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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deeplab2 | deeplab2-main/model/decoder/deeplabv3.py | # coding=utf-8
# Copyright 2023 The Deeplab2 Authors.
#
# 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 ... | 4,024 | 31.991803 | 80 | py |
deeplab2 | deeplab2-main/model/decoder/vip_deeplab_decoder.py | # coding=utf-8
# Copyright 2023 The Deeplab2 Authors.
#
# 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 ... | 14,356 | 44.147799 | 80 | py |
KdConv | KdConv-master/benchmark/LM/myCoTK/dataloader/bert_dataloader.py | '''
A module for BERT dataloader
'''
from cotk.dataloader import BERTLanguageProcessingBase
from cotk._utils import trim_before_target
import numpy as np
import os
import multiprocessing
from multiprocessing import Pool
import tqdm
import time
from itertools import chain
from collections import Counter
import json
impo... | 22,878 | 43.253385 | 128 | py |
KdConv | KdConv-master/benchmark/memhred/myCoTK/dataloader/bert_dataloader.py | '''
A module for BERT dataloader
'''
from cotk.dataloader import BERTLanguageProcessingBase
from cotk._utils import trim_before_target
import numpy as np
import os
import multiprocessing
from multiprocessing import Pool
import tqdm
import time
from itertools import chain
from collections import Counter
import json
impo... | 22,878 | 43.253385 | 128 | py |
KdConv | KdConv-master/benchmark/hred/myCoTK/dataloader/bert_dataloader.py | '''
A module for BERT dataloader
'''
from cotk.dataloader import BERTLanguageProcessingBase
from cotk._utils import trim_before_target
import numpy as np
import os
import multiprocessing
from multiprocessing import Pool
import tqdm
import time
from itertools import chain
from collections import Counter
import json
impo... | 22,878 | 43.253385 | 128 | py |
KdConv | KdConv-master/benchmark/membertret/run_BERTRetrieval.py | from __future__ import print_function
import argparse
import collections
import logging
import json
import math
import os
import random
import pickle
from tqdm import tqdm, trange
import re
import numpy as np
import torch
from torch import nn
from pytorch_pretrained_bert.modeling import BertPreTrainedModel, BertModel... | 20,914 | 45.169978 | 131 | py |
KdConv | KdConv-master/benchmark/membertret/myCoTK/dataloader/bert_dataloader.py | '''
A module for BERT dataloader
'''
from cotk.dataloader import BERTLanguageProcessingBase
from cotk._utils import trim_before_target
import numpy as np
import os
import multiprocessing
from multiprocessing import Pool
import tqdm
import time
from itertools import chain
from collections import Counter
import json
impo... | 22,878 | 43.253385 | 128 | py |
KdConv | KdConv-master/benchmark/myCoTK/dataloader/bert_dataloader.py | '''
A module for BERT dataloader
'''
from cotk.dataloader import BERTLanguageProcessingBase
from cotk._utils import trim_before_target
import numpy as np
import os
import multiprocessing
from multiprocessing import Pool
import tqdm
import time
from itertools import chain
from collections import Counter
import json
impo... | 22,878 | 43.253385 | 128 | py |
KdConv | KdConv-master/benchmark/seq2seq/myCoTK/dataloader/bert_dataloader.py | '''
A module for BERT dataloader
'''
from cotk.dataloader import BERTLanguageProcessingBase
from cotk._utils import trim_before_target
import numpy as np
import os
import multiprocessing
from multiprocessing import Pool
import tqdm
import time
from itertools import chain
from collections import Counter
import json
impo... | 22,878 | 43.253385 | 128 | py |
KdConv | KdConv-master/benchmark/memseq2seq/myCoTK/dataloader/bert_dataloader.py | '''
A module for BERT dataloader
'''
from cotk.dataloader import BERTLanguageProcessingBase
from cotk._utils import trim_before_target
import numpy as np
import os
import multiprocessing
from multiprocessing import Pool
import tqdm
import time
from itertools import chain
