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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palbert | palbert-main/src/utils/set_deterministic.py | import os
import random
import numpy as np
import torch
def set_seed(seed: int):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def set_deterministic_mode(seed):
set_seed(seed)
os.environ["PYTHONHASHSEED"... | 502 | 19.12 | 45 | py |
palbert | palbert-main/src/modeling/palbert_fast.py | # coding=utf-8
# Copyright 2022 Tinkoff, Google AI, Google Brain and the HuggingFace Inc. team.
#
# 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-... | 47,453 | 41.636119 | 165 | py |
mhml_calibration | mhml_calibration-main/prepare_endo_data.py | import os
import os.path as osp
from PIL import Image
from torchvision.transforms import Resize
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
rsz = Resize((512,512))
rootDir = 'labeled-images/'
outDir = 'data/kvasir/'
os.makedirs(outDir, exist_ok=True)
for dirName, subdir... | 1,560 | 33.688889 | 108 | py |
mhml_calibration | mhml_calibration-main/train_other.py | import os, json, sys, time, random, os.path as osp
import numpy as np
import torch
from tqdm import trange
import medmnist
from utils.data_handling import get_medmnist_loaders
from utils.data_handling import get_class_loaders
from utils.evaluation import evaluate_cls
from utils.get_model_v2 import get_arch
from utils.m... | 15,989 | 47.75 | 162 | py |
mhml_calibration | mhml_calibration-main/train_PMH.py | import os, json, sys, time, random, os.path as osp
import numpy as np
import torch
from tqdm import trange
import medmnist
from utils.data_handling import get_medmnist_loaders
from utils.data_handling import get_class_loaders
from utils.evaluation import evaluate_cls
from utils.get_model_v2 import get_arch
def set_see... | 17,363 | 48.329545 | 162 | py |
mhml_calibration | mhml_calibration-main/utils/get_model_v2.py | import sys
import torch
import torch.nn as nn
from torchvision.models import mobilenet_v2, MobileNet_V2_Weights
from torchvision.models import resnet18, ResNet18_Weights
from torchvision.models import resnet34, ResNet34_Weights
from torchvision.models import resnet50, ResNet50_Weights
from torchvision.models.convnext ... | 6,855 | 46.282759 | 123 | py |
mhml_calibration | mhml_calibration-main/utils/mixup.py |
import torch
import numpy as np
def mixup_data(x, y, alpha=0.4):
"""
Returns augmented image lam*x1 + (1-lam)*x2 by mixing x1 and x2
with coefficient lam simulated from beta(alpha, alpha)
:param x: (torch.tensor) images
:param y: (torch.tensor) labels
:param alpha: (float) parameter of the bet... | 1,281 | 31.05 | 95 | py |
mhml_calibration | mhml_calibration-main/utils/data_handling.py | from torch.utils.data import DataLoader
import numpy as np
from torch.utils.data.dataset import Dataset
import torchvision.transforms as tr
import pandas as pd
from PIL import Image
import os.path as osp
import medmnist
class ClassDataset(Dataset):
def __init__(self, csv_path, data_path, transforms, tg_size):
... | 5,934 | 42.639706 | 118 | py |
mhml_calibration | mhml_calibration-main/utils/dca_loss.py | # this file was downloaded from https://github.com/GB-TonyLiang/DCA
import torch
import torch.nn.functional as F
def cross_entropy_with_dca_loss(logits, labels, weights=None, alpha=1., beta=5.):
ce = F.cross_entropy(logits, labels, weight=weights)
softmaxes = F.softmax(logits, dim=1)
confidences, predicti... | 565 | 32.294118 | 81 | py |
mhml_calibration | mhml_calibration-main/utils/logit_margin_l1.py | # this file was downloaded from https://github.com/by-liu/MbLS
import torch
import torch.nn as nn
import torch.nn.functional as F
class LogitMarginL1(nn.Module):
"""Add marginal penalty to logits:
CE + alpha * max(0, max(l^n) - l^n - margin)
Args:
margin (float, optional): The margin value. D... | 3,663 | 38.397849 | 97 | py |
SKD | SKD-master/dataloader.py | from __future__ import print_function
import os
import argparse
import socket
import time
import sys
from tqdm import tqdm
import torch
import torch.optim as optim
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
import torch.nn.functional as F
from dataset.mini_imag... | 8,358 | 51.572327 | 109 | py |
SKD | SKD-master/train_distillation.py | """
the general training framework
"""
from __future__ import print_function
import os
import argparse
import socket
import time
import sys
from tqdm import tqdm
import mkl
import torch
import torch.optim as optim
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
impo... | 18,802 | 37.769072 | 143 | py |
SKD | SKD-master/train_selfsupervison.py | from __future__ import print_function
import os
import argparse
import socket
import time
import sys
from tqdm import tqdm
import mkl
# import tensorboard_logger as tb_logger
import torch
import torch.optim as optim
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
imp... | 16,453 | 37.990521 | 143 | py |
SKD | SKD-master/util.py | from __future__ import absolute_import
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
import os
import sys
from dataloader import get_dataloaders
class LabelSmoothing(nn.Module):
"""
NLL loss with label smoothing.
