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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actnn | actnn-main/image_classification/image_classification/preact_resnet.py | '''Pre-activation ResNet in PyTorch.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Identity Mappings in Deep Residual Networks. arXiv:1603.05027
'''
import math
import torch
import torch.nn as nn
from actnn import QModule
class PreActBlock(nn.Module):
expansion = 1
M = 2
def __ini... | 5,608 | 27.617347 | 92 | py |
actnn | actnn-main/image_classification/image_classification/mixup.py | import torch
import torch.nn as nn
import numpy as np
def mixup(alpha, num_classes, data, target):
with torch.no_grad():
bs = data.size(0)
c = np.random.beta(alpha, alpha)
perm = torch.randperm(bs).cuda()
md = c * data + (1-c) * data[perm, :]
mt = c * target + (1-c) * tar... | 1,524 | 26.727273 | 76 | py |
actnn | actnn-main/image_classification/image_classification/dataloaders.py | import os
import torch
import numpy as np
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from actnn import dataloader
DATA_BACKEND_CHOICES = ['pytorch']
# try:
# from nvidia.dali.plugin.pytorch import DALIClassificationIterator
# from nvidia.dali.pipeline import Pipeline
# ... | 15,400 | 40.850543 | 158 | py |
actnn | actnn-main/image_classification/image_classification/debug.py | from actnn import config, QScheme, QBNScheme
from .utils import *
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
from tqdm import tqdm
import numpy as np
import pickle
from matplotlib.colors import LogNorm
from copy import deepcopy
def get_var(model_and_loss, optimizer, val_loader, num_b... | 6,921 | 30.752294 | 132 | py |
actnn | actnn-main/image_classification/image_classification/smoothing.py | import torch
import torch.nn as nn
class LabelSmoothing(nn.Module):
"""
NLL loss with label smoothing.
"""
def __init__(self, smoothing=0.0):
"""
Constructor for the LabelSmoothing module.
:param smoothing: label smoothing factor
"""
super(LabelSmoothing, self).... | 762 | 27.259259 | 72 | py |
speedyfit | speedyfit-master/docs/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 2,913 | 33.690476 | 79 | py |
LPAE | LPAE-master/imagenet_resnet50/lpae_resnet50_imagenet_finetune.py |
import time
import argparse
import numpy as np
from PIL import Image
from distutils.util import strtobool
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#==... | 17,218 | 44.55291 | 160 | py |
LPAE | LPAE-master/imagenet_resnet50/wae_resnet50_imagenet_finetune.py |
import time
import argparse
import numpy as np
from PIL import Image
from distutils.util import strtobool
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#==... | 16,802 | 43.927807 | 159 | py |
LPAE | LPAE-master/imagenet_resnet50/resnet50_imagenet.py |
import time
import argparse
import numpy as np
from PIL import Image
from distutils.util import strtobool
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#==... | 13,251 | 43.619529 | 159 | py |
LPAE | LPAE-master/imagenet_resnet50/lpae_resnet50_imagenet.py |
import time
import argparse
import numpy as np
from PIL import Image
from distutils.util import strtobool
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#==... | 16,018 | 44.251412 | 159 | py |
LPAE | LPAE-master/imagenet_resnet50/wae_resnet50_imagenet.py |
import time
import argparse
import numpy as np
from PIL import Image
from distutils.util import strtobool
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#==... | 15,799 | 44.014245 | 159 | py |
LPAE | LPAE-master/celeba_srnet/wae_celeba.py |
import time
import math
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#===========================... | 10,669 | 44.021097 | 159 | py |
LPAE | LPAE-master/celeba_srnet/lpae_celeba.py |
import time
import math
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#===========================... | 10,946 | 45.385593 | 166 | py |
LPAE | LPAE-master/celeba_srnet/dataset.py | import torch
import torch.utils.data as data
from os import listdir
from os.path import join
from PIL import Image, ImageOps
import random
import torchvision.transforms as transforms
def is_image_file(filename):
return any(filename.endswith(extension) for extension in [".png", ".jpg", ".jpeg"])
def readlines... | 4,462 | 33.596899 | 126 | py |
LPAE | LPAE-master/celeba_srnet/wavesrnet_celeba.py |
import time
import math
import random
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from pytorch_wavelets import DWTForward, DWTInverse
import torchvision
import... | 37,474 | 42.025258 | 172 | py |
LPAE | LPAE-master/celeba_srnet/wae_srnet_celeba.py |
import time
import math
import random
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
from dataset i... | 22,282 | 42.267961 | 172 | py |
LPAE | LPAE-master/celeba_srnet/lpae_srnet_celeba.py |
import time
import math
import random
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
from dataset i... | 22,052 | 42.669307 | 172 | py |
LPAE | LPAE-master/naturalscene_vgg16/lpae_vgg16_naturalscene_finetune.py |
import time