from collections import Counter
import json
impo... | 22,878 | 43.253385 | 128 | py |
KdConv | KdConv-master/benchmark/bertret/run_BERTRetrieval.py | from __future__ import print_function
import argparse
import collections
import logging
import json
import math
import os
import random
import pickle
from tqdm import tqdm, trange
import re
import numpy as np
import torch
from torch import nn
from pytorch_pretrained_bert.modeling import BertPreTrainedModel, BertModel... | 17,005 | 43.171429 | 130 | py |
KdConv | KdConv-master/benchmark/bertret/myCoTK/dataloader/bert_dataloader.py | '''
A module for BERT dataloader
'''
from cotk.dataloader import BERTLanguageProcessingBase
from cotk._utils import trim_before_target
import numpy as np
import os
import multiprocessing
from multiprocessing import Pool
import tqdm
import time
from itertools import chain
from collections import Counter
import json
impo... | 22,878 | 43.253385 | 128 | py |
PaPi | PaPi-main/main.py | import os
import math
import time
import random
import shutil
import logging
import warnings
import argparse
import builtins
import numpy as np
import tensorboard_logger as tb_logger
import torch
import torch.nn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
im... | 19,833 | 38.668 | 223 | py |
PaPi | PaPi-main/utils/utils_mlp.py | # Modified from https://github.com/palm-ml/valen/blob/master/utils/models.py
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.init as init
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power ... | 1,931 | 25.833333 | 76 | py |
PaPi | PaPi-main/utils/cifar100.py | # Modified from https://github.com/hbzju/PiCO/blob/main/utils/cifar100.py
import os.path
import pickle
from typing import Any, Callable, Optional, Tuple
import numpy as np
from PIL import Image
import torch
from torch.utils.data import Dataset
import torchvision.transforms as transforms
import torchvision.datasets as... | 4,906 | 40.584746 | 131 | py |
PaPi | PaPi-main/utils/miniImagenet.py | # Modified from https://github.com/WZMIAOMIAO/deep-learning-for-image-processing
import os
import os.path
import sys
import numpy as np
from PIL import Image
import torch
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
import torchvision.datasets as dsets
from collections ... | 7,899 | 34.909091 | 115 | py |
PaPi | PaPi-main/utils/utils_algo.py | # https://github.com/hbzju/PiCO/blob/main/utils/utils_algo.py
import math
import pickle
import numpy as np
import copy
from scipy.special import comb
import torch
import torch.nn as nn
import torch.nn.functional as F
def generate_instancedependent_candidate_labels(model, train_X, train_Y):
with torch.no_grad()... | 10,135 | 30.092025 | 107 | py |
PaPi | PaPi-main/utils/resnet.py | # Modified from https://github.com/hbzju/PiCO/blob/main/resnet.py
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1, is_last=False):
super(BasicBlock, self).__init... | 6,779 | 32.9 | 104 | py |
PaPi | PaPi-main/utils/model.py | import numpy as np
from random import sample
import torch
import torch.nn as nn
import torch.nn.functional as F
class PaPi(nn.Module):
def __init__(self, args, base_encoder):
super().__init__()
pretrained = False
self.proto_weight = args.proto_m
self.encoder = base_enco... | 3,476 | 36.387097 | 135 | py |
PaPi | PaPi-main/utils/fmnist.py | import torch
from torch.utils.data import Dataset
import torchvision.transforms as transforms
import torchvision.datasets as dsets
from collections import OrderedDict
from .utils_mlp import mlp_partialize
from .utils_algo import generate_uniform_cv_candidate_labels, generate_instancedependent_candidate_labels
from .cu... | 4,294 | 38.40367 | 119 | py |
PaPi | PaPi-main/utils/cutout.py | # https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py
import numpy as np
import torch
class Cutout(object):
"""Randomly mask out one or more patches from an image.
Args:
n_holes (int): Number of patches to cut out of each image.