"""
def __init__(self, smoothing=0.0):
""... | 8,149 | 36.214612 | 119 | py |
SKD | SKD-master/eval_fewshot.py | from __future__ import print_function
import argparse
import socket
import time
import os
import mkl
import torch
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from models import model_pool
from models.util import create_model
from dataset.mini_imagenet import MetaImageNet
from datas... | 4,470 | 36.571429 | 259 | py |
SKD | SKD-master/dataset/tiered_imagenet.py | import os
import pickle
from PIL import Image
import numpy as np
import torch
from torch.utils.data import Dataset
import torchvision.transforms as transforms
class TieredImageNet(Dataset):
def __init__(self, args, partition='train', pretrain=True, is_sample=False, k=4096,
transform=None):
... | 8,565 | 39.215962 | 106 | py |
SKD | SKD-master/dataset/transform_cfg.py | from __future__ import print_function
import numpy as np
from PIL import Image
import torchvision.transforms as transforms
mean = [120.39586422 / 255.0, 115.59361427 / 255.0, 104.54012653 / 255.0]
std = [70.68188272 / 255.0, 68.27635443 / 255.0, 72.54505529 / 255.0]
normalize = transforms.Normalize(mean=mean, std=st... | 2,437 | 22 | 77 | py |
SKD | SKD-master/dataset/dataset_selfsupervision.py | import torch
import torch.utils.data
import numpy as np
class SSDatasetWrapper(torch.utils.data.Dataset):
def __init__(self, dset, opt):
self.dset = dset
self.opt = opt
def __getitem__(self, index):
image, target, item = self.dset[index]
if(not(self.opt.ssl)):... | 1,681 | 29.035714 | 104 | py |
SKD | SKD-master/dataset/mini_imagenet.py | import os
import pickle
from PIL import Image
import numpy as np
import torch
from torch.utils.data import Dataset
import torchvision.transforms as transforms
class ImageNet(Dataset):
def __init__(self, args, partition='train', pretrain=True, is_sample=False, k=4096,
transform=None):
supe... | 8,108 | 40.162437 | 106 | py |
SKD | SKD-master/dataset/cifar.py | from __future__ import print_function
import os
import pickle
from PIL import Image
import numpy as np
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
class CIFAR100(Dataset):
"""support FC100 and CIFAR-FS"""
def __init__(self, args, partition='train', pretrain=... | 12,761 | 37.672727 | 106 | py |
SKD | SKD-master/eval/cls_eval.py | from __future__ import print_function
import torch
import time
from tqdm import tqdm
from .util import AverageMeter, accuracy
import numpy as np
def validate(val_loader, model, criterion, opt):
"""One epoch validation"""
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top... | 8,244 | 39.615764 | 88 | py |
SKD | SKD-master/eval/util.py | import torch
import numpy as np
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self... | 974 | 24.657895 | 88 | py |
SKD | SKD-master/eval/meta_eval.py | from __future__ import print_function
import numpy as np
import scipy
from scipy.stats import t
from tqdm import tqdm
import torch
from sklearn import metrics
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import... | 12,501 | 38.438486 | 122 | py |
SKD | SKD-master/models/resnet_ssl.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.distributions import Bernoulli
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
clas... | 12,518 | 35.07781 | 129 | py |
SKD | SKD-master/models/resnet.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.distributions import Bernoulli
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
clas... | 12,076 | 34.730769 | 129 | py |
SKD | SKD-master/models/resnet_new.py | import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet50']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://do... | 5,734 | 30 | 82 | py |
SKD | SKD-master/models/resnet_sd.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch
__all__ = ['ResNet_StoDepth_lineardecay', 'resnet18_StoDepth_lineardecay', 'resnet34_StoDepth_lineardecay', 'resnet50_StoDepth_lineardecay', 'resnet101_StoDepth_lineardecay',
'resnet152_StoDepth_lineardecay']
model_urls = {
'... | 11,260 | 35.092949 | 174 | py |
SKD | SKD-master/models/wresnet.py | import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.autograd import Variable
