repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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CoMP | CoMP-main/comp/pl/comp.py | import logging
import pytorch_lightning as pl
import torch
import torch.distributions as dist
LARGE_NEGATIVE = -10000000.0
LOGGER = logging.getLogger(__name__)
LOGGER.setLevel(logging.INFO)
def pairwise_label_mask(m: torch.Tensor) -> torch.Tensor:
"""
Convert a batch_size x n_classes tensor, where each row ... | 5,207 | 31.962025 | 102 | py |
CoMP | CoMP-main/comp/pl/cvae.py | import logging
import pytorch_lightning as pl
import torch
import torch.distributions as tdist
from comp.nn.loss import GroupwiseMMD, RBF
LOGGER = logging.getLogger(__name__)
LOGGER.setLevel(logging.INFO)
class CVAE(pl.LightningModule):
"""Conditional VAE with flexible encoder and decoder"""
def __init__(... | 3,219 | 30.568627 | 87 | py |
CoMP | CoMP-main/comp/pl/trainer.py | from pytorch_lightning.callbacks import EarlyStopping, LambdaCallback, ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.trainer import Trainer
def create_trainer(
output_dir,
num_epochs,
gpus,
checkpoint_metric_name,
checkpoint_save_k=1,
checkpoint... | 1,797 | 32.296296 | 101 | py |
CoMP | CoMP-main/comp/data/loaders.py | import logging
import os
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
import torch
from torch.utils.data import (
DataLoader,
TensorDataset,
random_split,
)
LOGGER = logging.getLogger(__name__)
LOGGER.setLevel(logging.INFO)
def extract_labels(metadata_df, use_cuda):
labels = t... | 2,356 | 27.39759 | 90 | py |
bitwise-weight-training | bitwise-weight-training-main/localFunctions.py | import tensorflow as tf
import uuid
def fill_with_predefined(src):
def initializer(shape, dtype=None):
return tf.Variable(src, dtype=dtype, name=uuid.uuid4().hex)
return initializer
def activate(x, activationtype):
if activationtype is None:
return x
if 'relu' in activationtype:
... | 1,032 | 18.490566 | 67 | py |
bitwise-weight-training | bitwise-weight-training-main/ResNetBuilder.py | import tensorflow
import tensorflow.keras
from tensorflow.keras.layers import AveragePooling2D, Input, Flatten
from tensorflow.keras.layers import BatchNormalization, Activation
from tensorflow.keras.models import Model
from localLayers import QuantizedConv2D, QuantizedDense
from localFunctions import activate
def re... | 5,834 | 37.642384 | 148 | py |
bitwise-weight-training | bitwise-weight-training-main/localLayers.py | import utils
import tensorflow as tf
from tensorflow.keras.layers import Layer
from tensorflow.keras import backend as KB
from localFunctions import to_bit, to_sign, activate, fill_with_predefined
import numpy as np
def calc_scaling_factor(k, target):
current_std = np.std(k)
if current_std == 0:
prin... | 11,711 | 38.302013 | 153 | py |
bitwise-weight-training | bitwise-weight-training-main/Trainer.py | import argparse
import os
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0)
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # or any {'0', '1', '2'}
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
import utils, ... | 21,152 | 37.320652 | 175 | py |
stylegan-v | stylegan-v-master/src/legacy.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 16,515 | 50.451713 | 154 | py |
stylegan-v | stylegan-v-master/src/train.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 20,428 | 43.997797 | 207 | py |
stylegan-v | stylegan-v-master/src/training/mocogan.py | import functools
from typing import Tuple, List, Dict
import numpy as np
from torch import Tensor
import torch
import torch.nn as nn
from omegaconf import DictConfig, OmegaConf
from src.torch_utils import persistence
from src.training.networks import Discriminator as ImageDiscriminator
#-----------------------------... | 10,780 | 35.545763 | 160 | py |
stylegan-v | stylegan-v-master/src/training/logging.py | import os
from typing import List, Callable, Optional, Dict
from multiprocessing.pool import ThreadPool
from PIL import Image
import torch
from torch import Tensor
import numpy as np
import cv2
from tqdm import tqdm
from torchvision import utils
import torchvision.transforms.functional as TVF
#-----------------------... | 6,110 | 41.734266 | 132 | py |
stylegan-v | stylegan-v-master/src/training/loss.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 9,892 | 55.210227 | 164 | py |
stylegan-v | stylegan-v-master/src/training/augment.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 26,649 | 59.983982 | 366 | py |
