... 90% 4.63M 31s +505150K .......... .......... .......... .......... .......... 90% 183M 31s +505200K .......... .......... .......... .......... .......... 90% 1.18M 31s +505250K .......... .......... .......... .......... .......... 90% 93.1T 31s +505300K .......... .......... .......... .......... .......... 90% 335M 31s +505350K .......... .......... .......... .......... .......... 90% 707M 31s +505400K .......... .......... .......... .......... .......... 90% 611M 31s +505450K .......... .......... .......... .......... .......... 90% 1.68M 31s +505500K .......... .......... .......... .......... .......... 90% 93.1T 31s +505550K .......... .......... .......... .......... .......... 90% 400M 31s +505600K .......... .......... .......... .......... .......... 90% 564M 31s +505650K .......... .......... .......... .......... .......... 90% 7.46M 31s +505700K .......... .......... .......... .......... .......... 90% 3.75M 31s +505750K .......... .......... .......... .......... .......... 90% 7.01M 31s +505800K .......... .......... .......... .......... .......... 90% 1.41M 31s +505850K .......... .......... .......... .......... .......... 90% 4.78M 31s +505900K .......... .......... .......... .......... .......... 90% 95.4M 31s +505950K .......... .......... .......... .......... .......... 90% 15.9M 31s +506000K .......... .......... .......... .......... .......... 90% 8.55M 31s +506050K .......... .......... .......... .......... .......... 90% 1007K 31s +506100K .......... .......... .......... .......... .......... 90% 93.1T 31s +506150K .......... .......... .......... .......... .......... 90% 252M 31s +506200K .......... .......... .......... .......... .......... 90% 556M 31s +506250K .......... .......... .......... .......... .......... 90% 3.67M 31s +506300K .......... .......... .......... .......... .......... 90% 122M 31s +506350K .......... .......... .......... .......... .......... 90% 93.1T 31s +506400K .......... .......... .......... .......... .......... 90% 11.1M 31s +506450K .......... .......... .......... .......... .......... 90% 1.13M 30s +506500K .......... .......... .......... .......... .......... 90% 129M 30s +506550K .......... .......... .......... .......... .......... 90% 93.1T 30s +506600K .......... .......... .......... .......... .......... 90% 3.50M 30s +506650K .......... .......... .......... .......... .......... 90% 912K 30s +506700K .......... .......... .......... .......... .......... 90% 535M 30s +506750K .......... .......... .......... .......... .......... 90% 93.1T 30s +506800K .......... .......... .......... .......... .......... 90% 1.05M 30s +506850K .......... .......... .......... .......... .......... 90% 344M 30s +506900K .......... .......... .......... .......... .......... 90% 93.1T 30s +506950K .......... .......... .......... .......... .......... 90% 568M 30s +507000K .......... .......... .......... .......... .......... 90% 726M 30s +507050K .......... .......... .......... .......... .......... 90% 886M 30s +507100K .......... .......... .......... .......... .......... 90% 93.1T 30s +507150K .......... .......... .......... .......... .......... 90% 15.1M 30s +507200K .......... .......... .......... .......... .......... 90% 10.8M 30s +507250K .......... .......... .......... .......... .......... 90% 16.4M 30s +507300K .......... .......... .......... .......... .......... 90% 6.81M 30s +507350K .......... .......... .......... .......... .......... 90% 923K 30s +507400K .......... .......... .......... .......... .......... 90% 88.6M 30s +507450K .......... .......... .......... .......... .......... 90% 607M 30s +507500K .......... .......... .......... .......... .......... 90% 93.1T 30s +507550K .......... .......... .......... .......... .......... 90% 739M 30s +507600K .......... .......... .......... .......... .......... 90% 6.45M 30s +507650K .......... .......... .......... .......... .......... 90% 1.78M 30s +507700K .......... .......... .......... .......... .......... 90% 633K 30s +507750K .......... .......... .......... .......... .......... 90% 93.1T 30s +507800K .......... .......... .......... .......... .......... 90% 201M 30s +507850K .......... .......... .......... .......... .......... 90% 500M 30s +507900K .......... .......... .......... .......... .......... 90% 7.22M 30s +507950K .......... .......... .......... .......... .......... 90% 221M 30s +508000K .......... .......... .......... .......... .......... 90% 435M 30s +508050K .......... .......... .......... .......... .......... 90% 3.38M 30s +508100K .......... .......... .......... .......... .......... 90% 245M 29s +508150K .......... .......... .......... .......... .......... 90% 3.18M 29s +508200K .......... .......... .......... .......... .......... 90% 323M 29s +508250K .......... .......... .......... .......... .......... 90% 13.1M 29s +508300K .......... .......... .......... .......... .......... 90% 419M 29s +508350K .......... .......... .......... .......... .......... 90% 174M 29s +508400K .......... .......... .......... .......... .......... 90% 5.95M 29s +508450K .......... .......... .......... .......... .......... 90% 3.67M 29s +508500K .......... .......... .......... .......... .......... 90% 26.5M 29s +508550K .......... .......... .......... .......... .......... 90% 6.49M 29s +508600K .......... .......... .......... .......... .......... 90% 9.66M 29s +508650K .......... .......... .......... .......... .......... 90% 664K 29s +508700K .......... .......... .......... .......... .......... 90% 253M 29s +508750K .......... .......... .......... .......... .......... 90% 487M 29s +508800K .......... .......... .......... .......... .......... 90% 93.1T 29s +508850K .......... .......... .......... .......... .......... 90% 482M 29s +508900K .......... .......... .......... .......... .......... 90% 544M 29s +508950K .......... .......... .......... .......... .......... 90% 677M 29s +509000K .......... .......... .......... .......... .......... 90% 1.09M 29s +509050K .......... .......... .......... .......... .......... 90% 93.1T 29s +509100K .......... .......... .......... .......... .......... 90% 350M 29s +509150K .......... .......... .......... .......... .......... 90% 515M 29s +509200K .......... .......... .......... .......... .......... 90% 691M 29s +509250K .......... .......... .......... .......... .......... 90% 775K 29s +509300K .......... .......... .......... .......... .......... 90% 60.0M 29s +509350K .......... .......... .......... .......... .......... 90% 1.19M 29s +509400K .......... .......... .......... .......... .......... 90% 869K 29s +509450K .......... .......... .......... .......... .......... 90% 346M 29s +509500K .......... .......... .......... .......... .......... 90% 747M 29s +509550K .......... .......... .......... .......... .......... 90% 460M 29s +509600K .......... .......... .......... .......... .......... 90% 93.1T 29s +509650K .......... .......... .......... .......... .......... 90% 554M 29s +509700K .......... .......... .......... .......... .......... 90% 718K 29s +509750K .......... .......... .......... .......... .......... 90% 348M 29s +509800K .......... .......... .......... .......... .......... 90% 93.1T 28s +509850K .......... .......... .......... .......... .......... 90% 655M 28s +509900K .......... .......... .......... .......... .......... 90% 463M 28s +509950K .......... .......... .......... .......... .......... 90% 498M 28s +510000K .......... .......... .......... .......... .......... 90% 93.1T 28s +510050K .......... .......... .......... .......... .......... 90% 663M 28s +510100K .......... .......... .......... .......... .......... 90% 781M 28s +510150K .......... .......... .......... .......... .......... 90% 687M 28s +510200K .......... .......... .......... .......... .......... 90% 662M 28s +510250K .......... .......... .......... .......... .......... 90% 93.1T 28s +510300K .......... .......... .......... .......... .......... 90% 508K 28s +510350K .......... .......... .......... .......... .......... 90% 93.1T 28s +510400K .......... .......... .......... .......... .......... 90% 82.1K 28s +510450K .......... .......... .......... .......... .......... 90% 424K 28s +510500K .......... .......... .......... .......... .......... 90% 93.1T 28s +510550K .......... .......... .......... .......... .......... 90% 206K 28s +510600K .......... .......... .......... .......... .......... 90% 396K 28s +510650K .......... .......... .......... .......... .......... 91% 138M 28s +510700K .......... .......... .......... .......... .......... 91% 93.1T 28s +510750K .......... .......... .......... .......... .......... 91% 310M 28s +510800K .......... .......... .......... .......... .......... 91% 234K 28s +510850K .......... .......... .......... .......... .......... 91% 93.1T 28s +510900K .......... .......... .......... .......... .......... 91% 265M 28s +510950K .......... .......... .......... .......... .......... 91% 652M 28s +511000K .......... .......... .......... .......... .......... 91% 570M 28s +511050K .......... .......... .......... .......... .......... 91% 43.2K 28s +511100K .......... .......... .......... .......... .......... 91% 93.1T 28s +511150K .......... .......... .......... .......... .......... 91% 412M 28s +511200K .......... .......... .......... .......... .......... 91% 784M 28s +511250K .......... .......... .......... .......... .......... 91% 744M 28s +511300K .......... .......... .......... .......... .......... 91% 93.1T 28s +511350K .......... .......... .......... .......... .......... 91% 786M 28s +511400K .......... .......... .......... .......... .......... 91% 785M 28s +511450K .......... .......... .......... .......... .......... 91% 961M 28s +511500K .......... .......... .......... .......... .......... 91% 104K 28s +511550K .......... .......... .......... .......... .......... 91% 254M 28s +511600K .......... .......... .......... .......... .......... 91% 41.6K 28s +511650K .......... .......... .......... .......... .......... 91% 93.1T 28s +511700K .......... .......... .......... .......... .......... 91% 210M 28s +511750K .......... .......... .......... .......... .......... 91% 433M 28s +511800K .......... .......... .......... .......... .......... 91% 155K 28s +511850K .......... .......... .......... .......... .......... 91% 20.6K 28s +511900K .......... .......... .......... .......... .......... 91% 16.3K 28s +511950K .......... .......... .......... .......... .......... 91% 79.1M 28s +512000K .......... .......... .......... .......... .......... 91% 52.9K 28s +512050K .......... .......... .......... .......... .......... 91% 18.5K 29s +512100K .......... .......... .......... .......... .......... 91% 239K 29s +512150K .......... .......... .......... .......... .......... 91% 165K 29s +512200K .......... .......... .......... .......... .......... 91% 364M 28s +512250K .......... .......... .......... .......... .......... 91% 25.0K 29s +512300K .......... .......... .......... .......... .......... 91% 445K 29s +512350K .......... .......... .......... .......... .......... 91% 252K 29s +512400K .......... .......... .......... .......... .......... 91% 9.44K 29s +512450K .......... .......... .......... .......... .......... 91% 181M 29s +512500K .......... .......... .......... .......... .......... 91% 556M 29s +512550K .......... .......... .......... .......... .......... 91% 93.1T 29s +512600K .......... .......... .......... .......... .......... 91% 584M 29s +512650K .......... .......... 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.......... .......... .......... .......... 91% 620M 29s +513350K .......... .......... .......... .......... .......... 91% 403M 29s +513400K .......... .......... .......... .......... .......... 91% 548M 29s +513450K .......... .......... .......... .......... .......... 91% 93.1T 28s +513500K .......... .......... .......... .......... .......... 91% 564M 28s +513550K .......... .......... .......... .......... .......... 91% 599M 28s +513600K .......... .......... .......... .......... .......... 91% 578M 28s +513650K .......... .......... .......... .......... .......... 91% 455K 28s +513700K .......... .......... .......... .......... .......... 91% 93.1T 28s +513750K .......... .......... .......... .......... .......... 91% 163K 28s +513800K .......... .......... .......... .......... .......... 91% 370K 28s +513850K .......... .......... .......... .......... .......... 91% 376M 28s +513900K .......... .......... .......... .......... .......... 91% 318K 28s +513950K .......... .......... .......... .......... .......... 91% 286M 28s +514000K .......... .......... .......... .......... .......... 91% 597M 28s +514050K .......... .......... .......... .......... .......... 91% 735M 28s +514100K .......... .......... .......... .......... .......... 91% 23.8K 28s +514150K .......... .......... .......... .......... .......... 91% 122K 28s +514200K .......... .......... .......... .......... .......... 91% 28.3K 28s +514250K .......... .......... .......... .......... .......... 91% 352M 28s +514300K .......... .......... .......... .......... .......... 91% 109K 28s +514350K .......... .......... .......... .......... .......... 91% 292M 28s +514400K .......... .......... .......... .......... .......... 91% 752M 28s +514450K .......... .......... .......... .......... .......... 91% 86.9K 28s +514500K .......... .......... .......... .......... .......... 91% 93.1T 28s +514550K .......... .......... .......... .......... .......... 91% 432M 28s +514600K .......... .......... .......... .......... .......... 91% 566M 28s +514650K .......... .......... .......... .......... .......... 91% 169K 28s +514700K .......... .......... .......... .......... .......... 91% 143K 28s +514750K .......... .......... .......... .......... .......... 91% 181K 28s +514800K .......... .......... .......... .......... .......... 91% 114K 28s +514850K .......... .......... .......... .......... .......... 91% 334M 28s +514900K .......... .......... .......... .......... .......... 91% 93.1T 28s +514950K .......... .......... .......... .......... .......... 91% 32.9K 28s +515000K .......... .......... .......... .......... .......... 91% 93.1T 28s +515050K .......... .......... .......... .......... .......... 91% 5.78M 28s +515100K .......... .......... .......... .......... .......... 91% 93.1T 28s +515150K .......... .......... .......... .......... .......... 91% 2.26M 28s +515200K .......... .......... .......... .......... .......... 91% 2.73M 28s +515250K .......... .......... .......... .......... .......... 91% 93.1T 28s +515300K .......... .......... .......... .......... .......... 91% 417M 28s +515350K .......... .......... .......... .......... .......... 91% 481M 28s +515400K .......... .......... .......... .......... .......... 91% 447M 28s +515450K .......... .......... .......... .......... .......... 91% 93.1T 28s +515500K .......... .......... .......... .......... .......... 91% 597M 28s +515550K .......... .......... .......... .......... .......... 91% 1019M 28s +515600K .......... .......... .......... .......... .......... 91% 925M 28s +515650K .......... .......... .......... .......... .......... 91% 989M 28s +515700K .......... .......... .......... .......... .......... 91% 93.1T 28s +515750K .......... .......... .......... .......... .......... 91% 993M 28s +515800K .......... .......... .......... .......... .......... 91% 829M 28s +515850K .......... .......... .......... .......... .......... 91% 970M 28s +515900K .......... .......... .......... .......... .......... 91% 93.1T 28s +515950K .......... .......... .......... .......... .......... 91% 1013M 28s +516000K .......... .......... .......... .......... .......... 91% 813M 28s +516050K .......... .......... .......... .......... .......... 91% 1.01G 28s +516100K .......... .......... .......... .......... .......... 91% 970M 28s +516150K .......... .......... .......... .......... .......... 91% 93.1T 28s +516200K .......... .......... .......... .......... .......... 91% 1013M 28s +516250K .......... .......... .......... .......... .......... 91% 1.11G 27s +516300K .......... .......... .......... .......... .......... 92% 409K 27s +516350K .......... .......... .......... .......... .......... 92% 312M 27s +516400K .......... .......... .......... .......... .......... 92% 613M 27s +516450K .......... .......... .......... .......... .......... 92% 605M 27s +516500K .......... .......... .......... 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.......... .......... .......... .......... 92% 8.48M 27s +517850K .......... .......... .......... .......... .......... 92% 797K 26s +517900K .......... .......... .......... .......... .......... 92% 547M 26s +517950K .......... .......... .......... .......... .......... 92% 1.44M 26s +518000K .......... .......... .......... .......... .......... 92% 1022K 26s +518050K .......... .......... .......... .......... .......... 92% 4.39M 26s +518100K .......... .......... .......... .......... .......... 92% 809K 26s +518150K .......... .......... .......... .......... .......... 92% 2.00M 26s +518200K .......... .......... .......... .......... .......... 92% 3.68M 26s +518250K .......... .......... .......... .......... .......... 92% 1.31M 26s +518300K .......... .......... .......... .......... .......... 92% 1.19M 26s +518350K .......... .......... .......... .......... .......... 92% 6.76M 26s +518400K .......... .......... .......... .......... .......... 92% 1.80M 26s +518450K .......... .......... .......... .......... .......... 92% 1.94M 26s +518500K .......... .......... .......... .......... .......... 92% 1.24M 26s +518550K .......... .......... .......... .......... .......... 92% 1.83M 26s +518600K .......... .......... .......... .......... .......... 92% 2.86M 26s +518650K .......... .......... .......... .......... .......... 92% 741K 26s +518700K .......... .......... .......... .......... .......... 92% 1.26M 26s +518750K .......... .......... .......... .......... .......... 92% 12.9M 26s +518800K .......... .......... .......... .......... .......... 92% 1.26M 26s +518850K .......... .......... .......... .......... .......... 92% 649M 26s +518900K .......... .......... .......... .......... .......... 92% 1.26M 26s +518950K .......... .......... .......... .......... .......... 92% 45.5M 26s +519000K .......... .......... .......... .......... .......... 92% 710M 26s +519050K .......... .......... .......... .......... .......... 92% 974K 26s +519100K .......... .......... .......... .......... .......... 92% 517M 26s +519150K .......... .......... .......... .......... .......... 92% 744M 26s +519200K .......... .......... .......... .......... .......... 92% 93.1T 26s +519250K .......... .......... .......... .......... .......... 92% 769M 26s +519300K .......... .......... .......... .......... .......... 92% 935M 26s +519350K .......... .......... .......... .......... .......... 92% 1.86M 26s +519400K .......... .......... .......... .......... .......... 92% 17.5M 26s +519450K .......... .......... .......... .......... .......... 92% 2.78M 25s +519500K .......... .......... .......... .......... .......... 92% 3.02M 25s +519550K .......... .......... .......... .......... .......... 92% 93.1T 25s +519600K .......... .......... .......... .......... .......... 92% 792M 25s +519650K .......... .......... .......... .......... .......... 92% 2.03M 25s +519700K .......... .......... .......... .......... .......... 92% 4.57M 25s +519750K .......... .......... .......... .......... .......... 92% 4.63M 25s +519800K .......... .......... .......... .......... .......... 92% 5.03M 25s +519850K .......... .......... .......... .......... .......... 92% 637M 25s +519900K .......... .......... .......... .......... .......... 92% 3.24M 25s +519950K .......... .......... .......... .......... .......... 92% 2.75M 25s +520000K .......... .......... .......... .......... .......... 92% 1.26M 25s +520050K .......... .......... .......... .......... .......... 92% 496M 25s +520100K .......... .......... .......... .......... .......... 92% 654M 25s +520150K .......... .......... .......... .......... .......... 92% 1.14M 25s +520200K .......... .......... .......... .......... .......... 92% 93.1T 25s +520250K .......... .......... .......... .......... .......... 92% 585M 25s +520300K .......... .......... .......... .......... .......... 92% 935M 25s +520350K .......... .......... .......... .......... .......... 92% 1.74M 25s +520400K .......... .......... .......... .......... .......... 92% 523M 25s +520450K .......... .......... .......... .......... .......... 92% 93.1T 25s +520500K .......... .......... .......... .......... .......... 92% 491M 25s +520550K .......... .......... .......... .......... .......... 92% 1.94M 25s +520600K .......... .......... .......... .......... .......... 92% 552M 25s +520650K .......... .......... .......... .......... .......... 92% 138M 25s +520700K .......... .......... .......... .......... .......... 92% 568M 25s +520750K .......... .......... .......... .......... .......... 92% 2.65M 25s +520800K .......... .......... .......... .......... .......... 92% 40.3M 25s +520850K .......... .......... .......... .......... .......... 92% 47.1M 25s +520900K .......... .......... .......... .......... .......... 92% 3.14M 25s +520950K .......... .......... .......... .......... .......... 92% 682M 25s +521000K .......... .......... .......... .......... .......... 92% 2.20M 24s +521050K .......... .......... .......... .......... .......... 92% 93.1T 24s +521100K .......... .......... .......... .......... .......... 92% 2.64M 24s +521150K .......... .......... .......... .......... .......... 92% 754M 24s +521200K .......... .......... .......... .......... .......... 92% 3.13M 24s +521250K .......... .......... .......... .......... .......... 92% 93.1T 24s +521300K .......... .......... .......... .......... .......... 92% 900K 24s +521350K .......... .......... .......... .......... .......... 92% 93.1T 24s +521400K .......... .......... .......... .......... .......... 92% 461M 24s +521450K .......... .......... .......... .......... .......... 92% 691M 24s +521500K .......... .......... .......... .......... .......... 92% 55.8M 24s +521550K .......... .......... .......... .......... .......... 92% 1.52M 24s +521600K .......... .......... .......... .......... .......... 92% 93.1T 24s +521650K .......... .......... .......... .......... .......... 92% 425M 24s +521700K .......... .......... .......... .......... .......... 92% 6.14M 24s +521750K .......... .......... .......... .......... .......... 92% 568M 24s +521800K .......... .......... .......... .......... .......... 92% 1.42M 24s +521850K .......... .......... .......... .......... .......... 92% 6.86M 24s +521900K .......... .......... .......... .......... .......... 93% 942K 24s +521950K .......... .......... .......... .......... .......... 93% 93.1T 24s +522000K .......... .......... .......... .......... .......... 93% 408M 24s +522050K .......... .......... .......... .......... .......... 93% 661M 24s +522100K .......... .......... .......... .......... .......... 93% 500M 24s +522150K .......... .......... .......... .......... .......... 93% 93.1T 24s +522200K .......... .......... .......... .......... .......... 93% 540M 24s +522250K .......... .......... .......... .......... .......... 93% 536M 24s +522300K .......... .......... .......... .......... .......... 93% 2.20M 24s +522350K .......... .......... .......... .......... .......... 93% 59.4M 24s +522400K .......... .......... .......... .......... .......... 93% 659M 24s +522450K .......... .......... .......... .......... .......... 93% 732M 24s +522500K .......... .......... .......... .......... .......... 93% 93.1T 24s +522550K .......... .......... .......... .......... .......... 93% 1.12M 23s +522600K .......... .......... .......... .......... .......... 93% 447M 23s +522650K .......... .......... .......... .......... .......... 93% 1.12M 23s +522700K .......... .......... .......... .......... .......... 93% 450M 23s +522750K .......... .......... .......... .......... .......... 93% 93.1T 23s +522800K .......... .......... .......... .......... .......... 93% 676M 23s +522850K .......... .......... .......... .......... .......... 93% 671M 23s +522900K .......... .......... .......... .......... .......... 93% 804M 23s +522950K .......... .......... .......... .......... .......... 93% 1.15M 23s +523000K .......... .......... .......... .......... .......... 93% 466M 23s +523050K .......... .......... .......... .......... .......... 93% 627M 23s +523100K .......... .......... .......... .......... .......... 93% 93.1T 23s +523150K .......... .......... .......... .......... .......... 93% 122M 23s +523200K .......... .......... .......... .......... .......... 93% 727M 23s +523250K .......... .......... .......... .......... .......... 93% 9.35M 23s +523300K .......... .......... .......... .......... .......... 93% 5.64M 23s +523350K .......... .......... .......... .......... .......... 93% 26.2M 23s +523400K .......... .......... .......... .......... .......... 93% 93.1T 23s +523450K .......... .......... .......... .......... .......... 93% 1.18M 23s +523500K .......... .......... .......... .......... .......... 93% 482M 23s +523550K .......... .......... .......... .......... .......... 93% 93.1T 23s +523600K .......... .......... .......... .......... .......... 93% 612M 23s +523650K .......... .......... .......... .......... .......... 93% 602M 23s +523700K .......... .......... .......... .......... .......... 93% 2.14M 23s +523750K .......... .......... .......... .......... .......... 93% 6.35M 23s +523800K .......... .......... .......... .......... .......... 93% 364M 23s +523850K .......... .......... .......... .......... .......... 93% 8.54M 23s +523900K .......... .......... .......... .......... .......... 93% 364M 23s +523950K .......... .......... .......... .......... .......... 93% 93.1T 23s +524000K .......... .......... .......... .......... .......... 93% 13.4M 23s +524050K .......... .......... .......... .......... .......... 93% 6.34M 23s +524100K .......... .......... .......... .......... .......... 93% 1.90M 22s +524150K .......... .......... .......... .......... .......... 93% 129M 22s +524200K .......... .......... .......... .......... .......... 93% 518M 22s +524250K .......... .......... .......... .......... .......... 93% 730K 22s +524300K .......... .......... .......... .......... .......... 93% 93.1T 22s +524350K .......... .......... .......... .......... .......... 93% 395M 22s +524400K .......... .......... .......... .......... .......... 93% 1.80M 22s +524450K .......... .......... .......... .......... .......... 93% 93.1T 22s +524500K .......... .......... .......... .......... .......... 93% 1.19M 22s +524550K .......... .......... .......... .......... .......... 93% 3.85M 22s +524600K .......... .......... .......... .......... .......... 93% 93.1T 22s +524650K .......... .......... .......... .......... .......... 93% 502M 22s +524700K .......... .......... .......... .......... .......... 93% 2.09M 22s +524750K .......... .......... .......... .......... .......... 93% 551M 22s +524800K .......... .......... .......... .......... .......... 93% 93.1T 22s +524850K .......... .......... .......... .......... .......... 93% 9.95M 22s +524900K .......... .......... .......... .......... .......... 93% 4.18M 22s +524950K .......... .......... .......... .......... .......... 93% 1.68M 22s +525000K .......... .......... .......... .......... .......... 93% 5.39M 22s +525050K .......... .......... .......... .......... .......... 93% 66.9M 22s +525100K .......... .......... .......... .......... .......... 93% 4.93M 22s +525150K .......... .......... .......... .......... .......... 93% 6.73M 22s +525200K .......... .......... .......... .......... .......... 93% 8.59M 22s +525250K .......... .......... .......... .......... .......... 93% 10.7M 22s +525300K .......... .......... .......... .......... .......... 93% 4.67M 22s +525350K .......... .......... .......... .......... .......... 93% 3.63M 22s +525400K .......... .......... .......... .......... .......... 93% 34.4M 22s +525450K .......... .......... .......... .......... .......... 93% 93.1T 22s +525500K .......... .......... .......... .......... .......... 93% 3.67M 22s +525550K .......... .......... .......... .......... .......... 93% 93.1T 22s +525600K .......... .......... .......... .......... .......... 93% 2.21M 22s +525650K .......... .......... .......... .......... .......... 93% 497M 22s +525700K .......... .......... .......... .......... .......... 93% 93.1T 21s +525750K .......... .......... .......... .......... .......... 93% 1.63M 21s +525800K .......... .......... .......... .......... .......... 93% 491M 21s +525850K .......... .......... .......... .......... .......... 93% 776M 21s +525900K .......... .......... .......... .......... .......... 93% 93.1T 21s +525950K .......... .......... .......... .......... .......... 93% 397K 21s +526000K .......... .......... .......... .......... .......... 93% 411M 21s +526050K .......... .......... .......... .......... .......... 93% 1.42M 21s +526100K .......... .......... .......... .......... .......... 93% 93.1T 21s +526150K .......... .......... .......... .......... .......... 93% 468M 21s +526200K .......... .......... .......... .......... .......... 93% 636M 21s +526250K .......... .......... .......... .......... .......... 93% 570M 21s +526300K .......... .......... .......... .......... .......... 93% 16.0M 21s +526350K .......... .......... .......... .......... .......... 93% 1.72M 21s +526400K .......... .......... .......... .......... .......... 93% 439M 21s +526450K .......... .......... .......... .......... .......... 93% 93.1T 21s +526500K .......... .......... .......... .......... .......... 93% 5.43M 21s +526550K .......... .......... .......... .......... .......... 93% 558M 21s +526600K .......... .......... .......... .......... .......... 93% 93.1T 21s +526650K .......... .......... .......... .......... .......... 93% 651M 21s +526700K .......... .......... .......... .......... .......... 93% 680M 21s +526750K .......... .......... .......... .......... .......... 93% 639M 21s +526800K .......... .......... .......... .......... .......... 93% 682M 21s +526850K .......... .......... .......... .......... .......... 93% 93.1T 21s +526900K .......... .......... .......... .......... .......... 93% 2.07M 21s +526950K .......... .......... .......... .......... .......... 93% 93.1T 21s +527000K .......... .......... .......... .......... .......... 93% 496M 21s +527050K .......... .......... .......... .......... .......... 93% 648M 21s +527100K .......... .......... .......... .......... .......... 93% 700M 21s +527150K .......... .......... .......... .......... .......... 93% 93.1T 21s +527200K .......... .......... .......... .......... .......... 93% 1.80M 21s +527250K .......... .......... .......... .......... .......... 93% 548M 20s +527300K .......... .......... .......... .......... .......... 93% 770M 20s +527350K .......... .......... .......... .......... .......... 93% 19.0M 20s +527400K .......... .......... .......... .......... .......... 93% 2.54M 20s +527450K .......... .......... .......... .......... .......... 93% 16.6M 20s +527500K .......... .......... .......... .......... .......... 94% 6.36M 20s +527550K .......... .......... .......... .......... .......... 94% 5.97M 20s +527600K .......... .......... .......... .......... .......... 94% 793K 20s +527650K .......... .......... .......... .......... .......... 94% 337M 20s +527700K .......... .......... .......... .......... .......... 94% 623M 20s +527750K .......... .......... .......... .......... .......... 94% 93.1T 20s +527800K .......... .......... .......... .......... .......... 94% 650M 20s +527850K .......... .......... .......... .......... .......... 94% 1.92M 20s +527900K .......... .......... .......... .......... .......... 94% 3.76M 20s +527950K .......... .......... .......... .......... .......... 94% 431M 20s +528000K .......... .......... .......... .......... .......... 94% 93.1T 20s +528050K .......... .......... .......... .......... .......... 94% 554M 20s +528100K .......... .......... .......... .......... .......... 94% 646M 20s +528150K .......... .......... .......... .......... .......... 94% 3.81M 20s +528200K .......... .......... .......... .......... .......... 94% 1.89M 20s +528250K .......... .......... .......... .......... .......... 94% 381M 20s +528300K .......... .......... .......... .......... .......... 94% 808M 20s +528350K .......... .......... .......... .......... .......... 94% 2.02M 20s +528400K .......... .......... .......... .......... .......... 94% 394M 20s +528450K .......... .......... .......... .......... .......... 94% 920M 20s +528500K .......... .......... .......... .......... .......... 94% 33.6M 20s +528550K .......... .......... .......... .......... .......... 94% 1.06M 20s +528600K .......... .......... .......... .......... .......... 94% 93.1T 20s +528650K .......... .......... .......... .......... .......... 94% 477M 20s +528700K .......... .......... .......... .......... .......... 94% 1.97M 20s +528750K .......... .......... .......... .......... .......... 94% 381M 20s +528800K .......... .......... .......... .......... .......... 94% 579M 20s +528850K .......... .......... .......... .......... .......... 94% 3.59M 19s +528900K .......... .......... .......... .......... .......... 94% 93.1T 19s +528950K .......... .......... .......... .......... .......... 94% 440M 19s +529000K .......... .......... .......... .......... .......... 94% 904K 19s +529050K .......... .......... .......... .......... .......... 94% 49.7M 19s +529100K .......... .......... .......... .......... .......... 94% 27.1M 19s +529150K .......... .......... .......... .......... .......... 94% 93.1T 19s +529200K .......... .......... .......... .......... .......... 94% 60.1M 19s +529250K .......... .......... .......... .......... .......... 94% 664M 19s +529300K .......... .......... .......... .......... .......... 94% 2.73M 19s +529350K .......... .......... .......... 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.......... .......... .......... 94% 924K 19s +530050K .......... .......... .......... .......... .......... 94% 93.1T 19s +530100K .......... .......... .......... .......... .......... 94% 579M 19s +530150K .......... .......... .......... .......... .......... 94% 380M 19s +530200K .......... .......... .......... .......... .......... 94% 4.96M 19s +530250K .......... .......... .......... .......... .......... 94% 477M 19s +530300K .......... .......... .......... .......... .......... 94% 552M 19s +530350K .......... .......... .......... .......... .......... 94% 93.1T 19s +530400K .......... .......... .......... .......... .......... 94% 706M 19s +530450K .......... .......... .......... .......... .......... 94% 730M 18s +530500K .......... .......... .......... .......... .......... 94% 787M 18s +530550K .......... .......... .......... .......... .......... 94% 114M 18s +530600K .......... .......... .......... .......... .......... 94% 647M 18s +530650K .......... 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.......... .......... .......... .......... .......... 94% 93.1T 18s +531350K .......... .......... .......... .......... .......... 94% 1.77M 18s +531400K .......... .......... .......... .......... .......... 94% 93.1T 18s +531450K .......... .......... .......... .......... .......... 94% 377M 18s +531500K .......... .......... .......... .......... .......... 94% 603M 18s +531550K .......... .......... .......... .......... .......... 94% 656M 18s +531600K .......... .......... .......... .......... .......... 94% 93.1T 18s +531650K .......... .......... .......... .......... .......... 94% 647M 18s +531700K .......... .......... .......... .......... .......... 94% 1.74M 18s +531750K .......... .......... .......... .......... .......... 94% 93.1T 18s +531800K .......... .......... .......... .......... .......... 94% 536M 18s +531850K .......... .......... .......... .......... .......... 94% 1.32M 18s +531900K .......... .......... .......... .......... .......... 94% 477M 18s +531950K .......... .......... .......... .......... .......... 94% 93.1T 18s +532000K .......... .......... .......... .......... .......... 94% 578M 18s +532050K .......... .......... .......... .......... .......... 94% 24.8M 17s +532100K .......... .......... .......... .......... .......... 94% 38.7M 17s +532150K .......... .......... .......... .......... .......... 94% 1.70M 17s +532200K .......... .......... .......... .......... .......... 94% 93.1T 17s +532250K .......... .......... .......... .......... .......... 94% 360M 17s +532300K .......... .......... .......... .......... .......... 94% 605M 17s +532350K .......... .......... .......... .......... .......... 94% 594M 17s +532400K .......... .......... .......... .......... .......... 94% 854M 17s +532450K .......... .......... .......... .......... .......... 94% 7.37M 17s +532500K .......... .......... .......... .......... .......... 94% 2.77M 17s +532550K .......... .......... .......... .......... .......... 94% 4.49M 17s +532600K .......... .......... .......... .......... .......... 94% 4.57M 17s +532650K .......... .......... .......... .......... .......... 94% 4.27M 17s +532700K .......... .......... .......... .......... .......... 94% 4.36M 17s +532750K .......... .......... .......... .......... .......... 94% 4.31M 17s +532800K .......... .......... .......... .......... .......... 94% 3.66M 17s +532850K .......... .......... .......... .......... .......... 94% 944K 17s +532900K .......... .......... .......... .......... .......... 94% 431M 17s +532950K .......... .......... .......... .......... .......... 94% 738M 17s +533000K .......... .......... .......... .......... .......... 94% 93.1T 17s +533050K .......... .......... .......... .......... .......... 94% 768M 17s +533100K .......... .......... .......... .......... .......... 95% 1.07M 17s +533150K .......... .......... .......... .......... .......... 95% 38.2M 17s +533200K .......... .......... .......... .......... .......... 95% 93.1T 17s +533250K .......... .......... .......... .......... .......... 95% 599M 17s +533300K .......... .......... .......... .......... .......... 95% 7.50M 17s +533350K .......... .......... .......... .......... .......... 95% 1.37M 17s +533400K .......... .......... .......... .......... .......... 95% 93.1T 17s +533450K .......... .......... .......... .......... .......... 95% 281M 17s +533500K .......... .......... .......... .......... .......... 95% 1.31M 17s +533550K .......... .......... .......... .......... .......... 95% 42.3M 17s +533600K .......... .......... .......... .......... .......... 95% 411M 17s +533650K .......... .......... .......... .......... .......... 95% 1.48M 16s +533700K .......... .......... .......... .......... .......... 95% 93.1T 16s +533750K .......... .......... .......... .......... .......... 95% 604M 16s +533800K .......... .......... .......... .......... .......... 95% 1.32M 16s +533850K .......... .......... .......... 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.......... .......... .......... .......... 95% 588M 16s +535200K .......... .......... .......... .......... .......... 95% 3.20M 16s +535250K .......... .......... .......... .......... .......... 95% 17.5M 15s +535300K .......... .......... .......... .......... .......... 95% 1.63M 15s +535350K .......... .......... .......... .......... .......... 95% 2.90M 15s +535400K .......... .......... .......... .......... .......... 95% 29.4M 15s +535450K .......... .......... .......... .......... .......... 95% 5.21M 15s +535500K .......... .......... .......... .......... .......... 95% 4.37M 15s +535550K .......... .......... .......... .......... .......... 95% 2.04M 15s +535600K .......... .......... .......... .......... .......... 95% 264M 15s +535650K .......... .......... .......... .......... .......... 95% 8.31M 15s +535700K .......... .......... .......... .......... .......... 95% 1.46M 15s +535750K .......... .......... .......... .......... .......... 95% 93.1T 15s +535800K .......... .......... .......... .......... .......... 95% 15.4M 15s +535850K .......... .......... .......... .......... .......... 95% 1.43M 15s +535900K .......... .......... .......... .......... .......... 95% 16.0M 15s +535950K .......... .......... .......... .......... .......... 95% 590M 15s +536000K .......... .......... .......... .......... .......... 95% 565K 15s +536050K .......... .......... .......... .......... .......... 95% 7.85M 15s +536100K .......... .......... .......... .......... .......... 95% 2.79M 15s +536150K .......... .......... .......... .......... .......... 95% 6.70M 15s +536200K .......... .......... .......... .......... .......... 95% 1.59M 15s +536250K .......... .......... .......... .......... .......... 95% 1.47M 15s +536300K .......... .......... .......... .......... .......... 95% 62.2M 15s +536350K .......... .......... .......... .......... .......... 95% 571M 15s +536400K .......... .......... .......... .......... .......... 95% 175M 15s +536450K .......... .......... .......... .......... .......... 95% 4.49M 15s +536500K .......... .......... .......... .......... .......... 95% 4.69M 15s +536550K .......... .......... .......... .......... .......... 95% 3.12M 15s +536600K .......... .......... .......... .......... .......... 95% 1.89M 15s +536650K .......... .......... .......... .......... .......... 95% 632M 15s +536700K .......... .......... .......... .......... .......... 95% 2.37M 15s +536750K .......... .......... .......... .......... .......... 95% 93.1T 15s +536800K .......... .......... .......... .......... .......... 95% 14.1M 15s +536850K .......... .......... .......... .......... .......... 95% 6.09M 15s +536900K .......... .......... .......... .......... .......... 95% 5.16M 14s +536950K .......... .......... .......... .......... .......... 95% 3.44M 14s +537000K .......... .......... .......... .......... .......... 95% 2.36M 14s +537050K .......... .......... .......... .......... .......... 95% 4.02M 14s +537100K .......... .......... .......... .......... .......... 95% 88.5M 14s +537150K .......... .......... .......... .......... .......... 95% 754M 14s +537200K .......... .......... .......... .......... .......... 95% 3.29M 14s +537250K .......... .......... .......... .......... .......... 95% 2.84M 14s +537300K .......... .......... .......... .......... .......... 95% 28.6M 14s +537350K .......... .......... .......... .......... .......... 95% 6.19M 14s +537400K .......... .......... .......... .......... .......... 95% 5.48M 14s +537450K .......... .......... .......... .......... .......... 95% 5.72M 14s +537500K .......... .......... .......... .......... .......... 95% 6.35M 14s +537550K .......... .......... .......... .......... .......... 95% 2.06M 14s +537600K .......... .......... .......... .......... .......... 95% 553M 14s +537650K .......... .......... .......... .......... .......... 95% 2.20M 14s +537700K .......... .......... .......... .......... .......... 95% 93.1T 14s +537750K .......... .......... .......... .......... .......... 95% 1.76M 14s +537800K .......... .......... .......... .......... .......... 95% 377M 14s +537850K .......... .......... .......... .......... .......... 95% 93.1T 14s +537900K .......... .......... .......... .......... .......... 95% 3.55M 14s +537950K .......... .......... .......... .......... .......... 95% 483M 14s +538000K .......... .......... .......... .......... .......... 95% 93.1T 14s +538050K .......... .......... .......... .......... .......... 95% 1.35M 14s +538100K .......... .......... .......... .......... .......... 95% 44.4M 14s +538150K .......... .......... .......... .......... .......... 95% 478M 14s +538200K .......... .......... .......... .......... .......... 95% 704M 14s +538250K .......... .......... .......... .......... .......... 95% 1.65M 14s +538300K .......... .......... .......... .......... .......... 95% 3.22M 14s +538350K .......... .......... .......... 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.......... .......... .......... .......... .......... 96% 7.94M 13s +539700K .......... .......... .......... .......... .......... 96% 1.80M 13s +539750K .......... .......... .......... .......... .......... 96% 5.55M 13s +539800K .......... .......... .......... .......... .......... 96% 603M 13s +539850K .......... .......... .......... .......... .......... 96% 597M 13s +539900K .......... .......... .......... .......... .......... 96% 1.57M 13s +539950K .......... .......... .......... .......... .......... 96% 24.6M 13s +540000K .......... .......... .......... .......... .......... 96% 1.32M 13s +540050K .......... .......... .......... .......... .......... 96% 238M 13s +540100K .......... .......... .......... .......... .......... 96% 650M 13s +540150K .......... .......... .......... .......... .......... 96% 661M 12s +540200K .......... .......... .......... .......... .......... 96% 93.1T 12s +540250K .......... .......... .......... .......... .......... 96% 2.13M 12s +540300K .......... .......... .......... .......... .......... 96% 1.30M 12s +540350K .......... .......... .......... .......... .......... 96% 407M 12s +540400K .......... .......... .......... .......... .......... 96% 93.1T 12s +540450K .......... .......... .......... .......... .......... 96% 679M 12s +540500K .......... .......... .......... .......... .......... 96% 13.2M 12s +540550K .......... .......... .......... .......... .......... 96% 44.2M 12s +540600K .......... .......... .......... .......... .......... 96% 1.39M 12s +540650K .......... .......... .......... .......... .......... 96% 3.56M 12s +540700K .......... .......... .......... .......... .......... 96% 30.3M 12s +540750K .......... .......... .......... .......... .......... 96% 93.1T 12s +540800K .......... .......... .......... .......... .......... 96% 68.5M 12s +540850K .......... .......... .......... .......... .......... 96% 588M 12s +540900K .......... .......... .......... .......... .......... 96% 93.1T 12s +540950K .......... .......... .......... .......... .......... 96% 621M 12s +541000K .......... .......... .......... .......... .......... 96% 2.44M 12s +541050K .......... .......... .......... .......... .......... 96% 2.97M 12s +541100K .......... .......... .......... .......... .......... 96% 7.32M 12s +541150K .......... .......... .......... .......... .......... 96% 26.2M 12s +541200K .......... .......... .......... .......... .......... 96% 26.5M 12s +541250K .......... .......... .......... .......... .......... 96% 13.6M 12s +541300K .......... .......... .......... .......... .......... 96% 517M 12s +541350K .......... .......... .......... .......... .......... 96% 2.58M 12s +541400K .......... .......... .......... .......... .......... 96% 3.36M 12s +541450K .......... .......... .......... .......... .......... 96% 55.8M 12s +541500K .......... .......... .......... .......... .......... 96% 604M 12s +541550K .......... .......... .......... .......... 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+544800K .......... .......... .......... .......... .......... 97% 71.8M 10s +544850K .......... .......... .......... .......... .......... 97% 2.11M 10s +544900K .......... .......... .......... .......... .......... 97% 93.1T 10s +544950K .......... .......... .......... .......... .......... 97% 1015K 10s +545000K .......... .......... .......... .......... .......... 97% 1.30M 10s +545050K .......... .......... .......... .......... .......... 97% 1.49M 10s +545100K .......... .......... .......... .......... .......... 97% 438M 9s +545150K .......... .......... .......... .......... .......... 97% 93.1T 9s +545200K .......... .......... .......... .......... .......... 97% 643M 9s +545250K .......... .......... .......... .......... .......... 97% 663M 9s +545300K .......... .......... .......... .......... .......... 97% 672M 9s +545350K .......... .......... .......... .......... .......... 97% 93.1T 9s +545400K .......... .......... .......... .......... .......... 97% 683M 9s +545450K .......... .......... .......... .......... .......... 97% 373M 9s +545500K .......... .......... .......... .......... .......... 97% 93.1T 9s +545550K .......... .......... .......... .......... .......... 97% 681M 9s +545600K .......... .......... .......... .......... .......... 97% 801M 9s +545650K .......... .......... .......... .......... .......... 97% 3.87M 9s +545700K .......... .......... .......... .......... .......... 97% 516M 9s +545750K .......... .......... .......... .......... .......... 97% 689M 9s +545800K .......... .......... .......... .......... .......... 97% 608M 9s +545850K .......... .......... .......... .......... .......... 97% 93.1T 9s +545900K .......... .......... .......... .......... .......... 97% 647M 9s +545950K .......... .......... .......... .......... .......... 97% 698M 9s +546000K .......... .......... .......... .......... .......... 97% 10.3M 9s +546050K .......... .......... .......... .......... .......... 97% 2.99M 9s +546100K .......... .......... .......... .......... .......... 97% 526M 9s +546150K .......... .......... .......... .......... .......... 97% 2.07M 9s +546200K .......... .......... .......... .......... .......... 97% 843K 9s +546250K .......... .......... .......... .......... .......... 97% 439M 9s +546300K .......... .......... .......... .......... .......... 97% 624M 9s +546350K .......... .......... .......... .......... .......... 97% 93.1T 9s +546400K .......... .......... .......... .......... .......... 97% 599M 9s +546450K .......... .......... .......... .......... .......... 97% 2.56M 9s +546500K .......... .......... .......... .......... .......... 97% 398M 9s +546550K .......... .......... .......... .......... .......... 97% 93.1T 9s +546600K .......... .......... .......... .......... .......... 97% 546M 9s +546650K .......... .......... .......... .......... .......... 97% 557M 9s +546700K .......... .......... .......... .......... .......... 97% 115M 9s +546750K .......... .......... .......... .......... .......... 97% 93.1T 8s +546800K .......... .......... .......... .......... .......... 97% 516M 8s +546850K .......... .......... .......... .......... .......... 97% 593M 8s +546900K .......... .......... .......... .......... .......... 97% 336M 8s +546950K .......... .......... .......... .......... .......... 97% 93.1T 8s +547000K .......... .......... .......... .......... .......... 97% 2.32M 8s +547050K .......... .......... .......... .......... .......... 97% 561M 8s +547100K .......... .......... .......... .......... .......... 97% 36.2M 8s +547150K .......... .......... .......... .......... .......... 97% 4.37M 8s +547200K .......... .......... .......... .......... .......... 97% 3.84M 8s +547250K .......... .......... .......... .......... .......... 97% 435M 8s +547300K .......... .......... .......... .......... .......... 97% 645K 8s +547350K .......... .......... .......... .......... .......... 97% 71.2M 8s +547400K .......... .......... .......... .......... .......... 97% 560M 8s +547450K .......... .......... .......... .......... .......... 97% 651M 8s +547500K .......... .......... .......... .......... .......... 97% 93.1T 8s +547550K .......... .......... .......... .......... .......... 97% 554M 8s +547600K .......... .......... .......... .......... .......... 97% 2.06M 8s +547650K .......... .......... .......... .......... .......... 97% 1.33M 8s +547700K .......... .......... .......... .......... .......... 97% 458M 8s +547750K .......... .......... .......... .......... .......... 97% 93.1T 8s +547800K .......... .......... .......... .......... .......... 97% 686M 8s +547850K .......... .......... .......... .......... .......... 97% 700M 8s +547900K .......... .......... .......... .......... .......... 97% 649M 8s +547950K .......... .......... .......... .......... .......... 97% 719M 8s +548000K .......... .......... .......... .......... .......... 97% 93.1T 8s +548050K .......... .......... .......... .......... .......... 97% 2.40M 8s +548100K .......... .......... .......... .......... .......... 97% 93.1T 8s +548150K .......... .......... .......... .......... .......... 97% 428M 8s +548200K .......... .......... .......... .......... .......... 97% 566M 8s +548250K .......... .......... .......... .......... .......... 97% 565M 8s +548300K .......... .......... .......... .......... .......... 97% 596M 8s +548350K .......... .......... .......... .......... .......... 97% 93.1T 8s +548400K .......... .......... .......... .......... .......... 97% 121M 7s +548450K .......... .......... .......... .......... .......... 97% 2.44M 7s +548500K .......... .......... .......... .......... .......... 97% 412M 7s +548550K .......... .......... .......... .......... .......... 97% 454M 7s +548600K .......... .......... .......... .......... .......... 97% 93.1T 7s +548650K .......... .......... .......... .......... .......... 97% 2.15M 7s +548700K .......... .......... .......... .......... .......... 97% 93.1T 7s +548750K .......... .......... .......... .......... .......... 97% 272M 7s +548800K .......... .......... .......... .......... .......... 97% 1.08M 7s +548850K .......... .......... .......... .......... .......... 97% 93.1T 7s +548900K .......... .......... .......... .......... .......... 97% 401M 7s +548950K .......... .......... .......... .......... .......... 97% 635M 7s +549000K .......... .......... .......... .......... .......... 97% 1.21M 7s +549050K .......... .......... .......... .......... .......... 97% 7.55M 7s +549100K .......... .......... .......... .......... .......... 97% 597K 7s +549150K .......... .......... .......... .......... .......... 97% 408M 7s +549200K .......... .......... .......... .......... .......... 97% 93.1T 7s +549250K .......... .......... .......... .......... .......... 97% 412M 7s +549300K .......... .......... .......... .......... .......... 97% 294M 7s +549350K .......... .......... .......... .......... .......... 97% 558M 7s +549400K .......... .......... .......... .......... .......... 97% 638M 7s +549450K .......... .......... .......... .......... .......... 97% 646M 7s +549500K .......... .......... .......... .......... .......... 97% 93.1T 7s +549550K .......... .......... .......... .......... .......... 97% 641M 7s +549600K .......... .......... .......... .......... .......... 97% 661M 7s +549650K .......... .......... .......... .......... .......... 97% 440M 7s +549700K .......... .......... .......... .......... .......... 97% 12.2M 7s +549750K .......... .......... .......... .......... .......... 97% 93.1T 7s +549800K .......... .......... .......... .......... .......... 97% 1.44M 7s +549850K .......... .......... .......... .......... .......... 97% 444M 7s +549900K .......... .......... .......... .......... .......... 97% 93.1T 7s +549950K .......... .......... .......... .......... .......... 98% 586M 7s +550000K .......... .......... .......... .......... .......... 98% 692M 7s +550050K .......... .......... .......... .......... .......... 98% 684M 7s +550100K .......... .......... .......... .......... .......... 98% 93.1T 6s +550150K .......... .......... .......... .......... .......... 98% 680M 6s +550200K .......... .......... .......... .......... .......... 98% 670M 6s +550250K .......... .......... .......... .......... .......... 98% 310M 6s +550300K .......... .......... .......... .......... .......... 98% 834M 6s +550350K .......... .......... .......... .......... .......... 98% 8.77M 6s +550400K .......... .......... .......... .......... .......... 98% 500M 6s +550450K .......... .......... .......... .......... .......... 98% 2.37M 6s +550500K .......... .......... .......... .......... .......... 98% 365M 6s +550550K .......... .......... .......... .......... .......... 98% 1.70M 6s +550600K .......... .......... .......... .......... .......... 98% 93.1T 6s +550650K .......... .......... .......... .......... .......... 98% 482M 6s +550700K .......... .......... .......... .......... .......... 98% 3.16M 6s +550750K .......... .......... .......... .......... .......... 98% 114M 6s +550800K .......... .......... .......... .......... .......... 98% 67.0M 6s +550850K .......... .......... .......... .......... .......... 98% 216M 6s +550900K .......... .......... .......... .......... .......... 98% 899K 6s +550950K .......... .......... .......... .......... .......... 98% 425M 6s +551000K .......... .......... .......... .......... .......... 98% 620M 6s +551050K .......... .......... .......... .......... .......... 98% 93.1T 6s +551100K .......... .......... .......... .......... .......... 98% 616M 6s +551150K .......... .......... .......... .......... .......... 98% 2.53M 6s +551200K .......... .......... .......... .......... .......... 98% 54.7M 6s +551250K .......... .......... .......... .......... .......... 98% 50.9M 6s +551300K .......... .......... .......... .......... .......... 98% 171M 6s +551350K .......... .......... .......... .......... .......... 98% 1.06M 6s +551400K .......... .......... .......... .......... .......... 98% 469M 6s +551450K .......... .......... .......... .......... .......... 98% 504M 6s +551500K .......... .......... .......... .......... .......... 98% 93.1T 6s +551550K .......... .......... .......... .......... .......... 98% 615M 6s +551600K .......... .......... .......... .......... .......... 98% 649M 6s +551650K .......... .......... .......... .......... .......... 98% 626M 6s +551700K .......... .......... .......... .......... .......... 98% 7.24M 6s +551750K .......... .......... .......... .......... .......... 98% 79.6M 5s +551800K .......... .......... .......... .......... .......... 98% 7.14M 5s +551850K .......... .......... .......... .......... .......... 98% 1.21M 5s +551900K .......... .......... .......... .......... .......... 98% 417M 5s +551950K .......... .......... .......... .......... .......... 98% 565M 5s +552000K .......... .......... .......... .......... .......... 98% 510M 5s +552050K .......... .......... .......... .......... .......... 98% 93.1T 5s +552100K .......... .......... .......... .......... .......... 98% 617M 5s +552150K .......... .......... .......... .......... .......... 98% 678M 5s +552200K .......... .......... .......... .......... .......... 98% 3.92M 5s +552250K .......... .......... .......... .......... .......... 98% 17.7M 5s +552300K .......... .......... .......... .......... .......... 98% 111M 5s +552350K .......... .......... .......... .......... .......... 98% 8.08M 5s +552400K .......... .......... .......... .......... .......... 98% 5.81M 5s +552450K .......... .......... .......... .......... .......... 98% 12.5M 5s +552500K .......... .......... .......... .......... .......... 98% 13.3M 5s +552550K .......... .......... .......... .......... .......... 98% 3.69M 5s +552600K .......... .......... .......... .......... .......... 98% 4.57M 5s +552650K .......... .......... .......... .......... .......... 98% 1.14M 5s +552700K .......... .......... .......... .......... .......... 98% 448M 5s +552750K .......... .......... .......... .......... .......... 98% 93.1T 5s +552800K .......... .......... .......... .......... .......... 98% 525M 5s +552850K .......... .......... .......... .......... .......... 98% 684M 5s +552900K .......... .......... .......... .......... .......... 98% 6.97M 5s +552950K .......... .......... .......... .......... .......... 98% 617M 5s +553000K .......... .......... .......... .......... .......... 98% 2.30M 5s +553050K .......... .......... .......... .......... .......... 98% 3.22M 5s +553100K .......... .......... .......... .......... .......... 98% 24.2M 5s +553150K .......... .......... .......... .......... .......... 98% 10.8M 5s +553200K .......... .......... .......... .......... .......... 98% 25.1M 5s +553250K .......... .......... .......... .......... .......... 98% 73.9M 5s +553300K .......... .......... .......... .......... .......... 98% 719K 5s +553350K .......... .......... .......... .......... .......... 98% 388M 5s +553400K .......... .......... .......... .......... .......... 98% 451M 5s +553450K .......... .......... .......... .......... .......... 98% 93.1T 4s +553500K .......... .......... .......... .......... .......... 98% 522M 4s +553550K .......... .......... .......... .......... .......... 98% 628M 4s +553600K .......... .......... .......... .......... .......... 98% 632M 4s +553650K .......... .......... .......... .......... .......... 98% 655M 4s +553700K .......... .......... .......... .......... .......... 98% 93.1T 4s +553750K .......... .......... .......... .......... .......... 98% 642M 4s +553800K .......... .......... .......... .......... .......... 98% 649M 4s +553850K .......... .......... .......... .......... .......... 98% 687M 4s +553900K .......... .......... .......... .......... .......... 98% 3.57M 4s +553950K .......... .......... .......... .......... .......... 98% 45.4M 4s +554000K .......... .......... .......... .......... .......... 98% 5.17M 4s +554050K .......... .......... .......... .......... .......... 98% 93.1T 4s +554100K .......... .......... .......... .......... .......... 98% 6.17M 4s +554150K .......... .......... .......... .......... .......... 98% 93.1T 4s +554200K .......... .......... .......... .......... .......... 98% 5.39M 4s +554250K .......... .......... .......... .......... .......... 98% 4.60M 4s +554300K .......... .......... .......... .......... .......... 98% 642M 4s +554350K .......... .......... .......... .......... .......... 98% 2.09M 4s +554400K .......... .......... .......... .......... .......... 98% 398M 4s +554450K .......... .......... .......... .......... .......... 98% 14.8M 4s +554500K .......... .......... .......... .......... .......... 98% 3.13M 4s +554550K .......... .......... .......... .......... .......... 98% 68.0M 4s +554600K .......... .......... .......... .......... .......... 98% 517M 4s +554650K .......... .......... .......... .......... .......... 98% 5.23M 4s +554700K .......... .......... .......... .......... .......... 98% 10.7M 4s +554750K .......... .......... .......... .......... .......... 98% 15.6M 4s +554800K .......... .......... .......... .......... .......... 98% 3.78M 4s +554850K .......... .......... .......... .......... .......... 98% 22.2M 4s +554900K .......... .......... .......... .......... .......... 98% 550M 4s +554950K .......... .......... .......... .......... .......... 98% 566M 4s +555000K .......... .......... .......... .......... .......... 98% 1.65M 4s +555050K .......... .......... .......... .......... .......... 98% 8.15M 4s +555100K .......... .......... .......... .......... .......... 98% 549M 4s +555150K .......... .......... .......... .......... .......... 98% 56.0M 3s +555200K .......... .......... .......... .......... .......... 98% 28.6M 3s +555250K .......... .......... .......... .......... .......... 98% 2.86M 3s +555300K .......... .......... .......... .......... .......... 98% 555M 3s +555350K .......... .......... .......... .......... .......... 98% 4.69M 3s +555400K .......... .......... .......... .......... .......... 98% 93.1T 3s +555450K .......... .......... .......... .......... .......... 98% 11.1M 3s +555500K .......... .......... .......... .......... .......... 98% 575M 3s +555550K .......... .......... .......... .......... .......... 99% 2.18M 3s +555600K .......... .......... .......... .......... .......... 99% 5.40M 3s +555650K .......... .......... .......... .......... .......... 99% 19.7M 3s +555700K .......... .......... .......... .......... .......... 99% 4.09M 3s +555750K .......... .......... .......... .......... .......... 99% 17.1M 3s +555800K .......... .......... .......... .......... .......... 99% 27.5M 3s +555850K .......... .......... .......... .......... .......... 99% 525M 3s +555900K .......... .......... .......... .......... .......... 99% 3.32M 3s +555950K .......... .......... .......... .......... .......... 99% 455M 3s +556000K .......... .......... .......... .......... .......... 99% 2.21M 3s +556050K .......... .......... .......... .......... .......... 99% 93.1T 3s +556100K .......... .......... .......... .......... .......... 99% 1.08M 3s +556150K .......... .......... .......... .......... .......... 99% 422M 3s +556200K .......... .......... .......... .......... .......... 99% 93.1T 3s +556250K .......... .......... .......... .......... .......... 99% 672M 3s +556300K .......... .......... .......... .......... .......... 99% 654M 3s +556350K .......... .......... .......... .......... .......... 99% 679M 3s +556400K .......... .......... .......... .......... .......... 99% 95.3M 3s +556450K .......... .......... .......... .......... .......... 99% 6.08M 3s +556500K .......... .......... .......... .......... .......... 99% 1.38M 3s +556550K .......... .......... .......... .......... .......... 99% 93.1T 3s +556600K .......... .......... .......... .......... .......... 99% 392M 3s +556650K .......... .......... .......... .......... .......... 99% 4.70M 3s +556700K .......... .......... .......... .......... .......... 99% 4.84M 3s +556750K .......... .......... .......... .......... .......... 99% 33.4M 3s +556800K .......... .......... .......... .......... .......... 99% 93.1T 3s +556850K .......... .......... .......... .......... .......... 99% 589M 2s +556900K .......... .......... .......... .......... .......... 99% 7.24M 2s +556950K .......... .......... .......... .......... .......... 99% 575M 2s +557000K .......... .......... .......... .......... .......... 99% 1.53M 2s +557050K .......... .......... .......... .......... .......... 99% 583M 2s +557100K .......... .......... .......... .......... .......... 99% 8.18M 2s +557150K .......... .......... .......... .......... .......... 99% 5.58M 2s +557200K .......... .......... .......... .......... .......... 99% 73.2M 2s +557250K .......... .......... .......... .......... .......... 99% 6.34M 2s +557300K .......... .......... .......... .......... .......... 99% 4.87M 2s +557350K .......... .......... .......... .......... .......... 99% 2.39M 2s +557400K .......... .......... .......... .......... .......... 99% 93.1T 2s +557450K .......... .......... .......... .......... .......... 99% 3.25M 2s +557500K .......... .......... .......... .......... .......... 99% 93.1T 2s +557550K .......... .......... .......... .......... .......... 99% 91.9M 2s +557600K .......... .......... .......... .......... .......... 99% 93.1T 2s +557650K .......... .......... .......... .......... .......... 99% 37.3M 2s +557700K .......... .......... .......... .......... .......... 99% 667M 2s +557750K .......... .......... .......... .......... .......... 99% 5.09M 2s +557800K .......... .......... .......... .......... .......... 99% 2.47M 2s +557850K .......... .......... .......... .......... .......... 99% 7.34M 2s +557900K .......... .......... .......... .......... .......... 99% 3.84M 2s +557950K .......... .......... .......... .......... .......... 99% 438M 2s +558000K .......... .......... .......... .......... .......... 99% 697M 2s +558050K .......... .......... .......... .......... .......... 99% 81.0M 2s +558100K .......... .......... .......... .......... .......... 99% 3.48M 2s +558150K .......... .......... .......... .......... .......... 99% 7.68M 2s +558200K .......... .......... .......... .......... .......... 99% 1.30M 2s +558250K .......... .......... .......... .......... .......... 99% 93.1T 2s +558300K .......... .......... .......... .......... .......... 99% 459M 2s +558350K .......... .......... .......... .......... .......... 99% 673M 2s +558400K .......... .......... .......... .......... .......... 99% 11.4M 2s +558450K .......... .......... .......... .......... .......... 99% 4.92M 2s +558500K .......... .......... .......... .......... .......... 99% 28.2M 2s +558550K .......... .......... .......... .......... .......... 99% 4.22M 2s +558600K .......... .......... .......... .......... .......... 99% 5.84M 1s +558650K .......... .......... .......... .......... .......... 99% 12.0M 1s +558700K .......... .......... .......... .......... .......... 99% 93.1T 1s +558750K .......... .......... .......... .......... .......... 99% 17.3M 1s +558800K .......... .......... .......... .......... .......... 99% 716M 1s +558850K .......... .......... .......... .......... .......... 99% 84.0M 1s +558900K .......... .......... .......... .......... .......... 99% 856K 1s +558950K .......... .......... .......... .......... .......... 99% 93.1T 1s +559000K .......... .......... .......... .......... .......... 99% 429M 1s +559050K .......... .......... .......... .......... .......... 99% 644M 1s +559100K .......... .......... .......... .......... .......... 99% 608M 1s +559150K .......... .......... .......... .......... .......... 99% 93.1T 1s +559200K .......... .......... .......... .......... .......... 99% 37.2M 1s +559250K .......... .......... .......... .......... .......... 99% 2.33M 1s +559300K .......... .......... .......... .......... .......... 99% 21.5M 1s +559350K .......... .......... .......... .......... .......... 99% 563M 1s +559400K .......... .......... .......... .......... .......... 99% 5.51M 1s +559450K .......... .......... .......... .......... .......... 99% 7.05M 1s +559500K .......... .......... .......... .......... .......... 99% 5.33M 1s +559550K .......... .......... .......... .......... .......... 99% 93.1T 1s +559600K .......... .......... .......... .......... .......... 99% 12.4M 1s +559650K .......... .......... .......... .......... .......... 99% 725M 1s +559700K .......... .......... .......... .......... .......... 99% 344K 1s +559750K 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b/cutler/engine/defaults.py @@ -0,0 +1,726 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/engine/defaults.py + +""" +This file contains components with some default boilerplate logic user may need +in training / testing. They will not work for everyone, but many users may find them useful. + +The behavior of functions/classes in this file is subject to change, +since they are meant to represent the "common default behavior" people need in their projects. +""" + +import argparse +import logging +import os +import sys +import weakref +from collections import OrderedDict +from typing import Optional +import torch +from fvcore.nn.precise_bn import get_bn_modules +from omegaconf import OmegaConf +from torch.nn.parallel import DistributedDataParallel + +import data.transforms as T +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import CfgNode, LazyConfig +from detectron2.data import ( + MetadataCatalog, +) +from data import ( + build_detection_test_loader, + build_detection_train_loader, +) +from detectron2.evaluation import ( + DatasetEvaluator, + inference_on_dataset, + print_csv_format, + verify_results, +) +from modeling import build_model +from solver import build_lr_scheduler, build_optimizer +from detectron2.utils import comm +from detectron2.utils.collect_env import collect_env_info +from detectron2.utils.env import seed_all_rng +from detectron2.utils.events import CommonMetricPrinter, JSONWriter, TensorboardXWriter +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import setup_logger + +from detectron2.engine import hooks +from detectron2.engine import TrainerBase +from .train_loop import CustomAMPTrainer, CustomSimpleTrainer + +__all__ = [ + "create_ddp_model", + "default_argument_parser", + "default_setup", + "default_writers", + "DefaultPredictor", + "DefaultTrainer", +] + + +def create_ddp_model(model, *, fp16_compression=False, **kwargs): + """ + Create a DistributedDataParallel model if there are >1 processes. + + Args: + model: a torch.nn.Module + fp16_compression: add fp16 compression hooks to the ddp object. + See more at https://pytorch.org/docs/stable/ddp_comm_hooks.html#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook + kwargs: other arguments of :module:`torch.nn.parallel.DistributedDataParallel`. + """ # noqa + if comm.get_world_size() == 1: + return model + if "device_ids" not in kwargs: + kwargs["device_ids"] = [comm.get_local_rank()] + ddp = DistributedDataParallel(model, **kwargs) + if fp16_compression: + from torch.distributed.algorithms.ddp_comm_hooks import default as comm_hooks + + ddp.register_comm_hook(state=None, hook=comm_hooks.fp16_compress_hook) + return ddp + + +def default_argument_parser(epilog=None): + """ + Create a parser with some common arguments used by detectron2 users. + + Args: + epilog (str): epilog passed to ArgumentParser describing the usage. + + Returns: + argparse.ArgumentParser: + """ + parser = argparse.ArgumentParser( + epilog=epilog + or f""" +Examples: + +Run on single machine: + $ {sys.argv[0]} --num-gpus 8 --config-file cfg.yaml + +Change some config options: + $ {sys.argv[0]} --config-file cfg.yaml MODEL.WEIGHTS /path/to/weight.pth SOLVER.BASE_LR 0.001 + +Run on multiple machines: + (machine0)$ {sys.argv[0]} --machine-rank 0 --num-machines 2 --dist-url [--other-flags] + (machine1)$ {sys.argv[0]} --machine-rank 1 --num-machines 2 --dist-url [--other-flags] +""", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file") + parser.add_argument( + "--resume", + action="store_true", + help="Whether to attempt to resume from the checkpoint directory. " + "See documentation of `DefaultTrainer.resume_or_load()` for what it means.", + ) + parser.add_argument("--eval-only", action="store_true", help="perform evaluation only") + parser.add_argument("--num-gpus", type=int, default=1, help="number of gpus *per machine*") + parser.add_argument("--num-machines", type=int, default=1, help="total number of machines") + parser.add_argument( + "--machine-rank", type=int, default=0, help="the rank of this machine (unique per machine)" + ) + parser.add_argument( + "--test-dataset", type=str, default="", help="the dataset used for evaluation" + ) + parser.add_argument( + "--train-dataset", type=str, default="", help="the dataset used for training" + ) + parser.add_argument("--no-segm", action="store_true", help="perform evaluation on detection only") + # PyTorch still may leave orphan processes in multi-gpu training. + # Therefore we use a deterministic way to obtain port, + # so that users are aware of orphan processes by seeing the port occupied. + port = 2**15 + 2**14 + hash(os.getuid() if sys.platform != "win32" else 1) % 2**14 + parser.add_argument( + "--dist-url", + default="tcp://127.0.0.1:{}".format(port), + help="initialization URL for pytorch distributed backend. See " + "https://pytorch.org/docs/stable/distributed.html for details.", + ) + parser.add_argument( + "opts", + help=""" +Modify config options at the end of the command. For Yacs configs, use +space-separated "PATH.KEY VALUE" pairs. +For python-based LazyConfig, use "path.key=value". + """.strip(), + default=None, + nargs=argparse.REMAINDER, + ) + return parser + + +def _try_get_key(cfg, *keys, default=None): + """ + Try select keys from cfg until the first key that exists. Otherwise return default. + """ + if isinstance(cfg, CfgNode): + cfg = OmegaConf.create(cfg.dump()) + for k in keys: + none = object() + p = OmegaConf.select(cfg, k, default=none) + if p is not none: + return p + return default + + +def _highlight(code, filename): + try: + import pygments + except ImportError: + return code + + from pygments.lexers import Python3Lexer, YamlLexer + from pygments.formatters import Terminal256Formatter + + lexer = Python3Lexer() if filename.endswith(".py") else YamlLexer() + code = pygments.highlight(code, lexer, Terminal256Formatter(style="monokai")) + return code + + +def default_setup(cfg, args): + """ + Perform some basic common setups at the beginning of a job, including: + + 1. Set up the detectron2 logger + 2. Log basic information about environment, cmdline arguments, and config + 3. Backup the config to the output directory + + Args: + cfg (CfgNode or omegaconf.DictConfig): the full config to be used + args (argparse.NameSpace): the command line arguments to be logged + """ + output_dir = _try_get_key(cfg, "OUTPUT_DIR", "output_dir", "train.output_dir") + if comm.is_main_process() and output_dir: + PathManager.mkdirs(output_dir) + + rank = comm.get_rank() + setup_logger(output_dir, distributed_rank=rank, name="fvcore") + logger = setup_logger(output_dir, distributed_rank=rank) + + logger.info("Rank of current process: {}. World size: {}".format(rank, comm.get_world_size())) + logger.info("Environment info:\n" + collect_env_info()) + + logger.info("Command line arguments: " + str(args)) + if hasattr(args, "config_file") and args.config_file != "": + logger.info( + "Contents of args.config_file={}:\n{}".format( + args.config_file, + _highlight(PathManager.open(args.config_file, "r").read(), args.config_file), + ) + ) + + if comm.is_main_process() and output_dir: + # Note: some of our scripts may expect the existence of + # config.yaml in output directory + path = os.path.join(output_dir, "config.yaml") + if isinstance(cfg, CfgNode): + logger.info("Running with full config:\n{}".format(_highlight(cfg.dump(), ".yaml"))) + with PathManager.open(path, "w") as f: + f.write(cfg.dump()) + else: + LazyConfig.save(cfg, path) + logger.info("Full config saved to {}".format(path)) + + # make sure each worker has a different, yet deterministic seed if specified + seed = _try_get_key(cfg, "SEED", "train.seed", default=-1) + seed_all_rng(None if seed < 0 else seed + rank) + + # cudnn benchmark has large overhead. It shouldn't be used considering the small size of + # typical validation set. + if not (hasattr(args, "eval_only") and args.eval_only): + torch.backends.cudnn.benchmark = _try_get_key( + cfg, "CUDNN_BENCHMARK", "train.cudnn_benchmark", default=False + ) + + +def default_writers(output_dir: str, max_iter: Optional[int] = None): + """ + Build a list of :class:`EventWriter` to be used. + It now consists of a :class:`CommonMetricPrinter`, + :class:`TensorboardXWriter` and :class:`JSONWriter`. + + Args: + output_dir: directory to store JSON metrics and tensorboard events + max_iter: the total number of iterations + + Returns: + list[EventWriter]: a list of :class:`EventWriter` objects. + """ + PathManager.mkdirs(output_dir) + return [ + # It may not always print what you want to see, since it prints "common" metrics only. + CommonMetricPrinter(max_iter), + JSONWriter(os.path.join(output_dir, "metrics.json")), + TensorboardXWriter(output_dir), + ] + + +class DefaultPredictor: + """ + Create a simple end-to-end predictor with the given config that runs on + single device for a single input image. + + Compared to using the model directly, this class does the following additions: + + 1. Load checkpoint from `cfg.MODEL.WEIGHTS`. + 2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`. + 3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`. + 4. Take one input image and produce a single output, instead of a batch. + + This is meant for simple demo purposes, so it does the above steps automatically. + This is not meant for benchmarks or running complicated inference logic. + If you'd like to do anything more complicated, please refer to its source code as + examples to build and use the model manually. + + Attributes: + metadata (Metadata): the metadata of the underlying dataset, obtained from + cfg.DATASETS.TEST. + + Examples: + :: + pred = DefaultPredictor(cfg) + inputs = cv2.imread("input.jpg") + outputs = pred(inputs) + """ + + def __init__(self, cfg): + self.cfg = cfg.clone() # cfg can be modified by model + self.model = build_model(self.cfg) + self.model.eval() + if len(cfg.DATASETS.TEST): + self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0]) + + checkpointer = DetectionCheckpointer(self.model) + checkpointer.load(cfg.MODEL.WEIGHTS) + + self.aug = T.ResizeShortestEdge( + [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST + ) + + self.input_format = cfg.INPUT.FORMAT + assert self.input_format in ["RGB", "BGR"], self.input_format + + def __call__(self, original_image): + """ + Args: + original_image (np.ndarray): an image of shape (H, W, C) (in BGR order). + + Returns: + predictions (dict): + the output of the model for one image only. + See :doc:`/tutorials/models` for details about the format. + """ + with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258 + # Apply pre-processing to image. + if self.input_format == "RGB": + # whether the model expects BGR inputs or RGB + original_image = original_image[:, :, ::-1] + height, width = original_image.shape[:2] + image = self.aug.get_transform(original_image).apply_image(original_image) + image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) + + inputs = {"image": image, "height": height, "width": width} + predictions = self.model([inputs])[0] + return predictions + + +class DefaultTrainer(TrainerBase): + """ + A trainer with default training logic. It does the following: + + 1. Create a :class:`SimpleTrainer` using model, optimizer, dataloader + defined by the given config. Create a LR scheduler defined by the config. + 2. Load the last checkpoint or `cfg.MODEL.WEIGHTS`, if exists, when + `resume_or_load` is called. + 3. Register a few common hooks defined by the config. + + It is created to simplify the **standard model training workflow** and reduce code boilerplate + for users who only need the standard training workflow, with standard features. + It means this class makes *many assumptions* about your training logic that + may easily become invalid in a new research. In fact, any assumptions beyond those made in the + :class:`SimpleTrainer` are too much for research. + + The code of this class has been annotated about restrictive assumptions it makes. + When they do not work for you, you're encouraged to: + + 1. Overwrite methods of this class, OR: + 2. Use :class:`SimpleTrainer`, which only does minimal SGD training and + nothing else. You can then add your own hooks if needed. OR: + 3. Write your own training loop similar to `tools/plain_train_net.py`. + + See the :doc:`/tutorials/training` tutorials for more details. + + Note that the behavior of this class, like other functions/classes in + this file, is not stable, since it is meant to represent the "common default behavior". + It is only guaranteed to work well with the standard models and training workflow in detectron2. + To obtain more stable behavior, write your own training logic with other public APIs. + + Examples: + :: + trainer = DefaultTrainer(cfg) + trainer.resume_or_load() # load last checkpoint or MODEL.WEIGHTS + trainer.train() + + Attributes: + scheduler: + checkpointer (DetectionCheckpointer): + cfg (CfgNode): + """ + + def __init__(self, cfg): + """ + Args: + cfg (CfgNode): + """ + super().__init__() + logger = logging.getLogger("detectron2") + if not logger.isEnabledFor(logging.INFO): # setup_logger is not called for d2 + setup_logger() + cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size()) + + # Assume these objects must be constructed in this order. + model = self.build_model(cfg) + optimizer = self.build_optimizer(cfg, model) + data_loader = self.build_train_loader(cfg) + + model = create_ddp_model(model, broadcast_buffers=False) + if cfg.SOLVER.AMP.ENABLED: + self._trainer = CustomAMPTrainer(model, data_loader, optimizer, cfg=cfg) + else: + self._trainer = CustomSimpleTrainer(model, data_loader, optimizer, cfg=cfg) + + self.scheduler = self.build_lr_scheduler(cfg, optimizer) + self.checkpointer = DetectionCheckpointer( + # Assume you want to save checkpoints together with logs/statistics + model, + cfg.OUTPUT_DIR, + trainer=weakref.proxy(self), + ) + self.start_iter = 0 + self.max_iter = cfg.SOLVER.MAX_ITER + self.cfg = cfg + + self.register_hooks(self.build_hooks()) + + def resume_or_load(self, resume=True): + """ + If `resume==True` and `cfg.OUTPUT_DIR` contains the last checkpoint (defined by + a `last_checkpoint` file), resume from the file. Resuming means loading all + available states (eg. optimizer and scheduler) and update iteration counter + from the checkpoint. ``cfg.MODEL.WEIGHTS`` will not be used. + + Otherwise, this is considered as an independent training. The method will load model + weights from the file `cfg.MODEL.WEIGHTS` (but will not load other states) and start + from iteration 0. + + Args: + resume (bool): whether to do resume or not + """ + self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume) + if resume and self.checkpointer.has_checkpoint(): + # The checkpoint stores the training iteration that just finished, thus we start + # at the next iteration + self.start_iter = self.iter + 1 + + def build_hooks(self): + """ + Build a list of default hooks, including timing, evaluation, + checkpointing, lr scheduling, precise BN, writing events. + + Returns: + list[HookBase]: + """ + cfg = self.cfg.clone() + cfg.defrost() + cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN + + ret = [ + hooks.IterationTimer(), + hooks.LRScheduler(), + hooks.PreciseBN( + # Run at the same freq as (but before) evaluation. + cfg.TEST.EVAL_PERIOD, + self.model, + # Build a new data loader to not affect training + self.build_train_loader(cfg), + cfg.TEST.PRECISE_BN.NUM_ITER, + ) + if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model) + else None, + ] + + # Do PreciseBN before checkpointer, because it updates the model and need to + # be saved by checkpointer. + # This is not always the best: if checkpointing has a different frequency, + # some checkpoints may have more precise statistics than others. + if comm.is_main_process(): + ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD)) + + def test_and_save_results(): + self._last_eval_results = self.test(self.cfg, self.model) + return self._last_eval_results + + # Do evaluation after checkpointer, because then if it fails, + # we can use the saved checkpoint to debug. + ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results)) + + if comm.is_main_process(): + # Here the default print/log frequency of each writer is used. + # run writers in the end, so that evaluation metrics are written + ret.append(hooks.PeriodicWriter(self.build_writers(), period=20)) + return ret + + def build_writers(self): + """ + Build a list of writers to be used using :func:`default_writers()`. + If you'd like a different list of writers, you can overwrite it in + your trainer. + + Returns: + list[EventWriter]: a list of :class:`EventWriter` objects. + """ + return default_writers(self.cfg.OUTPUT_DIR, self.max_iter) + + def train(self): + """ + Run training. + + Returns: + OrderedDict of results, if evaluation is enabled. Otherwise None. + """ + super().train(self.start_iter, self.max_iter) + if len(self.cfg.TEST.EXPECTED_RESULTS) and comm.is_main_process(): + assert hasattr( + self, "_last_eval_results" + ), "No evaluation results obtained during training!" + verify_results(self.cfg, self._last_eval_results) + return self._last_eval_results + + def run_step(self): + self._trainer.iter = self.iter + self._trainer.run_step() + + def state_dict(self): + ret = super().state_dict() + ret["_trainer"] = self._trainer.state_dict() + return ret + + def load_state_dict(self, state_dict): + super().load_state_dict(state_dict) + self._trainer.load_state_dict(state_dict["_trainer"]) + + @classmethod + def build_model(cls, cfg): + """ + Returns: + torch.nn.Module: + + It now calls :func:`detectron2.modeling.build_model`. + Overwrite it if you'd like a different model. + """ + model = build_model(cfg) + logger = logging.getLogger(__name__) + logger.info("Model:\n{}".format(model)) + return model + + @classmethod + def build_optimizer(cls, cfg, model): + """ + Returns: + torch.optim.Optimizer: + + It now calls :func:`detectron2.solver.build_optimizer`. + Overwrite it if you'd like a different optimizer. + """ + return build_optimizer(cfg, model) + + @classmethod + def build_lr_scheduler(cls, cfg, optimizer): + """ + It now calls :func:`detectron2.solver.build_lr_scheduler`. + Overwrite it if you'd like a different scheduler. + """ + return build_lr_scheduler(cfg, optimizer) + + @classmethod + def build_train_loader(cls, cfg): + """ + Returns: + iterable + + It now calls :func:`detectron2.data.build_detection_train_loader`. + Overwrite it if you'd like a different data loader. + """ + return build_detection_train_loader(cfg) + + @classmethod + def build_test_loader(cls, cfg, dataset_name): + """ + Returns: + iterable + + It now calls :func:`detectron2.data.build_detection_test_loader`. + Overwrite it if you'd like a different data loader. + """ + return build_detection_test_loader(cfg, dataset_name) + + @classmethod + def build_evaluator(cls, cfg, dataset_name): + """ + Returns: + DatasetEvaluator or None + + It is not implemented by default. + """ + raise NotImplementedError( + """ +If you want DefaultTrainer to automatically run evaluation, +please implement `build_evaluator()` in subclasses (see train_net.py for example). +Alternatively, you can call evaluation functions yourself (see Colab balloon tutorial for example). +""" + ) + + @classmethod + def test(cls, cfg, model, evaluators=None): + """ + Evaluate the given model. The given model is expected to already contain + weights to evaluate. + + Args: + cfg (CfgNode): + model (nn.Module): + evaluators (list[DatasetEvaluator] or None): if None, will call + :meth:`build_evaluator`. Otherwise, must have the same length as + ``cfg.DATASETS.TEST``. + + Returns: + dict: a dict of result metrics + """ + logger = logging.getLogger(__name__) + if isinstance(evaluators, DatasetEvaluator): + evaluators = [evaluators] + if evaluators is not None: + assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format( + len(cfg.DATASETS.TEST), len(evaluators) + ) + + results = OrderedDict() + for idx, dataset_name in enumerate(cfg.DATASETS.TEST): + data_loader = cls.build_test_loader(cfg, dataset_name) + # When evaluators are passed in as arguments, + # implicitly assume that evaluators can be created before data_loader. + if evaluators is not None: + evaluator = evaluators[idx] + else: + try: + evaluator = cls.build_evaluator(cfg, dataset_name) + except NotImplementedError: + logger.warn( + "No evaluator found. Use `DefaultTrainer.test(evaluators=)`, " + "or implement its `build_evaluator` method." + ) + results[dataset_name] = {} + continue + results_i = inference_on_dataset(model, data_loader, evaluator) + results[dataset_name] = results_i + if comm.is_main_process(): + assert isinstance( + results_i, dict + ), "Evaluator must return a dict on the main process. Got {} instead.".format( + results_i + ) + logger.info("Evaluation results for {} in csv format:".format(dataset_name)) + print_csv_format(results_i) + + if len(results) == 1: + results = list(results.values())[0] + return results + + @staticmethod + def auto_scale_workers(cfg, num_workers: int): + """ + When the config is defined for certain number of workers (according to + ``cfg.SOLVER.REFERENCE_WORLD_SIZE``) that's different from the number of + workers currently in use, returns a new cfg where the total batch size + is scaled so that the per-GPU batch size stays the same as the + original ``IMS_PER_BATCH // REFERENCE_WORLD_SIZE``. + + Other config options are also scaled accordingly: + * training steps and warmup steps are scaled inverse proportionally. + * learning rate are scaled proportionally, following :paper:`ImageNet in 1h`. + + For example, with the original config like the following: + + .. code-block:: yaml + + IMS_PER_BATCH: 16 + BASE_LR: 0.1 + REFERENCE_WORLD_SIZE: 8 + MAX_ITER: 5000 + STEPS: (4000,) + CHECKPOINT_PERIOD: 1000 + + When this config is used on 16 GPUs instead of the reference number 8, + calling this method will return a new config with: + + .. code-block:: yaml + + IMS_PER_BATCH: 32 + BASE_LR: 0.2 + REFERENCE_WORLD_SIZE: 16 + MAX_ITER: 2500 + STEPS: (2000,) + CHECKPOINT_PERIOD: 500 + + Note that both the original config and this new config can be trained on 16 GPUs. + It's up to user whether to enable this feature (by setting ``REFERENCE_WORLD_SIZE``). + + Returns: + CfgNode: a new config. Same as original if ``cfg.SOLVER.REFERENCE_WORLD_SIZE==0``. + """ + old_world_size = cfg.SOLVER.REFERENCE_WORLD_SIZE + if old_world_size == 0 or old_world_size == num_workers: + return cfg + cfg = cfg.clone() + frozen = cfg.is_frozen() + cfg.defrost() + + assert ( + cfg.SOLVER.IMS_PER_BATCH % old_world_size == 0 + ), "Invalid REFERENCE_WORLD_SIZE in config!" + scale = num_workers / old_world_size + bs = cfg.SOLVER.IMS_PER_BATCH = int(round(cfg.SOLVER.IMS_PER_BATCH * scale)) + lr = cfg.SOLVER.BASE_LR = cfg.SOLVER.BASE_LR * scale + max_iter = cfg.SOLVER.MAX_ITER = int(round(cfg.SOLVER.MAX_ITER / scale)) + warmup_iter = cfg.SOLVER.WARMUP_ITERS = int(round(cfg.SOLVER.WARMUP_ITERS / scale)) + cfg.SOLVER.STEPS = tuple(int(round(s / scale)) for s in cfg.SOLVER.STEPS) + cfg.TEST.EVAL_PERIOD = int(round(cfg.TEST.EVAL_PERIOD / scale)) + cfg.SOLVER.CHECKPOINT_PERIOD = int(round(cfg.SOLVER.CHECKPOINT_PERIOD / scale)) + cfg.SOLVER.REFERENCE_WORLD_SIZE = num_workers # maintain invariant + logger = logging.getLogger(__name__) + logger.info( + f"Auto-scaling the config to batch_size={bs}, learning_rate={lr}, " + f"max_iter={max_iter}, warmup={warmup_iter}." + ) + + if frozen: + cfg.freeze() + return cfg + + +# Access basic attributes from the underlying trainer +for _attr in ["model", "data_loader", "optimizer"]: + setattr( + DefaultTrainer, + _attr, + property( + # getter + lambda self, x=_attr: getattr(self._trainer, x), + # setter + lambda self, value, x=_attr: setattr(self._trainer, x, value), + ), + ) diff --git a/cutler/engine/train_loop.py b/cutler/engine/train_loop.py new file mode 100644 index 0000000000000000000000000000000000000000..53cc24724659e292e17a5001f57348aed45f26c8 --- /dev/null +++ b/cutler/engine/train_loop.py @@ -0,0 +1,360 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/engine/train_loop.py and https://github.com/NVlabs/FreeSOLO/tree/main/freesolo/engine/trainer.py + +import torch +from torch.nn.parallel import DataParallel, DistributedDataParallel + +import numpy as np +import time +import torch +from torch.nn.parallel import DataParallel, DistributedDataParallel +import copy +import random +import torch.nn.functional as F +from detectron2.structures.instances import Instances +from detectron2.structures import BitMasks + +from detectron2.engine import SimpleTrainer + +__all__ = ["CustomSimpleTrainer", "CustomAMPTrainer"] + +class CustomSimpleTrainer(SimpleTrainer): + """ + A simple trainer for the most common type of task: + single-cost single-optimizer single-data-source iterative optimization, + optionally using data-parallelism. + It assumes that every step, you: + + 1. Compute the loss with a data from the data_loader. + 2. Compute the gradients with the above loss. + 3. Update the model with the optimizer. + + All other tasks during training (checkpointing, logging, evaluation, LR schedule) + are maintained by hooks, which can be registered by :meth:`TrainerBase.register_hooks`. + + If you want to do anything fancier than this, + either subclass TrainerBase and implement your own `run_step`, + or write your own training loop. + """ + + def __init__(self, model, data_loader, optimizer, cfg=None, use_copy_paste=False, + copy_paste_rate=-1, copy_paste_random_num=None, copy_paste_min_ratio=-1, + copy_paste_max_ratio=-1, visualize_copy_paste=False): + """ + Args: + model: a torch Module. Takes a data from data_loader and returns a + dict of losses. + data_loader: an iterable. Contains data to be used to call model. + optimizer: a torch optimizer. + """ + super().__init__(model, data_loader, optimizer) + + """ + We set the model to training mode in the trainer. + However it's valid to train a model that's in eval mode. + If you want your model (or a submodule of it) to behave + like evaluation during training, you can overwrite its train() method. + """ + self.cfg = cfg + # model.train() + + # self.model = model + # self.data_loader = data_loader + # to access the data loader iterator, call `self._data_loader_iter` + # self._data_loader_iter_obj = None + # self.optimizer = optimizer + + self.use_copy_paste = use_copy_paste if self.cfg is None else self.cfg.DATALOADER.COPY_PASTE + self.cfg_COPY_PASTE_RATE = copy_paste_rate if self.cfg is None else self.cfg.DATALOADER.COPY_PASTE_RATE + self.cfg_COPY_PASTE_RANDOM_NUM = copy_paste_random_num if self.cfg is None else self.cfg.DATALOADER.COPY_PASTE_RANDOM_NUM + self.cfg_COPY_PASTE_MIN_RATIO = copy_paste_min_ratio if self.cfg is None else self.cfg.DATALOADER.COPY_PASTE_MIN_RATIO + self.cfg_COPY_PASTE_MAX_RATIO = copy_paste_max_ratio if self.cfg is None else self.cfg.DATALOADER.COPY_PASTE_MAX_RATIO + self.cfg_VISUALIZE_COPY_PASTE = visualize_copy_paste if self.cfg is None else self.cfg.DATALOADER.VISUALIZE_COPY_PASTE + + def IoU(self, mask1, mask2): # only work when the batch size is 1 + mask1, mask2 = (mask1>0.5).to(torch.bool), (mask2>0.5).to(torch.bool) + intersection = torch.sum(mask1 * (mask1 == mask2), dim=[-1, -2]).squeeze() + union = torch.sum(mask1 + mask2, dim=[-1, -2]).squeeze() + return (intersection.to(torch.float) / union).mean().view(1, -1) + + def IoY(self, mask1, mask2): # only work when the batch size is 1 + # print(mask1.size(), mask2.size()) + mask1, mask2 = mask1.squeeze(), mask2.squeeze() + mask1, mask2 = (mask1>0.5).to(torch.bool), (mask2>0.5).to(torch.bool) + intersection = torch.sum(mask1 * (mask1 == mask2), dim=[-1, -2]).squeeze() + union = torch.sum(mask2, dim=[-1, -2]).squeeze() + return (intersection.to(torch.float) / union).mean().view(1, -1) + + def copy_and_paste(self, labeled_data, unlabeled_data): + new_unlabeled_data = [] + def mask_iou_matrix(x, y, mode='iou'): + x = x.reshape(x.shape[0], -1).float() + y = y.reshape(y.shape[0], -1).float() + inter_matrix = x @ y.transpose(1, 0) # n1xn2 + sum_x = x.sum(1)[:, None].expand(x.shape[0], y.shape[0]) + sum_y = y.sum(1)[None, :].expand(x.shape[0], y.shape[0]) + if mode == 'ioy': + iou_matrix = inter_matrix / (sum_y) # [1, 1] + else: + iou_matrix = inter_matrix / (sum_x + sum_y - inter_matrix) # [1, 1] + return iou_matrix + + def visualize_data(data, save_path = './sample.jpg'): + from data import detection_utils as utils + from detectron2.data import DatasetCatalog, MetadataCatalog + from detectron2.utils.visualizer import Visualizer + data["instances"] = data["instances"].to(device='cpu') + img = data["image"].permute(1, 2, 0).cpu().detach().numpy() + img = utils.convert_image_to_rgb(img, 'RGB') + metadata = MetadataCatalog.get('imagenet_train_tau0.15') + visualizer = Visualizer(img, metadata=metadata, scale=1.0) + target_fields = data["instances"].get_fields() + labels = [metadata.thing_classes[i] for i in target_fields["gt_classes"]] + vis = visualizer.overlay_instances( + labels=labels, + boxes=target_fields.get("gt_boxes"), # ("gt_boxes", None), + masks=target_fields.get("gt_masks"), # ("gt_masks", None), + keypoints=target_fields.get("gt_keypoints", None), + ) + print("Saving to {} ...".format(save_path)) + vis.save(save_path) + + for cur_labeled_data, cur_unlabeled_data in zip(labeled_data, unlabeled_data): + cur_labeled_instances = cur_labeled_data["instances"] + cur_labeled_image = cur_labeled_data["image"] + cur_unlabeled_instances = cur_unlabeled_data["instances"] + cur_unlabeled_image = cur_unlabeled_data["image"] + + num_labeled_instances = len(cur_labeled_instances) + copy_paste_rate = random.random() + + if self.cfg_COPY_PASTE_RATE >= copy_paste_rate and num_labeled_instances > 0: + if self.cfg_COPY_PASTE_RANDOM_NUM: + num_copy = 1 if num_labeled_instances == 1 else np.random.randint(1, max(1, num_labeled_instances)) + else: + num_copy = num_labeled_instances + else: + num_copy = 0 + if num_labeled_instances == 0 or num_copy == 0: + new_unlabeled_data.append(cur_unlabeled_data) + else: + # print("num_labeled_instances, num_copy: ", num_labeled_instances, num_copy) + choice = np.random.choice(num_labeled_instances, num_copy, replace=False) + copied_instances = cur_labeled_instances[choice].to(device=cur_unlabeled_instances.gt_boxes.device) + copied_masks = copied_instances.gt_masks + copied_boxes = copied_instances.gt_boxes + _, labeled_h, labeled_w = cur_labeled_image.shape + _, unlabeled_h, unlabeled_w = cur_unlabeled_image.shape + + # rescale the labeled image to align with unlabeled one. + if isinstance(copied_masks, torch.Tensor): + masks_new = copied_masks[None, ...].float() + else: + masks_new = copied_masks.tensor[None, ...].float() + # resize the masks with a random ratio from 0.5 to 1.0 + resize_ratio = random.uniform(self.cfg_COPY_PASTE_MIN_RATIO, self.cfg_COPY_PASTE_MAX_RATIO) + w_new = int(resize_ratio * unlabeled_w) + h_new = int(resize_ratio * unlabeled_h) + + w_shift = random.randint(0, unlabeled_w - w_new) + h_shift = random.randint(0, unlabeled_h - h_new) + + cur_labeled_image_new = F.interpolate(cur_labeled_image[None, ...].float(), size=(h_new, w_new), mode="bilinear", align_corners=False).byte().squeeze(0) + if isinstance(copied_masks, torch.Tensor): + masks_new = F.interpolate(copied_masks[None, ...].float(), size=(h_new, w_new), mode="bilinear", align_corners=False).bool().squeeze(0) + else: + masks_new = F.interpolate(copied_masks.tensor[None, ...].float(), size=(h_new, w_new), mode="bilinear", align_corners=False).bool().squeeze(0) + copied_boxes.scale(1. * unlabeled_w / labeled_w * resize_ratio, 1. * unlabeled_h / labeled_h * resize_ratio) + + if isinstance(cur_unlabeled_instances.gt_masks, torch.Tensor): + _, mask_w, mask_h = cur_unlabeled_instances.gt_masks.size() + else: + _, mask_w, mask_h = cur_unlabeled_instances.gt_masks.tensor.size() + masks_new_all = torch.zeros(num_copy, mask_w, mask_h) + image_new_all = torch.zeros_like(cur_unlabeled_image) + + image_new_all[:, h_shift:h_shift+h_new, w_shift:w_shift+w_new] += cur_labeled_image_new + masks_new_all[:, h_shift:h_shift+h_new, w_shift:w_shift+w_new] += masks_new + + cur_labeled_image = image_new_all.byte() #.squeeze(0) + if isinstance(copied_masks, torch.Tensor): + copied_masks = masks_new_all.bool() #.squeeze(0) + else: + copied_masks.tensor = masks_new_all.bool() #.squeeze(0) + copied_boxes.tensor[:, 0] += h_shift + copied_boxes.tensor[:, 2] += h_shift + copied_boxes.tensor[:, 1] += w_shift + copied_boxes.tensor[:, 3] += w_shift + + copied_instances.gt_masks = copied_masks + copied_instances.gt_boxes = copied_boxes + copied_instances._image_size = (unlabeled_h, unlabeled_w) + if len(cur_unlabeled_instances) == 0: + if isinstance(copied_instances.gt_masks, torch.Tensor): + alpha = copied_instances.gt_masks.sum(0) > 0 + else: + alpha = copied_instances.gt_masks.tensor.sum(0) > 0 + # merge image + alpha = alpha.cpu() + composited_image = (alpha * cur_labeled_image) + (~alpha * cur_unlabeled_image) + cur_unlabeled_data["image"] = composited_image + cur_unlabeled_data["instances"] = copied_instances + else: + # remove the copied object if iou greater than 0.5 + if isinstance(copied_masks, torch.Tensor): + iou_matrix = mask_iou_matrix(copied_masks, cur_unlabeled_instances.gt_masks, mode='ioy') # nxN + else: + iou_matrix = mask_iou_matrix(copied_masks.tensor, cur_unlabeled_instances.gt_masks.tensor, mode='ioy') # nxN + + keep = iou_matrix.max(1)[0] < 0.5 + if keep.sum() == 0: + new_unlabeled_data.append(cur_unlabeled_data) + continue + copied_instances = copied_instances[keep] + # update existing instances in unlabeled image + if isinstance(copied_instances.gt_masks, torch.Tensor): + alpha = copied_instances.gt_masks.sum(0) > 0 + cur_unlabeled_instances.gt_masks = ~alpha * cur_unlabeled_instances.gt_masks + areas_unlabeled = cur_unlabeled_instances.gt_masks.sum((1,2)) + else: + alpha = copied_instances.gt_masks.tensor.sum(0) > 0 + cur_unlabeled_instances.gt_masks.tensor = ~alpha * cur_unlabeled_instances.gt_masks.tensor + areas_unlabeled = cur_unlabeled_instances.gt_masks.tensor.sum((1,2)) + # merge image + alpha = alpha.cpu() + composited_image = (alpha * cur_labeled_image) + (~alpha * cur_unlabeled_image) + # merge instances + merged_instances = Instances.cat([cur_unlabeled_instances[areas_unlabeled > 0], copied_instances]) + # update boxes + if isinstance(merged_instances.gt_masks, torch.Tensor): + merged_instances.gt_boxes = BitMasks(merged_instances.gt_masks).get_bounding_boxes() + # merged_instances.gt_boxes = merged_instances.gt_masks.get_bounding_boxes() + else: + merged_instances.gt_boxes = merged_instances.gt_masks.get_bounding_boxes() + + cur_unlabeled_data["image"] = composited_image + cur_unlabeled_data["instances"] = merged_instances + if self.cfg_VISUALIZE_COPY_PASTE: + visualize_data(cur_unlabeled_data, save_path = 'sample_{}.jpg'.format(np.random.randint(5))) + new_unlabeled_data.append(cur_unlabeled_data) + return new_unlabeled_data + + def run_step(self): + """ + Implement the standard training logic described above. + """ + assert self.model.training, "[SimpleTrainer] model was changed to eval mode!" + start = time.perf_counter() + """ + If you want to do something with the data, you can wrap the dataloader. + """ + data = next(self._data_loader_iter) + # print(data, len(data)) + if self.use_copy_paste: + # print('using copy paste') + data = self.copy_and_paste(copy.deepcopy(data[::-1]), data) + data_time = time.perf_counter() - start + + """ + If you want to do something with the losses, you can wrap the model. + """ + loss_dict = self.model(data) + if isinstance(loss_dict, torch.Tensor): + losses = loss_dict + loss_dict = {"total_loss": loss_dict} + else: + losses = sum(loss_dict.values()) + + """ + If you need to accumulate gradients or do something similar, you can + wrap the optimizer with your custom `zero_grad()` method. + """ + if not torch.isnan(losses): + self.optimizer.zero_grad() + losses.backward() + else: + print('Nan loss. Skipped.') + + self._write_metrics(loss_dict, data_time) + + """ + If you need gradient clipping/scaling or other processing, you can + wrap the optimizer with your custom `step()` method. But it is + suboptimal as explained in https://arxiv.org/abs/2006.15704 Sec 3.2.4 + """ + self.optimizer.step() + + +class CustomAMPTrainer(CustomSimpleTrainer): + """ + Like :class:`SimpleTrainer`, but uses PyTorch's native automatic mixed precision + in the training loop. + """ + + def __init__(self, model, data_loader, optimizer, cfg=None, grad_scaler=None, use_copy_paste=False, + copy_paste_rate=-1, copy_paste_random_num=None, copy_paste_min_ratio=-1, + copy_paste_max_ratio=-1, visualize_copy_paste=False): + """ + Args: + model, data_loader, optimizer: same as in :class:`SimpleTrainer`. + grad_scaler: torch GradScaler to automatically scale gradients. + """ + unsupported = "AMPTrainer does not support single-process multi-device training!" + if isinstance(model, DistributedDataParallel): + assert not (model.device_ids and len(model.device_ids) > 1), unsupported + assert not isinstance(model, DataParallel), unsupported + + super().__init__(model, data_loader, optimizer, cfg=cfg, use_copy_paste=use_copy_paste, \ + copy_paste_rate=copy_paste_rate, copy_paste_random_num=copy_paste_random_num, \ + copy_paste_min_ratio=copy_paste_min_ratio, copy_paste_max_ratio=copy_paste_max_ratio, \ + visualize_copy_paste=visualize_copy_paste) + + if grad_scaler is None: + from torch.cuda.amp import GradScaler + + grad_scaler = GradScaler() + self.grad_scaler = grad_scaler + + def run_step(self): + """ + Implement the AMP training logic. + """ + assert self.model.training, "[AMPTrainer] model was changed to eval mode!" + assert torch.cuda.is_available(), "[AMPTrainer] CUDA is required for AMP training!" + from torch.cuda.amp import autocast + + start = time.perf_counter() + data = next(self._data_loader_iter) + if self.use_copy_paste: + # print('using copy paste') + data = self.copy_and_paste(copy.deepcopy(data[::-1]), data) + data_time = time.perf_counter() - start + + with autocast(): + loss_dict = self.model(data) + if isinstance(loss_dict, torch.Tensor): + losses = loss_dict + loss_dict = {"total_loss": loss_dict} + else: + losses = sum(loss_dict.values()) + + if not torch.isnan(losses): + self.optimizer.zero_grad() + self.grad_scaler.scale(losses).backward() + else: + print('Nan loss.') + + self._write_metrics(loss_dict, data_time) + + self.grad_scaler.step(self.optimizer) + self.grad_scaler.update() + + def state_dict(self): + ret = super().state_dict() + ret["grad_scaler"] = self.grad_scaler.state_dict() + return ret + + def load_state_dict(self, state_dict): + super().load_state_dict(state_dict) + self.grad_scaler.load_state_dict(state_dict["grad_scaler"]) diff --git a/cutler/evaluation/__init__.py b/cutler/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1ef0b0b14686b2f8c85b6684ff979c1ae917b995 --- /dev/null +++ b/cutler/evaluation/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. + +from .coco_evaluation import COCOEvaluator \ No newline at end of file diff --git a/cutler/evaluation/coco_evaluation.py b/cutler/evaluation/coco_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..ff272096cebbcd7eee2df11e4eb8434ee0ee32c1 --- /dev/null +++ b/cutler/evaluation/coco_evaluation.py @@ -0,0 +1,727 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/coco_evaluation.py +# supports evaluation of object detection only, although the prediction contains both segmentation and detection results. + +import contextlib +import copy +import io +import itertools +import json +import logging +import numpy as np +import os +import pickle +from collections import OrderedDict +import pycocotools.mask as mask_util +import torch +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from tabulate import tabulate + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.data.datasets.coco import convert_to_coco_json +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table + +from detectron2.evaluation.evaluator import DatasetEvaluator + +try: + from detectron2.evaluation.fast_eval_api import COCOeval_opt +except ImportError: + COCOeval_opt = COCOeval + + +class COCOEvaluator(DatasetEvaluator): + """ + Evaluate AR for object proposals, AP for instance detection/segmentation, AP + for keypoint detection outputs using COCO's metrics. + See http://cocodataset.org/#detection-eval and + http://cocodataset.org/#keypoints-eval to understand its metrics. + The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means + the metric cannot be computed (e.g. due to no predictions made). + + In addition to COCO, this evaluator is able to support any bounding box detection, + instance segmentation, or keypoint detection dataset. + """ + + def __init__( + self, + dataset_name, + tasks=None, + distributed=True, + output_dir=None, + *, + max_dets_per_image=None, + use_fast_impl=True, + kpt_oks_sigmas=(), + allow_cached_coco=True, + no_segm=False, + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have either the following corresponding metadata: + + "json_file": the path to the COCO format annotation + + Or it must be in detectron2's standard dataset format + so it can be converted to COCO format automatically. + tasks (tuple[str]): tasks that can be evaluated under the given + configuration. A task is one of "bbox", "segm", "keypoints". + By default, will infer this automatically from predictions. + distributed (True): if True, will collect results from all ranks and run evaluation + in the main process. + Otherwise, will only evaluate the results in the current process. + output_dir (str): optional, an output directory to dump all + results predicted on the dataset. The dump contains two files: + + 1. "instances_predictions.pth" a file that can be loaded with `torch.load` and + contains all the results in the format they are produced by the model. + 2. "coco_instances_results.json" a json file in COCO's result format. + max_dets_per_image (int): limit on the maximum number of detections per image. + By default in COCO, this limit is to 100, but this can be customized + to be greater, as is needed in evaluation metrics AP fixed and AP pool + (see https://arxiv.org/pdf/2102.01066.pdf) + This doesn't affect keypoint evaluation. + use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP. + Although the results should be very close to the official implementation in COCO + API, it is still recommended to compute results with the official API for use in + papers. The faster implementation also uses more RAM. + kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS. + See http://cocodataset.org/#keypoints-eval + When empty, it will use the defaults in COCO. + Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS. + allow_cached_coco (bool): Whether to use cached coco json from previous validation + runs. You should set this to False if you need to use different validation data. + Defaults to True. + """ + self._logger = logging.getLogger(__name__) + self._distributed = distributed + self._output_dir = output_dir + self.no_segm = no_segm + + if use_fast_impl and (COCOeval_opt is COCOeval): + self._logger.info("Fast COCO eval is not built. Falling back to official COCO eval.") + use_fast_impl = False + self._use_fast_impl = use_fast_impl + + # COCOeval requires the limit on the number of detections per image (maxDets) to be a list + # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the + # 3rd element (100) is used as the limit on the number of detections per image when + # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval, + # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults. + if max_dets_per_image is None: + max_dets_per_image = [1, 10, 100] + else: + max_dets_per_image = [1, 10, max_dets_per_image] + self._max_dets_per_image = max_dets_per_image + + if tasks is not None and isinstance(tasks, CfgNode): + kpt_oks_sigmas = ( + tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas + ) + self._logger.warn( + "COCO Evaluator instantiated using config, this is deprecated behavior." + " Please pass in explicit arguments instead." + ) + self._tasks = None # Infering it from predictions should be better + else: + self._tasks = tasks + + self._cpu_device = torch.device("cpu") + + self._metadata = MetadataCatalog.get(dataset_name) + if not hasattr(self._metadata, "json_file"): + if output_dir is None: + raise ValueError( + "output_dir must be provided to COCOEvaluator " + "for datasets not in COCO format." + ) + self._logger.info(f"Trying to convert '{dataset_name}' to COCO format ...") + + cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json") + self._metadata.json_file = cache_path + convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco) + + json_file = PathManager.get_local_path(self._metadata.json_file) + with contextlib.redirect_stdout(io.StringIO()): + self._coco_api = COCO(json_file) + + # Test set json files do not contain annotations (evaluation must be + # performed using the COCO evaluation server). + self._do_evaluation = "annotations" in self._coco_api.dataset + if self._do_evaluation: + self._kpt_oks_sigmas = kpt_oks_sigmas + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + prediction["instances"] = instances_to_coco_json(instances, input["image_id"]) + if "proposals" in output: + prediction["proposals"] = output["proposals"].to(self._cpu_device) + if len(prediction) > 1: + self._predictions.append(prediction) + + def evaluate(self, img_ids=None): + """ + Args: + img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset + """ + if self._distributed: + comm.synchronize() + predictions = comm.gather(self._predictions, dst=0) + predictions = list(itertools.chain(*predictions)) + + if not comm.is_main_process(): + return {} + else: + predictions = self._predictions + + if len(predictions) == 0: + self._logger.warning("[COCOEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "instances_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(predictions, f) + + self._results = OrderedDict() + if "proposals" in predictions[0]: + self._eval_box_proposals(predictions) + if "instances" in predictions[0]: + self._eval_predictions(predictions, img_ids=img_ids) + # Copy so the caller can do whatever with results + return copy.deepcopy(self._results) + + def _tasks_from_predictions(self, predictions): + """ + Get COCO API "tasks" (i.e. iou_type) from COCO-format predictions. + """ + tasks = {"bbox"} + for pred in predictions: + if "segmentation" in pred and not self.no_segm: + tasks.add("segm") + if "keypoints" in pred: + tasks.add("keypoints") + return sorted(tasks) + + def _eval_predictions(self, predictions, img_ids=None): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(coco_results) + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id + all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) + num_classes = len(all_contiguous_ids) + assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 + + reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} + for result in coco_results: + category_id = result["category_id"] + assert category_id < num_classes, ( + f"A prediction has class={category_id}, " + f"but the dataset only has {num_classes} classes and " + f"predicted class id should be in [0, {num_classes - 1}]." + ) + result["category_id"] = reverse_id_mapping[category_id] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "coco_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info( + "Evaluating predictions with {} COCO API...".format( + "unofficial" if self._use_fast_impl else "official" + ) + ) + for task in sorted(tasks): + assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!" + coco_eval = ( + _evaluate_predictions_on_coco( + self._coco_api, + coco_results, + task, + kpt_oks_sigmas=self._kpt_oks_sigmas, + use_fast_impl=self._use_fast_impl, + img_ids=img_ids, + max_dets_per_image=self._max_dets_per_image, + ) + if len(coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + + res = self._derive_coco_results( + coco_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res + + def _eval_box_proposals(self, predictions): + """ + Evaluate the box proposals in predictions. + Fill self._results with the metrics for "box_proposals" task. + """ + if self._output_dir: + # Saving generated box proposals to file. + # Predicted box_proposals are in XYXY_ABS mode. + bbox_mode = BoxMode.XYXY_ABS.value + ids, boxes, objectness_logits = [], [], [] + for prediction in predictions: + ids.append(prediction["image_id"]) + boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy()) + objectness_logits.append(prediction["proposals"].objectness_logits.numpy()) + + proposal_data = { + "boxes": boxes, + "objectness_logits": objectness_logits, + "ids": ids, + "bbox_mode": bbox_mode, + } + with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f: + pickle.dump(proposal_data, f) + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating bbox proposals ...") + res = {} + areas = {"all": "", "small": "s", "medium": "m", "large": "l"} + for limit in [100, 1000]: + for area, suffix in areas.items(): + stats = _evaluate_box_proposals(predictions, self._coco_api, area=area, limit=limit) + key = "AR{}@{:d}".format(suffix, limit) + res[key] = float(stats["ar"].item() * 100) + self._logger.info("Proposal metrics: \n" + create_small_table(res)) + self._results["box_proposals"] = res + + def _derive_coco_results(self, coco_eval, iou_type, class_names=None): + """ + Derive the desired score numbers from summarized COCOeval. + + Args: + coco_eval (None or COCOEval): None represents no predictions from model. + iou_type (str): + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + + metrics = { + "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "keypoints": ["AP", "AP50", "AP75", "APm", "APl"], + }[iou_type] + + if coco_eval is None: + self._logger.warn("No predictions from the model!") + return {metric: float("nan") for metric in metrics} + + # the standard metrics + results = { + metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan") + for idx, metric in enumerate(metrics) + } + self._logger.info( + "Evaluation results for {}: \n".format(iou_type) + create_small_table(results) + ) + if not np.isfinite(sum(results.values())): + self._logger.info("Some metrics cannot be computed and is shown as NaN.") + + if class_names is None or len(class_names) <= 1: + return results + # Compute per-category AP + # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa + precisions = coco_eval.eval["precision"] + # precision has dims (iou, recall, cls, area range, max dets) + assert len(class_names) == precisions.shape[2] + + results_per_category = [] + for idx, name in enumerate(class_names): + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + ap = np.mean(precision) if precision.size else float("nan") + results_per_category.append(("{}".format(name), float(ap * 100))) + + # tabulate it + N_COLS = min(6, len(results_per_category) * 2) + results_flatten = list(itertools.chain(*results_per_category)) + results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + results_2d, + tablefmt="pipe", + floatfmt=".3f", + headers=["category", "AP"] * (N_COLS // 2), + numalign="left", + ) + self._logger.info("Per-category {} AP: \n".format(iou_type) + table) + + results.update({"AP-" + name: ap for name, ap in results_per_category}) + return results + + +def instances_to_coco_json(instances, img_id): + """ + Dump an "Instances" object to a COCO-format json that's used for evaluation. + + Args: + instances (Instances): + img_id (int): the image id + + Returns: + list[dict]: list of json annotations in COCO format. + """ + num_instance = len(instances) + if num_instance == 0: + return [] + + boxes = instances.pred_boxes.tensor.numpy() + boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + boxes = boxes.tolist() + scores = instances.scores.tolist() + classes = instances.pred_classes.tolist() + + has_mask = instances.has("pred_masks") + if has_mask: + # use RLE to encode the masks, because they are too large and takes memory + # since this evaluator stores outputs of the entire dataset + rles = [ + mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0] + for mask in instances.pred_masks + ] + for rle in rles: + # "counts" is an array encoded by mask_util as a byte-stream. Python3's + # json writer which always produces strings cannot serialize a bytestream + # unless you decode it. Thankfully, utf-8 works out (which is also what + # the pycocotools/_mask.pyx does). + rle["counts"] = rle["counts"].decode("utf-8") + + has_keypoints = instances.has("pred_keypoints") + if has_keypoints: + keypoints = instances.pred_keypoints + + results = [] + for k in range(num_instance): + result = { + "image_id": img_id, + "category_id": classes[k], + "bbox": boxes[k], + "score": scores[k], + } + if has_mask: + result["segmentation"] = rles[k] + if has_keypoints: + # In COCO annotations, + # keypoints coordinates are pixel indices. + # However our predictions are floating point coordinates. + # Therefore we subtract 0.5 to be consistent with the annotation format. + # This is the inverse of data loading logic in `datasets/coco.py`. + keypoints[k][:, :2] -= 0.5 + result["keypoints"] = keypoints[k].flatten().tolist() + results.append(result) + return results + + +# inspired from Detectron: +# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa +def _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area="all", limit=None): + """ + Evaluate detection proposal recall metrics. This function is a much + faster alternative to the official COCO API recall evaluation code. However, + it produces slightly different results. + """ + # Record max overlap value for each gt box + # Return vector of overlap values + areas = { + "all": 0, + "small": 1, + "medium": 2, + "large": 3, + "96-128": 4, + "128-256": 5, + "256-512": 6, + "512-inf": 7, + } + area_ranges = [ + [0**2, 1e5**2], # all + [0**2, 32**2], # small + [32**2, 96**2], # medium + [96**2, 1e5**2], # large + [96**2, 128**2], # 96-128 + [128**2, 256**2], # 128-256 + [256**2, 512**2], # 256-512 + [512**2, 1e5**2], + ] # 512-inf + assert area in areas, "Unknown area range: {}".format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = [] + num_pos = 0 + + for prediction_dict in dataset_predictions: + predictions = prediction_dict["proposals"] + + # sort predictions in descending order + # TODO maybe remove this and make it explicit in the documentation + inds = predictions.objectness_logits.sort(descending=True)[1] + predictions = predictions[inds] + + ann_ids = coco_api.getAnnIds(imgIds=prediction_dict["image_id"]) + anno = coco_api.loadAnns(ann_ids) + gt_boxes = [ + BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) + for obj in anno + if obj["iscrowd"] == 0 + ] + gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes + gt_boxes = Boxes(gt_boxes) + gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0]) + + if len(gt_boxes) == 0 or len(predictions) == 0: + continue + + valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) + gt_boxes = gt_boxes[valid_gt_inds] + + num_pos += len(gt_boxes) + + if len(gt_boxes) == 0: + continue + + if limit is not None and len(predictions) > limit: + predictions = predictions[:limit] + + overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes) + + _gt_overlaps = torch.zeros(len(gt_boxes)) + for j in range(min(len(predictions), len(gt_boxes))): + # find which proposal box maximally covers each gt box + # and get the iou amount of coverage for each gt box + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ovr, gt_ind = max_overlaps.max(dim=0) + assert gt_ovr >= 0 + # find the proposal box that covers the best covered gt box + box_ind = argmax_overlaps[gt_ind] + # record the iou coverage of this gt box + _gt_overlaps[j] = overlaps[box_ind, gt_ind] + assert _gt_overlaps[j] == gt_ovr + # mark the proposal box and the gt box as used + overlaps[box_ind, :] = -1 + overlaps[:, gt_ind] = -1 + + # append recorded iou coverage level + gt_overlaps.append(_gt_overlaps) + gt_overlaps = ( + torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32) + ) + gt_overlaps, _ = torch.sort(gt_overlaps) + + if thresholds is None: + step = 0.05 + thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) + recalls = torch.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return { + "ar": ar, + "recalls": recalls, + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + +def _evaluate_predictions_on_coco( + coco_gt, + coco_results, + iou_type, + kpt_oks_sigmas=None, + use_fast_impl=True, + img_ids=None, + max_dets_per_image=None, +): + """ + Evaluate the coco results using COCOEval API. + """ + assert len(coco_results) > 0 + + if iou_type == "segm": + coco_results = copy.deepcopy(coco_results) + # When evaluating mask AP, if the results contain bbox, cocoapi will + # use the box area as the area of the instance, instead of the mask area. + # This leads to a different definition of small/medium/large. + # We remove the bbox field to let mask AP use mask area. + for c in coco_results: + c.pop("bbox", None) + + coco_dt = coco_gt.loadRes(coco_results) + coco_eval = (COCOeval_opt if use_fast_impl else COCOeval)(coco_gt, coco_dt, iou_type) + # For COCO, the default max_dets_per_image is [1, 10, 100]. + if max_dets_per_image is None: + max_dets_per_image = [1, 10, 100] # Default from COCOEval + else: + assert ( + len(max_dets_per_image) >= 3 + ), "COCOeval requires maxDets (and max_dets_per_image) to have length at least 3" + # In the case that user supplies a custom input for max_dets_per_image, + # apply COCOevalMaxDets to evaluate AP with the custom input. + if max_dets_per_image[2] != 100: + coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type) + if iou_type != "keypoints": + coco_eval.params.maxDets = max_dets_per_image + + if img_ids is not None: + coco_eval.params.imgIds = img_ids + + if iou_type == "keypoints": + # Use the COCO default keypoint OKS sigmas unless overrides are specified + if kpt_oks_sigmas: + assert hasattr(coco_eval.params, "kpt_oks_sigmas"), "pycocotools is too old!" + coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas) + # COCOAPI requires every detection and every gt to have keypoints, so + # we just take the first entry from both + num_keypoints_dt = len(coco_results[0]["keypoints"]) // 3 + num_keypoints_gt = len(next(iter(coco_gt.anns.values()))["keypoints"]) // 3 + num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas) + assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, ( + f"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. " + f"Ground truth contains {num_keypoints_gt} keypoints. " + f"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. " + "They have to agree with each other. For meaning of OKS, please refer to " + "http://cocodataset.org/#keypoints-eval." + ) + + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + + return coco_eval + + +class COCOevalMaxDets(COCOeval): + """ + Modified version of COCOeval for evaluating AP with a custom + maxDets (by default for COCO, maxDets is 100) + """ + + def summarize(self): + """ + Compute and display summary metrics for evaluation results given + a custom value for max_dets_per_image + """ + + def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100): + p = self.params + iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}" + titleStr = "Average Precision" if ap == 1 else "Average Recall" + typeStr = "(AP)" if ap == 1 else "(AR)" + iouStr = ( + "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1]) + if iouThr is None + else "{:0.2f}".format(iouThr) + ) + + aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] + mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] + if ap == 1: + # dimension of precision: [TxRxKxAxM] + s = self.eval["precision"] + # IoU + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, :, aind, mind] + else: + # dimension of recall: [TxKxAxM] + s = self.eval["recall"] + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, aind, mind] + if len(s[s > -1]) == 0: + mean_s = -1 + else: + mean_s = np.mean(s[s > -1]) + print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)) + return mean_s + + def _summarizeDets(): + stats = np.zeros((12,)) + # Evaluate AP using the custom limit on maximum detections per image + stats[0] = _summarize(1, maxDets=self.params.maxDets[2]) + stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2]) + stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2]) + stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2]) + stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2]) + stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) + stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) + stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2]) + stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2]) + return stats + + def _summarizeKps(): + stats = np.zeros((10,)) + stats[0] = _summarize(1, maxDets=20) + stats[1] = _summarize(1, maxDets=20, iouThr=0.5) + stats[2] = _summarize(1, maxDets=20, iouThr=0.75) + stats[3] = _summarize(1, maxDets=20, areaRng="medium") + stats[4] = _summarize(1, maxDets=20, areaRng="large") + stats[5] = _summarize(0, maxDets=20) + stats[6] = _summarize(0, maxDets=20, iouThr=0.5) + stats[7] = _summarize(0, maxDets=20, iouThr=0.75) + stats[8] = _summarize(0, maxDets=20, areaRng="medium") + stats[9] = _summarize(0, maxDets=20, areaRng="large") + return stats + + if not self.eval: + raise Exception("Please run accumulate() first") + iouType = self.params.iouType + if iouType == "segm" or iouType == "bbox": + summarize = _summarizeDets + elif iouType == "keypoints": + summarize = _summarizeKps + self.stats = summarize() + + def __str__(self): + self.summarize() diff --git a/cutler/model_zoo/configs/Base-RCNN-FPN.yaml b/cutler/model_zoo/configs/Base-RCNN-FPN.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3e020f2e7b2f26765be317f907126a1556621abf --- /dev/null +++ b/cutler/model_zoo/configs/Base-RCNN-FPN.yaml @@ -0,0 +1,42 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedRCNN" + BACKBONE: + NAME: "build_resnet_fpn_backbone" + RESNETS: + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + FPN: + IN_FEATURES: ["res2", "res3", "res4", "res5"] + ANCHOR_GENERATOR: + SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map + ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps) + RPN: + IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"] + PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level + PRE_NMS_TOPK_TEST: 1000 # Per FPN level + # Detectron1 uses 2000 proposals per-batch, + # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue) + # which is approximately 1000 proposals per-image since the default batch size for FPN is 2. + POST_NMS_TOPK_TRAIN: 1000 + POST_NMS_TOPK_TEST: 1000 + ROI_HEADS: + NAME: "StandardROIHeads" + IN_FEATURES: ["p2", "p3", "p4", "p5"] + ROI_BOX_HEAD: + NAME: "FastRCNNConvFCHead" + NUM_FC: 2 + POOLER_RESOLUTION: 7 + ROI_MASK_HEAD: + NAME: "MaskRCNNConvUpsampleHead" + NUM_CONV: 4 + POOLER_RESOLUTION: 14 +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (60000, 80000) + MAX_ITER: 90000 +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) +VERSION: 2 diff --git a/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_100perc.yaml b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_100perc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..64d5329d7f74b80219edbe08a1519799931f91e6 --- /dev/null +++ b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_100perc.yaml @@ -0,0 +1,40 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + WEIGHTS: "http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth" + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: False + FPN: + NORM: "SyncBN" + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_HEADS: + NAME: CustomCascadeROIHeads + RPN: + POST_NMS_TOPK_TRAIN: 2000 +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (60000, 80000) + MAX_ITER: 90000 + BASE_LR_MULTIPLIER: 2 + BASE_LR_MULTIPLIER_NAMES: ['roi_heads.mask_head.predictor', 'roi_heads.box_predictor.0.cls_score', 'roi_heads.box_predictor.0.bbox_pred', 'roi_heads.box_predictor.1.cls_score', 'roi_heads.box_predictor.1.bbox_pred', 'roi_heads.box_predictor.2.cls_score', 'roi_heads.box_predictor.2.bbox_pred'] +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) + MAX_SIZE_TRAIN: 1333 + MASK_FORMAT: "bitmask" + FORMAT: "RGB" +TEST: + PRECISE_BN: + ENABLED: True + EVAL_PERIOD: 5000 +OUTPUT_DIR: "output/100perc" \ No newline at end of file diff --git a/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_10perc.yaml b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_10perc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..304d422d3004b35a141faf64e0248fbcd3ba9f87 --- /dev/null +++ b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_10perc.yaml @@ -0,0 +1,40 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + WEIGHTS: "http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth" + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: False + FPN: + NORM: "SyncBN" + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_HEADS: + NAME: CustomCascadeROIHeads + RPN: + POST_NMS_TOPK_TRAIN: 2000 +DATASETS: + TRAIN: ("coco_semi_10perc",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.04 + STEPS: (6000, 8000) + MAX_ITER: 9000 + BASE_LR_MULTIPLIER: 4 + BASE_LR_MULTIPLIER_NAMES: ['roi_heads.mask_head.predictor', 'roi_heads.box_predictor.0.cls_score', 'roi_heads.box_predictor.0.bbox_pred', 'roi_heads.box_predictor.1.cls_score', 'roi_heads.box_predictor.1.bbox_pred', 'roi_heads.box_predictor.2.cls_score', 'roi_heads.box_predictor.2.bbox_pred'] +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) + MAX_SIZE_TRAIN: 1333 + MASK_FORMAT: "bitmask" + FORMAT: "RGB" +TEST: + PRECISE_BN: + ENABLED: True + EVAL_PERIOD: 5000 +OUTPUT_DIR: "output/10perc" \ No newline at end of file diff --git a/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_1perc.yaml b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_1perc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ca7a8298a096f65f7a564f691dfbd30f4f8f0aac --- /dev/null +++ b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_1perc.yaml @@ -0,0 +1,42 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + WEIGHTS: "http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth" + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: False + FPN: + NORM: "SyncBN" + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_HEADS: + NAME: CustomCascadeROIHeads + RPN: + POST_NMS_TOPK_TRAIN: 2000 +DATASETS: + TRAIN: ("coco_semi_1perc",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.04 + STEPS: (2400, 3200) + MAX_ITER: 3600 + WARMUP_FACTOR: 0.001 + WARMUP_ITERS: 1000 + BASE_LR_MULTIPLIER: 4 + BASE_LR_MULTIPLIER_NAMES: ['roi_heads.mask_head.predictor', 'roi_heads.box_predictor.0.cls_score', 'roi_heads.box_predictor.0.bbox_pred', 'roi_heads.box_predictor.1.cls_score', 'roi_heads.box_predictor.1.bbox_pred', 'roi_heads.box_predictor.2.cls_score', 'roi_heads.box_predictor.2.bbox_pred'] +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) + MAX_SIZE_TRAIN: 1333 + MASK_FORMAT: "bitmask" + FORMAT: "RGB" +TEST: + PRECISE_BN: + ENABLED: True + EVAL_PERIOD: 5000 +OUTPUT_DIR: "output/1perc" \ No newline at end of file diff --git a/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_20perc.yaml b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_20perc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f2cc00b1bb107b125a31c6fa8136d7d858344b49 --- /dev/null +++ b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_20perc.yaml @@ -0,0 +1,40 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + WEIGHTS: "http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth" + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: False + FPN: + NORM: "SyncBN" + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_HEADS: + NAME: CustomCascadeROIHeads + RPN: + POST_NMS_TOPK_TRAIN: 2000 +DATASETS: + TRAIN: ("coco_semi_20perc",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.04 + STEPS: (12000, 16000) + MAX_ITER: 18000 + BASE_LR_MULTIPLIER: 4 + BASE_LR_MULTIPLIER_NAMES: ['roi_heads.mask_head.predictor', 'roi_heads.box_predictor.0.cls_score', 'roi_heads.box_predictor.0.bbox_pred', 'roi_heads.box_predictor.1.cls_score', 'roi_heads.box_predictor.1.bbox_pred', 'roi_heads.box_predictor.2.cls_score', 'roi_heads.box_predictor.2.bbox_pred'] +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) + MAX_SIZE_TRAIN: 1333 + MASK_FORMAT: "bitmask" + FORMAT: "RGB" +TEST: + PRECISE_BN: + ENABLED: True + EVAL_PERIOD: 5000 +OUTPUT_DIR: "output/20perc" \ No newline at end of file diff --git a/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_2perc.yaml b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_2perc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c2c9571b6c521ecb4b325df6806f08e986da5f4a --- /dev/null +++ b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_2perc.yaml @@ -0,0 +1,42 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + WEIGHTS: "http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth" + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: False + FPN: + NORM: "SyncBN" + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_HEADS: + NAME: CustomCascadeROIHeads + RPN: + POST_NMS_TOPK_TRAIN: 2000 +DATASETS: + TRAIN: ("coco_semi_2perc",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.04 + STEPS: (2400, 3200) + MAX_ITER: 3600 + WARMUP_FACTOR: 0.001 + WARMUP_ITERS: 1000 + BASE_LR_MULTIPLIER: 4 + BASE_LR_MULTIPLIER_NAMES: ['roi_heads.mask_head.predictor', 'roi_heads.box_predictor.0.cls_score', 'roi_heads.box_predictor.0.bbox_pred', 'roi_heads.box_predictor.1.cls_score', 'roi_heads.box_predictor.1.bbox_pred', 'roi_heads.box_predictor.2.cls_score', 'roi_heads.box_predictor.2.bbox_pred'] +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) + MAX_SIZE_TRAIN: 1333 + MASK_FORMAT: "bitmask" + FORMAT: "RGB" +TEST: + PRECISE_BN: + ENABLED: True + EVAL_PERIOD: 5000 +OUTPUT_DIR: "output/2perc" \ No newline at end of file diff --git a/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_30perc.yaml b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_30perc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..30c7bc1e800a5a66646cefd90a091a20dc6d157a --- /dev/null +++ b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_30perc.yaml @@ -0,0 +1,40 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + WEIGHTS: "http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth" + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: False + FPN: + NORM: "SyncBN" + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_HEADS: + NAME: CustomCascadeROIHeads + RPN: + POST_NMS_TOPK_TRAIN: 2000 +DATASETS: + TRAIN: ("coco_semi_30perc",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.04 + STEPS: (18000, 24000) + MAX_ITER: 27000 + BASE_LR_MULTIPLIER: 4 + BASE_LR_MULTIPLIER_NAMES: ['roi_heads.mask_head.predictor', 'roi_heads.box_predictor.0.cls_score', 'roi_heads.box_predictor.0.bbox_pred', 'roi_heads.box_predictor.1.cls_score', 'roi_heads.box_predictor.1.bbox_pred', 'roi_heads.box_predictor.2.cls_score', 'roi_heads.box_predictor.2.bbox_pred'] +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) + MAX_SIZE_TRAIN: 1333 + MASK_FORMAT: "bitmask" + FORMAT: "RGB" +TEST: + PRECISE_BN: + ENABLED: True + EVAL_PERIOD: 5000 +OUTPUT_DIR: "output/30perc" \ No newline at end of file diff --git a/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_40perc.yaml b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_40perc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7afe89406e4f062bfdd5c021da176a8872709731 --- /dev/null +++ b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_40perc.yaml @@ -0,0 +1,40 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + WEIGHTS: "http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth" + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: False + FPN: + NORM: "SyncBN" + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_HEADS: + NAME: CustomCascadeROIHeads + RPN: + POST_NMS_TOPK_TRAIN: 2000 +DATASETS: + TRAIN: ("coco_semi_40perc",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.04 + STEPS: (24000, 32000) + MAX_ITER: 36000 + BASE_LR_MULTIPLIER: 4 + BASE_LR_MULTIPLIER_NAMES: ['roi_heads.mask_head.predictor', 'roi_heads.box_predictor.0.cls_score', 'roi_heads.box_predictor.0.bbox_pred', 'roi_heads.box_predictor.1.cls_score', 'roi_heads.box_predictor.1.bbox_pred', 'roi_heads.box_predictor.2.cls_score', 'roi_heads.box_predictor.2.bbox_pred'] +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) + MAX_SIZE_TRAIN: 1333 + MASK_FORMAT: "bitmask" + FORMAT: "RGB" +TEST: + PRECISE_BN: + ENABLED: True + EVAL_PERIOD: 5000 +OUTPUT_DIR: "output/40perc" \ No newline at end of file diff --git a/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_50perc.yaml b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_50perc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3f1872546190d4224f5e2b6a71abfd4181497236 --- /dev/null +++ b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_50perc.yaml @@ -0,0 +1,40 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + WEIGHTS: "http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth" + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: False + FPN: + NORM: "SyncBN" + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_HEADS: + NAME: CustomCascadeROIHeads + RPN: + POST_NMS_TOPK_TRAIN: 2000 +DATASETS: + TRAIN: ("coco_semi_50perc",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (30000, 40000) + MAX_ITER: 45000 + BASE_LR_MULTIPLIER: 2 + BASE_LR_MULTIPLIER_NAMES: ['roi_heads.mask_head.predictor', 'roi_heads.box_predictor.0.cls_score', 'roi_heads.box_predictor.0.bbox_pred', 'roi_heads.box_predictor.1.cls_score', 'roi_heads.box_predictor.1.bbox_pred', 'roi_heads.box_predictor.2.cls_score', 'roi_heads.box_predictor.2.bbox_pred'] +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) + MAX_SIZE_TRAIN: 1333 + MASK_FORMAT: "bitmask" + FORMAT: "RGB" +TEST: + PRECISE_BN: + ENABLED: True + EVAL_PERIOD: 5000 +OUTPUT_DIR: "output/50perc" \ No newline at end of file diff --git a/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_5perc.yaml b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_5perc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e8770f0926067e59d2c324845fa891148b478247 --- /dev/null +++ b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_5perc.yaml @@ -0,0 +1,42 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + WEIGHTS: "http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth" + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: False + FPN: + NORM: "SyncBN" + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_HEADS: + NAME: CustomCascadeROIHeads + RPN: + POST_NMS_TOPK_TRAIN: 2000 +DATASETS: + TRAIN: ("coco_semi_5perc",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.04 + STEPS: (3000, 4000) + MAX_ITER: 4500 + WARMUP_FACTOR: 0.001 + WARMUP_ITERS: 1000 + BASE_LR_MULTIPLIER: 4 + BASE_LR_MULTIPLIER_NAMES: ['roi_heads.mask_head.predictor', 'roi_heads.box_predictor.0.cls_score', 'roi_heads.box_predictor.0.bbox_pred', 'roi_heads.box_predictor.1.cls_score', 'roi_heads.box_predictor.1.bbox_pred', 'roi_heads.box_predictor.2.cls_score', 'roi_heads.box_predictor.2.bbox_pred'] +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) + MAX_SIZE_TRAIN: 1333 + MASK_FORMAT: "bitmask" + FORMAT: "RGB" +TEST: + PRECISE_BN: + ENABLED: True + EVAL_PERIOD: 5000 +OUTPUT_DIR: "output/5perc" \ No newline at end of file diff --git a/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_60perc.yaml b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_60perc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..250a3c75d446cdb19c13452ed0b90733923379b2 --- /dev/null +++ b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_60perc.yaml @@ -0,0 +1,40 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + WEIGHTS: "http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth" + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: False + FPN: + NORM: "SyncBN" + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_HEADS: + NAME: CustomCascadeROIHeads + RPN: + POST_NMS_TOPK_TRAIN: 2000 +DATASETS: + TRAIN: ("coco_semi_60perc",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (36000, 48000) + MAX_ITER: 54000 + BASE_LR_MULTIPLIER: 2 + BASE_LR_MULTIPLIER_NAMES: ['roi_heads.mask_head.predictor', 'roi_heads.box_predictor.0.cls_score', 'roi_heads.box_predictor.0.bbox_pred', 'roi_heads.box_predictor.1.cls_score', 'roi_heads.box_predictor.1.bbox_pred', 'roi_heads.box_predictor.2.cls_score', 'roi_heads.box_predictor.2.bbox_pred'] +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) + MAX_SIZE_TRAIN: 1333 + MASK_FORMAT: "bitmask" + FORMAT: "RGB" +TEST: + PRECISE_BN: + ENABLED: True + EVAL_PERIOD: 5000 +OUTPUT_DIR: "output/60perc" \ No newline at end of file diff --git a/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_80perc.yaml b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_80perc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0131dd6dd203adf0fe933ec333d7ea92ee3fa8e4 --- /dev/null +++ b/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_80perc.yaml @@ -0,0 +1,40 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +MODEL: + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + WEIGHTS: "http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth" + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: False + FPN: + NORM: "SyncBN" + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_HEADS: + NAME: CustomCascadeROIHeads + RPN: + POST_NMS_TOPK_TRAIN: 2000 +DATASETS: + TRAIN: ("coco_semi_80perc",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.02 + STEPS: (48000, 64000) + MAX_ITER: 72000 + BASE_LR_MULTIPLIER: 2 + BASE_LR_MULTIPLIER_NAMES: ['roi_heads.mask_head.predictor', 'roi_heads.box_predictor.0.cls_score', 'roi_heads.box_predictor.0.bbox_pred', 'roi_heads.box_predictor.1.cls_score', 'roi_heads.box_predictor.1.bbox_pred', 'roi_heads.box_predictor.2.cls_score', 'roi_heads.box_predictor.2.bbox_pred'] +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) + MAX_SIZE_TRAIN: 1333 + MASK_FORMAT: "bitmask" + FORMAT: "RGB" +TEST: + PRECISE_BN: + ENABLED: True + EVAL_PERIOD: 5000 +OUTPUT_DIR: "output/80perc" \ No newline at end of file diff --git a/cutler/model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml b/cutler/model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml new file mode 100644 index 0000000000000000000000000000000000000000..de425134256361c440bff4f46d42d526fa7696c0 --- /dev/null +++ b/cutler/model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml @@ -0,0 +1,61 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +DATALOADER: + COPY_PASTE: True + COPY_PASTE_RATE: 1.0 + VISUALIZE_COPY_PASTE: False + COPY_PASTE_RANDOM_NUM: True + COPY_PASTE_MIN_RATIO: 0.3 + COPY_PASTE_MAX_RATIO: 1.0 + NUM_WORKERS: 0 +MODEL: + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + WEIGHTS: 'http://dl.fbaipublicfiles.com/cutler/checkpoints/dino_RN50_pretrain_d2_format.pkl' + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: False + FPN: + NORM: "SyncBN" + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_HEADS: + NAME: CustomCascadeROIHeads + NUM_CLASSES: 1 + SCORE_THRESH_TEST: 0.0 + POSITIVE_FRACTION: 0.25 + USE_DROPLOSS: True + DROPLOSS_IOU_THRESH: 0.01 + RPN: + POST_NMS_TOPK_TRAIN: 4000 + NMS_THRESH: 0.65 +DATASETS: + TRAIN: ("imagenet_train",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.01 + WEIGHT_DECAY: 0.00005 + STEPS: (80000,) + MAX_ITER: 160000 + GAMMA: 0.02 + CLIP_GRADIENTS: + CLIP_TYPE: norm + CLIP_VALUE: 1.0 + ENABLED: true + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: (240, 320, 480, 640, 672, 704, 736, 768, 800, 1024) + MAX_SIZE_TRAIN: 1333 + MASK_FORMAT: "bitmask" + FORMAT: "RGB" +TEST: + PRECISE_BN: + ENABLED: True + NUM_ITER: 200 + DETECTIONS_PER_IMAGE: 100 +OUTPUT_DIR: "output/" \ No newline at end of file diff --git a/cutler/model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN_demo.yaml b/cutler/model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN_demo.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3d53504ad7ccc76e848c30f53d7b57c07c19bda4 --- /dev/null +++ b/cutler/model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN_demo.yaml @@ -0,0 +1,62 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +DATALOADER: + COPY_PASTE: True + COPY_PASTE_RATE: 1.0 + VISUALIZE_COPY_PASTE: False + COPY_PASTE_RANDOM_NUM: True + COPY_PASTE_MIN_RATIO: 0.3 + COPY_PASTE_MAX_RATIO: 1.0 + NUM_WORKERS: 0 +MODEL: + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + WEIGHTS: 'http://dl.fbaipublicfiles.com/cutler/checkpoints/dino_RN50_pretrain_d2_format.pkl' + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: False + FPN: + NORM: "SyncBN" + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_HEADS: + NAME: CustomCascadeROIHeads + NUM_CLASSES: 1 + SCORE_THRESH_TEST: 0.0 + POSITIVE_FRACTION: 0.25 + USE_DROPLOSS: True + DROPLOSS_IOU_THRESH: 0.01 + RPN: + POST_NMS_TOPK_TRAIN: 4000 + NMS_THRESH: 0.65 +DATASETS: + TRAIN: ("imagenet_train",) + TEST: ("imagenet_train",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.01 + WEIGHT_DECAY: 0.00005 + STEPS: (80000,) + MAX_ITER: 160000 + GAMMA: 0.02 + CLIP_GRADIENTS: + CLIP_TYPE: norm + CLIP_VALUE: 1.0 + ENABLED: true + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: (240, 320, 480, 640, 672, 704, 736, 768, 800, 1024) + MAX_SIZE_TRAIN: 1333 + MASK_FORMAT: "bitmask" + FORMAT: "RGB" +TEST: + PRECISE_BN: + ENABLED: True + NUM_ITER: 200 + DETECTIONS_PER_IMAGE: 100 +OUTPUT_DIR: "output/" diff --git a/cutler/model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN_self_train.yaml b/cutler/model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN_self_train.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e1903a368b3688111d437237e72c799522e600e2 --- /dev/null +++ b/cutler/model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN_self_train.yaml @@ -0,0 +1,60 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +DATALOADER: + COPY_PASTE: True + COPY_PASTE_RATE: 1.0 + VISUALIZE_COPY_PASTE: False + COPY_PASTE_RANDOM_NUM: True + COPY_PASTE_MIN_RATIO: 0.5 + COPY_PASTE_MAX_RATIO: 1.0 + NUM_WORKERS: 2 +MODEL: + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + WEIGHTS: 'http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_r1.pth' # round 1 + # WEIGHTS: 'http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_r2.pth' # round 2 + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: False + FPN: + NORM: "SyncBN" + ROI_BOX_HEAD: + CLS_AGNOSTIC_BBOX_REG: True + ROI_HEADS: + NAME: CustomCascadeROIHeads + NUM_CLASSES: 1 + SCORE_THRESH_TEST: 0.0 + POSITIVE_FRACTION: 0.25 + USE_DROPLOSS: False + DROPLOSS_IOU_THRESH: 0.01 +DATASETS: + TRAIN: ("imagenet_train_r1",) # round 1 + # TRAIN: ("imagenet_train_r2",) # round 2 +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.005 + STEPS: (79999,) + MAX_ITER: 80000 + GAMMA: 1.0 + CLIP_GRADIENTS: + CLIP_TYPE: norm + CLIP_VALUE: 1.0 + ENABLED: true + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: (240, 320, 480, 640, 672, 704, 736, 768, 800, 1024) + MAX_SIZE_TRAIN: 1333 + MASK_FORMAT: "bitmask" + FORMAT: "RGB" +TEST: + PRECISE_BN: + ENABLED: True + NUM_ITER: 200 + DETECTIONS_PER_IMAGE: 100 +OUTPUT_DIR: "output/self-train-r1/" # round 1 +# OUTPUT_DIR: "output/self-train-r2/" # round 2 \ No newline at end of file diff --git a/cutler/model_zoo/configs/CutLER-ImageNet/mask_rcnn_R_50_FPN.yaml b/cutler/model_zoo/configs/CutLER-ImageNet/mask_rcnn_R_50_FPN.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1843ac29975a9956128020a2f44caef3e3bb9c84 --- /dev/null +++ b/cutler/model_zoo/configs/CutLER-ImageNet/mask_rcnn_R_50_FPN.yaml @@ -0,0 +1,52 @@ +_BASE_: "../Base-RCNN-FPN.yaml" +DATALOADER: + COPY_PASTE: True + COPY_PASTE_RATE: 1.0 + VISUALIZE_COPY_PASTE: False + COPY_PASTE_RANDOM_NUM: True + COPY_PASTE_MIN_RATIO: 0.3 + COPY_PASTE_MAX_RATIO: 1.0 +MODEL: + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + WEIGHTS: 'http://dl.fbaipublicfiles.com/cutler/checkpoints/dino_RN50_pretrain_d2_format.pkl' + MASK_ON: True + BACKBONE: + FREEZE_AT: 0 + RESNETS: + DEPTH: 50 + NORM: "SyncBN" + STRIDE_IN_1X1: False + FPN: + NORM: "SyncBN" + ROI_HEADS: + NAME: "CustomStandardROIHeads" + NUM_CLASSES: 1 + SCORE_THRESH_TEST: 0.0 + USE_DROPLOSS: True + DROPLOSS_IOU_THRESH: 0.01 + RPN: + POST_NMS_TOPK_TRAIN: 4000 + NMS_THRESH: 0.65 +DATASETS: + TRAIN: ("imagenet_train",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.01 + WEIGHT_DECAY: 0.00005 + STEPS: (80000,) + MAX_ITER: 160000 + CLIP_GRADIENTS: + CLIP_TYPE: norm + CLIP_VALUE: 1.0 + ENABLED: true + NORM_TYPE: 2.0 +INPUT: + MIN_SIZE_TRAIN: (240, 320, 480, 640, 672, 704, 736, 768, 800, 1024) + MAX_SIZE_TRAIN: 1333 + MASK_FORMAT: "bitmask" + FORMAT: "RGB" +TEST: + PRECISE_BN: + ENABLED: True +OUTPUT_DIR: "output/" \ No newline at end of file diff --git a/cutler/modeling/__init__.py b/cutler/modeling/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6348b05cdbdb7c9236a5fa1f8e47fcf4a56d25a3 --- /dev/null +++ b/cutler/modeling/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. + +from .roi_heads import ( + ROI_HEADS_REGISTRY, + ROIHeads, + CustomStandardROIHeads, + FastRCNNOutputLayers, + build_roi_heads, +) +from .roi_heads.custom_cascade_rcnn import CustomCascadeROIHeads +from .roi_heads.fast_rcnn import FastRCNNOutputLayers +from .meta_arch.rcnn import GeneralizedRCNN, ProposalNetwork +from .meta_arch.build import build_model + +_EXCLUDE = {"ShapeSpec"} +__all__ = [k for k in globals().keys() if k not in _EXCLUDE and not k.startswith("_")] \ No newline at end of file diff --git a/cutler/modeling/__pycache__/__init__.cpython-312.pyc b/cutler/modeling/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5784a626356caa958fa5b61f3519c4883293f0cc Binary files /dev/null and b/cutler/modeling/__pycache__/__init__.cpython-312.pyc differ diff --git a/cutler/modeling/meta_arch/__init__.py b/cutler/modeling/meta_arch/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..301b618644875b914a3b2a436b40f886043e55b4 --- /dev/null +++ b/cutler/modeling/meta_arch/__init__.py @@ -0,0 +1,7 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/meta_arch/__init__.py + +from .build import META_ARCH_REGISTRY, build_model # isort:skip + +__all__ = list(globals().keys()) diff --git a/cutler/modeling/meta_arch/__pycache__/__init__.cpython-312.pyc b/cutler/modeling/meta_arch/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..27363aa29f4eaf4c1b8b785bdf55f7bbba2e76b4 Binary files /dev/null and b/cutler/modeling/meta_arch/__pycache__/__init__.cpython-312.pyc differ diff --git a/cutler/modeling/meta_arch/__pycache__/build.cpython-312.pyc b/cutler/modeling/meta_arch/__pycache__/build.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4af7a80be2061ea58ac0eb3a87bca3f84b2dac89 Binary files /dev/null and b/cutler/modeling/meta_arch/__pycache__/build.cpython-312.pyc differ diff --git a/cutler/modeling/meta_arch/__pycache__/rcnn.cpython-312.pyc b/cutler/modeling/meta_arch/__pycache__/rcnn.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..aa9bbedf111f1724f50ff17149bd5b2c6e46ea42 Binary files /dev/null and b/cutler/modeling/meta_arch/__pycache__/rcnn.cpython-312.pyc differ diff --git a/cutler/modeling/meta_arch/build.py b/cutler/modeling/meta_arch/build.py new file mode 100644 index 0000000000000000000000000000000000000000..448301b67f439558567236a74e29b6822c160dc9 --- /dev/null +++ b/cutler/modeling/meta_arch/build.py @@ -0,0 +1,27 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/meta_arch/build.py + +import torch + +from detectron2.utils.logger import _log_api_usage +from detectron2.utils.registry import Registry + +META_ARCH_REGISTRY = Registry("META_ARCH") # noqa F401 isort:skip +META_ARCH_REGISTRY.__doc__ = """ +Registry for meta-architectures, i.e. the whole model. + +The registered object will be called with `obj(cfg)` +and expected to return a `nn.Module` object. +""" + + +def build_model(cfg): + """ + Build the whole model architecture, defined by ``cfg.MODEL.META_ARCHITECTURE``. + Note that it does not load any weights from ``cfg``. + """ + meta_arch = cfg.MODEL.META_ARCHITECTURE + model = META_ARCH_REGISTRY.get(meta_arch)(cfg) + model.to(torch.device(cfg.MODEL.DEVICE)) + _log_api_usage("modeling.meta_arch." + meta_arch) + return model diff --git a/cutler/modeling/meta_arch/rcnn.py b/cutler/modeling/meta_arch/rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..de9f4051000c213e476ef5cbe6c4d4ec352677e4 --- /dev/null +++ b/cutler/modeling/meta_arch/rcnn.py @@ -0,0 +1,344 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/meta_arch/rcnn.py + +import logging +import numpy as np +from typing import Dict, List, Optional, Tuple +import torch +from torch import nn + +from detectron2.config import configurable +from detectron2.data.detection_utils import convert_image_to_rgb +from detectron2.layers import move_device_like +from detectron2.structures import ImageList, Instances +from detectron2.utils.events import get_event_storage +from detectron2.utils.logger import log_first_n + +from detectron2.modeling.backbone import Backbone, build_backbone +from detectron2.modeling.postprocessing import detector_postprocess +from detectron2.modeling.proposal_generator import build_proposal_generator +from ..roi_heads import build_roi_heads +from .build import META_ARCH_REGISTRY + +__all__ = ["GeneralizedRCNN", "ProposalNetwork"] + + +@META_ARCH_REGISTRY.register() +class GeneralizedRCNN(nn.Module): + """ + Generalized R-CNN. Any models that contains the following three components: + 1. Per-image feature extraction (aka backbone) + 2. Region proposal generation + 3. Per-region feature extraction and prediction + """ + + @configurable + def __init__( + self, + *, + backbone: Backbone, + proposal_generator: nn.Module, + roi_heads: nn.Module, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + input_format: Optional[str] = None, + vis_period: int = 0, + ): + """ + Args: + backbone: a backbone module, must follow detectron2's backbone interface + proposal_generator: a module that generates proposals using backbone features + roi_heads: a ROI head that performs per-region computation + pixel_mean, pixel_std: list or tuple with #channels element, representing + the per-channel mean and std to be used to normalize the input image + input_format: describe the meaning of channels of input. Needed by visualization + vis_period: the period to run visualization. Set to 0 to disable. + """ + super().__init__() + self.backbone = backbone + self.proposal_generator = proposal_generator + self.roi_heads = roi_heads + + self.input_format = input_format + self.vis_period = vis_period + if vis_period > 0: + assert input_format is not None, "input_format is required for visualization!" + + self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) + assert ( + self.pixel_mean.shape == self.pixel_std.shape + ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!" + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + return { + "backbone": backbone, + "proposal_generator": build_proposal_generator(cfg, backbone.output_shape()), + "roi_heads": build_roi_heads(cfg, backbone.output_shape()), + "input_format": cfg.INPUT.FORMAT, + "vis_period": cfg.VIS_PERIOD, + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + } + + @property + def device(self): + return self.pixel_mean.device + + def _move_to_current_device(self, x): + return move_device_like(x, self.pixel_mean) + + def visualize_training(self, batched_inputs, proposals): + """ + A function used to visualize images and proposals. It shows ground truth + bounding boxes on the original image and up to 20 top-scoring predicted + object proposals on the original image. Users can implement different + visualization functions for different models. + + Args: + batched_inputs (list): a list that contains input to the model. + proposals (list): a list that contains predicted proposals. Both + batched_inputs and proposals should have the same length. + """ + from detectron2.utils.visualizer import Visualizer + + storage = get_event_storage() + max_vis_prop = 20 + + for input, prop in zip(batched_inputs, proposals): + img = input["image"] + img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format) + v_gt = Visualizer(img, None) + v_gt = v_gt.overlay_instances(boxes=input["instances"].gt_boxes) + anno_img = v_gt.get_image() + box_size = min(len(prop.proposal_boxes), max_vis_prop) + v_pred = Visualizer(img, None) + v_pred = v_pred.overlay_instances( + boxes=prop.proposal_boxes[0:box_size].tensor.cpu().numpy() + ) + prop_img = v_pred.get_image() + vis_img = np.concatenate((anno_img, prop_img), axis=1) + vis_img = vis_img.transpose(2, 0, 1) + vis_name = "Left: GT bounding boxes; Right: Predicted proposals" + storage.put_image(vis_name, vis_img) + break # only visualize one image in a batch + + def forward(self, batched_inputs: List[Dict[str, torch.Tensor]]): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper` . + Each item in the list contains the inputs for one image. + For now, each item in the list is a dict that contains: + + * image: Tensor, image in (C, H, W) format. + * instances (optional): groundtruth :class:`Instances` + * proposals (optional): :class:`Instances`, precomputed proposals. + + Other information that's included in the original dicts, such as: + + * "height", "width" (int): the output resolution of the model, used in inference. + See :meth:`postprocess` for details. + + Returns: + list[dict]: + Each dict is the output for one input image. + The dict contains one key "instances" whose value is a :class:`Instances`. + The :class:`Instances` object has the following keys: + "pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints" + """ + if not self.training: + return self.inference(batched_inputs) + + images = self.preprocess_image(batched_inputs) + if "instances" in batched_inputs[0]: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + else: + gt_instances = None + + features = self.backbone(images.tensor) + + if self.proposal_generator is not None: + proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) + else: + assert "proposals" in batched_inputs[0] + proposals = [x["proposals"].to(self.device) for x in batched_inputs] + proposal_losses = {} + + _, detector_losses = self.roi_heads(images, features, proposals, gt_instances) + if self.vis_period > 0: + storage = get_event_storage() + if storage.iter % self.vis_period == 0: + self.visualize_training(batched_inputs, proposals) + + losses = {} + losses.update(detector_losses) + losses.update(proposal_losses) + return losses + + def inference( + self, + batched_inputs: List[Dict[str, torch.Tensor]], + detected_instances: Optional[List[Instances]] = None, + do_postprocess: bool = True, + ): + """ + Run inference on the given inputs. + + Args: + batched_inputs (list[dict]): same as in :meth:`forward` + detected_instances (None or list[Instances]): if not None, it + contains an `Instances` object per image. The `Instances` + object contains "pred_boxes" and "pred_classes" which are + known boxes in the image. + The inference will then skip the detection of bounding boxes, + and only predict other per-ROI outputs. + do_postprocess (bool): whether to apply post-processing on the outputs. + + Returns: + When do_postprocess=True, same as in :meth:`forward`. + Otherwise, a list[Instances] containing raw network outputs. + """ + assert not self.training + + images = self.preprocess_image(batched_inputs) + features = self.backbone(images.tensor) + + if detected_instances is None: + if self.proposal_generator is not None: + proposals, _ = self.proposal_generator(images, features, None) + else: + assert "proposals" in batched_inputs[0] + proposals = [x["proposals"].to(self.device) for x in batched_inputs] + + results, _ = self.roi_heads(images, features, proposals, None) + else: + detected_instances = [x.to(self.device) for x in detected_instances] + results = self.roi_heads.forward_with_given_boxes(features, detected_instances) + + if do_postprocess: + assert not torch.jit.is_scripting(), "Scripting is not supported for postprocess." + return GeneralizedRCNN._postprocess(results, batched_inputs, images.image_sizes) + else: + return results + + def preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]): + """ + Normalize, pad and batch the input images. + """ + images = [self._move_to_current_device(x["image"]) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors( + images, + self.backbone.size_divisibility, + padding_constraints=self.backbone.padding_constraints, + ) + return images + + @staticmethod + def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]], image_sizes): + """ + Rescale the output instances to the target size. + """ + # note: private function; subject to changes + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + instances, batched_inputs, image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"instances": r}) + return processed_results + + +@META_ARCH_REGISTRY.register() +class ProposalNetwork(nn.Module): + """ + A meta architecture that only predicts object proposals. + """ + + @configurable + def __init__( + self, + *, + backbone: Backbone, + proposal_generator: nn.Module, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + ): + """ + Args: + backbone: a backbone module, must follow detectron2's backbone interface + proposal_generator: a module that generates proposals using backbone features + pixel_mean, pixel_std: list or tuple with #channels element, representing + the per-channel mean and std to be used to normalize the input image + """ + super().__init__() + self.backbone = backbone + self.proposal_generator = proposal_generator + self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False) + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + return { + "backbone": backbone, + "proposal_generator": build_proposal_generator(cfg, backbone.output_shape()), + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + } + + @property + def device(self): + return self.pixel_mean.device + + def _move_to_current_device(self, x): + return move_device_like(x, self.pixel_mean) + + def forward(self, batched_inputs): + """ + Args: + Same as in :class:`GeneralizedRCNN.forward` + + Returns: + list[dict]: + Each dict is the output for one input image. + The dict contains one key "proposals" whose value is a + :class:`Instances` with keys "proposal_boxes" and "objectness_logits". + """ + images = [self._move_to_current_device(x["image"]) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors( + images, + self.backbone.size_divisibility, + padding_constraints=self.backbone.padding_constraints, + ) + features = self.backbone(images.tensor) + + if "instances" in batched_inputs[0]: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + elif "targets" in batched_inputs[0]: + log_first_n( + logging.WARN, "'targets' in the model inputs is now renamed to 'instances'!", n=10 + ) + gt_instances = [x["targets"].to(self.device) for x in batched_inputs] + else: + gt_instances = None + proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) + # In training, the proposals are not useful at all but we generate them anyway. + # This makes RPN-only models about 5% slower. + if self.training: + return proposal_losses + + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + proposals, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"proposals": r}) + return processed_results diff --git a/cutler/modeling/roi_heads/__init__.py b/cutler/modeling/roi_heads/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8edbaef3fb02d5c157819ae62414337f75c7ead4 --- /dev/null +++ b/cutler/modeling/roi_heads/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. + +from .roi_heads import ( + ROI_HEADS_REGISTRY, + ROIHeads, + Res5ROIHeads, + CustomStandardROIHeads, + build_roi_heads, + select_foreground_proposals, +) +from .custom_cascade_rcnn import CustomCascadeROIHeads +from .fast_rcnn import FastRCNNOutputLayers + +from . import custom_cascade_rcnn # isort:skip + +__all__ = list(globals().keys()) diff --git a/cutler/modeling/roi_heads/__pycache__/__init__.cpython-312.pyc b/cutler/modeling/roi_heads/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6fdc1713462b5b1794b08bca5de440ba71fcea9c Binary files /dev/null and b/cutler/modeling/roi_heads/__pycache__/__init__.cpython-312.pyc differ diff --git a/cutler/modeling/roi_heads/__pycache__/custom_cascade_rcnn.cpython-312.pyc b/cutler/modeling/roi_heads/__pycache__/custom_cascade_rcnn.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..835548464a456875737941a8a0a4a7012f098a0c Binary files /dev/null and b/cutler/modeling/roi_heads/__pycache__/custom_cascade_rcnn.cpython-312.pyc differ diff --git a/cutler/modeling/roi_heads/__pycache__/fast_rcnn.cpython-312.pyc b/cutler/modeling/roi_heads/__pycache__/fast_rcnn.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9eec5346181d00f7556b33cc7b7c25af8c390b48 Binary files /dev/null and b/cutler/modeling/roi_heads/__pycache__/fast_rcnn.cpython-312.pyc differ diff --git a/cutler/modeling/roi_heads/__pycache__/roi_heads.cpython-312.pyc b/cutler/modeling/roi_heads/__pycache__/roi_heads.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bf679158d23848c8bd62a0885eb6e051109a40d8 Binary files /dev/null and b/cutler/modeling/roi_heads/__pycache__/roi_heads.cpython-312.pyc differ diff --git a/cutler/modeling/roi_heads/custom_cascade_rcnn.py b/cutler/modeling/roi_heads/custom_cascade_rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..509249c918c3c05455535b8e17c4c591cbc3f25d --- /dev/null +++ b/cutler/modeling/roi_heads/custom_cascade_rcnn.py @@ -0,0 +1,338 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/roi_heads/cascade_rcnn.py + +from typing import List +import torch +from torch import nn +from torch.autograd.function import Function + +from detectron2.config import configurable +from detectron2.layers import ShapeSpec +from detectron2.structures import Boxes, pairwise_iou +from structures import pairwise_iou_max_scores +from detectron2.structures import Instances +from detectron2.utils.events import get_event_storage + +from detectron2.modeling.box_regression import Box2BoxTransform +from detectron2.modeling.matcher import Matcher +from detectron2.modeling.poolers import ROIPooler +from detectron2.modeling.roi_heads.box_head import build_box_head +from .fast_rcnn import FastRCNNOutputLayers, fast_rcnn_inference +from .roi_heads import ROI_HEADS_REGISTRY, CustomStandardROIHeads + +import torch.nn.functional as F + +class _ScaleGradient(Function): + @staticmethod + def forward(ctx, input, scale): + ctx.scale = scale + return input + + @staticmethod + def backward(ctx, grad_output): + return grad_output * ctx.scale, None + + +@ROI_HEADS_REGISTRY.register() +class CustomCascadeROIHeads(CustomStandardROIHeads): + """ + The ROI heads that implement :paper:`Cascade R-CNN`. + """ + + @configurable + def __init__( + self, + *, + box_in_features: List[str], + box_pooler: ROIPooler, + box_heads: List[nn.Module], + box_predictors: List[nn.Module], + proposal_matchers: List[Matcher], + **kwargs, + ): + """ + NOTE: this interface is experimental. + + Args: + box_pooler (ROIPooler): pooler that extracts region features from given boxes + box_heads (list[nn.Module]): box head for each cascade stage + box_predictors (list[nn.Module]): box predictor for each cascade stage + proposal_matchers (list[Matcher]): matcher with different IoU thresholds to + match boxes with ground truth for each stage. The first matcher matches + RPN proposals with ground truth, the other matchers use boxes predicted + by the previous stage as proposals and match them with ground truth. + """ + assert "proposal_matcher" not in kwargs, ( + "CustomCascadeROIHeads takes 'proposal_matchers=' for each stage instead " + "of one 'proposal_matcher='." + ) + # The first matcher matches RPN proposals with ground truth, done in the base class + kwargs["proposal_matcher"] = proposal_matchers[0] + num_stages = self.num_cascade_stages = len(box_heads) + box_heads = nn.ModuleList(box_heads) + box_predictors = nn.ModuleList(box_predictors) + assert len(box_predictors) == num_stages, f"{len(box_predictors)} != {num_stages}!" + assert len(proposal_matchers) == num_stages, f"{len(proposal_matchers)} != {num_stages}!" + super().__init__( + box_in_features=box_in_features, + box_pooler=box_pooler, + box_head=box_heads, + box_predictor=box_predictors, + **kwargs, + ) + self.proposal_matchers = proposal_matchers + + @classmethod + def from_config(cls, cfg, input_shape): + ret = super().from_config(cfg, input_shape) + ret.pop("proposal_matcher") + return ret + + @classmethod + def _init_box_head(cls, cfg, input_shape): + # fmt: off + in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + cascade_bbox_reg_weights = cfg.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS + cascade_ious = cfg.MODEL.ROI_BOX_CASCADE_HEAD.IOUS + assert len(cascade_bbox_reg_weights) == len(cascade_ious) + assert cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG, \ + "CustomCascadeROIHeads only support class-agnostic regression now!" + assert cascade_ious[0] == cfg.MODEL.ROI_HEADS.IOU_THRESHOLDS[0] + # fmt: on + + in_channels = [input_shape[f].channels for f in in_features] + # Check all channel counts are equal + assert len(set(in_channels)) == 1, in_channels + in_channels = in_channels[0] + + box_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + pooled_shape = ShapeSpec( + channels=in_channels, width=pooler_resolution, height=pooler_resolution + ) + + box_heads, box_predictors, proposal_matchers = [], [], [] + for match_iou, bbox_reg_weights in zip(cascade_ious, cascade_bbox_reg_weights): + box_head = build_box_head(cfg, pooled_shape) + box_heads.append(box_head) + box_predictors.append( + FastRCNNOutputLayers( + cfg, + box_head.output_shape, + box2box_transform=Box2BoxTransform(weights=bbox_reg_weights), + ) + ) + proposal_matchers.append(Matcher([match_iou], [0, 1], allow_low_quality_matches=False)) + return { + "box_in_features": in_features, + "box_pooler": box_pooler, + "box_heads": box_heads, + "box_predictors": box_predictors, + "proposal_matchers": proposal_matchers, + } + + def forward(self, images, features, proposals, targets=None): + del images + if self.training: + proposals = self.label_and_sample_proposals(proposals, targets) + + if self.training: + # Need targets to box head + losses = self._forward_box(features, proposals, targets) + losses.update(self._forward_mask(features, proposals)) + losses.update(self._forward_keypoint(features, proposals)) + return proposals, losses + else: + pred_instances = self._forward_box(features, proposals) + pred_instances = self.forward_with_given_boxes(features, pred_instances) + return pred_instances, {} + + def _forward_box(self, features, proposals, targets=None): + """ + Args: + features, targets: the same as in + Same as in :meth:`ROIHeads.forward`. + proposals (list[Instances]): the per-image object proposals with + their matching ground truth. + Each has fields "proposal_boxes", and "objectness_logits", + "gt_classes", "gt_boxes". + """ + features = [features[f] for f in self.box_in_features] + head_outputs = [] # (predictor, predictions, proposals) + prev_pred_boxes = None + image_sizes = [x.image_size for x in proposals] + for k in range(self.num_cascade_stages): + if k > 0: + # The output boxes of the previous stage are used to create the input + # proposals of the next stage. + proposals = self._create_proposals_from_boxes(prev_pred_boxes, image_sizes) + if self.training: + proposals = self._match_and_label_boxes(proposals, k, targets) + predictions = self._run_stage(features, proposals, k) + prev_pred_boxes = self.box_predictor[k].predict_boxes(predictions, proposals) + head_outputs.append((self.box_predictor[k], predictions, proposals)) + + no_gt_found = False + if self.training: + losses = {} + storage = get_event_storage() + for stage, (predictor, predictions, proposals) in enumerate(head_outputs): + no_gt_found = False + with storage.name_scope("stage{}".format(stage)): + if self.use_droploss: + try: + box_num_list = [len(x.gt_boxes) for x in proposals] + gt_num_list = [torch.unique(x.gt_boxes.tensor[:100], dim=0).size()[0] for x in proposals] + except: + box_num_list = [0 for x in proposals] + gt_num_list = [0 for x in proposals] + no_gt_found = True + + if not no_gt_found: + # NOTE: confidence score + prediction_score, predictions_delta = predictions[0], predictions[1] + prediction_score = F.softmax(prediction_score, dim=1)[:,0] + + # NOTE: maximum overlapping with GT (IoU) + proposal_boxes = Boxes.cat([x.proposal_boxes for x in proposals]) + predictions_bbox = predictor.box2box_transform.apply_deltas(predictions_delta, proposal_boxes.tensor) + idx_start = 0 + iou_max_list = [] + for idx, x in enumerate(proposals): + idx_end = idx_start + box_num_list[idx] + iou_max_list.append(pairwise_iou_max_scores(predictions_bbox[idx_start:idx_end], x.gt_boxes[:gt_num_list[idx]].tensor)) + idx_start = idx_end + iou_max = torch.cat(iou_max_list, dim=0) + + # NOTE: get the weight of each proposal + weights = iou_max.le(self.droploss_iou_thresh).float() + weights = 1 - weights.ge(1.0).float() + stage_losses = predictor.losses(predictions, proposals, weights=weights.detach()) + else: + stage_losses = predictor.losses(predictions, proposals) + else: + stage_losses = predictor.losses(predictions, proposals) + losses.update({k + "_stage{}".format(stage): v for k, v in stage_losses.items()}) + return losses + else: + # Each is a list[Tensor] of length #image. Each tensor is Ri x (K+1) + scores_per_stage = [h[0].predict_probs(h[1], h[2]) for h in head_outputs] + + # Average the scores across heads + scores = [ + sum(list(scores_per_image)) * (1.0 / self.num_cascade_stages) + for scores_per_image in zip(*scores_per_stage) + ] + # Use the boxes of the last head + predictor, predictions, proposals = head_outputs[-1] + boxes = predictor.predict_boxes(predictions, proposals) + pred_instances, _ = fast_rcnn_inference( + boxes, + scores, + image_sizes, + predictor.test_score_thresh, + predictor.test_nms_thresh, + predictor.test_topk_per_image, + ) + return pred_instances + + @torch.no_grad() + def _match_and_label_boxes(self, proposals, stage, targets): + """ + Match proposals with groundtruth using the matcher at the given stage. + Label the proposals as foreground or background based on the match. + + Args: + proposals (list[Instances]): One Instances for each image, with + the field "proposal_boxes". + stage (int): the current stage + targets (list[Instances]): the ground truth instances + + Returns: + list[Instances]: the same proposals, but with fields "gt_classes" and "gt_boxes" + """ + num_fg_samples, num_bg_samples = [], [] + for proposals_per_image, targets_per_image in zip(proposals, targets): + match_quality_matrix = pairwise_iou( + targets_per_image.gt_boxes, proposals_per_image.proposal_boxes + ) + # proposal_labels are 0 or 1 + matched_idxs, proposal_labels = self.proposal_matchers[stage](match_quality_matrix) + if len(targets_per_image) > 0: + gt_classes = targets_per_image.gt_classes[matched_idxs] + # Label unmatched proposals (0 label from matcher) as background (label=num_classes) + gt_classes[proposal_labels == 0] = self.num_classes + gt_boxes = targets_per_image.gt_boxes[matched_idxs] + else: + gt_classes = torch.zeros_like(matched_idxs) + self.num_classes + gt_boxes = Boxes( + targets_per_image.gt_boxes.tensor.new_zeros((len(proposals_per_image), 4)) + ) + proposals_per_image.gt_classes = gt_classes + proposals_per_image.gt_boxes = gt_boxes + + num_fg_samples.append((proposal_labels == 1).sum().item()) + num_bg_samples.append(proposal_labels.numel() - num_fg_samples[-1]) + + # Log the number of fg/bg samples in each stage + storage = get_event_storage() + storage.put_scalar( + "stage{}/roi_head/num_fg_samples".format(stage), + sum(num_fg_samples) / len(num_fg_samples), + ) + storage.put_scalar( + "stage{}/roi_head/num_bg_samples".format(stage), + sum(num_bg_samples) / len(num_bg_samples), + ) + return proposals + + def _run_stage(self, features, proposals, stage): + """ + Args: + features (list[Tensor]): #lvl input features to ROIHeads + proposals (list[Instances]): #image Instances, with the field "proposal_boxes" + stage (int): the current stage + + Returns: + Same output as `FastRCNNOutputLayers.forward()`. + """ + box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals]) + # The original implementation averages the losses among heads, + # but scale up the parameter gradients of the heads. + # This is equivalent to adding the losses among heads, + # but scale down the gradients on features. + if self.training: + box_features = _ScaleGradient.apply(box_features, 1.0 / self.num_cascade_stages) + box_features = self.box_head[stage](box_features) + return self.box_predictor[stage](box_features) + + def _create_proposals_from_boxes(self, boxes, image_sizes): + """ + Args: + boxes (list[Tensor]): per-image predicted boxes, each of shape Ri x 4 + image_sizes (list[tuple]): list of image shapes in (h, w) + + Returns: + list[Instances]: per-image proposals with the given boxes. + """ + # Just like RPN, the proposals should not have gradients + boxes = [Boxes(b.detach()) for b in boxes] + proposals = [] + for boxes_per_image, image_size in zip(boxes, image_sizes): + boxes_per_image.clip(image_size) + if self.training: + # do not filter empty boxes at inference time, + # because the scores from each stage need to be aligned and added later + boxes_per_image = boxes_per_image[boxes_per_image.nonempty()] + prop = Instances(image_size) + prop.proposal_boxes = boxes_per_image + proposals.append(prop) + return proposals diff --git a/cutler/modeling/roi_heads/fast_rcnn.py b/cutler/modeling/roi_heads/fast_rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..5342d62808be05e1ad5a5800ba42e4d58fa1ffcd --- /dev/null +++ b/cutler/modeling/roi_heads/fast_rcnn.py @@ -0,0 +1,587 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/roi_heads/fast_rcnn.py + +import logging +from typing import Callable, Dict, List, Optional, Tuple, Union +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.data.detection_utils import get_fed_loss_cls_weights +from detectron2.layers import ShapeSpec, batched_nms, cat, cross_entropy, nonzero_tuple +from detectron2.modeling.box_regression import Box2BoxTransform, _dense_box_regression_loss +from detectron2.structures import Instances, Boxes +from detectron2.utils.events import get_event_storage +from torch.nn import Parameter +import torch.nn.functional as F + +__all__ = ["fast_rcnn_inference", "FastRCNNOutputLayers"] + + +logger = logging.getLogger(__name__) + +""" +Shape shorthand in this module: + + N: number of images in the minibatch + R: number of ROIs, combined over all images, in the minibatch + Ri: number of ROIs in image i + K: number of foreground classes. E.g.,there are 80 foreground classes in COCO. + +Naming convention: + + deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box + transform (see :class:`box_regression.Box2BoxTransform`). + + pred_class_logits: predicted class scores in [-inf, +inf]; use + softmax(pred_class_logits) to estimate P(class). + + gt_classes: ground-truth classification labels in [0, K], where [0, K) represent + foreground object classes and K represents the background class. + + pred_proposal_deltas: predicted box2box transform deltas for transforming proposals + to detection box predictions. + + gt_proposal_deltas: ground-truth box2box transform deltas +""" + + +def fast_rcnn_inference( + boxes: List[torch.Tensor], + scores: List[torch.Tensor], + image_shapes: List[Tuple[int, int]], + score_thresh: float, + nms_thresh: float, + topk_per_image: int, +): + """ + Call `fast_rcnn_inference_single_image` for all images. + + Args: + boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic + boxes for each image. Element i has shape (Ri, K * 4) if doing + class-specific regression, or (Ri, 4) if doing class-agnostic + regression, where Ri is the number of predicted objects for image i. + This is compatible with the output of :meth:`FastRCNNOutputLayers.predict_boxes`. + scores (list[Tensor]): A list of Tensors of predicted class scores for each image. + Element i has shape (Ri, K + 1), where Ri is the number of predicted objects + for image i. Compatible with the output of :meth:`FastRCNNOutputLayers.predict_probs`. + image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch. + score_thresh (float): Only return detections with a confidence score exceeding this + threshold. + nms_thresh (float): The threshold to use for box non-maximum suppression. Value in [0, 1]. + topk_per_image (int): The number of top scoring detections to return. Set < 0 to return + all detections. + + Returns: + instances: (list[Instances]): A list of N instances, one for each image in the batch, + that stores the topk most confidence detections. + kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates + the corresponding boxes/scores index in [0, Ri) from the input, for image i. + """ + result_per_image = [ + fast_rcnn_inference_single_image( + boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image + ) + for scores_per_image, boxes_per_image, image_shape in zip(scores, boxes, image_shapes) + ] + return [x[0] for x in result_per_image], [x[1] for x in result_per_image] + + +def _log_classification_stats(pred_logits, gt_classes, prefix="fast_rcnn"): + """ + Log the classification metrics to EventStorage. + + Args: + pred_logits: Rx(K+1) logits. The last column is for background class. + gt_classes: R labels + """ + num_instances = gt_classes.numel() + if num_instances == 0: + return + pred_classes = pred_logits.argmax(dim=1) + bg_class_ind = pred_logits.shape[1] - 1 + + fg_inds = (gt_classes >= 0) & (gt_classes < bg_class_ind) + num_fg = fg_inds.nonzero().numel() + fg_gt_classes = gt_classes[fg_inds] + fg_pred_classes = pred_classes[fg_inds] + + num_false_negative = (fg_pred_classes == bg_class_ind).nonzero().numel() + num_accurate = (pred_classes == gt_classes).nonzero().numel() + fg_num_accurate = (fg_pred_classes == fg_gt_classes).nonzero().numel() + + storage = get_event_storage() + storage.put_scalar(f"{prefix}/cls_accuracy", num_accurate / num_instances) + if num_fg > 0: + storage.put_scalar(f"{prefix}/fg_cls_accuracy", fg_num_accurate / num_fg) + storage.put_scalar(f"{prefix}/false_negative", num_false_negative / num_fg) + + +def fast_rcnn_inference_single_image( + boxes, + scores, + image_shape: Tuple[int, int], + score_thresh: float, + nms_thresh: float, + topk_per_image: int, +): + """ + Single-image inference. Return bounding-box detection results by thresholding + on scores and applying non-maximum suppression (NMS). + + Args: + Same as `fast_rcnn_inference`, but with boxes, scores, and image shapes + per image. + + Returns: + Same as `fast_rcnn_inference`, but for only one image. + """ + valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) + if not valid_mask.all(): + boxes = boxes[valid_mask] + scores = scores[valid_mask] + + scores = scores[:, :-1] + num_bbox_reg_classes = boxes.shape[1] // 4 + # Convert to Boxes to use the `clip` function ... + boxes = Boxes(boxes.reshape(-1, 4)) + boxes.clip(image_shape) + boxes = boxes.tensor.view(-1, num_bbox_reg_classes, 4) # R x C x 4 + + # 1. Filter results based on detection scores. It can make NMS more efficient + # by filtering out low-confidence detections. + filter_mask = scores > score_thresh # R x K + # R' x 2. First column contains indices of the R predictions; + # Second column contains indices of classes. + filter_inds = filter_mask.nonzero() + if num_bbox_reg_classes == 1: + boxes = boxes[filter_inds[:, 0], 0] + else: + boxes = boxes[filter_mask] + scores = scores[filter_mask] + + # 2. Apply NMS for each class independently. + keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh) + if topk_per_image >= 0: + keep = keep[:topk_per_image] + boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep] + + result = Instances(image_shape) + result.pred_boxes = Boxes(boxes) + result.scores = scores + result.pred_classes = filter_inds[:, 1] + return result, filter_inds[:, 0] + +class NormedLinear(nn.Module): + def __init__(self, in_features, out_features): + super(NormedLinear, self).__init__() + self.weight = Parameter(torch.Tensor(in_features, out_features)) + self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5) + + def forward(self, x): + out = F.normalize(x, dim=1).mm(F.normalize(self.weight, dim=0)) + return out + +class FastRCNNOutputLayers(nn.Module): + """ + Two linear layers for predicting Fast R-CNN outputs: + + 1. proposal-to-detection box regression deltas + 2. classification scores + """ + + @configurable + def __init__( + self, + input_shape: ShapeSpec, + *, + box2box_transform, + num_classes: int, + test_score_thresh: float = 0.0, + test_nms_thresh: float = 0.5, + test_topk_per_image: int = 100, + cls_agnostic_bbox_reg: bool = False, + smooth_l1_beta: float = 0.0, + box_reg_loss_type: str = "smooth_l1", + loss_weight: Union[float, Dict[str, float]] = 1.0, + use_fed_loss: bool = False, + use_sigmoid_ce: bool = False, + get_fed_loss_cls_weights: Optional[Callable] = None, + fed_loss_num_classes: int = 50, + ): + """ + NOTE: this interface is experimental. + + Args: + input_shape (ShapeSpec): shape of the input feature to this module + box2box_transform (Box2BoxTransform or Box2BoxTransformRotated): + num_classes (int): number of foreground classes + test_score_thresh (float): threshold to filter predictions results. + test_nms_thresh (float): NMS threshold for prediction results. + test_topk_per_image (int): number of top predictions to produce per image. + cls_agnostic_bbox_reg (bool): whether to use class agnostic for bbox regression + smooth_l1_beta (float): transition point from L1 to L2 loss. Only used if + `box_reg_loss_type` is "smooth_l1" + box_reg_loss_type (str): Box regression loss type. One of: "smooth_l1", "giou", + "diou", "ciou" + loss_weight (float|dict): weights to use for losses. Can be single float for weighting + all losses, or a dict of individual weightings. Valid dict keys are: + * "loss_cls": applied to classification loss + * "loss_box_reg": applied to box regression loss + use_fed_loss (bool): whether to use federated loss which samples additional negative + classes to calculate the loss + use_sigmoid_ce (bool): whether to calculate the loss using weighted average of binary + cross entropy with logits. This could be used together with federated loss + get_fed_loss_cls_weights (Callable): a callable which takes dataset name and frequency + weight power, and returns the probabilities to sample negative classes for + federated loss. The implementation can be found in + detectron2/data/detection_utils.py + fed_loss_num_classes (int): number of federated classes to keep in total + """ + super().__init__() + if isinstance(input_shape, int): # some backward compatibility + input_shape = ShapeSpec(channels=input_shape) + self.num_classes = num_classes + input_size = input_shape.channels * (input_shape.width or 1) * (input_shape.height or 1) + # prediction layer for num_classes foreground classes and one background class (hence + 1) + self.cls_score = nn.Linear(input_size, num_classes + 1) + nn.init.normal_(self.cls_score.weight, std=0.01) + num_bbox_reg_classes = 1 if cls_agnostic_bbox_reg else num_classes + box_dim = len(box2box_transform.weights) + self.bbox_pred = nn.Linear(input_size, num_bbox_reg_classes * box_dim) + + nn.init.normal_(self.bbox_pred.weight, std=0.001) + for l in [self.cls_score, self.bbox_pred]: + nn.init.constant_(l.bias, 0) + + self.box2box_transform = box2box_transform + self.smooth_l1_beta = smooth_l1_beta + self.test_score_thresh = test_score_thresh + self.test_nms_thresh = test_nms_thresh + self.test_topk_per_image = test_topk_per_image + self.box_reg_loss_type = box_reg_loss_type + if isinstance(loss_weight, float): + loss_weight = {"loss_cls": loss_weight, "loss_box_reg": loss_weight} + self.loss_weight = loss_weight + self.use_fed_loss = use_fed_loss + self.use_sigmoid_ce = use_sigmoid_ce + self.fed_loss_num_classes = fed_loss_num_classes + + if self.use_fed_loss: + assert self.use_sigmoid_ce, "Please use sigmoid cross entropy loss with federated loss" + fed_loss_cls_weights = get_fed_loss_cls_weights() + assert ( + len(fed_loss_cls_weights) == self.num_classes + ), "Please check the provided fed_loss_cls_weights. Their size should match num_classes" + self.register_buffer("fed_loss_cls_weights", fed_loss_cls_weights) + + + @classmethod + def from_config(cls, cfg, input_shape): + return { + "input_shape": input_shape, + "box2box_transform": Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS), + # fmt: off + "num_classes" : cfg.MODEL.ROI_HEADS.NUM_CLASSES, + "cls_agnostic_bbox_reg" : cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG, + "smooth_l1_beta" : cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA, + "test_score_thresh" : cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST, + "test_nms_thresh" : cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST, + "test_topk_per_image" : cfg.TEST.DETECTIONS_PER_IMAGE, + "box_reg_loss_type" : cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE, + "loss_weight" : {"loss_box_reg": cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT}, # noqa + "use_fed_loss" : cfg.MODEL.ROI_BOX_HEAD.USE_FED_LOSS, + "use_sigmoid_ce" : cfg.MODEL.ROI_BOX_HEAD.USE_SIGMOID_CE, + "get_fed_loss_cls_weights" : lambda: get_fed_loss_cls_weights(dataset_names=cfg.DATASETS.TRAIN, freq_weight_power=cfg.MODEL.ROI_BOX_HEAD.FED_LOSS_FREQ_WEIGHT_POWER), # noqa + "fed_loss_num_classes" : cfg.MODEL.ROI_BOX_HEAD.FED_LOSS_NUM_CLASSES, + # fmt: on + } + + def forward(self, x): + """ + Args: + x: per-region features of shape (N, ...) for N bounding boxes to predict. + + Returns: + (Tensor, Tensor): + First tensor: shape (N,K+1), scores for each of the N box. Each row contains the + scores for K object categories and 1 background class. + + Second tensor: bounding box regression deltas for each box. Shape is shape (N,Kx4), + or (N,4) for class-agnostic regression. + """ + if x.dim() > 2: + x = torch.flatten(x, start_dim=1) + scores = self.cls_score(x) + proposal_deltas = self.bbox_pred(x) + return scores, proposal_deltas + + def losses(self, predictions, proposals, weights=None): + """ + Args: + predictions: return values of :meth:`forward()`. + proposals (list[Instances]): proposals that match the features that were used + to compute predictions. The fields ``proposal_boxes``, ``gt_boxes``, + ``gt_classes`` are expected. + weights: weights for reweighting the loss of each instance based on IoU + + Returns: + Dict[str, Tensor]: dict of losses + """ + scores, proposal_deltas = predictions + + # parse classification outputs + gt_classes = ( + cat([p.gt_classes for p in proposals], dim=0) if len(proposals) else torch.empty(0) + ) + _log_classification_stats(scores, gt_classes) + + # parse box regression outputs + if len(proposals): + proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0) # Nx4 + assert not proposal_boxes.requires_grad, "Proposals should not require gradients!" + # If "gt_boxes" does not exist, the proposals must be all negative and + # should not be included in regression loss computation. + # Here we just use proposal_boxes as an arbitrary placeholder because its + # value won't be used in self.box_reg_loss(). + gt_boxes = cat( + [(p.gt_boxes if p.has("gt_boxes") else p.proposal_boxes).tensor for p in proposals], + dim=0, + ) + else: + proposal_boxes = gt_boxes = torch.empty((0, 4), device=proposal_deltas.device) + + if self.use_sigmoid_ce: + loss_cls = self.sigmoid_cross_entropy_loss(scores, gt_classes) + else: + if weights != None: + loss_cls = (weights * cross_entropy(scores, gt_classes, reduction='none')).mean() + else: + loss_cls = cross_entropy(scores, gt_classes, reduction="mean") + + losses = { + "loss_cls": loss_cls, + "loss_box_reg": self.box_reg_loss( + proposal_boxes, gt_boxes, proposal_deltas, gt_classes + ), + } + return {k: v * self.loss_weight.get(k, 1.0) for k, v in losses.items()} + + # Implementation from https://github.com/xingyizhou/CenterNet2/blob/master/projects/CenterNet2/centernet/modeling/roi_heads/fed_loss.py # noqa + # with slight modifications + def get_fed_loss_classes(self, gt_classes, num_fed_loss_classes, num_classes, weight): + """ + Args: + gt_classes: a long tensor of shape R that contains the gt class label of each proposal. + num_fed_loss_classes: minimum number of classes to keep when calculating federated loss. + Will sample negative classes if number of unique gt_classes is smaller than this value. + num_classes: number of foreground classes + weight: probabilities used to sample negative classes + + Returns: + Tensor: + classes to keep when calculating the federated loss, including both unique gt + classes and sampled negative classes. + """ + unique_gt_classes = torch.unique(gt_classes) + prob = unique_gt_classes.new_ones(num_classes + 1).float() + prob[-1] = 0 + if len(unique_gt_classes) < num_fed_loss_classes: + prob[:num_classes] = weight.float().clone() + prob[unique_gt_classes] = 0 + sampled_negative_classes = torch.multinomial( + prob, num_fed_loss_classes - len(unique_gt_classes), replacement=False + ) + fed_loss_classes = torch.cat([unique_gt_classes, sampled_negative_classes]) + else: + fed_loss_classes = unique_gt_classes + return fed_loss_classes + + # Implementation from https://github.com/xingyizhou/CenterNet2/blob/master/projects/CenterNet2/centernet/modeling/roi_heads/custom_fast_rcnn.py#L113 # noqa + # with slight modifications + def sigmoid_cross_entropy_loss(self, pred_class_logits, gt_classes): + """ + Args: + pred_class_logits: shape (N, K+1), scores for each of the N box. Each row contains the + scores for K object categories and 1 background class + gt_classes: a long tensor of shape R that contains the gt class label of each proposal. + """ + if pred_class_logits.numel() == 0: + return pred_class_logits.new_zeros([1])[0] + + N = pred_class_logits.shape[0] + K = pred_class_logits.shape[1] - 1 + + target = pred_class_logits.new_zeros(N, K + 1) + target[range(len(gt_classes)), gt_classes] = 1 + target = target[:, :K] + + cls_loss = F.binary_cross_entropy_with_logits( + pred_class_logits[:, :-1], target, reduction="none" + ) + + if self.use_fed_loss: + fed_loss_classes = self.get_fed_loss_classes( + gt_classes, + num_fed_loss_classes=self.fed_loss_num_classes, + num_classes=K, + weight=self.fed_loss_cls_weights, + ) + fed_loss_classes_mask = fed_loss_classes.new_zeros(K + 1) + fed_loss_classes_mask[fed_loss_classes] = 1 + fed_loss_classes_mask = fed_loss_classes_mask[:K] + weight = fed_loss_classes_mask.view(1, K).expand(N, K).float() + else: + weight = 1 + + loss = torch.sum(cls_loss * weight) / N + return loss + + def box_reg_loss(self, proposal_boxes, gt_boxes, pred_deltas, gt_classes): + """ + Args: + proposal_boxes/gt_boxes are tensors with the same shape (R, 4 or 5). + pred_deltas has shape (R, 4 or 5), or (R, num_classes * (4 or 5)). + gt_classes is a long tensor of shape R, the gt class label of each proposal. + R shall be the number of proposals. + """ + box_dim = proposal_boxes.shape[1] # 4 or 5 + # Regression loss is only computed for foreground proposals (those matched to a GT) + fg_inds = nonzero_tuple((gt_classes >= 0) & (gt_classes < self.num_classes))[0] + if pred_deltas.shape[1] == box_dim: # cls-agnostic regression + fg_pred_deltas = pred_deltas[fg_inds] + else: + fg_pred_deltas = pred_deltas.view(-1, self.num_classes, box_dim)[ + fg_inds, gt_classes[fg_inds] + ] + + loss_box_reg = _dense_box_regression_loss( + [proposal_boxes[fg_inds]], + self.box2box_transform, + [fg_pred_deltas.unsqueeze(0)], + [gt_boxes[fg_inds]], + ..., + self.box_reg_loss_type, + self.smooth_l1_beta, + ) + + # The reg loss is normalized using the total number of regions (R), not the number + # of foreground regions even though the box regression loss is only defined on + # foreground regions. Why? Because doing so gives equal training influence to + # each foreground example. To see how, consider two different minibatches: + # (1) Contains a single foreground region + # (2) Contains 100 foreground regions + # If we normalize by the number of foreground regions, the single example in + # minibatch (1) will be given 100 times as much influence as each foreground + # example in minibatch (2). Normalizing by the total number of regions, R, + # means that the single example in minibatch (1) and each of the 100 examples + # in minibatch (2) are given equal influence. + return loss_box_reg / max(gt_classes.numel(), 1.0) # return 0 if empty + + def inference(self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]): + """ + Args: + predictions: return values of :meth:`forward()`. + proposals (list[Instances]): proposals that match the features that were + used to compute predictions. The ``proposal_boxes`` field is expected. + + Returns: + list[Instances]: same as `fast_rcnn_inference`. + list[Tensor]: same as `fast_rcnn_inference`. + """ + boxes = self.predict_boxes(predictions, proposals) + scores = self.predict_probs(predictions, proposals) + image_shapes = [x.image_size for x in proposals] + return fast_rcnn_inference( + boxes, + scores, + image_shapes, + self.test_score_thresh, + self.test_nms_thresh, + self.test_topk_per_image, + ) + + def predict_boxes_for_gt_classes(self, predictions, proposals): + """ + Args: + predictions: return values of :meth:`forward()`. + proposals (list[Instances]): proposals that match the features that were used + to compute predictions. The fields ``proposal_boxes``, ``gt_classes`` are expected. + + Returns: + list[Tensor]: + A list of Tensors of predicted boxes for GT classes in case of + class-specific box head. Element i of the list has shape (Ri, B), where Ri is + the number of proposals for image i and B is the box dimension (4 or 5) + """ + if not len(proposals): + return [] + scores, proposal_deltas = predictions + proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0) + N, B = proposal_boxes.shape + predict_boxes = self.box2box_transform.apply_deltas( + proposal_deltas, proposal_boxes + ) # Nx(KxB) + + K = predict_boxes.shape[1] // B + if K > 1: + gt_classes = torch.cat([p.gt_classes for p in proposals], dim=0) + # Some proposals are ignored or have a background class. Their gt_classes + # cannot be used as index. + gt_classes = gt_classes.clamp_(0, K - 1) + + predict_boxes = predict_boxes.view(N, K, B)[ + torch.arange(N, dtype=torch.long, device=predict_boxes.device), gt_classes + ] + num_prop_per_image = [len(p) for p in proposals] + return predict_boxes.split(num_prop_per_image) + + def predict_boxes( + self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances] + ): + """ + Args: + predictions: return values of :meth:`forward()`. + proposals (list[Instances]): proposals that match the features that were + used to compute predictions. The ``proposal_boxes`` field is expected. + + Returns: + list[Tensor]: + A list of Tensors of predicted class-specific or class-agnostic boxes + for each image. Element i has shape (Ri, K * B) or (Ri, B), where Ri is + the number of proposals for image i and B is the box dimension (4 or 5) + """ + if not len(proposals): + return [] + _, proposal_deltas = predictions + num_prop_per_image = [len(p) for p in proposals] + proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0) + predict_boxes = self.box2box_transform.apply_deltas( + proposal_deltas, + proposal_boxes, + ) # Nx(KxB) + return predict_boxes.split(num_prop_per_image) + + def predict_probs( + self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances] + ): + """ + Args: + predictions: return values of :meth:`forward()`. + proposals (list[Instances]): proposals that match the features that were + used to compute predictions. + + Returns: + list[Tensor]: + A list of Tensors of predicted class probabilities for each image. + Element i has shape (Ri, K + 1), where Ri is the number of proposals for image i. + """ + scores, _ = predictions + num_inst_per_image = [len(p) for p in proposals] + if self.use_sigmoid_ce: + probs = scores.sigmoid() + else: + probs = F.softmax(scores, dim=-1) + return probs.split(num_inst_per_image, dim=0) diff --git a/cutler/modeling/roi_heads/roi_heads.py b/cutler/modeling/roi_heads/roi_heads.py new file mode 100644 index 0000000000000000000000000000000000000000..f3ed51b3afe957a818ca39032851d69820e6d4cf --- /dev/null +++ b/cutler/modeling/roi_heads/roi_heads.py @@ -0,0 +1,926 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/roi_heads/roi_heads.py + +import inspect +import logging +import numpy as np +from typing import Dict, List, Optional, Tuple +import torch +from torch import nn + +from detectron2.config import configurable +from detectron2.layers import ShapeSpec, nonzero_tuple +from detectron2.structures import Boxes, pairwise_iou +from structures import pairwise_iou_max_scores +from detectron2.structures import ImageList, Instances +from detectron2.utils.events import get_event_storage +from detectron2.utils.registry import Registry + +from detectron2.modeling.backbone.resnet import BottleneckBlock, ResNet +from detectron2.modeling.matcher import Matcher +from detectron2.modeling.poolers import ROIPooler +from detectron2.modeling.proposal_generator.proposal_utils import add_ground_truth_to_proposals +from detectron2.modeling.sampling import subsample_labels +from detectron2.modeling.roi_heads.box_head import build_box_head +from .fast_rcnn import FastRCNNOutputLayers +from detectron2.modeling.roi_heads.keypoint_head import build_keypoint_head +from detectron2.modeling.roi_heads.mask_head import build_mask_head + +from detectron2.modeling.box_regression import Box2BoxTransform +import torch.nn.functional as F +from colored import fg + +blue, red = fg('blue'), fg('red') + +ROI_HEADS_REGISTRY = Registry("ROI_HEADS") +ROI_HEADS_REGISTRY.__doc__ = """ +Registry for ROI heads in a generalized R-CNN model. +ROIHeads take feature maps and region proposals, and +perform per-region computation. + +The registered object will be called with `obj(cfg, input_shape)`. +The call is expected to return an :class:`ROIHeads`. +""" + +logger = logging.getLogger(__name__) + + +def build_roi_heads(cfg, input_shape): + """ + Build ROIHeads defined by `cfg.MODEL.ROI_HEADS.NAME`. + """ + name = cfg.MODEL.ROI_HEADS.NAME + return ROI_HEADS_REGISTRY.get(name)(cfg, input_shape) + + +def select_foreground_proposals( + proposals: List[Instances], bg_label: int +) -> Tuple[List[Instances], List[torch.Tensor]]: + """ + Given a list of N Instances (for N images), each containing a `gt_classes` field, + return a list of Instances that contain only instances with `gt_classes != -1 && + gt_classes != bg_label`. + + Args: + proposals (list[Instances]): A list of N Instances, where N is the number of + images in the batch. + bg_label: label index of background class. + + Returns: + list[Instances]: N Instances, each contains only the selected foreground instances. + list[Tensor]: N boolean vector, correspond to the selection mask of + each Instances object. True for selected instances. + """ + assert isinstance(proposals, (list, tuple)) + assert isinstance(proposals[0], Instances) + assert proposals[0].has("gt_classes") + fg_proposals = [] + fg_selection_masks = [] + for proposals_per_image in proposals: + gt_classes = proposals_per_image.gt_classes + fg_selection_mask = (gt_classes != -1) & (gt_classes != bg_label) + fg_idxs = fg_selection_mask.nonzero().squeeze(1) + fg_proposals.append(proposals_per_image[fg_idxs]) + fg_selection_masks.append(fg_selection_mask) + return fg_proposals, fg_selection_masks + + +def select_proposals_with_visible_keypoints(proposals: List[Instances]) -> List[Instances]: + """ + Args: + proposals (list[Instances]): a list of N Instances, where N is the + number of images. + + Returns: + proposals: only contains proposals with at least one visible keypoint. + + Note that this is still slightly different from Detectron. + In Detectron, proposals for training keypoint head are re-sampled from + all the proposals with IOU>threshold & >=1 visible keypoint. + + Here, the proposals are first sampled from all proposals with + IOU>threshold, then proposals with no visible keypoint are filtered out. + This strategy seems to make no difference on Detectron and is easier to implement. + """ + ret = [] + all_num_fg = [] + for proposals_per_image in proposals: + # If empty/unannotated image (hard negatives), skip filtering for train + if len(proposals_per_image) == 0: + ret.append(proposals_per_image) + continue + gt_keypoints = proposals_per_image.gt_keypoints.tensor + # #fg x K x 3 + vis_mask = gt_keypoints[:, :, 2] >= 1 + xs, ys = gt_keypoints[:, :, 0], gt_keypoints[:, :, 1] + proposal_boxes = proposals_per_image.proposal_boxes.tensor.unsqueeze(dim=1) # #fg x 1 x 4 + kp_in_box = ( + (xs >= proposal_boxes[:, :, 0]) + & (xs <= proposal_boxes[:, :, 2]) + & (ys >= proposal_boxes[:, :, 1]) + & (ys <= proposal_boxes[:, :, 3]) + ) + selection = (kp_in_box & vis_mask).any(dim=1) + selection_idxs = nonzero_tuple(selection)[0] + all_num_fg.append(selection_idxs.numel()) + ret.append(proposals_per_image[selection_idxs]) + + storage = get_event_storage() + storage.put_scalar("keypoint_head/num_fg_samples", np.mean(all_num_fg)) + return ret + + +class ROIHeads(torch.nn.Module): + """ + ROIHeads perform all per-region computation in an R-CNN. + + It typically contains logic to + + 1. (in training only) match proposals with ground truth and sample them + 2. crop the regions and extract per-region features using proposals + 3. make per-region predictions with different heads + + It can have many variants, implemented as subclasses of this class. + This base class contains the logic to match/sample proposals. + But it is not necessary to inherit this class if the sampling logic is not needed. + """ + + @configurable + def __init__( + self, + *, + num_classes, + batch_size_per_image, + positive_fraction, + proposal_matcher, + proposal_append_gt=True, + ): + """ + NOTE: this interface is experimental. + + Args: + num_classes (int): number of foreground classes (i.e. background is not included) + batch_size_per_image (int): number of proposals to sample for training + positive_fraction (float): fraction of positive (foreground) proposals + to sample for training. + proposal_matcher (Matcher): matcher that matches proposals and ground truth + proposal_append_gt (bool): whether to include ground truth as proposals as well + """ + super().__init__() + self.batch_size_per_image = batch_size_per_image + self.positive_fraction = positive_fraction + self.num_classes = num_classes + self.proposal_matcher = proposal_matcher + self.proposal_append_gt = proposal_append_gt + + @classmethod + def from_config(cls, cfg): + return { + "batch_size_per_image": cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE, + "positive_fraction": cfg.MODEL.ROI_HEADS.POSITIVE_FRACTION, + "num_classes": cfg.MODEL.ROI_HEADS.NUM_CLASSES, + "proposal_append_gt": cfg.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT, + # Matcher to assign box proposals to gt boxes + "proposal_matcher": Matcher( + cfg.MODEL.ROI_HEADS.IOU_THRESHOLDS, + cfg.MODEL.ROI_HEADS.IOU_LABELS, + allow_low_quality_matches=False, + ), + } + + def _sample_proposals( + self, matched_idxs: torch.Tensor, matched_labels: torch.Tensor, gt_classes: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Based on the matching between N proposals and M groundtruth, + sample the proposals and set their classification labels. + + Args: + matched_idxs (Tensor): a vector of length N, each is the best-matched + gt index in [0, M) for each proposal. + matched_labels (Tensor): a vector of length N, the matcher's label + (one of cfg.MODEL.ROI_HEADS.IOU_LABELS) for each proposal. + gt_classes (Tensor): a vector of length M. + + Returns: + Tensor: a vector of indices of sampled proposals. Each is in [0, N). + Tensor: a vector of the same length, the classification label for + each sampled proposal. Each sample is labeled as either a category in + [0, num_classes) or the background (num_classes). + """ + has_gt = gt_classes.numel() > 0 + # Get the corresponding GT for each proposal + if has_gt: + gt_classes = gt_classes[matched_idxs] + # Label unmatched proposals (0 label from matcher) as background (label=num_classes) + gt_classes[matched_labels == 0] = self.num_classes + # Label ignore proposals (-1 label) + gt_classes[matched_labels == -1] = -1 + else: + gt_classes = torch.zeros_like(matched_idxs) + self.num_classes + + sampled_fg_idxs, sampled_bg_idxs = subsample_labels( + gt_classes, self.batch_size_per_image, self.positive_fraction, self.num_classes + ) + + sampled_idxs = torch.cat([sampled_fg_idxs, sampled_bg_idxs], dim=0) + return sampled_idxs, gt_classes[sampled_idxs] + + @torch.no_grad() + def label_and_sample_proposals( + self, proposals: List[Instances], targets: List[Instances] + ) -> List[Instances]: + """ + Prepare some proposals to be used to train the ROI heads. + It performs box matching between `proposals` and `targets`, and assigns + training labels to the proposals. + It returns ``self.batch_size_per_image`` random samples from proposals and groundtruth + boxes, with a fraction of positives that is no larger than + ``self.positive_fraction``. + + Args: + See :meth:`ROIHeads.forward` + + Returns: + list[Instances]: + length `N` list of `Instances`s containing the proposals + sampled for training. Each `Instances` has the following fields: + + - proposal_boxes: the proposal boxes + - gt_boxes: the ground-truth box that the proposal is assigned to + (this is only meaningful if the proposal has a label > 0; if label = 0 + then the ground-truth box is random) + + Other fields such as "gt_classes", "gt_masks", that's included in `targets`. + """ + # Augment proposals with ground-truth boxes. + # In the case of learned proposals (e.g., RPN), when training starts + # the proposals will be low quality due to random initialization. + # It's possible that none of these initial + # proposals have high enough overlap with the gt objects to be used + # as positive examples for the second stage components (box head, + # cls head, mask head). Adding the gt boxes to the set of proposals + # ensures that the second stage components will have some positive + # examples from the start of training. For RPN, this augmentation improves + # convergence and empirically improves box AP on COCO by about 0.5 + # points (under one tested configuration). + if self.proposal_append_gt: + proposals = add_ground_truth_to_proposals(targets, proposals) + + proposals_with_gt = [] + + num_fg_samples = [] + num_bg_samples = [] + for proposals_per_image, targets_per_image in zip(proposals, targets): + has_gt = len(targets_per_image) > 0 + match_quality_matrix = pairwise_iou( + targets_per_image.gt_boxes, proposals_per_image.proposal_boxes + ) + matched_idxs, matched_labels = self.proposal_matcher(match_quality_matrix) + sampled_idxs, gt_classes = self._sample_proposals( + matched_idxs, matched_labels, targets_per_image.gt_classes + ) + + # Set target attributes of the sampled proposals: + proposals_per_image = proposals_per_image[sampled_idxs] + proposals_per_image.gt_classes = gt_classes + + if has_gt: + sampled_targets = matched_idxs[sampled_idxs] + # We index all the attributes of targets that start with "gt_" + # and have not been added to proposals yet (="gt_classes"). + # NOTE: here the indexing waste some compute, because heads + # like masks, keypoints, etc, will filter the proposals again, + # (by foreground/background, or number of keypoints in the image, etc) + # so we essentially index the data twice. + for (trg_name, trg_value) in targets_per_image.get_fields().items(): + if trg_name.startswith("gt_") and not proposals_per_image.has(trg_name): + proposals_per_image.set(trg_name, trg_value[sampled_targets]) + # If no GT is given in the image, we don't know what a dummy gt value can be. + # Therefore the returned proposals won't have any gt_* fields, except for a + # gt_classes full of background label. + + num_bg_samples.append((gt_classes == self.num_classes).sum().item()) + num_fg_samples.append(gt_classes.numel() - num_bg_samples[-1]) + proposals_with_gt.append(proposals_per_image) + + # Log the number of fg/bg samples that are selected for training ROI heads + storage = get_event_storage() + storage.put_scalar("roi_head/num_fg_samples", np.mean(num_fg_samples)) + storage.put_scalar("roi_head/num_bg_samples", np.mean(num_bg_samples)) + + return proposals_with_gt + + def forward( + self, + images: ImageList, + features: Dict[str, torch.Tensor], + proposals: List[Instances], + targets: Optional[List[Instances]] = None, + ) -> Tuple[List[Instances], Dict[str, torch.Tensor]]: + """ + Args: + images (ImageList): + features (dict[str,Tensor]): input data as a mapping from feature + map name to tensor. Axis 0 represents the number of images `N` in + the input data; axes 1-3 are channels, height, and width, which may + vary between feature maps (e.g., if a feature pyramid is used). + proposals (list[Instances]): length `N` list of `Instances`. The i-th + `Instances` contains object proposals for the i-th input image, + with fields "proposal_boxes" and "objectness_logits". + targets (list[Instances], optional): length `N` list of `Instances`. The i-th + `Instances` contains the ground-truth per-instance annotations + for the i-th input image. Specify `targets` during training only. + It may have the following fields: + + - gt_boxes: the bounding box of each instance. + - gt_classes: the label for each instance with a category ranging in [0, #class]. + - gt_masks: PolygonMasks or BitMasks, the ground-truth masks of each instance. + - gt_keypoints: NxKx3, the groud-truth keypoints for each instance. + + Returns: + list[Instances]: length `N` list of `Instances` containing the + detected instances. Returned during inference only; may be [] during training. + + dict[str->Tensor]: + mapping from a named loss to a tensor storing the loss. Used during training only. + """ + raise NotImplementedError() + + +@ROI_HEADS_REGISTRY.register() +class Res5ROIHeads(ROIHeads): + """ + The ROIHeads in a typical "C4" R-CNN model, where + the box and mask head share the cropping and + the per-region feature computation by a Res5 block. + See :paper:`ResNet` Appendix A. + """ + + @configurable + def __init__( + self, + *, + in_features: List[str], + pooler: ROIPooler, + res5: nn.Module, + box_predictor: nn.Module, + mask_head: Optional[nn.Module] = None, + **kwargs, + ): + """ + NOTE: this interface is experimental. + + Args: + in_features (list[str]): list of backbone feature map names to use for + feature extraction + pooler (ROIPooler): pooler to extra region features from backbone + res5 (nn.Sequential): a CNN to compute per-region features, to be used by + ``box_predictor`` and ``mask_head``. Typically this is a "res5" + block from a ResNet. + box_predictor (nn.Module): make box predictions from the feature. + Should have the same interface as :class:`FastRCNNOutputLayers`. + mask_head (nn.Module): transform features to make mask predictions + """ + super().__init__(**kwargs) + self.in_features = in_features + self.pooler = pooler + if isinstance(res5, (list, tuple)): + res5 = nn.Sequential(*res5) + self.res5 = res5 + self.box_predictor = box_predictor + self.mask_on = mask_head is not None + if self.mask_on: + self.mask_head = mask_head + + @classmethod + def from_config(cls, cfg, input_shape): + # fmt: off + ret = super().from_config(cfg) + in_features = ret["in_features"] = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + pooler_scales = (1.0 / input_shape[in_features[0]].stride, ) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + mask_on = cfg.MODEL.MASK_ON + # fmt: on + assert not cfg.MODEL.KEYPOINT_ON + assert len(in_features) == 1 + + ret["pooler"] = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + + # Compatbility with old moco code. Might be useful. + # See notes in StandardROIHeads.from_config + if not inspect.ismethod(cls._build_res5_block): + logger.warning( + "The behavior of _build_res5_block may change. " + "Please do not depend on private methods." + ) + cls._build_res5_block = classmethod(cls._build_res5_block) + + ret["res5"], out_channels = cls._build_res5_block(cfg) + ret["box_predictor"] = FastRCNNOutputLayers( + cfg, ShapeSpec(channels=out_channels, height=1, width=1) + ) + + if mask_on: + ret["mask_head"] = build_mask_head( + cfg, + ShapeSpec(channels=out_channels, width=pooler_resolution, height=pooler_resolution), + ) + return ret + + @classmethod + def _build_res5_block(cls, cfg): + # fmt: off + stage_channel_factor = 2 ** 3 # res5 is 8x res2 + num_groups = cfg.MODEL.RESNETS.NUM_GROUPS + width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP + bottleneck_channels = num_groups * width_per_group * stage_channel_factor + out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS * stage_channel_factor + stride_in_1x1 = cfg.MODEL.RESNETS.STRIDE_IN_1X1 + norm = cfg.MODEL.RESNETS.NORM + assert not cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE[-1], \ + "Deformable conv is not yet supported in res5 head." + # fmt: on + + blocks = ResNet.make_stage( + BottleneckBlock, + 3, + stride_per_block=[2, 1, 1], + in_channels=out_channels // 2, + bottleneck_channels=bottleneck_channels, + out_channels=out_channels, + num_groups=num_groups, + norm=norm, + stride_in_1x1=stride_in_1x1, + ) + return nn.Sequential(*blocks), out_channels + + def _shared_roi_transform(self, features: List[torch.Tensor], boxes: List[Boxes]): + x = self.pooler(features, boxes) + return self.res5(x) + + def forward( + self, + images: ImageList, + features: Dict[str, torch.Tensor], + proposals: List[Instances], + targets: Optional[List[Instances]] = None, + ): + """ + See :meth:`ROIHeads.forward`. + """ + del images + + if self.training: + assert targets + proposals = self.label_and_sample_proposals(proposals, targets) + del targets + + proposal_boxes = [x.proposal_boxes for x in proposals] + box_features = self._shared_roi_transform( + [features[f] for f in self.in_features], proposal_boxes + ) + predictions = self.box_predictor(box_features.mean(dim=[2, 3])) + + if self.training: + del features + losses = self.box_predictor.losses(predictions, proposals) + if self.mask_on: + proposals, fg_selection_masks = select_foreground_proposals( + proposals, self.num_classes + ) + # Since the ROI feature transform is shared between boxes and masks, + # we don't need to recompute features. The mask loss is only defined + # on foreground proposals, so we need to select out the foreground + # features. + mask_features = box_features[torch.cat(fg_selection_masks, dim=0)] + del box_features + losses.update(self.mask_head(mask_features, proposals)) + return [], losses + else: + pred_instances, _ = self.box_predictor.inference(predictions, proposals) + pred_instances = self.forward_with_given_boxes(features, pred_instances) + return pred_instances, {} + + def forward_with_given_boxes( + self, features: Dict[str, torch.Tensor], instances: List[Instances] + ) -> List[Instances]: + """ + Use the given boxes in `instances` to produce other (non-box) per-ROI outputs. + + Args: + features: same as in `forward()` + instances (list[Instances]): instances to predict other outputs. Expect the keys + "pred_boxes" and "pred_classes" to exist. + + Returns: + instances (Instances): + the same `Instances` object, with extra + fields such as `pred_masks` or `pred_keypoints`. + """ + assert not self.training + assert instances[0].has("pred_boxes") and instances[0].has("pred_classes") + + if self.mask_on: + feature_list = [features[f] for f in self.in_features] + x = self._shared_roi_transform(feature_list, [x.pred_boxes for x in instances]) + return self.mask_head(x, instances) + else: + return instances + + +@ROI_HEADS_REGISTRY.register() +class CustomStandardROIHeads(ROIHeads): + """ + It's "standard" in a sense that there is no ROI transform sharing + or feature sharing between tasks. + Each head independently processes the input features by each head's + own pooler and head. + + This class is used by most models, such as FPN and C5. + To implement more models, you can subclass it and implement a different + :meth:`forward()` or a head. + """ + + @configurable + def __init__( + self, + *, + box_in_features: List[str], + box_pooler: ROIPooler, + box_head: nn.Module, + box_predictor: nn.Module, + mask_in_features: Optional[List[str]] = None, + mask_pooler: Optional[ROIPooler] = None, + mask_head: Optional[nn.Module] = None, + keypoint_in_features: Optional[List[str]] = None, + keypoint_pooler: Optional[ROIPooler] = None, + keypoint_head: Optional[nn.Module] = None, + train_on_pred_boxes: bool = False, + box2box_transform = Box2BoxTransform, + use_droploss: bool = False, + droploss_iou_thresh: float = 1.0, + **kwargs, + ): + """ + NOTE: this interface is experimental. + + Args: + box_in_features (list[str]): list of feature names to use for the box head. + box_pooler (ROIPooler): pooler to extra region features for box head + box_head (nn.Module): transform features to make box predictions + box_predictor (nn.Module): make box predictions from the feature. + Should have the same interface as :class:`FastRCNNOutputLayers`. + mask_in_features (list[str]): list of feature names to use for the mask + pooler or mask head. None if not using mask head. + mask_pooler (ROIPooler): pooler to extract region features from image features. + The mask head will then take region features to make predictions. + If None, the mask head will directly take the dict of image features + defined by `mask_in_features` + mask_head (nn.Module): transform features to make mask predictions + keypoint_in_features, keypoint_pooler, keypoint_head: similar to ``mask_*``. + train_on_pred_boxes (bool): whether to use proposal boxes or + predicted boxes from the box head to train other heads. + """ + super().__init__(**kwargs) + # keep self.in_features for backward compatibility + self.in_features = self.box_in_features = box_in_features + self.box_pooler = box_pooler + self.box_head = box_head + self.box_predictor = box_predictor + + self.mask_on = mask_in_features is not None + if self.mask_on: + self.mask_in_features = mask_in_features + self.mask_pooler = mask_pooler + self.mask_head = mask_head + + self.keypoint_on = keypoint_in_features is not None + if self.keypoint_on: + self.keypoint_in_features = keypoint_in_features + self.keypoint_pooler = keypoint_pooler + self.keypoint_head = keypoint_head + + self.train_on_pred_boxes = train_on_pred_boxes + self.use_droploss = use_droploss + self.box2box_transform = box2box_transform + self.droploss_iou_thresh = droploss_iou_thresh + + @classmethod + def from_config(cls, cfg, input_shape): + ret = super().from_config(cfg) + ret["train_on_pred_boxes"] = cfg.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES + # Subclasses that have not been updated to use from_config style construction + # may have overridden _init_*_head methods. In this case, those overridden methods + # will not be classmethods and we need to avoid trying to call them here. + # We test for this with ismethod which only returns True for bound methods of cls. + # Such subclasses will need to handle calling their overridden _init_*_head methods. + if cfg.MODEL.ROI_HEADS.USE_DROPLOSS: + ret['use_droploss'] = True + ret['droploss_iou_thresh'] = cfg.MODEL.ROI_HEADS.DROPLOSS_IOU_THRESH + ret['box2box_transform'] = Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS) + if inspect.ismethod(cls._init_box_head): + ret.update(cls._init_box_head(cfg, input_shape)) + if inspect.ismethod(cls._init_mask_head): + ret.update(cls._init_mask_head(cfg, input_shape)) + if inspect.ismethod(cls._init_keypoint_head): + ret.update(cls._init_keypoint_head(cfg, input_shape)) + return ret + + @classmethod + def _init_box_head(cls, cfg, input_shape): + # fmt: off + in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE + # fmt: on + + # If CustomStandardROIHeads is applied on multiple feature maps (as in FPN), + # then we share the same predictors and therefore the channel counts must be the same + in_channels = [input_shape[f].channels for f in in_features] + # Check all channel counts are equal + assert len(set(in_channels)) == 1, in_channels + in_channels = in_channels[0] + + box_pooler = ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + # Here we split "box head" and "box predictor", which is mainly due to historical reasons. + # They are used together so the "box predictor" layers should be part of the "box head". + # New subclasses of ROIHeads do not need "box predictor"s. + box_head = build_box_head( + cfg, ShapeSpec(channels=in_channels, height=pooler_resolution, width=pooler_resolution) + ) + box_predictor = FastRCNNOutputLayers(cfg, box_head.output_shape) + return { + "box_in_features": in_features, + "box_pooler": box_pooler, + "box_head": box_head, + "box_predictor": box_predictor, + } + + @classmethod + def _init_mask_head(cls, cfg, input_shape): + if not cfg.MODEL.MASK_ON: + return {} + # fmt: off + in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) + sampling_ratio = cfg.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_MASK_HEAD.POOLER_TYPE + # fmt: on + + in_channels = [input_shape[f].channels for f in in_features][0] + + ret = {"mask_in_features": in_features} + ret["mask_pooler"] = ( + ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + if pooler_type + else None + ) + if pooler_type: + shape = ShapeSpec( + channels=in_channels, width=pooler_resolution, height=pooler_resolution + ) + else: + shape = {f: input_shape[f] for f in in_features} + ret["mask_head"] = build_mask_head(cfg, shape) + return ret + + @classmethod + def _init_keypoint_head(cls, cfg, input_shape): + if not cfg.MODEL.KEYPOINT_ON: + return {} + # fmt: off + in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES + pooler_resolution = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION + pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) # noqa + sampling_ratio = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO + pooler_type = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE + # fmt: on + + in_channels = [input_shape[f].channels for f in in_features][0] + + ret = {"keypoint_in_features": in_features} + ret["keypoint_pooler"] = ( + ROIPooler( + output_size=pooler_resolution, + scales=pooler_scales, + sampling_ratio=sampling_ratio, + pooler_type=pooler_type, + ) + if pooler_type + else None + ) + if pooler_type: + shape = ShapeSpec( + channels=in_channels, width=pooler_resolution, height=pooler_resolution + ) + else: + shape = {f: input_shape[f] for f in in_features} + ret["keypoint_head"] = build_keypoint_head(cfg, shape) + return ret + + def forward( + self, + images: ImageList, + features: Dict[str, torch.Tensor], + proposals: List[Instances], + targets: Optional[List[Instances]] = None, + ) -> Tuple[List[Instances], Dict[str, torch.Tensor]]: + """ + See :class:`ROIHeads.forward`. + """ + del images + if self.training: + assert targets, "'targets' argument is required during training" + proposals = self.label_and_sample_proposals(proposals, targets) + del targets + + if self.training: + losses = self._forward_box(features, proposals) + # Usually the original proposals used by the box head are used by the mask, keypoint + # heads. But when `self.train_on_pred_boxes is True`, proposals will contain boxes + # predicted by the box head. + losses.update(self._forward_mask(features, proposals)) + losses.update(self._forward_keypoint(features, proposals)) + return proposals, losses + else: + pred_instances = self._forward_box(features, proposals) + # During inference cascaded prediction is used: the mask and keypoints heads are only + # applied to the top scoring box detections. + pred_instances = self.forward_with_given_boxes(features, pred_instances) + return pred_instances, {} + + def forward_with_given_boxes( + self, features: Dict[str, torch.Tensor], instances: List[Instances] + ) -> List[Instances]: + """ + Use the given boxes in `instances` to produce other (non-box) per-ROI outputs. + + This is useful for downstream tasks where a box is known, but need to obtain + other attributes (outputs of other heads). + Test-time augmentation also uses this. + + Args: + features: same as in `forward()` + instances (list[Instances]): instances to predict other outputs. Expect the keys + "pred_boxes" and "pred_classes" to exist. + + Returns: + list[Instances]: + the same `Instances` objects, with extra + fields such as `pred_masks` or `pred_keypoints`. + """ + assert not self.training + assert instances[0].has("pred_boxes") and instances[0].has("pred_classes") + + instances = self._forward_mask(features, instances) + instances = self._forward_keypoint(features, instances) + return instances + + def _forward_box(self, features: Dict[str, torch.Tensor], proposals: List[Instances]): + """ + Forward logic of the box prediction branch. If `self.train_on_pred_boxes is True`, + the function puts predicted boxes in the `proposal_boxes` field of `proposals` argument. + + Args: + features (dict[str, Tensor]): mapping from feature map names to tensor. + Same as in :meth:`ROIHeads.forward`. + proposals (list[Instances]): the per-image object proposals with + their matching ground truth. + Each has fields "proposal_boxes", and "objectness_logits", + "gt_classes", "gt_boxes". + + Returns: + In training, a dict of losses. + In inference, a list of `Instances`, the predicted instances. + """ + features = [features[f] for f in self.box_in_features] + box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals]) # torch.Size([512 * batch_size, 256, 7, 7]) + box_features = self.box_head(box_features) # torch.Size([512 * batch_size, 1024]) + predictions = self.box_predictor(box_features) # [torch.Size([512 * batch_size, 2]), torch.Size([512 * batch_size, 4])] + + no_gt_found = False + if self.use_droploss and self.training: + # the first K proposals are GT proposals + try: + box_num_list = [len(x.gt_boxes) for x in proposals] + gt_num_list = [torch.unique(x.gt_boxes.tensor[:100], dim=0).size()[0] for x in proposals] + except: + box_num_list = [0 for _ in proposals] + gt_num_list = [0 for _ in proposals] + no_gt_found = True + + if self.use_droploss and self.training and not no_gt_found: + # NOTE: maximum overlapping with GT (IoU) + predictions_delta = predictions[1] + proposal_boxes = Boxes.cat([x.proposal_boxes for x in proposals]) + predictions_bbox = self.box2box_transform.apply_deltas(predictions_delta, proposal_boxes.tensor) + idx_start = 0 + iou_max_list = [] + for idx, x in enumerate(proposals): + idx_end = idx_start + box_num_list[idx] + iou_max_list.append(pairwise_iou_max_scores(predictions_bbox[idx_start:idx_end], x.gt_boxes[:gt_num_list[idx]].tensor)) + idx_start = idx_end + iou_max = torch.cat(iou_max_list, dim=0) + + del box_features + + if self.training: + if self.use_droploss and not no_gt_found: + weights = iou_max.le(self.droploss_iou_thresh).float() + weights = 1 - weights.ge(1.0).float() + losses = self.box_predictor.losses(predictions, proposals, weights=weights.detach()) + else: + losses = self.box_predictor.losses(predictions, proposals) + if self.train_on_pred_boxes: # default is false + with torch.no_grad(): + pred_boxes = self.box_predictor.predict_boxes_for_gt_classes( + predictions, proposals + ) + for proposals_per_image, pred_boxes_per_image in zip(proposals, pred_boxes): + proposals_per_image.proposal_boxes = Boxes(pred_boxes_per_image) + return losses + else: + pred_instances, _ = self.box_predictor.inference(predictions, proposals) + return pred_instances + + def _forward_mask(self, features: Dict[str, torch.Tensor], instances: List[Instances]): + """ + Forward logic of the mask prediction branch. + + Args: + features (dict[str, Tensor]): mapping from feature map names to tensor. + Same as in :meth:`ROIHeads.forward`. + instances (list[Instances]): the per-image instances to train/predict masks. + In training, they can be the proposals. + In inference, they can be the boxes predicted by R-CNN box head. + + Returns: + In training, a dict of losses. + In inference, update `instances` with new fields "pred_masks" and return it. + """ + if not self.mask_on: + return {} if self.training else instances + + if self.training: + # head is only trained on positive proposals. + instances, _ = select_foreground_proposals(instances, self.num_classes) + + if self.mask_pooler is not None: + features = [features[f] for f in self.mask_in_features] + boxes = [x.proposal_boxes if self.training else x.pred_boxes for x in instances] + features = self.mask_pooler(features, boxes) + else: + features = {f: features[f] for f in self.mask_in_features} + return self.mask_head(features, instances) + + def _forward_keypoint(self, features: Dict[str, torch.Tensor], instances: List[Instances]): + """ + Forward logic of the keypoint prediction branch. + + Args: + features (dict[str, Tensor]): mapping from feature map names to tensor. + Same as in :meth:`ROIHeads.forward`. + instances (list[Instances]): the per-image instances to train/predict keypoints. + In training, they can be the proposals. + In inference, they can be the boxes predicted by R-CNN box head. + + Returns: + In training, a dict of losses. + In inference, update `instances` with new fields "pred_keypoints" and return it. + """ + if not self.keypoint_on: + return {} if self.training else instances + + if self.training: + # head is only trained on positive proposals with >=1 visible keypoints. + instances, _ = select_foreground_proposals(instances, self.num_classes) + instances = select_proposals_with_visible_keypoints(instances) + + if self.keypoint_pooler is not None: + features = [features[f] for f in self.keypoint_in_features] + boxes = [x.proposal_boxes if self.training else x.pred_boxes for x in instances] + features = self.keypoint_pooler(features, boxes) + else: + features = {f: features[f] for f in self.keypoint_in_features} + return self.keypoint_head(features, instances) diff --git a/cutler/solver/__init__.py b/cutler/solver/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a564c0b26b0f23cc8dd5dc7397b4ea376467927a --- /dev/null +++ b/cutler/solver/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. + +from .build import build_lr_scheduler, build_optimizer, get_default_optimizer_params + +__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/cutler/solver/__pycache__/__init__.cpython-312.pyc b/cutler/solver/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..72aaa63cb3faa5c6d3d1f55a9bb64a12c3ff7cea Binary files /dev/null and b/cutler/solver/__pycache__/__init__.cpython-312.pyc differ diff --git a/cutler/solver/__pycache__/build.cpython-312.pyc b/cutler/solver/__pycache__/build.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a960588f6e72938c21672b34dfbc63843cae5ae3 Binary files /dev/null and b/cutler/solver/__pycache__/build.cpython-312.pyc differ diff --git a/cutler/solver/build.py b/cutler/solver/build.py new file mode 100644 index 0000000000000000000000000000000000000000..4d49a2afe23cb035c0107553ed69b04a8412ffe6 --- /dev/null +++ b/cutler/solver/build.py @@ -0,0 +1,305 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/solver/build.py + +import copy +import itertools +import logging +from collections import defaultdict +from enum import Enum +from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Type, Union +import torch +from fvcore.common.param_scheduler import CosineParamScheduler, MultiStepParamScheduler + +from detectron2.config import CfgNode + +from detectron2.solver.lr_scheduler import LRMultiplier, WarmupParamScheduler + +_GradientClipperInput = Union[torch.Tensor, Iterable[torch.Tensor]] +_GradientClipper = Callable[[_GradientClipperInput], None] + + +class GradientClipType(Enum): + VALUE = "value" + NORM = "norm" + + +def _create_gradient_clipper(cfg: CfgNode) -> _GradientClipper: + """ + Creates gradient clipping closure to clip by value or by norm, + according to the provided config. + """ + cfg = copy.deepcopy(cfg) + + def clip_grad_norm(p: _GradientClipperInput): + torch.nn.utils.clip_grad_norm_(p, cfg.CLIP_VALUE, cfg.NORM_TYPE) + + def clip_grad_value(p: _GradientClipperInput): + torch.nn.utils.clip_grad_value_(p, cfg.CLIP_VALUE) + + _GRADIENT_CLIP_TYPE_TO_CLIPPER = { + GradientClipType.VALUE: clip_grad_value, + GradientClipType.NORM: clip_grad_norm, + } + return _GRADIENT_CLIP_TYPE_TO_CLIPPER[GradientClipType(cfg.CLIP_TYPE)] + + +def _generate_optimizer_class_with_gradient_clipping( + optimizer: Type[torch.optim.Optimizer], + *, + per_param_clipper: Optional[_GradientClipper] = None, + global_clipper: Optional[_GradientClipper] = None, +) -> Type[torch.optim.Optimizer]: + """ + Dynamically creates a new type that inherits the type of a given instance + and overrides the `step` method to add gradient clipping + """ + assert ( + per_param_clipper is None or global_clipper is None + ), "Not allowed to use both per-parameter clipping and global clipping" + + def optimizer_wgc_step(self, closure=None): + if per_param_clipper is not None: + for group in self.param_groups: + for p in group["params"]: + per_param_clipper(p) + else: + # global clipper for future use with detr + # (https://github.com/facebookresearch/detr/pull/287) + all_params = itertools.chain(*[g["params"] for g in self.param_groups]) + global_clipper(all_params) + super(type(self), self).step(closure) + + OptimizerWithGradientClip = type( + optimizer.__name__ + "WithGradientClip", + (optimizer,), + {"step": optimizer_wgc_step}, + ) + return OptimizerWithGradientClip + + +def maybe_add_gradient_clipping( + cfg: CfgNode, optimizer: Type[torch.optim.Optimizer] +) -> Type[torch.optim.Optimizer]: + """ + If gradient clipping is enabled through config options, wraps the existing + optimizer type to become a new dynamically created class OptimizerWithGradientClip + that inherits the given optimizer and overrides the `step` method to + include gradient clipping. + + Args: + cfg: CfgNode, configuration options + optimizer: type. A subclass of torch.optim.Optimizer + + Return: + type: either the input `optimizer` (if gradient clipping is disabled), or + a subclass of it with gradient clipping included in the `step` method. + """ + if not cfg.SOLVER.CLIP_GRADIENTS.ENABLED: + return optimizer + if isinstance(optimizer, torch.optim.Optimizer): + optimizer_type = type(optimizer) + else: + assert issubclass(optimizer, torch.optim.Optimizer), optimizer + optimizer_type = optimizer + + grad_clipper = _create_gradient_clipper(cfg.SOLVER.CLIP_GRADIENTS) + OptimizerWithGradientClip = _generate_optimizer_class_with_gradient_clipping( + optimizer_type, per_param_clipper=grad_clipper + ) + if isinstance(optimizer, torch.optim.Optimizer): + optimizer.__class__ = OptimizerWithGradientClip # a bit hacky, not recommended + return optimizer + else: + return OptimizerWithGradientClip + + +def build_optimizer(cfg: CfgNode, model: torch.nn.Module) -> torch.optim.Optimizer: + """ + Build an optimizer from config. + """ + params = get_default_optimizer_params( + model, + base_lr=cfg.SOLVER.BASE_LR, + base_lr_multiplier=cfg.SOLVER.BASE_LR_MULTIPLIER, + base_lr_multiplier_names=cfg.SOLVER.BASE_LR_MULTIPLIER_NAMES, + weight_decay_norm=cfg.SOLVER.WEIGHT_DECAY_NORM, + bias_lr_factor=cfg.SOLVER.BIAS_LR_FACTOR, + weight_decay_bias=cfg.SOLVER.WEIGHT_DECAY_BIAS, + ) + return maybe_add_gradient_clipping(cfg, torch.optim.SGD)( + params, + lr=cfg.SOLVER.BASE_LR, + momentum=cfg.SOLVER.MOMENTUM, + nesterov=cfg.SOLVER.NESTEROV, + weight_decay=cfg.SOLVER.WEIGHT_DECAY, + ) + + +def get_default_optimizer_params( + model: torch.nn.Module, + base_lr: Optional[float] = None, + base_lr_multiplier: Optional[float] = 1.0, + base_lr_multiplier_names: Optional[List[str]] = [], + weight_decay: Optional[float] = None, + weight_decay_norm: Optional[float] = None, + bias_lr_factor: Optional[float] = 1.0, + weight_decay_bias: Optional[float] = None, + lr_factor_func: Optional[Callable] = None, + overrides: Optional[Dict[str, Dict[str, float]]] = None, +) -> List[Dict[str, Any]]: + """ + Get default param list for optimizer, with support for a few types of + overrides. If no overrides needed, this is equivalent to `model.parameters()`. + + Args: + base_lr: lr for every group by default. Can be omitted to use the one in optimizer. + weight_decay: weight decay for every group by default. Can be omitted to use the one + in optimizer. + weight_decay_norm: override weight decay for params in normalization layers + bias_lr_factor: multiplier of lr for bias parameters. + weight_decay_bias: override weight decay for bias parameters. + lr_factor_func: function to calculate lr decay rate by mapping the parameter names to + corresponding lr decay rate. Note that setting this option requires + also setting ``base_lr``. + overrides: if not `None`, provides values for optimizer hyperparameters + (LR, weight decay) for module parameters with a given name; e.g. + ``{"embedding": {"lr": 0.01, "weight_decay": 0.1}}`` will set the LR and + weight decay values for all module parameters named `embedding`. + + For common detection models, ``weight_decay_norm`` is the only option + needed to be set. ``bias_lr_factor,weight_decay_bias`` are legacy settings + from Detectron1 that are not found useful. + + Example: + :: + torch.optim.SGD(get_default_optimizer_params(model, weight_decay_norm=0), + lr=0.01, weight_decay=1e-4, momentum=0.9) + """ + if overrides is None: + overrides = {} + defaults = {} + if base_lr is not None: + defaults["lr"] = base_lr + if weight_decay is not None: + defaults["weight_decay"] = weight_decay + bias_overrides = {} + if bias_lr_factor is not None and bias_lr_factor != 1.0: + # NOTE: unlike Detectron v1, we now by default make bias hyperparameters + # exactly the same as regular weights. + if base_lr is None: + raise ValueError("bias_lr_factor requires base_lr") + bias_overrides["lr"] = base_lr * bias_lr_factor + if weight_decay_bias is not None: + bias_overrides["weight_decay"] = weight_decay_bias + if len(bias_overrides): + if "bias" in overrides: + raise ValueError("Conflicting overrides for 'bias'") + overrides["bias"] = bias_overrides + if lr_factor_func is not None: + if base_lr is None: + raise ValueError("lr_factor_func requires base_lr") + norm_module_types = ( + torch.nn.BatchNorm1d, + torch.nn.BatchNorm2d, + torch.nn.BatchNorm3d, + torch.nn.SyncBatchNorm, + # NaiveSyncBatchNorm inherits from BatchNorm2d + torch.nn.GroupNorm, + torch.nn.InstanceNorm1d, + torch.nn.InstanceNorm2d, + torch.nn.InstanceNorm3d, + torch.nn.LayerNorm, + torch.nn.LocalResponseNorm, + ) + params: List[Dict[str, Any]] = [] + memo: Set[torch.nn.parameter.Parameter] = set() + for module_name, module in model.named_modules(): + for module_param_name, value in module.named_parameters(recurse=False): + if not value.requires_grad: + continue + # Avoid duplicating parameters + if value in memo: + continue + memo.add(value) + + hyperparams = copy.copy(defaults) + if isinstance(module, norm_module_types) and weight_decay_norm is not None: + hyperparams["weight_decay"] = weight_decay_norm + if lr_factor_func is not None: + hyperparams["lr"] *= lr_factor_func(f"{module_name}.{module_param_name}") + hyperparams.update(overrides.get(module_param_name, {})) + if module_name in base_lr_multiplier_names: + hyperparams["lr"] *= base_lr_multiplier + # print(" Checked: ", module_name, hyperparams["lr"]) + + params.append({"params": [value], **hyperparams}) + return reduce_param_groups(params) + + +def _expand_param_groups(params: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + # Transform parameter groups into per-parameter structure. + # Later items in `params` can overwrite parameters set in previous items. + ret = defaultdict(dict) + for item in params: + assert "params" in item + cur_params = {x: y for x, y in item.items() if x != "params"} + for param in item["params"]: + ret[param].update({"params": [param], **cur_params}) + return list(ret.values()) + + +def reduce_param_groups(params: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + # Reorganize the parameter groups and merge duplicated groups. + # The number of parameter groups needs to be as small as possible in order + # to efficiently use the PyTorch multi-tensor optimizer. Therefore instead + # of using a parameter_group per single parameter, we reorganize the + # parameter groups and merge duplicated groups. This approach speeds + # up multi-tensor optimizer significantly. + params = _expand_param_groups(params) + groups = defaultdict(list) # re-group all parameter groups by their hyperparams + for item in params: + cur_params = tuple((x, y) for x, y in item.items() if x != "params") + groups[cur_params].extend(item["params"]) + ret = [] + for param_keys, param_values in groups.items(): + cur = {kv[0]: kv[1] for kv in param_keys} + cur["params"] = param_values + ret.append(cur) + return ret + + +def build_lr_scheduler( + cfg: CfgNode, optimizer: torch.optim.Optimizer +) -> torch.optim.lr_scheduler._LRScheduler: + """ + Build a LR scheduler from config. + """ + name = cfg.SOLVER.LR_SCHEDULER_NAME + + if name == "WarmupMultiStepLR": + steps = [x for x in cfg.SOLVER.STEPS if x <= cfg.SOLVER.MAX_ITER] + if len(steps) != len(cfg.SOLVER.STEPS): + logger = logging.getLogger(__name__) + logger.warning( + "SOLVER.STEPS contains values larger than SOLVER.MAX_ITER. " + "These values will be ignored." + ) + sched = MultiStepParamScheduler( + values=[cfg.SOLVER.GAMMA**k for k in range(len(steps) + 1)], + milestones=steps, + num_updates=cfg.SOLVER.MAX_ITER, + ) + elif name == "WarmupCosineLR": + end_value = cfg.SOLVER.BASE_LR_END / cfg.SOLVER.BASE_LR + assert end_value >= 0.0 and end_value <= 1.0, end_value + sched = CosineParamScheduler(1, end_value) + else: + raise ValueError("Unknown LR scheduler: {}".format(name)) + + sched = WarmupParamScheduler( + sched, + cfg.SOLVER.WARMUP_FACTOR, + min(cfg.SOLVER.WARMUP_ITERS / cfg.SOLVER.MAX_ITER, 1.0), + cfg.SOLVER.WARMUP_METHOD, + ) + return LRMultiplier(optimizer, multiplier=sched, max_iter=cfg.SOLVER.MAX_ITER) diff --git a/cutler/structures/__init__.py b/cutler/structures/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a273d815bcf74fc6d20863222da3e14af3308bd8 --- /dev/null +++ b/cutler/structures/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. + +from .boxes import pairwise_iou_max_scores + +__all__ = [k for k in globals().keys() if not k.startswith("_")] + + +from detectron2.utils.env import fixup_module_metadata + +fixup_module_metadata(__name__, globals(), __all__) +del fixup_module_metadata diff --git a/cutler/structures/__pycache__/__init__.cpython-312.pyc b/cutler/structures/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e6beff711a4d849a1893eb52c4d0f4a20021a11f Binary files /dev/null and b/cutler/structures/__pycache__/__init__.cpython-312.pyc differ diff --git a/cutler/structures/__pycache__/boxes.cpython-312.pyc b/cutler/structures/__pycache__/boxes.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8d9639622864e74309477068e79215046204a14a Binary files /dev/null and b/cutler/structures/__pycache__/boxes.cpython-312.pyc differ diff --git a/cutler/structures/boxes.py b/cutler/structures/boxes.py new file mode 100644 index 0000000000000000000000000000000000000000..783181349ea7c4d81ee6ec7e7b1f1a1c05cc2bee --- /dev/null +++ b/cutler/structures/boxes.py @@ -0,0 +1,35 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/structures/boxes.py + +import torch + +def pairwise_iou_max_scores(boxes1: torch.Tensor, boxes2: torch.Tensor) -> torch.Tensor: + """ + Given two lists of boxes of size N and M, compute the IoU + (intersection over union) between **all** N x M pairs of boxes. + The box order must be (xmin, ymin, xmax, ymax). + + Args: + boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. + + Returns: + Tensor: IoU, sized [N,M]. + """ + area1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - boxes1[:, 1]) # [N] + area2 = (boxes2[:, 2] - boxes2[:, 0]) * (boxes2[:, 3] - boxes2[:, 1]) # [M] + + width_height = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) - torch.max( + boxes1[:, None, :2], boxes2[:, :2] + ) # [N,M,2] + + width_height.clamp_(min=0) # [N,M,2] + inter = width_height.prod(dim=2) # [N,M] + + # handle empty boxes + iou = torch.where( + inter > 0, + inter / (area1[:, None] + area2 - inter), + torch.zeros(1, dtype=inter.dtype, device=inter.device), + ) + iou_max, _ = torch.max(iou, dim=1) + return iou_max \ No newline at end of file diff --git a/cutler/tools/eval.sh b/cutler/tools/eval.sh new file mode 100644 index 0000000000000000000000000000000000000000..7bd7004205fb33a6d01071a1cdf104d211f4e5b1 --- /dev/null +++ b/cutler/tools/eval.sh @@ -0,0 +1,89 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. + +# link to the dataset folder, model weights and the config file. +export DETECTRON2_DATASETS=/path/to/DETECTRON2_DATASETS/ +model_weights="http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth" +config_file="model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml" +num_gpus=2 + +echo "========== start evaluating the model on all 11 datasets ==========" + +test_dataset='cls_agnostic_clipart' +echo "========== evaluating ${test_dataset} ==========" +python train_net.py --num-gpus ${num_gpus} \ + --config-file ${config_file} \ + --test-dataset ${test_dataset} --no-segm \ + --eval-only MODEL.WEIGHTS ${model_weights} + +test_dataset='cls_agnostic_watercolor' +echo "========== evaluating ${test_dataset} ==========" +python train_net.py --num-gpus ${num_gpus} \ + --config-file ${config_file} \ + --test-dataset ${test_dataset} --no-segm \ + --eval-only MODEL.WEIGHTS ${model_weights} + +test_dataset='cls_agnostic_comic' +echo "========== evaluating ${test_dataset} ==========" +python train_net.py --num-gpus ${num_gpus} \ + --config-file ${config_file} \ + --test-dataset ${test_dataset} --no-segm \ + --eval-only MODEL.WEIGHTS ${model_weights} + +test_dataset='cls_agnostic_voc' +echo "========== evaluating ${test_dataset} ==========" +python train_net.py --num-gpus ${num_gpus} \ + --config-file ${config_file} \ + --test-dataset ${test_dataset} --no-segm \ + --eval-only MODEL.WEIGHTS ${model_weights} + +test_dataset='cls_agnostic_objects365' +echo "========== evaluating ${test_dataset} ==========" +python train_net.py --num-gpus ${num_gpus} \ + --config-file ${config_file} \ + --test-dataset ${test_dataset} --no-segm \ + --eval-only MODEL.WEIGHTS ${model_weights} + +test_dataset='cls_agnostic_openimages' +echo "========== evaluating ${test_dataset} ==========" +python train_net.py --num-gpus ${num_gpus} \ + --config-file ${config_file} \ + --test-dataset ${test_dataset} --no-segm \ + --eval-only MODEL.WEIGHTS ${model_weights} + +test_dataset='cls_agnostic_kitti' +echo "========== evaluating ${test_dataset} ==========" +python train_net.py --num-gpus ${num_gpus} \ + --config-file ${config_file} \ + --test-dataset ${test_dataset} --no-segm \ + --eval-only MODEL.WEIGHTS ${model_weights} + +test_dataset='cls_agnostic_coco' +echo "========== evaluating ${test_dataset} ==========" +python train_net.py --num-gpus ${num_gpus} \ + --config-file ${config_file} \ + --test-dataset ${test_dataset} \ + --eval-only MODEL.WEIGHTS ${model_weights} + +test_dataset='cls_agnostic_coco20k' +echo "========== evaluating ${test_dataset} ==========" +python train_net.py --num-gpus ${num_gpus} \ + --config-file ${config_file} \ + --test-dataset ${test_dataset} \ + --eval-only MODEL.WEIGHTS ${model_weights} + +test_dataset='cls_agnostic_lvis' +echo "========== evaluating ${test_dataset} ==========" +# LVIS should set TEST.DETECTIONS_PER_IMAGE=300 +python train_net.py --num-gpus ${num_gpus} \ + --config-file ${config_file} \ + --test-dataset ${test_dataset} \ + --eval-only MODEL.WEIGHTS ${model_weights} TEST.DETECTIONS_PER_IMAGE 300 + +test_dataset='cls_agnostic_uvo' +echo "========== evaluating ${test_dataset} ==========" +python train_net.py --num-gpus ${num_gpus} \ + --config-file ${config_file} \ + --test-dataset ${test_dataset} \ + --eval-only MODEL.WEIGHTS ${model_weights} + +echo "========== evaluation is completed ==========" \ No newline at end of file diff --git a/cutler/tools/get_self_training_ann.py b/cutler/tools/get_self_training_ann.py new file mode 100644 index 0000000000000000000000000000000000000000..f2024d615a32e162d6e0f504e6b6a1eb9594c22b --- /dev/null +++ b/cutler/tools/get_self_training_ann.py @@ -0,0 +1,194 @@ +#!/usr/bin/env python +# Copyright (c) Meta Platforms, Inc. and affiliates. + +import os +import json +import tqdm +import torch +import datetime +import argparse +import pycocotools.mask as cocomask +from detectron2.utils.file_io import PathManager + +INFO = { + "description": "ImageNet-1K: Self-train", + "url": "", + "version": "1.0", + "year": 2022, + "contributor": "Xudong Wang", + "date_created": datetime.datetime.utcnow().isoformat(' ') +} + +LICENSES = [ + { + "id": 1, + "name": "Apache License", + "url": "https://github.com/facebookresearch/CutLER/blob/main/LICENSE" + } +] + +CATEGORIES = [ + { + 'id': 1, + 'name': 'fg', + 'supercategory': 'fg', + }, +] + +new_dict_filtered = { + "info": INFO, + "licenses": LICENSES, + "categories": CATEGORIES, + "images": [], + "annotations": [] +} + +category_info = { + "is_crowd": 0, + "id": 1 +} + + +def segmToRLE(segm, h, w): + if isinstance(segm, list): + # polygon -- a single object might consist of multiple parts + # we merge all parts into one mask rle code + rles = cocomask.frPyObjects(segm, h, w) + rle = cocomask.merge(rles) + elif isinstance(segm["counts"], list): + # uncompressed RLE + rle = cocomask.frPyObjects(segm, h, w) + else: + # rle + rle = segm + return rle + +def rle2mask(rle, height, width): + if "counts" in rle and isinstance(rle["counts"], list): + # if compact RLE, ignore this conversion + # Magic RLE format handling painfully discovered by looking at the + # COCO API showAnns function. + rle = cocomask.frPyObjects(rle, height, width) + mask = cocomask.decode(rle) + return mask + +def cocosegm2mask(segm, h, w): + rle = segmToRLE(segm, h, w) + mask = rle2mask(rle, h, w) + return mask + +def BatchIoU(masks1, masks2): + n1, n2 = masks1.size()[0], masks2.size()[0] + masks1, masks2 = (masks1>0.5).to(torch.bool), (masks2>0.5).to(torch.bool) + masks1_ = masks1[:,None,:,:,].expand(-1, n2, -1, -1) + masks2_ = masks2[None,:,:,:,].expand(n1, -1, -1, -1) + + intersection = torch.sum(masks1_ * (masks1_ == masks2_), dim=[-1, -2]) + union = torch.sum(masks1_ + masks2_, dim=[-1, -2]) + ious = intersection.to(torch.float) / union + return ious + +if __name__ == "__main__": + # load model arguments + parser = argparse.ArgumentParser(description='Generate json files for the self-training') + parser.add_argument('--new-pred', type=str, + default='output/inference/coco_instances_results.json', + help='Path to model predictions') + parser.add_argument('--prev-ann', type=str, + default='DETECTRON2_DATASETS/imagenet/annotations/cutler_imagenet1k_train.json', + help='Path to annotations in the previous round') + parser.add_argument('--save-path', type=str, + default='DETECTRON2_DATASETS/imagenet/annotations/cutler_imagenet1k_train_r1.json', + help='Path to save the generated annotation file') + # parser.add_argument('--n-rounds', type=int, default=1, + # help='N-th round of self-training') + parser.add_argument('--threshold', type=float, default=0.7, + help='Confidence score thresholds') + args = parser.parse_args() + + # load model predictions + new_pred = args.new_pred + with PathManager.open(new_pred, "r") as f: + predictions = json.load(f) + + # filter out low-confidence model predictions + THRESHOLD = args.threshold + pred_image_to_anns = {} + for id, ann in enumerate(predictions): + confidence_score = ann['score'] + if confidence_score >= THRESHOLD: + if ann['image_id'] in pred_image_to_anns: + pred_image_to_anns[ann['image_id']].append(ann) + else: + pred_image_to_anns[ann['image_id']] = [ann] + + # load psedu-masks used by the previous round + pseudo_ann_dict = json.load(open(args.prev_ann)) + pseudo_image_list = pseudo_ann_dict['images'] + pseudo_annotations = pseudo_ann_dict['annotations'] + + pseudo_image_to_anns = {} + for id, ann in enumerate(pseudo_annotations): + if ann['image_id'] in pseudo_image_to_anns: + pseudo_image_to_anns[ann['image_id']].append(ann) + else: + pseudo_image_to_anns[ann['image_id']] = [ann] + + # merge model predictions and the json file used by the previous round. + merged_anns = [] + num_preds, num_pseudo = 0, 0 + for k, anns_pseudo in tqdm.tqdm(pseudo_image_to_anns.items()): + masks = [] + for ann in anns_pseudo: + segm = ann['segmentation'] + mask = cocosegm2mask(segm, segm['size'][0], segm['size'][1]) + masks.append(torch.from_numpy(mask)) + pseudo_masks = torch.stack(masks, dim=0).cuda() + del masks + num_pseudo += len(anns_pseudo) + try: + anns_pred = pred_image_to_anns[k] + except: + merged_anns += anns_pseudo + continue + masks = [] + for ann in anns_pred: + segm = ann['segmentation'] + mask = cocosegm2mask(segm, segm['size'][0], segm['size'][1]) + masks.append(torch.from_numpy(mask)) + pred_masks = torch.stack(masks, dim=0).cuda() + num_preds += len(anns_pred) + try: + ious = BatchIoU(pseudo_masks, pred_masks) + iou_max, _ = ious.max(dim=1) + selected_index = (iou_max < 0.5).nonzero() + selected_pseudo = [anns_pseudo[i] for i in selected_index] + merged_anns += anns_pred + selected_pseudo + # if num_preds % 200000 == 0: + # print(len(merged_anns), num_preds, num_pseudo) + except: + merged_anns += anns_pseudo + + for key in pred_image_to_anns: + if key in pseudo_image_to_anns: + continue + else: + merged_anns += pred_image_to_anns[key] + + # re-generate annotation id + ann_id = 1 + for ann in merged_anns: + ann['id'] = ann_id + ann['area'] = ann['bbox'][-1] * ann['bbox'][-2] + ann['iscrowd'] = 0 + ann['width'] = ann['segmentation']['size'][0] + ann['height'] = ann['segmentation']['size'][1] + ann_id += 1 + + new_dict_filtered['images'] = pseudo_image_list + new_dict_filtered['annotations'] = merged_anns + + # save annotation file + # save_path = os.path.join(args.save_path, "cutler_imagenet1k_train_r{}.json".format(args.n_rounds)) + json.dump(new_dict_filtered, open(args.save_path, 'w')) + print("Done: {} images; {} anns.".format(len(new_dict_filtered['images']), len(new_dict_filtered['annotations']))) \ No newline at end of file diff --git a/cutler/tools/run_with_submitit.sh b/cutler/tools/run_with_submitit.sh new file mode 100644 index 0000000000000000000000000000000000000000..75aa4a43e440a0b144295042bdcd538aa6797084 --- /dev/null +++ b/cutler/tools/run_with_submitit.sh @@ -0,0 +1,4 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +sbatch tools/train-1node.sh \ + --config-file model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml \ + OUTPUT_DIR /path/to/output \ No newline at end of file diff --git a/cutler/tools/run_with_submitit_ssl.sh b/cutler/tools/run_with_submitit_ssl.sh new file mode 100644 index 0000000000000000000000000000000000000000..16887af06e877901bca491cdc57c581f53f46e7b --- /dev/null +++ b/cutler/tools/run_with_submitit_ssl.sh @@ -0,0 +1,3 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +sbatch tools/train-1node.sh \ +--config-file /private/home/xudongw/cutler-code-release/CutLER/cutler/model_zoo/configs/COCO-Semisupervised/cascade_mask_rcnn_R_50_FPN_50perc.yaml \ \ No newline at end of file diff --git a/cutler/tools/single-node_run.sh b/cutler/tools/single-node_run.sh new file mode 100755 index 0000000000000000000000000000000000000000..a9834086b174e57a27ac5d9c5ba7dbacc5fd617e --- /dev/null +++ b/cutler/tools/single-node_run.sh @@ -0,0 +1,9 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +#!/bin/bash +export DETECTRON2_DATASETS=/path/to/DETECTRON2_DATASETS/ +MASTER_NODE=$(scontrol show hostname "$SLURM_NODELIST" | head -n1) +DIST_URL="tcp://$MASTER_NODE:12399" +SOCKET_NAME=$(ip r | grep default | awk '{print $5}') +export GLOO_SOCKET_IFNAME=$SOCKET_NAME + +python -u train_net.py --num-gpus 8 --num-machines 1 --machine-rank "$SLURM_NODEID" --dist-url "$DIST_URL" "$@" \ No newline at end of file diff --git a/cutler/tools/train-1node.sh b/cutler/tools/train-1node.sh new file mode 100755 index 0000000000000000000000000000000000000000..a576eeb60a178b5f7679ddfd3ad22140081ffa85 --- /dev/null +++ b/cutler/tools/train-1node.sh @@ -0,0 +1,12 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +#!/bin/bash +#SBATCH -p devlab +#SBATCH --nodes=1 +#SBATCH --gres=gpu:8 +#SBATCH --gpus-per-node=8 +#SBATCH --cpus-per-task=80 +#SBATCH --mem=512G +#SBATCH --time 2000 +#SBATCH -o "submitit/slurm-%j.out" + +srun tools/single-node_run.sh $@ \ No newline at end of file diff --git a/cutler/train_net.py b/cutler/train_net.py new file mode 100755 index 0000000000000000000000000000000000000000..732f6e9095ffbaee629562a6a03bd159997d5ad1 --- /dev/null +++ b/cutler/train_net.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/tools/train_net.py + +""" +A main training script. + +This scripts reads a given config file and runs the training or evaluation. +It is an entry point that is made to train standard models in detectron2. + +In order to let one script support training of many models, +this script contains logic that are specific to these built-in models and therefore +may not be suitable for your own project. +For example, your research project perhaps only needs a single "evaluator". + +Therefore, we recommend you to use detectron2 as an library and take +this file as an example of how to use the library. +You may want to write your own script with your datasets and other customizations. +""" + +import logging +import os +from collections import OrderedDict + +import detectron2.utils.comm as comm +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import get_cfg +from config import add_cutler_config +from detectron2.data import MetadataCatalog +from engine import DefaultTrainer, default_argument_parser, default_setup +from detectron2.engine import hooks, launch +from detectron2.evaluation import ( + CityscapesInstanceEvaluator, + CityscapesSemSegEvaluator, + # COCOEvaluator, + COCOPanopticEvaluator, + DatasetEvaluators, + LVISEvaluator, + PascalVOCDetectionEvaluator, + SemSegEvaluator, + verify_results, +) +from evaluation import COCOEvaluator +from detectron2.modeling import GeneralizedRCNNWithTTA +import data # register new datasets +import modeling.roi_heads + +def build_evaluator(cfg, dataset_name, output_folder=None): + """ + Create evaluator(s) for a given dataset. + This uses the special metadata "evaluator_type" associated with each builtin dataset. + For your own dataset, you can simply create an evaluator manually in your + script and do not have to worry about the hacky if-else logic here. + """ + if output_folder is None: + output_folder = os.path.join(cfg.OUTPUT_DIR, "inference") + evaluator_list = [] + evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type + if evaluator_type in ["sem_seg", "coco_panoptic_seg"]: + evaluator_list.append( + SemSegEvaluator( + dataset_name, + distributed=True, + output_dir=output_folder, + ) + ) + if evaluator_type in ["coco", "coco_panoptic_seg"]: + evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder, no_segm=cfg.TEST.NO_SEGM)) + if evaluator_type == "coco_panoptic_seg": + evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder)) + if evaluator_type == "cityscapes_instance": + return CityscapesInstanceEvaluator(dataset_name) + if evaluator_type == "cityscapes_sem_seg": + return CityscapesSemSegEvaluator(dataset_name) + elif evaluator_type == "pascal_voc": + return PascalVOCDetectionEvaluator(dataset_name) + elif evaluator_type == "lvis": + return LVISEvaluator(dataset_name, output_dir=output_folder) + if len(evaluator_list) == 0: + raise NotImplementedError( + "no Evaluator for the dataset {} with the type {}".format(dataset_name, evaluator_type) + ) + elif len(evaluator_list) == 1: + return evaluator_list[0] + return DatasetEvaluators(evaluator_list) + +class Trainer(DefaultTrainer): + """ + We use the "DefaultTrainer" which contains pre-defined default logic for + standard training workflow. They may not work for you, especially if you + are working on a new research project. In that case you can write your + own training loop. You can use "tools/plain_train_net.py" as an example. + """ + + @classmethod + def build_evaluator(cls, cfg, dataset_name, output_folder=None): + return build_evaluator(cfg, dataset_name, output_folder) + + @classmethod + def test_with_TTA(cls, cfg, model): + logger = logging.getLogger("detectron2.trainer") + # In the end of training, run an evaluation with TTA + # Only support some R-CNN models. + logger.info("Running inference with test-time augmentation ...") + model = GeneralizedRCNNWithTTA(cfg, model) + evaluators = [ + cls.build_evaluator( + cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA") + ) + for name in cfg.DATASETS.TEST + ] + res = cls.test(cfg, model, evaluators) + res = OrderedDict({k + "_TTA": v for k, v in res.items()}) + return res + + +def setup(args): + """ + Create configs and perform basic setups. + """ + cfg = get_cfg() + add_cutler_config(cfg) + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + # FIXME: brute force changes to test datasets and evaluation tasks + if args.test_dataset != "": cfg.DATASETS.TEST = ((args.test_dataset),) + if args.train_dataset != "": cfg.DATASETS.TRAIN = ((args.train_dataset),) + cfg.TEST.NO_SEGM = args.no_segm + cfg.freeze() + default_setup(cfg, args) + return cfg + + +def main(args): + cfg = setup(args) + + if args.eval_only: + model = Trainer.build_model(cfg) + DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load( + cfg.MODEL.WEIGHTS, resume=args.resume + ) + res = Trainer.test(cfg, model) + if cfg.TEST.AUG.ENABLED: + res.update(Trainer.test_with_TTA(cfg, model)) + if comm.is_main_process(): + verify_results(cfg, res) + return res + + """ + If you'd like to do anything fancier than the standard training logic, + consider writing your own training loop (see plain_train_net.py) or + subclassing the trainer. + """ + trainer = Trainer(cfg) + trainer.resume_or_load(resume=args.resume) + if cfg.TEST.AUG.ENABLED: + trainer.register_hooks( + [hooks.EvalHook(0, lambda: trainer.test_with_TTA(cfg, trainer.model))] + ) + return trainer.train() + + +if __name__ == "__main__": + args = default_argument_parser().parse_args() + # print(args) + # args.opts = postprocess_args(args.opts) + # rint = random.randint(0, 10000) + # args.dist_url = args.dist_url.replace('12399', str(12399 + rint)) + print("Command Line Args:", args) + launch( + main, + args.num_gpus, + num_machines=args.num_machines, + machine_rank=args.machine_rank, + dist_url=args.dist_url, + args=(args,), + ) diff --git a/cutler_colab_demo.ipynb b/cutler_colab_demo.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..464c13905837a90a6f3fb78b039cb5391448f336 --- /dev/null +++ b/cutler_colab_demo.ipynb @@ -0,0 +1,641 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "W6xm1Gpmo0E_", + "outputId": "0a999c72-f2d2-4dac-da65-53145eca8bac" + }, + "outputs": [], + "source": [ + "# !git clone https://github.com/facebookresearch/CutLER.git" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ugDH5UwIo0FA", + "outputId": "2875eaee-d8d3-4ff7-9988-b1e926531aac" + }, + "outputs": [], + "source": [ + "# %cd CutLER/" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ZT0zKYpco0FB", + "outputId": "82f779a4-02e4-4100-cb57-a3b94749f051" + }, + "outputs": [], + "source": [ + "# !python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'\n", + "# !pip install git+https://github.com/cocodataset/panopticapi.git\n", + "# !pip install git+https://github.com/mcordts/cityscapesScripts.git\n", + "# !pip install -r requirements.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QLuhywKmo0FB", + "outputId": "0641cc96-75fa-4ed7-f970-d503f023eb95" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/rakib/Desktop/NAYM/Office_Projects/UNILEVER/unsupervised_CutLer/CutLER/cutler/demo\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rakib/miniconda3/envs/cutLer/lib/python3.12/site-packages/IPython/core/magics/osm.py:417: UserWarning: This is now an optional IPython functionality, setting dhist requires you to install the `pickleshare` library.\n", + " self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n" + ] + } + ], + "source": [ + "%cd /home/rakib/Desktop/NAYM/Office_Projects/UNILEVER/unsupervised_CutLer/CutLER/cutler/demo" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "DNdQki4co0FC" + }, + "outputs": [], + "source": [ + "import argparse\n", + "import multiprocessing as mp\n", + "import numpy as np\n", + "import os\n", + "import tempfile\n", + "import time\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from detectron2.config import get_cfg\n", + "from detectron2.data.detection_utils import read_image\n", + "from detectron2.utils.logger import setup_logger\n", + "import sys\n", + "sys.path.append('./')\n", + "sys.path.append('../')\n", + "from config import add_cutler_config\n", + "from predictor import VisualizationDemo" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "28jjJmR4o0FC" + }, + "outputs": [], + "source": [ + "def setup_cfg(args):\n", + " # load config from file and command-line arguments\n", + " cfg = get_cfg()\n", + " add_cutler_config(cfg)\n", + " # To use demo for Panoptic-DeepLab, please uncomment the following two lines.\n", + " # from detectron2.projects.panoptic_deeplab import add_panoptic_deeplab_config # noqa\n", + " # add_panoptic_deeplab_config(cfg)\n", + " cfg.merge_from_file(args.config_file)\n", + " cfg.merge_from_list(args.opts)\n", + " # Disable the use of SyncBN normalization when running on a CPU\n", + " # SyncBN is not supported on CPU and can cause errors, so we switch to BN instead\n", + " if cfg.MODEL.DEVICE == 'cuda' and cfg.MODEL.RESNETS.NORM == 'SyncBN':\n", + " cfg.MODEL.RESNETS.NORM = \"BN\"\n", + " cfg.MODEL.FPN.NORM = \"BN\"\n", + " # Set score_threshold for builtin models\n", + " cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold\n", + " cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold\n", + " cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold\n", + " cfg.freeze()\n", + " return cfg" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "dGs4L6lko0FD" + }, + "outputs": [], + "source": [ + "def get_parser(inputs):\n", + " parser = argparse.ArgumentParser(description=\"Detectron2 demo for builtin configs\")\n", + " parser.add_argument(\n", + " \"--config-file\",\n", + " default=\"configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml\",\n", + " metavar=\"FILE\",\n", + " help=\"path to config file\",\n", + " )\n", + " parser.add_argument(\"--webcam\", action=\"store_true\", help=\"Take inputs from webcam.\")\n", + " parser.add_argument(\"--video-input\", help=\"Path to video file.\")\n", + " parser.add_argument(\n", + " \"--input\", help=\"path to the input image\",\n", + " )\n", + " parser.add_argument(\n", + " \"--output\",\n", + " help=\"A file or directory to save output visualizations. \"\n", + " \"If not given, will show output in an OpenCV window.\",\n", + " )\n", + " parser.add_argument(\n", + " \"--confidence-threshold\",\n", + " type=float,\n", + " default=0.35,\n", + " help=\"Minimum score for instance predictions to be shown\",\n", + " )\n", + " parser.add_argument(\n", + " \"--opts\",\n", + " help=\"Modify config options using the command-line 'KEY VALUE' pairs\",\n", + " default=[],\n", + " nargs=argparse.REMAINDER,\n", + " )\n", + " args = parser.parse_args(inputs)\n", + " return args" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5dklS0uEo0FF", + "outputId": "452e9886-74b3-4fb7-c8c6-2c8cfec6f25a" + }, + "outputs": [], + "source": [ + "# !wget http://dl.fbaipublicfiles.com/cutler/checkpoints/cutler_cascade_final.pth" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "ZnEEaorqo0FF" + }, + "outputs": [], + "source": [ + "# provide arguments for running CutLER demo.\n", + "\n", + "# Note: Using ***CPU*** by default. to run on GPU, \n", + "# remove \"MODEL.DEVICE\", \n", + "# \"cpu\".\n", + "# Note: please specify a path with \"--input\" if you want to try your own images.\n", + "# Note: you can use a lower --confidence-threshold to get a higher recall.\n", + "mp.set_start_method(\"spawn\", \n", + "force=True)\n", + "inputs = [\n", + " '--config-file', \n", + " \"../model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN_demo.yaml\", \n", + " '--input', \n", + " \"/home/rakib/Desktop/NAYM/Office_Projects/UNILEVER/unsupervised_CutLer/img/20240122_124158_jpg.rf.eb4f7a2da650cd28210cf68340da977d.jpg\", \n", + " '--confidence-threshold', \n", + " '0.5', \n", + " \"--opts\", \n", + " \"MODEL.WEIGHTS\", \n", + " \"cutler_cascade_final.pth\", \n", + " \"MODEL.DEVICE\", \n", + " \"cuda\"\n", + " ]\n", + "args = get_parser(inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_-Z0jSyXo0FG", + "outputId": "197ab356-50d9-40a5-9b71-9e6cc4792131" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m[11/12 11:53:02 detectron2]: \u001b[0mArguments: Namespace(config_file='../model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN_demo.yaml', webcam=False, video_input=None, input='/home/rakib/Desktop/NAYM/Office_Projects/UNILEVER/unsupervised_CutLer/img/20240122_124158_jpg.rf.eb4f7a2da650cd28210cf68340da977d.jpg', output=None, confidence_threshold=0.5, opts=['MODEL.WEIGHTS', 'cutler_cascade_final.pth', 'MODEL.DEVICE', 'cuda'])\n", + "\u001b[32m[11/12 11:53:02 d2.checkpoint.detection_checkpoint]: \u001b[0m[DetectionCheckpointer] Loading from cutler_cascade_final.pth ...\n", + "\u001b[32m[11/12 11:53:02 fvcore.common.checkpoint]: \u001b[0m[Checkpointer] Loading from cutler_cascade_final.pth ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rakib/miniconda3/envs/cutLer/lib/python3.12/site-packages/fvcore/common/checkpoint.py:252: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " return torch.load(f, map_location=torch.device(\"cpu\"))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CUDNN_BENCHMARK: False\n", + "DATALOADER:\n", + " ASPECT_RATIO_GROUPING: True\n", + " COPY_PASTE: True\n", + " COPY_PASTE_MAX_RATIO: 1.0\n", + " COPY_PASTE_MIN_RATIO: 0.3\n", + " COPY_PASTE_RANDOM_NUM: True\n", + " COPY_PASTE_RATE: 1.0\n", + " FILTER_EMPTY_ANNOTATIONS: True\n", + " NUM_WORKERS: 0\n", + " REPEAT_SQRT: True\n", + " REPEAT_THRESHOLD: 0.0\n", + " SAMPLER_TRAIN: TrainingSampler\n", + " VISUALIZE_COPY_PASTE: False\n", + "DATASETS:\n", + " PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000\n", + " PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000\n", + " PROPOSAL_FILES_TEST: ()\n", + " PROPOSAL_FILES_TRAIN: ()\n", + " TEST: ('imagenet_train',)\n", + " TRAIN: ('imagenet_train',)\n", + "FLOAT32_PRECISION: \n", + "GLOBAL:\n", + " HACK: 1.0\n", + "INPUT:\n", + " CROP:\n", + " ENABLED: False\n", + " SIZE: [0.9, 0.9]\n", + " TYPE: relative_range\n", + " FORMAT: RGB\n", + " MASK_FORMAT: bitmask\n", + " MAX_SIZE_TEST: 1333\n", + " MAX_SIZE_TRAIN: 1333\n", + " MIN_SIZE_TEST: 800\n", + " MIN_SIZE_TRAIN: (240, 320, 480, 640, 672, 704, 736, 768, 800, 1024)\n", + " MIN_SIZE_TRAIN_SAMPLING: choice\n", + " RANDOM_FLIP: horizontal\n", + "MODEL:\n", + " ANCHOR_GENERATOR:\n", + " ANGLES: [[-90, 0, 90]]\n", + " ASPECT_RATIOS: [[0.5, 1.0, 2.0]]\n", + " NAME: DefaultAnchorGenerator\n", + " OFFSET: 0.0\n", + " SIZES: [[32], [64], [128], [256], [512]]\n", + " BACKBONE:\n", + " FREEZE_AT: 0\n", + " NAME: build_resnet_fpn_backbone\n", + " DEVICE: cuda\n", + " FPN:\n", + " FUSE_TYPE: sum\n", + " IN_FEATURES: ['res2', 'res3', 'res4', 'res5']\n", + " NORM: BN\n", + " OUT_CHANNELS: 256\n", + " KEYPOINT_ON: False\n", + " LOAD_PROPOSALS: False\n", + " MASK_ON: True\n", + " META_ARCHITECTURE: GeneralizedRCNN\n", + " PANOPTIC_FPN:\n", + " COMBINE:\n", + " ENABLED: True\n", + " INSTANCES_CONFIDENCE_THRESH: 0.5\n", + " OVERLAP_THRESH: 0.5\n", + " STUFF_AREA_LIMIT: 4096\n", + " INSTANCE_LOSS_WEIGHT: 1.0\n", + " PIXEL_MEAN: [123.675, 116.28, 103.53]\n", + " PIXEL_STD: [58.395, 57.12, 57.375]\n", + " PROPOSAL_GENERATOR:\n", + " MIN_SIZE: 0\n", + " NAME: RPN\n", + " RESNETS:\n", + " DEFORM_MODULATED: False\n", + " DEFORM_NUM_GROUPS: 1\n", + " DEFORM_ON_PER_STAGE: [False, False, False, False]\n", + " DEPTH: 50\n", + " NORM: BN\n", + " NUM_GROUPS: 1\n", + " OUT_FEATURES: ['res2', 'res3', 'res4', 'res5']\n", + " RES2_OUT_CHANNELS: 256\n", + " RES5_DILATION: 1\n", + " STEM_OUT_CHANNELS: 64\n", + " STRIDE_IN_1X1: False\n", + " WIDTH_PER_GROUP: 64\n", + " RETINANET:\n", + " BBOX_REG_LOSS_TYPE: smooth_l1\n", + " BBOX_REG_WEIGHTS: (1.0, 1.0, 1.0, 1.0)\n", + " FOCAL_LOSS_ALPHA: 0.25\n", + " FOCAL_LOSS_GAMMA: 2.0\n", + " IN_FEATURES: ['p3', 'p4', 'p5', 'p6', 'p7']\n", + " IOU_LABELS: [0, -1, 1]\n", + " IOU_THRESHOLDS: [0.4, 0.5]\n", + " NMS_THRESH_TEST: 0.5\n", + " NORM: \n", + " NUM_CLASSES: 80\n", + " NUM_CONVS: 4\n", + " PRIOR_PROB: 0.01\n", + " SCORE_THRESH_TEST: 0.5\n", + " SMOOTH_L1_LOSS_BETA: 0.1\n", + " TOPK_CANDIDATES_TEST: 1000\n", + " ROI_BOX_CASCADE_HEAD:\n", + " BBOX_REG_WEIGHTS: ((10.0, 10.0, 5.0, 5.0), (20.0, 20.0, 10.0, 10.0), (30.0, 30.0, 15.0, 15.0))\n", + " IOUS: (0.5, 0.6, 0.7)\n", + " ROI_BOX_HEAD:\n", + " BBOX_REG_LOSS_TYPE: smooth_l1\n", + " BBOX_REG_LOSS_WEIGHT: 1.0\n", + " BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)\n", + " CLS_AGNOSTIC_BBOX_REG: True\n", + " CONV_DIM: 256\n", + " FC_DIM: 1024\n", + " FED_LOSS_FREQ_WEIGHT_POWER: 0.5\n", + " FED_LOSS_NUM_CLASSES: 50\n", + " NAME: FastRCNNConvFCHead\n", + " NORM: \n", + " NUM_CONV: 0\n", + " NUM_FC: 2\n", + " POOLER_RESOLUTION: 7\n", + " POOLER_SAMPLING_RATIO: 0\n", + " POOLER_TYPE: ROIAlignV2\n", + " SMOOTH_L1_BETA: 0.0\n", + " TRAIN_ON_PRED_BOXES: False\n", + " USE_FED_LOSS: False\n", + " USE_SIGMOID_CE: False\n", + " ROI_HEADS:\n", + " BATCH_SIZE_PER_IMAGE: 512\n", + " DROPLOSS_IOU_THRESH: 0.01\n", + " IN_FEATURES: ['p2', 'p3', 'p4', 'p5']\n", + " IOU_LABELS: [0, 1]\n", + " IOU_THRESHOLDS: [0.5]\n", + " NAME: CustomCascadeROIHeads\n", + " NMS_THRESH_TEST: 0.5\n", + " NUM_CLASSES: 1\n", + " POSITIVE_FRACTION: 0.25\n", + " PROPOSAL_APPEND_GT: True\n", + " SCORE_THRESH_TEST: 0.5\n", + " USE_DROPLOSS: True\n", + " ROI_KEYPOINT_HEAD:\n", + " CONV_DIMS: (512, 512, 512, 512, 512, 512, 512, 512)\n", + " LOSS_WEIGHT: 1.0\n", + " MIN_KEYPOINTS_PER_IMAGE: 1\n", + " NAME: KRCNNConvDeconvUpsampleHead\n", + " NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: True\n", + " NUM_KEYPOINTS: 17\n", + " POOLER_RESOLUTION: 14\n", + " POOLER_SAMPLING_RATIO: 0\n", + " POOLER_TYPE: ROIAlignV2\n", + " ROI_MASK_HEAD:\n", + " CLS_AGNOSTIC_MASK: False\n", + " CONV_DIM: 256\n", + " NAME: MaskRCNNConvUpsampleHead\n", + " NORM: \n", + " NUM_CONV: 4\n", + " POOLER_RESOLUTION: 14\n", + " POOLER_SAMPLING_RATIO: 0\n", + " POOLER_TYPE: ROIAlignV2\n", + " RPN:\n", + " BATCH_SIZE_PER_IMAGE: 256\n", + " BBOX_REG_LOSS_TYPE: smooth_l1\n", + " BBOX_REG_LOSS_WEIGHT: 1.0\n", + " BBOX_REG_WEIGHTS: (1.0, 1.0, 1.0, 1.0)\n", + " BOUNDARY_THRESH: -1\n", + " CONV_DIMS: [-1]\n", + " HEAD_NAME: StandardRPNHead\n", + " IN_FEATURES: ['p2', 'p3', 'p4', 'p5', 'p6']\n", + " IOU_LABELS: [0, -1, 1]\n", + " IOU_THRESHOLDS: [0.3, 0.7]\n", + " LOSS_WEIGHT: 1.0\n", + " NMS_THRESH: 0.65\n", + " POSITIVE_FRACTION: 0.5\n", + " POST_NMS_TOPK_TEST: 1000\n", + " POST_NMS_TOPK_TRAIN: 4000\n", + " PRE_NMS_TOPK_TEST: 1000\n", + " PRE_NMS_TOPK_TRAIN: 2000\n", + " SMOOTH_L1_BETA: 0.0\n", + " SEM_SEG_HEAD:\n", + " COMMON_STRIDE: 4\n", + " CONVS_DIM: 128\n", + " IGNORE_VALUE: 255\n", + " IN_FEATURES: ['p2', 'p3', 'p4', 'p5']\n", + " LOSS_WEIGHT: 1.0\n", + " NAME: SemSegFPNHead\n", + " NORM: GN\n", + " NUM_CLASSES: 54\n", + " WEIGHTS: cutler_cascade_final.pth\n", + "OUTPUT_DIR: output/\n", + "SEED: -1\n", + "SOLVER:\n", + " AMP:\n", + " ENABLED: True\n", + " BASE_LR: 0.01\n", + " BASE_LR_END: 0.0\n", + " BASE_LR_MULTIPLIER: 1\n", + " BASE_LR_MULTIPLIER_NAMES: []\n", + " BIAS_LR_FACTOR: 1.0\n", + " CHECKPOINT_PERIOD: 5000\n", + " CLIP_GRADIENTS:\n", + " CLIP_TYPE: norm\n", + " CLIP_VALUE: 1.0\n", + " ENABLED: True\n", + " NORM_TYPE: 2.0\n", + " GAMMA: 0.02\n", + " IMS_PER_BATCH: 16\n", + " LR_SCHEDULER_NAME: WarmupMultiStepLR\n", + " MAX_ITER: 160000\n", + " MOMENTUM: 0.9\n", + " NESTEROV: False\n", + " NUM_DECAYS: 3\n", + " REFERENCE_WORLD_SIZE: 0\n", + " RESCALE_INTERVAL: False\n", + " STEPS: (80000,)\n", + " WARMUP_FACTOR: 0.001\n", + " WARMUP_ITERS: 1000\n", + " WARMUP_METHOD: linear\n", + " WEIGHT_DECAY: 5e-05\n", + " WEIGHT_DECAY_BIAS: None\n", + " WEIGHT_DECAY_NORM: 0.0\n", + "TEST:\n", + " AUG:\n", + " ENABLED: False\n", + " FLIP: True\n", + " MAX_SIZE: 4000\n", + " MIN_SIZES: (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)\n", + " DETECTIONS_PER_IMAGE: 100\n", + " EVAL_PERIOD: 0\n", + " EXPECTED_RESULTS: []\n", + " KEYPOINT_OKS_SIGMAS: []\n", + " NO_SEGM: False\n", + " PRECISE_BN:\n", + " ENABLED: True\n", + " NUM_ITER: 200\n", + "VERSION: 2\n", + "VIS_PERIOD: 0\n" + ] + } + ], + "source": [ + "setup_logger(name=\"fvcore\")\n", + "logger = setup_logger()\n", + "logger.info(\"Arguments: \" + str(args))\n", + "cfg = setup_cfg(args)\n", + "demo = VisualizationDemo(cfg)\n", + "print(cfg)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BdhUe5mVo0FG", + "outputId": "bbdd49a4-3c44-4fb2-a109-ba183f6bca1f" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rakib/miniconda3/envs/cutLer/lib/python3.12/site-packages/torch/functional.py:534: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3595.)\n", + " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m[11/12 11:53:03 detectron2]: \u001b[0m/home/rakib/Desktop/NAYM/Office_Projects/UNILEVER/unsupervised_CutLer/img/20240122_124158_jpg.rf.eb4f7a2da650cd28210cf68340da977d.jpg: detected 2 instances in 0.47s\n" + ] + } + ], + "source": [ + "# use PIL, to be consistent with evaluation\n", + "img = read_image(args.input, format=\"BGR\")\n", + "start_time = time.time()\n", + "predictions, visualized_output = demo.run_on_image(img)\n", + "logger.info(\n", + " \"{}: {} in {:.2f}s\".format(\n", + " args.input,\n", + " \"detected {} instances\".format(len(predictions[\"instances\"]))\n", + " if \"instances\" in predictions\n", + " else \"finished\",\n", + " time.time() - start_time,\n", + " )\n", + ")\n", + "\n", + "# save image to your local directory\n", + "if args.output:\n", + " if os.path.isdir(args.output):\n", + " assert os.path.isdir(args.output), args.output\n", + " out_filename = os.path.join(args.output, os.path.basename(args.input))\n", + " else:\n", + " assert len(args.input) == 1, \"Please specify a directory with args.output\"\n", + " out_filename = args.output\n", + " visualized_output.save(out_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 344 + }, + "id": "Qq45RaGSo0FH", + "outputId": "2d161c8e-67e3-4b42-ccfd-4556505ca0ae" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(visualized_output.get_image())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "cutLer", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/datasets/README.md b/datasets/README.md new file mode 100644 index 0000000000000000000000000000000000000000..30799608cd6752c4f9fc4e5cbf30c1fdd042c260 --- /dev/null +++ b/datasets/README.md @@ -0,0 +1,146 @@ +# Prepare Datasets for Training and Evaluating CutLER + +A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog) +for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc). +This document explains how to setup the builtin datasets so they can be used by the above APIs. [Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`, +and how to add new datasets to them. + +CutLER has builtin support for a few datasets. The datasets are assumed to exist in a directory specified by the environment variable `DETECTRON2_DATASETS`. Under this directory, detectron2 will look for datasets in the structure described below, if needed. +``` +$DETECTRON2_DATASETS/ + imagenet/ + coco/ + voc/ + kitti/ + ... +``` + +You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`. If left unset, the default is `./datasets` relative to your current working directory. + +All dataset annotation files should be converted to the COCO format. + +## Expected dataset structure for [ImageNet](https://image-net.org/download.php): +``` +imagenet/ + train/ + n01440764/*.JPEG + n01443537/*.JPEG + ... + annotations/ + imagenet_train_fixsize480_tau0.15_N3.json # generated by MaskCut +``` + +The ImageNet-1K samples should be downloaded from [here](https://image-net.org/download.php) and the pre-generated json file can be directly download from [here](http://dl.fbaipublicfiles.com/cutler/maskcut/imagenet_train_fixsize480_tau0.15_N3.json). +Or if you want to generate your own pseudo-masks, you can follow the instructions on [MaskCut](../README.md#1-maskcut). + + +## Expected dataset structure for [COCO, COCO20K, LVIS](https://cocodataset.org/#download): +``` +coco/ + annotations/ + instances_{train,val}2017.json + coco20k_trainval_gt.json + coco_cls_agnostic_instances_val2017.json + {1,2,5,10,20,30,40,50,60,80}perc_instances_train2017.json + lvis1.0_cocofied_val_cls_agnostic.json + {train,val}2017/ + 000000000139.jpg + 000000000285.jpg + ... + train2014/ + COCO_train2014_000000581921.jpg + COCO_train2014_000000581909.jpg + ... +``` + +Following previous work, we prepare annotations for semi-supervised learning by randomly sampling a subset of labeled images. +You can download these annotation filess needed for COCO semi-supervised learning experiments and for evaluating the performance of the models on COCO, COCO20K and LVIS: [coco-semi](http://dl.fbaipublicfiles.com/cutler/coco-smi/annotations.zip), [coco](http://dl.fbaipublicfiles.com/cutler/coco/coco_cls_agnostic_instances_val2017.json), [lvis](http://dl.fbaipublicfiles.com/cutler/coco/lvis1.0_cocofied_val_cls_agnostic.json) and [coco20k](http://dl.fbaipublicfiles.com/cutler/coco/coco20k_trainval_gt.json). + + +## Expected dataset structure for [VOC2007](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit): +``` +voc/ + annotations/ + trainvaltest_2007_cls_agnostic.json + VOC2007/ + JPEGImages/ + 000001.jpg + ... +``` +We provide pre-converted COCO-style annotation files [here](http://dl.fbaipublicfiles.com/cutler/voc/trainvaltest_2007_cls_agnostic.json) + + +## Expected dataset structure for [Objects365-V2](https://www.objects365.org/download.html): +``` +objects365/ + annotations/ + zhiyuan_objv2_val_cls_agnostic.json + val/ + 000000000139.jpg + 000000000285.jpg + ... +``` +You only need to download the validation set of Objects365 for evaluation. +We provide pre-converted COCO-style annotation files [here](http://dl.fbaipublicfiles.com/cutler/objects365/zhiyuan_objv2_val_cls_agnostic.json) + + +## Expected dataset structure for [OpenImages-V6](https://storage.googleapis.com/openimages/web/download_v6.html): +``` +openImages/ + annotations/ + openimages_val_cls_agnostic.json + validation/ + 47947b97662dc962.jpg + ... +``` +You only need to download the validation set of OpenImages for evaluation. +We provide pre-converted COCO-style annotation files [here](http://dl.fbaipublicfiles.com/cutler/openImages/openimages_val_cls_agnostic.json) + +## Expected dataset structure for [UVO](https://sites.google.com/view/unidentified-video-object/dataset): +``` +uvo/ + annotations/ + val_sparse_cleaned_cls_agnostic.json + all_UVO_frames/ + 000000000139.jpg + 000000000285.jpg + ... +``` +You only need to download the validation set of UVO for evaluation. +We provide pre-converted COCO-style annotation files [here](http://dl.fbaipublicfiles.com/cutler/uvo/val_sparse_cleaned_cls_agnostic.json) + +## Expected dataset structure for [KITTI](https://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=2d): +``` +kitti/ + annotations/ + trainval_cls_agnostic.json + JPEGImages/ + 001717.jpg + ... +``` +We provide pre-converted COCO-style annotation files [here](https://dl.fbaipublicfiles.com/cutler/kitti/trainval_cls_agnostic.json) + +## Expected dataset structure for [Watercolor, Comic, Clipart](https://github.com/naoto0804/cross-domain-detection): +``` +watercolor/ + annotations/ + traintest_cls_agnostic.json + JPEGImages/ + 163330523.jpg + ... +comic/ + annotations/ + traintest_cls_agnostic.json + JPEGImages/ + 161067391.jpg + ... +clipart/ + annotations/ + traintest_cls_agnostic.json + JPEGImages/ + 375390294.jpg + ... +``` +We provide pre-converted COCO-style annotation files here: [watercolor](http://dl.fbaipublicfiles.com/cutler/watercolor/traintest_cls_agnostic.json), [comic](http://dl.fbaipublicfiles.com/cutler/comic/traintest_cls_agnostic.json) and [clipart](http://dl.fbaipublicfiles.com/cutler/clipart/traintest_cls_agnostic.json). + +NOTE: ALL DATASETS FOLLOW THEIR ORIGINAL LICENSES. \ No newline at end of file diff --git a/img/1972a14f-ns_version_1_pic__53_jpg.rf.eb0559bd2c77831908e24908a87e2341.jpg 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validation data](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/): +``` +cd datasets/VOC2007/ +bash download_voc07.sh +``` +The dataset should be organized as : + +``` +./datasets/VOC2007/VOCdevkit + ├── VOC2007/ + ├── JPEGImages + ├── Annotations + ... +``` +### VOC2012 + +To download [Pascal VOC 2012 train and validation data](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/): +``` +cd datasets/VOC2012/ +bash download_voc12.sh +``` +The dataset should be organized as : + +``` +./datasets/VOC2012/VOCdevkit + ├── VOC2012/ + ├── JPEGImages + ├── Annotations + ... +``` + +### COCO +To download [COCO dataset](https://cocodataset.org/#home): +``` +cd datasets/COCO/ +bash download_coco.sh +``` +The dataset should be organized as : +``` +./datasets/COCO/ + ├── images/ + ├── annotations/ +``` + +### CUB +To download [CUB 200-2011](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html): +``` +cd datasets/CUB/ +bash download_cub.sh +``` + +The data should be organized as +``` +./datasets/CUB/CUB_200_2011 + ├── images/ + ├── images.txt + ├── image_sizes.txt + ... +``` +The `image_sizes.txt` file can be generated by `/dataset/CUB/compute_image_size.py` + + +### ImageNet +The data should be organized as +``` +./datasets/ImageNet/ + ├── ILSVRC + ├── Annoations + ├── Data + ├── Detection + ├── ImageSets + ├── LOC_synset_mapping.txt + ├── LOC_val_solution.csv + + ... +``` + +### ECSSD +To download [ECSSD](https://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/dataset.html): +``` +cd ../datasets/ECSSD +bash download_data.sh +``` + +The dataset should be organized as: + +``` +../datasets/ECSSD/ + ├── img + ├── gt +``` + +### DUTS +To download [DUTS](http://saliencydetection.net/duts/#org3aad434): +``` +cd ../datasets/DUTS_Test +bash download_data.sh +``` + +The dataset should be organized as: +``` +../datasets/DUTS_Test/ + ├── img + ├── gt +``` + + +### DUT-OMRON +To downlaod [DUT_OMRON](http://saliencydetection.net/dut-omron/#org96c3bab): +``` +cd ../datasets/DUT-OMRON +bash download_data.sh +``` + +The dataset should be organized as: +``` +../datasets/DUT-OMRON/ + ├── img + ├── gt +``` + + +## Download DINO checkpoint + +### DINO pretrained checkpoints + +We initialize the model weights by using dino pretrained weights. To download the dino model, please launch the following commands: +``` +wget https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth # vit-s/16 +wget https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth # vit-s/8 +wget https://dl.fbaipublicfiles.com/dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth # vit-b/16 +``` + +### DINO finetuned linear classifiers on ImageNet + +Since Dino offered finetuned linear classifiers on ImageNet, we can simply download them by following commands: +``` +wget https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_linearweights.pth #vit-s/16 +wget https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_pretrain/dino_deitsmall8_linearweights.pth # vit-s/8 +wget https://dl.fbaipublicfiles.com/dino/dino_vitbase16_pretrain/dino_vitbase16_linearweights.pth # vit-b/16 +``` diff --git a/maskcut/README.md b/maskcut/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ac4366ee659b8e9c59e5f9088e01df204dc18d15 --- /dev/null +++ b/maskcut/README.md @@ -0,0 +1,293 @@ +# (CVPR 2022) TokenCut +Pytorch implementation of **Tokencut**: + + +**Self-supervised Transformers for Unsupervised Object Discovery using Normalized Cut** + +*[Yangtao Wang](https://yangtaowang95.github.io), [Xi Shen](https://xishen0220.github.io/), [Shell Xu Hu](http://hushell.github.io/), [Yuan Yuan](https://yyuanad.github.io/), [James L. Crowley](http://crowley-coutaz.fr/jlc/jlc.html), [Dominique Vaufreydaz](https://research.vaufreydaz.org/)* + + +[[Project page](https://www.m-psi.fr/Papers/TokenCut2022/)] +[[ Github (Video Segmentation) ](https://github.com/YangtaoWANG95/TokenCut_video)] +[[Paper](https://arxiv.org/pdf/2202.11539.pdf)] +[![Colab demo](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/YangtaoWANG95/TokenCut/blob/master/inference_demo.ipynb) +[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/yangtaowang/TokenCut) + +

