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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CMW-Net | CMW-Net-main/section4/Feature-independent_Label_Noise/cmwn.py | from __future__ import print_function
import sys
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import random
import os
import argparse
import numpy as np
from PreResNet_MWN import *
import dataloader_cifar as dataloader
import torchne... | 13,922 | 35.543307 | 351 | py |
CMW-Net | CMW-Net-main/section4/Feature-dependent_Label_Noise/resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from torch.autograd import Variable
import torch.nn.init as init
def to_var(x, requires_grad=True):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x, requires_grad=requires_grad)
class MetaModule(nn.Module):
... | 12,870 | 33.975543 | 141 | py |
CMW-Net | CMW-Net-main/section4/Feature-dependent_Label_Noise/mwn.py | # -*- coding: utf-8 -*-
import os
import torch
import argparse
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from copy import deepcopy
# from pandas import Series
from resnet import ResNet34, VNet, CVNet, ACVNet
from cifar_train_val_test import CI... | 20,522 | 38.316092 | 200 | py |
CMW-Net | CMW-Net-main/section4/Feature-dependent_Label_Noise/cifar_train_val_test.py | # from data.cifar10_test import CIFAR10
from torch.utils.data import DataLoader
import torch
from PIL import Image
import os
import os.path
import numpy as np
import sys
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
import torch.utils.data as data
from torchvision.datasets.utils imp... | 10,626 | 36.953571 | 115 | py |
CMW-Net | CMW-Net-main/section6/webvision/dataloader_webvision_all.py | from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
import random
import numpy as np
from PIL import Image
import torch
import os
class imagenet_dataset(Dataset):
def __init__(self, root_dir, transform, num_class):
self.root = root_dir+'imagenet/val/'
self.... | 5,660 | 37.510204 | 153 | py |
CMW-Net | CMW-Net-main/section6/webvision/InceptionResNetV2.py | from __future__ import print_function, division, absolute_import
import torch
import torch.nn as nn
import os
import sys
class BasicConv2d(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_planes, ou... | 10,750 | 29.893678 | 89 | py |
CMW-Net | CMW-Net-main/section6/webvision/webvision.py | import argparse
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distr... | 26,469 | 44.093697 | 3,008 | py |
CMW-Net | CMW-Net-main/section6/webvision/trans_webvision.py | import argparse
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distr... | 25,991 | 43.813793 | 3,008 | py |
CMW-Net | CMW-Net-main/section6/Animal-10N/dataset.py | from __future__ import print_function
import torch
from torch.utils.data.dataset import Dataset
import numpy as np
import os
from PIL import Image
import PIL
from os import listdir
# https://github.com/kuangliu/pytorch-retinanet/blob/master/transform.py
def resize(img, size, max_size=1000):
'''Resize the input PI... | 2,293 | 27.320988 | 114 | py |
CMW-Net | CMW-Net-main/section6/Animal-10N/vgg_mwn.py | import torch
import torch.nn as nn
from typing import Union, List, Dict, Any, cast
import torch.nn.functional as F
from torch.autograd import Variable
import torch.nn.init as init
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_d... | 15,942 | 36.779621 | 113 | py |
CMW-Net | CMW-Net-main/section6/Animal-10N/cmwn.py | import sys
sys.path.append('..')
import os
import torch.nn as nn
import torch.nn.parallel
import random
import argparse
import numpy as np
from vgg import vgg19_bn
from dataset import Animal10
import torch.optim as optim
from torch.optim import lr_scheduler
import torchvision.transforms as transforms
import torch.nn.fu... | 13,623 | 33.57868 | 123 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/Resnet50_MWN_webvision.py | import os
import sys
import torch
import random
import torchnet
import argparse
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
# import dataloader_webvision as dataloader
from dataloaders import dataloader_webvision_v7 as dataloa... | 16,039 | 38.410319 | 187 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/models/resnet_mwn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.nn.init as init
def to_var(x, requires_grad=True):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x, requires_grad=requires_grad)
class MetaModule(nn.Module):
# adopted... | 13,856 | 34.530769 | 118 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/models/InceptionResNetV2.py | from __future__ import print_function, division, absolute_import
import torch
import torch.nn as nn
class BasicConv2d(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_planes, out_planes,
... | 9,427 | 29.911475 | 80 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/models/resnet.py | """ResNet in PyTorch.