stylegan-v | stylegan-v-master/src/training/dataset.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 20,180 | 39.605634 | 198 | py |
stylegan-v | stylegan-v-master/src/training/networks.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 34,316 | 49.764793 | 159 | py |
stylegan-v | stylegan-v-master/src/training/layers.py | import random
from typing import Dict, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from omegaconf import DictConfig
from src.torch_utils import persistence
from src.torch_utils.ops import bias_act, upfirdn2d, conv2d_resample
from src.torch_utils import misc
#... | 19,389 | 42.184855 | 164 | py |
stylegan-v | stylegan-v-master/src/training/training_loop.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 30,166 | 51.647469 | 195 | py |
stylegan-v | stylegan-v-master/src/training/motion.py | from typing import Dict
import numpy as np
import torch
import torch.nn as nn
from omegaconf import DictConfig
from src.torch_utils import misc
from src.torch_utils import persistence
from src.training.layers import (
MappingNetwork,
EqLRConv1d,
FullyConnectedLayer,
)
#-----------------------------------... | 12,147 | 52.991111 | 173 | py |
stylegan-v | stylegan-v-master/src/torch_utils/custom_ops.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 5,644 | 43.448819 | 146 | py |
stylegan-v | stylegan-v-master/src/torch_utils/training_stats.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 10,716 | 38.840149 | 118 | py |
stylegan-v | stylegan-v-master/src/torch_utils/persistence.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 9,721 | 37.579365 | 144 | py |
stylegan-v | stylegan-v-master/src/torch_utils/misc.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 11,817 | 41.974545 | 145 | py |
stylegan-v | stylegan-v-master/src/torch_utils/ops/bias_act.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 10,056 | 46.215962 | 185 | py |
stylegan-v | stylegan-v-master/src/torch_utils/ops/grid_sample_gradfix.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 3,299 | 38.285714 | 138 | py |
stylegan-v | stylegan-v-master/src/torch_utils/ops/conv2d_gradfix.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 7,685 | 43.947368 | 197 | py |
stylegan-v | stylegan-v-master/src/torch_utils/ops/upfirdn2d.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 16,287 | 41.306494 | 157 | py |
stylegan-v | stylegan-v-master/src/torch_utils/ops/conv2d_resample.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 7,591 | 47.356688 | 130 | py |
stylegan-v | stylegan-v-master/src/torch_utils/ops/fma.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 2,034 | 32.360656 | 105 | py |
stylegan-v | stylegan-v-master/src/deps/facial_recognition/model_irse.py | """
Copy-pasted from https://github.com/orpatashnik/StyleCLIP/tree/main/models/facial_recognition/model_irse.py
"""
from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
from .helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
"""
Modified Backbone ... | 3,427 | 37.516854 | 107 | py |
stylegan-v | stylegan-v-master/src/deps/facial_recognition/helpers.py | """
Copy-pasted from https://github.com/orpatashnik/StyleCLIP/tree/main/models/facial_recognition/helpers.py
"""
from collections import namedtuple
import torch
from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module
"""
ArcFace implementation from [TreB1eN](ht... | 4,234 | 33.153226 | 112 | py |
stylegan-v | stylegan-v-master/src/infra/launch.py | """
Run a __reproducible__ experiment on __allocated__ resources
It submits a slurm job(s) with the given hyperparams which will then execute `slurm_job.py`
This is the main entry-point
"""
import os
import subprocess
import re
import hydra
from omegaconf import DictConfig, OmegaConf
from pathlib import Path
from sr... | 4,736 | 41.294643 | 173 | py |
stylegan-v | stylegan-v-master/src/scripts/convert_videos_to_frames.py | """
Converts a dataset of mp4 videos into a dataset of video frames
I.e. a directory of mp4 files becomes a directory of directories of frames
This speeds up loading during training because we do not need
"""
import os
from typing import List
import argparse
from pathlib import Path
from multiprocessing import Pool
fro... | 3,975 | 36.509434 | 137 | py |
stylegan-v | stylegan-v-master/src/scripts/generate.py | """Generates a dataset of images using pretrained network pickle."""
