Instructions to use zeyuren2002/EvalMDE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zeyuren2002/EvalMDE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zeyuren2002/EvalMDE", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Add files using upload-large-folder tool
Browse filesThis view is limited to 50 files because it contains too many changes. See raw diff
- DepthMaster/README.md +201 -0
- DepthMaster/config/dataset/data_diode_all.yaml +4 -0
- DepthMaster/config/dataset/data_eth3d.yaml +4 -0
- DepthMaster/config/dataset/data_hypersim_test.yaml +5 -0
- DepthMaster/config/dataset/data_hypersim_train.yaml +4 -0
- DepthMaster/config/dataset/data_hypersim_val.yaml +4 -0
- DepthMaster/config/dataset/data_kitti_eigen_test.yaml +6 -0
- DepthMaster/config/dataset/data_kitti_val.yaml +6 -0
- DepthMaster/config/dataset/data_nyu_test.yaml +5 -0
- DepthMaster/config/dataset/data_nyu_train.yaml +5 -0
- DepthMaster/config/dataset/data_scannet_val.yaml +4 -0
- DepthMaster/config/dataset/data_vkitti_train.yaml +6 -0
- DepthMaster/config/dataset/data_vkitti_val.yaml +6 -0
- DepthMaster/config/dataset/dataset_train.yaml +18 -0
- DepthMaster/config/dataset/dataset_val.yaml +24 -0
- DepthMaster/config/dataset/dataset_vis.yaml +9 -0
- DepthMaster/config/logging.yaml +5 -0
- DepthMaster/config/model_sdv2.yaml +4 -0
- DepthMaster/config/train_s1.yaml +96 -0
- DepthMaster/config/train_s2.yaml +106 -0
- DepthMaster/config/wandb.yaml +3 -0
- DepthMaster/data_split/diode/diode_val_all_filename_list.txt +0 -0
- DepthMaster/data_split/diode/diode_val_indoor_filename_list.txt +325 -0
- DepthMaster/data_split/diode/diode_val_outdoor_filename_list.txt +446 -0
- DepthMaster/data_split/eth3d/eth3d_filename_list.txt +454 -0
- DepthMaster/data_split/hypersim/filename_list_test_filtered.txt +0 -0
- DepthMaster/data_split/hypersim/filename_list_test_filtered_100.txt +100 -0
- DepthMaster/data_split/hypersim/filename_list_train_filtered.txt +0 -0
- DepthMaster/data_split/hypersim/filename_list_val_filtered.txt +0 -0
- DepthMaster/data_split/hypersim/filename_list_val_filtered_small_80.txt +80 -0
- DepthMaster/data_split/hypersim/selected_vis_sample.txt +3 -0
- DepthMaster/data_split/kitti/eigen_test_files_with_gt.txt +0 -0
- DepthMaster/data_split/kitti/eigen_val_from_test.txt +116 -0
- DepthMaster/data_split/kitti/eigen_val_from_train_800.txt +0 -0
- DepthMaster/data_split/kitti/eigen_val_from_train_sub_100.txt +100 -0
- DepthMaster/data_split/nyu/labeled/filename_list_test.txt +654 -0
- DepthMaster/data_split/nyu/labeled/filename_list_train.txt +795 -0
- DepthMaster/data_split/nyu/labeled/filename_list_train_small_100.txt +100 -0
- DepthMaster/data_split/nyu/labeled/filename_list_train_test.txt +109 -0
- DepthMaster/data_split/scannet/scannet_val_sampled_list_800_1.txt +800 -0
- DepthMaster/data_split/vkitti/filename_list_debug.txt +1 -0
- DepthMaster/data_split/vkitti/vkitti_train.txt +0 -0
- DepthMaster/data_split/vkitti/vkitti_val.txt +0 -0
- DepthMaster/data_split/vkitti/vkitti_val_vae.txt +894 -0
- DepthMaster/depthmaster/__init__.py +26 -0
- DepthMaster/depthmaster/__pycache__/__init__.cpython-310.pyc +0 -0
- DepthMaster/depthmaster/__pycache__/depthmaster_pipeline.cpython-310.pyc +0 -0
- DepthMaster/depthmaster/depthmaster_pipeline.py +385 -0
- DepthMaster/depthmaster/modules/unet_2d_blocks.py +0 -0
- DepthMaster/depthmaster/modules/unet_2d_condition_s1.py +1317 -0
DepthMaster/README.md
ADDED
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|
| 1 |
+
<!-- # DepthMaster: Taming Diffusion Models for Monocular Depth Estimation
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
This repository represents the official implementation of the paper titled "DepthMaster: Taming Diffusion Models for Monocular Depth Estimation". -->
|
| 5 |
+
|
| 6 |
+
<!-- [](https://marigoldmonodepth.github.io)
|
| 7 |
+
[](https://arxiv.org/abs/2312.02145) -->
|
| 8 |
+
|
| 9 |
+
<!-- [](https://www.apache.org/licenses/LICENSE-2.0) -->
|
| 10 |
+
|
| 11 |
+
<h1 align="center"><strong>DepthMaster: Taming Diffusion Models for Monocular Depth Estimation</strong></h1>
|
| 12 |
+
<p align="center">
|
| 13 |
+
<a href="https://indu1ge.github.io/ziyangsong">Ziyang Song*</a>,
|
| 14 |
+
<a href="https://orcid.org/0009-0001-6677-0572">Zerong Wang*</a>,
|
| 15 |
+
<a href="https://orcid.org/0000-0001-7817-0665">Bo Li</a>,
|
| 16 |
+
<a href="https://orcid.org/0009-0007-1175-5918">Hao Zhang</a>,
|
| 17 |
+
<a href="https://ruijiezhu94.github.io/ruijiezhu/">Ruijie Zhu</a>,
|
| 18 |
+
<a href="https://orcid.org/0009-0004-3280-8490">Li Liu</a>,
|
| 19 |
+
<a href="https://pengtaojiang.github.io/">Peng-Tao Jiang†</a>,
|
| 20 |
+
<a href="http://staff.ustc.edu.cn/~tzzhang/">Tianzhu Zhang†</a>,
|
| 21 |
+
<br>
|
| 22 |
+
*Equal Contribution, †Corresponding Author
|
| 23 |
+
<br>
|
| 24 |
+
University of Science and Technology of China, vivo Mobile Communication Co., Ltd.
|
| 25 |
+
<br>
|
| 26 |
+
<b>TCSVT 2026</b>
|
| 27 |
+
</p>
|
| 28 |
+
<!-- [Ziyang Song*](https://indu1ge.github.io/ziyangsong),
|
| 29 |
+
[Zerong Wang*](),
|
| 30 |
+
[Bo Li](https://orcid.org/0000-0001-7817-0665),
|
| 31 |
+
[Hao Zhang](https://orcid.org/0009-0007-1175-5918),
|
| 32 |
+
[Ruijie Zhu](https://ruijiezhu94.github.io/ruijiezhu/),
|
| 33 |
+
[Li Liu](https://orcid.org/0009-0004-3280-8490)
|
| 34 |
+
[Tianzhu Zhang](http://staff.ustc.edu.cn/~tzzhang/)
|
| 35 |
+
[Peng-Tao Jiang](https://pengtaojiang.github.io/) -->
|
| 36 |
+
|
| 37 |
+
<div align="center">
|
| 38 |
+
<a href='https://arxiv.org/abs/2501.02576'><img src='https://img.shields.io/badge/Paper-arXiv-red'></a>
|
| 39 |
+
<!-- <a href='https://arxiv.org/abs/[]'><img src='https://img.shields.io/badge/arXiv-[]-b31b1b.svg'></a> -->
|
| 40 |
+
<a href='https://indu1ge.github.io/DepthMaster_page/'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
|
| 41 |
+
<a href='https://huggingface.co/zysong212/DepthMaster'><img src='https://img.shields.io/badge/🤗%20Hugging%20Face%20-Space-yellow'></a>
|
| 42 |
+
<a href='https://www.apache.org/licenses/LICENSE-2.0'><img src='https://img.shields.io/badge/License-Apache--2.0-929292'></a>
|
| 43 |
+
<!-- <a href='https://paperswithcode.com/sota/unsupervised-monocular-depth-estimation-on-7?p=ec-depth-exploring-the-consistency-of-self'><img src='https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/ec-depth-exploring-the-consistency-of-self/unsupervised-monocular-depth-estimation-on-7'></a> -->
|
| 44 |
+
</div>
|
| 45 |
+
|
| 46 |
+
|
| 47 |
+
<!-- We present Marigold, a diffusion model, and associated fine-tuning protocol for monocular depth estimation. Its core principle is to leverage the rich visual knowledge stored in modern generative image models. Our model, derived from Stable Diffusion and fine-tuned with synthetic data, can zero-shot transfer to unseen data, offering state-of-the-art monocular depth estimation results. -->
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+

|
| 51 |
+
|
| 52 |
+
<!-- >We present DepthMaster, a tamed single-step diffusion model designed to enhance the generalization and detail preservation abilities of depth estimation models. Through feature alignment, we effectively prevent the overfitting to texture details. By adaptively enhance -->
|
| 53 |
+
>We present DepthMaster, a tamed single-step diffusion model that customizes generative features in diffusion models to suit the discriminative depth estimation task. We introduce a Feature Alignment module to mitigate overfitting to texture and a Fourier Enhancement module to refine fine-grained details. DepthMaster exhibits state-of-the-art zero-shot performance and superior detail preservation ability, surpassing
|
| 54 |
+
other diffusion-based methods across various datasets.
|
| 55 |
+
## 📢 News
|
| 56 |
+
2026-04-02: [Paper](https://ieeexplore.ieee.org/document/11475488) is accepted by TCSVT. <br>
|
| 57 |
+
2025-01-15: Evaluation code is released. <br>
|
| 58 |
+
2025-01-02: [Paper](https://arxiv.org/abs/2501.02576) is released on arXiv. <br>
|
| 59 |
+
<!-- 2023-12-04: Added <a href="https://arxiv.org/abs/2312.02145"><img src="https://img.shields.io/badge/arXiv-PDF-b31b1b" height="16"></a>
|
| 60 |
+
paper and inference code (this repository). -->
|
| 61 |
+
|
| 62 |
+
## Installation
|
| 63 |
+
Please refer to [installation.md](./docs/installation.md) for installation.
|
| 64 |
+
|
| 65 |
+
## Checkpoint
|
| 66 |
+
The model can be downloaded [here](https://huggingface.co/zysong212/DepthMaster).
|
| 67 |
+
|
| 68 |
+
## 🏃 Testing on your images
|
| 69 |
+
|
| 70 |
+
### 📷 Prepare images
|
| 71 |
+
|
| 72 |
+
Place your images in a directory, for example, under `in_the_wild_example/input`, and run the following inference command.
|
| 73 |
+
|
| 74 |
+
```bash
|
| 75 |
+
bash scripts/infer.sh
|
| 76 |
+
```
|
| 77 |
+
|
| 78 |
+
|
| 79 |
+
You can find all results in `in_the_wild_example/output`. Enjoy!
|
| 80 |
+
|
| 81 |
+
|
| 82 |
+
<!-- ### ⬇ Checkpoint cache
|
| 83 |
+
|
| 84 |
+
By default, the [checkpoint](https://huggingface.co/prs-eth/marigold-v1-0) is stored in the Hugging Face cache.
|
| 85 |
+
The `HF_HOME` environment variable defines its location and can be overridden, e.g.:
|
| 86 |
+
|
| 87 |
+
```bash
|
| 88 |
+
export HF_HOME=$(pwd)/cache
|
| 89 |
+
```
|
| 90 |
+
|
| 91 |
+
Alternatively, use the following script to download the checkpoint weights locally:
|
| 92 |
+
|
| 93 |
+
```bash
|
| 94 |
+
bash script/download_weights.sh marigold-v1-0
|
| 95 |
+
# or LCM checkpoint
|
| 96 |
+
bash script/download_weights.sh marigold-lcm-v1-0
|
| 97 |
+
```
|
| 98 |
+
|
| 99 |
+
At inference, specify the checkpoint path:
|
| 100 |
+
|
| 101 |
+
```bash
|
| 102 |
+
python run.py \
|
| 103 |
+
--checkpoint checkpoint/marigold-v1-0 \
|
| 104 |
+
--denoise_steps 50 \
|
| 105 |
+
--ensemble_size 10 \
|
| 106 |
+
--input_rgb_dir input/in-the-wild_example\
|
| 107 |
+
--output_dir output/in-the-wild_example
|
| 108 |
+
``` -->
|
| 109 |
+
|
| 110 |
+
## 🦿 Evaluation on test datasets <a name="evaluation"></a>
|
| 111 |
+
|
| 112 |
+
Set data directory variable (also needed in evaluation scripts) and download [evaluation datasets](https://share.phys.ethz.ch/~pf/bingkedata/marigold/evaluation_dataset) following [Marigold](https://github.com/prs-eth/Marigold) into corresponding subfolders:
|
| 113 |
+
|
| 114 |
+
```bash
|
| 115 |
+
export BASE_DATA_DIR=<YOUR_DATA_DIR> # Set target data directory
|
| 116 |
+
|
| 117 |
+
wget -r -np -nH --cut-dirs=4 -R "index.html*" -P ${BASE_DATA_DIR} https://share.phys.ethz.ch/~pf/bingkedata/marigold/evaluation_dataset/
|
| 118 |
+
```
|
| 119 |
+
Download the model [here](https://huggingface.co/zysong212/DepthMaster) to `ckpt/eval` subfolder.
|
| 120 |
+
Run evaluation scripts, for example:
|
| 121 |
+
|
| 122 |
+
```bash
|
| 123 |
+
bash scripts/eval_kitti.sh
|
| 124 |
+
```
|
| 125 |
+
The evaluation results will be saved to `output\kitti`.
|
| 126 |
+
|
| 127 |
+
## 🏋️ Training
|
| 128 |
+
|
| 129 |
+
|
| 130 |
+
Modify the data directory in `train_s1.sh` and `train_s2.sh`:
|
| 131 |
+
|
| 132 |
+
```bash
|
| 133 |
+
BASE_DATA_DIR=YOUR_DATA_DIR # directory of training data
|
| 134 |
+
```
|
| 135 |
+
|
| 136 |
+
Prepare for [Hypersim](https://github.com/apple/ml-hypersim) and [Virtual KITTI 2](https://europe.naverlabs.com/research/computer-vision/proxy-virtual-worlds-vkitti-2/) datasets and save into `${BASE_DATA_DIR}` following [Marigold](https://github.com/prs-eth/Marigold?tab=readme-ov-file).
|
| 137 |
+
|
| 138 |
+
|
| 139 |
+
### The fist-stage training
|
| 140 |
+
Modify the checkpoint directory in `train_s1.sh`:
|
| 141 |
+
|
| 142 |
+
```bash
|
| 143 |
+
BASE_CKPT_DIR=YOUR_CHECKPOINT_DIR # directory of pretrained checkpoint
|
| 144 |
+
```
|
| 145 |
+
|
| 146 |
+
Download Stable Diffusion v2 [checkpoint](https://huggingface.co/stabilityai/stable-diffusion-2) into `${BASE_CKPT_DIR}`.
|
| 147 |
+
\
|
| 148 |
+
Download the checkpoint of [Depth-Anything-V2](https://github.com/DepthAnything/Depth-Anything-V2) into `checkpoints/`
|
| 149 |
+
|
| 150 |
+
Run training script
|
| 151 |
+
|
| 152 |
+
```bash
|
| 153 |
+
bash scripts/train_s1.sh
|
| 154 |
+
```
|
| 155 |
+
|
| 156 |
+
### The second-stage training
|
| 157 |
+
Modify the checkpoint directory in `train_s2.sh`:
|
| 158 |
+
|
| 159 |
+
```bash
|
| 160 |
+
BASE_CKPT_DIR=YOUR_FIRST_STAGE_CHECKPOINT_DIR # directory of your fist-stage checkpoint checkpoint
|
| 161 |
+
```
|
| 162 |
+
|
| 163 |
+
Run training script
|
| 164 |
+
|
| 165 |
+
```bash
|
| 166 |
+
bash scripts/train_s2.sh
|
| 167 |
+
```
|
| 168 |
+
|
| 169 |
+
|
| 170 |
+
<!-- Evaluating results
|
| 171 |
+
|
| 172 |
+
Only the U-Net is updated and saved during training. To use the inference pipeline with your training result, replace `unet` folder in Marigold checkpoints with that in the `checkpoint` output folder. Then refer to [this section](#evaluation) for evaluation. -->
|
| 173 |
+
|
| 174 |
+
|
| 175 |
+
## 🎓 Citation
|
| 176 |
+
|
| 177 |
+
Please cite our paper:
|
| 178 |
+
|
| 179 |
+
```bibtex
|
| 180 |
+
@article{song2026depthmaster,
|
| 181 |
+
title={Depthmaster: Taming diffusion models for monocular depth estimation},
|
| 182 |
+
author={Song, Ziyang and Wang, Zerong and Li, Bo and Zhang, Hao and Zhu, Ruijie and Liu, Li and Jiang, Peng-Tao and Zhang, Tianzhu},
|
| 183 |
+
journal={IEEE Transactions on Circuits and Systems for Video Technology},
|
| 184 |
+
year={2026},
|
| 185 |
+
publisher={IEEE}
|
| 186 |
+
}
|
| 187 |
+
```
|
| 188 |
+
|
| 189 |
+
## Acknowledgements
|
| 190 |
+
|
| 191 |
+
The code is based on [Marigold](https://github.com/prs-eth/Marigold).
|
| 192 |
+
\
|
| 193 |
+
The external encoder checkpoint is from [Depth-Anything-V2](https://github.com/DepthAnything/Depth-Anything-V2).
|
| 194 |
+
|
| 195 |
+
## 🎫 License
|
| 196 |
+
|
| 197 |
+
This work is licensed under the Apache License, Version 2.0 (as defined in the [LICENSE](LICENSE.txt)).
|
| 198 |
+
|
| 199 |
+
By downloading and using the code and model you agree to the terms in the [LICENSE](LICENSE.txt).
|
| 200 |
+
|
| 201 |
+
[](https://www.apache.org/licenses/LICENSE-2.0)
|
DepthMaster/config/dataset/data_diode_all.yaml
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
name: diode
|
| 2 |
+
disp_name: diode_val_all
|
| 3 |
+
dir: marigold_eval/diode/diode_val.tar
|
| 4 |
+
filenames: data_split/diode/diode_val_all_filename_list.txt
|
DepthMaster/config/dataset/data_eth3d.yaml
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
name: eth3d
|
| 2 |
+
disp_name: eth3d_full
|
| 3 |
+
dir: marigold_eval/eth3d/eth3d.tar
|
| 4 |
+
filenames: data_split/eth3d/eth3d_filename_list.txt
|
DepthMaster/config/dataset/data_hypersim_test.yaml
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
name: hypersim
|
| 2 |
+
disp_name: hypersim_test
|
| 3 |
+
dir: Hypersim/preprocessed/val
|
| 4 |
+
filenames: data_split/hypersim/filename_list_val_filtered_small_80.txt
|
| 5 |
+
tom_dir: none
|
DepthMaster/config/dataset/data_hypersim_train.yaml
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
name: hypersim
|
| 2 |
+
disp_name: hypersim_train
|
| 3 |
+
dir: hypersim/hypersim_processed_train.tar
|
| 4 |
+
filenames: data_split/hypersim/filename_list_train_filtered.txt
|
DepthMaster/config/dataset/data_hypersim_val.yaml
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
name: hypersim
|
| 2 |
+
disp_name: hypersim_val
|
| 3 |
+
dir: hypersim/hypersim_processed_val.tar
|
| 4 |
+
filenames: data_split/hypersim/filename_list_val_filtered.txt
|
DepthMaster/config/dataset/data_kitti_eigen_test.yaml
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
name: kitti
|
| 2 |
+
disp_name: kitti_eigen_test_full
|
| 3 |
+
dir: marigold_eval/kitti/kitti_eigen_split_test.tar
|
| 4 |
+
filenames: data_split/kitti/eigen_test_files_with_gt.txt
|
| 5 |
+
kitti_bm_crop: true
|
| 6 |
+
valid_mask_crop: eigen
|
DepthMaster/config/dataset/data_kitti_val.yaml
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
name: kitti
|
| 2 |
+
disp_name: kitti_val800_from_eigen_train
|
| 3 |
+
dir: kitti/kitti_sampled_val_800.tar
|
| 4 |
+
filenames: data_split/kitti/eigen_val_from_train_800.txt
|
| 5 |
+
kitti_bm_crop: true
|
| 6 |
+
valid_mask_crop: eigen
|
DepthMaster/config/dataset/data_nyu_test.yaml
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
name: nyu_v2
|
| 2 |
+
disp_name: nyu_test_full
|
| 3 |
+
dir: marigold_eval/nyuv2/nyu_labeled_extracted.tar
|
| 4 |
+
filenames: data_split/nyu/labeled/filename_list_test.txt
|
| 5 |
+
eigen_valid_mask: true
|
DepthMaster/config/dataset/data_nyu_train.yaml
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
name: nyu_v2
|
| 2 |
+
disp_name: nyu_train_full
|
| 3 |
+
dir: nyuv2/nyu_labeled_extracted.tar
|
| 4 |
+
filenames: data_split/nyu/labeled/filename_list_train.txt
|
| 5 |
+
eigen_valid_mask: true
|
DepthMaster/config/dataset/data_scannet_val.yaml
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
name: scannet
|
| 2 |
+
disp_name: scannet_val_800_1
|
| 3 |
+
dir: marigold_eval/scannet/scannet_val_sampled_800_1.tar
|
| 4 |
+
filenames: data_split/scannet/scannet_val_sampled_list_800_1.txt
|
DepthMaster/config/dataset/data_vkitti_train.yaml
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
name: vkitti
|
| 2 |
+
disp_name: vkitti_train
|
| 3 |
+
dir: vkitti/vkitti.tar
|
| 4 |
+
filenames: data_split/vkitti/vkitti_train.txt
|
| 5 |
+
kitti_bm_crop: true
|
| 6 |
+
valid_mask_crop: null # no valid_mask_crop for training
|
DepthMaster/config/dataset/data_vkitti_val.yaml
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
name: vkitti
|
| 2 |
+
disp_name: vkitti_val
|
| 3 |
+
dir: vkitti/vkitti.tar
|
| 4 |
+
filenames: data_split/vkitti/vkitti_val.txt
|
| 5 |
+
kitti_bm_crop: true
|
| 6 |
+
valid_mask_crop: eigen
|
DepthMaster/config/dataset/dataset_train.yaml
ADDED
|
@@ -0,0 +1,18 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
dataset:
|
| 2 |
+
train:
|
| 3 |
+
name: mixed
|
| 4 |
+
prob_ls: [0.9, 0.1]
|
| 5 |
+
dataset_list:
|
| 6 |
+
- name: hypersim
|
| 7 |
+
disp_name: hypersim_train
|
| 8 |
+
dir: hypersim/processed/train
|
| 9 |
+
filenames: data_split/hypersim/filename_list_train_filtered.txt
|
| 10 |
+
resize_to_hw:
|
| 11 |
+
- 480
|
| 12 |
+
- 640
|
| 13 |
+
- name: vkitti
|
| 14 |
+
disp_name: vkitti_train
|
| 15 |
+
dir: VKITTIv2
|
| 16 |
+
filenames: data_split/vkitti/vkitti_train.txt
|
| 17 |
+
kitti_bm_crop: true
|
| 18 |
+
valid_mask_crop: null
|
DepthMaster/config/dataset/dataset_val.yaml
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
dataset:
|
| 2 |
+
val:
|
| 3 |
+
# Smaller subsets for faster validation during training
|
| 4 |
+
# The first dataset is used to calculate main eval metric.
|
| 5 |
+
- name: hypersim
|
| 6 |
+
disp_name: hypersim_val_small_80
|
| 7 |
+
dir: hypersim/processed/val
|
| 8 |
+
filenames: data_split/hypersim/filename_list_val_filtered_small_80.txt
|
| 9 |
+
resize_to_hw:
|
| 10 |
+
- 480
|
| 11 |
+
- 640
|
| 12 |
+
|
| 13 |
+
- name: nyu_v2
|
| 14 |
+
disp_name: nyu_train_small_100
|
| 15 |
+
dir: marigold_eval/nyuv2/nyu_labeled_extracted.tar
|
| 16 |
+
filenames: data_split/nyu/labeled/filename_list_train_small_100.txt
|
| 17 |
+
eigen_valid_mask: true
|
| 18 |
+
|
| 19 |
+
- name: kitti
|
| 20 |
+
disp_name: kitti_val_from_train_sub_100
|
| 21 |
+
dir: marigold_eval/kitti/kitti_sampled_val_800.tar
|
| 22 |
+
filenames: data_split/kitti/eigen_val_from_train_sub_100.txt
|
| 23 |
+
kitti_bm_crop: true
|
| 24 |
+
valid_mask_crop: eigen
|
DepthMaster/config/dataset/dataset_vis.yaml
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
dataset:
|
| 2 |
+
vis:
|
| 3 |
+
- name: hypersim
|
| 4 |
+
disp_name: hypersim_vis
|
| 5 |
+
dir: hypersim/processed/val
|
| 6 |
+
filenames: data_split/hypersim/selected_vis_sample.txt
|
| 7 |
+
resize_to_hw:
|
| 8 |
+
- 480
|
| 9 |
+
- 640
|
DepthMaster/config/logging.yaml
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
logging:
|
| 2 |
+
filename: logging.log
|
| 3 |
+
format: ' %(asctime)s - %(levelname)s -%(filename)s - %(funcName)s >> %(message)s'
|
| 4 |
+
console_level: 20
|
| 5 |
+
file_level: 10
|
DepthMaster/config/model_sdv2.yaml
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model:
|
| 2 |
+
name: DepthMaster_pipeline
|
| 3 |
+
pretrained_path: initialization_v2
|
| 4 |
+
latent_scale_factor: 0.18215
|
DepthMaster/config/train_s1.yaml
ADDED
|
@@ -0,0 +1,96 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
base_config:
|
| 2 |
+
- config/logging.yaml
|
| 3 |
+
- config/wandb.yaml
|
| 4 |
+
- config/dataset/dataset_train.yaml
|
| 5 |
+
- config/dataset/dataset_val.yaml
|
| 6 |
+
- config/dataset/dataset_vis.yaml
|
| 7 |
+
- config/model_sdv2.yaml
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
pipeline:
|
| 11 |
+
name: DepthMasterPipeline
|
| 12 |
+
kwargs:
|
| 13 |
+
scale_invariant: true
|
| 14 |
+
shift_invariant: true
|
| 15 |
+
|
| 16 |
+
depth_normalization:
|
| 17 |
+
type: scale_shift_depth
|
| 18 |
+
clip: true
|
| 19 |
+
norm_min: -1.0
|
| 20 |
+
norm_max: 1.0
|
| 21 |
+
min_max_quantile: 0.02
|
| 22 |
+
|
| 23 |
+
augmentation:
|
| 24 |
+
lr_flip_p: 0.5
|
| 25 |
+
|
| 26 |
+
dataloader:
|
| 27 |
+
num_workers: 2
|
| 28 |
+
effective_batch_size: 32
|
| 29 |
+
max_train_batch_size: 8
|
| 30 |
+
seed: 2024 # to ensure continuity when resuming from checkpoint
|
| 31 |
+
|
| 32 |
+
# Training settings
|
| 33 |
+
trainer:
|
| 34 |
+
name: DepthMasterTrainerS1
|
| 35 |
+
training_noise_scheduler:
|
| 36 |
+
pretrained_path: initialization_v2
|
| 37 |
+
init_seed: 2024 # use null to train w/o seeding
|
| 38 |
+
save_period: 2000
|
| 39 |
+
backup_period: 2000
|
| 40 |
+
validation_period: 2000
|
| 41 |
+
visualization_period: 2000
|
| 42 |
+
|
| 43 |
+
multi_res_noise:
|
| 44 |
+
strength: 0.9
|
| 45 |
+
annealed: true
|
| 46 |
+
downscale_strategy: original
|
| 47 |
+
|
| 48 |
+
gt_depth_type: depth_raw_norm
|
| 49 |
+
gt_mask_type: valid_mask_raw
|
| 50 |
+
|
| 51 |
+
max_epoch: 10000 # a large enough number
|
| 52 |
+
max_iter: 20000 # usually converges at around 20k
|
| 53 |
+
|
| 54 |
+
optimizer:
|
| 55 |
+
name: Adam
|
| 56 |
+
|
| 57 |
+
loss:
|
| 58 |
+
name: mse_loss
|
| 59 |
+
kwargs:
|
| 60 |
+
reduction: mean
|
| 61 |
+
|
| 62 |
+
loss_feat_align:
|
| 63 |
+
lamda: 1
|
| 64 |
+
|
| 65 |
+
lr: 3.0e-05
|
| 66 |
+
lr_scheduler:
|
| 67 |
+
name: IterExponential
|
| 68 |
+
kwargs:
|
| 69 |
+
total_iter: 25000
|
| 70 |
+
final_ratio: 0.01
|
| 71 |
+
warmup_steps: 100
|
| 72 |
+
|
| 73 |
+
# Validation (and visualization) settings
|
| 74 |
+
validation:
|
| 75 |
+
ensemble_size: 1 # simplified setting for on-training validation
|
| 76 |
+
processing_res: 0
|
| 77 |
+
match_input_res: false
|
| 78 |
+
resample_method: bilinear
|
| 79 |
+
main_val_metric: abs_relative_difference
|
| 80 |
+
main_val_metric_goal: minimize
|
| 81 |
+
init_seed: 2024
|
| 82 |
+
|
| 83 |
+
eval:
|
| 84 |
+
alignment: least_square_sqrt_disp
|
| 85 |
+
align_max_res: null
|
| 86 |
+
eval_metrics:
|
| 87 |
+
- abs_relative_difference
|
| 88 |
+
- squared_relative_difference
|
| 89 |
+
- rmse_linear
|
| 90 |
+
- rmse_log
|
| 91 |
+
- log10
|
| 92 |
+
- delta1_acc
|
| 93 |
+
- delta2_acc
|
| 94 |
+
- delta3_acc
|
| 95 |
+
- i_rmse
|
| 96 |
+
- silog_rmse
|
DepthMaster/config/train_s2.yaml
ADDED
|
@@ -0,0 +1,106 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
base_config:
|
| 2 |
+
- config/logging.yaml
|
| 3 |
+
- config/wandb.yaml
|
| 4 |
+
- config/dataset/dataset_train.yaml
|
| 5 |
+
- config/dataset/dataset_val.yaml
|
| 6 |
+
- config/dataset/dataset_vis.yaml
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
model:
|
| 10 |
+
name: DepthMaster_pipeline
|
| 11 |
+
pretrained_path: stage1
|
| 12 |
+
latent_scale_factor: 0.18215
|
| 13 |
+
|
| 14 |
+
pipeline:
|
| 15 |
+
name: DepthMasterPipeline
|
| 16 |
+
kwargs:
|
| 17 |
+
scale_invariant: true
|
| 18 |
+
shift_invariant: true
|
| 19 |
+
|
| 20 |
+
depth_normalization:
|
| 21 |
+
type: scale_shift_depth
|
| 22 |
+
clip: true
|
| 23 |
+
norm_min: -1.0
|
| 24 |
+
norm_max: 1.0
|
| 25 |
+
min_max_quantile: 0.02
|
| 26 |
+
|
| 27 |
+
augmentation:
|
| 28 |
+
lr_flip_p: 0.5
|
| 29 |
+
|
| 30 |
+
dataloader:
|
| 31 |
+
num_workers: 2
|
| 32 |
+
effective_batch_size: 32
|
| 33 |
+
max_train_batch_size: 1
|
| 34 |
+
seed: 2024 # to ensure continuity when resuming from checkpoint
|
| 35 |
+
|
| 36 |
+
# Training settings
|
| 37 |
+
trainer:
|
| 38 |
+
name: DepthMasterTrainerS2
|
| 39 |
+
training_noise_scheduler:
|
| 40 |
+
pretrained_path: stage1
|
| 41 |
+
init_seed: 2024 # use null to train w/o seeding
|
| 42 |
+
save_period: 2000
|
| 43 |
+
backup_period: 2000
|
| 44 |
+
validation_period: 2000
|
| 45 |
+
visualization_period: 2000
|
| 46 |
+
|
| 47 |
+
multi_res_noise:
|
| 48 |
+
strength: 0.9
|
| 49 |
+
annealed: true
|
| 50 |
+
downscale_strategy: original
|
| 51 |
+
|
| 52 |
+
gt_depth_type: depth_raw_norm
|
| 53 |
+
gt_mask_type: valid_mask_raw
|
| 54 |
+
|
| 55 |
+
max_epoch: 10000 # a large enough number
|
| 56 |
+
max_iter: 20000 # usually converges at around 20k
|
| 57 |
+
|
| 58 |
+
optimizer:
|
| 59 |
+
name: Adam
|
| 60 |
+
|
| 61 |
+
loss:
|
| 62 |
+
name: mse_loss
|
| 63 |
+
kwargs:
|
| 64 |
+
reduction: mean
|
| 65 |
+
|
| 66 |
+
|
| 67 |
+
lr: 3.0e-06
|
| 68 |
+
lr_scheduler:
|
| 69 |
+
name: IterExponential
|
| 70 |
+
kwargs:
|
| 71 |
+
total_iter: 25000
|
| 72 |
+
final_ratio: 0.01
|
| 73 |
+
warmup_steps: 100
|
| 74 |
+
|
| 75 |
+
# Validation (and visualization) settings
|
| 76 |
+
validation:
|
| 77 |
+
ensemble_size: 1 # simplified setting for on-training validation
|
| 78 |
+
processing_res: 0
|
| 79 |
+
match_input_res: false
|
| 80 |
+
resample_method: bilinear
|
| 81 |
+
main_val_metric: abs_relative_difference
|
| 82 |
+
main_val_metric_goal: minimize
|
| 83 |
+
init_seed: 2024
|
| 84 |
+
|
| 85 |
+
eval:
|
| 86 |
+
alignment: least_square_sqrt_disp
|
| 87 |
+
align_max_res: null
|
| 88 |
+
eval_metrics:
|
| 89 |
+
- abs_relative_difference
|
| 90 |
+
- squared_relative_difference
|
| 91 |
+
- rmse_linear
|
| 92 |
+
- rmse_log
|
| 93 |
+
- log10
|
| 94 |
+
- delta1_acc
|
| 95 |
+
- delta2_acc
|
| 96 |
+
- delta3_acc
|
| 97 |
+
- i_rmse
|
| 98 |
+
- silog_rmse
|
| 99 |
+
|
| 100 |
+
grad_loss:
|
| 101 |
+
name: huber_loss
|
| 102 |
+
kwargs:
|
| 103 |
+
delta: 0.2
|
| 104 |
+
lamda: 1e-3
|
| 105 |
+
num: 3
|
| 106 |
+
thre: 0.04
|
DepthMaster/config/wandb.yaml
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
wandb:
|
| 2 |
+
# entity: your_entity
|
| 3 |
+
project: DepthMaster
|
DepthMaster/data_split/diode/diode_val_all_filename_list.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
DepthMaster/data_split/diode/diode_val_indoor_filename_list.txt
ADDED
|
@@ -0,0 +1,325 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
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|
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|
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|
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|
|
|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_200_010.png indoors/scene_00021/scan_00189/00021_00189_indoors_200_010_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_200_010_depth_mask.npy
|
| 2 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_240_030.png indoors/scene_00021/scan_00189/00021_00189_indoors_240_030_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_240_030_depth_mask.npy
|
| 3 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_210_000.png indoors/scene_00021/scan_00189/00021_00189_indoors_210_000_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_210_000_depth_mask.npy
|
| 4 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_350_030.png indoors/scene_00021/scan_00189/00021_00189_indoors_350_030_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_350_030_depth_mask.npy
|
| 5 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_010_010.png indoors/scene_00021/scan_00189/00021_00189_indoors_010_010_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_010_010_depth_mask.npy
|
| 6 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_230_020.png indoors/scene_00021/scan_00189/00021_00189_indoors_230_020_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_230_020_depth_mask.npy
|
| 7 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_150_030.png indoors/scene_00021/scan_00189/00021_00189_indoors_150_030_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_150_030_depth_mask.npy
|
| 8 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_000_040.png indoors/scene_00021/scan_00189/00021_00189_indoors_000_040_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_000_040_depth_mask.npy
|
| 9 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_220_030.png indoors/scene_00021/scan_00189/00021_00189_indoors_220_030_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_220_030_depth_mask.npy
|
| 10 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_340_020.png indoors/scene_00021/scan_00189/00021_00189_indoors_340_020_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_340_020_depth_mask.npy
|
| 11 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_000_000.png indoors/scene_00021/scan_00189/00021_00189_indoors_000_000_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_000_000_depth_mask.npy
|
| 12 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_350_010.png indoors/scene_00021/scan_00189/00021_00189_indoors_350_010_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_350_010_depth_mask.npy
|
| 13 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_010_030.png indoors/scene_00021/scan_00189/00021_00189_indoors_010_030_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_010_030_depth_mask.npy
|
| 14 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_130_000.png indoors/scene_00021/scan_00189/00021_00189_indoors_130_000_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_130_000_depth_mask.npy
|
| 15 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_230_000.png indoors/scene_00021/scan_00189/00021_00189_indoors_230_000_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_230_000_depth_mask.npy
|
| 16 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_220_010.png indoors/scene_00021/scan_00189/00021_00189_indoors_220_010_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_220_010_depth_mask.npy
|
| 17 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_340_000.png indoors/scene_00021/scan_00189/00021_00189_indoors_340_000_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_340_000_depth_mask.npy
|
| 18 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_000_020.png indoors/scene_00021/scan_00189/00021_00189_indoors_000_020_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_000_020_depth_mask.npy
|
| 19 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_160_020.png indoors/scene_00021/scan_00189/00021_00189_indoors_160_020_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_160_020_depth_mask.npy
|
| 20 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_190_020.png indoors/scene_00021/scan_00189/00021_00189_indoors_190_020_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_190_020_depth_mask.npy
|
| 21 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_210_020.png indoors/scene_00021/scan_00189/00021_00189_indoors_210_020_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_210_020_depth_mask.npy
|
| 22 |
+
indoors/scene_00021/scan_00189/00021_00189_indoors_330_010.png indoors/scene_00021/scan_00189/00021_00189_indoors_330_010_depth.npy indoors/scene_00021/scan_00189/00021_00189_indoors_330_010_depth_mask.npy
|
| 23 |
+
indoors/scene_00021/scan_00190/00021_00190_indoors_280_000.png indoors/scene_00021/scan_00190/00021_00190_indoors_280_000_depth.npy indoors/scene_00021/scan_00190/00021_00190_indoors_280_000_depth_mask.npy
|
| 24 |
+
indoors/scene_00021/scan_00190/00021_00190_indoors_150_030.png indoors/scene_00021/scan_00190/00021_00190_indoors_150_030_depth.npy indoors/scene_00021/scan_00190/00021_00190_indoors_150_030_depth_mask.npy
|
| 25 |
+
indoors/scene_00021/scan_00190/00021_00190_indoors_110_050.png indoors/scene_00021/scan_00190/00021_00190_indoors_110_050_depth.npy indoors/scene_00021/scan_00190/00021_00190_indoors_110_050_depth_mask.npy
|
| 26 |
+
indoors/scene_00021/scan_00190/00021_00190_indoors_050_030.png indoors/scene_00021/scan_00190/00021_00190_indoors_050_030_depth.npy indoors/scene_00021/scan_00190/00021_00190_indoors_050_030_depth_mask.npy
|
| 27 |
+
indoors/scene_00021/scan_00190/00021_00190_indoors_070_000.png indoors/scene_00021/scan_00190/00021_00190_indoors_070_000_depth.npy indoors/scene_00021/scan_00190/00021_00190_indoors_070_000_depth_mask.npy
|
| 28 |
+
indoors/scene_00021/scan_00190/00021_00190_indoors_140_020.png indoors/scene_00021/scan_00190/00021_00190_indoors_140_020_depth.npy indoors/scene_00021/scan_00190/00021_00190_indoors_140_020_depth_mask.npy
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indoors/scene_00020/scan_00185/00020_00185_indoors_340_020.png indoors/scene_00020/scan_00185/00020_00185_indoors_340_020_depth.npy indoors/scene_00020/scan_00185/00020_00185_indoors_340_020_depth_mask.npy
|
| 272 |
+
indoors/scene_00020/scan_00185/00020_00185_indoors_160_010.png indoors/scene_00020/scan_00185/00020_00185_indoors_160_010_depth.npy indoors/scene_00020/scan_00185/00020_00185_indoors_160_010_depth_mask.npy
|
| 273 |
+
indoors/scene_00020/scan_00185/00020_00185_indoors_070_000.png indoors/scene_00020/scan_00185/00020_00185_indoors_070_000_depth.npy indoors/scene_00020/scan_00185/00020_00185_indoors_070_000_depth_mask.npy
|
| 274 |
+
indoors/scene_00020/scan_00185/00020_00185_indoors_170_000.png indoors/scene_00020/scan_00185/00020_00185_indoors_170_000_depth.npy indoors/scene_00020/scan_00185/00020_00185_indoors_170_000_depth_mask.npy
|
| 275 |
+
indoors/scene_00020/scan_00185/00020_00185_indoors_270_000.png indoors/scene_00020/scan_00185/00020_00185_indoors_270_000_depth.npy indoors/scene_00020/scan_00185/00020_00185_indoors_270_000_depth_mask.npy
|
| 276 |
+
indoors/scene_00020/scan_00185/00020_00185_indoors_010_010.png indoors/scene_00020/scan_00185/00020_00185_indoors_010_010_depth.npy indoors/scene_00020/scan_00185/00020_00185_indoors_010_010_depth_mask.npy
|
| 277 |
+
indoors/scene_00020/scan_00185/00020_00185_indoors_350_030.png indoors/scene_00020/scan_00185/00020_00185_indoors_350_030_depth.npy indoors/scene_00020/scan_00185/00020_00185_indoors_350_030_depth_mask.npy
|
| 278 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_020_000.png indoors/scene_00020/scan_00186/00020_00186_indoors_020_000_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_020_000_depth_mask.npy
|
| 279 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_220_040.png indoors/scene_00020/scan_00186/00020_00186_indoors_220_040_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_220_040_depth_mask.npy
|
| 280 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_260_020.png indoors/scene_00020/scan_00186/00020_00186_indoors_260_020_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_260_020_depth_mask.npy
|
| 281 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_160_020.png indoors/scene_00020/scan_00186/00020_00186_indoors_160_020_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_160_020_depth_mask.npy
|
| 282 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_320_000.png indoors/scene_00020/scan_00186/00020_00186_indoors_320_000_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_320_000_depth_mask.npy
|
| 283 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_090_020.png indoors/scene_00020/scan_00186/00020_00186_indoors_090_020_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_090_020_depth_mask.npy
|
| 284 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_330_010.png indoors/scene_00020/scan_00186/00020_00186_indoors_330_010_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_330_010_depth_mask.npy
|
| 285 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_080_030.png indoors/scene_00020/scan_00186/00020_00186_indoors_080_030_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_080_030_depth_mask.npy
|
| 286 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_030_010.png indoors/scene_00020/scan_00186/00020_00186_indoors_030_010_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_030_010_depth_mask.npy
|
| 287 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_230_050.png indoors/scene_00020/scan_00186/00020_00186_indoors_230_050_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_230_050_depth_mask.npy
|
| 288 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_130_010.png indoors/scene_00020/scan_00186/00020_00186_indoors_130_010_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_130_010_depth_mask.npy
|
| 289 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_250_000.png indoors/scene_00020/scan_00186/00020_00186_indoors_250_000_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_250_000_depth_mask.npy
|
| 290 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_150_010.png indoors/scene_00020/scan_00186/00020_00186_indoors_150_010_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_150_010_depth_mask.npy
|
| 291 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_230_000.png indoors/scene_00020/scan_00186/00020_00186_indoors_230_000_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_230_000_depth_mask.npy
|
| 292 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_050_010.png indoors/scene_00020/scan_00186/00020_00186_indoors_050_010_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_050_010_depth_mask.npy
|
| 293 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_250_050.png indoors/scene_00020/scan_00186/00020_00186_indoors_250_050_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_250_050_depth_mask.npy
|
| 294 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_070_020.png indoors/scene_00020/scan_00186/00020_00186_indoors_070_020_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_070_020_depth_mask.npy
|
| 295 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_110_030.png indoors/scene_00020/scan_00186/00020_00186_indoors_110_030_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_110_030_depth_mask.npy
|
| 296 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_340_000.png indoors/scene_00020/scan_00186/00020_00186_indoors_340_000_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_340_000_depth_mask.npy
|
| 297 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_140_000.png indoors/scene_00020/scan_00186/00020_00186_indoors_140_000_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_140_000_depth_mask.npy
|
| 298 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_220_010.png indoors/scene_00020/scan_00186/00020_00186_indoors_220_010_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_220_010_depth_mask.npy
|
| 299 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_040_000.png indoors/scene_00020/scan_00186/00020_00186_indoors_040_000_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_040_000_depth_mask.npy
|
| 300 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_060_030.png indoors/scene_00020/scan_00186/00020_00186_indoors_060_030_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_060_030_depth_mask.npy
|
| 301 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_240_040.png indoors/scene_00020/scan_00186/00020_00186_indoors_240_040_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_240_040_depth_mask.npy
|