ImageNet-Style ResNet
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
Adapted from: https://github.com/bearpaw/pytorch-classification
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(n... | 7,493 | 33.376147 | 104 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/models/bit_models.py | # Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 8,622 | 42.771574 | 114 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/models/resnet_mwn_v3.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.nn.init as init
def to_var(x, requires_grad=True):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x, requires_grad=requires_grad)
class MetaModule(nn.Module):
# adopted... | 13,827 | 34.45641 | 105 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/models/resnet_mwn_v2.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.nn.init as init
def to_var(x, requires_grad=True):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x, requires_grad=requires_grad)
class MetaModule(nn.Module):
# adopted... | 13,825 | 34.451282 | 105 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/models/PreResNet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from models.drop import DropBlock2d
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(nn.Module):
... | 9,104 | 35.130952 | 104 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/models/resnet_mwn_v1.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.nn.init as init
def to_var(x, requires_grad=True):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x, requires_grad=requires_grad)
class MetaModule(nn.Module):
# adopted... | 13,827 | 34.45641 | 105 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/models/wideresnet.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0, activate_before_residual=False):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes, momentum=0.001)
... | 4,564 | 43.320388 | 118 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/models/drop.py | """ DropBlock, DropPath
PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
Papers:
DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
Code:
DropBlock impl ins... | 6,876 | 39.216374 | 118 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/dataloaders/dataloader_webvision_v5.py | import os
import torchvision.transforms as transforms
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from dataloaders.dataloader_clothing1M import sample_traning_set
class imagenet_dataset(Dataset):
def __init__(self, root_dir, transform, num_class):
self.root = root_dir+'imagene... | 11,406 | 40.32971 | 147 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/dataloaders/dataloader_clothing1M.py | import random
import numpy as np
import torch
import torchvision.transforms as transforms
from PIL import Image
from sklearn.metrics import roc_auc_score
from torch.utils.data import Dataset, DataLoader
def sample_traning_set(train_imgs, labels, num_class, num_samples):
random.shuffle(train_imgs)
class_num = ... | 12,139 | 45.872587 | 128 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/dataloaders/dataloader_webvision_v6.py | import os
import torchvision.transforms as transforms
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from dataloaders.dataloader_clothing1M import sample_traning_set
import torch
class imagenet_dataset(Dataset):
def __init__(self, root_dir, transform, num_class):
self.root = root_... | 11,100 | 39.663004 | 202 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/dataloaders/dataloader_webvision_all.py | from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
import random
import numpy as np
from PIL import Image
import torch
import os
class imagenet_dataset(Dataset):
def __init__(self, root_dir, transform, num_class):
self.root = root_dir+'imagenet/val/'
self.... | 8,747 | 42.522388 | 198 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/dataloaders/dataloader_webvision_v4.py | import os
import torchvision.transforms as transforms
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from dataloaders.dataloader_clothing1M import sample_traning_set
class imagenet_dataset(Dataset):
def __init__(self, root_dir, transform, num_class):
self.root = root_dir+'imagene... | 11,406 | 40.32971 | 147 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/dataloaders/dataloader_webvision.py | import os
import torchvision.transforms as transforms
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from dataloaders.dataloader_clothing1M import sample_traning_set
class imagenet_dataset(Dataset):
def __init__(self, root_dir, web_root, transform, num_class):
self.root = root_di... | 9,311 | 41.912442 | 119 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/dataloaders/dataloader_webvision_v8.py | import os
import torchvision.transforms as transforms
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from dataloaders.dataloader_clothing1M import sample_traning_set
import torch
class imagenet_dataset(Dataset):