import sys; sys.path.extend(['.', 'src'])
import os
import json
import random
import warnings
import click
from src import dnnlib
import numpy as np
import torch
from tqdm import tqdm
from omegaconf import OmegaConf
import legacy
from src.training.... | 7,858 | 50.366013 | 180 | py |
stylegan-v | stylegan-v-master/src/scripts/calc_metrics_for_dataset.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 6,770 | 38.829412 | 146 | py |
stylegan-v | stylegan-v-master/src/scripts/project.py | """
Given a dataset of images, it (optionally crops it) and embeds into the model
Also optionally generates random videos from the found w
"""
import sys; sys.path.extend(['.', 'src'])
import os
import re
import json
import random
from typing import List, Optional, Callable
from typing import List
from PIL import Ima... | 20,765 | 42.2625 | 146 | py |
stylegan-v | stylegan-v-master/src/scripts/profile_model.py | """
This script computes imgs/sec for a generator in the eval mode
for different batch sizes
"""
import sys; sys.path.extend(['..', '.', 'src'])
import time
import numpy as np
import torch
import torch.nn as nn
import hydra
from hydra.experimental import initialize
from omegaconf import DictConfig, OmegaConf
from tqdm... | 3,655 | 33.819048 | 153 | py |
stylegan-v | stylegan-v-master/src/scripts/frames_to_video_grid.py | """
Converts a directory of video frames into an mp4-grid
"""
import sys; sys.path.extend(['.'])
import os
import argparse
import random
import numpy as np
import torch
from torch import Tensor
import torchvision.transforms.functional as TVF
from torchvision import utils
from PIL import Image
from tqdm import tqdm
imp... | 3,209 | 39.632911 | 166 | py |
stylegan-v | stylegan-v-master/src/scripts/clip_edit.py | # import sys; sys.path.extend(['.', 'src', '/home/skoroki/StyleCLIP'])
import argparse
import math
import os
from typing import List
import json
import re
import random
import yaml
import itertools
import torchvision
from torch import optim
from PIL import Image
import click
import numpy as np
import torch
from tqdm i... | 15,919 | 38.405941 | 161 | py |
stylegan-v | stylegan-v-master/src/scripts/convert_video_to_dataset.py | """
Converts a dataset of mp4 videos into a dataset of video frames
I.e. a directory of mp4 files becomes a directory of directories of frames
This speeds up loading during training because we do not need
"""
import os
from typing import List
import argparse
from pathlib import Path
from multiprocessing import Pool
fro... | 3,759 | 41.727273 | 168 | py |
stylegan-v | stylegan-v-master/src/scripts/calc_metrics.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 11,305 | 44.043825 | 145 | py |
stylegan-v | stylegan-v-master/src/metrics/metric_utils.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 14,787 | 43.542169 | 153 | py |
stylegan-v | stylegan-v-master/src/metrics/frechet_video_distance.py | """
Frechet Video Distance (FVD). Matches the original tensorflow implementation from
https://github.com/google-research/google-research/blob/master/frechet_video_distance/frechet_video_distance.py
up to the upsampling operation. Note that this tf.hub I3D model is different from the one released in the I3D repo.
