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fairlex | fairlex-main/configs/supported.py | import torch
import torch.nn as nn
# metrics
from wilds.common.metrics.loss import ElementwiseLoss, MultiTaskLoss
from wilds.common.metrics.all_metrics import Metric, multiclass_logits_to_pred
from wilds.common.utils import minimum
import sklearn
class F1(Metric):
def __init__(self, prediction_fn=None, name=None... | 2,586 | 37.044118 | 114 | py |
fairlex | fairlex-main/dataloaders/ecthr_dataset.py | import torch
import pandas as pd
from datasets import load_dataset
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.utils import map_to_id_array
from configs.supported import F1, binary_logits_to_pred_v2
from wilds.common.grouper import CombinatorialGrouper
EAST_EUROPEAN_COUNTRIES = {'RUSSIA', '... | 6,292 | 40.401316 | 109 | py |
fairlex | fairlex-main/dataloaders/cail_dataset.py | import os
import json
import torch
import pandas as pd
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.utils import map_to_id_array
from wilds.common.metrics.all_metrics import F1, multiclass_logits_to_pred
from wilds.common.grouper import CombinatorialGrouper
REGIONS = {'Beijing': 0, 'Liaoning... | 6,162 | 38.50641 | 140 | py |
fairlex | fairlex-main/dataloaders/fscs_dataset.py | import torch
import pandas as pd
from datasets import load_dataset
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.utils import map_to_id_array
from wilds.common.metrics.all_metrics import F1, multiclass_logits_to_pred
from wilds.common.grouper import CombinatorialGrouper
LEGAL_AREAS = {'other'... | 5,657 | 40 | 152 | py |
fairlex | fairlex-main/dataloaders/scotus_dataset.py | import torch
import pandas as pd
from datasets import load_dataset
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.utils import map_to_id_array
from wilds.common.metrics.all_metrics import F1, multiclass_logits_to_pred
from wilds.common.grouper import CombinatorialGrouper
class SCOTUSDataset(W... | 5,169 | 38.769231 | 199 | py |
texture-vs-shape | texture-vs-shape-master/models/load_pretrained_models.py | """
Read PyTorch model from .pth.tar checkpoint.
"""
import os
import sys
from collections import OrderedDict
import torch
import torchvision
import torchvision.models
from torch.utils import model_zoo
def load_model(model_name):
model_urls = {
'resnet50_trained_on_SIN': 'https://bitbucket.org/robert... | 3,796 | 41.188889 | 295 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/load_datasets.py | import os
import sys
import numpy as np
import pandas as pd
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import random_split
def import_data(dataset_type):
if dataset_type == 'train':
transform_data = transforms.Compose([
transforms.RandomCr... | 2,024 | 27.928571 | 85 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/validate.py | import sys
from data.read_data import read_data
import json
import yaml
import torch
import os
import collections
def np_to_weights(weights_np):
weights = collections.OrderedDict()
for w in weights_np:
weights[w] = torch.tensor(weights_np[w])
return weights
def validate(model, settings, device):
... | 3,194 | 31.602041 | 91 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/train.py | from __future__ import print_function
import sys
import yaml
import torch
import os
import collections
from data.read_data import read_data
def weights_to_np(weights):
weights_np = collections.OrderedDict()
for w in weights:
weights_np[w] = weights[w].cpu().detach().numpy()
return weights_np
d... | 2,026 | 27.549296 | 77 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_model.py | from torch import nn
import torch
import torch.nn.functional as F
from models.pytorch_models import *
# Create an initial CNN Model
def create_seed_model(net='VGG16'):
try:
model = VGG(net)
except:
model = VGG('VGG16')
#loss = nn.NLLLoss()
loss = nn.CrossEntropyLoss()
optimizer =... | 416 | 17.954545 | 73 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/init_model.py | #from fedn.utils.pytorchmodel import PytorchModelHelper
from pytorch_model import create_seed_model
import torch
import numpy as np
if __name__ == '__main__':
# Create a seed model and push to Minio
model, _, _ = create_seed_model('VGG16')
outfile_name = "../../seed/VGG16_torch.npz"
np.savez_compressed(outfile_na... | 345 | 30.454545 | 56 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/dla.py | '''DLA in PyTorch.