length (int): The length (in pixels) of each squ... | 1,242 | 26.021739 | 82 | py |
PaPi | PaPi-main/utils/wide_resnet.py | # From https://github.com/xternalz/WideResNet-pytorch
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):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes... | 3,843 | 39.463158 | 116 | py |
PaPi | PaPi-main/utils/cifar10.py | # Modified from https://github.com/hbzju/PiCO/blob/main/utils/cifar10.py
import os.path
import pickle
from typing import Any, Callable, Optional, Tuple
import numpy as np
from PIL import Image
import torch
from torch.utils.data import Dataset
import torchvision.transforms as transforms
import torchvision.datasets as ... | 4,313 | 38.577982 | 140 | py |
PaPi | PaPi-main/utils/svhn.py | import numpy as np
from PIL import Image
import torch
from torch.utils.data import Dataset
import torchvision.transforms as transforms
import torchvision.datasets as dsets
from .wide_resnet import WideResNet
from .utils_algo import generate_uniform_cv_candidate_labels, generate_instancedependent_candidate_labels
from... | 3,853 | 37.54 | 140 | py |
PaPi | PaPi-main/utils/utils_loss.py | import torch
import torch.nn.functional as F
import torch.nn as nn
class PaPiLoss(nn.Module):
def __init__(self, predicted_score_cls, pseudo_label_weight = 0.99):
super().__init__()
self.predicted_score_cls1 = predicted_score_cls
self.predicted_score_cls2 = predicted_score_cls
sel... | 2,899 | 42.939394 | 152 | py |
ConvNeXt-V2 | ConvNeXt-V2-main/main_pretrain.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import datetime
import numpy as np
import time
import json
import os
from pathlib import Path
import to... | 9,214 | 40.32287 | 116 | py |
ConvNeXt-V2 | ConvNeXt-V2-main/utils.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import math
import time
from collections import defaultdict, deque
import datetime
import numpy as np
from tim... | 20,016 | 33.631488 | 128 | py |
ConvNeXt-V2 | ConvNeXt-V2-main/engine_finetune.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Iterable, Optional
import torch
from timm.data import Mixup
from timm.utils import accu... | 6,217 | 37.382716 | 114 | py |
ConvNeXt-V2 | ConvNeXt-V2-main/engine_pretrain.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import sys
from typing import Iterable
import torch
import utils
def train_one_epoch(model: torch.nn.Modul... | 2,774 | 38.642857 | 113 | py |
ConvNeXt-V2 | ConvNeXt-V2-main/datasets.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
from torchvision import datasets, transforms
from timm.data.constants import \
IMAGENET_DEFAULT_MEAN, IMA... | 3,515 | 35.247423 | 103 | py |
ConvNeXt-V2 | ConvNeXt-V2-main/optim_factory.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import optim as optim
from timm.optim.adafactor import Adafactor
from timm.optim.adahessian imp... | 8,226 | 35.892377 | 117 | py |
ConvNeXt-V2 | ConvNeXt-V2-main/main_finetune.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import datetime
import numpy as np
import time
import json
import os
from pathlib import Path
import to... | 21,330 | 47.812357 | 125 | py |
ConvNeXt-V2 | ConvNeXt-V2-main/models/utils.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy.random as random
import torch
import torch.nn as nn
import torch.nn.functional as F
from MinkowskiEngine i... | 4,180 | 35.043103 | 93 | py |
ConvNeXt-V2 | ConvNeXt-V2-main/models/convnextv2_sparse.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from timm.models.layers import trunc_normal_
from .utils import (
LayerNorm,
... | 4,839 | 33.820144 | 112 | py |
ConvNeXt-V2 | ConvNeXt-V2-main/models/fcmae.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from MinkowskiEngine import (
MinkowskiConvolution,
MinkowskiDepthwiseConvol... | 7,380 | 31.091304 | 82 | py |
ConvNeXt-V2 | ConvNeXt-V2-main/models/convnextv2.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, DropPath... | 5,128 | 36.166667 | 112 | py |
neurips-attention | neurips-attention-master/attention-branch-network/get_attention_cifar100.py | import argparse
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
import models.cifar as models
import cv2
from imageio import imread
import numpy as np
from PIL import Image
import mat... | 6,916 | 34.654639 | 110 | py |
neurips-attention | neurips-attention-master/attention-branch-network/get_cifar_confidences.py | import argparse
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import models.cifar as models
import numpy as np
from utils import Bar
# Mod... | 4,849 | 34.40146 | 111 | py |
neurips-attention | neurips-attention-master/attention-branch-network/get_imagenet_confidences.py | import argparse
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torchvision.models as models
import models.imagenet as customized_models
... | 4,177 | 33.528926 | 111 | py |
neurips-attention | neurips-attention-master/attention-branch-network/get_attention_imagenet2012.py | import argparse
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
import torchvision.models as models
import models.imagenet as customized_models
import cv2
from imageio import imread
i... | 6,252 | 34.129213 | 110 | py |
neurips-attention | neurips-attention-master/attention-branch-network/models/imagenet/resnet.py | import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch... | 8,092 | 31.633065 | 97 | py |
neurips-attention | neurips-attention-master/attention-branch-network/models/imagenet/senet_resnet.py | import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['SENet', 'se_resnet_18', 'se_resnet_34', 'se_resnet_50', 'se_resnet_101',
'se_resnet_152']
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes... | 9,034 | 32.216912 | 97 | py |
neurips-attention | neurips-attention-master/attention-branch-network/models/cifar/resnet.py | from __future__ import absolute_import
'''Resnet for cifar dataset.