"""
i... | 3,074 | 50.25 | 125 | py |
stylegan-v | stylegan-v-master/src/metrics/kernel_inception_distance.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 2,335 | 48.702128 | 118 | py |
stylegan-v | stylegan-v-master/src/metrics/video_inception_score.py | """Inception Score (IS) from the paper "Improved techniques for training
GANs". Matches the original implementation by Salimans et al. at
https://github.com/openai/improved-gan/blob/master/inception_score/model.py"""
import numpy as np
from . import metric_utils
#------------------------------------------------------... | 2,482 | 44.145455 | 126 | py |
stylegan-v | stylegan-v-master/src/metrics/inception_score.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 2,236 | 45.604167 | 126 | py |
stylegan-v | stylegan-v-master/src/metrics/metric_main.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 5,800 | 36.425806 | 128 | py |
DenseCL | DenseCL-main/tools/folder2lmdb_imagenet.py | # coding: utf-8
import argparse
import os
import os.path as osp
from PIL import Image
import six
import lmdb
import pyarrow as pa
import numpy as np
import torch.utils.data as data
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
def is_valid_file(filename):
x = "/".join(file... | 4,892 | 27.614035 | 84 | py |
DenseCL | DenseCL-main/tools/test.py | import argparse
import importlib
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from openselfsup.datasets import build_dataloader, build_dataset
from openselfsup.... | 3,944 | 31.073171 | 83 | py |
DenseCL | DenseCL-main/tools/extract.py | import argparse
import importlib
import numpy as np
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from openselfsup.utils import dist_forward_collect, nondist_for... | 6,703 | 35.63388 | 77 | py |
DenseCL | DenseCL-main/tools/upgrade_models.py | import torch
import argparse
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument(
'--save-path', type=str, required=True, help='destination file name')
args = parser.parse_args()
return args
def main():
ar... | 712 | 24.464286 | 77 | py |
DenseCL | DenseCL-main/tools/folder2lmdb_coco.py | # coding: utf-8
import argparse
import os
import os.path as osp
import lmdb
import pyarrow as pa
import json
import torch.utils.data as data
from torch.utils.data import DataLoader
class COCODataset(data.Dataset):
"""
pass.
"""
def __init__(self, infos, dpath):
file_names = list()
... | 3,476 | 25.953488 | 82 | py |
DenseCL | DenseCL-main/tools/extract_backbone_weights.py | import torch
import argparse
def parse_args():
parser = argparse.ArgumentParser(
description='This script extracts backbone weights from a checkpoint')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument(
'output', type=str, help='destination file name')
args ... | 948 | 28.65625 | 78 | py |
DenseCL | DenseCL-main/tools/train.py | from __future__ import division
import argparse
import importlib
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv import Config
from mmcv.runner import init_dist
from openselfsup import __version__
from openselfsup.apis import set_random_seed, train_model
from openselfsup.datasets import... | 4,838 | 32.839161 | 77 | py |
DenseCL | DenseCL-main/benchmarks/detection/convert-pretrain-to-detectron2.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import pickle as pkl
import sys
import torch
if __name__ == "__main__":
input = sys.argv[1]
obj = torch.load(input, map_location="cpu")
obj = obj["state_dict"]
newmodel = {}
for k, v in obj.items():
... | 980 | 25.513514 | 70 | py |
DenseCL | DenseCL-main/benchmarks/detection/train_net.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
from detectron2.evaluation i... | 2,755 | 31.423529 | 135 | py |
DenseCL | DenseCL-main/openselfsup/apis/inference.py | import warnings
import matplotlib.pyplot as plt
import cv2
import mmcv
import random
from PIL import Image
import numpy as np
import torch
from mmcv.runner import load_checkpoint
from mmcv.parallel import collate, scatter
from openselfsup.models import build_model
from openselfsup.utils import build_from_cfg
from o... | 2,568 | 29.223529 | 79 | py |
DenseCL | DenseCL-main/openselfsup/apis/train.py | import random
import re
from collections import OrderedDict
import numpy as np
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import DistSamplerSeedHook, Runner, obj_from_dict
from openselfsup.datasets import build_dataloader
from ope... | 10,222 | 34.870175 | 87 | py |
DenseCL | DenseCL-main/openselfsup/third_party/clustering.py | # This file is modified from
# https://github.com/facebookresearch/deepcluster/blob/master/clustering.py
import time
import numpy as np
import faiss
import torch
__all__ = ['Kmeans', 'PIC']
def preprocess_features(npdata, pca):
"""Preprocess an array of features.
Args:
npdata (np.array N * ndim): fe... | 9,309 | 29.12945 | 84 | py |
DenseCL | DenseCL-main/openselfsup/models/densecl.py | import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
@MODELS.register_module
class DenseCL(nn.Module):
'''DenseCL.