Reference:
Deep Layer Aggregation. https://arxiv.org/abs/1707.06484
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
sel... | 4,425 | 31.544118 | 83 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/shufflenetv2.py | '''ShuffleNetV2 in PyTorch.
See the paper "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class ShuffleBlock(nn.Module):
def __init__(self, groups=2):
super(ShuffleBlock, self).__init__()
... | 5,530 | 32.932515 | 107 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/regnet.py | '''RegNet in PyTorch.
Paper: "Designing Network Design Spaces".
Reference: https://github.com/keras-team/keras-applications/blob/master/keras_applications/efficientnet.py
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class SE(nn.Module):
'''Squeeze-and-Excitation block.'''
def __in... | 4,548 | 28.160256 | 106 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/efficientnet.py | '''EfficientNet in PyTorch.
Paper: "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks".
Reference: https://github.com/keras-team/keras-applications/blob/master/keras_applications/efficientnet.py
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
def swish(x):
return x ... | 5,719 | 31.5 | 106 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/pnasnet.py | '''PNASNet in PyTorch.
Paper: Progressive Neural Architecture Search
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class SepConv(nn.Module):
'''Separable Convolution.'''
def __init__(self, in_planes, out_planes, kernel_size, stride):
super(SepConv, self).__init__()
se... | 4,258 | 32.801587 | 105 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/resnet.py | '''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansi... | 4,218 | 30.721805 | 83 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/dla_simple.py | '''Simplified version of DLA in PyTorch.
Note this implementation is not identical to the original paper version.
But it seems works fine.
See dla.py for the original paper version.
Reference:
Deep Layer Aggregation. https://arxiv.org/abs/1707.06484
'''
import torch
import torch.nn as nn
import torch.nn.function... | 4,084 | 30.666667 | 83 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/mobilenetv2.py | '''MobileNetV2 in PyTorch.
See the paper "Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
'''expand + depthwise + pointwise'''
def __init... | 3,092 | 34.551724 | 114 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/vgg.py | '''VGG11/13/16/19 in Pytorch.'''
import torch
import torch.nn as nn
cfg = {
'VGG_mini': [8,'M', 512, 'M','M','M','M'],
'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG16': [64, 64, 'M', 1... | 1,489 | 29.408163 | 117 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/densenet.py | '''DenseNet in PyTorch.'''
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
def __init__(self, in_planes, growth_rate):
super(Bottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, 4*gr... | 3,542 | 31.805556 | 96 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/preact_resnet.py | '''Pre-activation ResNet in PyTorch.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Identity Mappings in Deep Residual Networks. arXiv:1603.05027
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class PreActBlock(nn.Module):
'''Pre-activation version of the BasicBlock.... | 4,078 | 33.277311 | 102 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/googlenet.py | '''GoogLeNet with PyTorch.'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Inception(nn.Module):
def __init__(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
super(Inception, self).__init__()
# 1x1 conv branch
self.b1 = nn.Sequential(
... | 3,221 | 28.833333 | 83 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/resnext.py | '''ResNeXt in PyTorch.
See the paper "Aggregated Residual Transformations for Deep Neural Networks" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
'''Grouped convolution block.'''
expansion = 2
def __init__(self, in_planes, cardinality=32... | 3,478 | 35.239583 | 129 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/senet.py | '''SENet in PyTorch.
SENet is the winner of ImageNet-2017. The paper is not released yet.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(... | 4,027 | 32.016393 | 102 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/shufflenet.py | '''ShuffleNet in PyTorch.
See the paper "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class ShuffleBlock(nn.Module):
def __init__(self, groups):
super(ShuffleBlock, self).__init... | 3,542 | 31.209091 | 126 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/lenet.py | '''LeNet in PyTorch.'''
import torch.nn as nn
import torch.nn.functional as F
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16*5*5, 120)
self.fc2 = nn.Linear... | 699 | 28.166667 | 43 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/mobilenet.py | '''MobileNet in PyTorch.