| 302 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_150_030.png indoors/scene_00020/scan_00186/00020_00186_indoors_150_030_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_150_030_depth_mask.npy
|
| 303 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_170_040.png indoors/scene_00020/scan_00186/00020_00186_indoors_170_040_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_170_040_depth_mask.npy
|
| 304 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_210_050.png indoors/scene_00020/scan_00186/00020_00186_indoors_210_050_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_210_050_depth_mask.npy
|
| 305 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_070_040.png indoors/scene_00020/scan_00186/00020_00186_indoors_070_040_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_070_040_depth_mask.npy
|
| 306 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_250_030.png indoors/scene_00020/scan_00186/00020_00186_indoors_250_030_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_250_030_depth_mask.npy
|
| 307 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_270_000.png indoors/scene_00020/scan_00186/00020_00186_indoors_270_000_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_270_000_depth_mask.npy
|
| 308 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_200_040.png indoors/scene_00020/scan_00186/00020_00186_indoors_200_040_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_200_040_depth_mask.npy
|
| 309 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_190_050.png indoors/scene_00020/scan_00186/00020_00186_indoors_190_050_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_190_050_depth_mask.npy
|
| 310 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_000_000.png indoors/scene_00020/scan_00186/00020_00186_indoors_000_000_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_000_000_depth_mask.npy
|
| 311 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_240_020.png indoors/scene_00020/scan_00186/00020_00186_indoors_240_020_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_240_020_depth_mask.npy
|
| 312 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_140_020.png indoors/scene_00020/scan_00186/00020_00186_indoors_140_020_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_140_020_depth_mask.npy
|
| 313 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_040_020.png indoors/scene_00020/scan_00186/00020_00186_indoors_040_020_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_040_020_depth_mask.npy
|
| 314 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_120_020.png indoors/scene_00020/scan_00186/00020_00186_indoors_120_020_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_120_020_depth_mask.npy
|
| 315 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_100_010.png indoors/scene_00020/scan_00186/00020_00186_indoors_100_010_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_100_010_depth_mask.npy
|
| 316 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_160_000.png indoors/scene_00020/scan_00186/00020_00186_indoors_160_000_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_160_000_depth_mask.npy
|
| 317 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_260_040.png indoors/scene_00020/scan_00186/00020_00186_indoors_260_040_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_260_040_depth_mask.npy
|
| 318 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_060_000.png indoors/scene_00020/scan_00186/00020_00186_indoors_060_000_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_060_000_depth_mask.npy
|
| 319 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_090_000.png indoors/scene_00020/scan_00186/00020_00186_indoors_090_000_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_090_000_depth_mask.npy
|
| 320 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_230_030.png indoors/scene_00020/scan_00186/00020_00186_indoors_230_030_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_230_030_depth_mask.npy
|
| 321 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_210_000.png indoors/scene_00020/scan_00186/00020_00186_indoors_210_000_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_210_000_depth_mask.npy
|
| 322 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_170_010.png indoors/scene_00020/scan_00186/00020_00186_indoors_170_010_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_170_010_depth_mask.npy
|
| 323 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_080_010.png indoors/scene_00020/scan_00186/00020_00186_indoors_080_010_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_080_010_depth_mask.npy
|
| 324 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_130_030.png indoors/scene_00020/scan_00186/00020_00186_indoors_130_030_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_130_030_depth_mask.npy
|
| 325 |
+
indoors/scene_00020/scan_00186/00020_00186_indoors_110_000.png indoors/scene_00020/scan_00186/00020_00186_indoors_110_000_depth.npy indoors/scene_00020/scan_00186/00020_00186_indoors_110_000_depth_mask.npy
|
DepthMaster/data_split/diode/diode_val_outdoor_filename_list.txt
ADDED
|
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| 1 |
+
outdoor/scene_00022/scan_00196/00022_00196_outdoor_350_020.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_350_020_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_350_020_depth_mask.npy
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| 2 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_010_000.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_010_000_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_010_000_depth_mask.npy
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| 3 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_080_050.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_080_050_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_080_050_depth_mask.npy
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| 4 |
+
outdoor/scene_00022/scan_00196/00022_00196_outdoor_230_030.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_230_030_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_230_030_depth_mask.npy
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| 5 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_010_040.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_010_040_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_010_040_depth_mask.npy
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| 6 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_170_010.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_170_010_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_170_010_depth_mask.npy
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| 7 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_080_010.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_080_010_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_080_010_depth_mask.npy
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| 8 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_220_020.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_220_020_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_220_020_depth_mask.npy
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| 9 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_040_030.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_040_030_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_040_030_depth_mask.npy
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| 10 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_320_020.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_320_020_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_320_020_depth_mask.npy
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| 11 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_000_010.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_000_010_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_000_010_depth_mask.npy
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| 12 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_120_020.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_120_020_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_120_020_depth_mask.npy
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| 13 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_090_040.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_090_040_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_090_040_depth_mask.npy
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| 14 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_190_010.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_190_010_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_190_010_depth_mask.npy
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| 15 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_200_000.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_200_000_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_200_000_depth_mask.npy
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| 16 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_060_010.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_060_010_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_060_010_depth_mask.npy
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| 17 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_300_000.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_300_000_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_300_000_depth_mask.npy
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| 18 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_020_030.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_020_030_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_020_030_depth_mask.npy
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| 19 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_240_020.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_240_020_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_240_020_depth_mask.npy
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| 20 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_060_050.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_060_050_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_060_050_depth_mask.npy
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| 21 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_300_040.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_300_040_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_300_040_depth_mask.npy
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| 22 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_100_000.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_100_000_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_100_000_depth_mask.npy
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| 23 |
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| 24 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_070_040.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_070_040_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_070_040_depth_mask.npy
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| 25 |
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| 26 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_180_000.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_180_000_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_180_000_depth_mask.npy
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| 27 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_210_010.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_210_010_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_210_010_depth_mask.npy
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| 28 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_070_000.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_070_000_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_070_000_depth_mask.npy
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| 29 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_310_010.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_310_010_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_310_010_depth_mask.npy
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| 30 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_020_050.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_020_050_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_020_050_depth_mask.npy
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| 31 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_300_020.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_300_020_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_300_020_depth_mask.npy
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| 32 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_060_030.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_060_030_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_060_030_depth_mask.npy
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| 33 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_340_000.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_340_000_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_340_000_depth_mask.npy
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| 34 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_240_000.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_240_000_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_240_000_depth_mask.npy
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| 35 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_290_030.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_290_030_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_290_030_depth_mask.npy
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| 36 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_250_010.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_250_010_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_250_010_depth_mask.npy
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| 37 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_030_040.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_030_040_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_030_040_depth_mask.npy
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| 38 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_180_020.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_180_020_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_180_020_depth_mask.npy
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| 39 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_310_030.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_310_030_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_310_030_depth_mask.npy
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| 40 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_070_020.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_070_020_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_070_020_depth_mask.npy
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| 41 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_010_020.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_010_020_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_010_020_depth_mask.npy
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| 42 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_050_040.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_050_040_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_050_040_depth_mask.npy
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| 43 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_230_010.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_230_010_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_230_010_depth_mask.npy
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| 44 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_330_010.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_330_010_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_330_010_depth_mask.npy
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| 45 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_080_030.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_080_030_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_080_030_depth_mask.npy
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| 46 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_220_000.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_220_000_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_220_000_depth_mask.npy
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| 47 |
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| 48 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_320_000.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_320_000_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_320_000_depth_mask.npy
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| 49 |
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outdoor/scene_00022/scan_00196/00022_00196_outdoor_090_020.png outdoor/scene_00022/scan_00196/00022_00196_outdoor_090_020_depth.npy outdoor/scene_00022/scan_00196/00022_00196_outdoor_090_020_depth_mask.npy
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| 50 |
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| 54 |
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outdoor/scene_00022/scan_00195/00022_00195_outdoor_320_040.png outdoor/scene_00022/scan_00195/00022_00195_outdoor_320_040_depth.npy outdoor/scene_00022/scan_00195/00022_00195_outdoor_320_040_depth_mask.npy
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outdoor/scene_00022/scan_00195/00022_00195_outdoor_220_000.png outdoor/scene_00022/scan_00195/00022_00195_outdoor_220_000_depth.npy outdoor/scene_00022/scan_00195/00022_00195_outdoor_220_000_depth_mask.npy
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| 61 |
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| 62 |
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outdoor/scene_00024/scan_00201/00024_00201_outdoor_180_030.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_180_030_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_180_030_depth_mask.npy
|
| 393 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_210_020.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_210_020_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_210_020_depth_mask.npy
|
| 394 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_130_010.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_130_010_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_130_010_depth_mask.npy
|
| 395 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_120_000.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_120_000_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_120_000_depth_mask.npy
|
| 396 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_140_050.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_140_050_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_140_050_depth_mask.npy
|
| 397 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_320_000.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_320_000_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_320_000_depth_mask.npy
|
| 398 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_190_020.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_190_020_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_190_020_depth_mask.npy
|
| 399 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_240_000.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_240_000_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_240_000_depth_mask.npy
|
| 400 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_100_020.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_100_020_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_100_020_depth_mask.npy
|
| 401 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_340_000.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_340_000_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_340_000_depth_mask.npy
|
| 402 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_000_020.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_000_020_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_000_020_depth_mask.npy
|
| 403 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_090_030.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_090_030_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_090_030_depth_mask.npy
|
| 404 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_130_040.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_130_040_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_130_040_depth_mask.npy
|
| 405 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_080_020.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_080_020_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_080_020_depth_mask.npy
|
| 406 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_170_020.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_170_020_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_170_020_depth_mask.npy
|
| 407 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_110_030.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_110_030_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_110_030_depth_mask.npy
|
| 408 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_030_000.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_030_000_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_030_000_depth_mask.npy
|
| 409 |
+
outdoor/scene_00024/scan_00201/00024_00201_outdoor_350_010.png outdoor/scene_00024/scan_00201/00024_00201_outdoor_350_010_depth.npy outdoor/scene_00024/scan_00201/00024_00201_outdoor_350_010_depth_mask.npy
|
| 410 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_340_030.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_340_030_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_340_030_depth_mask.npy
|
| 411 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_220_020.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_220_020_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_220_020_depth_mask.npy
|
| 412 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_140_030.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_140_030_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_140_030_depth_mask.npy
|
| 413 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_200_010.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_200_010_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_200_010_depth_mask.npy
|
| 414 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_190_000.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_190_000_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_190_000_depth_mask.npy
|
| 415 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_110_040.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_110_040_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_110_040_depth_mask.npy
|
| 416 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_070_010.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_070_010_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_070_010_depth_mask.npy
|
| 417 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_150_020.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_150_020_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_150_020_depth_mask.npy
|
| 418 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_210_000.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_210_000_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_210_000_depth_mask.npy
|
| 419 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_180_010.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_180_010_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_180_010_depth_mask.npy
|
| 420 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_310_040.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_310_040_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_310_040_depth_mask.npy
|
| 421 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_170_000.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_170_000_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_170_000_depth_mask.npy
|
| 422 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_130_020.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_130_020_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_130_020_depth_mask.npy
|
| 423 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_170_040.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_170_040_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_170_040_depth_mask.npy
|
| 424 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_120_030.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_120_030_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_120_030_depth_mask.npy
|
| 425 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_100_000.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_100_000_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_100_000_depth_mask.npy
|
| 426 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_200_040.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_200_040_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_200_040_depth_mask.npy
|
| 427 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_320_030.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_320_030_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_320_030_depth_mask.npy
|
| 428 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_160_010.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_160_010_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_160_010_depth_mask.npy
|
| 429 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_130_040.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_130_040_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_130_040_depth_mask.npy
|
| 430 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_170_020.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_170_020_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_170_020_depth_mask.npy
|
| 431 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_210_030.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_210_030_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_210_030_depth_mask.npy
|
| 432 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_330_040.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_330_040_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_330_040_depth_mask.npy
|
| 433 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_140_000.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_140_000_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_140_000_depth_mask.npy
|
| 434 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_160_030.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_160_030_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_160_030_depth_mask.npy
|
| 435 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_120_000.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_120_000_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_120_000_depth_mask.npy
|
| 436 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_100_030.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_100_030_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_100_030_depth_mask.npy
|
| 437 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_000_030.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_000_030_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_000_030_depth_mask.npy
|
| 438 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_220_040.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_220_040_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_220_040_depth_mask.npy
|
| 439 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_090_020.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_090_020_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_090_020_depth_mask.npy
|
| 440 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_300_030.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_300_030_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_300_030_depth_mask.npy
|
| 441 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_190_020.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_190_020_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_190_020_depth_mask.npy
|
| 442 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_080_030.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_080_030_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_080_030_depth_mask.npy
|
| 443 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_230_010.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_230_010_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_230_010_depth_mask.npy
|
| 444 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_180_030.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_180_030_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_180_030_depth_mask.npy
|
| 445 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_110_020.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_110_020_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_110_020_depth_mask.npy
|
| 446 |
+
outdoor/scene_00024/scan_00202/00024_00202_outdoor_150_040.png outdoor/scene_00024/scan_00202/00024_00202_outdoor_150_040_depth.npy outdoor/scene_00024/scan_00202/00024_00202_outdoor_150_040_depth_mask.npy
|
DepthMaster/data_split/eth3d/eth3d_filename_list.txt
ADDED
|
@@ -0,0 +1,454 @@
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rgb/electro/images/dslr_images/DSC_9299.JPG depth/electro_dslr_depth/electro/ground_truth_depth/dslr_images/DSC_9299.JPG
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rgb/electro/images/dslr_images/DSC_9300.JPG depth/electro_dslr_depth/electro/ground_truth_depth/dslr_images/DSC_9300.JPG
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rgb/electro/images/dslr_images/DSC_9303.JPG depth/electro_dslr_depth/electro/ground_truth_depth/dslr_images/DSC_9303.JPG
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DepthMaster/data_split/hypersim/filename_list_test_filtered.txt
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DepthMaster/data_split/hypersim/filename_list_test_filtered_100.txt
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+
ai_001_010/rgb_cam_00_fr0075.png ai_001_010/depth_plane_cam_00_fr0075.png
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| 2 |
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ai_001_010/rgb_cam_01_fr0051.png ai_001_010/depth_plane_cam_01_fr0051.png
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ai_001_010/rgb_cam_02_fr0027.png ai_001_010/depth_plane_cam_02_fr0027.png
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ai_003_007/rgb_cam_00_fr0003.png ai_003_007/depth_plane_cam_00_fr0003.png
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ai_003_007/rgb_cam_00_fr0079.png ai_003_007/depth_plane_cam_00_fr0079.png
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| 6 |
+
ai_003_007/rgb_cam_01_fr0055.png ai_003_007/depth_plane_cam_01_fr0055.png
|
| 7 |
+
ai_005_001/rgb_cam_00_fr0031.png ai_005_001/depth_plane_cam_00_fr0031.png
|
| 8 |
+
ai_005_010/rgb_cam_00_fr0007.png ai_005_010/depth_plane_cam_00_fr0007.png
|
| 9 |
+
ai_005_010/rgb_cam_00_fr0083.png ai_005_010/depth_plane_cam_00_fr0083.png
|
| 10 |
+
ai_008_005/rgb_cam_00_fr0059.png ai_008_005/depth_plane_cam_00_fr0059.png
|
| 11 |
+
ai_008_005/rgb_cam_01_fr0036.png ai_008_005/depth_plane_cam_01_fr0036.png
|
| 12 |
+
ai_008_007/rgb_cam_00_fr0012.png ai_008_007/depth_plane_cam_00_fr0012.png
|
| 13 |
+
ai_008_007/rgb_cam_00_fr0088.png ai_008_007/depth_plane_cam_00_fr0088.png
|
| 14 |
+
ai_008_007/rgb_cam_01_fr0064.png ai_008_007/depth_plane_cam_01_fr0064.png
|
| 15 |
+
ai_010_003/rgb_cam_00_fr0045.png ai_010_003/depth_plane_cam_00_fr0045.png
|
| 16 |
+
ai_010_003/rgb_cam_01_fr0021.png ai_010_003/depth_plane_cam_01_fr0021.png
|
| 17 |
+
ai_010_003/rgb_cam_01_fr0097.png ai_010_003/depth_plane_cam_01_fr0097.png
|
| 18 |
+
ai_010_004/rgb_cam_00_fr0073.png ai_010_004/depth_plane_cam_00_fr0073.png
|
| 19 |
+
ai_011_007/rgb_cam_00_fr0060.png ai_011_007/depth_plane_cam_00_fr0060.png
|
| 20 |
+
ai_011_007/rgb_cam_01_fr0041.png ai_011_007/depth_plane_cam_01_fr0041.png
|
| 21 |
+
ai_013_010/rgb_cam_00_fr0019.png ai_013_010/depth_plane_cam_00_fr0019.png
|
| 22 |
+
ai_013_010/rgb_cam_00_fr0095.png ai_013_010/depth_plane_cam_00_fr0095.png
|
| 23 |
+
ai_014_006/rgb_cam_00_fr0071.png ai_014_006/depth_plane_cam_00_fr0071.png
|
| 24 |
+
ai_015_010/rgb_cam_00_fr0047.png ai_015_010/depth_plane_cam_00_fr0047.png
|
| 25 |
+
ai_018_001/rgb_cam_00_fr0023.png ai_018_001/depth_plane_cam_00_fr0023.png
|
| 26 |
+
ai_018_001/rgb_cam_00_fr0099.png ai_018_001/depth_plane_cam_00_fr0099.png
|
| 27 |
+
ai_021_001/rgb_cam_00_fr0075.png ai_021_001/depth_plane_cam_00_fr0075.png
|
| 28 |
+
ai_021_001/rgb_cam_01_fr0051.png ai_021_001/depth_plane_cam_01_fr0051.png
|
| 29 |
+
ai_022_001/rgb_cam_00_fr0027.png ai_022_001/depth_plane_cam_00_fr0027.png
|
| 30 |
+
ai_022_003/rgb_cam_00_fr0003.png ai_022_003/depth_plane_cam_00_fr0003.png
|
| 31 |
+
ai_022_003/rgb_cam_00_fr0079.png ai_022_003/depth_plane_cam_00_fr0079.png
|
| 32 |
+
ai_022_003/rgb_cam_01_fr0055.png ai_022_003/depth_plane_cam_01_fr0055.png
|
| 33 |
+
ai_022_006/rgb_cam_00_fr0031.png ai_022_006/depth_plane_cam_00_fr0031.png
|
| 34 |
+
ai_023_005/rgb_cam_00_fr0007.png ai_023_005/depth_plane_cam_00_fr0007.png
|
| 35 |
+
ai_023_005/rgb_cam_00_fr0083.png ai_023_005/depth_plane_cam_00_fr0083.png
|
| 36 |
+
ai_023_005/rgb_cam_01_fr0059.png ai_023_005/depth_plane_cam_01_fr0059.png
|
| 37 |
+
ai_024_002/rgb_cam_00_fr0035.png ai_024_002/depth_plane_cam_00_fr0035.png
|
| 38 |
+
ai_024_003/rgb_cam_00_fr0011.png ai_024_003/depth_plane_cam_00_fr0011.png
|
| 39 |
+
ai_024_003/rgb_cam_00_fr0087.png ai_024_003/depth_plane_cam_00_fr0087.png
|
| 40 |
+
ai_024_005/rgb_cam_00_fr0063.png ai_024_005/depth_plane_cam_00_fr0063.png
|
| 41 |
+
ai_024_007/rgb_cam_00_fr0039.png ai_024_007/depth_plane_cam_00_fr0039.png
|
| 42 |
+
ai_024_007/rgb_cam_01_fr0016.png ai_024_007/depth_plane_cam_01_fr0016.png
|
| 43 |
+
ai_024_007/rgb_cam_01_fr0092.png ai_024_007/depth_plane_cam_01_fr0092.png
|
| 44 |
+
ai_026_002/rgb_cam_00_fr0068.png ai_026_002/depth_plane_cam_00_fr0068.png
|
| 45 |
+
ai_028_005/rgb_cam_00_fr0044.png ai_028_005/depth_plane_cam_00_fr0044.png
|
| 46 |
+
ai_028_006/rgb_cam_00_fr0020.png ai_028_006/depth_plane_cam_00_fr0020.png
|
| 47 |
+
ai_028_006/rgb_cam_00_fr0096.png ai_028_006/depth_plane_cam_00_fr0096.png
|
| 48 |
+
ai_030_001/rgb_cam_00_fr0072.png ai_030_001/depth_plane_cam_00_fr0072.png
|
| 49 |
+
ai_031_008/rgb_cam_00_fr0048.png ai_031_008/depth_plane_cam_00_fr0048.png
|
| 50 |
+
ai_033_002/rgb_cam_00_fr0024.png ai_033_002/depth_plane_cam_00_fr0024.png
|
| 51 |
+
ai_033_002/rgb_cam_01_fr0005.png ai_033_002/depth_plane_cam_01_fr0005.png
|
| 52 |
+
ai_033_002/rgb_cam_01_fr0082.png ai_033_002/depth_plane_cam_01_fr0082.png
|
| 53 |
+
ai_034_002/rgb_cam_00_fr0058.png ai_034_002/depth_plane_cam_00_fr0058.png
|
| 54 |
+
ai_034_002/rgb_cam_01_fr0036.png ai_034_002/depth_plane_cam_01_fr0036.png
|
| 55 |
+
ai_035_001/rgb_cam_00_fr0012.png ai_035_001/depth_plane_cam_00_fr0012.png
|
| 56 |
+
ai_035_001/rgb_cam_00_fr0088.png ai_035_001/depth_plane_cam_00_fr0088.png
|
| 57 |
+
ai_035_001/rgb_cam_01_fr0065.png ai_035_001/depth_plane_cam_01_fr0065.png
|
| 58 |
+
ai_035_005/rgb_cam_00_fr0041.png ai_035_005/depth_plane_cam_00_fr0041.png
|
| 59 |
+
ai_037_009/rgb_cam_00_fr0017.png ai_037_009/depth_plane_cam_00_fr0017.png
|
| 60 |
+
ai_037_009/rgb_cam_00_fr0093.png ai_037_009/depth_plane_cam_00_fr0093.png
|
| 61 |
+
ai_037_009/rgb_cam_01_fr0074.png ai_037_009/depth_plane_cam_01_fr0074.png
|
| 62 |
+
ai_039_002/rgb_cam_00_fr0050.png ai_039_002/depth_plane_cam_00_fr0050.png
|
| 63 |
+
ai_041_002/rgb_cam_00_fr0027.png ai_041_002/depth_plane_cam_00_fr0027.png
|
| 64 |
+
ai_041_002/rgb_cam_01_fr0004.png ai_041_002/depth_plane_cam_01_fr0004.png
|
| 65 |
+
ai_041_002/rgb_cam_01_fr0080.png ai_041_002/depth_plane_cam_01_fr0080.png
|
| 66 |
+
ai_041_004/rgb_cam_00_fr0056.png ai_041_004/depth_plane_cam_00_fr0056.png
|
| 67 |
+
ai_041_006/rgb_cam_00_fr0032.png ai_041_006/depth_plane_cam_00_fr0032.png
|
| 68 |
+
ai_041_007/rgb_cam_00_fr0008.png ai_041_007/depth_plane_cam_00_fr0008.png
|
| 69 |
+
ai_041_007/rgb_cam_00_fr0084.png ai_041_007/depth_plane_cam_00_fr0084.png
|
| 70 |
+
ai_044_004/rgb_cam_00_fr0060.png ai_044_004/depth_plane_cam_00_fr0060.png
|
| 71 |
+
ai_044_004/rgb_cam_01_fr0036.png ai_044_004/depth_plane_cam_01_fr0036.png
|
| 72 |
+
ai_048_008/rgb_cam_00_fr0012.png ai_048_008/depth_plane_cam_00_fr0012.png
|
| 73 |
+
ai_048_008/rgb_cam_00_fr0088.png ai_048_008/depth_plane_cam_00_fr0088.png
|
| 74 |
+
ai_048_008/rgb_cam_01_fr0064.png ai_048_008/depth_plane_cam_01_fr0064.png
|
| 75 |
+
ai_048_008/rgb_cam_02_fr0040.png ai_048_008/depth_plane_cam_02_fr0040.png
|
| 76 |
+
ai_048_008/rgb_cam_03_fr0016.png ai_048_008/depth_plane_cam_03_fr0016.png
|
| 77 |
+
ai_048_008/rgb_cam_03_fr0092.png ai_048_008/depth_plane_cam_03_fr0092.png
|
| 78 |
+
ai_048_010/rgb_cam_00_fr0068.png ai_048_010/depth_plane_cam_00_fr0068.png
|
| 79 |
+
ai_048_010/rgb_cam_01_fr0047.png ai_048_010/depth_plane_cam_01_fr0047.png
|
| 80 |
+
ai_048_010/rgb_cam_02_fr0023.png ai_048_010/depth_plane_cam_02_fr0023.png
|
| 81 |
+
ai_048_010/rgb_cam_03_fr0006.png ai_048_010/depth_plane_cam_03_fr0006.png
|
| 82 |
+
ai_048_010/rgb_cam_03_fr0082.png ai_048_010/depth_plane_cam_03_fr0082.png
|
| 83 |
+
ai_051_001/rgb_cam_00_fr0059.png ai_051_001/depth_plane_cam_00_fr0059.png
|
| 84 |
+
ai_051_001/rgb_cam_01_fr0035.png ai_051_001/depth_plane_cam_01_fr0035.png
|
| 85 |
+
ai_051_001/rgb_cam_02_fr0019.png ai_051_001/depth_plane_cam_02_fr0019.png
|
| 86 |
+
ai_051_001/rgb_cam_02_fr0099.png ai_051_001/depth_plane_cam_02_fr0099.png
|
| 87 |
+
ai_051_001/rgb_cam_03_fr0075.png ai_051_001/depth_plane_cam_03_fr0075.png
|
| 88 |
+
ai_051_001/rgb_cam_04_fr0051.png ai_051_001/depth_plane_cam_04_fr0051.png
|
| 89 |
+
ai_052_009/rgb_cam_00_fr0027.png ai_052_009/depth_plane_cam_00_fr0027.png
|
| 90 |
+
ai_052_009/rgb_cam_01_fr0054.png ai_052_009/depth_plane_cam_01_fr0054.png
|
| 91 |
+
ai_053_007/rgb_cam_00_fr0033.png ai_053_007/depth_plane_cam_00_fr0033.png
|
| 92 |
+
ai_053_007/rgb_cam_01_fr0009.png ai_053_007/depth_plane_cam_01_fr0009.png
|
| 93 |
+
ai_053_007/rgb_cam_01_fr0085.png ai_053_007/depth_plane_cam_01_fr0085.png
|
| 94 |
+
ai_053_010/rgb_cam_00_fr0061.png ai_053_010/depth_plane_cam_00_fr0061.png
|
| 95 |
+
ai_054_005/rgb_cam_00_fr0037.png ai_054_005/depth_plane_cam_00_fr0037.png
|
| 96 |
+
ai_054_005/rgb_cam_01_fr0013.png ai_054_005/depth_plane_cam_01_fr0013.png
|
| 97 |
+
ai_054_005/rgb_cam_01_fr0089.png ai_054_005/depth_plane_cam_01_fr0089.png
|
| 98 |
+
ai_054_007/rgb_cam_00_fr0065.png ai_054_007/depth_plane_cam_00_fr0065.png
|
| 99 |
+
ai_054_007/rgb_cam_01_fr0041.png ai_054_007/depth_plane_cam_01_fr0041.png
|
| 100 |
+
ai_054_007/rgb_cam_02_fr0017.png ai_054_007/depth_plane_cam_02_fr0017.png
|
DepthMaster/data_split/hypersim/filename_list_train_filtered.txt
ADDED
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The diff for this file is too large to render.
See raw diff
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DepthMaster/data_split/hypersim/filename_list_val_filtered.txt
ADDED
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The diff for this file is too large to render.