+ TokenCut teaser +

+ +If our project is helpful for your research, please consider citing : +``` +@inproceedings{wang2022tokencut, + title={Self-supervised Transformers for Unsupervised Object Discovery using Normalized Cut}, + author={Wang, Yangtao and Shen, Xi and Hu, Shell Xu and Yuan, Yuan and Crowley, James L. and Vaufreydaz, Dominique}, + booktitle={Conference on Computer Vision and Pattern Recognition} + year={2022} + } +``` + + +## Table of Content +* [1. Updates](#1-updates) +* [2. Installation](#2-installation) + * [2.1 Dependencies](#21-dependencies) + * [2.2 Data](#22-data) +* [3. Quick Start](#3-quick-start) + * [3.1 Detecting an object in one image](#31-detecting-an-object-in-one-image) + * [3.2 Segmenting a salient region in one image](#32-segmenting-a-salient-region-in-one-image) +* [4. Evaluation](#4-evaluation) + * [4.1 Unsupervised object discovery](#41-unsupervised-object-discovery) + * [4.2 Unsupervised saliency detection](#42-unsupervised-saliency-detection) + * [4.3 Weakly supervised object detection](#43-weakly-supervised-object-detection) +* [5. Acknowledgement](#5-acknowledgement) + +## 1. Updates + +***09/06/2022*** +Extension work of [TokeCut Video Segmentation](https://github.com/YangtaoWANG95/TokenCut_video) is realised! + +***03/10/2022*** +Creating a 480p Demo using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/yangtaowang/TokenCut) + +Internet image results: +

+ TokenCut visualizations + TokenCut visualizations + TokenCut visualizations + TokenCut visualizations +

+ +***02/26/2022*** +Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/TokenCut) + +***02/26/2022*** +A simple TokenCut Colab Demo is available. + +***02/21/2022*** +Initial commit: Code of TokenCut is released, including evaluation of unsupervised object discovery, unsupervised saliency object detection, weakly supervised object locolization. + +## 2. Installation +### 2.1 Dependencies + +This code was implemented with Python 3.7, PyTorch 1.7.1 and CUDA 11.2. Please refer to [the official installation](https://pytorch.org/get-started/previous-versions/). If CUDA 10.2 has been properly installed : +``` +pip install torch==1.7.1 torchvision==0.8.2 +``` + + +In order to install the additionnal dependencies, please launch the following command: + +``` +pip install -r requirements.txt +``` + + +### 2.2 Data + +We provide quick download commands in [DOWNLOAD_DATA.md](./DOWNLOAD_DATA.md) for VOC2007, VOC2012, COCO, CUB, ImageNet, ECSSD, DUTS and DUT-OMRON as well as DINO checkpoints. + + +## 3. Quick Start + +### 3.1 Detecting an object in one image + +We provide TokenCut visualization for bounding box prediction and attention map. Using `all` for all visualization results. + + +``` +python main_tokencut.py --image_path examples/VOC07_000036.jpg --visualize pred +python main_tokencut.py --image_path examples/VOC07_000036.jpg --visualize attn +python main_tokencut.py --image_path examples/VOC07_000036.jpg --visualize all +``` + +### 3.2 Segmenting a salient region in one image + +We provide TokenCut segmentation results as follows: + +``` +cd unsupervised_saliency_detection +python get_saliency.py --sigma-spatial 16 --sigma-luma 16 --sigma-chroma 8 --vit-arch small --patch-size 16 --img-path ../examples/VOC07_000036.jpg --out-dir ./output +``` + +## 4. Evaluation +Following are the different steps to reproduce the results of **TokenCut** presented in the paper. + +### 4.1 Unsupervised object discovery + + +

+ TokenCut visualizations + TokenCut visualizations + TokenCut visualizations +

+ +#### PASCAL-VOC +In order to apply TokenCut and compute corloc results (VOC07 68.8, VOC12 72.1), please launch: +``` +python main_tokencut.py --dataset VOC07 --set trainval +python main_tokencut.py --dataset VOC12 --set trainval +``` + +If you want to extract Dino features, which corresponds to [the KEY features in DINO](https://github.com/XiSHEN0220/LOST/blob/main/main_lost.py#L259-L261): +``` +mkdir features +python main_lost.py --dataset VOC07 --set trainval --save-feat-dir features/VOC2007 +``` + +#### COCO + +Results are provided given the 2014 annotations following previous works. The following command line allows you to get results on the subset of 20k images of the COCO dataset (corloc 58.8), following previous litterature. To be noted that the 20k images are a subset of the `train` set. +``` +python main_tokencut.py --dataset COCO20k --set train +``` + +#### Different models +We have tested the method on different setups of the VIT model, corloc results are presented in the following table (more can be found in the paper). + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
archpre-trainingdataset
VOC07VOC12COCO20k
ViT-S/16DINO68.872.158.8
ViT-S/8DINO67.371.660.7
ViT-B/16DINO68.872.459.0
+ + +Previous results on the dataset `VOC07` can be obtained by launching: +``` +python main_tokencut.py --dataset VOC07 --set trainval #VIT-S/16 +python main_tokencut.py --dataset VOC07 --set trainval --patch_size 8 #VIT-S/8 +python main_tokencut.py --dataset VOC07 --set trainval --arch vit_base #VIT-B/16 +``` + +### 4.2 Unsupervised saliency detection + +

+ TokenCut visualizations + TokenCut visualizations + TokenCut visualizations +

+ +To evaluate on ECSSD, DUTS, DUT_OMRON dataset: +``` +python get_saliency.py --out-dir ECSSD --sigma-spatial 16 --sigma-luma 16 --sigma-chroma 8 --nb-vis 1 --vit-arch small --patch-size 16 --dataset ECSSD + +python get_saliency.py --out-dir DUTS --sigma-spatial 16 --sigma-luma 16 --sigma-chroma 8 --nb-vis 1 --vit-arch small --patch-size 16 --dataset DUTS + +python get_saliency.py --out-dir DUT --sigma-spatial 16 --sigma-luma 16 --sigma-chroma 8 --nb-vis 1 --vit-arch small --patch-size 16 --dataset DUT +``` +This should give: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Method ECSSD DUTS DUT-OMRON
maxFIoUAccmaxFIoUAccmaxFIoUAcc
TokenCut80.371.291.867.257.690.360.053.388.0
TokenCut + BS87.477.293.475.562,491.469.761.889.7
+ +### 4.3 Weakly supervised object detection + +

+ TokenCut visualizations + TokenCut visualizations + TokenCut visualizations + +