import sys
import numpy as np
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)
def conv_in... | 3,419 | 30.962617 | 98 | py |
SKD | SKD-master/models/resnet_selfdist.py | import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3,
stride=stride, padding=1, bias=False)
def conv1x1(in_planes, planes, stride=1):
return nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bia... | 8,219 | 35.052632 | 118 | py |
SKD | SKD-master/models/convnet.py | from __future__ import print_function
import torch
import torch.nn as nn
class ConvNet(nn.Module):
def __init__(self, num_classes=-1):
super(ConvNet, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
... | 2,240 | 27.0125 | 86 | py |
SKD | SKD-master/distill/alias_multinomial.py | import torch
class AliasMethod(object):
'''
From: https://hips.seas.harvard.edu/blog/2013/03/03/the-alias-method-efficient-sampling-with-many-discrete-outcomes/
'''
def __init__(self, probs):
if probs.sum() > 1:
probs.div_(probs.sum())
K = len(probs)
self.prob ... | 1,915 | 28.9375 | 124 | py |
SKD | SKD-master/distill/util.py | from __future__ import print_function
import torch.nn as nn
class Embed(nn.Module):
"""Embedding module"""
def __init__(self, dim_in=1024, dim_out=128):
super(Embed, self).__init__()
self.linear = nn.Linear(dim_in, dim_out)
self.l2norm = Normalize(2)
def forward(self, x):
... | 1,573 | 24.387097 | 74 | py |
SKD | SKD-master/distill/NCEAverage.py | import torch
from torch.autograd import Function
from torch import nn
from .alias_multinomial import AliasMethod
import math
class NCESoftmax(nn.Module):
def __init__(self, inputSize, outputSize, K, T=0.07, momentum=0.5):
super(NCESoftmax, self).__init__()
self.nLem = outputSize
self.unig... | 15,956 | 40.88189 | 105 | py |
SKD | SKD-master/distill/NCECriterion.py | import torch
from torch import nn
eps = 1e-7
class NCECriterion(nn.Module):
def __init__(self, nLem):
super(NCECriterion, self).__init__()
self.nLem = nLem
def forward(self, x):
batchSize = x.size(0)
K = x.size(1) - 1
Pnt = 1 / float(self.nLem)
Pns = 1 / floa... | 924 | 23.342105 | 58 | py |
SKD | SKD-master/distill/criterion.py | from __future__ import print_function
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from scipy.stats import norm
from .NCEAverage import NCEAverage
from .NCEAverage import NCESoftmax
from .NCECriterion import NCECriterion
class DistillKL(nn.Module):
"""KL dive... | 2,908 | 28.683673 | 89 | py |
SeqNet | SeqNet-master/predict.py | ############Test
import argparse
import os
import tensorflow as tf
from keras.backend import tensorflow_backend
from utils import define_model, crop_prediction
from keras.layers import ReLU
from tqdm import tqdm
import numpy as np
from skimage.transform import resize
import cv2
from PIL import Image
def predict(ACT... | 5,313 | 38.656716 | 120 | py |
SeqNet | SeqNet-master/train.py | import numpy as np
import os
import time
from datetime import datetime
from keras.callbacks import TensorBoard, ModelCheckpoint
from keras.layers import ReLU
from keras.utils import plot_model
from utils import define_model, prepare_dataset
import tensorflow as tf
from keras.backend import tensorflow_backend
def train... | 2,609 | 39.78125 | 120 | py |
SeqNet | SeqNet-master/utils/define_model.py | import h5py
import keras.backend as K
import numpy as np
import os
import os.path
import tensorflow as tf
import threading
from PIL import Image
from keras import backend as K
from keras import losses
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from keras.layers import Input, MaxPoolin... | 23,879 | 47.340081 | 118 | py |
SeqNet | SeqNet-master/utils/data_augmentation.py | import numpy as np
from keras_preprocessing import image
def random_flip(img, masks, masks2, u=0.5):
if np.random.random() < u:
img = image.flip_axis(img, 1)
for i in range(masks.shape[0]):
masks[i] = image.flip_axis(masks[i], 1)
for i in range(masks2.shape[0]):
mas... | 5,142 | 38.561538 | 107 | py |
beacon-chain-spec | beacon-chain-spec-master/media/sphinx-docs/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# The Beacon Chain documentation build configuration file, created by