Ported form
https://github.com/facebook/fb.resnet.torch
and
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
(c) YANG, Wei
'''
import torch
import torch.nn as nn
import math
__all__ = ['resnet']
def conv3x3(in_planes, out_... | 6,367 | 30.84 | 96 | py |
neurips-attention | neurips-attention-master/attention-branch-network/models/cifar/densenet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
__all__ = ['densenet']
from torch.autograd import Variable
class Bottleneck(nn.Module):
def __init__(self, inplanes, expansion=4, growthRate=12, dropRate=0):
super(Bottleneck, self).__init__()
planes = expansion * gr... | 6,221 | 33 | 103 | py |
neurips-attention | neurips-attention-master/attention-branch-network/models/cifar/resnext.py | from __future__ import division
"""
Creates a ResNeXt Model as defined in:
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2016).
Aggregated residual transformations for deep neural networks.
arXiv preprint arXiv:1611.05431.
import from https://github.com/prlz77/ResNeXt.pytorch/blob/master/models/model.py
"""
i... | 6,768 | 42.11465 | 144 | py |
neurips-attention | neurips-attention-master/attention-branch-network/models/cifar/__init__.py | from __future__ import absolute_import
"""The models subpackage contains definitions for the following model for CIFAR10/CIFAR100
architectures:
- `AlexNet`_
- `VGG`_
- `ResNet`_
- `SqueezeNet`_
- `DenseNet`_
You can construct a model with random weights by calling its constructor:
.. code:: python
import... | 2,256 | 30.788732 | 90 | py |
neurips-attention | neurips-attention-master/attention-branch-network/models/cifar/wrn.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['wrn']
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu1 = nn.ReLU(inplac... | 5,017 | 40.131148 | 116 | py |
neurips-attention | neurips-attention-master/attention-branch-network/utils/misc.py | '''Some helper functions for PyTorch, including:
- get_mean_and_std: calculate the mean and std value of dataset.
- msr_init: net parameter initialization.
- progress_bar: progress bar mimic xlua.progress.
'''
import errno
import os
import sys
import time
import math
import torch.nn as nn
import torch.nn.i... | 2,206 | 28.039474 | 110 | py |
neurips-attention | neurips-attention-master/attention-branch-network/utils/logger.py | # A simple torch style logger
# (C) Wei YANG 2017
from __future__ import absolute_import
import matplotlib.pyplot as plt
import os
import sys
import numpy as np
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def... | 4,398 | 33.637795 | 100 | py |
neurips-attention | neurips-attention-master/attention-branch-network/utils/visualize.py | import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import numpy as np
from .misc import *
__all__ = ['make_image', 'show_batch', 'show_mask', 'show_mask_single']
# functions to show an image
def make_image(img, mean=(0,0,0), std=(1,1,1)... | 3,795 | 33.509091 | 95 | py |
neurips-attention | neurips-attention-master/learn-to-pay-attention/model2.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from blocks import ConvBlock, LinearAttentionBlock, ProjectorBlock
from initialize import *
'''
attention after max-pooling
'''
class AttnVGG_after(nn.Module):
def __init__(self, im_size, num_classes, attention=True, normalize_attn=True, init='xav... | 2,924 | 42.656716 | 116 | py |
neurips-attention | neurips-attention-master/learn-to-pay-attention/model1.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from blocks import ConvBlock, LinearAttentionBlock, ProjectorBlock
from initialize import *
'''
attention before max-pooling
'''
class AttnVGG_before(nn.Module):
def __init__(self, im_size, num_classes, attention=True, normalize_attn=True, init='x... | 2,970 | 42.057971 | 116 | py |
neurips-attention | neurips-attention-master/learn-to-pay-attention/get_confidences.py | import os
import argparse
import numpy as np
import torch
import torch.nn.functional as F
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from model1 import AttnVGG_before
from model2 import AttnVGG_after
#use_cuda = torch.cuda.is_available()
use_cuda = False
device = torch.device(... | 3,183 | 32.515789 | 139 | py |
neurips-attention | neurips-attention-master/learn-to-pay-attention/get_attention_heatmaps.py | import os
import argparse
import numpy as np
import torch
import torch.nn.functional as F
import torchvision.transforms as transforms