+ + +#### Fintune DINO small on CUB + +To finetune ViT-S/16 on CUB on a single node with 4 gpus for 1000 epochs run: +``` +python -m torch.distributed.launch --nproc_per_node=4 main.py --data_path /path/to/data --batch_size_per_gpu 256 --dataset cub --weight_decay 0.005 --pretrained_weights ./dino_deitsmall16_pretrain.pth --epoch 1000 --output_dir ./path/to/checkpoin --lr 2e-4 --warmup-epochs 50 --num_labels 200 --num_workers 16 --n_last_blocks 1 --avgpool_patchtokens true --arch vit_small --patch_size 16 +``` + +#### Evaluation on CUB +To evaluate a fine-tuned ViT-S/16 on CUB val with a single GPU run: +``` +python eval.py --pretrained_weights ./path/to/checkpoint --dataset cub --data_path ./path/to/data --batch_size_per_gpu 1 --no_center_crop +``` +This should give: +``` +Top1_cls: 79.12, top5_cls94.80, gt_loc: 0.914, top1_loc:0.723 +``` + +#### Evaluate on Imagenet + +To Evaluate ViT-S/16 finetuned on ImageNet val with a single GPU run: +``` +python eval.py --pretrained_weights /path/to/checkpoint --classifier_weights /path/to/linear_weights--dataset imagenet --data_path ./path/to/data --batch_size_per_gpu 1 --num_labels 1000 --batch_size_per_gpu 1 --no_center_crop --input_size 256 --tau 0.2 --patch_size 16 --arch vit_small +``` + +## 5. Acknowledgement + +TokenCut code is built on top of [LOST](https://github.com/valeoai/LOST), [DINO](https://github.com/facebookresearch/dino), [Segswap](https://github.com/XiSHEN0220/SegSwap), and [Bilateral_Sovlver](https://github.com/poolio/bilateral_solver). We would like to sincerely thanks those authors for their great works. + + diff --git a/maskcut/__init__.py b/maskcut/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/maskcut/__pycache__/colormap.cpython-312.pyc b/maskcut/__pycache__/colormap.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..48ec28660e3d207d07d00f0d0f7bb56654c2a790 Binary files /dev/null and b/maskcut/__pycache__/colormap.cpython-312.pyc differ diff --git a/maskcut/__pycache__/crf.cpython-312.pyc b/maskcut/__pycache__/crf.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7491b426f0ea3887775b05a17f9bd2777c858e8a Binary files /dev/null and b/maskcut/__pycache__/crf.cpython-312.pyc differ diff --git a/maskcut/__pycache__/dino.cpython-312.pyc b/maskcut/__pycache__/dino.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..58a4086e7cb25e1b32f93eedee935a9a45d8435b Binary files /dev/null and b/maskcut/__pycache__/dino.cpython-312.pyc differ diff --git a/maskcut/__pycache__/maskcut.cpython-312.pyc b/maskcut/__pycache__/maskcut.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..98e866445d43e531f571ce334c6210f7404eb98f Binary files /dev/null and b/maskcut/__pycache__/maskcut.cpython-312.pyc differ diff --git a/maskcut/colormap.py b/maskcut/colormap.py new file mode 100644 index 0000000000000000000000000000000000000000..c434b295103de461b987c81e153749366fde55ba --- /dev/null +++ b/maskcut/colormap.py @@ -0,0 +1,156 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# copied from https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/colormap.py + +""" +An awesome colormap for really neat visualizations. +Copied from Detectron, and removed gray colors. +""" + +import numpy as np +import random + +__all__ = ["colormap", "random_color", "random_colors"] + +# fmt: off +# RGB: +_COLORS = np.array( + [ + 0.000, 0.447, 0.741, + 0.850, 0.325, 0.098, + 0.929, 0.694, 0.125, + 0.494, 0.184, 0.556, + 0.466, 0.674, 0.188, + 0.301, 0.745, 0.933, + 0.635, 0.078, 0.184, + 0.300, 0.300, 0.300, + 0.600, 0.600, 0.600, + 1.000, 0.000, 0.000, + 1.000, 0.500, 0.000, + 0.749, 0.749, 0.000, + 0.000, 1.000, 0.000, + 0.000, 0.000, 1.000, + 0.667, 0.000, 1.000, + 0.333, 0.333, 0.000, + 0.333, 0.667, 0.000, + 0.333, 1.000, 0.000, + 0.667, 0.333, 0.000, + 0.667, 0.667, 0.000, + 0.667, 1.000, 0.000, + 1.000, 0.333, 0.000, + 1.000, 0.667, 0.000, + 1.000, 1.000, 0.000, + 0.000, 0.333, 0.500, + 0.000, 0.667, 0.500, + 0.000, 1.000, 0.500, + 0.333, 0.000, 0.500, + 0.333, 0.333, 0.500, + 0.333, 0.667, 0.500, + 0.333, 1.000, 0.500, + 0.667, 0.000, 0.500, + 0.667, 0.333, 0.500, + 0.667, 0.667, 0.500, + 0.667, 1.000, 0.500, + 1.000, 0.000, 0.500, + 1.000, 0.333, 0.500, + 1.000, 0.667, 0.500, + 1.000, 1.000, 0.500, + 0.000, 0.333, 1.000, + 0.000, 0.667, 1.000, + 0.000, 1.000, 1.000, + 0.333, 0.000, 1.000, + 0.333, 0.333, 1.000, + 0.333, 0.667, 1.000, + 0.333, 1.000, 1.000, + 0.667, 0.000, 1.000, + 0.667, 0.333, 1.000, + 0.667, 0.667, 1.000, + 0.667, 1.000, 1.000, + 1.000, 0.000, 1.000, + 1.000, 0.333, 1.000, + 1.000, 0.667, 1.000, + 0.333, 0.000, 0.000, + 0.500, 0.000, 0.000, + 0.667, 0.000, 0.000, + 0.833, 0.000, 0.000, + 1.000, 0.000, 0.000, + 0.000, 0.167, 0.000, + 0.000, 0.333, 0.000, + 0.000, 0.500, 0.000, + 0.000, 0.667, 0.000, + 0.000, 0.833, 0.000, + 0.000, 1.000, 0.000, + 0.000, 0.000, 0.167, + 0.000, 0.000, 0.333, + 0.000, 0.000, 0.500, + 0.000, 0.000, 0.667, + 0.000, 0.000, 0.833, + 0.000, 0.000, 1.000, + 0.000, 0.000, 0.000, + 0.143, 0.143, 0.143, + 0.857, 0.857, 0.857, + 1.000, 1.000, 1.000 + ] +).astype(np.float32).reshape(-1, 3) +# fmt: on + + +def colormap(rgb=False, maximum=255): + """ + Args: + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + Returns: + ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1] + """ + assert maximum in [255, 1], maximum + c = _COLORS * maximum + if not rgb: + c = c[:, ::-1] + return c + + +def random_color(rgb=False, maximum=255): + """ + Args: + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + Returns: + ndarray: a vector of 3 numbers + """ + idx = np.random.randint(0, len(_COLORS)) + ret = _COLORS[idx] * maximum + if not rgb: + ret = ret[::-1] + return ret + + +def random_colors(N, rgb=False, maximum=255): + """ + Args: + N (int): number of unique colors needed + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + Returns: + ndarray: a list of random_color + """ + indices = random.sample(range(len(_COLORS)), N) + ret = [_COLORS[i] * maximum for i in indices] + if not rgb: + ret = [x[::-1] for x in ret] + return ret + + +if __name__ == "__main__": + import cv2 + + size = 100 + H, W = 10, 10 + canvas = np.random.rand(H * size, W * size, 3).astype("float32") + for h in range(H): + for w in range(W): + idx = h * W + w + if idx >= len(_COLORS): + break + canvas[h * size : (h + 1) * size, w * size : (w + 1) * size] = _COLORS[idx] + cv2.imshow("a", canvas) + cv2.waitKey(0) \ No newline at end of file diff --git a/maskcut/crf.py b/maskcut/crf.py new file mode 100644 index 0000000000000000000000000000000000000000..f8164298800ddb5b4f72d9219d03a2e65955448b --- /dev/null +++ b/maskcut/crf.py @@ -0,0 +1,46 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# modfied by Xudong Wang based on https://github.com/lucasb-eyer/pydensecrf/blob/master/pydensecrf/tests/test_dcrf.py and third_party/TokenCut + +import numpy as np +import pydensecrf.densecrf as dcrf +import pydensecrf.utils as utils +import torch +import torch.nn.functional as F +import torchvision.transforms.functional as VF + +MAX_ITER = 10 +POS_W = 7 +POS_XY_STD = 3 +Bi_W = 10 +Bi_XY_STD = 50 +Bi_RGB_STD = 5 + +def densecrf(image, mask): + h, w = mask.shape + mask = mask.reshape(1, h, w) + fg = mask.astype(float) + bg = 1 - fg + output_logits = torch.from_numpy(np.concatenate((bg,fg), axis=0)) + + H, W = image.shape[:2] + image = np.ascontiguousarray(image) + + output_logits = F.interpolate(output_logits.unsqueeze(0), size=(H, W), mode="bilinear").squeeze() + output_probs = F.softmax(output_logits, dim=0).cpu().numpy() + + c = output_probs.shape[0] + h = output_probs.shape[1] + w = output_probs.shape[2] + + U = utils.unary_from_softmax(output_probs) + U = np.ascontiguousarray(U) + + d = dcrf.DenseCRF2D(w, h, c) + d.setUnaryEnergy(U) + d.addPairwiseGaussian(sxy=POS_XY_STD, compat=POS_W) + d.addPairwiseBilateral(sxy=Bi_XY_STD, srgb=Bi_RGB_STD, rgbim=image, compat=Bi_W) + + Q = d.inference(MAX_ITER) + Q = np.array(Q).reshape((c, h, w)) + MAP = np.argmax(Q, axis=0).reshape((h,w)).astype(np.float32) + return MAP diff --git a/maskcut/datasets.py b/maskcut/datasets.py new file mode 100755 index 0000000000000000000000000000000000000000..61368e02fd6f348b75fbe1bbe43f02c223386ed4 --- /dev/null +++ b/maskcut/datasets.py @@ -0,0 +1,390 @@ +""" +Datasets file. Code adapted from LOST: https://github.com/valeoai/LOST +""" +import os +import torch +import json +import torchvision +import numpy as np +import skimage.io + +from PIL import Image +from tqdm import tqdm +from skimage.transform import resize +from torchvision import transforms as pth_transforms + +# Image transformation applied to all images +transform = pth_transforms.Compose( + [ + pth_transforms.ToTensor(), + pth_transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ] +) + +class ImageDataset: + def __init__(self, image_path, resize=None): + + self.image_path = image_path + self.name = image_path.split("/")[-1] + + # Read the image + with open(image_path, "rb") as f: + img = Image.open(f) + img = img.convert("RGB") + + # Build a dataloader + if resize is not None: + transform_resize = pth_transforms.Compose( + [ + pth_transforms.ToTensor(), + pth_transforms.Resize(resize), + pth_transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ] + ) + img = transform_resize(img) + self.img_size = list(img.shape[-1:-3:-1]) + else: + img = transform(img) + self.img_size = list(img.shape[-1:-3:-1]) + self.dataloader = [[img, image_path]] + + def get_image_name(self, *args, **kwargs): + return self.image_path.split("/")[-1].split(".")[0] + + def load_image(self, *args, **kwargs): + return Image.open(self.image_path).convert("RGB").resize(self.img_size) + +class Dataset: + def __init__(self, dataset_name, dataset_set, remove_hards): + """ + Build the dataloader + """ + + self.dataset_name = dataset_name + self.set = dataset_set + + if dataset_name == "VOC07": + self.root_path = "datasets/VOC2007" + self.year = "2007" + elif dataset_name == "VOC12": + self.root_path = "datasets/VOC2012" + self.year = "2012" + elif dataset_name == "COCO20k": + self.year = "2014" + self.root_path = f"datasets/COCO/images/{dataset_set}{self.year}" + self.sel20k = 'datasets/coco_20k_filenames.txt' + # JSON file constructed based on COCO train2014 gt + self.all_annfile = "datasets/COCO/annotations/instances_train2014.json" + self.annfile = "datasets/instances_train2014_sel20k.json" + self.sel_20k = get_sel_20k(self.sel20k) + if not os.path.exists(self.annfile): + select_coco_20k(self.sel20k, self.all_annfile) + self.train2014 = get_train2014(self.annfile) + else: + raise ValueError("Unknown dataset.") + + if not os.path.exists(self.root_path): + raise ValueError("Please follow the README to setup the datasets.") + + self.name = f"{self.dataset_name}_{self.set}" + + # Build the dataloader + if "VOC" in dataset_name: + self.dataloader = torchvision.datasets.VOCDetection( + self.root_path, + year=self.year, + image_set=self.set, + transform=transform, + download=False, + ) + elif "COCO20k" == dataset_name: + self.dataloader = torchvision.datasets.CocoDetection( + self.root_path, annFile=self.annfile, transform=transform + ) + else: + raise ValueError("Unknown dataset.") + + # Set hards images that are not included + self.remove_hards = remove_hards + self.hards = [] + if remove_hards: + self.name += f"-nohards" + self.hards = self.get_hards() + print(f"Nb images discarded {len(self.hards)}") + + def load_image(self, im_name): + """ + Load the image corresponding to the im_name + """ + if "VOC" in self.dataset_name: + image = skimage.io.imread(f"./datasets/VOC{self.year}/VOCdevkit/VOC{self.year}/JPEGImages/{im_name}") + elif "COCO" in self.dataset_name: + #im_path = self.path_20k[self.sel_20k.index(im_name)] + #im_path = self.train2014['images'][self.sel_20k.index(im_name)]['file_name'] + #image = skimage.io.imread(f"./datasets/COCO/images/train2014/{im_path}") + image = skimage.io.imread(f"./datasets/COCO/images/train2014/{im_name}") + else: + raise ValueError("Unkown dataset.") + return image + + def get_image_name(self, inp): + """ + Return the image name + """ + if "VOC" in self.dataset_name: + im_name = inp["annotation"]["filename"] + elif "COCO" in self.dataset_name: + im_name = str(inp[0]["image_id"]) + im_name = self.train2014['images'][self.sel_20k.index(im_name)]['file_name'] + + return im_name + + def extract_gt(self, targets, im_name): + if "VOC" in self.dataset_name: + return extract_gt_VOC(targets, remove_hards=self.remove_hards) + elif "COCO" in self.dataset_name: + return extract_gt_COCO(targets, remove_iscrowd=True) + else: + raise ValueError("Unknown dataset") + + def extract_classes(self): + if "VOC" in self.dataset_name: + cls_path = f"classes_{self.set}_{self.year}.txt" + elif "COCO" in self.dataset_name: + cls_path = f"classes_{self.dataset}_{self.set}_{self.year}.txt" + + # Load if exists + if os.path.exists(cls_path): + all_classes = [] + with open(cls_path, "r") as f: + for line in f: + all_classes.append(line.strip()) + else: + print("Extract all classes from the dataset") + if "VOC" in self.dataset_name: + all_classes = self.extract_classes_VOC() + elif "COCO" in self.dataset_name: + all_classes = self.extract_classes_COCO() + + with open(cls_path, "w") as f: + for s in all_classes: + f.write(str(s) + "\n") + + return all_classes + + def extract_classes_VOC(self): + all_classes = [] + for im_id, inp in enumerate(tqdm(self.dataloader)): + objects = inp[1]["annotation"]["object"] + + for o in range(len(objects)): + if objects[o]["name"] not in all_classes: + all_classes.append(objects[o]["name"]) + + return all_classes + + def extract_classes_COCO(self): + all_classes = [] + for im_id, inp in enumerate(tqdm(self.dataloader)): + objects = inp[1] + + for o in range(len(objects)): + if objects[o]["category_id"] not in all_classes: + all_classes.append(objects[o]["category_id"]) + + return all_classes + + def get_hards(self): + hard_path = "datasets/hard_%s_%s_%s.txt" % (self.dataset_name, self.set, self.year) + if os.path.exists(hard_path): + hards = [] + with open(hard_path, "r") as f: + for line in f: + hards.append(int(line.strip())) + else: + print("Discover hard images that should be discarded") + + if "VOC" in self.dataset_name: + # set the hards + hards = discard_hard_voc(self.dataloader) + + with open(hard_path, "w") as f: + for s in hards: + f.write(str(s) + "\n") + + return hards + + +def discard_hard_voc(dataloader): + hards = [] + for im_id, inp in enumerate(tqdm(dataloader)): + objects = inp[1]["annotation"]["object"] + nb_obj = len(objects) + + hard = np.zeros(nb_obj) + for i, o in enumerate(range(nb_obj)): + hard[i] = ( + 1 + if (objects[o]["truncated"] == "1" or objects[o]["difficult"] == "1") + else 0 + ) + + # all images with only truncated or difficult objects + if np.sum(hard) == nb_obj: + hards.append(im_id) + return hards + + +def extract_gt_COCO(targets, remove_iscrowd=True): + objects = targets + nb_obj = len(objects) + + gt_bbxs = [] + gt_clss = [] + for o in range(nb_obj): + # Remove iscrowd boxes + if remove_iscrowd and objects[o]["iscrowd"] == 1: + continue + gt_cls = objects[o]["category_id"] + gt_clss.append(gt_cls) + bbx = objects[o]["bbox"] + x1y1x2y2 = [bbx[0], bbx[1], bbx[0] + bbx[2], bbx[1] + bbx[3]] + x1y1x2y2 = [int(round(x)) for x in x1y1x2y2] + gt_bbxs.append(x1y1x2y2) + + return np.asarray(gt_bbxs), gt_clss + + +def extract_gt_VOC(targets, remove_hards=False): + objects = targets["annotation"]["object"] + nb_obj = len(objects) + + gt_bbxs = [] + gt_clss = [] + for o in range(nb_obj): + if remove_hards and ( + objects[o]["truncated"] == "1" or objects[o]["difficult"] == "1" + ): + continue + gt_cls = objects[o]["name"] + gt_clss.append(gt_cls) + obj = objects[o]["bndbox"] + x1y1x2y2 = [ + int(obj["xmin"]), + int(obj["ymin"]), + int(obj["xmax"]), + int(obj["ymax"]), + ] + # Original annotations are integers in the range [1, W or H] + # Assuming they mean 1-based pixel indices (inclusive), + # a box with annotation (xmin=1, xmax=W) covers the whole image. + # In coordinate space this is represented by (xmin=0, xmax=W) + x1y1x2y2[0] -= 1 + x1y1x2y2[1] -= 1 + gt_bbxs.append(x1y1x2y2) + + return np.asarray(gt_bbxs), gt_clss + + +def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): + # https://github.com/ultralytics/yolov5/blob/develop/utils/general.py + # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 + box2 = box2.T + + # Get the coordinates of bounding boxes + if x1y1x2y2: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + else: # transform from xywh to xyxy + b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 + b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 + b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 + b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 + + # Intersection area + inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * ( + torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1) + ).clamp(0) + + # Union Area + w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps + w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps + union = w1 * h1 + w2 * h2 - inter + eps + + iou = inter / union + if GIoU or DIoU or CIoU: + cw = torch.max(b1_x2, b2_x2) - torch.min( + b1_x1, b2_x1 + ) # convex (smallest enclosing box) width + ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared + rho2 = ( + (b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2 + ) / 4 # center distance squared + if DIoU: + return iou - rho2 / c2 # DIoU + elif ( + CIoU + ): # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi ** 2) * torch.pow( + torch.atan(w2 / h2) - torch.atan(w1 / h1), 2 + ) + with torch.no_grad(): + alpha = v / (v - iou + (1 + eps)) + return iou - (rho2 / c2 + v * alpha) # CIoU + else: # GIoU https://arxiv.org/pdf/1902.09630.pdf + c_area = cw * ch + eps # convex area + return iou - (c_area - union) / c_area # GIoU + else: + return iou # IoU + +def get_sel_20k(sel_file): + # load selected images + with open(sel_file, "r") as f: + sel_20k = f.readlines() + sel_20k = [s.replace("\n", "") for s in sel_20k] + im20k = [str(int(s.split("_")[-1].split(".")[0])) for s in sel_20k] + return im20k + +def get_train2014(all_annotations_file): + # load all annotations + with open(all_annotations_file, "r") as f: + train2014 = json.load(f) + return train2014 + + + +def select_coco_20k(sel_file, all_annotations_file): + print('Building COCO 20k dataset.') + + # load all annotations + with open(all_annotations_file, "r") as f: + train2014 = json.load(f) + + # load selected images + with open(sel_file, "r") as f: + sel_20k = f.readlines() + sel_20k = [s.replace("\n", "") for s in sel_20k] + im20k = [str(int(s.split("_")[-1].split(".")[0])) for s in sel_20k] + + new_anno = [] + new_images = [] + + for i in tqdm(im20k): + new_anno.extend( + [a for a in train2014["annotations"] if a["image_id"] == int(i)] + ) + new_images.extend([a for a in train2014["images"] if a["id"] == int(i)]) + + train2014_20k = {} + train2014_20k["images"] = new_images + train2014_20k["annotations"] = new_anno + train2014_20k["categories"] = train2014["categories"] + + with open("datasets/instances_train2014_sel20k.json", "w") as outfile: + json.dump(train2014_20k, outfile) + + print(f'im20k :{im20k[0]}') + print('Done.') diff --git a/maskcut/datasets/COCO/download_coco.sh b/maskcut/datasets/COCO/download_coco.sh new file mode 100644 index 0000000000000000000000000000000000000000..ea31d39963f2a325ba52aec3eb8fdbe4398c71dd --- /dev/null +++ b/maskcut/datasets/COCO/download_coco.sh @@ -0,0 +1,18 @@ +mkdir images + +wget http://images.cocodataset.org/zips/train2014.zip +unzip train2014.zip ./images +rm train2014.zip + +wget http://images.cocodataset.org/zips/val2014.zip +unzip val2014.zip ./images +rm val2014.zip + +wget http://images.cocodataset.org/zips/test2014.zip +unzip test2014.zip ./images +rm test.2014.zip + +wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip +unzip annotations_trainval2014.zip +rm annotations_trainval2014.zip + diff --git a/maskcut/datasets/CUB/compute_image_size.py b/maskcut/datasets/CUB/compute_image_size.py new file mode 100644 index 0000000000000000000000000000000000000000..349f7640fb37863407b5121e0b306f0460eba11b --- /dev/null +++ b/maskcut/datasets/CUB/compute_image_size.py @@ -0,0 +1,23 @@ +import os +import cv2 +import pandas as pd + +def store_cub_image_sizes(root): + paths = pd.read_csv( + os.path.join(root, 'CUB_200_2011', 'images.txt'), + sep=' ', names=['id', 'path']) + + sizes = pd.DataFrame(columns=['id', 'width', 'height']) + for i in range(len(paths)): + path = paths.iloc[i].path + image_path = os.path.join(root, 'CUB_200_2011/images', path) + image = cv2.imread(image_path) + sizes.loc[i] = [paths.iloc[i].id, image.shape[1], image.shape[0]] + + save_path = os.path.join(root, 'CUB_200_2011', 'image_sizes.txt') + sizes.to_csv(path_or_buf=save_path, sep=' ', index=False, header=False) + +if __name__ == '__main__': + root = '.' + print('Storing image sizes of cub200 dataset') + store_cub_image_sizes(root) diff --git a/maskcut/datasets/CUB/download_cub.sh b/maskcut/datasets/CUB/download_cub.sh new file mode 100644 index 0000000000000000000000000000000000000000..91a43f3edb09055687cb70594d9de866d749d07f --- /dev/null +++ b/maskcut/datasets/CUB/download_cub.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +mkdir CUB +wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1hbzc_P1FuxMkcabkgn9ZKinBwW683j45' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1hbzc_P1FuxMkcabkgn9ZKinBwW683j45" -nc -O CUB/CUB_200_2011.tgz && rm -rf /tmp/cookies.txt +wget -nc -O CUB/CUBV2.tar "https://onedrive.live.com/download?cid=B7111B95B80CCC66&resid=B7111B95B80CCC66%2130812&authkey=AFMzb4akufUiWU0" +mkdir -p CUB/CUB_200_2011 +tar xvf CUB/CUB_200_2011.tgz -C CUB/ diff --git a/maskcut/datasets/DUTS_Test/download_data.sh b/maskcut/datasets/DUTS_Test/download_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..439a4ce2d0f57aa58cdb97cbc10b3986123cac9b --- /dev/null +++ b/maskcut/datasets/DUTS_Test/download_data.sh @@ -0,0 +1,6 @@ +wget http://saliencydetection.net/duts/download/DUTS-TE.zip +unzip DUTS-TE.zip +rm DUTS-TE.zip +mv DUTS-TE/DUTS-TE-Image/ img +mv DUTS-TE/DUTS-TE-Mask/ gt +rm -r DUTS-TE/ diff --git a/maskcut/datasets/DUT_OMRON/download_data.sh b/maskcut/datasets/DUT_OMRON/download_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..8e73a11f77eaee0a8b5a109cda41932a063dfd3f --- /dev/null +++ b/maskcut/datasets/DUT_OMRON/download_data.sh @@ -0,0 +1,9 @@ +wget http://saliencydetection.net/dut-omron/download/DUT-OMRON-image.zip +wget http://saliencydetection.net/dut-omron/download/DUT-OMRON-gt-pixelwise.zip.zip +unzip DUT-OMRON-gt-pixelwise.zip.zip +unzip DUT-OMRON-image.zip +rm DUT-OMRON-gt-pixelwise.zip.zip +rm DUT-OMRON-image.zip +mv DUT-OMRON-image/ img +mv pixelwiseGT-new-PNG/ gt +rm -r __MACOSX/ diff --git a/maskcut/datasets/ECSSD/download_data.sh b/maskcut/datasets/ECSSD/download_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..826e62ec4a0a2c8d815bd99eeb03e1927b2ce783 --- /dev/null +++ b/maskcut/datasets/ECSSD/download_data.sh @@ -0,0 +1,10 @@ +wget https://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/data/ECSSD/images.zip +wget https://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/data/ECSSD/ground_truth_mask.zip + +unzip images.zip +unzip ground_truth_mask.zip + +rm images.zip ground_truth_mask.zip + +mv images img +mv ground_truth_mask gt diff --git a/maskcut/datasets/ImageNet/Detection/imagenet1000_clsidx_to_labels.txt b/maskcut/datasets/ImageNet/Detection/imagenet1000_clsidx_to_labels.txt new file mode 100644 index 0000000000000000000000000000000000000000..12866db8c24e77acbbced6337a7716aa6c6318f4 --- /dev/null +++ b/maskcut/datasets/ImageNet/Detection/imagenet1000_clsidx_to_labels.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:acf75ef0abe89694b19056e0796401068b459c457baa30335f240c7692857355 +size 31675 diff --git a/maskcut/datasets/ImageNet/Detection/test.txt b/maskcut/datasets/ImageNet/Detection/test.txt new file mode 100644 index 0000000000000000000000000000000000000000..669795ffe16ce178dea46e9454375a86383174d9 --- /dev/null +++ b/maskcut/datasets/ImageNet/Detection/test.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fecd6a03eb9f74811e52791295f4ddf62d4b8b6b31de2c73eafae7e9fbd896ee +size 3200000 diff --git a/maskcut/datasets/ImageNet/Detection/train.txt b/maskcut/datasets/ImageNet/Detection/train.txt new file mode 100644 index 0000000000000000000000000000000000000000..0b8b9be9c61859351632e8a4009131ad36aae8de --- /dev/null +++ b/maskcut/datasets/ImageNet/Detection/train.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:55a0a92c2762655087e447ddd97dd27ba81dd1839a615e1bb598d04dac477d48 +size 43829433 diff --git a/maskcut/datasets/ImageNet/Detection/val.txt b/maskcut/datasets/ImageNet/Detection/val.txt new file mode 100644 index 0000000000000000000000000000000000000000..96dd02a97702afe71548f81dbc0e04ac84c255c5 --- /dev/null +++ b/maskcut/datasets/ImageNet/Detection/val.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:78b17d76293ec6b5a96ec2b7c0fefdcda40373b63c5e28309310aa620fa58a5e +size 2762694 diff --git a/maskcut/datasets/ImageNet/Detection/wnids.txt b/maskcut/datasets/ImageNet/Detection/wnids.txt new file mode 100644 index 0000000000000000000000000000000000000000..6ba412e3fd5c15b09a190bc15c1b6454f269fab6 --- /dev/null +++ b/maskcut/datasets/ImageNet/Detection/wnids.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:70002b0ff5de60a3a17a82dbfcff291931f96225ddf941ad2e182fc39e183d15 +size 10000 diff --git a/maskcut/datasets/VOC2007/download_voc07.sh b/maskcut/datasets/VOC2007/download_voc07.sh new file mode 100644 index 0000000000000000000000000000000000000000..fd180cd05349e4e4037e452651fcee1f24d2c7aa --- /dev/null +++ b/maskcut/datasets/VOC2007/download_voc07.sh @@ -0,0 +1,3 @@ +wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar +tar -xvf VOCtrainval_06-Nov-2007.tar +rm VOCtrainval_06-Nov-2007.tar \ No newline at end of file diff --git a/maskcut/datasets/VOC2012/download_voc12.sh b/maskcut/datasets/VOC2012/download_voc12.sh new file mode 100644 index 0000000000000000000000000000000000000000..8fd50132e3a25fac770b1ba1b60926999c5a268a --- /dev/null +++ b/maskcut/datasets/VOC2012/download_voc12.sh @@ -0,0 +1,4 @@ +wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar +tar -xvf VOCtrainval_11-May-2012.tar +rm VOCtrainval_11-May-2012.tar + diff --git a/maskcut/datasets/coco_20k_filenames.txt b/maskcut/datasets/coco_20k_filenames.txt new file mode 100644 index 0000000000000000000000000000000000000000..76584e99dc831653b366c3f5302f0a0d0e70c216 --- /dev/null +++ b/maskcut/datasets/coco_20k_filenames.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:74637e9b4f93f2ebef9b1f664aff53edd1f41ef08dad97bd22ce8e455052b335 +size 832314 diff --git a/maskcut/demo.py b/maskcut/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..a20629dea35c8becb74cecc6b24fd2cb4f4d2b25 --- /dev/null +++ b/maskcut/demo.py @@ -0,0 +1,108 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. + +import os +import sys +sys.path.append('../') +import argparse +import numpy as np +import PIL.Image as Image +import torch +from torchvision import transforms +from scipy import ndimage +from detectron2.utils.colormap import random_color + +import dino # model +from third_party.token_cut.unsupervised_saliency_detection import metric +from crf import densecrf +from maskcut import maskcut + +# Image transformation applied to all images +ToTensor = transforms.Compose([transforms.ToTensor(), + transforms.Normalize( + (0.485, 0.456, 0.406), + (0.229, 0.224, 0.225)),]) + +def vis_mask(input, mask, mask_color) : + fg = mask > 0.5 + rgb = np.copy(input) + rgb[fg] = (rgb[fg] * 0.3 + np.array(mask_color) * 0.7).astype(np.uint8) + return Image.fromarray(rgb) + +if __name__ == "__main__": + parser = argparse.ArgumentParser('MaskCut Demo') + # default arguments + parser.add_argument('--out-dir', type=str, help='output directory') + parser.add_argument('--vit-arch', type=str, default='small', choices=['base', 'small'], help='which architecture') + parser.add_argument('--vit-feat', type=str, default='k', choices=['k', 'q', 'v', 'kqv'], help='which features') + parser.add_argument('--patch-size', type=int, default=8, choices=[16, 8], help='patch size') + parser.add_argument('--img-path', type=str, default=None, help='single image visualization') + parser.add_argument('--tau', type=float, default=0.15, help='threshold used for producing binary graph') + + # additional arguments + parser.add_argument('--fixed_size', type=int, default=480, help='rescale the input images to a fixed size') + parser.add_argument('--pretrain_path', type=str, default=None, help='path to pretrained model') + parser.add_argument('--N', type=int, default=3, help='the maximum number of pseudo-masks per image') + parser.add_argument('--cpu', action='store_true', help='use cpu') + parser.add_argument('--output_path', type=str, default='', help='path to save outputs') + + args = parser.parse_args() + print (args) + + if args.pretrain_path is not None: + url = args.pretrain_path + if args.vit_arch == 'base' and args.patch_size == 8: + if args.pretrain_path is None: + url = "https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth" + feat_dim = 768 + elif args.vit_arch == 'small' and args.patch_size == 8: + if args.pretrain_path is None: + url = "https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_300ep_pretrain/dino_deitsmall8_300ep_pretrain.pth" + feat_dim = 384 + + backbone = dino.ViTFeat(url, feat_dim, args.vit_arch, args.vit_feat, args.patch_size) + + msg = 'Load {} pre-trained feature...'.format(args.vit_arch) + print (msg) + backbone.eval() + if not args.cpu: + backbone.cuda() + + bipartitions, _, I_new = maskcut(args.img_path, backbone, args.patch_size, args.tau, \ + N=args.N, fixed_size=args.fixed_size, cpu=args.cpu) + + I = Image.open(args.img_path).convert('RGB') + width, height = I.size + pseudo_mask_list = [] + for idx, bipartition in enumerate(bipartitions): + # post-process pesudo-masks with CRF + pseudo_mask = densecrf(np.array(I_new), bipartition) + pseudo_mask = ndimage.binary_fill_holes(pseudo_mask>=0.5) + + # filter out the mask that have a very different pseudo-mask after the CRF + if not args.cpu: + mask1 = torch.from_numpy(bipartition).cuda() + mask2 = torch.from_numpy(pseudo_mask).cuda() + else: + mask1 = torch.from_numpy(bipartition) + mask2 = torch.from_numpy(pseudo_mask) + if metric.IoU(mask1, mask2) < 0.5: + pseudo_mask = pseudo_mask * -1 + + # construct binary pseudo-masks + pseudo_mask[pseudo_mask < 0] = 0 + pseudo_mask = Image.fromarray(np.uint8(pseudo_mask*255)) + pseudo_mask = np.asarray(pseudo_mask.resize((width, height))) + + pseudo_mask = pseudo_mask.astype(np.uint8) + upper = np.max(pseudo_mask) + lower = np.min(pseudo_mask) + thresh = upper / 2.0 + pseudo_mask[pseudo_mask > thresh] = upper + pseudo_mask[pseudo_mask <= thresh] = lower + pseudo_mask_list.append(pseudo_mask) + + input = np.array(I) + for pseudo_mask in pseudo_mask_list: + input = vis_mask(input, pseudo_mask, random_color(rgb=True)) + input.save(os.path.join(args.output_path, "demo.jpg")) \ No newline at end of file diff --git a/maskcut/dino.py b/maskcut/dino.py new file mode 100644 index 0000000000000000000000000000000000000000..89d6e4ff52d554a3e4e4d6ade49670ae07c12264 --- /dev/null +++ b/maskcut/dino.py @@ -0,0 +1,346 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. + +""" +Copied from Dino repo. https://github.com/facebookresearch/dino +Mostly copy-paste from timm library. +https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py +""" +import math +from functools import partial + +import torch +import torch.nn as nn + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): + # type: (Tensor, float, float, float, float) -> Tensor + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +def drop_path(x, drop_prob: float = 0., training: bool = False): + if drop_prob == 0. or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x, attn + + +class Block(nn.Module): + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x, return_attention=False): + y, attn = self.attn(self.norm1(x)) + if return_attention: + return attn + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + num_patches = (img_size // patch_size) * (img_size // patch_size) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x): + B, C, H, W = x.shape + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class VisionTransformer(nn.Module): + """ Vision Transformer """ + def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., norm_layer=nn.LayerNorm, **kwargs): + super().__init__() + self.num_features = self.embed_dim = embed_dim + + self.patch_embed = PatchEmbed( + img_size=img_size[0], patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + + # Classifier head + self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def interpolate_pos_encoding(self, x, w, h): + npatch = x.shape[1] - 1 + N = self.pos_embed.shape[1] - 1 + if npatch == N and w == h: + return self.pos_embed + class_pos_embed = self.pos_embed[:, 0] + patch_pos_embed = self.pos_embed[:, 1:] + dim = x.shape[-1] + w0 = w // self.patch_embed.patch_size + h0 = h // self.patch_embed.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + w0, h0 = w0 + 0.1, h0 + 0.1 + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2), + scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), + mode='bicubic', + ) + assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) + + def prepare_tokens(self, x): + B, nc, w, h = x.shape + x = self.patch_embed(x) # patch linear embedding + + # add the [CLS] token to the embed patch tokens + cls_tokens = self.cls_token.expand(B, -1, -1) + x = torch.cat((cls_tokens, x), dim=1) + + # add positional encoding to each token + x = x + self.interpolate_pos_encoding(x, w, h) + + return self.pos_drop(x) + + def forward(self, x): + x = self.prepare_tokens(x) + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + return x[:, 0] + + def get_last_selfattention(self, x): + x = self.prepare_tokens(x) + for i, blk in enumerate(self.blocks): + if i < len(self.blocks) - 1: + x = blk(x) + else: + # return attention of the last block + return blk(x, return_attention=True) + + def get_intermediate_layers(self, x, n=1): + x = self.prepare_tokens(x) + # we return the output tokens from the `n` last blocks + output = [] + for i, blk in enumerate(self.blocks): + x = blk(x) + if len(self.blocks) - i <= n: + output.append(self.norm(x)) + return output + + +def vit_small(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def vit_base(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + +class ViTFeat(nn.Module): + """ Vision Transformer """ + def __init__(self, pretrained_pth, feat_dim, vit_arch = 'base', vit_feat = 'k', patch_size=16): + super().__init__() + if vit_arch == 'base' : + self.model = vit_base(patch_size=patch_size, num_classes=0) + + else : + self.model = vit_small(patch_size=patch_size, num_classes=0) + + self.feat_dim = feat_dim + self.vit_feat = vit_feat + self.patch_size = patch_size + +# state_dict = torch.load(pretrained_pth, map_location="cpu") + state_dict = torch.hub.load_state_dict_from_url(pretrained_pth) + self.model.load_state_dict(state_dict, strict=True) + print('Loading weight from {}'.format(pretrained_pth)) + + + def forward(self, img) : + feat_out = {} + def hook_fn_forward_qkv(module, input, output): + feat_out["qkv"] = output + + self.model._modules["blocks"][-1]._modules["attn"]._modules["qkv"].register_forward_hook(hook_fn_forward_qkv) + + + # Forward pass in the model + with torch.no_grad() : + h, w = img.shape[2], img.shape[3] + feat_h, feat_w = h // self.patch_size, w // self.patch_size + attentions = self.model.get_last_selfattention(img) + bs, nb_head, nb_token = attentions.shape[0], attentions.shape[1], attentions.shape[2] + qkv = ( + feat_out["qkv"] + .reshape(bs, nb_token, 3, nb_head, -1) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = qkv[0], qkv[1], qkv[2] + + k = k.transpose(1, 2).reshape(bs, nb_token, -1) + q = q.transpose(1, 2).reshape(bs, nb_token, -1) + v = v.transpose(1, 2).reshape(bs, nb_token, -1) + + # Modality selection + if self.vit_feat == "k": + feats = k[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + elif self.vit_feat == "q": + feats = q[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + elif self.vit_feat == "v": + feats = v[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + elif self.vit_feat == "kqv": + k = k[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + q = q[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + v = v[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + feats = torch.cat([k, q, v], dim=1) + return feats + + +if __name__ == "__main__": + vit_arch = 'base' + vit_feat = 'k' + + model = ViTFeat(vit_arch, vit_feat) + img = torch.cuda.FloatTensor(4, 3, 224, 224) + model.cuda() + # Forward pass in the model + feat = model(img) + print (feat.shape) diff --git a/maskcut/dino/__init__.py b/maskcut/dino/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..eb0d6a2338cc4ae729982b8e5f4cce83e09b6440 --- /dev/null +++ b/maskcut/dino/__init__.py @@ -0,0 +1,3 @@ +import sys +from os.path import dirname, join +sys.path.insert(0, join(dirname(__file__), '.')) diff --git a/maskcut/dino/utils.py b/maskcut/dino/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9586250123a125a83ea2679e121b1b0ef8089916 --- /dev/null +++ b/maskcut/dino/utils.py @@ -0,0 +1,829 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Misc functions. + +Mostly copy-paste from torchvision references or other public repos like DETR: +https://github.com/facebookresearch/detr/blob/master/util/misc.py +""" +import os +import sys +import time +import math +import random +import datetime +import subprocess +from collections import defaultdict, deque + +import numpy as np +import torch +from torch import nn +import torch.distributed as dist +from PIL import ImageFilter, ImageOps + + +class GaussianBlur(object): + """ + Apply Gaussian Blur to the PIL image. + """ + def __init__(self, p=0.5, radius_min=0.1, radius_max=2.): + self.prob = p + self.radius_min = radius_min + self.radius_max = radius_max + + def __call__(self, img): + do_it = random.random() <= self.prob + if not do_it: + return img + + return img.filter( + ImageFilter.GaussianBlur( + radius=random.uniform(self.radius_min, self.radius_max) + ) + ) + + +class Solarization(object): + """ + Apply Solarization to the PIL image. + """ + def __init__(self, p): + self.p = p + + def __call__(self, img): + if random.random() < self.p: + return ImageOps.solarize(img) + else: + return img + + +def load_pretrained_weights(model, pretrained_weights, checkpoint_key, model_name, patch_size): + if os.path.isfile(pretrained_weights): + state_dict = torch.load(pretrained_weights, map_location="cpu") + if checkpoint_key is not None and checkpoint_key in state_dict: + print(f"Take key {checkpoint_key} in provided checkpoint dict") + state_dict = state_dict[checkpoint_key] + # remove `module.` prefix + state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()} + # remove `backbone.` prefix induced by multicrop wrapper + state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()} + msg = model.load_state_dict(state_dict, strict=False) + print('Pretrained weights found at {} and loaded with msg: {}'.format(pretrained_weights, msg)) + else: + print("Please use the `--pretrained_weights` argument to indicate the path of the checkpoint to evaluate.") + url = None + if model_name == "vit_small" and patch_size == 16: + url = "dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth" + elif model_name == "vit_small" and patch_size == 8: + url = "dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth" + elif model_name == "vit_base" and patch_size == 16: + url = "dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth" + elif model_name == "vit_base" and patch_size == 8: + url = "dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth" + elif model_name == "xcit_small_12_p16": + url = "dino_xcit_small_12_p16_pretrain/dino_xcit_small_12_p16_pretrain.pth" + elif model_name == "xcit_small_12_p8": + url = "dino_xcit_small_12_p8_pretrain/dino_xcit_small_12_p8_pretrain.pth" + elif model_name == "xcit_medium_24_p16": + url = "dino_xcit_medium_24_p16_pretrain/dino_xcit_medium_24_p16_pretrain.pth" + elif model_name == "xcit_medium_24_p8": + url = "dino_xcit_medium_24_p8_pretrain/dino_xcit_medium_24_p8_pretrain.pth" + elif model_name == "resnet50": + url = "dino_resnet50_pretrain/dino_resnet50_pretrain.pth" + if url is not None: + print("Since no pretrained weights have been provided, we load the reference pretrained DINO weights.") + state_dict = torch.hub.load_state_dict_from_url(url="https://dl.fbaipublicfiles.com/dino/" + url) + model.load_state_dict(state_dict, strict=True) + else: + print("There is no reference weights available for this model => We use random weights.") + + +def load_pretrained_linear_weights(linear_classifier, model_name, patch_size): + url = None + if model_name == "vit_small" and patch_size == 16: + url = "dino_deitsmall16_pretrain/dino_deitsmall16_linearweights.pth" + elif model_name == "vit_small" and patch_size == 8: + url = "dino_deitsmall8_pretrain/dino_deitsmall8_linearweights.pth" + elif model_name == "vit_base" and patch_size == 16: + url = "dino_vitbase16_pretrain/dino_vitbase16_linearweights.pth" + elif model_name == "vit_base" and patch_size == 8: + url = "dino_vitbase8_pretrain/dino_vitbase8_linearweights.pth" + elif model_name == "resnet50": + url = "dino_resnet50_pretrain/dino_resnet50_linearweights.pth" + if url is not None: + print("We load the reference pretrained linear weights.") + state_dict = torch.hub.load_state_dict_from_url(url="https://dl.fbaipublicfiles.com/dino/" + url)["state_dict"] + linear_classifier.load_state_dict(state_dict, strict=True) + else: + print("We use random linear weights.") + + +def clip_gradients(model, clip): + norms = [] + for name, p in model.named_parameters(): + if p.grad is not None: + param_norm = p.grad.data.norm(2) + norms.append(param_norm.item()) + clip_coef = clip / (param_norm + 1e-6) + if clip_coef < 1: + p.grad.data.mul_(clip_coef) + return norms + + +def cancel_gradients_last_layer(epoch, model, freeze_last_layer): + if epoch >= freeze_last_layer: + return + for n, p in model.named_parameters(): + if "last_layer" in n: + p.grad = None + + +def restart_from_checkpoint(ckp_path, run_variables=None, **kwargs): + """ + Re-start from checkpoint + """ + if not os.path.isfile(ckp_path): + return + print("Found checkpoint at {}".format(ckp_path)) + + # open checkpoint file + checkpoint = torch.load(ckp_path, map_location="cpu") + + # key is what to look for in the checkpoint file + # value is the object to load + # example: {'state_dict': model} + for key, value in kwargs.items(): + if key in checkpoint and value is not None: + try: + msg = value.load_state_dict(checkpoint[key], strict=False) + print("=> loaded '{}' from checkpoint '{}' with msg {}".format(key, ckp_path, msg)) + except TypeError: + try: + msg = value.load_state_dict(checkpoint[key]) + print("=> loaded '{}' from checkpoint: '{}'".format(key, ckp_path)) + except ValueError: + print("=> failed to load '{}' from checkpoint: '{}'".format(key, ckp_path)) + else: + print("=> key '{}' not found in checkpoint: '{}'".format(key, ckp_path)) + + # re load variable important for the run + if run_variables is not None: + for var_name in run_variables: + if var_name in checkpoint: + run_variables[var_name] = checkpoint[var_name] + + +def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warmup_epochs=0, start_warmup_value=0): + warmup_schedule = np.array([]) + warmup_iters = warmup_epochs * niter_per_ep + if warmup_epochs > 0: + warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters) + + iters = np.arange(epochs * niter_per_ep - warmup_iters) + schedule = final_value + 0.5 * (base_value - final_value) * (1 + np.cos(np.pi * iters / len(iters))) + + schedule = np.concatenate((warmup_schedule, schedule)) + assert len(schedule) == epochs * niter_per_ep + return schedule + + +def bool_flag(s): + """ + Parse boolean arguments from the command line. + """ + FALSY_STRINGS = {"off", "false", "0"} + TRUTHY_STRINGS = {"on", "true", "1"} + if s.lower() in FALSY_STRINGS: + return False + elif s.lower() in TRUTHY_STRINGS: + return True + else: + raise argparse.ArgumentTypeError("invalid value for a boolean flag") + + +def fix_random_seeds(seed=31): + """ + Fix random seeds. + """ + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + np.random.seed(seed) + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.6f} ({global_avg:.6f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not is_dist_avail_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') + dist.barrier() + dist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value) + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that all processes + have the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.all_reduce(values) + if average: + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format( + type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append( + "{}: {}".format(name, str(meter)) + ) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None): + i = 0 + if not header: + header = '' + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt='{avg:.6f}') + data_time = SmoothedValue(fmt='{avg:.6f}') + space_fmt = ':' + str(len(str(len(iterable)))) + 'd' + if torch.cuda.is_available(): + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}', + 'max mem: {memory:.0f}' + ]) + else: + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}' + ]) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB)) + else: + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time))) + i += 1 + end = time.time() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('{} Total time: {} ({:.6f} s / it)'.format( + header, total_time_str, total_time / len(iterable))) + + +def get_sha(): + cwd = os.path.dirname(os.path.abspath(__file__)) + + def _run(command): + return subprocess.check_output(command, cwd=cwd).decode('ascii').strip() + sha = 'N/A' + diff = "clean" + branch = 'N/A' + try: + sha = _run(['git', 'rev-parse', 'HEAD']) + subprocess.check_output(['git', 'diff'], cwd=cwd) + diff = _run(['git', 'diff-index', 'HEAD']) + diff = "has uncommited changes" if diff else "clean" + branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) + except Exception: + pass + message = f"sha: {sha}, status: {diff}, branch: {branch}" + return message + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop('force', False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def init_distributed_mode(args): + # launched with torch.distributed.launch + if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ['WORLD_SIZE']) + args.gpu = int(os.environ['LOCAL_RANK']) + # launched with submitit on a slurm cluster + elif 'SLURM_PROCID' in os.environ: + args.rank = int(os.environ['SLURM_PROCID']) + args.gpu = args.rank % torch.cuda.device_count() + # launched naively with `python main_dino.py` + # we manually add MASTER_ADDR and MASTER_PORT to env variables + elif torch.cuda.is_available(): + print('Will run the code on one GPU.') + args.rank, args.gpu, args.world_size = 0, 0, 1 + os.environ['MASTER_ADDR'] = '127.0.0.1' + os.environ['MASTER_PORT'] = '29500' + else: + print('Does not support training without GPU.') + sys.exit(1) + + dist.init_process_group( + backend="nccl", + init_method=args.dist_url, + world_size=args.world_size, + rank=args.rank, + ) + + torch.cuda.set_device(args.gpu) + print('| distributed init (rank {}): {}'.format( + args.rank, args.dist_url), flush=True) + dist.barrier() + setup_for_distributed(args.rank == 0) + + +def accuracy(output, target, topk=(1,)): + """Computes the accuracy over the k top predictions for the specified values of k""" + maxk = max(topk) + batch_size = target.size(0) + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.reshape(1, -1).expand_as(pred)) + return [correct[:k].reshape(-1).float().sum(0) * 100. / batch_size for k in topk] + + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): + # type: (Tensor, float, float, float, float) -> Tensor + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +class LARS(torch.optim.Optimizer): + """ + Almost copy-paste from https://github.com/facebookresearch/barlowtwins/blob/main/main.py + """ + def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, eta=0.001, + weight_decay_filter=None, lars_adaptation_filter=None): + defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum, + eta=eta, weight_decay_filter=weight_decay_filter, + lars_adaptation_filter=lars_adaptation_filter) + super().__init__(params, defaults) + + @torch.no_grad() + def step(self): + for g in self.param_groups: + for p in g['params']: + dp = p.grad + + if dp is None: + continue + + if p.ndim != 1: + dp = dp.add(p, alpha=g['weight_decay']) + + if p.ndim != 1: + param_norm = torch.norm(p) + update_norm = torch.norm(dp) + one = torch.ones_like(param_norm) + q = torch.where(param_norm > 0., + torch.where(update_norm > 0, + (g['eta'] * param_norm / update_norm), one), one) + dp = dp.mul(q) + + param_state = self.state[p] + if 'mu' not in param_state: + param_state['mu'] = torch.zeros_like(p) + mu = param_state['mu'] + mu.mul_(g['momentum']).add_(dp) + + p.add_(mu, alpha=-g['lr']) + + +class MultiCropWrapper(nn.Module): + """ + Perform forward pass separately on each resolution input. + The inputs corresponding to a single resolution are clubbed and single + forward is run on the same resolution inputs. Hence we do several + forward passes = number of different resolutions used. We then + concatenate all the output features and run the head forward on these + concatenated features. + """ + def __init__(self, backbone, head): + super(MultiCropWrapper, self).__init__() + # disable layers dedicated to ImageNet labels classification + backbone.fc, backbone.head = nn.Identity(), nn.Identity() + self.backbone = backbone + self.head = head + + def forward(self, x): + # convert to list + if not isinstance(x, list): + x = [x] + idx_crops = torch.cumsum(torch.unique_consecutive( + torch.tensor([inp.shape[-1] for inp in x]), + return_counts=True, + )[1], 0) + start_idx, output = 0, torch.empty(0).to(x[0].device) + for end_idx in idx_crops: + _out = self.backbone(torch.cat(x[start_idx: end_idx])) + # The output is a tuple with XCiT model. See: + # https://github.com/facebookresearch/xcit/blob/master/xcit.py#L404-L405 + if isinstance(_out, tuple): + _out = _out[0] + # accumulate outputs + output = torch.cat((output, _out)) + start_idx = end_idx + # Run the head forward on the concatenated features. + return self.head(output) + + +def get_params_groups(model): + regularized = [] + not_regularized = [] + for name, param in model.named_parameters(): + if not param.requires_grad: + continue + # we do not regularize biases nor Norm parameters + if name.endswith(".bias") or len(param.shape) == 1: + not_regularized.append(param) + else: + regularized.append(param) + return [{'params': regularized}, {'params': not_regularized, 'weight_decay': 0.}] + + +def has_batchnorms(model): + bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm) + for name, module in model.named_modules(): + if isinstance(module, bn_types): + return True + return False + + +class PCA(): + """ + Class to compute and apply PCA. + """ + def __init__(self, dim=256, whit=0.5): + self.dim = dim + self.whit = whit + self.mean = None + + def train_pca(self, cov): + """ + Takes a covariance matrix (np.ndarray) as input. + """ + d, v = np.linalg.eigh(cov) + eps = d.max() * 1e-5 + n_0 = (d < eps).sum() + if n_0 > 0: + d[d < eps] = eps + + # total energy + totenergy = d.sum() + + # sort eigenvectors with eigenvalues order + idx = np.argsort(d)[::-1][:self.dim] + d = d[idx] + v = v[:, idx] + + print("keeping %.2f %% of the energy" % (d.sum() / totenergy * 100.0)) + + # for the whitening + d = np.diag(1. / d**self.whit) + + # principal components + self.dvt = np.dot(d, v.T) + + def apply(self, x): + # input is from numpy + if isinstance(x, np.ndarray): + if self.mean is not None: + x -= self.mean + return np.dot(self.dvt, x.T).T + + # input is from torch and is on GPU + if x.is_cuda: + if self.mean is not None: + x -= torch.cuda.FloatTensor(self.mean) + return torch.mm(torch.cuda.FloatTensor(self.dvt), x.transpose(0, 1)).transpose(0, 1) + + # input if from torch, on CPU + if self.mean is not None: + x -= torch.FloatTensor(self.mean) + return torch.mm(torch.FloatTensor(self.dvt), x.transpose(0, 1)).transpose(0, 1) + + +def compute_ap(ranks, nres): + """ + Computes average precision for given ranked indexes. + Arguments + --------- + ranks : zerro-based ranks of positive images + nres : number of positive images + Returns + ------- + ap : average precision + """ + + # number of images ranked by the system + nimgranks = len(ranks) + + # accumulate trapezoids in PR-plot + ap = 0 + + recall_step = 1. / nres + + for j in np.arange(nimgranks): + rank = ranks[j] + + if rank == 0: + precision_0 = 1. + else: + precision_0 = float(j) / rank + + precision_1 = float(j + 1) / (rank + 1) + + ap += (precision_0 + precision_1) * recall_step / 2. + + return ap + + +def compute_map(ranks, gnd, kappas=[]): + """ + Computes the mAP for a given set of returned results. + Usage: + map = compute_map (ranks, gnd) + computes mean average precsion (map) only + map, aps, pr, prs = compute_map (ranks, gnd, kappas) + computes mean average precision (map), average precision (aps) for each query + computes mean precision at kappas (pr), precision at kappas (prs) for each query + Notes: + 1) ranks starts from 0, ranks.shape = db_size X #queries + 2) The junk results (e.g., the query itself) should be declared in the gnd stuct array + 3) If there are no positive images for some query, that query is excluded from the evaluation + """ + + map = 0. + nq = len(gnd) # number of queries + aps = np.zeros(nq) + pr = np.zeros(len(kappas)) + prs = np.zeros((nq, len(kappas))) + nempty = 0 + + for i in np.arange(nq): + qgnd = np.array(gnd[i]['ok']) + + # no positive images, skip from the average + if qgnd.shape[0] == 0: + aps[i] = float('nan') + prs[i, :] = float('nan') + nempty += 1 + continue + + try: + qgndj = np.array(gnd[i]['junk']) + except: + qgndj = np.empty(0) + + # sorted positions of positive and junk images (0 based) + pos = np.arange(ranks.shape[0])[np.in1d(ranks[:,i], qgnd)] + junk = np.arange(ranks.shape[0])[np.in1d(ranks[:,i], qgndj)] + + k = 0; + ij = 0; + if len(junk): + # decrease positions of positives based on the number of + # junk images appearing before them + ip = 0 + while (ip < len(pos)): + while (ij < len(junk) and pos[ip] > junk[ij]): + k += 1 + ij += 1 + pos[ip] = pos[ip] - k + ip += 1 + + # compute ap + ap = compute_ap(pos, len(qgnd)) + map = map + ap + aps[i] = ap + + # compute precision @ k + pos += 1 # get it to 1-based + for j in np.arange(len(kappas)): + kq = min(max(pos), kappas[j]); + prs[i, j] = (pos <= kq).sum() / kq + pr = pr + prs[i, :] + + map = map / (nq - nempty) + pr = pr / (nq - nempty) + + return map, aps, pr, prs + + +def multi_scale(samples, model): + v = None + for s in [1, 1/2**(1/2), 1/2]: # we use 3 different scales + if s == 1: + inp = samples.clone() + else: + inp = nn.functional.interpolate(samples, scale_factor=s, mode='bilinear', align_corners=False) + feats = model(inp).clone() + if v is None: + v = feats + else: + v += feats + v /= 3 + v /= v.norm() + return v diff --git a/maskcut/dino/vision_transformer.py b/maskcut/dino/vision_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..b174cefee9c39ad2cda61bf2274fa877061d8e8a --- /dev/null +++ b/maskcut/dino/vision_transformer.py @@ -0,0 +1,311 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Mostly copy-paste from timm library. +https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py +""" +import math +from functools import partial + +import torch +import torch.nn as nn + +from utils import trunc_normal_ + + +def drop_path(x, drop_prob: float = 0., training: bool = False): + if drop_prob == 0. or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x, attn + + +class Block(nn.Module): + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x, return_attention=False): + y, attn = self.attn(self.norm1(x)) + if return_attention: + return attn + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + num_patches = (img_size // patch_size) * (img_size // patch_size) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x): + B, C, H, W = x.shape + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class VisionTransformer(nn.Module): + """ Vision Transformer """ + def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., norm_layer=nn.LayerNorm, **kwargs): + super().__init__() + self.num_features = self.embed_dim = embed_dim + + self.patch_embed = PatchEmbed( + img_size=img_size[0], patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + + # Classifier head + self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def interpolate_pos_encoding(self, x, w, h): + npatch = x.shape[1] - 1 + N = self.pos_embed.shape[1] - 1 + if npatch == N and w == h: + return self.pos_embed + class_pos_embed = self.pos_embed[:, 0] + patch_pos_embed = self.pos_embed[:, 1:] + dim = x.shape[-1] + w0 = w // self.patch_embed.patch_size + h0 = h // self.patch_embed.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + w0, h0 = w0 + 0.1, h0 + 0.1 + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2), + scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), + mode='bicubic', + ) + assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) + + def prepare_tokens(self, x): + B, nc, w, h = x.shape + x = self.patch_embed(x) # patch linear embedding + + # add the [CLS] token to the embed patch tokens + cls_tokens = self.cls_token.expand(B, -1, -1) + x = torch.cat((cls_tokens, x), dim=1) + + # add positional encoding to each token + x = x + self.interpolate_pos_encoding(x, w, h) + + return self.pos_drop(x) + + def forward(self, x): + x = self.prepare_tokens(x) + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + return x[:, 0] + + def get_last_selfattention(self, x): + x = self.prepare_tokens(x) + for i, blk in enumerate(self.blocks): + if i < len(self.blocks) - 1: + x = blk(x) + else: + # return attention of the last block + return blk(x, return_attention=True) + + def get_intermediate_layers(self, x, n=1): + x = self.prepare_tokens(x) + # we return the output tokens from the `n` last blocks + output = [] + for i, blk in enumerate(self.blocks): + x = blk(x) + if len(self.blocks) - i <= n: + output.append(self.norm(x)) + return output + + +def vit_tiny(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def vit_small(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def vit_base(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + +def moco_vit_small(**kwargs): + model = VisionTransformer( + patch_size=16, embed_dim=384, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + # model.default_cfg = _cfg() + return model + +def moco_vit_base(**kwargs): + model = VisionTransformer( + patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + #model.default_cfg = _cfg() + return model + +def mae_vit_base(**kwargs): + model = VisionTransformer( + patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +class DINOHead(nn.Module): + def __init__(self, in_dim, out_dim, use_bn=False, norm_last_layer=True, nlayers=3, hidden_dim=2048, bottleneck_dim=256): + super().__init__() + nlayers = max(nlayers, 1) + if nlayers == 1: + self.mlp = nn.Linear(in_dim, bottleneck_dim) + else: + layers = [nn.Linear(in_dim, hidden_dim)] + if use_bn: + layers.append(nn.BatchNorm1d(hidden_dim)) + layers.append(nn.GELU()) + for _ in range(nlayers - 2): + layers.append(nn.Linear(hidden_dim, hidden_dim)) + if use_bn: + layers.append(nn.BatchNorm1d(hidden_dim)) + layers.append(nn.GELU()) + layers.append(nn.Linear(hidden_dim, bottleneck_dim)) + self.mlp = nn.Sequential(*layers) + self.apply(self._init_weights) + self.last_layer = nn.utils.weight_norm(nn.Linear(bottleneck_dim, out_dim, bias=False)) + self.last_layer.weight_g.data.fill_(1) + if norm_last_layer: + self.last_layer.weight_g.requires_grad = False + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x): + x = self.mlp(x) + x = nn.functional.normalize(x, dim=-1, p=2) + x = self.last_layer(x) + return x diff --git a/maskcut/imgs/demo1.jpg b/maskcut/imgs/demo1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..081a1a959fb0e666224d9be8ff869ab6571ddcad --- /dev/null +++ b/maskcut/imgs/demo1.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26791b0963a272f39708b12ab5939372755242d38b6a0b4a16f690f3315d1346 +size 272408 diff --git a/maskcut/imgs/demo2.jpg b/maskcut/imgs/demo2.jpg new file mode 100644 index 0000000000000000000000000000000000000000..a63ff97f8f9a2708b6b49acc133d2916280fdc74 --- /dev/null +++ b/maskcut/imgs/demo2.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fafb2dbcab0fd3b9175d846e10abde31dc8cc15c295425527d75f90f882fe7e5 +size 261297 diff --git a/maskcut/imgs/demo3.jpg b/maskcut/imgs/demo3.jpg new file mode 100644 index 0000000000000000000000000000000000000000..91fc1a5492b0f4412374bbc5de91ed33a5dce186 --- /dev/null +++ b/maskcut/imgs/demo3.jpg @@ -0,0 +1,3 @@ +version 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3609076 diff --git a/maskcut/inference_demo.ipynb b/maskcut/inference_demo.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..755c75041630574d7df81250e0bb0506e9eeab6e --- /dev/null +++ b/maskcut/inference_demo.ipynb @@ -0,0 +1,129 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "TokenCut Demo.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9bxb7gkitwb7" + }, + "outputs": [], + "source": [ + "!git clone https://github.com/YangtaoWANG95/TokenCut\n", + "%cd TokenCut" + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install torch==1.7.1 torchvision==0.8.2\n", + "!pip install -r requirements.txt" + ], + "metadata": { + "id": "ncWpnDLPt6ZZ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Change this URL to any jpg image:\n", + "!cd examples && wget -O test.jpg https://i.imgur.com/G4wpITa.jpg" + ], + "metadata": { + "id": "QmloZNiE_co9" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!python main_tokencut.py --image_path examples/test.jpg --visualize all " + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2v3gHLihujra", + "outputId": "2946b730-6dd0-4632-aa99-bd16f3a4be69" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Since no pretrained weights have been provided, we load the reference pretrained DINO weights.\n", + "Pretrained weights found at dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth and loaded with msg: \n", + "Running TokenCut on the dataset test.jpg (exp: TokenCut-vit_small16_k)\n", + " 0% 0/1 [00:00" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {} + } + ] + } + ] +} diff --git a/maskcut/main_tokencut.py b/maskcut/main_tokencut.py new file mode 100755 index 0000000000000000000000000000000000000000..b24727be821ee0f4a735f543e653ec7c055f4b7f --- /dev/null +++ b/maskcut/main_tokencut.py @@ -0,0 +1,326 @@ +""" +Main experiment file. Code adapted from LOST: https://github.com/valeoai/LOST +""" +import os +import argparse +import random +import pickle + +import torch +import datetime +import torch.nn as nn +import numpy as np + +from tqdm import tqdm +from PIL import Image + +from networks import get_model +from datasets import ImageDataset, Dataset, bbox_iou +from visualizations import visualize_img, visualize_eigvec, visualize_predictions, visualize_predictions_gt +from object_discovery import ncut +import matplotlib.pyplot as plt +import time + +if __name__ == "__main__": + parser = argparse.ArgumentParser("Visualize Self-Attention maps") + parser.add_argument( + "--arch", + default="vit_small", + type=str, + choices=[ + "vit_tiny", + "vit_small", + "vit_base", + "moco_vit_small", + "moco_vit_base", + "mae_vit_base", + ], + help="Model architecture.", + ) + parser.add_argument( + "--patch_size", default=16, type=int, help="Patch resolution of the model." + ) + + # Use a dataset + parser.add_argument( + "--dataset", + default="VOC07", + type=str, + choices=[None, "VOC07", "VOC12", "COCO20k"], + help="Dataset name.", + ) + + parser.add_argument( + "--save-feat-dir", + type=str, + default=None, + help="if save-feat-dir is not None, only computing features and save it into save-feat-dir", + ) + + parser.add_argument( + "--set", + default="train", + type=str, + choices=["val", "train", "trainval", "test"], + help="Path of the image to load.", + ) + # Or use a single image + parser.add_argument( + "--image_path", + type=str, + default=None, + help="If want to apply only on one image, give file path.", + ) + + # Folder used to output visualizations and + parser.add_argument( + "--output_dir", type=str, default="outputs", help="Output directory to store predictions and visualizations." + ) + + # Evaluation setup + parser.add_argument("--no_hard", action="store_true", help="Only used in the case of the VOC_all setup (see the paper).") + parser.add_argument("--no_evaluation", action="store_true", help="Compute the evaluation.") + parser.add_argument("--save_predictions", default=True, type=bool, help="Save predicted bouding boxes.") + + # Visualization + parser.add_argument( + "--visualize", + type=str, + choices=["attn", "pred", "all", None], + default=None, + help="Select the different type of visualizations.", + ) + + # TokenCut parameters + parser.add_argument( + "--which_features", + type=str, + default="k", + choices=["k", "q", "v"], + help="Which features to use", + ) + parser.add_argument( + "--k_patches", + type=int, + default=100, + help="Number of patches with the lowest degree considered." + ) + parser.add_argument("--resize", type=int, default=None, help="Resize input image to fix size") + parser.add_argument("--tau", type=float, default=0.2, help="Tau for seperating the Graph.") + parser.add_argument("--eps", type=float, default=1e-5, help="Eps for defining the Graph.") + parser.add_argument("--no-binary-graph", action="store_true", default=False, help="Generate a binary graph where edge of the Graph will binary. Or using similarity score as edge weight.") + + # Use dino-seg proposed method + parser.add_argument("--dinoseg", action="store_true", help="Apply DINO-seg baseline.") + parser.add_argument("--dinoseg_head", type=int, default=4) + + args = parser.parse_args() + + if args.image_path is not None: + args.save_predictions = False + args.no_evaluation = True + args.dataset = None + + # ------------------------------------------------------------------------------------------------------- + # Dataset + + # If an image_path is given, apply the method only to the image + if args.image_path is not None: + dataset = ImageDataset(args.image_path, args.resize) + else: + dataset = Dataset(args.dataset, args.set, args.no_hard) + + # ------------------------------------------------------------------------------------------------------- + # Model + device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + #device = torch.device('cuda') + model = get_model(args.arch, args.patch_size, device) + + # ------------------------------------------------------------------------------------------------------- + # Directories + if args.image_path is None: + args.output_dir = os.path.join(args.output_dir, dataset.name) + os.makedirs(args.output_dir, exist_ok=True) + + # Naming + if args.dinoseg: + # Experiment with the baseline DINO-seg + if "vit" not in args.arch: + raise ValueError("DINO-seg can only be applied to tranformer networks.") + exp_name = f"{args.arch}-{args.patch_size}_dinoseg-head{args.dinoseg_head}" + else: + # Experiment with TokenCut + exp_name = f"TokenCut-{args.arch}" + if "vit" in args.arch: + exp_name += f"{args.patch_size}_{args.which_features}" + + print(f"Running TokenCut on the dataset {dataset.name} (exp: {exp_name})") + + # Visualization + if args.visualize: + vis_folder = f"{args.output_dir}/{exp_name}" + os.makedirs(vis_folder, exist_ok=True) + + if args.save_feat_dir is not None : + os.mkdir(args.save_feat_dir) + + # ------------------------------------------------------------------------------------------------------- + # Loop over images + preds_dict = {} + cnt = 0 + corloc = np.zeros(len(dataset.dataloader)) + + start_time = time.time() + pbar = tqdm(dataset.dataloader) + for im_id, inp in enumerate(pbar): + + # ------------ IMAGE PROCESSING ------------------------------------------- + img = inp[0] + + init_image_size = img.shape + + # Get the name of the image + im_name = dataset.get_image_name(inp[1]) + # Pass in case of no gt boxes in the image + if im_name is None: + continue + + # Padding the image with zeros to fit multiple of patch-size + size_im = ( + img.shape[0], + int(np.ceil(img.shape[1] / args.patch_size) * args.patch_size), + int(np.ceil(img.shape[2] / args.patch_size) * args.patch_size), + ) + paded = torch.zeros(size_im) + paded[:, : img.shape[1], : img.shape[2]] = img + img = paded + + # # Move to gpu + if device == torch.device('cuda'): + img = img.cuda(non_blocking=True) + # Size for transformers + w_featmap = img.shape[-2] // args.patch_size + h_featmap = img.shape[-1] // args.patch_size + + # ------------ GROUND-TRUTH ------------------------------------------- + if not args.no_evaluation: + gt_bbxs, gt_cls = dataset.extract_gt(inp[1], im_name) + + if gt_bbxs is not None: + # Discard images with no gt annotations + # Happens only in the case of VOC07 and VOC12 + if gt_bbxs.shape[0] == 0 and args.no_hard: + continue + + # ------------ EXTRACT FEATURES ------------------------------------------- + with torch.no_grad(): + + # ------------ FORWARD PASS ------------------------------------------- + if "vit" in args.arch: + # Store the outputs of qkv layer from the last attention layer + feat_out = {} + def hook_fn_forward_qkv(module, input, output): + feat_out["qkv"] = output + model._modules["blocks"][-1]._modules["attn"]._modules["qkv"].register_forward_hook(hook_fn_forward_qkv) + + # Forward pass in the model + attentions = model.get_last_selfattention(img[None, :, :, :]) + + # Scaling factor + scales = [args.patch_size, args.patch_size] + + # Dimensions + nb_im = attentions.shape[0] # Batch size + nh = attentions.shape[1] # Number of heads + nb_tokens = attentions.shape[2] # Number of tokens + + # Baseline: compute DINO segmentation technique proposed in the DINO paper + # and select the biggest component + if args.dinoseg: + pred = dino_seg(attentions, (w_featmap, h_featmap), args.patch_size, head=args.dinoseg_head) + pred = np.asarray(pred) + else: + # Extract the qkv features of the last attention layer + qkv = ( + feat_out["qkv"] + .reshape(nb_im, nb_tokens, 3, nh, -1 // nh) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = qkv[0], qkv[1], qkv[2] + k = k.transpose(1, 2).reshape(nb_im, nb_tokens, -1) + q = q.transpose(1, 2).reshape(nb_im, nb_tokens, -1) + v = v.transpose(1, 2).reshape(nb_im, nb_tokens, -1) + + # Modality selection + if args.which_features == "k": + #feats = k[:, 1:, :] + feats = k + elif args.which_features == "q": + #feats = q[:, 1:, :] + feats = q + elif args.which_features == "v": + #feats = v[:, 1:, :] + feats = v + + if args.save_feat_dir is not None : + np.save(os.path.join(args.save_feat_dir, im_name.replace('.jpg', '.npy').replace('.jpeg', '.npy').replace('.png', '.npy')), feats.cpu().numpy()) + continue + + else: + raise ValueError("Unknown model.") + + # ------------ Apply TokenCut ------------------------------------------- + if not args.dinoseg: + pred, objects, foreground, seed , bins, eigenvector= ncut(feats, [w_featmap, h_featmap], scales, init_image_size, args.tau, args.eps, im_name=im_name, no_binary_graph=args.no_binary_graph) + + if args.visualize == "pred" and args.no_evaluation : + image = dataset.load_image(im_name, size_im) + visualize_predictions(image, pred, vis_folder, im_name) + if args.visualize == "attn" and args.no_evaluation: + visualize_eigvec(eigenvector, vis_folder, im_name, [w_featmap, h_featmap], scales) + if args.visualize == "all" and args.no_evaluation: + image = dataset.load_image(im_name, size_im) + visualize_predictions(image, pred, vis_folder, im_name) + visualize_eigvec(eigenvector, vis_folder, im_name, [w_featmap, h_featmap], scales) + + # ------------ Visualizations ------------------------------------------- + # Save the prediction + preds_dict[im_name] = pred + + # Evaluation + if args.no_evaluation: + continue + + # Compare prediction to GT boxes + ious = bbox_iou(torch.from_numpy(pred), torch.from_numpy(gt_bbxs)) + + if torch.any(ious >= 0.5): + corloc[im_id] = 1 + vis_folder = f"{args.output_dir}/{exp_name}" + os.makedirs(vis_folder, exist_ok=True) + image = dataset.load_image(im_name) + #visualize_predictions(image, pred, vis_folder, im_name) + #visualize_eigvec(eigenvector, vis_folder, im_name, [w_featmap, h_featmap], scales) + + cnt += 1 + if cnt % 50 == 0: + pbar.set_description(f"Found {int(np.sum(corloc))}/{cnt}") + + end_time = time.time() + print(f'Time cost: {str(datetime.timedelta(milliseconds=int((end_time - start_time)*1000)))}') + # Save predicted bounding boxes + if args.save_predictions: + folder = f"{args.output_dir}/{exp_name}" + os.makedirs(folder, exist_ok=True) + filename = os.path.join(folder, "preds.pkl") + with open(filename, "wb") as f: + pickle.dump(preds_dict, f) + print("Predictions saved at %s" % filename) + + # Evaluate + if not args.no_evaluation: + print(f"corloc: {100*np.sum(corloc)/cnt:.2f} ({int(np.sum(corloc))}/{cnt})") + result_file = os.path.join(folder, 'results.txt') + with open(result_file, 'w') as f: + f.write('corloc,%.1f,,\n'%(100*np.sum(corloc)/cnt)) + print('File saved at %s'%result_file) diff --git a/maskcut/maskcut.py b/maskcut/maskcut.py new file mode 100644 index 0000000000000000000000000000000000000000..b46e11fe28650af428f8c830e214ec0b7b1dc393 --- /dev/null +++ b/maskcut/maskcut.py @@ -0,0 +1,416 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. + +import os +import sys +sys.path.append('../') +import argparse +import numpy as np +from tqdm import tqdm +import re +import datetime +import PIL +import PIL.Image as Image +import torch +import torch.nn.functional as F +from torchvision import transforms +from pycocotools import mask +import pycocotools.mask as mask_util +from scipy import ndimage +from scipy.linalg import eigh +import json + +import dino +# modfied by Xudong Wang based on third_party/TokenCut +sys.path.append('../') +sys.path.append('../third_party') +from token_cut.unsupervised_saliency_detection import utils, metric +from token_cut.unsupervised_saliency_detection.object_discovery import detect_box +# bilateral_solver codes are modfied based on https://github.com/poolio/bilateral_solver/blob/master/notebooks/bilateral_solver.ipynb +# from TokenCut.unsupervised_saliency_detection.bilateral_solver import BilateralSolver, BilateralGrid +# crf codes are are modfied based on https://github.com/lucasb-eyer/pydensecrf/blob/master/pydensecrf/tests/test_dcrf.py +from crf import densecrf + +# Image transformation applied to all images +ToTensor = transforms.Compose([transforms.ToTensor(), + transforms.Normalize( + (0.485, 0.456, 0.406), + (0.229, 0.224, 0.225)),]) + +def get_affinity_matrix(feats, tau, eps=1e-5): + # get affinity matrix via measuring patch-wise cosine similarity + feats = F.normalize(feats, p=2, dim=0) + A = (feats.transpose(0,1) @ feats).cpu().numpy() + # convert the affinity matrix to a binary one. + A = A > tau + A = np.where(A.astype(float) == 0, eps, A) + d_i = np.sum(A, axis=1) + D = np.diag(d_i) + return A, D + +def second_smallest_eigenvector(A, D): + # get the second smallest eigenvector from affinity matrix + _, eigenvectors = eigh(D-A, D, subset_by_index=[1,2]) + eigenvec = np.copy(eigenvectors[:, 0]) + second_smallest_vec = eigenvectors[:, 0] + return eigenvec, second_smallest_vec + +def get_salient_areas(second_smallest_vec): + # get the area corresponding to salient objects. + avg = np.sum(second_smallest_vec) / len(second_smallest_vec) + bipartition = second_smallest_vec > avg + return bipartition + +def check_num_fg_corners(bipartition, dims): + # check number of corners belonging to the foreground + bipartition_ = bipartition.reshape(dims) + top_l, top_r, bottom_l, bottom_r = bipartition_[0][0], bipartition_[0][-1], bipartition_[-1][0], bipartition_[-1][-1] + nc = int(top_l) + int(top_r) + int(bottom_l) + int(bottom_r) + return nc + +def get_masked_affinity_matrix(painting, feats, mask, ps): + # mask out affinity matrix based on the painting matrix + dim, num_patch = feats.size()[0], feats.size()[1] + painting = painting + mask.unsqueeze(0) + painting[painting > 0] = 1 + painting[painting <= 0] = 0 + feats = feats.clone().view(dim, ps, ps) + feats = ((1 - painting) * feats).view(dim, num_patch) + return feats, painting + +def maskcut_forward(feats, dims, scales, init_image_size, tau=0, N=3, cpu=False): + """ + Implementation of MaskCut. + Inputs + feats: the pixel/patche features of an image + dims: dimension of the map from which the features are used + scales: from image to map scale + init_image_size: size of the image + tau: thresold for graph construction + N: number of pseudo-masks per image. + """ + bipartitions = [] + eigvecs = [] + + for i in range(N): + if i == 0: + painting = torch.from_numpy(np.zeros(dims)) + if not cpu: painting = painting.cuda() + else: + feats, painting = get_masked_affinity_matrix(painting, feats, current_mask, ps) + + # construct the affinity matrix + A, D = get_affinity_matrix(feats, tau) + # get the second smallest eigenvector + eigenvec, second_smallest_vec = second_smallest_eigenvector(A, D) + # get salient area + bipartition = get_salient_areas(second_smallest_vec) + + # check if we should reverse the partition based on: + # 1) peak of the 2nd smallest eigvec 2) object centric bias + seed = np.argmax(np.abs(second_smallest_vec)) + nc = check_num_fg_corners(bipartition, dims) + if nc >= 3: + reverse = True + else: + reverse = bipartition[seed] != 1 + + if reverse: + # reverse bipartition, eigenvector and get new seed + eigenvec = eigenvec * -1 + bipartition = np.logical_not(bipartition) + seed = np.argmax(eigenvec) + else: + seed = np.argmax(second_smallest_vec) + + # get pxiels corresponding to the seed + bipartition = bipartition.reshape(dims).astype(float) + _, _, _, cc = detect_box(bipartition, seed, dims, scales=scales, initial_im_size=init_image_size) + pseudo_mask = np.zeros(dims) + pseudo_mask[cc[0],cc[1]] = 1 + pseudo_mask = torch.from_numpy(pseudo_mask) + if not cpu: pseudo_mask = pseudo_mask.to('cuda') + ps = pseudo_mask.shape[0] + + # check if the extra mask is heavily overlapped with the previous one or is too small. + if i >= 1: + ratio = torch.sum(pseudo_mask) / pseudo_mask.size()[0] / pseudo_mask.size()[1] + if metric.IoU(current_mask, pseudo_mask) > 0.5 or ratio <= 0.01: + pseudo_mask = np.zeros(dims) + pseudo_mask = torch.from_numpy(pseudo_mask) + if not cpu: pseudo_mask = pseudo_mask.to('cuda') + current_mask = pseudo_mask + + # mask out foreground areas in previous stages + masked_out = 0 if len(bipartitions) == 0 else np.sum(bipartitions, axis=0) + bipartition = F.interpolate(pseudo_mask.unsqueeze(0).unsqueeze(0), size=init_image_size, mode='nearest').squeeze() + bipartition_masked = bipartition.cpu().numpy() - masked_out + bipartition_masked[bipartition_masked <= 0] = 0 + bipartitions.append(bipartition_masked) + + # unsample the eigenvec + eigvec = second_smallest_vec.reshape(dims) + eigvec = torch.from_numpy(eigvec) + if not cpu: eigvec = eigvec.to('cuda') + eigvec = F.interpolate(eigvec.unsqueeze(0).unsqueeze(0), size=init_image_size, mode='nearest').squeeze() + eigvecs.append(eigvec.cpu().numpy()) + + return seed, bipartitions, eigvecs + +def maskcut(img_path, backbone,patch_size, tau, N=1, fixed_size=480, cpu=False) : + I = Image.open(img_path).convert('RGB') + bipartitions, eigvecs = [], [] + + I_new = I.resize((int(fixed_size), int(fixed_size)), PIL.Image.LANCZOS) + I_resize, w, h, feat_w, feat_h = utils.resize_pil(I_new, patch_size) + + tensor = ToTensor(I_resize).unsqueeze(0) + if not cpu: tensor = tensor.cuda() + feat = backbone(tensor)[0] + + _, bipartition, eigvec = maskcut_forward(feat, [feat_h, feat_w], [patch_size, patch_size], [h,w], tau, N=N, cpu=cpu) + + bipartitions += bipartition + eigvecs += eigvec + + return bipartitions, eigvecs, I_new + +def resize_binary_mask(array, new_size): + image = Image.fromarray(array.astype(np.uint8)*255) + image = image.resize(new_size) + return np.asarray(image).astype(np.bool_) + +def close_contour(contour): + if not np.array_equal(contour[0], contour[-1]): + contour = np.vstack((contour, contour[0])) + return contour + + +def create_image_info(image_id, file_name, image_size, + date_captured=datetime.datetime.utcnow().isoformat(' '), + license_id=1, coco_url="", flickr_url=""): + """Return image_info in COCO style + Args: + image_id: the image ID + file_name: the file name of each image + image_size: image size in the format of (width, height) + date_captured: the date this image info is created + license: license of this image + coco_url: url to COCO images if there is any + flickr_url: url to flickr if there is any + """ + image_info = { + "id": image_id, + "file_name": file_name, + "width": image_size[0], + "height": image_size[1], + "date_captured": date_captured, + "license": license_id, + "coco_url": coco_url, + "flickr_url": flickr_url + } + return image_info + + +def create_annotation_info(annotation_id, image_id, category_info, binary_mask, + image_size=None, bounding_box=None): + """Return annotation info in COCO style + Args: + annotation_id: the annotation ID + image_id: the image ID + category_info: the information on categories + binary_mask: a 2D binary numpy array where '1's represent the object + file_name: the file name of each image + image_size: image size in the format of (width, height) + bounding_box: the bounding box for detection task. If bounding_box is not provided, + we will generate one according to the binary mask. + """ + upper = np.max(binary_mask) + lower = np.min(binary_mask) + thresh = upper / 2.0 + binary_mask[binary_mask > thresh] = upper + binary_mask[binary_mask <= thresh] = lower + if image_size is not None: + binary_mask = resize_binary_mask(binary_mask.astype(np.uint8), image_size) + + binary_mask_encoded = mask.encode(np.asfortranarray(binary_mask.astype(np.uint8))) + + area = mask.area(binary_mask_encoded) + if area < 1: + return None + + if bounding_box is None: + bounding_box = mask.toBbox(binary_mask_encoded) + + rle = mask_util.encode(np.array(binary_mask[...,None], order="F", dtype="uint8"))[0] + rle['counts'] = rle['counts'].decode('ascii') + segmentation = rle + + annotation_info = { + "id": annotation_id, + "image_id": image_id, + "category_id": category_info["id"], + "iscrowd": 0, + "area": area.tolist(), + "bbox": bounding_box.tolist(), + "segmentation": segmentation, + "width": binary_mask.shape[1], + "height": binary_mask.shape[0], + } + + return annotation_info + +# necessay info used for coco style annotations +INFO = { + "description": "ImageNet-1K: pseudo-masks with MaskCut", + "url": "https://github.com/facebookresearch/CutLER", + "version": "1.0", + "year": 2023, + "contributor": "Xudong Wang", + "date_created": datetime.datetime.utcnow().isoformat(' ') +} + +LICENSES = [ + { + "id": 1, + "name": "Apache License", + "url": "https://github.com/facebookresearch/CutLER/blob/main/LICENSE" + } +] + +# only one class, i.e. foreground +CATEGORIES = [ + { + 'id': 1, + 'name': 'fg', + 'supercategory': 'fg', + }, +] + +convert = lambda text: int(text) if text.isdigit() else text.lower() +natrual_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ] + +output = { + "info": INFO, + "licenses": LICENSES, + "categories": CATEGORIES, + "images": [], + "annotations": []} + +category_info = { + "is_crowd": 0, + "id": 1 +} + +if __name__ == "__main__": + + parser = argparse.ArgumentParser('MaskCut script') + # default arguments + parser.add_argument('--out-dir', type=str, help='output directory') + parser.add_argument('--vit-arch', type=str, default='small', choices=['base', 'small'], help='which architecture') + parser.add_argument('--vit-feat', type=str, default='k', choices=['k', 'q', 'v', 'kqv'], help='which features') + parser.add_argument('--patch-size', type=int, default=16, choices=[16, 8], help='patch size') + parser.add_argument('--nb-vis', type=int, default=20, choices=[1, 200], help='nb of visualization') + parser.add_argument('--img-path', type=str, default=None, help='single image visualization') + + # additional arguments + parser.add_argument('--dataset-path', type=str, default="imagenet/train/", help='path to the dataset') + parser.add_argument('--tau', type=float, default=0.2, help='threshold used for producing binary graph') + parser.add_argument('--num-folder-per-job', type=int, default=1, help='the number of folders each job processes') + parser.add_argument('--job-index', type=int, default=0, help='the index of the job (for imagenet: in the range of 0 to 1000/args.num_folder_per_job-1)') + parser.add_argument('--fixed_size', type=int, default=480, help='rescale the input images to a fixed size') + parser.add_argument('--pretrain_path', type=str, default=None, help='path to pretrained model') + parser.add_argument('--N', type=int, default=3, help='the maximum number of pseudo-masks per image') + parser.add_argument('--cpu', action='store_true', help='use cpu') + + args = parser.parse_args() + + if args.pretrain_path is not None: + url = args.pretrain_path + if args.vit_arch == 'base' and args.patch_size == 8: + if args.pretrain_path is None: + url = "https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth" + feat_dim = 768 + elif args.vit_arch == 'small' and args.patch_size == 8: + if args.pretrain_path is None: + url = "https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_300ep_pretrain/dino_deitsmall8_300ep_pretrain.pth" + feat_dim = 384 + + backbone = dino.ViTFeat(url, feat_dim, args.vit_arch, args.vit_feat, args.patch_size) + + msg = 'Load {} pre-trained feature...'.format(args.vit_arch) + print (msg) + backbone.eval() + if not args.cpu: + backbone.cuda() + + img_folders = os.listdir(args.dataset_path) + + if args.out_dir is not None and not os.path.exists(args.out_dir) : + os.mkdir(args.out_dir) + + start_idx = max(args.job_index*args.num_folder_per_job, 0) + end_idx = min((args.job_index+1)*args.num_folder_per_job, len(img_folders)) + + image_id, segmentation_id = 1, 1 + image_names = [] + for img_folder in img_folders[start_idx:end_idx]: + args.img_dir = os.path.join(args.dataset_path, img_folder) + img_list = sorted(os.listdir(args.img_dir)) + + for img_name in tqdm(img_list) : + # get image path + img_path = os.path.join(args.img_dir, img_name) + # get pseudo-masks for each image using MaskCut + try: + bipartitions, _, I_new = maskcut(img_path, backbone, args.patch_size, \ + args.tau, N=args.N, fixed_size=args.fixed_size, cpu=args.cpu) + except: + print(f'Skipping {img_name}') + continue + + I = Image.open(img_path).convert('RGB') + width, height = I.size + for idx, bipartition in enumerate(bipartitions): + # post-process pesudo-masks with CRF + pseudo_mask = densecrf(np.array(I_new), bipartition) + pseudo_mask = ndimage.binary_fill_holes(pseudo_mask>=0.5) + + # filter out the mask that have a very different pseudo-mask after the CRF + mask1 = torch.from_numpy(bipartition) + mask2 = torch.from_numpy(pseudo_mask) + if not args.cpu: + mask1 = mask1.cuda() + mask2 = mask2.cuda() + if metric.IoU(mask1, mask2) < 0.5: + pseudo_mask = pseudo_mask * -1 + + # construct binary pseudo-masks + pseudo_mask[pseudo_mask < 0] = 0 + pseudo_mask = Image.fromarray(np.uint8(pseudo_mask*255)) + pseudo_mask = np.asarray(pseudo_mask.resize((width, height))) + + # create coco-style image info + if img_name not in image_names: + image_info = create_image_info( + image_id, "{}/{}".format(img_folder, img_name), (height, width, 3)) + output["images"].append(image_info) + image_names.append(img_name) + + # create coco-style annotation info + annotation_info = create_annotation_info( + segmentation_id, image_id, category_info, pseudo_mask.astype(np.uint8), None) + if annotation_info is not None: + output["annotations"].append(annotation_info) + segmentation_id += 1 + image_id += 1 + + # save annotations + if len(img_folders) == args.num_folder_per_job and args.job_index == 0: + json_name = '{}/imagenet_train_fixsize{}_tau{}_N{}.json'.format(args.out_dir, args.fixed_size, args.tau, args.N) + else: + json_name = '{}/imagenet_train_fixsize{}_tau{}_N{}_{}_{}.json'.format(args.out_dir, args.fixed_size, args.tau, args.N, start_idx, end_idx) + with open(json_name, 'w') as output_json_file: + json.dump(output, output_json_file) + print(f'dumping {json_name}') + print("Done: {} images; {} anns.".format(len(output['images']), len(output['annotations']))) \ No newline at end of file diff --git a/maskcut/maskcut_with_submitit.py b/maskcut/maskcut_with_submitit.py new file mode 100644 index 0000000000000000000000000000000000000000..caed08aa15ef731da09693541f22bd0ec346213e --- /dev/null +++ b/maskcut/maskcut_with_submitit.py @@ -0,0 +1,405 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. + +#!/usr/bin/env python3 +import os +import sys +sys.path.append('../') +import argparse +import numpy as np +from tqdm import tqdm +import re +import datetime +import PIL +import PIL.Image as Image +import torch +import torch.nn.functional as F +from torchvision import transforms +from itertools import groupby +from pycocotools import mask +import pycocotools.mask as mask_util +from scipy import ndimage +from scipy.linalg import eigh +import json + +import dino # model +from third_party.token_cut.unsupervised_saliency_detection import utils, metric +from third_party.token_cut.unsupervised_saliency_detection.object_discovery import detect_box +# Modified by XuDong Wang from third_party/TokenCut +# bilateral_solver codes are modfied based on https://github.com/poolio/bilateral_solver/blob/master/notebooks/bilateral_solver.ipynb +# from third_party.TokenCut.unsupervised_saliency_detection.bilateral_solver import BilateralSolver, BilateralGrid +# crf codes are are modfied based on https://github.com/lucasb-eyer/pydensecrf/blob/master/pydensecrf/tests/test_dcrf.py +from crf import densecrf + +# Image transformation applied to all images +ToTensor = transforms.Compose([transforms.ToTensor(), + transforms.Normalize( + (0.485, 0.456, 0.406), + (0.229, 0.224, 0.225)),]) + +def get_affinity_matrix(feats, tau, eps=1e-5): + # get affinity matrix via measuring patch-wise cosine similarity + feats = F.normalize(feats, p=2, dim=0) + A = (feats.transpose(0,1) @ feats).cpu().numpy() + # convert the affinity matrix to a binary one. + A = A > tau + A = np.where(A.astype(float) == 0, eps, A) + d_i = np.sum(A, axis=1) + D = np.diag(d_i) + return A, D + +def second_smallest_eigenvector(A, D): + # get the second smallest eigenvector from affinity matrix + _, eigenvectors = eigh(D-A, D, subset_by_index=[1,2]) + eigenvec = np.copy(eigenvectors[:, 0]) + second_smallest_vec = eigenvectors[:, 0] + return eigenvec, second_smallest_vec + +def get_salient_areas(second_smallest_vec): + # get the area corresponding to salient objects. + avg = np.sum(second_smallest_vec) / len(second_smallest_vec) + bipartition = second_smallest_vec > avg + return bipartition + +def check_num_fg_corners(bipartition, dims): + # check number of corners belonging to the foreground + bipartition_ = bipartition.reshape(dims) + top_l, top_r, bottom_l, bottom_r = bipartition_[0][0], bipartition_[0][-1], bipartition_[-1][0], bipartition_[-1][-1] + nc = int(top_l) + int(top_r) + int(bottom_l) + int(bottom_r) + return nc + +def get_masked_affinity_matrix(painting, feats, mask, ps): + # mask out affinity matrix based on the painting matrix + dim, num_patch = feats.size()[0], feats.size()[1] + painting = painting + mask.unsqueeze(0) + painting[painting > 0] = 1 + painting[painting <= 0] = 0 + feats = feats.clone().view(dim, ps, ps) + feats = ((1 - painting) * feats).view(dim, num_patch) + return feats, painting + +def maskcut_forward(feats, dims, scales, init_image_size, tau=0, N=3): + """ + Implementation of MaskCut. + Inputs + feats: the pixel/patche features of an image + dims: dimension of the map from which the features are used + scales: from image to map scale + init_image_size: size of the image + tau: thresold for graph construction + N: number of pseudo-masks per image. + """ + bipartitions = [] + eigvecs = [] + + for i in range(N): + if i == 0: + painting = torch.from_numpy(np.zeros(dims)).cuda() + else: + feats, painting = get_masked_affinity_matrix(painting, feats, current_mask, ps) + + # construct the affinity matrix + A, D = get_affinity_matrix(feats, tau) + # get the second smallest eigenvector + eigenvec, second_smallest_vec = second_smallest_eigenvector(A, D) + # get salient area + bipartition = get_salient_areas(second_smallest_vec) + + # check if we should reverse the partition based on: + # 1) peak of the 2nd smallest eigvec 2) object centric bias + seed = np.argmax(np.abs(second_smallest_vec)) + nc = check_num_fg_corners(bipartition, dims) + if nc >= 3: + reverse = True + else: + reverse = bipartition[seed] != 1 + + if reverse: + # reverse bipartition, eigenvector and get new seed + eigenvec = eigenvec * -1 + bipartition = np.logical_not(bipartition) + seed = np.argmax(eigenvec) + else: + seed = np.argmax(second_smallest_vec) + + # get pxiels corresponding to the seed + bipartition = bipartition.reshape(dims).astype(float) + _, _, _, cc = detect_box(bipartition, seed, dims, scales=scales, initial_im_size=init_image_size) ## We only extract the principal object BBox + pseudo_mask = np.zeros(dims) + pseudo_mask[cc[0],cc[1]] = 1 + pseudo_mask = torch.from_numpy(pseudo_mask).to('cuda') + ps = pseudo_mask.shape[0] + + # check if the extra mask is heavily overlapped with the previous one or is too small. + if i >= 1: + ratio = torch.sum(pseudo_mask) / pseudo_mask.size()[0] / pseudo_mask.size()[1] + if metric.IoU(current_mask, pseudo_mask) > 0.5 or ratio <= 0.01: + pseudo_mask = np.zeros(dims) + pseudo_mask = torch.from_numpy(pseudo_mask).to('cuda') + current_mask = pseudo_mask + + # mask out foreground areas in previous stages + masked_out = 0 if len(bipartitions) == 0 else np.sum(bipartitions, axis=0) + bipartition = F.interpolate(pseudo_mask.unsqueeze(0).unsqueeze(0), size=init_image_size, mode='nearest').squeeze() + bipartition_masked = bipartition.cpu().numpy() - masked_out + bipartition_masked[bipartition_masked <= 0] = 0 + bipartitions.append(bipartition_masked) + + # unsample the eigenvec + eigvec = second_smallest_vec.reshape(dims) + eigvec = torch.from_numpy(eigvec).to('cuda') + eigvec = F.interpolate(eigvec.unsqueeze(0).unsqueeze(0), size=init_image_size, mode='nearest').squeeze() + eigvecs.append(eigvec.cpu().numpy()) + + return seed, bipartitions, eigvecs + +def maskcut(img_path, backbone,patch_size, tau, N=1, fixed_size=480) : + I = Image.open(img_path).convert('RGB') + bipartitions, eigvecs = [], [] + + I_new = I.resize((int(fixed_size), int(fixed_size)), PIL.Image.LANCZOS) + I_resize, w, h, feat_w, feat_h = utils.resize_pil(I_new, patch_size) + + tensor = ToTensor(I_resize).unsqueeze(0).cuda() + feat = backbone(tensor)[0] + + _, bipartition, eigvec = maskcut_forward(feat, [feat_h, feat_w], [patch_size, patch_size], [h,w], tau, N=N) + + bipartitions += bipartition + eigvecs += eigvec + + return bipartitions, eigvecs, I_new + +def resize_binary_mask(array, new_size): + image = Image.fromarray(array.astype(np.uint8)*255) + image = image.resize(new_size) + return np.asarray(image).astype(np.bool_) + +def close_contour(contour): + if not np.array_equal(contour[0], contour[-1]): + contour = np.vstack((contour, contour[0])) + return contour + +def create_image_info(image_id, file_name, image_size, + date_captured=datetime.datetime.utcnow().isoformat(' '), + license_id=1, coco_url="", flickr_url=""): + """Return image_info in COCO style + Args: + image_id: the image ID + file_name: the file name of each image + image_size: image size in the format of (width, height) + date_captured: the date this image info is created + license: license of this image + coco_url: url to COCO images if there is any + flickr_url: url to flickr if there is any + """ + image_info = { + "id": image_id, + "file_name": file_name, + "width": image_size[0], + "height": image_size[1], + "date_captured": date_captured, + "license": license_id, + "coco_url": coco_url, + "flickr_url": flickr_url + } + return image_info + + +def create_annotation_info(annotation_id, image_id, category_info, binary_mask, + image_size=None, bounding_box=None): + """Return annotation info in COCO style + Args: + annotation_id: the annotation ID + image_id: the image ID + category_info: the information on categories + binary_mask: a 2D binary numpy array where '1's represent the object + file_name: the file name of each image + image_size: image size in the format of (width, height) + bounding_box: the bounding box for detection task. If bounding_box is not provided, + we will generate one according to the binary mask. + """ + upper = np.max(binary_mask) + lower = np.min(binary_mask) + thresh = upper / 2.0 + binary_mask[binary_mask > thresh] = upper + binary_mask[binary_mask <= thresh] = lower + if image_size is not None: + binary_mask = resize_binary_mask(binary_mask.astype(np.uint8), image_size) + + binary_mask_encoded = mask.encode(np.asfortranarray(binary_mask.astype(np.uint8))) + + area = mask.area(binary_mask_encoded) + if area < 1: + return None + + if bounding_box is None: + bounding_box = mask.toBbox(binary_mask_encoded) + + rle = mask_util.encode(np.array(binary_mask[...,None], order="F", dtype="uint8"))[0] + rle['counts'] = rle['counts'].decode('ascii') + segmentation = rle + + annotation_info = { + "id": annotation_id, + "image_id": image_id, + "category_id": category_info["id"], + "iscrowd": 0, + "area": area.tolist(), + "bbox": bounding_box.tolist(), + "segmentation": segmentation, + "width": binary_mask.shape[1], + "height": binary_mask.shape[0], + } + + return annotation_info + +# necessay info used for coco style annotations +INFO = { + "description": "ImageNet-1K: pseudo-masks with MaskCut", + "url": "https://github.com/facebookresearch/CutLER", + "version": "1.0", + "year": 2022, + "contributor": "Xudong Wang", + "date_created": datetime.datetime.utcnow().isoformat(' ') +} + +LICENSES = [ + { + "id": 1, + "name": "Apache License", + "url": "https://github.com/facebookresearch/CutLER/blob/main/LICENSE" + } +] + +# only one class, i.e. foreground +CATEGORIES = [ + { + 'id': 1, + 'name': 'fg', + 'supercategory': 'fg', + }, +] + +convert = lambda text: int(text) if text.isdigit() else text.lower() +natrual_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ] + +output = { + "info": INFO, + "licenses": LICENSES, + "categories": CATEGORIES, + "images": [], + "annotations": []} + +category_info = { + "is_crowd": 0, + "id": 1 +} + +def get_args_parser(): + parser = argparse.ArgumentParser('MaskCut script', add_help=False) + # default arguments + parser.add_argument('--out-dir', type=str, help='output directory') + parser.add_argument('--vit-arch', type=str, default='small', choices=['base', 'small'], help='which architecture') + parser.add_argument('--vit-feat', type=str, default='k', choices=['k', 'q', 'v', 'kqv'], help='which features') + parser.add_argument('--patch-size', type=int, default=16, choices=[16, 8], help='patch size') + parser.add_argument('--nb-vis', type=int, default=20, choices=[1, 200], help='nb of visualization') + parser.add_argument('--img-path', type=str, default=None, help='single image visualization') + + # additional arguments + parser.add_argument('--dataset-path', type=str, default="imagenet/train/", help='path to the dataset') + parser.add_argument('--tau', type=float, default=0.2, help='threshold used for producing binary graph') + parser.add_argument('--num-folder-per-job', type=int, default=1, help='the number of folders each job processes') + parser.add_argument('--job-index', type=int, default=0, help='the index of the job (for imagenet: in the range of 0 to 1000/args.num_folder_per_job-1)') + parser.add_argument('--fixed_size', type=int, default=480, help='rescale the input images to a fixed size') + parser.add_argument('--pretrain_path', type=str, default=None, help='path to pretrained model') + parser.add_argument('--N', type=int, default=3, help='the maximum number of pseudo-masks per image') + return parser + +## feature net +def main(args): + if args.pretrain_path is not None: + url = args.pretrain_path + if args.vit_arch == 'base' and args.patch_size == 8: + if args.pretrain_path is None: + url = "https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth" + feat_dim = 768 + elif args.vit_arch == 'small' and args.patch_size == 8: + if args.pretrain_path is None: + url = "https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_300ep_pretrain/dino_deitsmall8_300ep_pretrain.pth" + feat_dim = 384 + + backbone = dino.ViTFeat(url, feat_dim, args.vit_arch, args.vit_feat, args.patch_size) + + msg = 'Load {} pre-trained feature...'.format(args.vit_arch) + print (msg) + backbone.eval() + backbone.cuda() + + img_folders = os.listdir(args.dataset_path) + + if args.out_dir is not None and not os.path.exists(args.out_dir) : + os.mkdir(args.out_dir) + + start_idx = max(args.job_index*args.num_folder_per_job, 0) + end_idx = min((args.job_index+1)*args.num_folder_per_job, len(img_folders)) + + image_id, segmentation_id = 1, 1 + image_names = [] + for img_folder in img_folders[start_idx:end_idx]: + args.img_dir = os.path.join(args.dataset_path, img_folder) + img_list = sorted(os.listdir(args.img_dir)) + for img_name in tqdm(img_list) : + if image_id >= 20: + break + # get image path + img_path = os.path.join(args.img_dir, img_name) + # get bipartitions for each image + try: + bipartitions, _, I_new = maskcut(img_path, backbone, args.patch_size, args.tau, \ + N=args.N, fixed_size=args.fixed_size) + except: + print(f'Skipping {img_name}') + continue + + I = Image.open(img_path).convert('RGB') + width, height = I.size + for idx, bipartition in enumerate(bipartitions): + # post-process pesudo-masks with CRF + pseudo_mask = densecrf(np.array(I_new), bipartition) + pseudo_mask = ndimage.binary_fill_holes(pseudo_mask>=0.5) + + # filter out the mask that have a very different pseudo-mask after the CRF + mask1 = torch.from_numpy(bipartition).cuda() + mask2 = torch.from_numpy(pseudo_mask).cuda() + if metric.IoU(mask1, mask2) < 0.5: + pseudo_mask = pseudo_mask * -1 + + # construct binary pseudo-masks + pseudo_mask[pseudo_mask < 0] = 0 + pseudo_mask = Image.fromarray(np.uint8(pseudo_mask*255)) + pseudo_mask = np.asarray(pseudo_mask.resize((width, height))) + + # create coco-style image info + if img_name not in image_names: + image_info = create_image_info( + image_id, "{}/{}".format(img_folder, img_name), (height, width, 3)) + output["images"].append(image_info) + image_names.append(img_name) + + # create coco-style annotation info + annotation_info = create_annotation_info( + segmentation_id, image_id, category_info, pseudo_mask.astype(np.uint8), None) + if annotation_info is not None: + output["annotations"].append(annotation_info) + segmentation_id += 1 + image_id += 1 + + # dump annotations + if len(img_folders) == args.num_folder_per_job and args.job_index == 0: + json_name = '{}/imagenet_train_fixsize{}_tau{}_N{}.json'.format(args.out_dir, args.fixed_size, args.tau, args.N) + else: + json_name = '{}/imagenet_train_fixsize{}_tau{}_N{}_{}_{}.json'.format(args.out_dir, args.fixed_size, args.tau, args.N, start_idx, end_idx) + with open(json_name, 'w') as output_json_file: + json.dump(output, output_json_file) + print(f'dumping {json_name}') + print("Done: {} images; {} anns.".format(len(output['images']), len(output['annotations']))) diff --git a/maskcut/merge_jsons.py b/maskcut/merge_jsons.py new file mode 100644 index 0000000000000000000000000000000000000000..d50eaacc01e4ebb0e09d5a8d5dad4ed3df33702f --- /dev/null +++ b/maskcut/merge_jsons.py @@ -0,0 +1,98 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# merge all ImageNet annotation files as a single one. + +import os +import json +import argparse + +if __name__ == "__main__": + # load model arguments + parser = argparse.ArgumentParser(description='Merge json files') + parser.add_argument('--base-dir', type=str, + default='annotations/', + help='Dir to the generated annotation files with MaskCut') + parser.add_argument('--save-path', type=str, default="imagenet_train_fixsize480_tau0.15_N3.json", + help='Path to save the merged annotation file') + # following arguments should be consistent with maskcut.py or maskcut_with_submitit.py (if use submitit) + parser.add_argument('--num-folder-per-job', type=int, default=1, + help='Number of folders per json file') + parser.add_argument('--fixed-size', type=int, default=480, + help='rescale the input images to a fixed size') + parser.add_argument('--tau', type=float, default=0.15, help='threshold used for producing binary graph') + parser.add_argument('--N', type=int, default=3, help='the maximum number of pseudo-masks per image') + + args = parser.parse_args() + + base_name = 'imagenet_train_fixsize{}_tau{}_N{}'.format(args.fixed_size, args.tau, args.N) + + start_idx = 0 + every_k = args.num_folder_per_job + missed_folders = [] + tobe_merged_ann_dicts = [] + + # check if pseudo-masks for all 1000 ImageNet-1K folders are avaliable. + while start_idx < 1000: + end_idx = start_idx + every_k + filename = "{}_{}_{}.json".format(base_name, start_idx, end_idx) + tobe_merged = os.path.join(args.base_dir, filename) + if not os.path.isfile(tobe_merged): + end_idx = start_idx + 1 + tobe_merged_ = "{}_{}_{}.json".format(base_name, start_idx, end_idx) + if not os.path.isfile(tobe_merged_): + missed_folders.append(start_idx) + start_idx += 1 + continue + else: + tobe_merged = tobe_merged_ + start_idx += 1 + else: + start_idx += every_k + tobe_merged_ann_dict = json.load(open(tobe_merged)) + tobe_merged_ann_dicts.append(tobe_merged_ann_dict) + + print("Warning: these folders are not found: ", missed_folders) + + # filter out repeated image info + for idx, ann_dict in enumerate(tobe_merged_ann_dicts): + images = [] + images_ids = [] + for image in ann_dict['images']: + if image['id'] in images_ids: + continue + else: + images.append(image) + images_ids.append(image['id']) + ann_dict['images'] = images + + # re-generate image_id and segment_id, and combine annotation info and image info + # from all annotation files + base_ann_dict = tobe_merged_ann_dicts[0] + image_id = base_ann_dict['images'][-1]['id'] + 1 + segment_id = base_ann_dict['annotations'][-1]['id'] + 1 + segment_id_list = [ann['id'] for ann in base_ann_dict['annotations']] + for tobe_merged_ann_dict in tobe_merged_ann_dicts[1:]: + file_name_and_id = {} + for i, image in enumerate(tobe_merged_ann_dict['images']): + file_name_and_id[str(image['id'])] = image_id + image['id'] = image_id + base_ann_dict['images'].append(image) + image_id = image_id + 1 + + for i, annotation_info in enumerate(tobe_merged_ann_dict['annotations']): + annotation_info["image_id"] = file_name_and_id[str(annotation_info["image_id"])] + annotation_info["id"] = segment_id + annotation_info["iscrowd"] = 0 + segment_id_list.append(segment_id) + base_ann_dict['annotations'].append(annotation_info) + segment_id = segment_id + 1 + + segment_id = 1 + for ann in base_ann_dict['annotations']: + ann["id"] = segment_id + segment_id += 1 + + # save the final json file. + anns = [ann['id'] for ann in base_ann_dict['annotations']] + anns_image_id = [ann['image_id'] for ann in base_ann_dict['annotations']] + json.dump(base_ann_dict, open(args.save_path, 'w')) + print("Done: {} images; {} anns.".format(len(base_ann_dict['images']), len(base_ann_dict['annotations']))) diff --git a/maskcut/networks.py b/maskcut/networks.py new file mode 100755 index 0000000000000000000000000000000000000000..844a3b442fc1a88477052a7c2e1faf62acf9ae8a --- /dev/null +++ b/maskcut/networks.py @@ -0,0 +1,87 @@ +""" +Loads model. +Code adapted from LOST: https://github.com/valeoai/LOST +""" + +import torch +import torch.nn as nn + +from torchvision.models.resnet import resnet50 +from torchvision.models.vgg import vgg16 + +import dino.vision_transformer as vits +#import moco.vits as vits_moco + +def get_model(arch, patch_size, device): + + # Initialize model with pretraining + url = None + if "moco" in arch: + if arch == "moco_vit_small" and patch_size == 16: + url = "moco-v3/vit-s-300ep/vit-s-300ep.pth.tar" + elif arch == "moco_vit_base" and patch_size == 16: + url = "moco-v3/vit-b-300ep/vit-b-300ep.pth.tar" + model = vits.__dict__[arch](num_classes=0) + elif "mae" in arch: + if arch == "mae_vit_base" and patch_size == 16: + url = "mae/visualize/mae_visualize_vit_base.pth" + model = vits.__dict__[arch](num_classes=0) + elif "vit" in arch: + if arch == "vit_small" and patch_size == 16: + url = "dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth" + elif arch == "vit_small" and patch_size == 8: + url = "dino/dino_deitsmall8_300ep_pretrain/dino_deitsmall8_300ep_pretrain.pth" + elif arch == "vit_base" and patch_size == 16: + url = "dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth" + elif arch == "vit_base" and patch_size == 8: + url = "dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth" + elif arch == "resnet50": + url = "dino/dino_resnet50_pretrain/dino_resnet50_pretrain.pth" + model = vits.__dict__[arch](patch_size=patch_size, num_classes=0) + else: + raise NotImplementedError + + for p in model.parameters(): + p.requires_grad = False + + if url is not None: + print( + "Since no pretrained weights have been provided, we load the reference pretrained DINO weights." + ) + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/" + url + ) + if "moco" in arch: + state_dict = state_dict['state_dict'] + for k in list(state_dict.keys()): + # retain only base_encoder up to before the embedding layer + if k.startswith('module.base_encoder') and not k.startswith('module.base_encoder.head'): + # remove prefix + state_dict[k[len("module.base_encoder."):]] = state_dict[k] + # delete renamed or unused k + del state_dict[k] + elif "mae" in arch: + state_dict = state_dict['model'] + for k in list(state_dict.keys()): + # retain only base_encoder up to before the embedding layer + if k.startswith('decoder') or k.startswith('mask_token'): + # remove prefix + #state_dict[k[len("module.base_encoder."):]] = state_dict[k] + # delete renamed or unused k + del state_dict[k] + + msg = model.load_state_dict(state_dict, strict=True) + print( + "Pretrained weights found at {} and loaded with msg: {}".format( + url, msg + ) + ) + else: + print( + "There is no reference weights available for this model => We use random weights." + ) + + + model.eval() + model.to(device) + return model diff --git a/maskcut/object_discovery.py b/maskcut/object_discovery.py new file mode 100644 index 0000000000000000000000000000000000000000..2a24b2f6237f675ab2ed0651660deb9568c441ec --- /dev/null +++ b/maskcut/object_discovery.py @@ -0,0 +1,93 @@ +""" +Main functions for applying Normalized Cut. +Code adapted from LOST: https://github.com/valeoai/LOST +""" + +import torch +import torch.nn.functional as F +import numpy as np +from scipy.linalg import eigh +from scipy import ndimage + +def ncut(feats, dims, scales, init_image_size, tau = 0, eps=1e-5, im_name='', no_binary_graph=False): + """ + Implementation of NCut Method. + Inputs + feats: the pixel/patche features of an image + dims: dimension of the map from which the features are used + scales: from image to map scale + init_image_size: size of the image + tau: thresold for graph construction + eps: graph edge weight + im_name: image_name + no_binary_graph: ablation study for using similarity score as graph edge weight + """ + cls_token = feats[0,0:1,:].cpu().numpy() + + feats = feats[0,1:,:] + feats = F.normalize(feats, p=2) + A = (feats @ feats.transpose(1,0)) + A = A.cpu().numpy() + if no_binary_graph: + A[A tau + A = np.where(A.astype(float) == 0, eps, A) + d_i = np.sum(A, axis=1) + D = np.diag(d_i) + + # Print second and third smallest eigenvector + _, eigenvectors = eigh(D-A, D, subset_by_index=[1,2]) + eigenvec = np.copy(eigenvectors[:, 0]) + + # Using average point to compute bipartition + second_smallest_vec = eigenvectors[:, 0] + avg = np.sum(second_smallest_vec) / len(second_smallest_vec) + bipartition = second_smallest_vec > avg + + seed = np.argmax(np.abs(second_smallest_vec)) + + if bipartition[seed] != 1: + eigenvec = eigenvec * -1 + bipartition = np.logical_not(bipartition) + bipartition = bipartition.reshape(dims).astype(float) + + # predict BBox + pred, _, objects,cc = detect_box(bipartition, seed, dims, scales=scales, initial_im_size=init_image_size[1:]) ## We only extract the principal object BBox + mask = np.zeros(dims) + mask[cc[0],cc[1]] = 1 + + return np.asarray(pred), objects, mask, seed, None, eigenvec.reshape(dims) + +def detect_box(bipartition, seed, dims, initial_im_size=None, scales=None, principle_object=True): + """ + Extract a box corresponding to the seed patch. Among connected components extract from the affinity matrix, select the one corresponding to the seed patch. + """ + w_featmap, h_featmap = dims + objects, num_objects = ndimage.label(bipartition) + cc = objects[np.unravel_index(seed, dims)] + + + if principle_object: + mask = np.where(objects == cc) + # Add +1 because excluded max + ymin, ymax = min(mask[0]), max(mask[0]) + 1 + xmin, xmax = min(mask[1]), max(mask[1]) + 1 + # Rescale to image size + r_xmin, r_xmax = scales[1] * xmin, scales[1] * xmax + r_ymin, r_ymax = scales[0] * ymin, scales[0] * ymax + pred = [r_xmin, r_ymin, r_xmax, r_ymax] + + # Check not out of image size (used when padding) + if initial_im_size: + pred[2] = min(pred[2], initial_im_size[1]) + pred[3] = min(pred[3], initial_im_size[0]) + + # Coordinate predictions for the feature space + # Axis different then in image space + pred_feats = [ymin, xmin, ymax, xmax] + + return pred, pred_feats, objects, mask + else: + raise NotImplementedError + diff --git a/maskcut/predict.py b/maskcut/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..1a802f06789cf86aad5f87e95c6b669c60959a78 --- /dev/null +++ b/maskcut/predict.py @@ -0,0 +1,133 @@ +""" +download pretrained weights to ./weights +wget https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth +wget https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_300ep_pretrain/dino_deitsmall8_300ep_pretrain.pth +""" + +import sys + +sys.path.append("maskcut") +import numpy as np +import PIL.Image as Image +import torch +from scipy import ndimage +from colormap import random_color + +import dino +from third_party.token_cut.unsupervised_saliency_detection import metric +from crf import densecrf +from maskcut import maskcut + +from cog import BasePredictor, Input, Path + + +class Predictor(BasePredictor): + def setup(self): + """Load the model into memory to make running multiple predictions efficient""" + + # DINO pre-trained model + vit_features = "k" + self.patch_size = 8 + # adapted dino.ViTFeat to load from local pretrained_path + self.backbone_base = dino.ViTFeat( + "weights/dino_vitbase8_pretrain.pth", + 768, + "base", + vit_features, + self.patch_size, + ) + + self.backbone_small = dino.ViTFeat( + "weights/dino_deitsmall8_300ep_pretrain.pth", + 384, + "small", + vit_features, + self.patch_size, + ) + self.backbone_base.eval() + self.backbone_base.cuda() + self.backbone_small.eval() + self.backbone_small.cuda() + + def predict( + self, + image: Path = Input( + description="Input image", + ), + model: str = Input( + description="Choose the model architecture", + default="base", + choices=["small", "base"] + ), + n_pseudo_masks: int = Input( + description="The maximum number of pseudo-masks per image", + default=3, + ), + tau: float = Input( + description="Threshold used for producing binary graph", + default=0.15, + ), + ) -> Path: + """Run a single prediction on the model""" + + backbone = self.backbone_base if model == "base" else self.backbone_small + + # MaskCut hyperparameters + fixed_size = 480 + + # get pesudo-masks with MaskCut + bipartitions, _, I_new = maskcut( + str(image), + backbone, + self.patch_size, + tau, + N=n_pseudo_masks, + fixed_size=fixed_size, + cpu=False, + ) + + I = Image.open(str(image)).convert("RGB") + width, height = I.size + pseudo_mask_list = [] + for idx, bipartition in enumerate(bipartitions): + # post-process pesudo-masks with CRF + pseudo_mask = densecrf(np.array(I_new), bipartition) + pseudo_mask = ndimage.binary_fill_holes(pseudo_mask >= 0.5) + + # filter out the mask that have a very different pseudo-mask after the CRF + mask1 = torch.from_numpy(bipartition).cuda() + mask2 = torch.from_numpy(pseudo_mask).cuda() + + if metric.IoU(mask1, mask2) < 0.5: + pseudo_mask = pseudo_mask * -1 + + # construct binary pseudo-masks + pseudo_mask[pseudo_mask < 0] = 0 + pseudo_mask = Image.fromarray(np.uint8(pseudo_mask * 255)) + pseudo_mask = np.asarray(pseudo_mask.resize((width, height))) + + pseudo_mask = pseudo_mask.astype(np.uint8) + upper = np.max(pseudo_mask) + lower = np.min(pseudo_mask) + thresh = upper / 2.0 + pseudo_mask[pseudo_mask > thresh] = upper + pseudo_mask[pseudo_mask <= thresh] = lower + pseudo_mask_list.append(pseudo_mask) + + out = np.array(I) + for pseudo_mask in pseudo_mask_list: + + out = vis_mask(out, pseudo_mask, random_color(rgb=True)) + + output_path = f"/tmp/out.png" + + out.save(str(output_path)) + + return Path(output_path) + + +def vis_mask(input, mask, mask_color): + fg = mask > 0.5 + rgb = np.copy(input) + rgb[fg] = (rgb[fg] * 0.3 + np.array(mask_color) * 0.7).astype(np.uint8) + return Image.fromarray(rgb) diff --git a/maskcut/requirements.txt b/maskcut/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..3e700e33facf16daf2375313c6dfc57e0f8c56f2 --- /dev/null +++ b/maskcut/requirements.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9b350f220343a39c57dadd8e86a4ff4a07cc5cd45170ebe62d9ce354794042f9 +size 154 diff --git a/maskcut/run_maskcut_with_submitit.sh b/maskcut/run_maskcut_with_submitit.sh new file mode 100644 index 0000000000000000000000000000000000000000..fcc2fb912b479a88e9d43ab58e70871225b9e495 --- /dev/null +++ b/maskcut/run_maskcut_with_submitit.sh @@ -0,0 +1,15 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +python run_with_submitit_maskcut_array.py \ +--ngpus 1 \ +--nodes 1 \ +--timeout 1200 \ +--partition learnfair \ +--vit-arch base \ +--patch-size 8 \ +--dataset-path /path/to/imagenet/ \ +--tau 0.15 \ +--out-dir /path/to/save/annotations/ \ +--num-folder-per-job 2 \ +--job-index 0 \ +--fixed_size 480 \ +--N 3 \ diff --git a/maskcut/run_with_submitit_maskcut_array.py b/maskcut/run_with_submitit_maskcut_array.py new file mode 100644 index 0000000000000000000000000000000000000000..410c13df183b024580a371af27b7e1f1106160e5 --- /dev/null +++ b/maskcut/run_with_submitit_maskcut_array.py @@ -0,0 +1,164 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. + +""" +A script to run multinode training with submitit. +""" + +import sys +sys.path.append('./') +sys.path.append('./MaskCut') +sys.path.append('./third_party') + +import argparse +import os +import uuid +from pathlib import Path + +import maskcut_with_submitit as main_func +import submitit +import copy + +def parse_args(): + parent_parser = main_func.get_args_parser() + parser = argparse.ArgumentParser("Submitit for MaskCut", parents=[parent_parser]) + parser.add_argument("--ngpus", default=1, type=int, help="Number of gpus to request on each node") + parser.add_argument("--nodes", default=1, type=int, help="Number of nodes to request") + parser.add_argument("--timeout", default=1400, type=int, help="Duration of the job") + parser.add_argument("--job_dir", default="", type=str, help="Job dir. Leave empty for automatic.") + + parser.add_argument("--partition", default="learnfair", type=str, help="Partition where to submit") + parser.add_argument("--use_volta32", action='store_true', help="Big models? Use this") + parser.add_argument('--comment', default="", type=str, + help='Comment to pass to scheduler, e.g. priority message') + + # Removed the followings if the main file has it already + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') + parser.add_argument('--output_dir', default='', + help='path where to save, empty for no saving') + parser.add_argument('--device', default='cuda', + help='device to use for training / testing') + parser.add_argument('--seed', default=0, type=int) + parser.add_argument('--gpu', default=0, type=int) + parser.add_argument('--rank', default=0, type=int) + parser.add_argument('--resume', default='', help='resume from checkpoint') + parser.add_argument('--tolerance', default=1, type=int, help='tolerance for finding contours') + + return parser.parse_args() + + +def get_shared_folder() -> Path: + user = os.getenv("USER") + if Path("/checkpoint/").is_dir(): + p = Path(f"/checkpoint/{user}/experiments/maskcut/") + p.mkdir(exist_ok=True) + return p + raise RuntimeError("No shared folder available") + + +def get_init_file(): + # Init file must not exist, but it's parent dir must exist. + os.makedirs(str(get_shared_folder()), exist_ok=True) + init_file = get_shared_folder() / f"{uuid.uuid4().hex}_init" + if init_file.exists(): + os.remove(str(init_file)) + return init_file + +# Using a for loop for getting the array job and submit all jobs in one single array +class Trainer(object): + def __init__(self, args): + self.args = args + + def __call__(self): + + self._setup_gpu_args() + main_func.main(self.args) + + def checkpoint(self): + import os + import submitit + from pathlib import Path + + self.args.dist_url = get_init_file().as_uri() + checkpoint_file = os.path.join(self.args.output_dir, "checkpoint.pth") + if os.path.exists(checkpoint_file): + self.args.resume = checkpoint_file + print("Requeuing ", self.args) + empty_trainer = type(self)(self.args) + return submitit.helpers.DelayedSubmission(empty_trainer) + + def _setup_gpu_args(self): + import submitit + from pathlib import Path + + job_env = submitit.JobEnvironment() + self.args.output_dir = Path(str(self.args.output_dir).replace("%j", str(job_env.job_id))) + self.args.gpu = job_env.local_rank + self.args.rank = job_env.global_rank + self.args.world_size = job_env.num_tasks + print(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}") + + +def main(): + args = parse_args() + if args.job_dir == "": + args.job_dir = get_shared_folder() / "%j" + + # Note that the folder will depend on the job_id, to easily track experiments + executor = submitit.AutoExecutor(folder=args.job_dir, slurm_max_num_timeout=30) + + num_gpus_per_node = args.ngpus + nodes = args.nodes + timeout_min = args.timeout + + partition = args.partition + kwargs = {} + if args.use_volta32: + kwargs['slurm_constraint'] = 'volta32gb' + if args.comment: + kwargs['slurm_comment'] = args.comment + + executor.update_parameters( + mem_gb=40 * num_gpus_per_node, # 40 + gpus_per_node=num_gpus_per_node, + tasks_per_node=num_gpus_per_node, # one task per GPU + cpus_per_task=8, # default 8 + nodes=nodes, + timeout_min=timeout_min, # max is 60 * 72 + # Below are cluster dependent parameters + slurm_partition=partition, + slurm_signal_delay_s=120, + **kwargs + ) + + executor.update_parameters(name="MaskCut") + + # Since it is often necessary to submit over 100 jobs simutanously, + # using an array to submit these jobs is a more efficient way. + args.dist_url = get_init_file().as_uri() + args.output_dir = args.job_dir + print(args.output_dir) + + # list_folders = list(range(0, 500)) + end_idx = (1000 - args.job_index) // args.num_folder_per_job + 1 + list_folders = list(range(args.job_index, end_idx)) + jobs = [] + args_list = [] + for folder_index in list_folders: + args_copy = copy.deepcopy(args) + args_copy.job_index = folder_index + args_list.append(args_copy) + + with executor.batch(): + for args in args_list: + trainer = Trainer(args) + job = executor.submit(trainer) + jobs.append(job) + for job in jobs: + print("Submitted job_id:", job.job_id) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/maskcut/unsupervised_saliency_detection/__pycache__/metric.cpython-312.pyc b/maskcut/unsupervised_saliency_detection/__pycache__/metric.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2f52395899a21acc68ac940dd38859fae32d80fb Binary files /dev/null and b/maskcut/unsupervised_saliency_detection/__pycache__/metric.cpython-312.pyc differ diff --git a/maskcut/unsupervised_saliency_detection/bilateral_solver.py b/maskcut/unsupervised_saliency_detection/bilateral_solver.py new file mode 100755 index 0000000000000000000000000000000000000000..9fc1487162723689b9170b46847740013d718400 --- /dev/null +++ b/maskcut/unsupervised_saliency_detection/bilateral_solver.py @@ -0,0 +1,189 @@ +from scipy.sparse import diags +from scipy.sparse.linalg import cg +from scipy.sparse import csr_matrix +import numpy as np +from skimage.io import imread +from scipy import ndimage +import PIL.Image as Image + + +RGB_TO_YUV = np.array([ + [ 0.299, 0.587, 0.114], + [-0.168736, -0.331264, 0.5], + [ 0.5, -0.418688, -0.081312]]) +YUV_TO_RGB = np.array([ + [1.0, 0.0, 1.402], + [1.0, -0.34414, -0.71414], + [1.0, 1.772, 0.0]]) +YUV_OFFSET = np.array([0, 128.0, 128.0]).reshape(1, 1, -1) +MAX_VAL = 255.0 +def rgb2yuv(im): + return np.tensordot(im, RGB_TO_YUV, ([2], [1])) + YUV_OFFSET + +def yuv2rgb(im): + return np.tensordot(im.astype(float) - YUV_OFFSET, YUV_TO_RGB, ([2], [1])) + + +def get_valid_idx(valid, candidates): + """Find which values are present in a list and where they are located""" + locs = np.searchsorted(valid, candidates) + # Handle edge case where the candidate is larger than all valid values + locs = np.clip(locs, 0, len(valid) - 1) + # Identify which values are actually present + valid_idx = np.flatnonzero(valid[locs] == candidates) + locs = locs[valid_idx] + return valid_idx, locs + +class BilateralGrid(object): + def __init__(self, im, sigma_spatial=32, sigma_luma=8, sigma_chroma=8): + im_yuv = rgb2yuv(im) + # Compute 5-dimensional XYLUV bilateral-space coordinates + Iy, Ix = np.mgrid[:im.shape[0], :im.shape[1]] + x_coords = (Ix / sigma_spatial).astype(int) + y_coords = (Iy / sigma_spatial).astype(int) + luma_coords = (im_yuv[..., 0] /sigma_luma).astype(int) + chroma_coords = (im_yuv[..., 1:] / sigma_chroma).astype(int) + coords = np.dstack((x_coords, y_coords, luma_coords, chroma_coords)) + coords_flat = coords.reshape(-1, coords.shape[-1]) + self.npixels, self.dim = coords_flat.shape + # Hacky "hash vector" for coordinates, + # Requires all scaled coordinates be < MAX_VAL + self.hash_vec = (MAX_VAL**np.arange(self.dim)) + # Construct S and B matrix + self._compute_factorization(coords_flat) + + def _compute_factorization(self, coords_flat): + # Hash each coordinate in grid to a unique value + hashed_coords = self._hash_coords(coords_flat) + unique_hashes, unique_idx, idx = \ + np.unique(hashed_coords, return_index=True, return_inverse=True) + # Identify unique set of vertices + unique_coords = coords_flat[unique_idx] + self.nvertices = len(unique_coords) + # Construct sparse splat matrix that maps from pixels to vertices + self.S = csr_matrix((np.ones(self.npixels), (idx, np.arange(self.npixels)))) + # Construct sparse blur matrices. + # Note that these represent [1 0 1] blurs, excluding the central element + self.blurs = [] + for d in range(self.dim): + blur = 0.0 + for offset in (-1, 1): + offset_vec = np.zeros((1, self.dim)) + offset_vec[:, d] = offset + neighbor_hash = self._hash_coords(unique_coords + offset_vec) + valid_coord, idx = get_valid_idx(unique_hashes, neighbor_hash) + blur = blur + csr_matrix((np.ones((len(valid_coord),)), + (valid_coord, idx)), + shape=(self.nvertices, self.nvertices)) + self.blurs.append(blur) + + def _hash_coords(self, coord): + """Hacky function to turn a coordinate into a unique value""" + return np.dot(coord.reshape(-1, self.dim), self.hash_vec) + + def splat(self, x): + return self.S.dot(x) + + def slice(self, y): + return self.S.T.dot(y) + + def blur(self, x): + """Blur a bilateral-space vector with a 1 2 1 kernel in each dimension""" + assert x.shape[0] == self.nvertices + out = 2 * self.dim * x + for blur in self.blurs: + out = out + blur.dot(x) + return out + + def filter(self, x): + """Apply bilateral filter to an input x""" + return self.slice(self.blur(self.splat(x))) / \ + self.slice(self.blur(self.splat(np.ones_like(x)))) + + + + +def bistochastize(grid, maxiter=10): + """Compute diagonal matrices to bistochastize a bilateral grid""" + m = grid.splat(np.ones(grid.npixels)) + n = np.ones(grid.nvertices) + for i in range(maxiter): + n = np.sqrt(n * m / grid.blur(n)) + # Correct m to satisfy the assumption of bistochastization regardless + # of how many iterations have been run. + m = n * grid.blur(n) + Dm = diags(m, 0) + Dn = diags(n, 0) + return Dn, Dm + +class BilateralSolver(object): + def __init__(self, grid, params): + self.grid = grid + self.params = params + self.Dn, self.Dm = bistochastize(grid) + + def solve(self, x, w): + # Check that w is a vector or a nx1 matrix + if w.ndim == 2: + assert(w.shape[1] == 1) + elif w.dim == 1: + w = w.reshape(w.shape[0], 1) + A_smooth = (self.Dm - self.Dn.dot(self.grid.blur(self.Dn))) + w_splat = self.grid.splat(w) + A_data = diags(w_splat[:,0], 0) + A = self.params["lam"] * A_smooth + A_data + xw = x * w + b = self.grid.splat(xw) + # Use simple Jacobi preconditioner + A_diag = np.maximum(A.diagonal(), self.params["A_diag_min"]) + M = diags(1 / A_diag, 0) + # Flat initialization + y0 = self.grid.splat(xw) / w_splat + yhat = np.empty_like(y0) + for d in range(x.shape[-1]): + yhat[..., d], info = cg(A, b[..., d], x0=y0[..., d], M=M, maxiter=self.params["cg_maxiter"], tol=self.params["cg_tol"]) + xhat = self.grid.slice(yhat) + return xhat + +def bilateral_solver_output(img_pth, target, sigma_spatial = 24, sigma_luma = 4, sigma_chroma = 4) : + + reference = np.array(Image.open(img_pth).convert('RGB')) + h, w = target.shape + confidence = np.ones((h, w)) * 0.999 + + + + grid_params = { + 'sigma_luma' : sigma_luma, # Brightness bandwidth + 'sigma_chroma': sigma_chroma, # Color bandwidth + 'sigma_spatial': sigma_spatial # Spatial bandwidth + } + + bs_params = { + 'lam': 256, # The strength of the smoothness parameter + 'A_diag_min': 1e-5, # Clamp the diagonal of the A diagonal in the Jacobi preconditioner. + 'cg_tol': 1e-5, # The tolerance on the convergence in PCG + 'cg_maxiter': 25 # The number of PCG iterations + } + + grid = BilateralGrid(reference, **grid_params) + + t = target.reshape(-1, 1).astype(np.double) + c = confidence.reshape(-1, 1).astype(np.double) + + ## output solver, which is a soft value + output_solver = BilateralSolver(grid, bs_params).solve(t, c).reshape((h, w)) + + binary_solver = ndimage.binary_fill_holes(output_solver>0.5) + labeled, nr_objects = ndimage.label(binary_solver) + + nb_pixel = [np.sum(labeled == i) for i in range(nr_objects + 1)] + pixel_order = np.argsort(nb_pixel) + try : + binary_solver = labeled == pixel_order[-2] + except: + binary_solver = np.ones((h, w), dtype=bool) + + return output_solver, binary_solver + + \ No newline at end of file diff --git a/maskcut/unsupervised_saliency_detection/dino.py b/maskcut/unsupervised_saliency_detection/dino.py new file mode 100644 index 0000000000000000000000000000000000000000..18d50984c4b38d23c381575c155538de92a81635 --- /dev/null +++ b/maskcut/unsupervised_saliency_detection/dino.py @@ -0,0 +1,361 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Copied from Dino repo. https://github.com/facebookresearch/dino +Mostly copy-paste from timm library. +https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py +""" +import math +from functools import partial + +import torch +import torch.nn as nn + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): + # type: (Tensor, float, float, float, float) -> Tensor + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +def drop_path(x, drop_prob: float = 0., training: bool = False): + if drop_prob == 0. or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x, attn + + +class Block(nn.Module): + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x, return_attention=False): + y, attn = self.attn(self.norm1(x)) + if return_attention: + return attn + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + num_patches = (img_size // patch_size) * (img_size // patch_size) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x): + B, C, H, W = x.shape + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class VisionTransformer(nn.Module): + """ Vision Transformer """ + def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., norm_layer=nn.LayerNorm, **kwargs): + super().__init__() + self.num_features = self.embed_dim = embed_dim + + self.patch_embed = PatchEmbed( + img_size=img_size[0], patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + + # Classifier head + self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def interpolate_pos_encoding(self, x, w, h): + npatch = x.shape[1] - 1 + N = self.pos_embed.shape[1] - 1 + if npatch == N and w == h: + return self.pos_embed + class_pos_embed = self.pos_embed[:, 0] + patch_pos_embed = self.pos_embed[:, 1:] + dim = x.shape[-1] + w0 = w // self.patch_embed.patch_size + h0 = h // self.patch_embed.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + w0, h0 = w0 + 0.1, h0 + 0.1 + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2), + scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), + mode='bicubic', + ) + assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) + + def prepare_tokens(self, x): + B, nc, w, h = x.shape + x = self.patch_embed(x) # patch linear embedding + + # add the [CLS] token to the embed patch tokens + cls_tokens = self.cls_token.expand(B, -1, -1) + x = torch.cat((cls_tokens, x), dim=1) + + # add positional encoding to each token + x = x + self.interpolate_pos_encoding(x, w, h) + + return self.pos_drop(x) + + def forward(self, x): + x = self.prepare_tokens(x) + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + return x[:, 0] + + def get_last_selfattention(self, x): + x = self.prepare_tokens(x) + for i, blk in enumerate(self.blocks): + if i < len(self.blocks) - 1: + x = blk(x) + else: + # return attention of the last block + return blk(x, return_attention=True) + + def get_intermediate_layers(self, x, n=1): + x = self.prepare_tokens(x) + # we return the output tokens from the `n` last blocks + output = [] + for i, blk in enumerate(self.blocks): + x = blk(x) + if len(self.blocks) - i <= n: + output.append(self.norm(x)) + return output + + + +def vit_small(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def vit_base(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + + + +class ViTFeat(nn.Module): + """ Vision Transformer """ + def __init__(self, pretrained_pth, feat_dim, vit_arch = 'base', vit_feat = 'k', patch_size=16): + super().__init__() + if vit_arch == 'base' : + self.model = vit_base(patch_size=patch_size, num_classes=0) + + else : + self.model = vit_small(patch_size=patch_size, num_classes=0) + + self.feat_dim = feat_dim + self.vit_feat = vit_feat + self.patch_size = patch_size + +# state_dict = torch.load(pretrained_pth, map_location="cpu") + state_dict = torch.hub.load_state_dict_from_url("https://dl.fbaipublicfiles.com"+pretrained_pth) + self.model.load_state_dict(state_dict, strict=True) + print('Loading weight from {}'.format(pretrained_pth)) + + + def forward(self, img) : + feat_out = {} + def hook_fn_forward_qkv(module, input, output): + feat_out["qkv"] = output + + self.model._modules["blocks"][-1]._modules["attn"]._modules["qkv"].register_forward_hook(hook_fn_forward_qkv) + + + # Forward pass in the model + with torch.no_grad() : + h, w = img.shape[2], img.shape[3] + feat_h, feat_w = h // self.patch_size, w // self.patch_size + attentions = self.model.get_last_selfattention(img) + bs, nb_head, nb_token = attentions.shape[0], attentions.shape[1], attentions.shape[2] + qkv = ( + feat_out["qkv"] + .reshape(bs, nb_token, 3, nb_head, -1) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = qkv[0], qkv[1], qkv[2] + + k = k.transpose(1, 2).reshape(bs, nb_token, -1) + q = q.transpose(1, 2).reshape(bs, nb_token, -1) + v = v.transpose(1, 2).reshape(bs, nb_token, -1) + + # Modality selection + if self.vit_feat == "k": + feats = k[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + elif self.vit_feat == "q": + feats = q[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + elif self.vit_feat == "v": + feats = v[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + elif self.vit_feat == "kqv": + k = k[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + q = q[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + v = v[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + feats = torch.cat([k, q, v], dim=1) + return feats + + +if __name__ == "__main__": + vit_arch = 'base' + vit_feat = 'k' + + model = ViTFeat(vit_arch, vit_feat) + img = torch.cuda.FloatTensor(4, 3, 224, 224) + model.cuda() + # Forward pass in the model + feat = model(img) + print (feat.shape) diff --git a/maskcut/unsupervised_saliency_detection/get_saliency.py b/maskcut/unsupervised_saliency_detection/get_saliency.py new file mode 100755 index 0000000000000000000000000000000000000000..648a34dc4b19e966a69202dddf55d69d70bbefa7 --- /dev/null +++ b/maskcut/unsupervised_saliency_detection/get_saliency.py @@ -0,0 +1,191 @@ +import sys +sys.path.append('./model') +import dino # model + +import object_discovery as tokencut +import argparse +import utils +import bilateral_solver +import os + +from shutil import copyfile +import PIL.Image as Image +import cv2 +import numpy as np +from tqdm import tqdm + +from torchvision import transforms +import metric +import matplotlib.pyplot as plt +import skimage +import torch + +# Image transformation applied to all images +ToTensor = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.485, 0.456, 0.406), + (0.229, 0.224, 0.225)),]) + +def get_tokencut_binary_map(img_pth, backbone,patch_size, tau) : + I = Image.open(img_pth).convert('RGB') + I_resize, w, h, feat_w, feat_h = utils.resize_pil(I, patch_size) + + tensor = ToTensor(I_resize).unsqueeze(0).cuda() + feat = backbone(tensor)[0] + + seed, bipartition, eigvec = tokencut.ncut(feat, [feat_h, feat_w], [patch_size, patch_size], [h,w], tau) + return bipartition, eigvec + +def mask_color_compose(org, mask, mask_color = [173, 216, 230]) : + + mask_fg = mask > 0.5 + rgb = np.copy(org) + rgb[mask_fg] = (rgb[mask_fg] * 0.3 + np.array(mask_color) * 0.7).astype(np.uint8) + + return Image.fromarray(rgb) + + +parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + +## input / output dir +parser.add_argument('--out-dir', type=str, help='output directory') + +parser.add_argument('--vit-arch', type=str, default='small', choices=['base', 'small'], help='which architecture') + +parser.add_argument('--vit-feat', type=str, default='k', choices=['k', 'q', 'v', 'kqv'], help='which features') + +parser.add_argument('--patch-size', type=int, default=16, choices=[16, 8], help='patch size') + +parser.add_argument('--tau', type=float, default=0.2, help='Tau for tresholding graph') + +parser.add_argument('--sigma-spatial', type=float, default=16, help='sigma spatial in the bilateral solver') + +parser.add_argument('--sigma-luma', type=float, default=16, help='sigma luma in the bilateral solver') + +parser.add_argument('--sigma-chroma', type=float, default=8, help='sigma chroma in the bilateral solver') + + +parser.add_argument('--dataset', type=str, default=None, choices=['ECSSD', 'DUTS', 'DUT', None], help='which dataset?') + +parser.add_argument('--nb-vis', type=int, default=100, choices=[1, 200], help='nb of visualization') + +parser.add_argument('--img-path', type=str, default=None, help='single image visualization') + +args = parser.parse_args() +print (args) + +## feature net + +if args.vit_arch == 'base' and args.patch_size == 16: + url = "/dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth" + feat_dim = 768 +elif args.vit_arch == 'base' and args.patch_size == 8: + url = "/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth" + feat_dim = 768 +elif args.vit_arch == 'small' and args.patch_size == 16: + url = "/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth" + feat_dim = 384 +elif args.vit_arch == 'base' and args.patch_size == 8: + url = "/dino/dino_deitsmall8_300ep_pretrain/dino_deitsmall8_300ep_pretrain.pth" + +backbone = dino.ViTFeat(url, feat_dim, args.vit_arch, args.vit_feat, args.patch_size) +# resume_path = './model/dino_vitbase16_pretrain.pth' if args.patch_size == 16 else './model/dino_vitbase8_pretrain.pth' + +# feat_dim = 768 +# backbone = dino.ViTFeat(resume_path, feat_dim, args.vit_arch, args.vit_feat, args.patch_size) +# +#else : +# resume_path = './model/dino_deitsmall16_pretrain.pth' if args.patch_size == 16 else './model/dino_deitsmall8_pretrain.pth' +# feat_dim = 384 +# backbone = dino.ViTFeat(resume_path, feat_dim, args.vit_arch, args.vit_feat, args.patch_size) + + +msg = 'Load {} pre-trained feature...'.format(args.vit_arch) +print (msg) +backbone.eval() +backbone.cuda() + +if args.dataset == 'ECSSD' : + args.img_dir = '../datasets/ECSSD/img' + args.gt_dir = '../datasets/ECSSD/gt' + +elif args.dataset == 'DUTS' : + args.img_dir = '../datasets/DUTS_Test/img' + args.gt_dir = '../datasets/DUTS_Test/gt' + +elif args.dataset == 'DUT' : + args.img_dir = '../datasets/DUT_OMRON/img' + args.gt_dir = '../datasets/DUT_OMRON/gt' + +elif args.dataset is None : + args.gt_dir = None + + +print(args.dataset) + +if args.out_dir is not None and not os.path.exists(args.out_dir) : + os.mkdir(args.out_dir) + +if args.img_path is not None: + args.nb_vis = 1 + img_list = [args.img_path] +else: + img_list = sorted(os.listdir(args.img_dir)) + +count_vis = 0 +mask_lost = [] +mask_bfs = [] +gt = [] +for img_name in tqdm(img_list) : + if args.img_path is not None: + img_pth = img_name + img_name = img_name.split("/")[-1] + print(img_name) + else: + img_pth = os.path.join(args.img_dir, img_name) + + bipartition, eigvec = get_tokencut_binary_map(img_pth, backbone, args.patch_size, args.tau) + mask_lost.append(bipartition) + + output_solver, binary_solver = bilateral_solver.bilateral_solver_output(img_pth, bipartition, sigma_spatial = args.sigma_spatial, sigma_luma = args.sigma_luma, sigma_chroma = args.sigma_chroma) + mask1 = torch.from_numpy(bipartition).cuda() + mask2 = torch.from_numpy(binary_solver).cuda() + if metric.IoU(mask1, mask2) < 0.5: + binary_solver = binary_solver * -1 + mask_bfs.append(output_solver) + + if args.gt_dir is not None : + mask_gt = np.array(Image.open(os.path.join(args.gt_dir, img_name.replace('.jpg', '.png'))).convert('L')) + gt.append(mask_gt) + + if count_vis != args.nb_vis : + print(f'args.out_dir: {args.out_dir}, img_name: {img_name}') + out_name = os.path.join(args.out_dir, img_name) + out_lost = os.path.join(args.out_dir, img_name.replace('.jpg', '_tokencut.jpg')) + out_bfs = os.path.join(args.out_dir, img_name.replace('.jpg', '_tokencut_bfs.jpg')) + #out_eigvec = os.path.join(args.out_dir, img_name.replace('.jpg', '_tokencut_eigvec.jpg')) + + copyfile(img_pth, out_name) + org = np.array(Image.open(img_pth).convert('RGB')) + + #plt.imsave(fname=out_eigvec, arr=eigvec, cmap='cividis') + mask_color_compose(org, bipartition).save(out_lost) + mask_color_compose(org, binary_solver).save(out_bfs) + if args.gt_dir is not None : + out_gt = os.path.join(args.out_dir, img_name.replace('.jpg', '_gt.jpg')) + mask_color_compose(org, mask_gt).save(out_gt) + + + count_vis += 1 + else : + continue + +if args.gt_dir is not None and args.img_path is None: + print ('TokenCut evaluation:') + print (metric.metrics(mask_lost, gt)) + print ('\n') + + print ('TokenCut + bilateral solver evaluation:') + print (metric.metrics(mask_bfs, gt)) + print ('\n') + print ('\n') diff --git a/maskcut/unsupervised_saliency_detection/metric.py b/maskcut/unsupervised_saliency_detection/metric.py new file mode 100755 index 0000000000000000000000000000000000000000..974e868c765628f7f22e63935cce80d8ca604bbc --- /dev/null +++ b/maskcut/unsupervised_saliency_detection/metric.py @@ -0,0 +1,79 @@ +import numpy as np +import torch + + +def IoU(mask1, mask2): + mask1, mask2 = (mask1>0.5).to(torch.bool), (mask2>0.5).to(torch.bool) + intersection = torch.sum(mask1 * (mask1 == mask2), dim=[-1, -2]).squeeze() + union = torch.sum(mask1 + mask2, dim=[-1, -2]).squeeze() + return (intersection.to(torch.float) / union).mean().item() + + +def accuracy(mask1, mask2): + mask1, mask2 = (mask1>0.5).to(torch.bool), (mask2>0.5).to(torch.bool) + return torch.mean((mask1 == mask2).to(torch.float)).item() + + +def precision_recall(mask_gt, mask): + mask_gt, mask = mask_gt.to(torch.bool), mask.to(torch.bool) + true_positive = torch.sum(mask_gt * (mask_gt == mask), dim=[-1, -2]).squeeze() + mask_area = torch.sum(mask, dim=[-1, -2]).to(torch.float) + mask_gt_area = torch.sum(mask_gt, dim=[-1, -2]).to(torch.float) + + precision = true_positive / mask_area + precision[mask_area == 0.0] = 1.0 + + recall = true_positive / mask_gt_area + recall[mask_gt_area == 0.0] = 1.0 + + return precision.item(), recall.item() + + +def F_score(p, r, betta_sq=0.3): + f_scores = ((1 + betta_sq) * p * r) / (betta_sq * p + r) + f_scores[f_scores != f_scores] = 0.0 # handle nans + return f_scores + + +def F_max(precisions, recalls, betta_sq=0.3): + F = F_score(precisions, recalls, betta_sq) + return F.mean(dim=0).max().item() + +@torch.no_grad() +def metrics(pred, gt, stats=(IoU, accuracy, F_max), prob_bins=255): + avg_values = {} + precisions = [] + recalls = [] + out_dict = {} + + nb_sample = len(gt) + for step in range(nb_sample): + prediction, mask = torch.from_numpy(pred[step]), torch.from_numpy(gt[step]) + + for metric in stats: + method = metric.__name__ + if method not in avg_values and metric != F_max: + avg_values[method] = 0.0 + + if metric != F_max: + avg_values[method] += metric(mask, prediction) + else: + p, r = [], [] + splits = 2.0 * prediction.mean(dim=0) if prob_bins is None else \ + np.arange(0.0, 1.0, 1.0 / prob_bins) + + for split in splits: + pr = precision_recall(mask, prediction > split) + p.append(pr[0]) + r.append(pr[1]) + precisions.append(p) + recalls.append(r) + + for metric in stats: + method = metric.__name__ + if metric == F_max: + out_dict[method] = F_max(torch.tensor(precisions), torch.tensor(recalls)) + else: + out_dict[method] = avg_values[method] / nb_sample + + return out_dict \ No newline at end of file diff --git a/maskcut/unsupervised_saliency_detection/object_discovery.py b/maskcut/unsupervised_saliency_detection/object_discovery.py new file mode 100644 index 0000000000000000000000000000000000000000..453415e571c957d41b64d4c604d02f11ec35b438 --- /dev/null +++ b/maskcut/unsupervised_saliency_detection/object_discovery.py @@ -0,0 +1,97 @@ +import torch +import torch.nn.functional as F +import numpy as np +#from scipy.linalg.decomp import eig +import scipy +from scipy.linalg import eigh +from scipy import ndimage +#from sklearn.mixture import GaussianMixture +#from sklearn.cluster import KMeans + +def ncut(feats, dims, scales, init_image_size, tau = 0, eps=1e-5, im_name='', no_binary_graph=False): + """ + Implementation of NCut Method. + Inputs + feats: the pixel/patche features of an image + dims: dimension of the map from which the features are used + scales: from image to map scale + init_image_size: size of the image + tau: thresold for graph construction + eps: graph edge weight + im_name: image_name + no_binary_graph: ablation study for using similarity score as graph edge weight + """ + feats = F.normalize(feats, p=2, dim=0) + A = (feats.transpose(0,1) @ feats) + A = A.cpu().numpy() + if no_binary_graph: + A[A tau + A = np.where(A.astype(float) == 0, eps, A) + d_i = np.sum(A, axis=1) + D = np.diag(d_i) + + # Print second and third smallest eigenvector + _, eigenvectors = eigh(D-A, D, subset_by_index=[1,2]) + eigenvec = np.copy(eigenvectors[:, 0]) + + + # method1 avg + second_smallest_vec = eigenvectors[:, 0] + avg = np.sum(second_smallest_vec) / len(second_smallest_vec) + bipartition = second_smallest_vec > avg + + seed = np.argmax(np.abs(second_smallest_vec)) + + if bipartition[seed] != 1: + eigenvec = eigenvec * -1 + bipartition = np.logical_not(bipartition) + bipartition = bipartition.reshape(dims).astype(float) + + # predict BBox + pred, _, objects,cc = detect_box(bipartition, seed, dims, scales=scales, initial_im_size=init_image_size) ## We only extract the principal object BBox + mask = np.zeros(dims) + mask[cc[0],cc[1]] = 1 + + mask = torch.from_numpy(mask).to('cuda') +# mask = torch.from_numpy(bipartition).to('cuda') + bipartition = F.interpolate(mask.unsqueeze(0).unsqueeze(0), size=init_image_size, mode='nearest').squeeze() + + + eigvec = second_smallest_vec.reshape(dims) + eigvec = torch.from_numpy(eigvec).to('cuda') + eigvec = F.interpolate(eigvec.unsqueeze(0).unsqueeze(0), size=init_image_size, mode='nearest').squeeze() + return seed, bipartition.cpu().numpy(), eigvec.cpu().numpy() + +def detect_box(bipartition, seed, dims, initial_im_size=None, scales=None, principle_object=True): + """ + Extract a box corresponding to the seed patch. Among connected components extract from the affinity matrix, select the one corresponding to the seed patch. + """ + w_featmap, h_featmap = dims + objects, num_objects = ndimage.label(bipartition) + cc = objects[np.unravel_index(seed, dims)] + + + if principle_object: + mask = np.where(objects == cc) + # Add +1 because excluded max + ymin, ymax = min(mask[0]), max(mask[0]) + 1 + xmin, xmax = min(mask[1]), max(mask[1]) + 1 + # Rescale to image size + r_xmin, r_xmax = scales[1] * xmin, scales[1] * xmax + r_ymin, r_ymax = scales[0] * ymin, scales[0] * ymax + pred = [r_xmin, r_ymin, r_xmax, r_ymax] + + # Check not out of image size (used when padding) + if initial_im_size: + pred[2] = min(pred[2], initial_im_size[1]) + pred[3] = min(pred[3], initial_im_size[0]) + + # Coordinate predictions for the feature space + # Axis different then in image space + pred_feats = [ymin, xmin, ymax, xmax] + + return pred, pred_feats, objects, mask + else: + raise NotImplementedError diff --git a/maskcut/unsupervised_saliency_detection/utils.py b/maskcut/unsupervised_saliency_detection/utils.py new file mode 100755 index 0000000000000000000000000000000000000000..57341aa9c2f808f8a84988cc552a4f0ffe99981f --- /dev/null +++ b/maskcut/unsupervised_saliency_detection/utils.py @@ -0,0 +1,9 @@ +import PIL.Image as Image + +def resize_pil(I, patch_size=16) : + w, h = I.size + + new_w, new_h = int(round(w / patch_size)) * patch_size, int(round(h / patch_size)) * patch_size + feat_w, feat_h = new_w // patch_size, new_h // patch_size + + return I.resize((new_w, new_h), resample=Image.LANCZOS), w, h, feat_w, feat_h \ No newline at end of file diff --git a/maskcut/visualizations.py b/maskcut/visualizations.py new file mode 100755 index 0000000000000000000000000000000000000000..6f4908ec518521286b940adf62abe467f29be334 --- /dev/null +++ b/maskcut/visualizations.py @@ -0,0 +1,72 @@ +""" +Vis utilities. Code adapted from LOST: https://github.com/valeoai/LOST +""" +import cv2 +import torch +import skimage.io +import numpy as np +import torch.nn as nn +from PIL import Image +import scipy + +import matplotlib.pyplot as plt + +def visualize_img(image, vis_folder, im_name): + pltname = f"{vis_folder}/{im_name}" + Image.fromarray(image).save(pltname) + print(f"Original image saved at {pltname}.") + +def visualize_predictions(img, pred, vis_folder, im_name, save=True): + """ + Visualization of the predicted box and the corresponding seed patch. + """ + image = np.copy(img) + # Plot the box + cv2.rectangle( + image, + (int(pred[0]), int(pred[1])), + (int(pred[2]), int(pred[3])), + (255, 0, 0), 3, + ) + if save: + pltname = f"{vis_folder}/{im_name}_TokenCut_pred.jpg" + Image.fromarray(image).save(pltname) + print(f"Predictions saved at {pltname}.") + return image + +def visualize_predictions_gt(img, pred, gt, vis_folder, im_name, dim, scales, save=True): + """ + Visualization of the predicted box and the corresponding seed patch. + """ + image = np.copy(img) + # Plot the box + cv2.rectangle( + image, + (int(pred[0]), int(pred[1])), + (int(pred[2]), int(pred[3])), + (255, 0, 0), 3, + ) + # Plot the ground truth box + if len(gt>1): + for i in range(len(gt)): + cv2.rectangle( + image, + (int(gt[i][0]), int(gt[i][1])), + (int(gt[i][2]), int(gt[i][3])), + (0, 0, 255), 3, + ) + if save: + pltname = f"{vis_folder}/{im_name}_TokenCut_BBOX.jpg" + Image.fromarray(image).save(pltname) + #print(f"Predictions saved at {pltname}.") + return image + +def visualize_eigvec(eigvec, vis_folder, im_name, dim, scales, save=True): + """ + Visualization of the second smallest eigvector + """ + eigvec = scipy.ndimage.zoom(eigvec, scales, order=0, mode='nearest') + if save: + pltname = f"{vis_folder}/{im_name}_TokenCut_attn.jpg" + plt.imsave(fname=pltname, arr=eigvec, cmap='cividis') + print(f"Eigen attention saved at {pltname}.") diff --git a/maskcut/weakly_supvervised_detection/datasets.py b/maskcut/weakly_supvervised_detection/datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..04bab772709f75449e1aff4cfda375893e3426db --- /dev/null +++ b/maskcut/weakly_supvervised_detection/datasets.py @@ -0,0 +1,368 @@ +import os +import torch +import numpy as np +import pandas as pd +from PIL import Image +from tqdm import tqdm +from collections import defaultdict +from torchvision.datasets.folder import default_loader +from torchvision.datasets.utils import download_url +from torch.utils.data import Dataset +from torchvision import transforms + + +class CUB200(Dataset): + def __init__(self, root, is_train, transform=None, ori_size=False, input_size=224, center_crop=True): + self.root = root + self.is_train = is_train + self.ori_size = ori_size + if not ori_size and center_crop: + image_size = int(256/224*input_size) #TODO check + crop_size = input_size #TODO check + shift = (image_size - crop_size) // 2 + elif not ori_size and not center_crop: + image_size = input_size + crop_size = input_size + shift = 0 + self.data = self._load_data(image_size, crop_size, shift, center_crop) + self.transform = transform + + def _load_data(self, image_size, crop_size, shift, center_crop=True): + self._labelmap_path = os.path.join(self.root, 'CUB_200_2011', 'classes.txt') + + paths = pd.read_csv( + os.path.join(self.root, 'CUB_200_2011', 'images.txt'), + sep=' ', names=['id', 'path']) + labels = pd.read_csv( + os.path.join(self.root, 'CUB_200_2011', 'image_class_labels.txt'), + sep=' ', names=['id', 'label']) + splits = pd.read_csv( + os.path.join(self.root, 'CUB_200_2011', 'train_test_split.txt'), + sep=' ', names=['id', 'is_train']) + orig_image_sizes = pd.read_csv( + os.path.join(self.root, 'CUB_200_2011', 'image_sizes.txt'), + sep=' ', names=['id', 'width', 'height']) + bboxes = pd.read_csv( + os.path.join(self.root, 'CUB_200_2011', 'bounding_boxes.txt'), + sep=' ', names=['id', 'x', 'y', 'w', 'h']) + + if self.ori_size: + resized_bboxes = pd.DataFrame({'id': paths.id, + 'xmin': bboxes.x, + 'ymin': bboxes.y, + 'xmax': bboxes.x + bboxes.w, + 'ymax': bboxes.y + bboxes.h}) + else: + if center_crop: + resized_xmin = np.maximum( + (bboxes.x / orig_image_sizes.width * image_size - shift).astype(int), 0) + resized_ymin = np.maximum( + (bboxes.y / orig_image_sizes.height * image_size - shift).astype(int), 0) + resized_xmax = np.minimum( + ((bboxes.x + bboxes.w - 1) / orig_image_sizes.width * image_size - shift).astype(int), + crop_size - 1) + resized_ymax = np.minimum( + ((bboxes.y + bboxes.h - 1) / orig_image_sizes.height * image_size - shift).astype(int), + crop_size - 1) + else: + min_length = pd.concat([orig_image_sizes.width, orig_image_sizes.height], axis=1).min(axis=1) + resized_xmin = (bboxes.x / min_length * image_size).astype(int) + resized_ymin = (bboxes.y / min_length * image_size).astype(int) + resized_xmax = ((bboxes.x + bboxes.w - 1) / min_length * image_size).astype(int) + resized_ymax = ((bboxes.y + bboxes.h - 1) / min_length * image_size).astype(int) + + resized_bboxes = pd.DataFrame({'id': paths.id, + 'xmin': resized_xmin.values, + 'ymin': resized_ymin.values, + 'xmax': resized_xmax.values, + 'ymax': resized_ymax.values}) + + + data = paths.merge(labels, on='id')\ + .merge(splits, on='id')\ + .merge(resized_bboxes, on='id') + + if self.is_train: + data = data[data.is_train == 1] + else: + data = data[data.is_train == 0] + return data + + def __len__(self): + return len(self.data) + + # def _preprocess_bbox(self, origin_bbox, orig_image_size, center_crop=True): + # xmin, ymin, xmax, ymax = origin_bbox + # orig_width, orig_height = orig_image_size + # if center_crop: + # resized_xmin = np.maximum( + # (bboxes.x / orig_image_sizes.width * image_size - shift).astype(int), 0) + # resized_ymin = np.maximum( + # (bboxes.y / orig_image_sizes.height * image_size - shift).astype(int), 0) + # resized_xmax = np.minimum( + # ((bboxes.x + bboxes.w - 1) / orig_image_sizes.width * image_size - shift).astype(int), + # crop_size - 1) + # resized_ymax = np.minimum( + # ((bboxes.y + bboxes.h - 1) / orig_image_sizes.height * image_size - shift).astype(int), + # crop_size - 1) + # else: + # print(f'width: {orig_image_sizes.width}, height: {orig_image_sizes.height}') + # min_length = min(orig_image_sizes.width , orig_image_sizes.height) + # resized_xmin = int(bb / min_length * self.image_size) + # resized_ymin = int(ymin / min_length * self.image_size) + # resized_xmax = int(xmax / min_length * self.image_size) + # resized_ymax = int(ymax / min_length * self.image_size) + + # resized_bboxes = pd.DataFrame({'id': paths.id, + # 'xmin': resized_xmin.values, + # 'ymin': resized_ymin.values, + # 'xmax': resized_xmax.values, + # 'ymax': resized_ymax.values}) + + def __getitem__(self, idx): + sample = self.data.iloc[idx] + path = os.path.join(self.root, 'CUB_200_2011/images', sample.path) + image = Image.open(path).convert('RGB') + label = sample.label - 1 # label starts from 1 + gt_box = torch.tensor( + [sample.xmin, sample.ymin, sample.xmax, sample.ymax]) + + if self.transform is not None: + image = self.transform(image) + + return (image, label, gt_box) + + @property + def class_id_to_name(self): + if hasattr(self, '_class_id_to_name'): + return self._class_id_to_name + labelmap = pd.read_csv(self._labelmap_path, sep=' ', names=['label', 'name']) + labelmap['label'] = labelmap['label'].apply(lambda x: x - 1) + self._class_id_to_name = labelmap.set_index('label')['name'].to_dict() + return self._class_id_to_name + + @property + def class_name_to_id(self): + if hasattr(self, '_class_name_to_id'): + return self._class_name_to_id + self._class_name_to_id = {v: k for k, v in self.class_id_to_name.items()} + return self._class_name_to_id + + @property + def class_to_images(self): + if hasattr(self, '_class_to_images'): + return self._class_to_images + #self.log.warn('Create index...') + self._class_to_images = defaultdict(list) + for idx in tqdm(range(len(self))): + sample = self.data.iloc[idx] + label = sample.label - 1 + self._class_to_images[label].append(idx) + #self.log.warn('Done!') + return self._class_to_images + + +#class ImageNet(H5Dataset): +# def __init__(self, root, is_train, transform=None): +# self.root = root +# self.is_train = is_train +# tag = 'train' if is_train else 'val' +# self.h5_path = os.path.join(root, f'imagenet_{tag}.h5') +# +# super().__init__(self.h5_path, transform) + +class ImageNet(Dataset): + def __init__(self, root, is_train, transform=None, ori_size=False, input_size=224, center_crop=True): + self.root = root + self.is_train = is_train + self.ori_size = ori_size + self.center_crop = center_crop + if not ori_size and center_crop: + self.image_size = int(256/224 * input_size) + self.crop_size = input_size + self.shift = (self.image_size - self.crop_size) // 2 + elif not ori_size and not center_crop: + print('resize, without center crop') + self.image_size = input_size + + self._load_data() + self.transform = transform + + def _load_data(self): + self._labelmap_path = os.path.join( + self.root, 'ILSVRC/Detection', 'imagenet1000_clsidx_to_labels.txt') + + if self.is_train: + self.path = os.path.join(self.root, 'ILSVRC/Data/train') + self.metadata = pd.read_csv( + os.path.join(self.root, 'ILSVRC/Detection', 'train.txt'), + sep=' ', names=['path', 'label']) + else: + self.path = os.path.join(self.root, 'ILSVRC/Data/val') + self.metadata = pd.read_csv( + os.path.join(self.root, 'ILSVRC/Detection', 'val.txt'), + sep='\t', names=['path', 'label', 'xmin', 'ymin', 'xmax', 'ymax']) + self.wnids = pd.read_csv( + os.path.join(self.root, 'ILSVRC/Detection/', 'wnids.txt'), names=['dir_name']) + + def _preprocess_bbox(self, origin_bbox, orig_image_size, center_crop=True, image_path=None): + xmin, ymin, xmax, ymax = origin_bbox + orig_width, orig_height = orig_image_size + if center_crop: + resized_xmin = np.maximum( + int(xmin / orig_width * self.image_size - self.shift), 0) + resized_ymin = np.maximum( + int(ymin / orig_height * self.image_size - self.shift), 0) + resized_xmax = np.minimum( + int(xmax / orig_width * self.image_size - self.shift), self.crop_size - 1) + resized_ymax = np.minimum( + int(ymax / orig_height * self.image_size - self.shift), self.crop_size - 1) + else: + #print(f'ori W: {orig_width} ori H: {orig_height}, xmin: {xmin}, ymin: {ymin}, xmax: {xmax}, ymax: {ymax}, input_size: {self.image_size}, image_path: {image_path}') + min_length = min(orig_height, orig_width) + resized_xmin = int(xmin / min_length * self.image_size) + resized_ymin = int(ymin / min_length * self.image_size) + resized_xmax = int(xmax / min_length * self.image_size) + resized_ymax = int(ymax / min_length * self.image_size) + #print(f'output: xmin, ymin, xmax, ymax: {[resized_xmin, resized_ymin, resized_xmax, resized_ymax]}') + return [resized_xmin, resized_ymin, resized_xmax, resized_ymax] + + def __len__(self): + return len(self.metadata) + + def __getitem__(self, idx): + sample = self.metadata.iloc[idx] + if self.is_train: + image_path = os.path.join(self.path, sample.path) + else: + image_path = os.path.join( + self.path, self.wnids.iloc[int(sample.label)].dir_name, sample.path) + image = Image.open(image_path).convert('RGB') + label = sample.label + + # preprocess bbox + if self.is_train: + gt_box = torch.tensor([0., 0., 0., 0.]) + else: + origin_box = [sample.xmin, sample.ymin, sample.xmax, sample.ymax] + if self.ori_size: + gt_box = torch.tensor(origin_box) + else: + gt_box = torch.tensor( + self._preprocess_bbox(origin_box, image.size, self.center_crop, image_path)) + + if self.transform is not None: + image = self.transform(image) + + return (image, label, gt_box) + + @property + def class_id_to_name(self): + if hasattr(self, '_class_id_to_name'): + return self._class_id_to_name + with open(self._labelmap_path, 'r') as f: + self._class_id_to_name = eval(f.read()) + return self._class_id_to_name + + @property + def class_name_to_id(self): + if hasattr(self, '_class_name_to_id'): + return self._class_name_to_id + self._class_name_to_id = {v: k for k, v in self.class_id_to_name.items()} + return self._class_name_to_id + + @property + def wnid_list(self): + if hasattr(self, '_wnid_list'): + return self._wnid_list + self._wnid_list = self.wnids.dir_name.tolist() + return self._wnid_list + + @property + def class_to_images(self): + if hasattr(self, '_class_to_images'): + return self._class_to_images + self.log.warn('Create index...') + self._class_to_images = defaultdict(list) + for idx in tqdm(range(len(self))): + sample = self.metadata.iloc[idx] + label = sample.label + self._class_to_images[label].append(idx) + self.log.warn('Done!') + return self._class_to_images + + def verify_wnid(self, wnid): + is_valid = bool(re.match(u'^[n][0-9]{8}$', wnid)) + is_terminal = bool(wnid in self.wnids.dir_name.tolist()) + return is_valid and is_terminal + + def get_terminal_wnids(self, wnid): + page = requests.get("http://www.image-net.org/api/text/wordnet.structure.hyponym?wnid={}&full=1".format(wnid)) + str_wnids = str(BeautifulSoup(page.content, 'html.parser')) + split_wnids = re.split('\r\n-|\r\n', str_wnids) + return [_wnid for _wnid in split_wnids if self.verify_wnid(_wnid)] + + def get_image_ids(self, wnid): + terminal_wnids = self.get_terminal_wnids(wnid) + + image_ids = set() + for terminal_wnid in terminal_wnids: + class_id = self.wnid_list.index(terminal_wnid) + image_ids |= set(self.class_to_images[class_id]) + + return list(image_ids) + +DATASETS = { + 'cub': CUB200, + 'imagenet': ImageNet, +} + +LABELS = { + 'cub': 200, + 'imagenet': 1000, +} + +def build_dataset(is_train, args): + # Define arguments + data_name = args.dataset + root = args.data_path + batch_size = args.batch_size_per_gpu + num_workers = args.num_workers + + transform = build_transform(is_train, args) + dataset = DATASETS[data_name](root, is_train=is_train, transform=transform, ori_size=args.ori_size, input_size = args.input_size, center_crop = not args.no_center_crop) + + return dataset, LABELS[data_name] + +def build_transform(is_train, args): + resize_im = args.input_size > 32 + if is_train: + transform = transforms.Compose([ + transforms.RandomResizedCrop(args.input_size), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ]) + if not resize_im: + # replace RandomResizedCropAndInterpolation with + # RandomCrop + transform.transforms[0] = transforms.RandomCrop( + args.input_size, padding=4) + return transform + + t = [] + if resize_im and (not args.ori_size): + if args.no_center_crop: + t.append(transforms.Resize(args.input_size, interpolation=3)) + else: + size = int((256 / 224) * args.input_size) + t.append( + transforms.Resize(size, interpolation=3), # to maintain same ratio w.r.t. 224 images + ) + if not args.ori_size and not args.no_center_crop: + print('center crop') + t.append(transforms.CenterCrop(args.input_size)) + + t.append(transforms.ToTensor()) + t.append(transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))) + return transforms.Compose(t) diff --git a/maskcut/weakly_supvervised_detection/eval.py b/maskcut/weakly_supvervised_detection/eval.py new file mode 100644 index 0000000000000000000000000000000000000000..d6a1da2de383cd0552b1f418453ecc5831e2b2e7 --- /dev/null +++ b/maskcut/weakly_supvervised_detection/eval.py @@ -0,0 +1,197 @@ +import argparse +import os +import sys +import torch +import shutil +import utils +import numpy as np +import metrics +import torch.nn as nn +import torch.backends.cudnn as cudnn +import vision_transformer as vits + +from tqdm import tqdm +from datasets import build_dataset +from meters import AverageEpochMeter +from object_discovery import ncut, detect_box, get_feats +from visualization import visualize_fms, visualize_predictions_gt, visualize_img, visualize_eigvec + +from collections import OrderedDict +#from visualizations import visualize_img, visualize_eigvec, visualize_gt, visualize_fms, visualize_predictions, visualize_predictions_gt + +def evaluate(): + if args.device != 'cuda': + args.distributed = False + else: + utils.init_distributed_mode(args) + + print(args) + + device = torch.device(args.device) + cudnn.benchmark = True + + # =============build network ============== + if args.arch in vits.__dict__.keys(): + model = vits.__dict__[args.arch](patch_size=args.patch_size, num_classes=args.num_labels) + embed_dim = model.embed_dim * (args.n_last_blocks + int(args.avgpool_patchtokens)) + print(f'embed_dim: {embed_dim}') + else: + print(f"Unknow architecture: {args.arch}") + sys.exit(1) + + model.to(device) + model.eval() + + linear_classifier = LinearClassifier(embed_dim, num_labels=args.num_labels) + linear_classifier = linear_classifier.to(device) + + # ============ Load checkpoint ============= + checkpoint_model = torch.load(args.pretrained_weights, map_location='cpu') + if args.classifier_weights is None: + linear_classifier.load_state_dict(checkpoint_model['classifier']) + model.load_state_dict(checkpoint_model['model'], strict=False) + else: + model.load_state_dict(checkpoint_model) + checkpoint_classifier = torch.load(args.classifier_weights, map_location='cpu') + new_state_dict = OrderedDict() + new_state_dict = {k[7:]: v for k, v in checkpoint_classifier['state_dict'].items()} + # load params + linear_classifier.load_state_dict(new_state_dict) + print('Load from checkpoint done.') + + dataset_val, _ = build_dataset(is_train=False, args=args) + + if args.distributed: + sampler_val = torch.utils.data.distributed.DistributedSampler(dataset_val, shuffle=False) + else: + sampler_val = torch.utils.data.SequentialSampler(dataset_val) + + val_loader = torch.utils.data.DataLoader( + dataset_val, + sampler=sampler_val, + batch_size=args.batch_size_per_gpu, + num_workers=args.num_workers, + pin_memory=True, + ) + + + num_batches = len(val_loader) + scales = [args.patch_size, args.patch_size] + top1_cls_meter = AverageEpochMeter('Top-1 Cls') + top5_cls_meter = AverageEpochMeter('Top-5 Cls') + gt_loc_meter = AverageEpochMeter('GT-Known Loc') + top1_loc_meter = AverageEpochMeter('Top-1 Loc') + feat_out = {} + bbox_error = 0 + cls_error = 0 + skip = 0 + def hook_fn_forward_qkv(module, input, output): + feat_out["qkv"] = output + model._modules["blocks"][-1]._modules["attn"]._modules["qkv"].register_forward_hook(hook_fn_forward_qkv) + val_loader = tqdm(val_loader) + for i, (images, labels, gt_boxes) in enumerate(val_loader): + init_image_size = images[0].shape + images = utils.padding_img(images[0], args) + images = images.unsqueeze(0) + if images.shape[-1] > 1000 and images.shape[-2] > 1000: + skip = skip + 1 + continue + with torch.no_grad(): + images = images.to(device) + labels = labels.to(device) + + intermediate_output, shape= model.get_intermediate_layers(images, args.n_last_blocks) + output = torch.cat([x[:, 0] for x in intermediate_output], dim=-1) + if args.avgpool_patchtokens: + output = torch.cat((output.unsqueeze(-1), torch.mean(intermediate_output[-1][:, 1:], dim=1).unsqueeze(-1)), dim=-1) + output = output.reshape(output.shape[0], -1) + output = linear_classifier(output) + + batch_size = images.size(0) + top1_cls, top5_cls = utils.accuracy(output, labels, topk=(1, 5)) + top1_cls_meter.update(top1_cls, batch_size) + top5_cls_meter.update(top5_cls, batch_size) + + + + # ===== Generate Bbox========= + dims = (images[0].shape[-2] // args.patch_size, images[0].shape[-1] // args.patch_size) + k = get_feats(feat_out, shape) + bboxes, mask, seed, eigvec= ncut(k, dims, scales, init_image_size=init_image_size[1:], eps=args.eps, tau=args.tau) + bboxes_copy = np.copy(bboxes) + bboxes = torch.FloatTensor(bboxes).unsqueeze(0) + gt_loc, top1_loc = metrics.loc_accuracy(output, labels, gt_boxes, bboxes) + + if args.visual: + visualize_predictions_gt(images[0], bboxes_copy , gt_boxes, i, seed, dims, scales, './output/all') + visualize_eigvec(eigvec, './output/all', i, dims, scales) + + #visualize_fms(images[0], mask, seed, i, dims, scales) + if top1_loc == 0 and gt_loc == 0: + bbox_error = bbox_error + 1 + if args.visual: + # print(f'i: {i}, gt_boxes {gt_boxes}, prediction: {bboxes_copy}, image size: {images.shape}') + visualize_predictions_gt(images[0], bboxes_copy , gt_boxes, i, seed, dims, scales, './output/bbox_error') + + elif top1_loc == 0 and gt_loc == 1: + cls_error = cls_error +1 + if args.visual: + visualize_predictions_gt(images[0], bboxes_copy , gt_boxes, i, seed, dims, scales, './output/classification_error') + + gt_loc_meter.update(gt_loc, batch_size) + top1_loc_meter.update(top1_loc, batch_size) + top1_cls = top1_cls_meter.compute() + top5_cls = top5_cls_meter.compute() + gt_loc = gt_loc_meter.compute() + top1_loc = top1_loc_meter.compute() + val_loader.set_description(f'Top1_cls: {top1_cls:.4f}, top5_cls{top5_cls:.4f}, gt_loc: {gt_loc:.4f}, top1_loc:{top1_loc:.4f}') + print(f'Top1_cls: {top1_cls}, top5_cls{top5_cls}, gt_loc: {gt_loc}, top1_loc:{top1_loc}') + print(f'Bbox error: {bbox_error}, cls error: {cls_error}') + print(f'Skip large image: {skip}') + + +class LinearClassifier(nn.Module): + """Linear layer to train on top of frozen features""" + def __init__(self, dim, num_labels=1000): + super(LinearClassifier, self).__init__() + self.num_labels = num_labels + self.linear = nn.Linear(dim, num_labels) + self.linear.weight.data.normal_(mean=0.0, std=0.01) + self.linear.bias.data.zero_() + + def forward(self, x): + # flatten + x = x.view(x.size(0), -1) + # linear layer + return self.linear(x) + +if __name__=='__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--n_last_blocks', default=4, type=int, help="""Concatenate [CLS] tokens + for the `n` last blocks. We use `n=4` when evaluating ViT-Small and `n=1` with ViT-Base.""") + parser.add_argument('--avgpool_patchtokens', default=False, type=utils.bool_flag, + help="""Whether ot not to concatenate the global average pooled features to the [CLS] token. + We typically set this to False for ViT-Small and to True with ViT-Base.""") + parser.add_argument('--arch', default='vit_small', type=str, help='Architecture') + parser.add_argument('--dataset', default='cub', type=str, choices=['cub', 'imagenet'], help='Architecture') + parser.add_argument('--patch_size', default=16, type=int, help='Patch resolution of the model.') + parser.add_argument('--input_size', default=224, type=int, help='Input image size, default(224).') + parser.add_argument('--pretrained_weights', default='', type=str, help="Path to pretrained weights to evaluate.") + parser.add_argument('--classifier_weights', default=None, type=str, help="Path to linear classifier pretrained weights to evaluate.") + parser.add_argument('--batch_size_per_gpu', default=256, type=int, help='Per-GPU batch-size') + parser.add_argument("--dist_url", default="env://", type=str, help="""url used to set up + distributed training; see https://pytorch.org/docs/stable/distributed.html""") + parser.add_argument("--local_rank", default=0, type=int, help="Please ignore and do not set this argument.") + parser.add_argument('--data_path', default='/path/to/imagenet/', type=str) + parser.add_argument('--num_workers', default=10, type=int, help='Number of data loading workers per GPU.') + parser.add_argument('--val_freq', default=1, type=int, help="Epoch frequency for validation.") + parser.add_argument('--num_labels', default=200, type=int, help='Number of labels for linear classifier') + parser.add_argument('--device', default='cuda', help='device to use for training / testing') + parser.add_argument('--distributed', default=False, action='store_true', help='device to use for training / testing') + parser.add_argument('--ori_size', default=False, action='store_true', help='Evaluate on image raw size') + parser.add_argument('--visual', default=False, action='store_true', help='Visualize error examples on ./test') + parser.add_argument('--eps', default=1e-5, type=float, help='hyperparameter for tokencut') + parser.add_argument('--tau', default=0.05, type=float, help='hyperparamter for tokencut') + parser.add_argument('--no_center_crop', default=False, action='store_true', help='Center crop input image') + args = parser.parse_args() + evaluate() diff --git a/maskcut/weakly_supvervised_detection/finetune.py b/maskcut/weakly_supvervised_detection/finetune.py new file mode 100644 index 0000000000000000000000000000000000000000..938d6110429382d2c664b71cf004dbf8ed4fd5a3 --- /dev/null +++ b/maskcut/weakly_supvervised_detection/finetune.py @@ -0,0 +1,321 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import argparse +import json +from pathlib import Path + +import torch +from torch import nn +import torch.distributed as dist +import torch.backends.cudnn as cudnn +from torchvision import datasets +from torchvision import transforms as pth_transforms +from torchvision import models as torchvision_models + +import utils +import vision_transformer as vits +from datasets import CUB200 +from torch.utils.tensorboard import SummaryWriter + + +def eval_linear(args): + if args.device != 'cuda': + args.distributed = False + else: + utils.init_distributed_mode(args) + + print(args) + device = torch.device(args.device) + cudnn.benchmark = True + + # ============ building network ... ============ + # if the network is a Vision Transformer (i.e. vit_tiny, vit_small, vit_base) + if args.arch in vits.__dict__.keys(): + model = vits.__dict__[args.arch](patch_size=args.patch_size, num_classes=args.num_labels) + embed_dim = model.embed_dim * (args.n_last_blocks + int(args.avgpool_patchtokens)) + else: + print(f"Unknow architecture: {args.arch}") + sys.exit(1) + model.to(device) + model.eval() + + #model_without_ddp = model + #if args.distributed: + # model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) + # model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], fine_unused_parametters=True) + # model_without_ddp = model.module + n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) + print('number of params:', n_parameters) + + # load weights to evaluate + utils.load_pretrained_weights(model, args.pretrained_weights, args.checkpoint_key, args.arch, args.patch_size) + print(f"Model {args.arch} built.") + + linear_classifier = LinearClassifier(embed_dim, num_labels=args.num_labels) + linear_classifier = linear_classifier.to(device) + linear_classifier = nn.parallel.DistributedDataParallel(linear_classifier, device_ids=[args.gpu]) + linear_classifier_without_ddp = linear_classifier.module + + # ============ preparing data ... ============ + train_transform = pth_transforms.Compose([ + pth_transforms.RandomResizedCrop(args.input_size), + pth_transforms.RandomHorizontalFlip(), + pth_transforms.ToTensor(), + pth_transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ]) + val_transform = pth_transforms.Compose([ + pth_transforms.Resize(int((256/224)*args.input_size), interpolation=3), + pth_transforms.CenterCrop(args.input_size), + pth_transforms.ToTensor(), + pth_transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ]) + if args.dataset == 'cub': + dataset_train = CUB200(args.data_path, is_train=True, transform=train_transform, ori_size=args.ori_size, input_size=args.input_size) + dataset_val = CUB200(args.data_path, is_train=False,transform=val_transform, ori_size = args.ori_size, input_size = args.input_size) + else: + raise NotImplementedError + if args.distributed: + sampler_train = torch.utils.data.distributed.DistributedSampler(dataset_train) + sampler_val = torch.utils.data.distributed.DistributedSampler(dataset_val, shuffle=False) + else: + sampler_train = torch.utils.data.RandomSampler(dataset_train) + sampler_val = torch.utils.data.SequentialSampler(dataset_val) + + val_loader = torch.utils.data.DataLoader( + dataset_val, + sampler=sampler_val, + batch_size=args.batch_size_per_gpu, + num_workers=args.num_workers, + pin_memory=True, + ) + + #if args.evaluate: + # raise NotImplementederror + # utils.load_pretrained_linear_weights(linear_classifier, args.arch, args.patch_size) + # test_stats = validate_network(val_loader, model, linear_classifier, args.n_last_blocks, args.avgpool_patchtokens) + # print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%") + # return + + + train_loader = torch.utils.data.DataLoader( + dataset_train, + sampler=sampler_train, + batch_size=args.batch_size_per_gpu, + num_workers=args.num_workers, + pin_memory=True, + ) + print(f"Data loaded with {len(dataset_train)} train and {len(dataset_val)} val imgs.") + + # set optimizer + optimizer = torch.optim.SGD( + linear_classifier.parameters(), + args.lr * (args.batch_size_per_gpu * utils.get_world_size()) / 256., # linear scaling rule + momentum=0.9, + weight_decay=args.weight_decay, # we do not apply weight decay + ) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epochs, eta_min=0) + + if utils.is_main_process(): + writer = SummaryWriter(args.output_dir + '/log') + start_epoch = 0 + best_acc = 0 + print('Starting training') + + for epoch in range(start_epoch, args.epochs): + train_loader.sampler.set_epoch(epoch) + + train_stats = train(model, linear_classifier_without_ddp, device, optimizer, train_loader, epoch, args.n_last_blocks, args.avgpool_patchtokens) + scheduler.step() + + log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, + 'epoch': epoch} + if epoch % args.val_freq == 0 or epoch == args.epochs - 1: + test_stats = validate_network(val_loader, model, linear_classifier, device, args.n_last_blocks, args.avgpool_patchtokens) + print(f"Accuracy at epoch {epoch} of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%") + best_acc = max(best_acc, test_stats["acc1"]) + print(f'Max accuracy so far: {best_acc:.2f}%') + log_stats = {**{k: v for k, v in log_stats.items()}, + **{f'test_{k}': v for k, v in test_stats.items()}, + "best_acc": best_acc} + if utils.is_main_process(): + with (Path(args.output_dir) / "log.txt").open("a") as f: + f.write(json.dumps(log_stats) + "\n") + save_dict = { + "epoch": epoch + 1, + "model": model.state_dict(), + "classifier": linear_classifier_without_ddp.state_dict(), + "optimizer": optimizer.state_dict(), + "scheduler": scheduler.state_dict(), + "best_acc": best_acc, + } + + writer.add_scalar('Train_loss', train_stats['loss'], global_step=epoch) + writer.add_scalar('Learning_rate', train_stats['lr'], global_step=epoch) + writer.add_scalar('Train Acc_1', train_stats['acc1'], global_step=epoch) + writer.add_scalar('Acc_1', test_stats['acc1'], global_step=epoch) + writer.add_scalar('Acc_5', test_stats['acc5'], global_step=epoch) + + torch.save(save_dict, os.path.join(args.output_dir, "checkpoint.pth")) + + if best_acc < float(test_stats['acc1']): + best_acc = float(test_stats['acc1']) + shutil.copyfile(checkpoint_path, args.output_dir + '/model_best.pth') + print(f'Max accuracy so far: {best_acc:.2f}%') + print("Training of the supervised linear classifier on frozen features completed.\n" + "Top-1 test accuracy: {acc:.1f}".format(acc=best_acc)) + + +def train(model, linear_classifier, device, optimizer, loader, epoch, n, avgpool): + linear_classifier.train() + metric_logger = utils.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + for batch in metric_logger.log_every(loader, 20, header): + inp,target = batch[:2] + + # move to gpu + inp = inp.to(device, non_blocking=True) + target = target.to(device, non_blocking=True) + + # forward + with torch.no_grad(): + if "vit" in args.arch: + intermediate_output, _ = model.get_intermediate_layers(inp, n) + output = torch.cat([x[:, 0] for x in intermediate_output], dim=-1) + if avgpool: + output = torch.cat((output.unsqueeze(-1), torch.mean(intermediate_output[-1][:, 1:], dim=1).unsqueeze(-1)), dim=-1) + output = output.reshape(output.shape[0], -1) + else: + output = model(inp) + output = linear_classifier(output) + + # compute cross entropy loss + loss = nn.CrossEntropyLoss()(output, target) + + acc1, = utils.accuracy(output, target, topk=(1,)) + # compute the gradients + optimizer.zero_grad() + loss.backward() + + # step + optimizer.step() + + # log + torch.cuda.synchronize() + batch_size = inp.shape[0] + metric_logger.update(loss=loss.item()) + metric_logger.update(lr=optimizer.param_groups[0]["lr"]) + metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) + + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + + +@torch.no_grad() +def validate_network(val_loader, model, linear_classifier, device, n, avgpool): + linear_classifier.eval() + metric_logger = utils.MetricLogger(delimiter=" ") + header = 'Test:' + for batch in metric_logger.log_every(val_loader, 20, header): + inp, target = batch[:2] + # move to gpu + inp = inp.to(device, non_blocking=True) + target = target.to(device, non_blocking=True) + + # forward + with torch.no_grad(): + if "vit" in args.arch: + intermediate_output, _ = model.get_intermediate_layers(inp, n) + output = torch.cat([x[:, 0] for x in intermediate_output], dim=-1) + if avgpool: + output = torch.cat((output.unsqueeze(-1), torch.mean(intermediate_output[-1][:, 1:], dim=1).unsqueeze(-1)), dim=-1) + output = output.reshape(output.shape[0], -1) + else: + output = model(inp) + output = linear_classifier(output) + loss = nn.CrossEntropyLoss()(output, target) + + if linear_classifier.module.num_labels >= 5: + acc1, acc5 = utils.accuracy(output, target, topk=(1, 5)) + else: + acc1, = utils.accuracy(output, target, topk=(1,)) + + batch_size = inp.shape[0] + metric_logger.update(loss=loss.item()) + metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) + if linear_classifier.module.num_labels >= 5: + metric_logger.meters['acc5'].update(acc5.item(), n=batch_size) + if linear_classifier.module.num_labels >= 5: + print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}' + .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss)) + else: + print('* Acc@1 {top1.global_avg:.3f} loss {losses.global_avg:.3f}' + .format(top1=metric_logger.acc1, losses=metric_logger.loss)) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + + +class LinearClassifier(nn.Module): + """Linear layer to train on top of frozen features""" + def __init__(self, dim, num_labels=1000): + super(LinearClassifier, self).__init__() + self.num_labels = num_labels + self.linear = nn.Linear(dim, num_labels) + self.linear.weight.data.normal_(mean=0.0, std=0.01) + self.linear.bias.data.zero_() + + def forward(self, x): + # flatten + x = x.view(x.size(0), -1) + + # linear layer + return self.linear(x) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser('Evaluation with linear classification on ImageNet') + parser.add_argument('--n_last_blocks', default=4, type=int, help="""Concatenate [CLS] tokens + for the `n` last blocks. We use `n=4` when evaluating ViT-Small and `n=1` with ViT-Base.""") + parser.add_argument('--avgpool_patchtokens', default=False, type=utils.bool_flag, + help="""Whether ot not to concatenate the global average pooled features to the [CLS] token. + We typically set this to False for ViT-Small and to True with ViT-Base.""") + parser.add_argument('--arch', default='vit_small', type=str, help='Architecture') + parser.add_argument('--dataset', default='cub', type=str, help='Dataset') + parser.add_argument('--device', default='cuda', type=str, help='Deivce') + parser.add_argument('--patch_size', default=16, type=int, help='Patch resolution of the model.') + parser.add_argument('--pretrained_weights', default='', type=str, help="Path to pretrained weights to evaluate.") + parser.add_argument("--checkpoint_key", default="teacher", type=str, help='Key to use in the checkpoint (example: "teacher")') + parser.add_argument('--epochs', default=100, type=int, help='Number of epochs of training.') + parser.add_argument("--lr", default=0.001, type=float, help="""Learning rate at the beginning of + training (highest LR used during training). The learning rate is linearly scaled + with the batch size, and specified here for a reference batch size of 256. + We recommend tweaking the LR depending on the checkpoint evaluated.""") + parser.add_argument('--batch_size_per_gpu', default=128, type=int, help='Per-GPU batch-size') + parser.add_argument("--dist_url", default="env://", type=str, help="""url used to set up + distributed training; see https://pytorch.org/docs/stable/distributed.html""") + parser.add_argument("--local_rank", default=0, type=int, help="Please ignore and do not set this argument.") + parser.add_argument('--data_path', default='/path/to/imagenet/', type=str) + parser.add_argument('--num_workers', default=10, type=int, help='Number of data loading workers per GPU.') + parser.add_argument('--val_freq', default=1, type=int, help="Epoch frequency for validation.") + parser.add_argument('--output_dir', default=".", help='Path to save logs and checkpoints') + parser.add_argument('--num_labels', default=1000, type=int, help='Number of labels for linear classifier') + parser.add_argument('--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set') + parser.add_argument('--ori_size', default=False, action='store_true', help='Evaluate on image raw size') + parser.add_argument('--input_size', default=224, type=int, help='Input image size, default(224).') + parser.add_argument('--distributed', default=False, action='store_true', help='Using distributed data parallel') + parser.add_argument("--weight_decay", default=0.001, type=float, help='weight decay') + args = parser.parse_args() + eval_linear(args) diff --git a/maskcut/weakly_supvervised_detection/hubconf.py b/maskcut/weakly_supvervised_detection/hubconf.py new file mode 100644 index 0000000000000000000000000000000000000000..3709271ed2b52bb86fbeb70fc02bc47d1add207e --- /dev/null +++ b/maskcut/weakly_supvervised_detection/hubconf.py @@ -0,0 +1,151 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +from torchvision.models.resnet import resnet50 + +import vision_transformer as vits + +dependencies = ["torch", "torchvision"] + + +def dino_vits16(pretrained=True, **kwargs): + """ + ViT-Small/16x16 pre-trained with DINO. + Achieves 74.5% top-1 accuracy on ImageNet with k-NN classification. + """ + model = vits.__dict__["vit_small"](patch_size=16, num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model + + +def dino_vits8(pretrained=True, **kwargs): + """ + ViT-Small/8x8 pre-trained with DINO. + Achieves 78.3% top-1 accuracy on ImageNet with k-NN classification. + """ + model = vits.__dict__["vit_small"](patch_size=8, num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model + + +def dino_vitb16(pretrained=True, **kwargs): + """ + ViT-Base/16x16 pre-trained with DINO. + Achieves 76.1% top-1 accuracy on ImageNet with k-NN classification. + """ + model = vits.__dict__["vit_base"](patch_size=16, num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model + + +def dino_vitb8(pretrained=True, **kwargs): + """ + ViT-Base/8x8 pre-trained with DINO. + Achieves 77.4% top-1 accuracy on ImageNet with k-NN classification. + """ + model = vits.__dict__["vit_base"](patch_size=8, num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model + + +def dino_resnet50(pretrained=True, **kwargs): + """ + ResNet-50 pre-trained with DINO. + Achieves 75.3% top-1 accuracy on ImageNet linear evaluation benchmark (requires to train `fc`). + """ + model = resnet50(pretrained=False, **kwargs) + model.fc = torch.nn.Identity() + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_resnet50_pretrain/dino_resnet50_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=False) + return model + + +def dino_xcit_small_12_p16(pretrained=True, **kwargs): + """ + XCiT-Small-12/16 pre-trained with DINO. + """ + model = torch.hub.load('facebookresearch/xcit:main', "xcit_small_12_p16", num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_xcit_small_12_p16_pretrain/dino_xcit_small_12_p16_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model + + +def dino_xcit_small_12_p8(pretrained=True, **kwargs): + """ + XCiT-Small-12/8 pre-trained with DINO. + """ + model = torch.hub.load('facebookresearch/xcit:main', "xcit_small_12_p8", num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_xcit_small_12_p8_pretrain/dino_xcit_small_12_p8_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model + + +def dino_xcit_medium_24_p16(pretrained=True, **kwargs): + """ + XCiT-Medium-24/16 pre-trained with DINO. + """ + model = torch.hub.load('facebookresearch/xcit:main', "xcit_medium_24_p16", num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_xcit_medium_24_p16_pretrain/dino_xcit_medium_24_p16_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model + + +def dino_xcit_medium_24_p8(pretrained=True, **kwargs): + """ + XCiT-Medium-24/8 pre-trained with DINO. + """ + model = torch.hub.load('facebookresearch/xcit:main', "xcit_medium_24_p8", num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_xcit_medium_24_p8_pretrain/dino_xcit_medium_24_p8_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model diff --git a/maskcut/weakly_supvervised_detection/main.py b/maskcut/weakly_supvervised_detection/main.py new file mode 100644 index 0000000000000000000000000000000000000000..84587a333d9a0dd17b16a276527638c441a3325b --- /dev/null +++ b/maskcut/weakly_supvervised_detection/main.py @@ -0,0 +1,410 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import argparse +import json +from pathlib import Path +import time +import datetime + +import torch +from torch import nn +import torch.distributed as dist +import torch.backends.cudnn as cudnn +from torchvision import datasets +from torchvision import transforms as pth_transforms +from torchvision import models as torchvision_models + +import utils +import vision_transformer as vits + +from torch.utils.tensorboard import SummaryWriter +import shutil +import itertools +import numpy as np + +from timm.scheduler import create_scheduler +from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy +from timm.data import create_transform +from timm.data import Mixup + +from samplers import RASampler +from datasets import build_dataset + +def main(args): + if args.device != 'cuda': + args.distributed = False + else: + utils.init_distributed_mode(args) + + print(args) + + # ========fix seeds ======== + seed = args.seed + utils.get_rank() + torch.manual_seed(seed) + np.random.seed(seed) + + device = torch.device(args.device) + cudnn.benchmark = True + + # ============ building network ... ============ + # if the network is a Vision Transformer (i.e. vit_tiny, vit_small, vit_base) + if args.arch in vits.__dict__.keys(): + model = vits.__dict__[args.arch](patch_size=args.patch_size, num_classes=args.num_labels, adjacency_bp=args.adjacency_bp, temperature=args.temperature) + embed_dim = model.embed_dim * (args.n_last_blocks + int(args.avgpool_patchtokens)) + else: + print(f"Unknow architecture: {args.arch}") + sys.exit(1) + + model.to(device) + model.eval() + + model_without_ddp = model + #if args.distributed: + # model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) # TODO: where has unused params? + # #model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + # model_without_ddp = model.module + n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) + print('number of params:', n_parameters) + # load weights to evaluate + utils.load_pretrained_weights(model_without_ddp, args.pretrained_weights, args.checkpoint_key, args.arch, args.patch_size) + print(f"Model {args.arch} built.") + + linear_classifier = LinearClassifier(embed_dim, num_labels=args.num_labels) + linear_classifier = linear_classifier.cuda() + classifier_without_ddp = linear_classifier + linear_classifier = nn.parallel.DistributedDataParallel(linear_classifier, device_ids=[args.gpu]) + + # ============ Build dataset ============ + dataset_train, args.num_labels = build_dataset(is_train = True, args=args) + dataset_val, _ = build_dataset(is_train=False, args=args) + + num_tasks = utils.get_world_size() + global_rank = utils.get_rank() + + if args.distributed: + if args.data_aug and args.repeated_aug: + sampler_train = RASampler( + dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.distributed.DistributedSampler(dataset_train) + sampler_val = torch.utils.data.distributed.DistributedSampler(dataset_val, shuffle=False) + else: + sampler = torch.utils.data.RandomSampler(dataset_train) + sampler_val = torch.utils.data.SequentialSampler(dataset_val) + + train_loader = torch.utils.data.DataLoader( + dataset_train, + sampler=sampler_train, + batch_size=args.batch_size_per_gpu, + num_workers=args.num_workers, + pin_memory=True, + ) + val_loader = torch.utils.data.DataLoader( + dataset_val, + sampler=sampler_val, + batch_size=args.batch_size_per_gpu, + num_workers=args.num_workers, + pin_memory=True, + ) + print(f"Data loaded with {len(dataset_train)} train and {len(dataset_val)} val imgs.") + + if args.evaluate: + checkpoint = torch.load(args.checkpoint, map_location='cpu') + model_without_ddp.load_state_dict(checkpoint['model']) + test_stats = validate_network(val_loader, model_without_ddp, device) + print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%") + return + + # set optimizer + optimizer = torch.optim.SGD( + linear_classifier.parameters(), + args.lr * (args.batch_size_per_gpu * utils.get_world_size()) / 256., # linear scaling rule + momentum=0.9, + weight_decay=args.weight_decay, # we do not apply weight decay + ) + scheduler, _ = create_scheduler(args, optimizer) + + criterion = nn.CrossEntropyLoss() + # ----Mixup ----- + mixup_fn = None + smoothing = None + if args.data_aug: + print('Data augmentation: Mixup CutMix enable') + mixup_fn = Mixup( + mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax, + prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode, + label_smoothing=args.smoothing, num_classes=args.num_labels) + criterion = SoftTargetCrossEntropy() + + if utils.is_main_process(): + writer = SummaryWriter(args.output_dir + '/log') + start_epoch = 0 + best_acc = 0 + print("Starting training") + start_time = time.time() + for epoch in range(start_epoch, args.epochs): + if args.distributed: + train_loader.sampler.set_epoch(epoch) + + train_stats = train(model_without_ddp, device, optimizer, train_loader, epoch, criterion, linear_classifier, args.n_last_blocks, args.avgpool_patchtokens, mixup_fn) + + scheduler.step(epoch) + + log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, + 'epoch': epoch} + if epoch % args.val_freq == 0 or epoch == args.epochs - 1: + test_stats = validate_network(val_loader, model, device, linear_classifier, args.n_last_blocks, args.avgpool_patchtokens) + print(f"Accuracy at epoch {epoch} of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%") + log_stats = {**{k: v for k, v in log_stats.items()}, + **{f'test_{k}': v for k, v in test_stats.items()}} + if utils.is_main_process(): + with (Path(args.output_dir) / "log.txt").open("a") as f: + f.write(json.dumps(log_stats) + "\n") + save_dict = { + "epoch": epoch + 1, + "classifier": classifier_without_ddp.state_dict(), + "model": model_without_ddp.state_dict(), + "optimizer": optimizer.state_dict(), + "scheduler": scheduler.state_dict(), + "best_acc": best_acc, + } + + writer.add_scalar('Train_loss', train_stats['loss'], global_step=epoch) + writer.add_scalar('Learning_rate', train_stats['lr'], global_step=epoch) + writer.add_scalar('Train Acc_1', train_stats['acc1'], global_step=epoch) + writer.add_scalar('Acc_1', test_stats['acc1'], global_step=epoch) + writer.add_scalar('Acc_5', test_stats['acc5'], global_step=epoch) + + checkpoint_path = os.path.join(args.output_dir, "checkpoint.pth") + torch.save(save_dict, checkpoint_path) + + if best_acc < float(test_stats['acc1']): + best_acc = float(test_stats['acc1']) + shutil.copyfile(checkpoint_path, args.output_dir + '/model_best.pth') + print(f'Max accuracy so far: {best_acc:.2f}%') + print("Training of the TokenCut completed.\n" + "Top-1 test accuracy: {acc:.1f}".format(acc=best_acc)) + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print(f'Training time {total_time_str}') + + +def train(model, device, optimizer, loader, epoch, criterion, linear_classifier, n, avgpool, mixup_fn=None,): + linear_classifier.train() + metric_logger = utils.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + for batch in metric_logger.log_every(loader, 20, header): + inp, target = batch[:2] + # move to gpu + inp = inp.to(device, non_blocking=True) + target = target.to(device, non_blocking=True) + hard_target = target.clone() + if args.data_aug: + inp, target = mixup_fn(inp, target) + # forward + with torch.no_grad(): + intermediate_output,_ = model.get_intermediate_layers(inp, n) + output = torch.cat([x[:, 0] for x in intermediate_output], dim=-1) + if avgpool: + output = torch.cat((output.unsqueeze(-1), torch.mean(intermediate_output[-1][:, 1:], dim=1).unsqueeze(-1)), dim=-1) + output = output.reshape(output.shape[0], -1) + output = linear_classifier(output) + + # compute cross entropy loss + loss = criterion(output, target) + + acc1, = utils.accuracy(output, hard_target, topk=(1,)) + # compute the gradients + optimizer.zero_grad() + loss.backward() + + # step + optimizer.step() + + # log + torch.cuda.synchronize() + batch_size = inp.shape[0] + metric_logger.update(loss=loss.item()) + metric_logger.update(lr=optimizer.param_groups[0]["lr"]) + metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + + +@torch.no_grad() +def validate_network(val_loader, model, device, linear_classifier, n, avgpool): + linear_classifier.eval() + metric_logger = utils.MetricLogger(delimiter=" ") + header = 'Test:' + # for inp, target, _ in metric_logger.log_every(val_loader, 20, header): + for batch in metric_logger.log_every(val_loader, 20, header): + inp, target = batch[:2] + # move to gpu + inp = inp.to(device, non_blocking=True) + target = target.to(device, non_blocking=True) + + # forward + with torch.no_grad(): + intermediate_output,_ = model.get_intermediate_layers(inp, n) + output = torch.cat([x[:, 0] for x in intermediate_output], dim=-1) + if avgpool: + output = torch.cat((output.unsqueeze(-1), torch.mean(intermediate_output[-1][:, 1:], dim=1).unsqueeze(-1)), dim=-1) + output = output.reshape(output.shape[0], -1) + output = linear_classifier(output) + loss = nn.CrossEntropyLoss()(output, target) + + if linear_classifier.module.num_labels >= 5: + acc1, acc5 = utils.accuracy(output, target, topk=(1, 5)) + else: + acc1, = utils.accuracy(output, target, topk=(1,)) + + batch_size = inp.shape[0] + metric_logger.update(loss=loss.item()) + metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) + if linear_classifier.module.num_labels >= 5: + metric_logger.meters['acc5'].update(acc5.item(), n=batch_size) + if linear_classifier.module.num_labels >= 5: + print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}' + .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss)) + else: + print('* Acc@1 {top1.global_avg:.3f} loss {losses.global_avg:.3f}' + .format(top1=metric_logger.acc1, losses=metric_logger.loss)) + + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + +class LinearClassifier(nn.Module): + """Linear layer to train on top of frozen features""" + def __init__(self, dim, num_labels=1000): + super(LinearClassifier, self).__init__() + self.num_labels = num_labels + self.linear = nn.Linear(dim, num_labels) + self.linear.weight.data.normal_(mean=0.0, std=0.01) + self.linear.bias.data.zero_() + + def forward(self, x): + # flatten + x = x.view(x.size(0), -1) + + # linear layer + return self.linear(x) + +if __name__ == '__main__': + parser = argparse.ArgumentParser('Evaluation with linear classification on ImageNet') + parser.add_argument('--n_last_blocks', default=4, type=int, help="""Concatenate [CLS] tokens + for the `n` last blocks. We use `n=4` when evaluating ViT-Small and `n=1` with ViT-Base.""") + parser.add_argument('--avgpool_patchtokens', default=False, type=utils.bool_flag, + help="""Whether ot not to concatenate the global average pooled features to the [CLS] token. + We typically set this to False for ViT-Small and to True with ViT-Base.""") + parser.add_argument('--arch', default='vit_small', choices=['vit_small', 'vit_base'], type=str, help='Architecture') + parser.add_argument('--dataset', default='cub', type=str, choices=['cub', 'imagenet'], help='Architecture') + parser.add_argument('--patch_size', default=16, type=int, help='Patch resolution of the model.') + parser.add_argument('--input_size', default=224, type=int, help='Input image size, default(224).') + parser.add_argument('--pretrained_weights', default='', type=str, help="Path to pretrained weights to evaluate.") + parser.add_argument("--checkpoint_key", default="teacher", type=str, help='Key to use in the checkpoint (example: "teacher")') + parser.add_argument('--epochs', default=100, type=int, help='Number of epochs of training.') + parser.add_argument('--batch_size_per_gpu', default=128, type=int, help='Per-GPU batch-size') + parser.add_argument("--dist_url", default="env://", type=str, help="""url used to set up + distributed training; see https://pytorch.org/docs/stable/distributed.html""") + parser.add_argument("--local_rank", default=0, type=int, help="Please ignore and do not set this argument.") + parser.add_argument('--data_path', default='/path/to/imagenet/', type=str) + parser.add_argument('--num_workers', default=10, type=int, help='Number of data loading workers per GPU.') + parser.add_argument('--val_freq', default=1, type=int, help="Epoch frequency for validation.") + parser.add_argument('--output_dir', default="./checkpoints", help='Path to save logs and checkpoints') + parser.add_argument('--num_labels', default=1000, type=int, help='Number of labels for linear classifier') + parser.add_argument('--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set') + parser.add_argument('--weight_decay', default=0.1, type=float, help="weight_decay, default 0.1") + parser.add_argument('--device', default='cuda', help='device to use for training / testing') + parser.add_argument('--distributed', default=False, action='store_true', help='device to use for training / testing') + parser.add_argument('--adjacency_bp', default=False, action='store_true', help='whether backprop from adjacency matrix') + parser.add_argument('--temperature', default=1, type=int, help='Temperature for mask') + parser.add_argument('--seed', default=0, type=int) + + # ------ lr scheduler ------ + parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER', + help='LR scheduler (default: "cosine"') + parser.add_argument('--lr', type=float, default=1e-4, metavar='LR', + help="""Learning rate at the beginning of + training (highest LR used during training). The learning rate is linearly scaled + with the batch size, and specified here for a reference batch size of 256. + We recommend tweaking the LR depending on the checkpoint evaluated.""") + parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct', + help='learning rate noise on/off epoch percentages') + parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT', + help='learning rate noise limit percent (default: 0.67)') + parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV', + help='learning rate noise std-dev (default: 1.0)') + parser.add_argument('--warmup-lr', type=float, default=1e-6, metavar='LR', + help='warmup learning rate (default: 1e-6)') + parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR', + help='lower lr bound for cyclic schedulers that hit 0 (1e-5)') + parser.add_argument('--decay-epochs', type=float, default=5, metavar='N', + help='epoch interval to decay LR') + parser.add_argument('--warmup-epochs', type=int, default=5, metavar='N', + help='epochs to warmup LR, if scheduler supports') + parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N', + help='epochs to cooldown LR at min_lr, after cyclic schedule ends') + parser.add_argument('--patience-epochs', type=int, default=10, metavar='N', + help='patience epochs for Plateau LR scheduler (default: 10') + parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE', + help='LR decay rate (default: 0.1)') + # --------data aug--------- + parser.add_argument('--label-smooth-loss', default=False, action='store_true', help='use label smooth') + # * Random Erase params + parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT', + help='Random erase prob (default: 0.25)') + parser.add_argument('--remode', type=str, default='pixel', + help='Random erase mode (default: "pixel")') + parser.add_argument('--recount', type=int, default=1, + help='Random erase count (default: 1)') + parser.add_argument('--resplit', action='store_true', default=False, + help='Do not random erase first (clean) augmentation split') + + # * Mixup params + parser.add_argument('--mixup', type=float, default=0.8, + help='mixup alpha, mixup enabled if > 0. (default: 0.8)') + parser.add_argument('--cutmix', type=float, default=1.0, + help='cutmix alpha, cutmix enabled if > 0. (default: 1.0)') + parser.add_argument('--cutmix-minmax', type=float, nargs='+', default=None, + help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)') + parser.add_argument('--mixup-prob', type=float, default=1.0, + help='Probability of performing mixup or cutmix when either/both is enabled') + parser.add_argument('--mixup-switch-prob', type=float, default=0.5, + help='Probability of switching to cutmix when both mixup and cutmix enabled') + parser.add_argument('--mixup-mode', type=str, default='batch', + help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"') + + # Augmentation parameters + parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT', + help='Color jitter factor (default: 0.4)') + parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME', + help='Use AutoAugment policy. "v0" or "original". " + \ + "(default: rand-m9-mstd0.5-inc1)'), + parser.add_argument('--smoothing', type=float, default=0.1, help='Label smoothing (default: 0.1)') + parser.add_argument('--train-interpolation', type=str, default='bicubic', + help='Training interpolation (random, bilinear, bicubic default: "bicubic")') + + parser.add_argument('--repeated-aug', action='store_true') + parser.add_argument('--no-repeated-aug', action='store_false', dest='repeated_aug') + parser.set_defaults(repeated_aug=True) + + parser.add_argument('--no_center_crop', default=False, action='store_true', help='Center crop input image') + parser.add_argument('--data-aug', action='store_true', default=False, help='disable the data augmentations.') + parser.add_argument('--ori_size', default=False, action='store_true', help='Evaluate on image raw size') + args = parser.parse_args() + main(args) diff --git a/maskcut/weakly_supvervised_detection/meters.py b/maskcut/weakly_supvervised_detection/meters.py new file mode 100644 index 0000000000000000000000000000000000000000..cd74698eaf524397cea6829d289a1d9753596852 --- /dev/null +++ b/maskcut/weakly_supvervised_detection/meters.py @@ -0,0 +1,93 @@ +import numpy as np + +class AverageEpochMeter(object): + """Computes and stores the average and current value""" + def __init__(self, name, fmt=':f'): + self.name = name + self.fmt = fmt + self.reset() + + def reset(self): + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.sum += val * n + self.count += n + + def compute(self): + self.avg = self.sum / self.count + return self.avg + + def __str__(self): + fmtstr = '{name}: {avg' + self.fmt + '}' + return fmtstr.format(**self.__dict__) + +#class MultiAverageEpochMeter(object): +# def __init__(self, name, fmt=':f'): +# self.name = name +# self.fmt = fmt +# self.thresholds = np.arange(0., 1., 0.001) +# self.reset() +# +# def reset(self): +# self.avg = 0 +# self.sum = np.zeros(len(self.thresholds)) +# self.count = 0 +# +# def update(self, val, n=1): +# self.sum += val * n +# self.count += n +# +# def compute(self): +# self.avg = self.sum / self.count +# return self.avg.max() +# +# def __str__(self): +# fmtstr = '{name}: {avg' + self.fmt + '}' +# return fmtstr.format(**self.__dict__) +# +# +#class AUCEpochMeter(object): +# +# def __init__(self, name, fmt=':f'): +# self.name = name +# self.fmt = fmt +# self.thresholds = np.append(np.arange(0., 1., 0.001), [1., 2., 3.]) +# self.num_bins = len(self.thresholds) - 1 +# self.reset() +# +# def reset(self): +# self.gt_true_score_hist = np.zeros(self.num_bins, dtype=np.float) +# self.gt_false_score_hist = np.zeros(self.num_bins, dtype=np.float) +# +# def update(self, gt_true_hist, gt_false_hist): +# self.gt_true_score_hist += gt_true_hist +# self.gt_false_score_hist += gt_false_hist +# +# def compute(self): +# num_gt_true = self.gt_true_score_hist.sum() +# tp = self.gt_true_score_hist[::-1].cumsum() +# fn = num_gt_true - tp +# +# num_gt_false = self.gt_false_score_hist.sum() +# fp = self.gt_false_score_hist[::-1].cumsum() +# tn = num_gt_false - fp +# +# if ((tp + fn) <= 0).all(): +# raise RuntimeError("No positive ground truth in the eval set.") +# if ((tp + fp) <= 0).all(): +# raise RuntimeError("No positive prediction in the eval set.") +# +# non_zero_indices = (tp + fp) != 0 +# +# precision = tp / (tp + fp) +# recall = tp / (tp + fn) +# +# auc = (precision[1:] * np.diff(recall))[non_zero_indices[1:]].sum() +# return auc +# +# def __str__(self): +# fmtstr = '{name}: {avg' + self.fmt + '}' +# return fmtstr.format(**self.__dict__) diff --git a/maskcut/weakly_supvervised_detection/metrics.py b/maskcut/weakly_supvervised_detection/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..599eec204f3bb017735c5152be72e4ffea32e816 --- /dev/null +++ b/maskcut/weakly_supvervised_detection/metrics.py @@ -0,0 +1,41 @@ +import cv2 +import torch +import numpy as np + +def loc_accuracy(outputs, labels, gt_boxes, bboxes, iou_threshold=0.5): + if outputs is not None: + _, pred = torch.topk(outputs, k=1, dim=1, largest=True, sorted=True) + pred = pred.t() + correct = pred.eq(labels.view(1, -1).expand_as(pred)) + wrongs = [c == 0 for c in correct.cpu().numpy()][0] + + batch_size = len(gt_boxes) + gt_known, top1 = 0., 0. + for i, (gt_box, bbox) in enumerate(zip(gt_boxes, bboxes)): + iou_score = iou(gt_box, bbox) + + if iou_score >= iou_threshold: + gt_known += 1. + if outputs is not None and not wrongs[i]: + top1 += 1. + + gt_loc = gt_known / batch_size + top1_loc = top1 / batch_size + return gt_loc, top1_loc + +def iou(box1, box2): + """box: (xmin, ymin, xmax, ymax)""" + box1_xmin, box1_ymin, box1_xmax, box1_ymax = box1 + box2_xmin, box2_ymin, box2_xmax, box2_ymax = box2 + + inter_xmin = max(box1_xmin, box2_xmin) + inter_ymin = max(box1_ymin, box2_ymin) + inter_xmax = min(box1_xmax, box2_xmax) + inter_ymax = min(box1_ymax, box2_ymax) + + inter_area = (inter_xmax - inter_xmin + 1) * (inter_ymax - inter_ymin + 1) + box1_area = (box1_xmax - box1_xmin + 1) * (box1_ymax - box1_ymin + 1) + box2_area = (box2_xmax - box2_xmin + 1) * (box2_ymax - box2_ymin + 1) + + iou = inter_area / (box1_area + box2_area - inter_area).float() + return iou.item() diff --git a/maskcut/weakly_supvervised_detection/object_discovery.py b/maskcut/weakly_supvervised_detection/object_discovery.py new file mode 100644 index 0000000000000000000000000000000000000000..63f961741a8c89014b8c4243064373ba7926815a --- /dev/null +++ b/maskcut/weakly_supvervised_detection/object_discovery.py @@ -0,0 +1,108 @@ +""" +Main functions for applying Normalized Cut. +Code adapted from LOST: https://github.com/valeoai/LOST +""" +import torch +import torch.nn.functional as F +import numpy as np +#from scipy.linalg.decomp import eig +from scipy.linalg import eigh +from scipy import ndimage +from sklearn.cluster import KMeans +from sklearn.mixture import GaussianMixture + +def ncut(feats, dims, scales, init_image_size, eps=1e-5, tau=0.05): + #feats = feats[0,1:,:].detach().cpu().numpy() + feats = feats[0,1:,:] + feats = F.normalize(feats, p=2) + A = (feats @ feats.transpose(1,0)) + A = A.cpu().numpy() + + A = A > tau + A = np.where(A.astype(float) == 0, eps, A) + d_i = np.sum(A, axis=1) + D = np.diag(d_i) + + # Print second and third smallest eigenvector + _, eigenvectors = eigh(D-A, D, subset_by_index=[1,2]) + eigenvec = np.copy(eigenvectors[:, 0]) + + # method 2 avg + second_smallest_vec = eigenvectors[:, 0] + avg = np.sum(second_smallest_vec) / len(second_smallest_vec) + bipartition = second_smallest_vec > avg + + # method3 EM algo + #second_smallest_vec = eigenvectors[:, 0:1] + #bipartition = GMM(second_smallest_vec) + + # method 4 Kmeans + #second_smallest_vec = eigenvectors[:, 0:1] + #bipartition = Kmeans_partition(second_smallest_vec) + + seed = np.argmax(np.abs(second_smallest_vec)) + + if bipartition[seed] != 1: + bipartition = np.logical_not(bipartition) + eigenvec = eigenvec * -1 + bipartition = bipartition.reshape(dims).astype(float) + + # predict BBox + pred, _ = detect_box(bipartition, seed, dims, scales=scales, initial_im_size=init_image_size) ## We only extract the principal object BBox + + return np.asarray(pred), bipartition, seed, eigenvec.reshape(dims) + +def GMM(eigvec): + gmm = GaussianMixture(n_components=2, max_iter=300) + gmm.fit(eigvec) + partition = gmm.predict(eigvec) + return partition + +def Kmeans_partition(eigvec): + kmeans = KMeans(n_clusters=2, random_state=0).fit(eigvec) + return kmeans.labels_ + +def detect_box(bipartition, seed, dims, initial_im_size=None, scales=None, principle_object=True): + """ + Extract a box corresponding to the seed patch. Among connected components extract from the affinity matrix, select the one corresponding to the seed patch. + """ + + w_featmap, h_featmap = dims + objects, num_objects = ndimage.label(bipartition) + cc = objects[np.unravel_index(seed, dims)] + + + if principle_object: + mask = np.where(objects == cc) + # Add +1 because excluded max + ymin, ymax = min(mask[0]), max(mask[0]) + 1 + xmin, xmax = min(mask[1]), max(mask[1]) + 1 + # Rescale to image size + r_xmin, r_xmax = scales[1] * xmin, scales[1] * xmax + r_ymin, r_ymax = scales[0] * ymin, scales[0] * ymax + pred = [r_xmin, r_ymin, r_xmax, r_ymax] + + # Check not out of image size (used when padding) + if initial_im_size: + pred[2] = min(pred[2], initial_im_size[1]) + pred[3] = min(pred[3], initial_im_size[0]) + + # Coordinate predictions for the feature space + # Axis different then in image space + pred_feats = [ymin, xmin, ymax, xmax] + + return pred, pred_feats + else: + raise NotImplementedError + +def get_feats(feat_out, shape): + nb_im, nh, nb_tokens = shape[0:3] # Batch size, Number of heads, Number of tokens + qkv = ( + feat_out["qkv"] + .reshape(nb_im, nb_tokens, 3, nh, -1 // nh) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = qkv[0], qkv[1], qkv[2] + k = k.transpose(1, 2).reshape(nb_im, nb_tokens, -1) + return k + diff --git a/maskcut/weakly_supvervised_detection/requirements.txt b/maskcut/weakly_supvervised_detection/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..8ea5b7251f24582c9855374643b18185515edb8b --- /dev/null +++ b/maskcut/weakly_supvervised_detection/requirements.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ead6d92f44e5b0110140a8bd92c9097f11c6f95bcd7f6912c7b6ace33c35f99 +size 99 diff --git a/maskcut/weakly_supvervised_detection/samplers.py b/maskcut/weakly_supvervised_detection/samplers.py new file mode 100644 index 0000000000000000000000000000000000000000..e67dc6ddd2048a0032c1892540f3289bba4465ea --- /dev/null +++ b/maskcut/weakly_supvervised_detection/samplers.py @@ -0,0 +1,59 @@ +# Copyright (c) 2015-present, Facebook, Inc. +# All rights reserved. +import torch +import torch.distributed as dist +import math + + +class RASampler(torch.utils.data.Sampler): + """Sampler that restricts data loading to a subset of the dataset for distributed, + with repeated augmentation. + It ensures that different each augmented version of a sample will be visible to a + different process (GPU) + Heavily based on torch.utils.data.DistributedSampler + """ + + def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True): + if num_replicas is None: + if not dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + num_replicas = dist.get_world_size() + if rank is None: + if not dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + rank = dist.get_rank() + self.dataset = dataset + self.num_replicas = num_replicas + self.rank = rank + self.epoch = 0 + self.num_samples = int(math.ceil(len(self.dataset) * 3.0 / self.num_replicas)) + self.total_size = self.num_samples * self.num_replicas + # self.num_selected_samples = int(math.ceil(len(self.dataset) / self.num_replicas)) + self.num_selected_samples = int(math.floor(len(self.dataset) // 256 * 256 / self.num_replicas)) + self.shuffle = shuffle + + def __iter__(self): + # deterministically shuffle based on epoch + g = torch.Generator() + g.manual_seed(self.epoch) + if self.shuffle: + indices = torch.randperm(len(self.dataset), generator=g).tolist() + else: + indices = list(range(len(self.dataset))) + + # add extra samples to make it evenly divisible + indices = [ele for ele in indices for i in range(3)] + indices += indices[:(self.total_size - len(indices))] + assert len(indices) == self.total_size + + # subsample + indices = indices[self.rank:self.total_size:self.num_replicas] + assert len(indices) == self.num_samples + + return iter(indices[:self.num_selected_samples]) + + def __len__(self): + return self.num_selected_samples + + def set_epoch(self, epoch): + self.epoch = epoch diff --git a/maskcut/weakly_supvervised_detection/utils.py b/maskcut/weakly_supvervised_detection/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e318885f2c9b08522b0709710df6cfdca6fa0432 --- /dev/null +++ b/maskcut/weakly_supvervised_detection/utils.py @@ -0,0 +1,858 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Misc functions. + +Mostly copy-paste from torchvision references or other public repos like DETR: +https://github.com/facebookresearch/detr/blob/master/util/misc.py +""" +import os +import sys +import time +import math +import random +import datetime +import subprocess +from collections import defaultdict, deque + +import numpy as np +import torch +from torch import nn +import torch.distributed as dist +from PIL import ImageFilter, ImageOps + +from scipy import ndimage + +class GaussianBlur(object): + """ + Apply Gaussian Blur to the PIL image. + """ + def __init__(self, p=0.5, radius_min=0.1, radius_max=2.): + self.prob = p + self.radius_min = radius_min + self.radius_max = radius_max + + def __call__(self, img): + do_it = random.random() <= self.prob + if not do_it: + return img + + return img.filter( + ImageFilter.GaussianBlur( + radius=random.uniform(self.radius_min, self.radius_max) + ) + ) + + +class Solarization(object): + """ + Apply Solarization to the PIL image. + """ + def __init__(self, p): + self.p = p + + def __call__(self, img): + if random.random() < self.p: + return ImageOps.solarize(img) + else: + return img + + +def load_pretrained_weights(model, pretrained_weights, checkpoint_key, model_name, patch_size): + if os.path.isfile(pretrained_weights): + state_dict = torch.load(pretrained_weights, map_location="cpu") + if checkpoint_key is not None and checkpoint_key in state_dict: + print(f"Take key {checkpoint_key} in provided checkpoint dict") + state_dict = state_dict[checkpoint_key] + # remove `module.` prefix + state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()} + # remove `backbone.` prefix induced by multicrop wrapper + state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()} + msg = model.load_state_dict(state_dict, strict=False) + #print('Pretrained weights found at {} and loaded with msg: {}'.format(pretrained_weights, msg)) + print('Pretrained weights found at {} and loaded, (to see more info, uncomment line 82 in utils.py'.format(pretrained_weights)) + else: + print("Please use the `--pretrained_weights` argument to indicate the path of the checkpoint to evaluate.") + url = None + if model_name == "vit_small" and patch_size == 16: + url = "dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth" + elif model_name == "vit_small" and patch_size == 8: + url = "dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth" + elif model_name == "vit_base" and patch_size == 16: + url = "dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth" + elif model_name == "vit_base" and patch_size == 8: + url = "dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth" + if url is not None: + print("Since no pretrained weights have been provided, we load the reference pretrained DINO weights.") + state_dict = torch.hub.load_state_dict_from_url(url="https://dl.fbaipublicfiles.com/dino/" + url) + model_dict = model.state_dict() + pretrained_dict = {k:v for k,v in state_dict.items() if k in model_dict} + #print(f'load from pretrain model:') + #for k,v in pretrained_dict.items(): + # print(f'key:{k}') + + #print(f'keys not in pretrained model:') + #for k,v in model_dict.items(): + # if k not in pretrained_dict.keys(): + # print(f'not in key: {k}') + model_dict.update(pretrained_dict) + model.load_state_dict(model_dict) + #model.load_state_dict(state_dict, strict=False) + else: + print("There is no reference weights available for this model => We use random weights.") + + +def load_pretrained_linear_weights(linear_classifier, model_name, patch_size): + url = None + if model_name == "vit_small" and patch_size == 16: + url = "dino_deitsmall16_pretrain/dino_deitsmall16_linearweights.pth" + elif model_name == "vit_small" and patch_size == 8: + url = "dino_deitsmall8_pretrain/dino_deitsmall8_linearweights.pth" + elif model_name == "vit_base" and patch_size == 16: + url = "dino_vitbase16_pretrain/dino_vitbase16_linearweights.pth" + elif model_name == "vit_base" and patch_size == 8: + url = "dino_vitbase8_pretrain/dino_vitbase8_linearweights.pth" + if url is not None: + print("We load the reference pretrained linear weights.") + state_dict = torch.hub.load_state_dict_from_url(url="https://dl.fbaipublicfiles.com/dino/" + url)["state_dict"] + linear_classifier.load_state_dict(state_dict, strict=True) + else: + print("We use random linear weights.") + + +def clip_gradients(model, clip): + norms = [] + for name, p in model.named_parameters(): + if p.grad is not None: + param_norm = p.grad.data.norm(2) + norms.append(param_norm.item()) + clip_coef = clip / (param_norm + 1e-6) + if clip_coef < 1: + p.grad.data.mul_(clip_coef) + return norms + + +def cancel_gradients_last_layer(epoch, model, freeze_last_layer): + if epoch >= freeze_last_layer: + return + for n, p in model.named_parameters(): + if "last_layer" in n: + p.grad = None + + +def restart_from_checkpoint(ckp_path, run_variables=None, **kwargs): + """ + Re-start from checkpoint + """ + if not os.path.isfile(ckp_path): + return + print("Found checkpoint at {}".format(ckp_path)) + + # open checkpoint file + checkpoint = torch.load(ckp_path, map_location="cpu") + + # key is what to look for in the checkpoint file + # value is the object to load + # example: {'state_dict': model} + for key, value in kwargs.items(): + print(f'-key: {key}') + if key in checkpoint and value is not None: + try: + msg = value.load_state_dict(checkpoint[key], strict=False) + print("=> loaded '{}' from checkpoint '{}' with msg {}".format(key, ckp_path, msg)) + except TypeError: + try: + msg = value.load_state_dict(checkpoint[key]) + print("=> loaded '{}' from checkpoint: '{}'".format(key, ckp_path)) + except ValueError: + print("=> failed to load '{}' from checkpoint: '{}'".format(key, ckp_path)) + else: + print("=> key '{}' not found in checkpoint: '{}'".format(key, ckp_path)) + + # re load variable important for the run + if run_variables is not None: + for var_name in run_variables: + if var_name in checkpoint: + run_variables[var_name] = checkpoint[var_name] + + +def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warmup_epochs=0, start_warmup_value=0): + warmup_schedule = np.array([]) + warmup_iters = warmup_epochs * niter_per_ep + if warmup_epochs > 0: + warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters) + + iters = np.arange(epochs * niter_per_ep - warmup_iters) + schedule = final_value + 0.5 * (base_value - final_value) * (1 + np.cos(np.pi * iters / len(iters))) + + schedule = np.concatenate((warmup_schedule, schedule)) + assert len(schedule) == epochs * niter_per_ep + return schedule + + +def bool_flag(s): + """ + Parse boolean arguments from the command line. + """ + FALSY_STRINGS = {"off", "false", "0"} + TRUTHY_STRINGS = {"on", "true", "1"} + if s.lower() in FALSY_STRINGS: + return False + elif s.lower() in TRUTHY_STRINGS: + return True + else: + raise argparse.ArgumentTypeError("invalid value for a boolean flag") + + +def fix_random_seeds(seed=31): + """ + Fix random seeds. + """ + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + np.random.seed(seed) + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.6f} ({global_avg:.6f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not is_dist_avail_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') + dist.barrier() + dist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value) + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that all processes + have the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.all_reduce(values) + if average: + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format( + type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append( + "{}: {}".format(name, str(meter)) + ) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None): + i = 0 + if not header: + header = '' + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt='{avg:.6f}') + data_time = SmoothedValue(fmt='{avg:.6f}') + space_fmt = ':' + str(len(str(len(iterable)))) + 'd' + if torch.cuda.is_available(): + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}', + 'max mem: {memory:.0f}' + ]) + else: + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}' + ]) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB)) + else: + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time))) + i += 1 + end = time.time() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('{} Total time: {} ({:.6f} s / it)'.format( + header, total_time_str, total_time / len(iterable))) + + +def get_sha(): + cwd = os.path.dirname(os.path.abspath(__file__)) + + def _run(command): + return subprocess.check_output(command, cwd=cwd).decode('ascii').strip() + sha = 'N/A' + diff = "clean" + branch = 'N/A' + try: + sha = _run(['git', 'rev-parse', 'HEAD']) + subprocess.check_output(['git', 'diff'], cwd=cwd) + diff = _run(['git', 'diff-index', 'HEAD']) + diff = "has uncommited changes" if diff else "clean" + branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) + except Exception: + pass + message = f"sha: {sha}, status: {diff}, branch: {branch}" + return message + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop('force', False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def init_distributed_mode(args): + # launched with torch.distributed.launch + if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ['WORLD_SIZE']) + args.gpu = int(os.environ['LOCAL_RANK']) + # launched with submitit on a slurm cluster + elif 'SLURM_PROCID' in os.environ: + args.rank = int(os.environ['SLURM_PROCID']) + args.gpu = args.rank % torch.cuda.device_count() + # launched naively with `python main_dino.py` + # we manually add MASTER_ADDR and MASTER_PORT to env variables + elif torch.cuda.is_available(): + print('Will run the code on one GPU.') + args.rank, args.gpu, args.world_size = 0, 0, 1 + os.environ['MASTER_ADDR'] = '127.0.0.1' + os.environ['MASTER_PORT'] = '29500' + else: + print('Does not support training without GPU.') + sys.exit(1) + + dist.init_process_group( + backend="nccl", + init_method=args.dist_url, + world_size=args.world_size, + rank=args.rank, + ) + args.distributed = True + + torch.cuda.set_device(args.gpu) + print('| distributed init (rank {}): {}'.format( + args.rank, args.dist_url), flush=True) + dist.barrier() + #dist.barrier(device_ids=[args.gpu]) + setup_for_distributed(args.rank == 0) + + +def accuracy(output, target, topk=(1,)): + """Computes the accuracy over the k top predictions for the specified values of k""" + maxk = max(topk) + batch_size = target.size(0) + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.reshape(1, -1).expand_as(pred)) + return [correct[:k].reshape(-1).float().sum(0) * 100. / batch_size for k in topk] + + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): + # type: (Tensor, float, float, float, float) -> Tensor + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +class LARS(torch.optim.Optimizer): + """ + Almost copy-paste from https://github.com/facebookresearch/barlowtwins/blob/main/main.py + """ + def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, eta=0.001, + weight_decay_filter=None, lars_adaptation_filter=None): + defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum, + eta=eta, weight_decay_filter=weight_decay_filter, + lars_adaptation_filter=lars_adaptation_filter) + super().__init__(params, defaults) + + @torch.no_grad() + def step(self): + for g in self.param_groups: + for p in g['params']: + dp = p.grad + + if dp is None: + continue + + if p.ndim != 1: + dp = dp.add(p, alpha=g['weight_decay']) + + if p.ndim != 1: + param_norm = torch.norm(p) + update_norm = torch.norm(dp) + one = torch.ones_like(param_norm) + q = torch.where(param_norm > 0., + torch.where(update_norm > 0, + (g['eta'] * param_norm / update_norm), one), one) + dp = dp.mul(q) + + param_state = self.state[p] + if 'mu' not in param_state: + param_state['mu'] = torch.zeros_like(p) + mu = param_state['mu'] + mu.mul_(g['momentum']).add_(dp) + + p.add_(mu, alpha=-g['lr']) + + +class MultiCropWrapper(nn.Module): + """ + Perform forward pass separately on each resolution input. + The inputs corresponding to a single resolution are clubbed and single + forward is run on the same resolution inputs. Hence we do several + forward passes = number of different resolutions used. We then + concatenate all the output features and run the head forward on these + concatenated features. + """ + def __init__(self, backbone, head): + super(MultiCropWrapper, self).__init__() + # disable layers dedicated to ImageNet labels classification + backbone.fc, backbone.head = nn.Identity(), nn.Identity() + self.backbone = backbone + self.head = head + + def forward(self, x): + # convert to list + if not isinstance(x, list): + x = [x] + idx_crops = torch.cumsum(torch.unique_consecutive( + torch.tensor([inp.shape[-1] for inp in x]), + return_counts=True, + )[1], 0) + start_idx, output = 0, torch.empty(0).to(x[0].device) + for end_idx in idx_crops: + _out = self.backbone(torch.cat(x[start_idx: end_idx])) + # The output is a tuple with XCiT model. See: + # https://github.com/facebookresearch/xcit/blob/master/xcit.py#L404-L405 + if isinstance(_out, tuple): + _out = _out[0] + # accumulate outputs + output = torch.cat((output, _out)) + start_idx = end_idx + # Run the head forward on the concatenated features. + return self.head(output) + + +def get_params_groups(model): + regularized = [] + not_regularized = [] + for name, param in model.named_parameters(): + if not param.requires_grad: + continue + # we do not regularize biases nor Norm parameters + if name.endswith(".bias") or len(param.shape) == 1: + not_regularized.append(param) + else: + regularized.append(param) + return [{'params': regularized}, {'params': not_regularized, 'weight_decay': 0.}] + + +def has_batchnorms(model): + bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm) + for name, module in model.named_modules(): + if isinstance(module, bn_types): + return True + return False + + +class PCA(): + """ + Class to compute and apply PCA. + """ + def __init__(self, dim=256, whit=0.5): + self.dim = dim + self.whit = whit + self.mean = None + + def train_pca(self, cov): + """ + Takes a covariance matrix (np.ndarray) as input. + """ + d, v = np.linalg.eigh(cov) + eps = d.max() * 1e-5 + n_0 = (d < eps).sum() + if n_0 > 0: + d[d < eps] = eps + + # total energy + totenergy = d.sum() + + # sort eigenvectors with eigenvalues order + idx = np.argsort(d)[::-1][:self.dim] + d = d[idx] + v = v[:, idx] + + print("keeping %.2f %% of the energy" % (d.sum() / totenergy * 100.0)) + + # for the whitening + d = np.diag(1. / d**self.whit) + + # principal components + self.dvt = np.dot(d, v.T) + + def apply(self, x): + # input is from numpy + if isinstance(x, np.ndarray): + if self.mean is not None: + x -= self.mean + return np.dot(self.dvt, x.T).T + + # input is from torch and is on GPU + if x.is_cuda: + if self.mean is not None: + x -= torch.cuda.FloatTensor(self.mean) + return torch.mm(torch.cuda.FloatTensor(self.dvt), x.transpose(0, 1)).transpose(0, 1) + + # input if from torch, on CPU + if self.mean is not None: + x -= torch.FloatTensor(self.mean) + return torch.mm(torch.FloatTensor(self.dvt), x.transpose(0, 1)).transpose(0, 1) + + +def compute_ap(ranks, nres): + """ + Computes average precision for given ranked indexes. + Arguments + --------- + ranks : zerro-based ranks of positive images + nres : number of positive images + Returns + ------- + ap : average precision + """ + + # number of images ranked by the system + nimgranks = len(ranks) + + # accumulate trapezoids in PR-plot + ap = 0 + + recall_step = 1. / nres + + for j in np.arange(nimgranks): + rank = ranks[j] + + if rank == 0: + precision_0 = 1. + else: + precision_0 = float(j) / rank + + precision_1 = float(j + 1) / (rank + 1) + + ap += (precision_0 + precision_1) * recall_step / 2. + + return ap + + +def compute_map(ranks, gnd, kappas=[]): + """ + Computes the mAP for a given set of returned results. + Usage: + map = compute_map (ranks, gnd) + computes mean average precsion (map) only + map, aps, pr, prs = compute_map (ranks, gnd, kappas) + computes mean average precision (map), average precision (aps) for each query + computes mean precision at kappas (pr), precision at kappas (prs) for each query + Notes: + 1) ranks starts from 0, ranks.shape = db_size X #queries + 2) The junk results (e.g., the query itself) should be declared in the gnd stuct array + 3) If there are no positive images for some query, that query is excluded from the evaluation + """ + + map = 0. + nq = len(gnd) # number of queries + aps = np.zeros(nq) + pr = np.zeros(len(kappas)) + prs = np.zeros((nq, len(kappas))) + nempty = 0 + + for i in np.arange(nq): + qgnd = np.array(gnd[i]['ok']) + + # no positive images, skip from the average + if qgnd.shape[0] == 0: + aps[i] = float('nan') + prs[i, :] = float('nan') + nempty += 1 + continue + + try: + qgndj = np.array(gnd[i]['junk']) + except: + qgndj = np.empty(0) + + # sorted positions of positive and junk images (0 based) + pos = np.arange(ranks.shape[0])[np.in1d(ranks[:,i], qgnd)] + junk = np.arange(ranks.shape[0])[np.in1d(ranks[:,i], qgndj)] + + k = 0; + ij = 0; + if len(junk): + # decrease positions of positives based on the number of + # junk images appearing before them + ip = 0 + while (ip < len(pos)): + while (ij < len(junk) and pos[ip] > junk[ij]): + k += 1 + ij += 1 + pos[ip] = pos[ip] - k + ip += 1 + + # compute ap + ap = compute_ap(pos, len(qgnd)) + map = map + ap + aps[i] = ap + + # compute precision @ k + pos += 1 # get it to 1-based + for j in np.arange(len(kappas)): + kq = min(max(pos), kappas[j]); + prs[i, j] = (pos <= kq).sum() / kq + pr = pr + prs[i, :] + + map = map / (nq - nempty) + pr = pr / (nq - nempty) + + return map, aps, pr, prs + + +def multi_scale(samples, model): + v = None + for s in [1, 1/2**(1/2), 1/2]: # we use 3 different scales + if s == 1: + inp = samples.clone() + else: + inp = nn.functional.interpolate(samples, scale_factor=s, mode='bilinear', align_corners=False) + feats = model(inp).clone() + if v is None: + v = feats + else: + v += feats + v /= 3 + v /= v.norm() + return v + + + +def unnormalize_images(images): + mean = [0.485, 0.456, 0.406] + std = [0.229, 0.224, 0.225] + mean = torch.reshape(torch.tensor(mean), (1, 3, 1, 1)) + std = torch.reshape(torch.tensor(std), (1, 3, 1, 1)) + unnormalized_images = images.clone().detach().cpu() * std + mean + return unnormalized_images + +def padding_img(img, args): + # Padding the image with zeros to fit multiple of patch-size + size_im = ( + img.shape[0], + int(np.ceil(img.shape[1] / args.patch_size) * args.patch_size), + int(np.ceil(img.shape[2] / args.patch_size) * args.patch_size), + ) + paded = torch.zeros(size_im) + paded[:, : img.shape[1], : img.shape[2]] = img + img = paded + return img + + diff --git a/maskcut/weakly_supvervised_detection/vision_transformer.py b/maskcut/weakly_supvervised_detection/vision_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..583cdb4c781dbd44a6a52b94817c4902e9194fae --- /dev/null +++ b/maskcut/weakly_supvervised_detection/vision_transformer.py @@ -0,0 +1,294 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Mostly copy-paste from timm library. +https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py +""" +import math +from functools import partial + +import torch +import torch.nn as nn + +from utils import trunc_normal_ + + +def drop_path(x, drop_prob: float = 0., training: bool = False): + if drop_prob == 0. or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x, attn + + +class Block(nn.Module): + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x, return_attention=False): + y, attn= self.attn(self.norm1(x)) + if return_attention: + return attn + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + num_patches = (img_size // patch_size) * (img_size // patch_size) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x): + B, C, H, W = x.shape + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class VisionTransformer(nn.Module): + """ Vision Transformer """ + def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., norm_layer=nn.LayerNorm, **kwargs): + super().__init__() + self.num_features = self.embed_dim = embed_dim + + self.patch_embed = PatchEmbed( + img_size=img_size[0], patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + self.num_heads = num_heads + # Classifier head + #self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def interpolate_pos_encoding(self, x, w, h): + npatch = x.shape[1] - 1 + N = self.pos_embed.shape[1] - 1 + if npatch == N and w == h: + return self.pos_embed + class_pos_embed = self.pos_embed[:, 0] + patch_pos_embed = self.pos_embed[:, 1:] + dim = x.shape[-1] + w0 = w // self.patch_embed.patch_size + h0 = h // self.patch_embed.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + w0, h0 = w0 + 0.1, h0 + 0.1 + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2), + scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), + mode='bicubic', + ) + assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) + + def prepare_tokens(self, x): + B, nc, w, h = x.shape + x = self.patch_embed(x) # patch linear embedding + + # add the [CLS] token to the embed patch tokens + cls_tokens = self.cls_token.expand(B, -1, -1) + x = torch.cat((cls_tokens, x), dim=1) + + # add positional encoding to each token + x = x + self.interpolate_pos_encoding(x, w, h) + + return self.pos_drop(x) + + def forward(self, x): + x = self.prepare_tokens(x) + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + return x[:, 0] + + def get_last_selfattention(self, x): + x = self.prepare_tokens(x) + for i, blk in enumerate(self.blocks): + if i < len(self.blocks) - 1: + x = blk(x) + else: + # return attention of the last block + return blk(x, return_attention=True) + + def get_intermediate_layers(self, x, n=1): + x = self.prepare_tokens(x) + B,N,C = x.shape + # we return the output tokens from the `n` last blocks + output = [] + for i, blk in enumerate(self.blocks): + x = blk(x) + if len(self.blocks) - i <= n: + output.append(self.norm(x)) + #mask, seeds = self.get_mask(x, qkv) + #return output, mask, seeds + return output, (B, self.num_heads, N, C// self.num_heads) + + +def vit_tiny(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def vit_small(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def vit_base(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +class DINOHead(nn.Module): + def __init__(self, in_dim, out_dim, use_bn=False, norm_last_layer=True, nlayers=3, hidden_dim=2048, bottleneck_dim=256): + super().__init__() + nlayers = max(nlayers, 1) + if nlayers == 1: + self.mlp = nn.Linear(in_dim, bottleneck_dim) + else: + layers = [nn.Linear(in_dim, hidden_dim)] + if use_bn: + layers.append(nn.BatchNorm1d(hidden_dim)) + layers.append(nn.GELU()) + for _ in range(nlayers - 2): + layers.append(nn.Linear(hidden_dim, hidden_dim)) + if use_bn: + layers.append(nn.BatchNorm1d(hidden_dim)) + layers.append(nn.GELU()) + layers.append(nn.Linear(hidden_dim, bottleneck_dim)) + self.mlp = nn.Sequential(*layers) + self.apply(self._init_weights) + self.last_layer = nn.utils.weight_norm(nn.Linear(bottleneck_dim, out_dim, bias=False)) + self.last_layer.weight_g.data.fill_(1) + if norm_last_layer: + self.last_layer.weight_g.requires_grad = False + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x): + x = self.mlp(x) + x = nn.functional.normalize(x, dim=-1, p=2) + x = self.last_layer(x) + return x diff --git a/maskcut/weakly_supvervised_detection/visualization.py b/maskcut/weakly_supvervised_detection/visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..ac2a498fa172db8409dcd1556582cdaac2c5c215 --- /dev/null +++ b/maskcut/weakly_supvervised_detection/visualization.py @@ -0,0 +1,102 @@ +""" +Vis utilities. Code adapted from LOST: https://github.com/valeoai/LOST +""" +import cv2 +import numpy as np +from PIL import Image +import cv2 +import scipy +import numpy as np +import torch +import torch.nn as nn + +from utils import unnormalize_images +import matplotlib.pyplot as plt + +def visualize_img(image): + image = image.clone().detach().cpu().numpy() + #image = np.uint8(np.transpose(image, (1,2,0))) + print(f'image shape: {image.shape}') + image = np.uint8(image*256) + cv2.imwrite('./test/test_img.png', image) + +def visualize_fms(image, mask, seed, im_name, dim, scales, folder, save=True): + w_featmap, h_featmap = dim + image = image.clone().detach().cpu().numpy() + image = np.uint8(np.transpose(image, (1,2,0))*256) + mask = mask.reshape(w_featmap,h_featmap) + + mask = mask.reshape(w_featmap*h_featmap) + mask[seed] = 3 + mask = mask.reshape(w_featmap, h_featmap) + mask = scipy.ndimage.zoom(mask, scales, order=0, mode='nearest') + if save: + pltname1 = f"{folder}/LOST_{im_name}.png" + #Image.fromarray(image).save(pltname1) + cv2.imwrite(pltname1, image) + # print(f"Predictions saved at {pltname1}.") + pltname2 = f"{folder}/Mask_{im_name}.png" + plt.imsave(fname=pltname2, arr=mask) + # print(f"Predictions saved at {pltname2}.") + +def visualize_eigvec(eigvec, folder, im_name, dim, scales, save=True): + print(f'eigvec shape: {eigvec.shape}, scales: {scales}, dim: {dim}') + eigvec = scipy.ndimage.zoom(eigvec, scales, order=0, mode='nearest') + if save: + pltname= f"{folder}/{im_name}_ours_att.jpg" + plt.imsave(fname=pltname, arr=eigvec, cmap='cividis') + +def visualize_predictions_gt(image, pred, gt, im_name, seed, dim, scales, folder, output_name='ours_box', save=True): + """ + Visualization of the predicted box and the corresponding seed patch. + """ + image = unnormalize_images(image) + image = image[0].clone().detach().cpu().numpy() + image = np.transpose(image, (1,2,0)) + image = np.uint8(image*256) + image = np.ascontiguousarray(image, dtype=np.uint8) + + # Plot the box + cv2.rectangle( + image, + (int(pred[0]), int(pred[1])), + (int(pred[2]), int(pred[3])), + (255, 0, 0), 2 # RED + ) + # Plot the ground truth box + if len(gt>1): + for i in range(len(gt)): + cv2.rectangle( + image, + (int(gt[i][0]), int(gt[i][1])), + (int(gt[i][2]), int(gt[i][3])), + (0, 0, 255), 3, #BLUE + ) + + if save: + pltname = f"{folder}/{im_name}_{output_name}.jpg" + Image.fromarray(image).save(pltname) + # print(f"Predictions saved at {pltname}.") + return image + +def visualize_attn_LOST(A, dims, scales, folder, im_name): + """ + Visualization of the maps presented in Figure 2 of the paper. + """ + w_featmap, h_featmap = dims + + # Binarized similarity + binA = A.copy() + binA[binA < 0] = 0 + binA[binA > 0] = 1 + + # Save inverse degree + im_deg = ( + nn.functional.interpolate( + torch.from_numpy(1 / binA.sum(-1)).reshape(1, 1, w_featmap, h_featmap), + scale_factor=scales, + mode="nearest", + )[0][0].cpu().numpy() + ) + plt.imsave(fname=f"{folder}/{im_name}_lost_attn.jpg", arr=im_deg, cmap='cividis') + diff --git a/maskcut_colab_demo.ipynb b/maskcut_colab_demo.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..dcc5110a2406a9d47de8916999a324ec44b6c4ee --- /dev/null +++ b/maskcut_colab_demo.ipynb @@ -0,0 +1,312 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nCc6zrJ3cmt8" + }, + "outputs": [], + "source": [ + "# !git clone https://github.com/facebookresearch/CutLER.git" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "71Xs-_MPcmt8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/rakib/Desktop/NAYM/Office_Projects/UNILEVER/unsupervised_CutLer/CutLER/maskcut\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rakib/miniconda3/envs/cutLer/lib/python3.12/site-packages/IPython/core/magics/osm.py:417: UserWarning: This is now an optional IPython functionality, setting dhist requires you to install the `pickleshare` library.\n", + " self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n" + ] + } + ], + "source": [ + "%cd /home/rakib/Desktop/NAYM/Office_Projects/UNILEVER/unsupervised_CutLer/CutLER/maskcut" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cloning into 'TokenCut'...\n", + "remote: Enumerating objects: 212, done.\u001b[K\n", + "remote: Counting objects: 100% (62/62), done.\u001b[K\n", + "remote: Compressing objects: 100% (38/38), done.\u001b[K\n", + "remote: Total 212 (delta 42), reused 32 (delta 23), pack-reused 150 (from 1)\u001b[K\n", + "Receiving objects: 100% (212/212), 7.29 MiB | 642.00 KiB/s, done.\n", + "Resolving deltas: 100% (81/81), done.\n" + ] + } + ], + "source": [ + "# !git clone https://github.com/YangtaoWANG95/TokenCut.git" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-V0Y32Dbcmt9", + "outputId": "3ea5481b-5fee-4fa0-a0cf-082e16961c07" + }, + "outputs": [], + "source": [ + "# !pip install numpy\n", + "# !pip install opencv-python==4.6.0.66\n", + "# !pip install torch==1.8.1 torchvision==0.9.1\n", + "# !pip install scikit-image==0.19.2\n", + "# !pip install git+https://github.com/lucasb-eyer/pydensecrf.git\n", + "# !pip install tqdm\n", + "# !pip install pycocotools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4n3TREcdcmt9" + }, + "outputs": [], + "source": [ + "import sys\n", + "import numpy as np\n", + "import PIL.Image as Image\n", + "import torch\n", + "from torchvision import transforms\n", + "from scipy import ndimage\n", + "from colormap import random_color\n", + "sys.path.append('../')\n", + "import dino\n", + "from third_party.token_cut.unsupervised_saliency_detection import metric\n", + "from crf import densecrf\n", + "from maskcut import maskcut\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "xPG9nXywcmt_" + }, + "outputs": [], + "source": [ + "# Image transformation applied to all images\n", + "ToTensor = transforms.Compose([transforms.ToTensor(),\n", + " transforms.Normalize(\n", + " (0.485, 0.456, 0.406),\n", + " (0.229, 0.224, 0.225)),])\n", + "\n", + "def vis_mask(input, mask, mask_color) :\n", + " fg = mask > 0.5\n", + " rgb = np.copy(input)\n", + " rgb[fg] = (rgb[fg] * 0.3 + np.array(mask_color) * 0.7).astype(np.uint8)\n", + " return Image.fromarray(rgb)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "-cmiUTcncmt_" + }, + "outputs": [], + "source": [ + "# DINO hyperparameters\n", + "vit_arch = 'base'\n", + "vit_feat = 'k'\n", + "patch_size = 8\n", + "# DINO pre-trained model\n", + "url = \"https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth\"\n", + "feat_dim = 768\n", + "# MaskCut hyperparameters\n", + "fixed_size = 480\n", + "tau = 0.15\n", + "N = 3\n", + "# use cpu (cpu=True) or gpu (cpu=False)\n", + "cpu = True\n", + "# demo image path; you can change the img_path to try other demos or your own images.\n", + "img_path = '/home/rakib/Desktop/NAYM/Office_Projects/UNILEVER/unsupervised_CutLer/img/20240122_190024_jpg.rf.6df1ec8823c05ea695919201b694e063.jpg'" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KgybOQi-cmt_", + "outputId": "25a68f2f-ea2a-4f9d-9306-75e64a17de00" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading weight from https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth\n" + ] + } + ], + "source": [ + "# extract patch features with a pretrained DINO model\n", + "backbone = dino.ViTFeat(url, feat_dim, vit_arch, vit_feat, patch_size)\n", + "msg = 'Load {} pre-trained feature...'.format(vit_arch)\n", + "backbone.eval()\n", + "if not cpu:\n", + " backbone.cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "5YkV0tMfcmuA" + }, + "outputs": [], + "source": [ + "# get pesudo-masks with MaskCut\n", + "bipartitions, _, I_new = maskcut(img_path, backbone, patch_size, tau, N=N, fixed_size=fixed_size, cpu=cpu)\n", + "I = Image.open(img_path).convert('RGB')\n", + "width, height = I.size\n", + "pseudo_mask_list = []\n", + "for idx, bipartition in enumerate(bipartitions):\n", + " # post-process pesudo-masks with CRF\n", + " pseudo_mask = densecrf(np.array(I_new), bipartition)\n", + " pseudo_mask = ndimage.binary_fill_holes(pseudo_mask>=0.5)\n", + "\n", + " # filter out the mask that have a very different pseudo-mask after the CRF\n", + " if not cpu:\n", + " mask1 = torch.from_numpy(bipartition).cuda()\n", + " mask2 = torch.from_numpy(pseudo_mask).cuda()\n", + " else:\n", + " mask1 = torch.from_numpy(bipartition)\n", + " mask2 = torch.from_numpy(pseudo_mask)\n", + " if metric.IoU(mask1, mask2) < 0.5:\n", + " pseudo_mask = pseudo_mask * -1\n", + "\n", + " # construct binary pseudo-masks\n", + " pseudo_mask[pseudo_mask < 0] = 0\n", + " pseudo_mask = Image.fromarray(np.uint8(pseudo_mask*255))\n", + " pseudo_mask = np.asarray(pseudo_mask.resize((width, height)))\n", + "\n", + " pseudo_mask = pseudo_mask.astype(np.uint8)\n", + " upper = np.max(pseudo_mask)\n", + " lower = np.min(pseudo_mask)\n", + " thresh = upper / 2.0\n", + " pseudo_mask[pseudo_mask > thresh] = upper\n", + " pseudo_mask[pseudo_mask <= thresh] = lower\n", + " pseudo_mask_list.append(pseudo_mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "id": "jjWx5XL5cmuA", + "outputId": "8703c9dc-9f11-42a4-8282-6170d5cba55b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# pseudo-mask visualization\n", + "input = np.array(I)\n", + "for pseudo_mask in pseudo_mask_list:\n", + " input = vis_mask(input, pseudo_mask, random_color(rgb=True))\n", + "plt.imshow(input)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "cutLer", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..70f4d4c4916b31801fa132b2d652f61e40627b5d --- /dev/null +++ b/requirements.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:af10b7a06217284878f988ee7c22e06aabae48f1e8bc15504e3d750b147045d1 +size 95 diff --git a/test.ipynb b/test.ipynb index 7eda9a4dcfb3d8aa0ff63599fd15a4ff8aadee08..cbfc05f11287cfd6d1d2e5d8dfb26c93f6e2cf84 100644 --- a/test.ipynb +++ b/test.ipynb @@ -46,7 +46,9 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "!git clone --recursive https://github.com/facebookresearch/CutLER" + ] }, { "cell_type": "code", diff --git a/third_party/DOWNLOAD_DATA.md b/third_party/DOWNLOAD_DATA.md new file mode 100644 index 0000000000000000000000000000000000000000..831af65cd45ba58cec6dbd0ef66f94a8c590b5d1 --- /dev/null +++ b/third_party/DOWNLOAD_DATA.md @@ -0,0 +1,144 @@ +## Download datasets + +### VOC2007 +To download [Pascal VOC 2007 train and validation data](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/): +``` +cd datasets/VOC2007/ +bash download_voc07.sh +``` +The dataset should be organized as : + +``` +./datasets/VOC2007/VOCdevkit + ├── VOC2007/ + ├── JPEGImages + ├── Annotations + ... +``` +### VOC2012 + +To download [Pascal VOC 2012 train and validation data](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/): +``` +cd datasets/VOC2012/ +bash download_voc12.sh +``` +The dataset should be organized as : + +``` +./datasets/VOC2012/VOCdevkit + ├── VOC2012/ + ├── JPEGImages + ├── Annotations + ... +``` + +### COCO +To download [COCO dataset](https://cocodataset.org/#home): +``` +cd datasets/COCO/ +bash download_coco.sh +``` +The dataset should be organized as : +``` +./datasets/COCO/ + ├── images/ + ├── annotations/ +``` + +### CUB +To download [CUB 200-2011](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html): +``` +cd datasets/CUB/ +bash download_cub.sh +``` + +The data should be organized as +``` +./datasets/CUB/CUB_200_2011 + ├── images/ + ├── images.txt + ├── image_sizes.txt + ... +``` +The `image_sizes.txt` file can be generated by `/dataset/CUB/compute_image_size.py` + + +### ImageNet +The data should be organized as +``` +./datasets/ImageNet/ + ├── ILSVRC + ├── Annoations + ├── Data + ├── Detection + ├── ImageSets + ├── LOC_synset_mapping.txt + ├── LOC_val_solution.csv + + ... +``` + +### ECSSD +To download [ECSSD](https://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/dataset.html): +``` +cd ../datasets/ECSSD +bash download_data.sh +``` + +The dataset should be organized as: + +``` +../datasets/ECSSD/ + ├── img + ├── gt +``` + +### DUTS +To download [DUTS](http://saliencydetection.net/duts/#org3aad434): +``` +cd ../datasets/DUTS_Test +bash download_data.sh +``` + +The dataset should be organized as: +``` +../datasets/DUTS_Test/ + ├── img + ├── gt +``` + + +### DUT-OMRON +To downlaod [DUT_OMRON](http://saliencydetection.net/dut-omron/#org96c3bab): +``` +cd ../datasets/DUT-OMRON +bash download_data.sh +``` + +The dataset should be organized as: +``` +../datasets/DUT-OMRON/ + ├── img + ├── gt +``` + + +## Download DINO checkpoint + +### DINO pretrained checkpoints + +We initialize the model weights by using dino pretrained weights. To download the dino model, please launch the following commands: +``` +wget https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth # vit-s/16 +wget https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth # vit-s/8 +wget https://dl.fbaipublicfiles.com/dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth # vit-b/16 +``` + +### DINO finetuned linear classifiers on ImageNet + +Since Dino offered finetuned linear classifiers on ImageNet, we can simply download them by following commands: +``` +wget https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_linearweights.pth #vit-s/16 +wget https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_pretrain/dino_deitsmall8_linearweights.pth # vit-s/8 +wget https://dl.fbaipublicfiles.com/dino/dino_vitbase16_pretrain/dino_vitbase16_linearweights.pth # vit-b/16 +``` diff --git a/third_party/README.md b/third_party/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ac4366ee659b8e9c59e5f9088e01df204dc18d15 --- /dev/null +++ b/third_party/README.md @@ -0,0 +1,293 @@ +# (CVPR 2022) TokenCut +Pytorch implementation of **Tokencut**: + + +**Self-supervised Transformers for Unsupervised Object Discovery using Normalized Cut** + +*[Yangtao Wang](https://yangtaowang95.github.io), [Xi Shen](https://xishen0220.github.io/), [Shell Xu Hu](http://hushell.github.io/), [Yuan Yuan](https://yyuanad.github.io/), [James L. Crowley](http://crowley-coutaz.fr/jlc/jlc.html), [Dominique Vaufreydaz](https://research.vaufreydaz.org/)* + + +[[Project page](https://www.m-psi.fr/Papers/TokenCut2022/)] +[[ Github (Video Segmentation) ](https://github.com/YangtaoWANG95/TokenCut_video)] +[[Paper](https://arxiv.org/pdf/2202.11539.pdf)] +[![Colab demo](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/YangtaoWANG95/TokenCut/blob/master/inference_demo.ipynb) +[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/yangtaowang/TokenCut) + +