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|
|
|
DepthMaster/data_split/hypersim/filename_list_val_filtered_small_80.txt
ADDED
|
@@ -0,0 +1,80 @@
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|
| 1 |
+
ai_003_010/rgb_cam_00_fr0047.png ai_003_010/depth_plane_cam_00_fr0047.png
|
| 2 |
+
ai_003_010/rgb_cam_00_fr0048.png ai_003_010/depth_plane_cam_00_fr0048.png
|
| 3 |
+
ai_003_010/rgb_cam_01_fr0098.png ai_003_010/depth_plane_cam_01_fr0098.png
|
| 4 |
+
ai_004_003/rgb_cam_01_fr0008.png ai_004_003/depth_plane_cam_01_fr0008.png
|
| 5 |
+
ai_004_004/rgb_cam_00_fr0025.png ai_004_004/depth_plane_cam_00_fr0025.png
|
| 6 |
+
ai_004_004/rgb_cam_00_fr0046.png ai_004_004/depth_plane_cam_00_fr0046.png
|
| 7 |
+
ai_004_004/rgb_cam_00_fr0049.png ai_004_004/depth_plane_cam_00_fr0049.png
|
| 8 |
+
ai_004_004/rgb_cam_01_fr0023.png ai_004_004/depth_plane_cam_01_fr0023.png
|
| 9 |
+
ai_005_005/rgb_cam_00_fr0032.png ai_005_005/depth_plane_cam_00_fr0032.png
|
| 10 |
+
ai_006_007/rgb_cam_00_fr0022.png ai_006_007/depth_plane_cam_00_fr0022.png
|
| 11 |
+
ai_006_007/rgb_cam_00_fr0095.png ai_006_007/depth_plane_cam_00_fr0095.png
|
| 12 |
+
ai_007_001/rgb_cam_00_fr0044.png ai_007_001/depth_plane_cam_00_fr0044.png
|
| 13 |
+
ai_007_001/rgb_cam_00_fr0048.png ai_007_001/depth_plane_cam_00_fr0048.png
|
| 14 |
+
ai_009_007/rgb_cam_00_fr0017.png ai_009_007/depth_plane_cam_00_fr0017.png
|
| 15 |
+
ai_009_007/rgb_cam_00_fr0097.png ai_009_007/depth_plane_cam_00_fr0097.png
|
| 16 |
+
ai_009_009/rgb_cam_00_fr0094.png ai_009_009/depth_plane_cam_00_fr0094.png
|
| 17 |
+
ai_015_001/rgb_cam_00_fr0058.png ai_015_001/depth_plane_cam_00_fr0058.png
|
| 18 |
+
ai_015_001/rgb_cam_00_fr0089.png ai_015_001/depth_plane_cam_00_fr0089.png
|
| 19 |
+
ai_017_007/rgb_cam_01_fr0064.png ai_017_007/depth_plane_cam_01_fr0064.png
|
| 20 |
+
ai_018_005/rgb_cam_00_fr0014.png ai_018_005/depth_plane_cam_00_fr0014.png
|
| 21 |
+
ai_018_005/rgb_cam_00_fr0059.png ai_018_005/depth_plane_cam_00_fr0059.png
|
| 22 |
+
ai_022_010/rgb_cam_00_fr0097.png ai_022_010/depth_plane_cam_00_fr0097.png
|
| 23 |
+
ai_022_010/rgb_cam_00_fr0099.png ai_022_010/depth_plane_cam_00_fr0099.png
|
| 24 |
+
ai_023_003/rgb_cam_00_fr0013.png ai_023_003/depth_plane_cam_00_fr0013.png
|
| 25 |
+
ai_023_003/rgb_cam_00_fr0015.png ai_023_003/depth_plane_cam_00_fr0015.png
|
| 26 |
+
ai_023_003/rgb_cam_00_fr0036.png ai_023_003/depth_plane_cam_00_fr0036.png
|
| 27 |
+
ai_023_003/rgb_cam_00_fr0095.png ai_023_003/depth_plane_cam_00_fr0095.png
|
| 28 |
+
ai_023_003/rgb_cam_01_fr0029.png ai_023_003/depth_plane_cam_01_fr0029.png
|
| 29 |
+
ai_023_003/rgb_cam_01_fr0036.png ai_023_003/depth_plane_cam_01_fr0036.png
|
| 30 |
+
ai_023_003/rgb_cam_01_fr0071.png ai_023_003/depth_plane_cam_01_fr0071.png
|
| 31 |
+
ai_032_007/rgb_cam_00_fr0031.png ai_032_007/depth_plane_cam_00_fr0031.png
|
| 32 |
+
ai_032_007/rgb_cam_00_fr0040.png ai_032_007/depth_plane_cam_00_fr0040.png
|
| 33 |
+
ai_032_007/rgb_cam_00_fr0075.png ai_032_007/depth_plane_cam_00_fr0075.png
|
| 34 |
+
ai_035_003/rgb_cam_00_fr0054.png ai_035_003/depth_plane_cam_00_fr0054.png
|
| 35 |
+
ai_035_004/rgb_cam_00_fr0077.png ai_035_004/depth_plane_cam_00_fr0077.png
|
| 36 |
+
ai_038_009/rgb_cam_00_fr0031.png ai_038_009/depth_plane_cam_00_fr0031.png
|
| 37 |
+
ai_038_009/rgb_cam_01_fr0010.png ai_038_009/depth_plane_cam_01_fr0010.png
|
| 38 |
+
ai_038_009/rgb_cam_01_fr0088.png ai_038_009/depth_plane_cam_01_fr0088.png
|
| 39 |
+
ai_039_003/rgb_cam_01_fr0042.png ai_039_003/depth_plane_cam_01_fr0042.png
|
| 40 |
+
ai_039_003/rgb_cam_01_fr0097.png ai_039_003/depth_plane_cam_01_fr0097.png
|
| 41 |
+
ai_044_001/rgb_cam_00_fr0043.png ai_044_001/depth_plane_cam_00_fr0043.png
|
| 42 |
+
ai_044_001/rgb_cam_01_fr0018.png ai_044_001/depth_plane_cam_01_fr0018.png
|
| 43 |
+
ai_044_003/rgb_cam_01_fr0082.png ai_044_003/depth_plane_cam_01_fr0082.png
|
| 44 |
+
ai_044_003/rgb_cam_01_fr0087.png ai_044_003/depth_plane_cam_01_fr0087.png
|
| 45 |
+
ai_044_003/rgb_cam_02_fr0086.png ai_044_003/depth_plane_cam_02_fr0086.png
|
| 46 |
+
ai_044_003/rgb_cam_03_fr0022.png ai_044_003/depth_plane_cam_03_fr0022.png
|
| 47 |
+
ai_044_003/rgb_cam_03_fr0063.png ai_044_003/depth_plane_cam_03_fr0063.png
|
| 48 |
+
ai_045_008/rgb_cam_00_fr0015.png ai_045_008/depth_plane_cam_00_fr0015.png
|
| 49 |
+
ai_045_008/rgb_cam_00_fr0030.png ai_045_008/depth_plane_cam_00_fr0030.png
|
| 50 |
+
ai_045_008/rgb_cam_01_fr0029.png ai_045_008/depth_plane_cam_01_fr0029.png
|
| 51 |
+
ai_045_008/rgb_cam_01_fr0052.png ai_045_008/depth_plane_cam_01_fr0052.png
|
| 52 |
+
ai_045_008/rgb_cam_01_fr0088.png ai_045_008/depth_plane_cam_01_fr0088.png
|
| 53 |
+
ai_047_009/rgb_cam_00_fr0097.png ai_047_009/depth_plane_cam_00_fr0097.png
|
| 54 |
+
ai_048_001/rgb_cam_00_fr0014.png ai_048_001/depth_plane_cam_00_fr0014.png
|
| 55 |
+
ai_048_001/rgb_cam_00_fr0088.png ai_048_001/depth_plane_cam_00_fr0088.png
|
| 56 |
+
ai_048_001/rgb_cam_01_fr0045.png ai_048_001/depth_plane_cam_01_fr0045.png
|
| 57 |
+
ai_048_001/rgb_cam_02_fr0031.png ai_048_001/depth_plane_cam_02_fr0031.png
|
| 58 |
+
ai_048_001/rgb_cam_03_fr0005.png ai_048_001/depth_plane_cam_03_fr0005.png
|
| 59 |
+
ai_048_001/rgb_cam_03_fr0045.png ai_048_001/depth_plane_cam_03_fr0045.png
|
| 60 |
+
ai_048_001/rgb_cam_03_fr0054.png ai_048_001/depth_plane_cam_03_fr0054.png
|
| 61 |
+
ai_048_001/rgb_cam_03_fr0061.png ai_048_001/depth_plane_cam_03_fr0061.png
|
| 62 |
+
ai_050_002/rgb_cam_01_fr0016.png ai_050_002/depth_plane_cam_01_fr0016.png
|
| 63 |
+
ai_050_002/rgb_cam_02_fr0053.png ai_050_002/depth_plane_cam_02_fr0053.png
|
| 64 |
+
ai_050_002/rgb_cam_03_fr0082.png ai_050_002/depth_plane_cam_03_fr0082.png
|
| 65 |
+
ai_050_002/rgb_cam_04_fr0033.png ai_050_002/depth_plane_cam_04_fr0033.png
|
| 66 |
+
ai_051_004/rgb_cam_00_fr0028.png ai_051_004/depth_plane_cam_00_fr0028.png
|
| 67 |
+
ai_051_004/rgb_cam_01_fr0065.png ai_051_004/depth_plane_cam_01_fr0065.png
|
| 68 |
+
ai_051_004/rgb_cam_02_fr0054.png ai_051_004/depth_plane_cam_02_fr0054.png
|
| 69 |
+
ai_051_004/rgb_cam_02_fr0056.png ai_051_004/depth_plane_cam_02_fr0056.png
|
| 70 |
+
ai_051_004/rgb_cam_03_fr0037.png ai_051_004/depth_plane_cam_03_fr0037.png
|
| 71 |
+
ai_051_004/rgb_cam_04_fr0083.png ai_051_004/depth_plane_cam_04_fr0083.png
|
| 72 |
+
ai_051_004/rgb_cam_05_fr0003.png ai_051_004/depth_plane_cam_05_fr0003.png
|
| 73 |
+
ai_052_001/rgb_cam_00_fr0008.png ai_052_001/depth_plane_cam_00_fr0008.png
|
| 74 |
+
ai_052_003/rgb_cam_00_fr0097.png ai_052_003/depth_plane_cam_00_fr0097.png
|
| 75 |
+
ai_052_003/rgb_cam_01_fr0081.png ai_052_003/depth_plane_cam_01_fr0081.png
|
| 76 |
+
ai_052_007/rgb_cam_01_fr0001.png ai_052_007/depth_plane_cam_01_fr0001.png
|
| 77 |
+
ai_053_003/rgb_cam_00_fr0005.png ai_053_003/depth_plane_cam_00_fr0005.png
|
| 78 |
+
ai_053_005/rgb_cam_00_fr0080.png ai_053_005/depth_plane_cam_00_fr0080.png
|
| 79 |
+
ai_055_009/rgb_cam_01_fr0070.png ai_055_009/depth_plane_cam_01_fr0070.png
|
| 80 |
+
ai_055_009/rgb_cam_01_fr0086.png ai_055_009/depth_plane_cam_01_fr0086.png
|
DepthMaster/data_split/hypersim/selected_vis_sample.txt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
ai_015_004/rgb_cam_00_fr0002.png ai_015_004/depth_plane_cam_00_fr0002.png (val)
|
| 2 |
+
ai_044_003/rgb_cam_01_fr0063.png ai_044_003/depth_plane_cam_01_fr0063.png (val)
|
| 3 |
+
ai_052_003/rgb_cam_01_fr0076.png ai_052_003/depth_plane_cam_01_fr0076.png (val)
|
DepthMaster/data_split/kitti/eigen_test_files_with_gt.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
DepthMaster/data_split/kitti/eigen_val_from_test.txt
ADDED
|
@@ -0,0 +1,116 @@
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
2011_09_26/2011_09_26_drive_0002_sync/image_02/data/0000000027.png 2011_09_26_drive_0002_sync/proj_depth/groundtruth/image_02/0000000027.png 721.5377
|
| 2 |
+
2011_09_26/2011_09_26_drive_0002_sync/image_02/data/0000000072.png None 721.5377
|
| 3 |
+
2011_09_26/2011_09_26_drive_0002_sync/image_02/data/0000000021.png 2011_09_26_drive_0002_sync/proj_depth/groundtruth/image_02/0000000021.png 721.5377
|
| 4 |
+
2011_09_26/2011_09_26_drive_0002_sync/image_02/data/0000000006.png 2011_09_26_drive_0002_sync/proj_depth/groundtruth/image_02/0000000006.png 721.5377
|
| 5 |
+
2011_09_26/2011_09_26_drive_0009_sync/image_02/data/0000000064.png 2011_09_26_drive_0009_sync/proj_depth/groundtruth/image_02/0000000064.png 721.5377
|
| 6 |
+
2011_09_26/2011_09_26_drive_0009_sync/image_02/data/0000000160.png 2011_09_26_drive_0009_sync/proj_depth/groundtruth/image_02/0000000160.png 721.5377
|
| 7 |
+
2011_09_26/2011_09_26_drive_0009_sync/image_02/data/0000000260.png 2011_09_26_drive_0009_sync/proj_depth/groundtruth/image_02/0000000260.png 721.5377
|
| 8 |
+
2011_09_26/2011_09_26_drive_0009_sync/image_02/data/0000000356.png 2011_09_26_drive_0009_sync/proj_depth/groundtruth/image_02/0000000356.png 721.5377
|
| 9 |
+
2011_09_26/2011_09_26_drive_0013_sync/image_02/data/0000000115.png 2011_09_26_drive_0013_sync/proj_depth/groundtruth/image_02/0000000115.png 721.5377
|
| 10 |
+
2011_09_26/2011_09_26_drive_0013_sync/image_02/data/0000000085.png 2011_09_26_drive_0013_sync/proj_depth/groundtruth/image_02/0000000085.png 721.5377
|
| 11 |
+
2011_09_26/2011_09_26_drive_0013_sync/image_02/data/0000000100.png 2011_09_26_drive_0013_sync/proj_depth/groundtruth/image_02/0000000100.png 721.5377
|
| 12 |
+
2011_09_26/2011_09_26_drive_0013_sync/image_02/data/0000000005.png 2011_09_26_drive_0013_sync/proj_depth/groundtruth/image_02/0000000005.png 721.5377
|
| 13 |
+
2011_09_26/2011_09_26_drive_0020_sync/image_02/data/0000000057.png 2011_09_26_drive_0020_sync/proj_depth/groundtruth/image_02/0000000057.png 721.5377
|
| 14 |
+
2011_09_26/2011_09_26_drive_0020_sync/image_02/data/0000000084.png None 721.5377
|
| 15 |
+
2011_09_26/2011_09_26_drive_0020_sync/image_02/data/0000000009.png 2011_09_26_drive_0020_sync/proj_depth/groundtruth/image_02/0000000009.png 721.5377
|
| 16 |
+
2011_09_26/2011_09_26_drive_0020_sync/image_02/data/0000000078.png 2011_09_26_drive_0020_sync/proj_depth/groundtruth/image_02/0000000078.png 721.5377
|
| 17 |
+
2011_09_26/2011_09_26_drive_0023_sync/image_02/data/0000000090.png 2011_09_26_drive_0023_sync/proj_depth/groundtruth/image_02/0000000090.png 721.5377
|
| 18 |
+
2011_09_26/2011_09_26_drive_0023_sync/image_02/data/0000000450.png 2011_09_26_drive_0023_sync/proj_depth/groundtruth/image_02/0000000450.png 721.5377
|
| 19 |
+
2011_09_26/2011_09_26_drive_0023_sync/image_02/data/0000000396.png 2011_09_26_drive_0023_sync/proj_depth/groundtruth/image_02/0000000396.png 721.5377
|
| 20 |
+
2011_09_26/2011_09_26_drive_0023_sync/image_02/data/0000000342.png 2011_09_26_drive_0023_sync/proj_depth/groundtruth/image_02/0000000342.png 721.5377
|
| 21 |
+
2011_09_26/2011_09_26_drive_0027_sync/image_02/data/0000000077.png 2011_09_26_drive_0027_sync/proj_depth/groundtruth/image_02/0000000077.png 721.5377
|
| 22 |
+
2011_09_26/2011_09_26_drive_0027_sync/image_02/data/0000000042.png 2011_09_26_drive_0027_sync/proj_depth/groundtruth/image_02/0000000042.png 721.5377
|
| 23 |
+
2011_09_26/2011_09_26_drive_0027_sync/image_02/data/0000000168.png 2011_09_26_drive_0027_sync/proj_depth/groundtruth/image_02/0000000168.png 721.5377
|
| 24 |
+
2011_09_26/2011_09_26_drive_0027_sync/image_02/data/0000000182.png 2011_09_26_drive_0027_sync/proj_depth/groundtruth/image_02/0000000182.png 721.5377
|
| 25 |
+
2011_09_26/2011_09_26_drive_0027_sync/image_02/data/0000000028.png 2011_09_26_drive_0027_sync/proj_depth/groundtruth/image_02/0000000028.png 721.5377
|
| 26 |
+
2011_09_26/2011_09_26_drive_0029_sync/image_02/data/0000000366.png 2011_09_26_drive_0029_sync/proj_depth/groundtruth/image_02/0000000366.png 721.5377
|
| 27 |
+
2011_09_26/2011_09_26_drive_0029_sync/image_02/data/0000000196.png 2011_09_26_drive_0029_sync/proj_depth/groundtruth/image_02/0000000196.png 721.5377
|
| 28 |
+
2011_09_26/2011_09_26_drive_0029_sync/image_02/data/0000000126.png 2011_09_26_drive_0029_sync/proj_depth/groundtruth/image_02/0000000126.png 721.5377
|
| 29 |
+
2011_09_26/2011_09_26_drive_0029_sync/image_02/data/0000000042.png 2011_09_26_drive_0029_sync/proj_depth/groundtruth/image_02/0000000042.png 721.5377
|
| 30 |
+
2011_09_26/2011_09_26_drive_0036_sync/image_02/data/0000000352.png 2011_09_26_drive_0036_sync/proj_depth/groundtruth/image_02/0000000352.png 721.5377
|
| 31 |
+
2011_09_26/2011_09_26_drive_0036_sync/image_02/data/0000000448.png 2011_09_26_drive_0036_sync/proj_depth/groundtruth/image_02/0000000448.png 721.5377
|
| 32 |
+
2011_09_26/2011_09_26_drive_0036_sync/image_02/data/0000000416.png 2011_09_26_drive_0036_sync/proj_depth/groundtruth/image_02/0000000416.png 721.5377
|
| 33 |
+
2011_09_26/2011_09_26_drive_0036_sync/image_02/data/0000000384.png 2011_09_26_drive_0036_sync/proj_depth/groundtruth/image_02/0000000384.png 721.5377
|
| 34 |
+
2011_09_26/2011_09_26_drive_0046_sync/image_02/data/0000000015.png 2011_09_26_drive_0046_sync/proj_depth/groundtruth/image_02/0000000015.png 721.5377
|
| 35 |
+
2011_09_26/2011_09_26_drive_0046_sync/image_02/data/0000000045.png 2011_09_26_drive_0046_sync/proj_depth/groundtruth/image_02/0000000045.png 721.5377
|
| 36 |
+
2011_09_26/2011_09_26_drive_0046_sync/image_02/data/0000000075.png 2011_09_26_drive_0046_sync/proj_depth/groundtruth/image_02/0000000075.png 721.5377
|
| 37 |
+
2011_09_26/2011_09_26_drive_0046_sync/image_02/data/0000000105.png 2011_09_26_drive_0046_sync/proj_depth/groundtruth/image_02/0000000105.png 721.5377
|
| 38 |
+
2011_09_26/2011_09_26_drive_0048_sync/image_02/data/0000000002.png None 721.5377
|
| 39 |
+
2011_09_26/2011_09_26_drive_0048_sync/image_02/data/0000000008.png 2011_09_26_drive_0048_sync/proj_depth/groundtruth/image_02/0000000008.png 721.5377
|
| 40 |
+
2011_09_26/2011_09_26_drive_0048_sync/image_02/data/0000000014.png 2011_09_26_drive_0048_sync/proj_depth/groundtruth/image_02/0000000014.png 721.5377
|
| 41 |
+
2011_09_26/2011_09_26_drive_0048_sync/image_02/data/0000000020.png None 721.5377
|
| 42 |
+
2011_09_26/2011_09_26_drive_0052_sync/image_02/data/0000000026.png 2011_09_26_drive_0052_sync/proj_depth/groundtruth/image_02/0000000026.png 721.5377
|
| 43 |
+
2011_09_26/2011_09_26_drive_0052_sync/image_02/data/0000000018.png 2011_09_26_drive_0052_sync/proj_depth/groundtruth/image_02/0000000018.png 721.5377
|
| 44 |
+
2011_09_26/2011_09_26_drive_0052_sync/image_02/data/0000000052.png 2011_09_26_drive_0052_sync/proj_depth/groundtruth/image_02/0000000052.png 721.5377
|
| 45 |
+
2011_09_26/2011_09_26_drive_0052_sync/image_02/data/0000000002.png None 721.5377
|
| 46 |
+
2011_09_26/2011_09_26_drive_0056_sync/image_02/data/0000000253.png 2011_09_26_drive_0056_sync/proj_depth/groundtruth/image_02/0000000253.png 721.5377
|
| 47 |
+
2011_09_26/2011_09_26_drive_0056_sync/image_02/data/0000000077.png 2011_09_26_drive_0056_sync/proj_depth/groundtruth/image_02/0000000077.png 721.5377
|
| 48 |
+
2011_09_26/2011_09_26_drive_0056_sync/image_02/data/0000000275.png 2011_09_26_drive_0056_sync/proj_depth/groundtruth/image_02/0000000275.png 721.5377
|
| 49 |
+
2011_09_26/2011_09_26_drive_0056_sync/image_02/data/0000000209.png 2011_09_26_drive_0056_sync/proj_depth/groundtruth/image_02/0000000209.png 721.5377
|
| 50 |
+
2011_09_26/2011_09_26_drive_0059_sync/image_02/data/0000000344.png 2011_09_26_drive_0059_sync/proj_depth/groundtruth/image_02/0000000344.png 721.5377
|
| 51 |
+
2011_09_26/2011_09_26_drive_0059_sync/image_02/data/0000000028.png 2011_09_26_drive_0059_sync/proj_depth/groundtruth/image_02/0000000028.png 721.5377
|
| 52 |
+
2011_09_26/2011_09_26_drive_0059_sync/image_02/data/0000000182.png 2011_09_26_drive_0059_sync/proj_depth/groundtruth/image_02/0000000182.png 721.5377
|
| 53 |
+
2011_09_26/2011_09_26_drive_0059_sync/image_02/data/0000000196.png 2011_09_26_drive_0059_sync/proj_depth/groundtruth/image_02/0000000196.png 721.5377
|
| 54 |
+
2011_09_26/2011_09_26_drive_0064_sync/image_02/data/0000000308.png 2011_09_26_drive_0064_sync/proj_depth/groundtruth/image_02/0000000308.png 721.5377
|
| 55 |
+
2011_09_26/2011_09_26_drive_0064_sync/image_02/data/0000000176.png 2011_09_26_drive_0064_sync/proj_depth/groundtruth/image_02/0000000176.png 721.5377
|
| 56 |
+
2011_09_26/2011_09_26_drive_0064_sync/image_02/data/0000000440.png 2011_09_26_drive_0064_sync/proj_depth/groundtruth/image_02/0000000440.png 721.5377
|
| 57 |
+
2011_09_26/2011_09_26_drive_0064_sync/image_02/data/0000000550.png 2011_09_26_drive_0064_sync/proj_depth/groundtruth/image_02/0000000550.png 721.5377
|
| 58 |
+
2011_09_26/2011_09_26_drive_0084_sync/image_02/data/0000000283.png 2011_09_26_drive_0084_sync/proj_depth/groundtruth/image_02/0000000283.png 721.5377
|
| 59 |
+
2011_09_26/2011_09_26_drive_0084_sync/image_02/data/0000000153.png 2011_09_26_drive_0084_sync/proj_depth/groundtruth/image_02/0000000153.png 721.5377
|
| 60 |
+
2011_09_26/2011_09_26_drive_0084_sync/image_02/data/0000000179.png 2011_09_26_drive_0084_sync/proj_depth/groundtruth/image_02/0000000179.png 721.5377
|
| 61 |
+
2011_09_26/2011_09_26_drive_0084_sync/image_02/data/0000000062.png 2011_09_26_drive_0084_sync/proj_depth/groundtruth/image_02/0000000062.png 721.5377
|
| 62 |
+
2011_09_26/2011_09_26_drive_0084_sync/image_02/data/0000000231.png 2011_09_26_drive_0084_sync/proj_depth/groundtruth/image_02/0000000231.png 721.5377
|
| 63 |
+
2011_09_26/2011_09_26_drive_0086_sync/image_02/data/0000000034.png 2011_09_26_drive_0086_sync/proj_depth/groundtruth/image_02/0000000034.png 721.5377
|
| 64 |
+
2011_09_26/2011_09_26_drive_0086_sync/image_02/data/0000000628.png 2011_09_26_drive_0086_sync/proj_depth/groundtruth/image_02/0000000628.png 721.5377
|
| 65 |
+
2011_09_26/2011_09_26_drive_0086_sync/image_02/data/0000000358.png 2011_09_26_drive_0086_sync/proj_depth/groundtruth/image_02/0000000358.png 721.5377
|
| 66 |
+
2011_09_26/2011_09_26_drive_0086_sync/image_02/data/0000000466.png 2011_09_26_drive_0086_sync/proj_depth/groundtruth/image_02/0000000466.png 721.5377
|
| 67 |
+
2011_09_26/2011_09_26_drive_0093_sync/image_02/data/0000000064.png 2011_09_26_drive_0093_sync/proj_depth/groundtruth/image_02/0000000064.png 721.5377
|
| 68 |
+
2011_09_26/2011_09_26_drive_0093_sync/image_02/data/0000000160.png 2011_09_26_drive_0093_sync/proj_depth/groundtruth/image_02/0000000160.png 721.5377
|
| 69 |
+
2011_09_26/2011_09_26_drive_0093_sync/image_02/data/0000000256.png 2011_09_26_drive_0093_sync/proj_depth/groundtruth/image_02/0000000256.png 721.5377
|
| 70 |
+
2011_09_26/2011_09_26_drive_0093_sync/image_02/data/0000000385.png 2011_09_26_drive_0093_sync/proj_depth/groundtruth/image_02/0000000385.png 721.5377
|
| 71 |
+
2011_09_26/2011_09_26_drive_0096_sync/image_02/data/0000000057.png 2011_09_26_drive_0096_sync/proj_depth/groundtruth/image_02/0000000057.png 721.5377
|
| 72 |
+
2011_09_26/2011_09_26_drive_0096_sync/image_02/data/0000000171.png 2011_09_26_drive_0096_sync/proj_depth/groundtruth/image_02/0000000171.png 721.5377
|
| 73 |
+
2011_09_26/2011_09_26_drive_0096_sync/image_02/data/0000000285.png 2011_09_26_drive_0096_sync/proj_depth/groundtruth/image_02/0000000285.png 721.5377
|
| 74 |
+
2011_09_26/2011_09_26_drive_0096_sync/image_02/data/0000000399.png 2011_09_26_drive_0096_sync/proj_depth/groundtruth/image_02/0000000399.png 721.5377
|
| 75 |
+
2011_09_26/2011_09_26_drive_0101_sync/image_02/data/0000000760.png 2011_09_26_drive_0101_sync/proj_depth/groundtruth/image_02/0000000760.png 721.5377
|
| 76 |
+
2011_09_26/2011_09_26_drive_0101_sync/image_02/data/0000000624.png 2011_09_26_drive_0101_sync/proj_depth/groundtruth/image_02/0000000624.png 721.5377
|
| 77 |
+
2011_09_26/2011_09_26_drive_0101_sync/image_02/data/0000000454.png 2011_09_26_drive_0101_sync/proj_depth/groundtruth/image_02/0000000454.png 721.5377
|
| 78 |
+
2011_09_26/2011_09_26_drive_0101_sync/image_02/data/0000000828.png 2011_09_26_drive_0101_sync/proj_depth/groundtruth/image_02/0000000828.png 721.5377
|
| 79 |
+
2011_09_26/2011_09_26_drive_0106_sync/image_02/data/0000000035.png 2011_09_26_drive_0106_sync/proj_depth/groundtruth/image_02/0000000035.png 721.5377
|
| 80 |
+
2011_09_26/2011_09_26_drive_0106_sync/image_02/data/0000000083.png 2011_09_26_drive_0106_sync/proj_depth/groundtruth/image_02/0000000083.png 721.5377
|
| 81 |
+
2011_09_26/2011_09_26_drive_0106_sync/image_02/data/0000000131.png 2011_09_26_drive_0106_sync/proj_depth/groundtruth/image_02/0000000131.png 721.5377
|
| 82 |
+
2011_09_26/2011_09_26_drive_0106_sync/image_02/data/0000000179.png 2011_09_26_drive_0106_sync/proj_depth/groundtruth/image_02/0000000179.png 721.5377
|
| 83 |
+
2011_09_26/2011_09_26_drive_0117_sync/image_02/data/0000000312.png 2011_09_26_drive_0117_sync/proj_depth/groundtruth/image_02/0000000312.png 721.5377
|
| 84 |
+
2011_09_26/2011_09_26_drive_0117_sync/image_02/data/0000000598.png 2011_09_26_drive_0117_sync/proj_depth/groundtruth/image_02/0000000598.png 721.5377
|
| 85 |
+
2011_09_26/2011_09_26_drive_0117_sync/image_02/data/0000000468.png 2011_09_26_drive_0117_sync/proj_depth/groundtruth/image_02/0000000468.png 721.5377
|
| 86 |
+
2011_09_26/2011_09_26_drive_0117_sync/image_02/data/0000000546.png 2011_09_26_drive_0117_sync/proj_depth/groundtruth/image_02/0000000546.png 721.5377
|
| 87 |
+
2011_09_26/2011_09_26_drive_0117_sync/image_02/data/0000000052.png 2011_09_26_drive_0117_sync/proj_depth/groundtruth/image_02/0000000052.png 721.5377
|
| 88 |
+
2011_09_28/2011_09_28_drive_0002_sync/image_02/data/0000000018.png 2011_09_28_drive_0002_sync/proj_depth/groundtruth/image_02/0000000018.png 707.0493
|
| 89 |
+
2011_09_28/2011_09_28_drive_0002_sync/image_02/data/0000000039.png 2011_09_28_drive_0002_sync/proj_depth/groundtruth/image_02/0000000039.png 707.0493
|
| 90 |
+
2011_09_28/2011_09_28_drive_0002_sync/image_02/data/0000000048.png 2011_09_28_drive_0002_sync/proj_depth/groundtruth/image_02/0000000048.png 707.0493
|
| 91 |
+
2011_09_28/2011_09_28_drive_0002_sync/image_02/data/0000000087.png 2011_09_28_drive_0002_sync/proj_depth/groundtruth/image_02/0000000087.png 707.0493
|
| 92 |
+
2011_09_29/2011_09_29_drive_0071_sync/image_02/data/0000000360.png 2011_09_29_drive_0071_sync/proj_depth/groundtruth/image_02/0000000360.png 718.3351
|
| 93 |
+
2011_09_29/2011_09_29_drive_0071_sync/image_02/data/0000000216.png 2011_09_29_drive_0071_sync/proj_depth/groundtruth/image_02/0000000216.png 718.3351
|
| 94 |
+
2011_09_29/2011_09_29_drive_0071_sync/image_02/data/0000000108.png 2011_09_29_drive_0071_sync/proj_depth/groundtruth/image_02/0000000108.png 718.3351
|
| 95 |
+
2011_09_29/2011_09_29_drive_0071_sync/image_02/data/0000000144.png 2011_09_29_drive_0071_sync/proj_depth/groundtruth/image_02/0000000144.png 718.3351
|
| 96 |
+
2011_09_30/2011_09_30_drive_0016_sync/image_02/data/0000000022.png 2011_09_30_drive_0016_sync/proj_depth/groundtruth/image_02/0000000022.png 707.0912
|
| 97 |
+
2011_09_30/2011_09_30_drive_0016_sync/image_02/data/0000000220.png 2011_09_30_drive_0016_sync/proj_depth/groundtruth/image_02/0000000220.png 707.0912
|
| 98 |
+
2011_09_30/2011_09_30_drive_0016_sync/image_02/data/0000000110.png 2011_09_30_drive_0016_sync/proj_depth/groundtruth/image_02/0000000110.png 707.0912
|
| 99 |
+
2011_09_30/2011_09_30_drive_0016_sync/image_02/data/0000000264.png 2011_09_30_drive_0016_sync/proj_depth/groundtruth/image_02/0000000264.png 707.0912
|
| 100 |
+
2011_09_30/2011_09_30_drive_0018_sync/image_02/data/0000001391.png 2011_09_30_drive_0018_sync/proj_depth/groundtruth/image_02/0000001391.png 707.0912
|
| 101 |
+
2011_09_30/2011_09_30_drive_0018_sync/image_02/data/0000000749.png 2011_09_30_drive_0018_sync/proj_depth/groundtruth/image_02/0000000749.png 707.0912
|
| 102 |
+
2011_09_30/2011_09_30_drive_0018_sync/image_02/data/0000002740.png 2011_09_30_drive_0018_sync/proj_depth/groundtruth/image_02/0000002740.png 707.0912
|
| 103 |
+
2011_09_30/2011_09_30_drive_0018_sync/image_02/data/0000000000.png None 707.0912
|
| 104 |
+
2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000001040.png 2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000001040.png 707.0912
|
| 105 |
+
2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000794.png 2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000794.png 707.0912
|
| 106 |
+
2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000123.png 2011_09_30_drive_0027_sync/proj_depth/groundtruth/image_02/0000000123.png 707.0912
|
| 107 |
+
2011_09_30/2011_09_30_drive_0027_sync/image_02/data/0000000000.png None 707.0912
|
| 108 |
+
2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000002906.png 2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000002906.png 718.856
|
| 109 |
+
2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000004173.png 2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000004173.png 718.856
|
| 110 |
+
2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000003449.png 2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000003449.png 718.856
|
| 111 |
+
2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000001639.png 2011_10_03_drive_0027_sync/proj_depth/groundtruth/image_02/0000001639.png 718.856
|
| 112 |
+
2011_10_03/2011_10_03_drive_0027_sync/image_02/data/0000000000.png None 718.856
|
| 113 |
+
2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000480.png 2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000480.png 718.856
|
| 114 |
+
2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000736.png 2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000736.png 718.856
|
| 115 |
+
2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000544.png 2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000544.png 718.856
|
| 116 |
+
2011_10_03/2011_10_03_drive_0047_sync/image_02/data/0000000448.png 2011_10_03_drive_0047_sync/proj_depth/groundtruth/image_02/0000000448.png 718.856
|
DepthMaster/data_split/kitti/eigen_val_from_train_800.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
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|
DepthMaster/data_split/kitti/eigen_val_from_train_sub_100.txt
ADDED
|
@@ -0,0 +1,100 @@
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|
| 1 |
+
2011_09_26/2011_09_26_drive_0001_sync/image_02/data/0000000046.png 2011_09_26_drive_0001_sync/proj_depth/groundtruth/image_02/0000000046.png 721.5377
|
| 2 |
+
2011_09_26/2011_09_26_drive_0005_sync/image_02/data/0000000148.png 2011_09_26_drive_0005_sync/proj_depth/groundtruth/image_02/0000000148.png 721.5377
|
| 3 |
+
2011_09_26/2011_09_26_drive_0014_sync/image_02/data/0000000076.png 2011_09_26_drive_0014_sync/proj_depth/groundtruth/image_02/0000000076.png 721.5377
|
| 4 |
+
2011_09_26/2011_09_26_drive_0015_sync/image_02/data/0000000019.png 2011_09_26_drive_0015_sync/proj_depth/groundtruth/image_02/0000000019.png 721.5377
|
| 5 |
+
2011_09_26/2011_09_26_drive_0015_sync/image_02/data/0000000194.png 2011_09_26_drive_0015_sync/proj_depth/groundtruth/image_02/0000000194.png 721.5377
|
| 6 |
+
2011_09_26/2011_09_26_drive_0018_sync/image_02/data/0000000106.png 2011_09_26_drive_0018_sync/proj_depth/groundtruth/image_02/0000000106.png 721.5377
|
| 7 |
+
2011_09_26/2011_09_26_drive_0019_sync/image_02/data/0000000263.png 2011_09_26_drive_0019_sync/proj_depth/groundtruth/image_02/0000000263.png 721.5377
|
| 8 |
+
2011_09_26/2011_09_26_drive_0019_sync/image_02/data/0000000274.png 2011_09_26_drive_0019_sync/proj_depth/groundtruth/image_02/0000000274.png 721.5377
|
| 9 |
+
2011_09_26/2011_09_26_drive_0022_sync/image_02/data/0000000015.png 2011_09_26_drive_0022_sync/proj_depth/groundtruth/image_02/0000000015.png 721.5377
|
| 10 |
+
2011_09_26/2011_09_26_drive_0022_sync/image_02/data/0000000123.png 2011_09_26_drive_0022_sync/proj_depth/groundtruth/image_02/0000000123.png 721.5377
|
| 11 |
+
2011_09_26/2011_09_26_drive_0022_sync/image_02/data/0000000149.png 2011_09_26_drive_0022_sync/proj_depth/groundtruth/image_02/0000000149.png 721.5377
|
| 12 |
+
2011_09_26/2011_09_26_drive_0022_sync/image_02/data/0000000308.png 2011_09_26_drive_0022_sync/proj_depth/groundtruth/image_02/0000000308.png 721.5377
|
| 13 |
+
2011_09_26/2011_09_26_drive_0022_sync/image_02/data/0000000553.png 2011_09_26_drive_0022_sync/proj_depth/groundtruth/image_02/0000000553.png 721.5377
|
| 14 |
+
2011_09_26/2011_09_26_drive_0022_sync/image_02/data/0000000691.png 2011_09_26_drive_0022_sync/proj_depth/groundtruth/image_02/0000000691.png 721.5377
|
| 15 |
+
2011_09_26/2011_09_26_drive_0028_sync/image_02/data/0000000270.png 2011_09_26_drive_0028_sync/proj_depth/groundtruth/image_02/0000000270.png 721.5377
|
| 16 |
+
2011_09_26/2011_09_26_drive_0035_sync/image_02/data/0000000085.png 2011_09_26_drive_0035_sync/proj_depth/groundtruth/image_02/0000000085.png 721.5377
|
| 17 |
+
2011_09_26/2011_09_26_drive_0039_sync/image_02/data/0000000326.png 2011_09_26_drive_0039_sync/proj_depth/groundtruth/image_02/0000000326.png 721.5377
|
| 18 |
+
2011_09_26/2011_09_26_drive_0051_sync/image_02/data/0000000429.png 2011_09_26_drive_0051_sync/proj_depth/groundtruth/image_02/0000000429.png 721.5377
|
| 19 |
+
2011_09_26/2011_09_26_drive_0057_sync/image_02/data/0000000010.png 2011_09_26_drive_0057_sync/proj_depth/groundtruth/image_02/0000000010.png 721.5377
|
| 20 |
+
2011_09_26/2011_09_26_drive_0060_sync/image_02/data/0000000020.png 2011_09_26_drive_0060_sync/proj_depth/groundtruth/image_02/0000000020.png 721.5377
|
| 21 |
+
2011_09_26/2011_09_26_drive_0061_sync/image_02/data/0000000223.png 2011_09_26_drive_0061_sync/proj_depth/groundtruth/image_02/0000000223.png 721.5377
|
| 22 |
+
2011_09_26/2011_09_26_drive_0061_sync/image_02/data/0000000262.png 2011_09_26_drive_0061_sync/proj_depth/groundtruth/image_02/0000000262.png 721.5377
|
| 23 |
+
2011_09_26/2011_09_26_drive_0061_sync/image_02/data/0000000291.png 2011_09_26_drive_0061_sync/proj_depth/groundtruth/image_02/0000000291.png 721.5377
|
| 24 |
+
2011_09_26/2011_09_26_drive_0061_sync/image_02/data/0000000523.png 2011_09_26_drive_0061_sync/proj_depth/groundtruth/image_02/0000000523.png 721.5377
|
| 25 |
+
2011_09_26/2011_09_26_drive_0061_sync/image_02/data/0000000524.png 2011_09_26_drive_0061_sync/proj_depth/groundtruth/image_02/0000000524.png 721.5377
|
| 26 |
+
2011_09_26/2011_09_26_drive_0070_sync/image_02/data/0000000063.png 2011_09_26_drive_0070_sync/proj_depth/groundtruth/image_02/0000000063.png 721.5377
|
| 27 |
+
2011_09_26/2011_09_26_drive_0070_sync/image_02/data/0000000320.png 2011_09_26_drive_0070_sync/proj_depth/groundtruth/image_02/0000000320.png 721.5377
|
| 28 |
+
2011_09_26/2011_09_26_drive_0087_sync/image_02/data/0000000313.png 2011_09_26_drive_0087_sync/proj_depth/groundtruth/image_02/0000000313.png 721.5377
|
| 29 |
+
2011_09_26/2011_09_26_drive_0087_sync/image_02/data/0000000316.png 2011_09_26_drive_0087_sync/proj_depth/groundtruth/image_02/0000000316.png 721.5377
|
| 30 |
+
2011_09_26/2011_09_26_drive_0087_sync/image_02/data/0000000363.png 2011_09_26_drive_0087_sync/proj_depth/groundtruth/image_02/0000000363.png 721.5377
|
| 31 |
+
2011_09_26/2011_09_26_drive_0087_sync/image_02/data/0000000438.png 2011_09_26_drive_0087_sync/proj_depth/groundtruth/image_02/0000000438.png 721.5377
|
| 32 |
+
2011_09_26/2011_09_26_drive_0091_sync/image_02/data/0000000137.png 2011_09_26_drive_0091_sync/proj_depth/groundtruth/image_02/0000000137.png 721.5377
|
| 33 |
+
2011_09_26/2011_09_26_drive_0091_sync/image_02/data/0000000143.png 2011_09_26_drive_0091_sync/proj_depth/groundtruth/image_02/0000000143.png 721.5377
|
| 34 |
+
2011_09_26/2011_09_26_drive_0091_sync/image_02/data/0000000278.png 2011_09_26_drive_0091_sync/proj_depth/groundtruth/image_02/0000000278.png 721.5377
|
| 35 |
+
2011_09_26/2011_09_26_drive_0091_sync/image_02/data/0000000312.png 2011_09_26_drive_0091_sync/proj_depth/groundtruth/image_02/0000000312.png 721.5377
|
| 36 |
+
2011_09_26/2011_09_26_drive_0095_sync/image_02/data/0000000160.png 2011_09_26_drive_0095_sync/proj_depth/groundtruth/image_02/0000000160.png 721.5377
|
| 37 |
+
2011_09_26/2011_09_26_drive_0104_sync/image_02/data/0000000011.png 2011_09_26_drive_0104_sync/proj_depth/groundtruth/image_02/0000000011.png 721.5377
|
| 38 |
+
2011_09_26/2011_09_26_drive_0113_sync/image_02/data/0000000052.png 2011_09_26_drive_0113_sync/proj_depth/groundtruth/image_02/0000000052.png 721.5377
|
| 39 |
+
2011_09_26/2011_09_26_drive_0113_sync/image_02/data/0000000055.png 2011_09_26_drive_0113_sync/proj_depth/groundtruth/image_02/0000000055.png 721.5377
|
| 40 |
+
2011_09_29/2011_09_29_drive_0004_sync/image_02/data/0000000065.png 2011_09_29_drive_0004_sync/proj_depth/groundtruth/image_02/0000000065.png 718.3351
|
| 41 |
+
2011_09_30/2011_09_30_drive_0020_sync/image_02/data/0000000325.png 2011_09_30_drive_0020_sync/proj_depth/groundtruth/image_02/0000000325.png 707.0912
|
| 42 |
+
2011_09_30/2011_09_30_drive_0020_sync/image_02/data/0000000959.png 2011_09_30_drive_0020_sync/proj_depth/groundtruth/image_02/0000000959.png 707.0912
|
| 43 |
+
2011_09_30/2011_09_30_drive_0020_sync/image_02/data/0000001004.png 2011_09_30_drive_0020_sync/proj_depth/groundtruth/image_02/0000001004.png 707.0912
|
| 44 |
+
2011_09_30/2011_09_30_drive_0020_sync/image_02/data/0000001054.png 2011_09_30_drive_0020_sync/proj_depth/groundtruth/image_02/0000001054.png 707.0912
|
| 45 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000000545.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000000545.png 707.0912
|
| 46 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000000920.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000000920.png 707.0912
|
| 47 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000001593.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000001593.png 707.0912
|
| 48 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000001692.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000001692.png 707.0912
|
| 49 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000001806.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000001806.png 707.0912
|
| 50 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000001905.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000001905.png 707.0912
|
| 51 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000002714.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000002714.png 707.0912
|
| 52 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000002812.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000002812.png 707.0912
|
| 53 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000002838.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000002838.png 707.0912
|
| 54 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000003402.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000003402.png 707.0912
|
| 55 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000003700.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000003700.png 707.0912
|
| 56 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000004016.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000004016.png 707.0912
|
| 57 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000004276.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000004276.png 707.0912
|
| 58 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000004664.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000004664.png 707.0912
|
| 59 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000004772.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000004772.png 707.0912
|
| 60 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000004782.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000004782.png 707.0912
|
| 61 |
+
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000005095.png 2011_09_30_drive_0028_sync/proj_depth/groundtruth/image_02/0000005095.png 707.0912
|
| 62 |
+
2011_09_30/2011_09_30_drive_0033_sync/image_02/data/0000000319.png 2011_09_30_drive_0033_sync/proj_depth/groundtruth/image_02/0000000319.png 707.0912
|
| 63 |
+
2011_09_30/2011_09_30_drive_0033_sync/image_02/data/0000000355.png 2011_09_30_drive_0033_sync/proj_depth/groundtruth/image_02/0000000355.png 707.0912
|
| 64 |
+
2011_09_30/2011_09_30_drive_0033_sync/image_02/data/0000000500.png 2011_09_30_drive_0033_sync/proj_depth/groundtruth/image_02/0000000500.png 707.0912
|
| 65 |
+
2011_09_30/2011_09_30_drive_0033_sync/image_02/data/0000000682.png 2011_09_30_drive_0033_sync/proj_depth/groundtruth/image_02/0000000682.png 707.0912
|
| 66 |
+
2011_09_30/2011_09_30_drive_0033_sync/image_02/data/0000000710.png 2011_09_30_drive_0033_sync/proj_depth/groundtruth/image_02/0000000710.png 707.0912
|
| 67 |
+
2011_09_30/2011_09_30_drive_0033_sync/image_02/data/0000000896.png 2011_09_30_drive_0033_sync/proj_depth/groundtruth/image_02/0000000896.png 707.0912