See the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
'''Depthwise conv + Pointwise conv'''
def __init__(self, in_planes, out_... | 2,025 | 31.677419 | 123 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/models/pytorch_models/dpn.py | '''Dual Path Networks in PyTorch.'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer):
super(Bottleneck, self).__init__()
self.out_planes = out_planes
sel... | 3,562 | 34.989899 | 116 | py |
FEDn-client-cifar10-pytorch | FEDn-client-cifar10-pytorch-main/client/data/read_data.py | import pandas as pd
from sklearn.model_selection import train_test_split
import torch
import numpy as np
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import random_split
def read_data(filename, nr_examples=1000, batch_size=100, dataset_type='test'):
""" Helper function to re... | 1,510 | 34.97619 | 85 | py |
elo-rainbow | elo-rainbow-main/main.py | # -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2017 Kai Arulkumaran
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use,... | 11,997 | 47.772358 | 434 | py |
elo-rainbow | elo-rainbow-main/memory.py | # -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2017 Kai Arulkumaran
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use,... | 9,142 | 51.545977 | 434 | py |
elo-rainbow | elo-rainbow-main/search-client.py | from main import *
import requests
import json
import time
import numpy as np
import traceback
with open('server-addr', 'r') as f:
url = f.readline().strip()
print("URL: " + url)
p_data = None
if __name__ == '__main__':
torch.multiprocessing.set_start_method('spawn')
is_running = True
while... | 1,620 | 25.57377 | 105 | py |
elo-rainbow | elo-rainbow-main/model.py | # -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2017 Kai Arulkumaran
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use,... | 6,419 | 39.89172 | 434 | py |
elo-rainbow | elo-rainbow-main/eval-client.py | from main import *
import requests
import json
import time
import numpy as np
import traceback
with open('server-addr', 'r') as f:
url = f.readline().strip()
print("URL: " + url)
p_data = None
if __name__ == '__main__':
torch.multiprocessing.set_start_method('spawn')
while True:
try:
... | 721 | 18.513514 | 60 | py |
elo-rainbow | elo-rainbow-main/agent.py | # -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2017 Kai Arulkumaran
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use,... | 11,479 | 43.324324 | 434 | py |
elo-rainbow | elo-rainbow-main/env.py | # -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2017 Kai Arulkumaran
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use,... | 4,087 | 38.307692 | 434 | py |
NAA | NAA-main/responsegeneration/evaluate.py | import argparse
import json
import logging
import os
from pprint import pformat
from tqdm import tqdm
import torch
from transformers import GPT2Config, GPT2Tokenizer, GPT2LMHeadModel
from haystack.nodes.evaluator.evaluator import semantic_answer_similarity
from DataLoader.dataloader import *
from Evaluation.bleu impo... | 6,185 | 31.051813 | 123 | py |
NAA | NAA-main/responsegeneration/utils.py | import json
import os
import re
import string
import warnings
from glob import glob
from itertools import chain
from operator import itemgetter
import torch
import random
from tqdm.notebook import tqdm
import torch.nn.functional as F
##################################
# Data Formatting Util Functions #
###############... | 16,739 | 34.617021 | 111 | py |
NAA | NAA-main/responsegeneration/DataLoader/GeneralData.py | import os
from collections import defaultdict
from itertools import chain
import json
import random
import numpy as np
from tqdm import tqdm
import torch
from torch.utils.data import Dataset
import matplotlib.pyplot as plt
from utils import *
class GeneralMCDataset(Dataset):
def __init__(self, args, tokenizer, d... | 2,840 | 33.228916 | 125 | py |
NAA | NAA-main/responsegeneration/DataLoader/dataset_utils.py | import json
import os
import re
import string
import random
import warnings
from glob import glob
from itertools import chain
from operator import itemgetter
import torch
from torch.utils.data import Dataset, Sampler, DistributedSampler
from utils import *
#####################################
# Dataset/Dataloader Ut... | 12,519 | 32.297872 | 110 | py |
NAA | NAA-main/responsegeneration/DataLoader/dataloader.py | from torch.utils.data import DataLoader
import DataLoader.MARCOData as MARCOData
import DataLoader.SQuADData as SQuADData
import DataLoader.UbuntuData as UbuntuData
import DataLoader.GeneralData as GeneralData
from DataLoader.dataset_utils import *
from utils import *
def get_dataloaders(args, tokenizer):
"""
... | 5,815 | 35.35 | 109 | py |
NAA | NAA-main/responsegeneration/DataLoader/MARCOData.py | from itertools import chain
import ijson
import random
from tqdm import tqdm
from torch.utils.data import Dataset
from utils import *
from DataLoader.dataset_utils import *
# 'I'm sorry, I don't know' encoded by the gpt2 tokenizer
dontknow = [40, 1101, 7926, 11, 314, 836, 470, 760, 13]
class MARCODataset(Dataset):
... | 5,924 | 37.474026 | 123 | py |
NAA | NAA-main/responsegeneration/DataLoader/SQuADData.py | import random
from tqdm import tqdm
from torch.utils.data import Dataset
from utils import *
from DataLoader.dataset_utils import *
class SQuADDataset(Dataset):
def __init__(self, args, tokenizer, dataset_path):
"""
Initialize dataset for SQuAD dataset.
