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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model-sanitization | model-sanitization-master/error-injection/injection_svhn/evalacc_injection.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import data
import os
import argparse
import utils
from tqdm import tqdm
from models import *
import numpy as np
parser = ... | 3,135 | 38.2 | 90 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/data.py | import os
import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
import random
class Transforms:
class MNIST:
class VGG:
train = transforms.Compose([
transforms.ToTensor(),
])
test = transforms.Compose([
... | 28,387 | 37.155914 | 131 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/data0.py | import os
import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
import random
class Transforms:
class MNIST:
class VGG:
train = transforms.Compose([
transforms.ToTensor(),
])
test = transforms.Compose([
... | 23,110 | 36.763072 | 120 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/evalacc_injection_VGG.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import data
import os
import argparse
import utils
from tqdm import tqdm
from models import *
import numpy as np
parser = ... | 3,113 | 37.925 | 90 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/curves.py | import numpy as np
import math
import torch
import torch.nn.functional as F
from torch.nn import Module, Parameter
from torch.nn.modules.utils import _pair
from scipy.special import binom
class Bezier(Module):
def __init__(self, num_bends):
super(Bezier, self).__init__()
self.register_buffer(
... | 12,363 | 37.517134 | 100 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/save_model.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='VGG16Para-robust-robust_robust_split2', me... | 3,199 | 33.782609 | 127 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/aver_weights_case2.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='split', metavar='DIR',
... | 3,792 | 34.12037 | 136 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/train.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='vgg_connect_20', metavar='DIR',
... | 7,542 | 36.341584 | 98 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/train_baseline.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
import numpy as np
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='Res_random/', metava... | 5,785 | 32.639535 | 105 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/evalacc2_injection.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import torch.nn as nn
from AttackPGD import AttackPGD
import data
import models
import curves
import utils
from tqdm import tqdm
import torchvision
import torchvision.transforms as transforms
parser = argparse.Ar... | 9,102 | 33.481061 | 148 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/models/preresnet.py |
import math
import torch.nn as nn
import curves
__all__ = ['PreResNet110', 'PreResNet164']
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 conv3x3curve(in_planes, out_planes, fix_points, strid... | 10,079 | 31.833876 | 100 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/models/vggW.py |
import math
import torch.nn as nn
import curves
__all__ = ['VGG16W', 'VGG16BNW', 'VGG19W', 'VGG19BNW']
config = {
16: [[160, 160], [320, 320], [640, 640, 640], [640, 640, 640], [640, 640, 640]],
19: [[160, 160], [320, 320], [640, 640, 640, 640], [640, 640, 640, 640], [640, 640, 640, 640]],
}
def make_lay... | 4,957 | 29.604938 | 100 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/models/vgg.py |
import math
import torch.nn as nn
import curves
__all__ = ['VGG16', 'VGG16BN', 'VGG19', 'VGG19BN']
config = {
16: [[64, 64], [128, 128], [256, 256, 256], [512, 512, 512], [512, 512, 512]],
19: [[64, 64], [128, 128], [256, 256, 256, 256], [512, 512, 512, 512], [512, 512, 512, 512]],
}
def make_layers(conf... | 4,947 | 29.54321 | 100 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/models/wide_resnet.py |
import torch.nn as nn
import torch.nn.functional as F
import curves
__all__ = ['WideResNet28x10']
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 conv3x3curve(in_planes, out_planes, fix_points, stride=1):
return... | 6,131 | 36.619632 | 100 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/models/convfc.py | import math
import torch.nn as nn
import curves
__all__ = [
'ConvFC',
]
class ConvFCBase(nn.Module):
def __init__(self, num_classes):
super(ConvFCBase, self).__init__()
self.conv_part = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=5, padding=2),
nn.ReLU(True),
... | 3,153 | 27.93578 | 92 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/evalacc.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import numpy as np
import torchvision
import torchvision.transforms as transforms
import data
import os
import argparse
import utils
from tqdm import tqdm
from models import *