+ TokenCut teaser +

+ +If our project is helpful for your research, please consider citing : +``` +@inproceedings{wang2022tokencut, + title={Self-supervised Transformers for Unsupervised Object Discovery using Normalized Cut}, + author={Wang, Yangtao and Shen, Xi and Hu, Shell Xu and Yuan, Yuan and Crowley, James L. and Vaufreydaz, Dominique}, + booktitle={Conference on Computer Vision and Pattern Recognition} + year={2022} + } +``` + + +## Table of Content +* [1. Updates](#1-updates) +* [2. Installation](#2-installation) + * [2.1 Dependencies](#21-dependencies) + * [2.2 Data](#22-data) +* [3. Quick Start](#3-quick-start) + * [3.1 Detecting an object in one image](#31-detecting-an-object-in-one-image) + * [3.2 Segmenting a salient region in one image](#32-segmenting-a-salient-region-in-one-image) +* [4. Evaluation](#4-evaluation) + * [4.1 Unsupervised object discovery](#41-unsupervised-object-discovery) + * [4.2 Unsupervised saliency detection](#42-unsupervised-saliency-detection) + * [4.3 Weakly supervised object detection](#43-weakly-supervised-object-detection) +* [5. Acknowledgement](#5-acknowledgement) + +## 1. Updates + +***09/06/2022*** +Extension work of [TokeCut Video Segmentation](https://github.com/YangtaoWANG95/TokenCut_video) is realised! + +***03/10/2022*** +Creating a 480p Demo using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/yangtaowang/TokenCut) + +Internet image results: +

+ TokenCut visualizations + TokenCut visualizations + TokenCut visualizations + TokenCut visualizations +

+ +***02/26/2022*** +Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/TokenCut) + +***02/26/2022*** +A simple TokenCut Colab Demo is available. + +***02/21/2022*** +Initial commit: Code of TokenCut is released, including evaluation of unsupervised object discovery, unsupervised saliency object detection, weakly supervised object locolization. + +## 2. Installation +### 2.1 Dependencies + +This code was implemented with Python 3.7, PyTorch 1.7.1 and CUDA 11.2. Please refer to [the official installation](https://pytorch.org/get-started/previous-versions/). If CUDA 10.2 has been properly installed : +``` +pip install torch==1.7.1 torchvision==0.8.2 +``` + + +In order to install the additionnal dependencies, please launch the following command: + +``` +pip install -r requirements.txt +``` + + +### 2.2 Data + +We provide quick download commands in [DOWNLOAD_DATA.md](./DOWNLOAD_DATA.md) for VOC2007, VOC2012, COCO, CUB, ImageNet, ECSSD, DUTS and DUT-OMRON as well as DINO checkpoints. + + +## 3. Quick Start + +### 3.1 Detecting an object in one image + +We provide TokenCut visualization for bounding box prediction and attention map. Using `all` for all visualization results. + + +``` +python main_tokencut.py --image_path examples/VOC07_000036.jpg --visualize pred +python main_tokencut.py --image_path examples/VOC07_000036.jpg --visualize attn +python main_tokencut.py --image_path examples/VOC07_000036.jpg --visualize all +``` + +### 3.2 Segmenting a salient region in one image + +We provide TokenCut segmentation results as follows: + +``` +cd unsupervised_saliency_detection +python get_saliency.py --sigma-spatial 16 --sigma-luma 16 --sigma-chroma 8 --vit-arch small --patch-size 16 --img-path ../examples/VOC07_000036.jpg --out-dir ./output +``` + +## 4. Evaluation +Following are the different steps to reproduce the results of **TokenCut** presented in the paper. + +### 4.1 Unsupervised object discovery + + +

+ TokenCut visualizations + TokenCut visualizations + TokenCut visualizations +

+ +#### PASCAL-VOC +In order to apply TokenCut and compute corloc results (VOC07 68.8, VOC12 72.1), please launch: +``` +python main_tokencut.py --dataset VOC07 --set trainval +python main_tokencut.py --dataset VOC12 --set trainval +``` + +If you want to extract Dino features, which corresponds to [the KEY features in DINO](https://github.com/XiSHEN0220/LOST/blob/main/main_lost.py#L259-L261): +``` +mkdir features +python main_lost.py --dataset VOC07 --set trainval --save-feat-dir features/VOC2007 +``` + +#### COCO + +Results are provided given the 2014 annotations following previous works. The following command line allows you to get results on the subset of 20k images of the COCO dataset (corloc 58.8), following previous litterature. To be noted that the 20k images are a subset of the `train` set. +``` +python main_tokencut.py --dataset COCO20k --set train +``` + +#### Different models +We have tested the method on different setups of the VIT model, corloc results are presented in the following table (more can be found in the paper). + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
archpre-trainingdataset
VOC07VOC12COCO20k
ViT-S/16DINO68.872.158.8
ViT-S/8DINO67.371.660.7
ViT-B/16DINO68.872.459.0
+ + +Previous results on the dataset `VOC07` can be obtained by launching: +``` +python main_tokencut.py --dataset VOC07 --set trainval #VIT-S/16 +python main_tokencut.py --dataset VOC07 --set trainval --patch_size 8 #VIT-S/8 +python main_tokencut.py --dataset VOC07 --set trainval --arch vit_base #VIT-B/16 +``` + +### 4.2 Unsupervised saliency detection + +

+ TokenCut visualizations + TokenCut visualizations + TokenCut visualizations +

+ +To evaluate on ECSSD, DUTS, DUT_OMRON dataset: +``` +python get_saliency.py --out-dir ECSSD --sigma-spatial 16 --sigma-luma 16 --sigma-chroma 8 --nb-vis 1 --vit-arch small --patch-size 16 --dataset ECSSD + +python get_saliency.py --out-dir DUTS --sigma-spatial 16 --sigma-luma 16 --sigma-chroma 8 --nb-vis 1 --vit-arch small --patch-size 16 --dataset DUTS + +python get_saliency.py --out-dir DUT --sigma-spatial 16 --sigma-luma 16 --sigma-chroma 8 --nb-vis 1 --vit-arch small --patch-size 16 --dataset DUT +``` +This should give: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Method ECSSD DUTS DUT-OMRON
maxFIoUAccmaxFIoUAccmaxFIoUAcc
TokenCut80.371.291.867.257.690.360.053.388.0
TokenCut + BS87.477.293.475.562,491.469.761.889.7
+ +### 4.3 Weakly supervised object detection + +

+ TokenCut visualizations + TokenCut visualizations + TokenCut visualizations + +