Part of the code is borrowed from:
"https://github.com/facebookresearch/moco/blob/master/moco/builder.py".
'... | 9,158 | 34.777344 | 119 | py |
DenseCL | DenseCL-main/openselfsup/models/classification.py | import numpy as np
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
from .utils import Sobel
@MODELS.register_module
class Classification(nn.Module):
"""Simple image classification.
Args:
backbone (dict): Config dict for module of bac... | 3,370 | 31.413462 | 85 | py |
DenseCL | DenseCL-main/openselfsup/models/simclr.py | import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
from .utils import GatherLayer
@MODELS.register_module
class SimCLR(nn.Module):
"""SimCLR.
Implementation of "A Simple Framework for Contrastive Learning
of Visual Representatio... | 3,961 | 35.018182 | 88 | py |
DenseCL | DenseCL-main/openselfsup/models/rotation_pred.py | import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
@MODELS.register_module
class RotationPred(nn.Module):
"""Rotation prediction.
Implementation of "Unsupervised Representation Learning
by Predicting Image Rotations (https://arx... | 3,294 | 33.684211 | 79 | py |
DenseCL | DenseCL-main/openselfsup/models/deepcluster.py | import numpy as np
import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
from .utils import Sobel
@MODELS.register_module
class DeepCluster(nn.Module):
"""DeepCluster.
Implementation of "Deep Clustering for Unsupervised Learning
o... | 4,526 | 33.557252 | 88 | py |
DenseCL | DenseCL-main/openselfsup/models/relative_loc.py | import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
@MODELS.register_module
class RelativeLoc(nn.Module):
"""Relative patch location.
Implementation of "Unsupervised Visual Representation Learning
by Context Prediction (https://a... | 3,948 | 35.564815 | 88 | py |
DenseCL | DenseCL-main/openselfsup/models/moco.py | import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
@MODELS.register_module
class MOCO(nn.Module):
"""MOCO.
Implementation of "Momentum Contrast for Unsupervised Visual
Representation Learning (https://arxiv.org/abs/1911.05722)".... | 7,486 | 33.187215 | 88 | py |
DenseCL | DenseCL-main/openselfsup/models/npid.py | import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
@MODELS.register_module
class NPID(nn.Module):
"""NPID.
Implementation of "Unsupervised Feature Learning via Non-parametric
Instance Discrimination (https://arxiv.org/abs/1805.0... | 4,658 | 34.564885 | 88 | py |
DenseCL | DenseCL-main/openselfsup/models/byol.py | import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
@MODELS.register_module
class BYOL(nn.Module):
"""BYOL.
Implementation of "Bootstrap Your Own Latent: A New Approach to
Self-Supervised Learning (https://arxiv.org/abs/2006.0773... | 4,172 | 36.594595 | 88 | py |
DenseCL | DenseCL-main/openselfsup/models/builder.py | from torch import nn
from openselfsup.utils import build_from_cfg
from .registry import (BACKBONES, MODELS, NECKS, HEADS, MEMORIES, LOSSES)
def build(cfg, registry, default_args=None):
"""Build a module.
Args:
cfg (dict, list[dict]): The config of modules, it is either a dict
or a list o... | 1,274 | 21.368421 | 77 | py |
DenseCL | DenseCL-main/openselfsup/models/odc.py | import numpy as np
import torch
import torch.nn as nn
from openselfsup.utils import print_log
from . import builder
from .registry import MODELS
from .utils import Sobel
@MODELS.register_module
class ODC(nn.Module):
"""ODC.
Official implementation of
"Online Deep Clustering for Unsupervised Representati... | 5,322 | 34.966216 | 88 | py |
DenseCL | DenseCL-main/openselfsup/models/necks.py | import torch
import torch.nn as nn
from packaging import version
from mmcv.cnn import kaiming_init, normal_init
from .registry import NECKS
from .utils import build_norm_layer
def _init_weights(module, init_linear='normal', std=0.01, bias=0.):
assert init_linear in ['normal', 'kaiming'], \
"Undefined ini... | 12,802 | 31.168342 | 85 | py |
DenseCL | DenseCL-main/openselfsup/models/memories/simple_memory.py | import torch
import torch.nn as nn
import torch.distributed as dist
from mmcv.runner import get_dist_info
from openselfsup.utils import AliasMethod
from ..registry import MEMORIES
@MEMORIES.register_module
class SimpleMemory(nn.Module):
"""Simple memory bank for NPID.