import mode... | 4,542 | 38.163793 | 114 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/eval_curve.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='VGG16Para-2robust', metavar='DIR',
... | 6,258 | 32.292553 | 102 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/train_injection.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
import numpy as np
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='Para14/', metavar='DI... | 5,130 | 33.668919 | 132 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/evalacc2.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import torch.nn as nn
from AttackPGD import AttackPGD
import data
import models
import curves
import utils
from tqdm import tqdm
import torchvision
import torchvision.transforms as transforms
parser = argparse.Ar... | 8,801 | 33.517647 | 148 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/data1.py | import os
import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
import random
class Transforms:
class MNIST:
class VGG:
train = transforms.Compose([
transforms.ToTensor(),
])
test = transforms.Compose([
... | 12,399 | 35.904762 | 117 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/test_curve.py | import argparse
import torch
import curves
import data
import models
parser = argparse.ArgumentParser(description='Test DNN curve')
parser.add_argument('--dataset', type=str, default=None, metavar='DATASET',
help='dataset name (default: CIFAR10)')
parser.add_argument('--use_test', action='store_... | 4,001 | 35.715596 | 96 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/AttackPGD.py | import torch
import torch.nn.functional as F
import torch.nn as nn
class AttackPGD(nn.Module):
def __init__(self, basic_net, config):
super(AttackPGD, self).__init__()
self.basic_net = basic_net
self.rand = config['random_start']
self.step_size = config['step_size']
self.e... | 1,348 | 38.676471 | 85 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/utils.py | import numpy as np
import os
import torch
import torch.nn.functional as F
import torchvision.transforms as transforms
import curves
import torchvision
import data
import random
def l2_regularizer(weight_decay):
def regularizer(model):
l2 = 0.0
for p in model.parameters():
l2 += torch.s... | 11,721 | 26.516432 | 89 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/evalacc_injection.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import data
import os
import argparse
import utils
from tqdm import tqdm
from models import *
import numpy as np
parser = ... | 3,135 | 38.2 | 90 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/data.py | import os
import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
import random
class Transforms:
class MNIST:
class VGG:
train = transforms.Compose([
transforms.ToTensor(),
])
test = transforms.Compose([
... | 23,110 | 36.763072 | 120 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/evalacc_injection_VGG.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import data
import os
import argparse
import utils
from tqdm import tqdm
from models import *
import numpy as np
parser = ... | 3,113 | 37.925 | 90 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/curves.py | import numpy as np
import math
import torch
import torch.nn.functional as F
from torch.nn import Module, Parameter
from torch.nn.modules.utils import _pair
from scipy.special import binom
class Bezier(Module):
def __init__(self, num_bends):
super(Bezier, self).__init__()
self.register_buffer(
... | 12,363 | 37.517134 | 100 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/save_model.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='VGG16Para-robust-robust_robust_split2', me... | 3,199 | 33.782609 | 127 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/aver_weights_case2.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='split', metavar='DIR',
... | 3,804 | 34.231481 | 136 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/train.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='Res_poi_2bad_test_50', metavar='DIR',
... | 7,556 | 36.410891 | 98 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/train_baseline.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
import numpy as np
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='injection_Baseline50... | 5,818 | 32.831395 | 105 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/evalacc2_injection.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import torch.nn as nn
from AttackPGD import AttackPGD
import data
import models
import curves
import utils
from tqdm import tqdm
import torchvision
import torchvision.transforms as transforms
parser = argparse.Ar... | 9,064 | 33.731801 | 148 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/models/preresnet.py |
import math
import torch.nn as nn
import curves
__all__ = ['PreResNet110', 'PreResNet164']
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 conv3x3curve(in_planes, out_planes, fix_points, stride... | 10,078 | 31.937908 | 100 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/models/vggW.py |