def __init__(self, root_dir, transform, num_class):
self.root = root_... | 11,237 | 40.014599 | 202 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/dataloaders/dataloader_webvision_v3.py | import os
import torchvision.transforms as transforms
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from dataloaders.dataloader_clothing1M import sample_traning_set
class imagenet_dataset(Dataset):
def __init__(self, root_dir, transform, num_class):
self.root = root_dir+'imagene... | 11,406 | 40.32971 | 147 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/dataloaders/dataloader_webvision_v7.py | import os
import torchvision.transforms as transforms
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from dataloaders.dataloader_clothing1M import sample_traning_set
import torch
class imagenet_dataset(Dataset):
def __init__(self, root_dir, transform, num_class):
self.root = root_... | 11,100 | 39.663004 | 202 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/dataloaders/dataloader_webvision_v1.py | import os
import torchvision.transforms as transforms
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from dataloaders.dataloader_clothing1M import sample_traning_set
class imagenet_dataset(Dataset):
def __init__(self, root_dir, transform, num_class):
self.root = root_dir+'imagene... | 9,196 | 41.382488 | 119 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/dataloaders/dataloader_webvision_v2.py | import os
import torchvision.transforms as transforms
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from dataloaders.dataloader_clothing1M import sample_traning_set
class imagenet_dataset(Dataset):
def __init__(self, root_dir, transform, num_class):
self.root = root_dir+'imagene... | 10,897 | 40.124528 | 147 | py |
CMW-Net | CMW-Net-main/section5/mini_WebVision/dataloaders/dataloader_cifar.py | import json
import os
import pickle
import random
import numpy as np
import torchvision.transforms as transforms
from PIL import Image
from sklearn.metrics import roc_auc_score
from torch.utils.data import Dataset, DataLoader
def unpickle(file):
import _pickle as cPickle
with open(file, 'rb') as fo:
... | 16,765 | 47.880466 | 119 | py |
CMW-Net | CMW-Net-main/section5/ANIMAL-10N/acmwn.py | import sys
sys.path.append('..')
import os
import torch.nn as nn
import torch.nn.parallel
import random
import argparse
import numpy as np
from vgg_mwn import vgg19_bn, VNet
from dataset import Animal10
import torch.optim as optim
from torch.optim import lr_scheduler
import torchvision.transforms as transforms
import t... | 18,169 | 35.781377 | 156 | py |
CMW-Net | CMW-Net-main/section5/ANIMAL-10N/acmwn1.py | import sys
sys.path.append('..')
import os
import torch.nn as nn
import torch.nn.parallel
import random
import argparse
import numpy as np
from vgg_mwn import vgg19_bn, VNet
from dataset import Animal10
import torch.optim as optim
from torch.optim import lr_scheduler
import torchvision.transforms as transforms
import t... | 14,581 | 34.052885 | 156 | py |
CMW-Net | CMW-Net-main/section5/ANIMAL-10N/acmwn2.py | import sys
sys.path.append('..')
import os
import torch.nn as nn
import torch.nn.parallel
import random
import argparse
import numpy as np
from vgg_mwn import vgg19_bn, VNet
from dataset import Animal10
import torch.optim as optim
from torch.optim import lr_scheduler
import torchvision.transforms as transforms
import t... | 14,618 | 33.973684 | 156 | py |
CMW-Net | CMW-Net-main/section5/ANIMAL-10N/dataset.py | from __future__ import print_function
import torch
from torch.utils.data.dataset import Dataset
import numpy as np
import os
from PIL import Image
import PIL
from os import listdir
# https://github.com/kuangliu/pytorch-retinanet/blob/master/transform.py
def resize(img, size, max_size=1000):
'''Resize the input PI... | 2,293 | 27.320988 | 114 | py |
CMW-Net | CMW-Net-main/section5/ANIMAL-10N/vgg_mwn.py | import torch
import torch.nn as nn
from typing import Union, List, Dict, Any, cast
import torch.nn.functional as F
from torch.autograd import Variable
import torch.nn.init as init
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_d... | 15,942 | 36.779621 | 113 | py |
CMW-Net | CMW-Net-main/section5/WebFG-496/load_car.py | import torchvision
import torch
from torch.utils.data import Dataset
from torchvision import transforms
from torchvision.datasets import VisionDataset
from PIL import Image
import os
import os.path
import sys
def has_file_allowed_extension(filename, extensions):
"""Checks if a file is an allowed extension.
... | 7,779 | 34.363636 | 113 | py |
CMW-Net | CMW-Net-main/section5/WebFG-496/load_aircraft.py | import torchvision
import torch
from torch.utils.data import Dataset
from torchvision import transforms
from torchvision.datasets import VisionDataset
from PIL import Image
import os
import os.path
import sys
def has_file_allowed_extension(filename, extensions):
"""Checks if a file is an allowed extension.