"""
if args.second_loss == ... | 3,268 | 34.923077 | 123 | py |
NAA | NAA-main/responsegeneration/DataLoader/UbuntuData.py | import random
from tqdm import tqdm
from torch.utils.data import Dataset
from utils import *
from DataLoader.dataset_utils import *
class UbuntuBCDataset(Dataset):
"""
Ubuntu Dialogue Corpus dataset formatted for binary classification second head.
"""
def __init__(self, args, tokenizer, dataset_path... | 5,687 | 34.329193 | 85 | py |
BBB-Penetration-Prediction | BBB-Penetration-Prediction-main/rgcn.py | import pandas as pd
import torch
from torch.nn import Linear
from torch.nn import Parameter
import torch.nn.functional as F
from torch_geometric.nn import RGCNConv
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import roc_auc_score
fro... | 6,842 | 35.989189 | 99 | py |
BBB-Penetration-Prediction | BBB-Penetration-Prediction-main/graph.py | import pandas as pd
import numpy as np
import torch
import deepchem as dc
from sklearn.preprocessing import StandardScaler
from torch_geometric.data import Data
df_drug = pd.read_csv('drug_list_all.csv')
# edges
df_drugsim = pd.read_csv('drug_similarity.csv')
df_drugpro = pd.read_csv('drug_protein_interaction.csv')
... | 1,688 | 36.533333 | 83 | py |
BBB-Penetration-Prediction | BBB-Penetration-Prediction-main/rgcn_drugsim.py | import pandas as pd
import torch
from torch.nn import Linear
from torch.nn import Parameter
import torch.nn.functional as F
from torch_geometric.nn import RGCNConv
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import roc_auc_score
fro... | 6,850 | 36.032432 | 99 | py |
KD-MVS | KD-MVS-master/test.py | import argparse, os, time, sys, gc, cv2, signal
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from plyfile import PlyData, PlyElement
from multiprocessing import Pool
from functools import partial... | 20,267 | 45.808314 | 204 | py |
KD-MVS | KD-MVS-master/train_kd.py | import argparse, os, sys, time, gc, datetime
from models.module import cas_mvsnet_loss, cas_mvsnet_loss_kl
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from data... | 19,534 | 47.234568 | 162 | py |
KD-MVS | KD-MVS-master/train_unsup.py | import argparse, os, sys, time, gc, datetime
from models.module import unsup_loss
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from datasets import find_dataset_... | 18,762 | 46.864796 | 196 | py |
KD-MVS | KD-MVS-master/infer.py | import argparse, os, time, sys, gc, cv2, signal
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from plyfile import PlyData, PlyElement
from multiprocessing import Pool
from functools import partial... | 9,011 | 42.326923 | 204 | py |
KD-MVS | KD-MVS-master/tools/utils.py | import numpy as np
import torchvision.utils as vutils
import torch, random
import torch.nn.functional as F
import cv2
from typing import List, Union, Tuple, Dict
# from refile import *
import io
import os
import json
import numpy as np
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
class NanError(E... | 12,851 | 32.381818 | 107 | py |
KD-MVS | KD-MVS-master/models/cas_mvsnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .module import *
Align_Corners_Range = False
class DepthNet(nn.Module):
def __init__(self):
super(DepthNet, self).__init__()
def forward(self, features, proj_matrices, depth_values, num_depth, cost_regularization, prob_volume_ini... | 17,135 | 48.669565 | 177 | py |
KD-MVS | KD-MVS-master/models/unsup_modules.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class SSIM(nn.Module):
"""Layer to compute the SSIM loss between a pair of images
"""
def __init__(self):
super(SSIM, self).__init__()
self.mu_x_pool = nn.AvgPool2d(3, 1)
self.mu_y_pool = nn.AvgPool2d(3, 1)
... | 2,771 | 36.972603 | 90 | py |
KD-MVS | KD-MVS-master/models/module.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import time