|
| 68 |
+
2011_09_30/2011_09_30_drive_0033_sync/image_02/data/0000001197.png 2011_09_30_drive_0033_sync/proj_depth/groundtruth/image_02/0000001197.png 707.0912
|
| 69 |
+
2011_09_30/2011_09_30_drive_0033_sync/image_02/data/0000001508.png 2011_09_30_drive_0033_sync/proj_depth/groundtruth/image_02/0000001508.png 707.0912
|
| 70 |
+
2011_09_30/2011_09_30_drive_0033_sync/image_02/data/0000001512.png 2011_09_30_drive_0033_sync/proj_depth/groundtruth/image_02/0000001512.png 707.0912
|
| 71 |
+
2011_09_30/2011_09_30_drive_0034_sync/image_02/data/0000000029.png 2011_09_30_drive_0034_sync/proj_depth/groundtruth/image_02/0000000029.png 707.0912
|
| 72 |
+
2011_09_30/2011_09_30_drive_0034_sync/image_02/data/0000000171.png 2011_09_30_drive_0034_sync/proj_depth/groundtruth/image_02/0000000171.png 707.0912
|
| 73 |
+
2011_09_30/2011_09_30_drive_0034_sync/image_02/data/0000000193.png 2011_09_30_drive_0034_sync/proj_depth/groundtruth/image_02/0000000193.png 707.0912
|
| 74 |
+
2011_09_30/2011_09_30_drive_0034_sync/image_02/data/0000000389.png 2011_09_30_drive_0034_sync/proj_depth/groundtruth/image_02/0000000389.png 707.0912
|
| 75 |
+
2011_09_30/2011_09_30_drive_0034_sync/image_02/data/0000001141.png 2011_09_30_drive_0034_sync/proj_depth/groundtruth/image_02/0000001141.png 707.0912
|
| 76 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000000138.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000000138.png 718.856
|
| 77 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000000593.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000000593.png 718.856
|
| 78 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000001046.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000001046.png 718.856
|
| 79 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000001151.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000001151.png 718.856
|
| 80 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000001255.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000001255.png 718.856
|
| 81 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000001283.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000001283.png 718.856
|
| 82 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000001737.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000001737.png 718.856
|
| 83 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000001999.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000001999.png 718.856
|
| 84 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000002012.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000002012.png 718.856
|
| 85 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000002089.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000002089.png 718.856
|
| 86 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000002324.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000002324.png 718.856
|
| 87 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000002902.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000002902.png 718.856
|
| 88 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000002971.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000002971.png 718.856
|
| 89 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000003299.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000003299.png 718.856
|
| 90 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000003366.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000003366.png 718.856
|
| 91 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000003427.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000003427.png 718.856
|
| 92 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000003440.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000003440.png 718.856
|
| 93 |
+
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000004060.png 2011_10_03_drive_0034_sync/proj_depth/groundtruth/image_02/0000004060.png 718.856
|
| 94 |
+
2011_10_03/2011_10_03_drive_0042_sync/image_02/data/0000000525.png 2011_10_03_drive_0042_sync/proj_depth/groundtruth/image_02/0000000525.png 718.856
|
| 95 |
+
2011_10_03/2011_10_03_drive_0042_sync/image_02/data/0000000538.png 2011_10_03_drive_0042_sync/proj_depth/groundtruth/image_02/0000000538.png 718.856
|
| 96 |
+
2011_10_03/2011_10_03_drive_0042_sync/image_02/data/0000000648.png 2011_10_03_drive_0042_sync/proj_depth/groundtruth/image_02/0000000648.png 718.856
|
| 97 |
+
2011_10_03/2011_10_03_drive_0042_sync/image_02/data/0000000776.png 2011_10_03_drive_0042_sync/proj_depth/groundtruth/image_02/0000000776.png 718.856
|
| 98 |
+
2011_10_03/2011_10_03_drive_0042_sync/image_02/data/0000000779.png 2011_10_03_drive_0042_sync/proj_depth/groundtruth/image_02/0000000779.png 718.856
|
| 99 |
+
2011_10_03/2011_10_03_drive_0042_sync/image_02/data/0000001087.png 2011_10_03_drive_0042_sync/proj_depth/groundtruth/image_02/0000001087.png 718.856
|
| 100 |
+
2011_10_03/2011_10_03_drive_0042_sync/image_02/data/0000001107.png 2011_10_03_drive_0042_sync/proj_depth/groundtruth/image_02/0000001107.png 718.856
|
DepthMaster/data_split/nyu/labeled/filename_list_test.txt
ADDED
|
@@ -0,0 +1,654 @@
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test/kitchen_0004/rgb_0001.png test/kitchen_0004/depth_0001.png test/kitchen_0004/filled_0001.png
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test/kitchen_0004/rgb_0002.png test/kitchen_0004/depth_0002.png test/kitchen_0004/filled_0002.png
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test/office_0005/rgb_0009.png test/office_0005/depth_0009.png test/office_0005/filled_0009.png
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test/office_0007/rgb_0014.png test/office_0007/depth_0014.png test/office_0007/filled_0014.png
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test/office_0008/rgb_0015.png test/office_0008/depth_0015.png test/office_0008/filled_0015.png
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test/office_0008/rgb_0016.png test/office_0008/depth_0016.png test/office_0008/filled_0016.png
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test/office_0008/rgb_0017.png test/office_0008/depth_0017.png test/office_0008/filled_0017.png
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test/office_0008/rgb_0018.png test/office_0008/depth_0018.png test/office_0008/filled_0018.png
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test/office_0010/rgb_0021.png test/office_0010/depth_0021.png test/office_0010/filled_0021.png
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test/office_0013/rgb_0028.png test/office_0013/depth_0028.png test/office_0013/filled_0028.png
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test/office_0013/rgb_0029.png test/office_0013/depth_0029.png test/office_0013/filled_0029.png
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test/office_0013/rgb_0030.png test/office_0013/depth_0030.png test/office_0013/filled_0030.png
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test/office_0013/rgb_0031.png test/office_0013/depth_0031.png test/office_0013/filled_0031.png
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test/office_0013/rgb_0032.png test/office_0013/depth_0032.png test/office_0013/filled_0032.png
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test/office_0013/rgb_0033.png test/office_0013/depth_0033.png test/office_0013/filled_0033.png
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test/office_0013/rgb_0034.png test/office_0013/depth_0034.png test/office_0013/filled_0034.png
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test/office_0014/rgb_0035.png test/office_0014/depth_0035.png test/office_0014/filled_0035.png
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test/office_0014/rgb_0036.png test/office_0014/depth_0036.png test/office_0014/filled_0036.png
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test/office_0014/rgb_0037.png test/office_0014/depth_0037.png test/office_0014/filled_0037.png
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test/office_0014/rgb_0038.png test/office_0014/depth_0038.png test/office_0014/filled_0038.png
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test/office_0015/rgb_0039.png test/office_0015/depth_0039.png test/office_0015/filled_0039.png
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test/office_0015/rgb_0040.png test/office_0015/depth_0040.png test/office_0015/filled_0040.png
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test/office_0015/rgb_0041.png test/office_0015/depth_0041.png test/office_0015/filled_0041.png
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test/office_0015/rgb_0042.png test/office_0015/depth_0042.png test/office_0015/filled_0042.png
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test/office_0015/rgb_0043.png test/office_0015/depth_0043.png test/office_0015/filled_0043.png
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test/bathroom_0003/rgb_0046.png test/bathroom_0003/depth_0046.png test/bathroom_0003/filled_0046.png
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test/bathroom_0004/rgb_0047.png test/bathroom_0004/depth_0047.png test/bathroom_0004/filled_0047.png
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test/bedroom_0011/rgb_0056.png test/bedroom_0011/depth_0056.png test/bedroom_0011/filled_0056.png
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test/bedroom_0011/rgb_0057.png test/bedroom_0011/depth_0057.png test/bedroom_0011/filled_0057.png
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test/bedroom_0013/rgb_0059.png test/bedroom_0013/depth_0059.png test/bedroom_0013/filled_0059.png
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test/bedroom_0013/rgb_0060.png test/bedroom_0013/depth_0060.png test/bedroom_0013/filled_0060.png
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test/bedroom_0013/rgb_0061.png test/bedroom_0013/depth_0061.png test/bedroom_0013/filled_0061.png
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test/bookstore_0001/rgb_0086.png test/bookstore_0001/depth_0086.png test/bookstore_0001/filled_0086.png
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test/living_room_0008/rgb_0154.png test/living_room_0008/depth_0154.png test/living_room_0008/filled_0154.png
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test/living_room_0009/rgb_0155.png test/living_room_0009/depth_0155.png test/living_room_0009/filled_0155.png
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test/living_room_0013/rgb_0167.png test/living_room_0013/depth_0167.png test/living_room_0013/filled_0167.png
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test/living_room_0013/rgb_0168.png test/living_room_0013/depth_0168.png test/living_room_0013/filled_0168.png
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test/living_room_0014/rgb_0169.png test/living_room_0014/depth_0169.png test/living_room_0014/filled_0169.png
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test/bedroom_0003/rgb_0171.png test/bedroom_0003/depth_0171.png test/bedroom_0003/filled_0171.png
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test/bedroom_0003/rgb_0172.png test/bedroom_0003/depth_0172.png test/bedroom_0003/filled_0172.png
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test/bedroom_0007/rgb_0193.png test/bedroom_0007/depth_0193.png test/bedroom_0007/filled_0193.png
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| 87 |
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test/kitchen_0002/rgb_0195.png test/kitchen_0002/depth_0195.png test/kitchen_0002/filled_0195.png
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| 88 |
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| 89 |
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| 90 |
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| 91 |
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| 92 |
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| 93 |
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test/kitchen_0002/rgb_0201.png test/kitchen_0002/depth_0201.png test/kitchen_0002/filled_0201.png
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| 94 |
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test/kitchen_0002/rgb_0202.png test/kitchen_0002/depth_0202.png test/kitchen_0002/filled_0202.png
|
| 95 |
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test/living_room_0002/rgb_0207.png test/living_room_0002/depth_0207.png test/living_room_0002/filled_0207.png
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| 96 |
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test/living_room_0003/rgb_0208.png test/living_room_0003/depth_0208.png test/living_room_0003/filled_0208.png
|
| 97 |
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test/living_room_0003/rgb_0209.png test/living_room_0003/depth_0209.png test/living_room_0003/filled_0209.png
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| 98 |
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test/living_room_0003/rgb_0210.png test/living_room_0003/depth_0210.png test/living_room_0003/filled_0210.png
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| 99 |
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test/living_room_0003/rgb_0211.png test/living_room_0003/depth_0211.png test/living_room_0003/filled_0211.png
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| 100 |
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test/living_room_0003/rgb_0212.png test/living_room_0003/depth_0212.png test/living_room_0003/filled_0212.png
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| 101 |
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test/bedroom_0022/rgb_0220.png test/bedroom_0022/depth_0220.png test/bedroom_0022/filled_0220.png
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| 102 |
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test/bedroom_0024/rgb_0221.png test/bedroom_0024/depth_0221.png test/bedroom_0024/filled_0221.png
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| 103 |
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test/bedroom_0024/rgb_0222.png test/bedroom_0024/depth_0222.png test/bedroom_0024/filled_0222.png
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| 104 |
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test/kitchen_0015/rgb_0250.png test/kitchen_0015/depth_0250.png test/kitchen_0015/filled_0250.png
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| 105 |
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test/living_room_0021/rgb_0264.png test/living_room_0021/depth_0264.png test/living_room_0021/filled_0264.png
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| 106 |
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test/office_0016/rgb_0271.png test/office_0016/depth_0271.png test/office_0016/filled_0271.png
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| 107 |
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test/office_0017/rgb_0272.png test/office_0017/depth_0272.png test/office_0017/filled_0272.png
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| 108 |
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test/study_room_0001/rgb_0273.png test/study_room_0001/depth_0273.png test/study_room_0001/filled_0273.png
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| 109 |
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test/study_room_0006/rgb_0279.png test/study_room_0006/depth_0279.png test/study_room_0006/filled_0279.png
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| 110 |
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test/bedroom_0131/rgb_0280.png test/bedroom_0131/depth_0280.png test/bedroom_0131/filled_0280.png
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| 111 |
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test/bedroom_0131/rgb_0281.png test/bedroom_0131/depth_0281.png test/bedroom_0131/filled_0281.png
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| 112 |
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| 113 |
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test/bedroom_0131/rgb_0283.png test/bedroom_0131/depth_0283.png test/bedroom_0131/filled_0283.png
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| 114 |
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test/classroom_0001/rgb_0284.png test/classroom_0001/depth_0284.png test/classroom_0001/filled_0284.png
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| 115 |
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test/classroom_0001/rgb_0285.png test/classroom_0001/depth_0285.png test/classroom_0001/filled_0285.png
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| 116 |
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test/classroom_0007/rgb_0296.png test/classroom_0007/depth_0296.png test/classroom_0007/filled_0296.png
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| 117 |
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test/classroom_0007/rgb_0297.png test/classroom_0007/depth_0297.png test/classroom_0007/filled_0297.png
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| 118 |
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test/classroom_0007/rgb_0298.png test/classroom_0007/depth_0298.png test/classroom_0007/filled_0298.png
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| 119 |
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test/classroom_0008/rgb_0299.png test/classroom_0008/depth_0299.png test/classroom_0008/filled_0299.png
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| 120 |
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test/classroom_0008/rgb_0300.png test/classroom_0008/depth_0300.png test/classroom_0008/filled_0300.png
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| 121 |
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test/classroom_0009/rgb_0301.png test/classroom_0009/depth_0301.png test/classroom_0009/filled_0301.png
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| 122 |
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test/classroom_0009/rgb_0302.png test/classroom_0009/depth_0302.png test/classroom_0009/filled_0302.png
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| 123 |
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test/classroom_0014/rgb_0310.png test/classroom_0014/depth_0310.png test/classroom_0014/filled_0310.png
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| 124 |
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test/classroom_0014/rgb_0311.png test/classroom_0014/depth_0311.png test/classroom_0014/filled_0311.png
|
| 125 |
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test/classroom_0015/rgb_0312.png test/classroom_0015/depth_0312.png test/classroom_0015/filled_0312.png
|
| 126 |
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test/classroom_0017/rgb_0315.png test/classroom_0017/depth_0315.png test/classroom_0017/filled_0315.png
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| 127 |
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test/classroom_0017/rgb_0316.png test/classroom_0017/depth_0316.png test/classroom_0017/filled_0316.png
|
| 128 |
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test/classroom_0017/rgb_0317.png test/classroom_0017/depth_0317.png test/classroom_0017/filled_0317.png
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| 129 |
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test/classroom_0023/rgb_0325.png test/classroom_0023/depth_0325.png test/classroom_0023/filled_0325.png
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| 130 |
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test/classroom_0023/rgb_0326.png test/classroom_0023/depth_0326.png test/classroom_0023/filled_0326.png
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| 131 |
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| 132 |
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| 133 |
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test/classroom_0024/rgb_0329.png test/classroom_0024/depth_0329.png test/classroom_0024/filled_0329.png
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| 134 |
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test/classroom_0024/rgb_0330.png test/classroom_0024/depth_0330.png test/classroom_0024/filled_0330.png
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| 135 |
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| 136 |
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test/classroom_0026/rgb_0332.png test/classroom_0026/depth_0332.png test/classroom_0026/filled_0332.png
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| 140 |
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test/foyer_0001/rgb_0351.png test/foyer_0001/depth_0351.png test/foyer_0001/filled_0351.png
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| 141 |
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test/foyer_0001/rgb_0352.png test/foyer_0001/depth_0352.png test/foyer_0001/filled_0352.png
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| 142 |
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| 144 |
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| 594 |
+
test/living_room_0075/rgb_1330.png test/living_room_0075/depth_1330.png test/living_room_0075/filled_1330.png
|
| 595 |
+
test/living_room_0075/rgb_1331.png test/living_room_0075/depth_1331.png test/living_room_0075/filled_1331.png
|
| 596 |
+
test/living_room_0076/rgb_1332.png test/living_room_0076/depth_1332.png test/living_room_0076/filled_1332.png
|
| 597 |
+
test/living_room_0079/rgb_1335.png test/living_room_0079/depth_1335.png test/living_room_0079/filled_1335.png
|
| 598 |
+
test/living_room_0079/rgb_1336.png test/living_room_0079/depth_1336.png test/living_room_0079/filled_1336.png
|
| 599 |
+
test/living_room_0079/rgb_1337.png test/living_room_0079/depth_1337.png test/living_room_0079/filled_1337.png
|
| 600 |
+
test/living_room_0080/rgb_1338.png test/living_room_0080/depth_1338.png test/living_room_0080/filled_1338.png
|
| 601 |
+
test/living_room_0080/rgb_1339.png test/living_room_0080/depth_1339.png test/living_room_0080/filled_1339.png
|
| 602 |
+
test/living_room_0080/rgb_1340.png test/living_room_0080/depth_1340.png test/living_room_0080/filled_1340.png
|
| 603 |
+
test/dining_room_0003/rgb_1347.png test/dining_room_0003/depth_1347.png test/dining_room_0003/filled_1347.png
|
| 604 |
+
test/dining_room_0003/rgb_1348.png test/dining_room_0003/depth_1348.png test/dining_room_0003/filled_1348.png
|
| 605 |
+
test/dining_room_0003/rgb_1349.png test/dining_room_0003/depth_1349.png test/dining_room_0003/filled_1349.png
|
| 606 |
+
test/dining_room_0005/rgb_1353.png test/dining_room_0005/depth_1353.png test/dining_room_0005/filled_1353.png
|
| 607 |
+
test/dining_room_0005/rgb_1354.png test/dining_room_0005/depth_1354.png test/dining_room_0005/filled_1354.png
|
| 608 |
+
test/dining_room_0006/rgb_1355.png test/dining_room_0006/depth_1355.png test/dining_room_0006/filled_1355.png
|
| 609 |
+
test/dining_room_0006/rgb_1356.png test/dining_room_0006/depth_1356.png test/dining_room_0006/filled_1356.png
|
| 610 |
+
test/dining_room_0009/rgb_1364.png test/dining_room_0009/depth_1364.png test/dining_room_0009/filled_1364.png
|
| 611 |
+
test/dining_room_0009/rgb_1365.png test/dining_room_0009/depth_1365.png test/dining_room_0009/filled_1365.png
|
| 612 |
+
test/dining_room_0011/rgb_1368.png test/dining_room_0011/depth_1368.png test/dining_room_0011/filled_1368.png
|
| 613 |
+
test/dining_room_0011/rgb_1369.png test/dining_room_0011/depth_1369.png test/dining_room_0011/filled_1369.png
|
| 614 |
+
test/dining_room_0017/rgb_1384.png test/dining_room_0017/depth_1384.png test/dining_room_0017/filled_1384.png
|
| 615 |
+
test/dining_room_0017/rgb_1385.png test/dining_room_0017/depth_1385.png test/dining_room_0017/filled_1385.png
|
| 616 |
+
test/dining_room_0017/rgb_1386.png test/dining_room_0017/depth_1386.png test/dining_room_0017/filled_1386.png
|
| 617 |
+
test/dining_room_0018/rgb_1387.png test/dining_room_0018/depth_1387.png test/dining_room_0018/filled_1387.png
|
| 618 |
+
test/dining_room_0018/rgb_1388.png test/dining_room_0018/depth_1388.png test/dining_room_0018/filled_1388.png
|
| 619 |
+
test/dining_room_0018/rgb_1389.png test/dining_room_0018/depth_1389.png test/dining_room_0018/filled_1389.png
|
| 620 |
+
test/dining_room_0018/rgb_1390.png test/dining_room_0018/depth_1390.png test/dining_room_0018/filled_1390.png
|
| 621 |
+
test/dining_room_0018/rgb_1391.png test/dining_room_0018/depth_1391.png test/dining_room_0018/filled_1391.png
|
| 622 |
+
test/dining_room_0020/rgb_1394.png test/dining_room_0020/depth_1394.png test/dining_room_0020/filled_1394.png
|
| 623 |
+
test/dining_room_0020/rgb_1395.png test/dining_room_0020/depth_1395.png test/dining_room_0020/filled_1395.png
|
| 624 |
+
test/dining_room_0020/rgb_1396.png test/dining_room_0020/depth_1396.png test/dining_room_0020/filled_1396.png
|
| 625 |
+
test/dining_room_0021/rgb_1397.png test/dining_room_0021/depth_1397.png test/dining_room_0021/filled_1397.png
|
| 626 |
+
test/dining_room_0021/rgb_1398.png test/dining_room_0021/depth_1398.png test/dining_room_0021/filled_1398.png
|
| 627 |
+
test/dining_room_0021/rgb_1399.png test/dining_room_0021/depth_1399.png test/dining_room_0021/filled_1399.png
|
| 628 |
+
test/dining_room_0022/rgb_1400.png test/dining_room_0022/depth_1400.png test/dining_room_0022/filled_1400.png
|
| 629 |
+
test/dining_room_0022/rgb_1401.png test/dining_room_0022/depth_1401.png test/dining_room_0022/filled_1401.png
|
| 630 |
+
test/dining_room_0025/rgb_1407.png test/dining_room_0025/depth_1407.png test/dining_room_0025/filled_1407.png
|
| 631 |
+
test/dining_room_0025/rgb_1408.png test/dining_room_0025/depth_1408.png test/dining_room_0025/filled_1408.png
|
| 632 |
+
test/dining_room_0025/rgb_1409.png test/dining_room_0025/depth_1409.png test/dining_room_0025/filled_1409.png
|
| 633 |
+
test/dining_room_0025/rgb_1410.png test/dining_room_0025/depth_1410.png test/dining_room_0025/filled_1410.png
|
| 634 |
+
test/dining_room_0025/rgb_1411.png test/dining_room_0025/depth_1411.png test/dining_room_0025/filled_1411.png
|
| 635 |
+
test/dining_room_0026/rgb_1412.png test/dining_room_0026/depth_1412.png test/dining_room_0026/filled_1412.png
|
| 636 |
+
test/dining_room_0026/rgb_1413.png test/dining_room_0026/depth_1413.png test/dining_room_0026/filled_1413.png
|
| 637 |
+
test/dining_room_0026/rgb_1414.png test/dining_room_0026/depth_1414.png test/dining_room_0026/filled_1414.png
|
| 638 |
+
test/dining_room_0030/rgb_1421.png test/dining_room_0030/depth_1421.png test/dining_room_0030/filled_1421.png
|
| 639 |
+
test/dining_room_0030/rgb_1422.png test/dining_room_0030/depth_1422.png test/dining_room_0030/filled_1422.png
|
| 640 |
+
test/dining_room_0030/rgb_1423.png test/dining_room_0030/depth_1423.png test/dining_room_0030/filled_1423.png
|
| 641 |
+
test/dining_room_0030/rgb_1424.png test/dining_room_0030/depth_1424.png test/dining_room_0030/filled_1424.png
|
| 642 |
+
test/dining_room_0032/rgb_1430.png test/dining_room_0032/depth_1430.png test/dining_room_0032/filled_1430.png
|
| 643 |
+
test/dining_room_0032/rgb_1431.png test/dining_room_0032/depth_1431.png test/dining_room_0032/filled_1431.png
|
| 644 |
+
test/dining_room_0032/rgb_1432.png test/dining_room_0032/depth_1432.png test/dining_room_0032/filled_1432.png
|
| 645 |
+
test/dining_room_0032/rgb_1433.png test/dining_room_0032/depth_1433.png test/dining_room_0032/filled_1433.png
|
| 646 |
+
test/dining_room_0035/rgb_1441.png test/dining_room_0035/depth_1441.png test/dining_room_0035/filled_1441.png
|
| 647 |
+
test/dining_room_0035/rgb_1442.png test/dining_room_0035/depth_1442.png test/dining_room_0035/filled_1442.png
|
| 648 |
+
test/dining_room_0035/rgb_1443.png test/dining_room_0035/depth_1443.png test/dining_room_0035/filled_1443.png
|
| 649 |
+
test/dining_room_0036/rgb_1444.png test/dining_room_0036/depth_1444.png test/dining_room_0036/filled_1444.png
|
| 650 |
+
test/dining_room_0036/rgb_1445.png test/dining_room_0036/depth_1445.png test/dining_room_0036/filled_1445.png
|
| 651 |
+
test/dining_room_0036/rgb_1446.png test/dining_room_0036/depth_1446.png test/dining_room_0036/filled_1446.png
|
| 652 |
+
test/dining_room_0036/rgb_1447.png test/dining_room_0036/depth_1447.png test/dining_room_0036/filled_1447.png
|
| 653 |
+
test/dining_room_0036/rgb_1448.png test/dining_room_0036/depth_1448.png test/dining_room_0036/filled_1448.png
|
| 654 |
+
test/dining_room_0036/rgb_1449.png test/dining_room_0036/depth_1449.png test/dining_room_0036/filled_1449.png
|
DepthMaster/data_split/nyu/labeled/filename_list_train.txt
ADDED
|
@@ -0,0 +1,795 @@
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| 1 |
+
train/office_0003/rgb_0003.png train/office_0003/depth_0003.png train/office_0003/filled_0003.png
|
| 2 |
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train/office_0003/rgb_0004.png train/office_0003/depth_0004.png train/office_0003/filled_0004.png
|
| 3 |
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train/office_0004/rgb_0005.png train/office_0004/depth_0005.png train/office_0004/filled_0005.png
|
| 4 |
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train/office_0004/rgb_0006.png train/office_0004/depth_0006.png train/office_0004/filled_0006.png
|
| 5 |
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train/office_0004/rgb_0007.png train/office_0004/depth_0007.png train/office_0004/filled_0007.png
|
| 6 |
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train/office_0004/rgb_0008.png train/office_0004/depth_0008.png train/office_0004/filled_0008.png
|
| 7 |
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train/office_0006/rgb_0010.png train/office_0006/depth_0010.png train/office_0006/filled_0010.png
|
| 8 |
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train/office_0006/rgb_0011.png train/office_0006/depth_0011.png train/office_0006/filled_0011.png
|
| 9 |
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train/office_0006/rgb_0012.png train/office_0006/depth_0012.png train/office_0006/filled_0012.png
|
| 10 |
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train/office_0006/rgb_0013.png train/office_0006/depth_0013.png train/office_0006/filled_0013.png
|
| 11 |
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train/office_0009/rgb_0019.png train/office_0009/depth_0019.png train/office_0009/filled_0019.png
|
| 12 |
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train/office_0009/rgb_0020.png train/office_0009/depth_0020.png train/office_0009/filled_0020.png
|
| 13 |
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train/office_0011/rgb_0022.png train/office_0011/depth_0022.png train/office_0011/filled_0022.png
|
| 14 |
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train/office_0011/rgb_0023.png train/office_0011/depth_0023.png train/office_0011/filled_0023.png
|
| 15 |
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train/office_0011/rgb_0024.png train/office_0011/depth_0024.png train/office_0011/filled_0024.png
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| 16 |
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train/office_0011/rgb_0025.png train/office_0011/depth_0025.png train/office_0011/filled_0025.png
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| 17 |
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train/office_0012/rgb_0026.png train/office_0012/depth_0026.png train/office_0012/filled_0026.png
|
| 18 |
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train/office_0012/rgb_0027.png train/office_0012/depth_0027.png train/office_0012/filled_0027.png
|
| 19 |
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train/bathroom_0002/rgb_0044.png train/bathroom_0002/depth_0044.png train/bathroom_0002/filled_0044.png
|
| 20 |
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train/bathroom_0002/rgb_0045.png train/bathroom_0002/depth_0045.png train/bathroom_0002/filled_0045.png
|
| 21 |
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train/bathroom_0005/rgb_0048.png train/bathroom_0005/depth_0048.png train/bathroom_0005/filled_0048.png
|
| 22 |
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train/bathroom_0006/rgb_0049.png train/bathroom_0006/depth_0049.png train/bathroom_0006/filled_0049.png
|
| 23 |
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|
| 24 |
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train/living_room_0000/rgb_0051.png train/living_room_0000/depth_0051.png train/living_room_0000/filled_0051.png
|
| 25 |
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train/living_room_0000/rgb_0052.png train/living_room_0000/depth_0052.png train/living_room_0000/filled_0052.png
|
| 26 |
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train/living_room_0000/rgb_0053.png train/living_room_0000/depth_0053.png train/living_room_0000/filled_0053.png
|
| 27 |
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train/living_room_0000/rgb_0054.png train/living_room_0000/depth_0054.png train/living_room_0000/filled_0054.png
|
| 28 |
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train/living_room_0000/rgb_0055.png train/living_room_0000/depth_0055.png train/living_room_0000/filled_0055.png
|
| 29 |
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train/bedroom_0012/rgb_0058.png train/bedroom_0012/depth_0058.png train/bedroom_0012/filled_0058.png
|
| 30 |
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train/bedroom_0014/rgb_0064.png train/bedroom_0014/depth_0064.png train/bedroom_0014/filled_0064.png
|
| 31 |
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train/bedroom_0014/rgb_0065.png train/bedroom_0014/depth_0065.png train/bedroom_0014/filled_0065.png
|
| 32 |
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train/bedroom_0014/rgb_0066.png train/bedroom_0014/depth_0066.png train/bedroom_0014/filled_0066.png
|
| 33 |
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train/bedroom_0015/rgb_0067.png train/bedroom_0015/depth_0067.png train/bedroom_0015/filled_0067.png
|
| 34 |
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train/bedroom_0016/rgb_0068.png train/bedroom_0016/depth_0068.png train/bedroom_0016/filled_0068.png
|
| 35 |
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train/bedroom_0016/rgb_0069.png train/bedroom_0016/depth_0069.png train/bedroom_0016/filled_0069.png
|
| 36 |
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train/bedroom_0016/rgb_0070.png train/bedroom_0016/depth_0070.png train/bedroom_0016/filled_0070.png
|
| 37 |
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train/bedroom_0016/rgb_0071.png train/bedroom_0016/depth_0071.png train/bedroom_0016/filled_0071.png
|
| 38 |
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train/bedroom_0016/rgb_0072.png train/bedroom_0016/depth_0072.png train/bedroom_0016/filled_0072.png
|
| 39 |
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|
| 40 |
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train/bedroom_0017/rgb_0074.png train/bedroom_0017/depth_0074.png train/bedroom_0017/filled_0074.png
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| 41 |
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| 42 |
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| 43 |
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| 44 |
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| 45 |
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| 46 |
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| 47 |
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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| 52 |
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| 53 |
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| 54 |
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|
| 55 |
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|
| 56 |
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|
| 57 |
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train/bookstore_0002/rgb_0103.png train/bookstore_0002/depth_0103.png train/bookstore_0002/filled_0103.png
|
| 58 |
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train/bookstore_0000/rgb_0104.png train/bookstore_0000/depth_0104.png train/bookstore_0000/filled_0104.png
|
| 59 |
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| 60 |
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| 61 |
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| 62 |
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| 63 |
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| 64 |