... | 7,553 | 33.972222 | 113 | py |
CMW-Net | CMW-Net-main/section5/WebFG-496/load_meta.py | from torchvision.datasets import VisionDataset
from PIL import Image
import os
import os.path
import sys
import random
random.seed(0)
def has_file_allowed_extension(filename, extensions):
"""Checks if a file is an allowed extension.
Args:
filename (string): path to a file
extensions (tuple... | 8,180 | 35.522321 | 140 | py |
CMW-Net | CMW-Net-main/section5/WebFG-496/loss_plm.py | # -*- coding: utf-8 -*-
import torch
import torch.nn.functional as F
torch.manual_seed(0)
torch.cuda.manual_seed(0)
def peer_learning_loss(logits_1, logits_2, labels, drop_rate):
"""
:param logits_1: shape of (N, 200)
:param logits_2: shape of (N, 200)
:param labels: shape of (N,)
:... | 2,879 | 39 | 125 | py |
CMW-Net | CMW-Net-main/section5/WebFG-496/main_v13_v1.py | # -*- coding: utf-8 -*
import os
import time
import torch
import torchvision
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.nn as nn
import torch.optim as optim
import numpy as np
import argparse
from loss_plm import peer_learning_loss
from lr_scheduler import lr_scheduler
from bc... | 17,142 | 34.939203 | 191 | py |
CMW-Net | CMW-Net-main/section5/WebFG-496/resnet.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torchvision
torch.manual_seed(0)
torch.cuda.manual_seed(0)
torch.cuda.manual_seed_all(0)
class ResNet50(nn.Module):
"""
18: ([2, 2, 2, 2], basicblock),
34: ([3, 4, 6, 3], basicblock),
50: ([3, 4, 6, 3], bottleneck),
101: ([3, 4, ... | 1,782 | 28.716667 | 105 | py |
CMW-Net | CMW-Net-main/section5/WebFG-496/load_data.py | import torch.utils.data as data
from PIL import Image
import os
import os.path
IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm']
def is_image_file(filename):
"""Checks if a file is an image.
Args:
filename (string): path to a file
Returns:
bool: True if the filename en... | 3,948 | 28.916667 | 101 | py |
CMW-Net | CMW-Net-main/section5/WebFG-496/bcnn_mwn_v1.py | # -*- coding: utf-8 -*-
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.utils.model_zoo as model_zoo
import math
torch.manual_seed(0)
torch.cuda.manual_seed(0)
torch.cuda.manual_seed_all(0)
model_urls = {
'vgg11': 'https://d... | 14,445 | 33.477327 | 141 | py |
CMW-Net | CMW-Net-main/section5/WebFG-496/main_v13_v2.py | # -*- coding: utf-8 -*
import os
import time
import torch
import torchvision
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.nn as nn
import torch.optim as optim
import numpy as np
import argparse
from loss_plm import peer_learning_loss
from lr_scheduler import lr_scheduler
from bc... | 17,133 | 34.920335 | 191 | py |
CMW-Net | CMW-Net-main/section5/WebFG-496/main_v13.py | # -*- coding: utf-8 -*
import os
import time
import torch
import torchvision
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.nn as nn
import torch.optim as optim
import numpy as np
import argparse
from loss_plm import peer_learning_loss
from lr_scheduler import lr_scheduler
from bc... | 20,366 | 36.856877 | 191 | py |
wae_mdp | wae_mdp-master/variational_mdp.py | import os
from collections import namedtuple
import enum
from enum import Enum
from typing import Tuple, Optional, Callable, Dict, Iterator, NamedTuple, List
import numpy as np
import psutil
from absl import logging
import threading
from tf_agents.environments.wrappers import TimeLimit
from keras.saving.saved_model im... | 114,307 | 47.870457 | 120 | py |
wae_mdp | wae_mdp-master/wasserstein_mdp.py | import gc
import json
import os.path
from collections import namedtuple
import numpy as np
import tensorflow as tf
from typing import Tuple, Optional, Callable, NamedTuple, List, Union, Dict
import tensorflow.keras as tfk
import tensorflow.keras.layers as tfkl
from tensorflow.keras.utils import Progbar
from tensorflow... | 75,268 | 46.698986 | 129 | py |
wae_mdp | wae_mdp-master/variational_action_discretizer.py | import os
from typing import Tuple, Optional, List, Callable
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