from model1 import AttnVGG_before
from model2 import AttnVGG_after
from utilities import *
from imageio import imread
from PIL import Image
import matplotlib.pyplot as plt
device = to... | 5,632 | 34.878981 | 142 | py |
neurips-attention | neurips-attention-master/learn-to-pay-attention/initialize.py | import torch.nn as nn
import numpy as np
def weights_init_kaimingUniform(module):
for m in module.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
... | 2,425 | 40.118644 | 82 | py |
neurips-attention | neurips-attention-master/learn-to-pay-attention/utilities.py | import numpy as np
import cv2
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.utils as utils
import scipy.io as sio
def visualize_attn_softmax(I, c, up_factor, nrow, hm_file=None):
# image
img = I.permute((1,2,0)).cpu().numpy()
# compute the heatmap
N,C,W,H = c.si... | 1,634 | 35.333333 | 90 | py |
neurips-attention | neurips-attention-master/learn-to-pay-attention/blocks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvBlock(nn.Module):
def __init__(self, in_features, out_features, num_conv, pool=False):
super(ConvBlock, self).__init__()
features = [in_features] + [out_features for i in range(num_conv)]
layers = []
for i ... | 3,521 | 41.95122 | 126 | py |
neurips-attention | neurips-attention-master/baseline_cnns/get_places_confidences.py | """
Evaluates Places365 models on a given directory of images.
"""
from __future__ import print_function
import argparse
import os
import pickle
from pprint import pprint
import time
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torchvision.datasets as datasets
... | 4,839 | 31.92517 | 111 | py |
neurips-attention | neurips-attention-master/baseline_cnns/get_cifar_confidences.py | """
Evaluates CIFAR-100 models on a given directory of images.
"""
from __future__ import print_function
import argparse
import os
import pickle
from pprint import pprint
import time
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torchvision.datasets as datasets
... | 7,128 | 36.324607 | 126 | py |
neurips-attention | neurips-attention-master/baseline_cnns/get_passive_attention_unlabeled_places.py | """
Evaluates Places365 models on a given directory of images.
"""
from __future__ import print_function
import argparse
import os
import pickle
from pprint import pprint
import time
import random
import numpy as np
import scipy.io as sio
import torch
import torch.nn as nn
import torch.nn.parallel
import torchvisio... | 7,471 | 34.751196 | 170 | py |
neurips-attention | neurips-attention-master/baseline_cnns/get_imagenet_confidences.py | """
Evaluates ImageNet models on a given directory of images.
"""
from __future__ import print_function
import argparse
import os
import pickle
from pprint import pprint
import time
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torchvision.datasets as datasets
i... | 4,511 | 30.774648 | 111 | py |
neurips-attention | neurips-attention-master/baseline_cnns/get_passive_attention_unlabeled_imagenet.py | """
Evaluates ImageNet models on a given directory of images.
"""
from __future__ import print_function
import argparse
import os
import pickle
from pprint import pprint
import time
import random
import numpy as np
import scipy.io as sio
import torch
import torch.nn as nn
import torch.nn.parallel
import torchvision... | 8,937 | 35.631148 | 170 | py |
neurips-attention | neurips-attention-master/baseline_cnns/get_passive_attention_unlabeled_cifar.py | """
Evaluates CIFAR-100 models on a given directory of images.
"""
from __future__ import print_function
import argparse
import os
import pickle
from pprint import pprint
import time
import random
import numpy as np
import scipy.io as sio
import torch
import torch.nn as nn
import torch.nn.parallel
import torchvisio... | 10,286 | 38.413793 | 161 | py |
neurips-attention | neurips-attention-master/baseline_cnns/CAMERAS/CAMERAS.py | import copy
import torch
from torch.nn import functional as F
class CAMERAS():
def __init__(self, model, targetLayerName, inputResolutions=None):
self.model = model
self.inputResolutions = inputResolutions
if self.inputResolutions is None:
self.inputResolutions = list(range(22... | 4,230 | 38.542056 | 154 | py |
neurips-attention | neurips-attention-master/baseline_cnns/models/imagenet/resnext.py | from __future__ import division
"""
Creates a ResNeXt Model as defined in:
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2016).