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|
| 65 |
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|
| 66 |
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| 67 |
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| 68 |
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| 69 |
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|
| 70 |
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| 71 |
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|
| 72 |
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train/cafe_0001/rgb_0121.png train/cafe_0001/depth_0121.png train/cafe_0001/filled_0121.png
|
| 73 |
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train/cafe_0001/rgb_0122.png train/cafe_0001/depth_0122.png train/cafe_0001/filled_0122.png
|
| 74 |
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train/cafe_0001/rgb_0123.png train/cafe_0001/depth_0123.png train/cafe_0001/filled_0123.png
|
| 75 |
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train/cafe_0001/rgb_0124.png train/cafe_0001/depth_0124.png train/cafe_0001/filled_0124.png
|
| 76 |
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|
| 77 |
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|
| 78 |
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|
| 79 |
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|
| 80 |
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|
| 81 |
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|
| 82 |
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train/kitchen_0010/rgb_0141.png train/kitchen_0010/depth_0141.png train/kitchen_0010/filled_0141.png
|
| 83 |
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train/kitchen_0011/rgb_0142.png train/kitchen_0011/depth_0142.png train/kitchen_0011/filled_0142.png
|
| 84 |
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train/kitchen_0011/rgb_0143.png train/kitchen_0011/depth_0143.png train/kitchen_0011/filled_0143.png
|
| 85 |
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train/kitchen_0011/rgb_0144.png train/kitchen_0011/depth_0144.png train/kitchen_0011/filled_0144.png
|
| 86 |
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train/kitchen_0011/rgb_0145.png train/kitchen_0011/depth_0145.png train/kitchen_0011/filled_0145.png
|
| 87 |
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| 88 |
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train/living_room_0004/rgb_0147.png train/living_room_0004/depth_0147.png train/living_room_0004/filled_0147.png
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| 89 |
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train/living_room_0004/rgb_0148.png train/living_room_0004/depth_0148.png train/living_room_0004/filled_0148.png
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| 90 |
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| 91 |
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| 92 |
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| 93 |
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train/living_room_0006/rgb_0152.png train/living_room_0006/depth_0152.png train/living_room_0006/filled_0152.png
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| 94 |
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train/living_room_0010/rgb_0156.png train/living_room_0010/depth_0156.png train/living_room_0010/filled_0156.png
|
| 95 |
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train/living_room_0010/rgb_0157.png train/living_room_0010/depth_0157.png train/living_room_0010/filled_0157.png
|
| 96 |
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train/living_room_0010/rgb_0158.png train/living_room_0010/depth_0158.png train/living_room_0010/filled_0158.png
|
| 97 |
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train/living_room_0010/rgb_0159.png train/living_room_0010/depth_0159.png train/living_room_0010/filled_0159.png
|
| 98 |
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train/living_room_0011/rgb_0160.png train/living_room_0011/depth_0160.png train/living_room_0011/filled_0160.png
|
| 99 |
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train/living_room_0011/rgb_0161.png train/living_room_0011/depth_0161.png train/living_room_0011/filled_0161.png
|
| 100 |
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train/living_room_0011/rgb_0162.png train/living_room_0011/depth_0162.png train/living_room_0011/filled_0162.png
|
| 101 |
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train/living_room_0011/rgb_0163.png train/living_room_0011/depth_0163.png train/living_room_0011/filled_0163.png
|
| 102 |
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train/living_room_0012/rgb_0164.png train/living_room_0012/depth_0164.png train/living_room_0012/filled_0164.png
|
| 103 |
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train/living_room_0012/rgb_0165.png train/living_room_0012/depth_0165.png train/living_room_0012/filled_0165.png
|
| 104 |
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train/living_room_0012/rgb_0166.png train/living_room_0012/depth_0166.png train/living_room_0012/filled_0166.png
|
| 105 |
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train/bathroom_0001/rgb_0170.png train/bathroom_0001/depth_0170.png train/bathroom_0001/filled_0170.png
|
| 106 |
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train/bedroom_0004/rgb_0177.png train/bedroom_0004/depth_0177.png train/bedroom_0004/filled_0177.png
|
| 107 |
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train/bedroom_0004/rgb_0178.png train/bedroom_0004/depth_0178.png train/bedroom_0004/filled_0178.png
|
| 108 |
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train/bedroom_0004/rgb_0179.png train/bedroom_0004/depth_0179.png train/bedroom_0004/filled_0179.png
|
| 109 |
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| 674 |
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| 677 |
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| 678 |
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| 679 |
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| 698 |
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| 699 |
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| 700 |
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| 701 |
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| 702 |
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| 704 |
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| 705 |
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| 706 |
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| 707 |
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| 708 |
+
train/living_room_0047/rgb_1271.png train/living_room_0047/depth_1271.png train/living_room_0047/filled_1271.png
|
| 709 |
+
train/living_room_0047/rgb_1272.png train/living_room_0047/depth_1272.png train/living_room_0047/filled_1272.png
|
| 710 |
+
train/living_room_0047/rgb_1273.png train/living_room_0047/depth_1273.png train/living_room_0047/filled_1273.png
|
| 711 |
+
train/living_room_0047/rgb_1274.png train/living_room_0047/depth_1274.png train/living_room_0047/filled_1274.png
|
| 712 |
+
train/living_room_0050/rgb_1281.png train/living_room_0050/depth_1281.png train/living_room_0050/filled_1281.png
|
| 713 |
+
train/living_room_0050/rgb_1282.png train/living_room_0050/depth_1282.png train/living_room_0050/filled_1282.png
|
| 714 |
+
train/living_room_0050/rgb_1283.png train/living_room_0050/depth_1283.png train/living_room_0050/filled_1283.png
|
| 715 |
+
train/living_room_0050/rgb_1284.png train/living_room_0050/depth_1284.png train/living_room_0050/filled_1284.png
|
| 716 |
+
train/living_room_0055/rgb_1296.png train/living_room_0055/depth_1296.png train/living_room_0055/filled_1296.png
|
| 717 |
+
train/living_room_0058/rgb_1300.png train/living_room_0058/depth_1300.png train/living_room_0058/filled_1300.png
|
| 718 |
+
train/living_room_0058/rgb_1301.png train/living_room_0058/depth_1301.png train/living_room_0058/filled_1301.png
|
| 719 |
+
train/living_room_0062/rgb_1309.png train/living_room_0062/depth_1309.png train/living_room_0062/filled_1309.png
|
| 720 |
+
train/living_room_0062/rgb_1310.png train/living_room_0062/depth_1310.png train/living_room_0062/filled_1310.png
|
| 721 |
+
train/living_room_0062/rgb_1311.png train/living_room_0062/depth_1311.png train/living_room_0062/filled_1311.png
|
| 722 |
+
train/living_room_0063/rgb_1312.png train/living_room_0063/depth_1312.png train/living_room_0063/filled_1312.png
|
| 723 |
+
train/living_room_0063/rgb_1313.png train/living_room_0063/depth_1313.png train/living_room_0063/filled_1313.png
|
| 724 |
+
train/living_room_0067/rgb_1316.png train/living_room_0067/depth_1316.png train/living_room_0067/filled_1316.png
|
| 725 |
+
train/living_room_0068/rgb_1317.png train/living_room_0068/depth_1317.png train/living_room_0068/filled_1317.png
|
| 726 |
+
train/living_room_0068/rgb_1318.png train/living_room_0068/depth_1318.png train/living_room_0068/filled_1318.png
|
| 727 |
+
train/living_room_0068/rgb_1319.png train/living_room_0068/depth_1319.png train/living_room_0068/filled_1319.png
|
| 728 |
+
train/living_room_0069/rgb_1320.png train/living_room_0069/depth_1320.png train/living_room_0069/filled_1320.png
|
| 729 |
+
train/living_room_0069/rgb_1321.png train/living_room_0069/depth_1321.png train/living_room_0069/filled_1321.png
|
| 730 |
+
train/living_room_0069/rgb_1322.png train/living_room_0069/depth_1322.png train/living_room_0069/filled_1322.png
|
| 731 |
+
train/living_room_0069/rgb_1323.png train/living_room_0069/depth_1323.png train/living_room_0069/filled_1323.png
|
| 732 |
+
train/living_room_0070/rgb_1324.png train/living_room_0070/depth_1324.png train/living_room_0070/filled_1324.png
|
| 733 |
+
train/living_room_0070/rgb_1325.png train/living_room_0070/depth_1325.png train/living_room_0070/filled_1325.png
|
| 734 |
+
train/living_room_0070/rgb_1326.png train/living_room_0070/depth_1326.png train/living_room_0070/filled_1326.png
|
| 735 |
+
train/living_room_0071/rgb_1327.png train/living_room_0071/depth_1327.png train/living_room_0071/filled_1327.png
|
| 736 |
+
train/living_room_0071/rgb_1328.png train/living_room_0071/depth_1328.png train/living_room_0071/filled_1328.png
|
| 737 |
+
train/living_room_0078/rgb_1333.png train/living_room_0078/depth_1333.png train/living_room_0078/filled_1333.png
|
| 738 |
+
train/living_room_0078/rgb_1334.png train/living_room_0078/depth_1334.png train/living_room_0078/filled_1334.png
|
| 739 |
+
train/dining_room_0001/rgb_1341.png train/dining_room_0001/depth_1341.png train/dining_room_0001/filled_1341.png
|
| 740 |
+
train/dining_room_0001/rgb_1342.png train/dining_room_0001/depth_1342.png train/dining_room_0001/filled_1342.png
|
| 741 |
+
train/dining_room_0001/rgb_1343.png train/dining_room_0001/depth_1343.png train/dining_room_0001/filled_1343.png
|
| 742 |
+
train/dining_room_0001/rgb_1344.png train/dining_room_0001/depth_1344.png train/dining_room_0001/filled_1344.png
|
| 743 |
+
train/dining_room_0001/rgb_1345.png train/dining_room_0001/depth_1345.png train/dining_room_0001/filled_1345.png
|
| 744 |
+
train/dining_room_0002/rgb_1346.png train/dining_room_0002/depth_1346.png train/dining_room_0002/filled_1346.png
|
| 745 |
+
train/dining_room_0004/rgb_1350.png train/dining_room_0004/depth_1350.png train/dining_room_0004/filled_1350.png
|
| 746 |
+
train/dining_room_0004/rgb_1351.png train/dining_room_0004/depth_1351.png train/dining_room_0004/filled_1351.png
|
| 747 |
+
train/dining_room_0004/rgb_1352.png train/dining_room_0004/depth_1352.png train/dining_room_0004/filled_1352.png
|
| 748 |
+
train/dining_room_0007/rgb_1357.png train/dining_room_0007/depth_1357.png train/dining_room_0007/filled_1357.png
|
| 749 |
+
train/dining_room_0007/rgb_1358.png train/dining_room_0007/depth_1358.png train/dining_room_0007/filled_1358.png
|
| 750 |
+
train/dining_room_0007/rgb_1359.png train/dining_room_0007/depth_1359.png train/dining_room_0007/filled_1359.png
|
| 751 |
+
train/dining_room_0007/rgb_1360.png train/dining_room_0007/depth_1360.png train/dining_room_0007/filled_1360.png
|
| 752 |
+
train/dining_room_0008/rgb_1361.png train/dining_room_0008/depth_1361.png train/dining_room_0008/filled_1361.png
|
| 753 |
+
train/dining_room_0008/rgb_1362.png train/dining_room_0008/depth_1362.png train/dining_room_0008/filled_1362.png
|
| 754 |
+
train/dining_room_0008/rgb_1363.png train/dining_room_0008/depth_1363.png train/dining_room_0008/filled_1363.png
|
| 755 |
+
train/dining_room_0010/rgb_1366.png train/dining_room_0010/depth_1366.png train/dining_room_0010/filled_1366.png
|
| 756 |
+
train/dining_room_0010/rgb_1367.png train/dining_room_0010/depth_1367.png train/dining_room_0010/filled_1367.png
|
| 757 |
+
train/dining_room_0012/rgb_1370.png train/dining_room_0012/depth_1370.png train/dining_room_0012/filled_1370.png
|
| 758 |
+
train/dining_room_0012/rgb_1371.png train/dining_room_0012/depth_1371.png train/dining_room_0012/filled_1371.png
|
| 759 |
+
train/dining_room_0013/rgb_1372.png train/dining_room_0013/depth_1372.png train/dining_room_0013/filled_1372.png
|
| 760 |
+
train/dining_room_0013/rgb_1373.png train/dining_room_0013/depth_1373.png train/dining_room_0013/filled_1373.png
|
| 761 |
+
train/dining_room_0013/rgb_1374.png train/dining_room_0013/depth_1374.png train/dining_room_0013/filled_1374.png
|
| 762 |
+
train/dining_room_0013/rgb_1375.png train/dining_room_0013/depth_1375.png train/dining_room_0013/filled_1375.png
|
| 763 |
+
train/dining_room_0014/rgb_1376.png train/dining_room_0014/depth_1376.png train/dining_room_0014/filled_1376.png
|
| 764 |
+
train/dining_room_0014/rgb_1377.png train/dining_room_0014/depth_1377.png train/dining_room_0014/filled_1377.png
|
| 765 |
+
train/dining_room_0015/rgb_1378.png train/dining_room_0015/depth_1378.png train/dining_room_0015/filled_1378.png
|
| 766 |
+
train/dining_room_0015/rgb_1379.png train/dining_room_0015/depth_1379.png train/dining_room_0015/filled_1379.png
|
| 767 |
+
train/dining_room_0015/rgb_1380.png train/dining_room_0015/depth_1380.png train/dining_room_0015/filled_1380.png
|
| 768 |
+
train/dining_room_0015/rgb_1381.png train/dining_room_0015/depth_1381.png train/dining_room_0015/filled_1381.png
|
| 769 |
+
train/dining_room_0016/rgb_1382.png train/dining_room_0016/depth_1382.png train/dining_room_0016/filled_1382.png
|
| 770 |
+
train/dining_room_0016/rgb_1383.png train/dining_room_0016/depth_1383.png train/dining_room_0016/filled_1383.png
|
| 771 |
+
train/dining_room_0019/rgb_1392.png train/dining_room_0019/depth_1392.png train/dining_room_0019/filled_1392.png
|
| 772 |
+
train/dining_room_0019/rgb_1393.png train/dining_room_0019/depth_1393.png train/dining_room_0019/filled_1393.png
|
| 773 |
+
train/dining_room_0023/rgb_1402.png train/dining_room_0023/depth_1402.png train/dining_room_0023/filled_1402.png
|
| 774 |
+
train/dining_room_0023/rgb_1403.png train/dining_room_0023/depth_1403.png train/dining_room_0023/filled_1403.png
|
| 775 |
+
train/dining_room_0024/rgb_1404.png train/dining_room_0024/depth_1404.png train/dining_room_0024/filled_1404.png
|
| 776 |
+
train/dining_room_0024/rgb_1405.png train/dining_room_0024/depth_1405.png train/dining_room_0024/filled_1405.png
|
| 777 |
+
train/dining_room_0024/rgb_1406.png train/dining_room_0024/depth_1406.png train/dining_room_0024/filled_1406.png
|
| 778 |
+
train/dining_room_0028/rgb_1415.png train/dining_room_0028/depth_1415.png train/dining_room_0028/filled_1415.png
|
| 779 |
+
train/dining_room_0028/rgb_1416.png train/dining_room_0028/depth_1416.png train/dining_room_0028/filled_1416.png
|
| 780 |
+
train/dining_room_0029/rgb_1417.png train/dining_room_0029/depth_1417.png train/dining_room_0029/filled_1417.png
|
| 781 |
+
train/dining_room_0029/rgb_1418.png train/dining_room_0029/depth_1418.png train/dining_room_0029/filled_1418.png
|
| 782 |
+
train/dining_room_0029/rgb_1419.png train/dining_room_0029/depth_1419.png train/dining_room_0029/filled_1419.png
|
| 783 |
+
train/dining_room_0029/rgb_1420.png train/dining_room_0029/depth_1420.png train/dining_room_0029/filled_1420.png
|
| 784 |
+
train/dining_room_0031/rgb_1425.png train/dining_room_0031/depth_1425.png train/dining_room_0031/filled_1425.png
|
| 785 |
+
train/dining_room_0031/rgb_1426.png train/dining_room_0031/depth_1426.png train/dining_room_0031/filled_1426.png
|
| 786 |
+
train/dining_room_0031/rgb_1427.png train/dining_room_0031/depth_1427.png train/dining_room_0031/filled_1427.png
|
| 787 |
+
train/dining_room_0031/rgb_1428.png train/dining_room_0031/depth_1428.png train/dining_room_0031/filled_1428.png
|
| 788 |
+
train/dining_room_0031/rgb_1429.png train/dining_room_0031/depth_1429.png train/dining_room_0031/filled_1429.png
|
| 789 |
+
train/dining_room_0033/rgb_1434.png train/dining_room_0033/depth_1434.png train/dining_room_0033/filled_1434.png
|
| 790 |
+
train/dining_room_0033/rgb_1435.png train/dining_room_0033/depth_1435.png train/dining_room_0033/filled_1435.png
|
| 791 |
+
train/dining_room_0033/rgb_1436.png train/dining_room_0033/depth_1436.png train/dining_room_0033/filled_1436.png
|
| 792 |
+
train/dining_room_0034/rgb_1437.png train/dining_room_0034/depth_1437.png train/dining_room_0034/filled_1437.png
|
| 793 |
+
train/dining_room_0034/rgb_1438.png train/dining_room_0034/depth_1438.png train/dining_room_0034/filled_1438.png
|
| 794 |
+
train/dining_room_0034/rgb_1439.png train/dining_room_0034/depth_1439.png train/dining_room_0034/filled_1439.png
|
| 795 |
+
train/dining_room_0034/rgb_1440.png train/dining_room_0034/depth_1440.png train/dining_room_0034/filled_1440.png
|
DepthMaster/data_split/nyu/labeled/filename_list_train_small_100.txt
ADDED
|
@@ -0,0 +1,100 @@
|
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|
|
|
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|
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|
|
|
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|
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|
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|
|
|
|
|
|
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|
|
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|
|
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|
|
|
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|
|
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|
|
|
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|
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|
|
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|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
|
|
|
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|
|
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|
|
|
| 1 |
+
train/bathroom_0007/rgb_0649.png train/bathroom_0007/depth_0649.png train/bathroom_0007/filled_0649.png
|
| 2 |
+
train/bathroom_0010/rgb_0653.png train/bathroom_0010/depth_0653.png train/bathroom_0010/filled_0653.png
|
| 3 |
+
train/bathroom_0041/rgb_0719.png train/bathroom_0041/depth_0719.png train/bathroom_0041/filled_0719.png
|
| 4 |
+
train/bathroom_0045/rgb_0729.png train/bathroom_0045/depth_0729.png train/bathroom_0045/filled_0729.png
|
| 5 |
+
train/bathroom_0048/rgb_0736.png train/bathroom_0048/depth_0736.png train/bathroom_0048/filled_0736.png
|
| 6 |
+
train/bathroom_0056/rgb_0505.png train/bathroom_0056/depth_0505.png train/bathroom_0056/filled_0505.png
|
| 7 |
+
train/bedroom_0004/rgb_0178.png train/bedroom_0004/depth_0178.png train/bedroom_0004/filled_0178.png
|
| 8 |
+
train/bedroom_0016/rgb_0071.png train/bedroom_0016/depth_0071.png train/bedroom_0016/filled_0071.png
|
| 9 |
+
train/bedroom_0025/rgb_0910.png train/bedroom_0025/depth_0910.png train/bedroom_0025/filled_0910.png
|
| 10 |
+
train/bedroom_0026/rgb_0914.png train/bedroom_0026/depth_0914.png train/bedroom_0026/filled_0914.png
|
| 11 |
+
train/bedroom_0031/rgb_0929.png train/bedroom_0031/depth_0929.png train/bedroom_0031/filled_0929.png
|
| 12 |
+
train/bedroom_0034/rgb_0939.png train/bedroom_0034/depth_0939.png train/bedroom_0034/filled_0939.png
|
| 13 |
+
train/bedroom_0040/rgb_0954.png train/bedroom_0040/depth_0954.png train/bedroom_0040/filled_0954.png
|
| 14 |
+
train/bedroom_0042/rgb_0958.png train/bedroom_0042/depth_0958.png train/bedroom_0042/filled_0958.png
|
| 15 |
+
train/bedroom_0050/rgb_0978.png train/bedroom_0050/depth_0978.png train/bedroom_0050/filled_0978.png
|
| 16 |
+
train/bedroom_0051/rgb_0984.png train/bedroom_0051/depth_0984.png train/bedroom_0051/filled_0984.png
|
| 17 |
+
train/bedroom_0056/rgb_0997.png train/bedroom_0056/depth_0997.png train/bedroom_0056/filled_0997.png
|
| 18 |
+
train/bedroom_0060/rgb_1008.png train/bedroom_0060/depth_1008.png train/bedroom_0060/filled_1008.png
|
| 19 |
+
train/bedroom_0067/rgb_1029.png train/bedroom_0067/depth_1029.png train/bedroom_0067/filled_1029.png
|
| 20 |
+
train/bedroom_0072/rgb_1045.png train/bedroom_0072/depth_1045.png train/bedroom_0072/filled_1045.png
|
| 21 |
+
train/bedroom_0072/rgb_1046.png train/bedroom_0072/depth_1046.png train/bedroom_0072/filled_1046.png
|
| 22 |
+
train/bedroom_0079/rgb_1062.png train/bedroom_0079/depth_1062.png train/bedroom_0079/filled_1062.png
|
| 23 |
+
train/bedroom_0081/rgb_1072.png train/bedroom_0081/depth_1072.png train/bedroom_0081/filled_1072.png
|
| 24 |
+
train/bedroom_0096/rgb_1112.png train/bedroom_0096/depth_1112.png train/bedroom_0096/filled_1112.png
|
| 25 |
+
train/bedroom_0118/rgb_1173.png train/bedroom_0118/depth_1173.png train/bedroom_0118/filled_1173.png
|
| 26 |
+
train/bedroom_0129/rgb_1197.png train/bedroom_0129/depth_1197.png train/bedroom_0129/filled_1197.png
|
| 27 |
+
train/bedroom_0136/rgb_0527.png train/bedroom_0136/depth_0527.png train/bedroom_0136/filled_0527.png
|
| 28 |
+
train/bookstore_0000/rgb_0105.png train/bookstore_0000/depth_0105.png train/bookstore_0000/filled_0105.png
|
| 29 |
+
train/bookstore_0000/rgb_0107.png train/bookstore_0000/depth_0107.png train/bookstore_0000/filled_0107.png
|
| 30 |
+
train/bookstore_0002/rgb_0101.png train/bookstore_0002/depth_0101.png train/bookstore_0002/filled_0101.png
|
| 31 |
+
train/bookstore_0002/rgb_0103.png train/bookstore_0002/depth_0103.png train/bookstore_0002/filled_0103.png
|
| 32 |
+
train/classroom_0010/rgb_0305.png train/classroom_0010/depth_0305.png train/classroom_0010/filled_0305.png
|
| 33 |
+
train/classroom_0012/rgb_0309.png train/classroom_0012/depth_0309.png train/classroom_0012/filled_0309.png
|
| 34 |
+
train/conference_room_0001/rgb_0339.png train/conference_room_0001/depth_0339.png train/conference_room_0001/filled_0339.png
|
| 35 |
+
train/conference_room_0001/rgb_0341.png train/conference_room_0001/depth_0341.png train/conference_room_0001/filled_0341.png
|
| 36 |
+
train/dining_room_0002/rgb_1346.png train/dining_room_0002/depth_1346.png train/dining_room_0002/filled_1346.png
|
| 37 |
+
train/dining_room_0008/rgb_1363.png train/dining_room_0008/depth_1363.png train/dining_room_0008/filled_1363.png
|
| 38 |
+
train/dining_room_0012/rgb_1371.png train/dining_room_0012/depth_1371.png train/dining_room_0012/filled_1371.png
|
| 39 |
+
train/dining_room_0014/rgb_1377.png train/dining_room_0014/depth_1377.png train/dining_room_0014/filled_1377.png
|
| 40 |
+
train/dining_room_0015/rgb_1379.png train/dining_room_0015/depth_1379.png train/dining_room_0015/filled_1379.png
|
| 41 |
+
train/dining_room_0016/rgb_1382.png train/dining_room_0016/depth_1382.png train/dining_room_0016/filled_1382.png
|
| 42 |
+
train/dining_room_0031/rgb_1425.png train/dining_room_0031/depth_1425.png train/dining_room_0031/filled_1425.png
|
| 43 |
+
train/dining_room_0031/rgb_1426.png train/dining_room_0031/depth_1426.png train/dining_room_0031/filled_1426.png
|
| 44 |
+
train/dining_room_0033/rgb_1436.png train/dining_room_0033/depth_1436.png train/dining_room_0033/filled_1436.png
|
| 45 |
+
train/dining_room_0037/rgb_0548.png train/dining_room_0037/depth_0548.png train/dining_room_0037/filled_0548.png
|
| 46 |
+
train/furniture_store_0001/rgb_0224.png train/furniture_store_0001/depth_0224.png train/furniture_store_0001/filled_0224.png
|
| 47 |
+
train/furniture_store_0001/rgb_0237.png train/furniture_store_0001/depth_0237.png train/furniture_store_0001/filled_0237.png
|
| 48 |
+
train/furniture_store_0002/rgb_0249.png train/furniture_store_0002/depth_0249.png train/furniture_store_0002/filled_0249.png
|
| 49 |
+
train/home_office_0005/rgb_0368.png train/home_office_0005/depth_0368.png train/home_office_0005/filled_0368.png
|
| 50 |
+
train/home_office_0006/rgb_0374.png train/home_office_0006/depth_0374.png train/home_office_0006/filled_0374.png
|
| 51 |
+
train/home_office_0008/rgb_0380.png train/home_office_0008/depth_0380.png train/home_office_0008/filled_0380.png
|
| 52 |
+
train/home_office_0013/rgb_0554.png train/home_office_0013/depth_0554.png train/home_office_0013/filled_0554.png
|
| 53 |
+
train/kitchen_0010/rgb_0138.png train/kitchen_0010/depth_0138.png train/kitchen_0010/filled_0138.png
|
| 54 |
+
train/kitchen_0019/rgb_0750.png train/kitchen_0019/depth_0750.png train/kitchen_0019/filled_0750.png
|
| 55 |
+
train/kitchen_0019/rgb_0757.png train/kitchen_0019/depth_0757.png train/kitchen_0019/filled_0757.png
|
| 56 |
+
train/kitchen_0028/rgb_0788.png train/kitchen_0028/depth_0788.png train/kitchen_0028/filled_0788.png
|
| 57 |
+
train/kitchen_0028/rgb_0793.png train/kitchen_0028/depth_0793.png train/kitchen_0028/filled_0793.png
|
| 58 |
+
train/kitchen_0029/rgb_0799.png train/kitchen_0029/depth_0799.png train/kitchen_0029/filled_0799.png
|
| 59 |
+
train/kitchen_0033/rgb_0815.png train/kitchen_0033/depth_0815.png train/kitchen_0033/filled_0815.png
|
| 60 |
+
train/kitchen_0033/rgb_0816.png train/kitchen_0033/depth_0816.png train/kitchen_0033/filled_0816.png
|
| 61 |
+
train/kitchen_0037/rgb_0832.png train/kitchen_0037/depth_0832.png train/kitchen_0037/filled_0832.png
|
| 62 |
+
train/kitchen_0041/rgb_0849.png train/kitchen_0041/depth_0849.png train/kitchen_0041/filled_0849.png
|
| 63 |
+
train/kitchen_0047/rgb_0875.png train/kitchen_0047/depth_0875.png train/kitchen_0047/filled_0875.png
|
| 64 |
+
train/kitchen_0050/rgb_0887.png train/kitchen_0050/depth_0887.png train/kitchen_0050/filled_0887.png
|
| 65 |
+
train/kitchen_0051/rgb_0892.png train/kitchen_0051/depth_0892.png train/kitchen_0051/filled_0892.png
|
| 66 |
+
train/kitchen_0051/rgb_0893.png train/kitchen_0051/depth_0893.png train/kitchen_0051/filled_0893.png
|
| 67 |
+
train/kitchen_0052/rgb_0899.png train/kitchen_0052/depth_0899.png train/kitchen_0052/filled_0899.png
|
| 68 |
+
train/kitchen_0059/rgb_0573.png train/kitchen_0059/depth_0573.png train/kitchen_0059/filled_0573.png
|
| 69 |
+
train/living_room_0000/rgb_0050.png train/living_room_0000/depth_0050.png train/living_room_0000/filled_0050.png
|
| 70 |
+
train/living_room_0010/rgb_0156.png train/living_room_0010/depth_0156.png train/living_room_0010/filled_0156.png
|
| 71 |
+
train/living_room_0010/rgb_0158.png train/living_room_0010/depth_0158.png train/living_room_0010/filled_0158.png
|
| 72 |
+
train/living_room_0010/rgb_0159.png train/living_room_0010/depth_0159.png train/living_room_0010/filled_0159.png
|
| 73 |
+
train/living_room_0011/rgb_0162.png train/living_room_0011/depth_0162.png train/living_room_0011/filled_0162.png
|
| 74 |
+
train/living_room_0019/rgb_0258.png train/living_room_0019/depth_0258.png train/living_room_0019/filled_0258.png
|
| 75 |
+
train/living_room_0042/rgb_1251.png train/living_room_0042/depth_1251.png train/living_room_0042/filled_1251.png
|
| 76 |
+
train/living_room_0046/rgb_1268.png train/living_room_0046/depth_1268.png train/living_room_0046/filled_1268.png
|
| 77 |
+
train/living_room_0047/rgb_1272.png train/living_room_0047/depth_1272.png train/living_room_0047/filled_1272.png
|
| 78 |
+
train/living_room_0058/rgb_1301.png train/living_room_0058/depth_1301.png train/living_room_0058/filled_1301.png
|
| 79 |
+
train/living_room_0062/rgb_1310.png train/living_room_0062/depth_1310.png train/living_room_0062/filled_1310.png
|
| 80 |
+
train/living_room_0063/rgb_1313.png train/living_room_0063/depth_1313.png train/living_room_0063/filled_1313.png
|
| 81 |
+
train/living_room_0083/rgb_0588.png train/living_room_0083/depth_0588.png train/living_room_0083/filled_0588.png
|
| 82 |
+
train/living_room_0086/rgb_0601.png train/living_room_0086/depth_0601.png train/living_room_0086/filled_0601.png
|
| 83 |
+
train/office_0003/rgb_0004.png train/office_0003/depth_0004.png train/office_0003/filled_0004.png
|
| 84 |
+
train/office_0023/rgb_0623.png train/office_0023/depth_0623.png train/office_0023/filled_0623.png
|
| 85 |
+
train/office_0024/rgb_0627.png train/office_0024/depth_0627.png train/office_0024/filled_0627.png
|
| 86 |
+
train/office_kitchen_0003/rgb_0415.png train/office_kitchen_0003/depth_0415.png train/office_kitchen_0003/filled_0415.png
|
| 87 |
+
train/playroom_0002/rgb_0418.png train/playroom_0002/depth_0418.png train/playroom_0002/filled_0418.png
|
| 88 |
+
train/playroom_0003/rgb_0423.png train/playroom_0003/depth_0423.png train/playroom_0003/filled_0423.png
|
| 89 |
+
train/playroom_0003/rgb_0424.png train/playroom_0003/depth_0424.png train/playroom_0003/filled_0424.png
|
| 90 |
+
train/playroom_0003/rgb_0425.png train/playroom_0003/depth_0425.png train/playroom_0003/filled_0425.png
|
| 91 |
+
train/playroom_0004/rgb_0426.png train/playroom_0004/depth_0426.png train/playroom_0004/filled_0426.png
|
| 92 |
+
train/printer_room_0001/rgb_0451.png train/printer_room_0001/depth_0451.png train/printer_room_0001/filled_0451.png
|
| 93 |
+
train/reception_room_0001/rgb_0456.png train/reception_room_0001/depth_0456.png train/reception_room_0001/filled_0456.png
|
| 94 |
+
train/reception_room_0002/rgb_0459.png train/reception_room_0002/depth_0459.png train/reception_room_0002/filled_0459.png
|
| 95 |
+
train/reception_room_0004/rgb_0468.png train/reception_room_0004/depth_0468.png train/reception_room_0004/filled_0468.png
|
| 96 |
+
train/student_lounge_0001/rgb_0641.png train/student_lounge_0001/depth_0641.png train/student_lounge_0001/filled_0641.png
|
| 97 |
+
train/study_0003/rgb_0478.png train/study_0003/depth_0478.png train/study_0003/filled_0478.png
|
| 98 |
+
train/study_0005/rgb_0485.png train/study_0005/depth_0485.png train/study_0005/filled_0485.png
|
| 99 |
+
train/study_0008/rgb_0646.png train/study_0008/depth_0646.png train/study_0008/filled_0646.png
|
| 100 |
+
train/study_room_0004/rgb_0274.png train/study_room_0004/depth_0274.png train/study_room_0004/filled_0274.png
|
DepthMaster/data_split/nyu/labeled/filename_list_train_test.txt
ADDED
|
@@ -0,0 +1,109 @@
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
test/office_0008/rgb_0016.png test/office_0008/depth_0016.png test/office_0008/filled_0016.png
|
| 2 |
+
test/office_0013/rgb_0030.png test/office_0013/depth_0030.png test/office_0013/filled_0030.png
|
| 3 |
+
test/office_0014/rgb_0036.png test/office_0014/depth_0036.png test/office_0014/filled_0036.png
|
| 4 |
+
test/office_0015/rgb_0042.png test/office_0015/depth_0042.png test/office_0015/filled_0042.png
|
| 5 |
+
test/bedroom_0013/rgb_0059.png test/bedroom_0013/depth_0059.png test/bedroom_0013/filled_0059.png
|
| 6 |
+
test/bedroom_0018/rgb_0077.png test/bedroom_0018/depth_0077.png test/bedroom_0018/filled_0077.png
|
| 7 |
+
test/bookstore_0001/rgb_0087.png test/bookstore_0001/depth_0087.png test/bookstore_0001/filled_0087.png
|
| 8 |
+
test/bookstore_0001/rgb_0118.png test/bookstore_0001/depth_0118.png test/bookstore_0001/filled_0118.png
|
| 9 |
+
test/kitchen_0005/rgb_0129.png test/kitchen_0005/depth_0129.png test/kitchen_0005/filled_0129.png
|
| 10 |
+
test/living_room_0008/rgb_0153.png test/living_room_0008/depth_0153.png test/living_room_0008/filled_0153.png
|
| 11 |
+
test/bedroom_0003/rgb_0171.png test/bedroom_0003/depth_0171.png test/bedroom_0003/filled_0171.png
|
| 12 |
+
test/bedroom_0005/rgb_0180.png test/bedroom_0005/depth_0180.png test/bedroom_0005/filled_0180.png
|
| 13 |
+
test/bedroom_0006/rgb_0186.png test/bedroom_0006/depth_0186.png test/bedroom_0006/filled_0186.png
|
| 14 |
+
test/bedroom_0007/rgb_0192.png test/bedroom_0007/depth_0192.png test/bedroom_0007/filled_0192.png
|
| 15 |
+
test/kitchen_0002/rgb_0198.png test/kitchen_0002/depth_0198.png test/kitchen_0002/filled_0198.png
|
| 16 |
+
test/living_room_0003/rgb_0208.png test/living_room_0003/depth_0208.png test/living_room_0003/filled_0208.png
|
| 17 |
+
test/bedroom_0024/rgb_0221.png test/bedroom_0024/depth_0221.png test/bedroom_0024/filled_0221.png
|
| 18 |
+
test/study_room_0001/rgb_0273.png test/study_room_0001/depth_0273.png test/study_room_0001/filled_0273.png
|
| 19 |
+
test/classroom_0001/rgb_0284.png test/classroom_0001/depth_0284.png test/classroom_0001/filled_0284.png
|
| 20 |
+
test/classroom_0008/rgb_0300.png test/classroom_0008/depth_0300.png test/classroom_0008/filled_0300.png
|
| 21 |
+
test/classroom_0017/rgb_0315.png test/classroom_0017/depth_0315.png test/classroom_0017/filled_0315.png
|
| 22 |
+
test/classroom_0023/rgb_0328.png test/classroom_0023/depth_0328.png test/classroom_0023/filled_0328.png
|
| 23 |
+
test/computer_lab_0001/rgb_0334.png test/computer_lab_0001/depth_0334.png test/computer_lab_0001/filled_0334.png
|
| 24 |
+
test/home_office_0001/rgb_0357.png test/home_office_0001/depth_0357.png test/home_office_0001/filled_0357.png
|
| 25 |
+
test/home_office_0003/rgb_0363.png test/home_office_0003/depth_0363.png test/home_office_0003/filled_0363.png
|
| 26 |
+
test/home_office_0010/rgb_0388.png test/home_office_0010/depth_0388.png test/home_office_0010/filled_0388.png
|
| 27 |
+
test/office_kitchen_0002/rgb_0411.png test/office_kitchen_0002/depth_0411.png test/office_kitchen_0002/filled_0411.png
|
| 28 |
+
test/playroom_0005/rgb_0432.png test/playroom_0005/depth_0432.png test/playroom_0005/filled_0432.png
|
| 29 |
+
test/playroom_0007/rgb_0443.png test/playroom_0007/depth_0443.png test/playroom_0007/filled_0443.png
|
| 30 |
+
test/reception_room_0003/rgb_0462.png test/reception_room_0003/depth_0462.png test/reception_room_0003/filled_0462.png
|
| 31 |
+
test/study_0001/rgb_0470.png test/study_0001/depth_0470.png test/study_0001/filled_0470.png
|
| 32 |
+
test/study_0002/rgb_0476.png test/study_0002/depth_0476.png test/study_0002/filled_0476.png
|
| 33 |
+
test/bathroom_0060/rgb_0512.png test/bathroom_0060/depth_0512.png test/bathroom_0060/filled_0512.png
|
| 34 |
+
test/bedroom_0133/rgb_0519.png test/bedroom_0133/depth_0519.png test/bedroom_0133/filled_0519.png
|
| 35 |
+
test/bedroom_0135/rgb_0525.png test/bedroom_0135/depth_0525.png test/bedroom_0135/filled_0525.png
|
| 36 |
+
test/bedroom_0139/rgb_0538.png test/bedroom_0139/depth_0538.png test/bedroom_0139/filled_0538.png
|
| 37 |
+
test/home_office_0014/rgb_0556.png test/home_office_0014/depth_0556.png test/home_office_0014/filled_0556.png
|
| 38 |
+
test/kitchen_0056/rgb_0562.png test/kitchen_0056/depth_0562.png test/kitchen_0056/filled_0562.png
|
| 39 |
+
test/kitchen_0057/rgb_0568.png test/kitchen_0057/depth_0568.png test/kitchen_0057/filled_0568.png
|
| 40 |
+
test/living_room_0081/rgb_0581.png test/living_room_0081/depth_0581.png test/living_room_0081/filled_0581.png
|
| 41 |
+
test/living_room_0084/rgb_0594.png test/living_room_0084/depth_0594.png test/living_room_0084/filled_0594.png
|
| 42 |
+
test/office_0020/rgb_0612.png test/office_0020/depth_0612.png test/office_0020/filled_0612.png
|
| 43 |
+
test/office_0022/rgb_0621.png test/office_0022/depth_0621.png test/office_0022/filled_0621.png
|
| 44 |
+
test/office_0027/rgb_0638.png test/office_0027/depth_0638.png test/office_0027/filled_0638.png
|
| 45 |
+
test/bathroom_0012/rgb_0657.png test/bathroom_0012/depth_0657.png test/bathroom_0012/filled_0657.png
|
| 46 |
+
test/bathroom_0017/rgb_0670.png test/bathroom_0017/depth_0670.png test/bathroom_0017/filled_0670.png
|
| 47 |
+
test/bathroom_0021/rgb_0678.png test/bathroom_0021/depth_0678.png test/bathroom_0021/filled_0678.png
|
| 48 |
+
test/bathroom_0026/rgb_0688.png test/bathroom_0026/depth_0688.png test/bathroom_0026/filled_0688.png
|
| 49 |
+
test/bathroom_0031/rgb_0698.png test/bathroom_0031/depth_0698.png test/bathroom_0031/filled_0698.png
|
| 50 |
+
test/bathroom_0037/rgb_0710.png test/bathroom_0037/depth_0710.png test/bathroom_0037/filled_0710.png
|
| 51 |
+
test/bathroom_0043/rgb_0724.png test/bathroom_0043/depth_0724.png test/bathroom_0043/filled_0724.png
|
| 52 |
+