Args:
length (int): Number of f... | 2,305 | 33.939394 | 77 | py |
DenseCL | DenseCL-main/openselfsup/models/memories/odc_memory.py | import numpy as np
from sklearn.cluster import KMeans
import torch
import torch.nn as nn
import torch.distributed as dist
from mmcv.runner import get_dist_info
from ..registry import MEMORIES
@MEMORIES.register_module
class ODCMemory(nn.Module):
"""Memory modules for ODC.
Args:
length (int): Number... | 10,441 | 43.623932 | 81 | py |
DenseCL | DenseCL-main/openselfsup/models/utils/multi_pooling.py | import torch.nn as nn
class MultiPooling(nn.Module):
"""Pooling layers for features from multiple depth."""
POOL_PARAMS = {
'resnet50': [
dict(kernel_size=10, stride=10, padding=4),
dict(kernel_size=16, stride=8, padding=0),
dict(kernel_size=13, stride=5, padding=0... | 1,280 | 31.846154 | 66 | py |
DenseCL | DenseCL-main/openselfsup/models/utils/norm.py | import torch.nn as nn
norm_cfg = {
# format: layer_type: (abbreviation, module)
'BN': ('bn', nn.BatchNorm2d),
'SyncBN': ('bn', nn.SyncBatchNorm),
'GN': ('gn', nn.GroupNorm),
# and potentially 'SN'
}
def build_norm_layer(cfg, num_features, postfix=''):
"""Build normalization layer.
Args:
... | 1,684 | 29.089286 | 74 | py |
DenseCL | DenseCL-main/openselfsup/models/utils/scale.py | import torch
import torch.nn as nn
class Scale(nn.Module):
"""A learnable scale parameter."""
def __init__(self, scale=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float))
def forward(self, x):
return x * self.scale
| 305 | 20.857143 | 73 | py |
DenseCL | DenseCL-main/openselfsup/models/utils/sobel.py | import torch
import torch.nn as nn
class Sobel(nn.Module):
"""Sobel layer."""
def __init__(self):
super(Sobel, self).__init__()
grayscale = nn.Conv2d(3, 1, kernel_size=1, stride=1, padding=0)
grayscale.weight.data.fill_(1.0 / 3.0)
grayscale.bias.data.zero_()
sobel_filt... | 840 | 32.64 | 74 | py |
DenseCL | DenseCL-main/openselfsup/models/utils/conv_ws.py | import torch.nn as nn
import torch.nn.functional as F
def conv_ws_2d(input,
weight,
bias=None,
stride=1,
padding=0,
dilation=1,
groups=1,
eps=1e-5):
c_in = weight.size(0)
weight_flat = weight.view(c_in, -1... | 1,335 | 27.425532 | 79 | py |
DenseCL | DenseCL-main/openselfsup/models/utils/conv_module.py | import warnings
import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
from .conv_ws import ConvWS2d
from .norm import build_norm_layer
conv_cfg = {
'Conv': nn.Conv2d,
'ConvWS': ConvWS2d,
}
def build_conv_layer(cfg, *args, **kwargs):
"""Build convolution layer.
Args:
cfg (N... | 5,723 | 33.902439 | 78 | py |
DenseCL | DenseCL-main/openselfsup/models/utils/bbox.py | import torch
def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False):
"""Calculate overlap between two set of bboxes.
If ``is_aligned`` is ``False``, then calculate the ious between each bbox
of bboxes1 and bboxes2, otherwise the ious between each aligned pair of
bboxes1 and bboxes2.