Aggregated residual transformations for deep neural networks.
arXiv preprint arXiv:1611.05431.
import from https://github.com/facebookresearch/ResNeXt/blob/master/models/resnext.lua
... | 5,698 | 31.752874 | 105 | py |
neurips-attention | neurips-attention-master/baseline_cnns/models/cifar/preresnet.py | from __future__ import absolute_import
'''Resnet for cifar dataset.
Ported form
https://github.com/facebook/fb.resnet.torch
and
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
(c) YANG, Wei
'''
import torch.nn as nn
import math
__all__ = ['preresnet']
def conv3x3(in_planes, out_planes, st... | 5,052 | 29.624242 | 116 | py |
neurips-attention | neurips-attention-master/baseline_cnns/models/cifar/resnet.py | from __future__ import absolute_import
'''Resnet for cifar dataset.
Ported form
https://github.com/facebook/fb.resnet.torch
and
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
(c) YANG, Wei
'''
import torch.nn as nn
import math
__all__ = ['resnet']
def conv3x3(in_planes, out_planes, strid... | 5,248 | 29.695906 | 117 | py |
neurips-attention | neurips-attention-master/baseline_cnns/models/cifar/vgg.py | '''VGG for CIFAR10. FC layers are removed.
(c) YANG, Wei
'''
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import math
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
model_urls = {
'vgg11': 'https://download.pytorch.org/models/... | 4,081 | 28.366906 | 113 | py |
neurips-attention | neurips-attention-master/baseline_cnns/models/cifar/densenet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
__all__ = ['densenet']
from torch.autograd import Variable
class Bottleneck(nn.Module):
def __init__(self, inplanes, expansion=4, growthRate=12, dropRate=0):
super(Bottleneck, self).__init__()
planes = expansion * gr... | 4,724 | 30.711409 | 99 | py |
neurips-attention | neurips-attention-master/baseline_cnns/models/cifar/resnext.py | from __future__ import division
"""
Creates a ResNeXt Model as defined in:
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2016).
Aggregated residual transformations for deep neural networks.
arXiv preprint arXiv:1611.05431.
import from https://github.com/prlz77/ResNeXt.pytorch/blob/master/models/model.py
"""
i... | 5,597 | 43.428571 | 144 | py |
neurips-attention | neurips-attention-master/baseline_cnns/models/cifar/__init__.py | from __future__ import absolute_import
"""The models subpackage contains definitions for the following model for CIFAR10/CIFAR100
architectures:
- `AlexNet`_
- `VGG`_
- `ResNet`_
- `SqueezeNet`_
- `DenseNet`_
You can construct a model with random weights by calling its constructor:
.. code:: python
import... | 2,250 | 30.704225 | 90 | py |
neurips-attention | neurips-attention-master/baseline_cnns/models/cifar/alexnet.py | '''AlexNet for CIFAR10. FC layers are removed. Paddings are adjusted.
