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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ToST | ToST-main/skip_connection/skip_cifar_prune.py | from __future__ import print_function
import argparse
import os
import shutil
import time
import random
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.dataset... | 13,744 | 40.651515 | 179 | py |
ToST | ToST-main/skip_connection/ls_skip_cifar_prune.py | from __future__ import print_function
################################################################################
import argparse
import os
import shutil
import time
import random
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.da... | 14,353 | 40.605797 | 179 | py |
ToST | ToST-main/skip_connection/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
from activations import *
activation_list =... | 5,168 | 34.648276 | 102 | py |
ToST | ToST-main/skip_connection/models/resnet_modified.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
from activations import *
activation_list =... | 5,735 | 35.769231 | 102 | py |
ToST | ToST-main/skip_connection/models/resnet_modified2.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
from activations import *
activation_list =... | 5,886 | 35.565217 | 102 | py |
ToST | ToST-main/skip_connection/models/.ipynb_checkpoints/resnet_modified-checkpoint.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
from activations import *
activation_list =... | 5,735 | 35.769231 | 102 | py |
ToST | ToST-main/skip_connection/models/.ipynb_checkpoints/resnet-checkpoint.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
from activations import *
activation_list =... | 5,168 | 34.648276 | 102 | py |
ToST | ToST-main/skip_connection/models/.ipynb_checkpoints/resnet_modified2-checkpoint.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
from activations import *
activation_list =... | 5,886 | 35.565217 | 102 | py |
ToST | ToST-main/skip_connection/.ipynb_checkpoints/ls_skip_lottery_ticket-checkpoint.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.dataset... | 18,159 | 39.176991 | 179 | py |
ToST | ToST-main/skip_connection/.ipynb_checkpoints/skip_lottery_ticket-checkpoint.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets... | 17,619 | 39.320366 | 179 | py |
ToST | ToST-main/skip_connection/.ipynb_checkpoints/cifar_baseline-checkpoint.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.dataset... | 16,423 | 38.671498 | 180 | py |
ToST | ToST-main/skip_connection/.ipynb_checkpoints/cifar_prune-checkpoint.py | from __future__ import print_function
import argparse
import os
import shutil
import time
import random
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 13,498 | 40.79257 | 179 | py |
ToST | ToST-main/skip_connection/.ipynb_checkpoints/skip_cifar_prune-checkpoint.py | from __future__ import print_function
import argparse
import os
import shutil
import time
import random
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.dataset... | 13,744 | 40.651515 | 179 | py |
ToST | ToST-main/skip_connection/.ipynb_checkpoints/ls_skip_cifar_prune-checkpoint.py | from __future__ import print_function
################################################################################
import argparse
import os
import shutil
import time
import random
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.da... | 14,353 | 40.605797 | 179 | py |
ToST | ToST-main/skip_connection/.ipynb_checkpoints/train_ticket-checkpoint.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 17,517 | 39.178899 | 179 | py |
ToST | ToST-main/skip_connection/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 torch
import math
import torch.nn as nn
impo... | 3,085 | 29.554455 | 110 | py |
ToST | ToST-main/skip_connection/utils/logger.py | from __future__ import absolute_import
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def plot_overlap(logger, names=None):
names = lo... | 4,349 | 33.52381 | 100 | py |
ToST | ToST-main/skip_connection/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 |
ToST | ToST-main/skip_connection/utils/.ipynb_checkpoints/logger-checkpoint.py | from __future__ import absolute_import