+ + +#### Fintune DINO small on CUB + +To finetune ViT-S/16 on CUB on a single node with 4 gpus for 1000 epochs run: +``` +python -m torch.distributed.launch --nproc_per_node=4 main.py --data_path /path/to/data --batch_size_per_gpu 256 --dataset cub --weight_decay 0.005 --pretrained_weights ./dino_deitsmall16_pretrain.pth --epoch 1000 --output_dir ./path/to/checkpoin --lr 2e-4 --warmup-epochs 50 --num_labels 200 --num_workers 16 --n_last_blocks 1 --avgpool_patchtokens true --arch vit_small --patch_size 16 +``` + +#### Evaluation on CUB +To evaluate a fine-tuned ViT-S/16 on CUB val with a single GPU run: +``` +python eval.py --pretrained_weights ./path/to/checkpoint --dataset cub --data_path ./path/to/data --batch_size_per_gpu 1 --no_center_crop +``` +This should give: +``` +Top1_cls: 79.12, top5_cls94.80, gt_loc: 0.914, top1_loc:0.723 +``` + +#### Evaluate on Imagenet + +To Evaluate ViT-S/16 finetuned on ImageNet val with a single GPU run: +``` +python eval.py --pretrained_weights /path/to/checkpoint --classifier_weights /path/to/linear_weights--dataset imagenet --data_path ./path/to/data --batch_size_per_gpu 1 --num_labels 1000 --batch_size_per_gpu 1 --no_center_crop --input_size 256 --tau 0.2 --patch_size 16 --arch vit_small +``` + +## 5. Acknowledgement + +TokenCut code is built on top of [LOST](https://github.com/valeoai/LOST), [DINO](https://github.com/facebookresearch/dino), [Segswap](https://github.com/XiSHEN0220/SegSwap), and [Bilateral_Sovlver](https://github.com/poolio/bilateral_solver). We would like to sincerely thanks those authors for their great works. + + diff --git a/third_party/__init__.py b/third_party/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/datasets.py b/third_party/datasets.py new file mode 100755 index 0000000000000000000000000000000000000000..61368e02fd6f348b75fbe1bbe43f02c223386ed4 --- /dev/null +++ b/third_party/datasets.py @@ -0,0 +1,390 @@ +""" +Datasets file. Code adapted from LOST: https://github.com/valeoai/LOST +""" +import os +import torch +import json +import torchvision +import numpy as np +import skimage.io + +from PIL import Image +from tqdm import tqdm +from skimage.transform import resize +from torchvision import transforms as pth_transforms + +# Image transformation applied to all images +transform = pth_transforms.Compose( + [ + pth_transforms.ToTensor(), + pth_transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ] +) + +class ImageDataset: + def __init__(self, image_path, resize=None): + + self.image_path = image_path + self.name = image_path.split("/")[-1] + + # Read the image + with open(image_path, "rb") as f: + img = Image.open(f) + img = img.convert("RGB") + + # Build a dataloader + if resize is not None: + transform_resize = pth_transforms.Compose( + [ + pth_transforms.ToTensor(), + pth_transforms.Resize(resize), + pth_transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ] + ) + img = transform_resize(img) + self.img_size = list(img.shape[-1:-3:-1]) + else: + img = transform(img) + self.img_size = list(img.shape[-1:-3:-1]) + self.dataloader = [[img, image_path]] + + def get_image_name(self, *args, **kwargs): + return self.image_path.split("/")[-1].split(".")[0] + + def load_image(self, *args, **kwargs): + return Image.open(self.image_path).convert("RGB").resize(self.img_size) + +class Dataset: + def __init__(self, dataset_name, dataset_set, remove_hards): + """ + Build the dataloader + """ + + self.dataset_name = dataset_name + self.set = dataset_set + + if dataset_name == "VOC07": + self.root_path = "datasets/VOC2007" + self.year = "2007" + elif dataset_name == "VOC12": + self.root_path = "datasets/VOC2012" + self.year = "2012" + elif dataset_name == "COCO20k": + self.year = "2014" + self.root_path = f"datasets/COCO/images/{dataset_set}{self.year}" + self.sel20k = 'datasets/coco_20k_filenames.txt' + # JSON file constructed based on COCO train2014 gt + self.all_annfile = "datasets/COCO/annotations/instances_train2014.json" + self.annfile = "datasets/instances_train2014_sel20k.json" + self.sel_20k = get_sel_20k(self.sel20k) + if not os.path.exists(self.annfile): + select_coco_20k(self.sel20k, self.all_annfile) + self.train2014 = get_train2014(self.annfile) + else: + raise ValueError("Unknown dataset.") + + if not os.path.exists(self.root_path): + raise ValueError("Please follow the README to setup the datasets.") + + self.name = f"{self.dataset_name}_{self.set}" + + # Build the dataloader + if "VOC" in dataset_name: + self.dataloader = torchvision.datasets.VOCDetection( + self.root_path, + year=self.year, + image_set=self.set, + transform=transform, + download=False, + ) + elif "COCO20k" == dataset_name: + self.dataloader = torchvision.datasets.CocoDetection( + self.root_path, annFile=self.annfile, transform=transform + ) + else: + raise ValueError("Unknown dataset.") + + # Set hards images that are not included + self.remove_hards = remove_hards + self.hards = [] + if remove_hards: + self.name += f"-nohards" + self.hards = self.get_hards() + print(f"Nb images discarded {len(self.hards)}") + + def load_image(self, im_name): + """ + Load the image corresponding to the im_name + """ + if "VOC" in self.dataset_name: + image = skimage.io.imread(f"./datasets/VOC{self.year}/VOCdevkit/VOC{self.year}/JPEGImages/{im_name}") + elif "COCO" in self.dataset_name: + #im_path = self.path_20k[self.sel_20k.index(im_name)] + #im_path = self.train2014['images'][self.sel_20k.index(im_name)]['file_name'] + #image = skimage.io.imread(f"./datasets/COCO/images/train2014/{im_path}") + image = skimage.io.imread(f"./datasets/COCO/images/train2014/{im_name}") + else: + raise ValueError("Unkown dataset.") + return image + + def get_image_name(self, inp): + """ + Return the image name + """ + if "VOC" in self.dataset_name: + im_name = inp["annotation"]["filename"] + elif "COCO" in self.dataset_name: + im_name = str(inp[0]["image_id"]) + im_name = self.train2014['images'][self.sel_20k.index(im_name)]['file_name'] + + return im_name + + def extract_gt(self, targets, im_name): + if "VOC" in self.dataset_name: + return extract_gt_VOC(targets, remove_hards=self.remove_hards) + elif "COCO" in self.dataset_name: + return extract_gt_COCO(targets, remove_iscrowd=True) + else: + raise ValueError("Unknown dataset") + + def extract_classes(self): + if "VOC" in self.dataset_name: + cls_path = f"classes_{self.set}_{self.year}.txt" + elif "COCO" in self.dataset_name: + cls_path = f"classes_{self.dataset}_{self.set}_{self.year}.txt" + + # Load if exists + if os.path.exists(cls_path): + all_classes = [] + with open(cls_path, "r") as f: + for line in f: + all_classes.append(line.strip()) + else: + print("Extract all classes from the dataset") + if "VOC" in self.dataset_name: + all_classes = self.extract_classes_VOC() + elif "COCO" in self.dataset_name: + all_classes = self.extract_classes_COCO() + + with open(cls_path, "w") as f: + for s in all_classes: + f.write(str(s) + "\n") + + return all_classes + + def extract_classes_VOC(self): + all_classes = [] + for im_id, inp in enumerate(tqdm(self.dataloader)): + objects = inp[1]["annotation"]["object"] + + for o in range(len(objects)): + if objects[o]["name"] not in all_classes: + all_classes.append(objects[o]["name"]) + + return all_classes + + def extract_classes_COCO(self): + all_classes = [] + for im_id, inp in enumerate(tqdm(self.dataloader)): + objects = inp[1] + + for o in range(len(objects)): + if objects[o]["category_id"] not in all_classes: + all_classes.append(objects[o]["category_id"]) + + return all_classes + + def get_hards(self): + hard_path = "datasets/hard_%s_%s_%s.txt" % (self.dataset_name, self.set, self.year) + if os.path.exists(hard_path): + hards = [] + with open(hard_path, "r") as f: + for line in f: + hards.append(int(line.strip())) + else: + print("Discover hard images that should be discarded") + + if "VOC" in self.dataset_name: + # set the hards + hards = discard_hard_voc(self.dataloader) + + with open(hard_path, "w") as f: + for s in hards: + f.write(str(s) + "\n") + + return hards + + +def discard_hard_voc(dataloader): + hards = [] + for im_id, inp in enumerate(tqdm(dataloader)): + objects = inp[1]["annotation"]["object"] + nb_obj = len(objects) + + hard = np.zeros(nb_obj) + for i, o in enumerate(range(nb_obj)): + hard[i] = ( + 1 + if (objects[o]["truncated"] == "1" or objects[o]["difficult"] == "1") + else 0 + ) + + # all images with only truncated or difficult objects + if np.sum(hard) == nb_obj: + hards.append(im_id) + return hards + + +def extract_gt_COCO(targets, remove_iscrowd=True): + objects = targets + nb_obj = len(objects) + + gt_bbxs = [] + gt_clss = [] + for o in range(nb_obj): + # Remove iscrowd boxes + if remove_iscrowd and objects[o]["iscrowd"] == 1: + continue + gt_cls = objects[o]["category_id"] + gt_clss.append(gt_cls) + bbx = objects[o]["bbox"] + x1y1x2y2 = [bbx[0], bbx[1], bbx[0] + bbx[2], bbx[1] + bbx[3]] + x1y1x2y2 = [int(round(x)) for x in x1y1x2y2] + gt_bbxs.append(x1y1x2y2) + + return np.asarray(gt_bbxs), gt_clss + + +def extract_gt_VOC(targets, remove_hards=False): + objects = targets["annotation"]["object"] + nb_obj = len(objects) + + gt_bbxs = [] + gt_clss = [] + for o in range(nb_obj): + if remove_hards and ( + objects[o]["truncated"] == "1" or objects[o]["difficult"] == "1" + ): + continue + gt_cls = objects[o]["name"] + gt_clss.append(gt_cls) + obj = objects[o]["bndbox"] + x1y1x2y2 = [ + int(obj["xmin"]), + int(obj["ymin"]), + int(obj["xmax"]), + int(obj["ymax"]), + ] + # Original annotations are integers in the range [1, W or H] + # Assuming they mean 1-based pixel indices (inclusive), + # a box with annotation (xmin=1, xmax=W) covers the whole image. + # In coordinate space this is represented by (xmin=0, xmax=W) + x1y1x2y2[0] -= 1 + x1y1x2y2[1] -= 1 + gt_bbxs.append(x1y1x2y2) + + return np.asarray(gt_bbxs), gt_clss + + +def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): + # https://github.com/ultralytics/yolov5/blob/develop/utils/general.py + # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 + box2 = box2.T + + # Get the coordinates of bounding boxes + if x1y1x2y2: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + else: # transform from xywh to xyxy + b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 + b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 + b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 + b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 + + # Intersection area + inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * ( + torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1) + ).clamp(0) + + # Union Area + w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps + w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps + union = w1 * h1 + w2 * h2 - inter + eps + + iou = inter / union + if GIoU or DIoU or CIoU: + cw = torch.max(b1_x2, b2_x2) - torch.min( + b1_x1, b2_x1 + ) # convex (smallest enclosing box) width + ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared + rho2 = ( + (b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2 + ) / 4 # center distance squared + if DIoU: + return iou - rho2 / c2 # DIoU + elif ( + CIoU + ): # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi ** 2) * torch.pow( + torch.atan(w2 / h2) - torch.atan(w1 / h1), 2 + ) + with torch.no_grad(): + alpha = v / (v - iou + (1 + eps)) + return iou - (rho2 / c2 + v * alpha) # CIoU + else: # GIoU https://arxiv.org/pdf/1902.09630.pdf + c_area = cw * ch + eps # convex area + return iou - (c_area - union) / c_area # GIoU + else: + return iou # IoU + +def get_sel_20k(sel_file): + # load selected images + with open(sel_file, "r") as f: + sel_20k = f.readlines() + sel_20k = [s.replace("\n", "") for s in sel_20k] + im20k = [str(int(s.split("_")[-1].split(".")[0])) for s in sel_20k] + return im20k + +def get_train2014(all_annotations_file): + # load all annotations + with open(all_annotations_file, "r") as f: + train2014 = json.load(f) + return train2014 + + + +def select_coco_20k(sel_file, all_annotations_file): + print('Building COCO 20k dataset.') + + # load all annotations + with open(all_annotations_file, "r") as f: + train2014 = json.load(f) + + # load selected images + with open(sel_file, "r") as f: + sel_20k = f.readlines() + sel_20k = [s.replace("\n", "") for s in sel_20k] + im20k = [str(int(s.split("_")[-1].split(".")[0])) for s in sel_20k] + + new_anno = [] + new_images = [] + + for i in tqdm(im20k): + new_anno.extend( + [a for a in train2014["annotations"] if a["image_id"] == int(i)] + ) + new_images.extend([a for a in train2014["images"] if a["id"] == int(i)]) + + train2014_20k = {} + train2014_20k["images"] = new_images + train2014_20k["annotations"] = new_anno + train2014_20k["categories"] = train2014["categories"] + + with open("datasets/instances_train2014_sel20k.json", "w") as outfile: + json.dump(train2014_20k, outfile) + + print(f'im20k :{im20k[0]}') + print('Done.') diff --git a/third_party/datasets/COCO/download_coco.sh b/third_party/datasets/COCO/download_coco.sh new file mode 100644 index 0000000000000000000000000000000000000000..ea31d39963f2a325ba52aec3eb8fdbe4398c71dd --- /dev/null +++ b/third_party/datasets/COCO/download_coco.sh @@ -0,0 +1,18 @@ +mkdir images + +wget http://images.cocodataset.org/zips/train2014.zip +unzip train2014.zip ./images +rm train2014.zip + +wget http://images.cocodataset.org/zips/val2014.zip +unzip val2014.zip ./images +rm val2014.zip + +wget http://images.cocodataset.org/zips/test2014.zip +unzip test2014.zip ./images +rm test.2014.zip + +wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip +unzip annotations_trainval2014.zip +rm annotations_trainval2014.zip + diff --git a/third_party/datasets/CUB/compute_image_size.py b/third_party/datasets/CUB/compute_image_size.py new file mode 100644 index 0000000000000000000000000000000000000000..349f7640fb37863407b5121e0b306f0460eba11b --- /dev/null +++ b/third_party/datasets/CUB/compute_image_size.py @@ -0,0 +1,23 @@ +import os +import cv2 +import pandas as pd + +def store_cub_image_sizes(root): + paths = pd.read_csv( + os.path.join(root, 'CUB_200_2011', 'images.txt'), + sep=' ', names=['id', 'path']) + + sizes = pd.DataFrame(columns=['id', 'width', 'height']) + for i in range(len(paths)): + path = paths.iloc[i].path + image_path = os.path.join(root, 'CUB_200_2011/images', path) + image = cv2.imread(image_path) + sizes.loc[i] = [paths.iloc[i].id, image.shape[1], image.shape[0]] + + save_path = os.path.join(root, 'CUB_200_2011', 'image_sizes.txt') + sizes.to_csv(path_or_buf=save_path, sep=' ', index=False, header=False) + +if __name__ == '__main__': + root = '.' + print('Storing image sizes of cub200 dataset') + store_cub_image_sizes(root) diff --git a/third_party/datasets/CUB/download_cub.sh b/third_party/datasets/CUB/download_cub.sh new file mode 100644 index 0000000000000000000000000000000000000000..91a43f3edb09055687cb70594d9de866d749d07f --- /dev/null +++ b/third_party/datasets/CUB/download_cub.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +mkdir CUB +wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1hbzc_P1FuxMkcabkgn9ZKinBwW683j45' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1hbzc_P1FuxMkcabkgn9ZKinBwW683j45" -nc -O CUB/CUB_200_2011.tgz && rm -rf /tmp/cookies.txt +wget -nc -O CUB/CUBV2.tar "https://onedrive.live.com/download?cid=B7111B95B80CCC66&resid=B7111B95B80CCC66%2130812&authkey=AFMzb4akufUiWU0" +mkdir -p CUB/CUB_200_2011 +tar xvf CUB/CUB_200_2011.tgz -C CUB/ diff --git a/third_party/datasets/DUTS_Test/download_data.sh b/third_party/datasets/DUTS_Test/download_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..439a4ce2d0f57aa58cdb97cbc10b3986123cac9b --- /dev/null +++ b/third_party/datasets/DUTS_Test/download_data.sh @@ -0,0 +1,6 @@ +wget http://saliencydetection.net/duts/download/DUTS-TE.zip +unzip DUTS-TE.zip +rm DUTS-TE.zip +mv DUTS-TE/DUTS-TE-Image/ img +mv DUTS-TE/DUTS-TE-Mask/ gt +rm -r DUTS-TE/ diff --git a/third_party/datasets/DUT_OMRON/download_data.sh b/third_party/datasets/DUT_OMRON/download_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..8e73a11f77eaee0a8b5a109cda41932a063dfd3f --- /dev/null +++ b/third_party/datasets/DUT_OMRON/download_data.sh @@ -0,0 +1,9 @@ +wget http://saliencydetection.net/dut-omron/download/DUT-OMRON-image.zip +wget http://saliencydetection.net/dut-omron/download/DUT-OMRON-gt-pixelwise.zip.zip +unzip DUT-OMRON-gt-pixelwise.zip.zip +unzip DUT-OMRON-image.zip +rm DUT-OMRON-gt-pixelwise.zip.zip +rm DUT-OMRON-image.zip +mv DUT-OMRON-image/ img +mv pixelwiseGT-new-PNG/ gt +rm -r __MACOSX/ diff --git a/third_party/datasets/ECSSD/download_data.sh b/third_party/datasets/ECSSD/download_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..826e62ec4a0a2c8d815bd99eeb03e1927b2ce783 --- /dev/null +++ b/third_party/datasets/ECSSD/download_data.sh @@ -0,0 +1,10 @@ +wget https://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/data/ECSSD/images.zip +wget https://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/data/ECSSD/ground_truth_mask.zip + +unzip images.zip +unzip ground_truth_mask.zip + +rm images.zip ground_truth_mask.zip + +mv images img +mv ground_truth_mask gt diff --git a/third_party/datasets/ImageNet/Detection/imagenet1000_clsidx_to_labels.txt b/third_party/datasets/ImageNet/Detection/imagenet1000_clsidx_to_labels.txt new file mode 100644 index 0000000000000000000000000000000000000000..12866db8c24e77acbbced6337a7716aa6c6318f4 --- /dev/null +++ b/third_party/datasets/ImageNet/Detection/imagenet1000_clsidx_to_labels.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:acf75ef0abe89694b19056e0796401068b459c457baa30335f240c7692857355 +size 31675 diff --git a/third_party/datasets/ImageNet/Detection/test.txt b/third_party/datasets/ImageNet/Detection/test.txt new file mode 100644 index 0000000000000000000000000000000000000000..669795ffe16ce178dea46e9454375a86383174d9 --- /dev/null +++ b/third_party/datasets/ImageNet/Detection/test.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fecd6a03eb9f74811e52791295f4ddf62d4b8b6b31de2c73eafae7e9fbd896ee +size 3200000 diff --git a/third_party/datasets/ImageNet/Detection/train.txt b/third_party/datasets/ImageNet/Detection/train.txt new file mode 100644 index 0000000000000000000000000000000000000000..0b8b9be9c61859351632e8a4009131ad36aae8de --- /dev/null +++ b/third_party/datasets/ImageNet/Detection/train.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:55a0a92c2762655087e447ddd97dd27ba81dd1839a615e1bb598d04dac477d48 +size 43829433 diff --git a/third_party/datasets/ImageNet/Detection/val.txt b/third_party/datasets/ImageNet/Detection/val.txt new file mode 100644 index 0000000000000000000000000000000000000000..96dd02a97702afe71548f81dbc0e04ac84c255c5 --- /dev/null +++ b/third_party/datasets/ImageNet/Detection/val.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:78b17d76293ec6b5a96ec2b7c0fefdcda40373b63c5e28309310aa620fa58a5e +size 2762694 diff --git a/third_party/datasets/ImageNet/Detection/wnids.txt b/third_party/datasets/ImageNet/Detection/wnids.txt new file mode 100644 index 0000000000000000000000000000000000000000..6ba412e3fd5c15b09a190bc15c1b6454f269fab6 --- /dev/null +++ b/third_party/datasets/ImageNet/Detection/wnids.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:70002b0ff5de60a3a17a82dbfcff291931f96225ddf941ad2e182fc39e183d15 +size 10000 diff --git a/third_party/datasets/VOC2007/download_voc07.sh b/third_party/datasets/VOC2007/download_voc07.sh new file mode 100644 index 0000000000000000000000000000000000000000..fd180cd05349e4e4037e452651fcee1f24d2c7aa --- /dev/null +++ b/third_party/datasets/VOC2007/download_voc07.sh @@ -0,0 +1,3 @@ +wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar +tar -xvf VOCtrainval_06-Nov-2007.tar +rm VOCtrainval_06-Nov-2007.tar \ No newline at end of file diff --git a/third_party/datasets/VOC2012/download_voc12.sh b/third_party/datasets/VOC2012/download_voc12.sh new file mode 100644 index 0000000000000000000000000000000000000000..8fd50132e3a25fac770b1ba1b60926999c5a268a --- /dev/null +++ b/third_party/datasets/VOC2012/download_voc12.sh @@ -0,0 +1,4 @@ +wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar +tar -xvf VOCtrainval_11-May-2012.tar +rm VOCtrainval_11-May-2012.tar + diff --git a/third_party/datasets/coco_20k_filenames.txt b/third_party/datasets/coco_20k_filenames.txt new file mode 100644 index 0000000000000000000000000000000000000000..76584e99dc831653b366c3f5302f0a0d0e70c216 --- /dev/null +++ b/third_party/datasets/coco_20k_filenames.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:74637e9b4f93f2ebef9b1f664aff53edd1f41ef08dad97bd22ce8e455052b335 +size 832314 diff --git a/third_party/dino/__init__.py b/third_party/dino/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..eb0d6a2338cc4ae729982b8e5f4cce83e09b6440 --- /dev/null +++ b/third_party/dino/__init__.py @@ -0,0 +1,3 @@ +import sys +from os.path import dirname, join +sys.path.insert(0, join(dirname(__file__), '.')) diff --git a/third_party/dino/utils.py b/third_party/dino/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9586250123a125a83ea2679e121b1b0ef8089916 --- /dev/null +++ b/third_party/dino/utils.py @@ -0,0 +1,829 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Misc functions. + +Mostly copy-paste from torchvision references or other public repos like DETR: +https://github.com/facebookresearch/detr/blob/master/util/misc.py +""" +import os +import sys +import time +import math +import random +import datetime +import subprocess +from collections import defaultdict, deque + +import numpy as np +import torch +from torch import nn +import torch.distributed as dist +from PIL import ImageFilter, ImageOps + + +class GaussianBlur(object): + """ + Apply Gaussian Blur to the PIL image. + """ + def __init__(self, p=0.5, radius_min=0.1, radius_max=2.): + self.prob = p + self.radius_min = radius_min + self.radius_max = radius_max + + def __call__(self, img): + do_it = random.random() <= self.prob + if not do_it: + return img + + return img.filter( + ImageFilter.GaussianBlur( + radius=random.uniform(self.radius_min, self.radius_max) + ) + ) + + +class Solarization(object): + """ + Apply Solarization to the PIL image. + """ + def __init__(self, p): + self.p = p + + def __call__(self, img): + if random.random() < self.p: + return ImageOps.solarize(img) + else: + return img + + +def load_pretrained_weights(model, pretrained_weights, checkpoint_key, model_name, patch_size): + if os.path.isfile(pretrained_weights): + state_dict = torch.load(pretrained_weights, map_location="cpu") + if checkpoint_key is not None and checkpoint_key in state_dict: + print(f"Take key {checkpoint_key} in provided checkpoint dict") + state_dict = state_dict[checkpoint_key] + # remove `module.` prefix + state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()} + # remove `backbone.` prefix induced by multicrop wrapper + state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()} + msg = model.load_state_dict(state_dict, strict=False) + print('Pretrained weights found at {} and loaded with msg: {}'.format(pretrained_weights, msg)) + else: + print("Please use the `--pretrained_weights` argument to indicate the path of the checkpoint to evaluate.") + url = None + if model_name == "vit_small" and patch_size == 16: + url = "dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth" + elif model_name == "vit_small" and patch_size == 8: + url = "dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth" + elif model_name == "vit_base" and patch_size == 16: + url = "dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth" + elif model_name == "vit_base" and patch_size == 8: + url = "dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth" + elif model_name == "xcit_small_12_p16": + url = "dino_xcit_small_12_p16_pretrain/dino_xcit_small_12_p16_pretrain.pth" + elif model_name == "xcit_small_12_p8": + url = "dino_xcit_small_12_p8_pretrain/dino_xcit_small_12_p8_pretrain.pth" + elif model_name == "xcit_medium_24_p16": + url = "dino_xcit_medium_24_p16_pretrain/dino_xcit_medium_24_p16_pretrain.pth" + elif model_name == "xcit_medium_24_p8": + url = "dino_xcit_medium_24_p8_pretrain/dino_xcit_medium_24_p8_pretrain.pth" + elif model_name == "resnet50": + url = "dino_resnet50_pretrain/dino_resnet50_pretrain.pth" + if url is not None: + print("Since no pretrained weights have been provided, we load the reference pretrained DINO weights.") + state_dict = torch.hub.load_state_dict_from_url(url="https://dl.fbaipublicfiles.com/dino/" + url) + model.load_state_dict(state_dict, strict=True) + else: + print("There is no reference weights available for this model => We use random weights.") + + +def load_pretrained_linear_weights(linear_classifier, model_name, patch_size): + url = None + if model_name == "vit_small" and patch_size == 16: + url = "dino_deitsmall16_pretrain/dino_deitsmall16_linearweights.pth" + elif model_name == "vit_small" and patch_size == 8: + url = "dino_deitsmall8_pretrain/dino_deitsmall8_linearweights.pth" + elif model_name == "vit_base" and patch_size == 16: + url = "dino_vitbase16_pretrain/dino_vitbase16_linearweights.pth" + elif model_name == "vit_base" and patch_size == 8: + url = "dino_vitbase8_pretrain/dino_vitbase8_linearweights.pth" + elif model_name == "resnet50": + url = "dino_resnet50_pretrain/dino_resnet50_linearweights.pth" + if url is not None: + print("We load the reference pretrained linear weights.") + state_dict = torch.hub.load_state_dict_from_url(url="https://dl.fbaipublicfiles.com/dino/" + url)["state_dict"] + linear_classifier.load_state_dict(state_dict, strict=True) + else: + print("We use random linear weights.") + + +def clip_gradients(model, clip): + norms = [] + for name, p in model.named_parameters(): + if p.grad is not None: + param_norm = p.grad.data.norm(2) + norms.append(param_norm.item()) + clip_coef = clip / (param_norm + 1e-6) + if clip_coef < 1: + p.grad.data.mul_(clip_coef) + return norms + + +def cancel_gradients_last_layer(epoch, model, freeze_last_layer): + if epoch >= freeze_last_layer: + return + for n, p in model.named_parameters(): + if "last_layer" in n: + p.grad = None + + +def restart_from_checkpoint(ckp_path, run_variables=None, **kwargs): + """ + Re-start from checkpoint + """ + if not os.path.isfile(ckp_path): + return + print("Found checkpoint at {}".format(ckp_path)) + + # open checkpoint file + checkpoint = torch.load(ckp_path, map_location="cpu") + + # key is what to look for in the checkpoint file + # value is the object to load + # example: {'state_dict': model} + for key, value in kwargs.items(): + if key in checkpoint and value is not None: + try: + msg = value.load_state_dict(checkpoint[key], strict=False) + print("=> loaded '{}' from checkpoint '{}' with msg {}".format(key, ckp_path, msg)) + except TypeError: + try: + msg = value.load_state_dict(checkpoint[key]) + print("=> loaded '{}' from checkpoint: '{}'".format(key, ckp_path)) + except ValueError: + print("=> failed to load '{}' from checkpoint: '{}'".format(key, ckp_path)) + else: + print("=> key '{}' not found in checkpoint: '{}'".format(key, ckp_path)) + + # re load variable important for the run + if run_variables is not None: + for var_name in run_variables: + if var_name in checkpoint: + run_variables[var_name] = checkpoint[var_name] + + +def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warmup_epochs=0, start_warmup_value=0): + warmup_schedule = np.array([]) + warmup_iters = warmup_epochs * niter_per_ep + if warmup_epochs > 0: + warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters) + + iters = np.arange(epochs * niter_per_ep - warmup_iters) + schedule = final_value + 0.5 * (base_value - final_value) * (1 + np.cos(np.pi * iters / len(iters))) + + schedule = np.concatenate((warmup_schedule, schedule)) + assert len(schedule) == epochs * niter_per_ep + return schedule + + +def bool_flag(s): + """ + Parse boolean arguments from the command line. + """ + FALSY_STRINGS = {"off", "false", "0"} + TRUTHY_STRINGS = {"on", "true", "1"} + if s.lower() in FALSY_STRINGS: + return False + elif s.lower() in TRUTHY_STRINGS: + return True + else: + raise argparse.ArgumentTypeError("invalid value for a boolean flag") + + +def fix_random_seeds(seed=31): + """ + Fix random seeds. + """ + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + np.random.seed(seed) + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.6f} ({global_avg:.6f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not is_dist_avail_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') + dist.barrier() + dist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value) + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that all processes + have the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.all_reduce(values) + if average: + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format( + type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append( + "{}: {}".format(name, str(meter)) + ) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None): + i = 0 + if not header: + header = '' + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt='{avg:.6f}') + data_time = SmoothedValue(fmt='{avg:.6f}') + space_fmt = ':' + str(len(str(len(iterable)))) + 'd' + if torch.cuda.is_available(): + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}', + 'max mem: {memory:.0f}' + ]) + else: + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}' + ]) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB)) + else: + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time))) + i += 1 + end = time.time() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('{} Total time: {} ({:.6f} s / it)'.format( + header, total_time_str, total_time / len(iterable))) + + +def get_sha(): + cwd = os.path.dirname(os.path.abspath(__file__)) + + def _run(command): + return subprocess.check_output(command, cwd=cwd).decode('ascii').strip() + sha = 'N/A' + diff = "clean" + branch = 'N/A' + try: + sha = _run(['git', 'rev-parse', 'HEAD']) + subprocess.check_output(['git', 'diff'], cwd=cwd) + diff = _run(['git', 'diff-index', 'HEAD']) + diff = "has uncommited changes" if diff else "clean" + branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) + except Exception: + pass + message = f"sha: {sha}, status: {diff}, branch: {branch}" + return message + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop('force', False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def init_distributed_mode(args): + # launched with torch.distributed.launch + if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ['WORLD_SIZE']) + args.gpu = int(os.environ['LOCAL_RANK']) + # launched with submitit on a slurm cluster + elif 'SLURM_PROCID' in os.environ: + args.rank = int(os.environ['SLURM_PROCID']) + args.gpu = args.rank % torch.cuda.device_count() + # launched naively with `python main_dino.py` + # we manually add MASTER_ADDR and MASTER_PORT to env variables + elif torch.cuda.is_available(): + print('Will run the code on one GPU.') + args.rank, args.gpu, args.world_size = 0, 0, 1 + os.environ['MASTER_ADDR'] = '127.0.0.1' + os.environ['MASTER_PORT'] = '29500' + else: + print('Does not support training without GPU.') + sys.exit(1) + + dist.init_process_group( + backend="nccl", + init_method=args.dist_url, + world_size=args.world_size, + rank=args.rank, + ) + + torch.cuda.set_device(args.gpu) + print('| distributed init (rank {}): {}'.format( + args.rank, args.dist_url), flush=True) + dist.barrier() + setup_for_distributed(args.rank == 0) + + +def accuracy(output, target, topk=(1,)): + """Computes the accuracy over the k top predictions for the specified values of k""" + maxk = max(topk) + batch_size = target.size(0) + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.reshape(1, -1).expand_as(pred)) + return [correct[:k].reshape(-1).float().sum(0) * 100. / batch_size for k in topk] + + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): + # type: (Tensor, float, float, float, float) -> Tensor + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +class LARS(torch.optim.Optimizer): + """ + Almost copy-paste from https://github.com/facebookresearch/barlowtwins/blob/main/main.py + """ + def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, eta=0.001, + weight_decay_filter=None, lars_adaptation_filter=None): + defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum, + eta=eta, weight_decay_filter=weight_decay_filter, + lars_adaptation_filter=lars_adaptation_filter) + super().__init__(params, defaults) + + @torch.no_grad() + def step(self): + for g in self.param_groups: + for p in g['params']: + dp = p.grad + + if dp is None: + continue + + if p.ndim != 1: + dp = dp.add(p, alpha=g['weight_decay']) + + if p.ndim != 1: + param_norm = torch.norm(p) + update_norm = torch.norm(dp) + one = torch.ones_like(param_norm) + q = torch.where(param_norm > 0., + torch.where(update_norm > 0, + (g['eta'] * param_norm / update_norm), one), one) + dp = dp.mul(q) + + param_state = self.state[p] + if 'mu' not in param_state: + param_state['mu'] = torch.zeros_like(p) + mu = param_state['mu'] + mu.mul_(g['momentum']).add_(dp) + + p.add_(mu, alpha=-g['lr']) + + +class MultiCropWrapper(nn.Module): + """ + Perform forward pass separately on each resolution input. + The inputs corresponding to a single resolution are clubbed and single + forward is run on the same resolution inputs. Hence we do several + forward passes = number of different resolutions used. We then + concatenate all the output features and run the head forward on these + concatenated features. + """ + def __init__(self, backbone, head): + super(MultiCropWrapper, self).__init__() + # disable layers dedicated to ImageNet labels classification + backbone.fc, backbone.head = nn.Identity(), nn.Identity() + self.backbone = backbone + self.head = head + + def forward(self, x): + # convert to list + if not isinstance(x, list): + x = [x] + idx_crops = torch.cumsum(torch.unique_consecutive( + torch.tensor([inp.shape[-1] for inp in x]), + return_counts=True, + )[1], 0) + start_idx, output = 0, torch.empty(0).to(x[0].device) + for end_idx in idx_crops: + _out = self.backbone(torch.cat(x[start_idx: end_idx])) + # The output is a tuple with XCiT model. See: + # https://github.com/facebookresearch/xcit/blob/master/xcit.py#L404-L405 + if isinstance(_out, tuple): + _out = _out[0] + # accumulate outputs + output = torch.cat((output, _out)) + start_idx = end_idx + # Run the head forward on the concatenated features. + return self.head(output) + + +def get_params_groups(model): + regularized = [] + not_regularized = [] + for name, param in model.named_parameters(): + if not param.requires_grad: + continue + # we do not regularize biases nor Norm parameters + if name.endswith(".bias") or len(param.shape) == 1: + not_regularized.append(param) + else: + regularized.append(param) + return [{'params': regularized}, {'params': not_regularized, 'weight_decay': 0.}] + + +def has_batchnorms(model): + bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm) + for name, module in model.named_modules(): + if isinstance(module, bn_types): + return True + return False + + +class PCA(): + """ + Class to compute and apply PCA. + """ + def __init__(self, dim=256, whit=0.5): + self.dim = dim + self.whit = whit + self.mean = None + + def train_pca(self, cov): + """ + Takes a covariance matrix (np.ndarray) as input. + """ + d, v = np.linalg.eigh(cov) + eps = d.max() * 1e-5 + n_0 = (d < eps).sum() + if n_0 > 0: + d[d < eps] = eps + + # total energy + totenergy = d.sum() + + # sort eigenvectors with eigenvalues order + idx = np.argsort(d)[::-1][:self.dim] + d = d[idx] + v = v[:, idx] + + print("keeping %.2f %% of the energy" % (d.sum() / totenergy * 100.0)) + + # for the whitening + d = np.diag(1. / d**self.whit) + + # principal components + self.dvt = np.dot(d, v.T) + + def apply(self, x): + # input is from numpy + if isinstance(x, np.ndarray): + if self.mean is not None: + x -= self.mean + return np.dot(self.dvt, x.T).T + + # input is from torch and is on GPU + if x.is_cuda: + if self.mean is not None: + x -= torch.cuda.FloatTensor(self.mean) + return torch.mm(torch.cuda.FloatTensor(self.dvt), x.transpose(0, 1)).transpose(0, 1) + + # input if from torch, on CPU + if self.mean is not None: + x -= torch.FloatTensor(self.mean) + return torch.mm(torch.FloatTensor(self.dvt), x.transpose(0, 1)).transpose(0, 1) + + +def compute_ap(ranks, nres): + """ + Computes average precision for given ranked indexes. + Arguments + --------- + ranks : zerro-based ranks of positive images + nres : number of positive images + Returns + ------- + ap : average precision + """ + + # number of images ranked by the system + nimgranks = len(ranks) + + # accumulate trapezoids in PR-plot + ap = 0 + + recall_step = 1. / nres + + for j in np.arange(nimgranks): + rank = ranks[j] + + if rank == 0: + precision_0 = 1. + else: + precision_0 = float(j) / rank + + precision_1 = float(j + 1) / (rank + 1) + + ap += (precision_0 + precision_1) * recall_step / 2. + + return ap + + +def compute_map(ranks, gnd, kappas=[]): + """ + Computes the mAP for a given set of returned results. + Usage: + map = compute_map (ranks, gnd) + computes mean average precsion (map) only + map, aps, pr, prs = compute_map (ranks, gnd, kappas) + computes mean average precision (map), average precision (aps) for each query + computes mean precision at kappas (pr), precision at kappas (prs) for each query + Notes: + 1) ranks starts from 0, ranks.shape = db_size X #queries + 2) The junk results (e.g., the query itself) should be declared in the gnd stuct array + 3) If there are no positive images for some query, that query is excluded from the evaluation + """ + + map = 0. + nq = len(gnd) # number of queries + aps = np.zeros(nq) + pr = np.zeros(len(kappas)) + prs = np.zeros((nq, len(kappas))) + nempty = 0 + + for i in np.arange(nq): + qgnd = np.array(gnd[i]['ok']) + + # no positive images, skip from the average + if qgnd.shape[0] == 0: + aps[i] = float('nan') + prs[i, :] = float('nan') + nempty += 1 + continue + + try: + qgndj = np.array(gnd[i]['junk']) + except: + qgndj = np.empty(0) + + # sorted positions of positive and junk images (0 based) + pos = np.arange(ranks.shape[0])[np.in1d(ranks[:,i], qgnd)] + junk = np.arange(ranks.shape[0])[np.in1d(ranks[:,i], qgndj)] + + k = 0; + ij = 0; + if len(junk): + # decrease positions of positives based on the number of + # junk images appearing before them + ip = 0 + while (ip < len(pos)): + while (ij < len(junk) and pos[ip] > junk[ij]): + k += 1 + ij += 1 + pos[ip] = pos[ip] - k + ip += 1 + + # compute ap + ap = compute_ap(pos, len(qgnd)) + map = map + ap + aps[i] = ap + + # compute precision @ k + pos += 1 # get it to 1-based + for j in np.arange(len(kappas)): + kq = min(max(pos), kappas[j]); + prs[i, j] = (pos <= kq).sum() / kq + pr = pr + prs[i, :] + + map = map / (nq - nempty) + pr = pr / (nq - nempty) + + return map, aps, pr, prs + + +def multi_scale(samples, model): + v = None + for s in [1, 1/2**(1/2), 1/2]: # we use 3 different scales + if s == 1: + inp = samples.clone() + else: + inp = nn.functional.interpolate(samples, scale_factor=s, mode='bilinear', align_corners=False) + feats = model(inp).clone() + if v is None: + v = feats + else: + v += feats + v /= 3 + v /= v.norm() + return v diff --git a/third_party/dino/vision_transformer.py b/third_party/dino/vision_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..b174cefee9c39ad2cda61bf2274fa877061d8e8a --- /dev/null +++ b/third_party/dino/vision_transformer.py @@ -0,0 +1,311 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Mostly copy-paste from timm library. +https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py +""" +import math +from functools import partial + +import torch +import torch.nn as nn + +from utils import trunc_normal_ + + +def drop_path(x, drop_prob: float = 0., training: bool = False): + if drop_prob == 0. or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x, attn + + +class Block(nn.Module): + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x, return_attention=False): + y, attn = self.attn(self.norm1(x)) + if return_attention: + return attn + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + num_patches = (img_size // patch_size) * (img_size // patch_size) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x): + B, C, H, W = x.shape + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class VisionTransformer(nn.Module): + """ Vision Transformer """ + def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., norm_layer=nn.LayerNorm, **kwargs): + super().__init__() + self.num_features = self.embed_dim = embed_dim + + self.patch_embed = PatchEmbed( + img_size=img_size[0], patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + + # Classifier head + self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def interpolate_pos_encoding(self, x, w, h): + npatch = x.shape[1] - 1 + N = self.pos_embed.shape[1] - 1 + if npatch == N and w == h: + return self.pos_embed + class_pos_embed = self.pos_embed[:, 0] + patch_pos_embed = self.pos_embed[:, 1:] + dim = x.shape[-1] + w0 = w // self.patch_embed.patch_size + h0 = h // self.patch_embed.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + w0, h0 = w0 + 0.1, h0 + 0.1 + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2), + scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), + mode='bicubic', + ) + assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) + + def prepare_tokens(self, x): + B, nc, w, h = x.shape + x = self.patch_embed(x) # patch linear embedding + + # add the [CLS] token to the embed patch tokens + cls_tokens = self.cls_token.expand(B, -1, -1) + x = torch.cat((cls_tokens, x), dim=1) + + # add positional encoding to each token + x = x + self.interpolate_pos_encoding(x, w, h) + + return self.pos_drop(x) + + def forward(self, x): + x = self.prepare_tokens(x) + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + return x[:, 0] + + def get_last_selfattention(self, x): + x = self.prepare_tokens(x) + for i, blk in enumerate(self.blocks): + if i < len(self.blocks) - 1: + x = blk(x) + else: + # return attention of the last block + return blk(x, return_attention=True) + + def get_intermediate_layers(self, x, n=1): + x = self.prepare_tokens(x) + # we return the output tokens from the `n` last blocks + output = [] + for i, blk in enumerate(self.blocks): + x = blk(x) + if len(self.blocks) - i <= n: + output.append(self.norm(x)) + return output + + +def vit_tiny(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def vit_small(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def vit_base(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + +def moco_vit_small(**kwargs): + model = VisionTransformer( + patch_size=16, embed_dim=384, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + # model.default_cfg = _cfg() + return model + +def moco_vit_base(**kwargs): + model = VisionTransformer( + patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + #model.default_cfg = _cfg() + return model + +def mae_vit_base(**kwargs): + model = VisionTransformer( + patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +class DINOHead(nn.Module): + def __init__(self, in_dim, out_dim, use_bn=False, norm_last_layer=True, nlayers=3, hidden_dim=2048, bottleneck_dim=256): + super().__init__() + nlayers = max(nlayers, 1) + if nlayers == 1: + self.mlp = nn.Linear(in_dim, bottleneck_dim) + else: + layers = [nn.Linear(in_dim, hidden_dim)] + if use_bn: + layers.append(nn.BatchNorm1d(hidden_dim)) + layers.append(nn.GELU()) + for _ in range(nlayers - 2): + layers.append(nn.Linear(hidden_dim, hidden_dim)) + if use_bn: + layers.append(nn.BatchNorm1d(hidden_dim)) + layers.append(nn.GELU()) + layers.append(nn.Linear(hidden_dim, bottleneck_dim)) + self.mlp = nn.Sequential(*layers) + self.apply(self._init_weights) + self.last_layer = nn.utils.weight_norm(nn.Linear(bottleneck_dim, out_dim, bias=False)) + self.last_layer.weight_g.data.fill_(1) + if norm_last_layer: + self.last_layer.weight_g.requires_grad = False + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x): + x = self.mlp(x) + x = nn.functional.normalize(x, dim=-1, p=2) + x = self.last_layer(x) + return x diff --git a/third_party/inference_demo.ipynb b/third_party/inference_demo.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..755c75041630574d7df81250e0bb0506e9eeab6e --- /dev/null +++ b/third_party/inference_demo.ipynb @@ -0,0 +1,129 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "TokenCut Demo.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9bxb7gkitwb7" + }, + "outputs": [], + "source": [ + "!git clone https://github.com/YangtaoWANG95/TokenCut\n", + "%cd TokenCut" + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install torch==1.7.1 torchvision==0.8.2\n", + "!pip install -r requirements.txt" + ], + "metadata": { + "id": "ncWpnDLPt6ZZ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Change this URL to any jpg image:\n", + "!cd examples && wget -O test.jpg https://i.imgur.com/G4wpITa.jpg" + ], + "metadata": { + "id": "QmloZNiE_co9" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!python main_tokencut.py --image_path examples/test.jpg --visualize all " + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2v3gHLihujra", + "outputId": "2946b730-6dd0-4632-aa99-bd16f3a4be69" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Since no pretrained weights have been provided, we load the reference pretrained DINO weights.\n", + "Pretrained weights found at dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth and loaded with msg: \n", + "Running TokenCut on the dataset test.jpg (exp: TokenCut-vit_small16_k)\n", + " 0% 0/1 [00:00" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {} + } + ] + } + ] +} diff --git a/third_party/main_tokencut.py b/third_party/main_tokencut.py new file mode 100755 index 0000000000000000000000000000000000000000..b24727be821ee0f4a735f543e653ec7c055f4b7f --- /dev/null +++ b/third_party/main_tokencut.py @@ -0,0 +1,326 @@ +""" +Main experiment file. Code adapted from LOST: https://github.com/valeoai/LOST +""" +import os +import argparse +import random +import pickle + +import torch +import datetime +import torch.nn as nn +import numpy as np + +from tqdm import tqdm +from PIL import Image + +from networks import get_model +from datasets import ImageDataset, Dataset, bbox_iou +from visualizations import visualize_img, visualize_eigvec, visualize_predictions, visualize_predictions_gt +from object_discovery import ncut +import matplotlib.pyplot as plt +import time + +if __name__ == "__main__": + parser = argparse.ArgumentParser("Visualize Self-Attention maps") + parser.add_argument( + "--arch", + default="vit_small", + type=str, + choices=[ + "vit_tiny", + "vit_small", + "vit_base", + "moco_vit_small", + "moco_vit_base", + "mae_vit_base", + ], + help="Model architecture.", + ) + parser.add_argument( + "--patch_size", default=16, type=int, help="Patch resolution of the model." + ) + + # Use a dataset + parser.add_argument( + "--dataset", + default="VOC07", + type=str, + choices=[None, "VOC07", "VOC12", "COCO20k"], + help="Dataset name.", + ) + + parser.add_argument( + "--save-feat-dir", + type=str, + default=None, + help="if save-feat-dir is not None, only computing features and save it into save-feat-dir", + ) + + parser.add_argument( + "--set", + default="train", + type=str, + choices=["val", "train", "trainval", "test"], + help="Path of the image to load.", + ) + # Or use a single image + parser.add_argument( + "--image_path", + type=str, + default=None, + help="If want to apply only on one image, give file path.", + ) + + # Folder used to output visualizations and + parser.add_argument( + "--output_dir", type=str, default="outputs", help="Output directory to store predictions and visualizations." + ) + + # Evaluation setup + parser.add_argument("--no_hard", action="store_true", help="Only used in the case of the VOC_all setup (see the paper).") + parser.add_argument("--no_evaluation", action="store_true", help="Compute the evaluation.") + parser.add_argument("--save_predictions", default=True, type=bool, help="Save predicted bouding boxes.") + + # Visualization + parser.add_argument( + "--visualize", + type=str, + choices=["attn", "pred", "all", None], + default=None, + help="Select the different type of visualizations.", + ) + + # TokenCut parameters + parser.add_argument( + "--which_features", + type=str, + default="k", + choices=["k", "q", "v"], + help="Which features to use", + ) + parser.add_argument( + "--k_patches", + type=int, + default=100, + help="Number of patches with the lowest degree considered." + ) + parser.add_argument("--resize", type=int, default=None, help="Resize input image to fix size") + parser.add_argument("--tau", type=float, default=0.2, help="Tau for seperating the Graph.") + parser.add_argument("--eps", type=float, default=1e-5, help="Eps for defining the Graph.") + parser.add_argument("--no-binary-graph", action="store_true", default=False, help="Generate a binary graph where edge of the Graph will binary. Or using similarity score as edge weight.") + + # Use dino-seg proposed method + parser.add_argument("--dinoseg", action="store_true", help="Apply DINO-seg baseline.") + parser.add_argument("--dinoseg_head", type=int, default=4) + + args = parser.parse_args() + + if args.image_path is not None: + args.save_predictions = False + args.no_evaluation = True + args.dataset = None + + # ------------------------------------------------------------------------------------------------------- + # Dataset + + # If an image_path is given, apply the method only to the image + if args.image_path is not None: + dataset = ImageDataset(args.image_path, args.resize) + else: + dataset = Dataset(args.dataset, args.set, args.no_hard) + + # ------------------------------------------------------------------------------------------------------- + # Model + device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + #device = torch.device('cuda') + model = get_model(args.arch, args.patch_size, device) + + # ------------------------------------------------------------------------------------------------------- + # Directories + if args.image_path is None: + args.output_dir = os.path.join(args.output_dir, dataset.name) + os.makedirs(args.output_dir, exist_ok=True) + + # Naming + if args.dinoseg: + # Experiment with the baseline DINO-seg + if "vit" not in args.arch: + raise ValueError("DINO-seg can only be applied to tranformer networks.") + exp_name = f"{args.arch}-{args.patch_size}_dinoseg-head{args.dinoseg_head}" + else: + # Experiment with TokenCut + exp_name = f"TokenCut-{args.arch}" + if "vit" in args.arch: + exp_name += f"{args.patch_size}_{args.which_features}" + + print(f"Running TokenCut on the dataset {dataset.name} (exp: {exp_name})") + + # Visualization + if args.visualize: + vis_folder = f"{args.output_dir}/{exp_name}" + os.makedirs(vis_folder, exist_ok=True) + + if args.save_feat_dir is not None : + os.mkdir(args.save_feat_dir) + + # ------------------------------------------------------------------------------------------------------- + # Loop over images + preds_dict = {} + cnt = 0 + corloc = np.zeros(len(dataset.dataloader)) + + start_time = time.time() + pbar = tqdm(dataset.dataloader) + for im_id, inp in enumerate(pbar): + + # ------------ IMAGE PROCESSING ------------------------------------------- + img = inp[0] + + init_image_size = img.shape + + # Get the name of the image + im_name = dataset.get_image_name(inp[1]) + # Pass in case of no gt boxes in the image + if im_name is None: + continue + + # Padding the image with zeros to fit multiple of patch-size + size_im = ( + img.shape[0], + int(np.ceil(img.shape[1] / args.patch_size) * args.patch_size), + int(np.ceil(img.shape[2] / args.patch_size) * args.patch_size), + ) + paded = torch.zeros(size_im) + paded[:, : img.shape[1], : img.shape[2]] = img + img = paded + + # # Move to gpu + if device == torch.device('cuda'): + img = img.cuda(non_blocking=True) + # Size for transformers + w_featmap = img.shape[-2] // args.patch_size + h_featmap = img.shape[-1] // args.patch_size + + # ------------ GROUND-TRUTH ------------------------------------------- + if not args.no_evaluation: + gt_bbxs, gt_cls = dataset.extract_gt(inp[1], im_name) + + if gt_bbxs is not None: + # Discard images with no gt annotations + # Happens only in the case of VOC07 and VOC12 + if gt_bbxs.shape[0] == 0 and args.no_hard: + continue + + # ------------ EXTRACT FEATURES ------------------------------------------- + with torch.no_grad(): + + # ------------ FORWARD PASS ------------------------------------------- + if "vit" in args.arch: + # Store the outputs of qkv layer from the last attention layer + feat_out = {} + def hook_fn_forward_qkv(module, input, output): + feat_out["qkv"] = output + model._modules["blocks"][-1]._modules["attn"]._modules["qkv"].register_forward_hook(hook_fn_forward_qkv) + + # Forward pass in the model + attentions = model.get_last_selfattention(img[None, :, :, :]) + + # Scaling factor + scales = [args.patch_size, args.patch_size] + + # Dimensions + nb_im = attentions.shape[0] # Batch size + nh = attentions.shape[1] # Number of heads + nb_tokens = attentions.shape[2] # Number of tokens + + # Baseline: compute DINO segmentation technique proposed in the DINO paper + # and select the biggest component + if args.dinoseg: + pred = dino_seg(attentions, (w_featmap, h_featmap), args.patch_size, head=args.dinoseg_head) + pred = np.asarray(pred) + else: + # Extract the qkv features of the last attention layer + qkv = ( + feat_out["qkv"] + .reshape(nb_im, nb_tokens, 3, nh, -1 // nh) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = qkv[0], qkv[1], qkv[2] + k = k.transpose(1, 2).reshape(nb_im, nb_tokens, -1) + q = q.transpose(1, 2).reshape(nb_im, nb_tokens, -1) + v = v.transpose(1, 2).reshape(nb_im, nb_tokens, -1) + + # Modality selection + if args.which_features == "k": + #feats = k[:, 1:, :] + feats = k + elif args.which_features == "q": + #feats = q[:, 1:, :] + feats = q + elif args.which_features == "v": + #feats = v[:, 1:, :] + feats = v + + if args.save_feat_dir is not None : + np.save(os.path.join(args.save_feat_dir, im_name.replace('.jpg', '.npy').replace('.jpeg', '.npy').replace('.png', '.npy')), feats.cpu().numpy()) + continue + + else: + raise ValueError("Unknown model.") + + # ------------ Apply TokenCut ------------------------------------------- + if not args.dinoseg: + pred, objects, foreground, seed , bins, eigenvector= ncut(feats, [w_featmap, h_featmap], scales, init_image_size, args.tau, args.eps, im_name=im_name, no_binary_graph=args.no_binary_graph) + + if args.visualize == "pred" and args.no_evaluation : + image = dataset.load_image(im_name, size_im) + visualize_predictions(image, pred, vis_folder, im_name) + if args.visualize == "attn" and args.no_evaluation: + visualize_eigvec(eigenvector, vis_folder, im_name, [w_featmap, h_featmap], scales) + if args.visualize == "all" and args.no_evaluation: + image = dataset.load_image(im_name, size_im) + visualize_predictions(image, pred, vis_folder, im_name) + visualize_eigvec(eigenvector, vis_folder, im_name, [w_featmap, h_featmap], scales) + + # ------------ Visualizations ------------------------------------------- + # Save the prediction + preds_dict[im_name] = pred + + # Evaluation + if args.no_evaluation: + continue + + # Compare prediction to GT boxes + ious = bbox_iou(torch.from_numpy(pred), torch.from_numpy(gt_bbxs)) + + if torch.any(ious >= 0.5): + corloc[im_id] = 1 + vis_folder = f"{args.output_dir}/{exp_name}" + os.makedirs(vis_folder, exist_ok=True) + image = dataset.load_image(im_name) + #visualize_predictions(image, pred, vis_folder, im_name) + #visualize_eigvec(eigenvector, vis_folder, im_name, [w_featmap, h_featmap], scales) + + cnt += 1 + if cnt % 50 == 0: + pbar.set_description(f"Found {int(np.sum(corloc))}/{cnt}") + + end_time = time.time() + print(f'Time cost: {str(datetime.timedelta(milliseconds=int((end_time - start_time)*1000)))}') + # Save predicted bounding boxes + if args.save_predictions: + folder = f"{args.output_dir}/{exp_name}" + os.makedirs(folder, exist_ok=True) + filename = os.path.join(folder, "preds.pkl") + with open(filename, "wb") as f: + pickle.dump(preds_dict, f) + print("Predictions saved at %s" % filename) + + # Evaluate + if not args.no_evaluation: + print(f"corloc: {100*np.sum(corloc)/cnt:.2f} ({int(np.sum(corloc))}/{cnt})") + result_file = os.path.join(folder, 'results.txt') + with open(result_file, 'w') as f: + f.write('corloc,%.1f,,\n'%(100*np.sum(corloc)/cnt)) + print('File saved at %s'%result_file) diff --git a/third_party/networks.py b/third_party/networks.py new file mode 100755 index 0000000000000000000000000000000000000000..844a3b442fc1a88477052a7c2e1faf62acf9ae8a --- /dev/null +++ b/third_party/networks.py @@ -0,0 +1,87 @@ +""" +Loads model. +Code adapted from LOST: https://github.com/valeoai/LOST +""" + +import torch +import torch.nn as nn + +from torchvision.models.resnet import resnet50 +from torchvision.models.vgg import vgg16 + +import dino.vision_transformer as vits +#import moco.vits as vits_moco + +def get_model(arch, patch_size, device): + + # Initialize model with pretraining + url = None + if "moco" in arch: + if arch == "moco_vit_small" and patch_size == 16: + url = "moco-v3/vit-s-300ep/vit-s-300ep.pth.tar" + elif arch == "moco_vit_base" and patch_size == 16: + url = "moco-v3/vit-b-300ep/vit-b-300ep.pth.tar" + model = vits.__dict__[arch](num_classes=0) + elif "mae" in arch: + if arch == "mae_vit_base" and patch_size == 16: + url = "mae/visualize/mae_visualize_vit_base.pth" + model = vits.__dict__[arch](num_classes=0) + elif "vit" in arch: + if arch == "vit_small" and patch_size == 16: + url = "dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth" + elif arch == "vit_small" and patch_size == 8: + url = "dino/dino_deitsmall8_300ep_pretrain/dino_deitsmall8_300ep_pretrain.pth" + elif arch == "vit_base" and patch_size == 16: + url = "dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth" + elif arch == "vit_base" and patch_size == 8: + url = "dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth" + elif arch == "resnet50": + url = "dino/dino_resnet50_pretrain/dino_resnet50_pretrain.pth" + model = vits.__dict__[arch](patch_size=patch_size, num_classes=0) + else: + raise NotImplementedError + + for p in model.parameters(): + p.requires_grad = False + + if url is not None: + print( + "Since no pretrained weights have been provided, we load the reference pretrained DINO weights." + ) + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/" + url + ) + if "moco" in arch: + state_dict = state_dict['state_dict'] + for k in list(state_dict.keys()): + # retain only base_encoder up to before the embedding layer + if k.startswith('module.base_encoder') and not k.startswith('module.base_encoder.head'): + # remove prefix + state_dict[k[len("module.base_encoder."):]] = state_dict[k] + # delete renamed or unused k + del state_dict[k] + elif "mae" in arch: + state_dict = state_dict['model'] + for k in list(state_dict.keys()): + # retain only base_encoder up to before the embedding layer + if k.startswith('decoder') or k.startswith('mask_token'): + # remove prefix + #state_dict[k[len("module.base_encoder."):]] = state_dict[k] + # delete renamed or unused k + del state_dict[k] + + msg = model.load_state_dict(state_dict, strict=True) + print( + "Pretrained weights found at {} and loaded with msg: {}".format( + url, msg + ) + ) + else: + print( + "There is no reference weights available for this model => We use random weights." + ) + + + model.eval() + model.to(device) + return model diff --git a/third_party/object_discovery.py b/third_party/object_discovery.py new file mode 100644 index 0000000000000000000000000000000000000000..2a24b2f6237f675ab2ed0651660deb9568c441ec --- /dev/null +++ b/third_party/object_discovery.py @@ -0,0 +1,93 @@ +""" +Main functions for applying Normalized Cut. +Code adapted from LOST: https://github.com/valeoai/LOST +""" + +import torch +import torch.nn.functional as F +import numpy as np +from scipy.linalg import eigh +from scipy import ndimage + +def ncut(feats, dims, scales, init_image_size, tau = 0, eps=1e-5, im_name='', no_binary_graph=False): + """ + Implementation of NCut Method. + Inputs + feats: the pixel/patche features of an image + dims: dimension of the map from which the features are used + scales: from image to map scale + init_image_size: size of the image + tau: thresold for graph construction + eps: graph edge weight + im_name: image_name + no_binary_graph: ablation study for using similarity score as graph edge weight + """ + cls_token = feats[0,0:1,:].cpu().numpy() + + feats = feats[0,1:,:] + feats = F.normalize(feats, p=2) + A = (feats @ feats.transpose(1,0)) + A = A.cpu().numpy() + if no_binary_graph: + A[A tau + A = np.where(A.astype(float) == 0, eps, A) + d_i = np.sum(A, axis=1) + D = np.diag(d_i) + + # Print second and third smallest eigenvector + _, eigenvectors = eigh(D-A, D, subset_by_index=[1,2]) + eigenvec = np.copy(eigenvectors[:, 0]) + + # Using average point to compute bipartition + second_smallest_vec = eigenvectors[:, 0] + avg = np.sum(second_smallest_vec) / len(second_smallest_vec) + bipartition = second_smallest_vec > avg + + seed = np.argmax(np.abs(second_smallest_vec)) + + if bipartition[seed] != 1: + eigenvec = eigenvec * -1 + bipartition = np.logical_not(bipartition) + bipartition = bipartition.reshape(dims).astype(float) + + # predict BBox + pred, _, objects,cc = detect_box(bipartition, seed, dims, scales=scales, initial_im_size=init_image_size[1:]) ## We only extract the principal object BBox + mask = np.zeros(dims) + mask[cc[0],cc[1]] = 1 + + return np.asarray(pred), objects, mask, seed, None, eigenvec.reshape(dims) + +def detect_box(bipartition, seed, dims, initial_im_size=None, scales=None, principle_object=True): + """ + Extract a box corresponding to the seed patch. Among connected components extract from the affinity matrix, select the one corresponding to the seed patch. + """ + w_featmap, h_featmap = dims + objects, num_objects = ndimage.label(bipartition) + cc = objects[np.unravel_index(seed, dims)] + + + if principle_object: + mask = np.where(objects == cc) + # Add +1 because excluded max + ymin, ymax = min(mask[0]), max(mask[0]) + 1 + xmin, xmax = min(mask[1]), max(mask[1]) + 1 + # Rescale to image size + r_xmin, r_xmax = scales[1] * xmin, scales[1] * xmax + r_ymin, r_ymax = scales[0] * ymin, scales[0] * ymax + pred = [r_xmin, r_ymin, r_xmax, r_ymax] + + # Check not out of image size (used when padding) + if initial_im_size: + pred[2] = min(pred[2], initial_im_size[1]) + pred[3] = min(pred[3], initial_im_size[0]) + + # Coordinate predictions for the feature space + # Axis different then in image space + pred_feats = [ymin, xmin, ymax, xmax] + + return pred, pred_feats, objects, mask + else: + raise NotImplementedError + diff --git a/third_party/requirements.txt b/third_party/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..3e700e33facf16daf2375313c6dfc57e0f8c56f2 --- /dev/null +++ b/third_party/requirements.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9b350f220343a39c57dadd8e86a4ff4a07cc5cd45170ebe62d9ce354794042f9 +size 154 diff --git a/third_party/unsupervised_saliency_detection/__pycache__/metric.cpython-312.pyc b/third_party/unsupervised_saliency_detection/__pycache__/metric.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2f52395899a21acc68ac940dd38859fae32d80fb Binary files /dev/null and b/third_party/unsupervised_saliency_detection/__pycache__/metric.cpython-312.pyc differ diff --git a/third_party/unsupervised_saliency_detection/bilateral_solver.py b/third_party/unsupervised_saliency_detection/bilateral_solver.py new file mode 100755 index 0000000000000000000000000000000000000000..9fc1487162723689b9170b46847740013d718400 --- /dev/null +++ b/third_party/unsupervised_saliency_detection/bilateral_solver.py @@ -0,0 +1,189 @@ +from scipy.sparse import diags +from scipy.sparse.linalg import cg +from scipy.sparse import csr_matrix +import numpy as np +from skimage.io import imread +from scipy import ndimage +import PIL.Image as Image + + +RGB_TO_YUV = np.array([ + [ 0.299, 0.587, 0.114], + [-0.168736, -0.331264, 0.5], + [ 0.5, -0.418688, -0.081312]]) +YUV_TO_RGB = np.array([ + [1.0, 0.0, 1.402], + [1.0, -0.34414, -0.71414], + [1.0, 1.772, 0.0]]) +YUV_OFFSET = np.array([0, 128.0, 128.0]).reshape(1, 1, -1) +MAX_VAL = 255.0 +def rgb2yuv(im): + return np.tensordot(im, RGB_TO_YUV, ([2], [1])) + YUV_OFFSET + +def yuv2rgb(im): + return np.tensordot(im.astype(float) - YUV_OFFSET, YUV_TO_RGB, ([2], [1])) + + +def get_valid_idx(valid, candidates): + """Find which values are present in a list and where they are located""" + locs = np.searchsorted(valid, candidates) + # Handle edge case where the candidate is larger than all valid values + locs = np.clip(locs, 0, len(valid) - 1) + # Identify which values are actually present + valid_idx = np.flatnonzero(valid[locs] == candidates) + locs = locs[valid_idx] + return valid_idx, locs + +class BilateralGrid(object): + def __init__(self, im, sigma_spatial=32, sigma_luma=8, sigma_chroma=8): + im_yuv = rgb2yuv(im) + # Compute 5-dimensional XYLUV bilateral-space coordinates + Iy, Ix = np.mgrid[:im.shape[0], :im.shape[1]] + x_coords = (Ix / sigma_spatial).astype(int) + y_coords = (Iy / sigma_spatial).astype(int) + luma_coords = (im_yuv[..., 0] /sigma_luma).astype(int) + chroma_coords = (im_yuv[..., 1:] / sigma_chroma).astype(int) + coords = np.dstack((x_coords, y_coords, luma_coords, chroma_coords)) + coords_flat = coords.reshape(-1, coords.shape[-1]) + self.npixels, self.dim = coords_flat.shape + # Hacky "hash vector" for coordinates, + # Requires all scaled coordinates be < MAX_VAL + self.hash_vec = (MAX_VAL**np.arange(self.dim)) + # Construct S and B matrix + self._compute_factorization(coords_flat) + + def _compute_factorization(self, coords_flat): + # Hash each coordinate in grid to a unique value + hashed_coords = self._hash_coords(coords_flat) + unique_hashes, unique_idx, idx = \ + np.unique(hashed_coords, return_index=True, return_inverse=True) + # Identify unique set of vertices + unique_coords = coords_flat[unique_idx] + self.nvertices = len(unique_coords) + # Construct sparse splat matrix that maps from pixels to vertices + self.S = csr_matrix((np.ones(self.npixels), (idx, np.arange(self.npixels)))) + # Construct sparse blur matrices. + # Note that these represent [1 0 1] blurs, excluding the central element + self.blurs = [] + for d in range(self.dim): + blur = 0.0 + for offset in (-1, 1): + offset_vec = np.zeros((1, self.dim)) + offset_vec[:, d] = offset + neighbor_hash = self._hash_coords(unique_coords + offset_vec) + valid_coord, idx = get_valid_idx(unique_hashes, neighbor_hash) + blur = blur + csr_matrix((np.ones((len(valid_coord),)), + (valid_coord, idx)), + shape=(self.nvertices, self.nvertices)) + self.blurs.append(blur) + + def _hash_coords(self, coord): + """Hacky function to turn a coordinate into a unique value""" + return np.dot(coord.reshape(-1, self.dim), self.hash_vec) + + def splat(self, x): + return self.S.dot(x) + + def slice(self, y): + return self.S.T.dot(y) + + def blur(self, x): + """Blur a bilateral-space vector with a 1 2 1 kernel in each dimension""" + assert x.shape[0] == self.nvertices + out = 2 * self.dim * x + for blur in self.blurs: + out = out + blur.dot(x) + return out + + def filter(self, x): + """Apply bilateral filter to an input x""" + return self.slice(self.blur(self.splat(x))) / \ + self.slice(self.blur(self.splat(np.ones_like(x)))) + + + + +def bistochastize(grid, maxiter=10): + """Compute diagonal matrices to bistochastize a bilateral grid""" + m = grid.splat(np.ones(grid.npixels)) + n = np.ones(grid.nvertices) + for i in range(maxiter): + n = np.sqrt(n * m / grid.blur(n)) + # Correct m to satisfy the assumption of bistochastization regardless + # of how many iterations have been run. + m = n * grid.blur(n) + Dm = diags(m, 0) + Dn = diags(n, 0) + return Dn, Dm + +class BilateralSolver(object): + def __init__(self, grid, params): + self.grid = grid + self.params = params + self.Dn, self.Dm = bistochastize(grid) + + def solve(self, x, w): + # Check that w is a vector or a nx1 matrix + if w.ndim == 2: + assert(w.shape[1] == 1) + elif w.dim == 1: + w = w.reshape(w.shape[0], 1) + A_smooth = (self.Dm - self.Dn.dot(self.grid.blur(self.Dn))) + w_splat = self.grid.splat(w) + A_data = diags(w_splat[:,0], 0) + A = self.params["lam"] * A_smooth + A_data + xw = x * w + b = self.grid.splat(xw) + # Use simple Jacobi preconditioner + A_diag = np.maximum(A.diagonal(), self.params["A_diag_min"]) + M = diags(1 / A_diag, 0) + # Flat initialization + y0 = self.grid.splat(xw) / w_splat + yhat = np.empty_like(y0) + for d in range(x.shape[-1]): + yhat[..., d], info = cg(A, b[..., d], x0=y0[..., d], M=M, maxiter=self.params["cg_maxiter"], tol=self.params["cg_tol"]) + xhat = self.grid.slice(yhat) + return xhat + +def bilateral_solver_output(img_pth, target, sigma_spatial = 24, sigma_luma = 4, sigma_chroma = 4) : + + reference = np.array(Image.open(img_pth).convert('RGB')) + h, w = target.shape + confidence = np.ones((h, w)) * 0.999 + + + + grid_params = { + 'sigma_luma' : sigma_luma, # Brightness bandwidth + 'sigma_chroma': sigma_chroma, # Color bandwidth + 'sigma_spatial': sigma_spatial # Spatial bandwidth + } + + bs_params = { + 'lam': 256, # The strength of the smoothness parameter + 'A_diag_min': 1e-5, # Clamp the diagonal of the A diagonal in the Jacobi preconditioner. + 'cg_tol': 1e-5, # The tolerance on the convergence in PCG + 'cg_maxiter': 25 # The number of PCG iterations + } + + grid = BilateralGrid(reference, **grid_params) + + t = target.reshape(-1, 1).astype(np.double) + c = confidence.reshape(-1, 1).astype(np.double) + + ## output solver, which is a soft value + output_solver = BilateralSolver(grid, bs_params).solve(t, c).reshape((h, w)) + + binary_solver = ndimage.binary_fill_holes(output_solver>0.5) + labeled, nr_objects = ndimage.label(binary_solver) + + nb_pixel = [np.sum(labeled == i) for i in range(nr_objects + 1)] + pixel_order = np.argsort(nb_pixel) + try : + binary_solver = labeled == pixel_order[-2] + except: + binary_solver = np.ones((h, w), dtype=bool) + + return output_solver, binary_solver + + \ No newline at end of file diff --git a/third_party/unsupervised_saliency_detection/dino.py b/third_party/unsupervised_saliency_detection/dino.py new file mode 100644 index 0000000000000000000000000000000000000000..18d50984c4b38d23c381575c155538de92a81635 --- /dev/null +++ b/third_party/unsupervised_saliency_detection/dino.py @@ -0,0 +1,361 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Copied from Dino repo. https://github.com/facebookresearch/dino +Mostly copy-paste from timm library. +https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py +""" +import math +from functools import partial + +import torch +import torch.nn as nn + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): + # type: (Tensor, float, float, float, float) -> Tensor + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +def drop_path(x, drop_prob: float = 0., training: bool = False): + if drop_prob == 0. or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x, attn + + +class Block(nn.Module): + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x, return_attention=False): + y, attn = self.attn(self.norm1(x)) + if return_attention: + return attn + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + num_patches = (img_size // patch_size) * (img_size // patch_size) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x): + B, C, H, W = x.shape + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class VisionTransformer(nn.Module): + """ Vision Transformer """ + def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., norm_layer=nn.LayerNorm, **kwargs): + super().__init__() + self.num_features = self.embed_dim = embed_dim + + self.patch_embed = PatchEmbed( + img_size=img_size[0], patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + + # Classifier head + self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def interpolate_pos_encoding(self, x, w, h): + npatch = x.shape[1] - 1 + N = self.pos_embed.shape[1] - 1 + if npatch == N and w == h: + return self.pos_embed + class_pos_embed = self.pos_embed[:, 0] + patch_pos_embed = self.pos_embed[:, 1:] + dim = x.shape[-1] + w0 = w // self.patch_embed.patch_size + h0 = h // self.patch_embed.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + w0, h0 = w0 + 0.1, h0 + 0.1 + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2), + scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), + mode='bicubic', + ) + assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) + + def prepare_tokens(self, x): + B, nc, w, h = x.shape + x = self.patch_embed(x) # patch linear embedding + + # add the [CLS] token to the embed patch tokens + cls_tokens = self.cls_token.expand(B, -1, -1) + x = torch.cat((cls_tokens, x), dim=1) + + # add positional encoding to each token + x = x + self.interpolate_pos_encoding(x, w, h) + + return self.pos_drop(x) + + def forward(self, x): + x = self.prepare_tokens(x) + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + return x[:, 0] + + def get_last_selfattention(self, x): + x = self.prepare_tokens(x) + for i, blk in enumerate(self.blocks): + if i < len(self.blocks) - 1: + x = blk(x) + else: + # return attention of the last block + return blk(x, return_attention=True) + + def get_intermediate_layers(self, x, n=1): + x = self.prepare_tokens(x) + # we return the output tokens from the `n` last blocks + output = [] + for i, blk in enumerate(self.blocks): + x = blk(x) + if len(self.blocks) - i <= n: + output.append(self.norm(x)) + return output + + + +def vit_small(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def vit_base(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + + + +class ViTFeat(nn.Module): + """ Vision Transformer """ + def __init__(self, pretrained_pth, feat_dim, vit_arch = 'base', vit_feat = 'k', patch_size=16): + super().__init__() + if vit_arch == 'base' : + self.model = vit_base(patch_size=patch_size, num_classes=0) + + else : + self.model = vit_small(patch_size=patch_size, num_classes=0) + + self.feat_dim = feat_dim + self.vit_feat = vit_feat + self.patch_size = patch_size + +# state_dict = torch.load(pretrained_pth, map_location="cpu") + state_dict = torch.hub.load_state_dict_from_url("https://dl.fbaipublicfiles.com"+pretrained_pth) + self.model.load_state_dict(state_dict, strict=True) + print('Loading weight from {}'.format(pretrained_pth)) + + + def forward(self, img) : + feat_out = {} + def hook_fn_forward_qkv(module, input, output): + feat_out["qkv"] = output + + self.model._modules["blocks"][-1]._modules["attn"]._modules["qkv"].register_forward_hook(hook_fn_forward_qkv) + + + # Forward pass in the model + with torch.no_grad() : + h, w = img.shape[2], img.shape[3] + feat_h, feat_w = h // self.patch_size, w // self.patch_size + attentions = self.model.get_last_selfattention(img) + bs, nb_head, nb_token = attentions.shape[0], attentions.shape[1], attentions.shape[2] + qkv = ( + feat_out["qkv"] + .reshape(bs, nb_token, 3, nb_head, -1) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = qkv[0], qkv[1], qkv[2] + + k = k.transpose(1, 2).reshape(bs, nb_token, -1) + q = q.transpose(1, 2).reshape(bs, nb_token, -1) + v = v.transpose(1, 2).reshape(bs, nb_token, -1) + + # Modality selection + if self.vit_feat == "k": + feats = k[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + elif self.vit_feat == "q": + feats = q[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + elif self.vit_feat == "v": + feats = v[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + elif self.vit_feat == "kqv": + k = k[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + q = q[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + v = v[:, 1:].transpose(1, 2).reshape(bs, self.feat_dim, feat_h * feat_w) + feats = torch.cat([k, q, v], dim=1) + return feats + + +if __name__ == "__main__": + vit_arch = 'base' + vit_feat = 'k' + + model = ViTFeat(vit_arch, vit_feat) + img = torch.cuda.FloatTensor(4, 3, 224, 224) + model.cuda() + # Forward pass in the model + feat = model(img) + print (feat.shape) diff --git a/third_party/unsupervised_saliency_detection/get_saliency.py b/third_party/unsupervised_saliency_detection/get_saliency.py new file mode 100755 index 0000000000000000000000000000000000000000..648a34dc4b19e966a69202dddf55d69d70bbefa7 --- /dev/null +++ b/third_party/unsupervised_saliency_detection/get_saliency.py @@ -0,0 +1,191 @@ +import sys +sys.path.append('./model') +import dino # model + +import object_discovery as tokencut +import argparse +import utils +import bilateral_solver +import os + +from shutil import copyfile +import PIL.Image as Image +import cv2 +import numpy as np +from tqdm import tqdm + +from torchvision import transforms +import metric +import matplotlib.pyplot as plt +import skimage +import torch + +# Image transformation applied to all images +ToTensor = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.485, 0.456, 0.406), + (0.229, 0.224, 0.225)),]) + +def get_tokencut_binary_map(img_pth, backbone,patch_size, tau) : + I = Image.open(img_pth).convert('RGB') + I_resize, w, h, feat_w, feat_h = utils.resize_pil(I, patch_size) + + tensor = ToTensor(I_resize).unsqueeze(0).cuda() + feat = backbone(tensor)[0] + + seed, bipartition, eigvec = tokencut.ncut(feat, [feat_h, feat_w], [patch_size, patch_size], [h,w], tau) + return bipartition, eigvec + +def mask_color_compose(org, mask, mask_color = [173, 216, 230]) : + + mask_fg = mask > 0.5 + rgb = np.copy(org) + rgb[mask_fg] = (rgb[mask_fg] * 0.3 + np.array(mask_color) * 0.7).astype(np.uint8) + + return Image.fromarray(rgb) + + +parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + +## input / output dir +parser.add_argument('--out-dir', type=str, help='output directory') + +parser.add_argument('--vit-arch', type=str, default='small', choices=['base', 'small'], help='which architecture') + +parser.add_argument('--vit-feat', type=str, default='k', choices=['k', 'q', 'v', 'kqv'], help='which features') + +parser.add_argument('--patch-size', type=int, default=16, choices=[16, 8], help='patch size') + +parser.add_argument('--tau', type=float, default=0.2, help='Tau for tresholding graph') + +parser.add_argument('--sigma-spatial', type=float, default=16, help='sigma spatial in the bilateral solver') + +parser.add_argument('--sigma-luma', type=float, default=16, help='sigma luma in the bilateral solver') + +parser.add_argument('--sigma-chroma', type=float, default=8, help='sigma chroma in the bilateral solver') + + +parser.add_argument('--dataset', type=str, default=None, choices=['ECSSD', 'DUTS', 'DUT', None], help='which dataset?') + +parser.add_argument('--nb-vis', type=int, default=100, choices=[1, 200], help='nb of visualization') + +parser.add_argument('--img-path', type=str, default=None, help='single image visualization') + +args = parser.parse_args() +print (args) + +## feature net + +if args.vit_arch == 'base' and args.patch_size == 16: + url = "/dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth" + feat_dim = 768 +elif args.vit_arch == 'base' and args.patch_size == 8: + url = "/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth" + feat_dim = 768 +elif args.vit_arch == 'small' and args.patch_size == 16: + url = "/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth" + feat_dim = 384 +elif args.vit_arch == 'base' and args.patch_size == 8: + url = "/dino/dino_deitsmall8_300ep_pretrain/dino_deitsmall8_300ep_pretrain.pth" + +backbone = dino.ViTFeat(url, feat_dim, args.vit_arch, args.vit_feat, args.patch_size) +# resume_path = './model/dino_vitbase16_pretrain.pth' if args.patch_size == 16 else './model/dino_vitbase8_pretrain.pth' + +# feat_dim = 768 +# backbone = dino.ViTFeat(resume_path, feat_dim, args.vit_arch, args.vit_feat, args.patch_size) +# +#else : +# resume_path = './model/dino_deitsmall16_pretrain.pth' if args.patch_size == 16 else './model/dino_deitsmall8_pretrain.pth' +# feat_dim = 384 +# backbone = dino.ViTFeat(resume_path, feat_dim, args.vit_arch, args.vit_feat, args.patch_size) + + +msg = 'Load {} pre-trained feature...'.format(args.vit_arch) +print (msg) +backbone.eval() +backbone.cuda() + +if args.dataset == 'ECSSD' : + args.img_dir = '../datasets/ECSSD/img' + args.gt_dir = '../datasets/ECSSD/gt' + +elif args.dataset == 'DUTS' : + args.img_dir = '../datasets/DUTS_Test/img' + args.gt_dir = '../datasets/DUTS_Test/gt' + +elif args.dataset == 'DUT' : + args.img_dir = '../datasets/DUT_OMRON/img' + args.gt_dir = '../datasets/DUT_OMRON/gt' + +elif args.dataset is None : + args.gt_dir = None + + +print(args.dataset) + +if args.out_dir is not None and not os.path.exists(args.out_dir) : + os.mkdir(args.out_dir) + +if args.img_path is not None: + args.nb_vis = 1 + img_list = [args.img_path] +else: + img_list = sorted(os.listdir(args.img_dir)) + +count_vis = 0 +mask_lost = [] +mask_bfs = [] +gt = [] +for img_name in tqdm(img_list) : + if args.img_path is not None: + img_pth = img_name + img_name = img_name.split("/")[-1] + print(img_name) + else: + img_pth = os.path.join(args.img_dir, img_name) + + bipartition, eigvec = get_tokencut_binary_map(img_pth, backbone, args.patch_size, args.tau) + mask_lost.append(bipartition) + + output_solver, binary_solver = bilateral_solver.bilateral_solver_output(img_pth, bipartition, sigma_spatial = args.sigma_spatial, sigma_luma = args.sigma_luma, sigma_chroma = args.sigma_chroma) + mask1 = torch.from_numpy(bipartition).cuda() + mask2 = torch.from_numpy(binary_solver).cuda() + if metric.IoU(mask1, mask2) < 0.5: + binary_solver = binary_solver * -1 + mask_bfs.append(output_solver) + + if args.gt_dir is not None : + mask_gt = np.array(Image.open(os.path.join(args.gt_dir, img_name.replace('.jpg', '.png'))).convert('L')) + gt.append(mask_gt) + + if count_vis != args.nb_vis : + print(f'args.out_dir: {args.out_dir}, img_name: {img_name}') + out_name = os.path.join(args.out_dir, img_name) + out_lost = os.path.join(args.out_dir, img_name.replace('.jpg', '_tokencut.jpg')) + out_bfs = os.path.join(args.out_dir, img_name.replace('.jpg', '_tokencut_bfs.jpg')) + #out_eigvec = os.path.join(args.out_dir, img_name.replace('.jpg', '_tokencut_eigvec.jpg')) + + copyfile(img_pth, out_name) + org = np.array(Image.open(img_pth).convert('RGB')) + + #plt.imsave(fname=out_eigvec, arr=eigvec, cmap='cividis') + mask_color_compose(org, bipartition).save(out_lost) + mask_color_compose(org, binary_solver).save(out_bfs) + if args.gt_dir is not None : + out_gt = os.path.join(args.out_dir, img_name.replace('.jpg', '_gt.jpg')) + mask_color_compose(org, mask_gt).save(out_gt) + + + count_vis += 1 + else : + continue + +if args.gt_dir is not None and args.img_path is None: + print ('TokenCut evaluation:') + print (metric.metrics(mask_lost, gt)) + print ('\n') + + print ('TokenCut + bilateral solver evaluation:') + print (metric.metrics(mask_bfs, gt)) + print ('\n') + print ('\n') diff --git a/third_party/unsupervised_saliency_detection/metric.py b/third_party/unsupervised_saliency_detection/metric.py new file mode 100755 index 0000000000000000000000000000000000000000..974e868c765628f7f22e63935cce80d8ca604bbc --- /dev/null +++ b/third_party/unsupervised_saliency_detection/metric.py @@ -0,0 +1,79 @@ +import numpy as np +import torch + + +def IoU(mask1, mask2): + mask1, mask2 = (mask1>0.5).to(torch.bool), (mask2>0.5).to(torch.bool) + intersection = torch.sum(mask1 * (mask1 == mask2), dim=[-1, -2]).squeeze() + union = torch.sum(mask1 + mask2, dim=[-1, -2]).squeeze() + return (intersection.to(torch.float) / union).mean().item() + + +def accuracy(mask1, mask2): + mask1, mask2 = (mask1>0.5).to(torch.bool), (mask2>0.5).to(torch.bool) + return torch.mean((mask1 == mask2).to(torch.float)).item() + + +def precision_recall(mask_gt, mask): + mask_gt, mask = mask_gt.to(torch.bool), mask.to(torch.bool) + true_positive = torch.sum(mask_gt * (mask_gt == mask), dim=[-1, -2]).squeeze() + mask_area = torch.sum(mask, dim=[-1, -2]).to(torch.float) + mask_gt_area = torch.sum(mask_gt, dim=[-1, -2]).to(torch.float) + + precision = true_positive / mask_area + precision[mask_area == 0.0] = 1.0 + + recall = true_positive / mask_gt_area + recall[mask_gt_area == 0.0] = 1.0 + + return precision.item(), recall.item() + + +def F_score(p, r, betta_sq=0.3): + f_scores = ((1 + betta_sq) * p * r) / (betta_sq * p + r) + f_scores[f_scores != f_scores] = 0.0 # handle nans + return f_scores + + +def F_max(precisions, recalls, betta_sq=0.3): + F = F_score(precisions, recalls, betta_sq) + return F.mean(dim=0).max().item() + +@torch.no_grad() +def metrics(pred, gt, stats=(IoU, accuracy, F_max), prob_bins=255): + avg_values = {} + precisions = [] + recalls = [] + out_dict = {} + + nb_sample = len(gt) + for step in range(nb_sample): + prediction, mask = torch.from_numpy(pred[step]), torch.from_numpy(gt[step]) + + for metric in stats: + method = metric.__name__ + if method not in avg_values and metric != F_max: + avg_values[method] = 0.0 + + if metric != F_max: + avg_values[method] += metric(mask, prediction) + else: + p, r = [], [] + splits = 2.0 * prediction.mean(dim=0) if prob_bins is None else \ + np.arange(0.0, 1.0, 1.0 / prob_bins) + + for split in splits: + pr = precision_recall(mask, prediction > split) + p.append(pr[0]) + r.append(pr[1]) + precisions.append(p) + recalls.append(r) + + for metric in stats: + method = metric.__name__ + if metric == F_max: + out_dict[method] = F_max(torch.tensor(precisions), torch.tensor(recalls)) + else: + out_dict[method] = avg_values[method] / nb_sample + + return out_dict \ No newline at end of file diff --git a/third_party/unsupervised_saliency_detection/object_discovery.py b/third_party/unsupervised_saliency_detection/object_discovery.py new file mode 100644 index 0000000000000000000000000000000000000000..453415e571c957d41b64d4c604d02f11ec35b438 --- /dev/null +++ b/third_party/unsupervised_saliency_detection/object_discovery.py @@ -0,0 +1,97 @@ +import torch +import torch.nn.functional as F +import numpy as np +#from scipy.linalg.decomp import eig +import scipy +from scipy.linalg import eigh +from scipy import ndimage +#from sklearn.mixture import GaussianMixture +#from sklearn.cluster import KMeans + +def ncut(feats, dims, scales, init_image_size, tau = 0, eps=1e-5, im_name='', no_binary_graph=False): + """ + Implementation of NCut Method. + Inputs + feats: the pixel/patche features of an image + dims: dimension of the map from which the features are used + scales: from image to map scale + init_image_size: size of the image + tau: thresold for graph construction + eps: graph edge weight + im_name: image_name + no_binary_graph: ablation study for using similarity score as graph edge weight + """ + feats = F.normalize(feats, p=2, dim=0) + A = (feats.transpose(0,1) @ feats) + A = A.cpu().numpy() + if no_binary_graph: + A[A tau + A = np.where(A.astype(float) == 0, eps, A) + d_i = np.sum(A, axis=1) + D = np.diag(d_i) + + # Print second and third smallest eigenvector + _, eigenvectors = eigh(D-A, D, subset_by_index=[1,2]) + eigenvec = np.copy(eigenvectors[:, 0]) + + + # method1 avg + second_smallest_vec = eigenvectors[:, 0] + avg = np.sum(second_smallest_vec) / len(second_smallest_vec) + bipartition = second_smallest_vec > avg + + seed = np.argmax(np.abs(second_smallest_vec)) + + if bipartition[seed] != 1: + eigenvec = eigenvec * -1 + bipartition = np.logical_not(bipartition) + bipartition = bipartition.reshape(dims).astype(float) + + # predict BBox + pred, _, objects,cc = detect_box(bipartition, seed, dims, scales=scales, initial_im_size=init_image_size) ## We only extract the principal object BBox + mask = np.zeros(dims) + mask[cc[0],cc[1]] = 1 + + mask = torch.from_numpy(mask).to('cuda') +# mask = torch.from_numpy(bipartition).to('cuda') + bipartition = F.interpolate(mask.unsqueeze(0).unsqueeze(0), size=init_image_size, mode='nearest').squeeze() + + + eigvec = second_smallest_vec.reshape(dims) + eigvec = torch.from_numpy(eigvec).to('cuda') + eigvec = F.interpolate(eigvec.unsqueeze(0).unsqueeze(0), size=init_image_size, mode='nearest').squeeze() + return seed, bipartition.cpu().numpy(), eigvec.cpu().numpy() + +def detect_box(bipartition, seed, dims, initial_im_size=None, scales=None, principle_object=True): + """ + Extract a box corresponding to the seed patch. Among connected components extract from the affinity matrix, select the one corresponding to the seed patch. + """ + w_featmap, h_featmap = dims + objects, num_objects = ndimage.label(bipartition) + cc = objects[np.unravel_index(seed, dims)] + + + if principle_object: + mask = np.where(objects == cc) + # Add +1 because excluded max + ymin, ymax = min(mask[0]), max(mask[0]) + 1 + xmin, xmax = min(mask[1]), max(mask[1]) + 1 + # Rescale to image size + r_xmin, r_xmax = scales[1] * xmin, scales[1] * xmax + r_ymin, r_ymax = scales[0] * ymin, scales[0] * ymax + pred = [r_xmin, r_ymin, r_xmax, r_ymax] + + # Check not out of image size (used when padding) + if initial_im_size: + pred[2] = min(pred[2], initial_im_size[1]) + pred[3] = min(pred[3], initial_im_size[0]) + + # Coordinate predictions for the feature space + # Axis different then in image space + pred_feats = [ymin, xmin, ymax, xmax] + + return pred, pred_feats, objects, mask + else: + raise NotImplementedError diff --git a/third_party/unsupervised_saliency_detection/utils.py b/third_party/unsupervised_saliency_detection/utils.py new file mode 100755 index 0000000000000000000000000000000000000000..57341aa9c2f808f8a84988cc552a4f0ffe99981f --- /dev/null +++ b/third_party/unsupervised_saliency_detection/utils.py @@ -0,0 +1,9 @@ +import PIL.Image as Image + +def resize_pil(I, patch_size=16) : + w, h = I.size + + new_w, new_h = int(round(w / patch_size)) * patch_size, int(round(h / patch_size)) * patch_size + feat_w, feat_h = new_w // patch_size, new_h // patch_size + + return I.resize((new_w, new_h), resample=Image.LANCZOS), w, h, feat_w, feat_h \ No newline at end of file diff --git a/third_party/visualizations.py b/third_party/visualizations.py new file mode 100755 index 0000000000000000000000000000000000000000..6f4908ec518521286b940adf62abe467f29be334 --- /dev/null +++ b/third_party/visualizations.py @@ -0,0 +1,72 @@ +""" +Vis utilities. Code adapted from LOST: https://github.com/valeoai/LOST +""" +import cv2 +import torch +import skimage.io +import numpy as np +import torch.nn as nn +from PIL import Image +import scipy + +import matplotlib.pyplot as plt + +def visualize_img(image, vis_folder, im_name): + pltname = f"{vis_folder}/{im_name}" + Image.fromarray(image).save(pltname) + print(f"Original image saved at {pltname}.") + +def visualize_predictions(img, pred, vis_folder, im_name, save=True): + """ + Visualization of the predicted box and the corresponding seed patch. + """ + image = np.copy(img) + # Plot the box + cv2.rectangle( + image, + (int(pred[0]), int(pred[1])), + (int(pred[2]), int(pred[3])), + (255, 0, 0), 3, + ) + if save: + pltname = f"{vis_folder}/{im_name}_TokenCut_pred.jpg" + Image.fromarray(image).save(pltname) + print(f"Predictions saved at {pltname}.") + return image + +def visualize_predictions_gt(img, pred, gt, vis_folder, im_name, dim, scales, save=True): + """ + Visualization of the predicted box and the corresponding seed patch. + """ + image = np.copy(img) + # Plot the box + cv2.rectangle( + image, + (int(pred[0]), int(pred[1])), + (int(pred[2]), int(pred[3])), + (255, 0, 0), 3, + ) + # Plot the ground truth box + if len(gt>1): + for i in range(len(gt)): + cv2.rectangle( + image, + (int(gt[i][0]), int(gt[i][1])), + (int(gt[i][2]), int(gt[i][3])), + (0, 0, 255), 3, + ) + if save: + pltname = f"{vis_folder}/{im_name}_TokenCut_BBOX.jpg" + Image.fromarray(image).save(pltname) + #print(f"Predictions saved at {pltname}.") + return image + +def visualize_eigvec(eigvec, vis_folder, im_name, dim, scales, save=True): + """ + Visualization of the second smallest eigvector + """ + eigvec = scipy.ndimage.zoom(eigvec, scales, order=0, mode='nearest') + if save: + pltname = f"{vis_folder}/{im_name}_TokenCut_attn.jpg" + plt.imsave(fname=pltname, arr=eigvec, cmap='cividis') + print(f"Eigen attention saved at {pltname}.") diff --git a/third_party/weakly_supvervised_detection/datasets.py b/third_party/weakly_supvervised_detection/datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..04bab772709f75449e1aff4cfda375893e3426db --- /dev/null +++ b/third_party/weakly_supvervised_detection/datasets.py @@ -0,0 +1,368 @@ +import os +import torch +import numpy as np +import pandas as pd +from PIL import Image +from tqdm import tqdm +from collections import defaultdict +from torchvision.datasets.folder import default_loader +from torchvision.datasets.utils import download_url +from torch.utils.data import Dataset +from torchvision import transforms + + +class CUB200(Dataset): + def __init__(self, root, is_train, transform=None, ori_size=False, input_size=224, center_crop=True): + self.root = root + self.is_train = is_train + self.ori_size = ori_size + if not ori_size and center_crop: + image_size = int(256/224*input_size) #TODO check + crop_size = input_size #TODO check + shift = (image_size - crop_size) // 2 + elif not ori_size and not center_crop: + image_size = input_size + crop_size = input_size + shift = 0 + self.data = self._load_data(image_size, crop_size, shift, center_crop) + self.transform = transform + + def _load_data(self, image_size, crop_size, shift, center_crop=True): + self._labelmap_path = os.path.join(self.root, 'CUB_200_2011', 'classes.txt') + + paths = pd.read_csv( + os.path.join(self.root, 'CUB_200_2011', 'images.txt'), + sep=' ', names=['id', 'path']) + labels = pd.read_csv( + os.path.join(self.root, 'CUB_200_2011', 'image_class_labels.txt'), + sep=' ', names=['id', 'label']) + splits = pd.read_csv( + os.path.join(self.root, 'CUB_200_2011', 'train_test_split.txt'), + sep=' ', names=['id', 'is_train']) + orig_image_sizes = pd.read_csv( + os.path.join(self.root, 'CUB_200_2011', 'image_sizes.txt'), + sep=' ', names=['id', 'width', 'height']) + bboxes = pd.read_csv( + os.path.join(self.root, 'CUB_200_2011', 'bounding_boxes.txt'), + sep=' ', names=['id', 'x', 'y', 'w', 'h']) + + if self.ori_size: + resized_bboxes = pd.DataFrame({'id': paths.id, + 'xmin': bboxes.x, + 'ymin': bboxes.y, + 'xmax': bboxes.x + bboxes.w, + 'ymax': bboxes.y + bboxes.h}) + else: + if center_crop: + resized_xmin = np.maximum( + (bboxes.x / orig_image_sizes.width * image_size - shift).astype(int), 0) + resized_ymin = np.maximum( + (bboxes.y / orig_image_sizes.height * image_size - shift).astype(int), 0) + resized_xmax = np.minimum( + ((bboxes.x + bboxes.w - 1) / orig_image_sizes.width * image_size - shift).astype(int), + crop_size - 1) + resized_ymax = np.minimum( + ((bboxes.y + bboxes.h - 1) / orig_image_sizes.height * image_size - shift).astype(int), + crop_size - 1) + else: + min_length = pd.concat([orig_image_sizes.width, orig_image_sizes.height], axis=1).min(axis=1) + resized_xmin = (bboxes.x / min_length * image_size).astype(int) + resized_ymin = (bboxes.y / min_length * image_size).astype(int) + resized_xmax = ((bboxes.x + bboxes.w - 1) / min_length * image_size).astype(int) + resized_ymax = ((bboxes.y + bboxes.h - 1) / min_length * image_size).astype(int) + + resized_bboxes = pd.DataFrame({'id': paths.id, + 'xmin': resized_xmin.values, + 'ymin': resized_ymin.values, + 'xmax': resized_xmax.values, + 'ymax': resized_ymax.values}) + + + data = paths.merge(labels, on='id')\ + .merge(splits, on='id')\ + .merge(resized_bboxes, on='id') + + if self.is_train: + data = data[data.is_train == 1] + else: + data = data[data.is_train == 0] + return data + + def __len__(self): + return len(self.data) + + # def _preprocess_bbox(self, origin_bbox, orig_image_size, center_crop=True): + # xmin, ymin, xmax, ymax = origin_bbox + # orig_width, orig_height = orig_image_size + # if center_crop: + # resized_xmin = np.maximum( + # (bboxes.x / orig_image_sizes.width * image_size - shift).astype(int), 0) + # resized_ymin = np.maximum( + # (bboxes.y / orig_image_sizes.height * image_size - shift).astype(int), 0) + # resized_xmax = np.minimum( + # ((bboxes.x + bboxes.w - 1) / orig_image_sizes.width * image_size - shift).astype(int), + # crop_size - 1) + # resized_ymax = np.minimum( + # ((bboxes.y + bboxes.h - 1) / orig_image_sizes.height * image_size - shift).astype(int), + # crop_size - 1) + # else: + # print(f'width: {orig_image_sizes.width}, height: {orig_image_sizes.height}') + # min_length = min(orig_image_sizes.width , orig_image_sizes.height) + # resized_xmin = int(bb / min_length * self.image_size) + # resized_ymin = int(ymin / min_length * self.image_size) + # resized_xmax = int(xmax / min_length * self.image_size) + # resized_ymax = int(ymax / min_length * self.image_size) + + # resized_bboxes = pd.DataFrame({'id': paths.id, + # 'xmin': resized_xmin.values, + # 'ymin': resized_ymin.values, + # 'xmax': resized_xmax.values, + # 'ymax': resized_ymax.values}) + + def __getitem__(self, idx): + sample = self.data.iloc[idx] + path = os.path.join(self.root, 'CUB_200_2011/images', sample.path) + image = Image.open(path).convert('RGB') + label = sample.label - 1 # label starts from 1 + gt_box = torch.tensor( + [sample.xmin, sample.ymin, sample.xmax, sample.ymax]) + + if self.transform is not None: + image = self.transform(image) + + return (image, label, gt_box) + + @property + def class_id_to_name(self): + if hasattr(self, '_class_id_to_name'): + return self._class_id_to_name + labelmap = pd.read_csv(self._labelmap_path, sep=' ', names=['label', 'name']) + labelmap['label'] = labelmap['label'].apply(lambda x: x - 1) + self._class_id_to_name = labelmap.set_index('label')['name'].to_dict() + return self._class_id_to_name + + @property + def class_name_to_id(self): + if hasattr(self, '_class_name_to_id'): + return self._class_name_to_id + self._class_name_to_id = {v: k for k, v in self.class_id_to_name.items()} + return self._class_name_to_id + + @property + def class_to_images(self): + if hasattr(self, '_class_to_images'): + return self._class_to_images + #self.log.warn('Create index...') + self._class_to_images = defaultdict(list) + for idx in tqdm(range(len(self))): + sample = self.data.iloc[idx] + label = sample.label - 1 + self._class_to_images[label].append(idx) + #self.log.warn('Done!') + return self._class_to_images + + +#class ImageNet(H5Dataset): +# def __init__(self, root, is_train, transform=None): +# self.root = root +# self.is_train = is_train +# tag = 'train' if is_train else 'val' +# self.h5_path = os.path.join(root, f'imagenet_{tag}.h5') +# +# super().__init__(self.h5_path, transform) + +class ImageNet(Dataset): + def __init__(self, root, is_train, transform=None, ori_size=False, input_size=224, center_crop=True): + self.root = root + self.is_train = is_train + self.ori_size = ori_size + self.center_crop = center_crop + if not ori_size and center_crop: + self.image_size = int(256/224 * input_size) + self.crop_size = input_size + self.shift = (self.image_size - self.crop_size) // 2 + elif not ori_size and not center_crop: + print('resize, without center crop') + self.image_size = input_size + + self._load_data() + self.transform = transform + + def _load_data(self): + self._labelmap_path = os.path.join( + self.root, 'ILSVRC/Detection', 'imagenet1000_clsidx_to_labels.txt') + + if self.is_train: + self.path = os.path.join(self.root, 'ILSVRC/Data/train') + self.metadata = pd.read_csv( + os.path.join(self.root, 'ILSVRC/Detection', 'train.txt'), + sep=' ', names=['path', 'label']) + else: + self.path = os.path.join(self.root, 'ILSVRC/Data/val') + self.metadata = pd.read_csv( + os.path.join(self.root, 'ILSVRC/Detection', 'val.txt'), + sep='\t', names=['path', 'label', 'xmin', 'ymin', 'xmax', 'ymax']) + self.wnids = pd.read_csv( + os.path.join(self.root, 'ILSVRC/Detection/', 'wnids.txt'), names=['dir_name']) + + def _preprocess_bbox(self, origin_bbox, orig_image_size, center_crop=True, image_path=None): + xmin, ymin, xmax, ymax = origin_bbox + orig_width, orig_height = orig_image_size + if center_crop: + resized_xmin = np.maximum( + int(xmin / orig_width * self.image_size - self.shift), 0) + resized_ymin = np.maximum( + int(ymin / orig_height * self.image_size - self.shift), 0) + resized_xmax = np.minimum( + int(xmax / orig_width * self.image_size - self.shift), self.crop_size - 1) + resized_ymax = np.minimum( + int(ymax / orig_height * self.image_size - self.shift), self.crop_size - 1) + else: + #print(f'ori W: {orig_width} ori H: {orig_height}, xmin: {xmin}, ymin: {ymin}, xmax: {xmax}, ymax: {ymax}, input_size: {self.image_size}, image_path: {image_path}') + min_length = min(orig_height, orig_width) + resized_xmin = int(xmin / min_length * self.image_size) + resized_ymin = int(ymin / min_length * self.image_size) + resized_xmax = int(xmax / min_length * self.image_size) + resized_ymax = int(ymax / min_length * self.image_size) + #print(f'output: xmin, ymin, xmax, ymax: {[resized_xmin, resized_ymin, resized_xmax, resized_ymax]}') + return [resized_xmin, resized_ymin, resized_xmax, resized_ymax] + + def __len__(self): + return len(self.metadata) + + def __getitem__(self, idx): + sample = self.metadata.iloc[idx] + if self.is_train: + image_path = os.path.join(self.path, sample.path) + else: + image_path = os.path.join( + self.path, self.wnids.iloc[int(sample.label)].dir_name, sample.path) + image = Image.open(image_path).convert('RGB') + label = sample.label + + # preprocess bbox + if self.is_train: + gt_box = torch.tensor([0., 0., 0., 0.]) + else: + origin_box = [sample.xmin, sample.ymin, sample.xmax, sample.ymax] + if self.ori_size: + gt_box = torch.tensor(origin_box) + else: + gt_box = torch.tensor( + self._preprocess_bbox(origin_box, image.size, self.center_crop, image_path)) + + if self.transform is not None: + image = self.transform(image) + + return (image, label, gt_box) + + @property + def class_id_to_name(self): + if hasattr(self, '_class_id_to_name'): + return self._class_id_to_name + with open(self._labelmap_path, 'r') as f: + self._class_id_to_name = eval(f.read()) + return self._class_id_to_name + + @property + def class_name_to_id(self): + if hasattr(self, '_class_name_to_id'): + return self._class_name_to_id + self._class_name_to_id = {v: k for k, v in self.class_id_to_name.items()} + return self._class_name_to_id + + @property + def wnid_list(self): + if hasattr(self, '_wnid_list'): + return self._wnid_list + self._wnid_list = self.wnids.dir_name.tolist() + return self._wnid_list + + @property + def class_to_images(self): + if hasattr(self, '_class_to_images'): + return self._class_to_images + self.log.warn('Create index...') + self._class_to_images = defaultdict(list) + for idx in tqdm(range(len(self))): + sample = self.metadata.iloc[idx] + label = sample.label + self._class_to_images[label].append(idx) + self.log.warn('Done!') + return self._class_to_images + + def verify_wnid(self, wnid): + is_valid = bool(re.match(u'^[n][0-9]{8}$', wnid)) + is_terminal = bool(wnid in self.wnids.dir_name.tolist()) + return is_valid and is_terminal + + def get_terminal_wnids(self, wnid): + page = requests.get("http://www.image-net.org/api/text/wordnet.structure.hyponym?wnid={}&full=1".format(wnid)) + str_wnids = str(BeautifulSoup(page.content, 'html.parser')) + split_wnids = re.split('\r\n-|\r\n', str_wnids) + return [_wnid for _wnid in split_wnids if self.verify_wnid(_wnid)] + + def get_image_ids(self, wnid): + terminal_wnids = self.get_terminal_wnids(wnid) + + image_ids = set() + for terminal_wnid in terminal_wnids: + class_id = self.wnid_list.index(terminal_wnid) + image_ids |= set(self.class_to_images[class_id]) + + return list(image_ids) + +DATASETS = { + 'cub': CUB200, + 'imagenet': ImageNet, +} + +LABELS = { + 'cub': 200, + 'imagenet': 1000, +} + +def build_dataset(is_train, args): + # Define arguments + data_name = args.dataset + root = args.data_path + batch_size = args.batch_size_per_gpu + num_workers = args.num_workers + + transform = build_transform(is_train, args) + dataset = DATASETS[data_name](root, is_train=is_train, transform=transform, ori_size=args.ori_size, input_size = args.input_size, center_crop = not args.no_center_crop) + + return dataset, LABELS[data_name] + +def build_transform(is_train, args): + resize_im = args.input_size > 32 + if is_train: + transform = transforms.Compose([ + transforms.RandomResizedCrop(args.input_size), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ]) + if not resize_im: + # replace RandomResizedCropAndInterpolation with + # RandomCrop + transform.transforms[0] = transforms.RandomCrop( + args.input_size, padding=4) + return transform + + t = [] + if resize_im and (not args.ori_size): + if args.no_center_crop: + t.append(transforms.Resize(args.input_size, interpolation=3)) + else: + size = int((256 / 224) * args.input_size) + t.append( + transforms.Resize(size, interpolation=3), # to maintain same ratio w.r.t. 224 images + ) + if not args.ori_size and not args.no_center_crop: + print('center crop') + t.append(transforms.CenterCrop(args.input_size)) + + t.append(transforms.ToTensor()) + t.append(transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))) + return transforms.Compose(t) diff --git a/third_party/weakly_supvervised_detection/eval.py b/third_party/weakly_supvervised_detection/eval.py new file mode 100644 index 0000000000000000000000000000000000000000..d6a1da2de383cd0552b1f418453ecc5831e2b2e7 --- /dev/null +++ b/third_party/weakly_supvervised_detection/eval.py @@ -0,0 +1,197 @@ +import argparse +import os +import sys +import torch +import shutil +import utils +import numpy as np +import metrics +import torch.nn as nn +import torch.backends.cudnn as cudnn +import vision_transformer as vits + +from tqdm import tqdm +from datasets import build_dataset +from meters import AverageEpochMeter +from object_discovery import ncut, detect_box, get_feats +from visualization import visualize_fms, visualize_predictions_gt, visualize_img, visualize_eigvec + +from collections import OrderedDict +#from visualizations import visualize_img, visualize_eigvec, visualize_gt, visualize_fms, visualize_predictions, visualize_predictions_gt + +def evaluate(): + if args.device != 'cuda': + args.distributed = False + else: + utils.init_distributed_mode(args) + + print(args) + + device = torch.device(args.device) + cudnn.benchmark = True + + # =============build network ============== + if args.arch in vits.__dict__.keys(): + model = vits.__dict__[args.arch](patch_size=args.patch_size, num_classes=args.num_labels) + embed_dim = model.embed_dim * (args.n_last_blocks + int(args.avgpool_patchtokens)) + print(f'embed_dim: {embed_dim}') + else: + print(f"Unknow architecture: {args.arch}") + sys.exit(1) + + model.to(device) + model.eval() + + linear_classifier = LinearClassifier(embed_dim, num_labels=args.num_labels) + linear_classifier = linear_classifier.to(device) + + # ============ Load checkpoint ============= + checkpoint_model = torch.load(args.pretrained_weights, map_location='cpu') + if args.classifier_weights is None: + linear_classifier.load_state_dict(checkpoint_model['classifier']) + model.load_state_dict(checkpoint_model['model'], strict=False) + else: + model.load_state_dict(checkpoint_model) + checkpoint_classifier = torch.load(args.classifier_weights, map_location='cpu') + new_state_dict = OrderedDict() + new_state_dict = {k[7:]: v for k, v in checkpoint_classifier['state_dict'].items()} + # load params + linear_classifier.load_state_dict(new_state_dict) + print('Load from checkpoint done.') + + dataset_val, _ = build_dataset(is_train=False, args=args) + + if args.distributed: + sampler_val = torch.utils.data.distributed.DistributedSampler(dataset_val, shuffle=False) + else: + sampler_val = torch.utils.data.SequentialSampler(dataset_val) + + val_loader = torch.utils.data.DataLoader( + dataset_val, + sampler=sampler_val, + batch_size=args.batch_size_per_gpu, + num_workers=args.num_workers, + pin_memory=True, + ) + + + num_batches = len(val_loader) + scales = [args.patch_size, args.patch_size] + top1_cls_meter = AverageEpochMeter('Top-1 Cls') + top5_cls_meter = AverageEpochMeter('Top-5 Cls') + gt_loc_meter = AverageEpochMeter('GT-Known Loc') + top1_loc_meter = AverageEpochMeter('Top-1 Loc') + feat_out = {} + bbox_error = 0 + cls_error = 0 + skip = 0 + def hook_fn_forward_qkv(module, input, output): + feat_out["qkv"] = output + model._modules["blocks"][-1]._modules["attn"]._modules["qkv"].register_forward_hook(hook_fn_forward_qkv) + val_loader = tqdm(val_loader) + for i, (images, labels, gt_boxes) in enumerate(val_loader): + init_image_size = images[0].shape + images = utils.padding_img(images[0], args) + images = images.unsqueeze(0) + if images.shape[-1] > 1000 and images.shape[-2] > 1000: + skip = skip + 1 + continue + with torch.no_grad(): + images = images.to(device) + labels = labels.to(device) + + intermediate_output, shape= model.get_intermediate_layers(images, args.n_last_blocks) + output = torch.cat([x[:, 0] for x in intermediate_output], dim=-1) + if args.avgpool_patchtokens: + output = torch.cat((output.unsqueeze(-1), torch.mean(intermediate_output[-1][:, 1:], dim=1).unsqueeze(-1)), dim=-1) + output = output.reshape(output.shape[0], -1) + output = linear_classifier(output) + + batch_size = images.size(0) + top1_cls, top5_cls = utils.accuracy(output, labels, topk=(1, 5)) + top1_cls_meter.update(top1_cls, batch_size) + top5_cls_meter.update(top5_cls, batch_size) + + + + # ===== Generate Bbox========= + dims = (images[0].shape[-2] // args.patch_size, images[0].shape[-1] // args.patch_size) + k = get_feats(feat_out, shape) + bboxes, mask, seed, eigvec= ncut(k, dims, scales, init_image_size=init_image_size[1:], eps=args.eps, tau=args.tau) + bboxes_copy = np.copy(bboxes) + bboxes = torch.FloatTensor(bboxes).unsqueeze(0) + gt_loc, top1_loc = metrics.loc_accuracy(output, labels, gt_boxes, bboxes) + + if args.visual: + visualize_predictions_gt(images[0], bboxes_copy , gt_boxes, i, seed, dims, scales, './output/all') + visualize_eigvec(eigvec, './output/all', i, dims, scales) + + #visualize_fms(images[0], mask, seed, i, dims, scales) + if top1_loc == 0 and gt_loc == 0: + bbox_error = bbox_error + 1 + if args.visual: + # print(f'i: {i}, gt_boxes {gt_boxes}, prediction: {bboxes_copy}, image size: {images.shape}') + visualize_predictions_gt(images[0], bboxes_copy , gt_boxes, i, seed, dims, scales, './output/bbox_error') + + elif top1_loc == 0 and gt_loc == 1: + cls_error = cls_error +1 + if args.visual: + visualize_predictions_gt(images[0], bboxes_copy , gt_boxes, i, seed, dims, scales, './output/classification_error') + + gt_loc_meter.update(gt_loc, batch_size) + top1_loc_meter.update(top1_loc, batch_size) + top1_cls = top1_cls_meter.compute() + top5_cls = top5_cls_meter.compute() + gt_loc = gt_loc_meter.compute() + top1_loc = top1_loc_meter.compute() + val_loader.set_description(f'Top1_cls: {top1_cls:.4f}, top5_cls{top5_cls:.4f}, gt_loc: {gt_loc:.4f}, top1_loc:{top1_loc:.4f}') + print(f'Top1_cls: {top1_cls}, top5_cls{top5_cls}, gt_loc: {gt_loc}, top1_loc:{top1_loc}') + print(f'Bbox error: {bbox_error}, cls error: {cls_error}') + print(f'Skip large image: {skip}') + + +class LinearClassifier(nn.Module): + """Linear layer to train on top of frozen features""" + def __init__(self, dim, num_labels=1000): + super(LinearClassifier, self).__init__() + self.num_labels = num_labels + self.linear = nn.Linear(dim, num_labels) + self.linear.weight.data.normal_(mean=0.0, std=0.01) + self.linear.bias.data.zero_() + + def forward(self, x): + # flatten + x = x.view(x.size(0), -1) + # linear layer + return self.linear(x) + +if __name__=='__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--n_last_blocks', default=4, type=int, help="""Concatenate [CLS] tokens + for the `n` last blocks. We use `n=4` when evaluating ViT-Small and `n=1` with ViT-Base.""") + parser.add_argument('--avgpool_patchtokens', default=False, type=utils.bool_flag, + help="""Whether ot not to concatenate the global average pooled features to the [CLS] token. + We typically set this to False for ViT-Small and to True with ViT-Base.""") + parser.add_argument('--arch', default='vit_small', type=str, help='Architecture') + parser.add_argument('--dataset', default='cub', type=str, choices=['cub', 'imagenet'], help='Architecture') + parser.add_argument('--patch_size', default=16, type=int, help='Patch resolution of the model.') + parser.add_argument('--input_size', default=224, type=int, help='Input image size, default(224).') + parser.add_argument('--pretrained_weights', default='', type=str, help="Path to pretrained weights to evaluate.") + parser.add_argument('--classifier_weights', default=None, type=str, help="Path to linear classifier pretrained weights to evaluate.") + parser.add_argument('--batch_size_per_gpu', default=256, type=int, help='Per-GPU batch-size') + parser.add_argument("--dist_url", default="env://", type=str, help="""url used to set up + distributed training; see https://pytorch.org/docs/stable/distributed.html""") + parser.add_argument("--local_rank", default=0, type=int, help="Please ignore and do not set this argument.") + parser.add_argument('--data_path', default='/path/to/imagenet/', type=str) + parser.add_argument('--num_workers', default=10, type=int, help='Number of data loading workers per GPU.') + parser.add_argument('--val_freq', default=1, type=int, help="Epoch frequency for validation.") + parser.add_argument('--num_labels', default=200, type=int, help='Number of labels for linear classifier') + parser.add_argument('--device', default='cuda', help='device to use for training / testing') + parser.add_argument('--distributed', default=False, action='store_true', help='device to use for training / testing') + parser.add_argument('--ori_size', default=False, action='store_true', help='Evaluate on image raw size') + parser.add_argument('--visual', default=False, action='store_true', help='Visualize error examples on ./test') + parser.add_argument('--eps', default=1e-5, type=float, help='hyperparameter for tokencut') + parser.add_argument('--tau', default=0.05, type=float, help='hyperparamter for tokencut') + parser.add_argument('--no_center_crop', default=False, action='store_true', help='Center crop input image') + args = parser.parse_args() + evaluate() diff --git a/third_party/weakly_supvervised_detection/finetune.py b/third_party/weakly_supvervised_detection/finetune.py new file mode 100644 index 0000000000000000000000000000000000000000..938d6110429382d2c664b71cf004dbf8ed4fd5a3 --- /dev/null +++ b/third_party/weakly_supvervised_detection/finetune.py @@ -0,0 +1,321 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import argparse +import json +from pathlib import Path + +import torch +from torch import nn +import torch.distributed as dist +import torch.backends.cudnn as cudnn +from torchvision import datasets +from torchvision import transforms as pth_transforms +from torchvision import models as torchvision_models + +import utils +import vision_transformer as vits +from datasets import CUB200 +from torch.utils.tensorboard import SummaryWriter + + +def eval_linear(args): + if args.device != 'cuda': + args.distributed = False + else: + utils.init_distributed_mode(args) + + print(args) + device = torch.device(args.device) + cudnn.benchmark = True + + # ============ building network ... ============ + # if the network is a Vision Transformer (i.e. vit_tiny, vit_small, vit_base) + if args.arch in vits.__dict__.keys(): + model = vits.__dict__[args.arch](patch_size=args.patch_size, num_classes=args.num_labels) + embed_dim = model.embed_dim * (args.n_last_blocks + int(args.avgpool_patchtokens)) + else: + print(f"Unknow architecture: {args.arch}") + sys.exit(1) + model.to(device) + model.eval() + + #model_without_ddp = model + #if args.distributed: + # model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) + # model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], fine_unused_parametters=True) + # model_without_ddp = model.module + n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) + print('number of params:', n_parameters) + + # load weights to evaluate + utils.load_pretrained_weights(model, args.pretrained_weights, args.checkpoint_key, args.arch, args.patch_size) + print(f"Model {args.arch} built.") + + linear_classifier = LinearClassifier(embed_dim, num_labels=args.num_labels) + linear_classifier = linear_classifier.to(device) + linear_classifier = nn.parallel.DistributedDataParallel(linear_classifier, device_ids=[args.gpu]) + linear_classifier_without_ddp = linear_classifier.module + + # ============ preparing data ... ============ + train_transform = pth_transforms.Compose([ + pth_transforms.RandomResizedCrop(args.input_size), + pth_transforms.RandomHorizontalFlip(), + pth_transforms.ToTensor(), + pth_transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ]) + val_transform = pth_transforms.Compose([ + pth_transforms.Resize(int((256/224)*args.input_size), interpolation=3), + pth_transforms.CenterCrop(args.input_size), + pth_transforms.ToTensor(), + pth_transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ]) + if args.dataset == 'cub': + dataset_train = CUB200(args.data_path, is_train=True, transform=train_transform, ori_size=args.ori_size, input_size=args.input_size) + dataset_val = CUB200(args.data_path, is_train=False,transform=val_transform, ori_size = args.ori_size, input_size = args.input_size) + else: + raise NotImplementedError + if args.distributed: + sampler_train = torch.utils.data.distributed.DistributedSampler(dataset_train) + sampler_val = torch.utils.data.distributed.DistributedSampler(dataset_val, shuffle=False) + else: + sampler_train = torch.utils.data.RandomSampler(dataset_train) + sampler_val = torch.utils.data.SequentialSampler(dataset_val) + + val_loader = torch.utils.data.DataLoader( + dataset_val, + sampler=sampler_val, + batch_size=args.batch_size_per_gpu, + num_workers=args.num_workers, + pin_memory=True, + ) + + #if args.evaluate: + # raise NotImplementederror + # utils.load_pretrained_linear_weights(linear_classifier, args.arch, args.patch_size) + # test_stats = validate_network(val_loader, model, linear_classifier, args.n_last_blocks, args.avgpool_patchtokens) + # print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%") + # return + + + train_loader = torch.utils.data.DataLoader( + dataset_train, + sampler=sampler_train, + batch_size=args.batch_size_per_gpu, + num_workers=args.num_workers, + pin_memory=True, + ) + print(f"Data loaded with {len(dataset_train)} train and {len(dataset_val)} val imgs.") + + # set optimizer + optimizer = torch.optim.SGD( + linear_classifier.parameters(), + args.lr * (args.batch_size_per_gpu * utils.get_world_size()) / 256., # linear scaling rule + momentum=0.9, + weight_decay=args.weight_decay, # we do not apply weight decay + ) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epochs, eta_min=0) + + if utils.is_main_process(): + writer = SummaryWriter(args.output_dir + '/log') + start_epoch = 0 + best_acc = 0 + print('Starting training') + + for epoch in range(start_epoch, args.epochs): + train_loader.sampler.set_epoch(epoch) + + train_stats = train(model, linear_classifier_without_ddp, device, optimizer, train_loader, epoch, args.n_last_blocks, args.avgpool_patchtokens) + scheduler.step() + + log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, + 'epoch': epoch} + if epoch % args.val_freq == 0 or epoch == args.epochs - 1: + test_stats = validate_network(val_loader, model, linear_classifier, device, args.n_last_blocks, args.avgpool_patchtokens) + print(f"Accuracy at epoch {epoch} of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%") + best_acc = max(best_acc, test_stats["acc1"]) + print(f'Max accuracy so far: {best_acc:.2f}%') + log_stats = {**{k: v for k, v in log_stats.items()}, + **{f'test_{k}': v for k, v in test_stats.items()}, + "best_acc": best_acc} + if utils.is_main_process(): + with (Path(args.output_dir) / "log.txt").open("a") as f: + f.write(json.dumps(log_stats) + "\n") + save_dict = { + "epoch": epoch + 1, + "model": model.state_dict(), + "classifier": linear_classifier_without_ddp.state_dict(), + "optimizer": optimizer.state_dict(), + "scheduler": scheduler.state_dict(), + "best_acc": best_acc, + } + + writer.add_scalar('Train_loss', train_stats['loss'], global_step=epoch) + writer.add_scalar('Learning_rate', train_stats['lr'], global_step=epoch) + writer.add_scalar('Train Acc_1', train_stats['acc1'], global_step=epoch) + writer.add_scalar('Acc_1', test_stats['acc1'], global_step=epoch) + writer.add_scalar('Acc_5', test_stats['acc5'], global_step=epoch) + + torch.save(save_dict, os.path.join(args.output_dir, "checkpoint.pth")) + + if best_acc < float(test_stats['acc1']): + best_acc = float(test_stats['acc1']) + shutil.copyfile(checkpoint_path, args.output_dir + '/model_best.pth') + print(f'Max accuracy so far: {best_acc:.2f}%') + print("Training of the supervised linear classifier on frozen features completed.\n" + "Top-1 test accuracy: {acc:.1f}".format(acc=best_acc)) + + +def train(model, linear_classifier, device, optimizer, loader, epoch, n, avgpool): + linear_classifier.train() + metric_logger = utils.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + for batch in metric_logger.log_every(loader, 20, header): + inp,target = batch[:2] + + # move to gpu + inp = inp.to(device, non_blocking=True) + target = target.to(device, non_blocking=True) + + # forward + with torch.no_grad(): + if "vit" in args.arch: + intermediate_output, _ = model.get_intermediate_layers(inp, n) + output = torch.cat([x[:, 0] for x in intermediate_output], dim=-1) + if avgpool: + output = torch.cat((output.unsqueeze(-1), torch.mean(intermediate_output[-1][:, 1:], dim=1).unsqueeze(-1)), dim=-1) + output = output.reshape(output.shape[0], -1) + else: + output = model(inp) + output = linear_classifier(output) + + # compute cross entropy loss + loss = nn.CrossEntropyLoss()(output, target) + + acc1, = utils.accuracy(output, target, topk=(1,)) + # compute the gradients + optimizer.zero_grad() + loss.backward() + + # step + optimizer.step() + + # log + torch.cuda.synchronize() + batch_size = inp.shape[0] + metric_logger.update(loss=loss.item()) + metric_logger.update(lr=optimizer.param_groups[0]["lr"]) + metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) + + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + + +@torch.no_grad() +def validate_network(val_loader, model, linear_classifier, device, n, avgpool): + linear_classifier.eval() + metric_logger = utils.MetricLogger(delimiter=" ") + header = 'Test:' + for batch in metric_logger.log_every(val_loader, 20, header): + inp, target = batch[:2] + # move to gpu + inp = inp.to(device, non_blocking=True) + target = target.to(device, non_blocking=True) + + # forward + with torch.no_grad(): + if "vit" in args.arch: + intermediate_output, _ = model.get_intermediate_layers(inp, n) + output = torch.cat([x[:, 0] for x in intermediate_output], dim=-1) + if avgpool: + output = torch.cat((output.unsqueeze(-1), torch.mean(intermediate_output[-1][:, 1:], dim=1).unsqueeze(-1)), dim=-1) + output = output.reshape(output.shape[0], -1) + else: + output = model(inp) + output = linear_classifier(output) + loss = nn.CrossEntropyLoss()(output, target) + + if linear_classifier.module.num_labels >= 5: + acc1, acc5 = utils.accuracy(output, target, topk=(1, 5)) + else: + acc1, = utils.accuracy(output, target, topk=(1,)) + + batch_size = inp.shape[0] + metric_logger.update(loss=loss.item()) + metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) + if linear_classifier.module.num_labels >= 5: + metric_logger.meters['acc5'].update(acc5.item(), n=batch_size) + if linear_classifier.module.num_labels >= 5: + print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}' + .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss)) + else: + print('* Acc@1 {top1.global_avg:.3f} loss {losses.global_avg:.3f}' + .format(top1=metric_logger.acc1, losses=metric_logger.loss)) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + + +class LinearClassifier(nn.Module): + """Linear layer to train on top of frozen features""" + def __init__(self, dim, num_labels=1000): + super(LinearClassifier, self).__init__() + self.num_labels = num_labels + self.linear = nn.Linear(dim, num_labels) + self.linear.weight.data.normal_(mean=0.0, std=0.01) + self.linear.bias.data.zero_() + + def forward(self, x): + # flatten + x = x.view(x.size(0), -1) + + # linear layer + return self.linear(x) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser('Evaluation with linear classification on ImageNet') + parser.add_argument('--n_last_blocks', default=4, type=int, help="""Concatenate [CLS] tokens + for the `n` last blocks. We use `n=4` when evaluating ViT-Small and `n=1` with ViT-Base.""") + parser.add_argument('--avgpool_patchtokens', default=False, type=utils.bool_flag, + help="""Whether ot not to concatenate the global average pooled features to the [CLS] token. + We typically set this to False for ViT-Small and to True with ViT-Base.""") + parser.add_argument('--arch', default='vit_small', type=str, help='Architecture') + parser.add_argument('--dataset', default='cub', type=str, help='Dataset') + parser.add_argument('--device', default='cuda', type=str, help='Deivce') + parser.add_argument('--patch_size', default=16, type=int, help='Patch resolution of the model.') + parser.add_argument('--pretrained_weights', default='', type=str, help="Path to pretrained weights to evaluate.") + parser.add_argument("--checkpoint_key", default="teacher", type=str, help='Key to use in the checkpoint (example: "teacher")') + parser.add_argument('--epochs', default=100, type=int, help='Number of epochs of training.') + parser.add_argument("--lr", default=0.001, type=float, help="""Learning rate at the beginning of + training (highest LR used during training). The learning rate is linearly scaled + with the batch size, and specified here for a reference batch size of 256. + We recommend tweaking the LR depending on the checkpoint evaluated.""") + parser.add_argument('--batch_size_per_gpu', default=128, type=int, help='Per-GPU batch-size') + parser.add_argument("--dist_url", default="env://", type=str, help="""url used to set up + distributed training; see https://pytorch.org/docs/stable/distributed.html""") + parser.add_argument("--local_rank", default=0, type=int, help="Please ignore and do not set this argument.") + parser.add_argument('--data_path', default='/path/to/imagenet/', type=str) + parser.add_argument('--num_workers', default=10, type=int, help='Number of data loading workers per GPU.') + parser.add_argument('--val_freq', default=1, type=int, help="Epoch frequency for validation.") + parser.add_argument('--output_dir', default=".", help='Path to save logs and checkpoints') + parser.add_argument('--num_labels', default=1000, type=int, help='Number of labels for linear classifier') + parser.add_argument('--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set') + parser.add_argument('--ori_size', default=False, action='store_true', help='Evaluate on image raw size') + parser.add_argument('--input_size', default=224, type=int, help='Input image size, default(224).') + parser.add_argument('--distributed', default=False, action='store_true', help='Using distributed data parallel') + parser.add_argument("--weight_decay", default=0.001, type=float, help='weight decay') + args = parser.parse_args() + eval_linear(args) diff --git a/third_party/weakly_supvervised_detection/hubconf.py b/third_party/weakly_supvervised_detection/hubconf.py new file mode 100644 index 0000000000000000000000000000000000000000..3709271ed2b52bb86fbeb70fc02bc47d1add207e --- /dev/null +++ b/third_party/weakly_supvervised_detection/hubconf.py @@ -0,0 +1,151 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +from torchvision.models.resnet import resnet50 + +import vision_transformer as vits + +dependencies = ["torch", "torchvision"] + + +def dino_vits16(pretrained=True, **kwargs): + """ + ViT-Small/16x16 pre-trained with DINO. + Achieves 74.5% top-1 accuracy on ImageNet with k-NN classification. + """ + model = vits.__dict__["vit_small"](patch_size=16, num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model + + +def dino_vits8(pretrained=True, **kwargs): + """ + ViT-Small/8x8 pre-trained with DINO. + Achieves 78.3% top-1 accuracy on ImageNet with k-NN classification. + """ + model = vits.__dict__["vit_small"](patch_size=8, num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model + + +def dino_vitb16(pretrained=True, **kwargs): + """ + ViT-Base/16x16 pre-trained with DINO. + Achieves 76.1% top-1 accuracy on ImageNet with k-NN classification. + """ + model = vits.__dict__["vit_base"](patch_size=16, num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model + + +def dino_vitb8(pretrained=True, **kwargs): + """ + ViT-Base/8x8 pre-trained with DINO. + Achieves 77.4% top-1 accuracy on ImageNet with k-NN classification. + """ + model = vits.__dict__["vit_base"](patch_size=8, num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model + + +def dino_resnet50(pretrained=True, **kwargs): + """ + ResNet-50 pre-trained with DINO. + Achieves 75.3% top-1 accuracy on ImageNet linear evaluation benchmark (requires to train `fc`). + """ + model = resnet50(pretrained=False, **kwargs) + model.fc = torch.nn.Identity() + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_resnet50_pretrain/dino_resnet50_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=False) + return model + + +def dino_xcit_small_12_p16(pretrained=True, **kwargs): + """ + XCiT-Small-12/16 pre-trained with DINO. + """ + model = torch.hub.load('facebookresearch/xcit:main', "xcit_small_12_p16", num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_xcit_small_12_p16_pretrain/dino_xcit_small_12_p16_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model + + +def dino_xcit_small_12_p8(pretrained=True, **kwargs): + """ + XCiT-Small-12/8 pre-trained with DINO. + """ + model = torch.hub.load('facebookresearch/xcit:main', "xcit_small_12_p8", num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_xcit_small_12_p8_pretrain/dino_xcit_small_12_p8_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model + + +def dino_xcit_medium_24_p16(pretrained=True, **kwargs): + """ + XCiT-Medium-24/16 pre-trained with DINO. + """ + model = torch.hub.load('facebookresearch/xcit:main', "xcit_medium_24_p16", num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_xcit_medium_24_p16_pretrain/dino_xcit_medium_24_p16_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model + + +def dino_xcit_medium_24_p8(pretrained=True, **kwargs): + """ + XCiT-Medium-24/8 pre-trained with DINO. + """ + model = torch.hub.load('facebookresearch/xcit:main', "xcit_medium_24_p8", num_classes=0, **kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/dino/dino_xcit_medium_24_p8_pretrain/dino_xcit_medium_24_p8_pretrain.pth", + map_location="cpu", + ) + model.load_state_dict(state_dict, strict=True) + return model diff --git a/third_party/weakly_supvervised_detection/main.py b/third_party/weakly_supvervised_detection/main.py new file mode 100644 index 0000000000000000000000000000000000000000..84587a333d9a0dd17b16a276527638c441a3325b --- /dev/null +++ b/third_party/weakly_supvervised_detection/main.py @@ -0,0 +1,410 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import argparse +import json +from pathlib import Path +import time +import datetime + +import torch +from torch import nn +import torch.distributed as dist +import torch.backends.cudnn as cudnn +from torchvision import datasets +from torchvision import transforms as pth_transforms +from torchvision import models as torchvision_models + +import utils +import vision_transformer as vits + +from torch.utils.tensorboard import SummaryWriter +import shutil +import itertools +import numpy as np + +from timm.scheduler import create_scheduler +from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy +from timm.data import create_transform +from timm.data import Mixup + +from samplers import RASampler +from datasets import build_dataset + +def main(args): + if args.device != 'cuda': + args.distributed = False + else: + utils.init_distributed_mode(args) + + print(args) + + # ========fix seeds ======== + seed = args.seed + utils.get_rank() + torch.manual_seed(seed) + np.random.seed(seed) + + device = torch.device(args.device) + cudnn.benchmark = True + + # ============ building network ... ============ + # if the network is a Vision Transformer (i.e. vit_tiny, vit_small, vit_base) + if args.arch in vits.__dict__.keys(): + model = vits.__dict__[args.arch](patch_size=args.patch_size, num_classes=args.num_labels, adjacency_bp=args.adjacency_bp, temperature=args.temperature) + embed_dim = model.embed_dim * (args.n_last_blocks + int(args.avgpool_patchtokens)) + else: + print(f"Unknow architecture: {args.arch}") + sys.exit(1) + + model.to(device) + model.eval() + + model_without_ddp = model + #if args.distributed: + # model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) # TODO: where has unused params? + # #model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + # model_without_ddp = model.module + n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) + print('number of params:', n_parameters) + # load weights to evaluate + utils.load_pretrained_weights(model_without_ddp, args.pretrained_weights, args.checkpoint_key, args.arch, args.patch_size) + print(f"Model {args.arch} built.") + + linear_classifier = LinearClassifier(embed_dim, num_labels=args.num_labels) + linear_classifier = linear_classifier.cuda() + classifier_without_ddp = linear_classifier + linear_classifier = nn.parallel.DistributedDataParallel(linear_classifier, device_ids=[args.gpu]) + + # ============ Build dataset ============ + dataset_train, args.num_labels = build_dataset(is_train = True, args=args) + dataset_val, _ = build_dataset(is_train=False, args=args) + + num_tasks = utils.get_world_size() + global_rank = utils.get_rank() + + if args.distributed: + if args.data_aug and args.repeated_aug: + sampler_train = RASampler( + dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.distributed.DistributedSampler(dataset_train) + sampler_val = torch.utils.data.distributed.DistributedSampler(dataset_val, shuffle=False) + else: + sampler = torch.utils.data.RandomSampler(dataset_train) + sampler_val = torch.utils.data.SequentialSampler(dataset_val) + + train_loader = torch.utils.data.DataLoader( + dataset_train, + sampler=sampler_train, + batch_size=args.batch_size_per_gpu, + num_workers=args.num_workers, + pin_memory=True, + ) + val_loader = torch.utils.data.DataLoader( + dataset_val, + sampler=sampler_val, + batch_size=args.batch_size_per_gpu, + num_workers=args.num_workers, + pin_memory=True, + ) + print(f"Data loaded with {len(dataset_train)} train and {len(dataset_val)} val imgs.") + + if args.evaluate: + checkpoint = torch.load(args.checkpoint, map_location='cpu') + model_without_ddp.load_state_dict(checkpoint['model']) + test_stats = validate_network(val_loader, model_without_ddp, device) + print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%") + return + + # set optimizer + optimizer = torch.optim.SGD( + linear_classifier.parameters(), + args.lr * (args.batch_size_per_gpu * utils.get_world_size()) / 256., # linear scaling rule + momentum=0.9, + weight_decay=args.weight_decay, # we do not apply weight decay + ) + scheduler, _ = create_scheduler(args, optimizer) + + criterion = nn.CrossEntropyLoss() + # ----Mixup ----- + mixup_fn = None + smoothing = None + if args.data_aug: + print('Data augmentation: Mixup CutMix enable') + mixup_fn = Mixup( + mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax, + prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode, + label_smoothing=args.smoothing, num_classes=args.num_labels) + criterion = SoftTargetCrossEntropy() + + if utils.is_main_process(): + writer = SummaryWriter(args.output_dir + '/log') + start_epoch = 0 + best_acc = 0 + print("Starting training") + start_time = time.time() + for epoch in range(start_epoch, args.epochs): + if args.distributed: + train_loader.sampler.set_epoch(epoch) + + train_stats = train(model_without_ddp, device, optimizer, train_loader, epoch, criterion, linear_classifier, args.n_last_blocks, args.avgpool_patchtokens, mixup_fn) + + scheduler.step(epoch) + + log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, + 'epoch': epoch} + if epoch % args.val_freq == 0 or epoch == args.epochs - 1: + test_stats = validate_network(val_loader, model, device, linear_classifier, args.n_last_blocks, args.avgpool_patchtokens) + print(f"Accuracy at epoch {epoch} of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%") + log_stats = {**{k: v for k, v in log_stats.items()}, + **{f'test_{k}': v for k, v in test_stats.items()}} + if utils.is_main_process(): + with (Path(args.output_dir) / "log.txt").open("a") as f: + f.write(json.dumps(log_stats) + "\n") + save_dict = { + "epoch": epoch + 1, + "classifier": classifier_without_ddp.state_dict(), + "model": model_without_ddp.state_dict(), + "optimizer": optimizer.state_dict(), + "scheduler": scheduler.state_dict(), + "best_acc": best_acc, + } + + writer.add_scalar('Train_loss', train_stats['loss'], global_step=epoch) + writer.add_scalar('Learning_rate', train_stats['lr'], global_step=epoch) + writer.add_scalar('Train Acc_1', train_stats['acc1'], global_step=epoch) + writer.add_scalar('Acc_1', test_stats['acc1'], global_step=epoch) + writer.add_scalar('Acc_5', test_stats['acc5'], global_step=epoch) + + checkpoint_path = os.path.join(args.output_dir, "checkpoint.pth") + torch.save(save_dict, checkpoint_path) + + if best_acc < float(test_stats['acc1']): + best_acc = float(test_stats['acc1']) + shutil.copyfile(checkpoint_path, args.output_dir + '/model_best.pth') + print(f'Max accuracy so far: {best_acc:.2f}%') + print("Training of the TokenCut completed.\n" + "Top-1 test accuracy: {acc:.1f}".format(acc=best_acc)) + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print(f'Training time {total_time_str}') + + +def train(model, device, optimizer, loader, epoch, criterion, linear_classifier, n, avgpool, mixup_fn=None,): + linear_classifier.train() + metric_logger = utils.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + for batch in metric_logger.log_every(loader, 20, header): + inp, target = batch[:2] + # move to gpu + inp = inp.to(device, non_blocking=True) + target = target.to(device, non_blocking=True) + hard_target = target.clone() + if args.data_aug: + inp, target = mixup_fn(inp, target) + # forward + with torch.no_grad(): + intermediate_output,_ = model.get_intermediate_layers(inp, n) + output = torch.cat([x[:, 0] for x in intermediate_output], dim=-1) + if avgpool: + output = torch.cat((output.unsqueeze(-1), torch.mean(intermediate_output[-1][:, 1:], dim=1).unsqueeze(-1)), dim=-1) + output = output.reshape(output.shape[0], -1) + output = linear_classifier(output) + + # compute cross entropy loss + loss = criterion(output, target) + + acc1, = utils.accuracy(output, hard_target, topk=(1,)) + # compute the gradients + optimizer.zero_grad() + loss.backward() + + # step + optimizer.step() + + # log + torch.cuda.synchronize() + batch_size = inp.shape[0] + metric_logger.update(loss=loss.item()) + metric_logger.update(lr=optimizer.param_groups[0]["lr"]) + metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + + +@torch.no_grad() +def validate_network(val_loader, model, device, linear_classifier, n, avgpool): + linear_classifier.eval() + metric_logger = utils.MetricLogger(delimiter=" ") + header = 'Test:' + # for inp, target, _ in metric_logger.log_every(val_loader, 20, header): + for batch in metric_logger.log_every(val_loader, 20, header): + inp, target = batch[:2] + # move to gpu + inp = inp.to(device, non_blocking=True) + target = target.to(device, non_blocking=True) + + # forward + with torch.no_grad(): + intermediate_output,_ = model.get_intermediate_layers(inp, n) + output = torch.cat([x[:, 0] for x in intermediate_output], dim=-1) + if avgpool: + output = torch.cat((output.unsqueeze(-1), torch.mean(intermediate_output[-1][:, 1:], dim=1).unsqueeze(-1)), dim=-1) + output = output.reshape(output.shape[0], -1) + output = linear_classifier(output) + loss = nn.CrossEntropyLoss()(output, target) + + if linear_classifier.module.num_labels >= 5: + acc1, acc5 = utils.accuracy(output, target, topk=(1, 5)) + else: + acc1, = utils.accuracy(output, target, topk=(1,)) + + batch_size = inp.shape[0] + metric_logger.update(loss=loss.item()) + metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) + if linear_classifier.module.num_labels >= 5: + metric_logger.meters['acc5'].update(acc5.item(), n=batch_size) + if linear_classifier.module.num_labels >= 5: + print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}' + .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss)) + else: + print('* Acc@1 {top1.global_avg:.3f} loss {losses.global_avg:.3f}' + .format(top1=metric_logger.acc1, losses=metric_logger.loss)) + + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + +class LinearClassifier(nn.Module): + """Linear layer to train on top of frozen features""" + def __init__(self, dim, num_labels=1000): + super(LinearClassifier, self).__init__() + self.num_labels = num_labels + self.linear = nn.Linear(dim, num_labels) + self.linear.weight.data.normal_(mean=0.0, std=0.01) + self.linear.bias.data.zero_() + + def forward(self, x): + # flatten + x = x.view(x.size(0), -1) + + # linear layer + return self.linear(x) + +if __name__ == '__main__': + parser = argparse.ArgumentParser('Evaluation with linear classification on ImageNet') + parser.add_argument('--n_last_blocks', default=4, type=int, help="""Concatenate [CLS] tokens + for the `n` last blocks. We use `n=4` when evaluating ViT-Small and `n=1` with ViT-Base.""") + parser.add_argument('--avgpool_patchtokens', default=False, type=utils.bool_flag, + help="""Whether ot not to concatenate the global average pooled features to the [CLS] token. + We typically set this to False for ViT-Small and to True with ViT-Base.""") + parser.add_argument('--arch', default='vit_small', choices=['vit_small', 'vit_base'], type=str, help='Architecture') + parser.add_argument('--dataset', default='cub', type=str, choices=['cub', 'imagenet'], help='Architecture') + parser.add_argument('--patch_size', default=16, type=int, help='Patch resolution of the model.') + parser.add_argument('--input_size', default=224, type=int, help='Input image size, default(224).') + parser.add_argument('--pretrained_weights', default='', type=str, help="Path to pretrained weights to evaluate.") + parser.add_argument("--checkpoint_key", default="teacher", type=str, help='Key to use in the checkpoint (example: "teacher")') + parser.add_argument('--epochs', default=100, type=int, help='Number of epochs of training.') + parser.add_argument('--batch_size_per_gpu', default=128, type=int, help='Per-GPU batch-size') + parser.add_argument("--dist_url", default="env://", type=str, help="""url used to set up + distributed training; see https://pytorch.org/docs/stable/distributed.html""") + parser.add_argument("--local_rank", default=0, type=int, help="Please ignore and do not set this argument.") + parser.add_argument('--data_path', default='/path/to/imagenet/', type=str) + parser.add_argument('--num_workers', default=10, type=int, help='Number of data loading workers per GPU.') + parser.add_argument('--val_freq', default=1, type=int, help="Epoch frequency for validation.") + parser.add_argument('--output_dir', default="./checkpoints", help='Path to save logs and checkpoints') + parser.add_argument('--num_labels', default=1000, type=int, help='Number of labels for linear classifier') + parser.add_argument('--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set') + parser.add_argument('--weight_decay', default=0.1, type=float, help="weight_decay, default 0.1") + parser.add_argument('--device', default='cuda', help='device to use for training / testing') + parser.add_argument('--distributed', default=False, action='store_true', help='device to use for training / testing') + parser.add_argument('--adjacency_bp', default=False, action='store_true', help='whether backprop from adjacency matrix') + parser.add_argument('--temperature', default=1, type=int, help='Temperature for mask') + parser.add_argument('--seed', default=0, type=int) + + # ------ lr scheduler ------ + parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER', + help='LR scheduler (default: "cosine"') + parser.add_argument('--lr', type=float, default=1e-4, metavar='LR', + help="""Learning rate at the beginning of + training (highest LR used during training). The learning rate is linearly scaled + with the batch size, and specified here for a reference batch size of 256. + We recommend tweaking the LR depending on the checkpoint evaluated.""") + parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct', + help='learning rate noise on/off epoch percentages') + parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT', + help='learning rate noise limit percent (default: 0.67)') + parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV', + help='learning rate noise std-dev (default: 1.0)') + parser.add_argument('--warmup-lr', type=float, default=1e-6, metavar='LR', + help='warmup learning rate (default: 1e-6)') + parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR', + help='lower lr bound for cyclic schedulers that hit 0 (1e-5)') + parser.add_argument('--decay-epochs', type=float, default=5, metavar='N', + help='epoch interval to decay LR') + parser.add_argument('--warmup-epochs', type=int, default=5, metavar='N', + help='epochs to warmup LR, if scheduler supports') + parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N', + help='epochs to cooldown LR at min_lr, after cyclic schedule ends') + parser.add_argument('--patience-epochs', type=int, default=10, metavar='N', + help='patience epochs for Plateau LR scheduler (default: 10') + parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE', + help='LR decay rate (default: 0.1)') + # --------data aug--------- + parser.add_argument('--label-smooth-loss', default=False, action='store_true', help='use label smooth') + # * Random Erase params + parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT', + help='Random erase prob (default: 0.25)') + parser.add_argument('--remode', type=str, default='pixel', + help='Random erase mode (default: "pixel")') + parser.add_argument('--recount', type=int, default=1, + help='Random erase count (default: 1)') + parser.add_argument('--resplit', action='store_true', default=False, + help='Do not random erase first (clean) augmentation split') + + # * Mixup params + parser.add_argument('--mixup', type=float, default=0.8, + help='mixup alpha, mixup enabled if > 0. (default: 0.8)') + parser.add_argument('--cutmix', type=float, default=1.0, + help='cutmix alpha, cutmix enabled if > 0. (default: 1.0)') + parser.add_argument('--cutmix-minmax', type=float, nargs='+', default=None, + help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)') + parser.add_argument('--mixup-prob', type=float, default=1.0, + help='Probability of performing mixup or cutmix when either/both is enabled') + parser.add_argument('--mixup-switch-prob', type=float, default=0.5, + help='Probability of switching to cutmix when both mixup and cutmix enabled') + parser.add_argument('--mixup-mode', type=str, default='batch', + help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"') + + # Augmentation parameters + parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT', + help='Color jitter factor (default: 0.4)') + parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME', + help='Use AutoAugment policy. "v0" or "original". " + \ + "(default: rand-m9-mstd0.5-inc1)'), + parser.add_argument('--smoothing', type=float, default=0.1, help='Label smoothing (default: 0.1)') + parser.add_argument('--train-interpolation', type=str, default='bicubic', + help='Training interpolation (random, bilinear, bicubic default: "bicubic")') + + parser.add_argument('--repeated-aug', action='store_true') + parser.add_argument('--no-repeated-aug', action='store_false', dest='repeated_aug') + parser.set_defaults(repeated_aug=True) + + parser.add_argument('--no_center_crop', default=False, action='store_true', help='Center crop input image') + parser.add_argument('--data-aug', action='store_true', default=False, help='disable the data augmentations.') + parser.add_argument('--ori_size', default=False, action='store_true', help='Evaluate on image raw size') + args = parser.parse_args() + main(args) diff --git a/third_party/weakly_supvervised_detection/meters.py b/third_party/weakly_supvervised_detection/meters.py new file mode 100644 index 0000000000000000000000000000000000000000..cd74698eaf524397cea6829d289a1d9753596852 --- /dev/null +++ b/third_party/weakly_supvervised_detection/meters.py @@ -0,0 +1,93 @@ +import numpy as np + +class AverageEpochMeter(object): + """Computes and stores the average and current value""" + def __init__(self, name, fmt=':f'): + self.name = name + self.fmt = fmt + self.reset() + + def reset(self): + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.sum += val * n + self.count += n + + def compute(self): + self.avg = self.sum / self.count + return self.avg + + def __str__(self): + fmtstr = '{name}: {avg' + self.fmt + '}' + return fmtstr.format(**self.__dict__) + +#class MultiAverageEpochMeter(object): +# def __init__(self, name, fmt=':f'): +# self.name = name +# self.fmt = fmt +# self.thresholds = np.arange(0., 1., 0.001) +# self.reset() +# +# def reset(self): +# self.avg = 0 +# self.sum = np.zeros(len(self.thresholds)) +# self.count = 0 +# +# def update(self, val, n=1): +# self.sum += val * n +# self.count += n +# +# def compute(self): +# self.avg = self.sum / self.count +# return self.avg.max() +# +# def __str__(self): +# fmtstr = '{name}: {avg' + self.fmt + '}' +# return fmtstr.format(**self.__dict__) +# +# +#class AUCEpochMeter(object): +# +# def __init__(self, name, fmt=':f'): +# self.name = name +# self.fmt = fmt +# self.thresholds = np.append(np.arange(0., 1., 0.001), [1., 2., 3.]) +# self.num_bins = len(self.thresholds) - 1 +# self.reset() +# +# def reset(self): +# self.gt_true_score_hist = np.zeros(self.num_bins, dtype=np.float) +# self.gt_false_score_hist = np.zeros(self.num_bins, dtype=np.float) +# +# def update(self, gt_true_hist, gt_false_hist): +# self.gt_true_score_hist += gt_true_hist +# self.gt_false_score_hist += gt_false_hist +# +# def compute(self): +# num_gt_true = self.gt_true_score_hist.sum() +# tp = self.gt_true_score_hist[::-1].cumsum() +# fn = num_gt_true - tp +# +# num_gt_false = self.gt_false_score_hist.sum() +# fp = self.gt_false_score_hist[::-1].cumsum() +# tn = num_gt_false - fp +# +# if ((tp + fn) <= 0).all(): +# raise RuntimeError("No positive ground truth in the eval set.") +# if ((tp + fp) <= 0).all(): +# raise RuntimeError("No positive prediction in the eval set.") +# +# non_zero_indices = (tp + fp) != 0 +# +# precision = tp / (tp + fp) +# recall = tp / (tp + fn) +# +# auc = (precision[1:] * np.diff(recall))[non_zero_indices[1:]].sum() +# return auc +# +# def __str__(self): +# fmtstr = '{name}: {avg' + self.fmt + '}' +# return fmtstr.format(**self.__dict__) diff --git a/third_party/weakly_supvervised_detection/metrics.py b/third_party/weakly_supvervised_detection/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..599eec204f3bb017735c5152be72e4ffea32e816 --- /dev/null +++ b/third_party/weakly_supvervised_detection/metrics.py @@ -0,0 +1,41 @@ +import cv2 +import torch +import numpy as np + +def loc_accuracy(outputs, labels, gt_boxes, bboxes, iou_threshold=0.5): + if outputs is not None: + _, pred = torch.topk(outputs, k=1, dim=1, largest=True, sorted=True) + pred = pred.t() + correct = pred.eq(labels.view(1, -1).expand_as(pred)) + wrongs = [c == 0 for c in correct.cpu().numpy()][0] + + batch_size = len(gt_boxes) + gt_known, top1 = 0., 0. + for i, (gt_box, bbox) in enumerate(zip(gt_boxes, bboxes)): + iou_score = iou(gt_box, bbox) + + if iou_score >= iou_threshold: + gt_known += 1. + if outputs is not None and not wrongs[i]: + top1 += 1. + + gt_loc = gt_known / batch_size + top1_loc = top1 / batch_size + return gt_loc, top1_loc + +def iou(box1, box2): + """box: (xmin, ymin, xmax, ymax)""" + box1_xmin, box1_ymin, box1_xmax, box1_ymax = box1 + box2_xmin, box2_ymin, box2_xmax, box2_ymax = box2 + + inter_xmin = max(box1_xmin, box2_xmin) + inter_ymin = max(box1_ymin, box2_ymin) + inter_xmax = min(box1_xmax, box2_xmax) + inter_ymax = min(box1_ymax, box2_ymax) + + inter_area = (inter_xmax - inter_xmin + 1) * (inter_ymax - inter_ymin + 1) + box1_area = (box1_xmax - box1_xmin + 1) * (box1_ymax - box1_ymin + 1) + box2_area = (box2_xmax - box2_xmin + 1) * (box2_ymax - box2_ymin + 1) + + iou = inter_area / (box1_area + box2_area - inter_area).float() + return iou.item() diff --git a/third_party/weakly_supvervised_detection/object_discovery.py b/third_party/weakly_supvervised_detection/object_discovery.py new file mode 100644 index 0000000000000000000000000000000000000000..63f961741a8c89014b8c4243064373ba7926815a --- /dev/null +++ b/third_party/weakly_supvervised_detection/object_discovery.py @@ -0,0 +1,108 @@ +""" +Main functions for applying Normalized Cut. +Code adapted from LOST: https://github.com/valeoai/LOST +""" +import torch +import torch.nn.functional as F +import numpy as np +#from scipy.linalg.decomp import eig +from scipy.linalg import eigh +from scipy import ndimage +from sklearn.cluster import KMeans +from sklearn.mixture import GaussianMixture + +def ncut(feats, dims, scales, init_image_size, eps=1e-5, tau=0.05): + #feats = feats[0,1:,:].detach().cpu().numpy() + feats = feats[0,1:,:] + feats = F.normalize(feats, p=2) + A = (feats @ feats.transpose(1,0)) + A = A.cpu().numpy() + + A = A > tau + A = np.where(A.astype(float) == 0, eps, A) + d_i = np.sum(A, axis=1) + D = np.diag(d_i) + + # Print second and third smallest eigenvector + _, eigenvectors = eigh(D-A, D, subset_by_index=[1,2]) + eigenvec = np.copy(eigenvectors[:, 0]) + + # method 2 avg + second_smallest_vec = eigenvectors[:, 0] + avg = np.sum(second_smallest_vec) / len(second_smallest_vec) + bipartition = second_smallest_vec > avg + + # method3 EM algo + #second_smallest_vec = eigenvectors[:, 0:1] + #bipartition = GMM(second_smallest_vec) + + # method 4 Kmeans + #second_smallest_vec = eigenvectors[:, 0:1] + #bipartition = Kmeans_partition(second_smallest_vec) + + seed = np.argmax(np.abs(second_smallest_vec)) + + if bipartition[seed] != 1: + bipartition = np.logical_not(bipartition) + eigenvec = eigenvec * -1 + bipartition = bipartition.reshape(dims).astype(float) + + # predict BBox + pred, _ = detect_box(bipartition, seed, dims, scales=scales, initial_im_size=init_image_size) ## We only extract the principal object BBox + + return np.asarray(pred), bipartition, seed, eigenvec.reshape(dims) + +def GMM(eigvec): + gmm = GaussianMixture(n_components=2, max_iter=300) + gmm.fit(eigvec) + partition = gmm.predict(eigvec) + return partition + +def Kmeans_partition(eigvec): + kmeans = KMeans(n_clusters=2, random_state=0).fit(eigvec) + return kmeans.labels_ + +def detect_box(bipartition, seed, dims, initial_im_size=None, scales=None, principle_object=True): + """ + Extract a box corresponding to the seed patch. Among connected components extract from the affinity matrix, select the one corresponding to the seed patch. + """ + + w_featmap, h_featmap = dims + objects, num_objects = ndimage.label(bipartition) + cc = objects[np.unravel_index(seed, dims)] + + + if principle_object: + mask = np.where(objects == cc) + # Add +1 because excluded max + ymin, ymax = min(mask[0]), max(mask[0]) + 1 + xmin, xmax = min(mask[1]), max(mask[1]) + 1 + # Rescale to image size + r_xmin, r_xmax = scales[1] * xmin, scales[1] * xmax + r_ymin, r_ymax = scales[0] * ymin, scales[0] * ymax + pred = [r_xmin, r_ymin, r_xmax, r_ymax] + + # Check not out of image size (used when padding) + if initial_im_size: + pred[2] = min(pred[2], initial_im_size[1]) + pred[3] = min(pred[3], initial_im_size[0]) + + # Coordinate predictions for the feature space + # Axis different then in image space + pred_feats = [ymin, xmin, ymax, xmax] + + return pred, pred_feats + else: + raise NotImplementedError + +def get_feats(feat_out, shape): + nb_im, nh, nb_tokens = shape[0:3] # Batch size, Number of heads, Number of tokens + qkv = ( + feat_out["qkv"] + .reshape(nb_im, nb_tokens, 3, nh, -1 // nh) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = qkv[0], qkv[1], qkv[2] + k = k.transpose(1, 2).reshape(nb_im, nb_tokens, -1) + return k + diff --git a/third_party/weakly_supvervised_detection/requirements.txt b/third_party/weakly_supvervised_detection/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..8ea5b7251f24582c9855374643b18185515edb8b --- /dev/null +++ b/third_party/weakly_supvervised_detection/requirements.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ead6d92f44e5b0110140a8bd92c9097f11c6f95bcd7f6912c7b6ace33c35f99 +size 99 diff --git a/third_party/weakly_supvervised_detection/samplers.py b/third_party/weakly_supvervised_detection/samplers.py new file mode 100644 index 0000000000000000000000000000000000000000..e67dc6ddd2048a0032c1892540f3289bba4465ea --- /dev/null +++ b/third_party/weakly_supvervised_detection/samplers.py @@ -0,0 +1,59 @@ +# Copyright (c) 2015-present, Facebook, Inc. +# All rights reserved. +import torch +import torch.distributed as dist +import math + + +class RASampler(torch.utils.data.Sampler): + """Sampler that restricts data loading to a subset of the dataset for distributed, + with repeated augmentation. + It ensures that different each augmented version of a sample will be visible to a + different process (GPU) + Heavily based on torch.utils.data.DistributedSampler + """ + + def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True): + if num_replicas is None: + if not dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + num_replicas = dist.get_world_size() + if rank is None: + if not dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + rank = dist.get_rank() + self.dataset = dataset + self.num_replicas = num_replicas + self.rank = rank + self.epoch = 0 + self.num_samples = int(math.ceil(len(self.dataset) * 3.0 / self.num_replicas)) + self.total_size = self.num_samples * self.num_replicas + # self.num_selected_samples = int(math.ceil(len(self.dataset) / self.num_replicas)) + self.num_selected_samples = int(math.floor(len(self.dataset) // 256 * 256 / self.num_replicas)) + self.shuffle = shuffle + + def __iter__(self): + # deterministically shuffle based on epoch + g = torch.Generator() + g.manual_seed(self.epoch) + if self.shuffle: + indices = torch.randperm(len(self.dataset), generator=g).tolist() + else: + indices = list(range(len(self.dataset))) + + # add extra samples to make it evenly divisible + indices = [ele for ele in indices for i in range(3)] + indices += indices[:(self.total_size - len(indices))] + assert len(indices) == self.total_size + + # subsample + indices = indices[self.rank:self.total_size:self.num_replicas] + assert len(indices) == self.num_samples + + return iter(indices[:self.num_selected_samples]) + + def __len__(self): + return self.num_selected_samples + + def set_epoch(self, epoch): + self.epoch = epoch diff --git a/third_party/weakly_supvervised_detection/utils.py b/third_party/weakly_supvervised_detection/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e318885f2c9b08522b0709710df6cfdca6fa0432 --- /dev/null +++ b/third_party/weakly_supvervised_detection/utils.py @@ -0,0 +1,858 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Misc functions. + +Mostly copy-paste from torchvision references or other public repos like DETR: +https://github.com/facebookresearch/detr/blob/master/util/misc.py +""" +import os +import sys +import time +import math +import random +import datetime +import subprocess +from collections import defaultdict, deque + +import numpy as np +import torch +from torch import nn +import torch.distributed as dist +from PIL import ImageFilter, ImageOps + +from scipy import ndimage + +class GaussianBlur(object): + """ + Apply Gaussian Blur to the PIL image. + """ + def __init__(self, p=0.5, radius_min=0.1, radius_max=2.): + self.prob = p + self.radius_min = radius_min + self.radius_max = radius_max + + def __call__(self, img): + do_it = random.random() <= self.prob + if not do_it: + return img + + return img.filter( + ImageFilter.GaussianBlur( + radius=random.uniform(self.radius_min, self.radius_max) + ) + ) + + +class Solarization(object): + """ + Apply Solarization to the PIL image. + """ + def __init__(self, p): + self.p = p + + def __call__(self, img): + if random.random() < self.p: + return ImageOps.solarize(img) + else: + return img + + +def load_pretrained_weights(model, pretrained_weights, checkpoint_key, model_name, patch_size): + if os.path.isfile(pretrained_weights): + state_dict = torch.load(pretrained_weights, map_location="cpu") + if checkpoint_key is not None and checkpoint_key in state_dict: + print(f"Take key {checkpoint_key} in provided checkpoint dict") + state_dict = state_dict[checkpoint_key] + # remove `module.` prefix + state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()} + # remove `backbone.` prefix induced by multicrop wrapper + state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()} + msg = model.load_state_dict(state_dict, strict=False) + #print('Pretrained weights found at {} and loaded with msg: {}'.format(pretrained_weights, msg)) + print('Pretrained weights found at {} and loaded, (to see more info, uncomment line 82 in utils.py'.format(pretrained_weights)) + else: + print("Please use the `--pretrained_weights` argument to indicate the path of the checkpoint to evaluate.") + url = None + if model_name == "vit_small" and patch_size == 16: + url = "dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth" + elif model_name == "vit_small" and patch_size == 8: + url = "dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth" + elif model_name == "vit_base" and patch_size == 16: + url = "dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth" + elif model_name == "vit_base" and patch_size == 8: + url = "dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth" + if url is not None: + print("Since no pretrained weights have been provided, we load the reference pretrained DINO weights.") + state_dict = torch.hub.load_state_dict_from_url(url="https://dl.fbaipublicfiles.com/dino/" + url) + model_dict = model.state_dict() + pretrained_dict = {k:v for k,v in state_dict.items() if k in model_dict} + #print(f'load from pretrain model:') + #for k,v in pretrained_dict.items(): + # print(f'key:{k}') + + #print(f'keys not in pretrained model:') + #for k,v in model_dict.items(): + # if k not in pretrained_dict.keys(): + # print(f'not in key: {k}') + model_dict.update(pretrained_dict) + model.load_state_dict(model_dict) + #model.load_state_dict(state_dict, strict=False) + else: + print("There is no reference weights available for this model => We use random weights.") + + +def load_pretrained_linear_weights(linear_classifier, model_name, patch_size): + url = None + if model_name == "vit_small" and patch_size == 16: + url = "dino_deitsmall16_pretrain/dino_deitsmall16_linearweights.pth" + elif model_name == "vit_small" and patch_size == 8: + url = "dino_deitsmall8_pretrain/dino_deitsmall8_linearweights.pth" + elif model_name == "vit_base" and patch_size == 16: + url = "dino_vitbase16_pretrain/dino_vitbase16_linearweights.pth" + elif model_name == "vit_base" and patch_size == 8: + url = "dino_vitbase8_pretrain/dino_vitbase8_linearweights.pth" + if url is not None: + print("We load the reference pretrained linear weights.") + state_dict = torch.hub.load_state_dict_from_url(url="https://dl.fbaipublicfiles.com/dino/" + url)["state_dict"] + linear_classifier.load_state_dict(state_dict, strict=True) + else: + print("We use random linear weights.") + + +def clip_gradients(model, clip): + norms = [] + for name, p in model.named_parameters(): + if p.grad is not None: + param_norm = p.grad.data.norm(2) + norms.append(param_norm.item()) + clip_coef = clip / (param_norm + 1e-6) + if clip_coef < 1: + p.grad.data.mul_(clip_coef) + return norms + + +def cancel_gradients_last_layer(epoch, model, freeze_last_layer): + if epoch >= freeze_last_layer: + return + for n, p in model.named_parameters(): + if "last_layer" in n: + p.grad = None + + +def restart_from_checkpoint(ckp_path, run_variables=None, **kwargs): + """ + Re-start from checkpoint + """ + if not os.path.isfile(ckp_path): + return + print("Found checkpoint at {}".format(ckp_path)) + + # open checkpoint file + checkpoint = torch.load(ckp_path, map_location="cpu") + + # key is what to look for in the checkpoint file + # value is the object to load + # example: {'state_dict': model} + for key, value in kwargs.items(): + print(f'-key: {key}') + if key in checkpoint and value is not None: + try: + msg = value.load_state_dict(checkpoint[key], strict=False) + print("=> loaded '{}' from checkpoint '{}' with msg {}".format(key, ckp_path, msg)) + except TypeError: + try: + msg = value.load_state_dict(checkpoint[key]) + print("=> loaded '{}' from checkpoint: '{}'".format(key, ckp_path)) + except ValueError: + print("=> failed to load '{}' from checkpoint: '{}'".format(key, ckp_path)) + else: + print("=> key '{}' not found in checkpoint: '{}'".format(key, ckp_path)) + + # re load variable important for the run + if run_variables is not None: + for var_name in run_variables: + if var_name in checkpoint: + run_variables[var_name] = checkpoint[var_name] + + +def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warmup_epochs=0, start_warmup_value=0): + warmup_schedule = np.array([]) + warmup_iters = warmup_epochs * niter_per_ep + if warmup_epochs > 0: + warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters) + + iters = np.arange(epochs * niter_per_ep - warmup_iters) + schedule = final_value + 0.5 * (base_value - final_value) * (1 + np.cos(np.pi * iters / len(iters))) + + schedule = np.concatenate((warmup_schedule, schedule)) + assert len(schedule) == epochs * niter_per_ep + return schedule + + +def bool_flag(s): + """ + Parse boolean arguments from the command line. + """ + FALSY_STRINGS = {"off", "false", "0"} + TRUTHY_STRINGS = {"on", "true", "1"} + if s.lower() in FALSY_STRINGS: + return False + elif s.lower() in TRUTHY_STRINGS: + return True + else: + raise argparse.ArgumentTypeError("invalid value for a boolean flag") + + +def fix_random_seeds(seed=31): + """ + Fix random seeds. + """ + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + np.random.seed(seed) + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.6f} ({global_avg:.6f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not is_dist_avail_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') + dist.barrier() + dist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value) + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that all processes + have the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.all_reduce(values) + if average: + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format( + type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append( + "{}: {}".format(name, str(meter)) + ) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None): + i = 0 + if not header: + header = '' + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt='{avg:.6f}') + data_time = SmoothedValue(fmt='{avg:.6f}') + space_fmt = ':' + str(len(str(len(iterable)))) + 'd' + if torch.cuda.is_available(): + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}', + 'max mem: {memory:.0f}' + ]) + else: + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}' + ]) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB)) + else: + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time))) + i += 1 + end = time.time() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('{} Total time: {} ({:.6f} s / it)'.format( + header, total_time_str, total_time / len(iterable))) + + +def get_sha(): + cwd = os.path.dirname(os.path.abspath(__file__)) + + def _run(command): + return subprocess.check_output(command, cwd=cwd).decode('ascii').strip() + sha = 'N/A' + diff = "clean" + branch = 'N/A' + try: + sha = _run(['git', 'rev-parse', 'HEAD']) + subprocess.check_output(['git', 'diff'], cwd=cwd) + diff = _run(['git', 'diff-index', 'HEAD']) + diff = "has uncommited changes" if diff else "clean" + branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) + except Exception: + pass + message = f"sha: {sha}, status: {diff}, branch: {branch}" + return message + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop('force', False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def init_distributed_mode(args): + # launched with torch.distributed.launch + if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ['WORLD_SIZE']) + args.gpu = int(os.environ['LOCAL_RANK']) + # launched with submitit on a slurm cluster + elif 'SLURM_PROCID' in os.environ: + args.rank = int(os.environ['SLURM_PROCID']) + args.gpu = args.rank % torch.cuda.device_count() + # launched naively with `python main_dino.py` + # we manually add MASTER_ADDR and MASTER_PORT to env variables + elif torch.cuda.is_available(): + print('Will run the code on one GPU.') + args.rank, args.gpu, args.world_size = 0, 0, 1 + os.environ['MASTER_ADDR'] = '127.0.0.1' + os.environ['MASTER_PORT'] = '29500' + else: + print('Does not support training without GPU.') + sys.exit(1) + + dist.init_process_group( + backend="nccl", + init_method=args.dist_url, + world_size=args.world_size, + rank=args.rank, + ) + args.distributed = True + + torch.cuda.set_device(args.gpu) + print('| distributed init (rank {}): {}'.format( + args.rank, args.dist_url), flush=True) + dist.barrier() + #dist.barrier(device_ids=[args.gpu]) + setup_for_distributed(args.rank == 0) + + +def accuracy(output, target, topk=(1,)): + """Computes the accuracy over the k top predictions for the specified values of k""" + maxk = max(topk) + batch_size = target.size(0) + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.reshape(1, -1).expand_as(pred)) + return [correct[:k].reshape(-1).float().sum(0) * 100. / batch_size for k in topk] + + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): + # type: (Tensor, float, float, float, float) -> Tensor + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +class LARS(torch.optim.Optimizer): + """ + Almost copy-paste from https://github.com/facebookresearch/barlowtwins/blob/main/main.py + """ + def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, eta=0.001, + weight_decay_filter=None, lars_adaptation_filter=None): + defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum, + eta=eta, weight_decay_filter=weight_decay_filter, + lars_adaptation_filter=lars_adaptation_filter) + super().__init__(params, defaults) + + @torch.no_grad() + def step(self): + for g in self.param_groups: + for p in g['params']: + dp = p.grad + + if dp is None: + continue + + if p.ndim != 1: + dp = dp.add(p, alpha=g['weight_decay']) + + if p.ndim != 1: + param_norm = torch.norm(p) + update_norm = torch.norm(dp) + one = torch.ones_like(param_norm) + q = torch.where(param_norm > 0., + torch.where(update_norm > 0, + (g['eta'] * param_norm / update_norm), one), one) + dp = dp.mul(q) + + param_state = self.state[p] + if 'mu' not in param_state: + param_state['mu'] = torch.zeros_like(p) + mu = param_state['mu'] + mu.mul_(g['momentum']).add_(dp) + + p.add_(mu, alpha=-g['lr']) + + +class MultiCropWrapper(nn.Module): + """ + Perform forward pass separately on each resolution input. + The inputs corresponding to a single resolution are clubbed and single + forward is run on the same resolution inputs. Hence we do several + forward passes = number of different resolutions used. We then + concatenate all the output features and run the head forward on these + concatenated features. + """ + def __init__(self, backbone, head): + super(MultiCropWrapper, self).__init__() + # disable layers dedicated to ImageNet labels classification + backbone.fc, backbone.head = nn.Identity(), nn.Identity() + self.backbone = backbone + self.head = head + + def forward(self, x): + # convert to list + if not isinstance(x, list): + x = [x] + idx_crops = torch.cumsum(torch.unique_consecutive( + torch.tensor([inp.shape[-1] for inp in x]), + return_counts=True, + )[1], 0) + start_idx, output = 0, torch.empty(0).to(x[0].device) + for end_idx in idx_crops: + _out = self.backbone(torch.cat(x[start_idx: end_idx])) + # The output is a tuple with XCiT model. See: + # https://github.com/facebookresearch/xcit/blob/master/xcit.py#L404-L405 + if isinstance(_out, tuple): + _out = _out[0] + # accumulate outputs + output = torch.cat((output, _out)) + start_idx = end_idx + # Run the head forward on the concatenated features. + return self.head(output) + + +def get_params_groups(model): + regularized = [] + not_regularized = [] + for name, param in model.named_parameters(): + if not param.requires_grad: + continue + # we do not regularize biases nor Norm parameters + if name.endswith(".bias") or len(param.shape) == 1: + not_regularized.append(param) + else: + regularized.append(param) + return [{'params': regularized}, {'params': not_regularized, 'weight_decay': 0.}] + + +def has_batchnorms(model): + bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm) + for name, module in model.named_modules(): + if isinstance(module, bn_types): + return True + return False + + +class PCA(): + """ + Class to compute and apply PCA. + """ + def __init__(self, dim=256, whit=0.5): + self.dim = dim + self.whit = whit + self.mean = None + + def train_pca(self, cov): + """ + Takes a covariance matrix (np.ndarray) as input. + """ + d, v = np.linalg.eigh(cov) + eps = d.max() * 1e-5 + n_0 = (d < eps).sum() + if n_0 > 0: + d[d < eps] = eps + + # total energy + totenergy = d.sum() + + # sort eigenvectors with eigenvalues order + idx = np.argsort(d)[::-1][:self.dim] + d = d[idx] + v = v[:, idx] + + print("keeping %.2f %% of the energy" % (d.sum() / totenergy * 100.0)) + + # for the whitening + d = np.diag(1. / d**self.whit) + + # principal components + self.dvt = np.dot(d, v.T) + + def apply(self, x): + # input is from numpy + if isinstance(x, np.ndarray): + if self.mean is not None: + x -= self.mean + return np.dot(self.dvt, x.T).T + + # input is from torch and is on GPU + if x.is_cuda: + if self.mean is not None: + x -= torch.cuda.FloatTensor(self.mean) + return torch.mm(torch.cuda.FloatTensor(self.dvt), x.transpose(0, 1)).transpose(0, 1) + + # input if from torch, on CPU + if self.mean is not None: + x -= torch.FloatTensor(self.mean) + return torch.mm(torch.FloatTensor(self.dvt), x.transpose(0, 1)).transpose(0, 1) + + +def compute_ap(ranks, nres): + """ + Computes average precision for given ranked indexes. + Arguments + --------- + ranks : zerro-based ranks of positive images + nres : number of positive images + Returns + ------- + ap : average precision + """ + + # number of images ranked by the system + nimgranks = len(ranks) + + # accumulate trapezoids in PR-plot + ap = 0 + + recall_step = 1. / nres + + for j in np.arange(nimgranks): + rank = ranks[j] + + if rank == 0: + precision_0 = 1. + else: + precision_0 = float(j) / rank + + precision_1 = float(j + 1) / (rank + 1) + + ap += (precision_0 + precision_1) * recall_step / 2. + + return ap + + +def compute_map(ranks, gnd, kappas=[]): + """ + Computes the mAP for a given set of returned results. + Usage: + map = compute_map (ranks, gnd) + computes mean average precsion (map) only + map, aps, pr, prs = compute_map (ranks, gnd, kappas) + computes mean average precision (map), average precision (aps) for each query + computes mean precision at kappas (pr), precision at kappas (prs) for each query + Notes: + 1) ranks starts from 0, ranks.shape = db_size X #queries + 2) The junk results (e.g., the query itself) should be declared in the gnd stuct array + 3) If there are no positive images for some query, that query is excluded from the evaluation + """ + + map = 0. + nq = len(gnd) # number of queries + aps = np.zeros(nq) + pr = np.zeros(len(kappas)) + prs = np.zeros((nq, len(kappas))) + nempty = 0 + + for i in np.arange(nq): + qgnd = np.array(gnd[i]['ok']) + + # no positive images, skip from the average + if qgnd.shape[0] == 0: + aps[i] = float('nan') + prs[i, :] = float('nan') + nempty += 1 + continue + + try: + qgndj = np.array(gnd[i]['junk']) + except: + qgndj = np.empty(0) + + # sorted positions of positive and junk images (0 based) + pos = np.arange(ranks.shape[0])[np.in1d(ranks[:,i], qgnd)] + junk = np.arange(ranks.shape[0])[np.in1d(ranks[:,i], qgndj)] + + k = 0; + ij = 0; + if len(junk): + # decrease positions of positives based on the number of + # junk images appearing before them + ip = 0 + while (ip < len(pos)): + while (ij < len(junk) and pos[ip] > junk[ij]): + k += 1 + ij += 1 + pos[ip] = pos[ip] - k + ip += 1 + + # compute ap + ap = compute_ap(pos, len(qgnd)) + map = map + ap + aps[i] = ap + + # compute precision @ k + pos += 1 # get it to 1-based + for j in np.arange(len(kappas)): + kq = min(max(pos), kappas[j]); + prs[i, j] = (pos <= kq).sum() / kq + pr = pr + prs[i, :] + + map = map / (nq - nempty) + pr = pr / (nq - nempty) + + return map, aps, pr, prs + + +def multi_scale(samples, model): + v = None + for s in [1, 1/2**(1/2), 1/2]: # we use 3 different scales + if s == 1: + inp = samples.clone() + else: + inp = nn.functional.interpolate(samples, scale_factor=s, mode='bilinear', align_corners=False) + feats = model(inp).clone() + if v is None: + v = feats + else: + v += feats + v /= 3 + v /= v.norm() + return v + + + +def unnormalize_images(images): + mean = [0.485, 0.456, 0.406] + std = [0.229, 0.224, 0.225] + mean = torch.reshape(torch.tensor(mean), (1, 3, 1, 1)) + std = torch.reshape(torch.tensor(std), (1, 3, 1, 1)) + unnormalized_images = images.clone().detach().cpu() * std + mean + return unnormalized_images + +def padding_img(img, args): + # Padding the image with zeros to fit multiple of patch-size + size_im = ( + img.shape[0], + int(np.ceil(img.shape[1] / args.patch_size) * args.patch_size), + int(np.ceil(img.shape[2] / args.patch_size) * args.patch_size), + ) + paded = torch.zeros(size_im) + paded[:, : img.shape[1], : img.shape[2]] = img + img = paded + return img + + diff --git a/third_party/weakly_supvervised_detection/vision_transformer.py b/third_party/weakly_supvervised_detection/vision_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..583cdb4c781dbd44a6a52b94817c4902e9194fae --- /dev/null +++ b/third_party/weakly_supvervised_detection/vision_transformer.py @@ -0,0 +1,294 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Mostly copy-paste from timm library. +https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py +""" +import math +from functools import partial + +import torch +import torch.nn as nn + +from utils import trunc_normal_ + + +def drop_path(x, drop_prob: float = 0., training: bool = False): + if drop_prob == 0. or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x, attn + + +class Block(nn.Module): + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x, return_attention=False): + y, attn= self.attn(self.norm1(x)) + if return_attention: + return attn + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + num_patches = (img_size // patch_size) * (img_size // patch_size) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x): + B, C, H, W = x.shape + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class VisionTransformer(nn.Module): + """ Vision Transformer """ + def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., norm_layer=nn.LayerNorm, **kwargs): + super().__init__() + self.num_features = self.embed_dim = embed_dim + + self.patch_embed = PatchEmbed( + img_size=img_size[0], patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + self.num_heads = num_heads + # Classifier head + #self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def interpolate_pos_encoding(self, x, w, h): + npatch = x.shape[1] - 1 + N = self.pos_embed.shape[1] - 1 + if npatch == N and w == h: + return self.pos_embed + class_pos_embed = self.pos_embed[:, 0] + patch_pos_embed = self.pos_embed[:, 1:] + dim = x.shape[-1] + w0 = w // self.patch_embed.patch_size + h0 = h // self.patch_embed.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + w0, h0 = w0 + 0.1, h0 + 0.1 + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2), + scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), + mode='bicubic', + ) + assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) + + def prepare_tokens(self, x): + B, nc, w, h = x.shape + x = self.patch_embed(x) # patch linear embedding + + # add the [CLS] token to the embed patch tokens + cls_tokens = self.cls_token.expand(B, -1, -1) + x = torch.cat((cls_tokens, x), dim=1) + + # add positional encoding to each token + x = x + self.interpolate_pos_encoding(x, w, h) + + return self.pos_drop(x) + + def forward(self, x): + x = self.prepare_tokens(x) + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + return x[:, 0] + + def get_last_selfattention(self, x): + x = self.prepare_tokens(x) + for i, blk in enumerate(self.blocks): + if i < len(self.blocks) - 1: + x = blk(x) + else: + # return attention of the last block + return blk(x, return_attention=True) + + def get_intermediate_layers(self, x, n=1): + x = self.prepare_tokens(x) + B,N,C = x.shape + # we return the output tokens from the `n` last blocks + output = [] + for i, blk in enumerate(self.blocks): + x = blk(x) + if len(self.blocks) - i <= n: + output.append(self.norm(x)) + #mask, seeds = self.get_mask(x, qkv) + #return output, mask, seeds + return output, (B, self.num_heads, N, C// self.num_heads) + + +def vit_tiny(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def vit_small(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def vit_base(patch_size=16, **kwargs): + model = VisionTransformer( + patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, + qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +class DINOHead(nn.Module): + def __init__(self, in_dim, out_dim, use_bn=False, norm_last_layer=True, nlayers=3, hidden_dim=2048, bottleneck_dim=256): + super().__init__() + nlayers = max(nlayers, 1) + if nlayers == 1: + self.mlp = nn.Linear(in_dim, bottleneck_dim) + else: + layers = [nn.Linear(in_dim, hidden_dim)] + if use_bn: + layers.append(nn.BatchNorm1d(hidden_dim)) + layers.append(nn.GELU()) + for _ in range(nlayers - 2): + layers.append(nn.Linear(hidden_dim, hidden_dim)) + if use_bn: + layers.append(nn.BatchNorm1d(hidden_dim)) + layers.append(nn.GELU()) + layers.append(nn.Linear(hidden_dim, bottleneck_dim)) + self.mlp = nn.Sequential(*layers) + self.apply(self._init_weights) + self.last_layer = nn.utils.weight_norm(nn.Linear(bottleneck_dim, out_dim, bias=False)) + self.last_layer.weight_g.data.fill_(1) + if norm_last_layer: + self.last_layer.weight_g.requires_grad = False + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x): + x = self.mlp(x) + x = nn.functional.normalize(x, dim=-1, p=2) + x = self.last_layer(x) + return x diff --git a/third_party/weakly_supvervised_detection/visualization.py b/third_party/weakly_supvervised_detection/visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..ac2a498fa172db8409dcd1556582cdaac2c5c215 --- /dev/null +++ b/third_party/weakly_supvervised_detection/visualization.py @@ -0,0 +1,102 @@ +""" +Vis utilities. Code adapted from LOST: https://github.com/valeoai/LOST +""" +import cv2 +import numpy as np +from PIL import Image +import cv2 +import scipy +import numpy as np +import torch +import torch.nn as nn + +from utils import unnormalize_images +import matplotlib.pyplot as plt + +def visualize_img(image): + image = image.clone().detach().cpu().numpy() + #image = np.uint8(np.transpose(image, (1,2,0))) + print(f'image shape: {image.shape}') + image = np.uint8(image*256) + cv2.imwrite('./test/test_img.png', image) + +def visualize_fms(image, mask, seed, im_name, dim, scales, folder, save=True): + w_featmap, h_featmap = dim + image = image.clone().detach().cpu().numpy() + image = np.uint8(np.transpose(image, (1,2,0))*256) + mask = mask.reshape(w_featmap,h_featmap) + + mask = mask.reshape(w_featmap*h_featmap) + mask[seed] = 3 + mask = mask.reshape(w_featmap, h_featmap) + mask = scipy.ndimage.zoom(mask, scales, order=0, mode='nearest') + if save: + pltname1 = f"{folder}/LOST_{im_name}.png" + #Image.fromarray(image).save(pltname1) + cv2.imwrite(pltname1, image) + # print(f"Predictions saved at {pltname1}.") + pltname2 = f"{folder}/Mask_{im_name}.png" + plt.imsave(fname=pltname2, arr=mask) + # print(f"Predictions saved at {pltname2}.") + +def visualize_eigvec(eigvec, folder, im_name, dim, scales, save=True): + print(f'eigvec shape: {eigvec.shape}, scales: {scales}, dim: {dim}') + eigvec = scipy.ndimage.zoom(eigvec, scales, order=0, mode='nearest') + if save: + pltname= f"{folder}/{im_name}_ours_att.jpg" + plt.imsave(fname=pltname, arr=eigvec, cmap='cividis') + +def visualize_predictions_gt(image, pred, gt, im_name, seed, dim, scales, folder, output_name='ours_box', save=True): + """ + Visualization of the predicted box and the corresponding seed patch. + """ + image = unnormalize_images(image) + image = image[0].clone().detach().cpu().numpy() + image = np.transpose(image, (1,2,0)) + image = np.uint8(image*256) + image = np.ascontiguousarray(image, dtype=np.uint8) + + # Plot the box + cv2.rectangle( + image, + (int(pred[0]), int(pred[1])), + (int(pred[2]), int(pred[3])), + (255, 0, 0), 2 # RED + ) + # Plot the ground truth box + if len(gt>1): + for i in range(len(gt)): + cv2.rectangle( + image, + (int(gt[i][0]), int(gt[i][1])), + (int(gt[i][2]), int(gt[i][3])), + (0, 0, 255), 3, #BLUE + ) + + if save: + pltname = f"{folder}/{im_name}_{output_name}.jpg" + Image.fromarray(image).save(pltname) + # print(f"Predictions saved at {pltname}.") + return image + +def visualize_attn_LOST(A, dims, scales, folder, im_name): + """ + Visualization of the maps presented in Figure 2 of the paper. + """ + w_featmap, h_featmap = dims + + # Binarized similarity + binA = A.copy() + binA[binA < 0] = 0 + binA[binA > 0] = 1 + + # Save inverse degree + im_deg = ( + nn.functional.interpolate( + torch.from_numpy(1 / binA.sum(-1)).reshape(1, 1, w_featmap, h_featmap), + scale_factor=scales, + mode="nearest", + )[0][0].cpu().numpy() + ) + plt.imsave(fname=f"{folder}/{im_name}_lost_attn.jpg", arr=im_deg, cmap='cividis') + diff --git a/videocutler/INSTALL.md b/videocutler/INSTALL.md new file mode 100644 index 0000000000000000000000000000000000000000..0b41ae57faadc0612ed6802865714ac2e523e9dd --- /dev/null +++ b/videocutler/INSTALL.md @@ -0,0 +1,49 @@ +## Installation + +### Requirements +- Linux or macOS with Python ≥ 3.6 +- PyTorch ≥ 1.9 and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation. + Install them together at [pytorch.org](https://pytorch.org) to make sure of this. Note, please check + PyTorch version matches that is required by Detectron2. +- Detectron2: follow [Detectron2 installation instructions](https://detectron2.readthedocs.io/tutorials/install.html). +- OpenCV is optional but needed by demo and visualization +- `pip install -r requirements.txt` + +### Example conda environment setup + +```bash +conda create --name videocuter python=3.8 -y +conda activate videocuter +conda install pytorch==1.9.0 torchvision==0.10.0 cudatoolkit=11.1 -c pytorch -c nvidia +pip install -U opencv-python + +# under your working directory +git clone git@github.com:facebookresearch/detectron2.git +cd detectron2 +pip install -e . +pip install git+https://github.com/cocodataset/panopticapi.git +pip install git+https://github.com/mcordts/cityscapesScripts.git +``` + +### CUDA kernel for MSDeformAttn +After preparing the required environment, run the following command to compile CUDA kernel for MSDeformAttn: + +`CUDA_HOME` must be defined and points to the directory of the installed CUDA toolkit. +```bash +pip install -r videocutler/requirements.txt +cd videocutler/mask2former/modeling/pixel_decoder/ops +sh make.sh +``` + +#### Building on another system +To build on a system that does not have a GPU device but provide the drivers: +```bash +TORCH_CUDA_ARCH_LIST='8.0' FORCE_CUDA=1 python setup.py build install +``` +After preparing the required environment, run the following command to compile CUDA kernel for MSDeformAttn: + +`CUDA_HOME` must be defined and points to the directory of the installed CUDA toolkit. +```bash +cd videocutler/mask2former/modeling/pixel_decoder/ops +sh make.sh +``` diff --git a/videocutler/README.md b/videocutler/README.md new file mode 100644 index 0000000000000000000000000000000000000000..69b36fe216e086bbe3a3a44a55857bd3d9b5485f --- /dev/null +++ b/videocutler/README.md @@ -0,0 +1,119 @@ +# VideoCutLER: Unsupervised Video Instance Segmentation + +VideoCutLER is a simple unsupervised video instance segmentation (UVIS) method. ***We demonstrate that video instance segmentation models can be learned without using any human annotations, without relying on natural videos (ImageNet data alone is sufficient), and even without motion estimations!*** + +

+ +

+ +> [**VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation**](https://people.eecs.berkeley.edu/~xdwang/projects/VideoCutLER/videocutler.pdf) +> [Xudong Wang](https://people.eecs.berkeley.edu/~xdwang/), [Ishan Misra](https://imisra.github.io/), Ziyun Zeng, [Rohit Girdhar](https://rohitgirdhar.github.io/), [Trevor Darrell](https://people.eecs.berkeley.edu/~trevor/) +> UC Berkeley; FAIR, Meta AI +> CVPR 2024 + +[[`arxiv`](https://arxiv.org/abs/2308.14710)] [[`PDF`](https://people.eecs.berkeley.edu/~xdwang/projects/VideoCutLER/videocutler.pdf)] [[`bibtex`](#citation)] + + +## Installation +See [installation instructions](INSTALL.md). + + +## Dataset Preparation +See [Preparing Datasets for VideoCutLER](datasets/README.md). + + +## Method Overview +

+ +

+VideoCutLER has three main stages: +1) Firstly, we generate pseudo-masks for multiple objects in an image using MaskCut. +2) Then, we convert a random pair of images in the minibatch into a video with corresponding pseudo mask trajectories using ImageCut2Video. +3) Finally, we train an unsupervised video instance segmentation model using these mask trajectories. + + +## Inference Demo for VideoCutLER with Pre-trained Models + +We provide `demo_video/demo.py` that is able to demo builtin configs. Run it with: +``` +cd videocutler +python demo_video/demo.py \ + --config-file configs/imagenet_video/video_mask2former_R50_cls_agnostic.yaml \ + --input docs/demo-videos/99c6b1acf2/*.jpg \ + --confidence-threshold 0.8 \ + --output demos/ \ + --opts MODEL.WEIGHTS videocutler_m2f_rn50.pth +``` +Our trained VideoCutLER model on synthetic videos using ImageNet-1K can be obtained from [here](https://drive.google.com/file/d/11TACB8tOaAc-eXBo_i2arR_7qgGSeXRg/view?usp=drive_link). Then you should specify `MODEL.WEIGHTS` to the model checkpoint for evaluation. +Above command will run the inference and show visualizations in an OpenCV window, and save the results in the mp4 format. +For details of the command line arguments, see `demo.py -h` or look at its source code to understand its behavior. Some common arguments are: + +* To get a higher recall, use a smaller `--confidence-threshold`. +* To save each frame's segmentation result, add `--save-frames True` before `--opts`. +* To save each frame's segmentation masks, add `--save-masks True` before `--opts`. + +Following, we give some visualizations of the model predictions on the demo videos. +