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#=======================================... | 16,811 | 46.491525 | 159 | py |
LPAE | LPAE-master/naturalscene_vgg16/lpae_naturalscene.py |
import time
import math
import argparse
import numpy as np
from PIL import Image
from distutils.util import strtobool
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as tra... | 10,975 | 45.312236 | 166 | py |
LPAE | LPAE-master/naturalscene_vgg16/lpae_vgg16_naturalscene.py |
import time
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#=======================================... | 15,557 | 46.145455 | 159 | py |
LPAE | LPAE-master/naturalscene_vgg16/wae_papersetting_naturalscene.py |
import time
import math
import argparse
import numpy as np
from PIL import Image
from distutils.util import strtobool
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as tra... | 10,697 | 43.94958 | 159 | py |
LPAE | LPAE-master/naturalscene_vgg16/wae_vgg16_naturalscene.py |
import time
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#=======================================... | 15,409 | 45.838906 | 159 | py |
LPAE | LPAE-master/naturalscene_vgg16/vgg16_naturalscene.py |
import time
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#=======================================... | 11,781 | 45.203922 | 159 | py |
LPAE | LPAE-master/naturalscene_vgg16/wae_vgg16_naturalscene_finetune.py |
import time
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#=======================================... | 16,423 | 45.659091 | 159 | py |
LPAE | LPAE-master/naturalscene_resnet50/lpae_resnet50_naturalscene_finetune.py |
import time
import argparse
import numpy as np
from PIL import Image
from distutils.util import strtobool
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#==... | 16,934 | 44.16 | 164 | py |
LPAE | LPAE-master/naturalscene_resnet50/lpae_resnet50_naturalscene.py |
import time
import argparse
import numpy as np
from PIL import Image
from distutils.util import strtobool
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#==... | 15,670 | 43.646724 | 159 | py |
LPAE | LPAE-master/naturalscene_resnet50/resnet50_naturalscene.py |
import time
import argparse
import numpy as np
from PIL import Image
from distutils.util import strtobool
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#==... | 12,918 | 42.942177 | 159 | py |
LPAE | LPAE-master/naturalscene_resnet50/wae_resnet50_naturalscene_finetune.py |
import time
import argparse
import numpy as np
from PIL import Image
from distutils.util import strtobool
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#==... | 16,482 | 43.428571 | 159 | py |
LPAE | LPAE-master/naturalscene_resnet50/wae_resnet50_naturalscene.py |
import time
import argparse
import numpy as np
from PIL import Image
from distutils.util import strtobool
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#==... | 15,455 | 43.413793 | 159 | py |
LPAE | LPAE-master/div2k_srnet/wavesrnet_div2k.py |
import time
import math
import random
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from pytorch_wavelets import DWTForward, DWTInverse
import torchvision
import... | 37,442 | 41.988519 | 172 | py |
LPAE | LPAE-master/div2k_srnet/wae_div2k.py |
import time
import math
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#===========================... | 10,564 | 43.578059 | 147 | py |
LPAE | LPAE-master/div2k_srnet/wae_srnet_div2k.py |
import time
import math
import random
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
from dataset i... | 22,159 | 42.11284 | 172 | py |
LPAE | LPAE-master/div2k_srnet/dataset.py | import torch
import torch.utils.data as data
from os import listdir
from os.path import join
from PIL import Image, ImageOps
import random
import torchvision.transforms as transforms
def is_image_file(filename):
return any(filename.endswith(extension) for extension in [".png", ".jpg", ".jpeg"])
def readlines... | 4,752 | 33.442029 | 126 | py |
LPAE | LPAE-master/div2k_srnet/lpae_srnet_div2k.py |
import time
import math
import random
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
from dataset i... | 21,889 | 42.518887 | 172 | py |
LPAE | LPAE-master/div2k_srnet/lpae_div2k.py |
import time
import math
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#===========================... | 10,850 | 44.78481 | 166 | py |
LPAE | LPAE-master/imagenet_vgg16/lpae_imagenet.py |
import time
import math
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#===========================... | 10,955 | 45.227848 | 166 | py |
LPAE | LPAE-master/imagenet_vgg16/wae_vgg16_imagenet.py |
import time
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#=======================================... | 15,755 | 46.457831 | 159 | py |
LPAE | LPAE-master/imagenet_vgg16/wae_vgg16_imagenet_finetune.py |
import time
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#=======================================... | 16,758 | 46.208451 | 159 | py |
LPAE | LPAE-master/imagenet_vgg16/lpae_vgg16_imagenet_finetune.py |
import time
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#=======================================... | 17,100 | 46.901961 | 159 | py |