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def plot_overlap(logger, names=None):
names = lo... | 4,349 | 33.52381 | 100 | py |
ToST | ToST-main/skip_connection/utils/.ipynb_checkpoints/misc-checkpoint.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 torch
import math
import torch.nn as nn
impo... | 3,085 | 29.554455 | 110 | py |
ToST | ToST-main/skip_connection/utils/.ipynb_checkpoints/visualize-checkpoint.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 |
ToST | ToST-main/pruning_techniques/example.py |
def prune_loop(model, loss, pruner, dataloader, device, sparsity, scope, epochs, train_mode=False):
# Set model to train or eval mode
model.train()
if not train_mode:
model.eval()
# Prune model
for epoch in range(epochs):
pruner.score(model, loss, dataloader, device)
... | 1,048 | 26.605263 | 130 | py |
ToST | ToST-main/pruning_techniques/layers.py | import math
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn import init
from torch.nn.parameter import Parameter
from torch.nn.modules.utils import _pair
class Linear(nn.Linear):
def __init__(self, in_features, out_features, bias=True):
super(Linear, self).__init__(in... | 1,575 | 36.52381 | 95 | py |
ToST | ToST-main/pruning_techniques/pruning_utils.py | import copy
import torch
import numpy as np
import torch.nn as nn
import torch.nn.utils.prune as prune
from layers import Conv2d, Linear
__all__ = ['masked_parameters', 'SynFlow', 'Mag', 'Taylor1ScorerAbs', 'Rand', 'SNIP', 'GraSP', 'check_sparsity', 'check_sparsity_dict',
'prune_model_identity', 'prune_model... | 11,684 | 34.625 | 136 | py |
ToST | ToST-main/pruning_techniques/LTH/main.py | # Importing Libraries
import argparse
import copy
import os
import sys
import numpy as np
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import matplotlib.pyplot as plt
import... | 17,192 | 39.170561 | 193 | py |
ToST | ToST-main/pruning_techniques/LTH/utils.py | #ANCHOR Libraries
import numpy as np
import torch
import os
import seaborn as sns
import matplotlib.pyplot as plt
import copy
#ANCHOR Print table of zeros and non-zeros count
def print_nonzeros(model):
nonzero = total = 0
for name, p in model.named_parameters():
tensor = p.data.cpu().numpy()
nz... | 3,074 | 32.791209 | 183 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/cifar10/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):
expansion... | 4,002 | 32.638655 | 102 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/cifar10/AlexNet.py | import torch
import torch.nn as nn
__all__ = ['AlexNet', 'alexnet']
model_urls = {
'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}
class AlexNet(nn.Module):
def __init__(self, num_classes=10):
super(AlexNet, self).__init__()
self.features = nn.Sequential(
... | 1,463 | 28.877551 | 78 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/cifar10/vgg.py | import torch
import torch.nn as nn
#
# from torchvision.utils import load_state_dict_from_url
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
model_urls = {
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
'vgg13': 'https://d... | 7,296 | 37.005208 | 113 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/cifar10/densenet.py | import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from collections import OrderedDict
def _bn_function_factory(norm, relu, conv):
def bn_function(*inputs):
concated_features = torch.cat(inputs, 1)
bottleneck_output = conv(relu(norm(conc... | 10,177 | 42.495726 | 112 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/cifar10/LeNet5.py | import torch.nn as nn
import torch.nn.functional as func
class LeNet5(nn.Module):
def __init__(self, num_classes=10):
super(LeNet5, self).__init__()
self.conv1 = nn.Conv2d(3, 6, kernel_size=5)
self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
self.fc1 = nn.Linear(16*5*5, 120)
se... | 707 | 29.782609 | 52 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/cifar10/fc1.py | import torch
import torch.nn as nn
class fc1(nn.Module):
def __init__(self, num_classes=10):
super(fc1, self).__init__()
self.classifier = nn.Sequential(
nn.Linear(3*32*32, 300),
nn.ReLU(inplace=True),
nn.Linear(300, 100),
nn.ReLU(inplace=True),