# sphinx-quickstart on Mon Nov 6 15:38:57 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in ... | 5,542 | 29.794444 | 79 | py |
MaMaDroid2.0 | MaMaDroid2.0-main/MaMaDroid2.0/mamadroid_classifier_drebin_all_check_mbs.py | from __future__ import division
import csv
import pickle
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from sklearn.metrics import f1_score,confusion_matrix
import numpy as np
import random
import math
import sys,os
import time
from random im... | 15,010 | 40.239011 | 141 | py |
MaMaDroid2.0 | MaMaDroid2.0-main/MaMaDroid2.0/mamadroid_classifier_only_drebin.py | from __future__ import division
import csv
import pickle
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from sklearn.metrics import f1_score,confusion_matrix
import numpy as np
import random
import math
import sys,os
import time
from random im... | 17,571 | 39.865116 | 141 | py |
MaMaDroid2.0 | MaMaDroid2.0-main/MaMaDroid2.0/mamadroid_classifier_drebin_all.py | from __future__ import division
import csv
import pickle
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from sklearn.metrics import f1_score,confusion_matrix
import numpy as np
import random
import math
import sys,os
import time
from random im... | 17,559 | 40.317647 | 141 | py |
MaMaDroid2.0 | MaMaDroid2.0-main/MaMaDroid2.0/mamadroid_classifier_drebin_all_check_importance.py | from __future__ import division
import csv
import pickle
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from sklearn.metrics import f1_score,confusion_matrix
import numpy as np
import random
import math
import sys,os
import time
from random im... | 18,157 | 39.988713 | 141 | py |
MaMaDroid2.0 | MaMaDroid2.0-main/MaMaDroid2.0/mamadroid_classifier_no_drebin.py | from __future__ import division
import csv
import pickle
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from sklearn.metrics import f1_score,confusion_matrix
import numpy as np
import random
import math
import sys,os
import time
from random im... | 17,565 | 40.331765 | 141 | py |
MaMaDroid2.0 | MaMaDroid2.0-main/MaMaDroid2.0/mamadroid_classifier_drebin_all_check_mbs_only_perms.py | from __future__ import division
import csv
import pickle
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from sklearn.metrics import f1_score,confusion_matrix
import numpy as np
import random
import math
import sys,os
import time
from random im... | 14,411 | 39.943182 | 141 | py |
roar | roar-main/roar/estimation/misc.py | import numpy as np
import scipy.ndimage as ndimage
import torch
from roar.registry import ATTRIBUTES
from .attribute import BaseAttribute
@ATTRIBUTES.register_module('Sobl')
@ATTRIBUTES.register_module()
class Sobel(BaseAttribute):
def _sobel(self, img: torch.Tensor) -> torch.Tensor:
img = img.detach().... | 914 | 24.416667 | 78 | py |
roar | roar-main/roar/estimation/cam.py | from functools import reduce
from typing import Union
import torch
import torch.nn.functional as F
from mmengine.model import BaseModel, BaseModule
from roar.registry import ATTRIBUTES
from .attribute import BaseAttribute
def getattr_recursive(module, name):
return reduce(getattr, name.split('.'), module)
@AT... | 1,845 | 29.766667 | 79 | py |
roar | roar-main/roar/estimation/ensemble.py | from abc import abstractmethod
from functools import partial
import torch
from mmengine.model import BaseModel
from roar.registry import ATTRIBUTES
from .attribute import BaseAttribute
class EnsembleGradients(BaseAttribute):
REDUCTION = dict(mean=torch.mean, var=torch.var)
reduction = 'mean'
def __init... | 3,290 | 28.383929 | 78 | py |
roar | roar-main/roar/estimation/grad.py | import torch
from roar.registry import ATTRIBUTES
from .attribute import BaseAttribute
@ATTRIBUTES.register_module('Grad')
@ATTRIBUTES.register_module()
class Gradient(BaseAttribute):
@torch.enable_grad()
def _estimate(self, data_batch: dict) -> torch.Tensor:
grad = torch.autograd.grad(
... | 797 | 23.181818 | 71 | py |
roar | roar-main/roar/estimation/attribute.py | import copy
from abc import ABC, abstractmethod
from typing import Union
import torch
from mmengine.model import BaseModel
class BaseAttribute(ABC):
"""Base class for estimating a feature importance.