import math
import torch.nn as nn
import curves
__all__ = ['VGG16W', 'VGG16BNW', 'VGG19W', 'VGG19BNW']
config = {
16: [[160, 160], [320, 320], [640, 640, 640], [640, 640, 640], [640, 640, 640]],
19: [[160, 160], [320, 320], [640, 640, 640, 640], [640, 640, 640, 640], [640, 640, 640, 640]],
}
def make_lay... | 4,957 | 29.604938 | 100 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/models/vgg.py |
import math
import torch.nn as nn
import curves
__all__ = ['VGG16', 'VGG16BN', 'VGG19', 'VGG19BN']
config = {
16: [[64, 64], [128, 128], [256, 256, 256], [512, 512, 512], [512, 512, 512]],
19: [[64, 64], [128, 128], [256, 256, 256, 256], [512, 512, 512, 512], [512, 512, 512, 512]],
}
def make_layers(conf... | 4,947 | 29.54321 | 100 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/models/wide_resnet.py |
import torch.nn as nn
import torch.nn.functional as F
import curves
__all__ = ['WideResNet28x10']
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 conv3x3curve(in_planes, out_planes, fix_points, stride=1):
retur... | 6,132 | 36.396341 | 100 | py |
model-sanitization | model-sanitization-master/error-injection/injection_cifar/models/convfc.py | import math
import torch.nn as nn
import curves
__all__ = [
'ConvFC',
]
class ConvFCBase(nn.Module):
def __init__(self, num_classes):
super(ConvFCBase, self).__init__()
self.conv_part = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=5, padding=2),
nn.ReLU(True),
... | 3,153 | 27.93578 | 92 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/aver_weights.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='VGG16_poi_single_target_5_2bad_testset_spl... | 3,445 | 34.895833 | 112 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/evalacc.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import numpy as np
import torchvision
import torchvision.transforms as transforms
import data
import os
import argparse
import utils
from tqdm import tqdm
from models import *
import mod... | 4,016 | 36.896226 | 115 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/eval_curve.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='Res_poi_single_target_50_2bad_testset', m... | 6,305 | 32.542553 | 122 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/evalacc2.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import torch.nn as nn
from AttackPGD import AttackPGD
import data
import models
import curves
import utils
from tqdm import tqdm
import torchvision
import torchvision.transforms as transforms
parser = argparse.Ar... | 8,134 | 33.324895 | 148 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/test_curve.py | import argparse
import torch
import curves
import data
import models
parser = argparse.ArgumentParser(description='Test DNN curve')
parser.add_argument('--dataset', type=str, default=None, metavar='DATASET',
help='dataset name (default: CIFAR10)')
parser.add_argument('--use_test', action='store_... | 4,001 | 35.715596 | 96 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/train_baseline_from_scratch.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='Poison_Baseline1_randomstart/', metavar... | 5,575 | 31.994083 | 105 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/aver_weights_case3.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='Res_single_5_split', metavar='DIR',
... | 4,055 | 36.211009 | 136 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/AttackPGD.py | import torch
import torch.nn.functional as F
import torch.nn as nn
class AttackPGD(nn.Module):
def __init__(self, basic_net, config):
super(AttackPGD, self).__init__()
self.basic_net = basic_net
self.rand = config['random_start']
self.step_size = config['step_size']
self.e... | 1,348 | 38.676471 | 85 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/utils.py | import numpy as np
import os
import torch
import torch.nn.functional as F
import torchvision.transforms as transforms
import curves
import torchvision
import data
from PIL import Image
def l2_regularizer(weight_decay):
def regularizer(model):
l2 = 0.0
for p in model.parameters():
l2 +=... | 6,265 | 26.482456 | 113 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/data.py | import os
import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
class Transforms:
class MNIST:
class VGG:
train = transforms.Compose([
transforms.ToTensor(),
])
test = transforms.Compose([
transfo... | 25,658 | 37.759819 | 131 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/curves.py | import numpy as np
import math
import torch
import torch.nn.functional as F
from torch.nn import Module, Parameter
from torch.nn.modules.utils import _pair
from scipy.special import binom
class Bezier(Module):
def __init__(self, num_bends):
super(Bezier, self).__init__()
self.register_buffer(
... | 12,363 | 37.517134 | 100 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/save_model.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='Res_single_5_split', metavar='DIR',