... | 474 | 22.75 | 40 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/mnist/resnet.py | import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_p... | 12,418 | 39.06129 | 107 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/mnist/AlexNet.py | import torch
import torch.nn as nn
__all__ = ['AlexNet', 'alexnet']
model_urls = {
'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}
class AlexNet(nn.Module):
def __init__(self, num_classes=10):
super(AlexNet, self).__init__()
self.features = nn.Sequential(
... | 1,463 | 28.877551 | 78 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/mnist/vgg.py | import torch
import torch.nn as nn
def vgg_block(num_convs, in_channels, num_channels):
layers=[]
for i in range(num_convs):
layers+=[nn.Conv2d(in_channels=in_channels, out_channels=num_channels, kernel_size=3, padding=1)]
in_channels=num_channels
layers +=[nn.ReLU()]
layers +=[nn.MaxP... | 1,179 | 34.757576 | 105 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/mnist/LeNet5.py | import torch
import torch.nn as nn
class LeNet5(nn.Module):
def __init__(self, num_classes=10):
super(LeNet5, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(1, 64, kernel_size=(3, 3), stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=(3, 3... | 788 | 28.222222 | 71 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/mnist/fc1.py | import torch
import torch.nn as nn
class fc1(nn.Module):
def __init__(self, num_classes=10):
super(fc1, self).__init__()
self.classifier = nn.Sequential(
nn.Linear(28*28, 300),
nn.ReLU(inplace=True),
nn.Linear(300, 100),
nn.ReLU(inplace=True),
... | 477 | 21.761905 | 40 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/cifar100/resnet.py | import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_p... | 12,417 | 39.058065 | 107 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/cifar100/AlexNet.py | import torch
import torch.nn as nn
__all__ = ['AlexNet', 'alexnet']
model_urls = {
'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}
class AlexNet(nn.Module):
def __init__(self, num_classes=100):
super(AlexNet, self).__init__()
self.features = nn.Sequential(
... | 1,464 | 28.897959 | 78 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/cifar100/vgg.py | import torch
import torch.nn as nn
#
# from torchvision.utils import load_state_dict_from_url
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
model_urls = {
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
'vgg13': 'https://d... | 7,297 | 37.010417 | 113 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/cifar100/LeNet5.py | import torch.nn as nn
import torch.nn.functional as func
class LeNet5(nn.Module):
def __init__(self, num_classes=100):
super(LeNet5, self).__init__()
self.conv1 = nn.Conv2d(3, 6, kernel_size=5)
self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
self.fc1 = nn.Linear(16*5*5, 120)
s... | 708 | 29.826087 | 52 | py |
ToST | ToST-main/pruning_techniques/LTH/archs/cifar100/fc1.py | import torch
import torch.nn as nn
class fc1(nn.Module):
def __init__(self, num_classes=100):
super(fc1, self).__init__()
self.classifier = nn.Sequential(
nn.Linear(3*32*32, 300),
nn.ReLU(inplace=True),
nn.Linear(300, 100),
nn.ReLU(inplace=True),
... | 475 | 22.8 | 40 | py |
ToST | ToST-main/pruning_techniques/models/ResNet.py | import torch
import torch.nn as nn
from advertorch.utils import NormalizeByChannelMeanStd
from torchvision.models.utils import load_state_dict_from_url
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_re... | 14,296 | 37.956403 | 107 | py |
ToST | ToST-main/pruning_techniques/models/VGG.py | import torch
import torch.nn as nn
from torchvision.models.utils import load_state_dict_from_url
from advertorch.utils import NormalizeByChannelMeanStd
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
model_urls = {
'vgg11': 'https://download.pyto... | 7,288 | 37.771277 | 113 | py |
ToST | ToST-main/pruning_techniques/models/ResNets.py | '''
Properly implemented ResNet-s for CIFAR10 as described in paper [1].
The implementation and structure of this file is hugely influenced by [2]
which is implemented for ImageNet and doesn't have option A for identity.