Args:
model (BaseModel): The model used to estimate a feature importance
label (int | str): ... | 2,317 | 30.753425 | 74 | py |
TRamWAy | TRamWAy-master/tramway/feature/single_traj/utils_supervised.py | # -*- coding:utf-8 -*-
# Copyright © 2017-2019, Institut Pasteur
# Contributor: Maxime Duval
# This file is part of the TRamWAy software available at
# "https://github.com/DecBayComp/TRamWAy" and is distributed under
# the terms of the CeCILL license as circulated at the following URL
# "http://www.cecill.info/lic... | 20,304 | 37.67619 | 79 | py |
TRamWAy | TRamWAy-master/tramway/feature/single_traj/vae.py | # -*- coding:utf-8 -*-
# Copyright © 2017-2019, Institut Pasteur
# Contributor: Maxime Duval
# This file is part of the TRamWAy software available at
# "https://github.com/DecBayComp/TRamWAy" and is distributed under
# the terms of the CeCILL license as circulated at the following URL
# "http://www.cecill.info/lic... | 6,462 | 36.143678 | 79 | py |
TRamWAy | TRamWAy-master/tramway/feature/single_traj/__init__.py | from .rw_simulation import *
from .rw_features import *
from .batch_generation import *
from .batch_extraction import *
from .misc import *
try:
from .vae import *
except ImportError: # torch
pass
from .visualization import *
try:
from .utils_supervised import *
except ImportError: # torch
pass
"""
Usi... | 666 | 23.703704 | 74 | py |
TRamWAy | TRamWAy-master/tramway/localization/UNet/inference.py | # -*- coding: utf-8 -*-
# Copyright © 2020, Institut Pasteur
# Contributor: Jean-Baptiste Masson
# This file is part of the TRamWAy software available at
# "https://github.com/DecBayComp/TRamWAy" and is distributed under
# the terms of the CeCILL license as circulated at the following URL
# "http://www.cecill.info/... | 25,816 | 44.937722 | 181 | py |
TRamWAy | TRamWAy-master/tramway/localization/UNet/tf.py | # -*- coding: utf-8 -*-
# Copyright © 2020, Institut Pasteur
# Contributor: François Laurent
# This file is part of the TRamWAy software available at
# "https://github.com/DecBayComp/TRamWAy" and is distributed under
# the terms of the CeCILL license as circulated at the following URL
# "http://www.cecill.info/lice... | 2,492 | 29.777778 | 79 | py |
TRamWAy | TRamWAy-master/tramway/localization/UNet/utility_function_inference.py | # -*- coding: utf-8 -*-
# Copyright © 2020, Institut Pasteur
# Contributor: Jean-Baptiste Masson
# This file is part of the TRamWAy software available at
# "https://github.com/DecBayComp/TRamWAy" and is distributed under
# the terms of the CeCILL license as circulated at the following URL
# "http://www.cecill.info/... | 3,919 | 40.263158 | 89 | py |
TRamWAy | TRamWAy-master/doc/conf.py | # -*- coding: utf-8 -*-
#
# TRamWAy documentation build configuration file, created by
# sphinx-quickstart on Tue Jan 24 11:46:04 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# A... | 10,047 | 30.4 | 95 | py |
table-transformer | table-transformer-main/src/inference.py | from collections import OrderedDict, defaultdict
import json
import argparse
import sys
import xml.etree.ElementTree as ET
import os
import random
import io
import torch
from torchvision import transforms
from PIL import Image
from fitz import Rect
import numpy as np
import pandas as pd
import matplotlib
#matplotlib.u... | 38,569 | 40.075612 | 112 | py |
table-transformer | table-transformer-main/src/main.py | """
Copyright (C) 2021 Microsoft Corporation
"""
import os
import argparse
import json
from datetime import datetime
import string
import sys
import random
import numpy as np
import torch
from torch.utils.data import DataLoader
sys.path.append("../detr")
from engine import evaluate, train_one_epoch
from models import ... | 15,488 | 40.194149 | 156 | py |
table-transformer | table-transformer-main/src/table_datasets.py | """
Copyright (C) 2021 Microsoft Corporation
"""
import os
import sys
import random
import xml.etree.ElementTree as ET
from collections import defaultdict
import itertools
import math
import PIL
from PIL import Image, ImageFilter
import torch
from torchvision import transforms
from torchvision.transforms import functi... | 27,572 | 37.835211 | 132 | py |
table-transformer | table-transformer-main/src/eval.py | """
Copyright (C) 2021 Microsoft Corporation
"""
import os
import sys
from collections import Counter
import json
import statistics as stat
from datetime import datetime
import multiprocessing
from itertools import repeat
from functools import partial
import tqdm
import math
import torch
from torchvision import transf... | 29,547 | 41.271817 | 130 | py |
table-transformer | table-transformer-main/detr/main.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import datetime
import json
import random
import time
from pathlib import Path
import numpy as np
import torch
from torch.utils.data import DataLoader, DistributedSampler
import datasets
import util.misc as utils
from datasets impo... | 11,532 | 45.317269 | 116 | py |