test/bathroom_0046/rgb_0732.png test/bathroom_0046/depth_0732.png test/bathroom_0046/filled_0732.png
|
| 53 |
+
test/kitchen_0021/rgb_0760.png test/kitchen_0021/depth_0760.png test/kitchen_0021/filled_0760.png
|
| 54 |
+
test/kitchen_0022/rgb_0766.png test/kitchen_0022/depth_0766.png test/kitchen_0022/filled_0766.png
|
| 55 |
+
test/kitchen_0024/rgb_0772.png test/kitchen_0024/depth_0772.png test/kitchen_0024/filled_0772.png
|
| 56 |
+
test/kitchen_0025/rgb_0778.png test/kitchen_0025/depth_0778.png test/kitchen_0025/filled_0778.png
|
| 57 |
+
test/kitchen_0027/rgb_0784.png test/kitchen_0027/depth_0784.png test/kitchen_0027/filled_0784.png
|
| 58 |
+
test/kitchen_0030/rgb_0802.png test/kitchen_0030/depth_0802.png test/kitchen_0030/filled_0802.png
|
| 59 |
+
test/kitchen_0032/rgb_0813.png test/kitchen_0032/depth_0813.png test/kitchen_0032/filled_0813.png
|
| 60 |
+
test/kitchen_0038/rgb_0834.png test/kitchen_0038/depth_0834.png test/kitchen_0038/filled_0834.png
|
| 61 |
+
test/kitchen_0039/rgb_0840.png test/kitchen_0039/depth_0840.png test/kitchen_0039/filled_0840.png
|
| 62 |
+
test/kitchen_0040/rgb_0846.png test/kitchen_0040/depth_0846.png test/kitchen_0040/filled_0846.png
|
| 63 |
+
test/kitchen_0044/rgb_0859.png test/kitchen_0044/depth_0859.png test/kitchen_0044/filled_0859.png
|
| 64 |
+
test/kitchen_0046/rgb_0871.png test/kitchen_0046/depth_0871.png test/kitchen_0046/filled_0871.png
|
| 65 |
+
test/bedroom_0027/rgb_0919.png test/bedroom_0027/depth_0919.png test/bedroom_0027/filled_0919.png
|
| 66 |
+
test/bedroom_0032/rgb_0934.png test/bedroom_0032/depth_0934.png test/bedroom_0032/filled_0934.png
|
| 67 |
+
test/bedroom_0043/rgb_0960.png test/bedroom_0043/depth_0960.png test/bedroom_0043/filled_0960.png
|
| 68 |
+
test/bedroom_0048/rgb_0970.png test/bedroom_0048/depth_0970.png test/bedroom_0048/filled_0970.png
|
| 69 |
+
test/bedroom_0049/rgb_0976.png test/bedroom_0049/depth_0976.png test/bedroom_0049/filled_0976.png
|
| 70 |
+
test/bedroom_0055/rgb_0995.png test/bedroom_0055/depth_0995.png test/bedroom_0055/filled_0995.png
|
| 71 |
+
test/bedroom_0061/rgb_1011.png test/bedroom_0061/depth_1011.png test/bedroom_0061/filled_1011.png
|
| 72 |
+
test/bedroom_0068/rgb_1033.png test/bedroom_0068/depth_1033.png test/bedroom_0068/filled_1033.png
|
| 73 |
+
test/bedroom_0075/rgb_1052.png test/bedroom_0075/depth_1052.png test/bedroom_0075/filled_1052.png
|
| 74 |
+
test/bedroom_0084/rgb_1077.png test/bedroom_0084/depth_1077.png test/bedroom_0084/filled_1077.png
|
| 75 |
+
test/bedroom_0085/rgb_1083.png test/bedroom_0085/depth_1083.png test/bedroom_0085/filled_1083.png
|
| 76 |
+
test/bedroom_0088/rgb_1092.png test/bedroom_0088/depth_1092.png test/bedroom_0088/filled_1092.png
|
| 77 |
+
test/bedroom_0091/rgb_1099.png test/bedroom_0091/depth_1099.png test/bedroom_0091/filled_1099.png
|
| 78 |
+
test/bedroom_0095/rgb_1106.png test/bedroom_0095/depth_1106.png test/bedroom_0095/filled_1106.png
|
| 79 |
+
test/bedroom_0099/rgb_1119.png test/bedroom_0099/depth_1119.png test/bedroom_0099/filled_1119.png
|
| 80 |
+
test/bedroom_0102/rgb_1128.png test/bedroom_0102/depth_1128.png test/bedroom_0102/filled_1128.png
|
| 81 |
+
test/bedroom_0108/rgb_1144.png test/bedroom_0108/depth_1144.png test/bedroom_0108/filled_1144.png
|
| 82 |
+
test/bedroom_0110/rgb_1150.png test/bedroom_0110/depth_1150.png test/bedroom_0110/filled_1150.png
|
| 83 |
+
test/bedroom_0112/rgb_1156.png test/bedroom_0112/depth_1156.png test/bedroom_0112/filled_1156.png
|
| 84 |
+
test/bedroom_0115/rgb_1165.png test/bedroom_0115/depth_1165.png test/bedroom_0115/filled_1165.png
|
| 85 |
+
test/bedroom_0119/rgb_1175.png test/bedroom_0119/depth_1175.png test/bedroom_0119/filled_1175.png
|
| 86 |
+
test/bedroom_0123/rgb_1183.png test/bedroom_0123/depth_1183.png test/bedroom_0123/filled_1183.png
|
| 87 |
+
test/bedroom_0128/rgb_1196.png test/bedroom_0128/depth_1196.png test/bedroom_0128/filled_1196.png
|
| 88 |
+
test/living_room_0027/rgb_1206.png test/living_room_0027/depth_1206.png test/living_room_0027/filled_1206.png
|
| 89 |
+
test/living_room_0028/rgb_1212.png test/living_room_0028/depth_1212.png test/living_room_0028/filled_1212.png
|
| 90 |
+
test/living_room_0034/rgb_1226.png test/living_room_0034/depth_1226.png test/living_room_0034/filled_1226.png
|
| 91 |
+
test/living_room_0036/rgb_1234.png test/living_room_0036/depth_1234.png test/living_room_0036/filled_1234.png
|
| 92 |
+
test/living_room_0043/rgb_1254.png test/living_room_0043/depth_1254.png test/living_room_0043/filled_1254.png
|
| 93 |
+
test/living_room_0044/rgb_1260.png test/living_room_0044/depth_1260.png test/living_room_0044/filled_1260.png
|
| 94 |
+
test/living_room_0048/rgb_1275.png test/living_room_0048/depth_1275.png test/living_room_0048/filled_1275.png
|
| 95 |
+
test/living_room_0051/rgb_1285.png test/living_room_0051/depth_1285.png test/living_room_0051/filled_1285.png
|
| 96 |
+
test/living_room_0053/rgb_1291.png test/living_room_0053/depth_1291.png test/living_room_0053/filled_1291.png
|
| 97 |
+
test/living_room_0057/rgb_1298.png test/living_room_0057/depth_1298.png test/living_room_0057/filled_1298.png
|
| 98 |
+
test/living_room_0060/rgb_1306.png test/living_room_0060/depth_1306.png test/living_room_0060/filled_1306.png
|
| 99 |
+
test/living_room_0075/rgb_1330.png test/living_room_0075/depth_1330.png test/living_room_0075/filled_1330.png
|
| 100 |
+
test/living_room_0080/rgb_1338.png test/living_room_0080/depth_1338.png test/living_room_0080/filled_1338.png
|
| 101 |
+
test/dining_room_0005/rgb_1353.png test/dining_room_0005/depth_1353.png test/dining_room_0005/filled_1353.png
|
| 102 |
+
test/dining_room_0011/rgb_1368.png test/dining_room_0011/depth_1368.png test/dining_room_0011/filled_1368.png
|
| 103 |
+
test/dining_room_0018/rgb_1388.png test/dining_room_0018/depth_1388.png test/dining_room_0018/filled_1388.png
|
| 104 |
+
test/dining_room_0020/rgb_1396.png test/dining_room_0020/depth_1396.png test/dining_room_0020/filled_1396.png
|
| 105 |
+
test/dining_room_0025/rgb_1407.png test/dining_room_0025/depth_1407.png test/dining_room_0025/filled_1407.png
|
| 106 |
+
test/dining_room_0026/rgb_1413.png test/dining_room_0026/depth_1413.png test/dining_room_0026/filled_1413.png
|
| 107 |
+
test/dining_room_0032/rgb_1430.png test/dining_room_0032/depth_1430.png test/dining_room_0032/filled_1430.png
|
| 108 |
+
test/dining_room_0035/rgb_1443.png test/dining_room_0035/depth_1443.png test/dining_room_0035/filled_1443.png
|
| 109 |
+
test/dining_room_0036/rgb_1449.png test/dining_room_0036/depth_1449.png test/dining_room_0036/filled_1449.png
|
DepthMaster/data_split/scannet/scannet_val_sampled_list_800_1.txt
ADDED
|
@@ -0,0 +1,800 @@
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scene0050_01/color/002000.jpg scene0050_01/depth/002000.png
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scene0578_01/color/001200.jpg scene0578_01/depth/001200.png
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scene0580_01/color/002100.jpg scene0580_01/depth/002100.png
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| 719 |
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scene0144_00/color/000600.jpg scene0144_00/depth/000600.png
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scene0697_02/color/002100.jpg scene0697_02/depth/002100.png
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scene0025_01/color/001000.jpg scene0025_01/depth/001000.png
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scene0621_00/color/002000.jpg scene0621_00/depth/002000.png
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scene0086_01/color/000900.jpg scene0086_01/depth/000900.png
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scene0550_00/color/002200.jpg scene0550_00/depth/002200.png
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scene0015_00/color/001600.jpg scene0015_00/depth/001600.png
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scene0086_01/color/000500.jpg scene0086_01/depth/000500.png
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| 749 |
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scene0187_00/color/001400.jpg scene0187_00/depth/001400.png
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scene0653_00/color/000000.jpg scene0653_00/depth/000000.png
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| 755 |
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scene0077_01/color/000200.jpg scene0077_01/depth/000200.png
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scene0608_01/color/002600.jpg scene0608_01/depth/002600.png
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scene0338_02/color/000000.jpg scene0338_02/depth/000000.png
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scene0693_02/color/000000.jpg scene0693_02/depth/000000.png
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scene0257_00/color/000400.jpg scene0257_00/depth/000400.png
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scene0011_01/color/000900.jpg scene0011_01/depth/000900.png
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| 765 |
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scene0578_01/color/001100.jpg scene0578_01/depth/001100.png
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scene0435_00/color/000900.jpg scene0435_00/depth/000900.png
|
| 767 |
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scene0086_01/color/001100.jpg scene0086_01/depth/001100.png
|
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scene0427_00/color/000300.jpg scene0427_00/depth/000300.png
|
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scene0458_01/color/000200.jpg scene0458_01/depth/000200.png
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scene0663_00/color/002600.jpg scene0663_00/depth/002600.png
|
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scene0664_02/color/000700.jpg scene0664_02/depth/000700.png
|
| 772 |
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scene0670_00/color/002900.jpg scene0670_00/depth/002900.png
|
| 773 |
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scene0196_00/color/000500.jpg scene0196_00/depth/000500.png
|
| 774 |
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scene0575_01/color/000400.jpg scene0575_01/depth/000400.png
|
| 775 |
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scene0100_02/color/000100.jpg scene0100_02/depth/000100.png
|
| 776 |
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scene0606_01/color/001300.jpg scene0606_01/depth/001300.png
|
| 777 |
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scene0086_01/color/000600.jpg scene0086_01/depth/000600.png
|
| 778 |
+
scene0231_01/color/002700.jpg scene0231_01/depth/002700.png
|
| 779 |
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scene0697_01/color/002800.jpg scene0697_01/depth/002800.png
|
| 780 |
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scene0474_03/color/002600.jpg scene0474_03/depth/002600.png
|
| 781 |
+
scene0685_02/color/001900.jpg scene0685_02/depth/001900.png
|
| 782 |
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scene0435_01/color/001000.jpg scene0435_01/depth/001000.png
|
| 783 |
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scene0607_00/color/000100.jpg scene0607_00/depth/000100.png
|
| 784 |
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scene0353_01/color/000500.jpg scene0353_01/depth/000500.png
|
| 785 |
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scene0207_01/color/000500.jpg scene0207_01/depth/000500.png
|
| 786 |
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scene0575_02/color/000100.jpg scene0575_02/depth/000100.png
|
| 787 |
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scene0583_01/color/001500.jpg scene0583_01/depth/001500.png
|
| 788 |
+
scene0187_00/color/001900.jpg scene0187_00/depth/001900.png
|
| 789 |
+
scene0351_01/color/000100.jpg scene0351_01/depth/000100.png
|
| 790 |
+
scene0700_00/color/001000.jpg scene0700_00/depth/001000.png
|
| 791 |
+
scene0629_00/color/002300.jpg scene0629_00/depth/002300.png
|
| 792 |
+
scene0629_02/color/000200.jpg scene0629_02/depth/000200.png
|
| 793 |
+
scene0354_00/color/000200.jpg scene0354_00/depth/000200.png
|
| 794 |
+
scene0575_01/color/001200.jpg scene0575_01/depth/001200.png
|
| 795 |
+
scene0334_01/color/000800.jpg scene0334_01/depth/000800.png
|
| 796 |
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scene0378_01/color/000300.jpg scene0378_01/depth/000300.png
|
| 797 |
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scene0356_01/color/000100.jpg scene0356_01/depth/000100.png
|
| 798 |
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scene0064_00/color/000800.jpg scene0064_00/depth/000800.png
|
| 799 |
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scene0278_01/color/000400.jpg scene0278_01/depth/000400.png
|
| 800 |
+
scene0678_01/color/001300.jpg scene0678_01/depth/001300.png
|
DepthMaster/data_split/vkitti/filename_list_debug.txt
ADDED
|
@@ -0,0 +1 @@
|
|
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|
|
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|
| 1 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00114.jpg Scene01/clone/frames/depth/Camera_1/depth_00114.png
|
DepthMaster/data_split/vkitti/vkitti_train.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
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|
DepthMaster/data_split/vkitti/vkitti_val.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
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|
DepthMaster/data_split/vkitti/vkitti_val_vae.txt
ADDED
|
@@ -0,0 +1,894 @@
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| 1 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00000.jpg Scene01/clone/frames/depth/Camera_0/depth_00000.png
|
| 2 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00001.jpg Scene01/clone/frames/depth/Camera_0/depth_00001.png
|
| 3 |
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Scene01/clone/frames/rgb/Camera_0/rgb_00002.jpg Scene01/clone/frames/depth/Camera_0/depth_00002.png
|
| 4 |
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Scene01/clone/frames/rgb/Camera_0/rgb_00003.jpg Scene01/clone/frames/depth/Camera_0/depth_00003.png
|
| 5 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00004.jpg Scene01/clone/frames/depth/Camera_0/depth_00004.png
|
| 6 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00005.jpg Scene01/clone/frames/depth/Camera_0/depth_00005.png
|
| 7 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00006.jpg Scene01/clone/frames/depth/Camera_0/depth_00006.png
|
| 8 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00007.jpg Scene01/clone/frames/depth/Camera_0/depth_00007.png
|
| 9 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00008.jpg Scene01/clone/frames/depth/Camera_0/depth_00008.png
|
| 10 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00009.jpg Scene01/clone/frames/depth/Camera_0/depth_00009.png
|
| 11 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00010.jpg Scene01/clone/frames/depth/Camera_0/depth_00010.png
|
| 12 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00011.jpg Scene01/clone/frames/depth/Camera_0/depth_00011.png
|
| 13 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00012.jpg Scene01/clone/frames/depth/Camera_0/depth_00012.png
|
| 14 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00013.jpg Scene01/clone/frames/depth/Camera_0/depth_00013.png
|
| 15 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00014.jpg Scene01/clone/frames/depth/Camera_0/depth_00014.png
|
| 16 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00015.jpg Scene01/clone/frames/depth/Camera_0/depth_00015.png
|
| 17 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00016.jpg Scene01/clone/frames/depth/Camera_0/depth_00016.png
|
| 18 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00017.jpg Scene01/clone/frames/depth/Camera_0/depth_00017.png
|
| 19 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00018.jpg Scene01/clone/frames/depth/Camera_0/depth_00018.png
|
| 20 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00019.jpg Scene01/clone/frames/depth/Camera_0/depth_00019.png
|
| 21 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00020.jpg Scene01/clone/frames/depth/Camera_0/depth_00020.png
|
| 22 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00021.jpg Scene01/clone/frames/depth/Camera_0/depth_00021.png
|
| 23 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00022.jpg Scene01/clone/frames/depth/Camera_0/depth_00022.png
|
| 24 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00023.jpg Scene01/clone/frames/depth/Camera_0/depth_00023.png
|
| 25 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00024.jpg Scene01/clone/frames/depth/Camera_0/depth_00024.png
|
| 26 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00025.jpg Scene01/clone/frames/depth/Camera_0/depth_00025.png
|
| 27 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00026.jpg Scene01/clone/frames/depth/Camera_0/depth_00026.png
|
| 28 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00027.jpg Scene01/clone/frames/depth/Camera_0/depth_00027.png
|
| 29 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00028.jpg Scene01/clone/frames/depth/Camera_0/depth_00028.png
|
| 30 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00029.jpg Scene01/clone/frames/depth/Camera_0/depth_00029.png
|
| 31 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00030.jpg Scene01/clone/frames/depth/Camera_0/depth_00030.png
|
| 32 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00031.jpg Scene01/clone/frames/depth/Camera_0/depth_00031.png
|
| 33 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00032.jpg Scene01/clone/frames/depth/Camera_0/depth_00032.png
|
| 34 |
+
Scene01/clone/frames/rgb/Camera_0/rgb_00033.jpg Scene01/clone/frames/depth/Camera_0/depth_00033.png
|
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| 831 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00383.jpg Scene01/clone/frames/depth/Camera_1/depth_00383.png
|
| 832 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00384.jpg Scene01/clone/frames/depth/Camera_1/depth_00384.png
|
| 833 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00385.jpg Scene01/clone/frames/depth/Camera_1/depth_00385.png
|
| 834 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00386.jpg Scene01/clone/frames/depth/Camera_1/depth_00386.png
|
| 835 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00387.jpg Scene01/clone/frames/depth/Camera_1/depth_00387.png
|
| 836 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00388.jpg Scene01/clone/frames/depth/Camera_1/depth_00388.png
|
| 837 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00389.jpg Scene01/clone/frames/depth/Camera_1/depth_00389.png
|
| 838 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00390.jpg Scene01/clone/frames/depth/Camera_1/depth_00390.png
|
| 839 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00391.jpg Scene01/clone/frames/depth/Camera_1/depth_00391.png
|
| 840 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00392.jpg Scene01/clone/frames/depth/Camera_1/depth_00392.png
|
| 841 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00393.jpg Scene01/clone/frames/depth/Camera_1/depth_00393.png
|
| 842 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00394.jpg Scene01/clone/frames/depth/Camera_1/depth_00394.png
|
| 843 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00395.jpg Scene01/clone/frames/depth/Camera_1/depth_00395.png
|
| 844 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00396.jpg Scene01/clone/frames/depth/Camera_1/depth_00396.png
|
| 845 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00397.jpg Scene01/clone/frames/depth/Camera_1/depth_00397.png
|
| 846 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00398.jpg Scene01/clone/frames/depth/Camera_1/depth_00398.png
|
| 847 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00399.jpg Scene01/clone/frames/depth/Camera_1/depth_00399.png
|
| 848 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00400.jpg Scene01/clone/frames/depth/Camera_1/depth_00400.png
|
| 849 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00401.jpg Scene01/clone/frames/depth/Camera_1/depth_00401.png
|
| 850 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00402.jpg Scene01/clone/frames/depth/Camera_1/depth_00402.png
|
| 851 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00403.jpg Scene01/clone/frames/depth/Camera_1/depth_00403.png
|
| 852 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00404.jpg Scene01/clone/frames/depth/Camera_1/depth_00404.png
|
| 853 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00405.jpg Scene01/clone/frames/depth/Camera_1/depth_00405.png
|
| 854 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00406.jpg Scene01/clone/frames/depth/Camera_1/depth_00406.png
|
| 855 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00407.jpg Scene01/clone/frames/depth/Camera_1/depth_00407.png
|
| 856 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00408.jpg Scene01/clone/frames/depth/Camera_1/depth_00408.png
|
| 857 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00409.jpg Scene01/clone/frames/depth/Camera_1/depth_00409.png
|
| 858 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00410.jpg Scene01/clone/frames/depth/Camera_1/depth_00410.png
|
| 859 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00411.jpg Scene01/clone/frames/depth/Camera_1/depth_00411.png
|
| 860 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00412.jpg Scene01/clone/frames/depth/Camera_1/depth_00412.png
|
| 861 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00413.jpg Scene01/clone/frames/depth/Camera_1/depth_00413.png
|
| 862 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00414.jpg Scene01/clone/frames/depth/Camera_1/depth_00414.png
|
| 863 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00415.jpg Scene01/clone/frames/depth/Camera_1/depth_00415.png
|
| 864 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00416.jpg Scene01/clone/frames/depth/Camera_1/depth_00416.png
|
| 865 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00417.jpg Scene01/clone/frames/depth/Camera_1/depth_00417.png
|
| 866 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00418.jpg Scene01/clone/frames/depth/Camera_1/depth_00418.png
|
| 867 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00419.jpg Scene01/clone/frames/depth/Camera_1/depth_00419.png
|
| 868 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00420.jpg Scene01/clone/frames/depth/Camera_1/depth_00420.png
|
| 869 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00421.jpg Scene01/clone/frames/depth/Camera_1/depth_00421.png
|
| 870 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00422.jpg Scene01/clone/frames/depth/Camera_1/depth_00422.png
|
| 871 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00423.jpg Scene01/clone/frames/depth/Camera_1/depth_00423.png
|
| 872 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00424.jpg Scene01/clone/frames/depth/Camera_1/depth_00424.png
|
| 873 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00425.jpg Scene01/clone/frames/depth/Camera_1/depth_00425.png
|
| 874 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00426.jpg Scene01/clone/frames/depth/Camera_1/depth_00426.png
|
| 875 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00427.jpg Scene01/clone/frames/depth/Camera_1/depth_00427.png
|
| 876 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00428.jpg Scene01/clone/frames/depth/Camera_1/depth_00428.png
|
| 877 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00429.jpg Scene01/clone/frames/depth/Camera_1/depth_00429.png
|
| 878 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00430.jpg Scene01/clone/frames/depth/Camera_1/depth_00430.png
|
| 879 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00431.jpg Scene01/clone/frames/depth/Camera_1/depth_00431.png
|
| 880 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00432.jpg Scene01/clone/frames/depth/Camera_1/depth_00432.png
|
| 881 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00433.jpg Scene01/clone/frames/depth/Camera_1/depth_00433.png
|
| 882 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00434.jpg Scene01/clone/frames/depth/Camera_1/depth_00434.png
|
| 883 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00435.jpg Scene01/clone/frames/depth/Camera_1/depth_00435.png
|
| 884 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00436.jpg Scene01/clone/frames/depth/Camera_1/depth_00436.png
|
| 885 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00437.jpg Scene01/clone/frames/depth/Camera_1/depth_00437.png
|
| 886 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00438.jpg Scene01/clone/frames/depth/Camera_1/depth_00438.png
|
| 887 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00439.jpg Scene01/clone/frames/depth/Camera_1/depth_00439.png
|
| 888 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00440.jpg Scene01/clone/frames/depth/Camera_1/depth_00440.png
|
| 889 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00441.jpg Scene01/clone/frames/depth/Camera_1/depth_00441.png
|
| 890 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00442.jpg Scene01/clone/frames/depth/Camera_1/depth_00442.png
|
| 891 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00443.jpg Scene01/clone/frames/depth/Camera_1/depth_00443.png
|
| 892 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00444.jpg Scene01/clone/frames/depth/Camera_1/depth_00444.png
|
| 893 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00445.jpg Scene01/clone/frames/depth/Camera_1/depth_00445.png
|
| 894 |
+
Scene01/clone/frames/rgb/Camera_1/rgb_00446.jpg Scene01/clone/frames/depth/Camera_1/depth_00446.png
|
DepthMaster/depthmaster/__init__.py
ADDED
|
@@ -0,0 +1,26 @@
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
| 1 |
+
# Last modified: 2025-01-14
|
| 2 |
+
#
|
| 3 |
+
# Copyright 2025 Ziyang Song, USTC. All rights reserved.
|
| 4 |
+
#
|
| 5 |
+
# This file has been modified from the original version.
|
| 6 |
+
# Original copyright (c) 2023 Bingxin Ke, ETH Zurich. All rights reserved.
|
| 7 |
+
#
|
| 8 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 9 |
+
# you may not use this file except in compliance with the License.
|
| 10 |
+
# You may obtain a copy of the License at
|
| 11 |
+
#
|
| 12 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 13 |
+
#
|
| 14 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 15 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 16 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 17 |
+
# See the License for the specific language governing permissions and
|
| 18 |
+
# limitations under the License.
|
| 19 |
+
# --------------------------------------------------------------------------
|
| 20 |
+
# If you find this code useful, we kindly ask you to cite our paper in your work.
|
| 21 |
+
# Please find bibtex at: https://github.com/indu1ge/DepthMaster#-citation
|
| 22 |
+
# More information about the method can be found at https://indu1ge.github.io/DepthMaster_page
|
| 23 |
+
# --------------------------------------------------------------------------
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
from .depthmaster_pipeline import DepthMasterPipeline, DepthMasterDepthOutput # noqa: F401
|
DepthMaster/depthmaster/__pycache__/__init__.cpython-310.pyc
ADDED
|
Binary file (248 Bytes). View file
|
|
|
DepthMaster/depthmaster/__pycache__/depthmaster_pipeline.cpython-310.pyc
ADDED
|
Binary file (11.5 kB). View file
|
|
|
DepthMaster/depthmaster/depthmaster_pipeline.py
ADDED
|
@@ -0,0 +1,385 @@
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|
|
|
| 1 |
+
# Last modified: 2025-01-14
|
| 2 |
+
#
|
| 3 |
+
# Copyright 2025 Ziyang Song, USTC. All rights reserved.
|
| 4 |
+
#
|
| 5 |
+
# This file has been modified from the original version.
|
| 6 |
+
# Original copyright (c) 2023 Bingxin Ke, ETH Zurich. All rights reserved.
|
| 7 |
+
#
|
| 8 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 9 |
+
# you may not use this file except in compliance with the License.
|
| 10 |
+
# You may obtain a copy of the License at
|
| 11 |
+
#
|
| 12 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 13 |
+
#
|
| 14 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 15 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 16 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 17 |
+
# See the License for the specific language governing permissions and
|
| 18 |
+
# limitations under the License.
|
| 19 |
+
# --------------------------------------------------------------------------
|
| 20 |
+
# If you find this code useful, we kindly ask you to cite our paper in your work.
|
| 21 |
+
# Please find bibtex at: https://github.com/indu1ge/DepthMaster#-citation
|
| 22 |
+
# More information about the method can be found at https://indu1ge.github.io/DepthMaster_page
|
| 23 |
+
# --------------------------------------------------------------------------
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
import logging
|
| 27 |
+
from typing import Dict, Optional, Union
|
| 28 |
+
|
| 29 |
+
import numpy as np
|
| 30 |
+
import torch
|
| 31 |
+
from diffusers import (
|
| 32 |
+
AutoencoderKL,
|
| 33 |
+
DiffusionPipeline,
|
| 34 |
+
# UNet2DConditionModel,
|
| 35 |
+
)
|
| 36 |
+
from depthmaster.modules.unet_2d_condition_s2 import UNet2DConditionModel
|
| 37 |
+
from diffusers.utils import BaseOutput
|
| 38 |
+
from PIL import Image
|
| 39 |
+
from torch.utils.data import DataLoader, TensorDataset
|
| 40 |
+
from torchvision.transforms import InterpolationMode
|
| 41 |
+
from torchvision.transforms.functional import pil_to_tensor, resize
|
| 42 |
+
from tqdm.auto import tqdm
|
| 43 |
+
from transformers import CLIPTextModel, CLIPTokenizer
|
| 44 |
+
|
| 45 |
+
from .util.image_util import (
|
| 46 |
+
chw2hwc,
|
| 47 |
+
colorize_depth_maps,
|
| 48 |
+
get_tv_resample_method,
|
| 49 |
+
resize_max_res,
|
| 50 |
+
)
|
| 51 |
+
|
| 52 |
+
class DepthMasterDepthOutput(BaseOutput):
|
| 53 |
+
"""
|
| 54 |
+
Output class for monocular depth prediction pipeline.
|
| 55 |
+
|
| 56 |
+
Args:
|
| 57 |
+
depth_np (`np.ndarray`):
|
| 58 |
+
Predicted depth map, with depth values in the range of [0, 1].
|
| 59 |
+
depth_colored (`PIL.Image.Image`):
|
| 60 |
+
Colorized depth map, with the shape of [3, H, W] and values in [0, 1].
|
| 61 |
+
uncertainty (`None` or `np.ndarray`):
|
| 62 |
+
Uncalibrated uncertainty(MAD, median absolute deviation) coming from ensembling.
|
| 63 |
+
"""
|
| 64 |
+
|
| 65 |
+
depth_np: np.ndarray
|
| 66 |
+
depth_colored: Union[None, Image.Image]
|
| 67 |
+
uncertainty: Union[None, np.ndarray]
|
| 68 |
+
|
| 69 |
+
|
| 70 |
+
class DepthMasterPipeline(DiffusionPipeline):
|
| 71 |
+
"""
|
| 72 |
+
Pipeline for monocular depth estimation using DepthMaster.
|
| 73 |
+
|
| 74 |
+
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
|
| 75 |
+
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
|
| 76 |
+
|
| 77 |
+
Args:
|
| 78 |
+
unet (`UNet2DConditionModel`):
|
| 79 |
+
Conditional U-Net to denoise the depth latent, conditioned on image latent.
|
| 80 |
+
vae (`AutoencoderKL`):
|
| 81 |
+
Variational Auto-Encoder (VAE) Model to encode and decode images and depth maps
|
| 82 |
+
to and from latent representations.
|
| 83 |
+
scheduler (`DDIMScheduler`):
|
| 84 |
+
A scheduler to be used in combination with `unet` to denoise the encoded image latents.
|
| 85 |
+
text_encoder (`CLIPTextModel`):
|
| 86 |
+
Text-encoder, for empty text embedding.
|
| 87 |
+
tokenizer (`CLIPTokenizer`):
|
| 88 |
+
CLIP tokenizer.
|
| 89 |
+
scale_invariant (`bool`, *optional*):
|
| 90 |
+
A model property specifying whether the predicted depth maps are scale-invariant. This value must be set in
|
| 91 |
+
the model config. When used together with the `shift_invariant=True` flag, the model is also called
|
| 92 |
+
"affine-invariant". NB: overriding this value is not supported.
|
| 93 |
+
shift_invariant (`bool`, *optional*):
|
| 94 |
+
A model property specifying whether the predicted depth maps are shift-invariant. This value must be set in
|
| 95 |
+
the model config. When used together with the `scale_invariant=True` flag, the model is also called
|
| 96 |
+
"affine-invariant". NB: overriding this value is not supported.
|
| 97 |
+
default_denoising_steps (`int`, *optional*):
|
| 98 |
+
The minimum number of denoising diffusion steps that are required to produce a prediction of reasonable
|
| 99 |
+
quality with the given model. This value must be set in the model config. When the pipeline is called
|
| 100 |
+
without explicitly setting `num_inference_steps`, the default value is used. This is required to ensure
|
| 101 |
+
reasonable results with various model flavors compatible with the pipeline, such as those relying on very
|
| 102 |
+
short denoising schedules (`LCMScheduler`) and those with full diffusion schedules (`DDIMScheduler`).
|
| 103 |
+
default_processing_resolution (`int`, *optional*):
|
| 104 |
+
The recommended value of the `processing_resolution` parameter of the pipeline. This value must be set in
|
| 105 |
+
the model config. When the pipeline is called without explicitly setting `processing_resolution`, the
|
| 106 |
+
default value is used. This is required to ensure reasonable results with various model flavors trained
|
| 107 |
+
with varying optimal processing resolution values.
|
| 108 |
+
"""
|
| 109 |
+
|
| 110 |
+
rgb_latent_scale_factor = 0.18215
|
| 111 |
+
depth_latent_scale_factor = 0.18215
|
| 112 |
+
|
| 113 |
+
def __init__(
|
| 114 |
+
self,
|
| 115 |
+
unet: UNet2DConditionModel,
|
| 116 |
+
vae: AutoencoderKL,
|
| 117 |
+
text_encoder: CLIPTextModel,
|
| 118 |
+
tokenizer: CLIPTokenizer,
|
| 119 |
+
scale_invariant: Optional[bool] = True,
|
| 120 |
+
shift_invariant: Optional[bool] = True,
|
| 121 |
+
default_processing_resolution: Optional[int] = None,
|
| 122 |
+
):
|
| 123 |
+
super().__init__()
|
| 124 |
+
|
| 125 |
+
self.register_modules(
|
| 126 |
+
unet=unet,
|
| 127 |
+
vae=vae,
|
| 128 |
+
text_encoder=text_encoder,
|
| 129 |
+
tokenizer=tokenizer,
|
| 130 |
+
)
|
| 131 |
+
self.register_to_config(
|
| 132 |
+
scale_invariant=scale_invariant,
|
| 133 |
+
shift_invariant=shift_invariant,
|
| 134 |
+
default_processing_resolution=default_processing_resolution,
|
| 135 |
+
)
|
| 136 |
+
|
| 137 |
+
self.scale_invariant = scale_invariant
|
| 138 |
+
self.shift_invariant = shift_invariant
|
| 139 |
+
self.default_processing_resolution = default_processing_resolution
|
| 140 |
+
|
| 141 |
+
self.empty_text_embed = None
|
| 142 |
+
|
| 143 |
+
@torch.no_grad()
|
| 144 |
+
def __call__(
|
| 145 |
+
self,
|
| 146 |
+
input_image: Union[Image.Image, torch.Tensor],
|
| 147 |
+
processing_res: Optional[int] = None,
|
| 148 |
+
match_input_res: bool = True,
|
| 149 |
+
resample_method: str = "bilinear",
|
| 150 |
+
batch_size: int = 0,
|
| 151 |
+
color_map: str = "Spectral",
|
| 152 |
+
show_progress_bar: bool = True,
|
| 153 |
+
) -> DepthMasterDepthOutput:
|
| 154 |
+
"""
|
| 155 |
+
Function invoked when calling the pipeline.
|
| 156 |
+
|
| 157 |
+
Args:
|
| 158 |
+
input_image (`Image`):
|
| 159 |
+
Input RGB (or gray-scale) image.
|
| 160 |
+
processing_res (`int`, *optional*, defaults to `None`):
|
| 161 |
+
Effective processing resolution. When set to `0`, processes at the original image resolution. This
|
| 162 |
+
produces crisper predictions, but may also lead to the overall loss of global context. The default
|
| 163 |
+
value `None` resolves to the optimal value from the model config.
|
| 164 |
+
match_input_res (`bool`, *optional*, defaults to `True`):
|
| 165 |
+
Resize depth prediction to match input resolution.
|
| 166 |
+
Only valid if `processing_res` > 0.
|
| 167 |
+
resample_method: (`str`, *optional*, defaults to `bilinear`):
|
| 168 |
+
Resampling method used to resize images and depth predictions. This can be one of `bilinear`, `bicubic` or `nearest`, defaults to: `bilinear`.
|
| 169 |
+
batch_size (`int`, *optional*, defaults to `0`):
|
| 170 |
+
Inference batch size, no bigger than `num_ensemble`.
|
| 171 |
+
If set to 0, the script will automatically decide the proper batch size.
|
| 172 |
+
show_progress_bar (`bool`, *optional*, defaults to `True`):
|
| 173 |
+
Display a progress bar of diffusion denoising.
|
| 174 |
+
color_map (`str`, *optional*, defaults to `"Spectral"`, pass `None` to skip colorized depth map generation):
|
| 175 |
+
Colormap used to colorize the depth map.