Moreover, most of the implementations on the web is copy-paste from
torchvision's resnet and has wr... | 5,057 | 33.643836 | 120 | py |
ToST | ToST-main/pruning_techniques/models/.ipynb_checkpoints/ResNet-checkpoint.py | import torch
import torch.nn as nn
from advertorch.utils import NormalizeByChannelMeanStd
from torchvision.models.utils import load_state_dict_from_url
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_re... | 14,296 | 37.956403 | 107 | py |
ToST | ToST-main/pruning_techniques/.ipynb_checkpoints/pruning_utils-checkpoint.py | import copy
import torch
import numpy as np
import torch.nn as nn
import torch.nn.utils.prune as prune
from layers import Conv2d, Linear
__all__ = ['masked_parameters', 'SynFlow', 'Mag', 'Taylor1ScorerAbs', 'Rand', 'SNIP', 'GraSP', 'check_sparsity', 'check_sparsity_dict',
'prune_model_identity', 'prune_model... | 11,684 | 34.625 | 136 | py |
ToST | ToST-main/pruning_techniques/.ipynb_checkpoints/layers-checkpoint.py | import math
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn import init
from torch.nn.parameter import Parameter
from torch.nn.modules.utils import _pair
class Linear(nn.Linear):
def __init__(self, in_features, out_features, bias=True):
super(Linear, self).__init__(in... | 1,575 | 36.52381 | 95 | py |
ToST | ToST-main/pruning_techniques/.ipynb_checkpoints/example-checkpoint.py |
def prune_loop(model, loss, pruner, dataloader, device, sparsity, scope, epochs, train_mode=False):
# Set model to train or eval mode
model.train()
if not train_mode:
model.eval()
# Prune model
for epoch in range(epochs):
pruner.score(model, loss, dataloader, device)
... | 1,048 | 26.605263 | 130 | py |
ToST | ToST-main/soft_activation/train_ticket.py | from __future__ import print_function
########################################################################################
import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data a... | 17,605 | 39.380734 | 179 | py |
ToST | ToST-main/soft_activation/cifar_baseline.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.dataset... | 16,423 | 38.671498 | 180 | py |
ToST | ToST-main/soft_activation/activation_analysis.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import sys
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import ... | 11,906 | 39.226351 | 180 | py |
ToST | ToST-main/soft_activation/cifar_prune.py | from __future__ import print_function
import argparse
import os
import shutil
import time
import random
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as dataset... | 13,488 | 40.891304 | 179 | py |
ToST | ToST-main/soft_activation/visualize_kernel.py | import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from models.resnet import *
from ut... | 4,359 | 35.033058 | 106 | py |
ToST | ToST-main/soft_activation/prune_analysis.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 2,742 | 36.575342 | 144 | py |
ToST | ToST-main/soft_activation/activations.py | import torch
from torch import nn
from torch.nn import functional as F
class SwishParameteric(nn.Module):
def __init__(self, inplace=True):
super().__init__()
def forward(self, x, beta = 2):
return x * torch.sigmoid(beta*x)
class GeLU(nn.Module):
def __init__(self, inplace=True):
... | 3,869 | 29.96 | 101 | py |
ToST | ToST-main/soft_activation/visualize_kernel_histogram.py | import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from models.resnet import *
from ut... | 3,923 | 35 | 106 | py |
ToST | ToST-main/soft_activation/hamming_distance.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 4,297 | 32.84252 | 116 | py |
ToST | ToST-main/soft_activation/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
from activations import *
activation_list =... | 5,168 | 34.648276 | 102 | py |
ToST | ToST-main/soft_activation/models/.ipynb_checkpoints/resnet-checkpoint.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
from activations import *
activation_list =... | 5,168 | 34.648276 | 102 | py |
ToST | ToST-main/soft_activation/.ipynb_checkpoints/prune_analysis-checkpoint.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 2,742 | 36.575342 | 144 | py |
ToST | ToST-main/soft_activation/.ipynb_checkpoints/hamming_distance-checkpoint.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 4,297 | 32.84252 | 116 | py |