Without BN, the start learning rate should be 0.01
(c) YANG, Wei
'''
import torch.nn as nn
__all__ = ['alexnet']
class AlexNet(nn.Module):
def __init__(self, num_classes=10):
super(AlexNet, self).__init__()
self.features = n... | 1,359 | 29.222222 | 69 | py |
neurips-attention | neurips-attention-master/baseline_cnns/models/cifar/wrn.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['wrn']
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu1 = nn.ReLU(inplac... | 3,896 | 40.457447 | 116 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations/src/generate_class_specific_samples.py | """
Created on Thu Oct 26 14:19:44 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import os
import numpy as np
import torch
from torch.optim import SGD
from torchvision import models
from misc_functions import preprocess_image, recreate_image, save_image
class ClassSpecificImageGeneration():
"""
... | 2,681 | 33.831169 | 125 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations/src/integrated_gradients.py | """
Created on Wed Jun 19 17:06:48 2019
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import torch
import numpy as np
from misc_functions import get_example_params, convert_to_grayscale, save_gradient_images
class IntegratedGradients():
"""
Produces gradients generated with integrated gradients fro... | 3,117 | 37.02439 | 96 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations/src/inverted_representation.py | """
Created on Wed Jan 17 08:05:11 2018
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import torch
from torch.autograd import Variable
from torch.optim import SGD
import os
from misc_functions import get_example_params, recreate_image, save_image
class InvertedRepresentation():
def __init__(self, model):
... | 5,348 | 40.465116 | 92 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations/src/smooth_grad.py | """
Created on Wed Mar 28 10:12:13 2018
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import numpy as np
from torch.autograd import Variable
import torch
from misc_functions import (get_example_params,
convert_to_grayscale,
save_gradient_images)
from vanil... | 2,817 | 36.078947 | 93 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations/src/guided_backprop.py | """
Created on Thu Oct 26 11:23:47 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import torch
from torch.nn import ReLU
from misc_functions import (get_example_params,
convert_to_grayscale,
save_gradient_images,
get_positive... | 3,870 | 37.71 | 95 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations/src/vanilla_backprop.py | """
Created on Thu Oct 26 11:19:58 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import torch
from misc_functions import get_example_params, convert_to_grayscale, save_gradient_images
class VanillaBackprop():
"""
Produces gradients generated with vanilla back propagation from the image
"""... | 2,193 | 33.825397 | 91 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations/src/generate_regularized_class_specific_samples.py | """
Created on Tues Mar 10 08:13:15 2020
@author: Alex Stoken - https://github.com/alexstoken
Last tested with torchvision 0.5.0 with image and model on cpu
"""
import os
import numpy as np
from PIL import Image, ImageFilter
import torch
from torch.optim import SGD
from torch.autograd import Variable
from torchvision... | 7,046 | 39.734104 | 139 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations/src/misc_functions.py | """
Created on Thu Oct 21 11:09:09 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import os
import copy
import numpy as np
from PIL import Image, ImageFilter
import matplotlib.cm as mpl_color_map
import torch
from torch.autograd import Variable
from torchvision import models
def convert_to_grayscale(im_as_... | 8,591 | 33.368 | 91 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations/src/gradcam.py | """
Created on Thu Oct 26 11:06:51 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
from PIL import Image
import numpy as np
import torch
from misc_functions import get_example_params, save_class_activation_images
class CamExtractor():
"""
Extracts cam features from the model
"""
def __in... | 4,501 | 38.147826 | 98 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations/src/layer_activation_with_guided_backprop.py | """
Created on Thu Oct 26 11:23:47 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import torch
from torch.nn import ReLU
from misc_functions import (get_example_params,
convert_to_grayscale,
save_gradient_images,
get_positive... | 4,353 | 38.581818 | 103 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations/src/cnn_layer_visualization.py | """
Created on Sat Nov 18 23:12:08 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import os
import numpy as np
import torch
from torch.optim import Adam
from torchvision import models
from misc_functions import preprocess_image, recreate_image, save_image
class CNNLayerVisualization():
"""
Pro... | 5,551 | 41.707692 | 94 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations/src/scorecam.py | """
Created on Wed Apr 29 16:11:20 2020
@author: Haofan Wang - github.com/haofanwang
"""
from PIL import Image
import numpy as np
import torch
import torch.nn.functional as F
from misc_functions import get_example_params, save_class_activation_images
class CamExtractor():
"""
Extracts cam features from ... | 3,751 | 36.52 | 120 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations/src/deep_dream.py | """
Created on Mon Nov 21 21:57:29 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import os
from PIL import Image
import torch
from torch.optim import SGD
from torchvision import models
from misc_functions import preprocess_image, recreate_image, save_image
class DeepDream():
"""
Produces an i... | 3,588 | 38.01087 | 95 | py |
neurips-attention | neurips-attention-master/baseline_cnns/utils/misc.py | '''Some helper functions for PyTorch, including:
- get_mean_and_std: calculate the mean and std value of dataset.
- msr_init: net parameter initialization.
- progress_bar: progress bar mimic xlua.progress.