Arg... | 3,071 | 36.012048 | 79 | py |
DenseCL | DenseCL-main/openselfsup/models/utils/accuracy.py | import torch.nn as nn
def accuracy(pred, target, topk=1):
assert isinstance(topk, (int, tuple))
if isinstance(topk, int):
topk = (topk, )
return_single = True
else:
return_single = False
maxk = max(topk)
_, pred_label = pred.topk(maxk, dim=1)
pred_label = pred_label.t(... | 801 | 24.0625 | 69 | py |
DenseCL | DenseCL-main/openselfsup/models/utils/gather_layer.py | import torch
import torch.distributed as dist
class GatherLayer(torch.autograd.Function):
"""Gather tensors from all process, supporting backward propagation.
"""
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
output = [torch.zeros_like(input) \
for _ ... | 618 | 25.913043 | 72 | py |
DenseCL | DenseCL-main/openselfsup/models/backbones/resnet.py | import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from openselfsup.utils import get_root_logger
from ..registry import BACKBONES
from ..utils import build_conv_layer, build... | 13,648 | 30.74186 | 79 | py |
DenseCL | DenseCL-main/openselfsup/models/backbones/resnext.py | import math
import torch.nn as nn
from ..registry import BACKBONES
from ..utils import build_conv_layer, build_norm_layer
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
class Bottleneck(_Bottleneck):
def __init__(self, inplanes, planes, groups=1, base_width=4, **kwargs):
"""Bo... | 7,594 | 33.058296 | 79 | py |
DenseCL | DenseCL-main/openselfsup/models/heads/contrastive_head.py | import torch
import torch.nn as nn
from ..registry import HEADS
@HEADS.register_module
class ContrastiveHead(nn.Module):
"""Head for contrastive learning.
Args:
temperature (float): The temperature hyper-parameter that
controls the concentration level of the distribution.
Def... | 1,060 | 26.205128 | 65 | py |
DenseCL | DenseCL-main/openselfsup/models/heads/cls_head.py | import torch.nn as nn
from mmcv.cnn import kaiming_init, normal_init
from ..utils import accuracy
from ..registry import HEADS
@HEADS.register_module
class ClsHead(nn.Module):
"""Simplest classifier head, with only one fc layer.
"""
def __init__(self,
with_avg_pool=False,
... | 2,119 | 33.754098 | 78 | py |
DenseCL | DenseCL-main/openselfsup/models/heads/contrastive_weight_head.py | import torch
import torch.nn as nn
from ..registry import HEADS
@HEADS.register_module
class ContrastiveWeightHead(nn.Module):
'''Head for contrastive learning.
'''
def __init__(self, temperature=0.1):
super(ContrastiveWeightHead, self).__init__()
self.criterion = nn.CrossEntropyLoss()
... | 885 | 25.848485 | 60 | py |
DenseCL | DenseCL-main/openselfsup/models/heads/multi_cls_head.py | import torch.nn as nn
from ..utils import accuracy
from ..registry import HEADS
from ..utils import build_norm_layer, MultiPooling
@HEADS.register_module
class MultiClsHead(nn.Module):
"""Multiple classifier heads.
"""
FEAT_CHANNELS = {'resnet50': [64, 256, 512, 1024, 2048]}
FEAT_LAST_UNPOOL = {'res... | 2,682 | 32.962025 | 78 | py |
DenseCL | DenseCL-main/openselfsup/models/heads/latent_pred_head.py | import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from ..registry import HEADS
from .. import builder
@HEADS.register_module
class LatentPredictHead(nn.Module):
"""Head for contrastive learning.
"""
def __init__(self, predictor, size_average=True):
super(LatentPredictHead, self)... | 2,048 | 28.695652 | 72 | py |
DenseCL | DenseCL-main/openselfsup/datasets/base.py | from abc import ABCMeta, abstractmethod
import torch
from torch.utils.data import Dataset
from openselfsup.utils import print_log, build_from_cfg
from torchvision.transforms import Compose
from .registry import DATASETS, PIPELINES
from .builder import build_datasource
class BaseDataset(Dataset, metaclass=ABCMeta)... | 1,056 | 26.102564 | 76 | py |
DenseCL | DenseCL-main/openselfsup/datasets/classification.py | import torch
from openselfsup.utils import print_log
from .registry import DATASETS
from .base import BaseDataset
@DATASETS.register_module
class ClassificationDataset(BaseDataset):
"""Dataset for classification.