+ +

+ + +### Unsupervised Model Learning +We provide a script `train_net_video.py`, that is made to train all the configs provided in VideoCutLER. +To train a model with "train_net_video.py", first setup the ImageNet-1K dataset following [datasets/README.md](../datasets/README.md). + +Before training the detector, it is necessary to use MaskCut to generate pseudo-masks for all ImageNet data. +You can either use the pre-generated json file directly by downloading it from [here]() and placing it under "DETECTRON2_DATASETS/imagenet/annotations/", or generate your own pseudo-masks by following the instructions in [MaskCut](#1-maskcut). +You should download the pre-trained CutLER model from this [link](https://drive.google.com/file/d/1YFP14mCHBGR3SbepGiTv-OMeUIrtTPc3/view?usp=sharing) and then place it in the "videocutler/pretrain" directory, then run: +``` +cd videocutler +export DETECTRON2_DATASETS=/path/to/DETECTRON2_DATASETS/ +python train_net_video.py \ + --config-file configs/imagenet_video/video_mask2former_R50_cls_agnostic.yaml \ + SOLVER.BASE_LR 0.000005 SOLVER.IMS_PER_BATCH 16 MODEL.MASK_FORMER.DROPOUT 0.3 \ + OUTPUT_DIR OUTPUT-DIR/ \ +``` +For more options, see `python train_net_video.py -h`. + +If you want to train a model using multiple nodes, you may need to adjust [some model parameters](https://arxiv.org/abs/1706.02677) and some SBATCH command options in "train-1node.sh" and "single-node-video_run.sh", then run: +``` +cd videocutler +export DETECTRON2_DATASETS=/path/to/DETECTRON2_DATASETS/ +sbatch train-1node.sh \ + --config-file configs/imagenet_video/video_mask2former_R50_cls_agnostic.yaml \ + SOLVER.BASE_LR 0.000005 SOLVER.IMS_PER_BATCH 16 MODEL.MASK_FORMER.DROPOUT 0.3 \ + OUTPUT_DIR OUTPUT-DIR/ \ +``` + + +### Unsupervised Zero-shot Evaluation +To evaluate a model's performance on YouTubeVIS-2019 and YouTubeVIS-2021, please refer to [datasets/README.md](datasets/README.md) for instructions on preparing the datasets. Next, download the [model weights](https://drive.google.com/file/d/11TACB8tOaAc-eXBo_i2arR_7qgGSeXRg/view?usp=drive_link), specify the "model_weights", "config_file" and the path to "DETECTRON2_DATASETS", then run the following commands. +``` +export DETECTRON2_DATASETS=/PATH/TO/DETECTRON2_DATASETS/ +CUDA_VISIBLE_DEVICES=0,1,2,3 python train_net_video.py --num-gpus 4 \ + --config-file configs/imagenet_video/videocutler_eval_ytvis2019.yaml \ + --eval-only MODEL.WEIGHTS videocutler_m2f_rn50.pth \ + OUTPUT_DIR OUTPUT-DIR/ytvis_2019 + +python eval_ytvis.py --dataset-path ${DETECTRON2_DATASETS} --dataset-name 'ytvis_2019' --result-path 'OUTPUT-DIR/ytvis_2019/' +``` + + +## Ethical Considerations +VideoCutLER's wide range of video instance segmentation capabilities may introduce similar challenges to many other visual recognition methods. As the video can contain arbitrary instances, it may impact the model output. + + +## How to get support from us? +If you have any general questions, feel free to email us at [Xudong Wang](mailto:xdwang@eecs.berkeley.edu). If you have code or implementation-related questions, please feel free to send emails to us or open an issue in this codebase (We recommend that you open an issue in this codebase, because your questions may help others). + + +## Citation +If you find our work inspiring or use our codebase in your research, please consider giving a star ⭐ and a citation. +``` +@article{wang2023videocutler, + title={VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation}, + author={Wang, Xudong and Misra, Ishan and Zeng, Ziyun and Girdhar, Rohit and Darrell, Trevor}, + journal={arXiv preprint arXiv:2308.14710}, + year={2023} +} +``` diff --git a/videocutler/configs/imagenet/instance-segmentation/Base-COCO-InstanceSegmentation.yaml b/videocutler/configs/imagenet/instance-segmentation/Base-COCO-InstanceSegmentation.yaml new file mode 100644 index 0000000000000000000000000000000000000000..98943d9cca85e7445e8fe4c8725e7749a3b0422e --- /dev/null +++ b/videocutler/configs/imagenet/instance-segmentation/Base-COCO-InstanceSegmentation.yaml @@ -0,0 +1,47 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + STEPS: (327778, 355092) + MAX_ITER: 368750 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 10 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + IMAGE_SIZE: 1024 + MIN_SCALE: 0.1 + MAX_SCALE: 2.0 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "coco_instance_lsj" +TEST: + EVAL_PERIOD: 5000 +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 4 +VERSION: 2 diff --git a/videocutler/configs/imagenet/instance-segmentation/Base-imagenet-InstanceSegmentation.yaml b/videocutler/configs/imagenet/instance-segmentation/Base-imagenet-InstanceSegmentation.yaml new file mode 100644 index 0000000000000000000000000000000000000000..32824e57503e566d5e8e3749cfedc0797e848e42 --- /dev/null +++ b/videocutler/configs/imagenet/instance-segmentation/Base-imagenet-InstanceSegmentation.yaml @@ -0,0 +1,50 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("imagenet_train_tau0.15_fixsize480_w_painting3Inst_crf_centerprior_polygon",) + TEST: ("imagenet_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + STEPS: (80000,) + MAX_ITER: 160000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 10 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + IMAGE_SIZE: 1024 + MIN_SCALE: 0.1 + MAX_SCALE: 2.0 + # MASK_FORMAT: "bitmask" + FORMAT: "RGB" + DATASET_MAPPER_NAME: "coco_instance_lsj" +TEST: + PRECISE_BN: + ENABLED: True + EVAL_PERIOD: 5000 +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 4 +VERSION: 2 diff --git a/videocutler/configs/imagenet/instance-segmentation/mask2former_R50_imagenet.yaml b/videocutler/configs/imagenet/instance-segmentation/mask2former_R50_imagenet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..19412ba7dd367a5ce387549548ccf9fd440696fc --- /dev/null +++ b/videocutler/configs/imagenet/instance-segmentation/mask2former_R50_imagenet.yaml @@ -0,0 +1,87 @@ +_BASE_: Base-imagenet-InstanceSegmentation.yaml +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 8 + COPY_PASTE: True + COPY_PASTE_RATE: 1.0 + VISUALIZE_COPY_PASTE: False + COPY_PASTE_RANDOM_NUM: True + COPY_PASTE_MIN_RATIO: 0.3 + COPY_PASTE_MAX_RATIO: 1.0 +MODEL: + META_ARCHITECTURE: "MaskFormer" + SEM_SEG_HEAD: + NAME: "MaskFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 1 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + WEIGHTS: 'http://dl.fbaipublicfiles.com/cutler/checkpoints/dino_RN50_pretrain_d2_format.pkl' + MASK_FORMER: + POSITIVE_BANK_IOU_THRESH: 0.0 + TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 100 + NHEADS: 8 + DROPOUT: 0.3 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEST: + SEMANTIC_ON: False + INSTANCE_ON: True + PANOPTIC_ON: False + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 +DATASETS: + TRAIN: ("imagenet_train",) +INPUT: + IMAGE_SIZE: 896 + MIN_SCALE: 0.1 + MAX_SCALE: 2.0 + # MASK_FORMAT: "bitmask" + FORMAT: "RGB" + DATASET_MAPPER_NAME: "coco_instance_lsj" +TEST: + PRECISE_BN: + ENABLED: True + EVAL_PERIOD: 10000 + DETECTIONS_PER_IMAGE: 100 # Test MS-COCO: 100; Test LVIS: 300 +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.00002 + STEPS: (80000,) + MAX_ITER: 160000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 10 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +OUTPUT_DIR: "OUTPUT" \ No newline at end of file diff --git a/videocutler/configs/imagenet_video/Base-YouTubeVIS-VideoInstanceSegmentation.yaml b/videocutler/configs/imagenet_video/Base-YouTubeVIS-VideoInstanceSegmentation.yaml new file mode 100755 index 0000000000000000000000000000000000000000..76426ecb5236707ed71266d1b09908985d3f76f6 --- /dev/null +++ b/videocutler/configs/imagenet_video/Base-YouTubeVIS-VideoInstanceSegmentation.yaml @@ -0,0 +1,53 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + MASK_ON: True + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("ytvis_2019_train",) + TEST: ("ytvis_2019_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + STEPS: (4000,) + MAX_ITER: 6000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 10 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN_SAMPLING: "choice_by_clip" + RANDOM_FLIP: "flip_by_clip" + AUGMENTATIONS: [] + MIN_SIZE_TRAIN: (360, 480) + MIN_SIZE_TEST: 360 + CROP: + ENABLED: False + TYPE: "absolute_range" + SIZE: (600, 720) + FORMAT: "RGB" +TEST: + EVAL_PERIOD: 0 +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: False + NUM_WORKERS: 4 +VERSION: 2 diff --git a/videocutler/configs/imagenet_video/video_mask2former_R50_cls_agnostic.yaml b/videocutler/configs/imagenet_video/video_mask2former_R50_cls_agnostic.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3dd82ded4d844c0945888b0a2e04bcfc68f4b441 --- /dev/null +++ b/videocutler/configs/imagenet_video/video_mask2former_R50_cls_agnostic.yaml @@ -0,0 +1,86 @@ +_BASE_: Base-YouTubeVIS-VideoInstanceSegmentation.yaml +MODEL: + WEIGHTS: "pretrain/cutler_m2f_rn50.pth" + META_ARCHITECTURE: "VideoMaskFormer" + SEM_SEG_HEAD: + NAME: "MaskFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 1 # class-agnostic + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + MASK_FORMER: + TRANSFORMER_DECODER_NAME: "VideoMultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 100 + NHEADS: 8 + DROPOUT: 0.3 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEST: + SEMANTIC_ON: False + INSTANCE_ON: True + PANOPTIC_ON: False + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 +DATASETS: + TRAIN: ("imagenet_video_train_cls_agnostic",) + TEST: ("ytvis_2019_train",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.00002 + STEPS: (79999,) + MAX_ITER: 80000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 10 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: False + NUM_WORKERS: 0 + COPY_PASTE: True + COPY_PASTE_RATE: 1.0 + VISUALIZE_COPY_PASTE: False + COPY_PASTE_RANDOM_NUM: False + COPY_PASTE_MIN_RATIO: 0.8 + COPY_PASTE_MAX_RATIO: 1.0 +INPUT: + SAMPLING_FRAME_NUM: 3 + MIN_SIZE_TRAIN_SAMPLING: "choice_by_clip" + RANDOM_FLIP: "flip_by_clip" + AUGMENTATIONS: ['brightness', 'contrast', 'rotation'] + MIN_SIZE_TRAIN: (360, 480) + MIN_SIZE_TEST: 360 + CROP: + ENABLED: True + TYPE: "absolute_range" + SIZE: (600, 720) +OUTPUT_DIR: "OUTPUT/" \ No newline at end of file diff --git a/videocutler/configs/imagenet_video/videocutler_eval_ytvis2019.yaml b/videocutler/configs/imagenet_video/videocutler_eval_ytvis2019.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a9fbf60738702b5eaee9184f3d773527956ce499 --- /dev/null +++ b/videocutler/configs/imagenet_video/videocutler_eval_ytvis2019.yaml @@ -0,0 +1,3 @@ +_BASE_: video_mask2former_R50_cls_agnostic.yaml +DATASETS: + TEST: ("ytvis_2019_train",) \ No newline at end of file diff --git a/videocutler/configs/imagenet_video/videocutler_eval_ytvis2021.yaml b/videocutler/configs/imagenet_video/videocutler_eval_ytvis2021.yaml new file mode 100644 index 0000000000000000000000000000000000000000..00b5f5203b1f2c0ed5016371a96c2fde1e8701dd --- /dev/null +++ b/videocutler/configs/imagenet_video/videocutler_eval_ytvis2021.yaml @@ -0,0 +1,3 @@ +_BASE_: video_mask2former_R50_cls_agnostic.yaml +DATASETS: + TEST: ("ytvis_2021_train",) \ No newline at end of file diff --git a/videocutler/datasets/README.md b/videocutler/datasets/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0c6e5d901370171330612002a262a8ff4f255911 --- /dev/null +++ b/videocutler/datasets/README.md @@ -0,0 +1,46 @@ +# Prepare Datasets for VideoCutLER + +A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog) +for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc). +This document explains how to setup the builtin datasets so they can be used by the above APIs. +[Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`, +and how to add new datasets to them. + +VideoCutLER has builtin support for a few datasets. +The datasets are assumed to exist in a directory specified by the environment variable +`DETECTRON2_DATASETS`. +Under this directory, detectron2 will look for datasets in the structure described below, if needed. +``` +$DETECTRON2_DATASETS/ + imagenet/ + ytvis_2019/ + ytvis_2021/ +``` + +You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`. +If left unset, the default is `./datasets` relative to your current working directory. + +Please check expected dataset structure for ImageNet-1K at [here](../../datasets/README.md). You can directly [download](https://drive.google.com/file/d/1gllHvrZQNVXphnk-IQxMcXh87Qs86ofT/view?usp=sharing) the pre-processed ImageNet-1K annotations produced by MaskCut in YouTubeVIS format and place it under the "imagenet/annotations/" directory. + +Alternatively, you can refer to the instructions on generating pseudo-masks using MaskCut at [here](../../README.md#generating-annotations-for-imagenet-1k-with-maskcut). You'll need to convert these annotations into the [YouTubeVIS](https://competitions.codalab.org/competitions/20128) format (MaskCut provides MSCOCO format annotations). This format conversion is a necessary step to ensure compatibility with the training process of VideoCutLER. + + +## Expected dataset structure for [YouTubeVIS 2019](https://competitions.codalab.org/competitions/20128): + +``` +ytvis_2019/ + {train,valid,test}.json + {train,valid,test}/ + Annotations/ + JPEGImages/ +``` + +## Expected dataset structure for [YouTubeVIS 2021](https://competitions.codalab.org/competitions/28988): + +``` +ytvis_2021/ + {train,valid,test}.json + {train,valid,test}/ + Annotations/ + JPEGImages/ +``` diff --git a/videocutler/datasets/ade20k_instance_catid_mapping.txt b/videocutler/datasets/ade20k_instance_catid_mapping.txt new file mode 100644 index 0000000000000000000000000000000000000000..2b709cbb9dd36bd040ace3360094cb7f4cff32b7 --- /dev/null +++ b/videocutler/datasets/ade20k_instance_catid_mapping.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:183cc9f84212753d5f251cfb2242599acc895d332ad806cd181a827d2691c3cd +size 1344 diff --git a/videocutler/datasets/ade20k_instance_imgCatIds.json b/videocutler/datasets/ade20k_instance_imgCatIds.json new file mode 100644 index 0000000000000000000000000000000000000000..d142da440f6987d7be6d28a436f4322ef0d51ae5 --- /dev/null +++ b/videocutler/datasets/ade20k_instance_imgCatIds.json @@ -0,0 +1 @@ +{"images": [{"file_name": "ADE_train_00000001.jpg", "width": 683, "id": 0, "height": 512}, {"file_name": "ADE_train_00000002.jpg", "width": 711, "id": 1, "height": 512}, {"file_name": "ADE_train_00000003.jpg", "width": 683, "id": 2, "height": 512}, {"file_name": "ADE_train_00000004.jpg", "width": 384, "id": 3, "height": 251}, {"file_name": "ADE_train_00000005.jpg", "width": 683, "id": 4, "height": 512}, {"file_name": "ADE_train_00000006.jpg", "width": 512, "id": 5, "height": 973}, {"file_name": "ADE_train_00000007.jpg", "width": 512, "id": 6, "height": 735}, {"file_name": "ADE_train_00000008.jpg", "width": 538, "id": 7, "height": 512}, {"file_name": "ADE_train_00000009.jpg", "width": 683, "id": 8, "height": 512}, {"file_name": "ADE_train_00000010.jpg", "width": 512, "id": 9, "height": 764}, {"file_name": "ADE_train_00000011.jpg", "width": 356, "id": 10, "height": 247}, {"file_name": "ADE_train_00000012.jpg", "width": 512, "id": 11, "height": 683}, {"file_name": "ADE_train_00000013.jpg", "width": 683, "id": 12, "height": 512}, {"file_name": "ADE_train_00000014.jpg", "width": 683, "id": 13, "height": 512}, {"file_name": "ADE_train_00000015.jpg", "width": 637, "id": 14, "height": 512}, {"file_name": "ADE_train_00000016.jpg", "width": 683, "id": 15, "height": 512}, {"file_name": "ADE_train_00000017.jpg", "width": 512, "id": 16, "height": 683}, {"file_name": "ADE_train_00000018.jpg", "width": 770, "id": 17, "height": 512}, {"file_name": "ADE_train_00000019.jpg", "width": 640, "id": 18, "height": 431}, {"file_name": "ADE_train_00000020.jpg", "width": 512, "id": 19, "height": 517}, {"file_name": "ADE_train_00000021.jpg", "width": 233, "id": 20, "height": 311}, 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"name": "traffic light"}, {"id": 91, "name": "tray"}, {"id": 92, "name": "ashcan"}, {"id": 93, "name": "fan"}, {"id": 94, "name": "plate"}, {"id": 95, "name": "monitor"}, {"id": 96, "name": "bulletin board"}, {"id": 97, "name": "radiator"}, {"id": 98, "name": "glass"}, {"id": 99, "name": "clock"}, {"id": 100, "name": "flag"}]} \ No newline at end of file diff --git a/videocutler/datasets/prepare_ade20k_ins_seg.py b/videocutler/datasets/prepare_ade20k_ins_seg.py new file mode 100644 index 0000000000000000000000000000000000000000..e4e951adcd84dbd08b3d6570aee56887bf1c69a6 --- /dev/null +++ b/videocutler/datasets/prepare_ade20k_ins_seg.py @@ -0,0 +1,112 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +import glob +import json +import os +from collections import Counter + +import numpy as np +import tqdm +from panopticapi.utils import IdGenerator, save_json +from PIL import Image +import pycocotools.mask as mask_util + + +if __name__ == "__main__": + dataset_dir = os.getenv("DETECTRON2_DATASETS", "datasets") + + for name, dirname in [("train", "training"), ("val", "validation")]: + image_dir = os.path.join(dataset_dir, f"ADEChallengeData2016/images/{dirname}/") + instance_dir = os.path.join( + dataset_dir, f"ADEChallengeData2016/annotations_instance/{dirname}/" + ) + + # img_id = 0 + ann_id = 1 + + # json + out_file = os.path.join(dataset_dir, f"ADEChallengeData2016/ade20k_instance_{name}.json") + + # json config + instance_config_file = "datasets/ade20k_instance_imgCatIds.json" + with open(instance_config_file) as f: + category_dict = json.load(f)["categories"] + + # load catid mapping + # it is important to share category id for both instance and panoptic annotations + mapping_file = "datasets/ade20k_instance_catid_mapping.txt" + with open(mapping_file) as f: + map_id = {} + for i, line in enumerate(f.readlines()): + if i == 0: + continue + ins_id, sem_id, _ = line.strip().split() + # shift id by 1 because we want it to start from 0! + # ignore_label becomes 255 + map_id[int(ins_id)] = int(sem_id) - 1 + + for cat in category_dict: + cat["id"] = map_id[cat["id"]] + + filenames = sorted(glob.glob(os.path.join(image_dir, "*.jpg"))) + + ann_dict = {} + images = [] + annotations = [] + + for idx, filename in enumerate(tqdm.tqdm(filenames)): + image = {} + image_id = os.path.basename(filename).split(".")[0] + + image["id"] = image_id + image["file_name"] = os.path.basename(filename) + + original_format = np.array(Image.open(filename)) + image["width"] = original_format.shape[1] + image["height"] = original_format.shape[0] + + images.append(image) + + filename_instance = os.path.join(instance_dir, image_id + ".png") + ins_seg = np.asarray(Image.open(filename_instance)) + assert ins_seg.dtype == np.uint8 + + instance_cat_ids = ins_seg[..., 0] + # instance id starts from 1! + # because 0 is reserved as VOID label + instance_ins_ids = ins_seg[..., 1] + + # process things + for thing_id in np.unique(instance_ins_ids): + if thing_id == 0: + continue + mask = instance_ins_ids == thing_id + instance_cat_id = np.unique(instance_cat_ids[mask]) + assert len(instance_cat_id) == 1 + + anno = {} + anno['id'] = ann_id + ann_id += 1 + anno['image_id'] = image['id'] + anno["iscrowd"] = int(0) + anno["category_id"] = int(map_id[instance_cat_id[0]]) + + inds = np.nonzero(mask) + ymin, ymax = inds[0].min(), inds[0].max() + xmin, xmax = inds[1].min(), inds[1].max() + anno["bbox"] = [int(xmin), int(ymin), int(xmax - xmin + 1), int(ymax - ymin + 1)] + # if xmax <= xmin or ymax <= ymin: + # continue + rle = mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0] + rle["counts"] = rle["counts"].decode("utf-8") + anno["segmentation"] = rle + anno["area"] = int(mask_util.area(rle)) + annotations.append(anno) + + # save this + ann_dict['images'] = images + ann_dict['categories'] = category_dict + ann_dict['annotations'] = annotations + + save_json(ann_dict, out_file) diff --git a/videocutler/datasets/prepare_ade20k_pan_seg.py b/videocutler/datasets/prepare_ade20k_pan_seg.py new file mode 100644 index 0000000000000000000000000000000000000000..8b1b03281dd4307c6dad2dc7e2505aacad3e72a3 --- /dev/null +++ b/videocutler/datasets/prepare_ade20k_pan_seg.py @@ -0,0 +1,500 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +import glob +import json +import os +from collections import Counter + +import numpy as np +import tqdm +from panopticapi.utils import IdGenerator, save_json +from PIL import Image + +ADE20K_SEM_SEG_CATEGORIES = [ + "wall", + "building", + "sky", + "floor", + "tree", + "ceiling", + "road, route", + "bed", + "window ", + "grass", + "cabinet", + "sidewalk, pavement", + "person", + "earth, ground", + "door", + "table", + "mountain, mount", + "plant", + "curtain", + "chair", + "car", + "water", + "painting, picture", + "sofa", + "shelf", + "house", + "sea", + "mirror", + "rug", + "field", + "armchair", + "seat", + "fence", + "desk", + "rock, stone", + "wardrobe, closet, press", + "lamp", + "tub", + "rail", + "cushion", + "base, pedestal, stand", + "box", + "column, pillar", + "signboard, sign", + "chest of drawers, chest, bureau, dresser", + "counter", + "sand", + "sink", + "skyscraper", + "fireplace", + "refrigerator, icebox", + "grandstand, covered stand", + "path", + "stairs", + "runway", + "case, display case, showcase, vitrine", + "pool table, billiard table, snooker table", + "pillow", + "screen door, screen", + "stairway, staircase", + "river", + "bridge, span", + "bookcase", + "blind, screen", + "coffee table", + "toilet, can, commode, crapper, pot, potty, stool, throne", + "flower", + "book", + "hill", + "bench", + "countertop", + "stove", + "palm, palm tree", + "kitchen island", + "computer", + "swivel chair", + "boat", + "bar", + "arcade machine", + "hovel, hut, hutch, shack, shanty", + "bus", + "towel", + "light", + "truck", + "tower", + "chandelier", + "awning, sunshade, sunblind", + "street lamp", + "booth", + "tv", + "plane", + "dirt track", + "clothes", + "pole", + "land, ground, soil", + "bannister, banister, balustrade, balusters, handrail", + "escalator, moving staircase, moving stairway", + "ottoman, pouf, pouffe, puff, hassock", + "bottle", + "buffet, counter, sideboard", + "poster, posting, placard, notice, bill, card", + "stage", + "van", + "ship", + "fountain", + "conveyer belt, conveyor belt, conveyer, conveyor, transporter", + "canopy", + "washer, automatic washer, washing machine", + "plaything, toy", + "pool", + "stool", + "barrel, cask", + "basket, handbasket", + "falls", + "tent", + "bag", + "minibike, motorbike", + "cradle", + "oven", + "ball", + "food, solid food", + "step, stair", + "tank, storage tank", + "trade name", + "microwave", + "pot", + "animal", + "bicycle", + "lake", + "dishwasher", + "screen", + "blanket, cover", + "sculpture", + "hood, exhaust hood", + "sconce", + "vase", + "traffic light", + "tray", + "trash can", + "fan", + "pier", + "crt screen", + "plate", + "monitor", + "bulletin board", + "shower", + "radiator", + "glass, drinking glass", + "clock", + "flag", # noqa +] + +PALETTE = [ + [120, 120, 120], + [180, 120, 120], + [6, 230, 230], + [80, 50, 50], + [4, 200, 3], + [120, 120, 80], + [140, 140, 140], + [204, 5, 255], + [230, 230, 230], + [4, 250, 7], + [224, 5, 255], + [235, 255, 7], + [150, 5, 61], + [120, 120, 70], + [8, 255, 51], + [255, 6, 82], + [143, 255, 140], + [204, 255, 4], + [255, 51, 7], + [204, 70, 3], + [0, 102, 200], + [61, 230, 250], + [255, 6, 51], + [11, 102, 255], + [255, 7, 71], + [255, 9, 224], + [9, 7, 230], + [220, 220, 220], + [255, 9, 92], + [112, 9, 255], + [8, 255, 214], + [7, 255, 224], + [255, 184, 6], + [10, 255, 71], + [255, 41, 10], + [7, 255, 255], + [224, 255, 8], + [102, 8, 255], + [255, 61, 6], + [255, 194, 7], + [255, 122, 8], + [0, 255, 20], + [255, 8, 41], + [255, 5, 153], + [6, 51, 255], + [235, 12, 255], + [160, 150, 20], + [0, 163, 255], + [140, 140, 200], + [250, 10, 15], + [20, 255, 0], + [31, 255, 0], + [255, 31, 0], + [255, 224, 0], + [153, 255, 0], + [0, 0, 255], + [255, 71, 0], + [0, 235, 255], + [0, 173, 255], + [31, 0, 255], + [11, 200, 200], + [255, 82, 0], + [0, 255, 245], + [0, 61, 255], + [0, 255, 112], + [0, 255, 133], + [255, 0, 0], + [255, 163, 0], + [255, 102, 0], + [194, 255, 0], + [0, 143, 255], + [51, 255, 0], + [0, 82, 255], + [0, 255, 41], + [0, 255, 173], + [10, 0, 255], + [173, 255, 0], + [0, 255, 153], + [255, 92, 0], + [255, 0, 255], + [255, 0, 245], + [255, 0, 102], + [255, 173, 0], + [255, 0, 20], + [255, 184, 184], + [0, 31, 255], + [0, 255, 61], + [0, 71, 255], + [255, 0, 204], + [0, 255, 194], + [0, 255, 82], + [0, 10, 255], + [0, 112, 255], + [51, 0, 255], + [0, 194, 255], + [0, 122, 255], + [0, 255, 163], + [255, 153, 0], + [0, 255, 10], + [255, 112, 0], + [143, 255, 0], + [82, 0, 255], + [163, 255, 0], + [255, 235, 0], + [8, 184, 170], + [133, 0, 255], + [0, 255, 92], + [184, 0, 255], + [255, 0, 31], + [0, 184, 255], + [0, 214, 255], + [255, 0, 112], + [92, 255, 0], + [0, 224, 255], + [112, 224, 255], + [70, 184, 160], + [163, 0, 255], + [153, 0, 255], + [71, 255, 0], + [255, 0, 163], + [255, 204, 0], + [255, 0, 143], + [0, 255, 235], + [133, 255, 0], + [255, 0, 235], + [245, 0, 255], + [255, 0, 122], + [255, 245, 0], + [10, 190, 212], + [214, 255, 0], + [0, 204, 255], + [20, 0, 255], + [255, 255, 0], + [0, 153, 255], + [0, 41, 255], + [0, 255, 204], + [41, 0, 255], + [41, 255, 0], + [173, 0, 255], + [0, 245, 255], + [71, 0, 255], + [122, 0, 255], + [0, 255, 184], + [0, 92, 255], + [184, 255, 0], + [0, 133, 255], + [255, 214, 0], + [25, 194, 194], + [102, 255, 0], + [92, 0, 255], +] + + +if __name__ == "__main__": + dataset_dir = os.getenv("DETECTRON2_DATASETS", "datasets") + + for name, dirname in [("train", "training"), ("val", "validation")]: + image_dir = os.path.join(dataset_dir, f"ADEChallengeData2016/images/{dirname}/") + semantic_dir = os.path.join(dataset_dir, f"ADEChallengeData2016/annotations/{dirname}/") + instance_dir = os.path.join( + dataset_dir, f"ADEChallengeData2016/annotations_instance/{dirname}/" + ) + + # folder to store panoptic PNGs + out_folder = os.path.join(dataset_dir, f"ADEChallengeData2016/ade20k_panoptic_{name}/") + # json with segmentations information + out_file = os.path.join(dataset_dir, f"ADEChallengeData2016/ade20k_panoptic_{name}.json") + + if not os.path.isdir(out_folder): + print("Creating folder {} for panoptic segmentation PNGs".format(out_folder)) + os.mkdir(out_folder) + + # json config + config_file = "datasets/ade20k_instance_imgCatIds.json" + with open(config_file) as f: + config = json.load(f) + + # load catid mapping + mapping_file = "datasets/ade20k_instance_catid_mapping.txt" + with open(mapping_file) as f: + map_id = {} + for i, line in enumerate(f.readlines()): + if i == 0: + continue + ins_id, sem_id, _ = line.strip().split() + # shift id by 1 because we want it to start from 0! + # ignore_label becomes 255 + map_id[int(ins_id) - 1] = int(sem_id) - 1 + + ADE20K_150_CATEGORIES = [] + for cat_id, cat_name in enumerate(ADE20K_SEM_SEG_CATEGORIES): + ADE20K_150_CATEGORIES.append( + { + "name": cat_name, + "id": cat_id, + "isthing": int(cat_id in map_id.values()), + "color": PALETTE[cat_id], + } + ) + categories_dict = {cat["id"]: cat for cat in ADE20K_150_CATEGORIES} + + panoptic_json_categories = ADE20K_150_CATEGORIES[:] + panoptic_json_images = [] + panoptic_json_annotations = [] + + filenames = sorted(glob.glob(os.path.join(image_dir, "*.jpg"))) + for idx, filename in enumerate(tqdm.tqdm(filenames)): + panoptic_json_image = {} + panoptic_json_annotation = {} + + image_id = os.path.basename(filename).split(".")[0] + + panoptic_json_image["id"] = image_id + panoptic_json_image["file_name"] = os.path.basename(filename) + + original_format = np.array(Image.open(filename)) + panoptic_json_image["width"] = original_format.shape[1] + panoptic_json_image["height"] = original_format.shape[0] + + pan_seg = np.zeros( + (original_format.shape[0], original_format.shape[1], 3), dtype=np.uint8 + ) + id_generator = IdGenerator(categories_dict) + + filename_semantic = os.path.join(semantic_dir, image_id + ".png") + filename_instance = os.path.join(instance_dir, image_id + ".png") + + sem_seg = np.asarray(Image.open(filename_semantic)) + ins_seg = np.asarray(Image.open(filename_instance)) + + assert sem_seg.dtype == np.uint8 + assert ins_seg.dtype == np.uint8 + + semantic_cat_ids = sem_seg - 1 + instance_cat_ids = ins_seg[..., 0] - 1 + # instance id starts from 1! + # because 0 is reserved as VOID label + instance_ins_ids = ins_seg[..., 1] + + segm_info = [] + + # NOTE: there is some overlap between semantic and instance annotation + # thus we paste stuffs first + + # process stuffs + for semantic_cat_id in np.unique(semantic_cat_ids): + if semantic_cat_id == 255: + continue + if categories_dict[semantic_cat_id]["isthing"]: + continue + mask = semantic_cat_ids == semantic_cat_id + # should not have any overlap + assert pan_seg[mask].sum() == 0 + + segment_id, color = id_generator.get_id_and_color(semantic_cat_id) + pan_seg[mask] = color + + area = np.sum(mask) # segment area computation + # bbox computation for a segment + hor = np.sum(mask, axis=0) + hor_idx = np.nonzero(hor)[0] + x = hor_idx[0] + width = hor_idx[-1] - x + 1 + vert = np.sum(mask, axis=1) + vert_idx = np.nonzero(vert)[0] + y = vert_idx[0] + height = vert_idx[-1] - y + 1 + bbox = [int(x), int(y), int(width), int(height)] + + segm_info.append( + { + "id": int(segment_id), + "category_id": int(semantic_cat_id), + "area": int(area), + "bbox": bbox, + "iscrowd": 0, + } + ) + + # process things + for thing_id in np.unique(instance_ins_ids): + if thing_id == 0: + continue + mask = instance_ins_ids == thing_id + instance_cat_id = np.unique(instance_cat_ids[mask]) + assert len(instance_cat_id) == 1 + + semantic_cat_id = map_id[instance_cat_id[0]] + + segment_id, color = id_generator.get_id_and_color(semantic_cat_id) + pan_seg[mask] = color + + area = np.sum(mask) # segment area computation + # bbox computation for a segment + hor = np.sum(mask, axis=0) + hor_idx = np.nonzero(hor)[0] + x = hor_idx[0] + width = hor_idx[-1] - x + 1 + vert = np.sum(mask, axis=1) + vert_idx = np.nonzero(vert)[0] + y = vert_idx[0] + height = vert_idx[-1] - y + 1 + bbox = [int(x), int(y), int(width), int(height)] + + segm_info.append( + { + "id": int(segment_id), + "category_id": int(semantic_cat_id), + "area": int(area), + "bbox": bbox, + "iscrowd": 0, + } + ) + + panoptic_json_annotation = { + "image_id": image_id, + "file_name": image_id + ".png", + "segments_info": segm_info, + } + + Image.fromarray(pan_seg).save(os.path.join(out_folder, image_id + ".png")) + + panoptic_json_images.append(panoptic_json_image) + panoptic_json_annotations.append(panoptic_json_annotation) + + # save this + d = { + "images": panoptic_json_images, + "annotations": panoptic_json_annotations, + "categories": panoptic_json_categories, + } + + save_json(d, out_file) diff --git a/videocutler/datasets/prepare_ade20k_sem_seg.py b/videocutler/datasets/prepare_ade20k_sem_seg.py new file mode 100644 index 0000000000000000000000000000000000000000..b0edfeb340edaff45beb14b3f9438aef2c65e78f --- /dev/null +++ b/videocutler/datasets/prepare_ade20k_sem_seg.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +import os +from pathlib import Path + +import numpy as np +import tqdm +from PIL import Image + + +def convert(input, output): + img = np.asarray(Image.open(input)) + assert img.dtype == np.uint8 + img = img - 1 # 0 (ignore) becomes 255. others are shifted by 1 + Image.fromarray(img).save(output) + + +if __name__ == "__main__": + dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "ADEChallengeData2016" + for name in ["training", "validation"]: + annotation_dir = dataset_dir / "annotations" / name + output_dir = dataset_dir / "annotations_detectron2" / name + output_dir.mkdir(parents=True, exist_ok=True) + for file in tqdm.tqdm(list(annotation_dir.iterdir())): + output_file = output_dir / file.name + convert(file, output_file) diff --git a/videocutler/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py b/videocutler/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py new file mode 100644 index 0000000000000000000000000000000000000000..3090c9bc2f9a63156a4132e89c635613691eb350 --- /dev/null +++ b/videocutler/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import functools +import json +import multiprocessing as mp +import numpy as np +import os +import time +from fvcore.common.download import download +from panopticapi.utils import rgb2id +from PIL import Image + +from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES + + +def _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map): + panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32) + panoptic = rgb2id(panoptic) + output = np.zeros_like(panoptic, dtype=np.uint8) + 255 + for seg in segments: + cat_id = seg["category_id"] + new_cat_id = id_map[cat_id] + output[panoptic == seg["id"]] = new_cat_id + Image.fromarray(output).save(output_semantic) + + +def separate_coco_semantic_from_panoptic(panoptic_json, panoptic_root, sem_seg_root, categories): + """ + Create semantic segmentation annotations from panoptic segmentation + annotations, to be used by PanopticFPN. + It maps all thing categories to class 0, and maps all unlabeled pixels to class 255. + It maps all stuff categories to contiguous ids starting from 1. + Args: + panoptic_json (str): path to the panoptic json file, in COCO's format. + panoptic_root (str): a directory with panoptic annotation files, in COCO's format. + sem_seg_root (str): a directory to output semantic annotation files + categories (list[dict]): category metadata. Each dict needs to have: + "id": corresponds to the "category_id" in the json annotations + "isthing": 0 or 1 + """ + os.makedirs(sem_seg_root, exist_ok=True) + + id_map = {} # map from category id to id in the output semantic annotation + assert len(categories) <= 254 + for i, k in enumerate(categories): + id_map[k["id"]] = i + # what is id = 0? + # id_map[0] = 255 + print(id_map) + + with open(panoptic_json) as f: + obj = json.load(f) + + pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4)) + + def iter_annotations(): + for anno in obj["annotations"]: + file_name = anno["file_name"] + segments = anno["segments_info"] + input = os.path.join(panoptic_root, file_name) + output = os.path.join(sem_seg_root, file_name) + yield input, output, segments + + print("Start writing to {} ...".format(sem_seg_root)) + start = time.time() + pool.starmap( + functools.partial(_process_panoptic_to_semantic, id_map=id_map), + iter_annotations(), + chunksize=100, + ) + print("Finished. time: {:.2f}s".format(time.time() - start)) + + +if __name__ == "__main__": + dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "coco") + for s in ["val2017", "train2017"]: + separate_coco_semantic_from_panoptic( + os.path.join(dataset_dir, "annotations/panoptic_{}.json".format(s)), + os.path.join(dataset_dir, "panoptic_{}".format(s)), + os.path.join(dataset_dir, "panoptic_semseg_{}".format(s)), + COCO_CATEGORIES, + ) diff --git a/videocutler/demo.sh b/videocutler/demo.sh new file mode 100644 index 0000000000000000000000000000000000000000..baaa6862939b1111809d60ec663b24b72b90874d --- /dev/null +++ b/videocutler/demo.sh @@ -0,0 +1,8 @@ +python demo_video/demo.py \ + --config-file configs/imagenet_video/video_mask2former_R50_cls_agnostic.yaml \ + --input docs/demo-videos/99c6b1acf2/*.jpg \ + --confidence-threshold 0.8 \ + --output demos/ \ + # --save-frames True \ + # --save-masks True \ + --opts MODEL.WEIGHTS videocutler_m2f_rn50.pth \ No newline at end of file diff --git a/videocutler/demo/README.md b/videocutler/demo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b32a57e08d320114efd12c307f587d9db28dbcd8 --- /dev/null +++ b/videocutler/demo/README.md @@ -0,0 +1,5 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +## VideoCutLER Demo + +We provide a command line tool to run a simple demo of builtin configs. +The usage is explained in [GETTING_STARTED.md](../GETTING_STARTED.md). diff --git a/videocutler/demo/demo.py b/videocutler/demo/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..1b0b4d9db88555b99cec9ba6ef703a9ba9e7323d --- /dev/null +++ b/videocutler/demo/demo.py @@ -0,0 +1,194 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from: https://github.com/facebookresearch/detectron2/blob/master/demo/demo.py +import argparse +import glob +import multiprocessing as mp +import os + +# fmt: off +import sys +sys.path.insert(1, os.path.join(sys.path[0], '..')) +# fmt: on + +import tempfile +import time +import warnings + +import cv2 +import numpy as np +import tqdm + +from detectron2.config import get_cfg +from detectron2.data.detection_utils import read_image +from detectron2.projects.deeplab import add_deeplab_config +from detectron2.utils.logger import setup_logger + +from mask2former import add_maskformer2_config +from predictor import VisualizationDemo + + +# constants +WINDOW_NAME = "mask2former demo" + + +def setup_cfg(args): + # load config from file and command-line arguments + cfg = get_cfg() + add_deeplab_config(cfg) + add_maskformer2_config(cfg) + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + cfg.freeze() + return cfg + + +def get_parser(): + parser = argparse.ArgumentParser(description="maskformer2 demo for builtin configs") + parser.add_argument( + "--config-file", + default="configs/coco/panoptic-segmentation/maskformer2_R50_bs16_50ep.yaml", + metavar="FILE", + help="path to config file", + ) + parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.") + parser.add_argument("--video-input", help="Path to video file.") + parser.add_argument( + "--input", + nargs="+", + help="A list of space separated input images; " + "or a single glob pattern such as 'directory/*.jpg'", + ) + parser.add_argument( + "--output", + help="A file or directory to save output visualizations. " + "If not given, will show output in an OpenCV window.", + ) + + parser.add_argument( + "--confidence-threshold", + type=float, + default=0.5, + help="Minimum score for instance predictions to be shown", + ) + parser.add_argument( + "--opts", + help="Modify config options using the command-line 'KEY VALUE' pairs", + default=[], + nargs=argparse.REMAINDER, + ) + return parser + + +def test_opencv_video_format(codec, file_ext): + with tempfile.TemporaryDirectory(prefix="video_format_test") as dir: + filename = os.path.join(dir, "test_file" + file_ext) + writer = cv2.VideoWriter( + filename=filename, + fourcc=cv2.VideoWriter_fourcc(*codec), + fps=float(30), + frameSize=(10, 10), + isColor=True, + ) + [writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)] + writer.release() + if os.path.isfile(filename): + return True + return False + + +if __name__ == "__main__": + mp.set_start_method("spawn", force=True) + args = get_parser().parse_args() + setup_logger(name="fvcore") + logger = setup_logger() + logger.info("Arguments: " + str(args)) + + cfg = setup_cfg(args) + + demo = VisualizationDemo(cfg) + + if args.input: + if len(args.input) == 1: + args.input = glob.glob(os.path.expanduser(args.input[0])) + assert args.input, "The input path(s) was not found" + for path in tqdm.tqdm(args.input, disable=not args.output): + # use PIL, to be consistent with evaluation + img = read_image(path, format="BGR") + start_time = time.time() + predictions, visualized_output = demo.run_on_image(img) + logger.info( + "{}: {} in {:.2f}s".format( + path, + "detected {} instances".format(len(predictions["instances"])) + if "instances" in predictions + else "finished", + time.time() - start_time, + ) + ) + + if args.output: + if os.path.isdir(args.output): + assert os.path.isdir(args.output), args.output + out_filename = os.path.join(args.output, os.path.basename(path)) + else: + assert len(args.input) == 1, "Please specify a directory with args.output" + out_filename = args.output + visualized_output.save(out_filename) + else: + cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) + cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1]) + if cv2.waitKey(0) == 27: + break # esc to quit + elif args.webcam: + assert args.input is None, "Cannot have both --input and --webcam!" + assert args.output is None, "output not yet supported with --webcam!" + cam = cv2.VideoCapture(0) + for vis in tqdm.tqdm(demo.run_on_video(cam)): + cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) + cv2.imshow(WINDOW_NAME, vis) + if cv2.waitKey(1) == 27: + break # esc to quit + cam.release() + cv2.destroyAllWindows() + elif args.video_input: + video = cv2.VideoCapture(args.video_input) + width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) + frames_per_second = video.get(cv2.CAP_PROP_FPS) + num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) + basename = os.path.basename(args.video_input) + codec, file_ext = ( + ("x264", ".mkv") if test_opencv_video_format("x264", ".mkv") else ("mp4v", ".mp4") + ) + if codec == ".mp4v": + warnings.warn("x264 codec not available, switching to mp4v") + if args.output: + if os.path.isdir(args.output): + output_fname = os.path.join(args.output, basename) + output_fname = os.path.splitext(output_fname)[0] + file_ext + else: + output_fname = args.output + assert not os.path.isfile(output_fname), output_fname + output_file = cv2.VideoWriter( + filename=output_fname, + # some installation of opencv may not support x264 (due to its license), + # you can try other format (e.g. MPEG) + fourcc=cv2.VideoWriter_fourcc(*codec), + fps=float(frames_per_second), + frameSize=(width, height), + isColor=True, + ) + assert os.path.isfile(args.video_input) + for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames): + if args.output: + output_file.write(vis_frame) + else: + cv2.namedWindow(basename, cv2.WINDOW_NORMAL) + cv2.imshow(basename, vis_frame) + if cv2.waitKey(1) == 27: + break # esc to quit + video.release() + if args.output: + output_file.release() + else: + cv2.destroyAllWindows() diff --git a/videocutler/demo/predictor.py b/videocutler/demo/predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..189ec7976f4283b7f3116b6e15b3191fb8fe969f --- /dev/null +++ b/videocutler/demo/predictor.py @@ -0,0 +1,219 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Copied from: https://github.com/facebookresearch/detectron2/blob/master/demo/predictor.py +import atexit +import bisect +import multiprocessing as mp +from collections import deque + +import cv2 +import torch + +from detectron2.data import MetadataCatalog +from detectron2.engine.defaults import DefaultPredictor +from detectron2.utils.video_visualizer import VideoVisualizer +from detectron2.utils.visualizer import ColorMode, Visualizer + + +class VisualizationDemo(object): + def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False): + """ + Args: + cfg (CfgNode): + instance_mode (ColorMode): + parallel (bool): whether to run the model in different processes from visualization. + Useful since the visualization logic can be slow. + """ + self.metadata = MetadataCatalog.get( + cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else "__unused" + ) + self.cpu_device = torch.device("cpu") + self.instance_mode = instance_mode + + self.parallel = parallel + if parallel: + num_gpu = torch.cuda.device_count() + self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu) + else: + self.predictor = DefaultPredictor(cfg) + + def run_on_image(self, image): + """ + Args: + image (np.ndarray): an image of shape (H, W, C) (in BGR order). + This is the format used by OpenCV. + Returns: + predictions (dict): the output of the model. + vis_output (VisImage): the visualized image output. + """ + vis_output = None + predictions = self.predictor(image) + # Convert image from OpenCV BGR format to Matplotlib RGB format. + image = image[:, :, ::-1] + visualizer = Visualizer(image, self.metadata, instance_mode=self.instance_mode) + if "panoptic_seg" in predictions: + panoptic_seg, segments_info = predictions["panoptic_seg"] + vis_output = visualizer.draw_panoptic_seg_predictions( + panoptic_seg.to(self.cpu_device), segments_info + ) + else: + if "sem_seg" in predictions: + vis_output = visualizer.draw_sem_seg( + predictions["sem_seg"].argmax(dim=0).to(self.cpu_device) + ) + if "instances" in predictions: + instances = predictions["instances"].to(self.cpu_device) + vis_output = visualizer.draw_instance_predictions(predictions=instances) + + return predictions, vis_output + + def _frame_from_video(self, video): + while video.isOpened(): + success, frame = video.read() + if success: + yield frame + else: + break + + def run_on_video(self, video): + """ + Visualizes predictions on frames of the input video. + Args: + video (cv2.VideoCapture): a :class:`VideoCapture` object, whose source can be + either a webcam or a video file. + Yields: + ndarray: BGR visualizations of each video frame. + """ + video_visualizer = VideoVisualizer(self.metadata, self.instance_mode) + + def process_predictions(frame, predictions): + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + if "panoptic_seg" in predictions: + panoptic_seg, segments_info = predictions["panoptic_seg"] + vis_frame = video_visualizer.draw_panoptic_seg_predictions( + frame, panoptic_seg.to(self.cpu_device), segments_info + ) + elif "instances" in predictions: + predictions = predictions["instances"].to(self.cpu_device) + vis_frame = video_visualizer.draw_instance_predictions(frame, predictions) + elif "sem_seg" in predictions: + vis_frame = video_visualizer.draw_sem_seg( + frame, predictions["sem_seg"].argmax(dim=0).to(self.cpu_device) + ) + + # Converts Matplotlib RGB format to OpenCV BGR format + vis_frame = cv2.cvtColor(vis_frame.get_image(), cv2.COLOR_RGB2BGR) + return vis_frame + + frame_gen = self._frame_from_video(video) + if self.parallel: + buffer_size = self.predictor.default_buffer_size + + frame_data = deque() + + for cnt, frame in enumerate(frame_gen): + frame_data.append(frame) + self.predictor.put(frame) + + if cnt >= buffer_size: + frame = frame_data.popleft() + predictions = self.predictor.get() + yield process_predictions(frame, predictions) + + while len(frame_data): + frame = frame_data.popleft() + predictions = self.predictor.get() + yield process_predictions(frame, predictions) + else: + for frame in frame_gen: + yield process_predictions(frame, self.predictor(frame)) + + +class AsyncPredictor: + """ + A predictor that runs the model asynchronously, possibly on >1 GPUs. + Because rendering the visualization takes considerably amount of time, + this helps improve throughput a little bit when rendering videos. + """ + + class _StopToken: + pass + + class _PredictWorker(mp.Process): + def __init__(self, cfg, task_queue, result_queue): + self.cfg = cfg + self.task_queue = task_queue + self.result_queue = result_queue + super().__init__() + + def run(self): + predictor = DefaultPredictor(self.cfg) + + while True: + task = self.task_queue.get() + if isinstance(task, AsyncPredictor._StopToken): + break + idx, data = task + result = predictor(data) + self.result_queue.put((idx, result)) + + def __init__(self, cfg, num_gpus: int = 1): + """ + Args: + cfg (CfgNode): + num_gpus (int): if 0, will run on CPU + """ + num_workers = max(num_gpus, 1) + self.task_queue = mp.Queue(maxsize=num_workers * 3) + self.result_queue = mp.Queue(maxsize=num_workers * 3) + self.procs = [] + for gpuid in range(max(num_gpus, 1)): + cfg = cfg.clone() + cfg.defrost() + cfg.MODEL.DEVICE = "cuda:{}".format(gpuid) if num_gpus > 0 else "cpu" + self.procs.append( + AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue) + ) + + self.put_idx = 0 + self.get_idx = 0 + self.result_rank = [] + self.result_data = [] + + for p in self.procs: + p.start() + atexit.register(self.shutdown) + + def put(self, image): + self.put_idx += 1 + self.task_queue.put((self.put_idx, image)) + + def get(self): + self.get_idx += 1 # the index needed for this request + if len(self.result_rank) and self.result_rank[0] == self.get_idx: + res = self.result_data[0] + del self.result_data[0], self.result_rank[0] + return res + + while True: + # make sure the results are returned in the correct order + idx, res = self.result_queue.get() + if idx == self.get_idx: + return res + insert = bisect.bisect(self.result_rank, idx) + self.result_rank.insert(insert, idx) + self.result_data.insert(insert, res) + + def __len__(self): + return self.put_idx - self.get_idx + + def __call__(self, image): + self.put(image) + return self.get() + + def shutdown(self): + for _ in self.procs: + self.task_queue.put(AsyncPredictor._StopToken()) + + @property + def default_buffer_size(self): + return len(self.procs) * 5 diff --git a/videocutler/demo_video/colormap.py b/videocutler/demo_video/colormap.py new file mode 100644 index 0000000000000000000000000000000000000000..ddca838306b8c6837be3c6cf051769697fce6f30 --- /dev/null +++ b/videocutler/demo_video/colormap.py @@ -0,0 +1,170 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# copied from https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/colormap.py + +""" +An awesome colormap for really neat visualizations. +Copied from Detectron, and removed gray colors. +""" + +import numpy as np +import random + +__all__ = ["colormap", "random_color", "random_colors"] + +# fmt: off +# RGB: +_COLORS = np.array( + [ + 0.000, 0.447, 0.741, + 0.850, 0.325, 0.098, + 0.929, 0.694, 0.125, + 0.494, 0.184, 0.556, + 0.466, 0.674, 0.188, + 0.301, 0.745, 0.933, + 0.635, 0.078, 0.184, + 0.300, 0.300, 0.300, + 0.600, 0.600, 0.600, + 1.000, 0.000, 0.000, + 1.000, 0.500, 0.000, + 0.749, 0.749, 0.000, + 0.000, 1.000, 0.000, + 0.000, 0.000, 1.000, + 0.667, 0.000, 1.000, + 0.333, 0.333, 0.000, + 0.333, 0.667, 0.000, + 0.333, 1.000, 0.000, + 0.667, 0.333, 0.000, + 0.667, 0.667, 0.000, + 0.667, 1.000, 0.000, + 1.000, 0.333, 0.000, + 1.000, 0.667, 0.000, + 1.000, 1.000, 0.000, + 0.000, 0.333, 0.500, + 0.000, 0.667, 0.500, + 0.000, 1.000, 0.500, + 0.333, 0.000, 0.500, + 0.333, 0.333, 0.500, + 0.333, 0.667, 0.500, + 0.333, 1.000, 0.500, + 0.667, 0.000, 0.500, + 0.667, 0.333, 0.500, + 0.667, 0.667, 0.500, + 0.667, 1.000, 0.500, + 1.000, 0.000, 0.500, + 1.000, 0.333, 0.500, + 1.000, 0.667, 0.500, + 1.000, 1.000, 0.500, + 0.000, 0.333, 1.000, + 0.000, 0.667, 1.000, + 0.000, 1.000, 1.000, + 0.333, 0.000, 1.000, + 0.333, 0.333, 1.000, + 0.333, 0.667, 1.000, + 0.333, 1.000, 1.000, + 0.667, 0.000, 1.000, + 0.667, 0.333, 1.000, + 0.667, 0.667, 1.000, + 0.667, 1.000, 1.000, + 1.000, 0.000, 1.000, + 1.000, 0.333, 1.000, + 1.000, 0.667, 1.000, + 0.333, 0.000, 0.000, + 0.500, 0.000, 0.000, + 0.667, 0.000, 0.000, + 0.833, 0.000, 0.000, + 1.000, 0.000, 0.000, + 0.000, 0.167, 0.000, + 0.000, 0.333, 0.000, + 0.000, 0.500, 0.000, + 0.000, 0.667, 0.000, + 0.000, 0.833, 0.000, + 0.000, 1.000, 0.000, + 0.000, 0.000, 0.167, + 0.000, 0.000, 0.333, + 0.000, 0.000, 0.500, + 0.000, 0.000, 0.667, + 0.000, 0.000, 0.833, + 0.000, 0.000, 1.000, + 0.000, 0.000, 0.000, + 0.143, 0.143, 0.143, + 0.857, 0.857, 0.857, + 1.000, 1.000, 1.000 + ] +).astype(np.float32).reshape(-1, 3) +# fmt: on + + +def colormap(rgb=False, maximum=255): + """ + Args: + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + Returns: + ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1] + """ + assert maximum in [255, 1], maximum + c = _COLORS * maximum + if not rgb: + c = c[:, ::-1] + return c + + +def random_color(rgb=False, maximum=255): + """ + Args: + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + Returns: + ndarray: a vector of 3 numbers + """ + idx = np.random.randint(0, len(_COLORS)) + ret = _COLORS[idx] * maximum + if not rgb: + ret = ret[::-1] + return ret + + +def random_colors(N, rgb=False, maximum=255): + """ + Args: + N (int): number of unique colors needed + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + Returns: + ndarray: a list of random_color + """ + indices = random.sample(range(len(_COLORS)), N) + ret = [_COLORS[i] * maximum for i in indices] + if not rgb: + ret = [x[::-1] for x in ret] + return ret + +def select_colors(rgb=False, maximum=255, indices=[0]): + """ + Args: + N (int): number of unique colors needed + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + Returns: + ndarray: a list of random_color + """ + # indices = random.sample(range(len(_COLORS)), N) + ret = [_COLORS[i] * maximum for i in indices] + if not rgb: + ret = [x[::-1] for x in ret] + return ret + +if __name__ == "__main__": + import cv2 + + size = 100 + H, W = 10, 10 + canvas = np.random.rand(H * size, W * size, 3).astype("float32") + for h in range(H): + for w in range(W): + idx = h * W + w + if idx >= len(_COLORS): + break + canvas[h * size : (h + 1) * size, w * size : (w + 1) * size] = _COLORS[idx] + cv2.imshow("a", canvas) + cv2.waitKey(0) \ No newline at end of file diff --git a/videocutler/demo_video/demo.py b/videocutler/demo_video/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..41d1dd33a3b17b9add892e69b53f8e115132f6bd --- /dev/null +++ b/videocutler/demo_video/demo.py @@ -0,0 +1,227 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang + +import argparse +import glob +import multiprocessing as mp +import os + +# fmt: off +import sys +sys.path.insert(1, os.path.join(sys.path[0], '..')) +# fmt: on + +import tempfile +import time + +import cv2 +import numpy as np + +from torch.cuda.amp import autocast + +from detectron2.config import get_cfg +from detectron2.data.detection_utils import read_image +from detectron2.projects.deeplab import add_deeplab_config +from detectron2.utils.logger import setup_logger + +from mask2former import add_maskformer2_config +from mask2former_video import add_maskformer2_video_config +from predictor import VisualizationDemo + +from PIL import Image + +# constants +WINDOW_NAME = "VideoCutLER video demo" + + +def setup_cfg(args): + # load config from file and command-line arguments + cfg = get_cfg() + add_deeplab_config(cfg) + add_maskformer2_config(cfg) + add_maskformer2_video_config(cfg) + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + cfg.freeze() + return cfg + + +def get_parser(): + parser = argparse.ArgumentParser(description="maskformer2 demo for builtin configs") + parser.add_argument( + "--config-file", + default="configs/youtubevis_2019/video_maskformer2_R50_bs16_8ep.yaml", + metavar="FILE", + help="path to config file", + ) + parser.add_argument("--video-input", help="Path to video file.") + parser.add_argument( + "--input", + nargs="+", + help="A list of space separated input images; " + "or a single glob pattern such as 'directory/*.jpg'" + "this will be treated as frames of a video", + ) + parser.add_argument( + "--output", + help="A file or directory to save output visualizations. " + "If not given, will show output in an OpenCV window.", + ) + + parser.add_argument( + "--save-frames", + default=False, + help="Save frame level image outputs.", + ) + parser.add_argument( + "--save-masks", + default=False, + help="Save frame level image masks.", + ) + parser.add_argument( + "--confidence-threshold", + type=float, + default=0.5, + help="Minimum score for instance predictions to be shown", + ) + parser.add_argument( + "--opts", + help="Modify config options using the command-line 'KEY VALUE' pairs", + default=[], + nargs=argparse.REMAINDER, + ) + return parser + + +def test_opencv_video_format(codec, file_ext): + with tempfile.TemporaryDirectory(prefix="video_format_test") as dir: + filename = os.path.join(dir, "test_file" + file_ext) + writer = cv2.VideoWriter( + filename=filename, + fourcc=cv2.VideoWriter_fourcc(*codec), + fps=float(30), + frameSize=(10, 10), + isColor=True, + ) + [writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)] + writer.release() + if os.path.isfile(filename): + return True + return False + +PALETTE = [0, 0, 0, 128, 0, 0, 0, 128, 0, 128, 128, 0, 0, 0, 128, 128, 0, 128, 0, 128, 128, 128, 128, 128, 64, 0, 0, 191, 0, 0, 64, 128, 0, 191, 128, 0, 64, 0, 128] + +def save_masks(masks, file_path): + # n_masks = len(masks) + # width, height = masks[0].shape + mask_image = np.zeros(masks[0].shape, dtype=np.uint8) + for i, mask in enumerate(masks): + mask_image[mask!=0] = i + 1 + mask_image = Image.fromarray(mask_image, mode="P") + mask_image.putpalette(PALETTE) + mask_image.save(file_path) + +if __name__ == "__main__": + mp.set_start_method("spawn", force=True) + args = get_parser().parse_args() + setup_logger(name="fvcore") + logger = setup_logger() + logger.info("Arguments: " + str(args)) + + cfg = setup_cfg(args) + + demo = VisualizationDemo(cfg) + + if args.output: + os.makedirs(args.output, exist_ok=True) + + if args.input: + folder = args.input + video_name = folder[0].split("/")[-2] + + if len(folder) == 1: + args.input = sorted(glob.glob(os.path.expanduser(folder[0]))) + assert args.input, "The input path(s) was not found" + + vid_frames = [] + for path in args.input: + img = read_image(path, format="BGR") + vid_frames.append(img) + + start_time = time.time() + with autocast(): + predictions, visualized_output = demo.run_on_video(vid_frames, confidence_threshold=args.confidence_threshold) + + selected_idx = [idx for idx, score in enumerate(predictions["pred_scores"]) if score >= args.confidence_threshold] + predictions["pred_scores"] = [predictions["pred_scores"][idx] for idx in selected_idx] + predictions["pred_labels"] = [predictions["pred_labels"][idx] for idx in selected_idx] + predictions["pred_masks"] = [predictions["pred_masks"][idx] for idx in selected_idx] + + logger.info( + "detected {} instances per frame in {:.2f}s".format( + len(predictions["pred_scores"]), time.time() - start_time + ) + ) + + if args.output: + # save image-level predictions + if args.save_frames: + frame_index = 0 + for path, _vis_output in zip(args.input, visualized_output): + video_folder_path = os.path.join(args.output, video_name) + os.makedirs(video_folder_path, exist_ok=True) + out_filename = os.path.join(video_folder_path, os.path.basename(path)) + _vis_output.save(out_filename) + # get masks for frame frame_index and save them + if args.save_masks: + pseudo_masks = [predictions["pred_masks"][inst_id][frame_index] for inst_id in range(len(predictions["pred_masks"]))] + save_masks(pseudo_masks, os.path.join(video_folder_path, "mask_" + os.path.basename(path)).replace(".jpg", ".png")) + frame_index += 1 + + # save mp4 + H, W = visualized_output[0].height, visualized_output[0].width + cap = cv2.VideoCapture(-1) + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + out = cv2.VideoWriter(os.path.join(args.output, video_name + "_visualization.mp4"), fourcc, 10.0, (W, H), True) + for _vis_output in visualized_output: + frame = _vis_output.get_image()[:, :, ::-1] + out.write(frame) + cap.release() + out.release() + + elif args.video_input: + video = cv2.VideoCapture(args.video_input) + + vid_frames = [] + while video.isOpened(): + success, frame = video.read() + if success: + vid_frames.append(frame) + else: + break + + start_time = time.time() + with autocast(): + predictions, visualized_output = demo.run_on_video(vid_frames) + logger.info( + "detected {} instances per frame in {:.2f}s".format( + len(predictions["pred_scores"]), time.time() - start_time + ) + ) + + if args.output: + if args.save_frames: + for idx, _vis_output in enumerate(visualized_output): + out_filename = os.path.join(args.output, f"{idx}.jpg") + _vis_output.save(out_filename) + + H, W = visualized_output[0].height, visualized_output[0].width + + cap = cv2.VideoCapture(-1) + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + out = cv2.VideoWriter(os.path.join(args.output, "visualization.mp4"), fourcc, 10.0, (W, H), True) + for _vis_output in visualized_output: + frame = _vis_output.get_image()[:, :, ::-1] + out.write(frame) + cap.release() + out.release() diff --git a/videocutler/demo_video/predictor.py b/videocutler/demo_video/predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..6ffd3147cec281d56a3b0ee3135e483066abf52c --- /dev/null +++ b/videocutler/demo_video/predictor.py @@ -0,0 +1,215 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from: https://github.com/sukjunhwang/IFC/blob/master/projects/IFC/demo/predictor.py + +import atexit +import bisect +import multiprocessing as mp +from collections import deque +import cv2 +import torch + +from visualizer import TrackVisualizer + +from detectron2.data import MetadataCatalog +from detectron2.engine.defaults import DefaultPredictor +from detectron2.structures import Instances +from detectron2.utils.video_visualizer import VideoVisualizer +from detectron2.utils.visualizer import ColorMode + + +class VisualizationDemo(object): + def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False): + """ + Args: + cfg (CfgNode): + instance_mode (ColorMode): + parallel (bool): whether to run the model in different processes from visualization. + Useful since the visualization logic can be slow. + """ + self.metadata = MetadataCatalog.get( + cfg.DATASETS.TRAIN[0] if len(cfg.DATASETS.TRAIN) else "__unused" + ) + self.cpu_device = torch.device("cpu") + self.instance_mode = instance_mode + + self.parallel = parallel + if parallel: + num_gpu = torch.cuda.device_count() + self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu) + else: + self.predictor = VideoPredictor(cfg) + + def run_on_video(self, frames, confidence_threshold=0, inst_id=0): + """ + Args: + frames (List[np.ndarray]): a list of images of shape (H, W, C) (in BGR order). + This is the format used by OpenCV. + Returns: + predictions (dict): the output of the model. + vis_output (VisImage): the visualized image output. + """ + vis_output = None + predictions = self.predictor(frames) + + image_size = predictions["image_size"] + pred_scores = predictions["pred_scores"] + pred_labels = predictions["pred_labels"] + pred_masks = predictions["pred_masks"] + + selected_idx = [idx for idx, score in enumerate(pred_scores) if score >= confidence_threshold] + pred_scores = [pred_scores[idx] for idx in selected_idx] + pred_labels = [pred_labels[idx] for idx in selected_idx] + pred_masks = [pred_masks[idx] for idx in selected_idx] + + frame_masks = list(zip(*pred_masks)) + total_vis_output = [] + for frame_idx in range(len(frames)): + frame = frames[frame_idx][:, :, ::-1] + visualizer = TrackVisualizer(frame, self.metadata, instance_mode=self.instance_mode, inst_id=inst_id) + ins = Instances(image_size) + if len(pred_scores) > 0: + ins.scores = pred_scores + ins.pred_classes = pred_labels + ins.pred_masks = torch.stack(frame_masks[frame_idx], dim=0) + + vis_output = visualizer.draw_instance_predictions(predictions=ins) + total_vis_output.append(vis_output) + + return predictions, total_vis_output + + +class VideoPredictor(DefaultPredictor): + """ + Create a simple end-to-end predictor with the given config that runs on + single device for a single input image. + Compared to using the model directly, this class does the following additions: + 1. Load checkpoint from `cfg.MODEL.WEIGHTS`. + 2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`. + 3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`. + 4. Take one input image and produce a single output, instead of a batch. + If you'd like to do anything more fancy, please refer to its source code + as examples to build and use the model manually. + Attributes: + metadata (Metadata): the metadata of the underlying dataset, obtained from + cfg.DATASETS.TEST. + Examples: + :: + pred = DefaultPredictor(cfg) + inputs = cv2.imread("input.jpg") + outputs = pred(inputs) + """ + def __call__(self, frames): + """ + Args: + original_image (np.ndarray): an image of shape (H, W, C) (in BGR order). + Returns: + predictions (dict): + the output of the model for one image only. + See :doc:`/tutorials/models` for details about the format. + """ + with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258 + input_frames = [] + for original_image in frames: + # Apply pre-processing to image. + if self.input_format == "RGB": + # whether the model expects BGR inputs or RGB + original_image = original_image[:, :, ::-1] + height, width = original_image.shape[:2] + image = self.aug.get_transform(original_image).apply_image(original_image) + image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) + input_frames.append(image) + + inputs = {"image": input_frames, "height": height, "width": width} + predictions = self.model([inputs]) + return predictions + + +class AsyncPredictor: + """ + A predictor that runs the model asynchronously, possibly on >1 GPUs. + Because rendering the visualization takes considerably amount of time, + this helps improve throughput when rendering videos. + """ + + class _StopToken: + pass + + class _PredictWorker(mp.Process): + def __init__(self, cfg, task_queue, result_queue): + self.cfg = cfg + self.task_queue = task_queue + self.result_queue = result_queue + super().__init__() + + def run(self): + predictor = VideoPredictor(self.cfg) + + while True: + task = self.task_queue.get() + if isinstance(task, AsyncPredictor._StopToken): + break + idx, data = task + result = predictor(data) + self.result_queue.put((idx, result)) + + def __init__(self, cfg, num_gpus: int = 1): + """ + Args: + cfg (CfgNode): + num_gpus (int): if 0, will run on CPU + """ + num_workers = max(num_gpus, 1) + self.task_queue = mp.Queue(maxsize=num_workers * 3) + self.result_queue = mp.Queue(maxsize=num_workers * 3) + self.procs = [] + for gpuid in range(max(num_gpus, 1)): + cfg = cfg.clone() + cfg.defrost() + cfg.MODEL.DEVICE = "cuda:{}".format(gpuid) if num_gpus > 0 else "cpu" + self.procs.append( + AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue) + ) + + self.put_idx = 0 + self.get_idx = 0 + self.result_rank = [] + self.result_data = [] + + for p in self.procs: + p.start() + atexit.register(self.shutdown) + + def put(self, image): + self.put_idx += 1 + self.task_queue.put((self.put_idx, image)) + + def get(self): + self.get_idx += 1 # the index needed for this request + if len(self.result_rank) and self.result_rank[0] == self.get_idx: + res = self.result_data[0] + del self.result_data[0], self.result_rank[0] + return res + + while True: + # make sure the results are returned in the correct order + idx, res = self.result_queue.get() + if idx == self.get_idx: + return res + insert = bisect.bisect(self.result_rank, idx) + self.result_rank.insert(insert, idx) + self.result_data.insert(insert, res) + + def __len__(self): + return self.put_idx - self.get_idx + + def __call__(self, image): + self.put(image) + return self.get() + + def shutdown(self): + for _ in self.procs: + self.task_queue.put(AsyncPredictor._StopToken()) + + @property + def default_buffer_size(self): + return len(self.procs) * 5 diff --git a/videocutler/demo_video/visualizer.py b/videocutler/demo_video/visualizer.py new file mode 100644 index 0000000000000000000000000000000000000000..b1b0546ec0d54fcdadf77c16c9c1c88b952b8d41 --- /dev/null +++ b/videocutler/demo_video/visualizer.py @@ -0,0 +1,94 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from : https://github.com/sukjunhwang/IFC/blob/master/projects/IFC/demo/visualizer.py + +import torch +import numpy as np +import matplotlib.colors as mplc + +from detectron2.utils.visualizer import ColorMode, GenericMask, Visualizer, _create_text_labels +from colormap import random_colors, select_colors, colormap +import random + +_ID_JITTERS = [[0.9047944201469568, 0.3241718265806123, 0.33443746665210006], [0.4590171386127151, 0.9095038146383864, 0.3143840671974788], [0.4769356899795538, 0.5044406738441948, 0.5354530846360839], [0.00820945625670777, 0.24099210193126785, 0.15471834055332978], [0.6195684374237388, 0.4020380013509799, 0.26100266066404676], [0.08281237756545068, 0.05900744492710419, 0.06106221202154216], [0.2264886829978755, 0.04925271007292076, 0.10214429345996079], [0.1888247470009874, 0.11275000298612425, 0.46112894830685514], [0.37415767691880975, 0.844284596118331, 0.950471611180866], [0.3817344218157631, 0.3483259270707101, 0.6572989333690541], [0.2403115731054466, 0.03078280287279167, 0.5385975692534737], [0.7035076951650824, 0.12352084932325424, 0.12873080308790197], [0.12607434914489934, 0.111244793010015, 0.09333334699716023], [0.6551607300342269, 0.7003064103554443, 0.4131794512286162], [0.13592107365596595, 0.5390702818232149, 0.004540643174930525], [0.38286244894454347, 0.709142545393449, 0.529074791609835], [0.4279376583651734, 0.5634708596431771, 0.8505569717104301], [0.3460488523902999, 0.464769595519293, 0.6676839675477276], [0.8544063246675081, 0.5041190233407755, 0.9081217697141578], [0.9207009090747208, 0.2403865944739051, 0.05375410999863772], [0.6515786136947107, 0.6299918449948327, 0.45292029442034387], [0.986174217295693, 0.2424849846977214, 0.3981993323108266], [0.22101915872994693, 0.3408589198278038, 0.006381420347677524], [0.3159785813515982, 0.1145748921741011, 0.595754317197274], [0.10263421488052715, 0.5864139253490858, 0.23908000741142432], [0.8272999391532938, 0.6123527260897751, 0.3365197327803193], [0.5269583712937912, 0.25668929554516506, 0.7888411215078127], [0.2433880265410031, 0.7240751234287827, 0.8483215810528648], [0.7254601709704898, 0.8316525547295984, 0.9325253855921963], [0.5574483824856672, 0.2935331727879944, 0.6594839453793155], [0.6209642371433579, 0.054030693198821256, 0.5080873988178534], [0.9055507077365624, 0.12865888619203514, 0.9309191861440005], [0.9914469722960537, 0.3074114506206205, 0.8762107657323488], [0.4812682518247371, 0.15055826298548158, 0.9656340505308308], [0.6459219454316445, 0.9144794010251625, 0.751338812155106], [0.860840174209798, 0.8844626353077639, 0.3604624506769899], [0.8194991672032272, 0.926399617787601, 0.8059222327343247], [0.6540413175393658, 0.04579445254618297, 0.26891917826531275], [0.37778835833987046, 0.36247927666109536, 0.7989799305827889], [0.22738304978177726, 0.9038018263773739, 0.6970838854138303], [0.6362015495896184, 0.527680794236961, 0.5570915425178721], [0.6436401915860954, 0.6316925317144524, 0.9137151236993912], [0.04161828388587163, 0.3832413349082706, 0.6880829921949752], [0.7768167825719299, 0.8933821497682587, 0.7221278391266809], [0.8632760876301346, 0.3278628094906323, 0.8421587587114462], [0.8556499133262127, 0.6497385872901932, 0.5436895688477963], [0.9861940318610894, 0.03562313777386272, 0.9183454677106616], [0.8042586091176366, 0.6167222703170994, 0.24181981557207644], [0.9504247117633057, 0.3454233714011461, 0.6883727005547743], [0.9611909135491202, 0.46384154263898114, 0.32700443315058914], [0.523542176970206, 0.446222414615845, 0.9067402987747814], [0.7536954008682911, 0.6675512338797588, 0.22538238957839196], [0.1554052265688285, 0.05746097492966129, 0.8580358872587424], [0.8540838640971405, 0.9165504335482566, 0.6806982829158964], [0.7065090319405029, 0.8683059983962002, 0.05167128320624026], [0.39134812961899124, 0.8910075505622979, 0.7639815712623922], [0.1578117311479783, 0.20047326898284668, 0.9220177338840568], [0.2017488993096358, 0.6949259970936679, 0.8729196864798128], [0.5591089340651949, 0.15576770423813258, 0.1469857469387812], [0.14510398622626974, 0.24451497734532168, 0.46574271993578786], [0.13286397822351492, 0.4178244533944635, 0.03728728952131943], [0.556463206310225, 0.14027595183361663, 0.2731537988657907], [0.4093837966398032, 0.8015225687789814, 0.8033567296903834], [0.527442563956637, 0.902232617214431, 0.7066626674362227], [0.9058355503297827, 0.34983989180213004, 0.8353262183839384], [0.7108382186953104, 0.08591307895133471, 0.21434688012521974], [0.22757345065207668, 0.7943075496583976, 0.2992305547627421], [0.20454109788173636, 0.8251670332103687, 0.012981987094547232], [0.7672562637297392, 0.005429019973062554, 0.022163616037108702], [0.37487345910117564, 0.5086240194440863, 0.9061216063654387], [0.9878004014101087, 0.006345852772772331, 0.17499753379350858], [0.030061528704491303, 0.1409704315546606, 0.3337131835834506], [0.5022506782611504, 0.5448435505388706, 0.40584238936140726], [0.39560774627423445, 0.8905943695833262, 0.5850815030921116], [0.058615671926786406, 0.5365713844300387, 0.1620457551256279], [0.41843842882069693, 0.1536005983609976, 0.3127878501592438], [0.05947621790155899, 0.5412421167331932, 0.2611322146455659], [0.5196159938235607, 0.7066461551682705, 0.970261497412556], [0.30443031606149007, 0.45158581060034975, 0.4331841153149706], [0.8848298403933996, 0.7241791700943656, 0.8917110054596072], [0.5720260591898779, 0.3072801598203052, 0.8891066705989902], [0.13964015336177327, 0.2531778096760302, 0.5703756837403124], [0.2156307542329836, 0.4139947500641685, 0.87051676884144], [0.10800455881891169, 0.05554646035458266, 0.2947027428551443], [0.35198009410633857, 0.365849666213808, 0.06525787683513773], [0.5223264108118847, 0.9032195574351178, 0.28579084943315025], [0.7607724246546966, 0.3087194381828555, 0.6253235528354899], [0.5060485442077824, 0.19173600467625274, 0.9931175692203702], [0.5131805830323746, 0.07719515392040577, 0.923212006754969], [0.3629762141280106, 0.02429179642710888, 0.6963754952399983], [0.7542592485456767, 0.6478893299494212, 0.3424965345400731], [0.49944574453364454, 0.6775665366832825, 0.33758796076989583], [0.010621818120767679, 0.8221571611173205, 0.5186257457566332], [0.5857910304290109, 0.7178133992025467, 0.9729243483606071], [0.16987399482717613, 0.9942570210657463, 0.18120758122552927], [0.016362572521240848, 0.17582788603087263, 0.7255176922640298], [0.10981764283706419, 0.9078582203470377, 0.7638063718334003], [0.9252097840441119, 0.3330197086990039, 0.27888705301420136], [0.12769972651171546, 0.11121470804891687, 0.12710743734391716], [0.5753520518360334, 0.2763862879599456, 0.6115636613363361]] + + +class TrackVisualizer(Visualizer): + def __init__(self, img_rgb, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE, inst_id=0): + super().__init__( + img_rgb, metadata=metadata, scale=scale, instance_mode=instance_mode + ) + self.cpu_device = torch.device("cpu") + self.inst_id = inst_id + + def _jitter(self, color, id): + """ + Randomly modifies given color to produce a slightly different color than the color given. + Args: + color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color + picked. The values in the list are in the [0.0, 1.0] range. + Returns: + jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the + color after being jittered. The values in the list are in the [0.0, 1.0] range. + """ + color = mplc.to_rgb(color) + vec = _ID_JITTERS[id] + # better to do it in another color space + vec = vec / np.linalg.norm(vec) * 0.5 + res = np.clip(vec + color, 0, 1) + return tuple(res) + + def draw_instance_predictions(self, predictions): + """ + Draw instance-level prediction results on an image. + Args: + predictions (Instances): the output of an instance detection/segmentation + model. Following fields will be used to draw: + "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle"). + Returns: + output (VisImage): image object with visualizations. + """ + preds = predictions.to(self.cpu_device) + + boxes = preds.pred_boxes if preds.has("pred_boxes") else None + scores = preds.scores if preds.has("scores") else None + classes = preds.pred_classes if preds.has("pred_classes") else None + labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None)) + if labels is not None: + # labels = ["[{}] ".format(_id) + l for _id, l in enumerate(labels)] + labels = ["[{}] ".format(_id) for _id, l in enumerate(labels)] + + if preds.has("pred_masks"): + masks = np.asarray(preds.pred_masks) + masks = [GenericMask(x, self.output.height, self.output.width) for x in masks] + else: + masks = None + + if classes is None: + return self.output + + colors = colormap(rgb=True, maximum=1) + index_start = self.inst_id % len(colors) + # select len(classes) colors from the list of colors + if index_start + len(classes) >= len(colors): + colors = colors[:len(classes)] + else: + colors = colors[index_start:index_start+len(classes)] + alpha = 0.5 # DEFAULT 0.5 + + if self._instance_mode == ColorMode.IMAGE_BW: + self.output.img = self._create_grayscale_image( + (preds.pred_masks.any(dim=0) > 0).numpy() + if preds.has("pred_masks") + else None + ) + alpha = 0.5 # DEFAULT 0.3 + + self.overlay_instances( + masks=masks, + boxes=boxes, + labels=labels, + assigned_colors=colors, + alpha=alpha, + ) + + return self.output diff --git a/videocutler/eval.sh b/videocutler/eval.sh new file mode 100644 index 0000000000000000000000000000000000000000..01db90350168a738f59f664d431ccd45fb800e6e --- /dev/null +++ b/videocutler/eval.sh @@ -0,0 +1,17 @@ +export DETECTRON2_DATASETS=/shared/xudongw/DATASETS/ + +###### eval YouTubeVIS-2019 ###### +CUDA_VISIBLE_DEVICES=0,1,2,3 python train_net_video.py --num-gpus 4 \ + --config-file configs/imagenet_video/videocutler_eval_ytvis2019.yaml \ + --eval-only MODEL.WEIGHTS videocutler_m2f_rn50.pth \ + OUTPUT_DIR OUTPUT/ytvis_2019 + +python eval_ytvis.py --dataset-path ${DETECTRON2_DATASETS} --dataset-name 'ytvis_2019' --result-path 'OUTPUT/ytvis_2019/' + +###### eval YouTubeVIS-2021 ###### +# CUDA_VISIBLE_DEVICES=0,1,2,3 python train_net_video.py --num-gpus 4 \ +# --config-file configs/imagenet_video/videocutler_eval_ytvis2021.yaml \ +# --eval-only MODEL.WEIGHTS videocutler_m2f_rn50.pth \ +# OUTPUT_DIR OUTPUT/ytvis_2021/ + +# python eval_ytvis.py --dataset-path ${DETECTRON2_DATASETS} --dataset-name 'ytvis_2021' --result-path 'OUTPUT/ytvis_2021/' \ No newline at end of file diff --git a/videocutler/eval_ytvis.py b/videocutler/eval_ytvis.py new file mode 100644 index 0000000000000000000000000000000000000000..14687ad5a80f9048ee7e58bae92cd8ec617a2b1e --- /dev/null +++ b/videocutler/eval_ytvis.py @@ -0,0 +1,46 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from detectron2 and cocoapi + +import argparse +import os + +from mask2former_video.data_video.datasets.ytvis_api.ytvoseval import YTVOSeval +from mask2former_video.data_video.datasets.ytvis_api.ytvos import YTVOS + +def print_and_summary(cocoEval): + str_print = "" + for key in cocoEval.stats: + str_print += "{:.2f},".format(key*100) + return str_print + +def get_parser(): + parser = argparse.ArgumentParser(description="eval configs") + parser.add_argument( + "--dataset-path", default="DATASETS", help="path to the annotation file", + ) + parser.add_argument( + "--dataset-name", default="ytvis_2019", help="path to the annotation file", + ) + parser.add_argument( + "--result-path", default="OUTPUT", help="path to the the result file", + ) + return parser + +if __name__ == "__main__": + args = get_parser().parse_args() + + annFile = os.path.join(args.dataset_path, args.dataset_name, 'train.json') + cocoGt=YTVOS(annFile) + + resFile = os.path.join(args.result_path, 'inference/results.json') + cocoDt=cocoGt.loadRes(resFile) + + annType = 'segm' + print('Running demo for {} results.'.format(annType)) + cocoEval = YTVOSeval(cocoGt,cocoDt,annType) + cocoEval.params.useCats = 0 + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + copypaste = print_and_summary(cocoEval) + print(copypaste) \ No newline at end of file diff --git a/videocutler/mask2former/__init__.py b/videocutler/mask2former/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9b405c83bd2e8fa186a556a7db450af86c28c79b --- /dev/null +++ b/videocutler/mask2former/__init__.py @@ -0,0 +1,26 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from . import data # register all new datasets +from . import modeling + +# config +from .config import add_maskformer2_config + +# dataset loading +from .data.dataset_mappers.coco_instance_new_baseline_dataset_mapper import COCOInstanceNewBaselineDatasetMapper +from .data.dataset_mappers.coco_panoptic_new_baseline_dataset_mapper import COCOPanopticNewBaselineDatasetMapper +from .data.dataset_mappers.mask_former_instance_dataset_mapper import ( + MaskFormerInstanceDatasetMapper, +) +from .data.dataset_mappers.mask_former_panoptic_dataset_mapper import ( + MaskFormerPanopticDatasetMapper, +) +from .data.dataset_mappers.mask_former_semantic_dataset_mapper import ( + MaskFormerSemanticDatasetMapper, +) + +# models +from .maskformer_model import MaskFormer +from .test_time_augmentation import SemanticSegmentorWithTTA + +# evaluation +from .evaluation.instance_evaluation import InstanceSegEvaluator diff --git a/videocutler/mask2former/config.py b/videocutler/mask2former/config.py new file mode 100644 index 0000000000000000000000000000000000000000..3c3291a72432f6b509230a36c8fadcd4c023ec5a --- /dev/null +++ b/videocutler/mask2former/config.py @@ -0,0 +1,115 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +from detectron2.config import CfgNode as CN + + +def add_maskformer2_config(cfg): + """ + Add config for MASK_FORMER. + """ + # NOTE: configs from original maskformer + # data config + # select the dataset mapper + cfg.INPUT.DATASET_MAPPER_NAME = "mask_former_semantic" + # Color augmentation + cfg.INPUT.COLOR_AUG_SSD = False + # We retry random cropping until no single category in semantic segmentation GT occupies more + # than `SINGLE_CATEGORY_MAX_AREA` part of the crop. + cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0 + # Pad image and segmentation GT in dataset mapper. + cfg.INPUT.SIZE_DIVISIBILITY = -1 + + # solver config + # weight decay on embedding + cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0 + # optimizer + cfg.SOLVER.OPTIMIZER = "ADAMW" + cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1 + + # mask_former model config + cfg.MODEL.MASK_FORMER = CN() + + # loss + cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION = True + cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT = 0.1 + cfg.MODEL.MASK_FORMER.CLASS_WEIGHT = 1.0 + cfg.MODEL.MASK_FORMER.DICE_WEIGHT = 1.0 + cfg.MODEL.MASK_FORMER.MASK_WEIGHT = 20.0 + cfg.MODEL.MASK_FORMER.POSITIVE_BANK_IOU_THRESH = 0.01 + + # transformer config + cfg.MODEL.MASK_FORMER.NHEADS = 8 + cfg.MODEL.MASK_FORMER.DROPOUT = 0.1 + cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = 2048 + cfg.MODEL.MASK_FORMER.ENC_LAYERS = 0 + cfg.MODEL.MASK_FORMER.DEC_LAYERS = 6 + cfg.MODEL.MASK_FORMER.PRE_NORM = False + + cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256 + cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = 100 + + cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE = "res5" + cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ = False + + # mask_former inference config + cfg.MODEL.MASK_FORMER.TEST = CN() + cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True + cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = False + cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = False + cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD = 0.0 + cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD = 0.0 + cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False + + # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet) + # you can use this config to override + cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY = 32 + + # pixel decoder config + cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256 + # adding transformer in pixel decoder + cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0 + # pixel decoder + cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = "BasePixelDecoder" + + # swin transformer backbone + cfg.MODEL.SWIN = CN() + cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224 + cfg.MODEL.SWIN.PATCH_SIZE = 4 + cfg.MODEL.SWIN.EMBED_DIM = 96 + cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2] + cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24] + cfg.MODEL.SWIN.WINDOW_SIZE = 7 + cfg.MODEL.SWIN.MLP_RATIO = 4.0 + cfg.MODEL.SWIN.QKV_BIAS = True + cfg.MODEL.SWIN.QK_SCALE = None + cfg.MODEL.SWIN.DROP_RATE = 0.0 + cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0 + cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3 + cfg.MODEL.SWIN.APE = False + cfg.MODEL.SWIN.PATCH_NORM = True + cfg.MODEL.SWIN.OUT_FEATURES = ["res2", "res3", "res4", "res5"] + cfg.MODEL.SWIN.USE_CHECKPOINT = False + + # NOTE: maskformer2 extra configs + # transformer module + cfg.MODEL.MASK_FORMER.TRANSFORMER_DECODER_NAME = "MultiScaleMaskedTransformerDecoder" + + # LSJ aug + cfg.INPUT.IMAGE_SIZE = 1024 + cfg.INPUT.MIN_SCALE = 0.1 + cfg.INPUT.MAX_SCALE = 2.0 + + # MSDeformAttn encoder configs + cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["res3", "res4", "res5"] + cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4 + cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8 + + # point loss configs + # Number of points sampled during training for a mask point head. + cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS = 112 * 112 + # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the + # original paper. + cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO = 3.0 + # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in + # the original paper. + cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75 diff --git a/videocutler/mask2former/data/__init__.py b/videocutler/mask2former/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..63ba265b1effc69f1eef16e57a04db8902ee347e --- /dev/null +++ b/videocutler/mask2former/data/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from . import datasets diff --git a/videocutler/mask2former/data/dataset_mappers/__init__.py b/videocutler/mask2former/data/dataset_mappers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9020c2df23e2af280b7bb168b996ae9eaf312eb8 --- /dev/null +++ b/videocutler/mask2former/data/dataset_mappers/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. diff --git a/videocutler/mask2former/data/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py b/videocutler/mask2former/data/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py new file mode 100644 index 0000000000000000000000000000000000000000..83b2de05ab0e9edaa5cc3f419b1bf1622c960b71 --- /dev/null +++ b/videocutler/mask2former/data/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py @@ -0,0 +1,194 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/Mask2Former/tree/main/mask2former/data/dataset_mappers/dataset_mapper.py +import copy +import logging + +import numpy as np +import torch + +from detectron2.config import configurable +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.data.transforms import TransformGen +from detectron2.structures import BitMasks, Instances + +from pycocotools import mask as coco_mask + +__all__ = ["COCOInstanceNewBaselineDatasetMapper"] + + +def convert_coco_poly_to_mask(segmentations, height, width): + masks = [] + for polygons in segmentations: + rles = coco_mask.frPyObjects(polygons, height, width) + mask = coco_mask.decode(rles) + if len(mask.shape) < 3: + mask = mask[..., None] + mask = torch.as_tensor(mask, dtype=torch.uint8) + mask = mask.any(dim=2) + masks.append(mask) + if masks: + masks = torch.stack(masks, dim=0) + else: + masks = torch.zeros((0, height, width), dtype=torch.uint8) + return masks + + +def build_transform_gen(cfg, is_train): + """ + Create a list of default :class:`Augmentation` from config. + Now it includes resizing and flipping. + Returns: + list[Augmentation] + """ + assert is_train, "Only support training augmentation" + image_size = cfg.INPUT.IMAGE_SIZE + min_scale = cfg.INPUT.MIN_SCALE + max_scale = cfg.INPUT.MAX_SCALE + + augmentation = [] + + if cfg.INPUT.RANDOM_FLIP != "none": + augmentation.append( + T.RandomFlip( + horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal", + vertical=cfg.INPUT.RANDOM_FLIP == "vertical", + ) + ) + + augmentation.extend([ + T.ResizeScale( + min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size + ), + T.FixedSizeCrop(crop_size=(image_size, image_size)), + ]) + + return augmentation + + +# This is specifically designed for the COCO dataset. +class COCOInstanceNewBaselineDatasetMapper: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by MaskFormer. + + This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies geometric transforms to the image and annotation + 3. Find and applies suitable cropping to the image and annotation + 4. Prepare image and annotation to Tensors + """ + + @configurable + def __init__( + self, + is_train=True, + *, + tfm_gens, + image_format, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: for training or inference + augmentations: a list of augmentations or deterministic transforms to apply + tfm_gens: data augmentation + image_format: an image format supported by :func:`detection_utils.read_image`. + """ + self.tfm_gens = tfm_gens + logging.getLogger(__name__).info( + "[COCOInstanceNewBaselineDatasetMapper] Full TransformGens used in training: {}".format(str(self.tfm_gens)) + ) + + self.img_format = image_format + self.is_train = is_train + + @classmethod + def from_config(cls, cfg, is_train=True): + # Build augmentation + tfm_gens = build_transform_gen(cfg, is_train) + + ret = { + "is_train": is_train, + "tfm_gens": tfm_gens, + "image_format": cfg.INPUT.FORMAT, + } + return ret + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + image = utils.read_image(dataset_dict["file_name"], format=self.img_format) + utils.check_image_size(dataset_dict, image) + + # TODO: get padding mask + # by feeding a "segmentation mask" to the same transforms + padding_mask = np.ones(image.shape[:2]) + + image, transforms = T.apply_transform_gens(self.tfm_gens, image) + # the crop transformation has default padding value 0 for segmentation + padding_mask = transforms.apply_segmentation(padding_mask) + padding_mask = ~ padding_mask.astype(bool) + + image_shape = image.shape[:2] # h, w + + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + dataset_dict["padding_mask"] = torch.as_tensor(np.ascontiguousarray(padding_mask)) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + return dataset_dict + + if "annotations" in dataset_dict: + # USER: Modify this if you want to keep them for some reason. + for anno in dataset_dict["annotations"]: + # Let's always keep mask + # if not self.mask_on: + # anno.pop("segmentation", None) + anno.pop("keypoints", None) + + # USER: Implement additional transformations if you have other types of data + # annos = [ + # utils.transform_instance_annotations(obj, transforms, image_shape) + # for obj in dataset_dict.pop("annotations") + # ] + annos = [ + utils.transform_instance_annotations(obj, transforms, image_shape) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + # NOTE: does not support BitMask due to augmentation + # Current BitMask cannot handle empty objects + instances = utils.annotations_to_instances(annos, image_shape) + # print('instances: ', instances) + # After transforms such as cropping are applied, the bounding box may no longer + # tightly bound the object. As an example, imagine a triangle object + # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight + # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to + # the intersection of original bounding box and the cropping box. + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + # Need to filter empty instances first (due to augmentation) + instances = utils.filter_empty_instances(instances) + # Generate masks from polygon + h, w = instances.image_size + # image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float) + if hasattr(instances, 'gt_masks'): + gt_masks = instances.gt_masks + gt_masks = convert_coco_poly_to_mask(gt_masks.polygons, h, w) + instances.gt_masks = gt_masks + dataset_dict["instances"] = instances + + return dataset_dict diff --git a/videocutler/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py b/videocutler/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py new file mode 100644 index 0000000000000000000000000000000000000000..2c9676f909c6edd24329ae61874f1e227050bc21 --- /dev/null +++ b/videocutler/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py @@ -0,0 +1,165 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Copied from https://github.com/facebookresearch/Mask2Former/tree/main/mask2former/data/dataset_mappers/ +import copy +import logging + +import numpy as np +import torch + +from detectron2.config import configurable +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.data.transforms import TransformGen +from detectron2.structures import BitMasks, Boxes, Instances + +__all__ = ["COCOPanopticNewBaselineDatasetMapper"] + + +def build_transform_gen(cfg, is_train): + """ + Create a list of default :class:`Augmentation` from config. + Now it includes resizing and flipping. + Returns: + list[Augmentation] + """ + assert is_train, "Only support training augmentation" + image_size = cfg.INPUT.IMAGE_SIZE + min_scale = cfg.INPUT.MIN_SCALE + max_scale = cfg.INPUT.MAX_SCALE + + augmentation = [] + + if cfg.INPUT.RANDOM_FLIP != "none": + augmentation.append( + T.RandomFlip( + horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal", + vertical=cfg.INPUT.RANDOM_FLIP == "vertical", + ) + ) + + augmentation.extend([ + T.ResizeScale( + min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size + ), + T.FixedSizeCrop(crop_size=(image_size, image_size)), + ]) + + return augmentation + + +# This is specifically designed for the COCO dataset. +class COCOPanopticNewBaselineDatasetMapper: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by MaskFormer. + + This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies geometric transforms to the image and annotation + 3. Find and applies suitable cropping to the image and annotation + 4. Prepare image and annotation to Tensors + """ + + @configurable + def __init__( + self, + is_train=True, + *, + tfm_gens, + image_format, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: for training or inference + augmentations: a list of augmentations or deterministic transforms to apply + crop_gen: crop augmentation + tfm_gens: data augmentation + image_format: an image format supported by :func:`detection_utils.read_image`. + """ + self.tfm_gens = tfm_gens + logging.getLogger(__name__).info( + "[COCOPanopticNewBaselineDatasetMapper] Full TransformGens used in training: {}".format( + str(self.tfm_gens) + ) + ) + + self.img_format = image_format + self.is_train = is_train + + @classmethod + def from_config(cls, cfg, is_train=True): + # Build augmentation + tfm_gens = build_transform_gen(cfg, is_train) + + ret = { + "is_train": is_train, + "tfm_gens": tfm_gens, + "image_format": cfg.INPUT.FORMAT, + } + return ret + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + image = utils.read_image(dataset_dict["file_name"], format=self.img_format) + utils.check_image_size(dataset_dict, image) + + image, transforms = T.apply_transform_gens(self.tfm_gens, image) + image_shape = image.shape[:2] # h, w + + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + return dataset_dict + + if "pan_seg_file_name" in dataset_dict: + pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB") + segments_info = dataset_dict["segments_info"] + + # apply the same transformation to panoptic segmentation + pan_seg_gt = transforms.apply_segmentation(pan_seg_gt) + + from panopticapi.utils import rgb2id + + pan_seg_gt = rgb2id(pan_seg_gt) + + instances = Instances(image_shape) + classes = [] + masks = [] + for segment_info in segments_info: + class_id = segment_info["category_id"] + if not segment_info["iscrowd"]: + classes.append(class_id) + masks.append(pan_seg_gt == segment_info["id"]) + + classes = np.array(classes) + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1])) + instances.gt_boxes = Boxes(torch.zeros((0, 4))) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + instances.gt_boxes = masks.get_bounding_boxes() + + dataset_dict["instances"] = instances + + return dataset_dict diff --git a/videocutler/mask2former/data/dataset_mappers/mask_former_instance_dataset_mapper.py b/videocutler/mask2former/data/dataset_mappers/mask_former_instance_dataset_mapper.py new file mode 100644 index 0000000000000000000000000000000000000000..81222181cb606d8d5dbd13540e61244a34cecde2 --- /dev/null +++ b/videocutler/mask2former/data/dataset_mappers/mask_former_instance_dataset_mapper.py @@ -0,0 +1,182 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by XuDong from https://github.com/facebookresearch/Mask2Former/tree/main/mask2former/data/dataset_mappers/ + +import copy +import logging + +import numpy as np +import pycocotools.mask as mask_util +import torch +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.projects.point_rend import ColorAugSSDTransform +from detectron2.structures import BitMasks, Instances, polygons_to_bitmask + +__all__ = ["MaskFormerInstanceDatasetMapper"] + + +class MaskFormerInstanceDatasetMapper: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by MaskFormer for instance segmentation. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies geometric transforms to the image and annotation + 3. Find and applies suitable cropping to the image and annotation + 4. Prepare image and annotation to Tensors + """ + + @configurable + def __init__( + self, + is_train=True, + *, + augmentations, + image_format, + size_divisibility, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: for training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + size_divisibility: pad image size to be divisible by this value + """ + self.is_train = is_train + self.tfm_gens = augmentations + self.img_format = image_format + self.size_divisibility = size_divisibility + + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}") + + @classmethod + def from_config(cls, cfg, is_train=True): + # Build augmentation + augs = [ + T.ResizeShortestEdge( + cfg.INPUT.MIN_SIZE_TRAIN, + cfg.INPUT.MAX_SIZE_TRAIN, + cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING, + ) + ] + if cfg.INPUT.CROP.ENABLED: + augs.append( + T.RandomCrop( + cfg.INPUT.CROP.TYPE, + cfg.INPUT.CROP.SIZE, + ) + ) + if cfg.INPUT.COLOR_AUG_SSD: + augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT)) + augs.append(T.RandomFlip()) + + ret = { + "is_train": is_train, + "augmentations": augs, + "image_format": cfg.INPUT.FORMAT, + "size_divisibility": cfg.INPUT.SIZE_DIVISIBILITY, + } + return ret + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + assert self.is_train, "MaskFormerPanopticDatasetMapper should only be used for training!" + + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + image = utils.read_image(dataset_dict["file_name"], format=self.img_format) + utils.check_image_size(dataset_dict, image) + + aug_input = T.AugInput(image) + aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input) + image = aug_input.image + + # transform instnace masks + assert "annotations" in dataset_dict + for anno in dataset_dict["annotations"]: + anno.pop("keypoints", None) + + annos = [ + utils.transform_instance_annotations(obj, transforms, image.shape[:2]) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + + if len(annos): + assert "segmentation" in annos[0] + segms = [obj["segmentation"] for obj in annos] + masks = [] + for segm in segms: + if isinstance(segm, list): + # polygon + masks.append(polygons_to_bitmask(segm, *image.shape[:2])) + elif isinstance(segm, dict): + # COCO RLE + masks.append(mask_util.decode(segm)) + elif isinstance(segm, np.ndarray): + assert segm.ndim == 2, "Expect segmentation of 2 dimensions, got {}.".format( + segm.ndim + ) + # mask array + masks.append(segm) + else: + raise ValueError( + "Cannot convert segmentation of type '{}' to BitMasks!" + "Supported types are: polygons as list[list[float] or ndarray]," + " COCO-style RLE as a dict, or a binary segmentation mask " + " in a 2D numpy array of shape HxW.".format(type(segm)) + ) + + # Pad image and segmentation label here! + image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + masks = [torch.from_numpy(np.ascontiguousarray(x)) for x in masks] + + classes = [int(obj["category_id"]) for obj in annos] + classes = torch.tensor(classes, dtype=torch.int64) + + if self.size_divisibility > 0: + image_size = (image.shape[-2], image.shape[-1]) + padding_size = [ + 0, + self.size_divisibility - image_size[1], + 0, + self.size_divisibility - image_size[0], + ] + # pad image + image = F.pad(image, padding_size, value=128).contiguous() + # pad mask + masks = [F.pad(x, padding_size, value=0).contiguous() for x in masks] + + image_shape = (image.shape[-2], image.shape[-1]) # h, w + + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = image + + # Prepare per-category binary masks + instances = Instances(image_shape) + instances.gt_classes = classes + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, image.shape[-2], image.shape[-1])) + else: + masks = BitMasks(torch.stack(masks)) + instances.gt_masks = masks.tensor + + dataset_dict["instances"] = instances + + return dataset_dict diff --git a/videocutler/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py b/videocutler/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py new file mode 100644 index 0000000000000000000000000000000000000000..9e54083cb4eecc82a4765b725697fd54aa81e189 --- /dev/null +++ b/videocutler/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py @@ -0,0 +1,167 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by XuDong from https://github.com/facebookresearch/Mask2Former/tree/main/mask2former/data/dataset_mappers/ + +import copy +import logging + +import numpy as np +import torch +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.structures import BitMasks, Instances + +from .mask_former_semantic_dataset_mapper import MaskFormerSemanticDatasetMapper + +__all__ = ["MaskFormerPanopticDatasetMapper"] + + +class MaskFormerPanopticDatasetMapper(MaskFormerSemanticDatasetMapper): + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by MaskFormer for panoptic segmentation. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies geometric transforms to the image and annotation + 3. Find and applies suitable cropping to the image and annotation + 4. Prepare image and annotation to Tensors + """ + + @configurable + def __init__( + self, + is_train=True, + *, + augmentations, + image_format, + ignore_label, + size_divisibility, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: for training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + ignore_label: the label that is ignored to evaluation + size_divisibility: pad image size to be divisible by this value + """ + super().__init__( + is_train, + augmentations=augmentations, + image_format=image_format, + ignore_label=ignore_label, + size_divisibility=size_divisibility, + ) + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + assert self.is_train, "MaskFormerPanopticDatasetMapper should only be used for training!" + + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + image = utils.read_image(dataset_dict["file_name"], format=self.img_format) + utils.check_image_size(dataset_dict, image) + + # semantic segmentation + if "sem_seg_file_name" in dataset_dict: + # PyTorch transformation not implemented for uint16, so converting it to double first + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double") + else: + sem_seg_gt = None + + # panoptic segmentation + if "pan_seg_file_name" in dataset_dict: + pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB") + segments_info = dataset_dict["segments_info"] + else: + pan_seg_gt = None + segments_info = None + + if pan_seg_gt is None: + raise ValueError( + "Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.".format( + dataset_dict["file_name"] + ) + ) + + aug_input = T.AugInput(image, sem_seg=sem_seg_gt) + aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input) + image = aug_input.image + if sem_seg_gt is not None: + sem_seg_gt = aug_input.sem_seg + + # apply the same transformation to panoptic segmentation + pan_seg_gt = transforms.apply_segmentation(pan_seg_gt) + + from panopticapi.utils import rgb2id + + pan_seg_gt = rgb2id(pan_seg_gt) + + # Pad image and segmentation label here! + image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + if sem_seg_gt is not None: + sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long")) + pan_seg_gt = torch.as_tensor(pan_seg_gt.astype("long")) + + if self.size_divisibility > 0: + image_size = (image.shape[-2], image.shape[-1]) + padding_size = [ + 0, + self.size_divisibility - image_size[1], + 0, + self.size_divisibility - image_size[0], + ] + image = F.pad(image, padding_size, value=128).contiguous() + if sem_seg_gt is not None: + sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous() + pan_seg_gt = F.pad( + pan_seg_gt, padding_size, value=0 + ).contiguous() # 0 is the VOID panoptic label + + image_shape = (image.shape[-2], image.shape[-1]) # h, w + + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = image + if sem_seg_gt is not None: + dataset_dict["sem_seg"] = sem_seg_gt.long() + + if "annotations" in dataset_dict: + raise ValueError("Pemantic segmentation dataset should not have 'annotations'.") + + # Prepare per-category binary masks + pan_seg_gt = pan_seg_gt.numpy() + instances = Instances(image_shape) + classes = [] + masks = [] + for segment_info in segments_info: + class_id = segment_info["category_id"] + if not segment_info["iscrowd"]: + classes.append(class_id) + masks.append(pan_seg_gt == segment_info["id"]) + + classes = np.array(classes) + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1])) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + + dataset_dict["instances"] = instances + + return dataset_dict diff --git a/videocutler/mask2former/data/dataset_mappers/mask_former_semantic_dataset_mapper.py b/videocutler/mask2former/data/dataset_mappers/mask_former_semantic_dataset_mapper.py new file mode 100644 index 0000000000000000000000000000000000000000..b2e1240a7763605b004919df34d64c64767d1106 --- /dev/null +++ b/videocutler/mask2former/data/dataset_mappers/mask_former_semantic_dataset_mapper.py @@ -0,0 +1,186 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by XuDong from https://github.com/facebookresearch/Mask2Former/tree/main/mask2former/data/dataset_mappers/ + +import copy +import logging + +import numpy as np +import torch +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.data import MetadataCatalog +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.projects.point_rend import ColorAugSSDTransform +from detectron2.structures import BitMasks, Instances + +__all__ = ["MaskFormerSemanticDatasetMapper"] + + +class MaskFormerSemanticDatasetMapper: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by MaskFormer for semantic segmentation. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies geometric transforms to the image and annotation + 3. Find and applies suitable cropping to the image and annotation + 4. Prepare image and annotation to Tensors + """ + + @configurable + def __init__( + self, + is_train=True, + *, + augmentations, + image_format, + ignore_label, + size_divisibility, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: for training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + ignore_label: the label that is ignored to evaluation + size_divisibility: pad image size to be divisible by this value + """ + self.is_train = is_train + self.tfm_gens = augmentations + self.img_format = image_format + self.ignore_label = ignore_label + self.size_divisibility = size_divisibility + + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}") + + @classmethod + def from_config(cls, cfg, is_train=True): + # Build augmentation + augs = [ + T.ResizeShortestEdge( + cfg.INPUT.MIN_SIZE_TRAIN, + cfg.INPUT.MAX_SIZE_TRAIN, + cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING, + ) + ] + if cfg.INPUT.CROP.ENABLED: + augs.append( + T.RandomCrop_CategoryAreaConstraint( + cfg.INPUT.CROP.TYPE, + cfg.INPUT.CROP.SIZE, + cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA, + cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE, + ) + ) + if cfg.INPUT.COLOR_AUG_SSD: + augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT)) + augs.append(T.RandomFlip()) + + # Assume always applies to the training set. + dataset_names = cfg.DATASETS.TRAIN + meta = MetadataCatalog.get(dataset_names[0]) + ignore_label = meta.ignore_label + + ret = { + "is_train": is_train, + "augmentations": augs, + "image_format": cfg.INPUT.FORMAT, + "ignore_label": ignore_label, + "size_divisibility": cfg.INPUT.SIZE_DIVISIBILITY, + } + return ret + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + assert self.is_train, "MaskFormerSemanticDatasetMapper should only be used for training!" + + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + image = utils.read_image(dataset_dict["file_name"], format=self.img_format) + utils.check_image_size(dataset_dict, image) + + if "sem_seg_file_name" in dataset_dict: + # PyTorch transformation not implemented for uint16, so converting it to double first + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double") + else: + sem_seg_gt = None + + if sem_seg_gt is None: + raise ValueError( + "Cannot find 'sem_seg_file_name' for semantic segmentation dataset {}.".format( + dataset_dict["file_name"] + ) + ) + + aug_input = T.AugInput(image, sem_seg=sem_seg_gt) + aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input) + image = aug_input.image + sem_seg_gt = aug_input.sem_seg + + # Pad image and segmentation label here! + image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + if sem_seg_gt is not None: + sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long")) + + if self.size_divisibility > 0: + image_size = (image.shape[-2], image.shape[-1]) + padding_size = [ + 0, + self.size_divisibility - image_size[1], + 0, + self.size_divisibility - image_size[0], + ] + image = F.pad(image, padding_size, value=128).contiguous() + if sem_seg_gt is not None: + sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous() + + image_shape = (image.shape[-2], image.shape[-1]) # h, w + + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = image + + if sem_seg_gt is not None: + dataset_dict["sem_seg"] = sem_seg_gt.long() + + if "annotations" in dataset_dict: + raise ValueError("Semantic segmentation dataset should not have 'annotations'.") + + # Prepare per-category binary masks + if sem_seg_gt is not None: + sem_seg_gt = sem_seg_gt.numpy() + instances = Instances(image_shape) + classes = np.unique(sem_seg_gt) + # remove ignored region + classes = classes[classes != self.ignore_label] + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + + masks = [] + for class_id in classes: + masks.append(sem_seg_gt == class_id) + + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1])) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + + dataset_dict["instances"] = instances + + return dataset_dict diff --git a/videocutler/mask2former/data/datasets/__init__.py b/videocutler/mask2former/data/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..403a678e3ba6655135f36e788ad53587f05d6d1e --- /dev/null +++ b/videocutler/mask2former/data/datasets/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from . import ( + register_ade20k_full, + register_ade20k_panoptic, + register_coco_stuff_10k, + register_mapillary_vistas, + register_coco_panoptic_annos_semseg, + register_ade20k_instance, + register_mapillary_vistas_panoptic, +) diff --git a/videocutler/mask2former/data/datasets/register_ade20k_full.py b/videocutler/mask2former/data/datasets/register_ade20k_full.py new file mode 100644 index 0000000000000000000000000000000000000000..7121a22227583b29a6e167b560703e33371f1081 --- /dev/null +++ b/videocutler/mask2former/data/datasets/register_ade20k_full.py @@ -0,0 +1,964 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets import load_sem_seg + +ADE20K_SEM_SEG_FULL_CATEGORIES = [ + {"name": "wall", "id": 2978, "trainId": 0}, + {"name": "building, edifice", "id": 312, "trainId": 1}, + {"name": "sky", "id": 2420, "trainId": 2}, + {"name": "tree", "id": 2855, "trainId": 3}, + {"name": "road, route", "id": 2131, "trainId": 4}, + {"name": "floor, flooring", "id": 976, "trainId": 5}, + {"name": "ceiling", "id": 447, "trainId": 6}, + {"name": "bed", "id": 165, "trainId": 7}, + {"name": "sidewalk, pavement", "id": 2377, "trainId": 8}, + {"name": "earth, ground", "id": 838, "trainId": 9}, + {"name": "cabinet", "id": 350, "trainId": 10}, + {"name": "person, individual, someone, somebody, mortal, soul", "id": 1831, "trainId": 11}, + {"name": "grass", "id": 1125, "trainId": 12}, + {"name": "windowpane, window", "id": 3055, "trainId": 13}, + {"name": "car, auto, automobile, machine, motorcar", "id": 401, "trainId": 14}, + {"name": "mountain, mount", "id": 1610, "trainId": 15}, + {"name": "plant, flora, plant life", "id": 1910, "trainId": 16}, + {"name": "table", "id": 2684, "trainId": 17}, + {"name": "chair", "id": 471, "trainId": 18}, + {"name": "curtain, drape, drapery, mantle, pall", "id": 687, "trainId": 19}, + {"name": "door", "id": 774, "trainId": 20}, + {"name": "sofa, couch, lounge", "id": 2473, "trainId": 21}, + {"name": "sea", "id": 2264, "trainId": 22}, + {"name": "painting, picture", "id": 1735, "trainId": 23}, + {"name": "water", "id": 2994, "trainId": 24}, + {"name": "mirror", "id": 1564, "trainId": 25}, + {"name": "house", "id": 1276, "trainId": 26}, + {"name": "rug, carpet, carpeting", "id": 2178, "trainId": 27}, + {"name": "shelf", "id": 2329, "trainId": 28}, + {"name": "armchair", "id": 57, "trainId": 29}, + {"name": "fence, fencing", "id": 907, "trainId": 30}, + {"name": "field", "id": 913, "trainId": 31}, + {"name": "lamp", "id": 1395, "trainId": 32}, + {"name": "rock, stone", "id": 2138, "trainId": 33}, + {"name": "seat", "id": 2272, "trainId": 34}, + {"name": "river", "id": 2128, "trainId": 35}, + {"name": "desk", "id": 724, "trainId": 36}, + {"name": "bathtub, bathing tub, bath, tub", "id": 155, "trainId": 37}, + {"name": "railing, rail", "id": 2053, "trainId": 38}, + {"name": "signboard, sign", "id": 2380, "trainId": 39}, + {"name": "cushion", "id": 689, "trainId": 40}, + {"name": "path", "id": 1788, "trainId": 41}, + {"name": "work surface", "id": 3087, "trainId": 42}, + {"name": "stairs, steps", "id": 2530, "trainId": 43}, + {"name": "column, pillar", "id": 581, "trainId": 44}, + {"name": "sink", "id": 2388, "trainId": 45}, + {"name": "wardrobe, closet, press", "id": 2985, "trainId": 46}, + {"name": "snow", "id": 2454, "trainId": 47}, + {"name": "refrigerator, icebox", "id": 2096, "trainId": 48}, + {"name": "base, pedestal, stand", "id": 137, "trainId": 49}, + {"name": "bridge, span", "id": 294, "trainId": 50}, + {"name": "blind, screen", "id": 212, "trainId": 51}, + {"name": "runway", "id": 2185, "trainId": 52}, + {"name": "cliff, drop, drop-off", "id": 524, "trainId": 53}, + {"name": "sand", "id": 2212, "trainId": 54}, + {"name": "fireplace, hearth, open fireplace", "id": 943, "trainId": 55}, + {"name": "pillow", "id": 1869, "trainId": 56}, + {"name": "screen door, screen", "id": 2251, "trainId": 57}, + {"name": "toilet, can, commode, crapper, pot, potty, stool, throne", "id": 2793, "trainId": 58}, + {"name": "skyscraper", "id": 2423, "trainId": 59}, + {"name": "grandstand, covered stand", "id": 1121, "trainId": 60}, + {"name": "box", "id": 266, "trainId": 61}, + {"name": "pool table, billiard table, snooker table", "id": 1948, "trainId": 62}, + {"name": "palm, palm tree", "id": 1744, "trainId": 63}, + {"name": "double door", "id": 783, "trainId": 64}, + {"name": "coffee table, cocktail table", "id": 571, "trainId": 65}, + {"name": "counter", "id": 627, "trainId": 66}, + {"name": "countertop", "id": 629, "trainId": 67}, + {"name": "chest of drawers, chest, bureau, dresser", "id": 491, "trainId": 68}, + {"name": "kitchen island", "id": 1374, "trainId": 69}, + {"name": "boat", "id": 223, "trainId": 70}, + {"name": "waterfall, falls", "id": 3016, "trainId": 71}, + { + "name": "stove, kitchen stove, range, kitchen range, cooking stove", + "id": 2598, + "trainId": 72, + }, + {"name": "flower", "id": 978, "trainId": 73}, + {"name": "bookcase", "id": 239, "trainId": 74}, + {"name": "controls", "id": 608, "trainId": 75}, + {"name": "book", "id": 236, "trainId": 76}, + {"name": "stairway, staircase", "id": 2531, "trainId": 77}, + {"name": "streetlight, street lamp", "id": 2616, "trainId": 78}, + { + "name": "computer, computing machine, computing device, data processor, electronic computer, information processing system", + "id": 591, + "trainId": 79, + }, + { + "name": "bus, autobus, coach, charabanc, double-decker, jitney, motorbus, motorcoach, omnibus, passenger vehicle", + "id": 327, + "trainId": 80, + }, + {"name": "swivel chair", "id": 2679, "trainId": 81}, + {"name": "light, light source", "id": 1451, "trainId": 82}, + {"name": "bench", "id": 181, "trainId": 83}, + {"name": "case, display case, showcase, vitrine", "id": 420, "trainId": 84}, + {"name": "towel", "id": 2821, "trainId": 85}, + {"name": "fountain", "id": 1023, "trainId": 86}, + {"name": "embankment", "id": 855, "trainId": 87}, + { + "name": "television receiver, television, television set, tv, tv set, idiot box, boob tube, telly, goggle box", + "id": 2733, + "trainId": 88, + }, + {"name": "van", "id": 2928, "trainId": 89}, + {"name": "hill", "id": 1240, "trainId": 90}, + {"name": "awning, sunshade, sunblind", "id": 77, "trainId": 91}, + {"name": "poster, posting, placard, notice, bill, card", "id": 1969, "trainId": 92}, + {"name": "truck, motortruck", "id": 2880, "trainId": 93}, + {"name": "airplane, aeroplane, plane", "id": 14, "trainId": 94}, + {"name": "pole", "id": 1936, "trainId": 95}, + {"name": "tower", "id": 2828, "trainId": 96}, + {"name": "court", "id": 631, "trainId": 97}, + {"name": "ball", "id": 103, "trainId": 98}, + { + "name": "aircraft carrier, carrier, flattop, attack aircraft carrier", + "id": 3144, + "trainId": 99, + }, + {"name": "buffet, counter, sideboard", "id": 308, "trainId": 100}, + {"name": "hovel, hut, hutch, shack, shanty", "id": 1282, "trainId": 101}, + {"name": "apparel, wearing apparel, dress, clothes", "id": 38, "trainId": 102}, + {"name": "minibike, motorbike", "id": 1563, "trainId": 103}, + {"name": "animal, animate being, beast, brute, creature, fauna", "id": 29, "trainId": 104}, + {"name": "chandelier, pendant, pendent", "id": 480, "trainId": 105}, + {"name": "step, stair", "id": 2569, "trainId": 106}, + {"name": "booth, cubicle, stall, kiosk", "id": 247, "trainId": 107}, + {"name": "bicycle, bike, wheel, cycle", "id": 187, "trainId": 108}, + {"name": "doorframe, doorcase", "id": 778, "trainId": 109}, + {"name": "sconce", "id": 2243, "trainId": 110}, + {"name": "pond", "id": 1941, "trainId": 111}, + {"name": "trade name, brand name, brand, marque", "id": 2833, "trainId": 112}, + {"name": "bannister, banister, balustrade, balusters, handrail", "id": 120, "trainId": 113}, + {"name": "bag", "id": 95, "trainId": 114}, + {"name": "traffic light, traffic signal, stoplight", "id": 2836, "trainId": 115}, + {"name": "gazebo", "id": 1087, "trainId": 116}, + {"name": "escalator, moving staircase, moving stairway", "id": 868, "trainId": 117}, + {"name": "land, ground, soil", "id": 1401, "trainId": 118}, + {"name": "board, plank", "id": 220, "trainId": 119}, + {"name": "arcade machine", "id": 47, "trainId": 120}, + {"name": "eiderdown, duvet, continental quilt", "id": 843, "trainId": 121}, + {"name": "bar", "id": 123, "trainId": 122}, + {"name": "stall, stand, sales booth", "id": 2537, "trainId": 123}, + {"name": "playground", "id": 1927, "trainId": 124}, + {"name": "ship", "id": 2337, "trainId": 125}, + {"name": "ottoman, pouf, pouffe, puff, hassock", "id": 1702, "trainId": 126}, + { + "name": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", + "id": 64, + "trainId": 127, + }, + {"name": "bottle", "id": 249, "trainId": 128}, + {"name": "cradle", "id": 642, "trainId": 129}, + {"name": "pot, flowerpot", "id": 1981, "trainId": 130}, + { + "name": "conveyer belt, conveyor belt, conveyer, conveyor, transporter", + "id": 609, + "trainId": 131, + }, + {"name": "train, railroad train", "id": 2840, "trainId": 132}, + {"name": "stool", "id": 2586, "trainId": 133}, + {"name": "lake", "id": 1393, "trainId": 134}, + {"name": "tank, storage tank", "id": 2704, "trainId": 135}, + {"name": "ice, water ice", "id": 1304, "trainId": 136}, + {"name": "basket, handbasket", "id": 146, "trainId": 137}, + {"name": "manhole", "id": 1494, "trainId": 138}, + {"name": "tent, collapsible shelter", "id": 2739, "trainId": 139}, + {"name": "canopy", "id": 389, "trainId": 140}, + {"name": "microwave, microwave oven", "id": 1551, "trainId": 141}, + {"name": "barrel, cask", "id": 131, "trainId": 142}, + {"name": "dirt track", "id": 738, "trainId": 143}, + {"name": "beam", "id": 161, "trainId": 144}, + {"name": "dishwasher, dish washer, dishwashing machine", "id": 747, "trainId": 145}, + {"name": "plate", "id": 1919, "trainId": 146}, + {"name": "screen, crt screen", "id": 3109, "trainId": 147}, + {"name": "ruins", "id": 2179, "trainId": 148}, + {"name": "washer, automatic washer, washing machine", "id": 2989, "trainId": 149}, + {"name": "blanket, cover", "id": 206, "trainId": 150}, + {"name": "plaything, toy", "id": 1930, "trainId": 151}, + {"name": "food, solid food", "id": 1002, "trainId": 152}, + {"name": "screen, silver screen, projection screen", "id": 2254, "trainId": 153}, + {"name": "oven", "id": 1708, "trainId": 154}, + {"name": "stage", "id": 2526, "trainId": 155}, + {"name": "beacon, lighthouse, beacon light, pharos", "id": 160, "trainId": 156}, + {"name": "umbrella", "id": 2901, "trainId": 157}, + {"name": "sculpture", "id": 2262, "trainId": 158}, + {"name": "aqueduct", "id": 44, "trainId": 159}, + {"name": "container", "id": 597, "trainId": 160}, + {"name": "scaffolding, staging", "id": 2235, "trainId": 161}, + {"name": "hood, exhaust hood", "id": 1260, "trainId": 162}, + {"name": "curb, curbing, kerb", "id": 682, "trainId": 163}, + {"name": "roller coaster", "id": 2151, "trainId": 164}, + {"name": "horse, equus caballus", "id": 3107, "trainId": 165}, + {"name": "catwalk", "id": 432, "trainId": 166}, + {"name": "glass, drinking glass", "id": 1098, "trainId": 167}, + {"name": "vase", "id": 2932, "trainId": 168}, + {"name": "central reservation", "id": 461, "trainId": 169}, + {"name": "carousel", "id": 410, "trainId": 170}, + {"name": "radiator", "id": 2046, "trainId": 171}, + {"name": "closet", "id": 533, "trainId": 172}, + {"name": "machine", "id": 1481, "trainId": 173}, + {"name": "pier, wharf, wharfage, dock", "id": 1858, "trainId": 174}, + {"name": "fan", "id": 894, "trainId": 175}, + {"name": "inflatable bounce game", "id": 1322, "trainId": 176}, + {"name": "pitch", "id": 1891, "trainId": 177}, + {"name": "paper", "id": 1756, "trainId": 178}, + {"name": "arcade, colonnade", "id": 49, "trainId": 179}, + {"name": "hot tub", "id": 1272, "trainId": 180}, + {"name": "helicopter", "id": 1229, "trainId": 181}, + {"name": "tray", "id": 2850, "trainId": 182}, + {"name": "partition, divider", "id": 1784, "trainId": 183}, + {"name": "vineyard", "id": 2962, "trainId": 184}, + {"name": "bowl", "id": 259, "trainId": 185}, + {"name": "bullring", "id": 319, "trainId": 186}, + {"name": "flag", "id": 954, "trainId": 187}, + {"name": "pot", "id": 1974, "trainId": 188}, + {"name": "footbridge, overcrossing, pedestrian bridge", "id": 1013, "trainId": 189}, + {"name": "shower", "id": 2356, "trainId": 190}, + {"name": "bag, traveling bag, travelling bag, grip, suitcase", "id": 97, "trainId": 191}, + {"name": "bulletin board, notice board", "id": 318, "trainId": 192}, + {"name": "confessional booth", "id": 592, "trainId": 193}, + {"name": "trunk, tree trunk, bole", "id": 2885, "trainId": 194}, + {"name": "forest", "id": 1017, "trainId": 195}, + {"name": "elevator door", "id": 851, "trainId": 196}, + {"name": "laptop, laptop computer", "id": 1407, "trainId": 197}, + {"name": "instrument panel", "id": 1332, "trainId": 198}, + {"name": "bucket, pail", "id": 303, "trainId": 199}, + {"name": "tapestry, tapis", "id": 2714, "trainId": 200}, + {"name": "platform", "id": 1924, "trainId": 201}, + {"name": "jacket", "id": 1346, "trainId": 202}, + {"name": "gate", "id": 1081, "trainId": 203}, + {"name": "monitor, monitoring device", "id": 1583, "trainId": 204}, + { + "name": "telephone booth, phone booth, call box, telephone box, telephone kiosk", + "id": 2727, + "trainId": 205, + }, + {"name": "spotlight, spot", "id": 2509, "trainId": 206}, + {"name": "ring", "id": 2123, "trainId": 207}, + {"name": "control panel", "id": 602, "trainId": 208}, + {"name": "blackboard, chalkboard", "id": 202, "trainId": 209}, + {"name": "air conditioner, air conditioning", "id": 10, "trainId": 210}, + {"name": "chest", "id": 490, "trainId": 211}, + {"name": "clock", "id": 530, "trainId": 212}, + {"name": "sand dune", "id": 2213, "trainId": 213}, + {"name": "pipe, pipage, piping", "id": 1884, "trainId": 214}, + {"name": "vault", "id": 2934, "trainId": 215}, + {"name": "table football", "id": 2687, "trainId": 216}, + {"name": "cannon", "id": 387, "trainId": 217}, + {"name": "swimming pool, swimming bath, natatorium", "id": 2668, "trainId": 218}, + {"name": "fluorescent, fluorescent fixture", "id": 982, "trainId": 219}, + {"name": "statue", "id": 2547, "trainId": 220}, + { + "name": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", + "id": 1474, + "trainId": 221, + }, + {"name": "exhibitor", "id": 877, "trainId": 222}, + {"name": "ladder", "id": 1391, "trainId": 223}, + {"name": "carport", "id": 414, "trainId": 224}, + {"name": "dam", "id": 698, "trainId": 225}, + {"name": "pulpit", "id": 2019, "trainId": 226}, + {"name": "skylight, fanlight", "id": 2422, "trainId": 227}, + {"name": "water tower", "id": 3010, "trainId": 228}, + {"name": "grill, grille, grillwork", "id": 1139, "trainId": 229}, + {"name": "display board", "id": 753, "trainId": 230}, + {"name": "pane, pane of glass, window glass", "id": 1747, "trainId": 231}, + {"name": "rubbish, trash, scrap", "id": 2175, "trainId": 232}, + {"name": "ice rink", "id": 1301, "trainId": 233}, + {"name": "fruit", "id": 1033, "trainId": 234}, + {"name": "patio", "id": 1789, "trainId": 235}, + {"name": "vending machine", "id": 2939, "trainId": 236}, + {"name": "telephone, phone, telephone set", "id": 2730, "trainId": 237}, + {"name": "net", "id": 1652, "trainId": 238}, + { + "name": "backpack, back pack, knapsack, packsack, rucksack, haversack", + "id": 90, + "trainId": 239, + }, + {"name": "jar", "id": 1349, "trainId": 240}, + {"name": "track", "id": 2830, "trainId": 241}, + {"name": "magazine", "id": 1485, "trainId": 242}, + {"name": "shutter", "id": 2370, "trainId": 243}, + {"name": "roof", "id": 2155, "trainId": 244}, + {"name": "banner, streamer", "id": 118, "trainId": 245}, + {"name": "landfill", "id": 1402, "trainId": 246}, + {"name": "post", "id": 1957, "trainId": 247}, + {"name": "altarpiece, reredos", "id": 3130, "trainId": 248}, + {"name": "hat, chapeau, lid", "id": 1197, "trainId": 249}, + {"name": "arch, archway", "id": 52, "trainId": 250}, + {"name": "table game", "id": 2688, "trainId": 251}, + {"name": "bag, handbag, pocketbook, purse", "id": 96, "trainId": 252}, + {"name": "document, written document, papers", "id": 762, "trainId": 253}, + {"name": "dome", "id": 772, "trainId": 254}, + {"name": "pier", "id": 1857, "trainId": 255}, + {"name": "shanties", "id": 2315, "trainId": 256}, + {"name": "forecourt", "id": 1016, "trainId": 257}, + {"name": "crane", "id": 643, "trainId": 258}, + {"name": "dog, domestic dog, canis familiaris", "id": 3105, "trainId": 259}, + {"name": "piano, pianoforte, forte-piano", "id": 1849, "trainId": 260}, + {"name": "drawing", "id": 791, "trainId": 261}, + {"name": "cabin", "id": 349, "trainId": 262}, + { + "name": "ad, advertisement, advertizement, advertising, advertizing, advert", + "id": 6, + "trainId": 263, + }, + {"name": "amphitheater, amphitheatre, coliseum", "id": 3114, "trainId": 264}, + {"name": "monument", "id": 1587, "trainId": 265}, + {"name": "henhouse", "id": 1233, "trainId": 266}, + {"name": "cockpit", "id": 559, "trainId": 267}, + {"name": "heater, warmer", "id": 1223, "trainId": 268}, + {"name": "windmill, aerogenerator, wind generator", "id": 3049, "trainId": 269}, + {"name": "pool", "id": 1943, "trainId": 270}, + {"name": "elevator, lift", "id": 853, "trainId": 271}, + {"name": "decoration, ornament, ornamentation", "id": 709, "trainId": 272}, + {"name": "labyrinth", "id": 1390, "trainId": 273}, + {"name": "text, textual matter", "id": 2748, "trainId": 274}, + {"name": "printer", "id": 2007, "trainId": 275}, + {"name": "mezzanine, first balcony", "id": 1546, "trainId": 276}, + {"name": "mattress", "id": 1513, "trainId": 277}, + {"name": "straw", "id": 2600, "trainId": 278}, + {"name": "stalls", "id": 2538, "trainId": 279}, + {"name": "patio, terrace", "id": 1790, "trainId": 280}, + {"name": "billboard, hoarding", "id": 194, "trainId": 281}, + {"name": "bus stop", "id": 326, "trainId": 282}, + {"name": "trouser, pant", "id": 2877, "trainId": 283}, + {"name": "console table, console", "id": 594, "trainId": 284}, + {"name": "rack", "id": 2036, "trainId": 285}, + {"name": "notebook", "id": 1662, "trainId": 286}, + {"name": "shrine", "id": 2366, "trainId": 287}, + {"name": "pantry", "id": 1754, "trainId": 288}, + {"name": "cart", "id": 418, "trainId": 289}, + {"name": "steam shovel", "id": 2553, "trainId": 290}, + {"name": "porch", "id": 1951, "trainId": 291}, + {"name": "postbox, mailbox, letter box", "id": 1963, "trainId": 292}, + {"name": "figurine, statuette", "id": 918, "trainId": 293}, + {"name": "recycling bin", "id": 2086, "trainId": 294}, + {"name": 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{"name": "roll", "id": 2149, "trainId": 672}, + {"name": "runner", "id": 2183, "trainId": 673}, + {"name": "engine", "id": 858, "trainId": 674}, + {"name": "inflatable glove", "id": 1324, "trainId": 675}, + {"name": "games", "id": 1055, "trainId": 676}, + {"name": "pallets", "id": 1741, "trainId": 677}, + {"name": "baskets", "id": 149, "trainId": 678}, + {"name": "coop", "id": 615, "trainId": 679}, + {"name": "dvd player", "id": 825, "trainId": 680}, + {"name": "rocking horse", "id": 2143, "trainId": 681}, + {"name": "buckets", "id": 304, "trainId": 682}, + {"name": "bread rolls", "id": 283, "trainId": 683}, + {"name": "shawl", "id": 2322, "trainId": 684}, + {"name": "watering can", "id": 3017, "trainId": 685}, + {"name": "spotlights", "id": 2510, "trainId": 686}, + {"name": "post-it", "id": 1960, "trainId": 687}, + {"name": "bowls", "id": 265, "trainId": 688}, + {"name": "security camera", "id": 2282, "trainId": 689}, + {"name": "runner cloth", "id": 2184, "trainId": 690}, + {"name": "lock", "id": 1461, "trainId": 691}, + {"name": "alarm, warning device, alarm system", "id": 3113, "trainId": 692}, + {"name": "side", "id": 2372, "trainId": 693}, + {"name": "roulette", "id": 2166, "trainId": 694}, + {"name": "bone", "id": 232, "trainId": 695}, + {"name": "cutlery", "id": 693, "trainId": 696}, + {"name": "pool balls", "id": 1945, "trainId": 697}, + {"name": "wheels", "id": 3039, "trainId": 698}, + {"name": "spice rack", "id": 2494, "trainId": 699}, + {"name": "plant pots", "id": 1908, "trainId": 700}, + {"name": "towel ring", "id": 2827, "trainId": 701}, + {"name": "bread box", "id": 280, "trainId": 702}, + {"name": "video", "id": 2950, "trainId": 703}, + {"name": "funfair", "id": 1044, "trainId": 704}, + {"name": "breads", "id": 288, "trainId": 705}, + {"name": "tripod", "id": 2863, "trainId": 706}, + {"name": "ironing board", "id": 1342, "trainId": 707}, + {"name": "skimmer", "id": 2409, "trainId": 708}, + {"name": "hollow", "id": 1258, "trainId": 709}, + {"name": "scratching post", "id": 2249, "trainId": 710}, + {"name": "tricycle", "id": 2862, "trainId": 711}, + {"name": "file box", "id": 920, "trainId": 712}, + {"name": "mountain pass", "id": 1607, "trainId": 713}, + {"name": "tombstones", "id": 2802, "trainId": 714}, + {"name": "cooker", "id": 610, "trainId": 715}, + {"name": "card game, cards", "id": 3129, "trainId": 716}, + {"name": "golf bag", "id": 1108, "trainId": 717}, + {"name": "towel paper", "id": 2823, "trainId": 718}, + {"name": "chaise lounge", "id": 476, "trainId": 719}, + {"name": "sun", "id": 2641, "trainId": 720}, + {"name": "toilet paper holder", "id": 2788, "trainId": 721}, + {"name": "rake", "id": 2070, "trainId": 722}, + {"name": "key", "id": 1368, "trainId": 723}, + {"name": "umbrella stand", "id": 2903, "trainId": 724}, + {"name": "dartboard", "id": 699, "trainId": 725}, + {"name": "transformer", "id": 2844, "trainId": 726}, + {"name": "fireplace utensils", "id": 942, "trainId": 727}, + {"name": "sweatshirts", "id": 2663, "trainId": 728}, + { + "name": "cellular telephone, cellular phone, cellphone, cell, mobile phone", + "id": 457, + "trainId": 729, + }, + {"name": "tallboy", "id": 2701, "trainId": 730}, + {"name": "stapler", "id": 2540, "trainId": 731}, + {"name": "sauna", "id": 2231, "trainId": 732}, + {"name": "test tube", "id": 2746, "trainId": 733}, + {"name": "palette", "id": 1738, "trainId": 734}, + {"name": "shopping carts", "id": 2350, "trainId": 735}, + {"name": "tools", "id": 2808, "trainId": 736}, + {"name": "push button, push, button", "id": 2025, "trainId": 737}, + {"name": "star", "id": 2541, "trainId": 738}, + {"name": "roof rack", "id": 2156, "trainId": 739}, + {"name": "barbed wire", "id": 126, "trainId": 740}, + {"name": "spray", "id": 2512, "trainId": 741}, + {"name": "ear", "id": 831, "trainId": 742}, + {"name": "sponge", "id": 2503, "trainId": 743}, + {"name": "racket", "id": 2039, "trainId": 744}, + {"name": "tins", "id": 2774, "trainId": 745}, + {"name": "eyeglasses", "id": 886, "trainId": 746}, + {"name": "file", "id": 919, "trainId": 747}, + {"name": "scarfs", "id": 2240, "trainId": 748}, + {"name": "sugar bowl", "id": 2636, "trainId": 749}, + {"name": "flip flop", "id": 963, "trainId": 750}, + {"name": "headstones", "id": 1218, "trainId": 751}, + {"name": "laptop bag", "id": 1406, "trainId": 752}, + {"name": "leash", "id": 1420, "trainId": 753}, + {"name": "climbing frame", "id": 526, "trainId": 754}, + {"name": "suit hanger", "id": 2639, "trainId": 755}, + {"name": "floor spotlight", "id": 975, "trainId": 756}, + {"name": "plate rack", "id": 1921, "trainId": 757}, + {"name": "sewer", "id": 2305, "trainId": 758}, + {"name": "hard drive", "id": 1193, "trainId": 759}, + {"name": "sprinkler", "id": 2517, "trainId": 760}, + {"name": "tools box", "id": 2809, "trainId": 761}, + {"name": "necklace", "id": 1647, "trainId": 762}, + {"name": "bulbs", "id": 314, "trainId": 763}, + {"name": "steel industry", "id": 2560, "trainId": 764}, + {"name": "club", "id": 545, "trainId": 765}, + {"name": "jack", "id": 1345, "trainId": 766}, + {"name": "door bars", "id": 775, "trainId": 767}, + { + "name": "control panel, instrument panel, control board, board, panel", + "id": 603, + "trainId": 768, + }, + {"name": "hairbrush", "id": 1163, "trainId": 769}, + {"name": "napkin holder", "id": 1641, "trainId": 770}, + {"name": "office", "id": 1678, "trainId": 771}, + {"name": "smoke detector", "id": 2450, "trainId": 772}, + {"name": "utensils", "id": 2915, "trainId": 773}, + {"name": "apron", "id": 42, "trainId": 774}, + {"name": "scissors", "id": 2242, "trainId": 775}, + {"name": "terminal", "id": 2741, "trainId": 776}, + {"name": "grinder", "id": 1143, "trainId": 777}, + {"name": "entry phone", "id": 862, "trainId": 778}, + {"name": "newspaper stand", "id": 1654, "trainId": 779}, + {"name": "pepper shaker", "id": 1826, "trainId": 780}, + {"name": "onions", "id": 1689, "trainId": 781}, + { + "name": "central processing unit, cpu, c p u , central processor, processor, mainframe", + "id": 3124, + "trainId": 782, + }, + {"name": "tape", "id": 2710, "trainId": 783}, + {"name": "bat", "id": 152, "trainId": 784}, + {"name": "coaster", "id": 549, "trainId": 785}, + {"name": "calculator", "id": 360, "trainId": 786}, + {"name": "potatoes", "id": 1982, "trainId": 787}, + {"name": "luggage rack", "id": 1478, "trainId": 788}, + {"name": "salt", "id": 2203, "trainId": 789}, + {"name": "street number", "id": 2612, "trainId": 790}, + {"name": "viewpoint", "id": 2956, "trainId": 791}, + {"name": "sword", "id": 2681, "trainId": 792}, + {"name": "cd", "id": 437, "trainId": 793}, + {"name": "rowing machine", "id": 2171, "trainId": 794}, + {"name": "plug", "id": 1933, "trainId": 795}, + {"name": "andiron, firedog, dog, dog-iron", "id": 3110, "trainId": 796}, + {"name": "pepper", "id": 1824, "trainId": 797}, + {"name": "tongs", "id": 2803, "trainId": 798}, + {"name": "bonfire", "id": 234, "trainId": 799}, + {"name": "dog dish", "id": 764, "trainId": 800}, + {"name": "belt", "id": 177, "trainId": 801}, + {"name": "dumbbells", "id": 817, "trainId": 802}, + {"name": "videocassette recorder, vcr", "id": 3145, "trainId": 803}, + {"name": "hook", "id": 1262, "trainId": 804}, + {"name": "envelopes", "id": 864, "trainId": 805}, + {"name": "shower faucet", "id": 2359, "trainId": 806}, + {"name": "watch", "id": 2992, "trainId": 807}, + {"name": "padlock", "id": 1725, "trainId": 808}, + {"name": "swimming pool ladder", "id": 2667, "trainId": 809}, + {"name": "spanners", "id": 2484, "trainId": 810}, + {"name": "gravy boat", "id": 1133, "trainId": 811}, + {"name": "notice board", "id": 1667, "trainId": 812}, + {"name": "trash bags", "id": 2847, "trainId": 813}, + {"name": "fire alarm", "id": 932, "trainId": 814}, + {"name": "ladle", "id": 1392, "trainId": 815}, + {"name": "stethoscope", "id": 2573, "trainId": 816}, + {"name": "rocket", "id": 2140, "trainId": 817}, + {"name": "funnel", "id": 1046, "trainId": 818}, + {"name": "bowling pins", "id": 264, "trainId": 819}, + {"name": "valve", "id": 2927, "trainId": 820}, + {"name": "thermometer", "id": 2752, "trainId": 821}, + {"name": "cups", "id": 679, "trainId": 822}, + {"name": "spice jar", "id": 2493, "trainId": 823}, + {"name": "night light", "id": 1658, "trainId": 824}, + {"name": "soaps", "id": 2466, "trainId": 825}, + {"name": "games table", "id": 1057, "trainId": 826}, + {"name": "slotted spoon", "id": 2444, "trainId": 827}, + {"name": "reel", "id": 2093, "trainId": 828}, + {"name": "scourer", "id": 2248, "trainId": 829}, + {"name": "sleeping robe", "id": 2432, "trainId": 830}, + {"name": "desk mat", "id": 726, "trainId": 831}, + {"name": "dumbbell", "id": 816, "trainId": 832}, + {"name": "hammer", "id": 1171, "trainId": 833}, + {"name": "tie", "id": 2766, "trainId": 834}, + {"name": "typewriter", "id": 2900, "trainId": 835}, + {"name": "shaker", "id": 2313, "trainId": 836}, + {"name": "cheese dish", "id": 488, "trainId": 837}, + {"name": "sea star", "id": 2265, "trainId": 838}, + {"name": "racquet", "id": 2043, "trainId": 839}, + {"name": "butane gas cylinder", "id": 332, "trainId": 840}, + {"name": "paper weight", "id": 1771, "trainId": 841}, + {"name": "shaving brush", "id": 2320, "trainId": 842}, + {"name": "sunglasses", "id": 2646, "trainId": 843}, + {"name": "gear shift", "id": 1089, "trainId": 844}, + {"name": "towel rail", "id": 2826, "trainId": 845}, + {"name": "adding machine, totalizer, totaliser", "id": 3148, "trainId": 846}, +] + + +def _get_ade20k_full_meta(): + # Id 0 is reserved for ignore_label, we change ignore_label for 0 + # to 255 in our pre-processing, so all ids are shifted by 1. + stuff_ids = [k["id"] for k in ADE20K_SEM_SEG_FULL_CATEGORIES] + assert len(stuff_ids) == 847, len(stuff_ids) + + # For semantic segmentation, this mapping maps from contiguous stuff id + # (in [0, 91], used in models) to ids in the dataset (used for processing results) + stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)} + stuff_classes = [k["name"] for k in ADE20K_SEM_SEG_FULL_CATEGORIES] + + ret = { + "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, + "stuff_classes": stuff_classes, + } + return ret + + +def register_all_ade20k_full(root): + root = os.path.join(root, "ADE20K_2021_17_01") + meta = _get_ade20k_full_meta() + for name, dirname in [("train", "training"), ("val", "validation")]: + image_dir = os.path.join(root, "images_detectron2", dirname) + gt_dir = os.path.join(root, "annotations_detectron2", dirname) + name = f"ade20k_full_sem_seg_{name}" + DatasetCatalog.register( + name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="tif", image_ext="jpg") + ) + MetadataCatalog.get(name).set( + stuff_classes=meta["stuff_classes"][:], + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + ignore_label=65535, # NOTE: gt is saved in 16-bit TIFF images + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_ade20k_full(_root) diff --git a/videocutler/mask2former/data/datasets/register_ade20k_instance.py b/videocutler/mask2former/data/datasets/register_ade20k_instance.py new file mode 100644 index 0000000000000000000000000000000000000000..1ded7095cde756dfa1d94c25b2f7d1d2e5da6313 --- /dev/null +++ b/videocutler/mask2former/data/datasets/register_ade20k_instance.py @@ -0,0 +1,53 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import json +import logging +import numpy as np +import os +from PIL import Image + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets.coco import load_coco_json, register_coco_instances +from detectron2.utils.file_io import PathManager + +ADE_CATEGORIES = [{'id': 7, 'name': 'bed'}, {'id': 8, 'name': 'windowpane'}, {'id': 10, 'name': 'cabinet'}, {'id': 12, 'name': 'person'}, {'id': 14, 'name': 'door'}, {'id': 15, 'name': 'table'}, {'id': 18, 'name': 'curtain'}, {'id': 19, 'name': 'chair'}, {'id': 20, 'name': 'car'}, {'id': 22, 'name': 'painting'}, {'id': 23, 'name': 'sofa'}, {'id': 24, 'name': 'shelf'}, {'id': 27, 'name': 'mirror'}, {'id': 30, 'name': 'armchair'}, {'id': 31, 'name': 'seat'}, {'id': 32, 'name': 'fence'}, {'id': 33, 'name': 'desk'}, {'id': 35, 'name': 'wardrobe'}, {'id': 36, 'name': 'lamp'}, {'id': 37, 'name': 'bathtub'}, {'id': 38, 'name': 'railing'}, {'id': 39, 'name': 'cushion'}, {'id': 41, 'name': 'box'}, {'id': 42, 'name': 'column'}, {'id': 43, 'name': 'signboard'}, {'id': 44, 'name': 'chest of drawers'}, {'id': 45, 'name': 'counter'}, {'id': 47, 'name': 'sink'}, {'id': 49, 'name': 'fireplace'}, {'id': 50, 'name': 'refrigerator'}, {'id': 53, 'name': 'stairs'}, {'id': 55, 'name': 'case'}, {'id': 56, 'name': 'pool table'}, {'id': 57, 'name': 'pillow'}, {'id': 58, 'name': 'screen door'}, {'id': 62, 'name': 'bookcase'}, {'id': 64, 'name': 'coffee table'}, {'id': 65, 'name': 'toilet'}, {'id': 66, 'name': 'flower'}, {'id': 67, 'name': 'book'}, {'id': 69, 'name': 'bench'}, {'id': 70, 'name': 'countertop'}, {'id': 71, 'name': 'stove'}, {'id': 72, 'name': 'palm'}, {'id': 73, 'name': 'kitchen island'}, {'id': 74, 'name': 'computer'}, {'id': 75, 'name': 'swivel chair'}, {'id': 76, 'name': 'boat'}, {'id': 78, 'name': 'arcade machine'}, {'id': 80, 'name': 'bus'}, {'id': 81, 'name': 'towel'}, {'id': 82, 'name': 'light'}, {'id': 83, 'name': 'truck'}, {'id': 85, 'name': 'chandelier'}, {'id': 86, 'name': 'awning'}, {'id': 87, 'name': 'streetlight'}, {'id': 88, 'name': 'booth'}, {'id': 89, 'name': 'television receiver'}, {'id': 90, 'name': 'airplane'}, {'id': 92, 'name': 'apparel'}, {'id': 93, 'name': 'pole'}, {'id': 95, 'name': 'bannister'}, {'id': 97, 'name': 'ottoman'}, {'id': 98, 'name': 'bottle'}, {'id': 102, 'name': 'van'}, {'id': 103, 'name': 'ship'}, {'id': 104, 'name': 'fountain'}, {'id': 107, 'name': 'washer'}, {'id': 108, 'name': 'plaything'}, {'id': 110, 'name': 'stool'}, {'id': 111, 'name': 'barrel'}, {'id': 112, 'name': 'basket'}, {'id': 115, 'name': 'bag'}, {'id': 116, 'name': 'minibike'}, {'id': 118, 'name': 'oven'}, {'id': 119, 'name': 'ball'}, {'id': 120, 'name': 'food'}, {'id': 121, 'name': 'step'}, {'id': 123, 'name': 'trade name'}, {'id': 124, 'name': 'microwave'}, {'id': 125, 'name': 'pot'}, {'id': 126, 'name': 'animal'}, {'id': 127, 'name': 'bicycle'}, {'id': 129, 'name': 'dishwasher'}, {'id': 130, 'name': 'screen'}, {'id': 132, 'name': 'sculpture'}, {'id': 133, 'name': 'hood'}, {'id': 134, 'name': 'sconce'}, {'id': 135, 'name': 'vase'}, {'id': 136, 'name': 'traffic light'}, {'id': 137, 'name': 'tray'}, {'id': 138, 'name': 'ashcan'}, {'id': 139, 'name': 'fan'}, {'id': 142, 'name': 'plate'}, {'id': 143, 'name': 'monitor'}, {'id': 144, 'name': 'bulletin board'}, {'id': 146, 'name': 'radiator'}, {'id': 147, 'name': 'glass'}, {'id': 148, 'name': 'clock'}, {'id': 149, 'name': 'flag'}] + + +_PREDEFINED_SPLITS = { + # point annotations without masks + "ade20k_instance_train": ( + "ADEChallengeData2016/images/training", + "ADEChallengeData2016/ade20k_instance_train.json", + ), + "ade20k_instance_val": ( + "ADEChallengeData2016/images/validation", + "ADEChallengeData2016/ade20k_instance_val.json", + ), +} + + +def _get_ade_instances_meta(): + thing_ids = [k["id"] for k in ADE_CATEGORIES] + assert len(thing_ids) == 100, len(thing_ids) + # Mapping from the incontiguous ADE category id to an id in [0, 99] + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in ADE_CATEGORIES] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + } + return ret + + +def register_all_ade20k_instance(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_ade_instances_meta(), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_ade20k_instance(_root) diff --git a/videocutler/mask2former/data/datasets/register_ade20k_panoptic.py b/videocutler/mask2former/data/datasets/register_ade20k_panoptic.py new file mode 100644 index 0000000000000000000000000000000000000000..a76c999f96c58b2f44ab363a55dcc1c8c7f1b074 --- /dev/null +++ b/videocutler/mask2former/data/datasets/register_ade20k_panoptic.py @@ -0,0 +1,390 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import json +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.utils.file_io import PathManager + +ADE20K_150_CATEGORIES = [ + {"color": [120, 120, 120], "id": 0, "isthing": 0, "name": "wall"}, + {"color": [180, 120, 120], "id": 1, "isthing": 0, "name": "building"}, + {"color": [6, 230, 230], "id": 2, "isthing": 0, "name": "sky"}, + {"color": [80, 50, 50], "id": 3, "isthing": 0, "name": "floor"}, + {"color": [4, 200, 3], "id": 4, "isthing": 0, "name": "tree"}, + {"color": [120, 120, 80], "id": 5, "isthing": 0, "name": "ceiling"}, + {"color": [140, 140, 140], "id": 6, "isthing": 0, "name": "road, route"}, + {"color": [204, 5, 255], "id": 7, "isthing": 1, "name": "bed"}, + {"color": [230, 230, 230], "id": 8, "isthing": 1, "name": "window "}, + {"color": [4, 250, 7], "id": 9, "isthing": 0, "name": "grass"}, + {"color": [224, 5, 255], "id": 10, "isthing": 1, "name": "cabinet"}, + {"color": [235, 255, 7], "id": 11, "isthing": 0, "name": "sidewalk, pavement"}, + {"color": [150, 5, 61], "id": 12, "isthing": 1, "name": "person"}, + {"color": [120, 120, 70], "id": 13, "isthing": 0, "name": "earth, ground"}, + {"color": [8, 255, 51], "id": 14, "isthing": 1, "name": "door"}, + {"color": [255, 6, 82], "id": 15, "isthing": 1, "name": "table"}, + {"color": [143, 255, 140], "id": 16, "isthing": 0, "name": "mountain, mount"}, + {"color": [204, 255, 4], "id": 17, "isthing": 0, "name": "plant"}, + {"color": [255, 51, 7], "id": 18, "isthing": 1, "name": "curtain"}, + {"color": [204, 70, 3], "id": 19, "isthing": 1, "name": "chair"}, + {"color": [0, 102, 200], "id": 20, "isthing": 1, "name": "car"}, + {"color": [61, 230, 250], "id": 21, "isthing": 0, "name": "water"}, + {"color": [255, 6, 51], "id": 22, "isthing": 1, "name": "painting, picture"}, + {"color": [11, 102, 255], "id": 23, "isthing": 1, "name": "sofa"}, + {"color": [255, 7, 71], "id": 24, "isthing": 1, "name": "shelf"}, + {"color": [255, 9, 224], "id": 25, "isthing": 0, "name": "house"}, + {"color": [9, 7, 230], "id": 26, "isthing": 0, "name": "sea"}, + {"color": [220, 220, 220], "id": 27, "isthing": 1, "name": "mirror"}, + {"color": [255, 9, 92], "id": 28, "isthing": 0, "name": "rug"}, + {"color": [112, 9, 255], "id": 29, "isthing": 0, "name": "field"}, + {"color": [8, 255, 214], "id": 30, "isthing": 1, "name": "armchair"}, + {"color": [7, 255, 224], "id": 31, "isthing": 1, "name": "seat"}, + {"color": [255, 184, 6], "id": 32, "isthing": 1, "name": "fence"}, + {"color": [10, 255, 71], "id": 33, "isthing": 1, "name": "desk"}, + {"color": [255, 41, 10], "id": 34, "isthing": 0, "name": "rock, stone"}, + {"color": [7, 255, 255], "id": 35, "isthing": 1, "name": "wardrobe, closet, press"}, + {"color": [224, 255, 8], "id": 36, "isthing": 1, "name": "lamp"}, + {"color": [102, 8, 255], "id": 37, "isthing": 1, "name": "tub"}, + {"color": [255, 61, 6], "id": 38, "isthing": 1, "name": "rail"}, + {"color": [255, 194, 7], "id": 39, "isthing": 1, "name": "cushion"}, + {"color": [255, 122, 8], "id": 40, "isthing": 0, "name": "base, pedestal, stand"}, + {"color": [0, 255, 20], "id": 41, "isthing": 1, "name": "box"}, + {"color": [255, 8, 41], "id": 42, "isthing": 1, "name": "column, pillar"}, + {"color": [255, 5, 153], "id": 43, "isthing": 1, "name": "signboard, sign"}, + { + "color": [6, 51, 255], + "id": 44, + "isthing": 1, + "name": "chest of drawers, chest, bureau, dresser", + }, + {"color": [235, 12, 255], "id": 45, "isthing": 1, "name": "counter"}, + {"color": [160, 150, 20], "id": 46, "isthing": 0, "name": "sand"}, + {"color": [0, 163, 255], "id": 47, "isthing": 1, "name": "sink"}, + {"color": [140, 140, 140], "id": 48, "isthing": 0, "name": "skyscraper"}, + {"color": [250, 10, 15], "id": 49, "isthing": 1, "name": "fireplace"}, + {"color": [20, 255, 0], "id": 50, "isthing": 1, "name": "refrigerator, icebox"}, + {"color": [31, 255, 0], "id": 51, "isthing": 0, "name": "grandstand, covered stand"}, + {"color": [255, 31, 0], "id": 52, "isthing": 0, "name": "path"}, + {"color": [255, 224, 0], "id": 53, "isthing": 1, "name": "stairs"}, + {"color": [153, 255, 0], "id": 54, "isthing": 0, "name": "runway"}, + {"color": [0, 0, 255], "id": 55, "isthing": 1, "name": "case, display case, showcase, vitrine"}, + { + "color": [255, 71, 0], + "id": 56, + "isthing": 1, + "name": "pool table, billiard table, snooker table", + }, + {"color": [0, 235, 255], "id": 57, "isthing": 1, "name": "pillow"}, + {"color": [0, 173, 255], "id": 58, "isthing": 1, "name": "screen door, screen"}, + {"color": [31, 0, 255], "id": 59, "isthing": 0, "name": "stairway, staircase"}, + {"color": [11, 200, 200], "id": 60, "isthing": 0, "name": "river"}, + {"color": [255, 82, 0], "id": 61, "isthing": 0, "name": "bridge, span"}, + {"color": [0, 255, 245], "id": 62, "isthing": 1, "name": "bookcase"}, + {"color": [0, 61, 255], "id": 63, "isthing": 0, "name": "blind, screen"}, + {"color": [0, 255, 112], "id": 64, "isthing": 1, "name": "coffee table"}, + { + "color": [0, 255, 133], + "id": 65, + "isthing": 1, + "name": "toilet, can, commode, crapper, pot, potty, stool, throne", + }, + {"color": [255, 0, 0], "id": 66, "isthing": 1, "name": "flower"}, + {"color": [255, 163, 0], "id": 67, "isthing": 1, "name": "book"}, + {"color": [255, 102, 0], "id": 68, "isthing": 0, "name": "hill"}, + {"color": [194, 255, 0], "id": 69, "isthing": 1, "name": "bench"}, + {"color": [0, 143, 255], "id": 70, "isthing": 1, "name": "countertop"}, + {"color": [51, 255, 0], "id": 71, "isthing": 1, "name": "stove"}, + {"color": [0, 82, 255], "id": 72, "isthing": 1, "name": "palm, palm tree"}, + {"color": [0, 255, 41], "id": 73, "isthing": 1, "name": "kitchen island"}, + {"color": [0, 255, 173], "id": 74, "isthing": 1, "name": "computer"}, + {"color": [10, 0, 255], "id": 75, "isthing": 1, "name": "swivel chair"}, + {"color": [173, 255, 0], "id": 76, "isthing": 1, "name": "boat"}, + {"color": [0, 255, 153], "id": 77, "isthing": 0, "name": "bar"}, + {"color": [255, 92, 0], "id": 78, "isthing": 1, "name": "arcade machine"}, + {"color": [255, 0, 255], "id": 79, "isthing": 0, "name": "hovel, hut, hutch, shack, shanty"}, + {"color": [255, 0, 245], "id": 80, "isthing": 1, "name": "bus"}, + {"color": [255, 0, 102], "id": 81, "isthing": 1, "name": "towel"}, + {"color": [255, 173, 0], "id": 82, "isthing": 1, "name": "light"}, + {"color": [255, 0, 20], "id": 83, "isthing": 1, "name": "truck"}, + {"color": [255, 184, 184], "id": 84, "isthing": 0, "name": "tower"}, + {"color": [0, 31, 255], "id": 85, "isthing": 1, "name": "chandelier"}, + {"color": [0, 255, 61], "id": 86, "isthing": 1, "name": "awning, sunshade, sunblind"}, + {"color": [0, 71, 255], "id": 87, "isthing": 1, "name": "street lamp"}, + {"color": [255, 0, 204], "id": 88, "isthing": 1, "name": "booth"}, + {"color": [0, 255, 194], "id": 89, "isthing": 1, "name": "tv"}, + {"color": [0, 255, 82], "id": 90, "isthing": 1, "name": "plane"}, + {"color": [0, 10, 255], "id": 91, "isthing": 0, "name": "dirt track"}, + {"color": [0, 112, 255], "id": 92, "isthing": 1, "name": "clothes"}, + {"color": [51, 0, 255], "id": 93, "isthing": 1, "name": "pole"}, + {"color": [0, 194, 255], "id": 94, "isthing": 0, "name": "land, ground, soil"}, + { + "color": [0, 122, 255], + "id": 95, + "isthing": 1, + "name": "bannister, banister, balustrade, balusters, handrail", + }, + { + "color": [0, 255, 163], + "id": 96, + "isthing": 0, + "name": "escalator, moving staircase, moving stairway", + }, + { + "color": [255, 153, 0], + "id": 97, + "isthing": 1, + "name": "ottoman, pouf, pouffe, puff, hassock", + }, + {"color": [0, 255, 10], "id": 98, "isthing": 1, "name": "bottle"}, + {"color": [255, 112, 0], "id": 99, "isthing": 0, "name": "buffet, counter, sideboard"}, + { + "color": [143, 255, 0], + "id": 100, + "isthing": 0, + "name": "poster, posting, placard, notice, bill, card", + }, + {"color": [82, 0, 255], "id": 101, "isthing": 0, "name": "stage"}, + {"color": [163, 255, 0], "id": 102, "isthing": 1, "name": "van"}, + {"color": [255, 235, 0], "id": 103, "isthing": 1, "name": "ship"}, + {"color": [8, 184, 170], "id": 104, "isthing": 1, "name": "fountain"}, + { + "color": [133, 0, 255], + "id": 105, + "isthing": 0, + "name": "conveyer belt, conveyor belt, conveyer, conveyor, transporter", + }, + {"color": [0, 255, 92], "id": 106, "isthing": 0, "name": "canopy"}, + { + "color": [184, 0, 255], + "id": 107, + "isthing": 1, + "name": "washer, automatic washer, washing machine", + }, + {"color": [255, 0, 31], "id": 108, "isthing": 1, "name": "plaything, toy"}, + {"color": [0, 184, 255], "id": 109, "isthing": 0, "name": "pool"}, + {"color": [0, 214, 255], "id": 110, "isthing": 1, "name": "stool"}, + {"color": [255, 0, 112], "id": 111, "isthing": 1, "name": "barrel, cask"}, + {"color": [92, 255, 0], "id": 112, "isthing": 1, "name": "basket, handbasket"}, + {"color": [0, 224, 255], "id": 113, "isthing": 0, "name": "falls"}, + {"color": [112, 224, 255], "id": 114, "isthing": 0, "name": "tent"}, + {"color": [70, 184, 160], "id": 115, "isthing": 1, "name": "bag"}, + {"color": [163, 0, 255], "id": 116, "isthing": 1, "name": "minibike, motorbike"}, + {"color": [153, 0, 255], "id": 117, "isthing": 0, "name": "cradle"}, + {"color": [71, 255, 0], "id": 118, "isthing": 1, "name": "oven"}, + {"color": [255, 0, 163], "id": 119, "isthing": 1, "name": "ball"}, + {"color": [255, 204, 0], "id": 120, "isthing": 1, "name": "food, solid food"}, + {"color": [255, 0, 143], "id": 121, "isthing": 1, "name": "step, stair"}, + {"color": [0, 255, 235], "id": 122, "isthing": 0, "name": "tank, storage tank"}, + {"color": [133, 255, 0], "id": 123, "isthing": 1, "name": "trade name"}, + {"color": [255, 0, 235], "id": 124, "isthing": 1, "name": "microwave"}, + {"color": [245, 0, 255], "id": 125, "isthing": 1, "name": "pot"}, + {"color": [255, 0, 122], "id": 126, "isthing": 1, "name": "animal"}, + {"color": [255, 245, 0], "id": 127, "isthing": 1, "name": "bicycle"}, + {"color": [10, 190, 212], "id": 128, "isthing": 0, "name": "lake"}, + {"color": [214, 255, 0], "id": 129, "isthing": 1, "name": "dishwasher"}, + {"color": [0, 204, 255], "id": 130, "isthing": 1, "name": "screen"}, + {"color": [20, 0, 255], "id": 131, "isthing": 0, "name": "blanket, cover"}, + {"color": [255, 255, 0], "id": 132, "isthing": 1, "name": "sculpture"}, + {"color": [0, 153, 255], "id": 133, "isthing": 1, "name": "hood, exhaust hood"}, + {"color": [0, 41, 255], "id": 134, "isthing": 1, "name": "sconce"}, + {"color": [0, 255, 204], "id": 135, "isthing": 1, "name": "vase"}, + {"color": [41, 0, 255], "id": 136, "isthing": 1, "name": "traffic light"}, + {"color": [41, 255, 0], "id": 137, "isthing": 1, "name": "tray"}, + {"color": [173, 0, 255], "id": 138, "isthing": 1, "name": "trash can"}, + {"color": [0, 245, 255], "id": 139, "isthing": 1, "name": "fan"}, + {"color": [71, 0, 255], "id": 140, "isthing": 0, "name": "pier"}, + {"color": [122, 0, 255], "id": 141, "isthing": 0, "name": "crt screen"}, + {"color": [0, 255, 184], "id": 142, "isthing": 1, "name": "plate"}, + {"color": [0, 92, 255], "id": 143, "isthing": 1, "name": "monitor"}, + {"color": [184, 255, 0], "id": 144, "isthing": 1, "name": "bulletin board"}, + {"color": [0, 133, 255], "id": 145, "isthing": 0, "name": "shower"}, + {"color": [255, 214, 0], "id": 146, "isthing": 1, "name": "radiator"}, + {"color": [25, 194, 194], "id": 147, "isthing": 1, "name": "glass, drinking glass"}, + {"color": [102, 255, 0], "id": 148, "isthing": 1, "name": "clock"}, + {"color": [92, 0, 255], "id": 149, "isthing": 1, "name": "flag"}, +] + +ADE20k_COLORS = [k["color"] for k in ADE20K_150_CATEGORIES] + +MetadataCatalog.get("ade20k_sem_seg_train").set( + stuff_colors=ADE20k_COLORS[:], +) + +MetadataCatalog.get("ade20k_sem_seg_val").set( + stuff_colors=ADE20k_COLORS[:], +) + + +def load_ade20k_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". + gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017". + json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json". + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + + def _convert_category_id(segment_info, meta): + if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: + segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = True + else: + segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = False + return segment_info + + with PathManager.open(json_file) as f: + json_info = json.load(f) + + ret = [] + for ann in json_info["annotations"]: + image_id = ann["image_id"] + # TODO: currently we assume image and label has the same filename but + # different extension, and images have extension ".jpg" for COCO. Need + # to make image extension a user-provided argument if we extend this + # function to support other COCO-like datasets. + image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg") + label_file = os.path.join(gt_dir, ann["file_name"]) + sem_label_file = os.path.join(semseg_dir, ann["file_name"]) + segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]] + ret.append( + { + "file_name": image_file, + "image_id": image_id, + "pan_seg_file_name": label_file, + "sem_seg_file_name": sem_label_file, + "segments_info": segments_info, + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"] + assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"] + assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"] + return ret + + +def register_ade20k_panoptic( + name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None +): + """ + Register a "standard" version of ADE20k panoptic segmentation dataset named `name`. + The dictionaries in this registered dataset follows detectron2's standard format. + Hence it's called "standard". + Args: + name (str): the name that identifies a dataset, + e.g. "ade20k_panoptic_train" + metadata (dict): extra metadata associated with this dataset. + image_root (str): directory which contains all the images + panoptic_root (str): directory which contains panoptic annotation images in COCO format + panoptic_json (str): path to the json panoptic annotation file in COCO format + sem_seg_root (none): not used, to be consistent with + `register_coco_panoptic_separated`. + instances_json (str): path to the json instance annotation file + """ + panoptic_name = name + DatasetCatalog.register( + panoptic_name, + lambda: load_ade20k_panoptic_json( + panoptic_json, image_root, panoptic_root, semantic_root, metadata + ), + ) + MetadataCatalog.get(panoptic_name).set( + panoptic_root=panoptic_root, + image_root=image_root, + panoptic_json=panoptic_json, + json_file=instances_json, + evaluator_type="ade20k_panoptic_seg", + ignore_label=255, + label_divisor=1000, + **metadata, + ) + + +_PREDEFINED_SPLITS_ADE20K_PANOPTIC = { + "ade20k_panoptic_train": ( + "ADEChallengeData2016/images/training", + "ADEChallengeData2016/ade20k_panoptic_train", + "ADEChallengeData2016/ade20k_panoptic_train.json", + "ADEChallengeData2016/annotations_detectron2/training", + "ADEChallengeData2016/ade20k_instance_train.json", + ), + "ade20k_panoptic_val": ( + "ADEChallengeData2016/images/validation", + "ADEChallengeData2016/ade20k_panoptic_val", + "ADEChallengeData2016/ade20k_panoptic_val.json", + "ADEChallengeData2016/annotations_detectron2/validation", + "ADEChallengeData2016/ade20k_instance_val.json", + ), +} + + +def get_metadata(): + meta = {} + # The following metadata maps contiguous id from [0, #thing categories + + # #stuff categories) to their names and colors. We have to replica of the + # same name and color under "thing_*" and "stuff_*" because the current + # visualization function in D2 handles thing and class classes differently + # due to some heuristic used in Panoptic FPN. We keep the same naming to + # enable reusing existing visualization functions. + thing_classes = [k["name"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1] + thing_colors = [k["color"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1] + stuff_classes = [k["name"] for k in ADE20K_150_CATEGORIES] + stuff_colors = [k["color"] for k in ADE20K_150_CATEGORIES] + + meta["thing_classes"] = thing_classes + meta["thing_colors"] = thing_colors + meta["stuff_classes"] = stuff_classes + meta["stuff_colors"] = stuff_colors + + # Convert category id for training: + # category id: like semantic segmentation, it is the class id for each + # pixel. Since there are some classes not used in evaluation, the category + # id is not always contiguous and thus we have two set of category ids: + # - original category id: category id in the original dataset, mainly + # used for evaluation. + # - contiguous category id: [0, #classes), in order to train the linear + # softmax classifier. + thing_dataset_id_to_contiguous_id = {} + stuff_dataset_id_to_contiguous_id = {} + + for i, cat in enumerate(ADE20K_150_CATEGORIES): + if cat["isthing"]: + thing_dataset_id_to_contiguous_id[cat["id"]] = i + # else: + # stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + # in order to use sem_seg evaluator + stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id + meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id + + return meta + + +def register_all_ade20k_panoptic(root): + metadata = get_metadata() + for ( + prefix, + (image_root, panoptic_root, panoptic_json, semantic_root, instance_json), + ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items(): + # The "standard" version of COCO panoptic segmentation dataset, + # e.g. used by Panoptic-DeepLab + register_ade20k_panoptic( + prefix, + metadata, + os.path.join(root, image_root), + os.path.join(root, panoptic_root), + os.path.join(root, semantic_root), + os.path.join(root, panoptic_json), + os.path.join(root, instance_json), + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_ade20k_panoptic(_root) diff --git a/videocutler/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py b/videocutler/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py new file mode 100644 index 0000000000000000000000000000000000000000..eecd413d4ed028f94e3aad9fc6bad231e850b5da --- /dev/null +++ b/videocutler/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py @@ -0,0 +1,181 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import json +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets import load_sem_seg +from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES +from detectron2.utils.file_io import PathManager + + +_PREDEFINED_SPLITS_COCO_PANOPTIC = { + "coco_2017_train_panoptic": ( + # This is the original panoptic annotation directory + "coco/panoptic_train2017", + "coco/annotations/panoptic_train2017.json", + # This directory contains semantic annotations that are + # converted from panoptic annotations. + # It is used by PanopticFPN. + # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py + # to create these directories. + "coco/panoptic_semseg_train2017", + ), + "coco_2017_val_panoptic": ( + "coco/panoptic_val2017", + "coco/annotations/panoptic_val2017.json", + "coco/panoptic_semseg_val2017", + ), +} + + +def get_metadata(): + meta = {} + # The following metadata maps contiguous id from [0, #thing categories + + # #stuff categories) to their names and colors. We have to replica of the + # same name and color under "thing_*" and "stuff_*" because the current + # visualization function in D2 handles thing and class classes differently + # due to some heuristic used in Panoptic FPN. We keep the same naming to + # enable reusing existing visualization functions. + thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1] + thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1] + stuff_classes = [k["name"] for k in COCO_CATEGORIES] + stuff_colors = [k["color"] for k in COCO_CATEGORIES] + + meta["thing_classes"] = thing_classes + meta["thing_colors"] = thing_colors + meta["stuff_classes"] = stuff_classes + meta["stuff_colors"] = stuff_colors + + # Convert category id for training: + # category id: like semantic segmentation, it is the class id for each + # pixel. Since there are some classes not used in evaluation, the category + # id is not always contiguous and thus we have two set of category ids: + # - original category id: category id in the original dataset, mainly + # used for evaluation. + # - contiguous category id: [0, #classes), in order to train the linear + # softmax classifier. + thing_dataset_id_to_contiguous_id = {} + stuff_dataset_id_to_contiguous_id = {} + + for i, cat in enumerate(COCO_CATEGORIES): + if cat["isthing"]: + thing_dataset_id_to_contiguous_id[cat["id"]] = i + # else: + # stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + # in order to use sem_seg evaluator + stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id + meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id + + return meta + + +def load_coco_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". + gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017". + json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json". + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + + def _convert_category_id(segment_info, meta): + if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: + segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = True + else: + segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = False + return segment_info + + with PathManager.open(json_file) as f: + json_info = json.load(f) + + ret = [] + for ann in json_info["annotations"]: + image_id = int(ann["image_id"]) + # TODO: currently we assume image and label has the same filename but + # different extension, and images have extension ".jpg" for COCO. Need + # to make image extension a user-provided argument if we extend this + # function to support other COCO-like datasets. + image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg") + label_file = os.path.join(gt_dir, ann["file_name"]) + sem_label_file = os.path.join(semseg_dir, ann["file_name"]) + segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]] + ret.append( + { + "file_name": image_file, + "image_id": image_id, + "pan_seg_file_name": label_file, + "sem_seg_file_name": sem_label_file, + "segments_info": segments_info, + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"] + assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"] + assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"] + return ret + + +def register_coco_panoptic_annos_sem_seg( + name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json +): + panoptic_name = name + delattr(MetadataCatalog.get(panoptic_name), "thing_classes") + delattr(MetadataCatalog.get(panoptic_name), "thing_colors") + MetadataCatalog.get(panoptic_name).set( + thing_classes=metadata["thing_classes"], + thing_colors=metadata["thing_colors"], + # thing_dataset_id_to_contiguous_id=metadata["thing_dataset_id_to_contiguous_id"], + ) + + # the name is "coco_2017_train_panoptic_with_sem_seg" and "coco_2017_val_panoptic_with_sem_seg" + semantic_name = name + "_with_sem_seg" + DatasetCatalog.register( + semantic_name, + lambda: load_coco_panoptic_json(panoptic_json, image_root, panoptic_root, sem_seg_root, metadata), + ) + MetadataCatalog.get(semantic_name).set( + sem_seg_root=sem_seg_root, + panoptic_root=panoptic_root, + image_root=image_root, + panoptic_json=panoptic_json, + json_file=instances_json, + evaluator_type="coco_panoptic_seg", + ignore_label=255, + label_divisor=1000, + **metadata, + ) + + +def register_all_coco_panoptic_annos_sem_seg(root): + for ( + prefix, + (panoptic_root, panoptic_json, semantic_root), + ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items(): + prefix_instances = prefix[: -len("_panoptic")] + instances_meta = MetadataCatalog.get(prefix_instances) + image_root, instances_json = instances_meta.image_root, instances_meta.json_file + + register_coco_panoptic_annos_sem_seg( + prefix, + get_metadata(), + image_root, + os.path.join(root, panoptic_root), + os.path.join(root, panoptic_json), + os.path.join(root, semantic_root), + instances_json, + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_coco_panoptic_annos_sem_seg(_root) diff --git a/videocutler/mask2former/data/datasets/register_coco_stuff_10k.py b/videocutler/mask2former/data/datasets/register_coco_stuff_10k.py new file mode 100644 index 0000000000000000000000000000000000000000..a1ec0375858ada8e4270b534fcd58106254c7fa9 --- /dev/null +++ b/videocutler/mask2former/data/datasets/register_coco_stuff_10k.py @@ -0,0 +1,223 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets import load_sem_seg + +COCO_CATEGORIES = [ + {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"}, + {"color": [119, 11, 32], "isthing": 1, "id": 2, "name": "bicycle"}, + {"color": [0, 0, 142], "isthing": 1, "id": 3, "name": "car"}, + {"color": [0, 0, 230], "isthing": 1, "id": 4, "name": "motorcycle"}, + {"color": [106, 0, 228], "isthing": 1, "id": 5, "name": "airplane"}, + {"color": [0, 60, 100], "isthing": 1, "id": 6, "name": "bus"}, + {"color": [0, 80, 100], "isthing": 1, "id": 7, "name": "train"}, + {"color": [0, 0, 70], "isthing": 1, "id": 8, "name": "truck"}, + {"color": [0, 0, 192], "isthing": 1, "id": 9, "name": "boat"}, + {"color": [250, 170, 30], "isthing": 1, "id": 10, "name": "traffic light"}, + {"color": [100, 170, 30], "isthing": 1, "id": 11, "name": "fire hydrant"}, + {"color": [220, 220, 0], "isthing": 1, "id": 13, "name": "stop sign"}, + {"color": [175, 116, 175], "isthing": 1, "id": 14, "name": "parking meter"}, + {"color": [250, 0, 30], "isthing": 1, "id": 15, "name": "bench"}, + {"color": [165, 42, 42], "isthing": 1, "id": 16, "name": "bird"}, + {"color": [255, 77, 255], "isthing": 1, "id": 17, "name": "cat"}, + {"color": [0, 226, 252], "isthing": 1, "id": 18, "name": "dog"}, + {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"}, + {"color": [0, 82, 0], "isthing": 1, "id": 20, "name": "sheep"}, + {"color": [120, 166, 157], "isthing": 1, "id": 21, "name": "cow"}, + {"color": [110, 76, 0], "isthing": 1, "id": 22, "name": "elephant"}, + {"color": [174, 57, 255], "isthing": 1, "id": 23, "name": "bear"}, + {"color": [199, 100, 0], "isthing": 1, "id": 24, "name": "zebra"}, + {"color": [72, 0, 118], "isthing": 1, "id": 25, "name": "giraffe"}, + {"color": [255, 179, 240], "isthing": 1, "id": 27, "name": "backpack"}, + {"color": [0, 125, 92], "isthing": 1, "id": 28, "name": "umbrella"}, + {"color": [209, 0, 151], "isthing": 1, "id": 31, "name": "handbag"}, + {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "tie"}, + {"color": [0, 220, 176], "isthing": 1, "id": 33, "name": "suitcase"}, + {"color": [255, 99, 164], "isthing": 1, "id": 34, "name": "frisbee"}, + {"color": [92, 0, 73], "isthing": 1, "id": 35, "name": "skis"}, + {"color": [133, 129, 255], "isthing": 1, "id": 36, "name": "snowboard"}, + {"color": [78, 180, 255], "isthing": 1, "id": 37, "name": "sports ball"}, + {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "kite"}, + {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "baseball bat"}, + {"color": [45, 89, 255], "isthing": 1, "id": 40, "name": "baseball glove"}, + {"color": [134, 134, 103], "isthing": 1, "id": 41, "name": "skateboard"}, + {"color": [145, 148, 174], "isthing": 1, "id": 42, "name": "surfboard"}, + {"color": [255, 208, 186], "isthing": 1, "id": 43, "name": "tennis racket"}, + {"color": [197, 226, 255], "isthing": 1, "id": 44, "name": "bottle"}, + {"color": [171, 134, 1], "isthing": 1, "id": 46, "name": "wine glass"}, + {"color": [109, 63, 54], "isthing": 1, "id": 47, "name": "cup"}, + {"color": [207, 138, 255], "isthing": 1, "id": 48, "name": "fork"}, + {"color": [151, 0, 95], "isthing": 1, "id": 49, "name": "knife"}, + {"color": [9, 80, 61], "isthing": 1, "id": 50, "name": "spoon"}, + {"color": [84, 105, 51], "isthing": 1, "id": 51, "name": "bowl"}, + {"color": [74, 65, 105], "isthing": 1, "id": 52, "name": "banana"}, + {"color": [166, 196, 102], "isthing": 1, "id": 53, "name": "apple"}, + {"color": [208, 195, 210], "isthing": 1, "id": 54, "name": "sandwich"}, + {"color": [255, 109, 65], "isthing": 1, "id": 55, "name": "orange"}, + {"color": [0, 143, 149], "isthing": 1, "id": 56, "name": "broccoli"}, + {"color": [179, 0, 194], "isthing": 1, "id": 57, "name": "carrot"}, + {"color": [209, 99, 106], "isthing": 1, "id": 58, "name": "hot dog"}, + {"color": [5, 121, 0], "isthing": 1, "id": 59, "name": "pizza"}, + {"color": [227, 255, 205], "isthing": 1, "id": 60, "name": "donut"}, + {"color": [147, 186, 208], "isthing": 1, "id": 61, "name": "cake"}, + {"color": [153, 69, 1], "isthing": 1, "id": 62, "name": "chair"}, + {"color": [3, 95, 161], "isthing": 1, "id": 63, "name": "couch"}, + {"color": [163, 255, 0], "isthing": 1, "id": 64, "name": "potted plant"}, + {"color": [119, 0, 170], "isthing": 1, "id": 65, "name": "bed"}, + {"color": [0, 182, 199], "isthing": 1, "id": 67, "name": "dining table"}, + {"color": [0, 165, 120], "isthing": 1, "id": 70, "name": "toilet"}, + {"color": [183, 130, 88], "isthing": 1, "id": 72, "name": "tv"}, + {"color": [95, 32, 0], "isthing": 1, "id": 73, "name": "laptop"}, + {"color": [130, 114, 135], "isthing": 1, "id": 74, "name": "mouse"}, + {"color": [110, 129, 133], "isthing": 1, "id": 75, "name": "remote"}, + {"color": [166, 74, 118], "isthing": 1, "id": 76, "name": "keyboard"}, + {"color": [219, 142, 185], "isthing": 1, "id": 77, "name": "cell phone"}, + {"color": [79, 210, 114], "isthing": 1, "id": 78, "name": "microwave"}, + {"color": [178, 90, 62], "isthing": 1, "id": 79, "name": "oven"}, + {"color": [65, 70, 15], "isthing": 1, "id": 80, "name": "toaster"}, + {"color": [127, 167, 115], "isthing": 1, "id": 81, "name": "sink"}, + {"color": [59, 105, 106], "isthing": 1, "id": 82, "name": "refrigerator"}, + {"color": [142, 108, 45], "isthing": 1, "id": 84, "name": "book"}, + {"color": [196, 172, 0], "isthing": 1, "id": 85, "name": "clock"}, + {"color": [95, 54, 80], "isthing": 1, "id": 86, "name": "vase"}, + {"color": [128, 76, 255], "isthing": 1, "id": 87, "name": "scissors"}, + {"color": [201, 57, 1], "isthing": 1, "id": 88, "name": "teddy bear"}, + {"color": [246, 0, 122], "isthing": 1, "id": 89, "name": "hair drier"}, + {"color": [191, 162, 208], "isthing": 1, "id": 90, "name": "toothbrush"}, + {"id": 92, "name": "banner", "supercategory": "textile"}, + {"id": 93, "name": "blanket", "supercategory": "textile"}, + {"id": 94, "name": "branch", "supercategory": "plant"}, + {"id": 95, "name": "bridge", "supercategory": "building"}, + {"id": 96, "name": "building-other", "supercategory": "building"}, + {"id": 97, "name": "bush", "supercategory": "plant"}, + {"id": 98, "name": "cabinet", "supercategory": "furniture-stuff"}, + {"id": 99, "name": "cage", "supercategory": "structural"}, + {"id": 100, "name": "cardboard", "supercategory": "raw-material"}, + {"id": 101, "name": "carpet", "supercategory": "floor"}, + {"id": 102, "name": "ceiling-other", "supercategory": "ceiling"}, + {"id": 103, "name": "ceiling-tile", "supercategory": "ceiling"}, + {"id": 104, "name": "cloth", "supercategory": "textile"}, + {"id": 105, "name": "clothes", "supercategory": "textile"}, + {"id": 106, "name": "clouds", "supercategory": "sky"}, + {"id": 107, "name": "counter", "supercategory": "furniture-stuff"}, + {"id": 108, "name": "cupboard", "supercategory": "furniture-stuff"}, + {"id": 109, "name": "curtain", "supercategory": "textile"}, + {"id": 110, "name": "desk-stuff", "supercategory": "furniture-stuff"}, + {"id": 111, "name": "dirt", "supercategory": "ground"}, + {"id": 112, "name": "door-stuff", "supercategory": "furniture-stuff"}, + {"id": 113, "name": "fence", "supercategory": "structural"}, + {"id": 114, "name": "floor-marble", "supercategory": "floor"}, + {"id": 115, "name": "floor-other", "supercategory": "floor"}, + {"id": 116, "name": "floor-stone", "supercategory": "floor"}, + {"id": 117, "name": "floor-tile", "supercategory": "floor"}, + {"id": 118, "name": "floor-wood", "supercategory": "floor"}, + {"id": 119, "name": "flower", "supercategory": "plant"}, + {"id": 120, "name": "fog", "supercategory": "water"}, + {"id": 121, "name": "food-other", "supercategory": "food-stuff"}, + {"id": 122, "name": "fruit", "supercategory": "food-stuff"}, + {"id": 123, "name": "furniture-other", "supercategory": "furniture-stuff"}, + {"id": 124, "name": "grass", "supercategory": "plant"}, + {"id": 125, "name": "gravel", "supercategory": "ground"}, + {"id": 126, "name": "ground-other", "supercategory": "ground"}, + {"id": 127, "name": "hill", "supercategory": "solid"}, + {"id": 128, "name": "house", "supercategory": "building"}, + {"id": 129, "name": "leaves", "supercategory": "plant"}, + {"id": 130, "name": "light", "supercategory": "furniture-stuff"}, + {"id": 131, "name": "mat", "supercategory": "textile"}, + {"id": 132, "name": "metal", "supercategory": "raw-material"}, + {"id": 133, "name": "mirror-stuff", "supercategory": "furniture-stuff"}, + {"id": 134, "name": "moss", "supercategory": "plant"}, + {"id": 135, "name": "mountain", "supercategory": "solid"}, + {"id": 136, "name": "mud", "supercategory": "ground"}, + {"id": 137, "name": "napkin", "supercategory": "textile"}, + {"id": 138, "name": "net", "supercategory": "structural"}, + {"id": 139, "name": "paper", "supercategory": "raw-material"}, + {"id": 140, "name": "pavement", "supercategory": "ground"}, + {"id": 141, "name": "pillow", "supercategory": "textile"}, + {"id": 142, "name": "plant-other", "supercategory": "plant"}, + {"id": 143, "name": "plastic", "supercategory": "raw-material"}, + {"id": 144, "name": "platform", "supercategory": "ground"}, + {"id": 145, "name": "playingfield", "supercategory": "ground"}, + {"id": 146, "name": "railing", "supercategory": "structural"}, + {"id": 147, "name": "railroad", "supercategory": "ground"}, + {"id": 148, "name": "river", "supercategory": "water"}, + {"id": 149, "name": "road", "supercategory": "ground"}, + {"id": 150, "name": "rock", "supercategory": "solid"}, + {"id": 151, "name": "roof", "supercategory": "building"}, + {"id": 152, "name": "rug", "supercategory": "textile"}, + {"id": 153, "name": "salad", "supercategory": "food-stuff"}, + {"id": 154, "name": "sand", "supercategory": "ground"}, + {"id": 155, "name": "sea", "supercategory": "water"}, + {"id": 156, "name": "shelf", "supercategory": "furniture-stuff"}, + {"id": 157, "name": "sky-other", "supercategory": "sky"}, + {"id": 158, "name": "skyscraper", "supercategory": "building"}, + {"id": 159, "name": "snow", "supercategory": "ground"}, + {"id": 160, "name": "solid-other", "supercategory": "solid"}, + {"id": 161, "name": "stairs", "supercategory": "furniture-stuff"}, + {"id": 162, "name": "stone", "supercategory": "solid"}, + {"id": 163, "name": "straw", "supercategory": "plant"}, + {"id": 164, "name": "structural-other", "supercategory": "structural"}, + {"id": 165, "name": "table", "supercategory": "furniture-stuff"}, + {"id": 166, "name": "tent", "supercategory": "building"}, + {"id": 167, "name": "textile-other", "supercategory": "textile"}, + {"id": 168, "name": "towel", "supercategory": "textile"}, + {"id": 169, "name": "tree", "supercategory": "plant"}, + {"id": 170, "name": "vegetable", "supercategory": "food-stuff"}, + {"id": 171, "name": "wall-brick", "supercategory": "wall"}, + {"id": 172, "name": "wall-concrete", "supercategory": "wall"}, + {"id": 173, "name": "wall-other", "supercategory": "wall"}, + {"id": 174, "name": "wall-panel", "supercategory": "wall"}, + {"id": 175, "name": "wall-stone", "supercategory": "wall"}, + {"id": 176, "name": "wall-tile", "supercategory": "wall"}, + {"id": 177, "name": "wall-wood", "supercategory": "wall"}, + {"id": 178, "name": "water-other", "supercategory": "water"}, + {"id": 179, "name": "waterdrops", "supercategory": "water"}, + {"id": 180, "name": "window-blind", "supercategory": "window"}, + {"id": 181, "name": "window-other", "supercategory": "window"}, + {"id": 182, "name": "wood", "supercategory": "solid"}, +] + + +def _get_coco_stuff_meta(): + # Id 0 is reserved for ignore_label, we change ignore_label for 0 + # to 255 in our pre-processing. + stuff_ids = [k["id"] for k in COCO_CATEGORIES] + assert len(stuff_ids) == 171, len(stuff_ids) + + # For semantic segmentation, this mapping maps from contiguous stuff id + # (in [0, 91], used in models) to ids in the dataset (used for processing results) + stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)} + stuff_classes = [k["name"] for k in COCO_CATEGORIES] + + ret = { + "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, + "stuff_classes": stuff_classes, + } + return ret + + +def register_all_coco_stuff_10k(root): + root = os.path.join(root, "coco", "coco_stuff_10k") + meta = _get_coco_stuff_meta() + for name, image_dirname, sem_seg_dirname in [ + ("train", "images_detectron2/train", "annotations_detectron2/train"), + ("test", "images_detectron2/test", "annotations_detectron2/test"), + ]: + image_dir = os.path.join(root, image_dirname) + gt_dir = os.path.join(root, sem_seg_dirname) + name = f"coco_2017_{name}_stuff_10k_sem_seg" + DatasetCatalog.register( + name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg") + ) + MetadataCatalog.get(name).set( + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + ignore_label=255, + **meta, + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_coco_stuff_10k(_root) diff --git a/videocutler/mask2former/data/datasets/register_mapillary_vistas.py b/videocutler/mask2former/data/datasets/register_mapillary_vistas.py new file mode 100644 index 0000000000000000000000000000000000000000..ce3874b65d943c333d093abd6998500f8a3775f5 --- /dev/null +++ b/videocutler/mask2former/data/datasets/register_mapillary_vistas.py @@ -0,0 +1,507 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets import load_sem_seg + +MAPILLARY_VISTAS_SEM_SEG_CATEGORIES = [ + { + "color": [165, 42, 42], + "instances": True, + "readable": "Bird", + "name": "animal--bird", + "evaluate": True, + }, + { + "color": [0, 192, 0], + "instances": True, + "readable": "Ground Animal", + "name": "animal--ground-animal", + "evaluate": True, + }, + { + "color": [196, 196, 196], + "instances": False, + "readable": "Curb", + "name": "construction--barrier--curb", + "evaluate": True, + }, + { + "color": [190, 153, 153], + "instances": False, + "readable": "Fence", + "name": "construction--barrier--fence", + "evaluate": True, + }, + { + "color": [180, 165, 180], + "instances": False, + "readable": "Guard Rail", + "name": "construction--barrier--guard-rail", + "evaluate": True, + }, + { + "color": [90, 120, 150], + "instances": False, + "readable": "Barrier", + "name": "construction--barrier--other-barrier", + "evaluate": True, + }, + { + "color": [102, 102, 156], + "instances": False, + "readable": "Wall", + "name": "construction--barrier--wall", + "evaluate": True, + }, + { + "color": [128, 64, 255], + "instances": False, + "readable": "Bike Lane", + "name": "construction--flat--bike-lane", + "evaluate": True, + }, + { + "color": [140, 140, 200], + "instances": True, + "readable": "Crosswalk - Plain", + "name": "construction--flat--crosswalk-plain", + "evaluate": True, + }, + { + "color": [170, 170, 170], + "instances": False, + "readable": "Curb Cut", + "name": "construction--flat--curb-cut", + "evaluate": True, + }, + { + "color": [250, 170, 160], + "instances": False, + "readable": "Parking", + "name": "construction--flat--parking", + "evaluate": True, + }, + { + "color": [96, 96, 96], + "instances": False, + "readable": "Pedestrian Area", + "name": "construction--flat--pedestrian-area", + "evaluate": True, + }, + { + "color": [230, 150, 140], + "instances": False, + "readable": "Rail Track", + "name": "construction--flat--rail-track", + "evaluate": True, + }, + { + "color": [128, 64, 128], + "instances": False, + "readable": "Road", + "name": "construction--flat--road", + "evaluate": True, + }, + { + "color": [110, 110, 110], + "instances": False, + "readable": "Service Lane", + "name": "construction--flat--service-lane", + "evaluate": True, + }, + { + "color": [244, 35, 232], + "instances": False, + "readable": "Sidewalk", + "name": "construction--flat--sidewalk", + "evaluate": True, + }, + { + "color": [150, 100, 100], + "instances": False, + "readable": "Bridge", + "name": "construction--structure--bridge", + "evaluate": True, + }, + { + "color": [70, 70, 70], + "instances": False, + "readable": "Building", + "name": "construction--structure--building", + "evaluate": True, + }, + { + "color": [150, 120, 90], + "instances": False, + "readable": "Tunnel", + "name": "construction--structure--tunnel", + "evaluate": True, + }, + { + "color": [220, 20, 60], + "instances": True, + "readable": "Person", + "name": "human--person", + "evaluate": True, + }, + { + "color": [255, 0, 0], + "instances": True, + "readable": "Bicyclist", + "name": "human--rider--bicyclist", + "evaluate": True, + }, + { + "color": [255, 0, 100], + "instances": True, + "readable": "Motorcyclist", + "name": "human--rider--motorcyclist", + "evaluate": True, + }, + { + "color": [255, 0, 200], + "instances": True, + "readable": "Other Rider", + "name": "human--rider--other-rider", + "evaluate": True, + }, + { + "color": [200, 128, 128], + "instances": True, + "readable": "Lane Marking - Crosswalk", + "name": "marking--crosswalk-zebra", + "evaluate": True, + }, + { + "color": [255, 255, 255], + "instances": False, + "readable": "Lane Marking - General", + "name": "marking--general", + "evaluate": True, + }, + { + "color": [64, 170, 64], + "instances": False, + "readable": "Mountain", + "name": "nature--mountain", + "evaluate": True, + }, + { + "color": [230, 160, 50], + "instances": False, + "readable": "Sand", + "name": "nature--sand", + "evaluate": True, + }, + { + "color": [70, 130, 180], + "instances": False, + "readable": "Sky", + "name": "nature--sky", + "evaluate": True, + }, + { + "color": [190, 255, 255], + "instances": False, + "readable": "Snow", + "name": "nature--snow", + "evaluate": True, + }, + { + "color": [152, 251, 152], + "instances": False, + "readable": "Terrain", + "name": "nature--terrain", + "evaluate": True, + }, + { + "color": [107, 142, 35], + "instances": False, + "readable": "Vegetation", + "name": "nature--vegetation", + "evaluate": True, + }, + { + "color": [0, 170, 30], + "instances": False, + "readable": "Water", + "name": "nature--water", + "evaluate": True, + }, + { + "color": [255, 255, 128], + "instances": True, + "readable": "Banner", + "name": "object--banner", + "evaluate": True, + }, + { + "color": [250, 0, 30], + "instances": True, + "readable": "Bench", + "name": "object--bench", + "evaluate": True, + }, + { + "color": [100, 140, 180], + "instances": True, + "readable": "Bike Rack", + "name": "object--bike-rack", + "evaluate": True, + }, + { + "color": [220, 220, 220], + "instances": True, + "readable": "Billboard", + "name": "object--billboard", + "evaluate": True, + }, + { + "color": [220, 128, 128], + "instances": True, + "readable": "Catch Basin", + "name": "object--catch-basin", + "evaluate": True, + }, + { + "color": [222, 40, 40], + "instances": True, + "readable": "CCTV Camera", + "name": "object--cctv-camera", + "evaluate": True, + }, + { + "color": [100, 170, 30], + "instances": True, + "readable": "Fire Hydrant", + "name": "object--fire-hydrant", + "evaluate": True, + }, + { + "color": [40, 40, 40], + "instances": True, + "readable": "Junction Box", + "name": "object--junction-box", + "evaluate": True, + }, + { + "color": [33, 33, 33], + "instances": True, + "readable": "Mailbox", + "name": "object--mailbox", + "evaluate": True, + }, + { + "color": [100, 128, 160], + "instances": True, + "readable": "Manhole", + "name": "object--manhole", + "evaluate": True, + }, + { + "color": [142, 0, 0], + "instances": True, + "readable": "Phone Booth", + "name": "object--phone-booth", + "evaluate": True, + }, + { + "color": [70, 100, 150], + "instances": False, + "readable": "Pothole", + "name": "object--pothole", + "evaluate": True, + }, + { + "color": [210, 170, 100], + "instances": True, + "readable": "Street Light", + "name": "object--street-light", + "evaluate": True, + }, + { + "color": [153, 153, 153], + "instances": True, + "readable": "Pole", + "name": "object--support--pole", + "evaluate": True, + }, + { + "color": [128, 128, 128], + "instances": True, + "readable": "Traffic Sign Frame", + "name": "object--support--traffic-sign-frame", + "evaluate": True, + }, + { + "color": [0, 0, 80], + "instances": True, + "readable": "Utility Pole", + "name": "object--support--utility-pole", + "evaluate": True, + }, + { + "color": [250, 170, 30], + "instances": True, + "readable": "Traffic Light", + "name": "object--traffic-light", + "evaluate": True, + }, + { + "color": [192, 192, 192], + "instances": True, + "readable": "Traffic Sign (Back)", + "name": "object--traffic-sign--back", + "evaluate": True, + }, + { + "color": [220, 220, 0], + "instances": True, + "readable": "Traffic Sign (Front)", + "name": "object--traffic-sign--front", + "evaluate": True, + }, + { + "color": [140, 140, 20], + "instances": True, + "readable": "Trash Can", + "name": "object--trash-can", + "evaluate": True, + }, + { + "color": [119, 11, 32], + "instances": True, + "readable": "Bicycle", + "name": "object--vehicle--bicycle", + "evaluate": True, + }, + { + "color": [150, 0, 255], + "instances": True, + "readable": "Boat", + "name": "object--vehicle--boat", + "evaluate": True, + }, + { + "color": [0, 60, 100], + "instances": True, + "readable": "Bus", + "name": "object--vehicle--bus", + "evaluate": True, + }, + { + "color": [0, 0, 142], + "instances": True, + "readable": "Car", + "name": "object--vehicle--car", + "evaluate": True, + }, + { + "color": [0, 0, 90], + "instances": True, + "readable": "Caravan", + "name": "object--vehicle--caravan", + "evaluate": True, + }, + { + "color": [0, 0, 230], + "instances": True, + "readable": "Motorcycle", + "name": "object--vehicle--motorcycle", + "evaluate": True, + }, + { + "color": [0, 80, 100], + "instances": False, + "readable": "On Rails", + "name": "object--vehicle--on-rails", + "evaluate": True, + }, + { + "color": [128, 64, 64], + "instances": True, + "readable": "Other Vehicle", + "name": "object--vehicle--other-vehicle", + "evaluate": True, + }, + { + "color": [0, 0, 110], + "instances": True, + "readable": "Trailer", + "name": "object--vehicle--trailer", + "evaluate": True, + }, + { + "color": [0, 0, 70], + "instances": True, + "readable": "Truck", + "name": "object--vehicle--truck", + "evaluate": True, + }, + { + "color": [0, 0, 192], + "instances": True, + "readable": "Wheeled Slow", + "name": "object--vehicle--wheeled-slow", + "evaluate": True, + }, + { + "color": [32, 32, 32], + "instances": False, + "readable": "Car Mount", + "name": "void--car-mount", + "evaluate": True, + }, + { + "color": [120, 10, 10], + "instances": False, + "readable": "Ego Vehicle", + "name": "void--ego-vehicle", + "evaluate": True, + }, + { + "color": [0, 0, 0], + "instances": False, + "readable": "Unlabeled", + "name": "void--unlabeled", + "evaluate": False, + }, +] + + +def _get_mapillary_vistas_meta(): + stuff_classes = [k["readable"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k["evaluate"]] + assert len(stuff_classes) == 65 + + stuff_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k["evaluate"]] + assert len(stuff_colors) == 65 + + ret = { + "stuff_classes": stuff_classes, + "stuff_colors": stuff_colors, + } + return ret + + +def register_all_mapillary_vistas(root): + root = os.path.join(root, "mapillary_vistas") + meta = _get_mapillary_vistas_meta() + for name, dirname in [("train", "training"), ("val", "validation")]: + image_dir = os.path.join(root, dirname, "images") + gt_dir = os.path.join(root, dirname, "labels") + name = f"mapillary_vistas_sem_seg_{name}" + DatasetCatalog.register( + name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg") + ) + MetadataCatalog.get(name).set( + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + ignore_label=65, # different from other datasets, Mapillary Vistas sets ignore_label to 65 + **meta, + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_mapillary_vistas(_root) diff --git a/videocutler/mask2former/data/datasets/register_mapillary_vistas_panoptic.py b/videocutler/mask2former/data/datasets/register_mapillary_vistas_panoptic.py new file mode 100644 index 0000000000000000000000000000000000000000..0123185583f03ba1715da6e0b1eb24f71c12adda --- /dev/null +++ b/videocutler/mask2former/data/datasets/register_mapillary_vistas_panoptic.py @@ -0,0 +1,508 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import json +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.utils.file_io import PathManager + + +MAPILLARY_VISTAS_SEM_SEG_CATEGORIES = [ + {'color': [165, 42, 42], + 'id': 1, + 'isthing': 1, + 'name': 'Bird', + 'supercategory': 'animal--bird'}, + {'color': [0, 192, 0], + 'id': 2, + 'isthing': 1, + 'name': 'Ground Animal', + 'supercategory': 'animal--ground-animal'}, + {'color': [196, 196, 196], + 'id': 3, + 'isthing': 0, + 'name': 'Curb', + 'supercategory': 'construction--barrier--curb'}, + {'color': [190, 153, 153], + 'id': 4, + 'isthing': 0, + 'name': 'Fence', + 'supercategory': 'construction--barrier--fence'}, + {'color': [180, 165, 180], + 'id': 5, + 'isthing': 0, + 'name': 'Guard Rail', + 'supercategory': 'construction--barrier--guard-rail'}, + {'color': [90, 120, 150], + 'id': 6, + 'isthing': 0, + 'name': 'Barrier', + 'supercategory': 'construction--barrier--other-barrier'}, + {'color': [102, 102, 156], + 'id': 7, + 'isthing': 0, + 'name': 'Wall', + 'supercategory': 'construction--barrier--wall'}, + {'color': [128, 64, 255], + 'id': 8, + 'isthing': 0, + 'name': 'Bike Lane', + 'supercategory': 'construction--flat--bike-lane'}, + {'color': [140, 140, 200], + 'id': 9, + 'isthing': 1, + 'name': 'Crosswalk - Plain', + 'supercategory': 'construction--flat--crosswalk-plain'}, + {'color': [170, 170, 170], + 'id': 10, + 'isthing': 0, + 'name': 'Curb Cut', + 'supercategory': 'construction--flat--curb-cut'}, + {'color': [250, 170, 160], + 'id': 11, + 'isthing': 0, + 'name': 'Parking', + 'supercategory': 'construction--flat--parking'}, + {'color': [96, 96, 96], + 'id': 12, + 'isthing': 0, + 'name': 'Pedestrian Area', + 'supercategory': 'construction--flat--pedestrian-area'}, + {'color': [230, 150, 140], + 'id': 13, + 'isthing': 0, + 'name': 'Rail Track', + 'supercategory': 'construction--flat--rail-track'}, + {'color': [128, 64, 128], + 'id': 14, + 'isthing': 0, + 'name': 'Road', + 'supercategory': 'construction--flat--road'}, + {'color': [110, 110, 110], + 'id': 15, + 'isthing': 0, + 'name': 'Service Lane', + 'supercategory': 'construction--flat--service-lane'}, + {'color': [244, 35, 232], + 'id': 16, + 'isthing': 0, + 'name': 'Sidewalk', + 'supercategory': 'construction--flat--sidewalk'}, + {'color': [150, 100, 100], + 'id': 17, + 'isthing': 0, + 'name': 'Bridge', + 'supercategory': 'construction--structure--bridge'}, + {'color': [70, 70, 70], + 'id': 18, + 'isthing': 0, + 'name': 'Building', + 'supercategory': 'construction--structure--building'}, + {'color': [150, 120, 90], + 'id': 19, + 'isthing': 0, + 'name': 'Tunnel', + 'supercategory': 'construction--structure--tunnel'}, + {'color': [220, 20, 60], + 'id': 20, + 'isthing': 1, + 'name': 'Person', + 'supercategory': 'human--person'}, + {'color': [255, 0, 0], + 'id': 21, + 'isthing': 1, + 'name': 'Bicyclist', + 'supercategory': 'human--rider--bicyclist'}, + {'color': [255, 0, 100], + 'id': 22, + 'isthing': 1, + 'name': 'Motorcyclist', + 'supercategory': 'human--rider--motorcyclist'}, + {'color': [255, 0, 200], + 'id': 23, + 'isthing': 1, + 'name': 'Other Rider', + 'supercategory': 'human--rider--other-rider'}, + {'color': [200, 128, 128], + 'id': 24, + 'isthing': 1, + 'name': 'Lane Marking - Crosswalk', + 'supercategory': 'marking--crosswalk-zebra'}, + {'color': [255, 255, 255], + 'id': 25, + 'isthing': 0, + 'name': 'Lane Marking - General', + 'supercategory': 'marking--general'}, + {'color': [64, 170, 64], + 'id': 26, + 'isthing': 0, + 'name': 'Mountain', + 'supercategory': 'nature--mountain'}, + {'color': [230, 160, 50], + 'id': 27, + 'isthing': 0, + 'name': 'Sand', + 'supercategory': 'nature--sand'}, + {'color': [70, 130, 180], + 'id': 28, + 'isthing': 0, + 'name': 'Sky', + 'supercategory': 'nature--sky'}, + {'color': [190, 255, 255], + 'id': 29, + 'isthing': 0, + 'name': 'Snow', + 'supercategory': 'nature--snow'}, + {'color': [152, 251, 152], + 'id': 30, + 'isthing': 0, + 'name': 'Terrain', + 'supercategory': 'nature--terrain'}, + {'color': [107, 142, 35], + 'id': 31, + 'isthing': 0, + 'name': 'Vegetation', + 'supercategory': 'nature--vegetation'}, + {'color': [0, 170, 30], + 'id': 32, + 'isthing': 0, + 'name': 'Water', + 'supercategory': 'nature--water'}, + {'color': [255, 255, 128], + 'id': 33, + 'isthing': 1, + 'name': 'Banner', + 'supercategory': 'object--banner'}, + {'color': [250, 0, 30], + 'id': 34, + 'isthing': 1, + 'name': 'Bench', + 'supercategory': 'object--bench'}, + {'color': [100, 140, 180], + 'id': 35, + 'isthing': 1, + 'name': 'Bike Rack', + 'supercategory': 'object--bike-rack'}, + {'color': [220, 220, 220], + 'id': 36, + 'isthing': 1, + 'name': 'Billboard', + 'supercategory': 'object--billboard'}, + {'color': [220, 128, 128], + 'id': 37, + 'isthing': 1, + 'name': 'Catch Basin', + 'supercategory': 'object--catch-basin'}, + {'color': [222, 40, 40], + 'id': 38, + 'isthing': 1, + 'name': 'CCTV Camera', + 'supercategory': 'object--cctv-camera'}, + {'color': [100, 170, 30], + 'id': 39, + 'isthing': 1, + 'name': 'Fire Hydrant', + 'supercategory': 'object--fire-hydrant'}, + {'color': [40, 40, 40], + 'id': 40, + 'isthing': 1, + 'name': 'Junction Box', + 'supercategory': 'object--junction-box'}, + {'color': [33, 33, 33], + 'id': 41, + 'isthing': 1, + 'name': 'Mailbox', + 'supercategory': 'object--mailbox'}, + {'color': [100, 128, 160], + 'id': 42, + 'isthing': 1, + 'name': 'Manhole', + 'supercategory': 'object--manhole'}, + {'color': [142, 0, 0], + 'id': 43, + 'isthing': 1, + 'name': 'Phone Booth', + 'supercategory': 'object--phone-booth'}, + {'color': [70, 100, 150], + 'id': 44, + 'isthing': 0, + 'name': 'Pothole', + 'supercategory': 'object--pothole'}, + {'color': [210, 170, 100], + 'id': 45, + 'isthing': 1, + 'name': 'Street Light', + 'supercategory': 'object--street-light'}, + {'color': [153, 153, 153], + 'id': 46, + 'isthing': 1, + 'name': 'Pole', + 'supercategory': 'object--support--pole'}, + {'color': [128, 128, 128], + 'id': 47, + 'isthing': 1, + 'name': 'Traffic Sign Frame', + 'supercategory': 'object--support--traffic-sign-frame'}, + {'color': [0, 0, 80], + 'id': 48, + 'isthing': 1, + 'name': 'Utility Pole', + 'supercategory': 'object--support--utility-pole'}, + {'color': [250, 170, 30], + 'id': 49, + 'isthing': 1, + 'name': 'Traffic Light', + 'supercategory': 'object--traffic-light'}, + {'color': [192, 192, 192], + 'id': 50, + 'isthing': 1, + 'name': 'Traffic Sign (Back)', + 'supercategory': 'object--traffic-sign--back'}, + {'color': [220, 220, 0], + 'id': 51, + 'isthing': 1, + 'name': 'Traffic Sign (Front)', + 'supercategory': 'object--traffic-sign--front'}, + {'color': [140, 140, 20], + 'id': 52, + 'isthing': 1, + 'name': 'Trash Can', + 'supercategory': 'object--trash-can'}, + {'color': [119, 11, 32], + 'id': 53, + 'isthing': 1, + 'name': 'Bicycle', + 'supercategory': 'object--vehicle--bicycle'}, + {'color': [150, 0, 255], + 'id': 54, + 'isthing': 1, + 'name': 'Boat', + 'supercategory': 'object--vehicle--boat'}, + {'color': [0, 60, 100], + 'id': 55, + 'isthing': 1, + 'name': 'Bus', + 'supercategory': 'object--vehicle--bus'}, + {'color': [0, 0, 142], + 'id': 56, + 'isthing': 1, + 'name': 'Car', + 'supercategory': 'object--vehicle--car'}, + {'color': [0, 0, 90], + 'id': 57, + 'isthing': 1, + 'name': 'Caravan', + 'supercategory': 'object--vehicle--caravan'}, + {'color': [0, 0, 230], + 'id': 58, + 'isthing': 1, + 'name': 'Motorcycle', + 'supercategory': 'object--vehicle--motorcycle'}, + {'color': [0, 80, 100], + 'id': 59, + 'isthing': 0, + 'name': 'On Rails', + 'supercategory': 'object--vehicle--on-rails'}, + {'color': [128, 64, 64], + 'id': 60, + 'isthing': 1, + 'name': 'Other Vehicle', + 'supercategory': 'object--vehicle--other-vehicle'}, + {'color': [0, 0, 110], + 'id': 61, + 'isthing': 1, + 'name': 'Trailer', + 'supercategory': 'object--vehicle--trailer'}, + {'color': [0, 0, 70], + 'id': 62, + 'isthing': 1, + 'name': 'Truck', + 'supercategory': 'object--vehicle--truck'}, + {'color': [0, 0, 192], + 'id': 63, + 'isthing': 1, + 'name': 'Wheeled Slow', + 'supercategory': 'object--vehicle--wheeled-slow'}, + {'color': [32, 32, 32], + 'id': 64, + 'isthing': 0, + 'name': 'Car Mount', + 'supercategory': 'void--car-mount'}, + {'color': [120, 10, 10], + 'id': 65, + 'isthing': 0, + 'name': 'Ego Vehicle', + 'supercategory': 'void--ego-vehicle'} +] + + +def load_mapillary_vistas_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". + gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017". + json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json". + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + + def _convert_category_id(segment_info, meta): + if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: + segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = True + else: + segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = False + return segment_info + + with PathManager.open(json_file) as f: + json_info = json.load(f) + + ret = [] + for ann in json_info["annotations"]: + image_id = ann["image_id"] + # TODO: currently we assume image and label has the same filename but + # different extension, and images have extension ".jpg" for COCO. Need + # to make image extension a user-provided argument if we extend this + # function to support other COCO-like datasets. + image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg") + label_file = os.path.join(gt_dir, ann["file_name"]) + sem_label_file = os.path.join(semseg_dir, ann["file_name"]) + segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]] + ret.append( + { + "file_name": image_file, + "image_id": image_id, + "pan_seg_file_name": label_file, + "sem_seg_file_name": sem_label_file, + "segments_info": segments_info, + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"] + assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"] + assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"] + return ret + + +def register_mapillary_vistas_panoptic( + name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None +): + """ + Register a "standard" version of ADE20k panoptic segmentation dataset named `name`. + The dictionaries in this registered dataset follows detectron2's standard format. + Hence it's called "standard". + Args: + name (str): the name that identifies a dataset, + e.g. "ade20k_panoptic_train" + metadata (dict): extra metadata associated with this dataset. + image_root (str): directory which contains all the images + panoptic_root (str): directory which contains panoptic annotation images in COCO format + panoptic_json (str): path to the json panoptic annotation file in COCO format + sem_seg_root (none): not used, to be consistent with + `register_coco_panoptic_separated`. + instances_json (str): path to the json instance annotation file + """ + panoptic_name = name + DatasetCatalog.register( + panoptic_name, + lambda: load_mapillary_vistas_panoptic_json( + panoptic_json, image_root, panoptic_root, semantic_root, metadata + ), + ) + MetadataCatalog.get(panoptic_name).set( + panoptic_root=panoptic_root, + image_root=image_root, + panoptic_json=panoptic_json, + json_file=instances_json, + evaluator_type="mapillary_vistas_panoptic_seg", + ignore_label=65, # different from other datasets, Mapillary Vistas sets ignore_label to 65 + label_divisor=1000, + **metadata, + ) + + +_PREDEFINED_SPLITS_ADE20K_PANOPTIC = { + "mapillary_vistas_panoptic_train": ( + "mapillary_vistas/training/images", + "mapillary_vistas/training/panoptic", + "mapillary_vistas/training/panoptic/panoptic_2018.json", + "mapillary_vistas/training/labels", + ), + "mapillary_vistas_panoptic_val": ( + "mapillary_vistas/validation/images", + "mapillary_vistas/validation/panoptic", + "mapillary_vistas/validation/panoptic/panoptic_2018.json", + "mapillary_vistas/validation/labels", + ), +} + + +def get_metadata(): + meta = {} + # The following metadata maps contiguous id from [0, #thing categories + + # #stuff categories) to their names and colors. We have to replica of the + # same name and color under "thing_*" and "stuff_*" because the current + # visualization function in D2 handles thing and class classes differently + # due to some heuristic used in Panoptic FPN. We keep the same naming to + # enable reusing existing visualization functions. + thing_classes = [k["name"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES] + thing_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES] + stuff_classes = [k["name"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES] + stuff_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES] + + meta["thing_classes"] = thing_classes + meta["thing_colors"] = thing_colors + meta["stuff_classes"] = stuff_classes + meta["stuff_colors"] = stuff_colors + + # Convert category id for training: + # category id: like semantic segmentation, it is the class id for each + # pixel. Since there are some classes not used in evaluation, the category + # id is not always contiguous and thus we have two set of category ids: + # - original category id: category id in the original dataset, mainly + # used for evaluation. + # - contiguous category id: [0, #classes), in order to train the linear + # softmax classifier. + thing_dataset_id_to_contiguous_id = {} + stuff_dataset_id_to_contiguous_id = {} + + for i, cat in enumerate(MAPILLARY_VISTAS_SEM_SEG_CATEGORIES): + if cat["isthing"]: + thing_dataset_id_to_contiguous_id[cat["id"]] = i + # else: + # stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + # in order to use sem_seg evaluator + stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id + meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id + + return meta + + +def register_all_mapillary_vistas_panoptic(root): + metadata = get_metadata() + for ( + prefix, + (image_root, panoptic_root, panoptic_json, semantic_root), + ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items(): + # The "standard" version of COCO panoptic segmentation dataset, + # e.g. used by Panoptic-DeepLab + register_mapillary_vistas_panoptic( + prefix, + metadata, + os.path.join(root, image_root), + os.path.join(root, panoptic_root), + os.path.join(root, semantic_root), + os.path.join(root, panoptic_json), + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_mapillary_vistas_panoptic(_root) diff --git a/videocutler/mask2former/evaluation/__init__.py b/videocutler/mask2former/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/videocutler/mask2former/evaluation/instance_evaluation.py b/videocutler/mask2former/evaluation/instance_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..bc2facec351e5f6ee965ee9acb4394f12c023f54 --- /dev/null +++ b/videocutler/mask2former/evaluation/instance_evaluation.py @@ -0,0 +1,107 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import contextlib +import copy +import io +import itertools +import json +import logging +import numpy as np +import os +import pickle +from collections import OrderedDict +import pycocotools.mask as mask_util +import torch +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from tabulate import tabulate + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.data.datasets.coco import convert_to_coco_json +from detectron2.evaluation.coco_evaluation import COCOEvaluator, _evaluate_predictions_on_coco +from detectron2.evaluation.fast_eval_api import COCOeval_opt +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table + + +# modified from COCOEvaluator for instance segmetnat +class InstanceSegEvaluator(COCOEvaluator): + """ + Evaluate AR for object proposals, AP for instance detection/segmentation, AP + for keypoint detection outputs using COCO's metrics. + See http://cocodataset.org/#detection-eval and + http://cocodataset.org/#keypoints-eval to understand its metrics. + The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means + the metric cannot be computed (e.g. due to no predictions made). + + In addition to COCO, this evaluator is able to support any bounding box detection, + instance segmentation, or keypoint detection dataset. + """ + + def _eval_predictions(self, predictions, img_ids=None): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(coco_results) + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id + # all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) + # num_classes = len(all_contiguous_ids) + # assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 + + reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} + for result in coco_results: + category_id = result["category_id"] + # assert category_id < num_classes, ( + # f"A prediction has class={category_id}, " + # f"but the dataset only has {num_classes} classes and " + # f"predicted class id should be in [0, {num_classes - 1}]." + # ) + assert category_id in reverse_id_mapping, ( + f"A prediction has class={category_id}, " + f"but the dataset only has class ids in {dataset_id_to_contiguous_id}." + ) + result["category_id"] = reverse_id_mapping[category_id] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "coco_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info( + "Evaluating predictions with {} COCO API...".format( + "unofficial" if self._use_fast_impl else "official" + ) + ) + for task in sorted(tasks): + assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!" + coco_eval = ( + _evaluate_predictions_on_coco( + self._coco_api, + coco_results, + task, + kpt_oks_sigmas=self._kpt_oks_sigmas, + use_fast_impl=self._use_fast_impl, + img_ids=img_ids, + max_dets_per_image=self._max_dets_per_image, + ) + if len(coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + + res = self._derive_coco_results( + coco_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res diff --git a/videocutler/mask2former/maskformer_model.py b/videocutler/mask2former/maskformer_model.py new file mode 100644 index 0000000000000000000000000000000000000000..a73fe5b9f7eee40e6702b527b8680eca04a0c2f2 --- /dev/null +++ b/videocutler/mask2former/maskformer_model.py @@ -0,0 +1,381 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import Tuple + +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.data import MetadataCatalog +from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head +from detectron2.modeling.backbone import Backbone +from detectron2.modeling.postprocessing import sem_seg_postprocess +from detectron2.structures import Boxes, ImageList, Instances, BitMasks +from detectron2.utils.memory import retry_if_cuda_oom + +from .modeling.criterion import SetCriterion +from .modeling.matcher import HungarianMatcher + + +@META_ARCH_REGISTRY.register() +class MaskFormer(nn.Module): + """ + Main class for mask classification semantic segmentation architectures. + """ + + @configurable + def __init__( + self, + *, + backbone: Backbone, + sem_seg_head: nn.Module, + criterion: nn.Module, + num_queries: int, + object_mask_threshold: float, + overlap_threshold: float, + metadata, + size_divisibility: int, + sem_seg_postprocess_before_inference: bool, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + # inference + semantic_on: bool, + panoptic_on: bool, + instance_on: bool, + test_topk_per_image: int, + ): + """ + Args: + backbone: a backbone module, must follow detectron2's backbone interface + sem_seg_head: a module that predicts semantic segmentation from backbone features + criterion: a module that defines the loss + num_queries: int, number of queries + object_mask_threshold: float, threshold to filter query based on classification score + for panoptic segmentation inference + overlap_threshold: overlap threshold used in general inference for panoptic segmentation + metadata: dataset meta, get `thing` and `stuff` category names for panoptic + segmentation inference + size_divisibility: Some backbones require the input height and width to be divisible by a + specific integer. We can use this to override such requirement. + sem_seg_postprocess_before_inference: whether to resize the prediction back + to original input size before semantic segmentation inference or after. + For high-resolution dataset like Mapillary, resizing predictions before + inference will cause OOM error. + pixel_mean, pixel_std: list or tuple with #channels element, representing + the per-channel mean and std to be used to normalize the input image + semantic_on: bool, whether to output semantic segmentation prediction + instance_on: bool, whether to output instance segmentation prediction + panoptic_on: bool, whether to output panoptic segmentation prediction + test_topk_per_image: int, instance segmentation parameter, keep topk instances per image + """ + super().