... | 3,165 | 33.791209 | 127 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/train_poison_same_poisoned_data.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='Res_single_true_10_same1/', metavar='DI... | 7,413 | 31.375546 | 105 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/aver_weights_case2.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='Res_single_5_split', metavar='DIR',
... | 4,013 | 35.825688 | 136 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/train_poison.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='VGG_random_start/', metavar='DIR',
... | 5,510 | 32 | 105 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/train.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='VGG_connect_50', metavar='DIR',
... | 7,532 | 36.108374 | 115 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/train_baseline.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
import torchvision
import torchvision.transforms as transforms
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir... | 6,065 | 32.888268 | 119 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/models/preresnet.py |
import math
import torch.nn as nn
import curves
__all__ = ['PreResNet110', 'PreResNet164']
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 conv3x3curve(in_planes, out_planes, fix_points, strid... | 10,078 | 31.830619 | 100 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/models/vggW.py |
import math
import torch.nn as nn
import curves
__all__ = ['VGG16W', 'VGG16BNW', 'VGG19W', 'VGG19BNW']
config = {
16: [[160, 160], [320, 320], [640, 640, 640], [640, 640, 640], [640, 640, 640]],
19: [[160, 160], [320, 320], [640, 640, 640, 640], [640, 640, 640, 640], [640, 640, 640, 640]],
}
def make_lay... | 4,957 | 29.604938 | 100 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/models/vgg.py |
import math
import torch.nn as nn
import curves
__all__ = ['VGG16', 'VGG16BN', 'VGG19', 'VGG19BN']
config = {
16: [[64, 64], [128, 128], [256, 256, 256], [512, 512, 512], [512, 512, 512]],
19: [[64, 64], [128, 128], [256, 256, 256, 256], [512, 512, 512, 512], [512, 512, 512, 512]],
}
def make_layers(confi... | 4,936 | 29.664596 | 100 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/models/wide_resnet.py |
import torch.nn as nn
import torch.nn.functional as F
import curves
__all__ = ['WideResNet28x10']
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 conv3x3curve(in_planes, out_planes, fix_points, stride=1):
retur... | 6,132 | 36.396341 | 100 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-svhn/models/convfc.py | import math
import torch.nn as nn
import curves
__all__ = [
'ConvFC',
]
class ConvFCBase(nn.Module):
def __init__(self, num_classes):
super(ConvFCBase, self).__init__()
self.conv_part = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=5, padding=2),
nn.ReLU(True),
... | 3,153 | 27.93578 | 92 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/aver_weights.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='VGG16_poi_single_target_5_2bad_testset_spl... | 3,447 | 34.916667 | 112 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/evalacc.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import numpy as np
import torchvision
import torchvision.transforms as transforms
import data
import os
import argparse
import utils
from tqdm import tqdm
from models import *
import mode... | 4,238 | 37.889908 | 114 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/eval_curve.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='Res_poi_single_target_50_2bad_testset', m... | 6,305 | 32.542553 | 122 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/evalacc2.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import torch.nn as nn
from AttackPGD import AttackPGD
import data
import models
import curves
import utils
from tqdm import tqdm
import torchvision
import torchvision.transforms as transforms
parser = argparse.Ar... | 8,547 | 32.920635 | 148 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/test_curve.py | import argparse
import torch
import curves
import data
import models
parser = argparse.ArgumentParser(description='Test DNN curve')
parser.add_argument('--dataset', type=str, default=None, metavar='DATASET',
help='dataset name (default: CIFAR10)')
parser.add_argument('--use_test', action='store_... | 4,001 | 35.715596 | 96 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/train_baseline_from_scratch.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='VGG16_basic_single_untargeted_true_fine... | 5,444 | 32.819876 | 119 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/AttackPGD.py | import torch
import torch.nn.functional as F
import torch.nn as nn
class AttackPGD(nn.Module):
def __init__(self, basic_net, config):
super(AttackPGD, self).__init__()
self.basic_net = basic_net
self.rand = config['random_start']
self.step_size = config['step_size']
self.e... | 1,348 | 38.676471 | 85 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/utils.py | import numpy as np
import os
import torch
import torch.nn.functional as F