import sys
from .unsup_homo import *
from .unsup_modules import *
def init_bn(module):
if module.weight is not None:
nn.init.ones_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
return
... | 49,364 | 43.593496 | 149 | py |
KD-MVS | KD-MVS-master/models/unsup_homo.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# from config import device
device = torch.device("cuda" if torch.cuda.is_available() else 'cpu')
def inverse_warping(img, left_cam, right_cam, depth):
# img: [batch_size, height, width, channels]
# cameras (K, R, t)
R_left = left_cam[:, ... | 8,534 | 42.324873 | 126 | py |
KD-MVS | KD-MVS-master/datasets/dtu_kd.py | from torch.utils.data import Dataset
import numpy as np
import os, cv2, time, math
from PIL import Image
from datasets.data_io import *
# By Yikang Ding (2022.02)
class MVSDataset(Dataset):
def __init__(self, datapath, pseudo_depth, listfile, mode, nviews=5, ndepths=192, interval_scale=1.06, **kwargs):
sup... | 8,699 | 38.189189 | 119 | py |
KD-MVS | KD-MVS-master/datasets/dtu_yao.py | from torch.utils.data import Dataset
import numpy as np
import os, cv2, time, math
from PIL import Image
from datasets.data_io import *
# the DTU dataset preprocessed by Yao Yao (only for training)
class MVSDataset(Dataset):
def __init__(self, datapath, listfile, mode, nviews, ndepths=192, interval_scale=1.06, **k... | 7,012 | 37.322404 | 119 | py |
KD-MVS | KD-MVS-master/datasets/dtu_ut.py | from torch.utils.data import Dataset
import torch
import numpy as np
import os, cv2, time, math
from PIL import Image
from torchvision import transforms
from datasets.data_io import *
class RandomGamma():
def __init__(self, min_gamma=0.7, max_gamma=1.5, clip_image=False):
self._min_gamma = min_gamma
... | 7,366 | 36.586735 | 119 | py |
KD-MVS | KD-MVS-master/datasets/dtu_yao_test.py | from torch.utils.data import Dataset
import numpy as np
import os, cv2, time, math
from PIL import Image
from datasets.data_io import *
class MVSDataset(Dataset):
def __init__(self, datapath, listfile, mode, nviews, ndepths=192, interval_scale=1.06, **kwargs):
super(MVSDataset, self).__init__()
sel... | 6,964 | 37.269231 | 119 | py |
KD-MVS | KD-MVS-master/datasets/general_eval.py | from torch.utils.data import Dataset
import numpy as np
import os, cv2, time
from PIL import Image
from datasets.data_io import *
s_h, s_w = 0, 0
class MVSDataset(Dataset):
def __init__(self, datapath, listfile, mode, nviews, ndepths=192, interval_scale=1.06, **kwargs):
super(MVSDataset, self).__init__()
... | 7,173 | 37.98913 | 118 | py |
Semi-Leak | Semi-Leak-master/mia_augmented.py | from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
import numpy as np
import os
import pickle as pkl
from torch.utils.data import WeightedRandomSampler, DataLoader
from scipy.spatial.... | 21,842 | 41.829412 | 164 | py |
Semi-Leak | Semi-Leak-master/query_target.py | # import needed library
import os
import random
import warnings
import numpy as np
import torch
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.multiprocessing as mp
from utils import net_builder
from datasets.ssl_dataset import SSLDataset
from datasets.data_utils import get_data_loader
imp... | 14,504 | 40.442857 | 164 | py |
Semi-Leak | Semi-Leak-master/flexmatch.py | import os
import logging
import random
import warnings
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
from utils import net_builder, get_logger, count_parameters, over_write_args_fr... | 15,769 | 41.621622 | 164 | py |
Semi-Leak | Semi-Leak-master/utils.py | import os
import time
from torch.utils.tensorboard import SummaryWriter
import logging
import yaml
def over_write_args_from_file(args, yml):
if yml == '':
return
with open(yml, 'r', encoding='utf-8') as f:
dic = yaml.load(f.read(), Loader=yaml.Loader)
for k in dic:
setattr(... | 3,896 | 33.486726 | 122 | py |