__init__() + self.backbone = backbone + self.sem_seg_head = sem_seg_head + self.criterion = criterion + self.num_queries = num_queries + self.overlap_threshold = overlap_threshold + self.object_mask_threshold = object_mask_threshold + self.metadata = metadata + if size_divisibility < 0: + # use backbone size_divisibility if not set + size_divisibility = self.backbone.size_divisibility + self.size_divisibility = size_divisibility + self.sem_seg_postprocess_before_inference = sem_seg_postprocess_before_inference + self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False) + + # additional args + self.semantic_on = semantic_on + self.instance_on = instance_on + self.panoptic_on = panoptic_on + self.test_topk_per_image = test_topk_per_image + + if not self.semantic_on: + assert self.sem_seg_postprocess_before_inference + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape()) + + # Loss parameters: + deep_supervision = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION + no_object_weight = cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT + + # loss weights + class_weight = cfg.MODEL.MASK_FORMER.CLASS_WEIGHT + dice_weight = cfg.MODEL.MASK_FORMER.DICE_WEIGHT + mask_weight = cfg.MODEL.MASK_FORMER.MASK_WEIGHT + + # building criterion + matcher = HungarianMatcher( + cost_class=class_weight, + cost_mask=mask_weight, + cost_dice=dice_weight, + num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS, + thresh=cfg.MODEL.MASK_FORMER.POSITIVE_BANK_IOU_THRESH, + ) + + weight_dict = {"loss_ce": class_weight, "loss_mask": mask_weight, "loss_dice": dice_weight} + + if deep_supervision: + dec_layers = cfg.MODEL.MASK_FORMER.DEC_LAYERS + aux_weight_dict = {} + for i in range(dec_layers - 1): + aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) + weight_dict.update(aux_weight_dict) + + losses = ["labels", "masks"] + + criterion = SetCriterion( + sem_seg_head.num_classes, + matcher=matcher, + weight_dict=weight_dict, + eos_coef=no_object_weight, + losses=losses, + num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS, + oversample_ratio=cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO, + importance_sample_ratio=cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO, + ) + + return { + "backbone": backbone, + "sem_seg_head": sem_seg_head, + "criterion": criterion, + "num_queries": cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES, + "object_mask_threshold": cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD, + "overlap_threshold": cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD, + "metadata": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), + "size_divisibility": cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY, + "sem_seg_postprocess_before_inference": ( + cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE + or cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON + or cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON + ), + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + # inference + "semantic_on": cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON, + "instance_on": cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON, + "panoptic_on": cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON, + "test_topk_per_image": cfg.TEST.DETECTIONS_PER_IMAGE, + } + + @property + def device(self): + return self.pixel_mean.device + + def forward(self, batched_inputs): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper`. + Each item in the list contains the inputs for one image. + For now, each item in the list is a dict that contains: + * "image": Tensor, image in (C, H, W) format. + * "instances": per-region ground truth + * Other information that's included in the original dicts, such as: + "height", "width" (int): the output resolution of the model (may be different + from input resolution), used in inference. + Returns: + list[dict]: + each dict has the results for one image. The dict contains the following keys: + + * "sem_seg": + A Tensor that represents the + per-pixel segmentation prediced by the head. + The prediction has shape KxHxW that represents the logits of + each class for each pixel. + * "panoptic_seg": + A tuple that represent panoptic output + panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment. + segments_info (list[dict]): Describe each segment in `panoptic_seg`. + Each dict contains keys "id", "category_id", "isthing". + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, self.size_divisibility) + + features = self.backbone(images.tensor) + outputs = self.sem_seg_head(features) + + if self.training: + # mask classification target + if "instances" in batched_inputs[0]: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + targets = self.prepare_targets(gt_instances, images) + else: + targets = None + + # bipartite matching-based loss + losses = self.criterion(outputs, targets) + + for k in list(losses.keys()): + if k in self.criterion.weight_dict: + losses[k] *= self.criterion.weight_dict[k] + else: + # remove this loss if not specified in `weight_dict` + losses.pop(k) + return losses + else: + mask_cls_results = outputs["pred_logits"] + mask_pred_results = outputs["pred_masks"] + # upsample masks + mask_pred_results = F.interpolate( + mask_pred_results, + size=(images.tensor.shape[-2], images.tensor.shape[-1]), + mode="bilinear", + align_corners=False, + ) + + del outputs + + processed_results = [] + for mask_cls_result, mask_pred_result, input_per_image, image_size in zip( + mask_cls_results, mask_pred_results, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + processed_results.append({}) + + if self.sem_seg_postprocess_before_inference: + mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)( + mask_pred_result, image_size, height, width + ) + mask_cls_result = mask_cls_result.to(mask_pred_result) + + # semantic segmentation inference + if self.semantic_on: + r = retry_if_cuda_oom(self.semantic_inference)(mask_cls_result, mask_pred_result) + if not self.sem_seg_postprocess_before_inference: + r = retry_if_cuda_oom(sem_seg_postprocess)(r, image_size, height, width) + processed_results[-1]["sem_seg"] = r + + # panoptic segmentation inference + if self.panoptic_on: + panoptic_r = retry_if_cuda_oom(self.panoptic_inference)(mask_cls_result, mask_pred_result) + processed_results[-1]["panoptic_seg"] = panoptic_r + + # instance segmentation inference + if self.instance_on: + instance_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result) + processed_results[-1]["instances"] = instance_r + + return processed_results + + def prepare_targets(self, targets, images): + h_pad, w_pad = images.tensor.shape[-2:] + new_targets = [] + for targets_per_image in targets: + # pad gt + gt_masks = targets_per_image.gt_masks + padded_masks = torch.zeros((gt_masks.shape[0], h_pad, w_pad), dtype=gt_masks.dtype, device=gt_masks.device) + padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks + new_targets.append( + { + "labels": targets_per_image.gt_classes, + "masks": padded_masks, + } + ) + return new_targets + + def semantic_inference(self, mask_cls, mask_pred): + mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1] + mask_pred = mask_pred.sigmoid() + semseg = torch.einsum("qc,qhw->chw", mask_cls, mask_pred) + return semseg + + def panoptic_inference(self, mask_cls, mask_pred): + scores, labels = F.softmax(mask_cls, dim=-1).max(-1) + mask_pred = mask_pred.sigmoid() + + keep = labels.ne(self.sem_seg_head.num_classes) & (scores > self.object_mask_threshold) + cur_scores = scores[keep] + cur_classes = labels[keep] + cur_masks = mask_pred[keep] + cur_mask_cls = mask_cls[keep] + cur_mask_cls = cur_mask_cls[:, :-1] + + cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks + + h, w = cur_masks.shape[-2:] + panoptic_seg = torch.zeros((h, w), dtype=torch.int32, device=cur_masks.device) + segments_info = [] + + current_segment_id = 0 + + if cur_masks.shape[0] == 0: + # We didn't detect any mask :( + return panoptic_seg, segments_info + else: + # take argmax + cur_mask_ids = cur_prob_masks.argmax(0) + stuff_memory_list = {} + for k in range(cur_classes.shape[0]): + pred_class = cur_classes[k].item() + isthing = pred_class in self.metadata.thing_dataset_id_to_contiguous_id.values() + mask_area = (cur_mask_ids == k).sum().item() + original_area = (cur_masks[k] >= 0.5).sum().item() + mask = (cur_mask_ids == k) & (cur_masks[k] >= 0.5) + + if mask_area > 0 and original_area > 0 and mask.sum().item() > 0: + if mask_area / original_area < self.overlap_threshold: + continue + + # merge stuff regions + if not isthing: + if int(pred_class) in stuff_memory_list.keys(): + panoptic_seg[mask] = stuff_memory_list[int(pred_class)] + continue + else: + stuff_memory_list[int(pred_class)] = current_segment_id + 1 + + current_segment_id += 1 + panoptic_seg[mask] = current_segment_id + + segments_info.append( + { + "id": current_segment_id, + "isthing": bool(isthing), + "category_id": int(pred_class), + } + ) + + return panoptic_seg, segments_info + + def instance_inference(self, mask_cls, mask_pred): + # mask_pred is already processed to have the same shape as original input + image_size = mask_pred.shape[-2:] + + # [Q, K] + scores = F.softmax(mask_cls, dim=-1)[:, :-1] + labels = torch.arange(self.sem_seg_head.num_classes, device=self.device).unsqueeze(0).repeat(self.num_queries, 1).flatten(0, 1) + # scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.num_queries, sorted=False) + scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.test_topk_per_image, sorted=False) + labels_per_image = labels[topk_indices] + + topk_indices = topk_indices // self.sem_seg_head.num_classes + # mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1) + mask_pred = mask_pred[topk_indices] + + # if this is panoptic segmentation, we only keep the "thing" classes + if self.panoptic_on: + keep = torch.zeros_like(scores_per_image).bool() + for i, lab in enumerate(labels_per_image): + keep[i] = lab in self.metadata.thing_dataset_id_to_contiguous_id.values() + + scores_per_image = scores_per_image[keep] + labels_per_image = labels_per_image[keep] + mask_pred = mask_pred[keep] + + result = Instances(image_size) + # mask (before sigmoid) + result.pred_masks = (mask_pred > 0).float() + result.pred_boxes = Boxes(torch.zeros(mask_pred.size(0), 4)) + # Uncomment the following to get boxes from masks (this is slow) + # result.pred_boxes = BitMasks(mask_pred > 0).get_bounding_boxes() + + # calculate average mask prob + mask_scores_per_image = (mask_pred.sigmoid().flatten(1) * result.pred_masks.flatten(1)).sum(1) / (result.pred_masks.flatten(1).sum(1) + 1e-6) + result.scores = scores_per_image * mask_scores_per_image + result.pred_classes = labels_per_image + return result diff --git a/videocutler/mask2former/modeling/__init__.py b/videocutler/mask2former/modeling/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7aed7beac4a880371b14b368f64227a0d129e7c7 --- /dev/null +++ b/videocutler/mask2former/modeling/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .backbone.swin import D2SwinTransformer +from .pixel_decoder.fpn import BasePixelDecoder +from .pixel_decoder.msdeformattn import MSDeformAttnPixelDecoder +from .meta_arch.mask_former_head import MaskFormerHead +from .meta_arch.per_pixel_baseline import PerPixelBaselineHead, PerPixelBaselinePlusHead diff --git a/videocutler/mask2former/modeling/backbone/__init__.py b/videocutler/mask2former/modeling/backbone/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9020c2df23e2af280b7bb168b996ae9eaf312eb8 --- /dev/null +++ b/videocutler/mask2former/modeling/backbone/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. diff --git a/videocutler/mask2former/modeling/backbone/swin.py b/videocutler/mask2former/modeling/backbone/swin.py new file mode 100644 index 0000000000000000000000000000000000000000..3b099d84396ac31d22881e5b6c9e53d2d0abaef3 --- /dev/null +++ b/videocutler/mask2former/modeling/backbone/swin.py @@ -0,0 +1,770 @@ +# -------------------------------------------------------- +# Swin Transformer +# Copyright (c) 2021 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ze Liu, Yutong Lin, Yixuan Wei +# -------------------------------------------------------- + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation/blob/main/mmseg/models/backbones/swin_transformer.py + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ + +from detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec + + +class Mlp(nn.Module): + """Multilayer perceptron.""" + + def __init__( + self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0 + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + """Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """Forward function. + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = ( + self.qkv(x) + .reshape(B_, N, 3, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[ + self.relative_position_index.view(-1) + ].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class SwinTransformerBlock(nn.Module): + """Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop + ) + + self.H = None + self.W = None + + def forward(self, x, mask_matrix): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + mask_matrix: Attention mask for cyclic shift. + """ + B, L, C = x.shape + H, W = self.H, self.W + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + attn_mask = mask_matrix + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition( + shifted_x, self.window_size + ) # nW*B, window_size, window_size, C + x_windows = x_windows.view( + -1, self.window_size * self.window_size, C + ) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + +class PatchMerging(nn.Module): + """Patch Merging Layer + Args: + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C) + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of feature channels + depth (int): Depths of this stage. + num_heads (int): Number of attention head. + window_size (int): Local window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + depth, + num_heads, + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + ): + super().__init__() + self.window_size = window_size + self.shift_size = window_size // 2 + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + + # calculate attention mask for SW-MSA + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition( + img_mask, self.window_size + ) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, float(0.0) + ) + + for blk in self.blocks: + blk.H, blk.W = H, W + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, attn_mask) + else: + x = blk(x, attn_mask) + if self.downsample is not None: + x_down = self.downsample(x, H, W) + Wh, Ww = (H + 1) // 2, (W + 1) // 2 + return x, H, W, x_down, Wh, Ww + else: + return x, H, W, x, H, W + + +class PatchEmbed(nn.Module): + """Image to Patch Embedding + Args: + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + patch_size = to_2tuple(patch_size) + self.patch_size = patch_size + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + """Forward function.""" + # padding + _, _, H, W = x.size() + if W % self.patch_size[1] != 0: + x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) + if H % self.patch_size[0] != 0: + x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) + + x = self.proj(x) # B C Wh Ww + if self.norm is not None: + Wh, Ww = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) + + return x + + +class SwinTransformer(nn.Module): + """Swin Transformer backbone. + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + pretrain_img_size (int): Input image size for training the pretrained model, + used in absolute postion embedding. Default 224. + patch_size (int | tuple(int)): Patch size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + depths (tuple[int]): Depths of each Swin Transformer stage. + num_heads (tuple[int]): Number of attention head of each stage. + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Default: 0.2. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. + patch_norm (bool): If True, add normalization after patch embedding. Default: True. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + pretrain_img_size=224, + patch_size=4, + in_chans=3, + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.2, + norm_layer=nn.LayerNorm, + ape=False, + patch_norm=True, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + use_checkpoint=False, + ): + super().__init__() + + self.pretrain_img_size = pretrain_img_size + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.out_indices = out_indices + self.frozen_stages = frozen_stages + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + pretrain_img_size = to_2tuple(pretrain_img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + pretrain_img_size[0] // patch_size[0], + pretrain_img_size[1] // patch_size[1], + ] + + self.absolute_pos_embed = nn.Parameter( + torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] # stochastic depth decay rule + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = BasicLayer( + dim=int(embed_dim * 2 ** i_layer), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], + norm_layer=norm_layer, + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint, + ) + self.layers.append(layer) + + num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] + self.num_features = num_features + + # add a norm layer for each output + for i_layer in out_indices: + layer = norm_layer(num_features[i_layer]) + layer_name = f"norm{i_layer}" + self.add_module(layer_name, layer) + + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.ape: + self.absolute_pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + self.pos_drop.eval() + for i in range(0, self.frozen_stages - 1): + m = self.layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + + def _init_weights(m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward(self, x): + """Forward function.""" + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate( + self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic" + ) + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + outs = {} + for i in range(self.num_layers): + layer = self.layers[i] + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) + + if i in self.out_indices: + norm_layer = getattr(self, f"norm{i}") + x_out = norm_layer(x_out) + + out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() + outs["res{}".format(i + 2)] = out + + return outs + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(SwinTransformer, self).train(mode) + self._freeze_stages() + + +@BACKBONE_REGISTRY.register() +class D2SwinTransformer(SwinTransformer, Backbone): + def __init__(self, cfg, input_shape): + + pretrain_img_size = cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE + patch_size = cfg.MODEL.SWIN.PATCH_SIZE + in_chans = 3 + embed_dim = cfg.MODEL.SWIN.EMBED_DIM + depths = cfg.MODEL.SWIN.DEPTHS + num_heads = cfg.MODEL.SWIN.NUM_HEADS + window_size = cfg.MODEL.SWIN.WINDOW_SIZE + mlp_ratio = cfg.MODEL.SWIN.MLP_RATIO + qkv_bias = cfg.MODEL.SWIN.QKV_BIAS + qk_scale = cfg.MODEL.SWIN.QK_SCALE + drop_rate = cfg.MODEL.SWIN.DROP_RATE + attn_drop_rate = cfg.MODEL.SWIN.ATTN_DROP_RATE + drop_path_rate = cfg.MODEL.SWIN.DROP_PATH_RATE + norm_layer = nn.LayerNorm + ape = cfg.MODEL.SWIN.APE + patch_norm = cfg.MODEL.SWIN.PATCH_NORM + use_checkpoint = cfg.MODEL.SWIN.USE_CHECKPOINT + + super().__init__( + pretrain_img_size, + patch_size, + in_chans, + embed_dim, + depths, + num_heads, + window_size, + mlp_ratio, + qkv_bias, + qk_scale, + drop_rate, + attn_drop_rate, + drop_path_rate, + norm_layer, + ape, + patch_norm, + use_checkpoint=use_checkpoint, + ) + + self._out_features = cfg.MODEL.SWIN.OUT_FEATURES + + self._out_feature_strides = { + "res2": 4, + "res3": 8, + "res4": 16, + "res5": 32, + } + self._out_feature_channels = { + "res2": self.num_features[0], + "res3": self.num_features[1], + "res4": self.num_features[2], + "res5": self.num_features[3], + } + + def forward(self, x): + """ + Args: + x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. + Returns: + dict[str->Tensor]: names and the corresponding features + """ + assert ( + x.dim() == 4 + ), f"SwinTransformer takes an input of shape (N, C, H, W). Got {x.shape} instead!" + outputs = {} + y = super().forward(x) + for k in y.keys(): + if k in self._out_features: + outputs[k] = y[k] + return outputs + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } + + @property + def size_divisibility(self): + return 32 diff --git a/videocutler/mask2former/modeling/criterion.py b/videocutler/mask2former/modeling/criterion.py new file mode 100644 index 0000000000000000000000000000000000000000..878ae754d1a108084644bfaebb3409fa6849cf13 --- /dev/null +++ b/videocutler/mask2former/modeling/criterion.py @@ -0,0 +1,263 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/detr.py +""" +MaskFormer criterion. +""" +import logging + +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.utils.comm import get_world_size +from detectron2.projects.point_rend.point_features import ( + get_uncertain_point_coords_with_randomness, + point_sample, +) + +from ..utils.misc import is_dist_avail_and_initialized, nested_tensor_from_tensor_list + + +def dice_loss( + inputs: torch.Tensor, + targets: torch.Tensor, + num_masks: float, + ): + """ + Compute the DICE loss, similar to generalized IOU for masks + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * (inputs * targets).sum(-1) + denominator = inputs.sum(-1) + targets.sum(-1) + loss = 1 - (numerator + 1) / (denominator + 1) + return loss.sum() / num_masks + + +dice_loss_jit = torch.jit.script( + dice_loss +) # type: torch.jit.ScriptModule + + +def sigmoid_ce_loss( + inputs: torch.Tensor, + targets: torch.Tensor, + num_masks: float, + ): + """ + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + Returns: + Loss tensor + """ + loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + + return loss.mean(1).sum() / num_masks + + +sigmoid_ce_loss_jit = torch.jit.script( + sigmoid_ce_loss +) # type: torch.jit.ScriptModule + + +def calculate_uncertainty(logits): + """ + We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the + foreground class in `classes`. + Args: + logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or + class-agnostic, where R is the total number of predicted masks in all images and C is + the number of foreground classes. The values are logits. + Returns: + scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with + the most uncertain locations having the highest uncertainty score. + """ + assert logits.shape[1] == 1 + gt_class_logits = logits.clone() + return -(torch.abs(gt_class_logits)) + + +class SetCriterion(nn.Module): + """This class computes the loss for DETR. + The process happens in two steps: + 1) we compute hungarian assignment between ground truth boxes and the outputs of the model + 2) we supervise each pair of matched ground-truth / prediction (supervise class and box) + """ + + def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses, + num_points, oversample_ratio, importance_sample_ratio): + """Create the criterion. + Parameters: + num_classes: number of object categories, omitting the special no-object category + matcher: module able to compute a matching between targets and proposals + weight_dict: dict containing as key the names of the losses and as values their relative weight. + eos_coef: relative classification weight applied to the no-object category + losses: list of all the losses to be applied. See get_loss for list of available losses. + """ + super().__init__() + self.num_classes = num_classes + self.matcher = matcher + self.weight_dict = weight_dict + self.eos_coef = eos_coef + self.losses = losses + empty_weight = torch.ones(self.num_classes + 1) + empty_weight[-1] = self.eos_coef + self.register_buffer("empty_weight", empty_weight) + + # pointwise mask loss parameters + self.num_points = num_points + self.oversample_ratio = oversample_ratio + self.importance_sample_ratio = importance_sample_ratio + + def loss_labels(self, outputs, targets, indices, num_masks): + """Classification loss (NLL) + targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes] + """ + assert "pred_logits" in outputs + src_logits = outputs["pred_logits"].float() + + idx = self._get_src_permutation_idx(indices) + target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) + target_classes = torch.full( + src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device + ) + target_classes[idx] = target_classes_o + + loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight) + losses = {"loss_ce": loss_ce} + return losses + + def loss_masks(self, outputs, targets, indices, num_masks): + """Compute the losses related to the masks: the focal loss and the dice loss. + targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w] + """ + assert "pred_masks" in outputs + + src_idx = self._get_src_permutation_idx(indices) + tgt_idx = self._get_tgt_permutation_idx(indices) + src_masks = outputs["pred_masks"] + src_masks = src_masks[src_idx] + masks = [t["masks"] for t in targets] + # TODO use valid to mask invalid areas due to padding in loss + target_masks, valid = nested_tensor_from_tensor_list(masks).decompose() + target_masks = target_masks.to(src_masks) + target_masks = target_masks[tgt_idx] + + # No need to upsample predictions as we are using normalized coordinates :) + # N x 1 x H x W + src_masks = src_masks[:, None] + target_masks = target_masks[:, None] + + with torch.no_grad(): + # sample point_coords + point_coords = get_uncertain_point_coords_with_randomness( + src_masks, + lambda logits: calculate_uncertainty(logits), + self.num_points, + self.oversample_ratio, + self.importance_sample_ratio, + ) + # get gt labels + point_labels = point_sample( + target_masks, + point_coords, + align_corners=False, + ).squeeze(1) + + point_logits = point_sample( + src_masks, + point_coords, + align_corners=False, + ).squeeze(1) + + losses = { + "loss_mask": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks), + "loss_dice": dice_loss_jit(point_logits, point_labels, num_masks), + } + + del src_masks + del target_masks + return losses + + def _get_src_permutation_idx(self, indices): + # permute predictions following indices + batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)]) + src_idx = torch.cat([src for (src, _) in indices]) + return batch_idx, src_idx + + def _get_tgt_permutation_idx(self, indices): + # permute targets following indices + batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]) + tgt_idx = torch.cat([tgt for (_, tgt) in indices]) + return batch_idx, tgt_idx + + def get_loss(self, loss, outputs, targets, indices, num_masks): + loss_map = { + 'labels': self.loss_labels, + 'masks': self.loss_masks, + } + assert loss in loss_map, f"do you really want to compute {loss} loss?" + return loss_map[loss](outputs, targets, indices, num_masks) + + def forward(self, outputs, targets): + """This performs the loss computation. + Parameters: + outputs: dict of tensors, see the output specification of the model for the format + targets: list of dicts, such that len(targets) == batch_size. + The expected keys in each dict depends on the losses applied, see each loss' doc + """ + outputs_without_aux = {k: v for k, v in outputs.items() if k != "aux_outputs"} + + # Retrieve the matching between the outputs of the last layer and the targets + indices = self.matcher(outputs_without_aux, targets) + + # Compute the average number of target boxes accross all nodes, for normalization purposes + num_masks = sum(len(t["labels"]) for t in targets) + num_masks = torch.as_tensor( + [num_masks], dtype=torch.float, device=next(iter(outputs.values())).device + ) + if is_dist_avail_and_initialized(): + torch.distributed.all_reduce(num_masks) + num_masks = torch.clamp(num_masks / get_world_size(), min=1).item() + + # Compute all the requested losses + losses = {} + for loss in self.losses: + losses.update(self.get_loss(loss, outputs, targets, indices, num_masks)) + + # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. + if "aux_outputs" in outputs: + for i, aux_outputs in enumerate(outputs["aux_outputs"]): + indices = self.matcher(aux_outputs, targets) + for loss in self.losses: + l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks) + l_dict = {k + f"_{i}": v for k, v in l_dict.items()} + losses.update(l_dict) + + return losses + + def __repr__(self): + head = "Criterion " + self.__class__.__name__ + body = [ + "matcher: {}".format(self.matcher.__repr__(_repr_indent=8)), + "losses: {}".format(self.losses), + "weight_dict: {}".format(self.weight_dict), + "num_classes: {}".format(self.num_classes), + "eos_coef: {}".format(self.eos_coef), + "num_points: {}".format(self.num_points), + "oversample_ratio: {}".format(self.oversample_ratio), + "importance_sample_ratio: {}".format(self.importance_sample_ratio), + ] + _repr_indent = 4 + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/videocutler/mask2former/modeling/matcher.py b/videocutler/mask2former/modeling/matcher.py new file mode 100644 index 0000000000000000000000000000000000000000..5d7efedb9cb89cd8bf2014e8c5a1c3285141da83 --- /dev/null +++ b/videocutler/mask2former/modeling/matcher.py @@ -0,0 +1,207 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/matcher.py +""" +Modules to compute the matching cost and solve the corresponding LSAP. +""" +import torch +import torch.nn.functional as F +from scipy.optimize import linear_sum_assignment +from torch import nn +from torch.cuda.amp import autocast + +from detectron2.projects.point_rend.point_features import point_sample + + +def batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor, thresh: float): + """ + Compute the DICE loss, similar to generalized IOU for masks + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * torch.einsum("nc,mc->nm", inputs, targets) + denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :] + loss = 1 - (numerator + 1) / (denominator + 1) + + if targets.size()[0] != 0: + hw = inputs.shape[1] + drop_weights = torch.einsum("nc,mc->nm", inputs.ge(0.0).float(), targets).max(-1)[0] / hw + drop_weights = drop_weights.ge(thresh).float().detach()[:,None] + loss = drop_weights * loss + + return loss + + +batch_dice_loss_jit = torch.jit.script( + batch_dice_loss +) # type: torch.jit.ScriptModule + + +def batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor, thresh: float,): + """ + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + Returns: + Loss tensor + """ + hw = inputs.shape[1] + + pos = F.binary_cross_entropy_with_logits( + inputs, torch.ones_like(inputs), reduction="none" + ) + # print("pos: ", pos.size()) + neg = F.binary_cross_entropy_with_logits( + inputs, torch.zeros_like(inputs), reduction="none" + ) + # print("neg: ", neg.size()) + + loss = torch.einsum("nc,mc->nm", pos, targets) + torch.einsum( + "nc,mc->nm", neg, (1 - targets) + ) + if targets.size()[0] != 0: + drop_weights = torch.einsum("nc,mc->nm", inputs.ge(0.0).float(), targets).max(-1)[0] / hw + drop_weights = drop_weights.ge(thresh).float().detach()[:,None] + loss = drop_weights * loss + # print("drop_weights: ", drop_weights.size(), drop_weights.sum()) + + return loss / hw + + +batch_sigmoid_ce_loss_jit = torch.jit.script( + batch_sigmoid_ce_loss +) # type: torch.jit.ScriptModule + + +class HungarianMatcher(nn.Module): + """This class computes an assignment between the targets and the predictions of the network + + For efficiency reasons, the targets don't include the no_object. Because of this, in general, + there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, + while the others are un-matched (and thus treated as non-objects). + """ + # used in mask2former/maskformer_model.py + def __init__(self, cost_class: float = 1, cost_mask: float = 1, cost_dice: float = 1, num_points: int = 0, thresh: float = 0.01): + """Creates the matcher + + Params: + cost_class: This is the relative weight of the classification error in the matching cost + cost_mask: This is the relative weight of the focal loss of the binary mask in the matching cost + cost_dice: This is the relative weight of the dice loss of the binary mask in the matching cost + """ + super().__init__() + self.cost_class = cost_class + self.cost_mask = cost_mask + self.cost_dice = cost_dice + + assert cost_class != 0 or cost_mask != 0 or cost_dice != 0, "all costs cant be 0" + + self.num_points = num_points + self.thresh = thresh + + @torch.no_grad() + def memory_efficient_forward(self, outputs, targets): + """More memory-friendly matching""" + bs, num_queries = outputs["pred_logits"].shape[:2] + + indices = [] + + # Iterate through batch size + for b in range(bs): + + out_prob = outputs["pred_logits"][b].softmax(-1) # [num_queries, num_classes] + tgt_ids = targets[b]["labels"] + + # Compute the classification cost. Contrary to the loss, we don't use the NLL, + # but approximate it in 1 - proba[target class]. + # The 1 is a constant that doesn't change the matching, it can be ommitted. + cost_class = -out_prob[:, tgt_ids] + + out_mask = outputs["pred_masks"][b] # [num_queries, H_pred, W_pred] + # gt masks are already padded when preparing target + tgt_mask = targets[b]["masks"].to(out_mask) + + out_mask = out_mask[:, None] + tgt_mask = tgt_mask[:, None] + # all masks share the same set of points for efficient matching! + point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device) + # get gt labels + tgt_mask = point_sample( + tgt_mask, + point_coords.repeat(tgt_mask.shape[0], 1, 1), + align_corners=False, + ).squeeze(1) + + out_mask = point_sample( + out_mask, + point_coords.repeat(out_mask.shape[0], 1, 1), + align_corners=False, + ).squeeze(1) + + with autocast(enabled=False): + out_mask = out_mask.float() + tgt_mask = tgt_mask.float() + + if out_mask.shape[0] == 0 or tgt_mask.shape[0] == 0: + cost_mask = batch_sigmoid_ce_loss(out_mask, tgt_mask, thresh=self.thresh) + cost_dice = batch_dice_loss(out_mask, tgt_mask, thresh=self.thresh) + else: + # Compute the focal loss between masks + cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask, thresh=self.thresh) + # Compute the dice loss betwen masks + cost_dice = batch_dice_loss_jit(out_mask, tgt_mask, thresh=self.thresh) + # Final cost matrix + C = ( + self.cost_mask * cost_mask + + self.cost_class * cost_class + + self.cost_dice * cost_dice + ) + C = C.reshape(num_queries, -1).cpu() + + indices.append(linear_sum_assignment(C)) + + return [ + (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) + for i, j in indices + ] + + @torch.no_grad() + def forward(self, outputs, targets): + """Performs the matching + + Params: + outputs: This is a dict that contains at least these entries: + "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits + "pred_masks": Tensor of dim [batch_size, num_queries, H_pred, W_pred] with the predicted masks + + targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing: + "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth + objects in the target) containing the class labels + "masks": Tensor of dim [num_target_boxes, H_gt, W_gt] containing the target masks + + Returns: + A list of size batch_size, containing tuples of (index_i, index_j) where: + - index_i is the indices of the selected predictions (in order) + - index_j is the indices of the corresponding selected targets (in order) + For each batch element, it holds: + len(index_i) = len(index_j) = min(num_queries, num_target_boxes) + """ + return self.memory_efficient_forward(outputs, targets) + + def __repr__(self, _repr_indent=4): + head = "Matcher " + self.__class__.__name__ + body = [ + "cost_class: {}".format(self.cost_class), + "cost_mask: {}".format(self.cost_mask), + "cost_dice: {}".format(self.cost_dice), + ] + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/videocutler/mask2former/modeling/meta_arch/__init__.py b/videocutler/mask2former/modeling/meta_arch/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9020c2df23e2af280b7bb168b996ae9eaf312eb8 --- /dev/null +++ b/videocutler/mask2former/modeling/meta_arch/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. diff --git a/videocutler/mask2former/modeling/meta_arch/mask_former_head.py b/videocutler/mask2former/modeling/meta_arch/mask_former_head.py new file mode 100644 index 0000000000000000000000000000000000000000..4e484d1a0be526a8d27d449f561c8495c3446a02 --- /dev/null +++ b/videocutler/mask2former/modeling/meta_arch/mask_former_head.py @@ -0,0 +1,132 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +from copy import deepcopy +from typing import Callable, Dict, List, Optional, Tuple, Union + +import fvcore.nn.weight_init as weight_init +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ShapeSpec, get_norm +from detectron2.modeling import SEM_SEG_HEADS_REGISTRY + +from ..transformer_decoder.maskformer_transformer_decoder import build_transformer_decoder +from ..pixel_decoder.fpn import build_pixel_decoder + + +@SEM_SEG_HEADS_REGISTRY.register() +class MaskFormerHead(nn.Module): + + _version = 2 + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + version = local_metadata.get("version", None) + if version is None or version < 2: + # Do not warn if train from scratch + scratch = True + logger = logging.getLogger(__name__) + for k in list(state_dict.keys()): + newk = k + # if "sem_seg_head" in k and not k.startswith(prefix + "predictor"): + # newk = k.replace(prefix, prefix + "pixel_decoder.") + # logger.debug(f"{k} ==> {newk}") + if newk != k: + state_dict[newk] = state_dict[k] + del state_dict[k] + scratch = False + + if not scratch: + logger.warning( + f"Weight format of {self.__class__.__name__} have changed! " + "Please upgrade your models. Applying automatic conversion now ..." + ) + + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + num_classes: int, + pixel_decoder: nn.Module, + loss_weight: float = 1.0, + ignore_value: int = -1, + # extra parameters + transformer_predictor: nn.Module, + transformer_in_feature: str, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + num_classes: number of classes to predict + pixel_decoder: the pixel decoder module + loss_weight: loss weight + ignore_value: category id to be ignored during training. + transformer_predictor: the transformer decoder that makes prediction + transformer_in_feature: input feature name to the transformer_predictor + """ + super().__init__() + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + self.in_features = [k for k, v in input_shape] + feature_strides = [v.stride for k, v in input_shape] + feature_channels = [v.channels for k, v in input_shape] + + self.ignore_value = ignore_value + self.common_stride = 4 + self.loss_weight = loss_weight + + self.pixel_decoder = pixel_decoder + self.predictor = transformer_predictor + self.transformer_in_feature = transformer_in_feature + + self.num_classes = num_classes + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + # figure out in_channels to transformer predictor + if cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "transformer_encoder": + transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM + elif cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "pixel_embedding": + transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + elif cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "multi_scale_pixel_decoder": # for maskformer2 + transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM + else: + transformer_predictor_in_channels = input_shape[cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE].channels + + return { + "input_shape": { + k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES + }, + "ignore_value": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE, + "num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES, + "pixel_decoder": build_pixel_decoder(cfg, input_shape), + "loss_weight": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT, + "transformer_in_feature": cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE, + "transformer_predictor": build_transformer_decoder( + cfg, + transformer_predictor_in_channels, + mask_classification=True, + ), + } + + def forward(self, features, mask=None): + return self.layers(features, mask) + + def layers(self, features, mask=None): + mask_features, transformer_encoder_features, multi_scale_features = self.pixel_decoder.forward_features(features) + if self.transformer_in_feature == "multi_scale_pixel_decoder": + predictions = self.predictor(multi_scale_features, mask_features, mask) + else: + if self.transformer_in_feature == "transformer_encoder": + assert ( + transformer_encoder_features is not None + ), "Please use the TransformerEncoderPixelDecoder." + predictions = self.predictor(transformer_encoder_features, mask_features, mask) + elif self.transformer_in_feature == "pixel_embedding": + predictions = self.predictor(mask_features, mask_features, mask) + else: + predictions = self.predictor(features[self.transformer_in_feature], mask_features, mask) + return predictions diff --git a/videocutler/mask2former/modeling/meta_arch/per_pixel_baseline.py b/videocutler/mask2former/modeling/meta_arch/per_pixel_baseline.py new file mode 100644 index 0000000000000000000000000000000000000000..4ce7573e0ff97e7fdeef0ea94928def6e263ab1d --- /dev/null +++ b/videocutler/mask2former/modeling/meta_arch/per_pixel_baseline.py @@ -0,0 +1,243 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +from typing import Callable, Dict, List, Optional, Tuple, Union + +import fvcore.nn.weight_init as weight_init +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ShapeSpec, get_norm +from detectron2.modeling import SEM_SEG_HEADS_REGISTRY + +from ..transformer_decoder.maskformer_transformer_decoder import StandardTransformerDecoder +from ..pixel_decoder.fpn import build_pixel_decoder + + +@SEM_SEG_HEADS_REGISTRY.register() +class PerPixelBaselineHead(nn.Module): + + _version = 2 + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + version = local_metadata.get("version", None) + if version is None or version < 2: + logger = logging.getLogger(__name__) + # Do not warn if train from scratch + scratch = True + logger = logging.getLogger(__name__) + for k in list(state_dict.keys()): + newk = k + if "sem_seg_head" in k and not k.startswith(prefix + "predictor"): + newk = k.replace(prefix, prefix + "pixel_decoder.") + # logger.warning(f"{k} ==> {newk}") + if newk != k: + state_dict[newk] = state_dict[k] + del state_dict[k] + scratch = False + + if not scratch: + logger.warning( + f"Weight format of {self.__class__.__name__} have changed! " + "Please upgrade your models. Applying automatic conversion now ..." + ) + + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + num_classes: int, + pixel_decoder: nn.Module, + loss_weight: float = 1.0, + ignore_value: int = -1, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + num_classes: number of classes to predict + pixel_decoder: the pixel decoder module + loss_weight: loss weight + ignore_value: category id to be ignored during training. + """ + super().__init__() + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + self.in_features = [k for k, v in input_shape] + feature_strides = [v.stride for k, v in input_shape] + feature_channels = [v.channels for k, v in input_shape] + + self.ignore_value = ignore_value + self.common_stride = 4 + self.loss_weight = loss_weight + + self.pixel_decoder = pixel_decoder + self.predictor = Conv2d( + self.pixel_decoder.mask_dim, num_classes, kernel_size=1, stride=1, padding=0 + ) + weight_init.c2_msra_fill(self.predictor) + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + return { + "input_shape": { + k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES + }, + "ignore_value": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE, + "num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES, + "pixel_decoder": build_pixel_decoder(cfg, input_shape), + "loss_weight": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT, + } + + def forward(self, features, targets=None): + """ + Returns: + In training, returns (None, dict of losses) + In inference, returns (CxHxW logits, {}) + """ + x = self.layers(features) + if self.training: + return None, self.losses(x, targets) + else: + x = F.interpolate( + x, scale_factor=self.common_stride, mode="bilinear", align_corners=False + ) + return x, {} + + def layers(self, features): + x, _, _ = self.pixel_decoder.forward_features(features) + x = self.predictor(x) + return x + + def losses(self, predictions, targets): + predictions = predictions.float() # https://github.com/pytorch/pytorch/issues/48163 + predictions = F.interpolate( + predictions, scale_factor=self.common_stride, mode="bilinear", align_corners=False + ) + loss = F.cross_entropy( + predictions, targets, reduction="mean", ignore_index=self.ignore_value + ) + losses = {"loss_sem_seg": loss * self.loss_weight} + return losses + + +@SEM_SEG_HEADS_REGISTRY.register() +class PerPixelBaselinePlusHead(PerPixelBaselineHead): + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + version = local_metadata.get("version", None) + if version is None or version < 2: + # Do not warn if train from scratch + scratch = True + logger = logging.getLogger(__name__) + for k in list(state_dict.keys()): + newk = k + if "sem_seg_head" in k and not k.startswith(prefix + "predictor"): + newk = k.replace(prefix, prefix + "pixel_decoder.") + logger.debug(f"{k} ==> {newk}") + if newk != k: + state_dict[newk] = state_dict[k] + del state_dict[k] + scratch = False + + if not scratch: + logger.warning( + f"Weight format of {self.__class__.__name__} have changed! " + "Please upgrade your models. Applying automatic conversion now ..." + ) + + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + # extra parameters + transformer_predictor: nn.Module, + transformer_in_feature: str, + deep_supervision: bool, + # inherit parameters + num_classes: int, + pixel_decoder: nn.Module, + loss_weight: float = 1.0, + ignore_value: int = -1, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + transformer_predictor: the transformer decoder that makes prediction + transformer_in_feature: input feature name to the transformer_predictor + deep_supervision: whether or not to add supervision to the output of + every transformer decoder layer + num_classes: number of classes to predict + pixel_decoder: the pixel decoder module + loss_weight: loss weight + ignore_value: category id to be ignored during training. + """ + super().__init__( + input_shape, + num_classes=num_classes, + pixel_decoder=pixel_decoder, + loss_weight=loss_weight, + ignore_value=ignore_value, + ) + + del self.predictor + + self.predictor = transformer_predictor + self.transformer_in_feature = transformer_in_feature + self.deep_supervision = deep_supervision + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + ret = super().from_config(cfg, input_shape) + ret["transformer_in_feature"] = cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE + if cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "transformer_encoder": + in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM + else: + in_channels = input_shape[ret["transformer_in_feature"]].channels + ret["transformer_predictor"] = StandardTransformerDecoder( + cfg, in_channels, mask_classification=False + ) + ret["deep_supervision"] = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION + return ret + + def forward(self, features, targets=None): + """ + Returns: + In training, returns (None, dict of losses) + In inference, returns (CxHxW logits, {}) + """ + x, aux_outputs = self.layers(features) + if self.training: + if self.deep_supervision: + losses = self.losses(x, targets) + for i, aux_output in enumerate(aux_outputs): + losses["loss_sem_seg" + f"_{i}"] = self.losses( + aux_output["pred_masks"], targets + )["loss_sem_seg"] + return None, losses + else: + return None, self.losses(x, targets) + else: + x = F.interpolate( + x, scale_factor=self.common_stride, mode="bilinear", align_corners=False + ) + return x, {} + + def layers(self, features): + mask_features, transformer_encoder_features, _ = self.pixel_decoder.forward_features(features) + if self.transformer_in_feature == "transformer_encoder": + assert ( + transformer_encoder_features is not None + ), "Please use the TransformerEncoderPixelDecoder." + predictions = self.predictor(transformer_encoder_features, mask_features) + else: + predictions = self.predictor(features[self.transformer_in_feature], mask_features) + if self.deep_supervision: + return predictions["pred_masks"], predictions["aux_outputs"] + else: + return predictions["pred_masks"], None diff --git a/videocutler/mask2former/modeling/pixel_decoder/__init__.py b/videocutler/mask2former/modeling/pixel_decoder/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9020c2df23e2af280b7bb168b996ae9eaf312eb8 --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. diff --git a/videocutler/mask2former/modeling/pixel_decoder/fpn.py b/videocutler/mask2former/modeling/pixel_decoder/fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..7df65a178ce4a105d5c803ff5aa18aa56c44d374 --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/fpn.py @@ -0,0 +1,312 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +from typing import Callable, Dict, List, Optional, Tuple, Union + +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_ +from torch.cuda.amp import autocast + +from detectron2.config import configurable +from detectron2.layers import Conv2d, DeformConv, ShapeSpec, get_norm +from detectron2.modeling import SEM_SEG_HEADS_REGISTRY + +from ..transformer_decoder.position_encoding import PositionEmbeddingSine +from ..transformer_decoder.transformer import TransformerEncoder, TransformerEncoderLayer, _get_clones, _get_activation_fn + + +def build_pixel_decoder(cfg, input_shape): + """ + Build a pixel decoder from `cfg.MODEL.MASK_FORMER.PIXEL_DECODER_NAME`. + """ + name = cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME + model = SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape) + forward_features = getattr(model, "forward_features", None) + if not callable(forward_features): + raise ValueError( + "Only SEM_SEG_HEADS with forward_features method can be used as pixel decoder. " + f"Please implement forward_features for {name} to only return mask features." + ) + return model + + +# This is a modified FPN decoder. +@SEM_SEG_HEADS_REGISTRY.register() +class BasePixelDecoder(nn.Module): + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + conv_dim: int, + mask_dim: int, + norm: Optional[Union[str, Callable]] = None, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + conv_dims: number of output channels for the intermediate conv layers. + mask_dim: number of output channels for the final conv layer. + norm (str or callable): normalization for all conv layers + """ + super().__init__() + + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5" + feature_channels = [v.channels for k, v in input_shape] + + lateral_convs = [] + output_convs = [] + + use_bias = norm == "" + for idx, in_channels in enumerate(feature_channels): + if idx == len(self.in_features) - 1: + output_norm = get_norm(norm, conv_dim) + output_conv = Conv2d( + in_channels, + conv_dim, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + activation=F.relu, + ) + weight_init.c2_xavier_fill(output_conv) + self.add_module("layer_{}".format(idx + 1), output_conv) + + lateral_convs.append(None) + output_convs.append(output_conv) + else: + lateral_norm = get_norm(norm, conv_dim) + output_norm = get_norm(norm, conv_dim) + + lateral_conv = Conv2d( + in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm + ) + output_conv = Conv2d( + conv_dim, + conv_dim, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + activation=F.relu, + ) + weight_init.c2_xavier_fill(lateral_conv) + weight_init.c2_xavier_fill(output_conv) + self.add_module("adapter_{}".format(idx + 1), lateral_conv) + self.add_module("layer_{}".format(idx + 1), output_conv) + + lateral_convs.append(lateral_conv) + output_convs.append(output_conv) + # Place convs into top-down order (from low to high resolution) + # to make the top-down computation in forward clearer. + self.lateral_convs = lateral_convs[::-1] + self.output_convs = output_convs[::-1] + + self.mask_dim = mask_dim + self.mask_features = Conv2d( + conv_dim, + mask_dim, + kernel_size=3, + stride=1, + padding=1, + ) + weight_init.c2_xavier_fill(self.mask_features) + + self.maskformer_num_feature_levels = 3 # always use 3 scales + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + ret = {} + ret["input_shape"] = { + k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES + } + ret["conv_dim"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM + ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + ret["norm"] = cfg.MODEL.SEM_SEG_HEAD.NORM + return ret + + def forward_features(self, features): + multi_scale_features = [] + num_cur_levels = 0 + # Reverse feature maps into top-down order (from low to high resolution) + for idx, f in enumerate(self.in_features[::-1]): + x = features[f] + lateral_conv = self.lateral_convs[idx] + output_conv = self.output_convs[idx] + if lateral_conv is None: + y = output_conv(x) + else: + cur_fpn = lateral_conv(x) + # Following FPN implementation, we use nearest upsampling here + y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest") + y = output_conv(y) + if num_cur_levels < self.maskformer_num_feature_levels: + multi_scale_features.append(y) + num_cur_levels += 1 + return self.mask_features(y), None, multi_scale_features + + def forward(self, features, targets=None): + logger = logging.getLogger(__name__) + logger.warning("Calling forward() may cause unpredicted behavior of PixelDecoder module.") + return self.forward_features(features) + + +class TransformerEncoderOnly(nn.Module): + def __init__( + self, + d_model=512, + nhead=8, + num_encoder_layers=6, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + ): + super().__init__() + + encoder_layer = TransformerEncoderLayer( + d_model, nhead, dim_feedforward, dropout, activation, normalize_before + ) + encoder_norm = nn.LayerNorm(d_model) if normalize_before else None + self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) + + self._reset_parameters() + + self.d_model = d_model + self.nhead = nhead + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, src, mask, pos_embed): + # flatten NxCxHxW to HWxNxC + bs, c, h, w = src.shape + src = src.flatten(2).permute(2, 0, 1) + pos_embed = pos_embed.flatten(2).permute(2, 0, 1) + if mask is not None: + mask = mask.flatten(1) + + memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) + return memory.permute(1, 2, 0).view(bs, c, h, w) + + +# This is a modified FPN decoder with extra Transformer encoder that processes the lowest-resolution feature map. +@SEM_SEG_HEADS_REGISTRY.register() +class TransformerEncoderPixelDecoder(BasePixelDecoder): + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + transformer_dropout: float, + transformer_nheads: int, + transformer_dim_feedforward: int, + transformer_enc_layers: int, + transformer_pre_norm: bool, + conv_dim: int, + mask_dim: int, + norm: Optional[Union[str, Callable]] = None, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + transformer_dropout: dropout probability in transformer + transformer_nheads: number of heads in transformer + transformer_dim_feedforward: dimension of feedforward network + transformer_enc_layers: number of transformer encoder layers + transformer_pre_norm: whether to use pre-layernorm or not + conv_dims: number of output channels for the intermediate conv layers. + mask_dim: number of output channels for the final conv layer. + norm (str or callable): normalization for all conv layers + """ + super().__init__(input_shape, conv_dim=conv_dim, mask_dim=mask_dim, norm=norm) + + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5" + feature_strides = [v.stride for k, v in input_shape] + feature_channels = [v.channels for k, v in input_shape] + + in_channels = feature_channels[len(self.in_features) - 1] + self.input_proj = Conv2d(in_channels, conv_dim, kernel_size=1) + weight_init.c2_xavier_fill(self.input_proj) + self.transformer = TransformerEncoderOnly( + d_model=conv_dim, + dropout=transformer_dropout, + nhead=transformer_nheads, + dim_feedforward=transformer_dim_feedforward, + num_encoder_layers=transformer_enc_layers, + normalize_before=transformer_pre_norm, + ) + N_steps = conv_dim // 2 + self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True) + + # update layer + use_bias = norm == "" + output_norm = get_norm(norm, conv_dim) + output_conv = Conv2d( + conv_dim, + conv_dim, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + activation=F.relu, + ) + weight_init.c2_xavier_fill(output_conv) + delattr(self, "layer_{}".format(len(self.in_features))) + self.add_module("layer_{}".format(len(self.in_features)), output_conv) + self.output_convs[0] = output_conv + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + ret = super().from_config(cfg, input_shape) + ret["transformer_dropout"] = cfg.MODEL.MASK_FORMER.DROPOUT + ret["transformer_nheads"] = cfg.MODEL.MASK_FORMER.NHEADS + ret["transformer_dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD + ret[ + "transformer_enc_layers" + ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config + ret["transformer_pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM + return ret + + def forward_features(self, features): + multi_scale_features = [] + num_cur_levels = 0 + # Reverse feature maps into top-down order (from low to high resolution) + for idx, f in enumerate(self.in_features[::-1]): + x = features[f] + lateral_conv = self.lateral_convs[idx] + output_conv = self.output_convs[idx] + if lateral_conv is None: + transformer = self.input_proj(x) + pos = self.pe_layer(x) + transformer = self.transformer(transformer, None, pos) + y = output_conv(transformer) + # save intermediate feature as input to Transformer decoder + transformer_encoder_features = transformer + else: + cur_fpn = lateral_conv(x) + # Following FPN implementation, we use nearest upsampling here + y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest") + y = output_conv(y) + if num_cur_levels < self.maskformer_num_feature_levels: + multi_scale_features.append(y) + num_cur_levels += 1 + return self.mask_features(y), transformer_encoder_features, multi_scale_features + + def forward(self, features, targets=None): + logger = logging.getLogger(__name__) + logger.warning("Calling forward() may cause unpredicted behavior of PixelDecoder module.") + return self.forward_features(features) diff --git a/videocutler/mask2former/modeling/pixel_decoder/msdeformattn.py b/videocutler/mask2former/modeling/pixel_decoder/msdeformattn.py new file mode 100644 index 0000000000000000000000000000000000000000..0ff1a81a3ed0c05464dad2143830bacac5951dfe --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/msdeformattn.py @@ -0,0 +1,358 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +from typing import Callable, Dict, List, Optional, Tuple, Union + +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_ +from torch.cuda.amp import autocast + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ShapeSpec, get_norm +from detectron2.modeling import SEM_SEG_HEADS_REGISTRY + +from ..transformer_decoder.position_encoding import PositionEmbeddingSine +from ..transformer_decoder.transformer import _get_clones, _get_activation_fn +from .ops.modules import MSDeformAttn + + +# MSDeformAttn Transformer encoder in deformable detr +class MSDeformAttnTransformerEncoderOnly(nn.Module): + def __init__(self, d_model=256, nhead=8, + num_encoder_layers=6, dim_feedforward=1024, dropout=0.1, + activation="relu", + num_feature_levels=4, enc_n_points=4, + ): + super().__init__() + + self.d_model = d_model + self.nhead = nhead + + encoder_layer = MSDeformAttnTransformerEncoderLayer(d_model, dim_feedforward, + dropout, activation, + num_feature_levels, nhead, enc_n_points) + self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers) + + self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model)) + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + for m in self.modules(): + if isinstance(m, MSDeformAttn): + m._reset_parameters() + normal_(self.level_embed) + + def get_valid_ratio(self, mask): + _, H, W = mask.shape + valid_H = torch.sum(~mask[:, :, 0], 1) + valid_W = torch.sum(~mask[:, 0, :], 1) + valid_ratio_h = valid_H.float() / H + valid_ratio_w = valid_W.float() / W + valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) + return valid_ratio + + def forward(self, srcs, pos_embeds): + masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs] + # prepare input for encoder + src_flatten = [] + mask_flatten = [] + lvl_pos_embed_flatten = [] + spatial_shapes = [] + for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)): + bs, c, h, w = src.shape + spatial_shape = (h, w) + spatial_shapes.append(spatial_shape) + src = src.flatten(2).transpose(1, 2) + mask = mask.flatten(1) + pos_embed = pos_embed.flatten(2).transpose(1, 2) + lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1) + lvl_pos_embed_flatten.append(lvl_pos_embed) + src_flatten.append(src) + mask_flatten.append(mask) + src_flatten = torch.cat(src_flatten, 1) + mask_flatten = torch.cat(mask_flatten, 1) + lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) + spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device) + level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1])) + valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1) + + # encoder + memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten) + + return memory, spatial_shapes, level_start_index + + +class MSDeformAttnTransformerEncoderLayer(nn.Module): + def __init__(self, + d_model=256, d_ffn=1024, + dropout=0.1, activation="relu", + n_levels=4, n_heads=8, n_points=4): + super().__init__() + + # self attention + self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points) + self.dropout1 = nn.Dropout(dropout) + self.norm1 = nn.LayerNorm(d_model) + + # ffn + self.linear1 = nn.Linear(d_model, d_ffn) + self.activation = _get_activation_fn(activation) + self.dropout2 = nn.Dropout(dropout) + self.linear2 = nn.Linear(d_ffn, d_model) + self.dropout3 = nn.Dropout(dropout) + self.norm2 = nn.LayerNorm(d_model) + + @staticmethod + def with_pos_embed(tensor, pos): + return tensor if pos is None else tensor + pos + + def forward_ffn(self, src): + src2 = self.linear2(self.dropout2(self.activation(self.linear1(src)))) + src = src + self.dropout3(src2) + src = self.norm2(src) + return src + + def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None): + # self attention + src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask) + src = src + self.dropout1(src2) + src = self.norm1(src) + + # ffn + src = self.forward_ffn(src) + + return src + + +class MSDeformAttnTransformerEncoder(nn.Module): + def __init__(self, encoder_layer, num_layers): + super().__init__() + self.layers = _get_clones(encoder_layer, num_layers) + self.num_layers = num_layers + + @staticmethod + def get_reference_points(spatial_shapes, valid_ratios, device): + reference_points_list = [] + for lvl, (H_, W_) in enumerate(spatial_shapes): + + ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device), + torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device)) + ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_) + ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_) + ref = torch.stack((ref_x, ref_y), -1) + reference_points_list.append(ref) + reference_points = torch.cat(reference_points_list, 1) + reference_points = reference_points[:, :, None] * valid_ratios[:, None] + return reference_points + + def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None): + output = src + reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device) + for _, layer in enumerate(self.layers): + output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask) + + return output + + +@SEM_SEG_HEADS_REGISTRY.register() +class MSDeformAttnPixelDecoder(nn.Module): + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + transformer_dropout: float, + transformer_nheads: int, + transformer_dim_feedforward: int, + transformer_enc_layers: int, + conv_dim: int, + mask_dim: int, + norm: Optional[Union[str, Callable]] = None, + # deformable transformer encoder args + transformer_in_features: List[str], + common_stride: int, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + transformer_dropout: dropout probability in transformer + transformer_nheads: number of heads in transformer + transformer_dim_feedforward: dimension of feedforward network + transformer_enc_layers: number of transformer encoder layers + conv_dims: number of output channels for the intermediate conv layers. + mask_dim: number of output channels for the final conv layer. + norm (str or callable): normalization for all conv layers + """ + super().__init__() + transformer_input_shape = { + k: v for k, v in input_shape.items() if k in transformer_in_features + } + + # this is the input shape of pixel decoder + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5" + self.feature_strides = [v.stride for k, v in input_shape] + self.feature_channels = [v.channels for k, v in input_shape] + + # this is the input shape of transformer encoder (could use less features than pixel decoder + transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: x[1].stride) + self.transformer_in_features = [k for k, v in transformer_input_shape] # starting from "res2" to "res5" + transformer_in_channels = [v.channels for k, v in transformer_input_shape] + self.transformer_feature_strides = [v.stride for k, v in transformer_input_shape] # to decide extra FPN layers + + self.transformer_num_feature_levels = len(self.transformer_in_features) + if self.transformer_num_feature_levels > 1: + input_proj_list = [] + # from low resolution to high resolution (res5 -> res2) + for in_channels in transformer_in_channels[::-1]: + input_proj_list.append(nn.Sequential( + nn.Conv2d(in_channels, conv_dim, kernel_size=1), + nn.GroupNorm(32, conv_dim), + )) + self.input_proj = nn.ModuleList(input_proj_list) + else: + self.input_proj = nn.ModuleList([ + nn.Sequential( + nn.Conv2d(transformer_in_channels[-1], conv_dim, kernel_size=1), + nn.GroupNorm(32, conv_dim), + )]) + + for proj in self.input_proj: + nn.init.xavier_uniform_(proj[0].weight, gain=1) + nn.init.constant_(proj[0].bias, 0) + + self.transformer = MSDeformAttnTransformerEncoderOnly( + d_model=conv_dim, + dropout=transformer_dropout, + nhead=transformer_nheads, + dim_feedforward=transformer_dim_feedforward, + num_encoder_layers=transformer_enc_layers, + num_feature_levels=self.transformer_num_feature_levels, + ) + N_steps = conv_dim // 2 + self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True) + + self.mask_dim = mask_dim + # use 1x1 conv instead + self.mask_features = Conv2d( + conv_dim, + mask_dim, + kernel_size=1, + stride=1, + padding=0, + ) + weight_init.c2_xavier_fill(self.mask_features) + + self.maskformer_num_feature_levels = 3 # always use 3 scales + self.common_stride = common_stride + + # extra fpn levels + stride = min(self.transformer_feature_strides) + self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride)) + + lateral_convs = [] + output_convs = [] + + use_bias = norm == "" + for idx, in_channels in enumerate(self.feature_channels[:self.num_fpn_levels]): + lateral_norm = get_norm(norm, conv_dim) + output_norm = get_norm(norm, conv_dim) + + lateral_conv = Conv2d( + in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm + ) + output_conv = Conv2d( + conv_dim, + conv_dim, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + activation=F.relu, + ) + weight_init.c2_xavier_fill(lateral_conv) + weight_init.c2_xavier_fill(output_conv) + self.add_module("adapter_{}".format(idx + 1), lateral_conv) + self.add_module("layer_{}".format(idx + 1), output_conv) + + lateral_convs.append(lateral_conv) + output_convs.append(output_conv) + # Place convs into top-down order (from low to high resolution) + # to make the top-down computation in forward clearer. + self.lateral_convs = lateral_convs[::-1] + self.output_convs = output_convs[::-1] + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + ret = {} + ret["input_shape"] = { + k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES + } + ret["conv_dim"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM + ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + ret["norm"] = cfg.MODEL.SEM_SEG_HEAD.NORM + ret["transformer_dropout"] = cfg.MODEL.MASK_FORMER.DROPOUT + ret["transformer_nheads"] = cfg.MODEL.MASK_FORMER.NHEADS + # ret["transformer_dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD + ret["transformer_dim_feedforward"] = 1024 # use 1024 for deformable transformer encoder + ret[ + "transformer_enc_layers" + ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config + ret["transformer_in_features"] = cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES + ret["common_stride"] = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE + return ret + + @autocast(enabled=False) + def forward_features(self, features): + srcs = [] + pos = [] + # Reverse feature maps into top-down order (from low to high resolution) + for idx, f in enumerate(self.transformer_in_features[::-1]): + x = features[f].float() # deformable detr does not support half precision + srcs.append(self.input_proj[idx](x)) + pos.append(self.pe_layer(x)) + + y, spatial_shapes, level_start_index = self.transformer(srcs, pos) + bs = y.shape[0] + + split_size_or_sections = [None] * self.transformer_num_feature_levels + for i in range(self.transformer_num_feature_levels): + if i < self.transformer_num_feature_levels - 1: + split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i] + else: + split_size_or_sections[i] = y.shape[1] - level_start_index[i] + y = torch.split(y, split_size_or_sections, dim=1) + + out = [] + multi_scale_features = [] + num_cur_levels = 0 + for i, z in enumerate(y): + out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1])) + + # append `out` with extra FPN levels + # Reverse feature maps into top-down order (from low to high resolution) + for idx, f in enumerate(self.in_features[:self.num_fpn_levels][::-1]): + x = features[f].float() + lateral_conv = self.lateral_convs[idx] + output_conv = self.output_convs[idx] + cur_fpn = lateral_conv(x) + # Following FPN implementation, we use nearest upsampling here + y = cur_fpn + F.interpolate(out[-1], size=cur_fpn.shape[-2:], mode="bilinear", align_corners=False) + y = output_conv(y) + out.append(y) + + for o in out: + if num_cur_levels < self.maskformer_num_feature_levels: + multi_scale_features.append(o) + num_cur_levels += 1 + + return self.mask_features(out[-1]), out[0], multi_scale_features diff --git a/videocutler/mask2former/modeling/pixel_decoder/ops/functions/__init__.py b/videocutler/mask2former/modeling/pixel_decoder/ops/functions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2b06b5ac538b63bdb9a6c82e4635b95bb5491d5b --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/ops/functions/__init__.py @@ -0,0 +1,13 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from .ms_deform_attn_func import MSDeformAttnFunction + diff --git a/videocutler/mask2former/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py b/videocutler/mask2former/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py new file mode 100644 index 0000000000000000000000000000000000000000..94a36ab85b7c5f9ecee342db91a5d5731740740f --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py @@ -0,0 +1,72 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch +import torch.nn.functional as F +from torch.autograd import Function +from torch.autograd.function import once_differentiable + +try: + import MultiScaleDeformableAttention as MSDA +except ModuleNotFoundError as e: + info_string = ( + "\n\nPlease compile MultiScaleDeformableAttention CUDA op with the following commands:\n" + "\t`cd mask2former/modeling/pixel_decoder/ops`\n" + "\t`sh make.sh`\n" + ) + raise ModuleNotFoundError(info_string) + + +class MSDeformAttnFunction(Function): + @staticmethod + def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step): + ctx.im2col_step = im2col_step + output = MSDA.ms_deform_attn_forward( + value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step) + ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors + grad_value, grad_sampling_loc, grad_attn_weight = \ + MSDA.ms_deform_attn_backward( + value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step) + + return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None + + +def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights): + # for debug and test only, + # need to use cuda version instead + N_, S_, M_, D_ = value.shape + _, Lq_, M_, L_, P_, _ = sampling_locations.shape + value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1) + sampling_grids = 2 * sampling_locations - 1 + sampling_value_list = [] + for lid_, (H_, W_) in enumerate(value_spatial_shapes): + # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_ + value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_) + # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2 + sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1) + # N_*M_, D_, Lq_, P_ + sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_, + mode='bilinear', padding_mode='zeros', align_corners=False) + sampling_value_list.append(sampling_value_l_) + # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_) + attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_) + output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_) + return output.transpose(1, 2).contiguous() diff --git a/videocutler/mask2former/modeling/pixel_decoder/ops/make.sh b/videocutler/mask2former/modeling/pixel_decoder/ops/make.sh new file mode 100755 index 0000000000000000000000000000000000000000..7b38cdbf48f3571d986a33e7563b517952b51bb2 --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/ops/make.sh @@ -0,0 +1,13 @@ +#!/usr/bin/env bash +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +python setup.py build install diff --git a/videocutler/mask2former/modeling/pixel_decoder/ops/modules/__init__.py b/videocutler/mask2former/modeling/pixel_decoder/ops/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6fdbf03359958f3d67ab00f879bf6b61a6c8f06a --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/ops/modules/__init__.py @@ -0,0 +1,12 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from .ms_deform_attn import MSDeformAttn diff --git a/videocutler/mask2former/modeling/pixel_decoder/ops/modules/ms_deform_attn.py b/videocutler/mask2former/modeling/pixel_decoder/ops/modules/ms_deform_attn.py new file mode 100644 index 0000000000000000000000000000000000000000..e7b4c42ea504a0859ccadd72646919c941e72f73 --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/ops/modules/ms_deform_attn.py @@ -0,0 +1,125 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import warnings +import math + +import torch +from torch import nn +import torch.nn.functional as F +from torch.nn.init import xavier_uniform_, constant_ + +from ..functions import MSDeformAttnFunction +from ..functions.ms_deform_attn_func import ms_deform_attn_core_pytorch + + +def _is_power_of_2(n): + if (not isinstance(n, int)) or (n < 0): + raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))) + return (n & (n-1) == 0) and n != 0 + + +class MSDeformAttn(nn.Module): + def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4): + """ + Multi-Scale Deformable Attention Module + :param d_model hidden dimension + :param n_levels number of feature levels + :param n_heads number of attention heads + :param n_points number of sampling points per attention head per feature level + """ + super().__init__() + if d_model % n_heads != 0: + raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads)) + _d_per_head = d_model // n_heads + # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation + if not _is_power_of_2(_d_per_head): + warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 " + "which is more efficient in our CUDA implementation.") + + self.im2col_step = 128 + + self.d_model = d_model + self.n_levels = n_levels + self.n_heads = n_heads + self.n_points = n_points + + self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2) + self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points) + self.value_proj = nn.Linear(d_model, d_model) + self.output_proj = nn.Linear(d_model, d_model) + + self._reset_parameters() + + def _reset_parameters(self): + constant_(self.sampling_offsets.weight.data, 0.) + thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1) + for i in range(self.n_points): + grid_init[:, :, i, :] *= i + 1 + with torch.no_grad(): + self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) + constant_(self.attention_weights.weight.data, 0.) + constant_(self.attention_weights.bias.data, 0.) + xavier_uniform_(self.value_proj.weight.data) + constant_(self.value_proj.bias.data, 0.) + xavier_uniform_(self.output_proj.weight.data) + constant_(self.output_proj.bias.data, 0.) + + def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None): + """ + :param query (N, Length_{query}, C) + :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area + or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes + :param input_flatten (N, \sum_{l=0}^{L-1} H_l \cdot W_l, C) + :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})] + :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}] + :param input_padding_mask (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements + + :return output (N, Length_{query}, C) + """ + N, Len_q, _ = query.shape + N, Len_in, _ = input_flatten.shape + assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in + + value = self.value_proj(input_flatten) + if input_padding_mask is not None: + value = value.masked_fill(input_padding_mask[..., None], float(0)) + value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads) + sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2) + attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points) + attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points) + # N, Len_q, n_heads, n_levels, n_points, 2 + if reference_points.shape[-1] == 2: + offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1) + sampling_locations = reference_points[:, :, None, :, None, :] \ + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + elif reference_points.shape[-1] == 4: + sampling_locations = reference_points[:, :, None, :, None, :2] \ + + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 + else: + raise ValueError( + 'Last dim of reference_points must be 2 or 4, but get {} instead.'.format(reference_points.shape[-1])) + try: + output = MSDeformAttnFunction.apply( + value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step) + except: + # CPU + output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights) + # # For FLOPs calculation only + # output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights) + output = self.output_proj(output) + return output diff --git a/videocutler/mask2former/modeling/pixel_decoder/ops/setup.py b/videocutler/mask2former/modeling/pixel_decoder/ops/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..3b57ad313ac8f9b6586892142da8ba943e516cec --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/ops/setup.py @@ -0,0 +1,78 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +import os +import glob + +import torch + +from torch.utils.cpp_extension import CUDA_HOME +from torch.utils.cpp_extension import CppExtension +from torch.utils.cpp_extension import CUDAExtension + +from setuptools import find_packages +from setuptools import setup + +requirements = ["torch", "torchvision"] + +def get_extensions(): + this_dir = os.path.dirname(os.path.abspath(__file__)) + extensions_dir = os.path.join(this_dir, "src") + + main_file = glob.glob(os.path.join(extensions_dir, "*.cpp")) + source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp")) + source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu")) + + sources = main_file + source_cpu + extension = CppExtension + extra_compile_args = {"cxx": []} + define_macros = [] + + # Force cuda since torch ask for a device, not if cuda is in fact available. + if (os.environ.get('FORCE_CUDA') or torch.cuda.is_available()) and CUDA_HOME is not None: + extension = CUDAExtension + sources += source_cuda + define_macros += [("WITH_CUDA", None)] + extra_compile_args["nvcc"] = [ + "-DCUDA_HAS_FP16=1", + "-D__CUDA_NO_HALF_OPERATORS__", + "-D__CUDA_NO_HALF_CONVERSIONS__", + "-D__CUDA_NO_HALF2_OPERATORS__", + ] + else: + if CUDA_HOME is None: + raise NotImplementedError('CUDA_HOME is None. Please set environment variable CUDA_HOME.') + else: + raise NotImplementedError('No CUDA runtime is found. Please set FORCE_CUDA=1 or test it by running torch.cuda.is_available().') + + sources = [os.path.join(extensions_dir, s) for s in sources] + include_dirs = [extensions_dir] + ext_modules = [ + extension( + "MultiScaleDeformableAttention", + sources, + include_dirs=include_dirs, + define_macros=define_macros, + extra_compile_args=extra_compile_args, + ) + ] + return ext_modules + +setup( + name="MultiScaleDeformableAttention", + version="1.0", + author="Weijie Su", + url="https://github.com/fundamentalvision/Deformable-DETR", + description="PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention", + packages=find_packages(exclude=("configs", "tests",)), + ext_modules=get_extensions(), + cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, +) diff --git a/videocutler/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp b/videocutler/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp new file mode 100644 index 0000000000000000000000000000000000000000..48757e2b0156b2c1513b615d2a17e5aee5172ae7 --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp @@ -0,0 +1,46 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#include + +#include +#include + + +at::Tensor +ms_deform_attn_cpu_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + AT_ERROR("Not implement on cpu"); +} + +std::vector +ms_deform_attn_cpu_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + AT_ERROR("Not implement on cpu"); +} + diff --git a/videocutler/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h b/videocutler/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h new file mode 100644 index 0000000000000000000000000000000000000000..51bb27e9ee828f967e8aa854c2d55574040c6d7e --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h @@ -0,0 +1,38 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#pragma once +#include + +at::Tensor +ms_deform_attn_cpu_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step); + +std::vector +ms_deform_attn_cpu_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step); + + diff --git a/videocutler/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu b/videocutler/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..0c465dab3d636dfd6a44523c63f148b6e15084d9 --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu @@ -0,0 +1,158 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#include +#include "cuda/ms_deform_im2col_cuda.cuh" + +#include +#include +#include +#include + + +at::Tensor ms_deform_attn_cuda_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); + AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); + AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); + AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); + AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); + + AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); + AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); + AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); + AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); + AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); + + const int batch = value.size(0); + const int spatial_size = value.size(1); + const int num_heads = value.size(2); + const int channels = value.size(3); + + const int num_levels = spatial_shapes.size(0); + + const int num_query = sampling_loc.size(1); + const int num_point = sampling_loc.size(4); + + const int im2col_step_ = std::min(batch, im2col_step); + + AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); + + auto output = at::zeros({batch, num_query, num_heads, channels}, value.options()); + + const int batch_n = im2col_step_; + auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); + auto per_value_size = spatial_size * num_heads * channels; + auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; + auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; + for (int n = 0; n < batch/im2col_step_; ++n) + { + auto columns = output_n.select(0, n); + AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] { + ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(), + value.data() + n * im2col_step_ * per_value_size, + spatial_shapes.data(), + level_start_index.data(), + sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + attn_weight.data() + n * im2col_step_ * per_attn_weight_size, + batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, + columns.data()); + + })); + } + + output = output.view({batch, num_query, num_heads*channels}); + + return output; +} + + +std::vector ms_deform_attn_cuda_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + + AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); + AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); + AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); + AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); + AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); + AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous"); + + AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); + AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); + AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); + AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); + AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); + AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor"); + + const int batch = value.size(0); + const int spatial_size = value.size(1); + const int num_heads = value.size(2); + const int channels = value.size(3); + + const int num_levels = spatial_shapes.size(0); + + const int num_query = sampling_loc.size(1); + const int num_point = sampling_loc.size(4); + + const int im2col_step_ = std::min(batch, im2col_step); + + AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); + + auto grad_value = at::zeros_like(value); + auto grad_sampling_loc = at::zeros_like(sampling_loc); + auto grad_attn_weight = at::zeros_like(attn_weight); + + const int batch_n = im2col_step_; + auto per_value_size = spatial_size * num_heads * channels; + auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; + auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; + auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); + + for (int n = 0; n < batch/im2col_step_; ++n) + { + auto grad_output_g = grad_output_n.select(0, n); + AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] { + ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(), + grad_output_g.data(), + value.data() + n * im2col_step_ * per_value_size, + spatial_shapes.data(), + level_start_index.data(), + sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + attn_weight.data() + n * im2col_step_ * per_attn_weight_size, + batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, + grad_value.data() + n * im2col_step_ * per_value_size, + grad_sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + grad_attn_weight.data() + n * im2col_step_ * per_attn_weight_size); + + })); + } + + return { + grad_value, grad_sampling_loc, grad_attn_weight + }; +} \ No newline at end of file diff --git a/videocutler/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h b/videocutler/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h new file mode 100644 index 0000000000000000000000000000000000000000..4f0658e8668a11f0e7d71deff9adac71884f2e87 --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h @@ -0,0 +1,35 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#pragma once +#include + +at::Tensor ms_deform_attn_cuda_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step); + +std::vector ms_deform_attn_cuda_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step); + diff --git a/videocutler/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_im2col_cuda.cuh b/videocutler/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_im2col_cuda.cuh new file mode 100644 index 0000000000000000000000000000000000000000..c04e0d4ab97d25c1756fcd8d08dd1e5a6d280b7c --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_im2col_cuda.cuh @@ -0,0 +1,1332 @@ +/*! +************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************** +* Modified from DCN (https://github.com/msracver/Deformable-ConvNets) +* Copyright (c) 2018 Microsoft +************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#include +#include +#include + +#include +#include + +#include + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N, const int num_threads) +{ + return (N + num_threads - 1) / num_threads; +} + + +template +__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + } + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + + +template +__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c, + const scalar_t &top_grad, + const scalar_t &attn_weight, + scalar_t* &grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + const scalar_t top_grad_value = top_grad * attn_weight; + scalar_t grad_h_weight = 0, grad_w_weight = 0; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + grad_h_weight -= hw * v1; + grad_w_weight -= hh * v1; + atomicAdd(grad_value+ptr1, w1*top_grad_value); + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + grad_h_weight -= lw * v2; + grad_w_weight += hh * v2; + atomicAdd(grad_value+ptr2, w2*top_grad_value); + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + grad_h_weight += hw * v3; + grad_w_weight -= lh * v3; + atomicAdd(grad_value+ptr3, w3*top_grad_value); + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + grad_h_weight += lw * v4; + grad_w_weight += lh * v4; + atomicAdd(grad_value+ptr4, w4*top_grad_value); + } + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + *grad_attn_weight = top_grad * val; + *grad_sampling_loc = width * grad_w_weight * top_grad_value; + *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value; +} + + +template +__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c, + const scalar_t &top_grad, + const scalar_t &attn_weight, + scalar_t* &grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + const scalar_t top_grad_value = top_grad * attn_weight; + scalar_t grad_h_weight = 0, grad_w_weight = 0; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + grad_h_weight -= hw * v1; + grad_w_weight -= hh * v1; + atomicAdd(grad_value+ptr1, w1*top_grad_value); + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + grad_h_weight -= lw * v2; + grad_w_weight += hh * v2; + atomicAdd(grad_value+ptr2, w2*top_grad_value); + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + grad_h_weight += hw * v3; + grad_w_weight -= lh * v3; + atomicAdd(grad_value+ptr3, w3*top_grad_value); + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + grad_h_weight += lw * v4; + grad_w_weight += lh * v4; + atomicAdd(grad_value+ptr4, w4*top_grad_value); + } + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + atomicAdd(grad_attn_weight, top_grad * val); + atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value); + atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value); +} + + +template +__global__ void ms_deformable_im2col_gpu_kernel(const int n, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *data_col) +{ + CUDA_KERNEL_LOOP(index, n) + { + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + scalar_t *data_col_ptr = data_col + index; + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + scalar_t col = 0; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride); + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight; + } + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + } + } + *data_col_ptr = col; + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; + __shared__ scalar_t cache_grad_attn_weight[blockSize]; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + if (tid == 0) + { + scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; + int sid=2; + for (unsigned int tid = 1; tid < blockSize; ++tid) + { + _grad_w += cache_grad_sampling_loc[sid]; + _grad_h += cache_grad_sampling_loc[sid + 1]; + _grad_a += cache_grad_attn_weight[tid]; + sid += 2; + } + + + *grad_sampling_loc = _grad_w; + *(grad_sampling_loc + 1) = _grad_h; + *grad_attn_weight = _grad_a; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; + __shared__ scalar_t cache_grad_attn_weight[blockSize]; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockSize/2; s>0; s>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + } + __syncthreads(); + } + + if (tid == 0) + { + *grad_sampling_loc = cache_grad_sampling_loc[0]; + *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; + *grad_attn_weight = cache_grad_attn_weight[0]; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + if (tid == 0) + { + scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; + int sid=2; + for (unsigned int tid = 1; tid < blockDim.x; ++tid) + { + _grad_w += cache_grad_sampling_loc[sid]; + _grad_h += cache_grad_sampling_loc[sid + 1]; + _grad_a += cache_grad_attn_weight[tid]; + sid += 2; + } + + + *grad_sampling_loc = _grad_w; + *(grad_sampling_loc + 1) = _grad_h; + *grad_attn_weight = _grad_a; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + if (tid + (s << 1) < spre) + { + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; + } + } + __syncthreads(); + } + + if (tid == 0) + { + *grad_sampling_loc = cache_grad_sampling_loc[0]; + *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; + *grad_attn_weight = cache_grad_attn_weight[0]; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + if (tid + (s << 1) < spre) + { + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; + } + } + __syncthreads(); + } + + if (tid == 0) + { + atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]); + atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]); + atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]); + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear_gm( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + grad_sampling_loc, grad_attn_weight); + } + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +void ms_deformable_im2col_cuda(cudaStream_t stream, + const scalar_t* data_value, + const int64_t* data_spatial_shapes, + const int64_t* data_level_start_index, + const scalar_t* data_sampling_loc, + const scalar_t* data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t* data_col) +{ + const int num_kernels = batch_size * num_query * num_heads * channels; + const int num_actual_kernels = batch_size * num_query * num_heads * channels; + const int num_threads = CUDA_NUM_THREADS; + ms_deformable_im2col_gpu_kernel + <<>>( + num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, + batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in ms_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); + } + +} + +template +void ms_deformable_col2im_cuda(cudaStream_t stream, + const scalar_t* grad_col, + const scalar_t* data_value, + const int64_t * data_spatial_shapes, + const int64_t * data_level_start_index, + const scalar_t * data_sampling_loc, + const scalar_t * data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t* grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels; + const int num_kernels = batch_size * num_query * num_heads * channels; + const int num_actual_kernels = batch_size * num_query * num_heads * channels; + if (channels > 1024) + { + if ((channels & 1023) == 0) + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + else + { + ms_deformable_col2im_gpu_kernel_gm + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + } + else{ + switch(channels) + { + case 1: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 2: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 4: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 8: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 16: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 32: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 64: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 128: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 256: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 512: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 1024: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + default: + if (channels < 64) + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + else + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + } + } + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in ms_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); + } + +} \ No newline at end of file diff --git a/videocutler/mask2former/modeling/pixel_decoder/ops/src/ms_deform_attn.h b/videocutler/mask2former/modeling/pixel_decoder/ops/src/ms_deform_attn.h new file mode 100644 index 0000000000000000000000000000000000000000..2f80a1b294c55b37d13bb3558ff7aeadba3b37de --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/ops/src/ms_deform_attn.h @@ -0,0 +1,67 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#pragma once + +#include "cpu/ms_deform_attn_cpu.h" + +#ifdef WITH_CUDA +#include "cuda/ms_deform_attn_cuda.h" +#endif + + +at::Tensor +ms_deform_attn_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + if (value.type().is_cuda()) + { +#ifdef WITH_CUDA + return ms_deform_attn_cuda_forward( + value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + +std::vector +ms_deform_attn_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + if (value.type().is_cuda()) + { +#ifdef WITH_CUDA + return ms_deform_attn_cuda_backward( + value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + diff --git a/videocutler/mask2former/modeling/pixel_decoder/ops/src/vision.cpp b/videocutler/mask2former/modeling/pixel_decoder/ops/src/vision.cpp new file mode 100644 index 0000000000000000000000000000000000000000..4a08821e0121a77556aa7a263ec8ebfa928b13b6 --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/ops/src/vision.cpp @@ -0,0 +1,21 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#include "ms_deform_attn.h" + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward"); + m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward"); +} diff --git a/videocutler/mask2former/modeling/pixel_decoder/ops/test.py b/videocutler/mask2former/modeling/pixel_decoder/ops/test.py new file mode 100644 index 0000000000000000000000000000000000000000..6e1b545459f6fd3235767e721eb5a1090ae14bef --- /dev/null +++ b/videocutler/mask2former/modeling/pixel_decoder/ops/test.py @@ -0,0 +1,92 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import time +import torch +import torch.nn as nn +from torch.autograd import gradcheck + +from functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch + + +N, M, D = 1, 2, 2 +Lq, L, P = 2, 2, 2 +shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda() +level_start_index = torch.cat((shapes.new_zeros((1, )), shapes.prod(1).cumsum(0)[:-1])) +S = sum([(H*W).item() for H, W in shapes]) + + +torch.manual_seed(3) + + +@torch.no_grad() +def check_forward_equal_with_pytorch_double(): + value = torch.rand(N, S, M, D).cuda() * 0.01 + sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda() + attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5 + attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True) + im2col_step = 2 + output_pytorch = ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double()).detach().cpu() + output_cuda = MSDeformAttnFunction.apply(value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step).detach().cpu() + fwdok = torch.allclose(output_cuda, output_pytorch) + max_abs_err = (output_cuda - output_pytorch).abs().max() + max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max() + + print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}') + + +@torch.no_grad() +def check_forward_equal_with_pytorch_float(): + value = torch.rand(N, S, M, D).cuda() * 0.01 + sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda() + attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5 + attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True) + im2col_step = 2 + output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu() + output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu() + fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3) + max_abs_err = (output_cuda - output_pytorch).abs().max() + max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max() + + print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}') + + +def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True): + + value = torch.rand(N, S, M, channels).cuda() * 0.01 + sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda() + attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5 + attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True) + im2col_step = 2 + func = MSDeformAttnFunction.apply + + value.requires_grad = grad_value + sampling_locations.requires_grad = grad_sampling_loc + attention_weights.requires_grad = grad_attn_weight + + gradok = gradcheck(func, (value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step)) + + print(f'* {gradok} check_gradient_numerical(D={channels})') + + +if __name__ == '__main__': + check_forward_equal_with_pytorch_double() + check_forward_equal_with_pytorch_float() + + for channels in [30, 32, 64, 71, 1025, 2048, 3096]: + check_gradient_numerical(channels, True, True, True) + + + diff --git a/videocutler/mask2former/modeling/transformer_decoder/__init__.py b/videocutler/mask2former/modeling/transformer_decoder/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ddcf38e78f3bbb2380b0a246000bcb5e5b385619 --- /dev/null +++ b/videocutler/mask2former/modeling/transformer_decoder/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .maskformer_transformer_decoder import StandardTransformerDecoder +from .mask2former_transformer_decoder import MultiScaleMaskedTransformerDecoder diff --git a/videocutler/mask2former/modeling/transformer_decoder/mask2former_transformer_decoder.py b/videocutler/mask2former/modeling/transformer_decoder/mask2former_transformer_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..52594f62693e6bf48a4c140ba2fe7131a0317774 --- /dev/null +++ b/videocutler/mask2former/modeling/transformer_decoder/mask2former_transformer_decoder.py @@ -0,0 +1,461 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py +import logging +import fvcore.nn.weight_init as weight_init +from typing import Optional +import torch +from torch import nn, Tensor +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d + +from .position_encoding import PositionEmbeddingSine +from .maskformer_transformer_decoder import TRANSFORMER_DECODER_REGISTRY + + +class SelfAttentionLayer(nn.Module): + + def __init__(self, d_model, nhead, dropout=0.0, + activation="relu", normalize_before=False): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + + self.norm = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + q = k = self.with_pos_embed(tgt, query_pos) + tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + tgt = self.norm(tgt) + + return tgt + + def forward_pre(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.norm(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + + return tgt + + def forward(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(tgt, tgt_mask, + tgt_key_padding_mask, query_pos) + return self.forward_post(tgt, tgt_mask, + tgt_key_padding_mask, query_pos) + + +class CrossAttentionLayer(nn.Module): + + def __init__(self, d_model, nhead, dropout=0.0, + activation="relu", normalize_before=False): + super().__init__() + self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + + self.norm = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt, memory, + memory_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + tgt = self.norm(tgt) + + return tgt + + def forward_pre(self, tgt, memory, + memory_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.norm(tgt) + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + + return tgt + + def forward(self, tgt, memory, + memory_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(tgt, memory, memory_mask, + memory_key_padding_mask, pos, query_pos) + return self.forward_post(tgt, memory, memory_mask, + memory_key_padding_mask, pos, query_pos) + + +class FFNLayer(nn.Module): + + def __init__(self, d_model, dim_feedforward=2048, dropout=0.0, + activation="relu", normalize_before=False): + super().__init__() + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm = nn.LayerNorm(d_model) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt): + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) + tgt = tgt + self.dropout(tgt2) + tgt = self.norm(tgt) + return tgt + + def forward_pre(self, tgt): + tgt2 = self.norm(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout(tgt2) + return tgt + + def forward(self, tgt): + if self.normalize_before: + return self.forward_pre(tgt) + return self.forward_post(tgt) + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(F"activation should be relu/gelu, not {activation}.") + + +class MLP(nn.Module): + """ Very simple multi-layer perceptron (also called FFN)""" + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +@TRANSFORMER_DECODER_REGISTRY.register() +class MultiScaleMaskedTransformerDecoder(nn.Module): + + _version = 2 + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + version = local_metadata.get("version", None) + if version is None or version < 2: + # Do not warn if train from scratch + scratch = True + logger = logging.getLogger(__name__) + for k in list(state_dict.keys()): + newk = k + if "static_query" in k: + newk = k.replace("static_query", "query_feat") + if newk != k: + state_dict[newk] = state_dict[k] + del state_dict[k] + scratch = False + + if not scratch: + logger.warning( + f"Weight format of {self.__class__.__name__} have changed! " + "Please upgrade your models. Applying automatic conversion now ..." + ) + + @configurable + def __init__( + self, + in_channels, + mask_classification=True, + *, + num_classes: int, + hidden_dim: int, + num_queries: int, + nheads: int, + dim_feedforward: int, + dec_layers: int, + pre_norm: bool, + mask_dim: int, + enforce_input_project: bool, + ): + """ + NOTE: this interface is experimental. + Args: + in_channels: channels of the input features + mask_classification: whether to add mask classifier or not + num_classes: number of classes + hidden_dim: Transformer feature dimension + num_queries: number of queries + nheads: number of heads + dim_feedforward: feature dimension in feedforward network + enc_layers: number of Transformer encoder layers + dec_layers: number of Transformer decoder layers + pre_norm: whether to use pre-LayerNorm or not + mask_dim: mask feature dimension + enforce_input_project: add input project 1x1 conv even if input + channels and hidden dim is identical + """ + super().__init__() + + assert mask_classification, "Only support mask classification model" + self.mask_classification = mask_classification + + # positional encoding + N_steps = hidden_dim // 2 + self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True) + + # define Transformer decoder here + self.num_heads = nheads + self.num_layers = dec_layers + self.transformer_self_attention_layers = nn.ModuleList() + self.transformer_cross_attention_layers = nn.ModuleList() + self.transformer_ffn_layers = nn.ModuleList() + + for _ in range(self.num_layers): + self.transformer_self_attention_layers.append( + SelfAttentionLayer( + d_model=hidden_dim, + nhead=nheads, + dropout=0.0, + normalize_before=pre_norm, + ) + ) + + self.transformer_cross_attention_layers.append( + CrossAttentionLayer( + d_model=hidden_dim, + nhead=nheads, + dropout=0.0, + normalize_before=pre_norm, + ) + ) + + self.transformer_ffn_layers.append( + FFNLayer( + d_model=hidden_dim, + dim_feedforward=dim_feedforward, + dropout=0.0, + normalize_before=pre_norm, + ) + ) + + self.decoder_norm = nn.LayerNorm(hidden_dim) + + self.num_queries = num_queries + # learnable query features + self.query_feat = nn.Embedding(num_queries, hidden_dim) + # learnable query p.e. + self.query_embed = nn.Embedding(num_queries, hidden_dim) + + # level embedding (we always use 3 scales) + self.num_feature_levels = 3 + self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim) + self.input_proj = nn.ModuleList() + for _ in range(self.num_feature_levels): + if in_channels != hidden_dim or enforce_input_project: + self.input_proj.append(Conv2d(in_channels, hidden_dim, kernel_size=1)) + weight_init.c2_xavier_fill(self.input_proj[-1]) + else: + self.input_proj.append(nn.Sequential()) + + # output FFNs + if self.mask_classification: + self.class_embed = nn.Linear(hidden_dim, num_classes + 1) + self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3) + + @classmethod + def from_config(cls, cfg, in_channels, mask_classification): + ret = {} + ret["in_channels"] = in_channels + ret["mask_classification"] = mask_classification + + ret["num_classes"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES + ret["hidden_dim"] = cfg.MODEL.MASK_FORMER.HIDDEN_DIM + ret["num_queries"] = cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES + # Transformer parameters: + ret["nheads"] = cfg.MODEL.MASK_FORMER.NHEADS + ret["dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD + + # NOTE: because we add learnable query features which requires supervision, + # we add minus 1 to decoder layers to be consistent with our loss + # implementation: that is, number of auxiliary losses is always + # equal to number of decoder layers. With learnable query features, the number of + # auxiliary losses equals number of decoders plus 1. + assert cfg.MODEL.MASK_FORMER.DEC_LAYERS >= 1 + ret["dec_layers"] = cfg.MODEL.MASK_FORMER.DEC_LAYERS - 1 + ret["pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM + ret["enforce_input_project"] = cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ + + ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + + return ret + + def forward(self, x, mask_features, mask = None): + # x is a list of multi-scale feature + assert len(x) == self.num_feature_levels + src = [] + pos = [] + size_list = [] + + # disable mask, it does not affect performance + del mask + + for i in range(self.num_feature_levels): + size_list.append(x[i].shape[-2:]) + pos.append(self.pe_layer(x[i], None).flatten(2)) + src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None]) + + # flatten NxCxHxW to HWxNxC + pos[-1] = pos[-1].permute(2, 0, 1) + src[-1] = src[-1].permute(2, 0, 1) + + _, bs, _ = src[0].shape + + # QxNxC + query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1) + output = self.query_feat.weight.unsqueeze(1).repeat(1, bs, 1) + + predictions_class = [] + predictions_mask = [] + + # prediction heads on learnable query features + outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[0]) + predictions_class.append(outputs_class) + predictions_mask.append(outputs_mask) + + for i in range(self.num_layers): + level_index = i % self.num_feature_levels + attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False + # attention: cross-attention first + output = self.transformer_cross_attention_layers[i]( + output, src[level_index], + memory_mask=attn_mask, + memory_key_padding_mask=None, # here we do not apply masking on padded region + pos=pos[level_index], query_pos=query_embed + ) + + output = self.transformer_self_attention_layers[i]( + output, tgt_mask=None, + tgt_key_padding_mask=None, + query_pos=query_embed + ) + + # FFN + output = self.transformer_ffn_layers[i]( + output + ) + + outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels]) + predictions_class.append(outputs_class) + predictions_mask.append(outputs_mask) + + assert len(predictions_class) == self.num_layers + 1 + + out = { + 'pred_logits': predictions_class[-1], + 'pred_masks': predictions_mask[-1], + 'aux_outputs': self._set_aux_loss( + predictions_class if self.mask_classification else None, predictions_mask + ) + } + return out + + def forward_prediction_heads(self, output, mask_features, attn_mask_target_size): + decoder_output = self.decoder_norm(output) + decoder_output = decoder_output.transpose(0, 1) + outputs_class = self.class_embed(decoder_output) + mask_embed = self.mask_embed(decoder_output) + outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features) + + # NOTE: prediction is of higher-resolution + # [B, Q, H, W] -> [B, Q, H*W] -> [B, h, Q, H*W] -> [B*h, Q, HW] + attn_mask = F.interpolate(outputs_mask, size=attn_mask_target_size, mode="bilinear", align_corners=False) + # must use bool type + # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged. + attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool() + attn_mask = attn_mask.detach() + + return outputs_class, outputs_mask, attn_mask + + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_seg_masks): + # this is a workaround to make torchscript happy, as torchscript + # doesn't support dictionary with non-homogeneous values, such + # as a dict having both a Tensor and a list. + if self.mask_classification: + return [ + {"pred_logits": a, "pred_masks": b} + for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1]) + ] + else: + return [{"pred_masks": b} for b in outputs_seg_masks[:-1]] diff --git a/videocutler/mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py b/videocutler/mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..79f09fa43f2f5a33c3422a6bb999b20763ab8b5e --- /dev/null +++ b/videocutler/mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py @@ -0,0 +1,188 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d +from detectron2.utils.registry import Registry + +from .position_encoding import PositionEmbeddingSine +from .transformer import Transformer + + +TRANSFORMER_DECODER_REGISTRY = Registry("TRANSFORMER_MODULE") +TRANSFORMER_DECODER_REGISTRY.__doc__ = """ +Registry for transformer module in MaskFormer. +""" + + +def build_transformer_decoder(cfg, in_channels, mask_classification=True): + """ + Build a instance embedding branch from `cfg.MODEL.INS_EMBED_HEAD.NAME`. + """ + name = cfg.MODEL.MASK_FORMER.TRANSFORMER_DECODER_NAME + return TRANSFORMER_DECODER_REGISTRY.get(name)(cfg, in_channels, mask_classification) + + +@TRANSFORMER_DECODER_REGISTRY.register() +class StandardTransformerDecoder(nn.Module): + @configurable + def __init__( + self, + in_channels, + mask_classification=True, + *, + num_classes: int, + hidden_dim: int, + num_queries: int, + nheads: int, + dropout: float, + dim_feedforward: int, + enc_layers: int, + dec_layers: int, + pre_norm: bool, + deep_supervision: bool, + mask_dim: int, + enforce_input_project: bool, + ): + """ + NOTE: this interface is experimental. + Args: + in_channels: channels of the input features + mask_classification: whether to add mask classifier or not + num_classes: number of classes + hidden_dim: Transformer feature dimension + num_queries: number of queries + nheads: number of heads + dropout: dropout in Transformer + dim_feedforward: feature dimension in feedforward network + enc_layers: number of Transformer encoder layers + dec_layers: number of Transformer decoder layers + pre_norm: whether to use pre-LayerNorm or not + deep_supervision: whether to add supervision to every decoder layers + mask_dim: mask feature dimension + enforce_input_project: add input project 1x1 conv even if input + channels and hidden dim is identical + """ + super().__init__() + + self.mask_classification = mask_classification + + # positional encoding + N_steps = hidden_dim // 2 + self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True) + + transformer = Transformer( + d_model=hidden_dim, + dropout=dropout, + nhead=nheads, + dim_feedforward=dim_feedforward, + num_encoder_layers=enc_layers, + num_decoder_layers=dec_layers, + normalize_before=pre_norm, + return_intermediate_dec=deep_supervision, + ) + + self.num_queries = num_queries + self.transformer = transformer + hidden_dim = transformer.d_model + + self.query_embed = nn.Embedding(num_queries, hidden_dim) + + if in_channels != hidden_dim or enforce_input_project: + self.input_proj = Conv2d(in_channels, hidden_dim, kernel_size=1) + weight_init.c2_xavier_fill(self.input_proj) + else: + self.input_proj = nn.Sequential() + self.aux_loss = deep_supervision + + # output FFNs + if self.mask_classification: + self.class_embed = nn.Linear(hidden_dim, num_classes + 1) + self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3) + + @classmethod + def from_config(cls, cfg, in_channels, mask_classification): + ret = {} + ret["in_channels"] = in_channels + ret["mask_classification"] = mask_classification + + ret["num_classes"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES + ret["hidden_dim"] = cfg.MODEL.MASK_FORMER.HIDDEN_DIM + ret["num_queries"] = cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES + # Transformer parameters: + ret["nheads"] = cfg.MODEL.MASK_FORMER.NHEADS + ret["dropout"] = cfg.MODEL.MASK_FORMER.DROPOUT + ret["dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD + ret["enc_layers"] = cfg.MODEL.MASK_FORMER.ENC_LAYERS + ret["dec_layers"] = cfg.MODEL.MASK_FORMER.DEC_LAYERS + ret["pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM + ret["deep_supervision"] = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION + ret["enforce_input_project"] = cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ + + ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + + return ret + + def forward(self, x, mask_features, mask=None): + if mask is not None: + mask = F.interpolate(mask[None].float(), size=x.shape[-2:]).to(torch.bool)[0] + pos = self.pe_layer(x, mask) + + src = x + hs, memory = self.transformer(self.input_proj(src), mask, self.query_embed.weight, pos) + + if self.mask_classification: + outputs_class = self.class_embed(hs) + out = {"pred_logits": outputs_class[-1]} + else: + out = {} + + if self.aux_loss: + # [l, bs, queries, embed] + mask_embed = self.mask_embed(hs) + outputs_seg_masks = torch.einsum("lbqc,bchw->lbqhw", mask_embed, mask_features) + out["pred_masks"] = outputs_seg_masks[-1] + out["aux_outputs"] = self._set_aux_loss( + outputs_class if self.mask_classification else None, outputs_seg_masks + ) + else: + # FIXME h_boxes takes the last one computed, keep this in mind + # [bs, queries, embed] + mask_embed = self.mask_embed(hs[-1]) + outputs_seg_masks = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features) + out["pred_masks"] = outputs_seg_masks + return out + + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_seg_masks): + # this is a workaround to make torchscript happy, as torchscript + # doesn't support dictionary with non-homogeneous values, such + # as a dict having both a Tensor and a list. + if self.mask_classification: + return [ + {"pred_logits": a, "pred_masks": b} + for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1]) + ] + else: + return [{"pred_masks": b} for b in outputs_seg_masks[:-1]] + + +class MLP(nn.Module): + """Very simple multi-layer perceptron (also called FFN)""" + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList( + nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]) + ) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x diff --git a/videocutler/mask2former/modeling/transformer_decoder/position_encoding.py b/videocutler/mask2former/modeling/transformer_decoder/position_encoding.py new file mode 100644 index 0000000000000000000000000000000000000000..f32532e070e67b2cd25771aea1ad10e7e5a5dc69 --- /dev/null +++ b/videocutler/mask2former/modeling/transformer_decoder/position_encoding.py @@ -0,0 +1,64 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# # Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/position_encoding.py +""" +Various positional encodings for the transformer. +""" +import math + +import torch +from torch import nn + + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, x, mask=None): + if mask is None: + mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) + not_mask = ~mask + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack( + (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos_y = torch.stack( + (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + def __repr__(self, _repr_indent=4): + head = "Positional encoding " + self.__class__.__name__ + body = [ + "num_pos_feats: {}".format(self.num_pos_feats), + "temperature: {}".format(self.temperature), + "normalize: {}".format(self.normalize), + "scale: {}".format(self.scale), + ] + # _repr_indent = 4 + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/videocutler/mask2former/modeling/transformer_decoder/transformer.py b/videocutler/mask2former/modeling/transformer_decoder/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..ea8caa0108f5e136a9739320ab69a3e1b6f40298 --- /dev/null +++ b/videocutler/mask2former/modeling/transformer_decoder/transformer.py @@ -0,0 +1,369 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/transformer.py +""" +Transformer class. + +Copy-paste from torch.nn.Transformer with modifications: + * positional encodings are passed in MHattention + * extra LN at the end of encoder is removed + * decoder returns a stack of activations from all decoding layers +""" +import copy +from typing import List, Optional + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + + +class Transformer(nn.Module): + def __init__( + self, + d_model=512, + nhead=8, + num_encoder_layers=6, + num_decoder_layers=6, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + return_intermediate_dec=False, + ): + super().__init__() + + encoder_layer = TransformerEncoderLayer( + d_model, nhead, dim_feedforward, dropout, activation, normalize_before + ) + encoder_norm = nn.LayerNorm(d_model) if normalize_before else None + self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) + + decoder_layer = TransformerDecoderLayer( + d_model, nhead, dim_feedforward, dropout, activation, normalize_before + ) + decoder_norm = nn.LayerNorm(d_model) + self.decoder = TransformerDecoder( + decoder_layer, + num_decoder_layers, + decoder_norm, + return_intermediate=return_intermediate_dec, + ) + + self._reset_parameters() + + self.d_model = d_model + self.nhead = nhead + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, src, mask, query_embed, pos_embed): + # flatten NxCxHxW to HWxNxC + bs, c, h, w = src.shape + src = src.flatten(2).permute(2, 0, 1) + pos_embed = pos_embed.flatten(2).permute(2, 0, 1) + query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) + if mask is not None: + mask = mask.flatten(1) + + tgt = torch.zeros_like(query_embed) + memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) + hs = self.decoder( + tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed + ) + return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w) + + +class TransformerEncoder(nn.Module): + def __init__(self, encoder_layer, num_layers, norm=None): + super().__init__() + self.layers = _get_clones(encoder_layer, num_layers) + self.num_layers = num_layers + self.norm = norm + + def forward( + self, + src, + mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + output = src + + for layer in self.layers: + output = layer( + output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos + ) + + if self.norm is not None: + output = self.norm(output) + + return output + + +class TransformerDecoder(nn.Module): + def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): + super().__init__() + self.layers = _get_clones(decoder_layer, num_layers) + self.num_layers = num_layers + self.norm = norm + self.return_intermediate = return_intermediate + + def forward( + self, + tgt, + memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + ): + output = tgt + + intermediate = [] + + for layer in self.layers: + output = layer( + output, + memory, + tgt_mask=tgt_mask, + memory_mask=memory_mask, + tgt_key_padding_mask=tgt_key_padding_mask, + memory_key_padding_mask=memory_key_padding_mask, + pos=pos, + query_pos=query_pos, + ) + if self.return_intermediate: + intermediate.append(self.norm(output)) + + if self.norm is not None: + output = self.norm(output) + if self.return_intermediate: + intermediate.pop() + intermediate.append(output) + + if self.return_intermediate: + return torch.stack(intermediate) + + return output.unsqueeze(0) + + +class TransformerEncoderLayer(nn.Module): + def __init__( + self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + ): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post( + self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + q = k = self.with_pos_embed(src, pos) + src2 = self.self_attn( + q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask + )[0] + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + src = self.norm2(src) + return src + + def forward_pre( + self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + src2 = self.norm1(src) + q = k = self.with_pos_embed(src2, pos) + src2 = self.self_attn( + q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask + )[0] + src = src + self.dropout1(src2) + src2 = self.norm2(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) + src = src + self.dropout2(src2) + return src + + def forward( + self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + if self.normalize_before: + return self.forward_pre(src, src_mask, src_key_padding_mask, pos) + return self.forward_post(src, src_mask, src_key_padding_mask, pos) + + +class TransformerDecoderLayer(nn.Module): + def __init__( + self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + ): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.norm3 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.dropout3 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post( + self, + tgt, + memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + ): + q = k = self.with_pos_embed(tgt, query_pos) + tgt2 = self.self_attn( + q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask + )[0] + tgt = tgt + self.dropout1(tgt2) + tgt = self.norm1(tgt) + tgt2 = self.multihead_attn( + query=self.with_pos_embed(tgt, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, + attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask, + )[0] + tgt = tgt + self.dropout2(tgt2) + tgt = self.norm2(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) + tgt = tgt + self.dropout3(tgt2) + tgt = self.norm3(tgt) + return tgt + + def forward_pre( + self, + tgt, + memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + ): + tgt2 = self.norm1(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn( + q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask + )[0] + tgt = tgt + self.dropout1(tgt2) + tgt2 = self.norm2(tgt) + tgt2 = self.multihead_attn( + query=self.with_pos_embed(tgt2, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, + attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask, + )[0] + tgt = tgt + self.dropout2(tgt2) + tgt2 = self.norm3(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout3(tgt2) + return tgt + + def forward( + self, + tgt, + memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + ): + if self.normalize_before: + return self.forward_pre( + tgt, + memory, + tgt_mask, + memory_mask, + tgt_key_padding_mask, + memory_key_padding_mask, + pos, + query_pos, + ) + return self.forward_post( + tgt, + memory, + tgt_mask, + memory_mask, + tgt_key_padding_mask, + memory_key_padding_mask, + pos, + query_pos, + ) + + +def _get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(f"activation should be relu/gelu, not {activation}.") diff --git a/videocutler/mask2former/test_time_augmentation.py b/videocutler/mask2former/test_time_augmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..b02568d1b1ed32efb9316b5c4d53c4d71e5cef78 --- /dev/null +++ b/videocutler/mask2former/test_time_augmentation.py @@ -0,0 +1,103 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import logging +from itertools import count + +import numpy as np +import torch +from fvcore.transforms import HFlipTransform +from torch import nn +from torch.nn.parallel import DistributedDataParallel + +from detectron2.data.detection_utils import read_image +from detectron2.modeling import DatasetMapperTTA + + +__all__ = [ + "SemanticSegmentorWithTTA", +] + + +class SemanticSegmentorWithTTA(nn.Module): + """ + A SemanticSegmentor with test-time augmentation enabled. + Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`. + """ + + def __init__(self, cfg, model, tta_mapper=None, batch_size=1): + """ + Args: + cfg (CfgNode): + model (SemanticSegmentor): a SemanticSegmentor to apply TTA on. + tta_mapper (callable): takes a dataset dict and returns a list of + augmented versions of the dataset dict. Defaults to + `DatasetMapperTTA(cfg)`. + batch_size (int): batch the augmented images into this batch size for inference. + """ + super().__init__() + if isinstance(model, DistributedDataParallel): + model = model.module + self.cfg = cfg.clone() + + self.model = model + + if tta_mapper is None: + tta_mapper = DatasetMapperTTA(cfg) + self.tta_mapper = tta_mapper + self.batch_size = batch_size + + def __call__(self, batched_inputs): + """ + Same input/output format as :meth:`SemanticSegmentor.forward` + """ + + def _maybe_read_image(dataset_dict): + ret = copy.copy(dataset_dict) + if "image" not in ret: + image = read_image(ret.pop("file_name"), self.model.input_format) + image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW + ret["image"] = image + if "height" not in ret and "width" not in ret: + ret["height"] = image.shape[1] + ret["width"] = image.shape[2] + return ret + + processed_results = [] + for x in batched_inputs: + result = self._inference_one_image(_maybe_read_image(x)) + processed_results.append(result) + return processed_results + + def _inference_one_image(self, input): + """ + Args: + input (dict): one dataset dict with "image" field being a CHW tensor + Returns: + dict: one output dict + """ + orig_shape = (input["height"], input["width"]) + augmented_inputs, tfms = self._get_augmented_inputs(input) + + final_predictions = None + count_predictions = 0 + for input, tfm in zip(augmented_inputs, tfms): + count_predictions += 1 + with torch.no_grad(): + if final_predictions is None: + if any(isinstance(t, HFlipTransform) for t in tfm.transforms): + final_predictions = self.model([input])[0].pop("sem_seg").flip(dims=[2]) + else: + final_predictions = self.model([input])[0].pop("sem_seg") + else: + if any(isinstance(t, HFlipTransform) for t in tfm.transforms): + final_predictions += self.model([input])[0].pop("sem_seg").flip(dims=[2]) + else: + final_predictions += self.model([input])[0].pop("sem_seg") + + final_predictions = final_predictions / count_predictions + return {"sem_seg": final_predictions} + + def _get_augmented_inputs(self, input): + augmented_inputs = self.tta_mapper(input) + tfms = [x.pop("transforms") for x in augmented_inputs] + return augmented_inputs, tfms diff --git a/videocutler/mask2former/utils/__init__.py b/videocutler/mask2former/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9020c2df23e2af280b7bb168b996ae9eaf312eb8 --- /dev/null +++ b/videocutler/mask2former/utils/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. diff --git a/videocutler/mask2former/utils/misc.py b/videocutler/mask2former/utils/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..874d9805b482f52bbffc1be620e36e0cffc07c46 --- /dev/null +++ b/videocutler/mask2former/utils/misc.py @@ -0,0 +1,111 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/util/misc.py +""" +Misc functions, including distributed helpers. + +Mostly copy-paste from torchvision references. +""" +from typing import List, Optional + +import torch +import torch.distributed as dist +import torchvision +from torch import Tensor + + +def _max_by_axis(the_list): + # type: (List[List[int]]) -> List[int] + maxes = the_list[0] + for sublist in the_list[1:]: + for index, item in enumerate(sublist): + maxes[index] = max(maxes[index], item) + return maxes + + +class NestedTensor(object): + def __init__(self, tensors, mask: Optional[Tensor]): + self.tensors = tensors + self.mask = mask + + def to(self, device): + # type: (Device) -> NestedTensor # noqa + cast_tensor = self.tensors.to(device) + mask = self.mask + if mask is not None: + assert mask is not None + cast_mask = mask.to(device) + else: + cast_mask = None + return NestedTensor(cast_tensor, cast_mask) + + def decompose(self): + return self.tensors, self.mask + + def __repr__(self): + return str(self.tensors) + + +def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): + # TODO make this more general + if tensor_list[0].ndim == 3: + if torchvision._is_tracing(): + # nested_tensor_from_tensor_list() does not export well to ONNX + # call _onnx_nested_tensor_from_tensor_list() instead + return _onnx_nested_tensor_from_tensor_list(tensor_list) + + # TODO make it support different-sized images + max_size = _max_by_axis([list(img.shape) for img in tensor_list]) + # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) + batch_shape = [len(tensor_list)] + max_size + b, c, h, w = batch_shape + dtype = tensor_list[0].dtype + device = tensor_list[0].device + tensor = torch.zeros(batch_shape, dtype=dtype, device=device) + mask = torch.ones((b, h, w), dtype=torch.bool, device=device) + for img, pad_img, m in zip(tensor_list, tensor, mask): + pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + m[: img.shape[1], : img.shape[2]] = False + else: + raise ValueError("not supported") + return NestedTensor(tensor, mask) + + +# _onnx_nested_tensor_from_tensor_list() is an implementation of +# nested_tensor_from_tensor_list() that is supported by ONNX tracing. +@torch.jit.unused +def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor: + max_size = [] + for i in range(tensor_list[0].dim()): + max_size_i = torch.max( + torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32) + ).to(torch.int64) + max_size.append(max_size_i) + max_size = tuple(max_size) + + # work around for + # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + # m[: img.shape[1], :img.shape[2]] = False + # which is not yet supported in onnx + padded_imgs = [] + padded_masks = [] + for img in tensor_list: + padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] + padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) + padded_imgs.append(padded_img) + + m = torch.zeros_like(img[0], dtype=torch.int, device=img.device) + padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1) + padded_masks.append(padded_mask.to(torch.bool)) + + tensor = torch.stack(padded_imgs) + mask = torch.stack(padded_masks) + + return NestedTensor(tensor, mask=mask) + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True diff --git a/videocutler/mask2former_video/__init__.py b/videocutler/mask2former_video/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1fbb263363489699fdfb9d7bcd36cbf7627c66f4 --- /dev/null +++ b/videocutler/mask2former_video/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from . import modeling + +# config +from .config import add_maskformer2_video_config + +# models +from .video_maskformer_model import VideoMaskFormer + +# video +from .data_video import ( + YTVISDatasetMapper, + YTVISEvaluator, + build_detection_train_loader, + build_detection_test_loader, + get_detection_dataset_dicts, +) + +# copy-paste +from .engine import * \ No newline at end of file diff --git a/videocutler/mask2former_video/config.py b/videocutler/mask2former_video/config.py new file mode 100644 index 0000000000000000000000000000000000000000..745929b918d7bf5a2e9e51ff6e4b8fc609a3151f --- /dev/null +++ b/videocutler/mask2former_video/config.py @@ -0,0 +1,24 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/Mask2Former/tree/main/mask2former_video + +from detectron2.config import CfgNode as CN + + +def add_maskformer2_video_config(cfg): + # video data + # DataLoader + cfg.INPUT.SAMPLING_FRAME_NUM = 2 + cfg.INPUT.SAMPLING_FRAME_RANGE = 20 + cfg.INPUT.SAMPLING_FRAME_SHUFFLE = False + cfg.INPUT.AUGMENTATIONS = [] # "brightness", "contrast", "saturation", "rotation" + + cfg.DATALOADER.COPY_PASTE = False + cfg.DATALOADER.COPY_PASTE_RATE = 0.0 + cfg.DATALOADER.COPY_PASTE_MIN_RATIO = 0.5 + cfg.DATALOADER.COPY_PASTE_MAX_RATIO = 1.0 + cfg.DATALOADER.COPY_PASTE_RANDOM_NUM = True # random select number of instances + cfg.DATALOADER.VISUALIZE_COPY_PASTE = False + + cfg.SOLVER.BASE_LR_MULTIPLIER = 1 + cfg.SOLVER.BASE_LR_MULTIPLIER_NAMES = [] diff --git a/videocutler/mask2former_video/data_video/__init__.py b/videocutler/mask2former_video/data_video/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..3085ed74c61784b0601e535cf7bee00358faefc1 --- /dev/null +++ b/videocutler/mask2former_video/data_video/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/sukjunhwang/IFC + +from .dataset_mapper import YTVISDatasetMapper, CocoClipDatasetMapper +from .build import * + +from .datasets import * +from .ytvis_eval import YTVISEvaluator diff --git a/videocutler/mask2former_video/data_video/augmentation.py b/videocutler/mask2former_video/data_video/augmentation.py new file mode 100755 index 0000000000000000000000000000000000000000..5739721fdf97a8faf418e42bc4238cc7eb36047b --- /dev/null +++ b/videocutler/mask2former_video/data_video/augmentation.py @@ -0,0 +1,167 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/sukjunhwang/IFC + +import numpy as np +import logging +import sys +from fvcore.transforms.transform import ( + HFlipTransform, + NoOpTransform, + VFlipTransform, +) +from PIL import Image + +from detectron2.data import transforms as T + + +class ResizeShortestEdge(T.Augmentation): + """ + Scale the shorter edge to the given size, with a limit of `max_size` on the longer edge. + If `max_size` is reached, then downscale so that the longer edge does not exceed max_size. + """ + + def __init__( + self, short_edge_length, max_size=sys.maxsize, sample_style="range", interp=Image.BILINEAR, clip_frame_cnt=1 + ): + """ + Args: + short_edge_length (list[int]): If ``sample_style=="range"``, + a [min, max] interval from which to sample the shortest edge length. + If ``sample_style=="choice"``, a list of shortest edge lengths to sample from. + max_size (int): maximum allowed longest edge length. + sample_style (str): either "range" or "choice". + """ + super().__init__() + assert sample_style in ["range", "choice", "range_by_clip", "choice_by_clip"], sample_style + + self.is_range = ("range" in sample_style) + if isinstance(short_edge_length, int): + short_edge_length = (short_edge_length, short_edge_length) + if self.is_range: + assert len(short_edge_length) == 2, ( + "short_edge_length must be two values using 'range' sample style." + f" Got {short_edge_length}!" + ) + self._cnt = 0 + self._init(locals()) + + def get_transform(self, image): + if self._cnt % self.clip_frame_cnt == 0: + if self.is_range: + self.size = np.random.randint(self.short_edge_length[0], self.short_edge_length[1] + 1) + else: + self.size = np.random.choice(self.short_edge_length) + if self.size == 0: + return NoOpTransform() + + self._cnt = 0 # avoiding overflow + self._cnt += 1 + + h, w = image.shape[:2] + + scale = self.size * 1.0 / min(h, w) + if h < w: + newh, neww = self.size, scale * w + else: + newh, neww = scale * h, self.size + if max(newh, neww) > self.max_size: + scale = self.max_size * 1.0 / max(newh, neww) + newh = newh * scale + neww = neww * scale + neww = int(neww + 0.5) + newh = int(newh + 0.5) + return T.ResizeTransform(h, w, newh, neww, self.interp) + + +class RandomFlip(T.Augmentation): + """ + Flip the image horizontally or vertically with the given probability. + """ + + def __init__(self, prob=0.5, *, horizontal=True, vertical=False, clip_frame_cnt=1): + """ + Args: + prob (float): probability of flip. + horizontal (boolean): whether to apply horizontal flipping + vertical (boolean): whether to apply vertical flipping + """ + super().__init__() + + if horizontal and vertical: + raise ValueError("Cannot do both horiz and vert. Please use two Flip instead.") + if not horizontal and not vertical: + raise ValueError("At least one of horiz or vert has to be True!") + self._cnt = 0 + + self._init(locals()) + + def get_transform(self, image): + if self._cnt % self.clip_frame_cnt == 0: + self.do = self._rand_range() < self.prob + self._cnt = 0 # avoiding overflow + self._cnt += 1 + + h, w = image.shape[:2] + + if self.do: + if self.horizontal: + return HFlipTransform(w) + elif self.vertical: + return VFlipTransform(h) + else: + return NoOpTransform() + + +def build_augmentation(cfg, is_train): + logger = logging.getLogger(__name__) + aug_list = [] + if is_train: + # Crop + if cfg.INPUT.CROP.ENABLED: + aug_list.append(T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE)) + + # Resize + min_size = cfg.INPUT.MIN_SIZE_TRAIN + max_size = cfg.INPUT.MAX_SIZE_TRAIN + sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING + ms_clip_frame_cnt = cfg.INPUT.SAMPLING_FRAME_NUM if "by_clip" in cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING else 1 + aug_list.append(ResizeShortestEdge(min_size, max_size, sample_style, clip_frame_cnt=ms_clip_frame_cnt)) + + # Flip + if cfg.INPUT.RANDOM_FLIP != "none": + if cfg.INPUT.RANDOM_FLIP == "flip_by_clip": + flip_clip_frame_cnt = cfg.INPUT.SAMPLING_FRAME_NUM + else: + flip_clip_frame_cnt = 1 + + aug_list.append( + # NOTE using RandomFlip modified for the support of flip maintenance + RandomFlip( + horizontal=(cfg.INPUT.RANDOM_FLIP == "horizontal") or (cfg.INPUT.RANDOM_FLIP == "flip_by_clip"), + vertical=cfg.INPUT.RANDOM_FLIP == "vertical", + clip_frame_cnt=flip_clip_frame_cnt, + ) + ) + + # Additional augmentations : brightness, contrast, saturation, rotation + augmentations = cfg.INPUT.AUGMENTATIONS + if "brightness" in augmentations: + aug_list.append(T.RandomBrightness(0.9, 1.1)) + if "contrast" in augmentations: + aug_list.append(T.RandomContrast(0.9, 1.1)) + if "saturation" in augmentations: + aug_list.append(T.RandomSaturation(0.9, 1.1)) + if "rotation" in augmentations: + aug_list.append( + T.RandomRotation( + [-15, 15], expand=False, center=[(0.4, 0.4), (0.6, 0.6)], sample_style="range" + ) + ) + else: + # Resize + min_size = cfg.INPUT.MIN_SIZE_TEST + max_size = cfg.INPUT.MAX_SIZE_TEST + sample_style = "choice" + aug_list.append(T.ResizeShortestEdge(min_size, max_size, sample_style)) + + return aug_list diff --git a/videocutler/mask2former_video/data_video/build.py b/videocutler/mask2former_video/data_video/build.py new file mode 100755 index 0000000000000000000000000000000000000000..a4f37e48fcc228a756e0108291fa09b1b2e2c6f1 --- /dev/null +++ b/videocutler/mask2former_video/data_video/build.py @@ -0,0 +1,250 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/sukjunhwang/IFC + +import itertools +import logging +import torch.utils.data + +from detectron2.config import CfgNode, configurable +from detectron2.data.build import ( + build_batch_data_loader, + load_proposals_into_dataset, + trivial_batch_collator, +) +from detectron2.data.catalog import DatasetCatalog +from detectron2.data.common import DatasetFromList, MapDataset +from detectron2.data.dataset_mapper import DatasetMapper +from detectron2.data.samplers import InferenceSampler, TrainingSampler +from detectron2.utils.comm import get_world_size + + +def _compute_num_images_per_worker(cfg: CfgNode): + num_workers = get_world_size() + images_per_batch = cfg.SOLVER.IMS_PER_BATCH + assert ( + images_per_batch % num_workers == 0 + ), "SOLVER.IMS_PER_BATCH ({}) must be divisible by the number of workers ({}).".format( + images_per_batch, num_workers + ) + assert ( + images_per_batch >= num_workers + ), "SOLVER.IMS_PER_BATCH ({}) must be larger than the number of workers ({}).".format( + images_per_batch, num_workers + ) + images_per_worker = images_per_batch // num_workers + return images_per_worker + + +def filter_images_with_only_crowd_annotations(dataset_dicts, dataset_names): + """ + Filter out images with none annotations or only crowd annotations + (i.e., images without non-crowd annotations). + A common training-time preprocessing on COCO dataset. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 Dataset format. + + Returns: + list[dict]: the same format, but filtered. + """ + num_before = len(dataset_dicts) + + def valid(anns): + for ann in anns: + if isinstance(ann, list): + for instance in ann: + if instance.get("iscrowd", 0) == 0: + return True + else: + if ann.get("iscrowd", 0) == 0: + return True + return False + + dataset_dicts = [x for x in dataset_dicts if valid(x["annotations"])] + num_after = len(dataset_dicts) + logger = logging.getLogger(__name__) + logger.info( + "Removed {} images with no usable annotations. {} images left.".format( + num_before - num_after, num_after + ) + ) + return dataset_dicts + + +def get_detection_dataset_dicts( + dataset_names, filter_empty=True, proposal_files=None +): + """ + Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation. + + Args: + dataset_names (str or list[str]): a dataset name or a list of dataset names + filter_empty (bool): whether to filter out images without instance annotations + proposal_files (list[str]): if given, a list of object proposal files + that match each dataset in `dataset_names`. + + Returns: + list[dict]: a list of dicts following the standard dataset dict format. + """ + if isinstance(dataset_names, str): + dataset_names = [dataset_names] + assert len(dataset_names) + dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names] + for dataset_name, dicts in zip(dataset_names, dataset_dicts): + assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) + + if proposal_files is not None: + assert len(dataset_names) == len(proposal_files) + # load precomputed proposals from proposal files + dataset_dicts = [ + load_proposals_into_dataset(dataset_i_dicts, proposal_file) + for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files) + ] + + dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts)) + + has_instances = "annotations" in dataset_dicts[0] + if filter_empty and has_instances: + dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts, dataset_names) + + assert len(dataset_dicts), "No valid data found in {}.".format(",".join(dataset_names)) + return dataset_dicts + + +def _train_loader_from_config(cfg, mapper, *, dataset=None, sampler=None): + if dataset is None: + dataset = get_detection_dataset_dicts( + cfg.DATASETS.TRAIN, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + + if mapper is None: + mapper = DatasetMapper(cfg, True) + + if sampler is None: + sampler_name = cfg.DATALOADER.SAMPLER_TRAIN + logger = logging.getLogger(__name__) + logger.info("Using training sampler {}".format(sampler_name)) + sampler = TrainingSampler(len(dataset)) + + return { + "dataset": dataset, + "sampler": sampler, + "mapper": mapper, + "total_batch_size": cfg.SOLVER.IMS_PER_BATCH, + "aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING, + "num_workers": cfg.DATALOADER.NUM_WORKERS, + } + + +# TODO can allow dataset as an iterable or IterableDataset to make this function more general +@configurable(from_config=_train_loader_from_config) +def build_detection_train_loader( + dataset, *, mapper, sampler=None, total_batch_size, aspect_ratio_grouping=True, num_workers=0 +): + """ + Build a dataloader for object detection with some default features. + This interface is experimental. + + Args: + dataset (list or torch.utils.data.Dataset): a list of dataset dicts, + or a map-style pytorch dataset. They can be obtained by using + :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper (callable): a callable which takes a sample (dict) from dataset and + returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=True)``. + sampler (torch.utils.data.sampler.Sampler or None): a sampler that + produces indices to be applied on ``dataset``. + Default to :class:`TrainingSampler`, which coordinates a random shuffle + sequence across all workers. + total_batch_size (int): total batch size across all workers. Batching + simply puts data into a list. + aspect_ratio_grouping (bool): whether to group images with similar + aspect ratio for efficiency. When enabled, it requires each + element in dataset be a dict with keys "width" and "height". + num_workers (int): number of parallel data loading workers + + Returns: + torch.utils.data.DataLoader: a dataloader. Each output from it is a + ``list[mapped_element]`` of length ``total_batch_size / num_workers``, + where ``mapped_element`` is produced by the ``mapper``. + """ + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset(dataset, mapper) + if sampler is None: + sampler = TrainingSampler(len(dataset)) + assert isinstance(sampler, torch.utils.data.sampler.Sampler) + return build_batch_data_loader( + dataset, + sampler, + total_batch_size, + aspect_ratio_grouping=aspect_ratio_grouping, + num_workers=num_workers, + ) + + +def _test_loader_from_config(cfg, dataset_name, mapper=None): + """ + Uses the given `dataset_name` argument (instead of the names in cfg), because the + standard practice is to evaluate each test set individually (not combining them). + """ + dataset = get_detection_dataset_dicts( + [dataset_name], + filter_empty=False, + proposal_files=[ + cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(dataset_name)] + ] + if cfg.MODEL.LOAD_PROPOSALS + else None, + ) + if mapper is None: + mapper = DatasetMapper(cfg, False) + return {"dataset": dataset, "mapper": mapper, "num_workers": cfg.DATALOADER.NUM_WORKERS} + + +@configurable(from_config=_test_loader_from_config) +def build_detection_test_loader(dataset, *, mapper, num_workers=0): + """ + Similar to `build_detection_train_loader`, but uses a batch size of 1. + This interface is experimental. + + Args: + dataset (list or torch.utils.data.Dataset): a list of dataset dicts, + or a map-style pytorch dataset. They can be obtained by using + :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper (callable): a callable which takes a sample (dict) from dataset + and returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``. + num_workers (int): number of parallel data loading workers + + Returns: + DataLoader: a torch DataLoader, that loads the given detection + dataset, with test-time transformation and batching. + + Examples: + :: + data_loader = build_detection_test_loader( + DatasetRegistry.get("my_test"), + mapper=DatasetMapper(...)) + + # or, instantiate with a CfgNode: + data_loader = build_detection_test_loader(cfg, "my_test") + """ + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset(dataset, mapper) + sampler = InferenceSampler(len(dataset)) + # Always use 1 image per worker during inference since this is the + # standard when reporting inference time in papers. + batch_sampler = torch.utils.data.sampler.BatchSampler(sampler, 1, drop_last=False) + data_loader = torch.utils.data.DataLoader( + dataset, + num_workers=num_workers, + batch_sampler=batch_sampler, + collate_fn=trivial_batch_collator, + ) + return data_loader diff --git a/videocutler/mask2former_video/data_video/dataset_mapper.py b/videocutler/mask2former_video/data_video/dataset_mapper.py new file mode 100755 index 0000000000000000000000000000000000000000..d3f9513d3af000b7dbb321cd5f721122d701cedf --- /dev/null +++ b/videocutler/mask2former_video/data_video/dataset_mapper.py @@ -0,0 +1,415 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/sukjunhwang/IFC + +import copy +import logging +import random +import numpy as np +from typing import List, Union +import torch + +from detectron2.config import configurable +from detectron2.structures import ( + BitMasks, + Boxes, + BoxMode, + Instances, +) + +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T + +from .augmentation import build_augmentation + +__all__ = ["YTVISDatasetMapper", "CocoClipDatasetMapper"] + + +def filter_empty_instances(instances, by_box=True, by_mask=True, box_threshold=1e-5): + """ + Filter out empty instances in an `Instances` object. + + Args: + instances (Instances): + by_box (bool): whether to filter out instances with empty boxes + by_mask (bool): whether to filter out instances with empty masks + box_threshold (float): minimum width and height to be considered non-empty + + Returns: + Instances: the filtered instances. + """ + assert by_box or by_mask + r = [] + if by_box: + r.append(instances.gt_boxes.nonempty(threshold=box_threshold)) + if instances.has("gt_masks") and by_mask: + r.append(instances.gt_masks.nonempty()) + + if not r: + return instances + m = r[0] + for x in r[1:]: + m = m & x + + instances.gt_ids[~m] = -1 + return instances + + +def _get_dummy_anno(num_classes): + return { + "iscrowd": 0, + "category_id": num_classes, + "id": -1, + "bbox": np.array([0, 0, 0, 0]), + "bbox_mode": BoxMode.XYXY_ABS, + "segmentation": [np.array([0.0] * 6)] + } + + +def ytvis_annotations_to_instances(annos, image_size): + """ + Create an :class:`Instances` object used by the models, + from instance annotations in the dataset dict. + + Args: + annos (list[dict]): a list of instance annotations in one image, each + element for one instance. + image_size (tuple): height, width + + Returns: + Instances: + It will contain fields "gt_boxes", "gt_classes", "gt_ids", + "gt_masks", if they can be obtained from `annos`. + This is the format that builtin models expect. + """ + boxes = [BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS) for obj in annos] + target = Instances(image_size) + target.gt_boxes = Boxes(boxes) + + classes = [int(obj["category_id"]) for obj in annos] + classes = torch.tensor(classes, dtype=torch.int64) + target.gt_classes = classes + + ids = [int(obj["id"]) for obj in annos] + ids = torch.tensor(ids, dtype=torch.int64) + target.gt_ids = ids + + if len(annos) and "segmentation" in annos[0]: + segms = [obj["segmentation"] for obj in annos] + masks = [] + for segm in segms: + assert segm.ndim == 2, "Expect segmentation of 2 dimensions, got {}.".format( + segm.ndim + ) + # mask array + masks.append(segm) + # torch.from_numpy does not support array with negative stride. + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x)) for x in masks]) + ) + target.gt_masks = masks + + return target + +class SizeMismatchError(ValueError): + """ + When loaded image has difference width/height compared with annotation. + """ + +def check_image_size(dataset_dict, image): + """ + Raise an error if the image does not match the size specified in the dict. + """ + if "width" in dataset_dict or "height" in dataset_dict: + image_wh = (image.shape[1], image.shape[0]) + expected_wh = (dataset_dict["width"], dataset_dict["height"]) + if not image_wh == expected_wh: + expected_wh = (dataset_dict["height"], dataset_dict["width"]) + dataset_dict["height"], dataset_dict["width"] = dataset_dict["width"], dataset_dict["height"] + if image_wh != expected_wh: + raise SizeMismatchError( + "Mismatched image shape{}, got {}, expect {}.".format( + " for image " + dataset_dict["file_name"] + if "file_name" in dataset_dict + else "", + image_wh, + expected_wh, + ) + + " Please check the width/height in your annotation." + ) + + # To ensure bbox always remap to original image size + if "width" not in dataset_dict: + dataset_dict["width"] = image.shape[1] + if "height" not in dataset_dict: + dataset_dict["height"] = image.shape[0] + +class YTVISDatasetMapper: + """ + A callable which takes a dataset dict in YouTube-VIS Dataset format, + and map it into a format used by the model. + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + image_format: str, + use_instance_mask: bool = False, + sampling_frame_num: int = 2, + sampling_frame_range: int = 5, + sampling_frame_shuffle: bool = False, + num_classes: int = 40, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + """ + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.sampling_frame_num = sampling_frame_num + self.sampling_frame_range = sampling_frame_range + self.sampling_frame_shuffle = sampling_frame_shuffle + self.num_classes = num_classes + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + augs = build_augmentation(cfg, is_train) + + sampling_frame_num = cfg.INPUT.SAMPLING_FRAME_NUM + sampling_frame_range = cfg.INPUT.SAMPLING_FRAME_RANGE + sampling_frame_shuffle = cfg.INPUT.SAMPLING_FRAME_SHUFFLE + + ret = { + "is_train": is_train, + "augmentations": augs, + "image_format": cfg.INPUT.FORMAT, + "use_instance_mask": cfg.MODEL.MASK_ON, + "sampling_frame_num": sampling_frame_num, + "sampling_frame_range": sampling_frame_range, + "sampling_frame_shuffle": sampling_frame_shuffle, + "num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES, + } + + return ret + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one video, in YTVIS Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + # TODO consider examining below deepcopy as it costs huge amount of computations. + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + + video_length = dataset_dict["length"] + if self.is_train: + ref_frame = random.randrange(video_length) + + start_idx = max(0, ref_frame-self.sampling_frame_range) + end_idx = min(video_length, ref_frame+self.sampling_frame_range + 1) + + selected_idx = np.random.choice( + np.array(list(range(start_idx, ref_frame)) + list(range(ref_frame+1, end_idx))), + self.sampling_frame_num - 1, + ) + selected_idx = selected_idx.tolist() + [ref_frame] + selected_idx = sorted(selected_idx) + if self.sampling_frame_shuffle: + random.shuffle(selected_idx) + else: + selected_idx = range(video_length) + + video_annos = dataset_dict.pop("annotations", None) + file_names = dataset_dict.pop("file_names", None) + + if self.is_train: + _ids = set() + for frame_idx in selected_idx: + _ids.update([anno["id"] for anno in video_annos[frame_idx]]) + ids = dict() + for i, _id in enumerate(_ids): + ids[_id] = i + + dataset_dict["image"] = [] + dataset_dict["instances"] = [] + dataset_dict["file_names"] = [] + for frame_idx in selected_idx: + dataset_dict["file_names"].append(file_names[frame_idx]) + + # Read image + image = utils.read_image(file_names[frame_idx], format=self.image_format) + check_image_size(dataset_dict, image) + + aug_input = T.AugInput(image) + transforms = self.augmentations(aug_input) + image = aug_input.image + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"].append(torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))) + + if (video_annos is None) or (not self.is_train): + continue + + # NOTE copy() is to prevent annotations getting changed from applying augmentations + _frame_annos = [] + for anno in video_annos[frame_idx]: + _anno = {} + for k, v in anno.items(): + _anno[k] = copy.deepcopy(v) + _frame_annos.append(_anno) + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations(obj, transforms, image_shape) + for obj in _frame_annos + if obj.get("iscrowd", 0) == 0 + ] + sorted_annos = [_get_dummy_anno(self.num_classes) for _ in range(len(ids))] + + for _anno in annos: + idx = ids[_anno["id"]] + sorted_annos[idx] = _anno + _gt_ids = [_anno["id"] for _anno in sorted_annos] + + instances = utils.annotations_to_instances(sorted_annos, image_shape, mask_format="bitmask") + instances.gt_ids = torch.tensor(_gt_ids) + if instances.has("gt_masks"): + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + instances = filter_empty_instances(instances) + else: + instances.gt_masks = BitMasks(torch.empty((0, *image_shape))) + dataset_dict["instances"].append(instances) + + return dataset_dict + + +class CocoClipDatasetMapper: + """ + A callable which takes a COCO image which converts into multiple frames, + and map it into a format used by the model. + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + image_format: str, + use_instance_mask: bool = False, + sampling_frame_num: int = 2, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + """ + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.sampling_frame_num = sampling_frame_num + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + augs = build_augmentation(cfg, is_train) + + sampling_frame_num = cfg.INPUT.SAMPLING_FRAME_NUM + + ret = { + "is_train": is_train, + "augmentations": augs, + "image_format": cfg.INPUT.FORMAT, + "use_instance_mask": cfg.MODEL.MASK_ON, + "sampling_frame_num": sampling_frame_num, + } + + return ret + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + + img_annos = dataset_dict.pop("annotations", None) + file_name = dataset_dict.pop("file_name", None) + original_image = utils.read_image(file_name, format=self.image_format) + + dataset_dict["image"] = [] + dataset_dict["instances"] = [] + dataset_dict["file_names"] = [file_name] * self.sampling_frame_num + for _ in range(self.sampling_frame_num): + utils.check_image_size(dataset_dict, original_image) + + aug_input = T.AugInput(original_image) + transforms = self.augmentations(aug_input) + image = aug_input.image + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"].append(torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))) + + if (img_annos is None) or (not self.is_train): + continue + + _img_annos = [] + for anno in img_annos: + _anno = {} + for k, v in anno.items(): + _anno[k] = copy.deepcopy(v) + _img_annos.append(_anno) + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations(obj, transforms, image_shape) + for obj in _img_annos + if obj.get("iscrowd", 0) == 0 + ] + _gt_ids = list(range(len(annos))) + for idx in range(len(annos)): + if len(annos[idx]["segmentation"]) == 0: + annos[idx]["segmentation"] = [np.array([0.0] * 6)] + + instances = utils.annotations_to_instances(annos, image_shape, mask_format="bitmask") + instances.gt_ids = torch.tensor(_gt_ids) + if instances.has("gt_masks"): + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + instances = filter_empty_instances(instances) + else: + instances.gt_masks = BitMasks(torch.empty((0, *image_shape))) + dataset_dict["instances"].append(instances) + + return dataset_dict diff --git a/videocutler/mask2former_video/data_video/datasets/__init__.py b/videocutler/mask2former_video/data_video/datasets/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..e18d90b9f7762b2cf362b177faa1e53b580cbeca --- /dev/null +++ b/videocutler/mask2former_video/data_video/datasets/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/Mask2Former/tree/main/mask2former_video + +from . import builtin # ensure the builtin datasets are registered + +__all__ = [k for k in globals().keys() if "builtin" not in k and not k.startswith("_")] diff --git a/videocutler/mask2former_video/data_video/datasets/builtin.py b/videocutler/mask2former_video/data_video/datasets/builtin.py new file mode 100755 index 0000000000000000000000000000000000000000..15cee7dc3639e3532831c0538248aae0f7050213 --- /dev/null +++ b/videocutler/mask2former_video/data_video/datasets/builtin.py @@ -0,0 +1,89 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/Mask2Former/tree/main/mask2former_video + +import os + +from .ytvis import ( + register_ytvis_instances, + _get_ytvis_2019_instances_meta, + _get_ytvis_2021_instances_meta, + _get_imagenet_cls_agnostic_instances_meta, +) + +# ==== Predefined splits for YTVIS 2019 =========== +_PREDEFINED_SPLITS_YTVIS_2019 = { + "ytvis_2019_train": ("ytvis_2019/train/JPEGImages", + "ytvis_2019/train.json"), + "ytvis_2019_val": ("ytvis_2019/valid/JPEGImages", + "ytvis_2019/valid.json"), + "ytvis_2019_test": ("ytvis_2019/test/JPEGImages", + "ytvis_2019/test.json"), + "ytvis_2019_train_5perc": ("ytvis_2019/train/JPEGImages", + "ytvis_2019/train_5percent.json"), + "ytvis_2019_train_10perc": ("ytvis_2019/train/JPEGImages", + "ytvis_2019/train_10percent.json"), + "ytvis_2019_train_20perc": ("ytvis_2019/train/JPEGImages", + "ytvis_2019/train_20percent.json"), + "ytvis_2019_train_30perc": ("ytvis_2019/train/JPEGImages", + "ytvis_2019/train_30percent.json"), + "ytvis_2019_train_40perc": ("ytvis_2019/train/JPEGImages", + "ytvis_2019/train_40percent.json"), + "ytvis_2019_train_50perc": ("ytvis_2019/train/JPEGImages", + "ytvis_2019/train_50percent.json"), +} + +# ==== Predefined splits for YTVIS 2021 =========== +_PREDEFINED_SPLITS_YTVIS_2021 = { + "ytvis_2021_train": ("ytvis_2021/train/JPEGImages", + "ytvis_2021/train.json"), + "ytvis_2021_val": ("ytvis_2021/valid/JPEGImages", + "ytvis_2021/valid.json"), + "ytvis_2021_test": ("ytvis_2021/test/JPEGImages", + "ytvis_2021/test.json"), + "ytvis_2021_minus_2019_train": ("ytvis_2021/train/JPEGImages", + "ytvis_2021/instances_val_sub.json"), +} + +_PREDEFINED_SPLITS_ImageNet_CLS_AGNOSTIC = { + "imagenet_video_train_cls_agnostic": ("imagenet/train", + "imagenet/annotations/video_imagenet_train_fixsize480_tau0.15_N3.json"), +} + + +def register_all_ytvis_2019(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS_YTVIS_2019.items(): + # Assume pre-defined datasets live in `./datasets`. + register_ytvis_instances( + key, + _get_ytvis_2019_instances_meta(), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +def register_all_ytvis_2021(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS_YTVIS_2021.items(): + # Assume pre-defined datasets live in `./datasets`. + register_ytvis_instances( + key, + _get_ytvis_2021_instances_meta(), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + +def register_all_imagenet_cls_agnostic(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS_ImageNet_CLS_AGNOSTIC.items(): + # Assume pre-defined datasets live in `./datasets`. + register_ytvis_instances( + key, + _get_imagenet_cls_agnostic_instances_meta(), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + +if __name__.endswith(".builtin"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + register_all_ytvis_2019(_root) + register_all_ytvis_2021(_root) + register_all_imagenet_cls_agnostic(_root) \ No newline at end of file diff --git a/videocutler/mask2former_video/data_video/datasets/ytvis.py b/videocutler/mask2former_video/data_video/datasets/ytvis.py new file mode 100755 index 0000000000000000000000000000000000000000..69f0a8e010f70f4b2b75151c7431b0af91871edd --- /dev/null +++ b/videocutler/mask2former_video/data_video/datasets/ytvis.py @@ -0,0 +1,356 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/Mask2Former/tree/main/mask2former_video + + +import contextlib +import io +import json +import logging +import numpy as np +import os +import pycocotools.mask as mask_util +from fvcore.common.file_io import PathManager +from fvcore.common.timer import Timer + +from detectron2.structures import Boxes, BoxMode, PolygonMasks +from detectron2.data import DatasetCatalog, MetadataCatalog + +""" +This file contains functions to parse YTVIS dataset of +COCO-format annotations into dicts in "Detectron2 format". +""" + +logger = logging.getLogger(__name__) + +__all__ = ["load_ytvis_json", "register_ytvis_instances"] + + +YTVIS_CATEGORIES_2019 = [ + {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"}, + {"color": [0, 82, 0], "isthing": 1, "id": 2, "name": "giant_panda"}, + {"color": [119, 11, 32], "isthing": 1, "id": 3, "name": "lizard"}, + {"color": [165, 42, 42], "isthing": 1, "id": 4, "name": "parrot"}, + {"color": [134, 134, 103], "isthing": 1, "id": 5, "name": "skateboard"}, + {"color": [0, 0, 142], "isthing": 1, "id": 6, "name": "sedan"}, + {"color": [255, 109, 65], "isthing": 1, "id": 7, "name": "ape"}, + {"color": [0, 226, 252], "isthing": 1, "id": 8, "name": "dog"}, + {"color": [5, 121, 0], "isthing": 1, "id": 9, "name": "snake"}, + {"color": [0, 60, 100], "isthing": 1, "id": 10, "name": "monkey"}, + {"color": [250, 170, 30], "isthing": 1, "id": 11, "name": "hand"}, + {"color": [100, 170, 30], "isthing": 1, "id": 12, "name": "rabbit"}, + {"color": [179, 0, 194], "isthing": 1, "id": 13, "name": "duck"}, + {"color": [255, 77, 255], "isthing": 1, "id": 14, "name": "cat"}, + {"color": [120, 166, 157], "isthing": 1, "id": 15, "name": "cow"}, + {"color": [73, 77, 174], "isthing": 1, "id": 16, "name": "fish"}, + {"color": [0, 80, 100], "isthing": 1, "id": 17, "name": "train"}, + {"color": [182, 182, 255], "isthing": 1, "id": 18, "name": "horse"}, + {"color": [0, 143, 149], "isthing": 1, "id": 19, "name": "turtle"}, + {"color": [174, 57, 255], "isthing": 1, "id": 20, "name": "bear"}, + {"color": [0, 0, 230], "isthing": 1, "id": 21, "name": "motorbike"}, + {"color": [72, 0, 118], "isthing": 1, "id": 22, "name": "giraffe"}, + {"color": [255, 179, 240], "isthing": 1, "id": 23, "name": "leopard"}, + {"color": [0, 125, 92], "isthing": 1, "id": 24, "name": "fox"}, + {"color": [209, 0, 151], "isthing": 1, "id": 25, "name": "deer"}, + {"color": [188, 208, 182], "isthing": 1, "id": 26, "name": "owl"}, + {"color": [145, 148, 174], "isthing": 1, "id": 27, "name": "surfboard"}, + {"color": [106, 0, 228], "isthing": 1, "id": 28, "name": "airplane"}, + {"color": [0, 0, 70], "isthing": 1, "id": 29, "name": "truck"}, + {"color": [199, 100, 0], "isthing": 1, "id": 30, "name": "zebra"}, + {"color": [166, 196, 102], "isthing": 1, "id": 31, "name": "tiger"}, + {"color": [110, 76, 0], "isthing": 1, "id": 32, "name": "elephant"}, + {"color": [133, 129, 255], "isthing": 1, "id": 33, "name": "snowboard"}, + {"color": [0, 0, 192], "isthing": 1, "id": 34, "name": "boat"}, + {"color": [183, 130, 88], "isthing": 1, "id": 35, "name": "shark"}, + {"color": [130, 114, 135], "isthing": 1, "id": 36, "name": "mouse"}, + {"color": [107, 142, 35], "isthing": 1, "id": 37, "name": "frog"}, + {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "eagle"}, + {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "earless_seal"}, + {"color": [255, 208, 186], "isthing": 1, "id": 40, "name": "tennis_racket"}, +] + + +IMAGENET_CATEGORIES_cls_agnostic = [ + # {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "fg"}, + # {"color": [120, 166, 157], "isthing": 1, "id": 1, "name": "fg"}, + {"color": [73, 77, 174], "isthing": 1, "id": 1, "name": "fg"}, + # {"color": [199, 100, 0], "isthing": 1, "id": 2, "name": "fg"}, +] + + +YTVIS_CATEGORIES_2021 = [ + {"color": [106, 0, 228], "isthing": 1, "id": 1, "name": "airplane"}, + {"color": [174, 57, 255], "isthing": 1, "id": 2, "name": "bear"}, + {"color": [255, 109, 65], "isthing": 1, "id": 3, "name": "bird"}, + {"color": [0, 0, 192], "isthing": 1, "id": 4, "name": "boat"}, + {"color": [0, 0, 142], "isthing": 1, "id": 5, "name": "car"}, + {"color": [255, 77, 255], "isthing": 1, "id": 6, "name": "cat"}, + {"color": [120, 166, 157], "isthing": 1, "id": 7, "name": "cow"}, + {"color": [209, 0, 151], "isthing": 1, "id": 8, "name": "deer"}, + {"color": [0, 226, 252], "isthing": 1, "id": 9, "name": "dog"}, + {"color": [179, 0, 194], "isthing": 1, "id": 10, "name": "duck"}, + {"color": [174, 255, 243], "isthing": 1, "id": 11, "name": "earless_seal"}, + {"color": [110, 76, 0], "isthing": 1, "id": 12, "name": "elephant"}, + {"color": [73, 77, 174], "isthing": 1, "id": 13, "name": "fish"}, + {"color": [250, 170, 30], "isthing": 1, "id": 14, "name": "flying_disc"}, + {"color": [0, 125, 92], "isthing": 1, "id": 15, "name": "fox"}, + {"color": [107, 142, 35], "isthing": 1, "id": 16, "name": "frog"}, + {"color": [0, 82, 0], "isthing": 1, "id": 17, "name": "giant_panda"}, + {"color": [72, 0, 118], "isthing": 1, "id": 18, "name": "giraffe"}, + {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"}, + {"color": [255, 179, 240], "isthing": 1, "id": 20, "name": "leopard"}, + {"color": [119, 11, 32], "isthing": 1, "id": 21, "name": "lizard"}, + {"color": [0, 60, 100], "isthing": 1, "id": 22, "name": "monkey"}, + {"color": [0, 0, 230], "isthing": 1, "id": 23, "name": "motorbike"}, + {"color": [130, 114, 135], "isthing": 1, "id": 24, "name": "mouse"}, + {"color": [165, 42, 42], "isthing": 1, "id": 25, "name": "parrot"}, + {"color": [220, 20, 60], "isthing": 1, "id": 26, "name": "person"}, + {"color": [100, 170, 30], "isthing": 1, "id": 27, "name": "rabbit"}, + {"color": [183, 130, 88], "isthing": 1, "id": 28, "name": "shark"}, + {"color": [134, 134, 103], "isthing": 1, "id": 29, "name": "skateboard"}, + {"color": [5, 121, 0], "isthing": 1, "id": 30, "name": "snake"}, + {"color": [133, 129, 255], "isthing": 1, "id": 31, "name": "snowboard"}, + {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "squirrel"}, + {"color": [145, 148, 174], "isthing": 1, "id": 33, "name": "surfboard"}, + {"color": [255, 208, 186], "isthing": 1, "id": 34, "name": "tennis_racket"}, + {"color": [166, 196, 102], "isthing": 1, "id": 35, "name": "tiger"}, + {"color": [0, 80, 100], "isthing": 1, "id": 36, "name": "train"}, + {"color": [0, 0, 70], "isthing": 1, "id": 37, "name": "truck"}, + {"color": [0, 143, 149], "isthing": 1, "id": 38, "name": "turtle"}, + {"color": [0, 228, 0], "isthing": 1, "id": 39, "name": "whale"}, + {"color": [199, 100, 0], "isthing": 1, "id": 40, "name": "zebra"}, +] + + +def _get_ytvis_2019_instances_meta(): + thing_ids = [k["id"] for k in YTVIS_CATEGORIES_2019 if k["isthing"] == 1] + thing_colors = [k["color"] for k in YTVIS_CATEGORIES_2019 if k["isthing"] == 1] + assert len(thing_ids) == 40, len(thing_ids) + # Mapping from the incontiguous YTVIS category id to an id in [0, 39] + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in YTVIS_CATEGORIES_2019 if k["isthing"] == 1] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + "thing_colors": thing_colors, + } + return ret + +def _get_ytvis_2021_instances_meta(): + thing_ids = [k["id"] for k in YTVIS_CATEGORIES_2021 if k["isthing"] == 1] + thing_colors = [k["color"] for k in YTVIS_CATEGORIES_2021 if k["isthing"] == 1] + assert len(thing_ids) == 40, len(thing_ids) + # Mapping from the incontiguous YTVIS category id to an id in [0, 39] + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in YTVIS_CATEGORIES_2021 if k["isthing"] == 1] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + "thing_colors": thing_colors, + } + return ret + +def _get_imagenet_cls_agnostic_instances_meta(): + thing_ids = [k["id"] for k in IMAGENET_CATEGORIES_cls_agnostic if k["isthing"] == 1] + thing_colors = [k["color"] for k in IMAGENET_CATEGORIES_cls_agnostic if k["isthing"] == 1] + assert len(thing_ids) == 1, len(thing_ids) + # Mapping from the incontiguous YTVIS category id to an id in [0, 39] + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in IMAGENET_CATEGORIES_cls_agnostic if k["isthing"] == 1] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + "thing_colors": thing_colors, + } + return ret + +def load_ytvis_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): + from .ytvis_api.ytvos import YTVOS + + timer = Timer() + json_file = PathManager.get_local_path(json_file) + with contextlib.redirect_stdout(io.StringIO()): + ytvis_api = YTVOS(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + + id_map = None + if dataset_name is not None: + meta = MetadataCatalog.get(dataset_name) + cat_ids = sorted(ytvis_api.getCatIds()) + cats = ytvis_api.loadCats(cat_ids) + # The categories in a custom json file may not be sorted. + thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])] + meta.thing_classes = thing_classes + + # In COCO, certain category ids are artificially removed, + # and by convention they are always ignored. + # We deal with COCO's id issue and translate + # the category ids to contiguous ids in [0, 80). + + # It works by looking at the "categories" field in the json, therefore + # if users' own json also have incontiguous ids, we'll + # apply this mapping as well but print a warning. + if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): + if "coco" not in dataset_name: + logger.warning( + """ +Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. +""" + ) + id_map = {v: i for i, v in enumerate(cat_ids)} + meta.thing_dataset_id_to_contiguous_id = id_map + + # sort indices for reproducible results + vid_ids = sorted(ytvis_api.vids.keys()) + # vids is a list of dicts, each looks something like: + # {'license': 1, + # 'flickr_url': ' ', + # 'file_names': ['ff25f55852/00000.jpg', 'ff25f55852/00005.jpg', ..., 'ff25f55852/00175.jpg'], + # 'height': 720, + # 'width': 1280, + # 'length': 36, + # 'date_captured': '2019-04-11 00:55:41.903902', + # 'id': 2232} + vids = ytvis_api.loadVids(vid_ids) + + anns = [ytvis_api.vidToAnns[vid_id] for vid_id in vid_ids] + total_num_valid_anns = sum([len(x) for x in anns]) + total_num_anns = len(ytvis_api.anns) + if total_num_valid_anns < total_num_anns: + logger.warning( + f"{json_file} contains {total_num_anns} annotations, but only " + f"{total_num_valid_anns} of them match to images in the file." + ) + + vids_anns = list(zip(vids, anns)) + logger.info("Loaded {} videos in YTVIS format from {}".format(len(vids_anns), json_file)) + + dataset_dicts = [] + + ann_keys = ["iscrowd", "category_id", "id"] + (extra_annotation_keys or []) + + num_instances_without_valid_segmentation = 0 + + for (vid_dict, anno_dict_list) in vids_anns: + record = {} + record["file_names"] = [os.path.join(image_root, vid_dict["file_names"][i]) for i in range(vid_dict["length"])] + record["height"] = vid_dict["height"] + record["width"] = vid_dict["width"] + record["length"] = vid_dict["length"] + video_id = record["video_id"] = vid_dict["id"] + + video_objs = [] + for frame_idx in range(record["length"]): + frame_objs = [] + for anno in anno_dict_list: + assert anno["video_id"] == video_id + + obj = {key: anno[key] for key in ann_keys if key in anno} + + _bboxes = anno.get("bboxes", None) + _segm = anno.get("segmentations", None) + + if not (_bboxes and _segm and _bboxes[frame_idx] and _segm[frame_idx]): + continue + + bbox = _bboxes[frame_idx] + segm = _segm[frame_idx] + + obj["bbox"] = bbox + obj["bbox_mode"] = BoxMode.XYWH_ABS + + if isinstance(segm, dict): + if isinstance(segm["counts"], list): + # convert to compressed RLE + segm = mask_util.frPyObjects(segm, *segm["size"]) + elif segm: + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + num_instances_without_valid_segmentation += 1 + continue # ignore this instance + obj["segmentation"] = segm + + if id_map: + obj["category_id"] = id_map[obj["category_id"]] + frame_objs.append(obj) + video_objs.append(frame_objs) + record["annotations"] = video_objs + dataset_dicts.append(record) + + if num_instances_without_valid_segmentation > 0: + logger.warning( + "Filtered out {} instances without valid segmentation. ".format( + num_instances_without_valid_segmentation + ) + + "There might be issues in your dataset generation process. " + "A valid polygon should be a list[float] with even length >= 6." + ) + return dataset_dicts + + +def register_ytvis_instances(name, metadata, json_file, image_root): + """ + Register a dataset in YTVIS's json annotation format for + instance tracking. + + Args: + name (str): the name that identifies a dataset, e.g. "ytvis_train". + metadata (dict): extra metadata associated with this dataset. You can + leave it as an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str or path-like): directory which contains all the images. + """ + assert isinstance(name, str), name + assert isinstance(json_file, (str, os.PathLike)), json_file + assert isinstance(image_root, (str, os.PathLike)), image_root + # 1. register a function which returns dicts + DatasetCatalog.register(name, lambda: load_ytvis_json(json_file, image_root, name)) + + # 2. Optionally, add metadata about this dataset, + # since they might be useful in evaluation, visualization or logging + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="ytvis", **metadata + ) + + +if __name__ == "__main__": + """ + Test the YTVIS json dataset loader. + """ + from detectron2.utils.logger import setup_logger + from detectron2.utils.visualizer import Visualizer + import detectron2.data.datasets # noqa # add pre-defined metadata + import sys + from PIL import Image + + logger = setup_logger(name=__name__) + #assert sys.argv[3] in DatasetCatalog.list() + meta = MetadataCatalog.get("ytvis_2019_train") + + json_file = "./datasets/ytvis/instances_train_sub.json" + image_root = "./datasets/ytvis/train/JPEGImages" + dicts = load_ytvis_json(json_file, image_root, dataset_name="ytvis_2019_train") + logger.info("Done loading {} samples.".format(len(dicts))) + + dirname = "ytvis-data-vis" + os.makedirs(dirname, exist_ok=True) + + def extract_frame_dic(dic, frame_idx): + import copy + frame_dic = copy.deepcopy(dic) + annos = frame_dic.get("annotations", None) + if annos: + frame_dic["annotations"] = annos[frame_idx] + + return frame_dic + + for d in dicts: + vid_name = d["file_names"][0].split('/')[-2] + os.makedirs(os.path.join(dirname, vid_name), exist_ok=True) + for idx, file_name in enumerate(d["file_names"]): + img = np.array(Image.open(file_name)) + visualizer = Visualizer(img, metadata=meta) + vis = visualizer.draw_dataset_dict(extract_frame_dic(d, idx)) + fpath = os.path.join(dirname, vid_name, file_name.split('/')[-1]) + vis.save(fpath) diff --git a/videocutler/mask2former_video/data_video/datasets/ytvis_api/__init__.py b/videocutler/mask2former_video/data_video/datasets/ytvis_api/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d7c96243d4b8477b2c1d22bb835a7e3effd8d6b5 --- /dev/null +++ b/videocutler/mask2former_video/data_video/datasets/ytvis_api/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/youtubevos/cocoapi diff --git a/videocutler/mask2former_video/data_video/datasets/ytvis_api/ytvos.py b/videocutler/mask2former_video/data_video/datasets/ytvis_api/ytvos.py new file mode 100644 index 0000000000000000000000000000000000000000..f8f8792d40f5a692115170672295746035318e7b --- /dev/null +++ b/videocutler/mask2former_video/data_video/datasets/ytvis_api/ytvos.py @@ -0,0 +1,290 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/youtubevos/cocoapi + +__author__ = 'ychfan' +# Interface for accessing the YouTubeVIS dataset. + +# The following API functions are defined: +# YTVOS - YTVOS api class that loads YouTubeVIS annotation file and prepare data structures. +# decodeMask - Decode binary mask M encoded via run-length encoding. +# encodeMask - Encode binary mask M using run-length encoding. +# getAnnIds - Get ann ids that satisfy given filter conditions. +# getCatIds - Get cat ids that satisfy given filter conditions. +# getImgIds - Get img ids that satisfy given filter conditions. +# loadAnns - Load anns with the specified ids. +# loadCats - Load cats with the specified ids. +# loadImgs - Load imgs with the specified ids. +# annToMask - Convert segmentation in an annotation to binary mask. +# loadRes - Load algorithm results and create API for accessing them. + +# Microsoft COCO Toolbox. version 2.0 +# Data, paper, and tutorials available at: http://mscoco.org/ +# Code written by Piotr Dollar and Tsung-Yi Lin, 2014. +# Licensed under the Simplified BSD License [see bsd.txt] + +import json +import time +import matplotlib.pyplot as plt +from matplotlib.collections import PatchCollection +from matplotlib.patches import Polygon +import numpy as np +import copy +import itertools +from pycocotools import mask as maskUtils +import os +from collections import defaultdict +import sys +PYTHON_VERSION = sys.version_info[0] +if PYTHON_VERSION == 2: + from urllib import urlretrieve +elif PYTHON_VERSION == 3: + from urllib.request import urlretrieve + + +def _isArrayLike(obj): + return hasattr(obj, '__iter__') and hasattr(obj, '__len__') + + +class YTVOS: + def __init__(self, annotation_file=None): + """ + Constructor of Microsoft COCO helper class for reading and visualizing annotations. + :param annotation_file (str): location of annotation file + :param image_folder (str): location to the folder that hosts images. + :return: + """ + # load dataset + self.dataset,self.anns,self.cats,self.vids = dict(),dict(),dict(),dict() + self.vidToAnns, self.catToVids = defaultdict(list), defaultdict(list) + if not annotation_file == None: + print('loading annotations into memory...') + tic = time.time() + dataset = json.load(open(annotation_file, 'r')) + assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset)) + print('Done (t={:0.2f}s)'.format(time.time()- tic)) + self.dataset = dataset + self.createIndex() + + def createIndex(self): + # create index + print('creating index...') + anns, cats, vids = {}, {}, {} + vidToAnns,catToVids = defaultdict(list),defaultdict(list) + if 'annotations' in self.dataset: + for ann in self.dataset['annotations']: + vidToAnns[ann['video_id']].append(ann) + anns[ann['id']] = ann + + if 'videos' in self.dataset: + for vid in self.dataset['videos']: + vids[vid['id']] = vid + + if 'categories' in self.dataset: + for cat in self.dataset['categories']: + cats[cat['id']] = cat + + if 'annotations' in self.dataset and 'categories' in self.dataset: + for ann in self.dataset['annotations']: + catToVids[ann['category_id']].append(ann['video_id']) + + print('index created!') + + # create class members + self.anns = anns + self.vidToAnns = vidToAnns + self.catToVids = catToVids + self.vids = vids + self.cats = cats + + def info(self): + """ + Print information about the annotation file. + :return: + """ + for key, value in self.dataset['info'].items(): + print('{}: {}'.format(key, value)) + + def getAnnIds(self, vidIds=[], catIds=[], areaRng=[], iscrowd=None): + """ + Get ann ids that satisfy given filter conditions. default skips that filter + :param vidIds (int array) : get anns for given vids + catIds (int array) : get anns for given cats + areaRng (float array) : get anns for given area range (e.g. [0 inf]) + iscrowd (boolean) : get anns for given crowd label (False or True) + :return: ids (int array) : integer array of ann ids + """ + vidIds = vidIds if _isArrayLike(vidIds) else [vidIds] + catIds = catIds if _isArrayLike(catIds) else [catIds] + + if len(vidIds) == len(catIds) == len(areaRng) == 0: + anns = self.dataset['annotations'] + else: + if not len(vidIds) == 0: + lists = [self.vidToAnns[vidId] for vidId in vidIds if vidId in self.vidToAnns] + anns = list(itertools.chain.from_iterable(lists)) + else: + anns = self.dataset['annotations'] + anns = anns if len(catIds) == 0 else [ann for ann in anns if ann['category_id'] in catIds] + anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['avg_area'] > areaRng[0] and ann['avg_area'] < areaRng[1]] + if not iscrowd == None: + ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd] + else: + ids = [ann['id'] for ann in anns] + return ids + + def getCatIds(self, catNms=[], supNms=[], catIds=[]): + """ + filtering parameters. default skips that filter. + :param catNms (str array) : get cats for given cat names + :param supNms (str array) : get cats for given supercategory names + :param catIds (int array) : get cats for given cat ids + :return: ids (int array) : integer array of cat ids + """ + catNms = catNms if _isArrayLike(catNms) else [catNms] + supNms = supNms if _isArrayLike(supNms) else [supNms] + catIds = catIds if _isArrayLike(catIds) else [catIds] + + if len(catNms) == len(supNms) == len(catIds) == 0: + cats = self.dataset['categories'] + else: + cats = self.dataset['categories'] + cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name'] in catNms] + cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms] + cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id'] in catIds] + ids = [cat['id'] for cat in cats] + return ids + + def getVidIds(self, vidIds=[], catIds=[]): + ''' + Get vid ids that satisfy given filter conditions. + :param vidIds (int array) : get vids for given ids + :param catIds (int array) : get vids with all given cats + :return: ids (int array) : integer array of vid ids + ''' + vidIds = vidIds if _isArrayLike(vidIds) else [vidIds] + catIds = catIds if _isArrayLike(catIds) else [catIds] + + if len(vidIds) == len(catIds) == 0: + ids = self.vids.keys() + else: + ids = set(vidIds) + for i, catId in enumerate(catIds): + if i == 0 and len(ids) == 0: + ids = set(self.catToVids[catId]) + else: + ids &= set(self.catToVids[catId]) + return list(ids) + + def loadAnns(self, ids=[]): + """ + Load anns with the specified ids. + :param ids (int array) : integer ids specifying anns + :return: anns (object array) : loaded ann objects + """ + if _isArrayLike(ids): + return [self.anns[id] for id in ids] + elif type(ids) == int: + return [self.anns[ids]] + + def loadCats(self, ids=[]): + """ + Load cats with the specified ids. + :param ids (int array) : integer ids specifying cats + :return: cats (object array) : loaded cat objects + """ + if _isArrayLike(ids): + return [self.cats[id] for id in ids] + elif type(ids) == int: + return [self.cats[ids]] + + def loadVids(self, ids=[]): + """ + Load anns with the specified ids. + :param ids (int array) : integer ids specifying vid + :return: vids (object array) : loaded vid objects + """ + if _isArrayLike(ids): + return [self.vids[id] for id in ids] + elif type(ids) == int: + return [self.vids[ids]] + + + def loadRes(self, resFile): + """ + Load result file and return a result api object. + :param resFile (str) : file name of result file + :return: res (obj) : result api object + """ + res = YTVOS() + res.dataset['videos'] = [img for img in self.dataset['videos']] + + print('Loading and preparing results...') + tic = time.time() + if type(resFile) == str or (PYTHON_VERSION == 2 and type(resFile) == unicode): + anns = json.load(open(resFile)) + elif type(resFile) == np.ndarray: + anns = self.loadNumpyAnnotations(resFile) + else: + anns = resFile + assert type(anns) == list, 'results in not an array of objects' + annsVidIds = [ann['video_id'] for ann in anns] + assert set(annsVidIds) == (set(annsVidIds) & set(self.getVidIds())), \ + 'Results do not correspond to current coco set' + if 'segmentations' in anns[0]: + res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) + for id, ann in enumerate(anns): + ann['areas'] = [] + if not 'bboxes' in ann: + ann['bboxes'] = [] + for seg in ann['segmentations']: + # now only support compressed RLE format as segmentation results + if seg: + ann['areas'].append(maskUtils.area(seg)) + if len(ann['bboxes']) < len(ann['areas']): + ann['bboxes'].append(maskUtils.toBbox(seg)) + else: + ann['areas'].append(None) + if len(ann['bboxes']) < len(ann['areas']): + ann['bboxes'].append(None) + ann['id'] = id+1 + l = [a for a in ann['areas'] if a] + if len(l)==0: + ann['avg_area'] = 0 + else: + ann['avg_area'] = np.array(l).mean() + ann['iscrowd'] = 0 + print('DONE (t={:0.2f}s)'.format(time.time()- tic)) + + res.dataset['annotations'] = anns + res.createIndex() + return res + + def annToRLE(self, ann, frameId): + """ + Convert annotation which can be polygons, uncompressed RLE to RLE. + :return: binary mask (numpy 2D array) + """ + t = self.vids[ann['video_id']] + h, w = t['height'], t['width'] + segm = ann['segmentations'][frameId] + if type(segm) == list: + # polygon -- a single object might consist of multiple parts + # we merge all parts into one mask rle code + rles = maskUtils.frPyObjects(segm, h, w) + rle = maskUtils.merge(rles) + elif type(segm['counts']) == list: + # uncompressed RLE + rle = maskUtils.frPyObjects(segm, h, w) + else: + # rle + rle = segm + return rle + + def annToMask(self, ann, frameId): + """ + Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask. + :return: binary mask (numpy 2D array) + """ + rle = self.annToRLE(ann, frameId) + m = maskUtils.decode(rle) + return m diff --git a/videocutler/mask2former_video/data_video/datasets/ytvis_api/ytvoseval.py b/videocutler/mask2former_video/data_video/datasets/ytvis_api/ytvoseval.py new file mode 100644 index 0000000000000000000000000000000000000000..9511d830af806d49554ecb00dc89f4a6469a0da8 --- /dev/null +++ b/videocutler/mask2former_video/data_video/datasets/ytvis_api/ytvoseval.py @@ -0,0 +1,567 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/youtubevos/cocoapi + +__author__ = 'ychfan' + +import numpy as np +import datetime +import time +from collections import defaultdict +from pycocotools import mask as maskUtils +import copy + +class YTVOSeval: + # Interface for evaluating video instance segmentation on the YouTubeVIS dataset. + # + # The usage for YTVOSeval is as follows: + # cocoGt=..., cocoDt=... # load dataset and results + # E = YTVOSeval(cocoGt,cocoDt); # initialize YTVOSeval object + # E.params.recThrs = ...; # set parameters as desired + # E.evaluate(); # run per image evaluation + # E.accumulate(); # accumulate per image results + # E.summarize(); # display summary metrics of results + # For example usage see evalDemo.m and http://mscoco.org/. + # + # The evaluation parameters are as follows (defaults in brackets): + # imgIds - [all] N img ids to use for evaluation + # catIds - [all] K cat ids to use for evaluation + # iouThrs - [.5:.05:.95] T=10 IoU thresholds for evaluation + # recThrs - [0:.01:1] R=101 recall thresholds for evaluation + # areaRng - [...] A=4 object area ranges for evaluation + # maxDets - [1 10 100] M=3 thresholds on max detections per image + # iouType - ['segm'] set iouType to 'segm', 'bbox' or 'keypoints' + # iouType replaced the now DEPRECATED useSegm parameter. + # useCats - [1] if true use category labels for evaluation + # Note: if useCats=0 category labels are ignored as in proposal scoring. + # Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified. + # + # evaluate(): evaluates detections on every image and every category and + # concats the results into the "evalImgs" with fields: + # dtIds - [1xD] id for each of the D detections (dt) + # gtIds - [1xG] id for each of the G ground truths (gt) + # dtMatches - [TxD] matching gt id at each IoU or 0 + # gtMatches - [TxG] matching dt id at each IoU or 0 + # dtScores - [1xD] confidence of each dt + # gtIgnore - [1xG] ignore flag for each gt + # dtIgnore - [TxD] ignore flag for each dt at each IoU + # + # accumulate(): accumulates the per-image, per-category evaluation + # results in "evalImgs" into the dictionary "eval" with fields: + # params - parameters used for evaluation + # date - date evaluation was performed + # counts - [T,R,K,A,M] parameter dimensions (see above) + # precision - [TxRxKxAxM] precision for every evaluation setting + # recall - [TxKxAxM] max recall for every evaluation setting + # Note: precision and recall==-1 for settings with no gt objects. + # + # See also coco, mask, pycocoDemo, pycocoEvalDemo + # + # Microsoft COCO Toolbox. version 2.0 + # Data, paper, and tutorials available at: http://mscoco.org/ + # Code written by Piotr Dollar and Tsung-Yi Lin, 2015. + # Licensed under the Simplified BSD License [see coco/license.txt] + def __init__(self, cocoGt=None, cocoDt=None, iouType='segm'): + ''' + Initialize CocoEval using coco APIs for gt and dt + :param cocoGt: coco object with ground truth annotations + :param cocoDt: coco object with detection results + :return: None + ''' + if not iouType: + print('iouType not specified. use default iouType segm') + self.cocoGt = cocoGt # ground truth COCO API + self.cocoDt = cocoDt # detections COCO API + self.params = {} # evaluation parameters + self.evalVids = defaultdict(list) # per-image per-category evaluation results [KxAxI] elements + self.eval = {} # accumulated evaluation results + self._gts = defaultdict(list) # gt for evaluation + self._dts = defaultdict(list) # dt for evaluation + self.params = Params(iouType=iouType) # parameters + self._paramsEval = {} # parameters for evaluation + self.stats = [] # result summarization + self.ious = {} # ious between all gts and dts + if not cocoGt is None: + self.params.vidIds = sorted(cocoGt.getVidIds()) + self.params.catIds = sorted(cocoGt.getCatIds()) + + + def _prepare(self): + ''' + Prepare ._gts and ._dts for evaluation based on params + :return: None + ''' + def _toMask(anns, coco): + # modify ann['segmentation'] by reference + for ann in anns: + for i, a in enumerate(ann['segmentations']): + if a: + rle = coco.annToRLE(ann, i) + ann['segmentations'][i] = rle + l = [a for a in ann['areas'] if a] + if len(l)==0: + ann['avg_area'] = 0 + else: + ann['avg_area'] = np.array(l).mean() + p = self.params + if p.useCats: + gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(vidIds=p.vidIds, catIds=p.catIds)) + dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(vidIds=p.vidIds, catIds=p.catIds)) + else: + gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(vidIds=p.vidIds)) + dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(vidIds=p.vidIds)) + + # convert ground truth to mask if iouType == 'segm' + if p.iouType == 'segm': + _toMask(gts, self.cocoGt) + _toMask(dts, self.cocoDt) + # set ignore flag + for gt in gts: + gt['ignore'] = gt['ignore'] if 'ignore' in gt else 0 + gt['ignore'] = 'iscrowd' in gt and gt['iscrowd'] + if p.iouType == 'keypoints': + gt['ignore'] = (gt['num_keypoints'] == 0) or gt['ignore'] + self._gts = defaultdict(list) # gt for evaluation + self._dts = defaultdict(list) # dt for evaluation + for gt in gts: + self._gts[gt['video_id'], gt['category_id']].append(gt) + for dt in dts: + self._dts[dt['video_id'], dt['category_id']].append(dt) + self.evalVids = defaultdict(list) # per-image per-category evaluation results + self.eval = {} # accumulated evaluation results + + def evaluate(self): + ''' + Run per image evaluation on given images and store results (a list of dict) in self.evalVids + :return: None + ''' + tic = time.time() + print('Running per image evaluation...') + p = self.params + # add backward compatibility if useSegm is specified in params + if not p.useSegm is None: + p.iouType = 'segm' if p.useSegm == 1 else 'bbox' + print('useSegm (deprecated) is not None. Running {} evaluation'.format(p.iouType)) + print('Evaluate annotation type *{}*'.format(p.iouType)) + p.vidIds = list(np.unique(p.vidIds)) + if p.useCats: + p.catIds = list(np.unique(p.catIds)) + p.maxDets = sorted(p.maxDets) + self.params=p + + self._prepare() + # loop through images, area range, max detection number + catIds = p.catIds if p.useCats else [-1] + + if p.iouType == 'segm' or p.iouType == 'bbox': + computeIoU = self.computeIoU + elif p.iouType == 'keypoints': + computeIoU = self.computeOks + self.ious = {(vidId, catId): computeIoU(vidId, catId) \ + for vidId in p.vidIds + for catId in catIds} + + evaluateVid = self.evaluateVid + maxDet = p.maxDets[-1] + + + self.evalImgs = [evaluateVid(vidId, catId, areaRng, maxDet) + for catId in catIds + for areaRng in p.areaRng + for vidId in p.vidIds + ] + self._paramsEval = copy.deepcopy(self.params) + toc = time.time() + print('DONE (t={:0.2f}s).'.format(toc-tic)) + + def computeIoU(self, vidId, catId): + p = self.params + if p.useCats: + gt = self._gts[vidId,catId] + dt = self._dts[vidId,catId] + else: + gt = [_ for cId in p.catIds for _ in self._gts[vidId,cId]] + dt = [_ for cId in p.catIds for _ in self._dts[vidId,cId]] + if len(gt) == 0 and len(dt) ==0: + return [] + inds = np.argsort([-d['score'] for d in dt], kind='mergesort') + dt = [dt[i] for i in inds] + if len(dt) > p.maxDets[-1]: + dt=dt[0:p.maxDets[-1]] + + if p.iouType == 'segm': + g = [g['segmentations'] for g in gt] + d = [d['segmentations'] for d in dt] + elif p.iouType == 'bbox': + g = [g['bboxes'] for g in gt] + d = [d['bboxes'] for d in dt] + else: + raise Exception('unknown iouType for iou computation') + + # compute iou between each dt and gt region + iscrowd = [int(o['iscrowd']) for o in gt] + #ious = maskUtils.iou(d,g,iscrowd) + def iou_seq(d_seq, g_seq): + i = .0 + u = .0 + for d, g in zip(d_seq, g_seq): + if d and g: + i += maskUtils.area(maskUtils.merge([d, g], True)) + u += maskUtils.area(maskUtils.merge([d, g], False)) + elif not d and g: + u += maskUtils.area(g) + elif d and not g: + u += maskUtils.area(d) + if not u > .0: + print("Mask sizes in video {} and category {} may not match!".format(vidId, catId)) + iou = i / u if u > .0 else .0 + return iou + ious = np.zeros([len(d), len(g)]) + for i, j in np.ndindex(ious.shape): + ious[i, j] = iou_seq(d[i], g[j]) + #print(vidId, catId, ious.shape, ious) + return ious + + def computeOks(self, imgId, catId): + p = self.params + # dimention here should be Nxm + gts = self._gts[imgId, catId] + dts = self._dts[imgId, catId] + inds = np.argsort([-d['score'] for d in dts], kind='mergesort') + dts = [dts[i] for i in inds] + if len(dts) > p.maxDets[-1]: + dts = dts[0:p.maxDets[-1]] + # if len(gts) == 0 and len(dts) == 0: + if len(gts) == 0 or len(dts) == 0: + return [] + ious = np.zeros((len(dts), len(gts))) + sigmas = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62,.62, 1.07, 1.07, .87, .87, .89, .89])/10.0 + vars = (sigmas * 2)**2 + k = len(sigmas) + # compute oks between each detection and ground truth object + for j, gt in enumerate(gts): + # create bounds for ignore regions(double the gt bbox) + g = np.array(gt['keypoints']) + xg = g[0::3]; yg = g[1::3]; vg = g[2::3] + k1 = np.count_nonzero(vg > 0) + bb = gt['bbox'] + x0 = bb[0] - bb[2]; x1 = bb[0] + bb[2] * 2 + y0 = bb[1] - bb[3]; y1 = bb[1] + bb[3] * 2 + for i, dt in enumerate(dts): + d = np.array(dt['keypoints']) + xd = d[0::3]; yd = d[1::3] + if k1>0: + # measure the per-keypoint distance if keypoints visible + dx = xd - xg + dy = yd - yg + else: + # measure minimum distance to keypoints in (x0,y0) & (x1,y1) + z = np.zeros((k)) + dx = np.max((z, x0-xd),axis=0)+np.max((z, xd-x1),axis=0) + dy = np.max((z, y0-yd),axis=0)+np.max((z, yd-y1),axis=0) + e = (dx**2 + dy**2) / vars / (gt['avg_area']+np.spacing(1)) / 2 + if k1 > 0: + e=e[vg > 0] + ious[i, j] = np.sum(np.exp(-e)) / e.shape[0] + return ious + + def evaluateVid(self, vidId, catId, aRng, maxDet): + ''' + perform evaluation for single category and image + :return: dict (single image results) + ''' + p = self.params + if p.useCats: + gt = self._gts[vidId,catId] + dt = self._dts[vidId,catId] + else: + gt = [_ for cId in p.catIds for _ in self._gts[vidId,cId]] + dt = [_ for cId in p.catIds for _ in self._dts[vidId,cId]] + if len(gt) == 0 and len(dt) ==0: + return None + + for g in gt: + if g['ignore'] or (g['avg_area']aRng[1]): + g['_ignore'] = 1 + else: + g['_ignore'] = 0 + + # sort dt highest score first, sort gt ignore last + gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort') + gt = [gt[i] for i in gtind] + dtind = np.argsort([-d['score'] for d in dt], kind='mergesort') + dt = [dt[i] for i in dtind[0:maxDet]] + iscrowd = [int(o['iscrowd']) for o in gt] + # load computed ious + ious = self.ious[vidId, catId][:, gtind] if len(self.ious[vidId, catId]) > 0 else self.ious[vidId, catId] + + T = len(p.iouThrs) + G = len(gt) + D = len(dt) + gtm = np.zeros((T,G)) + dtm = np.zeros((T,D)) + gtIg = np.array([g['_ignore'] for g in gt]) + dtIg = np.zeros((T,D)) + if not len(ious)==0: + for tind, t in enumerate(p.iouThrs): + for dind, d in enumerate(dt): + # information about best match so far (m=-1 -> unmatched) + iou = min([t,1-1e-10]) + m = -1 + for gind, g in enumerate(gt): + # if this gt already matched, and not a crowd, continue + if gtm[tind,gind]>0 and not iscrowd[gind]: + continue + # if dt matched to reg gt, and on ignore gt, stop + if m>-1 and gtIg[m]==0 and gtIg[gind]==1: + break + # continue to next gt unless better match made + if ious[dind,gind] < iou: + continue + # if match successful and best so far, store appropriately + iou=ious[dind,gind] + m=gind + # if match made store id of match for both dt and gt + if m ==-1: + continue + dtIg[tind,dind] = gtIg[m] + dtm[tind,dind] = gt[m]['id'] + gtm[tind,m] = d['id'] + # set unmatched detections outside of area range to ignore + a = np.array([d['avg_area']aRng[1] for d in dt]).reshape((1, len(dt))) + dtIg = np.logical_or(dtIg, np.logical_and(dtm==0, np.repeat(a,T,0))) + # store results for given image and category + return { + 'video_id': vidId, + 'category_id': catId, + 'aRng': aRng, + 'maxDet': maxDet, + 'dtIds': [d['id'] for d in dt], + 'gtIds': [g['id'] for g in gt], + 'dtMatches': dtm, + 'gtMatches': gtm, + 'dtScores': [d['score'] for d in dt], + 'gtIgnore': gtIg, + 'dtIgnore': dtIg, + } + + def accumulate(self, p = None): + ''' + Accumulate per image evaluation results and store the result in self.eval + :param p: input params for evaluation + :return: None + ''' + print('Accumulating evaluation results...') + tic = time.time() + if not self.evalImgs: + print('Please run evaluate() first') + # allows input customized parameters + if p is None: + p = self.params + p.catIds = p.catIds if p.useCats == 1 else [-1] + T = len(p.iouThrs) + R = len(p.recThrs) + K = len(p.catIds) if p.useCats else 1 + A = len(p.areaRng) + M = len(p.maxDets) + precision = -np.ones((T,R,K,A,M)) # -1 for the precision of absent categories + recall = -np.ones((T,K,A,M)) + scores = -np.ones((T,R,K,A,M)) + + # create dictionary for future indexing + _pe = self._paramsEval + catIds = _pe.catIds if _pe.useCats else [-1] + setK = set(catIds) + setA = set(map(tuple, _pe.areaRng)) + setM = set(_pe.maxDets) + setI = set(_pe.vidIds) + # get inds to evaluate + k_list = [n for n, k in enumerate(p.catIds) if k in setK] + m_list = [m for n, m in enumerate(p.maxDets) if m in setM] + a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA] + i_list = [n for n, i in enumerate(p.vidIds) if i in setI] + I0 = len(_pe.vidIds) + A0 = len(_pe.areaRng) + # retrieve E at each category, area range, and max number of detections + for k, k0 in enumerate(k_list): + Nk = k0*A0*I0 + for a, a0 in enumerate(a_list): + Na = a0*I0 + for m, maxDet in enumerate(m_list): + E = [self.evalImgs[Nk + Na + i] for i in i_list] + E = [e for e in E if not e is None] + if len(E) == 0: + continue + dtScores = np.concatenate([e['dtScores'][0:maxDet] for e in E]) + + # different sorting method generates slightly different results. + # mergesort is used to be consistent as Matlab implementation. + inds = np.argsort(-dtScores, kind='mergesort') + dtScoresSorted = dtScores[inds] + + dtm = np.concatenate([e['dtMatches'][:,0:maxDet] for e in E], axis=1)[:,inds] + dtIg = np.concatenate([e['dtIgnore'][:,0:maxDet] for e in E], axis=1)[:,inds] + gtIg = np.concatenate([e['gtIgnore'] for e in E]) + npig = np.count_nonzero(gtIg==0 ) + if npig == 0: + continue + tps = np.logical_and( dtm, np.logical_not(dtIg) ) + fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) ) + + tp_sum = np.cumsum(tps, axis=1).astype(dtype=float) + fp_sum = np.cumsum(fps, axis=1).astype(dtype=float) + for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): + tp = np.array(tp) + fp = np.array(fp) + nd = len(tp) + rc = tp / npig + pr = tp / (fp+tp+np.spacing(1)) + q = np.zeros((R,)) + ss = np.zeros((R,)) + + if nd: + recall[t,k,a,m] = rc[-1] + else: + recall[t,k,a,m] = 0 + + # numpy is slow without cython optimization for accessing elements + # use python array gets significant speed improvement + pr = pr.tolist(); q = q.tolist() + + for i in range(nd-1, 0, -1): + if pr[i] > pr[i-1]: + pr[i-1] = pr[i] + + inds = np.searchsorted(rc, p.recThrs, side='left') + try: + for ri, pi in enumerate(inds): + q[ri] = pr[pi] + ss[ri] = dtScoresSorted[pi] + except: + pass + precision[t,:,k,a,m] = np.array(q) + scores[t,:,k,a,m] = np.array(ss) + self.eval = { + 'params': p, + 'counts': [T, R, K, A, M], + 'date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), + 'precision': precision, + 'recall': recall, + 'scores': scores, + } + toc = time.time() + print('DONE (t={:0.2f}s).'.format( toc-tic)) + + def summarize(self): + ''' + Compute and display summary metrics for evaluation results. + Note this functin can *only* be applied on the default parameter setting + ''' + def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ): + p = self.params + iStr = ' {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}' + titleStr = 'Average Precision' if ap == 1 else 'Average Recall' + typeStr = '(AP)' if ap==1 else '(AR)' + iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \ + if iouThr is None else '{:0.2f}'.format(iouThr) + + aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] + mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] + if ap == 1: + # dimension of precision: [TxRxKxAxM] + s = self.eval['precision'] + # IoU + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:,:,:,aind,mind] + else: + # dimension of recall: [TxKxAxM] + s = self.eval['recall'] + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:,:,aind,mind] + if len(s[s>-1])==0: + mean_s = -1 + else: + mean_s = np.mean(s[s>-1]) + print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)) + return mean_s + def _summarizeDets(): + stats = np.zeros((12,)) + stats[0] = _summarize(1) + stats[1] = _summarize(1, iouThr=.5, maxDets=self.params.maxDets[2]) + stats[2] = _summarize(1, iouThr=.75, maxDets=self.params.maxDets[2]) + stats[3] = _summarize(1, areaRng='small', maxDets=self.params.maxDets[2]) + stats[4] = _summarize(1, areaRng='medium', maxDets=self.params.maxDets[2]) + stats[5] = _summarize(1, areaRng='large', maxDets=self.params.maxDets[2]) + stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) + stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) + stats[9] = _summarize(0, areaRng='small', maxDets=self.params.maxDets[2]) + stats[10] = _summarize(0, areaRng='medium', maxDets=self.params.maxDets[2]) + stats[11] = _summarize(0, areaRng='large', maxDets=self.params.maxDets[2]) + return stats + def _summarizeKps(): + stats = np.zeros((10,)) + stats[0] = _summarize(1, maxDets=20) + stats[1] = _summarize(1, maxDets=20, iouThr=.5) + stats[2] = _summarize(1, maxDets=20, iouThr=.75) + stats[3] = _summarize(1, maxDets=20, areaRng='medium') + stats[4] = _summarize(1, maxDets=20, areaRng='large') + stats[5] = _summarize(0, maxDets=20) + stats[6] = _summarize(0, maxDets=20, iouThr=.5) + stats[7] = _summarize(0, maxDets=20, iouThr=.75) + stats[8] = _summarize(0, maxDets=20, areaRng='medium') + stats[9] = _summarize(0, maxDets=20, areaRng='large') + return stats + if not self.eval: + raise Exception('Please run accumulate() first') + iouType = self.params.iouType + if iouType == 'segm' or iouType == 'bbox': + summarize = _summarizeDets + elif iouType == 'keypoints': + summarize = _summarizeKps + self.stats = summarize() + + def __str__(self): + self.summarize() + +class Params: + ''' + Params for coco evaluation api + ''' + def setDetParams(self): + self.vidIds = [] + self.catIds = [] + # np.arange causes trouble. the data point on arange is slightly larger than the true value + #self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True) + #self.recThrs = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True) + self.iouThrs = np.linspace(.5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True) + self.recThrs = np.linspace(.0, 1.00, int(np.round((1.00 - .0) / .01)) + 1, endpoint=True) + self.maxDets = [1, 10, 100] + self.areaRng = [[0 ** 2, 1e5 ** 2], [0 ** 2, 128 ** 2], [ 128 ** 2, 256 ** 2], [256 ** 2, 1e5 ** 2]] + self.areaRngLbl = ['all', 'small', 'medium', 'large'] + self.useCats = 1 + + def setKpParams(self): + self.vidIds = [] + self.catIds = [] + # np.arange causes trouble. the data point on arange is slightly larger than the true value + self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True) + self.recThrs = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True) + self.maxDets = [20] + self.areaRng = [[0 ** 2, 1e5 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]] + self.areaRngLbl = ['all', 'medium', 'large'] + self.useCats = 1 + + def __init__(self, iouType='segm'): + if iouType == 'segm' or iouType == 'bbox': + self.setDetParams() + elif iouType == 'keypoints': + self.setKpParams() + else: + raise Exception('iouType not supported') + self.iouType = iouType + # useSegm is deprecated + self.useSegm = None diff --git a/videocutler/mask2former_video/data_video/ytvis_eval.py b/videocutler/mask2former_video/data_video/ytvis_eval.py new file mode 100755 index 0000000000000000000000000000000000000000..8d977ef9e639a4c0734a57cc75d34da7d22f7c5d --- /dev/null +++ b/videocutler/mask2former_video/data_video/ytvis_eval.py @@ -0,0 +1,327 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/sukjunhwang/IFC + +import contextlib +import copy +import io +import itertools +import json +import logging +import numpy as np +import os +from collections import OrderedDict +import pycocotools.mask as mask_util +import torch +from .datasets.ytvis_api.ytvos import YTVOS +from .datasets.ytvis_api.ytvoseval import YTVOSeval +from tabulate import tabulate + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.evaluation import DatasetEvaluator +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table + + +class YTVISEvaluator(DatasetEvaluator): + """ + Evaluate AR for object proposals, AP for instance detection/segmentation, AP + for keypoint detection outputs using COCO's metrics. + See http://cocodataset.org/#detection-eval and + http://cocodataset.org/#keypoints-eval to understand its metrics. + + In addition to COCO, this evaluator is able to support any bounding box detection, + instance segmentation, or keypoint detection dataset. + """ + + def __init__( + self, + dataset_name, + tasks=None, + distributed=True, + output_dir=None, + *, + use_fast_impl=True, + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have either the following corresponding metadata: + + "json_file": the path to the COCO format annotation + + Or it must be in detectron2's standard dataset format + so it can be converted to COCO format automatically. + tasks (tuple[str]): tasks that can be evaluated under the given + configuration. A task is one of "bbox", "segm", "keypoints". + By default, will infer this automatically from predictions. + distributed (True): if True, will collect results from all ranks and run evaluation + in the main process. + Otherwise, will only evaluate the results in the current process. + output_dir (str): optional, an output directory to dump all + results predicted on the dataset. The dump contains two files: + + 1. "instances_predictions.pth" a file in torch serialization + format that contains all the raw original predictions. + 2. "coco_instances_results.json" a json file in COCO's result + format. + use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP. + Although the results should be very close to the official implementation in COCO + API, it is still recommended to compute results with the official API for use in + papers. The faster implementation also uses more RAM. + """ + self._logger = logging.getLogger(__name__) + self._distributed = distributed + self._output_dir = output_dir + self._use_fast_impl = use_fast_impl + + if tasks is not None and isinstance(tasks, CfgNode): + self._logger.warning( + "COCO Evaluator instantiated using config, this is deprecated behavior." + " Please pass in explicit arguments instead." + ) + self._tasks = None # Infering it from predictions should be better + else: + self._tasks = tasks + + self._cpu_device = torch.device("cpu") + + self._metadata = MetadataCatalog.get(dataset_name) + + json_file = PathManager.get_local_path(self._metadata.json_file) + with contextlib.redirect_stdout(io.StringIO()): + self._ytvis_api = YTVOS(json_file) + + # Test set json files do not contain annotations (evaluation must be + # performed using the COCO evaluation server). + self._do_evaluation = "annotations" in self._ytvis_api.dataset + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + prediction = instances_to_coco_json_video(inputs, outputs) + self._predictions.extend(prediction) + + def evaluate(self): + """ + Args: + img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset + """ + if self._distributed: + comm.synchronize() + predictions = comm.gather(self._predictions, dst=0) + predictions = list(itertools.chain(*predictions)) + + if not comm.is_main_process(): + return {} + else: + predictions = self._predictions + + if len(predictions) == 0: + self._logger.warning("[COCOEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "instances_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(predictions, f) + + self._results = OrderedDict() + self._eval_predictions(predictions) + # Copy so the caller can do whatever with results + return copy.deepcopy(self._results) + + def _eval_predictions(self, predictions): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for YTVIS format ...") + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id + all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) + num_classes = len(all_contiguous_ids) + assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 + + reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} + for result in predictions: + category_id = result["category_id"] + assert category_id < num_classes, ( + f"A prediction has class={category_id}, " + f"but the dataset only has {num_classes} classes and " + f"predicted class id should be in [0, {num_classes - 1}]." + ) + result["category_id"] = reverse_id_mapping[category_id] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(predictions)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + coco_eval = ( + _evaluate_predictions_on_coco( + self._ytvis_api, + predictions, + ) + if len(predictions) > 0 + else None # cocoapi does not handle empty results very well + ) + + res = self._derive_coco_results( + coco_eval, class_names=self._metadata.get("thing_classes") + ) + self._results["segm"] = res + + def _derive_coco_results(self, coco_eval, class_names=None): + """ + Derive the desired score numbers from summarized COCOeval. + Args: + coco_eval (None or COCOEval): None represents no predictions from model. + iou_type (str): + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + Returns: + a dict of {metric name: score} + """ + + metrics = ["AP", "AP50", "AP75", "APs", "APm", "APl", "AR1", "AR10"] + + if coco_eval is None: + self._logger.warn("No predictions from the model!") + return {metric: float("nan") for metric in metrics} + + # the standard metrics + results = { + metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan") + for idx, metric in enumerate(metrics) + } + self._logger.info( + "Evaluation results for {}: \n".format("segm") + create_small_table(results) + ) + if not np.isfinite(sum(results.values())): + self._logger.info("Some metrics cannot be computed and is shown as NaN.") + + if class_names is None or len(class_names) <= 1: + return results + # Compute per-category AP + # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa + precisions = coco_eval.eval["precision"] + # precision has dims (iou, recall, cls, area range, max dets) + assert len(class_names) == precisions.shape[2] + + results_per_category = [] + for idx, name in enumerate(class_names): + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + ap = np.mean(precision) if precision.size else float("nan") + results_per_category.append(("{}".format(name), float(ap * 100))) + + # tabulate it + N_COLS = min(6, len(results_per_category) * 2) + results_flatten = list(itertools.chain(*results_per_category)) + results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + results_2d, + tablefmt="pipe", + floatfmt=".3f", + headers=["category", "AP"] * (N_COLS // 2), + numalign="left", + ) + self._logger.info("Per-category {} AP: \n".format("segm") + table) + + results.update({"AP-" + name: ap for name, ap in results_per_category}) + return results + + +def instances_to_coco_json_video(inputs, outputs): + """ + Dump an "Instances" object to a COCO-format json that's used for evaluation. + + Args: + instances (Instances): + video_id (int): the image id + + Returns: + list[dict]: list of json annotations in COCO format. + """ + assert len(inputs) == 1, "More than one inputs are loaded for inference!" + + video_id = inputs[0]["video_id"] + video_length = inputs[0]["length"] + + scores = outputs["pred_scores"] + labels = outputs["pred_labels"] + masks = outputs["pred_masks"] + + ytvis_results = [] + for instance_id, (s, l, m) in enumerate(zip(scores, labels, masks)): + segms = [ + mask_util.encode(np.array(_mask[:, :, None], order="F", dtype="uint8"))[0] + for _mask in m + ] + for rle in segms: + rle["counts"] = rle["counts"].decode("utf-8") + + res = { + "video_id": video_id, + "score": s, + "category_id": l, + "segmentations": segms, + } + ytvis_results.append(res) + + return ytvis_results + + +def _evaluate_predictions_on_coco( + coco_gt, + coco_results, + img_ids=None, +): + """ + Evaluate the coco results using COCOEval API. + """ + assert len(coco_results) > 0 + + coco_results = copy.deepcopy(coco_results) + # When evaluating mask AP, if the results contain bbox, cocoapi will + # use the box area as the area of the instance, instead of the mask area. + # This leads to a different definition of small/medium/large. + # We remove the bbox field to let mask AP use mask area. + for c in coco_results: + c.pop("bbox", None) + + coco_dt = coco_gt.loadRes(coco_results) + coco_eval = YTVOSeval(coco_gt, coco_dt) + # For COCO, the default max_dets_per_image is [1, 10, 100]. + max_dets_per_image = [1, 10, 100] # Default from COCOEval + coco_eval.params.maxDets = max_dets_per_image + + if img_ids is not None: + coco_eval.params.imgIds = img_ids + + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + + return coco_eval diff --git a/videocutler/mask2former_video/engine/__init__.py b/videocutler/mask2former_video/engine/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..02b5fa56466a1b5ee44fd5adeda393ef0c73d8ba --- /dev/null +++ b/videocutler/mask2former_video/engine/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. + +from .train_loop import * + +__all__ = [k for k in globals().keys() if not k.startswith("_")] + +from .defaults import * \ No newline at end of file diff --git a/videocutler/mask2former_video/engine/defaults.py b/videocutler/mask2former_video/engine/defaults.py new file mode 100644 index 0000000000000000000000000000000000000000..c3c925ba0a05358ec27803d13a4c0f98f005206e --- /dev/null +++ b/videocutler/mask2former_video/engine/defaults.py @@ -0,0 +1,729 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/engine/defaults.py + +""" +This file contains components with some default boilerplate logic user may need +in training / testing. They will not work for everyone, but many users may find them useful. + +The behavior of functions/classes in this file is subject to change, +since they are meant to represent the "common default behavior" people need in their projects. +""" + +import argparse +import logging +import os +import sys +import weakref +from collections import OrderedDict +from typing import Optional +import torch +from fvcore.nn.precise_bn import get_bn_modules +from omegaconf import OmegaConf +from torch.nn.parallel import DistributedDataParallel + +import detectron2.data.transforms as T +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import CfgNode, LazyConfig +from detectron2.data import ( + MetadataCatalog, +) +from detectron2.data import ( + build_detection_test_loader, + build_detection_train_loader, +) +from detectron2.evaluation import ( + DatasetEvaluator, + inference_on_dataset, + print_csv_format, + verify_results, +) +from detectron2.modeling import build_model +from detectron2.solver import build_lr_scheduler, build_optimizer +from detectron2.utils import comm +from detectron2.utils.collect_env import collect_env_info +from detectron2.utils.env import seed_all_rng +from detectron2.utils.events import CommonMetricPrinter, JSONWriter, TensorboardXWriter +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import setup_logger + +from detectron2.engine import hooks +from detectron2.engine import TrainerBase +from .train_loop import CustomAMPTrainer, CustomSimpleTrainer + +__all__ = [ + "create_ddp_model", + "default_argument_parser", + "default_setup", + "default_writers", + "DefaultPredictor", + "DefaultTrainer", +] + + +def create_ddp_model(model, *, fp16_compression=False, **kwargs): + """ + Create a DistributedDataParallel model if there are >1 processes. + + Args: + model: a torch.nn.Module + fp16_compression: add fp16 compression hooks to the ddp object. + See more at https://pytorch.org/docs/stable/ddp_comm_hooks.html#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook + kwargs: other arguments of :module:`torch.nn.parallel.DistributedDataParallel`. + """ # noqa + if comm.get_world_size() == 1: + return model + if "device_ids" not in kwargs: + kwargs["device_ids"] = [comm.get_local_rank()] + ddp = DistributedDataParallel(model, **kwargs) + if fp16_compression: + from torch.distributed.algorithms.ddp_comm_hooks import default as comm_hooks + + ddp.register_comm_hook(state=None, hook=comm_hooks.fp16_compress_hook) + return ddp + + +def default_argument_parser(epilog=None): + """ + Create a parser with some common arguments used by detectron2 users. + + Args: + epilog (str): epilog passed to ArgumentParser describing the usage. + + Returns: + argparse.ArgumentParser: + """ + parser = argparse.ArgumentParser( + epilog=epilog + or f""" +Examples: + +Run on single machine: + $ {sys.argv[0]} --num-gpus 8 --config-file cfg.yaml + +Change some config options: + $ {sys.argv[0]} --config-file cfg.yaml MODEL.WEIGHTS /path/to/weight.pth SOLVER.BASE_LR 0.001 + +Run on multiple machines: + (machine0)$ {sys.argv[0]} --machine-rank 0 --num-machines 2 --dist-url [--other-flags] + (machine1)$ {sys.argv[0]} --machine-rank 1 --num-machines 2 --dist-url [--other-flags] +""", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file") + parser.add_argument( + "--resume", + action="store_true", + help="Whether to attempt to resume from the checkpoint directory. " + "See documentation of `DefaultTrainer.resume_or_load()` for what it means.", + ) + parser.add_argument("--eval-only", action="store_true", help="perform evaluation only") + parser.add_argument("--num-gpus", type=int, default=1, help="number of gpus *per machine*") + parser.add_argument("--num-machines", type=int, default=1, help="total number of machines") + parser.add_argument( + "--machine-rank", type=int, default=0, help="the rank of this machine (unique per machine)" + ) + parser.add_argument( + "--test-dataset", type=str, default="", help="the dataset used for evaluation" + ) + parser.add_argument( + "--train-dataset", type=str, default="", help="the dataset used for training" + ) + parser.add_argument( + "--wandb-name", type=str, default="", help="the wandb project name" + ) + parser.add_argument("--no-segm", action="store_true", help="perform evaluation on detection only") + # PyTorch still may leave orphan processes in multi-gpu training. + # Therefore we use a deterministic way to obtain port, + # so that users are aware of orphan processes by seeing the port occupied. + port = 2**15 + 2**14 + hash(os.getuid() if sys.platform != "win32" else 1) % 2**14 + parser.add_argument( + "--dist-url", + default="tcp://127.0.0.1:{}".format(port), + help="initialization URL for pytorch distributed backend. See " + "https://pytorch.org/docs/stable/distributed.html for details.", + ) + parser.add_argument( + "opts", + help=""" +Modify config options at the end of the command. For Yacs configs, use +space-separated "PATH.KEY VALUE" pairs. +For python-based LazyConfig, use "path.key=value". + """.strip(), + default=None, + nargs=argparse.REMAINDER, + ) + return parser + + +def _try_get_key(cfg, *keys, default=None): + """ + Try select keys from cfg until the first key that exists. Otherwise return default. + """ + if isinstance(cfg, CfgNode): + cfg = OmegaConf.create(cfg.dump()) + for k in keys: + none = object() + p = OmegaConf.select(cfg, k, default=none) + if p is not none: + return p + return default + + +def _highlight(code, filename): + try: + import pygments + except ImportError: + return code + + from pygments.lexers import Python3Lexer, YamlLexer + from pygments.formatters import Terminal256Formatter + + lexer = Python3Lexer() if filename.endswith(".py") else YamlLexer() + code = pygments.highlight(code, lexer, Terminal256Formatter(style="monokai")) + return code + + +def default_setup(cfg, args): + """ + Perform some basic common setups at the beginning of a job, including: + + 1. Set up the detectron2 logger + 2. Log basic information about environment, cmdline arguments, and config + 3. Backup the config to the output directory + + Args: + cfg (CfgNode or omegaconf.DictConfig): the full config to be used + args (argparse.NameSpace): the command line arguments to be logged + """ + output_dir = _try_get_key(cfg, "OUTPUT_DIR", "output_dir", "train.output_dir") + if comm.is_main_process() and output_dir: + PathManager.mkdirs(output_dir) + + rank = comm.get_rank() + setup_logger(output_dir, distributed_rank=rank, name="fvcore") + logger = setup_logger(output_dir, distributed_rank=rank) + + logger.info("Rank of current process: {}. World size: {}".format(rank, comm.get_world_size())) + logger.info("Environment info:\n" + collect_env_info()) + + logger.info("Command line arguments: " + str(args)) + if hasattr(args, "config_file") and args.config_file != "": + logger.info( + "Contents of args.config_file={}:\n{}".format( + args.config_file, + _highlight(PathManager.open(args.config_file, "r").read(), args.config_file), + ) + ) + + if comm.is_main_process() and output_dir: + # Note: some of our scripts may expect the existence of + # config.yaml in output directory + path = os.path.join(output_dir, "config.yaml") + if isinstance(cfg, CfgNode): + logger.info("Running with full config:\n{}".format(_highlight(cfg.dump(), ".yaml"))) + with PathManager.open(path, "w") as f: + f.write(cfg.dump()) + else: + LazyConfig.save(cfg, path) + logger.info("Full config saved to {}".format(path)) + + # make sure each worker has a different, yet deterministic seed if specified + seed = _try_get_key(cfg, "SEED", "train.seed", default=-1) + seed_all_rng(None if seed < 0 else seed + rank) + + # cudnn benchmark has large overhead. It shouldn't be used considering the small size of + # typical validation set. + if not (hasattr(args, "eval_only") and args.eval_only): + torch.backends.cudnn.benchmark = _try_get_key( + cfg, "CUDNN_BENCHMARK", "train.cudnn_benchmark", default=False + ) + + +def default_writers(output_dir: str, max_iter: Optional[int] = None): + """ + Build a list of :class:`EventWriter` to be used. + It now consists of a :class:`CommonMetricPrinter`, + :class:`TensorboardXWriter` and :class:`JSONWriter`. + + Args: + output_dir: directory to store JSON metrics and tensorboard events + max_iter: the total number of iterations + + Returns: + list[EventWriter]: a list of :class:`EventWriter` objects. + """ + PathManager.mkdirs(output_dir) + return [ + # It may not always print what you want to see, since it prints "common" metrics only. + CommonMetricPrinter(max_iter), + JSONWriter(os.path.join(output_dir, "metrics.json")), + TensorboardXWriter(output_dir), + ] + + +class DefaultPredictor: + """ + Create a simple end-to-end predictor with the given config that runs on + single device for a single input image. + + Compared to using the model directly, this class does the following additions: + + 1. Load checkpoint from `cfg.MODEL.WEIGHTS`. + 2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`. + 3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`. + 4. Take one input image and produce a single output, instead of a batch. + + This is meant for simple demo purposes, so it does the above steps automatically. + This is not meant for benchmarks or running complicated inference logic. + If you'd like to do anything more complicated, please refer to its source code as + examples to build and use the model manually. + + Attributes: + metadata (Metadata): the metadata of the underlying dataset, obtained from + cfg.DATASETS.TEST. + + Examples: + :: + pred = DefaultPredictor(cfg) + inputs = cv2.imread("input.jpg") + outputs = pred(inputs) + """ + + def __init__(self, cfg): + self.cfg = cfg.clone() # cfg can be modified by model + self.model = build_model(self.cfg) + self.model.eval() + if len(cfg.DATASETS.TEST): + self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0]) + + checkpointer = DetectionCheckpointer(self.model) + checkpointer.load(cfg.MODEL.WEIGHTS) + + self.aug = T.ResizeShortestEdge( + [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST + ) + + self.input_format = cfg.INPUT.FORMAT + assert self.input_format in ["RGB", "BGR"], self.input_format + + def __call__(self, original_image): + """ + Args: + original_image (np.ndarray): an image of shape (H, W, C) (in BGR order). + + Returns: + predictions (dict): + the output of the model for one image only. + See :doc:`/tutorials/models` for details about the format. + """ + with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258 + # Apply pre-processing to image. + if self.input_format == "RGB": + # whether the model expects BGR inputs or RGB + original_image = original_image[:, :, ::-1] + height, width = original_image.shape[:2] + image = self.aug.get_transform(original_image).apply_image(original_image) + image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) + + inputs = {"image": image, "height": height, "width": width} + predictions = self.model([inputs])[0] + return predictions + + +class DefaultTrainer(TrainerBase): + """ + A trainer with default training logic. It does the following: + + 1. Create a :class:`SimpleTrainer` using model, optimizer, dataloader + defined by the given config. Create a LR scheduler defined by the config. + 2. Load the last checkpoint or `cfg.MODEL.WEIGHTS`, if exists, when + `resume_or_load` is called. + 3. Register a few common hooks defined by the config. + + It is created to simplify the **standard model training workflow** and reduce code boilerplate + for users who only need the standard training workflow, with standard features. + It means this class makes *many assumptions* about your training logic that + may easily become invalid in a new research. In fact, any assumptions beyond those made in the + :class:`SimpleTrainer` are too much for research. + + The code of this class has been annotated about restrictive assumptions it makes. + When they do not work for you, you're encouraged to: + + 1. Overwrite methods of this class, OR: + 2. Use :class:`SimpleTrainer`, which only does minimal SGD training and + nothing else. You can then add your own hooks if needed. OR: + 3. Write your own training loop similar to `tools/plain_train_net.py`. + + See the :doc:`/tutorials/training` tutorials for more details. + + Note that the behavior of this class, like other functions/classes in + this file, is not stable, since it is meant to represent the "common default behavior". + It is only guaranteed to work well with the standard models and training workflow in detectron2. + To obtain more stable behavior, write your own training logic with other public APIs. + + Examples: + :: + trainer = DefaultTrainer(cfg) + trainer.resume_or_load() # load last checkpoint or MODEL.WEIGHTS + trainer.train() + + Attributes: + scheduler: + checkpointer (DetectionCheckpointer): + cfg (CfgNode): + """ + + def __init__(self, cfg): + """ + Args: + cfg (CfgNode): + """ + super().__init__() + logger = logging.getLogger("detectron2") + if not logger.isEnabledFor(logging.INFO): # setup_logger is not called for d2 + setup_logger() + cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size()) + + # Assume these objects must be constructed in this order. + model = self.build_model(cfg) + optimizer = self.build_optimizer(cfg, model) + data_loader = self.build_train_loader(cfg) + + model = create_ddp_model(model, broadcast_buffers=False) + if cfg.SOLVER.AMP.ENABLED: + self._trainer = CustomAMPTrainer(model, data_loader, optimizer, cfg=cfg) + else: + self._trainer = CustomSimpleTrainer(model, data_loader, optimizer, cfg=cfg) + + self.scheduler = self.build_lr_scheduler(cfg, optimizer) + self.checkpointer = DetectionCheckpointer( + # Assume you want to save checkpoints together with logs/statistics + model, + cfg.OUTPUT_DIR, + trainer=weakref.proxy(self), + ) + self.start_iter = 0 + self.max_iter = cfg.SOLVER.MAX_ITER + self.cfg = cfg + + self.register_hooks(self.build_hooks()) + + def resume_or_load(self, resume=True): + """ + If `resume==True` and `cfg.OUTPUT_DIR` contains the last checkpoint (defined by + a `last_checkpoint` file), resume from the file. Resuming means loading all + available states (eg. optimizer and scheduler) and update iteration counter + from the checkpoint. ``cfg.MODEL.WEIGHTS`` will not be used. + + Otherwise, this is considered as an independent training. The method will load model + weights from the file `cfg.MODEL.WEIGHTS` (but will not load other states) and start + from iteration 0. + + Args: + resume (bool): whether to do resume or not + """ + self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume) + if resume and self.checkpointer.has_checkpoint(): + # The checkpoint stores the training iteration that just finished, thus we start + # at the next iteration + self.start_iter = self.iter + 1 + + def build_hooks(self): + """ + Build a list of default hooks, including timing, evaluation, + checkpointing, lr scheduling, precise BN, writing events. + + Returns: + list[HookBase]: + """ + cfg = self.cfg.clone() + cfg.defrost() + cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN + + ret = [ + hooks.IterationTimer(), + hooks.LRScheduler(), + hooks.PreciseBN( + # Run at the same freq as (but before) evaluation. + cfg.TEST.EVAL_PERIOD, + self.model, + # Build a new data loader to not affect training + self.build_train_loader(cfg), + cfg.TEST.PRECISE_BN.NUM_ITER, + ) + if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model) + else None, + ] + + # Do PreciseBN before checkpointer, because it updates the model and need to + # be saved by checkpointer. + # This is not always the best: if checkpointing has a different frequency, + # some checkpoints may have more precise statistics than others. + if comm.is_main_process(): + ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD)) + + def test_and_save_results(): + self._last_eval_results = self.test(self.cfg, self.model) + return self._last_eval_results + + # Do evaluation after checkpointer, because then if it fails, + # we can use the saved checkpoint to debug. + ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results)) + + if comm.is_main_process(): + # Here the default print/log frequency of each writer is used. + # run writers in the end, so that evaluation metrics are written + ret.append(hooks.PeriodicWriter(self.build_writers(), period=20)) + return ret + + def build_writers(self): + """ + Build a list of writers to be used using :func:`default_writers()`. + If you'd like a different list of writers, you can overwrite it in + your trainer. + + Returns: + list[EventWriter]: a list of :class:`EventWriter` objects. + """ + return default_writers(self.cfg.OUTPUT_DIR, self.max_iter) + + def train(self): + """ + Run training. + + Returns: + OrderedDict of results, if evaluation is enabled. Otherwise None. + """ + super().train(self.start_iter, self.max_iter) + if len(self.cfg.TEST.EXPECTED_RESULTS) and comm.is_main_process(): + assert hasattr( + self, "_last_eval_results" + ), "No evaluation results obtained during training!" + verify_results(self.cfg, self._last_eval_results) + return self._last_eval_results + + def run_step(self): + self._trainer.iter = self.iter + self._trainer.run_step() + + def state_dict(self): + ret = super().state_dict() + ret["_trainer"] = self._trainer.state_dict() + return ret + + def load_state_dict(self, state_dict): + super().load_state_dict(state_dict) + self._trainer.load_state_dict(state_dict["_trainer"]) + + @classmethod + def build_model(cls, cfg): + """ + Returns: + torch.nn.Module: + + It now calls :func:`detectron2.modeling.build_model`. + Overwrite it if you'd like a different model. + """ + model = build_model(cfg) + logger = logging.getLogger(__name__) + logger.info("Model:\n{}".format(model)) + return model + + @classmethod + def build_optimizer(cls, cfg, model): + """ + Returns: + torch.optim.Optimizer: + + It now calls :func:`detectron2.solver.build_optimizer`. + Overwrite it if you'd like a different optimizer. + """ + return build_optimizer(cfg, model) + + @classmethod + def build_lr_scheduler(cls, cfg, optimizer): + """ + It now calls :func:`detectron2.solver.build_lr_scheduler`. + Overwrite it if you'd like a different scheduler. + """ + return build_lr_scheduler(cfg, optimizer) + + @classmethod + def build_train_loader(cls, cfg): + """ + Returns: + iterable + + It now calls :func:`detectron2.data.build_detection_train_loader`. + Overwrite it if you'd like a different data loader. + """ + return build_detection_train_loader(cfg) + + @classmethod + def build_test_loader(cls, cfg, dataset_name): + """ + Returns: + iterable + + It now calls :func:`detectron2.data.build_detection_test_loader`. + Overwrite it if you'd like a different data loader. + """ + return build_detection_test_loader(cfg, dataset_name) + + @classmethod + def build_evaluator(cls, cfg, dataset_name): + """ + Returns: + DatasetEvaluator or None + + It is not implemented by default. + """ + raise NotImplementedError( + """ +If you want DefaultTrainer to automatically run evaluation, +please implement `build_evaluator()` in subclasses (see train_net.py for example). +Alternatively, you can call evaluation functions yourself (see Colab balloon tutorial for example). +""" + ) + + @classmethod + def test(cls, cfg, model, evaluators=None): + """ + Evaluate the given model. The given model is expected to already contain + weights to evaluate. + + Args: + cfg (CfgNode): + model (nn.Module): + evaluators (list[DatasetEvaluator] or None): if None, will call + :meth:`build_evaluator`. Otherwise, must have the same length as + ``cfg.DATASETS.TEST``. + + Returns: + dict: a dict of result metrics + """ + logger = logging.getLogger(__name__) + if isinstance(evaluators, DatasetEvaluator): + evaluators = [evaluators] + if evaluators is not None: + assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format( + len(cfg.DATASETS.TEST), len(evaluators) + ) + + results = OrderedDict() + for idx, dataset_name in enumerate(cfg.DATASETS.TEST): + data_loader = cls.build_test_loader(cfg, dataset_name) + # When evaluators are passed in as arguments, + # implicitly assume that evaluators can be created before data_loader. + if evaluators is not None: + evaluator = evaluators[idx] + else: + try: + evaluator = cls.build_evaluator(cfg, dataset_name) + except NotImplementedError: + logger.warn( + "No evaluator found. Use `DefaultTrainer.test(evaluators=)`, " + "or implement its `build_evaluator` method." + ) + results[dataset_name] = {} + continue + results_i = inference_on_dataset(model, data_loader, evaluator) + results[dataset_name] = results_i + if comm.is_main_process(): + assert isinstance( + results_i, dict + ), "Evaluator must return a dict on the main process. Got {} instead.".format( + results_i + ) + logger.info("Evaluation results for {} in csv format:".format(dataset_name)) + print_csv_format(results_i) + + if len(results) == 1: + results = list(results.values())[0] + return results + + @staticmethod + def auto_scale_workers(cfg, num_workers: int): + """ + When the config is defined for certain number of workers (according to + ``cfg.SOLVER.REFERENCE_WORLD_SIZE``) that's different from the number of + workers currently in use, returns a new cfg where the total batch size + is scaled so that the per-GPU batch size stays the same as the + original ``IMS_PER_BATCH // REFERENCE_WORLD_SIZE``. + + Other config options are also scaled accordingly: + * training steps and warmup steps are scaled inverse proportionally. + * learning rate are scaled proportionally, following :paper:`ImageNet in 1h`. + + For example, with the original config like the following: + + .. code-block:: yaml + + IMS_PER_BATCH: 16 + BASE_LR: 0.1 + REFERENCE_WORLD_SIZE: 8 + MAX_ITER: 5000 + STEPS: (4000,) + CHECKPOINT_PERIOD: 1000 + + When this config is used on 16 GPUs instead of the reference number 8, + calling this method will return a new config with: + + .. code-block:: yaml + + IMS_PER_BATCH: 32 + BASE_LR: 0.2 + REFERENCE_WORLD_SIZE: 16 + MAX_ITER: 2500 + STEPS: (2000,) + CHECKPOINT_PERIOD: 500 + + Note that both the original config and this new config can be trained on 16 GPUs. + It's up to user whether to enable this feature (by setting ``REFERENCE_WORLD_SIZE``). + + Returns: + CfgNode: a new config. Same as original if ``cfg.SOLVER.REFERENCE_WORLD_SIZE==0``. + """ + old_world_size = cfg.SOLVER.REFERENCE_WORLD_SIZE + if old_world_size == 0 or old_world_size == num_workers: + return cfg + cfg = cfg.clone() + frozen = cfg.is_frozen() + cfg.defrost() + + assert ( + cfg.SOLVER.IMS_PER_BATCH % old_world_size == 0 + ), "Invalid REFERENCE_WORLD_SIZE in config!" + scale = num_workers / old_world_size + bs = cfg.SOLVER.IMS_PER_BATCH = int(round(cfg.SOLVER.IMS_PER_BATCH * scale)) + lr = cfg.SOLVER.BASE_LR = cfg.SOLVER.BASE_LR * scale + max_iter = cfg.SOLVER.MAX_ITER = int(round(cfg.SOLVER.MAX_ITER / scale)) + warmup_iter = cfg.SOLVER.WARMUP_ITERS = int(round(cfg.SOLVER.WARMUP_ITERS / scale)) + cfg.SOLVER.STEPS = tuple(int(round(s / scale)) for s in cfg.SOLVER.STEPS) + cfg.TEST.EVAL_PERIOD = int(round(cfg.TEST.EVAL_PERIOD / scale)) + cfg.SOLVER.CHECKPOINT_PERIOD = int(round(cfg.SOLVER.CHECKPOINT_PERIOD / scale)) + cfg.SOLVER.REFERENCE_WORLD_SIZE = num_workers # maintain invariant + logger = logging.getLogger(__name__) + logger.info( + f"Auto-scaling the config to batch_size={bs}, learning_rate={lr}, " + f"max_iter={max_iter}, warmup={warmup_iter}." + ) + + if frozen: + cfg.freeze() + return cfg + + +# Access basic attributes from the underlying trainer +for _attr in ["model", "data_loader", "optimizer"]: + setattr( + DefaultTrainer, + _attr, + property( + # getter + lambda self, x=_attr: getattr(self._trainer, x), + # setter + lambda self, value, x=_attr: setattr(self._trainer, x, value), + ), + ) diff --git a/videocutler/mask2former_video/engine/train_loop.py b/videocutler/mask2former_video/engine/train_loop.py new file mode 100644 index 0000000000000000000000000000000000000000..cfb10b64e2e11cb51619455e42fb511f5403124c --- /dev/null +++ b/videocutler/mask2former_video/engine/train_loop.py @@ -0,0 +1,396 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Modified by XuDong Wang from https://github.com/facebookresearch/detectron2/blob/main/detectron2/engine/train_loop.py + +import torch +from torch.nn.parallel import DataParallel, DistributedDataParallel + +import numpy as np +import time +import torch +from torch.nn.parallel import DataParallel, DistributedDataParallel +import copy +import random +import torch.nn.functional as F +from detectron2.structures.instances import Instances +from detectron2.structures import BitMasks + +from detectron2.engine import SimpleTrainer + +__all__ = ["CustomSimpleTrainer", "CustomAMPTrainer"] + +class CustomSimpleTrainer(SimpleTrainer): + """ + A simple trainer for the most common type of task: + single-cost single-optimizer single-data-source iterative optimization, + optionally using data-parallelism. + It assumes that every step, you: + + 1. Compute the loss with a data from the data_loader. + 2. Compute the gradients with the above loss. + 3. Update the model with the optimizer. + + All other tasks during training (checkpointing, logging, evaluation, LR schedule) + are maintained by hooks, which can be registered by :meth:`TrainerBase.register_hooks`. + + If you want to do anything fancier than this, + either subclass TrainerBase and implement your own `run_step`, + or write your own training loop. + """ + + def __init__(self, model, data_loader, optimizer, cfg=None, use_copy_paste=False, + copy_paste_rate=-1, copy_paste_random_num=None, copy_paste_min_ratio=-1, + copy_paste_max_ratio=-1, visualize_copy_paste=False): + """ + Args: + model: a torch Module. Takes a data from data_loader and returns a + dict of losses. + data_loader: an iterable. Contains data to be used to call model. + optimizer: a torch optimizer. + """ + super().__init__(model, data_loader, optimizer) + + """ + We set the model to training mode in the trainer. + However it's valid to train a model that's in eval mode. + If you want your model (or a submodule of it) to behave + like evaluation during training, you can overwrite its train() method. + """ + self.cfg = cfg + # model.train() + + # self.model = model + # self.data_loader = data_loader + # to access the data loader iterator, call `self._data_loader_iter` + # self._data_loader_iter_obj = None + # self.optimizer = optimizer + + self.use_copy_paste = use_copy_paste if self.cfg is None else self.cfg.DATALOADER.COPY_PASTE + self.cfg_COPY_PASTE_RATE = copy_paste_rate if self.cfg is None else self.cfg.DATALOADER.COPY_PASTE_RATE + self.cfg_COPY_PASTE_RANDOM_NUM = copy_paste_random_num if self.cfg is None else self.cfg.DATALOADER.COPY_PASTE_RANDOM_NUM + self.cfg_COPY_PASTE_MIN_RATIO = copy_paste_min_ratio if self.cfg is None else self.cfg.DATALOADER.COPY_PASTE_MIN_RATIO + self.cfg_COPY_PASTE_MAX_RATIO = copy_paste_max_ratio if self.cfg is None else self.cfg.DATALOADER.COPY_PASTE_MAX_RATIO + self.cfg_VISUALIZE_COPY_PASTE = visualize_copy_paste if self.cfg is None else self.cfg.DATALOADER.VISUALIZE_COPY_PASTE + print("copy_paste hyper-params:", self.use_copy_paste, self.cfg_COPY_PASTE_RATE, self.cfg_COPY_PASTE_RANDOM_NUM) + + def IoU(self, mask1, mask2): # only work when the batch size is 1 + mask1, mask2 = (mask1>0.5).to(torch.bool), (mask2>0.5).to(torch.bool) + intersection = torch.sum(mask1 * (mask1 == mask2), dim=[-1, -2]).squeeze() + union = torch.sum(mask1 + mask2, dim=[-1, -2]).squeeze() + return (intersection.to(torch.float) / union).mean().view(1, -1) + + def IoY(self, mask1, mask2): # only work when the batch size is 1 + mask1, mask2 = mask1.squeeze(), mask2.squeeze() + mask1, mask2 = (mask1>0.5).to(torch.bool), (mask2>0.5).to(torch.bool) + intersection = torch.sum(mask1 * (mask1 == mask2), dim=[-1, -2]).squeeze() + union = torch.sum(mask2, dim=[-1, -2]).squeeze() + return (intersection.to(torch.float) / union).mean().view(1, -1) + + def copy_and_paste(self, sources, targets): + def mask_iou_matrix(x, y, mode='iou'): + x = x.reshape(x.shape[0], -1).float() + y = y.reshape(y.shape[0], -1).float() + inter_matrix = x @ y.transpose(1, 0) # n1xn2 + sum_x = x.sum(1)[:, None].expand(x.shape[0], y.shape[0]) + sum_y = y.sum(1)[None, :].expand(x.shape[0], y.shape[0]) + if mode == 'ioy': + iou_matrix = inter_matrix / (sum_y) # [1, 1] + else: + iou_matrix = inter_matrix / (sum_x + sum_y - inter_matrix) # [1, 1] + return iou_matrix + + def visualize_data(data, save_path = './sample.jpg'): + from detectron2.data import detection_utils as utils + from detectron2.data import DatasetCatalog, MetadataCatalog + from detectron2.utils.visualizer import Visualizer + data["instances"] = data["instances"].to(device='cpu') + img = data["image"].permute(1, 2, 0).cpu().detach().numpy() + img = utils.convert_image_to_rgb(img, 'RGB') + metadata = MetadataCatalog.get('ytvis_2019_train_cls_agnostic') + visualizer = Visualizer(img, metadata=metadata, scale=1.0) + target_fields = data["instances"].get_fields() + labels = [metadata.thing_classes[i] for i in target_fields["gt_classes"]] + vis = visualizer.overlay_instances( + labels=labels, + boxes=target_fields.get("gt_boxes"), # ("gt_boxes", None), + masks=target_fields.get("gt_masks"), # ("gt_masks", None), + keypoints=target_fields.get("gt_keypoints", None), + ) + print("Saving to {} ...".format(save_path)) + vis.save(save_path) + + new_targets = [] + target_data_copy = [] + for source_data, target_data in zip(sources, targets): + # data dict is created by mask2former_video/data_video/dataset_mapper.py + # data['instances']: list of instances [[frame1 instances],[frame2 instances]...[frameN instances]] + source_instances = source_data["instances"] + source_image_list = source_data["image"] + target_instances_list = target_data["instances"] + target_image_list = target_data["image"] + target_data_copy.append(copy.deepcopy(target_data)) + + num_source_instances = len(source_instances[0]) + copy_paste_rate = random.random() + if self.cfg_COPY_PASTE_RATE >= copy_paste_rate and num_source_instances > 0: + if self.cfg_COPY_PASTE_RANDOM_NUM: + num_copy = 1 if num_source_instances == 1 else np.random.randint(1, max(1, num_source_instances)) + else: + num_copy = num_source_instances + else: + num_copy = 0 + + if num_source_instances == 0 or num_copy == 0: + new_targets.append(target_data) + continue + else: + choice = np.random.choice(num_source_instances, num_copy, replace=False) + # randomly choose instances from the first frame and copy all these selected instances + frame_id = np.random.randint(1, max(1, len(source_instances))) - 1 + copied_instances = source_instances[frame_id][choice].to(device=target_instances_list[frame_id].gt_boxes.device) + + # paste these instances to ALL frames in the same video + target_instances_list_new = [] + target_image_list_new = [] + for f in range(len(target_instances_list)): + # Use DEEPCOPY, otherwise, the objects will be in-place edited + target_instances = copy.deepcopy(target_instances_list[f]) + target_image = copy.deepcopy(target_image_list[f]) + copied_masks = copy.deepcopy(copied_instances.gt_masks) + copied_boxes = copy.deepcopy(copied_instances.gt_boxes) + _, labeled_h, labeled_w = source_image_list[frame_id].shape + _, unlabeled_h, unlabeled_w = target_image.shape + + # rescale the labeled image to align with unlabeled one. + if isinstance(copied_masks, torch.Tensor): + masks_new = copied_masks[None, ...].float() + else: + masks_new = copied_masks.tensor[None, ...].float() + # resize the masks with a random ratio from 0.5 to 1.0 + resize_ratio = random.uniform(self.cfg_COPY_PASTE_MIN_RATIO, self.cfg_COPY_PASTE_MAX_RATIO) + w_new = int(resize_ratio * unlabeled_w) + h_new = int(resize_ratio * unlabeled_h) + + # randomly shift the masks，so that the masks are not always in the center of the image + w_shift = random.randint(0, max(0, unlabeled_w - w_new)) + h_shift = random.randint(0, max(0, unlabeled_h - h_new)) + + source_image_new = F.interpolate(source_image_list[frame_id][None, ...].float(), size=(h_new, w_new), mode="bilinear", align_corners=False).byte().squeeze(0) + if isinstance(copied_masks, torch.Tensor): + masks_new = F.interpolate(copied_masks[None, ...].float(), size=(h_new, w_new), mode="bilinear", align_corners=False).bool().squeeze(0) + else: + masks_new = F.interpolate(copied_masks.tensor[None, ...].float(), size=(h_new, w_new), mode="bilinear", align_corners=False).bool().squeeze(0) + copied_boxes.scale(1. * unlabeled_w / labeled_w * resize_ratio, 1. * unlabeled_h / labeled_h * resize_ratio) + + if isinstance(target_instances.gt_masks, torch.Tensor): + _, mask_w, mask_h = target_instances.gt_masks.size() + else: + _, mask_w, mask_h = target_instances.gt_masks.tensor.size() + + masks_new_all = torch.zeros(num_copy, mask_w, mask_h) + image_new_all = torch.zeros_like(target_image) + + image_new_all[:, h_shift:h_shift+h_new, w_shift:w_shift+w_new] += source_image_new + masks_new_all[:, h_shift:h_shift+h_new, w_shift:w_shift+w_new] += masks_new + + source_image = image_new_all.byte() #.squeeze(0) + if isinstance(copied_masks, torch.Tensor): + copied_masks = masks_new_all.bool() #.squeeze(0) + else: + copied_masks.tensor = masks_new_all.bool() #.squeeze(0) + copied_boxes.tensor[:, 0] += h_shift + copied_boxes.tensor[:, 2] += h_shift + copied_boxes.tensor[:, 1] += w_shift + copied_boxes.tensor[:, 3] += w_shift + + copied_instances.gt_masks = copied_masks + copied_instances.gt_boxes = copied_boxes + copied_instances._image_size = (unlabeled_h, unlabeled_w) + if len(target_instances) == 0: + if isinstance(copied_instances.gt_masks, torch.Tensor): + alpha = copied_instances.gt_masks.sum(0) > 0 + else: + alpha = copied_instances.gt_masks.tensor.sum(0) > 0 + # merge image + alpha = alpha.cpu() + composited_image = (alpha * source_image) + (~alpha * target_image) + + target_image_list_new.append(composited_image) + target_instances_list_new.append(copied_instances) + else: + # remove the copied object if iou greater than 0.5 + if isinstance(copied_masks, torch.Tensor): + iou_matrix = mask_iou_matrix(copied_masks, target_instances.gt_masks, mode='ioy') # nxN + else: + iou_matrix = mask_iou_matrix(copied_masks.tensor, target_instances.gt_masks.tensor, mode='ioy') # nxN + + # check if the iou is greater than 0.5. for each video, all frames should have + # the same amount of instances and gt masks (can be None). + if f == 0: + keep = iou_matrix.max(1)[0] < 0.5 + sum_keep = keep.sum() + else: + keep = iou_matrix.max(1)[0] < 2.0 + + if sum_keep < keep.size()[0]: + target_image_list_new.append(target_image) + target_instances_list_new.append(target_instances) + continue + + copied_instances = copied_instances[keep] + # update existing instances in unlabeled image + if isinstance(copied_instances.gt_masks, torch.Tensor): + alpha = copied_instances.gt_masks.sum(0) > 0 + target_instances.gt_masks = ~alpha * target_instances.gt_masks + areas_unlabeled = target_instances.gt_masks.sum((1,2)) + else: + alpha = copied_instances.gt_masks.tensor.sum(0) > 0 + target_instances.gt_masks.tensor = ~alpha * target_instances.gt_masks.tensor + areas_unlabeled = target_instances.gt_masks.tensor.sum((1,2)) + # merge image + alpha = alpha.cpu() + composited_image = (alpha * source_image) + (~alpha * target_image) + # merge instances + merged_instances = Instances.cat([target_instances[areas_unlabeled > 0], copied_instances]) + # update boxes + if isinstance(merged_instances.gt_masks, torch.Tensor): + merged_instances.gt_boxes = BitMasks(merged_instances.gt_masks).get_bounding_boxes() + else: + merged_instances.gt_boxes = merged_instances.gt_masks.get_bounding_boxes() + + target_image_list_new.append(composited_image) + target_instances_list_new.append(merged_instances) + + if self.cfg_VISUALIZE_COPY_PASTE: + visualize_data(target_data, save_path = 'sample_{}.jpg'.format(np.random.randint(5))) + + target_data["image"] = target_image_list_new + target_data["instances"] = target_instances_list_new + new_targets.append(target_data) + + new_targets_outputs = [] + for t, new_target in enumerate(new_targets): + n_insts = [len(new_target["instances"][i]) for i in range(len(new_target["instances"]))] + try: + assert len(set(n_insts)) == 1, "The number of instances should be the same for all frames in the same video." + new_targets_outputs.append(new_target) + except: + new_targets_outputs.append(target_data_copy[t]) + return new_targets_outputs + + def run_step(self): + """ + Implement the standard training logic described above. + """ + assert self.model.training, "[SimpleTrainer] model was changed to eval mode!" + start = time.perf_counter() + """ + If you want to do something with the data, you can wrap the dataloader. + """ + data = next(self._data_loader_iter) + if self.use_copy_paste: + data = self.copy_and_paste(copy.deepcopy(data[::-1]), data) + data_time = time.perf_counter() - start + + """ + If you want to do something with the losses, you can wrap the model. + """ + loss_dict = self.model(data) + if isinstance(loss_dict, torch.Tensor): + losses = loss_dict + loss_dict = {"total_loss": loss_dict} + else: + losses = sum(loss_dict.values()) + + """ + If you need to accumulate gradients or do something similar, you can + wrap the optimizer with your custom `zero_grad()` method. + """ + if not torch.isnan(losses): + self.optimizer.zero_grad() + losses.backward() + else: + print('Nan loss. Skipped.') + + self._write_metrics(loss_dict, data_time) + + """ + If you need gradient clipping/scaling or other processing, you can + wrap the optimizer with your custom `step()` method. But it is + suboptimal as explained in https://arxiv.org/abs/2006.15704 Sec 3.2.4 + """ + self.optimizer.step() + + +class CustomAMPTrainer(CustomSimpleTrainer): + """ + Like :class:`SimpleTrainer`, but uses PyTorch's native automatic mixed precision + in the training loop. + """ + + def __init__(self, model, data_loader, optimizer, cfg=None, grad_scaler=None, use_copy_paste=False, + copy_paste_rate=-1, copy_paste_random_num=None, copy_paste_min_ratio=-1, + copy_paste_max_ratio=-1, visualize_copy_paste=False): + """ + Args: + model, data_loader, optimizer: same as in :class:`SimpleTrainer`. + grad_scaler: torch GradScaler to automatically scale gradients. + """ + unsupported = "AMPTrainer does not support single-process multi-device training!" + if isinstance(model, DistributedDataParallel): + assert not (model.device_ids and len(model.device_ids) > 1), unsupported + assert not isinstance(model, DataParallel), unsupported + print("INFO: use AMPTrainer.") + + super().__init__(model, data_loader, optimizer, cfg=cfg, use_copy_paste=use_copy_paste, \ + copy_paste_rate=copy_paste_rate, copy_paste_random_num=copy_paste_random_num, \ + copy_paste_min_ratio=copy_paste_min_ratio, copy_paste_max_ratio=copy_paste_max_ratio, \ + visualize_copy_paste=visualize_copy_paste) + + if grad_scaler is None: + from torch.cuda.amp import GradScaler + + grad_scaler = GradScaler() + self.grad_scaler = grad_scaler + + def run_step(self): + """ + Implement the AMP training logic. + """ + assert self.model.training, "[AMPTrainer] model was changed to eval mode!" + assert torch.cuda.is_available(), "[AMPTrainer] CUDA is required for AMP training!" + from torch.cuda.amp import autocast + + start = time.perf_counter() + data = next(self._data_loader_iter) + if self.use_copy_paste: + data = self.copy_and_paste(copy.deepcopy(data[::-1]), data) + data_time = time.perf_counter() - start + + with autocast(): + loss_dict = self.model(data) + if isinstance(loss_dict, torch.Tensor): + losses = loss_dict + loss_dict = {"total_loss": loss_dict} + else: + losses = sum(loss_dict.values()) + + if not torch.isnan(losses): + self.optimizer.zero_grad() + self.grad_scaler.scale(losses).backward() + else: + print('Nan loss.') + + self._write_metrics(loss_dict, data_time) + + self.grad_scaler.step(self.optimizer) + self.grad_scaler.update() + + def state_dict(self): + ret = super().state_dict() + ret["grad_scaler"] = self.grad_scaler.state_dict() + return ret + + def load_state_dict(self, state_dict): + super().load_state_dict(state_dict) + self.grad_scaler.load_state_dict(state_dict["grad_scaler"]) diff --git a/videocutler/mask2former_video/modeling/__init__.py b/videocutler/mask2former_video/modeling/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9cc855fb05ad6ad02824f6f307539845a3cea192 --- /dev/null +++ b/videocutler/mask2former_video/modeling/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .transformer_decoder.video_mask2former_transformer_decoder import VideoMultiScaleMaskedTransformerDecoder diff --git a/videocutler/mask2former_video/modeling/criterion.py b/videocutler/mask2former_video/modeling/criterion.py new file mode 100644 index 0000000000000000000000000000000000000000..176a55c09eef5a8db7dd87bf187404f2cbe98489 --- /dev/null +++ b/videocutler/mask2former_video/modeling/criterion.py @@ -0,0 +1,259 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/detr.py +""" +MaskFormer criterion. +""" +import logging + +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.utils.comm import get_world_size +from detectron2.projects.point_rend.point_features import ( + get_uncertain_point_coords_with_randomness, + point_sample, +) + +from mask2former.utils.misc import is_dist_avail_and_initialized + + +def dice_loss( + inputs: torch.Tensor, + targets: torch.Tensor, + num_masks: float, + ): + """ + Compute the DICE loss, similar to generalized IOU for masks + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * (inputs * targets).sum(-1) + denominator = inputs.sum(-1) + targets.sum(-1) + loss = 1 - (numerator + 1) / (denominator + 1) + return loss.sum() / num_masks + + +dice_loss_jit = torch.jit.script( + dice_loss +) # type: torch.jit.ScriptModule + + +def sigmoid_ce_loss( + inputs: torch.Tensor, + targets: torch.Tensor, + num_masks: float, + ): + """ + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + Returns: + Loss tensor + """ + loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + + return loss.mean(1).sum() / num_masks + + +sigmoid_ce_loss_jit = torch.jit.script( + sigmoid_ce_loss +) # type: torch.jit.ScriptModule + + +def calculate_uncertainty(logits): + """ + We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the + foreground class in `classes`. + Args: + logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or + class-agnostic, where R is the total number of predicted masks in all images and C is + the number of foreground classes. The values are logits. + Returns: + scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with + the most uncertain locations having the highest uncertainty score. + """ + assert logits.shape[1] == 1 + gt_class_logits = logits.clone() + return -(torch.abs(gt_class_logits)) + + +class VideoSetCriterion(nn.Module): + """This class computes the loss for DETR. + The process happens in two steps: + 1) we compute hungarian assignment between ground truth boxes and the outputs of the model + 2) we supervise each pair of matched ground-truth / prediction (supervise class and box) + """ + + def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses, + num_points, oversample_ratio, importance_sample_ratio): + """Create the criterion. + Parameters: + num_classes: number of object categories, omitting the special no-object category + matcher: module able to compute a matching between targets and proposals + weight_dict: dict containing as key the names of the losses and as values their relative weight. + eos_coef: relative classification weight applied to the no-object category + losses: list of all the losses to be applied. See get_loss for list of available losses. + """ + super().__init__() + self.num_classes = num_classes + self.matcher = matcher + self.weight_dict = weight_dict + self.eos_coef = eos_coef + self.losses = losses + empty_weight = torch.ones(self.num_classes + 1) + empty_weight[-1] = self.eos_coef + self.register_buffer("empty_weight", empty_weight) + + # pointwise mask loss parameters + self.num_points = num_points + self.oversample_ratio = oversample_ratio + self.importance_sample_ratio = importance_sample_ratio + + def loss_labels(self, outputs, targets, indices, num_masks): + """Classification loss (NLL) + targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes] + """ + assert "pred_logits" in outputs + src_logits = outputs["pred_logits"].float() + + idx = self._get_src_permutation_idx(indices) + target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) + target_classes = torch.full( + src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device + ) + target_classes[idx] = target_classes_o + + loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight) + losses = {"loss_ce": loss_ce} + return losses + + def loss_masks(self, outputs, targets, indices, num_masks): + """Compute the losses related to the masks: the focal loss and the dice loss. + targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w] + """ + assert "pred_masks" in outputs + + src_idx = self._get_src_permutation_idx(indices) + src_masks = outputs["pred_masks"] + src_masks = src_masks[src_idx] + # Modified to handle video + target_masks = torch.cat([t['masks'][i] for t, (_, i) in zip(targets, indices)]).to(src_masks) + + # No need to upsample predictions as we are using normalized coordinates :) + # NT x 1 x H x W + src_masks = src_masks.flatten(0, 1)[:, None] + target_masks = target_masks.flatten(0, 1)[:, None] + + with torch.no_grad(): + # sample point_coords + point_coords = get_uncertain_point_coords_with_randomness( + src_masks, + lambda logits: calculate_uncertainty(logits), + self.num_points, + self.oversample_ratio, + self.importance_sample_ratio, + ) + # get gt labels + point_labels = point_sample( + target_masks, + point_coords, + align_corners=False, + ).squeeze(1) + + point_logits = point_sample( + src_masks, + point_coords, + align_corners=False, + ).squeeze(1) + + losses = { + "loss_mask": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks), + "loss_dice": dice_loss_jit(point_logits, point_labels, num_masks), + } + + del src_masks + del target_masks + return losses + + def _get_src_permutation_idx(self, indices): + # permute predictions following indices + batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)]) + src_idx = torch.cat([src for (src, _) in indices]) + return batch_idx, src_idx + + def _get_tgt_permutation_idx(self, indices): + # permute targets following indices + batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]) + tgt_idx = torch.cat([tgt for (_, tgt) in indices]) + return batch_idx, tgt_idx + + def get_loss(self, loss, outputs, targets, indices, num_masks): + loss_map = { + 'labels': self.loss_labels, + 'masks': self.loss_masks, + } + assert loss in loss_map, f"do you really want to compute {loss} loss?" + return loss_map[loss](outputs, targets, indices, num_masks) + + def forward(self, outputs, targets): + """This performs the loss computation. + Parameters: + outputs: dict of tensors, see the output specification of the model for the format + targets: list of dicts, such that len(targets) == batch_size. + The expected keys in each dict depends on the losses applied, see each loss' doc + """ + outputs_without_aux = {k: v for k, v in outputs.items() if k != "aux_outputs"} + + # Retrieve the matching between the outputs of the last layer and the targets + indices = self.matcher(outputs_without_aux, targets) + + # Compute the average number of target boxes accross all nodes, for normalization purposes + num_masks = sum(len(t["labels"]) for t in targets) + num_masks = torch.as_tensor( + [num_masks], dtype=torch.float, device=next(iter(outputs.values())).device + ) + if is_dist_avail_and_initialized(): + torch.distributed.all_reduce(num_masks) + num_masks = torch.clamp(num_masks / get_world_size(), min=1).item() + + # Compute all the requested losses + losses = {} + for loss in self.losses: + losses.update(self.get_loss(loss, outputs, targets, indices, num_masks)) + + # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. + if "aux_outputs" in outputs: + for i, aux_outputs in enumerate(outputs["aux_outputs"]): + indices = self.matcher(aux_outputs, targets) + for loss in self.losses: + l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks) + l_dict = {k + f"_{i}": v for k, v in l_dict.items()} + losses.update(l_dict) + + return losses + + def __repr__(self): + head = "Criterion " + self.__class__.__name__ + body = [ + "matcher: {}".format(self.matcher.__repr__(_repr_indent=8)), + "losses: {}".format(self.losses), + "weight_dict: {}".format(self.weight_dict), + "num_classes: {}".format(self.num_classes), + "eos_coef: {}".format(self.eos_coef), + "num_points: {}".format(self.num_points), + "oversample_ratio: {}".format(self.oversample_ratio), + "importance_sample_ratio: {}".format(self.importance_sample_ratio), + ] + _repr_indent = 4 + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/videocutler/mask2former_video/modeling/matcher.py b/videocutler/mask2former_video/modeling/matcher.py new file mode 100644 index 0000000000000000000000000000000000000000..a47091a71ac43649f9e60080565bb18122c0b413 --- /dev/null +++ b/videocutler/mask2former_video/modeling/matcher.py @@ -0,0 +1,193 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/matcher.py +""" +Modules to compute the matching cost and solve the corresponding LSAP. +""" +import torch +import torch.nn.functional as F +from scipy.optimize import linear_sum_assignment +from torch import nn +from torch.cuda.amp import autocast + +from detectron2.projects.point_rend.point_features import point_sample + + +def batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor): + """ + Compute the DICE loss, similar to generalized IOU for masks + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * torch.einsum("nc,mc->nm", inputs, targets) + denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :] + loss = 1 - (numerator + 1) / (denominator + 1) + return loss + + +batch_dice_loss_jit = torch.jit.script( + batch_dice_loss +) # type: torch.jit.ScriptModule + + +def batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor): + """ + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + Returns: + Loss tensor + """ + hw = inputs.shape[1] + + pos = F.binary_cross_entropy_with_logits( + inputs, torch.ones_like(inputs), reduction="none" + ) + neg = F.binary_cross_entropy_with_logits( + inputs, torch.zeros_like(inputs), reduction="none" + ) + + loss = torch.einsum("nc,mc->nm", pos, targets) + torch.einsum( + "nc,mc->nm", neg, (1 - targets) + ) + + return loss / hw + + +batch_sigmoid_ce_loss_jit = torch.jit.script( + batch_sigmoid_ce_loss +) # type: torch.jit.ScriptModule + + +class VideoHungarianMatcher(nn.Module): + """This class computes an assignment between the targets and the predictions of the network + + For efficiency reasons, the targets don't include the no_object. Because of this, in general, + there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, + while the others are un-matched (and thus treated as non-objects). + """ + + def __init__(self, cost_class: float = 1, cost_mask: float = 1, cost_dice: float = 1, num_points: int = 0): + """Creates the matcher + + Params: + cost_class: This is the relative weight of the classification error in the matching cost + cost_mask: This is the relative weight of the focal loss of the binary mask in the matching cost + cost_dice: This is the relative weight of the dice loss of the binary mask in the matching cost + """ + super().__init__() + self.cost_class = cost_class + self.cost_mask = cost_mask + self.cost_dice = cost_dice + + assert cost_class != 0 or cost_mask != 0 or cost_dice != 0, "all costs cant be 0" + + self.num_points = num_points + + @torch.no_grad() + def memory_efficient_forward(self, outputs, targets): + """More memory-friendly matching""" + bs, num_queries = outputs["pred_logits"].shape[:2] + + indices = [] + + # Iterate through batch size + for b in range(bs): + + out_prob = outputs["pred_logits"][b].softmax(-1) # [num_queries, num_classes] + tgt_ids = targets[b]["labels"] + + # Compute the classification cost. Contrary to the loss, we don't use the NLL, + # but approximate it in 1 - proba[target class]. + # The 1 is a constant that doesn't change the matching, it can be ommitted. + cost_class = -out_prob[:, tgt_ids] + + out_mask = outputs["pred_masks"][b] # [num_queries, T, H_pred, W_pred] + # gt masks are already padded when preparing target + tgt_mask = targets[b]["masks"].to(out_mask) # [num_gts, T, H_pred, W_pred] + + # out_mask = out_mask[:, None] + # tgt_mask = tgt_mask[:, None] + # all masks share the same set of points for efficient matching! + point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device) + # get gt labels + tgt_mask = point_sample( + tgt_mask, + point_coords.repeat(tgt_mask.shape[0], 1, 1), + align_corners=False, + ).flatten(1) + + out_mask = point_sample( + out_mask, + point_coords.repeat(out_mask.shape[0], 1, 1), + align_corners=False, + ).flatten(1) + + with autocast(enabled=False): + out_mask = out_mask.float() + tgt_mask = tgt_mask.float() + # Compute the focal loss between masks + if out_mask.shape[0] == 0 or tgt_mask.shape[0] == 0: + cost_mask = batch_sigmoid_ce_loss(out_mask, tgt_mask) + cost_dice = batch_dice_loss(out_mask, tgt_mask) + else: + cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask) + + # Compute the dice loss betwen masks + cost_dice = batch_dice_loss_jit(out_mask, tgt_mask) + + # Final cost matrix + C = ( + self.cost_mask * cost_mask + + self.cost_class * cost_class + + self.cost_dice * cost_dice + ) + C = C.reshape(num_queries, -1).cpu() + + indices.append(linear_sum_assignment(C)) + + return [ + (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) + for i, j in indices + ] + + @torch.no_grad() + def forward(self, outputs, targets): + """Performs the matching + + Params: + outputs: This is a dict that contains at least these entries: + "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits + "pred_masks": Tensor of dim [batch_size, num_queries, H_pred, W_pred] with the predicted masks + + targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing: + "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth + objects in the target) containing the class labels + "masks": Tensor of dim [num_target_boxes, H_gt, W_gt] containing the target masks + + Returns: + A list of size batch_size, containing tuples of (index_i, index_j) where: + - index_i is the indices of the selected predictions (in order) + - index_j is the indices of the corresponding selected targets (in order) + For each batch element, it holds: + len(index_i) = len(index_j) = min(num_queries, num_target_boxes) + """ + return self.memory_efficient_forward(outputs, targets) + + def __repr__(self, _repr_indent=4): + head = "Matcher " + self.__class__.__name__ + body = [ + "cost_class: {}".format(self.cost_class), + "cost_mask: {}".format(self.cost_mask), + "cost_dice: {}".format(self.cost_dice), + ] + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/videocutler/mask2former_video/modeling/transformer_decoder/__init__.py b/videocutler/mask2former_video/modeling/transformer_decoder/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c1971bbb1e4216f43e28a511f166a31ba68b3fbd --- /dev/null +++ b/videocutler/mask2former_video/modeling/transformer_decoder/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .video_mask2former_transformer_decoder import VideoMultiScaleMaskedTransformerDecoder diff --git a/videocutler/mask2former_video/modeling/transformer_decoder/position_encoding.py b/videocutler/mask2former_video/modeling/transformer_decoder/position_encoding.py new file mode 100644 index 0000000000000000000000000000000000000000..b0ebc769de4ed6a86069fcbc9b1e819d5f6b4bc8 --- /dev/null +++ b/videocutler/mask2former_video/modeling/transformer_decoder/position_encoding.py @@ -0,0 +1,57 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# # Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/position_encoding.py +""" +Various positional encodings for the transformer. +""" +import math + +import torch +from torch import nn + + +class PositionEmbeddingSine3D(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, x, mask=None): + # b, t, c, h, w + assert x.dim() == 5, f"{x.shape} should be a 5-dimensional Tensor, got {x.dim()}-dimensional Tensor instead" + if mask is None: + mask = torch.zeros((x.size(0), x.size(1), x.size(3), x.size(4)), device=x.device, dtype=torch.bool) + not_mask = ~mask + z_embed = not_mask.cumsum(1, dtype=torch.float32) + y_embed = not_mask.cumsum(2, dtype=torch.float32) + x_embed = not_mask.cumsum(3, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + z_embed = z_embed / (z_embed[:, -1:, :, :] + eps) * self.scale + y_embed = y_embed / (y_embed[:, :, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + dim_t_z = torch.arange((self.num_pos_feats * 2), dtype=torch.float32, device=x.device) + dim_t_z = self.temperature ** (2 * (dim_t_z // 2) / (self.num_pos_feats * 2)) + + pos_x = x_embed[:, :, :, :, None] / dim_t + pos_y = y_embed[:, :, :, :, None] / dim_t + pos_z = z_embed[:, :, :, :, None] / dim_t_z + pos_x = torch.stack((pos_x[:, :, :, :, 0::2].sin(), pos_x[:, :, :, :, 1::2].cos()), dim=5).flatten(4) + pos_y = torch.stack((pos_y[:, :, :, :, 0::2].sin(), pos_y[:, :, :, :, 1::2].cos()), dim=5).flatten(4) + pos_z = torch.stack((pos_z[:, :, :, :, 0::2].sin(), pos_z[:, :, :, :, 1::2].cos()), dim=5).flatten(4) + pos = (torch.cat((pos_y, pos_x), dim=4) + pos_z).permute(0, 1, 4, 2, 3) # b, t, c, h, w + return pos diff --git a/videocutler/mask2former_video/modeling/transformer_decoder/video_mask2former_transformer_decoder.py b/videocutler/mask2former_video/modeling/transformer_decoder/video_mask2former_transformer_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..06d4e8511e99faa3b104b8588cba6163f548135e --- /dev/null +++ b/videocutler/mask2former_video/modeling/transformer_decoder/video_mask2former_transformer_decoder.py @@ -0,0 +1,474 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py +import logging +import fvcore.nn.weight_init as weight_init +from typing import Optional +import torch +from torch import nn, Tensor +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d + +from mask2former.modeling.transformer_decoder.maskformer_transformer_decoder import TRANSFORMER_DECODER_REGISTRY + +from .position_encoding import PositionEmbeddingSine3D + + +class SelfAttentionLayer(nn.Module): + + def __init__(self, d_model, nhead, dropout=0.0, + activation="relu", normalize_before=False): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + + self.norm = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + q = k = self.with_pos_embed(tgt, query_pos) + tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + tgt = self.norm(tgt) + + return tgt + + def forward_pre(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.norm(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + + return tgt + + def forward(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(tgt, tgt_mask, + tgt_key_padding_mask, query_pos) + return self.forward_post(tgt, tgt_mask, + tgt_key_padding_mask, query_pos) + + +class CrossAttentionLayer(nn.Module): + + def __init__(self, d_model, nhead, dropout=0.0, + activation="relu", normalize_before=False): + super().__init__() + self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + + self.norm = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt, memory, + memory_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + tgt = self.norm(tgt) + + return tgt + + def forward_pre(self, tgt, memory, + memory_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.norm(tgt) + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + + return tgt + + def forward(self, tgt, memory, + memory_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(tgt, memory, memory_mask, + memory_key_padding_mask, pos, query_pos) + return self.forward_post(tgt, memory, memory_mask, + memory_key_padding_mask, pos, query_pos) + + +class FFNLayer(nn.Module): + + def __init__(self, d_model, dim_feedforward=2048, dropout=0.0, + activation="relu", normalize_before=False): + super().__init__() + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm = nn.LayerNorm(d_model) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt): + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) + tgt = tgt + self.dropout(tgt2) + tgt = self.norm(tgt) + return tgt + + def forward_pre(self, tgt): + tgt2 = self.norm(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout(tgt2) + return tgt + + def forward(self, tgt): + if self.normalize_before: + return self.forward_pre(tgt) + return self.forward_post(tgt) + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(F"activation should be relu/gelu, not {activation}.") + + +class MLP(nn.Module): + """ Very simple multi-layer perceptron (also called FFN)""" + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +@TRANSFORMER_DECODER_REGISTRY.register() +class VideoMultiScaleMaskedTransformerDecoder(nn.Module): + + _version = 2 + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + version = local_metadata.get("version", None) + if version is None or version < 2: + # Do not warn if train from scratch + scratch = True + logger = logging.getLogger(__name__) + for k in list(state_dict.keys()): + newk = k + if "static_query" in k: + newk = k.replace("static_query", "query_feat") + if newk != k: + state_dict[newk] = state_dict[k] + del state_dict[k] + scratch = False + + if not scratch: + logger.warning( + f"Weight format of {self.__class__.__name__} have changed! " + "Please upgrade your models. Applying automatic conversion now ..." + ) + + @configurable + def __init__( + self, + in_channels, + mask_classification=True, + *, + num_classes: int, + hidden_dim: int, + num_queries: int, + nheads: int, + dim_feedforward: int, + dec_layers: int, + pre_norm: bool, + mask_dim: int, + enforce_input_project: bool, + # video related + num_frames, + ): + """ + NOTE: this interface is experimental. + Args: + in_channels: channels of the input features + mask_classification: whether to add mask classifier or not + num_classes: number of classes + hidden_dim: Transformer feature dimension + num_queries: number of queries + nheads: number of heads + dim_feedforward: feature dimension in feedforward network + enc_layers: number of Transformer encoder layers + dec_layers: number of Transformer decoder layers + pre_norm: whether to use pre-LayerNorm or not + mask_dim: mask feature dimension + enforce_input_project: add input project 1x1 conv even if input + channels and hidden dim is identical + """ + super().__init__() + + assert mask_classification, "Only support mask classification model" + self.mask_classification = mask_classification + + self.num_frames = num_frames + + # positional encoding + N_steps = hidden_dim // 2 + self.pe_layer = PositionEmbeddingSine3D(N_steps, normalize=True) + + # define Transformer decoder here + self.num_heads = nheads + self.num_layers = dec_layers + self.transformer_self_attention_layers = nn.ModuleList() + self.transformer_cross_attention_layers = nn.ModuleList() + self.transformer_ffn_layers = nn.ModuleList() + + for _ in range(self.num_layers): + self.transformer_self_attention_layers.append( + SelfAttentionLayer( + d_model=hidden_dim, + nhead=nheads, + dropout=0.0, + normalize_before=pre_norm, + ) + ) + + self.transformer_cross_attention_layers.append( + CrossAttentionLayer( + d_model=hidden_dim, + nhead=nheads, + dropout=0.0, + normalize_before=pre_norm, + ) + ) + + self.transformer_ffn_layers.append( + FFNLayer( + d_model=hidden_dim, + dim_feedforward=dim_feedforward, + dropout=0.0, + normalize_before=pre_norm, + ) + ) + + self.decoder_norm = nn.LayerNorm(hidden_dim) + + self.num_queries = num_queries + # learnable query features + self.query_feat = nn.Embedding(num_queries, hidden_dim) + # learnable query p.e. + self.query_embed = nn.Embedding(num_queries, hidden_dim) + + # level embedding (we always use 3 scales) + self.num_feature_levels = 3 + self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim) + self.input_proj = nn.ModuleList() + for _ in range(self.num_feature_levels): + if in_channels != hidden_dim or enforce_input_project: + self.input_proj.append(Conv2d(in_channels, hidden_dim, kernel_size=1)) + weight_init.c2_xavier_fill(self.input_proj[-1]) + else: + self.input_proj.append(nn.Sequential()) + + # output FFNs + if self.mask_classification: + self.class_embed = nn.Linear(hidden_dim, num_classes + 1) + self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3) + + @classmethod + def from_config(cls, cfg, in_channels, mask_classification): + ret = {} + ret["in_channels"] = in_channels + ret["mask_classification"] = mask_classification + + ret["num_classes"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES + ret["hidden_dim"] = cfg.MODEL.MASK_FORMER.HIDDEN_DIM + ret["num_queries"] = cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES + # Transformer parameters: + ret["nheads"] = cfg.MODEL.MASK_FORMER.NHEADS + ret["dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD + + # NOTE: because we add learnable query features which requires supervision, + # we add minus 1 to decoder layers to be consistent with our loss + # implementation: that is, number of auxiliary losses is always + # equal to number of decoder layers. With learnable query features, the number of + # auxiliary losses equals number of decoders plus 1. + assert cfg.MODEL.MASK_FORMER.DEC_LAYERS >= 1 + ret["dec_layers"] = cfg.MODEL.MASK_FORMER.DEC_LAYERS - 1 + ret["pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM + ret["enforce_input_project"] = cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ + + ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + + ret["num_frames"] = cfg.INPUT.SAMPLING_FRAME_NUM + + return ret + + def forward(self, x, mask_features, mask = None): + bt, c_m, h_m, w_m = mask_features.shape + bs = bt // self.num_frames if self.training else 1 + t = bt // bs + mask_features = mask_features.view(bs, t, c_m, h_m, w_m) + + # x is a list of multi-scale feature + assert len(x) == self.num_feature_levels + src = [] + pos = [] + size_list = [] + + # disable mask, it does not affect performance + del mask + + for i in range(self.num_feature_levels): + size_list.append(x[i].shape[-2:]) + pos.append(self.pe_layer(x[i].view(bs, t, -1, size_list[-1][0], size_list[-1][1]), None).flatten(3)) + src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None]) + + # NTxCxHW => NxTxCxHW => (TxHW)xNxC + _, c, hw = src[-1].shape + pos[-1] = pos[-1].view(bs, t, c, hw).permute(1, 3, 0, 2).flatten(0, 1) + src[-1] = src[-1].view(bs, t, c, hw).permute(1, 3, 0, 2).flatten(0, 1) + + # QxNxC + query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1) + output = self.query_feat.weight.unsqueeze(1).repeat(1, bs, 1) + + predictions_class = [] + predictions_mask = [] + + # prediction heads on learnable query features + outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[0]) + predictions_class.append(outputs_class) + predictions_mask.append(outputs_mask) + + for i in range(self.num_layers): + level_index = i % self.num_feature_levels + attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False + # attention: cross-attention first + output = self.transformer_cross_attention_layers[i]( + output, src[level_index], + memory_mask=attn_mask, + memory_key_padding_mask=None, # here we do not apply masking on padded region + pos=pos[level_index], query_pos=query_embed + ) + + output = self.transformer_self_attention_layers[i]( + output, tgt_mask=None, + tgt_key_padding_mask=None, + query_pos=query_embed + ) + + # FFN + output = self.transformer_ffn_layers[i]( + output + ) + + outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels]) + predictions_class.append(outputs_class) + predictions_mask.append(outputs_mask) + + assert len(predictions_class) == self.num_layers + 1 + + out = { + 'pred_logits': predictions_class[-1], + 'pred_masks': predictions_mask[-1], + 'aux_outputs': self._set_aux_loss( + predictions_class if self.mask_classification else None, predictions_mask + ) + } + return out + + def forward_prediction_heads(self, output, mask_features, attn_mask_target_size): + decoder_output = self.decoder_norm(output) + decoder_output = decoder_output.transpose(0, 1) + outputs_class = self.class_embed(decoder_output) + mask_embed = self.mask_embed(decoder_output) + outputs_mask = torch.einsum("bqc,btchw->bqthw", mask_embed, mask_features) + b, q, t, _, _ = outputs_mask.shape + + # NOTE: prediction is of higher-resolution + # [B, Q, T, H, W] -> [B, Q, T*H*W] -> [B, h, Q, T*H*W] -> [B*h, Q, T*HW] + attn_mask = F.interpolate(outputs_mask.flatten(0, 1), size=attn_mask_target_size, mode="bilinear", align_corners=False).view( + b, q, t, attn_mask_target_size[0], attn_mask_target_size[1]) + # must use bool type + # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged. + attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool() + attn_mask = attn_mask.detach() + + return outputs_class, outputs_mask, attn_mask + + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_seg_masks): + # this is a workaround to make torchscript happy, as torchscript + # doesn't support dictionary with non-homogeneous values, such + # as a dict having both a Tensor and a list. + if self.mask_classification: + return [ + {"pred_logits": a, "pred_masks": b} + for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1]) + ] + else: + return [{"pred_masks": b} for b in outputs_seg_masks[:-1]] diff --git a/videocutler/mask2former_video/utils/__init__.py b/videocutler/mask2former_video/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9020c2df23e2af280b7bb168b996ae9eaf312eb8 --- /dev/null +++ b/videocutler/mask2former_video/utils/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. diff --git a/videocutler/mask2former_video/utils/memory.py b/videocutler/mask2former_video/utils/memory.py new file mode 100644 index 0000000000000000000000000000000000000000..7ee5f15d279f85f1738f16e1a5a613d79e26f89a --- /dev/null +++ b/videocutler/mask2former_video/utils/memory.py @@ -0,0 +1,80 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +from contextlib import contextmanager +from functools import wraps +import torch +from torch.cuda.amp import autocast + +__all__ = ["retry_if_cuda_oom"] + + +@contextmanager +def _ignore_torch_cuda_oom(): + """ + A context which ignores CUDA OOM exception from pytorch. + """ + try: + yield + except RuntimeError as e: + # NOTE: the string may change? + if "CUDA out of memory. " in str(e): + pass + else: + raise + + +def retry_if_cuda_oom(func): + """ + Makes a function retry itself after encountering + pytorch's CUDA OOM error. + It will first retry after calling `torch.cuda.empty_cache()`. + If that still fails, it will then retry by trying to convert inputs to CPUs. + In this case, it expects the function to dispatch to CPU implementation. + The return values may become CPU tensors as well and it's user's + responsibility to convert it back to CUDA tensor if needed. + Args: + func: a stateless callable that takes tensor-like objects as arguments + Returns: + a callable which retries `func` if OOM is encountered. + Examples: + :: + output = retry_if_cuda_oom(some_torch_function)(input1, input2) + # output may be on CPU even if inputs are on GPU + Note: + 1. When converting inputs to CPU, it will only look at each argument and check + if it has `.device` and `.to` for conversion. Nested structures of tensors + are not supported. + 2. Since the function might be called more than once, it has to be + stateless. + """ + + def maybe_to_cpu(x): + try: + like_gpu_tensor = x.device.type == "cuda" and hasattr(x, "to") + except AttributeError: + like_gpu_tensor = False + if like_gpu_tensor: + return x.to(device="cpu").to(torch.float32) + else: + return x + + @wraps(func) + def wrapped(*args, **kwargs): + with _ignore_torch_cuda_oom(): + return func(*args, **kwargs) + + # Clear cache and retry + torch.cuda.empty_cache() + with _ignore_torch_cuda_oom(): + return func(*args, **kwargs) + + # Try on CPU. This slows down the code significantly, therefore print a notice. + logger = logging.getLogger(__name__) + logger.info("Attempting to copy inputs to CPU due to CUDA OOM") + new_args = (maybe_to_cpu(x) for x in args) + new_kwargs = {k: maybe_to_cpu(v) for k, v in kwargs.items()} + with autocast(enabled=False): + return func(*new_args, **new_kwargs) + + return wrapped diff --git a/videocutler/mask2former_video/video_maskformer_model.py b/videocutler/mask2former_video/video_maskformer_model.py new file mode 100644 index 0000000000000000000000000000000000000000..4e10fccedcaf434195e0e974642d087e2790870d --- /dev/null +++ b/videocutler/mask2former_video/video_maskformer_model.py @@ -0,0 +1,288 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import math +from typing import Tuple + +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.data import MetadataCatalog +from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head +from detectron2.modeling.backbone import Backbone +from detectron2.modeling.postprocessing import sem_seg_postprocess +from detectron2.structures import Boxes, ImageList, Instances, BitMasks + +from .modeling.criterion import VideoSetCriterion +from .modeling.matcher import VideoHungarianMatcher +from .utils.memory import retry_if_cuda_oom + +logger = logging.getLogger(__name__) + + +@META_ARCH_REGISTRY.register() +class VideoMaskFormer(nn.Module): + """ + Main class for mask classification semantic segmentation architectures. + """ + + @configurable + def __init__( + self, + *, + backbone: Backbone, + sem_seg_head: nn.Module, + criterion: nn.Module, + num_queries: int, + object_mask_threshold: float, + overlap_threshold: float, + metadata, + size_divisibility: int, + sem_seg_postprocess_before_inference: bool, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + # video + num_frames, + ): + """ + Args: + backbone: a backbone module, must follow detectron2's backbone interface + sem_seg_head: a module that predicts semantic segmentation from backbone features + criterion: a module that defines the loss + num_queries: int, number of queries + object_mask_threshold: float, threshold to filter query based on classification score + for panoptic segmentation inference + overlap_threshold: overlap threshold used in general inference for panoptic segmentation + metadata: dataset meta, get `thing` and `stuff` category names for panoptic + segmentation inference + size_divisibility: Some backbones require the input height and width to be divisible by a + specific integer. We can use this to override such requirement. + sem_seg_postprocess_before_inference: whether to resize the prediction back + to original input size before semantic segmentation inference or after. + For high-resolution dataset like Mapillary, resizing predictions before + inference will cause OOM error. + pixel_mean, pixel_std: list or tuple with #channels element, representing + the per-channel mean and std to be used to normalize the input image + semantic_on: bool, whether to output semantic segmentation prediction + instance_on: bool, whether to output instance segmentation prediction + panoptic_on: bool, whether to output panoptic segmentation prediction + test_topk_per_image: int, instance segmentation parameter, keep topk instances per image + """ + super().__init__() + self.backbone = backbone + self.sem_seg_head = sem_seg_head + self.criterion = criterion + self.num_queries = num_queries + self.overlap_threshold = overlap_threshold + self.object_mask_threshold = object_mask_threshold + self.metadata = metadata + if size_divisibility < 0: + # use backbone size_divisibility if not set + size_divisibility = self.backbone.size_divisibility + self.size_divisibility = size_divisibility + self.sem_seg_postprocess_before_inference = sem_seg_postprocess_before_inference + self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False) + + self.num_frames = num_frames + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape()) + + # Loss parameters: + deep_supervision = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION + no_object_weight = cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT + + # loss weights + class_weight = cfg.MODEL.MASK_FORMER.CLASS_WEIGHT + dice_weight = cfg.MODEL.MASK_FORMER.DICE_WEIGHT + mask_weight = cfg.MODEL.MASK_FORMER.MASK_WEIGHT + + # building criterion + matcher = VideoHungarianMatcher( + cost_class=class_weight, + cost_mask=mask_weight, + cost_dice=dice_weight, + num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS, + ) + + weight_dict = {"loss_ce": class_weight, "loss_mask": mask_weight, "loss_dice": dice_weight} + + if deep_supervision: + dec_layers = cfg.MODEL.MASK_FORMER.DEC_LAYERS + aux_weight_dict = {} + for i in range(dec_layers - 1): + aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) + weight_dict.update(aux_weight_dict) + + losses = ["labels", "masks"] + + criterion = VideoSetCriterion( + sem_seg_head.num_classes, + matcher=matcher, + weight_dict=weight_dict, + eos_coef=no_object_weight, + losses=losses, + num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS, + oversample_ratio=cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO, + importance_sample_ratio=cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO, + ) + + return { + "backbone": backbone, + "sem_seg_head": sem_seg_head, + "criterion": criterion, + "num_queries": cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES, + "object_mask_threshold": cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD, + "overlap_threshold": cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD, + "metadata": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), + "size_divisibility": cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY, + "sem_seg_postprocess_before_inference": True, + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + # video + "num_frames": cfg.INPUT.SAMPLING_FRAME_NUM, + } + + @property + def device(self): + return self.pixel_mean.device + + def forward(self, batched_inputs): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper`. + Each item in the list contains the inputs for one image. + For now, each item in the list is a dict that contains: + * "image": Tensor, image in (C, H, W) format. + * "instances": per-region ground truth + * Other information that's included in the original dicts, such as: + "height", "width" (int): the output resolution of the model (may be different + from input resolution), used in inference. + Returns: + list[dict]: + each dict has the results for one image. The dict contains the following keys: + + * "sem_seg": + A Tensor that represents the + per-pixel segmentation prediced by the head. + The prediction has shape KxHxW that represents the logits of + each class for each pixel. + * "panoptic_seg": + A tuple that represent panoptic output + panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment. + segments_info (list[dict]): Describe each segment in `panoptic_seg`. + Each dict contains keys "id", "category_id", "isthing". + """ + images = [] + for video in batched_inputs: + for frame in video["image"]: + images.append(frame.to(self.device)) + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, self.size_divisibility) + + features = self.backbone(images.tensor) + outputs = self.sem_seg_head(features) + + if self.training: + # mask classification target + targets = self.prepare_targets(batched_inputs, images) + + # bipartite matching-based loss + losses = self.criterion(outputs, targets) + + for k in list(losses.keys()): + if k in self.criterion.weight_dict: + losses[k] *= self.criterion.weight_dict[k] + else: + # remove this loss if not specified in `weight_dict` + losses.pop(k) + return losses + else: + mask_cls_results = outputs["pred_logits"] + mask_pred_results = outputs["pred_masks"] + + mask_cls_result = mask_cls_results[0] + # upsample masks + mask_pred_result = retry_if_cuda_oom(F.interpolate)( + mask_pred_results[0], + size=(images.tensor.shape[-2], images.tensor.shape[-1]), + mode="bilinear", + align_corners=False, + ) + + del outputs + + input_per_image = batched_inputs[0] + image_size = images.image_sizes[0] # image size without padding after data augmentation + + height = input_per_image.get("height", image_size[0]) # raw image size before data augmentation + width = input_per_image.get("width", image_size[1]) + + return retry_if_cuda_oom(self.inference_video)(mask_cls_result, mask_pred_result, image_size, height, width) + + def prepare_targets(self, targets, images): + h_pad, w_pad = images.tensor.shape[-2:] + gt_instances = [] + for i, targets_per_video in enumerate(targets): + _num_instance = len(targets_per_video["instances"][0]) + mask_shape = [_num_instance, self.num_frames, h_pad, w_pad] + gt_masks_per_video = torch.zeros(mask_shape, dtype=torch.bool, device=self.device) + + gt_ids_per_video = [] + for f_i, targets_per_frame in enumerate(targets_per_video["instances"]): + targets_per_frame = targets_per_frame.to(self.device) + h, w = targets_per_frame.image_size + + gt_ids_per_video.append(targets_per_frame.gt_ids[:, None]) + gt_masks_per_video[:, f_i, :h, :w] = targets_per_frame.gt_masks.tensor + + gt_ids_per_video = torch.cat(gt_ids_per_video, dim=1) + valid_idx = (gt_ids_per_video != -1).any(dim=-1) + + gt_classes_per_video = targets_per_frame.gt_classes[valid_idx] # N, + gt_ids_per_video = gt_ids_per_video[valid_idx] # N, num_frames + + gt_instances.append({"labels": gt_classes_per_video, "ids": gt_ids_per_video}) + gt_masks_per_video = gt_masks_per_video[valid_idx].float() # N, num_frames, H, W + gt_instances[-1].update({"masks": gt_masks_per_video}) + + return gt_instances + + def inference_video(self, pred_cls, pred_masks, img_size, output_height, output_width): + if len(pred_cls) > 0: + scores = F.softmax(pred_cls, dim=-1)[:, :-1] + labels = torch.arange(self.sem_seg_head.num_classes, device=self.device).unsqueeze(0).repeat(self.num_queries, 1).flatten(0, 1) + # keep top-10 predictions + # TODO: make it configurable + scores_per_image, topk_indices = scores.flatten(0, 1).topk(10, sorted=False) + labels_per_image = labels[topk_indices] + topk_indices = topk_indices // self.sem_seg_head.num_classes + pred_masks = pred_masks[topk_indices] + + pred_masks = pred_masks[:, :, : img_size[0], : img_size[1]] + pred_masks = F.interpolate( + pred_masks, size=(output_height, output_width), mode="bilinear", align_corners=False + ) + + masks = pred_masks > 0. + + out_scores = scores_per_image.tolist() + out_labels = labels_per_image.tolist() + out_masks = [m for m in masks.cpu()] + else: + out_scores = [] + out_labels = [] + out_masks = [] + + video_output = { + "image_size": (output_height, output_width), + "pred_scores": out_scores, + "pred_labels": out_labels, + "pred_masks": out_masks, + } + + return video_output diff --git a/videocutler/predict.py b/videocutler/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..453479c2429b65aa37bb8f8cdad70101ea7f6988 --- /dev/null +++ b/videocutler/predict.py @@ -0,0 +1,54 @@ +import sys +sys.path.insert(0, "Mask2Former") +import tempfile +from pathlib import Path +import numpy as np +import cv2 +import cog + +# import some common detectron2 utilities +from detectron2.config import CfgNode as CN +from detectron2.engine import DefaultPredictor +from detectron2.config import get_cfg +from detectron2.utils.visualizer import Visualizer, ColorMode +from detectron2.data import MetadataCatalog +from detectron2.projects.deeplab import add_deeplab_config + +# import Mask2Former project +from mask2former import add_maskformer2_config + + +class Predictor(cog.Predictor): + def setup(self): + cfg = get_cfg() + add_deeplab_config(cfg) + add_maskformer2_config(cfg) + cfg.merge_from_file("Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml") + cfg.MODEL.WEIGHTS = 'model_final_f07440.pkl' + cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True + cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = True + cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = True + self.predictor = DefaultPredictor(cfg) + self.coco_metadata = MetadataCatalog.get("coco_2017_val_panoptic") + + + @cog.input( + "image", + type=Path, + help="Input image for segmentation. Output will be the concatenation of Panoptic segmentation (top), " + "instance segmentation (middle), and semantic segmentation (bottom).", + ) + def predict(self, image): + im = cv2.imread(str(image)) + outputs = self.predictor(im) + v = Visualizer(im[:, :, ::-1], self.coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW) + panoptic_result = v.draw_panoptic_seg(outputs["panoptic_seg"][0].to("cpu"), + outputs["panoptic_seg"][1]).get_image() + v = Visualizer(im[:, :, ::-1], self.coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW) + instance_result = v.draw_instance_predictions(outputs["instances"].to("cpu")).get_image() + v = Visualizer(im[:, :, ::-1], self.coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW) + semantic_result = v.draw_sem_seg(outputs["sem_seg"].argmax(0).to("cpu")).get_image() + result = np.concatenate((panoptic_result, instance_result, semantic_result), axis=0)[:, :, ::-1] + out_path = Path(tempfile.mkdtemp()) / "out.png" + cv2.imwrite(str(out_path), result) + return out_path diff --git a/videocutler/requirements.txt b/videocutler/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..807eca71efb65d6ff9df15bc1ecd645c22b8ec25 --- /dev/null +++ b/videocutler/requirements.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0299a9b2dbbfb7962bdcab0402948c378820dcb7f066219b1debdde48d068487 +size 53 diff --git a/videocutler/single-node-video_run.sh b/videocutler/single-node-video_run.sh new file mode 100644 index 0000000000000000000000000000000000000000..e2656d2dcf878067f956f439d11f4ffbccaf37ce --- /dev/null +++ b/videocutler/single-node-video_run.sh @@ -0,0 +1,7 @@ +#!/bin/bash +MASTER_NODE=$(scontrol show hostname "$SLURM_NODELIST" | head -n1) +DIST_URL="tcp://$MASTER_NODE:12392" +SOCKET_NAME=$(ip r | grep default | awk '{print $5}') +export GLOO_SOCKET_IFNAME=ens32 + +python -u train_net_video.py --num-gpus 8 --num-machines 1 --machine-rank "$SLURM_NODEID" --dist-url "$DIST_URL" "$@" diff --git a/videocutler/tools/README.md b/videocutler/tools/README.md new file mode 100644 index 0000000000000000000000000000000000000000..309f767b8f3ae10208107e72032f64d18f429a8c --- /dev/null +++ b/videocutler/tools/README.md @@ -0,0 +1,79 @@ +This directory contains few tools for MaskFormer. + +* `convert-torchvision-to-d2.py` + +Tool to convert torchvision pre-trained weights for D2. + +``` +wget https://download.pytorch.org/models/resnet101-63fe2227.pth +python tools/convert-torchvision-to-d2.py resnet101-63fe2227.pth R-101.pkl +``` + +* `convert-pretrained-swin-model-to-d2.py` + +Tool to convert Swin Transformer pre-trained weights for D2. + +``` +pip install timm + +wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth +python tools/convert-pretrained-swin-model-to-d2.py swin_tiny_patch4_window7_224.pth swin_tiny_patch4_window7_224.pkl + +wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth +python tools/convert-pretrained-swin-model-to-d2.py swin_small_patch4_window7_224.pth swin_small_patch4_window7_224.pkl + +wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth +python tools/convert-pretrained-swin-model-to-d2.py swin_base_patch4_window12_384_22k.pth swin_base_patch4_window12_384_22k.pkl + +wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth +python tools/convert-pretrained-swin-model-to-d2.py swin_large_patch4_window12_384_22k.pth swin_large_patch4_window12_384_22k.pkl +``` + +* `evaluate_pq_for_semantic_segmentation.py` + +Tool to evaluate PQ (PQ-stuff) for semantic segmentation predictions. + +Usage: + +``` +python tools/evaluate_pq_for_semantic_segmentation.py --dataset-name ade20k_sem_seg_val --json-file OUTPUT_DIR/inference/sem_seg_predictions.json +``` + +where `OUTPUT_DIR` is set in the config file. + +* `evaluate_coco_boundary_ap.py` + +Tool to evaluate Boundary AP for instance segmentation predictions. + +Usage: + +``` +python tools/coco_instance_evaluation.py --gt-json-file COCO_GT_JSON --dt-json-file COCO_DT_JSON +``` + +To install Boundary IoU API, run: + +``` +pip install git+https://github.com/bowenc0221/boundary-iou-api.git +``` + +* `analyze_model.py` + +Tool to analyze model parameters and flops. + +Usage for semantic segmentation (ADE20K only, use with caution!): + +``` +python tools/analyze_model.py --num-inputs 1 --tasks flop --use-fixed-input-size --config-file CONFIG_FILE +``` + +Note that, for semantic segmentation (ADE20K only), we use a dummy image with fixed size that equals to `cfg.INPUT.CROP.SIZE[0] x cfg.INPUT.CROP.SIZE[0]`. +Please do not use `--use-fixed-input-size` for calculating FLOPs on other datasets like Cityscapes! + +Usage for panoptic and instance segmentation: + +``` +python tools/analyze_model.py --num-inputs 100 --tasks flop --config-file CONFIG_FILE +``` + +Note that, for panoptic and instance segmentation, we compute the average flops over 100 real validation images. diff --git a/videocutler/tools/analyze_model.py b/videocutler/tools/analyze_model.py new file mode 100644 index 0000000000000000000000000000000000000000..a92227e2a530bbdcbdf6d72bc9ed93886f24a7bb --- /dev/null +++ b/videocutler/tools/analyze_model.py @@ -0,0 +1,177 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/facebookresearch/detectron2/blob/main/tools/analyze_model.py + +import logging +import numpy as np +from collections import Counter +import tqdm +from fvcore.nn import flop_count_table # can also try flop_count_str + +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate +from detectron2.data import build_detection_test_loader +from detectron2.engine import default_argument_parser +from detectron2.modeling import build_model +from detectron2.projects.deeplab import add_deeplab_config +from detectron2.utils.analysis import ( + FlopCountAnalysis, + activation_count_operators, + parameter_count_table, +) +from detectron2.utils.logger import setup_logger + +# fmt: off +import os +import sys +sys.path.insert(1, os.path.join(sys.path[0], '..')) +# fmt: on + +from mask2former import add_maskformer2_config + +logger = logging.getLogger("detectron2") + + +def setup(args): + if args.config_file.endswith(".yaml"): + cfg = get_cfg() + add_deeplab_config(cfg) + add_maskformer2_config(cfg) + cfg.merge_from_file(args.config_file) + cfg.DATALOADER.NUM_WORKERS = 0 + cfg.merge_from_list(args.opts) + cfg.freeze() + else: + cfg = LazyConfig.load(args.config_file) + cfg = LazyConfig.apply_overrides(cfg, args.opts) + setup_logger(name="fvcore") + setup_logger() + return cfg + + +def do_flop(cfg): + if isinstance(cfg, CfgNode): + data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0]) + model = build_model(cfg) + DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS) + else: + data_loader = instantiate(cfg.dataloader.test) + model = instantiate(cfg.model) + model.to(cfg.train.device) + DetectionCheckpointer(model).load(cfg.train.init_checkpoint) + model.eval() + + counts = Counter() + total_flops = [] + for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa + if args.use_fixed_input_size and isinstance(cfg, CfgNode): + import torch + crop_size = cfg.INPUT.CROP.SIZE[0] + data[0]["image"] = torch.zeros((3, crop_size, crop_size)) + flops = FlopCountAnalysis(model, data) + if idx > 0: + flops.unsupported_ops_warnings(False).uncalled_modules_warnings(False) + counts += flops.by_operator() + total_flops.append(flops.total()) + + logger.info("Flops table computed from only one input sample:\n" + flop_count_table(flops)) + logger.info( + "Average GFlops for each type of operators:\n" + + str([(k, v / (idx + 1) / 1e9) for k, v in counts.items()]) + ) + logger.info( + "Total GFlops: {:.1f}±{:.1f}".format(np.mean(total_flops) / 1e9, np.std(total_flops) / 1e9) + ) + + +def do_activation(cfg): + if isinstance(cfg, CfgNode): + data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0]) + model = build_model(cfg) + DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS) + else: + data_loader = instantiate(cfg.dataloader.test) + model = instantiate(cfg.model) + model.to(cfg.train.device) + DetectionCheckpointer(model).load(cfg.train.init_checkpoint) + model.eval() + + counts = Counter() + total_activations = [] + for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa + count = activation_count_operators(model, data) + counts += count + total_activations.append(sum(count.values())) + logger.info( + "(Million) Activations for Each Type of Operators:\n" + + str([(k, v / idx) for k, v in counts.items()]) + ) + logger.info( + "Total (Million) Activations: {}±{}".format( + np.mean(total_activations), np.std(total_activations) + ) + ) + + +def do_parameter(cfg): + if isinstance(cfg, CfgNode): + model = build_model(cfg) + else: + model = instantiate(cfg.model) + logger.info("Parameter Count:\n" + parameter_count_table(model, max_depth=5)) + + +def do_structure(cfg): + if isinstance(cfg, CfgNode): + model = build_model(cfg) + else: + model = instantiate(cfg.model) + logger.info("Model Structure:\n" + str(model)) + + +if __name__ == "__main__": + parser = default_argument_parser( + epilog=""" +Examples: +To show parameters of a model: +$ ./analyze_model.py --tasks parameter \\ + --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml +Flops and activations are data-dependent, therefore inputs and model weights +are needed to count them: +$ ./analyze_model.py --num-inputs 100 --tasks flop \\ + --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \\ + MODEL.WEIGHTS /path/to/model.pkl +""" + ) + parser.add_argument( + "--tasks", + choices=["flop", "activation", "parameter", "structure"], + required=True, + nargs="+", + ) + parser.add_argument( + "-n", + "--num-inputs", + default=100, + type=int, + help="number of inputs used to compute statistics for flops/activations, " + "both are data dependent.", + ) + parser.add_argument( + "--use-fixed-input-size", + action="store_true", + help="use fixed input size when calculating flops", + ) + args = parser.parse_args() + assert not args.eval_only + assert args.num_gpus == 1 + + cfg = setup(args) + + for task in args.tasks: + { + "flop": do_flop, + "activation": do_activation, + "parameter": do_parameter, + "structure": do_structure, + }[task](cfg) diff --git a/videocutler/tools/convert-pretrained-swin-model-to-d2.py b/videocutler/tools/convert-pretrained-swin-model-to-d2.py new file mode 100644 index 0000000000000000000000000000000000000000..8fbaeab990743e60bfe9f6197cbf3bf8585abcb6 --- /dev/null +++ b/videocutler/tools/convert-pretrained-swin-model-to-d2.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import pickle as pkl +import sys + +import torch + +""" +Usage: + # download pretrained swin model: + wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth + # run the conversion + ./convert-pretrained-model-to-d2.py swin_tiny_patch4_window7_224.pth swin_tiny_patch4_window7_224.pkl + # Then, use swin_tiny_patch4_window7_224.pkl with the following changes in config: +MODEL: + WEIGHTS: "/path/to/swin_tiny_patch4_window7_224.pkl" +INPUT: + FORMAT: "RGB" +""" + +if __name__ == "__main__": + input = sys.argv[1] + + obj = torch.load(input, map_location="cpu")["model"] + + res = {"model": obj, "__author__": "third_party", "matching_heuristics": True} + + with open(sys.argv[2], "wb") as f: + pkl.dump(res, f) diff --git a/videocutler/tools/convert-torchvision-to-d2.py b/videocutler/tools/convert-torchvision-to-d2.py new file mode 100644 index 0000000000000000000000000000000000000000..060390a2c26297d7d9ee97ceb601453c9372f1d5 --- /dev/null +++ b/videocutler/tools/convert-torchvision-to-d2.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. + +import pickle as pkl +import sys + +import torch + +""" +Usage: + # download one of the ResNet{18,34,50,101,152} models from torchvision: + wget https://download.pytorch.org/models/resnet50-19c8e357.pth -O r50.pth + # run the conversion + ./convert-torchvision-to-d2.py r50.pth r50.pkl + # Then, use r50.pkl with the following changes in config: +MODEL: + WEIGHTS: "/path/to/r50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STRIDE_IN_1X1: False +INPUT: + FORMAT: "RGB" +""" + +if __name__ == "__main__": + input = sys.argv[1] + + obj = torch.load(input, map_location="cpu") + + newmodel = {} + for k in list(obj.keys()): + old_k = k + if "layer" not in k: + k = "stem." + k + for t in [1, 2, 3, 4]: + k = k.replace("layer{}".format(t), "res{}".format(t + 1)) + for t in [1, 2, 3]: + k = k.replace("bn{}".format(t), "conv{}.norm".format(t)) + k = k.replace("downsample.0", "shortcut") + k = k.replace("downsample.1", "shortcut.norm") + print(old_k, "->", k) + newmodel[k] = obj.pop(old_k).detach().numpy() + + res = {"model": newmodel, "__author__": "torchvision", "matching_heuristics": True} + + with open(sys.argv[2], "wb") as f: + pkl.dump(res, f) + if obj: + print("Unconverted keys:", obj.keys()) diff --git a/videocutler/tools/evaluate_coco_boundary_ap.py b/videocutler/tools/evaluate_coco_boundary_ap.py new file mode 100644 index 0000000000000000000000000000000000000000..1e96b5d2f7d3f0987098904e8f5d97854906d58a --- /dev/null +++ b/videocutler/tools/evaluate_coco_boundary_ap.py @@ -0,0 +1,49 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# Modified by Bowen Cheng from: https://github.com/bowenc0221/boundary-iou-api/blob/master/tools/coco_instance_evaluation.py + +""" +Evaluation for COCO val2017: +python ./tools/coco_instance_evaluation.py \ + --gt-json-file COCO_GT_JSON \ + --dt-json-file COCO_DT_JSON +""" +import argparse +import json + +from boundary_iou.coco_instance_api.coco import COCO +from boundary_iou.coco_instance_api.cocoeval import COCOeval + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--gt-json-file", default="") + parser.add_argument("--dt-json-file", default="") + parser.add_argument("--iou-type", default="boundary") + parser.add_argument("--dilation-ratio", default="0.020", type=float) + args = parser.parse_args() + print(args) + + annFile = args.gt_json_file + resFile = args.dt_json_file + dilation_ratio = args.dilation_ratio + if args.iou_type == "boundary": + get_boundary = True + else: + get_boundary = False + cocoGt = COCO(annFile, get_boundary=get_boundary, dilation_ratio=dilation_ratio) + + # remove box predictions + resFile = json.load(open(resFile)) + for c in resFile: + c.pop("bbox", None) + + cocoDt = cocoGt.loadRes(resFile) + cocoEval = COCOeval(cocoGt, cocoDt, iouType=args.iou_type, dilation_ratio=dilation_ratio) + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + + +if __name__ == '__main__': + main() diff --git a/videocutler/tools/evaluate_pq_for_semantic_segmentation.py b/videocutler/tools/evaluate_pq_for_semantic_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..d22d735b632d885bb213de7804dad68b6068a8d0 --- /dev/null +++ b/videocutler/tools/evaluate_pq_for_semantic_segmentation.py @@ -0,0 +1,245 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. + +import argparse +import json +import os +from collections import defaultdict +from tqdm import tqdm + +import numpy as np +import torch + +from detectron2.data import MetadataCatalog +from detectron2.data.detection_utils import read_image +from detectron2.utils.file_io import PathManager +from pycocotools import mask as maskUtils + +from panopticapi.evaluation import PQStat + + +def default_argument_parser(): + """ + Creates a parser with some common arguments used by analysis tools. + Returns: + argparse.ArgumentParser: + """ + parser = argparse.ArgumentParser(description="Evaluate PQ metric for semantic segmentation.") + # NOTE: currently does not support Cityscapes, you need to convert + # Cityscapes prediction format to Detectron2 prediction format. + parser.add_argument( + "--dataset-name", + default="ade20k_sem_seg_val", + choices=["ade20k_sem_seg_val", "coco_2017_test_stuff_10k_sem_seg", "ade20k_full_sem_seg_val"], + help="dataset name you want to evaluate") + parser.add_argument("--json-file", default="", help="path to detection json file") + + return parser + + +# Modified from the official panoptic api: https://github.com/cocodataset/panopticapi/blob/master/panopticapi/evaluation.py +def pq_compute_single_image(segm_gt, segm_dt, categories, ignore_label): + pq_stat = PQStat() + VOID = ignore_label + OFFSET = 256 * 256 * 256 + + pan_gt = segm_gt + pan_pred = segm_dt + + gt_ann = {'segments_info': []} + labels, labels_cnt = np.unique(segm_gt, return_counts=True) + for cat_id, cnt in zip(labels, labels_cnt): + if cat_id == VOID: + continue + gt_ann['segments_info'].append( + {"id": cat_id, "category_id": cat_id, "area": cnt, "iscrowd": 0} + ) + + pred_ann = {'segments_info': []} + for cat_id in np.unique(segm_dt): + pred_ann['segments_info'].append({"id": cat_id, "category_id": cat_id}) + + gt_segms = {el['id']: el for el in gt_ann['segments_info']} + pred_segms = {el['id']: el for el in pred_ann['segments_info']} + + # predicted segments area calculation + prediction sanity checks + pred_labels_set = set(el['id'] for el in pred_ann['segments_info']) + labels, labels_cnt = np.unique(pan_pred, return_counts=True) + for label, label_cnt in zip(labels, labels_cnt): + if label not in pred_segms: + if label == VOID: + continue + raise KeyError('In the image with ID {} segment with ID {} is presented in PNG and not presented in JSON.'.format(image_id, label)) + pred_segms[label]['area'] = label_cnt + pred_labels_set.remove(label) + if pred_segms[label]['category_id'] not in categories: + raise KeyError('In the image with ID {} segment with ID {} has unknown category_id {}.'.format(image_id, label, pred_segms[label]['category_id'])) + if len(pred_labels_set) != 0: + raise KeyError('In the image with ID {} the following segment IDs {} are presented in JSON and not presented in PNG.'.format(image_id, list(pred_labels_set))) + + # confusion matrix calculation + pan_gt_pred = pan_gt.astype(np.uint64) * OFFSET + pan_pred.astype(np.uint64) + gt_pred_map = {} + labels, labels_cnt = np.unique(pan_gt_pred, return_counts=True) + for label, intersection in zip(labels, labels_cnt): + gt_id = label // OFFSET + pred_id = label % OFFSET + gt_pred_map[(gt_id, pred_id)] = intersection + + # count all matched pairs + gt_matched = set() + pred_matched = set() + for label_tuple, intersection in gt_pred_map.items(): + gt_label, pred_label = label_tuple + if gt_label not in gt_segms: + continue + if pred_label not in pred_segms: + continue + if gt_segms[gt_label]['iscrowd'] == 1: + continue + if gt_segms[gt_label]['category_id'] != pred_segms[pred_label]['category_id']: + continue + + union = pred_segms[pred_label]['area'] + gt_segms[gt_label]['area'] - intersection - gt_pred_map.get((VOID, pred_label), 0) + iou = intersection / union + if iou > 0.5: + pq_stat[gt_segms[gt_label]['category_id']].tp += 1 + pq_stat[gt_segms[gt_label]['category_id']].iou += iou + gt_matched.add(gt_label) + pred_matched.add(pred_label) + + # count false positives + crowd_labels_dict = {} + for gt_label, gt_info in gt_segms.items(): + if gt_label in gt_matched: + continue + # crowd segments are ignored + if gt_info['iscrowd'] == 1: + crowd_labels_dict[gt_info['category_id']] = gt_label + continue + pq_stat[gt_info['category_id']].fn += 1 + + # count false positives + for pred_label, pred_info in pred_segms.items(): + if pred_label in pred_matched: + continue + # intersection of the segment with VOID + intersection = gt_pred_map.get((VOID, pred_label), 0) + # plus intersection with corresponding CROWD region if it exists + if pred_info['category_id'] in crowd_labels_dict: + intersection += gt_pred_map.get((crowd_labels_dict[pred_info['category_id']], pred_label), 0) + # predicted segment is ignored if more than half of the segment correspond to VOID and CROWD regions + if intersection / pred_info['area'] > 0.5: + continue + pq_stat[pred_info['category_id']].fp += 1 + + return pq_stat + + +def main(): + parser = default_argument_parser() + args = parser.parse_args() + + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + json_file = args.json_file + + with open(json_file) as f: + predictions = json.load(f) + + imgToAnns = defaultdict(list) + for pred in predictions: + image_id = os.path.basename(pred["file_name"]).split(".")[0] + imgToAnns[image_id].append( + {"category_id" : pred["category_id"], "segmentation" : pred["segmentation"]} + ) + + image_ids = list(imgToAnns.keys()) + + meta = MetadataCatalog.get(args.dataset_name) + class_names = meta.stuff_classes + num_classes = len(meta.stuff_classes) + ignore_label = meta.ignore_label + conf_matrix = np.zeros((num_classes + 1, num_classes + 1), dtype=np.int64) + + categories = {} + for i in range(num_classes): + categories[i] = {"id": i, "name": class_names[i], "isthing": 0} + + pq_stat = PQStat() + + for image_id in tqdm(image_ids): + if args.dataset_name == "ade20k_sem_seg_val": + gt_dir = os.path.join(_root, "ADEChallengeData2016", "annotations_detectron2", "validation") + segm_gt = read_image(os.path.join(gt_dir, image_id + ".png")).copy().astype(np.int64) + elif args.dataset_name == "coco_2017_test_stuff_10k_sem_seg": + gt_dir = os.path.join(_root, "coco", "coco_stuff_10k", "annotations_detectron2", "test") + segm_gt = read_image(os.path.join(gt_dir, image_id + ".png")).copy().astype(np.int64) + elif args.dataset_name == "ade20k_full_sem_seg_val": + gt_dir = os.path.join(_root, "ADE20K_2021_17_01", "annotations_detectron2", "validation") + segm_gt = read_image(os.path.join(gt_dir, image_id + ".tif")).copy().astype(np.int64) + else: + raise ValueError(f"Unsupported dataset {args.dataset_name}") + + # get predictions + segm_dt = np.zeros_like(segm_gt) + anns = imgToAnns[image_id] + for ann in anns: + # map back category_id + if hasattr(meta, "stuff_dataset_id_to_contiguous_id"): + if ann["category_id"] in meta.stuff_dataset_id_to_contiguous_id: + category_id = meta.stuff_dataset_id_to_contiguous_id[ann["category_id"]] + else: + category_id = ann["category_id"] + mask = maskUtils.decode(ann["segmentation"]) + segm_dt[mask > 0] = category_id + + # miou + gt = segm_gt.copy() + pred = segm_dt.copy() + gt[gt == ignore_label] = num_classes + conf_matrix += np.bincount( + (num_classes + 1) * pred.reshape(-1) + gt.reshape(-1), + minlength=conf_matrix.size, + ).reshape(conf_matrix.shape) + + # pq + pq_stat_single = pq_compute_single_image(segm_gt, segm_dt, categories, meta.ignore_label) + pq_stat += pq_stat_single + + metrics = [("All", None), ("Stuff", False)] + results = {} + for name, isthing in metrics: + results[name], per_class_results = pq_stat.pq_average(categories, isthing=isthing) + if name == 'All': + results['per_class'] = per_class_results + print("{:10s}| {:>5s} {:>5s} {:>5s} {:>5s}".format("", "PQ", "SQ", "RQ", "N")) + print("-" * (10 + 7 * 4)) + + for name, _isthing in metrics: + print("{:10s}| {:5.1f} {:5.1f} {:5.1f} {:5d}".format( + name, + 100 * results[name]['pq'], + 100 * results[name]['sq'], + 100 * results[name]['rq'], + results[name]['n']) + ) + + # calculate miou + acc = np.full(num_classes, np.nan, dtype=np.float64) + iou = np.full(num_classes, np.nan, dtype=np.float64) + tp = conf_matrix.diagonal()[:-1].astype(np.float64) + pos_gt = np.sum(conf_matrix[:-1, :-1], axis=0).astype(np.float64) + pos_pred = np.sum(conf_matrix[:-1, :-1], axis=1).astype(np.float64) + acc_valid = pos_gt > 0 + acc[acc_valid] = tp[acc_valid] / pos_gt[acc_valid] + iou_valid = (pos_gt + pos_pred) > 0 + union = pos_gt + pos_pred - tp + iou[acc_valid] = tp[acc_valid] / union[acc_valid] + miou = np.sum(iou[acc_valid]) / np.sum(iou_valid) + + print("") + print(f"mIoU: {miou}") + + +if __name__ == '__main__': + main() diff --git a/videocutler/train-1node.sh b/videocutler/train-1node.sh new file mode 100644 index 0000000000000000000000000000000000000000..6a4ed3c2c31b1722e9b31ab2b73ef7ea6f32afad --- /dev/null +++ b/videocutler/train-1node.sh @@ -0,0 +1,11 @@ +#!/bin/bash +#SBATCH -p learnfair +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --gres=gpu:8 +#SBATCH --gpus-per-node=8 +#SBATCH --cpus-per-task=48 +#SBATCH --time 10000 +#SBATCH -o "submitit/videocutler/slurm-%j.out" + +srun single-node-video_run.sh $@ \ No newline at end of file diff --git a/videocutler/train_net.py b/videocutler/train_net.py new file mode 100644 index 0000000000000000000000000000000000000000..09563c12df8e40c04008adb48a8bf7f4e60b8a51 --- /dev/null +++ b/videocutler/train_net.py @@ -0,0 +1,350 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +MaskFormer Training Script. + +This script is a simplified version of the training script in detectron2/tools. +""" +try: + # ignore ShapelyDeprecationWarning from fvcore + from shapely.errors import ShapelyDeprecationWarning + import warnings + warnings.filterwarnings('ignore', category=ShapelyDeprecationWarning) +except: + pass + +import copy +import itertools +import logging +import os + +from collections import OrderedDict +from typing import Any, Dict, List, Set + +import torch + +import detectron2.utils.comm as comm +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import get_cfg +from detectron2.data import MetadataCatalog, build_detection_train_loader +from detectron2.engine import ( + DefaultTrainer, + default_argument_parser, + default_setup, + launch, +) +from detectron2.evaluation import ( + CityscapesInstanceEvaluator, + CityscapesSemSegEvaluator, + COCOEvaluator, + COCOPanopticEvaluator, + DatasetEvaluators, + LVISEvaluator, + SemSegEvaluator, + verify_results, +) +from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler +from detectron2.solver.build import maybe_add_gradient_clipping +from detectron2.utils.logger import setup_logger + +# MaskFormer +from mask2former import ( + COCOInstanceNewBaselineDatasetMapper, + COCOPanopticNewBaselineDatasetMapper, + InstanceSegEvaluator, + MaskFormerInstanceDatasetMapper, + MaskFormerPanopticDatasetMapper, + MaskFormerSemanticDatasetMapper, + SemanticSegmentorWithTTA, + add_maskformer2_config, +) + +import random + +# setup wandb +import wandb + +class Trainer(DefaultTrainer): + """ + Extension of the Trainer class adapted to MaskFormer. + """ + + @classmethod + def build_evaluator(cls, cfg, dataset_name, output_folder=None): + """ + Create evaluator(s) for a given dataset. + This uses the special metadata "evaluator_type" associated with each + builtin dataset. For your own dataset, you can simply create an + evaluator manually in your script and do not have to worry about the + hacky if-else logic here. + """ + if output_folder is None: + output_folder = os.path.join(cfg.OUTPUT_DIR, "inference") + evaluator_list = [] + evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type + # semantic segmentation + if evaluator_type in ["sem_seg", "ade20k_panoptic_seg"]: + evaluator_list.append( + SemSegEvaluator( + dataset_name, + distributed=True, + output_dir=output_folder, + ) + ) + # instance segmentation + if evaluator_type == "coco": + evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder)) + # panoptic segmentation + if evaluator_type in [ + "coco_panoptic_seg", + "ade20k_panoptic_seg", + "cityscapes_panoptic_seg", + "mapillary_vistas_panoptic_seg", + ]: + if cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON: + evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder)) + # COCO + if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON: + evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder)) + if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON: + evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder)) + # Mapillary Vistas + if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON: + evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder)) + if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON: + evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder)) + # Cityscapes + if evaluator_type == "cityscapes_instance": + assert ( + torch.cuda.device_count() > comm.get_rank() + ), "CityscapesEvaluator currently do not work with multiple machines." + return CityscapesInstanceEvaluator(dataset_name) + if evaluator_type == "cityscapes_sem_seg": + assert ( + torch.cuda.device_count() > comm.get_rank() + ), "CityscapesEvaluator currently do not work with multiple machines." + return CityscapesSemSegEvaluator(dataset_name) + if evaluator_type == "cityscapes_panoptic_seg": + if cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON: + assert ( + torch.cuda.device_count() > comm.get_rank() + ), "CityscapesEvaluator currently do not work with multiple machines." + evaluator_list.append(CityscapesSemSegEvaluator(dataset_name)) + if cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON: + assert ( + torch.cuda.device_count() > comm.get_rank() + ), "CityscapesEvaluator currently do not work with multiple machines." + evaluator_list.append(CityscapesInstanceEvaluator(dataset_name)) + # ADE20K + if evaluator_type == "ade20k_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON: + evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder)) + # LVIS + if evaluator_type == "lvis": + return LVISEvaluator(dataset_name, output_dir=output_folder) + if len(evaluator_list) == 0: + raise NotImplementedError( + "no Evaluator for the dataset {} with the type {}".format( + dataset_name, evaluator_type + ) + ) + elif len(evaluator_list) == 1: + return evaluator_list[0] + return DatasetEvaluators(evaluator_list) + + @classmethod + def build_train_loader(cls, cfg): + # Semantic segmentation dataset mapper + if cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_semantic": + mapper = MaskFormerSemanticDatasetMapper(cfg, True) + return build_detection_train_loader(cfg, mapper=mapper) + # Panoptic segmentation dataset mapper + elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_panoptic": + mapper = MaskFormerPanopticDatasetMapper(cfg, True) + return build_detection_train_loader(cfg, mapper=mapper) + # Instance segmentation dataset mapper + elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_instance": + mapper = MaskFormerInstanceDatasetMapper(cfg, True) + return build_detection_train_loader(cfg, mapper=mapper) + # coco instance segmentation lsj new baseline + elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_instance_lsj": + mapper = COCOInstanceNewBaselineDatasetMapper(cfg, True) + return build_detection_train_loader(cfg, mapper=mapper) + # coco panoptic segmentation lsj new baseline + elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_panoptic_lsj": + mapper = COCOPanopticNewBaselineDatasetMapper(cfg, True) + return build_detection_train_loader(cfg, mapper=mapper) + else: + mapper = None + return build_detection_train_loader(cfg, mapper=mapper) + + @classmethod + def build_lr_scheduler(cls, cfg, optimizer): + """ + It now calls :func:`detectron2.solver.build_lr_scheduler`. + Overwrite it if you'd like a different scheduler. + """ + return build_lr_scheduler(cfg, optimizer) + + @classmethod + def build_optimizer(cls, cfg, model): + weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM + weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED + + defaults = {} + defaults["lr"] = cfg.SOLVER.BASE_LR + defaults["weight_decay"] = cfg.SOLVER.WEIGHT_DECAY + + norm_module_types = ( + torch.nn.BatchNorm1d, + torch.nn.BatchNorm2d, + torch.nn.BatchNorm3d, + torch.nn.SyncBatchNorm, + # NaiveSyncBatchNorm inherits from BatchNorm2d + torch.nn.GroupNorm, + torch.nn.InstanceNorm1d, + torch.nn.InstanceNorm2d, + torch.nn.InstanceNorm3d, + torch.nn.LayerNorm, + torch.nn.LocalResponseNorm, + ) + + params: List[Dict[str, Any]] = [] + memo: Set[torch.nn.parameter.Parameter] = set() + for module_name, module in model.named_modules(): + for module_param_name, value in module.named_parameters(recurse=False): + if not value.requires_grad: + continue + # Avoid duplicating parameters + if value in memo: + continue + memo.add(value) + + hyperparams = copy.copy(defaults) + if "backbone" in module_name: + hyperparams["lr"] = hyperparams["lr"] * cfg.SOLVER.BACKBONE_MULTIPLIER + if ( + "relative_position_bias_table" in module_param_name + or "absolute_pos_embed" in module_param_name + ): + print(module_param_name) + hyperparams["weight_decay"] = 0.0 + if isinstance(module, norm_module_types): + hyperparams["weight_decay"] = weight_decay_norm + if isinstance(module, torch.nn.Embedding): + hyperparams["weight_decay"] = weight_decay_embed + params.append({"params": [value], **hyperparams}) + + def maybe_add_full_model_gradient_clipping(optim): + # detectron2 doesn't have full model gradient clipping now + clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE + enable = ( + cfg.SOLVER.CLIP_GRADIENTS.ENABLED + and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model" + and clip_norm_val > 0.0 + ) + + class FullModelGradientClippingOptimizer(optim): + def step(self, closure=None): + all_params = itertools.chain(*[x["params"] for x in self.param_groups]) + torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val) + super().step(closure=closure) + + return FullModelGradientClippingOptimizer if enable else optim + + optimizer_type = cfg.SOLVER.OPTIMIZER + if optimizer_type == "SGD": + optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)( + params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM + ) + elif optimizer_type == "ADAMW": + optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)( + params, cfg.SOLVER.BASE_LR + ) + else: + raise NotImplementedError(f"no optimizer type {optimizer_type}") + if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model": + optimizer = maybe_add_gradient_clipping(cfg, optimizer) + return optimizer + + @classmethod + def test_with_TTA(cls, cfg, model): + logger = logging.getLogger("detectron2.trainer") + # In the end of training, run an evaluation with TTA. + logger.info("Running inference with test-time augmentation ...") + model = SemanticSegmentorWithTTA(cfg, model) + evaluators = [ + cls.build_evaluator( + cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA") + ) + for name in cfg.DATASETS.TEST + ] + res = cls.test(cfg, model, evaluators) + res = OrderedDict({k + "_TTA": v for k, v in res.items()}) + return res + + +def setup(args): + """ + Create configs and perform basic setups. + """ + cfg = get_cfg() + # for poly lr schedule + add_deeplab_config(cfg) + add_maskformer2_config(cfg) + cfg.merge_from_file(args.config_file) + # use shell script commands to define testing datasets + if 'DATASETS.TEST' in args.opts: + dataset_opt_idx = args.opts.index('DATASETS.TEST') + args.opts[dataset_opt_idx+1] = (args.opts[dataset_opt_idx+1],) + cfg.merge_from_list(args.opts) + if args.test_dataset != "": cfg.DATASETS.TEST = ((args.test_dataset),) + if args.train_dataset != "": cfg.DATASETS.TRAIN = ((args.train_dataset),) + cfg.freeze() + default_setup(cfg, args) + # Setup logger for "mask_former" module + setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="mask2former") + return cfg + + +def main(args): + cfg = setup(args) + + if not args.eval_only: + if comm.is_main_process(): # only on main process + wandb.init( + # set the wandb project where this run will be logged + project="CutLER-M2F", + sync_tensorboard=True, + name=args.wandb_name, + entity="xdwang", + ) + + if args.eval_only: + model = Trainer.build_model(cfg) + DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load( + cfg.MODEL.WEIGHTS, resume=args.resume + ) + res = Trainer.test(cfg, model) + if cfg.TEST.AUG.ENABLED: + res.update(Trainer.test_with_TTA(cfg, model)) + if comm.is_main_process(): + verify_results(cfg, res) + return res + + trainer = Trainer(cfg) + trainer.resume_or_load(resume=args.resume) + return trainer.train() + + +if __name__ == "__main__": + args = default_argument_parser().parse_args() + rint = random.randint(0, 10000) + args.dist_url = args.dist_url.replace('12399', str(12399 + rint)) + print("Command Line Args:", args) + launch( + main, + args.num_gpus, + num_machines=args.num_machines, + machine_rank=args.machine_rank, + dist_url=args.dist_url, + args=(args,), + ) diff --git a/videocutler/train_net_video.py b/videocutler/train_net_video.py new file mode 100644 index 0000000000000000000000000000000000000000..f5d816a348a69bf0eb581783edfd518b38205d85 --- /dev/null +++ b/videocutler/train_net_video.py @@ -0,0 +1,325 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +MaskFormer Training Script. + +This script is a simplified version of the training script in detectron2/tools. +""" +try: + # ignore ShapelyDeprecationWarning from fvcore + from shapely.errors import ShapelyDeprecationWarning + import warnings + warnings.filterwarnings('ignore', category=ShapelyDeprecationWarning) +except: + pass + +import copy +import itertools +import logging +import os + +from collections import OrderedDict +from typing import Any, Dict, List, Set + +import torch + +import detectron2.utils.comm as comm +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import get_cfg +from detectron2.data import MetadataCatalog +from detectron2.engine import ( + # DefaultTrainer, + default_argument_parser, + default_setup, + launch, +) +from detectron2.evaluation import ( + DatasetEvaluator, + inference_on_dataset, + print_csv_format, + verify_results, +) +from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler +from detectron2.solver.build import maybe_add_gradient_clipping +from detectron2.utils.logger import setup_logger + +# MaskFormer +from mask2former import add_maskformer2_config +from mask2former_video import ( + YTVISDatasetMapper, + YTVISEvaluator, + add_maskformer2_video_config, + build_detection_train_loader, + build_detection_test_loader, + get_detection_dataset_dicts, +) +# additional settings +from mask2former_video.engine import DefaultTrainer +import wandb + +# setup wandb + +class Trainer(DefaultTrainer): + """ + Extension of the Trainer class adapted to MaskFormer. + """ + + @classmethod + def build_evaluator(cls, cfg, dataset_name, output_folder=None): + """ + Create evaluator(s) for a given dataset. + This uses the special metadata "evaluator_type" associated with each builtin dataset. + For your own dataset, you can simply create an evaluator manually in your + script and do not have to worry about the hacky if-else logic here. + """ + if output_folder is None: + output_folder = os.path.join(cfg.OUTPUT_DIR, "inference") + os.makedirs(output_folder, exist_ok=True) + + return YTVISEvaluator(dataset_name, cfg, True, output_folder) + + @classmethod + def build_train_loader(cls, cfg): + dataset_name = cfg.DATASETS.TRAIN[0] + mapper = YTVISDatasetMapper(cfg, is_train=True) + + dataset_dict = get_detection_dataset_dicts( + dataset_name, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + + return build_detection_train_loader(cfg, mapper=mapper, dataset=dataset_dict) + + @classmethod + def build_test_loader(cls, cfg, dataset_name): + dataset_name = cfg.DATASETS.TEST[0] + mapper = YTVISDatasetMapper(cfg, is_train=False) + return build_detection_test_loader(cfg, dataset_name, mapper=mapper) + + @classmethod + def build_lr_scheduler(cls, cfg, optimizer): + """ + It now calls :func:`detectron2.solver.build_lr_scheduler`. + Overwrite it if you'd like a different scheduler. + """ + return build_lr_scheduler(cfg, optimizer) + + @classmethod + def build_optimizer(cls, cfg, model): + weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM + weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED + + base_lr_multiplier_names = cfg.SOLVER.BASE_LR_MULTIPLIER_NAMES + base_lr_multiplier = cfg.SOLVER.BASE_LR_MULTIPLIER + + defaults = {} + defaults["lr"] = cfg.SOLVER.BASE_LR + defaults["weight_decay"] = cfg.SOLVER.WEIGHT_DECAY + + norm_module_types = ( + torch.nn.BatchNorm1d, + torch.nn.BatchNorm2d, + torch.nn.BatchNorm3d, + torch.nn.SyncBatchNorm, + # NaiveSyncBatchNorm inherits from BatchNorm2d + torch.nn.GroupNorm, + torch.nn.InstanceNorm1d, + torch.nn.InstanceNorm2d, + torch.nn.InstanceNorm3d, + torch.nn.LayerNorm, + torch.nn.LocalResponseNorm, + ) + + params: List[Dict[str, Any]] = [] + memo: Set[torch.nn.parameter.Parameter] = set() + for module_name, module in model.named_modules(): + for module_param_name, value in module.named_parameters(recurse=False): + if not value.requires_grad: + continue + # Avoid duplicating parameters + if value in memo: + continue + memo.add(value) + + hyperparams = copy.copy(defaults) + if "backbone" in module_name: + hyperparams["lr"] = hyperparams["lr"] * cfg.SOLVER.BACKBONE_MULTIPLIER + if ( + "relative_position_bias_table" in module_param_name + or "absolute_pos_embed" in module_param_name + ): + print(module_param_name) + hyperparams["weight_decay"] = 0.0 + if isinstance(module, norm_module_types): + hyperparams["weight_decay"] = weight_decay_norm + if isinstance(module, torch.nn.Embedding): + hyperparams["weight_decay"] = weight_decay_embed + if module_name in base_lr_multiplier_names: + hyperparams["lr"] *= base_lr_multiplier + print(" Checked: ", module_name, hyperparams["lr"]) + params.append({"params": [value], **hyperparams}) + + def maybe_add_full_model_gradient_clipping(optim): + # detectron2 doesn't have full model gradient clipping now + clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE + enable = ( + cfg.SOLVER.CLIP_GRADIENTS.ENABLED + and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model" + and clip_norm_val > 0.0 + ) + + class FullModelGradientClippingOptimizer(optim): + def step(self, closure=None): + all_params = itertools.chain(*[x["params"] for x in self.param_groups]) + torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val) + super().step(closure=closure) + + return FullModelGradientClippingOptimizer if enable else optim + + optimizer_type = cfg.SOLVER.OPTIMIZER + if optimizer_type == "SGD": + optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)( + params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM + ) + elif optimizer_type == "ADAMW": + optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)( + params, cfg.SOLVER.BASE_LR + ) + else: + raise NotImplementedError(f"no optimizer type {optimizer_type}") + if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model": + optimizer = maybe_add_gradient_clipping(cfg, optimizer) + return optimizer + + @classmethod + def test(cls, cfg, model, evaluators=None): + """ + Evaluate the given model. The given model is expected to already contain + weights to evaluate. + Args: + cfg (CfgNode): + model (nn.Module): + evaluators (list[DatasetEvaluator] or None): if None, will call + :meth:`build_evaluator`. Otherwise, must have the same length as + ``cfg.DATASETS.TEST``. + Returns: + dict: a dict of result metrics + """ + from torch.cuda.amp import autocast + logger = logging.getLogger(__name__) + if isinstance(evaluators, DatasetEvaluator): + evaluators = [evaluators] + if evaluators is not None: + assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format( + len(cfg.DATASETS.TEST), len(evaluators) + ) + + results = OrderedDict() + for idx, dataset_name in enumerate(cfg.DATASETS.TEST): + data_loader = cls.build_test_loader(cfg, dataset_name) + # When evaluators are passed in as arguments, + # implicitly assume that evaluators can be created before data_loader. + if evaluators is not None: + evaluator = evaluators[idx] + else: + try: + evaluator = cls.build_evaluator(cfg, dataset_name) + except NotImplementedError: + logger.warn( + "No evaluator found. Use `DefaultTrainer.test(evaluators=)`, " + "or implement its `build_evaluator` method." + ) + results[dataset_name] = {} + continue + with autocast(): + results_i = inference_on_dataset(model, data_loader, evaluator) + results[dataset_name] = results_i + if comm.is_main_process(): + assert isinstance( + results_i, dict + ), "Evaluator must return a dict on the main process. Got {} instead.".format( + results_i + ) + logger.info("Evaluation results for {} in csv format:".format(dataset_name)) + print_csv_format(results_i) + + if len(results) == 1: + results = list(results.values())[0] + return results + + +def setup(args): + """ + Create configs and perform basic setups. + """ + cfg = get_cfg() + # for poly lr schedule + add_deeplab_config(cfg) + add_maskformer2_config(cfg) + add_maskformer2_video_config(cfg) + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + # NOTE: need to add following lines to detectron2/detectron2/enginee/defaults.py + # parser.add_argument("--train-dataset", default="", help="training dataset") + # parser.add_argument("--test-dataset", default="", help="testing dataset") + # parser.add_argument("--steps", default=0, help="number of steps to train") + # NOTE: need to change detectron2/detectron2/data/transforms/transform.py + # replace all assert img.shape[:2] == (self.h, self.w) to the following lines + # try: + # img.shape[:2] == (self.h, self.w) + # except: + # (self.h, self.w) = (self.w, self.h) + # assert img.shape[:2] == (self.h, self.w) + if args.test_dataset != "": cfg.DATASETS.TEST = ((args.test_dataset),) + if args.train_dataset != "": cfg.DATASETS.TRAIN = ((args.train_dataset),) + if args.steps != 0: cfg.SOLVER.STEPS = (int(args.steps),) + cfg.freeze() + default_setup(cfg, args) + # Setup logger for "mask_former" module + setup_logger(name="mask2former") + setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="mask2former_video") + return cfg + + +def main(args): + cfg = setup(args) + + # start a new wandb run to track this script + if not args.eval_only or args.wandb_name != "": + if comm.is_main_process(): # only on main process + wandb.init( + # set the wandb project where this run will be logged + project="VideoCutLER", + sync_tensorboard=True, + name=args.wandb_name, + entity="xdwang", + ) + + if args.eval_only: + model = Trainer.build_model(cfg) + DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load( + cfg.MODEL.WEIGHTS, resume=args.resume + ) + res = Trainer.test(cfg, model) + if cfg.TEST.AUG.ENABLED: + raise NotImplementedError + if comm.is_main_process(): + verify_results(cfg, res) + return res + + trainer = Trainer(cfg) + trainer.resume_or_load(resume=args.resume) + return trainer.train() + + +if __name__ == "__main__": + args = default_argument_parser().parse_args() + print("Command Line Args:", args) + launch( + main, + args.num_gpus, + num_machines=args.num_machines, + machine_rank=args.machine_rank, + dist_url=args.dist_url, + args=(args,), + )