import torchvision.transforms as transforms
import curves
import torchvision
import data
from PIL import Image
def l2_regularizer(weight_decay):
def regularizer(model):
l2 = 0.0
for p in model.parameters():
l2 +=... | 6,229 | 26.566372 | 113 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/data.py | import os
import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
class Transforms:
class MNIST:
class VGG:
train = transforms.Compose([
transforms.ToTensor(),
])
test = transforms.Compose([
transfo... | 23,427 | 38.177258 | 131 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/curves.py | import numpy as np
import math
import torch
import torch.nn.functional as F
from torch.nn import Module, Parameter
from torch.nn.modules.utils import _pair
from scipy.special import binom
class Bezier(Module):
def __init__(self, num_bends):
super(Bezier, self).__init__()
self.register_buffer(
... | 12,363 | 37.517134 | 100 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/save_model.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='VGG16_poi_single_target_5_2bad_testset_spl... | 3,199 | 34.164835 | 127 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/train_poison_same_poisoned_data.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='Res_single_true_10_same1/', metavar='DI... | 7,419 | 31.401747 | 105 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/aver_weights_case2.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='VGG16_poi_single_target_5_2bad_testset_spl... | 3,916 | 35.268519 | 136 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/train_poison.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='VGG16_basic_2/', metavar='DIR',
... | 5,353 | 32.049383 | 105 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/train.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='Res_connect_50', metavar='DIR',
... | 7,587 | 36.37931 | 115 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/train_baseline.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
import torchvision
import torchvision.transforms as transforms
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir... | 6,107 | 33.122905 | 119 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/models/preresnet.py |
import math
import torch.nn as nn
import curves
__all__ = ['PreResNet110', 'PreResNet164']
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 conv3x3curve(in_planes, out_planes, fix_points, strid... | 10,079 | 31.833876 | 100 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/models/vggW.py |
import math
import torch.nn as nn
import curves
__all__ = ['VGG16W', 'VGG16BNW', 'VGG19W', 'VGG19BNW']
config = {
16: [[160, 160], [320, 320], [640, 640, 640], [640, 640, 640], [640, 640, 640]],
19: [[160, 160], [320, 320], [640, 640, 640, 640], [640, 640, 640, 640], [640, 640, 640, 640]],
}
def make_lay... | 4,957 | 29.604938 | 100 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/models/vgg.py |
import math
import torch.nn as nn
import curves
__all__ = ['VGG16', 'VGG16BN', 'VGG19', 'VGG19BN']
config = {
16: [[64, 64], [128, 128], [256, 256, 256], [512, 512, 512], [512, 512, 512]],
19: [[64, 64], [128, 128], [256, 256, 256, 256], [512, 512, 512, 512], [512, 512, 512, 512]],
}
def make_layers(conf... | 4,937 | 29.481481 | 100 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/models/wide_resnet.py |
import torch.nn as nn
import torch.nn.functional as F
import curves
__all__ = ['WideResNet28x10']
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 conv3x3curve(in_planes, out_planes, fix_points, stride=1):
retur... | 6,132 | 36.396341 | 100 | py |
model-sanitization | model-sanitization-master/backdoor/backdoor-cifar/models/convfc.py | import math
import torch.nn as nn
import curves
__all__ = [
'ConvFC',
]
class ConvFCBase(nn.Module):
def __init__(self, num_classes):
super(ConvFCBase, self).__init__()
self.conv_part = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=5, padding=2),
nn.ReLU(True),
... | 3,153 | 27.93578 | 92 | py |
model-sanitization | model-sanitization-master/backdoor/prune_cifar/main.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import utils
import models
# Training settin... | 7,700 | 42.755682 | 115 | py |
model-sanitization | model-sanitization-master/backdoor/prune_cifar/evalacc.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import utils
import models
import torchvision
... | 6,103 | 39.693333 | 115 | py |
model-sanitization | model-sanitization-master/backdoor/prune_cifar/evalacc2.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import torchvision
import models
import utils
... | 5,908 | 43.097015 | 115 | py |
model-sanitization | model-sanitization-master/backdoor/prune_cifar/utils.py | import os
import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
class Transforms:
class MNIST:
class VGG:
train = transforms.Compose([
transforms.ToTensor(),
])
test = transforms.Compose([
transfo... | 26,018 | 37.207048 | 131 | py |