"""
def __init__(self, data_source, pipeline):
super(ClassificationDataset, self).... | 1,435 | 33.190476 | 74 | py |
DenseCL | DenseCL-main/openselfsup/datasets/rotation_pred.py | import torch
from PIL import Image
from .registry import DATASETS
from .base import BaseDataset
def rotate(img):
"""Rotate input image with 0, 90, 180, and 270 degrees.
Args:
img (Tensor): input image of shape (C, H, W).
Returns:
list[Tensor]: A list of four rotated images.
"""
... | 1,288 | 27.021739 | 78 | py |
DenseCL | DenseCL-main/openselfsup/datasets/relative_loc.py | from openselfsup.utils import build_from_cfg
import torch
from PIL import Image
from torchvision.transforms import Compose, RandomCrop
import torchvision.transforms.functional as TF
from .registry import DATASETS, PIPELINES
from .base import BaseDataset
def image_to_patches(img):
"""Crop split_per_side x split_... | 2,327 | 34.272727 | 94 | py |
DenseCL | DenseCL-main/openselfsup/datasets/dataset_wrappers.py | import numpy as np
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .registry import DATASETS
@DATASETS.register_module
class ConcatDataset(_ConcatDataset):
"""A wrapper of concatenated dataset.
Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
concat the group flag for... | 1,639 | 28.285714 | 78 | py |
DenseCL | DenseCL-main/openselfsup/datasets/byol.py | import torch
from torch.utils.data import Dataset
from openselfsup.utils import build_from_cfg
from torchvision.transforms import Compose
from .registry import DATASETS, PIPELINES
from .builder import build_datasource
@DATASETS.register_module
class BYOLDataset(Dataset):
"""Dataset for BYOL.
"""
def _... | 1,076 | 28.916667 | 74 | py |
DenseCL | DenseCL-main/openselfsup/datasets/contrastive.py | import torch
from PIL import Image
from .registry import DATASETS
from .base import BaseDataset
@DATASETS.register_module
class ContrastiveDataset(BaseDataset):
"""Dataset for contrastive learning methods that forward
two views of the image at a time (MoCo, SimCLR).
"""
def __init__(self, data_so... | 972 | 32.551724 | 78 | py |
DenseCL | DenseCL-main/openselfsup/datasets/data_sources/coco_lmdb.py | # coding: utf-8
import os.path as osp
from PIL import Image
import six
import lmdb
import pyarrow as pa
import torch.utils.data as data
from ..registry import DATASOURCES
def loads_pyarrow(buf):
"""
Args:
buf: the output of `dumps`.
"""
return pa.deserialize(buf)
@DATASOURCES.register_modu... | 1,890 | 25.263889 | 109 | py |
DenseCL | DenseCL-main/openselfsup/datasets/data_sources/imagenet_lmdb.py | # coding: utf-8
import os.path as osp
from PIL import Image
import six
import lmdb
import pyarrow as pa
import torch.utils.data as data
from ..registry import DATASOURCES
def loads_pyarrow(buf):
"""
Args:
buf: the output of `dumps`.
"""
return pa.deserialize(buf)
@DATASOURCES.register_modul... | 2,079 | 26.368421 | 109 | py |
DenseCL | DenseCL-main/openselfsup/datasets/data_sources/cifar.py | from PIL import Image
from torchvision.datasets import CIFAR10, CIFAR100
from ..registry import DATASOURCES
@DATASOURCES.register_module
class Cifar10(object):
CLASSES = [
'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog',
'horse', 'ship', 'truck'
]
def __init__(self, root... | 1,614 | 27.839286 | 71 | py |
DenseCL | DenseCL-main/openselfsup/datasets/loader/sampler.py | from __future__ import division
import math
import numpy as np
import torch
from mmcv.runner import get_dist_info
from torch.utils.data import DistributedSampler as _DistributedSampler
from torch.utils.data import Sampler
class DistributedSampler(_DistributedSampler):
def __init__(self,
dataset... | 10,628 | 34.079208 | 92 | py |
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