|
| 176 |
+
Returns:
|
| 177 |
+
`DepthMasterDepthOutput`: Output class for DepthMaster monocular depth prediction pipeline, including:
|
| 178 |
+
- **depth_np** (`np.ndarray`) Predicted depth map, with depth values in the range of [0, 1]
|
| 179 |
+
- **depth_colored** (`PIL.Image.Image`) Colorized depth map, with the shape of [3, H, W] and values in [0, 1], None if `color_map` is `None`
|
| 180 |
+
"""
|
| 181 |
+
# Model-specific optimal default values leading to fast and reasonable results.
|
| 182 |
+
if processing_res is None:
|
| 183 |
+
processing_res = self.default_processing_resolution
|
| 184 |
+
|
| 185 |
+
assert processing_res >= 0
|
| 186 |
+
|
| 187 |
+
resample_method: InterpolationMode = get_tv_resample_method(resample_method)
|
| 188 |
+
|
| 189 |
+
# ----------------- Image Preprocess -----------------
|
| 190 |
+
# Convert to torch tensor
|
| 191 |
+
if isinstance(input_image, Image.Image):
|
| 192 |
+
input_image = input_image.convert("RGB")
|
| 193 |
+
# convert to torch tensor [H, W, rgb] -> [rgb, H, W]
|
| 194 |
+
rgb = pil_to_tensor(input_image)
|
| 195 |
+
rgb = rgb.unsqueeze(0) # [1, rgb, H, W]
|
| 196 |
+
elif isinstance(input_image, torch.Tensor):
|
| 197 |
+
rgb = input_image
|
| 198 |
+
else:
|
| 199 |
+
raise TypeError(f"Unknown input type: {type(input_image) = }")
|
| 200 |
+
input_size = rgb.shape
|
| 201 |
+
assert (
|
| 202 |
+
4 == rgb.dim() and 3 == input_size[-3]
|
| 203 |
+
), f"Wrong input shape {input_size}, expected [1, rgb, H, W]"
|
| 204 |
+
# --------------- Image Processing ------------------------
|
| 205 |
+
# Resize image
|
| 206 |
+
if processing_res > 0:
|
| 207 |
+
rgb = resize_max_res(
|
| 208 |
+
rgb,
|
| 209 |
+
max_edge_resolution=processing_res,
|
| 210 |
+
resample_method=resample_method,
|
| 211 |
+
)
|
| 212 |
+
|
| 213 |
+
# Normalize rgb values
|
| 214 |
+
rgb_norm: torch.Tensor = rgb / 255.0 * 2.0 - 1.0 # [0, 255] -> [-1, 1]
|
| 215 |
+
rgb_norm = rgb_norm.to(self.dtype)
|
| 216 |
+
assert rgb_norm.min() >= -1.0 and rgb_norm.max() <= 1.0
|
| 217 |
+
|
| 218 |
+
# ----------------- Predicting depth -----------------
|
| 219 |
+
# Batch repeated input image
|
| 220 |
+
duplicated_rgb = rgb_norm.expand(1, -1, -1, -1)
|
| 221 |
+
single_rgb_dataset = TensorDataset(duplicated_rgb)
|
| 222 |
+
# find the batch size
|
| 223 |
+
if batch_size > 0:
|
| 224 |
+
_bs = batch_size
|
| 225 |
+
else:
|
| 226 |
+
_bs = 1
|
| 227 |
+
|
| 228 |
+
single_rgb_loader = DataLoader(
|
| 229 |
+
single_rgb_dataset, batch_size=_bs, shuffle=False
|
| 230 |
+
)
|
| 231 |
+
|
| 232 |
+
# Predict depth maps (batched)
|
| 233 |
+
depth_pred_ls = []
|
| 234 |
+
if show_progress_bar:
|
| 235 |
+
iterable = tqdm(
|
| 236 |
+
single_rgb_loader, desc=" " * 2 + "Inference batches", leave=False
|
| 237 |
+
)
|
| 238 |
+
else:
|
| 239 |
+
iterable = single_rgb_loader
|
| 240 |
+
for batch in iterable:
|
| 241 |
+
(batched_img,) = batch # here the image is still around 0-1
|
| 242 |
+
depth_pred_raw = self.single_infer(
|
| 243 |
+
rgb_in=batched_img,
|
| 244 |
+
)
|
| 245 |
+
depth_pred_ls.append(depth_pred_raw.detach())
|
| 246 |
+
depth_preds = torch.concat(depth_pred_ls, dim=0)
|
| 247 |
+
torch.cuda.empty_cache() # clear vram cache for ensembling
|
| 248 |
+
|
| 249 |
+
depth_pred = depth_preds
|
| 250 |
+
pred_uncert = None
|
| 251 |
+
|
| 252 |
+
# Resize back to original resolution
|
| 253 |
+
if match_input_res:
|
| 254 |
+
depth_pred = resize(
|
| 255 |
+
depth_pred,
|
| 256 |
+
input_size[-2:],
|
| 257 |
+
interpolation=resample_method,
|
| 258 |
+
antialias=True,
|
| 259 |
+
)
|
| 260 |
+
|
| 261 |
+
# Convert to numpy
|
| 262 |
+
depth_pred = depth_pred.squeeze()
|
| 263 |
+
depth_pred = depth_pred.cpu().numpy()
|
| 264 |
+
if pred_uncert is not None:
|
| 265 |
+
pred_uncert = pred_uncert.squeeze().cpu().numpy()
|
| 266 |
+
|
| 267 |
+
# Clip output range
|
| 268 |
+
depth_pred = depth_pred.clip(0, 1)
|
| 269 |
+
|
| 270 |
+
# Colorize
|
| 271 |
+
if color_map is not None:
|
| 272 |
+
depth_colored = colorize_depth_maps(
|
| 273 |
+
depth_pred, 0, 1, cmap=color_map
|
| 274 |
+
).squeeze() # [3, H, W], value in (0, 1)
|
| 275 |
+
depth_colored = (depth_colored * 255).astype(np.uint8)
|
| 276 |
+
depth_colored_hwc = chw2hwc(depth_colored)
|
| 277 |
+
depth_colored_img = Image.fromarray(depth_colored_hwc)
|
| 278 |
+
else:
|
| 279 |
+
depth_colored_img = None
|
| 280 |
+
|
| 281 |
+
return DepthMasterDepthOutput(
|
| 282 |
+
depth_np=depth_pred,
|
| 283 |
+
depth_colored=depth_colored_img,
|
| 284 |
+
uncertainty=pred_uncert,
|
| 285 |
+
)
|
| 286 |
+
|
| 287 |
+
|
| 288 |
+
def encode_empty_text(self):
|
| 289 |
+
"""
|
| 290 |
+
Encode text embedding for empty prompt
|
| 291 |
+
"""
|
| 292 |
+
prompt = ""
|
| 293 |
+
text_inputs = self.tokenizer(
|
| 294 |
+
prompt,
|
| 295 |
+
padding="do_not_pad",
|
| 296 |
+
max_length=self.tokenizer.model_max_length,
|
| 297 |
+
truncation=True,
|
| 298 |
+
return_tensors="pt",
|
| 299 |
+
)
|
| 300 |
+
text_input_ids = text_inputs.input_ids.to(self.text_encoder.device) #[1,2]
|
| 301 |
+
self.empty_text_embed = self.text_encoder(text_input_ids)[0].to(self.dtype) #[1,2,1024]
|
| 302 |
+
|
| 303 |
+
@torch.no_grad()
|
| 304 |
+
def single_infer(
|
| 305 |
+
self,
|
| 306 |
+
rgb_in: torch.Tensor,
|
| 307 |
+
) -> torch.Tensor:
|
| 308 |
+
"""
|
| 309 |
+
Perform an individual depth prediction without ensembling.
|
| 310 |
+
|
| 311 |
+
Args:
|
| 312 |
+
rgb_in (`torch.Tensor`):
|
| 313 |
+
Input RGB image.
|
| 314 |
+
Returns:
|
| 315 |
+
`torch.Tensor`: Predicted depth map.
|
| 316 |
+
"""
|
| 317 |
+
device = self.device
|
| 318 |
+
rgb_in = rgb_in.to(device)
|
| 319 |
+
|
| 320 |
+
# Encode image
|
| 321 |
+
rgb_latent = self.encode_rgb(rgb_in) # 1/8 Resolution with a channel nums of 4.
|
| 322 |
+
|
| 323 |
+
|
| 324 |
+
# Batched empty text embedding
|
| 325 |
+
if self.empty_text_embed is None:
|
| 326 |
+
self.encode_empty_text()
|
| 327 |
+
batch_empty_text_embed = self.empty_text_embed.repeat(
|
| 328 |
+
(rgb_latent.shape[0], 1, 1)
|
| 329 |
+
).to(device) # [B, 2, 1024]
|
| 330 |
+
|
| 331 |
+
|
| 332 |
+
unet_output = self.unet(
|
| 333 |
+
rgb_latent,
|
| 334 |
+
1,
|
| 335 |
+
encoder_hidden_states=batch_empty_text_embed,
|
| 336 |
+
).sample # [B, 4, h, w]
|
| 337 |
+
|
| 338 |
+
torch.cuda.empty_cache()
|
| 339 |
+
depth = self.decode_depth(unet_output) # [B, 1, h, w]
|
| 340 |
+
|
| 341 |
+
# clip prediction
|
| 342 |
+
depth = torch.clip(depth, -1.0, 1.0)
|
| 343 |
+
# shift to [0, 1]
|
| 344 |
+
depth = (depth + 1.0) / 2.0
|
| 345 |
+
|
| 346 |
+
return depth
|
| 347 |
+
|
| 348 |
+
def encode_rgb(self, rgb_in: torch.Tensor) -> torch.Tensor:
|
| 349 |
+
"""
|
| 350 |
+
Encode RGB image into latent.
|
| 351 |
+
|
| 352 |
+
Args:
|
| 353 |
+
rgb_in (`torch.Tensor`):
|
| 354 |
+
Input RGB image to be encoded.
|
| 355 |
+
|
| 356 |
+
Returns:
|
| 357 |
+
`torch.Tensor`: Image latent.
|
| 358 |
+
"""
|
| 359 |
+
# encode
|
| 360 |
+
h = self.vae.encoder(rgb_in)
|
| 361 |
+
moments = self.vae.quant_conv(h)
|
| 362 |
+
mean, logvar = torch.chunk(moments, 2, dim=1)
|
| 363 |
+
# scale latent
|
| 364 |
+
rgb_latent = mean * self.rgb_latent_scale_factor
|
| 365 |
+
return rgb_latent
|
| 366 |
+
|
| 367 |
+
def decode_depth(self, depth_latent: torch.Tensor) -> torch.Tensor:
|
| 368 |
+
"""
|
| 369 |
+
Decode depth latent into depth map.
|
| 370 |
+
|
| 371 |
+
Args:
|
| 372 |
+
depth_latent (`torch.Tensor`):
|
| 373 |
+
Depth latent to be decoded.
|
| 374 |
+
|
| 375 |
+
Returns:
|
| 376 |
+
`torch.Tensor`: Decoded depth map.
|
| 377 |
+
"""
|
| 378 |
+
# scale latent
|
| 379 |
+
depth_latent = depth_latent / self.depth_latent_scale_factor
|
| 380 |
+
# decode
|
| 381 |
+
z = self.vae.post_quant_conv(depth_latent)
|
| 382 |
+
stacked = self.vae.decoder(z)
|
| 383 |
+
# mean of output channels
|
| 384 |
+
depth_mean = stacked.mean(dim=1, keepdim=True)
|
| 385 |
+
return depth_mean
|
DepthMaster/depthmaster/modules/unet_2d_blocks.py
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
DepthMaster/depthmaster/modules/unet_2d_condition_s1.py
ADDED
|
@@ -0,0 +1,1317 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
# Copyright 2024 The HuggingFace Team. All rights reserved.
|
| 2 |
+
#
|
| 3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
+
# you may not use this file except in compliance with the License.
|
| 5 |
+
# You may obtain a copy of the License at
|
| 6 |
+
#
|
| 7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
+
#
|
| 9 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
+
# See the License for the specific language governing permissions and
|
| 13 |
+
# limitations under the License.
|
| 14 |
+
from dataclasses import dataclass
|
| 15 |
+
from typing import Any, Dict, List, Optional, Tuple, Union
|
| 16 |
+
|
| 17 |
+
import torch
|
| 18 |
+
import torch.nn as nn
|
| 19 |
+
import torch.utils.checkpoint
|
| 20 |
+
|
| 21 |
+
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
| 22 |
+
from diffusers.loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin
|
| 23 |
+
from diffusers.loaders.single_file_model import FromOriginalModelMixin
|
| 24 |
+
from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers
|
| 25 |
+
from diffusers.models.activations import get_activation
|
| 26 |
+
from diffusers.models.attention_processor import (
|
| 27 |
+
ADDED_KV_ATTENTION_PROCESSORS,
|
| 28 |
+
CROSS_ATTENTION_PROCESSORS,
|
| 29 |
+
Attention,
|
| 30 |
+
AttentionProcessor,
|
| 31 |
+
AttnAddedKVProcessor,
|
| 32 |
+
AttnProcessor,
|
| 33 |
+
FusedAttnProcessor2_0,
|
| 34 |
+
)
|
| 35 |
+
from diffusers.models.embeddings import (
|
| 36 |
+
GaussianFourierProjection,
|
| 37 |
+
GLIGENTextBoundingboxProjection,
|
| 38 |
+
ImageHintTimeEmbedding,
|
| 39 |
+
ImageProjection,
|
| 40 |
+
ImageTimeEmbedding,
|
| 41 |
+
TextImageProjection,
|
| 42 |
+
TextImageTimeEmbedding,
|
| 43 |
+
TextTimeEmbedding,
|
| 44 |
+
TimestepEmbedding,
|
| 45 |
+
Timesteps,
|
| 46 |
+
)
|
| 47 |
+
from diffusers.models.modeling_utils import ModelMixin
|
| 48 |
+
from depthmaster.modules.unet_2d_blocks import (
|
| 49 |
+
get_down_block,
|
| 50 |
+
get_mid_block,
|
| 51 |
+
get_up_block,
|
| 52 |
+
)
|
| 53 |
+
|
| 54 |
+
|
| 55 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
| 56 |
+
|
| 57 |
+
|
| 58 |
+
@dataclass
|
| 59 |
+
class UNet2DConditionOutput(BaseOutput):
|
| 60 |
+
"""
|
| 61 |
+
The output of [`UNet2DConditionModel`].
|
| 62 |
+
|
| 63 |
+
Args:
|
| 64 |
+
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
|
| 65 |
+
The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
|
| 66 |
+
"""
|
| 67 |
+
|
| 68 |
+
sample: torch.Tensor = None
|
| 69 |
+
feat_64: torch.Tensor = None
|
| 70 |
+
|
| 71 |
+
|
| 72 |
+
class UNet2DConditionModel(
|
| 73 |
+
ModelMixin, ConfigMixin, FromOriginalModelMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin
|
| 74 |
+
):
|
| 75 |
+
r"""
|
| 76 |
+
A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
|
| 77 |
+
shaped output.
|
| 78 |
+
|
| 79 |
+
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
|
| 80 |
+
for all models (such as downloading or saving).
|
| 81 |
+
|
| 82 |
+
Parameters:
|
| 83 |
+
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
|
| 84 |
+
Height and width of input/output sample.
|
| 85 |
+
in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
|
| 86 |
+
out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
|
| 87 |
+
center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
|
| 88 |
+
flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
|
| 89 |
+
Whether to flip the sin to cos in the time embedding.
|
| 90 |
+
freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
|
| 91 |
+
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
|
| 92 |
+
The tuple of downsample blocks to use.
|
| 93 |
+
mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
|
| 94 |
+
Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or
|
| 95 |
+
`UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
|
| 96 |
+
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
|
| 97 |
+
The tuple of upsample blocks to use.
|
| 98 |
+
only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
|
| 99 |
+
Whether to include self-attention in the basic transformer blocks, see
|
| 100 |
+
[`~models.attention.BasicTransformerBlock`].
|
| 101 |
+
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
|
| 102 |
+
The tuple of output channels for each block.
|
| 103 |
+
layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
|
| 104 |
+
downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
|
| 105 |
+
mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
|
| 106 |
+
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
|
| 107 |
+
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
|
| 108 |
+
norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
|
| 109 |
+
If `None`, normalization and activation layers is skipped in post-processing.
|
| 110 |
+
norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
|
| 111 |
+
cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
|
| 112 |
+
The dimension of the cross attention features.
|
| 113 |
+
transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
|
| 114 |
+
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
|
| 115 |
+
[`~models.unets.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unets.unet_2d_blocks.CrossAttnUpBlock2D`],
|
| 116 |
+
[`~models.unets.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
|
| 117 |
+
reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):
|
| 118 |
+
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling
|
| 119 |
+
blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for
|
| 120 |
+
[`~models.unets.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unets.unet_2d_blocks.CrossAttnUpBlock2D`],
|
| 121 |
+
[`~models.unets.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
|
| 122 |
+
encoder_hid_dim (`int`, *optional*, defaults to None):
|
| 123 |
+
If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
|
| 124 |
+
dimension to `cross_attention_dim`.
|
| 125 |
+
encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
|
| 126 |
+
If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
|
| 127 |
+
embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
|
| 128 |
+
attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
|
| 129 |
+
num_attention_heads (`int`, *optional*):
|
| 130 |
+
The number of attention heads. If not defined, defaults to `attention_head_dim`
|
| 131 |
+
resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
|
| 132 |
+
for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
|
| 133 |
+
class_embed_type (`str`, *optional*, defaults to `None`):
|
| 134 |
+
The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
|
| 135 |
+
`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
|
| 136 |
+
addition_embed_type (`str`, *optional*, defaults to `None`):
|
| 137 |
+
Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
|
| 138 |
+
"text". "text" will use the `TextTimeEmbedding` layer.
|
| 139 |
+
addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
|
| 140 |
+
Dimension for the timestep embeddings.
|
| 141 |
+
num_class_embeds (`int`, *optional*, defaults to `None`):
|
| 142 |
+
Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
|
| 143 |
+
class conditioning with `class_embed_type` equal to `None`.
|
| 144 |
+
time_embedding_type (`str`, *optional*, defaults to `positional`):
|
| 145 |
+
The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
|
| 146 |
+
time_embedding_dim (`int`, *optional*, defaults to `None`):
|
| 147 |
+
An optional override for the dimension of the projected time embedding.
|
| 148 |
+
time_embedding_act_fn (`str`, *optional*, defaults to `None`):
|
| 149 |
+
Optional activation function to use only once on the time embeddings before they are passed to the rest of
|
| 150 |
+
the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
|
| 151 |
+
timestep_post_act (`str`, *optional*, defaults to `None`):
|
| 152 |
+
The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
|
| 153 |
+
time_cond_proj_dim (`int`, *optional*, defaults to `None`):
|
| 154 |
+
The dimension of `cond_proj` layer in the timestep embedding.
|
| 155 |
+
conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
|
| 156 |
+
conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
|
| 157 |
+
projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
|
| 158 |
+
`class_embed_type="projection"`. Required when `class_embed_type="projection"`.
|
| 159 |
+
class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
|
| 160 |
+
embeddings with the class embeddings.
|
| 161 |
+
mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
|
| 162 |
+
Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
|
| 163 |
+
`only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
|
| 164 |
+
`only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
|
| 165 |
+
otherwise.
|
| 166 |
+
"""
|
| 167 |
+
|
| 168 |
+
_supports_gradient_checkpointing = True
|
| 169 |
+
_no_split_modules = ["BasicTransformerBlock", "ResnetBlock2D", "CrossAttnUpBlock2D"]
|
| 170 |
+
|
| 171 |
+
@register_to_config
|
| 172 |
+
def __init__(
|
| 173 |
+
self,
|
| 174 |
+
sample_size: Optional[int] = None,
|
| 175 |
+
in_channels: int = 4,
|
| 176 |
+
out_channels: int = 4,
|
| 177 |
+
center_input_sample: bool = False,
|
| 178 |
+
flip_sin_to_cos: bool = True,
|
| 179 |
+
freq_shift: int = 0,
|
| 180 |
+
down_block_types: Tuple[str] = (
|
| 181 |
+
"CrossAttnDownBlock2D",
|
| 182 |
+
"CrossAttnDownBlock2D",
|
| 183 |
+
"CrossAttnDownBlock2D",
|
| 184 |
+
"DownBlock2D",
|
| 185 |
+
),
|
| 186 |
+
mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
|
| 187 |
+
up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
|
| 188 |
+
only_cross_attention: Union[bool, Tuple[bool]] = False,
|
| 189 |
+
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
|
| 190 |
+
layers_per_block: Union[int, Tuple[int]] = 2,
|
| 191 |
+
downsample_padding: int = 1,
|
| 192 |
+
mid_block_scale_factor: float = 1,
|
| 193 |
+
dropout: float = 0.0,
|
| 194 |
+
act_fn: str = "silu",
|
| 195 |
+
norm_num_groups: Optional[int] = 32,
|
| 196 |
+
norm_eps: float = 1e-5,
|
| 197 |
+
cross_attention_dim: Union[int, Tuple[int]] = 1280,
|
| 198 |
+
transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
|
| 199 |
+
reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
|
| 200 |
+
encoder_hid_dim: Optional[int] = None,
|
| 201 |
+
encoder_hid_dim_type: Optional[str] = None,
|
| 202 |
+
attention_head_dim: Union[int, Tuple[int]] = 8,
|
| 203 |
+
num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
|
| 204 |
+
dual_cross_attention: bool = False,
|
| 205 |
+
use_linear_projection: bool = False,
|
| 206 |
+
class_embed_type: Optional[str] = None,
|
| 207 |
+
addition_embed_type: Optional[str] = None,
|
| 208 |
+
addition_time_embed_dim: Optional[int] = None,
|
| 209 |
+
num_class_embeds: Optional[int] = None,
|
| 210 |
+
upcast_attention: bool = False,
|
| 211 |
+
resnet_time_scale_shift: str = "default",
|
| 212 |
+
resnet_skip_time_act: bool = False,
|
| 213 |
+
resnet_out_scale_factor: float = 1.0,
|
| 214 |
+
time_embedding_type: str = "positional",
|
| 215 |
+
time_embedding_dim: Optional[int] = None,
|
| 216 |
+
time_embedding_act_fn: Optional[str] = None,
|
| 217 |
+
timestep_post_act: Optional[str] = None,
|
| 218 |
+
time_cond_proj_dim: Optional[int] = None,
|
| 219 |
+
conv_in_kernel: int = 3,
|
| 220 |
+
conv_out_kernel: int = 3,
|
| 221 |
+
projection_class_embeddings_input_dim: Optional[int] = None,
|
| 222 |
+
attention_type: str = "default",
|
| 223 |
+
class_embeddings_concat: bool = False,
|
| 224 |
+
mid_block_only_cross_attention: Optional[bool] = None,
|
| 225 |
+
cross_attention_norm: Optional[str] = None,
|
| 226 |
+
addition_embed_type_num_heads: int = 64,
|
| 227 |
+
):
|
| 228 |
+
super().__init__()
|
| 229 |
+
# print('loaded correct file')
|
| 230 |
+
|
| 231 |
+
self.sample_size = sample_size
|
| 232 |
+
|
| 233 |
+
if num_attention_heads is not None:
|
| 234 |
+
raise ValueError(
|
| 235 |
+
"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
|
| 236 |
+
)
|
| 237 |
+
|
| 238 |
+
# If `num_attention_heads` is not defined (which is the case for most models)
|
| 239 |
+
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
|
| 240 |
+
# The reason for this behavior is to correct for incorrectly named variables that were introduced
|
| 241 |
+
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
|
| 242 |
+
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
|
| 243 |
+
# which is why we correct for the naming here.
|
| 244 |
+
num_attention_heads = num_attention_heads or attention_head_dim
|
| 245 |
+
|
| 246 |
+
# Check inputs
|
| 247 |
+
self._check_config(
|
| 248 |
+
down_block_types=down_block_types,
|
| 249 |
+
up_block_types=up_block_types,
|
| 250 |
+
only_cross_attention=only_cross_attention,
|
| 251 |
+
block_out_channels=block_out_channels,
|
| 252 |
+
layers_per_block=layers_per_block,
|
| 253 |
+
cross_attention_dim=cross_attention_dim,
|
| 254 |
+
transformer_layers_per_block=transformer_layers_per_block,
|
| 255 |
+
reverse_transformer_layers_per_block=reverse_transformer_layers_per_block,
|
| 256 |
+
attention_head_dim=attention_head_dim,
|
| 257 |
+
num_attention_heads=num_attention_heads,
|
| 258 |
+
)
|
| 259 |
+
|
| 260 |
+
# input
|
| 261 |
+
conv_in_padding = (conv_in_kernel - 1) // 2
|
| 262 |
+
self.conv_in = nn.Conv2d(
|
| 263 |
+
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
|
| 264 |
+
)
|
| 265 |
+
|
| 266 |
+
# time
|
| 267 |
+
time_embed_dim, timestep_input_dim = self._set_time_proj(
|
| 268 |
+
time_embedding_type,
|
| 269 |
+
block_out_channels=block_out_channels,
|
| 270 |
+
flip_sin_to_cos=flip_sin_to_cos,
|
| 271 |
+
freq_shift=freq_shift,
|
| 272 |
+
time_embedding_dim=time_embedding_dim,
|
| 273 |
+
)
|
| 274 |
+
|
| 275 |
+
self.time_embedding = TimestepEmbedding(
|
| 276 |
+
timestep_input_dim,
|
| 277 |
+
time_embed_dim,
|
| 278 |
+
act_fn=act_fn,
|
| 279 |
+
post_act_fn=timestep_post_act,
|
| 280 |
+
cond_proj_dim=time_cond_proj_dim,
|
| 281 |
+
)
|
| 282 |
+
|
| 283 |
+
self._set_encoder_hid_proj(
|
| 284 |
+
encoder_hid_dim_type,
|
| 285 |
+
cross_attention_dim=cross_attention_dim,
|
| 286 |
+
encoder_hid_dim=encoder_hid_dim,
|
| 287 |
+
)
|
| 288 |
+
|
| 289 |
+
# class embedding
|
| 290 |
+
self._set_class_embedding(
|
| 291 |
+
class_embed_type,
|
| 292 |
+
act_fn=act_fn,
|
| 293 |
+
num_class_embeds=num_class_embeds,
|
| 294 |
+
projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
|
| 295 |
+
time_embed_dim=time_embed_dim,
|
| 296 |
+
timestep_input_dim=timestep_input_dim,
|
| 297 |
+
)
|
| 298 |
+
|
| 299 |
+
self._set_add_embedding(
|
| 300 |
+
addition_embed_type,
|
| 301 |
+
addition_embed_type_num_heads=addition_embed_type_num_heads,
|
| 302 |
+
addition_time_embed_dim=addition_time_embed_dim,
|
| 303 |
+
cross_attention_dim=cross_attention_dim,
|
| 304 |
+
encoder_hid_dim=encoder_hid_dim,
|
| 305 |
+
flip_sin_to_cos=flip_sin_to_cos,
|
| 306 |
+
freq_shift=freq_shift,
|
| 307 |
+
projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
|
| 308 |
+
time_embed_dim=time_embed_dim,
|
| 309 |
+
)
|
| 310 |
+
|
| 311 |
+
if time_embedding_act_fn is None:
|
| 312 |
+
self.time_embed_act = None
|
| 313 |
+
else:
|
| 314 |
+
self.time_embed_act = get_activation(time_embedding_act_fn)
|
| 315 |
+
|
| 316 |
+
self.down_blocks = nn.ModuleList([])
|
| 317 |
+
self.up_blocks = nn.ModuleList([])
|
| 318 |
+
|
| 319 |
+
if isinstance(only_cross_attention, bool):
|
| 320 |
+
if mid_block_only_cross_attention is None:
|
| 321 |
+
mid_block_only_cross_attention = only_cross_attention
|
| 322 |
+
|
| 323 |
+
only_cross_attention = [only_cross_attention] * len(down_block_types)
|
| 324 |
+
|
| 325 |
+
if mid_block_only_cross_attention is None:
|
| 326 |
+
mid_block_only_cross_attention = False
|
| 327 |
+
|
| 328 |
+
if isinstance(num_attention_heads, int):
|
| 329 |
+
num_attention_heads = (num_attention_heads,) * len(down_block_types)
|
| 330 |
+
|
| 331 |
+
if isinstance(attention_head_dim, int):
|
| 332 |
+
attention_head_dim = (attention_head_dim,) * len(down_block_types)
|
| 333 |
+
|
| 334 |
+
if isinstance(cross_attention_dim, int):
|
| 335 |
+
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
|
| 336 |
+
|
| 337 |
+
if isinstance(layers_per_block, int):
|
| 338 |
+
layers_per_block = [layers_per_block] * len(down_block_types)
|
| 339 |
+
|
| 340 |
+
if isinstance(transformer_layers_per_block, int):
|
| 341 |
+
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
|
| 342 |
+
|
| 343 |
+
if class_embeddings_concat:
|
| 344 |
+
# The time embeddings are concatenated with the class embeddings. The dimension of the
|
| 345 |
+
# time embeddings passed to the down, middle, and up blocks is twice the dimension of the
|
| 346 |
+
# regular time embeddings
|
| 347 |
+
blocks_time_embed_dim = time_embed_dim * 2
|
| 348 |
+
else:
|
| 349 |
+
blocks_time_embed_dim = time_embed_dim
|
| 350 |
+
|
| 351 |
+
# down
|
| 352 |
+
output_channel = block_out_channels[0]
|
| 353 |
+
for i, down_block_type in enumerate(down_block_types):
|
| 354 |
+
input_channel = output_channel
|
| 355 |
+
output_channel = block_out_channels[i]
|
| 356 |
+
is_final_block = i == len(block_out_channels) - 1
|
| 357 |
+
|
| 358 |
+
down_block = get_down_block(
|
| 359 |
+
down_block_type,
|
| 360 |
+
num_layers=layers_per_block[i],
|
| 361 |
+
transformer_layers_per_block=transformer_layers_per_block[i],
|
| 362 |
+
in_channels=input_channel,
|
| 363 |
+
out_channels=output_channel,
|
| 364 |
+
temb_channels=blocks_time_embed_dim,
|
| 365 |
+
add_downsample=not is_final_block,
|
| 366 |
+
resnet_eps=norm_eps,
|
| 367 |
+
resnet_act_fn=act_fn,
|
| 368 |
+
resnet_groups=norm_num_groups,
|
| 369 |
+
cross_attention_dim=cross_attention_dim[i],
|
| 370 |
+
num_attention_heads=num_attention_heads[i],
|
| 371 |
+
downsample_padding=downsample_padding,
|
| 372 |
+
dual_cross_attention=dual_cross_attention,
|
| 373 |
+
use_linear_projection=use_linear_projection,
|
| 374 |
+
only_cross_attention=only_cross_attention[i],
|
| 375 |
+
upcast_attention=upcast_attention,
|
| 376 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
| 377 |
+
attention_type=attention_type,
|
| 378 |
+
resnet_skip_time_act=resnet_skip_time_act,
|
| 379 |
+
resnet_out_scale_factor=resnet_out_scale_factor,
|
| 380 |
+
cross_attention_norm=cross_attention_norm,
|
| 381 |
+
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
|
| 382 |
+
dropout=dropout,
|
| 383 |
+
)
|
| 384 |
+
self.down_blocks.append(down_block)
|
| 385 |
+
|
| 386 |
+
# mid
|
| 387 |
+
self.mid_block = get_mid_block(
|
| 388 |
+
mid_block_type,
|
| 389 |
+
temb_channels=blocks_time_embed_dim,
|
| 390 |
+
in_channels=block_out_channels[-1],
|
| 391 |
+
resnet_eps=norm_eps,
|
| 392 |
+
resnet_act_fn=act_fn,
|
| 393 |
+
resnet_groups=norm_num_groups,
|
| 394 |
+
output_scale_factor=mid_block_scale_factor,
|
| 395 |
+
transformer_layers_per_block=transformer_layers_per_block[-1],
|
| 396 |
+
num_attention_heads=num_attention_heads[-1],
|
| 397 |
+
cross_attention_dim=cross_attention_dim[-1],
|
| 398 |
+
dual_cross_attention=dual_cross_attention,
|
| 399 |
+
use_linear_projection=use_linear_projection,
|
| 400 |
+
mid_block_only_cross_attention=mid_block_only_cross_attention,
|
| 401 |
+
upcast_attention=upcast_attention,
|
| 402 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
| 403 |
+
attention_type=attention_type,
|
| 404 |
+
resnet_skip_time_act=resnet_skip_time_act,
|
| 405 |
+
cross_attention_norm=cross_attention_norm,
|
| 406 |
+
attention_head_dim=attention_head_dim[-1],
|
| 407 |
+
dropout=dropout,
|
| 408 |
+
)
|
| 409 |
+
|
| 410 |
+
# count how many layers upsample the images
|
| 411 |
+
self.num_upsamplers = 0
|
| 412 |
+
|
| 413 |
+
# up
|
| 414 |
+
reversed_block_out_channels = list(reversed(block_out_channels))
|
| 415 |
+
reversed_num_attention_heads = list(reversed(num_attention_heads))
|
| 416 |
+
reversed_layers_per_block = list(reversed(layers_per_block))
|
| 417 |
+
reversed_cross_attention_dim = list(reversed(cross_attention_dim))
|
| 418 |
+
reversed_transformer_layers_per_block = (
|
| 419 |
+
list(reversed(transformer_layers_per_block))
|
| 420 |
+
if reverse_transformer_layers_per_block is None
|
| 421 |
+
else reverse_transformer_layers_per_block
|
| 422 |
+
)
|
| 423 |
+
only_cross_attention = list(reversed(only_cross_attention))
|
| 424 |
+
|
| 425 |
+
output_channel = reversed_block_out_channels[0]
|
| 426 |
+
for i, up_block_type in enumerate(up_block_types):
|
| 427 |
+
is_final_block = i == len(block_out_channels) - 1
|
| 428 |
+
|
| 429 |
+
prev_output_channel = output_channel
|
| 430 |
+
output_channel = reversed_block_out_channels[i]
|
| 431 |
+
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
|
| 432 |
+
|
| 433 |
+
# add upsample block for all BUT final layer
|
| 434 |
+
if not is_final_block:
|
| 435 |
+
add_upsample = True
|
| 436 |
+
self.num_upsamplers += 1
|
| 437 |
+
else:
|
| 438 |
+
add_upsample = False
|
| 439 |
+
|
| 440 |
+
up_block = get_up_block(
|
| 441 |
+
up_block_type,
|
| 442 |
+
num_layers=reversed_layers_per_block[i] + 1,
|
| 443 |
+
transformer_layers_per_block=reversed_transformer_layers_per_block[i],
|
| 444 |
+
in_channels=input_channel,
|
| 445 |
+
out_channels=output_channel,
|
| 446 |
+
prev_output_channel=prev_output_channel,
|
| 447 |
+
temb_channels=blocks_time_embed_dim,
|
| 448 |
+
add_upsample=add_upsample,
|
| 449 |
+
resnet_eps=norm_eps,
|
| 450 |
+
resnet_act_fn=act_fn,
|
| 451 |
+
resolution_idx=i,
|
| 452 |
+
resnet_groups=norm_num_groups,
|
| 453 |
+
cross_attention_dim=reversed_cross_attention_dim[i],
|
| 454 |
+
num_attention_heads=reversed_num_attention_heads[i],
|
| 455 |
+
dual_cross_attention=dual_cross_attention,
|
| 456 |
+
use_linear_projection=use_linear_projection,
|
| 457 |
+
only_cross_attention=only_cross_attention[i],
|
| 458 |
+
upcast_attention=upcast_attention,
|
| 459 |
+
resnet_time_scale_shift=resnet_time_scale_shift,
|
| 460 |
+
attention_type=attention_type,
|
| 461 |
+
resnet_skip_time_act=resnet_skip_time_act,
|
| 462 |
+
resnet_out_scale_factor=resnet_out_scale_factor,
|
| 463 |
+
cross_attention_norm=cross_attention_norm,
|
| 464 |
+
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
|
| 465 |
+
dropout=dropout,
|
| 466 |
+
)
|
| 467 |
+
self.up_blocks.append(up_block)
|
| 468 |
+
prev_output_channel = output_channel
|
| 469 |
+
|
| 470 |
+
# out
|
| 471 |
+
if norm_num_groups is not None:
|
| 472 |
+
self.conv_norm_out = nn.GroupNorm(
|
| 473 |
+
num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
|
| 474 |
+
)
|
| 475 |
+
|
| 476 |
+
self.conv_act = get_activation(act_fn)
|
| 477 |
+
|
| 478 |
+
else:
|
| 479 |
+
self.conv_norm_out = None
|
| 480 |
+
self.conv_act = None
|
| 481 |
+
|
| 482 |
+
conv_out_padding = (conv_out_kernel - 1) // 2
|
| 483 |
+
self.conv_out = nn.Conv2d(
|
| 484 |
+
block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
|
| 485 |
+
)
|
| 486 |
+
|
| 487 |
+
self._set_pos_net_if_use_gligen(attention_type=attention_type, cross_attention_dim=cross_attention_dim)
|
| 488 |
+
|
| 489 |
+
def _check_config(
|
| 490 |
+
self,
|
| 491 |
+
down_block_types: Tuple[str],
|
| 492 |
+
up_block_types: Tuple[str],
|
| 493 |
+
only_cross_attention: Union[bool, Tuple[bool]],
|
| 494 |
+
block_out_channels: Tuple[int],
|
| 495 |
+
layers_per_block: Union[int, Tuple[int]],
|
| 496 |
+
cross_attention_dim: Union[int, Tuple[int]],
|
| 497 |
+
transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]],
|
| 498 |
+
reverse_transformer_layers_per_block: bool,
|
| 499 |
+
attention_head_dim: int,
|
| 500 |
+
num_attention_heads: Optional[Union[int, Tuple[int]]],
|
| 501 |
+
):
|
| 502 |
+
if len(down_block_types) != len(up_block_types):
|
| 503 |
+
raise ValueError(
|
| 504 |
+
f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
|
| 505 |
+
)
|
| 506 |
+
|
| 507 |
+
if len(block_out_channels) != len(down_block_types):
|
| 508 |
+
raise ValueError(
|
| 509 |
+
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
|
| 510 |
+
)
|
| 511 |
+
|
| 512 |
+
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
|
| 513 |
+
raise ValueError(
|
| 514 |
+
f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
|
| 515 |
+
)
|
| 516 |
+
|
| 517 |
+
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
|
| 518 |
+
raise ValueError(
|
| 519 |
+
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
|
| 520 |
+
)
|
| 521 |
+
|
| 522 |
+
if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
|
| 523 |
+
raise ValueError(
|
| 524 |
+
f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
|
| 525 |
+
)
|
| 526 |
+
|
| 527 |
+
if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
|
| 528 |
+
raise ValueError(
|
| 529 |
+
f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
|
| 530 |
+
)
|
| 531 |
+
|
| 532 |
+
if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
|
| 533 |
+
raise ValueError(
|
| 534 |
+
f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
|
| 535 |
+
)
|
| 536 |
+
if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:
|
| 537 |
+
for layer_number_per_block in transformer_layers_per_block:
|
| 538 |
+
if isinstance(layer_number_per_block, list):
|
| 539 |
+
raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.")