import tf_agents
from tensorflow.keras import Model
from tensorflow.keras.models import Sequential, model_from_json
from tensorflow.keras.layers import Input, Concatenate, Reshap... | 60,245 | 53.12938 | 151 | py |
wae_mdp | wae_mdp-master/train.py | import functools
import importlib
import json
import os
import random
import sys
from collections import namedtuple
import datetime
from typing import Optional, List
import numpy as np
import tensorflow as tf
import tf_agents
from absl import app, flags
from tensorflow.keras.layers import Dense
from tensorflow.keras i... | 59,660 | 42.045455 | 129 | py |
wae_mdp | wae_mdp-master/reinforcement_learning/sac_training.py | import functools
import json
import os
import sys
path = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, path + '/../')
from collections import namedtuple
from typing import Tuple, Callable, Optional, List
import threading
import timeit
import datetime
import numpy as np
import random
try:
import r... | 30,588 | 41.25 | 120 | py |
wae_mdp | wae_mdp-master/reinforcement_learning/dqn_training.py | import math
import os
import sys
path = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, path + '/../')
from typing import Tuple, Callable, Optional, List
import functools
import threading
import datetime
try:
import reverb
except ImportError as ie:
print(ie, "Reverb is not installed on your sys... | 20,414 | 39.187008 | 120 | py |
wae_mdp | wae_mdp-master/reinforcement_learning/c51_training.py | import math
import functools
import tf_agents
import os
import sys
from typing import Tuple, Callable, Optional, List
import threading
import datetime
from absl import app
from absl import flags
import PIL
import tensorflow as tf
from tensorflow.python.keras.engine import sequential
from tensorflow.python.keras.utils... | 14,707 | 37.807388 | 120 | py |
wae_mdp | wae_mdp-master/util/replay_buffer_tools.py | from abc import ABC, abstractmethod
import tensorflow as tf
import os
class PriorityHandler(ABC):
def __init__(self):
self.max_priority = None
@abstractmethod
def update_priority(self, key, value, **kwargs):
return NotImplemented
@abstractmethod
def load_or_initialize_checkpoint... | 4,653 | 38.777778 | 116 | py |
wae_mdp | wae_mdp-master/util/nn.py | from typing import NamedTuple, Tuple, Callable, Optional
import tensorflow as tf
import tensorflow.keras as tfk
import tensorflow.keras.layers as tfkl
import tensorflow_probability.python.bijectors as tfb
from tensorflow_probability.python import bijectors as tfb
class ModelArchitecture(NamedTuple):
hidden_units... | 1,791 | 32.811321 | 113 | py |
wae_mdp | wae_mdp-master/layers/autoregressive_bernoulli.py | from typing import Union, Tuple, Callable, Optional
import tensorflow as tf
from tensorflow import keras as tfk
from tensorflow.keras import layers as tfkl
import tensorflow_probability.python.bijectors as tfb
import tensorflow_probability.python.distributions as tfd
import tensorflow_probability.python.layers as tfpl... | 14,852 | 38.927419 | 119 | py |
wae_mdp | wae_mdp-master/layers/lipschitz_functions.py | from typing import Optional
from tensorflow import keras as tfk
import tensorflow.keras.layers as tfkl
class SteadyStateLipschitzFunction(tfk.Model):
def __init__(
self,
latent_state: tfk.Input,
next_latent_state: tfk.Input,
steady_state_lipschitz_network: tfk.Mode... | 2,153 | 37.464286 | 114 | py |
wae_mdp | wae_mdp-master/layers/steady_state_network.py | from typing import Optional, Callable, Union, Tuple
import tensorflow as tf
import tensorflow.keras as tfk
import tensorflow.keras.layers as tfkl
import tensorflow_probability.python.bijectors as tfb
import tensorflow_probability.python.distributions as tfd
from tf_agents.typing.types import Float
from layers.autoreg... | 3,086 | 35.317647 | 94 | py |
wae_mdp | wae_mdp-master/layers/latent_policy.py | import tensorflow as tf
import tensorflow.keras as tfk
import tensorflow.keras.layers as tfkl
import tensorflow_probability.python.bijectors as tfb