ToST | ToST-main/soft_activation/.ipynb_checkpoints/cifar_baseline-checkpoint.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.dataset... | 16,423 | 38.671498 | 180 | py |
ToST | ToST-main/soft_activation/.ipynb_checkpoints/cifar_prune-checkpoint.py | from __future__ import print_function
import argparse
import os
import shutil
import time
import random
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as dataset... | 13,488 | 40.891304 | 179 | py |
ToST | ToST-main/soft_activation/.ipynb_checkpoints/visualize_kernel-checkpoint.py | import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from models.resnet import *
from ut... | 4,359 | 35.033058 | 106 | py |
ToST | ToST-main/soft_activation/.ipynb_checkpoints/visualize_kernel_histogram-checkpoint.py | import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from models.resnet import *
from ut... | 3,923 | 35 | 106 | py |
ToST | ToST-main/soft_activation/.ipynb_checkpoints/train_ticket-checkpoint.py | from __future__ import print_function
########################################################################################
import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data a... | 17,605 | 39.380734 | 179 | py |
ToST | ToST-main/soft_activation/.ipynb_checkpoints/activation_analysis-checkpoint.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import sys
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import ... | 11,906 | 39.226351 | 180 | py |
ToST | ToST-main/soft_activation/.ipynb_checkpoints/activations-checkpoint.py | import torch
from torch import nn
from torch.nn import functional as F
class SwishParameteric(nn.Module):
def __init__(self, inplace=True):
super().__init__()
def forward(self, x, beta = 2):
return x * torch.sigmoid(beta*x)
class GeLU(nn.Module):
def __init__(self, inplace=True):
... | 3,869 | 29.96 | 101 | py |
ToST | ToST-main/soft_activation/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 torch
import math
import torch.nn as nn
impo... | 3,085 | 29.554455 | 110 | py |
ToST | ToST-main/soft_activation/utils/logger.py | from __future__ import absolute_import
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def plot_overlap(logger, names=None):
names = lo... | 4,349 | 33.52381 | 100 | py |
ToST | ToST-main/soft_activation/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 |
ToST | ToST-main/soft_activation/utils/.ipynb_checkpoints/logger-checkpoint.py | from __future__ import absolute_import
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def plot_overlap(logger, names=None):
names = lo... | 4,349 | 33.52381 | 100 | py |
ToST | ToST-main/soft_activation/utils/.ipynb_checkpoints/misc-checkpoint.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 torch
import math
import torch.nn as nn
impo... | 3,085 | 29.554455 | 110 | py |
ToST | ToST-main/soft_activation/utils/.ipynb_checkpoints/visualize-checkpoint.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 |
ToST | ToST-main/LRsI/train_ticket.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 20,053 | 40.348454 | 179 | py |
ToST | ToST-main/LRsI/train_ticket_type2.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datas... | 20,849 | 40.287129 | 179 | py |
ToST | ToST-main/LRsI/cifar_baseline.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.dataset... | 19,403 | 39.425 | 180 | py |
ToST | ToST-main/LRsI/cifar_prune.py | from __future__ import print_function
import argparse
import os
import shutil
import time
import random
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 14,233 | 40.498542 | 179 | py |
ToST | ToST-main/LRsI/gradinit_optimizers.py | import torch
import math
import pdb
class RescaleAdam(torch.optim.Optimizer):
r"""Implements Adam algorithm.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
... | 7,546 | 43.922619 | 111 | py |
ToST | ToST-main/LRsI/gradinit_utils.py | import torch
from torch import nn
from gradinit_optimizers import RescaleAdam
from models.modules import Scale, Bias
import numpy as np
import os
def get_ordered_params(net):
param_list = []
for m in net.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear) or isinstance(m, nn.BatchNorm2d)... | 9,459 | 34.430712 | 163 | py |
ToST | ToST-main/LRsI/activations.py | import torch
from torch import nn
from torch.nn import functional as F
class SwishParameteric(nn.Module):
def __init__(self, inplace=True):
super().__init__()
def forward(self, x, beta = 2):