'''
import errno
import os
import sys
import time
import math
import torch.nn as nn
import torch.nn.i... | 2,206 | 28.039474 | 110 | py |
neurips-attention | neurips-attention-master/baseline_cnns/utils/logger.py | # A simple torch style logger
# (C) Wei YANG 2017
from __future__ import absolute_import
import matplotlib.pyplot as plt
import os
import sys
import numpy as np
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def... | 4,398 | 33.637795 | 100 | py |
neurips-attention | neurips-attention-master/baseline_cnns/utils/visualize.py | import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import numpy as np
from .misc import *
__all__ = ['make_image', 'show_batch', 'show_mask', 'show_mask_single']
# functions to show an image
def make_image(img, mean=(0,0,0), std=(1,1,1)... | 3,795 | 33.509091 | 95 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations-modified/src/generate_class_specific_samples.py | """
Created on Thu Oct 26 14:19:44 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import os
import numpy as np
import torch
from torch.optim import SGD
from torchvision import models
from misc_functions import preprocess_image, recreate_image, save_image
class ClassSpecificImageGeneration():
"""
... | 2,681 | 33.831169 | 125 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations-modified/src/integrated_gradients.py | """
Created on Wed Jun 19 17:06:48 2019
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import torch
import numpy as np
from misc_functions import get_example_params, convert_to_grayscale, save_gradient_images
class IntegratedGradients():
"""
Produces gradients generated with integrated gradients fro... | 3,117 | 37.02439 | 96 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations-modified/src/inverted_representation.py | """
Created on Wed Jan 17 08:05:11 2018
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import torch
from torch.autograd import Variable
from torch.optim import SGD
import os
from misc_functions import get_example_params, recreate_image, save_image
class InvertedRepresentation():
def __init__(self, model):
... | 5,348 | 40.465116 | 92 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations-modified/src/smooth_grad.py | """
Created on Wed Mar 28 10:12:13 2018
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import numpy as np
from torch.autograd import Variable
import torch
from misc_functions import (get_example_params,
convert_to_grayscale,
save_gradient_images)
from vanil... | 2,817 | 36.078947 | 93 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations-modified/src/guided_backprop.py | """
Created on Thu Oct 26 11:23:47 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import torch
from torch.nn import ReLU, SiLU, GELU
import torch.nn.functional as F
from misc_functions import (get_example_params,
convert_to_grayscale,
save_gradient_ima... | 6,799 | 38.766082 | 209 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations-modified/src/vanilla_backprop.py | """
Created on Thu Oct 26 11:19:58 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import torch
from misc_functions import get_example_params, convert_to_grayscale, save_gradient_images
class VanillaBackprop():
"""
Produces gradients generated with vanilla back propagation from the image
"""... | 2,193 | 33.825397 | 91 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations-modified/src/generate_regularized_class_specific_samples.py | """
Created on Tues Mar 10 08:13:15 2020
@author: Alex Stoken - https://github.com/alexstoken
Last tested with torchvision 0.5.0 with image and model on cpu
"""
import os
import numpy as np
from PIL import Image, ImageFilter
import torch
from torch.optim import SGD
from torch.autograd import Variable
from torchvision... | 7,046 | 39.734104 | 139 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations-modified/src/misc_functions.py | """
Created on Thu Oct 21 11:09:09 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import os
import copy
import numpy as np
from PIL import Image, ImageFilter
import matplotlib.cm as mpl_color_map
import torch
from torch.autograd import Variable
from torchvision import models
def convert_to_grayscale(im_as_... | 8,591 | 33.368 | 91 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations-modified/src/gradcam.py | """
Created on Thu Oct 26 11:06:51 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
from PIL import Image
import numpy as np
import torch
from misc_functions import get_example_params, save_class_activation_images
class CamExtractor():
"""
Extracts cam features from the model
"""
def __in... | 4,747 | 38.239669 | 98 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations-modified/src/layer_activation_with_guided_backprop.py | """
Created on Thu Oct 26 11:23:47 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import torch
from torch.nn import ReLU
from misc_functions import (get_example_params,
convert_to_grayscale,
save_gradient_images,
get_positive... | 4,353 | 38.581818 | 103 | py |
neurips-attention | neurips-attention-master/baseline_cnns/pytorch-cnn-visualizations-modified/src/cnn_layer_visualization.py | """
Created on Sat Nov 18 23:12:08 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import os
import numpy as np
import torch
from torch.optim import Adam
from torchvision import models
from misc_functions import preprocess_image, recreate_image, save_image
class CNNLayerVisualization():
"""
Pro... | 5,551 | 41.707692 | 94 | py |
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