import tensorflow_probability.python.distributions as tfd
from tf_agents.typing.types import Float
from layers.base_models import DiscreteDistributionModel
class LatentP... | 1,548 | 32.673913 | 81 | py |
wae_mdp | wae_mdp-master/layers/decoders.py | from typing import Optional, Union, Tuple
import numpy as np
import tensorflow as tf
from tensorflow import keras as tfk
import tensorflow.keras.layers as tfkl
import tensorflow_probability.python.distributions as tfd
from tf_agents.typing.types import Float
from layers.base_models import DistributionModel
class St... | 5,599 | 36.837838 | 97 | py |
wae_mdp | wae_mdp-master/layers/base_models.py | import abc
import tensorflow.keras as tfk
import tensorflow_probability.python.distributions as tfd
class DiscreteDistributionModel(tfk.Model):
@abc.abstractmethod
def relaxed_distribution(self, *args, **kwargs) -> tfd.Distribution:
return NotImplemented
@abc.abstractmethod
def discrete_dis... | 1,011 | 27.914286 | 73 | py |
wae_mdp | wae_mdp-master/layers/encoders.py | from typing import Optional, Callable, Union, Tuple
import enum
import tensorflow as tf
import tensorflow.keras as tfk
import tensorflow.keras.layers as tfkl
import tensorflow_probability.python.bijectors as tfb
import tensorflow_probability.python.distributions as tfd
from tf_agents.typing.types import Float
from la... | 14,551 | 35.747475 | 109 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CS-CMNIST/domainbed/sweep_train.py | import argparse
import collections
import json
import os
import random
import sys
import time
import uuid
import pdb
import numpy as np
import torch
import torch.utils.data
import datasets
import hparams_registry
import algorithms
import misc
from fast_data_loader import InfiniteDataLoader, FastDataLoader
'''
pytho... | 16,218 | 45.606322 | 155 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CS-CMNIST/domainbed/misc.py | """
Things that don't belong anywhere else
"""
import hashlib
import json
import os
import sys
from shutil import copyfile
import pdb
import numpy as np
import torch
import tqdm
from collections import Counter
def make_weights_for_balanced_classes(dataset):
counts = Counter()
classes = []
for _, y in data... | 4,236 | 26.335484 | 90 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CS-CMNIST/domainbed/utils.py | import pdb
from torchvision import transforms
import torch
import math
class Dataset(object):
r"""An abstract class representing a :class:`Dataset`.
All datasets that represent a map from keys to data samples should subclass
it. All subclasses should overrite :meth:`__getitem__`, supporting fetching a
... | 6,608 | 37.649123 | 102 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CS-CMNIST/domainbed/networks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models
import pdb
import misc
import wide_resnet
class MLP(nn.Module):
"""Just an MLP"""
def __init__(self, n_inputs, n_outputs, hparams):
super(MLP, self).__init__()
self.input = nn.Linear(n_inputs, hparams... | 7,446 | 28.434783 | 109 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CS-CMNIST/domainbed/wide_resnet.py | """
From https://github.com/meliketoy/wide-resnet.pytorch
"""
import sys
import pdb
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torch.autograd import Variable
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(
in_pl... | 3,503 | 29.736842 | 79 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CS-CMNIST/domainbed/datasets.py | import os
from collections import defaultdict
import PIL
from PIL import Image, ImageFile
# from domainbed.lib.corrupted_cifar10_protocol import CORRUPTED_CIFAR10_PROTOCOL
import h5py
import numpy as np
import torch
from utils import TensorDataset
from utils import TensorDataset
from utils import TensorDataset, save... | 10,644 | 33.787582 | 117 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CS-CMNIST/domainbed/fast_data_loader.py | import torch
class _InfiniteSampler(torch.utils.data.Sampler):
"""Wraps another Sampler to yield an infinite stream."""