Semi-Leak | Semi-Leak-master/mia_normal.py | from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
import numpy as np
import os
import pickle as pkl
from torch.utils.data import WeightedRandomSampler, DataLoader
import time
torch.... | 38,549 | 43.669757 | 210 | py |
Semi-Leak | Semi-Leak-master/train_utils.py | import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
from torch.optim.lr_scheduler import LambdaLR
import torch.nn.functional as F
import math
import time
import os
from copy import deepcopy
from torch.optim.optimizer import Optimizer, required
import copy
from custom_writer import Cust... | 14,475 | 33.222222 | 128 | py |
Semi-Leak | Semi-Leak-master/fullysupervised.py | # import needed library
import os
import logging
import random
import warnings
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
from utils import net_builder, get_logger, count_parame... | 14,678 | 40.466102 | 164 | py |
Semi-Leak | Semi-Leak-master/fixmatch.py | import os
import logging
import random
import warnings
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
from utils import net_builder, get_logger, count_parameters, over_write_args_fr... | 15,438 | 40.953804 | 164 | py |
Semi-Leak | Semi-Leak-master/uda.py | import os
import logging
import random
import warnings
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
from utils import net_builder, get_logger, count_parameters, over_write_args_fr... | 15,722 | 41.380054 | 164 | py |
Semi-Leak | Semi-Leak-master/custom_writer.py | import datetime
from pathlib import Path
from typing import Sequence, Union, Tuple
import numpy as np
import json
import torch
import os
class CustomWriter(object):
'''
Custom Writer for training record.
Parameters:
-----------
log_dir : pathlib.Path or str, path to save logs.
enabled : bool, ... | 5,788 | 32.270115 | 109 | py |
Semi-Leak | Semi-Leak-master/models/uda/uda_utils.py | import torch
import math
import torch.nn.functional as F
from train_utils import ce_loss
import numpy as np
class Get_Scalar:
def __init__(self, value):
self.value = value
def get_value(self, iter):
return self.value
def __call__(self, iter):
return self.value
def TSA(schedule,... | 2,476 | 31.168831 | 108 | py |
Semi-Leak | Semi-Leak-master/models/uda/uda.py | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from torch.cuda.amp import autocast, GradScaler
from collections import Counter
import os
import contextlib
from train_utils import AverageMeter
from .uda_utils import consistency_loss, TSA, Get_Sc... | 14,734 | 39.592287 | 136 | py |
Semi-Leak | Semi-Leak-master/models/fullysupervised/fullysupervised.py | import torch
import numpy as np
import torch.nn.functional as F
from torch.cuda.amp import autocast, GradScaler
import os
import contextlib
from train_utils import ce_loss, wd_loss, EMA, Bn_Controller
from sklearn.metrics import *
from copy import deepcopy
torch.set_num_threads(1)
class FullySupervised:
def _... | 11,073 | 35.308197 | 108 | py |
Semi-Leak | Semi-Leak-master/models/fullysupervised/fullysupervised_utils.py | import torch
import torch.nn.functional as F
from train_utils import ce_loss
class Get_Scalar:
def __init__(self, value):
self.value = value
def get_value(self, iter):
return self.value
def __call__(self, iter):
return self.value
| 270 | 17.066667 | 31 | py |
Semi-Leak | Semi-Leak-master/models/fixmatch/fixmatch.py | import pickle
from re import S
import torch
import numpy as np
import pandas as pd
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from torch.cuda.amp import autocast, GradScaler
from collections import Counter
import os
import contextlib
from .fixmatch_utils import consisten... | 13,267 | 38.724551 | 134 | py |
Semi-Leak | Semi-Leak-master/models/fixmatch/fixmatch_utils.py | import torch
import torch.nn.functional as F