table-transformer | table-transformer-main/detr/engine.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Train and eval functions used in main.py
"""
import math
import os
import sys
from typing import Iterable
import torch
import util.misc as utils
from datasets.coco_eval import CocoEvaluator
from datasets.panoptic_eval import PanopticEvaluator
... | 6,832 | 42.801282 | 103 | py |
table-transformer | table-transformer-main/detr/hubconf.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
from models.backbone import Backbone, Joiner
from models.detr import DETR, PostProcess
from models.position_encoding import PositionEmbeddingSine
from models.segmentation import DETRsegm, PostProcessPanoptic
from models.transformer imp... | 6,265 | 36.076923 | 117 | py |
table-transformer | table-transformer-main/detr/engine_multi.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Train and eval functions used in main.py
"""
import math
import os
import sys
from typing import Iterable
import torch
import util.misc as utils
from datasets.coco_eval import CocoEvaluator
from datasets.panoptic_eval import PanopticEvaluator
... | 7,600 | 43.976331 | 129 | py |
table-transformer | table-transformer-main/detr/test_all.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import io
import unittest
import torch
from models.matcher import HungarianMatcher
from models.position_encoding import PositionEmbeddingSine, PositionEmbeddingLearned
from models.backbone import Backbone, Joiner, BackboneBase
from util import box... | 7,359 | 41.543353 | 119 | py |
table-transformer | table-transformer-main/detr/models/detr_multi.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
DETR model and criterion classes.
"""
import torch
import torch.nn.functional as F
from torch import nn
from util import box_ops
from util.misc import (NestedTensor, nested_tensor_from_tensor_list,
accuracy, get_world_siz... | 16,679 | 46.252125 | 113 | py |
table-transformer | table-transformer-main/detr/models/detr.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
DETR model and criterion classes.
"""
import torch
import torch.nn.functional as F
from torch import nn
from util import box_ops
from util.misc import (NestedTensor, nested_tensor_from_tensor_list,
accuracy, get_world_siz... | 16,514 | 46.185714 | 113 | py |
table-transformer | table-transformer-main/detr/models/matcher.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Modules to compute the matching cost and solve the corresponding LSAP.
"""
import torch
from scipy.optimize import linear_sum_assignment
from torch import nn
from util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou
class HungarianMatc... | 4,270 | 48.091954 | 119 | py |
table-transformer | table-transformer-main/detr/models/segmentation.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
This file provides the definition of the convolutional heads used to predict masks, as well as the losses
"""
import io
from collections import defaultdict
from typing import List, Optional
import torch
import torch.nn as nn
import torch.nn.fun... | 15,566 | 41.766484 | 120 | py |
table-transformer | table-transformer-main/detr/models/position_encoding.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Various positional encodings for the transformer.
"""
import math
import torch
from torch import nn
from util.misc import NestedTensor
class PositionEmbeddingSine(nn.Module):
"""
This is a more standard version of the position embeddi... | 3,336 | 36.077778 | 103 | py |
table-transformer | table-transformer-main/detr/models/backbone.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Backbone modules.
"""
from collections import OrderedDict
import torch
import torch.nn.functional as F
import torchvision
from torch import nn
from torchvision.models._utils import IntermediateLayerGetter
from typing import Dict, List
from uti... | 4,424 | 35.875 | 113 | py |
table-transformer | table-transformer-main/detr/models/transformer.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
DETR Transformer class.
Copy-paste from torch.nn.Transformer with modifications:
* positional encodings are passed in MHattention
* extra LN at the end of encoder is removed
* decoder returns a stack of activations from all decoding... | 12,162 | 39.815436 | 98 | py |
table-transformer | table-transformer-main/detr/d2/converter.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Helper script to convert models trained with the main version of DETR to be used with the Detectron2 version.
"""
import json
import argparse
import numpy as np
import torch
def parse_args():
parser = argparse.ArgumentParser("D2 model con... | 2,590 | 36.014286 | 114 | py |
table-transformer | table-transformer-main/detr/d2/train_net.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
DETR Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
import os
import sys
# fmt: off
sys.path.insert(1, os.path.join(sys.path[0], '..'))