def __init__(self, sampler):
self.sampler = sampler
def __iter__(self):
while True:
for batch in self.sampler:
yield batch
class InfiniteData... | 1,894 | 28.609375 | 79 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CS-CMNIST/domainbed/algorithms.py | import copy
import numpy as np
import math
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import pdb
import torch.nn.functional as F
import networks
# from ..domainbed.lib.misc import random_pairs_of_minibatches
ALGORITHMS = [
#'ERM',
#'IRM',
#'IBERM'... | 24,915 | 36.132638 | 133 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CS-CMNIST/domainbed/scripts/download.py | from torchvision.datasets import MNIST
# import xml.etree.ElementTree as ET
from zipfile import ZipFile
import argparse
import tarfile
import shutil
# import gdown
# import uuid
import json
import os
# utils #######################################################################
def stage_path(data_dir, name):
... | 8,410 | 31.727626 | 109 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/train_new.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import collections
import json
import os
import random
import sys
import time
import uuid
import numpy as np
import PIL
import torch
import torchvision
import torch.utils.data
import datasets
import hparams_registry
import algorit... | 10,298 | 37.718045 | 87 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/command_launchers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
A command launcher launches a list of commands on a cluster; implement your own
launcher to add support for your cluster. We've provided an example launcher
which runs all commands serially on the local machine.
"""
import subprocess
import ti... | 1,787 | 27.83871 | 79 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/sweep.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Run sweeps
"""
import argparse
import copy
import getpass
import hashlib
import json
import os
import random
import shutil
import time
import uuid
import numpy as np
import torch
import datasets
import hparams_registry
import algorithms
impo... | 7,271 | 35.913706 | 121 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/download.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from torchvision.datasets import MNIST
import xml.etree.ElementTree as ET
from zipfile import ZipFile
import argparse
import tarfile
import shutil
import gdown
import uuid
import json
import os
from wilds.datasets.camelyon17_dataset import Camelyo... | 9,103 | 32.105455 | 108 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/misc.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Things that don't belong anywhere else
"""
import hashlib
import json
import os
import sys
from shutil import copyfile
from collections import OrderedDict, defaultdict
from numbers import Number
import operator
import numpy as np
import torch... | 6,708 | 28.425439 | 99 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/networks.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models
import wide_resnet
import copy
def remove_batch_norm_from_resnet(model):
fuse = torch.nn.utils.fusion.fuse_conv_bn_eval
model.eval()
model.... | 7,126 | 30.122271 | 80 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/wide_resnet.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
From https://github.com/meliketoy/wide-resnet.pytorch
"""
import sys
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torch.autograd import Variable
def conv3x3(in_plane... | 3,242 | 29.885714 | 79 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/datasets.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import torch
from PIL import Image, ImageFile
from torchvision import transforms
import torchvision.datasets.folder
from torch.utils.data import TensorDataset
from torch.utils import data
from torch.utils.data import Subset
from torchvis... | 12,309 | 34.784884 | 105 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/helpers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
DEBUG_DATASETS = ['Debug28', 'Debug224']
def make_minibatches(dataset, batch_size):
"""Test helper to make a minibatches array like train.py"""
minibatches = []
for env in dataset:
X = torch.stack([env[i][0] for i... | 509 | 30.875 | 70 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/train.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import collections
import json
import os
import random
import sys
import time
import uuid
import numpy as np
import PIL
import torch
import torchvision
import torch.utils.data
import datasets
import hparams_registry
import algorit... | 10,387 | 37.474074 | 87 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/fast_data_loader.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
class _InfiniteSampler(torch.utils.data.Sampler):
"""Wraps another Sampler to yield an infinite stream."""
def __init__(self, sampler):
self.sampler = sampler
def __iter__(self):
while True:
fo... | 2,156 | 28.148649 | 79 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/algorithms.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd as autograd
from torch.autograd import Variable
import math
import copy
import numpy as np
from collections import defaultdict, OrderedDict
try:
from back... | 75,150 | 37.303262 | 139 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/test/test_datasets.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""Unit tests."""
import argparse
import itertools
import json
import os
import subprocess
import sys
import time
import unittest
import uuid
import torch
from domainbed import datasets
from domainbed import hparams_registry
from domainbed impor... | 1,454 | 28.1 | 80 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/test/test_networks.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import itertools
import json
import os
import subprocess
import sys
import time
import unittest
import uuid
import torch
from domainbed import datasets
from domainbed import hparams_registry
from domainbed import algorithms
from d... | 1,181 | 30.105263 | 79 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/test/test_models.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""Unit tests."""