from train_utils import ce_loss
class Get_Scalar:
def __init__(self, value):
self.value = value
def get_value(self, iter):
return self.value
def __call__(self, iter):
return self.value
def consistency_loss(logits_s, logits_w, name='c... | 1,532 | 32.326087 | 95 | py |
Semi-Leak | Semi-Leak-master/models/nets/resnet_update.py | '''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion... | 4,385 | 31.014599 | 83 | py |
Semi-Leak | Semi-Leak-master/models/nets/wrn_var.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
momentum = 0.001
def mish(x):
"""Mish: A Self Regularized Non-Monotonic Neural Activation Function (https://arxiv.org/abs/1908.08681)"""
return x * torch.tanh(F.softplus(x))
class PSBatchNorm2d(nn.BatchNorm2d):
"""How Does B... | 6,542 | 38.896341 | 119 | py |
Semi-Leak | Semi-Leak-master/models/nets/resnet50.py | """
from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv... | 9,580 | 35.291667 | 111 | py |
Semi-Leak | Semi-Leak-master/models/nets/wrn.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
momentum = 0.001
def mish(x):
"""Mish: A Self Regularized Non-Monotonic Neural Activation Function (https://arxiv.org/abs/1908.08681)"""
return x * torch.tanh(F.softplus(x))
class PSBatchNorm2d(nn.BatchNorm2d):
"""How Does B... | 11,068 | 39.39781 | 119 | py |
Semi-Leak | Semi-Leak-master/models/flexmatch/flexmatch_utils.py | import torch
import math
import torch.nn.functional as F
import numpy as np
from train_utils import ce_loss
class Get_Scalar:
def __init__(self, value):
self.value = value
def get_value(self, iter):
return self.value
def __call__(self, iter):
return self.value
def consistency_... | 2,221 | 35.42623 | 123 | py |
Semi-Leak | Semi-Leak-master/models/flexmatch/flexmatch.py | import pickle
import json
import torch
import numpy as np
import pandas as pd
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from torch.cuda.amp import autocast, GradScaler
from collections import Counter
import os
import contextlib
from train_utils import AverageMeter
from .... | 15,760 | 40.259162 | 136 | py |
Semi-Leak | Semi-Leak-master/datasets/ssl_dataset.py | import torch
from .data_utils import split_ssl_data, sample_labeled_data
from .dataset import BasicDataset, BaseDataset
from collections import Counter
import torchvision
import numpy as np
from torchvision import transforms
import json
import os
from .augmentation.randaugment import RandAugment
from torch.utils.data... | 11,585 | 54.971014 | 152 | py |
Semi-Leak | Semi-Leak-master/datasets/data_utils.py | import torch
import torchvision
from torchvision import datasets
from torch.utils.data import sampler, DataLoader
from torch.utils.data.sampler import BatchSampler
import torch.distributed as dist
import numpy as np
import json
import os
from datasets.DistributedProxySampler import DistributedProxySampler
def split_... | 7,741 | 34.190909 | 103 | py |
Semi-Leak | Semi-Leak-master/datasets/dataset.py | from torchvision import transforms
from torch.utils.data import Dataset
from .data_utils import get_onehot
from .augmentation.randaugment import RandAugment
import torchvision
from PIL import Image
import numpy as np
import copy
class BasicDataset(Dataset):
"""
BasicDataset returns a pair of image and labels... | 8,260 | 36.211712 | 106 | py |
Semi-Leak | Semi-Leak-master/datasets/DistributedProxySampler.py | # copyright: https://github.com/pytorch/pytorch/issues/23430#issuecomment-562350407
import math
import torch
from torch.utils.data.distributed import DistributedSampler
class DistributedProxySampler(DistributedSampler):
"""Sampler that restricts data loading to a subset of input sampler indices.
It is espec... | 1,788 | 35.510204 | 83 | py |
Semi-Leak | Semi-Leak-master/datasets/augmentation/randaugment.py | # copyright: https://github.com/ildoonet/pytorch-randaugment