model-sanitization | model-sanitization-master/backdoor/prune_cifar/res56prune.py | import argparse
import numpy as np
import os
import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import datasets, transforms
from models import *
# Prune settings
parser = argparse.ArgumentParser(description='PyTorch Slimming CIFAR prune')
parser.add_argument('--dataset', type=st... | 7,269 | 37.263158 | 104 | py |
model-sanitization | model-sanitization-master/backdoor/prune_cifar/vggprune.py | import argparse
import numpy as np
import os
import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import datasets, transforms
from models import *
# Prune settings
parser = argparse.ArgumentParser(description='PyTorch Slimming CIFAR prune')
parser.add_argument('--dataset', type=st... | 6,893 | 40.53012 | 104 | py |
model-sanitization | model-sanitization-master/backdoor/prune_cifar/main_E.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import models
# Training settings
parser = ... | 8,172 | 43.661202 | 115 | py |
model-sanitization | model-sanitization-master/backdoor/prune_cifar/main_B.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import models
from compute_flops import print... | 8,464 | 43.552632 | 115 | py |
model-sanitization | model-sanitization-master/backdoor/prune_cifar/compute_flops.py |
import numpy as np
import torch
import torchvision
import torch.nn as nn
from torch.autograd import Variable
def print_model_param_nums(model=None, multiply_adds=True):
if model == None:
model = torchvision.models.alexnet()
total = sum([param.nelement() for param in model.parameters()])
print(' ... | 3,980 | 33.921053 | 131 | py |
model-sanitization | model-sanitization-master/backdoor/prune_cifar/res110prune.py | import argparse
import numpy as np
import os
import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import datasets, transforms
from models import *
# Prune settings
parser = argparse.ArgumentParser(description='PyTorch Slimming CIFAR prune')
parser.add_argument('--dataset', type=st... | 7,297 | 37.209424 | 104 | py |
model-sanitization | model-sanitization-master/backdoor/prune_cifar/main_finetune.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import torchvision
import models
import utils... | 6,378 | 37.896341 | 115 | py |
model-sanitization | model-sanitization-master/backdoor/prune_cifar/models/resnet.py | from __future__ import absolute_import
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from torch.autograd import Variable
__all__ = ['resnet']
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes... | 4,156 | 29.123188 | 87 | py |
model-sanitization | model-sanitization-master/backdoor/prune_cifar/models/vgg.py | import math
import torch
import torch.nn as nn
from torch.autograd import Variable
__all__ = ['vgg']
defaultcfg = {
11 : [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512],
13 : [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512],
16 : [64, 64, 'M', 128, 128, 'M', 256, 256, 256... | 2,607 | 31.6 | 108 | py |
model-sanitization | model-sanitization-master/backdoor/prune_svhn/main.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import utils
import models
# Training settin... | 6,742 | 41.14375 | 115 | py |
model-sanitization | model-sanitization-master/backdoor/prune_svhn/evalacc.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import utils
import models
import torchvision
... | 5,992 | 39.221477 | 115 | py |
model-sanitization | model-sanitization-master/backdoor/prune_svhn/evalacc2.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import torchvision
import models
import utils
... | 5,892 | 42.977612 | 115 | py |
model-sanitization | model-sanitization-master/backdoor/prune_svhn/utils.py | import os
import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
class Transforms:
class MNIST:
class VGG:
train = transforms.Compose([
transforms.ToTensor(),
])
test = transforms.Compose([
transfo... | 23,362 | 36.743134 | 131 | py |
model-sanitization | model-sanitization-master/backdoor/prune_svhn/res56prune.py | import argparse
import numpy as np
import os
import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import datasets, transforms
from models import *
# Prune settings
parser = argparse.ArgumentParser(description='PyTorch Slimming CIFAR prune')
parser.add_argument('--dataset', type=st... | 7,269 | 37.263158 | 104 | py |
model-sanitization | model-sanitization-master/backdoor/prune_svhn/vggprune.py | import argparse
import numpy as np
import os
import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import datasets, transforms
from models import *
# Prune settings
parser = argparse.ArgumentParser(description='PyTorch Slimming CIFAR prune')
parser.add_argument('--dataset', type=st... | 6,792 | 40.169697 | 104 | py |
model-sanitization | model-sanitization-master/backdoor/prune_svhn/main_E.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import models
# Training settings
parser = ... | 8,172 | 43.661202 | 115 | py |
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