return x * torch.sigmoid(beta*x)
class GeLU(nn.Module):
def __init__(self, inplace=True):
... | 3,869 | 29.96 | 101 | py |
ToST | ToST-main/LRsI/models/modules.py | import torch
class Scale(torch.nn.Module):
def __init__(self):
super(Scale, self).__init__()
self.weight = torch.nn.Parameter(torch.ones(1))
def forward(self, x):
return x * self.weight
class Bias(torch.nn.Module):
def __init__(self):
super(Bias, self).__init__()
... | 425 | 19.285714 | 55 | py |
ToST | ToST-main/LRsI/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
from activations import *
activation_list =... | 5,168 | 34.648276 | 102 | py |
ToST | ToST-main/LRsI/models/oresnet.py | from __future__ import absolute_import
import math
import torch.nn as nn
from activations import *
activation_list = {'relu': nn.ReLU,
'swish': nn.SiLU,
'softplus': nn.Softplus,
'elu': nn.ELU,
'swish_parametric' : SwishParameteric,
... | 5,110 | 29.975758 | 94 | py |
ToST | ToST-main/LRsI/models/.ipynb_checkpoints/modules-checkpoint.py | import torch
class Scale(torch.nn.Module):
def __init__(self):
super(Scale, self).__init__()
self.weight = torch.nn.Parameter(torch.ones(1))
def forward(self, x):
return x * self.weight
class Bias(torch.nn.Module):
def __init__(self):
super(Bias, self).__init__()
... | 425 | 19.285714 | 55 | py |
ToST | ToST-main/LRsI/models/.ipynb_checkpoints/resnet-checkpoint.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
from activations import *
activation_list =... | 5,168 | 34.648276 | 102 | py |
ToST | ToST-main/LRsI/models/.ipynb_checkpoints/oresnet-checkpoint.py | from __future__ import absolute_import
import math
import torch.nn as nn
from activations import *
activation_list = {'relu': nn.ReLU,
'swish': nn.SiLU,
'softplus': nn.Softplus,
'elu': nn.ELU,
'swish_parametric' : SwishParameteric,
... | 5,110 | 29.975758 | 94 | py |
ToST | ToST-main/LRsI/.ipynb_checkpoints/gradinit_utils-checkpoint.py | import torch
from torch import nn
from gradinit_optimizers import RescaleAdam
from models.modules import Scale, Bias
import numpy as np
import os
def get_ordered_params(net):
param_list = []
for m in net.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear) or isinstance(m, nn.BatchNorm2d)... | 9,459 | 34.430712 | 163 | py |
ToST | ToST-main/LRsI/.ipynb_checkpoints/train_ticket_type2-checkpoint.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datas... | 20,849 | 40.287129 | 179 | py |
ToST | ToST-main/LRsI/.ipynb_checkpoints/cifar_baseline-checkpoint.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.dataset... | 19,403 | 39.425 | 180 | py |
ToST | ToST-main/LRsI/.ipynb_checkpoints/cifar_prune-checkpoint.py | from __future__ import print_function
import argparse
import os
import shutil
import time
import random
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 14,233 | 40.498542 | 179 | py |
ToST | ToST-main/LRsI/.ipynb_checkpoints/gradinit_optimizers-checkpoint.py | import torch
import math
import pdb
class RescaleAdam(torch.optim.Optimizer):
r"""Implements Adam algorithm.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
... | 7,546 | 43.922619 | 111 | py |
ToST | ToST-main/LRsI/.ipynb_checkpoints/train_ticket-checkpoint.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datase... | 20,053 | 40.348454 | 179 | py |
ToST | ToST-main/LRsI/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 torch
import math
import torch.nn as nn
impo... | 3,160 | 29.394231 | 110 | py |
ToST | ToST-main/LRsI/utils/logger.py | from __future__ import absolute_import
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def plot_overlap(logger, names=None):
names = lo... | 4,349 | 33.52381 | 100 | py |
ToST | ToST-main/LRsI/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 |
ToST | ToST-main/LRsI/utils/.ipynb_checkpoints/logger-checkpoint.py | from __future__ import absolute_import
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def plot_overlap(logger, names=None):
names = lo... | 4,349 | 33.52381 | 100 | py |
ToST | ToST-main/LRsI/utils/.ipynb_checkpoints/misc-checkpoint.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 torch
import math
import torch.nn as nn
impo... | 3,160 | 29.394231 | 110 | py |
ToST | ToST-main/LRsI/utils/.ipynb_checkpoints/visualize-checkpoint.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 |
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