# code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
# This code is modified version of one of ildoonet, for randaugmentation of fixmatch.
import random
import PIL
import PIL.ImageOp... | 4,956 | 23.909548 | 102 | py |
VIBUS | VIBUS-master/pretrain/main.py | # Change dataloader multiprocess start method to anything not fork
import open3d as o3d
import torch.multiprocessing as mp
try:
mp.set_start_method('forkserver') # Reuse process created
except RuntimeError:
pass
import os
import sys
import json
import logging
from easydict import EasyDict as edict
# Torch packa... | 5,055 | 31.203822 | 97 | py |
VIBUS | VIBUS-master/pretrain/ddp_main_save.py | # Change dataloader multiprocess start method to anything not fork
import open3d as o3d
import numpy as np
import torch.multiprocessing as mp
try:
mp.set_start_method('spawn') # Reuse process created
except RuntimeError:
pass
import torch.distributed as dist
import os
import sys
import json
import random
impor... | 8,484 | 33.77459 | 101 | py |
VIBUS | VIBUS-master/pretrain/ddp_main.py | # Change dataloader multiprocess start method to anything not fork
import open3d as o3d
import numpy as np
import torch.multiprocessing as mp
try:
mp.set_start_method('spawn') # Reuse process created
except RuntimeError:
pass
import torch.distributed as dist
import os
import sys
import json
import random
impor... | 8,683 | 34.016129 | 101 | py |
VIBUS | VIBUS-master/pretrain/run.py | import os
import sys
import open3d as o3d
import numpy as np
import logging
import torch
import torch.multiprocessing as mp
try:
mp.set_start_method('forkserver') # Reuse process created
except RuntimeError:
pass
import torch.distributed as dist
from config import get_config
from lib.test import test
from lib.... | 11,133 | 72.735099 | 6,051 | py |
VIBUS | VIBUS-master/pretrain/config.py | import argparse
def str2opt(arg):
assert arg in ['SGD', 'Adam']
return arg
def str2scheduler(arg):
assert arg in ['StepLR', 'PolyLR', 'ExpLR', 'SquaredLR']
return arg
def str2bool(v):
return v.lower() in ('true', '1')
def str2list(l):
return [int(i) for i in l.split(',')]
def add_argument_group(na... | 10,902 | 43.684426 | 180 | py |
VIBUS | VIBUS-master/pretrain/models/res16unet.py | from models.resnet import ResNetBase, get_norm
from models.modules.common import ConvType, NormType, conv, conv_tr
from models.modules.resnet_block import BasicBlock, Bottleneck
import torch.nn as nn
from MinkowskiEngine import MinkowskiReLU
import MinkowskiEngine.MinkowskiOps as me
import MinkowskiEngine as ME
class... | 10,544 | 26.107969 | 92 | py |
VIBUS | VIBUS-master/pretrain/models/resnet.py | import torch.nn as nn
import MinkowskiEngine as ME
from models.model import Model
from models.modules.common import ConvType, NormType, get_norm, conv, sum_pool
from models.modules.resnet_block import BasicBlock, Bottleneck
class ResNetBase(Model):
BLOCK = None
LAYERS = ()
INIT_DIM = 64
PLANES = (64, 128, 2... | 5,352 | 23.668203 | 94 | py |
VIBUS | VIBUS-master/pretrain/models/conditional_random_fields.py | import torch
import torch.nn as nn
from torch.autograd import Variable
from MinkowskiEngine import SparseTensor, MinkowskiConvolution, MinkowskiConvolutionFunction, convert_to_int_tensor
from MinkowskiEngine import convert_region_type as me_convert_region_type
from models.model import HighDimensionalModel
from models... | 6,094 | 35.065089 | 115 | py |
VIBUS | VIBUS-master/pretrain/models/resunet.py | from models.resnet import ResNetBase, get_norm
from models.modules.common import ConvType, NormType, conv, conv_tr
from models.modules.resnet_block import BasicBlock, BasicBlockINBN, Bottleneck
import torch.nn as nn
import MinkowskiEngine as ME
from MinkowskiEngine import MinkowskiReLU
import MinkowskiEngine.Minkowsk... | 14,938 | 26.767658 | 91 | py |
VIBUS | VIBUS-master/pretrain/models/wrapper.py | import random
from torch.nn import Module
from MinkowskiEngine import SparseTensor
class Wrapper(Module):
"""
Wrapper for the segmentation networks.
"""
OUT_PIXEL_DIST = -1
def __init__(self, NetClass, in_nchannel, out_nchannel, config):
super(Wrapper, self).__init__()
self.initialize_filter(NetCl... | 950 | 29.677419 | 80 | py |
VIBUS | VIBUS-master/pretrain/models/modules/senet_block.py | import torch.nn as nn
import MinkowskiEngine as ME
from models.modules.common import ConvType, NormType
from models.modules.resnet_block import BasicBlock, Bottleneck
class SELayer(nn.Module):
def __init__(self, channel, reduction=16, D=-1):
# Global coords does not require coords_key
super(SELayer, self... | 3,081 | 22 | 90 | py |
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