import argparse
import itertools
import json
import os
import subprocess
import sys
import time
import unittest
import uuid
import torch
from domainbed import datasets
from domainbed import hparams_registry
from domainbed impor... | 1,427 | 31.454545 | 91 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/test/test_model_selection.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""Unit tests."""
import argparse
import itertools
import json
import os
import subprocess
import sys
import time
import unittest
import uuid
import torch
import model_selection
from query import Q
from parameterized import parameterized
def m... | 4,305 | 29.539007 | 77 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/test/scripts/test_train.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# import argparse
# import itertools
import json
import os
import subprocess
# import sys
# import time
import unittest
import uuid
import torch
# import datasets
# import hparams_registry
# import algorithms
# import networks
# from parameterize... | 1,636 | 31.74 | 80 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/test/scripts/test_sweep.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import itertools
import json
import os
import subprocess
import sys
import time
import unittest
import uuid
import torch
from domainbed import datasets
from domainbed import hparams_registry
from domainbed import algorithms
from d... | 5,140 | 38.546154 | 80 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/test/scripts/test_collect_results.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import itertools
import json
import os
import subprocess
import sys
import time
import unittest
import uuid
import torch
from domainbed import datasets
from domainbed import hparams_registry
from domainbed import algorithms
from d... | 3,677 | 31.263158 | 96 | py |
Joint-covariate-alignment-and-concept-alignment-for-domain-generalization | Joint-covariate-alignment-and-concept-alignment-for-domain-generalization-main/CMNIST/scripts/save_images.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Save some representative images from each dataset to disk.
"""
import random
import torch
import argparse
from domainbed import hparams_registry
from domainbed import datasets
import imageio
import os
from tqdm import tqdm
if __name__ == '__ma... | 2,029 | 38.803922 | 113 | py |
FeatureImportanceDL | FeatureImportanceDL-master/example.py | import numpy as np
import os
import tensorflow.keras as keras
from tensorflow.keras import backend as K
from src.FeatureSelector import FeatureSelector
from DataGenerator import generate_data, get_one_hot
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
# Dataset parameters
N_TRAIN_SAMPLES = 512
N_VAL_SAMPLES = 256
N_TEST_S... | 2,886 | 40.242857 | 109 | py |
FeatureImportanceDL | FeatureImportanceDL-master/src/Selector.py | import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Input, Flatten
from tensorflow.keras import backend as K
from tensorflow.keras.callbacks import TensorBoard
class SelectorNetwork:
def __init__(self, mask_batch_size, tensorboard_logs_di... | 4,025 | 36.277778 | 97 | py |
FeatureImportanceDL | FeatureImportanceDL-master/src/FeatureSelector.py | import datetime
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import backend as K
from .MaskOptimizer import MaskOptimizer
from .Operator import OperatorNetwork
from .Selector import SelectorNetwork
logs_base_dir = "./logs"
os.makedirs(logs_base_dir, exist_ok=True)
def mean_squared_erro... | 6,931 | 46.479452 | 123 | py |
FeatureImportanceDL | FeatureImportanceDL-master/src/Operator.py | import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Input, Flatten, Reshape, Conv2D, MaxPool2D
from tensorflow.keras.callbacks import TensorBoard
class OperatorNetwork:
def __init__(self, x_batch_size, mask_batch_size, tensorboard_logs_di... | 10,348 | 46.255708 | 119 | py |
GAIN | GAIN-master/data_loader.py | # coding=utf-8
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dis... | 1,595 | 28.555556 | 74 | py |
JTA-Dataset | JTA-Dataset-master/to_poses.py | # -*- coding: utf-8 -*-
import numpy as np
from threading import Thread
import time
from path import Path
import json
from joint import Joint
from pose import Pose
import click
import sys
assert sys.version_info >= (3, 6), '[!] This script requires Python >= 3.6'
def get_pose(frame_data, person_id):
# type: (np... | 3,556 | 29.401709 | 107 | py |
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