|
| 540 |
+
|
| 541 |
+
def _set_time_proj(
|
| 542 |
+
self,
|
| 543 |
+
time_embedding_type: str,
|
| 544 |
+
block_out_channels: int,
|
| 545 |
+
flip_sin_to_cos: bool,
|
| 546 |
+
freq_shift: float,
|
| 547 |
+
time_embedding_dim: int,
|
| 548 |
+
) -> Tuple[int, int]:
|
| 549 |
+
if time_embedding_type == "fourier":
|
| 550 |
+
time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
|
| 551 |
+
if time_embed_dim % 2 != 0:
|
| 552 |
+
raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
|
| 553 |
+
self.time_proj = GaussianFourierProjection(
|
| 554 |
+
time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
|
| 555 |
+
)
|
| 556 |
+
timestep_input_dim = time_embed_dim
|
| 557 |
+
elif time_embedding_type == "positional":
|
| 558 |
+
time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
|
| 559 |
+
|
| 560 |
+
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
|
| 561 |
+
timestep_input_dim = block_out_channels[0]
|
| 562 |
+
else:
|
| 563 |
+
raise ValueError(
|
| 564 |
+
f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
|
| 565 |
+
)
|
| 566 |
+
|
| 567 |
+
return time_embed_dim, timestep_input_dim
|
| 568 |
+
|
| 569 |
+
def _set_encoder_hid_proj(
|
| 570 |
+
self,
|
| 571 |
+
encoder_hid_dim_type: Optional[str],
|
| 572 |
+
cross_attention_dim: Union[int, Tuple[int]],
|
| 573 |
+
encoder_hid_dim: Optional[int],
|
| 574 |
+
):
|
| 575 |
+
if encoder_hid_dim_type is None and encoder_hid_dim is not None:
|
| 576 |
+
encoder_hid_dim_type = "text_proj"
|
| 577 |
+
self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
|
| 578 |
+
logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
|
| 579 |
+
|
| 580 |
+
if encoder_hid_dim is None and encoder_hid_dim_type is not None:
|
| 581 |
+
raise ValueError(
|
| 582 |
+
f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
|
| 583 |
+
)
|
| 584 |
+
|
| 585 |
+
if encoder_hid_dim_type == "text_proj":
|
| 586 |
+
self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
|
| 587 |
+
elif encoder_hid_dim_type == "text_image_proj":
|
| 588 |
+
# image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
|
| 589 |
+
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
|
| 590 |
+
# case when `addition_embed_type == "text_image_proj"` (Kandinsky 2.1)`
|
| 591 |
+
self.encoder_hid_proj = TextImageProjection(
|
| 592 |
+
text_embed_dim=encoder_hid_dim,
|
| 593 |
+
image_embed_dim=cross_attention_dim,
|
| 594 |
+
cross_attention_dim=cross_attention_dim,
|
| 595 |
+
)
|
| 596 |
+
elif encoder_hid_dim_type == "image_proj":
|
| 597 |
+
# Kandinsky 2.2
|
| 598 |
+
self.encoder_hid_proj = ImageProjection(
|
| 599 |
+
image_embed_dim=encoder_hid_dim,
|
| 600 |
+
cross_attention_dim=cross_attention_dim,
|
| 601 |
+
)
|
| 602 |
+
elif encoder_hid_dim_type is not None:
|
| 603 |
+
raise ValueError(
|
| 604 |
+
f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
|
| 605 |
+
)
|
| 606 |
+
else:
|
| 607 |
+
self.encoder_hid_proj = None
|
| 608 |
+
|
| 609 |
+
def _set_class_embedding(
|
| 610 |
+
self,
|
| 611 |
+
class_embed_type: Optional[str],
|
| 612 |
+
act_fn: str,
|
| 613 |
+
num_class_embeds: Optional[int],
|
| 614 |
+
projection_class_embeddings_input_dim: Optional[int],
|
| 615 |
+
time_embed_dim: int,
|
| 616 |
+
timestep_input_dim: int,
|
| 617 |
+
):
|
| 618 |
+
if class_embed_type is None and num_class_embeds is not None:
|
| 619 |
+
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
|
| 620 |
+
elif class_embed_type == "timestep":
|
| 621 |
+
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
|
| 622 |
+
elif class_embed_type == "identity":
|
| 623 |
+
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
|
| 624 |
+
elif class_embed_type == "projection":
|
| 625 |
+
if projection_class_embeddings_input_dim is None:
|
| 626 |
+
raise ValueError(
|
| 627 |
+
"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
|
| 628 |
+
)
|
| 629 |
+
# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
|
| 630 |
+
# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
|
| 631 |
+
# 2. it projects from an arbitrary input dimension.
|
| 632 |
+
#
|
| 633 |
+
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
|
| 634 |
+
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
|
| 635 |
+
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
|
| 636 |
+
self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
| 637 |
+
elif class_embed_type == "simple_projection":
|
| 638 |
+
if projection_class_embeddings_input_dim is None:
|
| 639 |
+
raise ValueError(
|
| 640 |
+
"`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
|
| 641 |
+
)
|
| 642 |
+
self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
|
| 643 |
+
else:
|
| 644 |
+
self.class_embedding = None
|
| 645 |
+
|
| 646 |
+
def _set_add_embedding(
|
| 647 |
+
self,
|
| 648 |
+
addition_embed_type: str,
|
| 649 |
+
addition_embed_type_num_heads: int,
|
| 650 |
+
addition_time_embed_dim: Optional[int],
|
| 651 |
+
flip_sin_to_cos: bool,
|
| 652 |
+
freq_shift: float,
|
| 653 |
+
cross_attention_dim: Optional[int],
|
| 654 |
+
encoder_hid_dim: Optional[int],
|
| 655 |
+
projection_class_embeddings_input_dim: Optional[int],
|
| 656 |
+
time_embed_dim: int,
|
| 657 |
+
):
|
| 658 |
+
if addition_embed_type == "text":
|
| 659 |
+
if encoder_hid_dim is not None:
|
| 660 |
+
text_time_embedding_from_dim = encoder_hid_dim
|
| 661 |
+
else:
|
| 662 |
+
text_time_embedding_from_dim = cross_attention_dim
|
| 663 |
+
|
| 664 |
+
self.add_embedding = TextTimeEmbedding(
|
| 665 |
+
text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
|
| 666 |
+
)
|
| 667 |
+
elif addition_embed_type == "text_image":
|
| 668 |
+
# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
|
| 669 |
+
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
|
| 670 |
+
# case when `addition_embed_type == "text_image"` (Kandinsky 2.1)`
|
| 671 |
+
self.add_embedding = TextImageTimeEmbedding(
|
| 672 |
+
text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
|
| 673 |
+
)
|
| 674 |
+
elif addition_embed_type == "text_time":
|
| 675 |
+
self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
|
| 676 |
+
self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
| 677 |
+
elif addition_embed_type == "image":
|
| 678 |
+
# Kandinsky 2.2
|
| 679 |
+
self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
|
| 680 |
+
elif addition_embed_type == "image_hint":
|
| 681 |
+
# Kandinsky 2.2 ControlNet
|
| 682 |
+
self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
|
| 683 |
+
elif addition_embed_type is not None:
|
| 684 |
+
raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
|
| 685 |
+
|
| 686 |
+
def _set_pos_net_if_use_gligen(self, attention_type: str, cross_attention_dim: int):
|
| 687 |
+
if attention_type in ["gated", "gated-text-image"]:
|
| 688 |
+
positive_len = 768
|
| 689 |
+
if isinstance(cross_attention_dim, int):
|
| 690 |
+
positive_len = cross_attention_dim
|
| 691 |
+
elif isinstance(cross_attention_dim, (list, tuple)):
|
| 692 |
+
positive_len = cross_attention_dim[0]
|
| 693 |
+
|
| 694 |
+
feature_type = "text-only" if attention_type == "gated" else "text-image"
|
| 695 |
+
self.position_net = GLIGENTextBoundingboxProjection(
|
| 696 |
+
positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type
|
| 697 |
+
)
|
| 698 |
+
|
| 699 |
+
@property
|
| 700 |
+
def attn_processors(self) -> Dict[str, AttentionProcessor]:
|
| 701 |
+
r"""
|
| 702 |
+
Returns:
|
| 703 |
+
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
| 704 |
+
indexed by its weight name.
|
| 705 |
+
"""
|
| 706 |
+
# set recursively
|
| 707 |
+
processors = {}
|
| 708 |
+
|
| 709 |
+
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
| 710 |
+
if hasattr(module, "get_processor"):
|
| 711 |
+
processors[f"{name}.processor"] = module.get_processor()
|
| 712 |
+
|
| 713 |
+
for sub_name, child in module.named_children():
|
| 714 |
+
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
| 715 |
+
|
| 716 |
+
return processors
|
| 717 |
+
|
| 718 |
+
for name, module in self.named_children():
|
| 719 |
+
fn_recursive_add_processors(name, module, processors)
|
| 720 |
+
|
| 721 |
+
return processors
|
| 722 |
+
|
| 723 |
+
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
|
| 724 |
+
r"""
|
| 725 |
+
Sets the attention processor to use to compute attention.
|
| 726 |
+
|
| 727 |
+
Parameters:
|
| 728 |
+
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
|
| 729 |
+
The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
| 730 |
+
for **all** `Attention` layers.
|
| 731 |
+
|
| 732 |
+
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
|
| 733 |
+
processor. This is strongly recommended when setting trainable attention processors.
|
| 734 |
+
|
| 735 |
+
"""
|
| 736 |
+
count = len(self.attn_processors.keys())
|
| 737 |
+
|
| 738 |
+
if isinstance(processor, dict) and len(processor) != count:
|
| 739 |
+
raise ValueError(
|
| 740 |
+
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
| 741 |
+
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
| 742 |
+
)
|
| 743 |
+
|
| 744 |
+
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
| 745 |
+
if hasattr(module, "set_processor"):
|
| 746 |
+
if not isinstance(processor, dict):
|
| 747 |
+
module.set_processor(processor)
|
| 748 |
+
else:
|
| 749 |
+
module.set_processor(processor.pop(f"{name}.processor"))
|
| 750 |
+
|
| 751 |
+
for sub_name, child in module.named_children():
|
| 752 |
+
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
| 753 |
+
|
| 754 |
+
for name, module in self.named_children():
|
| 755 |
+
fn_recursive_attn_processor(name, module, processor)
|
| 756 |
+
|
| 757 |
+
def set_default_attn_processor(self):
|
| 758 |
+
"""
|
| 759 |
+
Disables custom attention processors and sets the default attention implementation.
|
| 760 |
+
"""
|
| 761 |
+
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
| 762 |
+
processor = AttnAddedKVProcessor()
|
| 763 |
+
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
| 764 |
+
processor = AttnProcessor()
|
| 765 |
+
else:
|
| 766 |
+
raise ValueError(
|
| 767 |
+
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
|
| 768 |
+
)
|
| 769 |
+
|
| 770 |
+
self.set_attn_processor(processor)
|
| 771 |
+
|
| 772 |
+
def set_attention_slice(self, slice_size: Union[str, int, List[int]] = "auto"):
|
| 773 |
+
r"""
|
| 774 |
+
Enable sliced attention computation.
|
| 775 |
+
|
| 776 |
+
When this option is enabled, the attention module splits the input tensor in slices to compute attention in
|
| 777 |
+
several steps. This is useful for saving some memory in exchange for a small decrease in speed.
|
| 778 |
+
|
| 779 |
+
Args:
|
| 780 |
+
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
|
| 781 |
+
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
|
| 782 |
+
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
|
| 783 |
+
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
|
| 784 |
+
must be a multiple of `slice_size`.
|
| 785 |
+
"""
|
| 786 |
+
sliceable_head_dims = []
|
| 787 |
+
|
| 788 |
+
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
|
| 789 |
+
if hasattr(module, "set_attention_slice"):
|
| 790 |
+
sliceable_head_dims.append(module.sliceable_head_dim)
|
| 791 |
+
|
| 792 |
+
for child in module.children():
|
| 793 |
+
fn_recursive_retrieve_sliceable_dims(child)
|
| 794 |
+
|
| 795 |
+
# retrieve number of attention layers
|
| 796 |
+
for module in self.children():
|
| 797 |
+
fn_recursive_retrieve_sliceable_dims(module)
|
| 798 |
+
|
| 799 |
+
num_sliceable_layers = len(sliceable_head_dims)
|
| 800 |
+
|
| 801 |
+
if slice_size == "auto":
|
| 802 |
+
# half the attention head size is usually a good trade-off between
|
| 803 |
+
# speed and memory
|
| 804 |
+
slice_size = [dim // 2 for dim in sliceable_head_dims]
|
| 805 |
+
elif slice_size == "max":
|
| 806 |
+
# make smallest slice possible
|
| 807 |
+
slice_size = num_sliceable_layers * [1]
|
| 808 |
+
|
| 809 |
+
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
|
| 810 |
+
|
| 811 |
+
if len(slice_size) != len(sliceable_head_dims):
|
| 812 |
+
raise ValueError(
|
| 813 |
+
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
|
| 814 |
+
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
|
| 815 |
+
)
|
| 816 |
+
|
| 817 |
+
for i in range(len(slice_size)):
|
| 818 |
+
size = slice_size[i]
|
| 819 |
+
dim = sliceable_head_dims[i]
|
| 820 |
+
if size is not None and size > dim:
|
| 821 |
+
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
|
| 822 |
+
|
| 823 |
+
# Recursively walk through all the children.
|
| 824 |
+
# Any children which exposes the set_attention_slice method
|
| 825 |
+
# gets the message
|
| 826 |
+
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
|
| 827 |
+
if hasattr(module, "set_attention_slice"):
|
| 828 |
+
module.set_attention_slice(slice_size.pop())
|
| 829 |
+
|
| 830 |
+
for child in module.children():
|
| 831 |
+
fn_recursive_set_attention_slice(child, slice_size)
|
| 832 |
+
|
| 833 |
+
reversed_slice_size = list(reversed(slice_size))
|
| 834 |
+
for module in self.children():
|
| 835 |
+
fn_recursive_set_attention_slice(module, reversed_slice_size)
|
| 836 |
+
|
| 837 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
| 838 |
+
if hasattr(module, "gradient_checkpointing"):
|
| 839 |
+
module.gradient_checkpointing = value
|
| 840 |
+
|
| 841 |
+
def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
|
| 842 |
+
r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
|
| 843 |
+
|
| 844 |
+
The suffixes after the scaling factors represent the stage blocks where they are being applied.
|
| 845 |
+
|
| 846 |
+
Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that
|
| 847 |
+
are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
|
| 848 |
+
|
| 849 |
+
Args:
|
| 850 |
+
s1 (`float`):
|
| 851 |
+
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
|
| 852 |
+
mitigate the "oversmoothing effect" in the enhanced denoising process.
|
| 853 |
+
s2 (`float`):
|
| 854 |
+
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
|
| 855 |
+
mitigate the "oversmoothing effect" in the enhanced denoising process.
|
| 856 |
+
b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
|
| 857 |
+
b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
|
| 858 |
+
"""
|
| 859 |
+
for i, upsample_block in enumerate(self.up_blocks):
|
| 860 |
+
setattr(upsample_block, "s1", s1)
|
| 861 |
+
setattr(upsample_block, "s2", s2)
|
| 862 |
+
setattr(upsample_block, "b1", b1)
|
| 863 |
+
setattr(upsample_block, "b2", b2)
|
| 864 |
+
|
| 865 |
+
def disable_freeu(self):
|
| 866 |
+
"""Disables the FreeU mechanism."""
|
| 867 |
+
freeu_keys = {"s1", "s2", "b1", "b2"}
|
| 868 |
+
for i, upsample_block in enumerate(self.up_blocks):
|
| 869 |
+
for k in freeu_keys:
|
| 870 |
+
if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
|
| 871 |
+
setattr(upsample_block, k, None)
|
| 872 |
+
|
| 873 |
+
def fuse_qkv_projections(self):
|
| 874 |
+
"""
|
| 875 |
+
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
|
| 876 |
+
are fused. For cross-attention modules, key and value projection matrices are fused.
|
| 877 |
+
|
| 878 |
+
<Tip warning={true}>
|
| 879 |
+
|
| 880 |
+
This API is 🧪 experimental.
|
| 881 |
+
|
| 882 |
+
</Tip>
|
| 883 |
+
"""
|
| 884 |
+
self.original_attn_processors = None
|
| 885 |
+
|
| 886 |
+
for _, attn_processor in self.attn_processors.items():
|
| 887 |
+
if "Added" in str(attn_processor.__class__.__name__):
|
| 888 |
+
raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
|
| 889 |
+
|
| 890 |
+
self.original_attn_processors = self.attn_processors
|
| 891 |
+
|
| 892 |
+
for module in self.modules():
|
| 893 |
+
if isinstance(module, Attention):
|
| 894 |
+
module.fuse_projections(fuse=True)
|
| 895 |
+
|
| 896 |
+
self.set_attn_processor(FusedAttnProcessor2_0())
|
| 897 |
+
|
| 898 |
+
def unfuse_qkv_projections(self):
|
| 899 |
+
"""Disables the fused QKV projection if enabled.
|
| 900 |
+
|
| 901 |
+
<Tip warning={true}>
|
| 902 |
+
|
| 903 |
+
This API is 🧪 experimental.
|
| 904 |
+
|
| 905 |
+
</Tip>
|
| 906 |
+
|
| 907 |
+
"""
|
| 908 |
+
if self.original_attn_processors is not None:
|
| 909 |
+
self.set_attn_processor(self.original_attn_processors)
|
| 910 |
+
|
| 911 |
+
def get_time_embed(
|
| 912 |
+
self, sample: torch.Tensor, timestep: Union[torch.Tensor, float, int]
|
| 913 |
+
) -> Optional[torch.Tensor]:
|
| 914 |
+
timesteps = timestep
|
| 915 |
+
if not torch.is_tensor(timesteps):
|
| 916 |
+
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
|
| 917 |
+
# This would be a good case for the `match` statement (Python 3.10+)
|
| 918 |
+
is_mps = sample.device.type == "mps"
|
| 919 |
+
if isinstance(timestep, float):
|
| 920 |
+
dtype = torch.float32 if is_mps else torch.float64
|
| 921 |
+
else:
|
| 922 |
+
dtype = torch.int32 if is_mps else torch.int64
|
| 923 |
+
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
| 924 |
+
elif len(timesteps.shape) == 0:
|
| 925 |
+
timesteps = timesteps[None].to(sample.device)
|
| 926 |
+
|
| 927 |
+
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
| 928 |
+
timesteps = timesteps.expand(sample.shape[0])
|
| 929 |
+
|
| 930 |
+
t_emb = self.time_proj(timesteps)
|
| 931 |
+
# `Timesteps` does not contain any weights and will always return f32 tensors
|
| 932 |
+
# but time_embedding might actually be running in fp16. so we need to cast here.
|
| 933 |
+
# there might be better ways to encapsulate this.
|
| 934 |
+
t_emb = t_emb.to(dtype=sample.dtype)
|
| 935 |
+
return t_emb
|
| 936 |
+
|
| 937 |
+
def get_class_embed(self, sample: torch.Tensor, class_labels: Optional[torch.Tensor]) -> Optional[torch.Tensor]:
|
| 938 |
+
class_emb = None
|
| 939 |
+
if self.class_embedding is not None:
|
| 940 |
+
if class_labels is None:
|
| 941 |
+
raise ValueError("class_labels should be provided when num_class_embeds > 0")
|
| 942 |
+
|
| 943 |
+
if self.config.class_embed_type == "timestep":
|
| 944 |
+
class_labels = self.time_proj(class_labels)
|
| 945 |
+
|
| 946 |
+
# `Timesteps` does not contain any weights and will always return f32 tensors
|
| 947 |
+
# there might be better ways to encapsulate this.
|
| 948 |
+
class_labels = class_labels.to(dtype=sample.dtype)
|
| 949 |
+
|
| 950 |
+
class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
|
| 951 |
+
return class_emb
|
| 952 |
+
|
| 953 |
+
def get_aug_embed(
|
| 954 |
+
self, emb: torch.Tensor, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any]
|
| 955 |
+
) -> Optional[torch.Tensor]:
|
| 956 |
+
aug_emb = None
|
| 957 |
+
if self.config.addition_embed_type == "text":
|
| 958 |
+
aug_emb = self.add_embedding(encoder_hidden_states)
|
| 959 |
+
elif self.config.addition_embed_type == "text_image":
|
| 960 |
+
# Kandinsky 2.1 - style
|
| 961 |
+
if "image_embeds" not in added_cond_kwargs:
|
| 962 |
+
raise ValueError(
|
| 963 |
+
f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
|
| 964 |
+
)
|
| 965 |
+
|
| 966 |
+
image_embs = added_cond_kwargs.get("image_embeds")
|
| 967 |
+
text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
|
| 968 |
+
aug_emb = self.add_embedding(text_embs, image_embs)
|
| 969 |
+
elif self.config.addition_embed_type == "text_time":
|
| 970 |
+
# SDXL - style
|
| 971 |
+
if "text_embeds" not in added_cond_kwargs:
|
| 972 |
+
raise ValueError(
|
| 973 |
+
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
|
| 974 |
+
)
|
| 975 |
+
text_embeds = added_cond_kwargs.get("text_embeds")
|
| 976 |
+
if "time_ids" not in added_cond_kwargs:
|
| 977 |
+
raise ValueError(
|
| 978 |
+
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
|
| 979 |
+
)
|
| 980 |
+
time_ids = added_cond_kwargs.get("time_ids")
|
| 981 |
+
time_embeds = self.add_time_proj(time_ids.flatten())
|
| 982 |
+
time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
|
| 983 |
+
add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
|
| 984 |
+
add_embeds = add_embeds.to(emb.dtype)
|
| 985 |
+
aug_emb = self.add_embedding(add_embeds)
|
| 986 |
+
elif self.config.addition_embed_type == "image":
|
| 987 |
+
# Kandinsky 2.2 - style
|
| 988 |
+
if "image_embeds" not in added_cond_kwargs:
|
| 989 |
+
raise ValueError(
|
| 990 |
+
f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
|
| 991 |
+
)
|
| 992 |
+
image_embs = added_cond_kwargs.get("image_embeds")
|
| 993 |
+
aug_emb = self.add_embedding(image_embs)
|
| 994 |
+
elif self.config.addition_embed_type == "image_hint":
|
| 995 |
+
# Kandinsky 2.2 - style
|
| 996 |
+
if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
|
| 997 |
+
raise ValueError(
|
| 998 |
+
f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
|
| 999 |
+
)
|
| 1000 |
+
image_embs = added_cond_kwargs.get("image_embeds")
|
| 1001 |
+
hint = added_cond_kwargs.get("hint")
|
| 1002 |
+
aug_emb = self.add_embedding(image_embs, hint)
|
| 1003 |
+
return aug_emb
|
| 1004 |
+
|
| 1005 |
+
def process_encoder_hidden_states(
|
| 1006 |
+
self, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any]
|
| 1007 |
+
) -> torch.Tensor:
|
| 1008 |
+
if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
|
| 1009 |
+
encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
|
| 1010 |
+
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
|
| 1011 |
+
# Kandinsky 2.1 - style
|
| 1012 |
+
if "image_embeds" not in added_cond_kwargs:
|
| 1013 |
+
raise ValueError(
|
| 1014 |
+
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
|
| 1015 |
+
)
|
| 1016 |
+
|
| 1017 |
+
image_embeds = added_cond_kwargs.get("image_embeds")
|
| 1018 |
+
encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
|
| 1019 |
+
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
|
| 1020 |
+
# Kandinsky 2.2 - style
|
| 1021 |
+
if "image_embeds" not in added_cond_kwargs:
|
| 1022 |
+
raise ValueError(
|
| 1023 |
+
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
|
| 1024 |
+
)
|
| 1025 |
+
image_embeds = added_cond_kwargs.get("image_embeds")
|
| 1026 |
+
encoder_hidden_states = self.encoder_hid_proj(image_embeds)
|
| 1027 |
+
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
|
| 1028 |
+
if "image_embeds" not in added_cond_kwargs:
|
| 1029 |
+
raise ValueError(
|
| 1030 |
+
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
|
| 1031 |
+
)
|
| 1032 |
+
|
| 1033 |
+
if hasattr(self, "text_encoder_hid_proj") and self.text_encoder_hid_proj is not None:
|
| 1034 |
+
encoder_hidden_states = self.text_encoder_hid_proj(encoder_hidden_states)
|
| 1035 |
+
|
| 1036 |
+
image_embeds = added_cond_kwargs.get("image_embeds")
|
| 1037 |
+
image_embeds = self.encoder_hid_proj(image_embeds)
|
| 1038 |
+
encoder_hidden_states = (encoder_hidden_states, image_embeds)
|
| 1039 |
+
return encoder_hidden_states
|
| 1040 |
+
|
| 1041 |
+
def forward(
|
| 1042 |
+
self,
|
| 1043 |
+
sample: torch.Tensor,
|
| 1044 |
+
timestep: Union[torch.Tensor, float, int],
|
| 1045 |
+
encoder_hidden_states: torch.Tensor,
|
| 1046 |
+
class_labels: Optional[torch.Tensor] = None,
|
| 1047 |
+
timestep_cond: Optional[torch.Tensor] = None,
|
| 1048 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1049 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
| 1050 |
+
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
|
| 1051 |
+
down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
|
| 1052 |
+
mid_block_additional_residual: Optional[torch.Tensor] = None,
|
| 1053 |
+
down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
|
| 1054 |
+
encoder_attention_mask: Optional[torch.Tensor] = None,
|
| 1055 |
+
return_dict: bool = True,
|
| 1056 |
+
) -> Union[UNet2DConditionOutput, Tuple]:
|
| 1057 |
+
r"""
|
| 1058 |
+
The [`UNet2DConditionModel`] forward method.
|
| 1059 |
+
|
| 1060 |
+
Args:
|
| 1061 |
+
sample (`torch.Tensor`):
|
| 1062 |
+
The noisy input tensor with the following shape `(batch, channel, height, width)`.
|
| 1063 |
+
timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.
|
| 1064 |
+
encoder_hidden_states (`torch.Tensor`):
|
| 1065 |
+
The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
|
| 1066 |
+
class_labels (`torch.Tensor`, *optional*, defaults to `None`):
|
| 1067 |
+
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
|
| 1068 |
+
timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
|
| 1069 |
+
Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
|
| 1070 |
+
through the `self.time_embedding` layer to obtain the timestep embeddings.
|
| 1071 |
+
attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
|
| 1072 |
+
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
|
| 1073 |
+
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
|
| 1074 |
+
negative values to the attention scores corresponding to "discard" tokens.
|
| 1075 |
+
cross_attention_kwargs (`dict`, *optional*):
|
| 1076 |
+
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
| 1077 |
+
`self.processor` in
|
| 1078 |
+
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
| 1079 |
+
added_cond_kwargs: (`dict`, *optional*):
|
| 1080 |
+
A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
|
| 1081 |
+
are passed along to the UNet blocks.
|
| 1082 |
+
down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
|
| 1083 |
+
A tuple of tensors that if specified are added to the residuals of down unet blocks.
|
| 1084 |
+
mid_block_additional_residual: (`torch.Tensor`, *optional*):
|
| 1085 |
+
A tensor that if specified is added to the residual of the middle unet block.
|
| 1086 |
+
down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
|
| 1087 |
+
additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)
|
| 1088 |
+
encoder_attention_mask (`torch.Tensor`):
|
| 1089 |
+
A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
|
| 1090 |
+
`True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
|
| 1091 |
+
which adds large negative values to the attention scores corresponding to "discard" tokens.
|
| 1092 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
| 1093 |
+
Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
|
| 1094 |
+
tuple.
|
| 1095 |
+
|
| 1096 |
+
Returns:
|
| 1097 |
+
[`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
|
| 1098 |
+
If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned,
|
| 1099 |
+
otherwise a `tuple` is returned where the first element is the sample tensor.
|
| 1100 |
+
"""
|
| 1101 |
+
# By default samples have to be AT least a multiple of the overall upsampling factor.
|
| 1102 |
+
# The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
|
| 1103 |
+
# However, the upsampling interpolation output size can be forced to fit any upsampling size
|
| 1104 |
+
# on the fly if necessary.
|
| 1105 |
+
default_overall_up_factor = 2**self.num_upsamplers
|
| 1106 |
+
|
| 1107 |
+
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
|
| 1108 |
+
forward_upsample_size = False
|
| 1109 |
+
upsample_size = None
|
| 1110 |
+
|
| 1111 |
+
for dim in sample.shape[-2:]:
|
| 1112 |
+
if dim % default_overall_up_factor != 0:
|
| 1113 |
+
# Forward upsample size to force interpolation output size.
|
| 1114 |
+
forward_upsample_size = True
|
| 1115 |
+
break
|
| 1116 |
+
|
| 1117 |
+
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension
|
| 1118 |
+
# expects mask of shape:
|
| 1119 |
+
# [batch, key_tokens]
|
| 1120 |
+
# adds singleton query_tokens dimension:
|
| 1121 |
+
# [batch, 1, key_tokens]
|
| 1122 |
+
# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
|
| 1123 |
+
# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
|
| 1124 |
+
# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
|
| 1125 |
+
if attention_mask is not None:
|
| 1126 |
+
# assume that mask is expressed as:
|
| 1127 |
+
# (1 = keep, 0 = discard)
|
| 1128 |
+
# convert mask into a bias that can be added to attention scores:
|
| 1129 |
+
# (keep = +0, discard = -10000.0)
|
| 1130 |
+
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
| 1131 |
+
attention_mask = attention_mask.unsqueeze(1)
|
| 1132 |
+
|
| 1133 |
+
# convert encoder_attention_mask to a bias the same way we do for attention_mask
|
| 1134 |
+
if encoder_attention_mask is not None:
|
| 1135 |
+
encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
|
| 1136 |
+
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
|
| 1137 |
+
|
| 1138 |
+
# 0. center input if necessary
|
| 1139 |
+
if self.config.center_input_sample:
|
| 1140 |
+
sample = 2 * sample - 1.0
|
| 1141 |
+
|
| 1142 |
+
# 1. time
|
| 1143 |
+
t_emb = self.get_time_embed(sample=sample, timestep=timestep)
|
| 1144 |
+
emb = self.time_embedding(t_emb, timestep_cond)
|
| 1145 |
+
aug_emb = None
|
| 1146 |
+
|
| 1147 |
+
class_emb = self.get_class_embed(sample=sample, class_labels=class_labels)
|
| 1148 |
+
if class_emb is not None:
|
| 1149 |
+
if self.config.class_embeddings_concat:
|
| 1150 |
+
emb = torch.cat([emb, class_emb], dim=-1)
|
| 1151 |
+
else:
|
| 1152 |
+
emb = emb + class_emb
|
| 1153 |
+
|
| 1154 |
+
aug_emb = self.get_aug_embed(
|
| 1155 |
+
emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
|
| 1156 |
+
)
|
| 1157 |
+
if self.config.addition_embed_type == "image_hint":
|
| 1158 |
+
aug_emb, hint = aug_emb
|
| 1159 |
+
sample = torch.cat([sample, hint], dim=1)
|
| 1160 |
+
|
| 1161 |
+
emb = emb + aug_emb if aug_emb is not None else emb
|
| 1162 |
+
|
| 1163 |
+
if self.time_embed_act is not None:
|
| 1164 |
+
emb = self.time_embed_act(emb)
|
| 1165 |
+
|
| 1166 |
+
encoder_hidden_states = self.process_encoder_hidden_states(
|
| 1167 |
+
encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
|
| 1168 |
+
)
|
| 1169 |
+
|
| 1170 |
+
# 2. pre-process
|
| 1171 |
+
sample = self.conv_in(sample)
|
| 1172 |
+
|
| 1173 |
+
# 2.5 GLIGEN position net
|
| 1174 |
+
if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None:
|
| 1175 |
+
cross_attention_kwargs = cross_attention_kwargs.copy()
|
| 1176 |
+
gligen_args = cross_attention_kwargs.pop("gligen")
|
| 1177 |
+
cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)}
|
| 1178 |
+
|
| 1179 |
+
# 3. down
|
| 1180 |
+
# we're popping the `scale` instead of getting it because otherwise `scale` will be propagated
|
| 1181 |
+
# to the internal blocks and will raise deprecation warnings. this will be confusing for our users.
|
| 1182 |
+
if cross_attention_kwargs is not None:
|
| 1183 |
+
cross_attention_kwargs = cross_attention_kwargs.copy()
|
| 1184 |
+
lora_scale = cross_attention_kwargs.pop("scale", 1.0)
|
| 1185 |
+
else:
|
| 1186 |
+
lora_scale = 1.0
|
| 1187 |
+
|
| 1188 |
+
if USE_PEFT_BACKEND:
|
| 1189 |
+
# weight the lora layers by setting `lora_scale` for each PEFT layer
|
| 1190 |
+
scale_lora_layers(self, lora_scale)
|
| 1191 |
+
|
| 1192 |
+
is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
|
| 1193 |
+
# using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets
|
| 1194 |
+
is_adapter = down_intrablock_additional_residuals is not None
|
| 1195 |
+
# maintain backward compatibility for legacy usage, where
|
| 1196 |
+
# T2I-Adapter and ControlNet both use down_block_additional_residuals arg
|
| 1197 |
+
# but can only use one or the other
|
| 1198 |
+
if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:
|
| 1199 |
+
deprecate(
|
| 1200 |
+
"T2I should not use down_block_additional_residuals",
|
| 1201 |
+
"1.3.0",
|
| 1202 |
+
"Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \
|
| 1203 |
+
and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \
|
| 1204 |
+
for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ",
|
| 1205 |
+
standard_warn=False,
|
| 1206 |
+
)
|
| 1207 |
+
down_intrablock_additional_residuals = down_block_additional_residuals
|
| 1208 |
+
is_adapter = True
|
| 1209 |
+
|
| 1210 |
+
down_block_res_samples = (sample,)
|
| 1211 |
+
for downsample_block in self.down_blocks:
|
| 1212 |
+
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
| 1213 |
+
# For t2i-adapter CrossAttnDownBlock2D
|
| 1214 |
+
additional_residuals = {}
|
| 1215 |
+
if is_adapter and len(down_intrablock_additional_residuals) > 0:
|
| 1216 |
+
additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)
|
| 1217 |
+
|
| 1218 |
+
sample, res_samples = downsample_block(
|
| 1219 |
+
hidden_states=sample,
|
| 1220 |
+
temb=emb,
|
| 1221 |
+
encoder_hidden_states=encoder_hidden_states,
|
| 1222 |
+
attention_mask=attention_mask,
|
| 1223 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
| 1224 |
+
encoder_attention_mask=encoder_attention_mask,
|
| 1225 |
+
**additional_residuals,
|
| 1226 |
+
)
|
| 1227 |
+
else:
|
| 1228 |
+
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
|
| 1229 |
+
if is_adapter and len(down_intrablock_additional_residuals) > 0:
|
| 1230 |
+
sample += down_intrablock_additional_residuals.pop(0)
|
| 1231 |
+
|
| 1232 |
+
down_block_res_samples += res_samples
|
| 1233 |
+
|
| 1234 |
+
if is_controlnet:
|
| 1235 |
+
new_down_block_res_samples = ()
|
| 1236 |
+
|
| 1237 |
+
for down_block_res_sample, down_block_additional_residual in zip(
|
| 1238 |
+
down_block_res_samples, down_block_additional_residuals
|
| 1239 |
+
):
|
| 1240 |
+
down_block_res_sample = down_block_res_sample + down_block_additional_residual
|
| 1241 |
+
new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
|
| 1242 |
+
|
| 1243 |
+
down_block_res_samples = new_down_block_res_samples
|
| 1244 |
+
|
| 1245 |
+
# 4. mid
|
| 1246 |
+
if self.mid_block is not None:
|
| 1247 |
+
if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
|
| 1248 |
+
sample = self.mid_block(
|
| 1249 |
+
sample,
|
| 1250 |
+
emb,
|
| 1251 |
+
encoder_hidden_states=encoder_hidden_states,
|
| 1252 |
+
attention_mask=attention_mask,
|
| 1253 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
| 1254 |
+
encoder_attention_mask=encoder_attention_mask,
|
| 1255 |
+
)
|
| 1256 |
+
else:
|
| 1257 |
+
sample = self.mid_block(sample, emb)
|
| 1258 |
+
|
| 1259 |
+
# To support T2I-Adapter-XL
|
| 1260 |
+
if (
|
| 1261 |
+
is_adapter
|
| 1262 |
+
and len(down_intrablock_additional_residuals) > 0
|
| 1263 |
+
and sample.shape == down_intrablock_additional_residuals[0].shape
|
| 1264 |
+
):
|
| 1265 |
+
sample += down_intrablock_additional_residuals.pop(0)
|
| 1266 |
+
|
| 1267 |
+
if is_controlnet:
|
| 1268 |
+
sample = sample + mid_block_additional_residual
|
| 1269 |
+
|
| 1270 |
+
feat_64 = sample
|
| 1271 |
+
|
| 1272 |
+
|
| 1273 |
+
# 5. up
|
| 1274 |
+
for i, upsample_block in enumerate(self.up_blocks):
|
| 1275 |
+
is_final_block = i == len(self.up_blocks) - 1
|
| 1276 |
+
|
| 1277 |
+
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
|
| 1278 |
+
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
|
| 1279 |
+
|
| 1280 |
+
# if we have not reached the final block and need to forward the
|
| 1281 |
+
# upsample size, we do it here
|
| 1282 |
+
if not is_final_block and forward_upsample_size:
|
| 1283 |
+
upsample_size = down_block_res_samples[-1].shape[2:]
|
| 1284 |
+
|
| 1285 |
+
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
|
| 1286 |
+
sample = upsample_block(
|
| 1287 |
+
hidden_states=sample,
|
| 1288 |
+
temb=emb,
|
| 1289 |
+
res_hidden_states_tuple=res_samples,
|
| 1290 |
+
encoder_hidden_states=encoder_hidden_states,
|
| 1291 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
| 1292 |
+
upsample_size=upsample_size,
|
| 1293 |
+
attention_mask=attention_mask,
|
| 1294 |
+
encoder_attention_mask=encoder_attention_mask,
|
| 1295 |
+
)
|
| 1296 |
+
else:
|
| 1297 |
+
sample = upsample_block(
|
| 1298 |
+
hidden_states=sample,
|
| 1299 |
+
temb=emb,
|
| 1300 |
+
res_hidden_states_tuple=res_samples,
|
| 1301 |
+
upsample_size=upsample_size,
|
| 1302 |
+
)
|
| 1303 |
+
|
| 1304 |
+
# 6. post-process
|
| 1305 |
+
if self.conv_norm_out:
|
| 1306 |
+
sample = self.conv_norm_out(sample)
|
| 1307 |
+
sample = self.conv_act(sample)
|
| 1308 |
+
sample = self.conv_out(sample)
|
| 1309 |
+
|
| 1310 |
+
if USE_PEFT_BACKEND:
|
| 1311 |
+
# remove `lora_scale` from each PEFT layer
|
| 1312 |
+
unscale_lora_layers(self, lora_scale)
|
| 1313 |
+
|
| 1314 |
+
if not return_dict:
|
| 1315 |
+
return (sample,)
|
| 1316 |
+
|
| 1317 |
+
return UNet2DConditionOutput(sample=sample, feat_64=feat_64)
|