# fmt: on
import time
from typing import Any, Dict, ... | 5,076 | 31.754839 | 115 | py |
table-transformer | table-transformer-main/detr/d2/detr/detr.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import logging
import math
from typing import List
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from scipy.optimize import linear_sum_assignment
from torch import nn
from detectron2.layers import... | 11,143 | 41.534351 | 118 | py |
table-transformer | table-transformer-main/detr/d2/detr/dataset_mapper.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import copy
import logging
import numpy as np
import torch
from detectron2.data import detection_utils as utils
from detectron2.data import transforms as T
from detectron2.data.transforms import TransformGen
__all__ = ["DetrDatasetMapper"]
def ... | 4,570 | 36.162602 | 111 | py |
table-transformer | table-transformer-main/detr/util/plot_utils.py | """
Plotting utilities to visualize training logs.
"""
import torch
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from pathlib import Path, PurePath
def plot_logs(logs, fields=('class_error', 'loss_bbox_unscaled', 'mAP'), ewm_col=0, log_name='log.txt'):
'''
Func... | 4,514 | 40.805556 | 120 | py |
table-transformer | table-transformer-main/detr/util/misc.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Misc functions, including distributed helpers.
Mostly copy-paste from torchvision references.
"""
import os
import subprocess
import time
from collections import defaultdict, deque
import datetime
import pickle
from typing import Optional, List... | 15,305 | 31.635394 | 116 | py |
table-transformer | table-transformer-main/detr/util/box_ops.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Utilities for bounding box manipulation and GIoU.
"""
import torch
from torchvision.ops.boxes import box_area
def box_cxcywh_to_xyxy(x):
x_c, y_c, w, h = x.unbind(-1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
(x_c + 0.5 * w), (y_... | 2,561 | 27.786517 | 110 | py |
table-transformer | table-transformer-main/detr/datasets/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch.utils.data
import torchvision
from .coco import build as build_coco
def get_coco_api_from_dataset(dataset):
for _ in range(10):
# if isinstance(dataset, torchvision.datasets.CocoDetection):
# break
if ... | 897 | 33.538462 | 70 | py |
table-transformer | table-transformer-main/detr/datasets/coco_eval.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
COCO evaluator that works in distributed mode.
Mostly copy-paste from https://github.com/pytorch/vision/blob/edfd5a7/references/detection/coco_eval.py
The difference is that there is less copy-pasting from pycocotools
in the end of the file, as... | 8,735 | 32.860465 | 103 | py |
table-transformer | table-transformer-main/detr/datasets/coco_panoptic.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import json
from pathlib import Path
import numpy as np
import torch
from PIL import Image
from panopticapi.utils import rgb2id
from util.box_ops import masks_to_boxes
from .coco import make_coco_transforms
class CocoPanoptic:
def __init__(... | 3,723 | 36.24 | 111 | py |
table-transformer | table-transformer-main/detr/datasets/coco.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
COCO dataset which returns image_id for evaluation.
Mostly copy-paste from https://github.com/pytorch/vision/blob/13b35ff/references/detection/coco_utils.py
"""
from pathlib import Path
import torch
import torch.utils.data
import torchvision
f... | 5,253 | 32.044025 | 118 | py |
table-transformer | table-transformer-main/detr/datasets/transforms.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Transforms and data augmentation for both image + bbox.
"""
import random
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
from util.box_ops import box_xyxy_to_cxcywh
from util.misc impor... | 8,669 | 30.299639 | 104 | py |
GPGrid | GPGrid-master/script/section_gp-5d-train-validate.py | #!/usr/bin/env python
# coding: utf-8
# # Adopted training inputs:
#
# Accroding to the tests, below are selected for traning GP models:
#
# - EEP factor: f = 0.18 This number gives uniform distributions
#
# - Kernel: Mat32
#
# - Normalization: p = (p - p.mean)/... | 25,398 | 27.506173 | 118 | py |
GPGrid | GPGrid-master/script/section_gp-5d-test.py | #!/usr/bin/env python
# coding: utf-8
# # Adopted training inputs:
#
# Accroding to the tests, below are selected for traning GP models:
#
# - EEP factor: f = 0.18 This number gives uniform distributions
#
# - Kernel: Mat32
#
# - Normalization: p = (p - p.mean)/... | 21,980 | 26.788875 | 136 | py |
GPGrid | GPGrid-master/script/section_gp-5d-predict.py | # # S0: Loading packages
import math
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
import os
# In[3]:
import torch
import tqdm
import gpytorch
from torch.nn import Linear
from gpytorch.means import ConstantMean, LinearMean
from gpytorch.kernels import RBFKernel, ScaleKernel
from gpytor... | 9,606 | 32.127586 | 146 | py |
GPGrid | GPGrid-master/script/SVI_gp-5d-sys-train.py | import math
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
import os
# ! pip install ipywidgets
# ! pip install jupyterlab
# ! jupyter nbextension enable --py widgetsnbextension
# ! jupyter labextension install jupyterlab-manager
# ! pip install tqdm
# In[4]:
import torch
import tqdm
... | 10,885 | 25.55122 | 130 | py |
dae-factuality | dae-factuality-main/main.py | import argparse
import json
import logging
import os
import random
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
impor... | 18,072 | 37.453191 | 118 | py |
dae-factuality | dae-factuality-main/evaluate_factuality.py | import argparse
import os
import json
import numpy as np
import torch
import utils
from sklearn.utils.extmath import softmax
from preprocessing_utils import get_tokens, get_relevant_deps_and_context
from transformers import (
BertConfig,
BertTokenizer,
ElectraConfig,
ElectraTokenizer,
)
MODEL_CLASSES =... | 7,762 | 35.106977 | 115 | py |
dae-factuality | dae-factuality-main/utils.py | from torch import nn
import torch, os, logging, csv, copy, math
from transformers.modeling_bert import BertPreTrainedModel, BertModel
from transformers.modeling_electra import ElectraPreTrainedModel, ElectraModel
from torch.utils.data import TensorDataset
from torch.nn import CrossEntropyLoss
logger = logging.getLogge... | 15,291 | 39.348285 | 120 | py |
EIGNN | EIGNN-main/models_chains.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from functions import *
from layers import ImplicitGraph, IDM_SGC
from torch.nn import Parameter
from utils import get_spectral_rad, SparseDropout
import torch.sparse as sparse
from torch_geometric.nn import GCNConv, GATConv, SGConv, APPNP, GCN2Conv, Ju... | 9,088 | 33.298113 | 106 | py |
EIGNN | EIGNN-main/train_EIGNN_chains.py | from __future__ import division
from __future__ import print_function
import os
#os.environ["CUDA_VISIBLE_DEVICES"] = "1"
import time
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import ipdb
from utils import accuracy, clip_gradient... | 7,454 | 37.035714 | 120 | py |
EIGNN | EIGNN-main/models_heterophilic.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from layers import ImplicitGraph, IDM_SGC
from torch.nn import Parameter
from utils import get_spectral_rad, SparseDropout
import torch.sparse as sparse
from torch_geometric.nn import GCNConv, GATConv, SGConv, APPNP, JumpingKnowledge, GCN2Conv
from torc... | 7,364 | 33.097222 | 102 | py |
EIGNN | EIGNN-main/functions.py | import torch
import numpy as np
import scipy.sparse as sp
from torch.autograd import Function
from utils import sparse_mx_to_torch_sparse_tensor
import ipdb
class ImplicitFunction(Function):
#ImplicitFunction.apply(input, A, U, self.X_0, self.W, self.Omega_1, self.Omega_2)
@staticmethod
def forward(ctx, W,... | 3,590 | 32.560748 | 117 | py |
EIGNN | EIGNN-main/utils.py | import sys
import numpy as np
import scipy.sparse as sp
import torch
# import torch_sparse
import pickle as pkl
import networkx as nx
from normalization import fetch_normalization, row_normalize, aug_normalized_adjacency
from time import perf_counter
import ipdb
from sklearn import metrics
def parse_index_file(filen... | 6,446 | 32.753927 | 121 | py |
EIGNN | EIGNN-main/train_EIGNN_heterophilic.py | from __future__ import division
from __future__ import print_function
import os
#os.environ["CUDA_VISIBLE_DEVICES"] = "1"
import time
import argparse
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import ipdb
from utils import accuracy,... | 9,375 | 37.743802 | 118 | py |
EIGNN | EIGNN-main/datasets_utils.py | import sys
import numpy as np
import scipy.sparse as sp
import torch
# import torch_sparse
import pickle as pkl
import networkx as nx
from normalization import fetch_normalization, row_normalize, aug_normalized_adjacency
from time import perf_counter
import ipdb
from utils import *
import os.path as osp
from torch_spa... | 16,280 | 41.069767 | 109 | py |
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