LPAE | LPAE-master/imagenet_vgg16/lpae_vgg16_imagenet.py |
import time
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#=======================================... | 15,880 | 46.690691 | 159 | py |
LPAE | LPAE-master/imagenet_vgg16/vgg16_imagenet.py |
import time
import math
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#===========================... | 12,302 | 45.779468 | 159 | py |
LPAE | LPAE-master/imagenet_vgg16/wae_imagenet.py |
import time
import math
import argparse
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
#===========================... | 10,430 | 43.961207 | 159 | py |
attackbox | attackbox-master/paper_model.py | import torch
import torch.nn as nn
from torch.autograd import Variable
class BasicCNN(nn.Module):
def __init__(self):
super(BasicCNN, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(1, 20, kernel_size=5),
nn.ReLU(),
nn.MaxPool2d(2, stride=2),
... | 2,846 | 31.724138 | 117 | py |
attackbox | attackbox-master/allmodels.py | import time
import random
import numpy as np
import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable
from torch.utils.data.dataset import Dataset
import os
from PIL import Image
#import pretrainedmodels
#import pretrainedm... | 27,317 | 33.667513 | 161 | py |
attackbox | attackbox-master/test_attack.py | import torch
#from wideresnet import *
import os, argparse, logging, sys, shutil
import numpy as np
import utils
import shutil
import matplotlib.pyplot as plt
from attack import *
from models import PytorchModel
from paper_model import vgg16, BasicCNN
from allmodels import MNIST, load_model, load_mnist_data, load_cifar... | 9,283 | 38.008403 | 123 | py |
attackbox | attackbox-master/utils.py | import torch
import os
import shutil
def distance(x_adv, x, norm='l2'):
diff = (x_adv - x).view(x.size(0), -1)
if norm == 'l2':
out = torch.sqrt(torch.sum(diff * diff)).item()
return out
elif norm == 'linf':
out = torch.sum(torch.max(torch.abs(diff), 1)[0]).item()
return out... | 985 | 30.806452 | 83 | py |
attackbox | attackbox-master/vecattack.py | import torch
from torch.autograd import Variable
import torch.optim as optim
from utils import mulvt
class CWattack(object):
def __init__(self,model):
self.model = model
def get_loss(self,xi,label_onehot_v, c, modifier, TARGETED):
#print(c.size(),modifier.size())
#loss1 = c*torch.sum(m... | 2,958 | 35.9875 | 90 | py |
attackbox | attackbox-master/models.py | import torch
import numpy as np
from torch.autograd import Variable
class PytorchModel(object):
def __init__(self,model, bounds, num_classes):
self.model = model
self.model.eval()
self.bounds = bounds
self.num_classes = num_classes
self.num_queries = 0
def predict(s... | 2,516 | 33.479452 | 96 | py |
attackbox | attackbox-master/nes_attack.py | import torch
from torch.autograd import Variable
import torch.optim as optim
from utils import mulvt
zero_iters=50
label_only_sigma = 0.5
batch_per_gpu = 4
sigma = 0.001
momentum = 0.1
min_lr = 5e-5
goal_epsilon = 0.1
delta_e = 0.01
class NES(object):
def __init__(self,model):
self.model = model
def on... | 3,288 | 35.544444 | 88 | py |
attackbox | attackbox-master/sign_sgd/test_foolbox.py | import argparse
import os
import sys
from numpy import linalg as LA
import foolbox, random
from OPT_attack import OPT_attack
from OPT_attack_lf import OPT_attack_lf
from OPT_attack_sign_SGD import OPT_attack_sign_SGD
from OPT_attack_sign_SGD_lf import OPT_attack_sign_SGD_lf
from models import PytorchModel
import torch... | 6,268 | 42.234483 | 178 | py |
attackbox | attackbox-master/sign_sgd/evolutionary.py | import torch
import scipy
import numpy as np
import scipy.misc
import PIL
class Evolutionary(object):
def __init__(self,model, train_dataset=None):
self.model = model
def predict(self, x):
return self.model.predict_label(torch.tensor(x.reshape(3, 32, 32), dtype=torch.float))
def loss(... | 4,189 | 31.734375 | 113 | py |
attackbox | attackbox-master/sign_sgd/OPT_attack_lf.py | from utils import mulvt
import time
import torch
import numpy as np
from numpy import linalg as LA
import random
class OPT_attack_lf(object):
def __init__(self, model, train_dataset=None):
self.model = model
self.train_dataset = train_dataset
def attack_untargeted(self, x0, y0, alpha = 0.2, b... | 18,766 | 38.929787 | 129 | py |
attackbox | attackbox-master/sign_sgd/allmodels.py | import time
import random
import numpy as np
import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable
from torch.utils.data.dataset import Dataset
import os
from PIL import Image
#import pretrainedmodels
#import pretrainedm... | 21,820 | 33.969551 | 161 | py |
attackbox | attackbox-master/sign_sgd/attack.py | import argparse
import os
import sys
from OPT_attack import OPT_attack
from OPT_attack_lf import OPT_attack_lf
from OPT_attack_sign_SGD import OPT_attack_sign_SGD
from OPT_attack_sign_SGD_lf import OPT_attack_sign_SGD_lf
from models import PytorchModel
import torch
import torchvision.models as models
from allmodels im... | 5,244 | 38.43609 | 119 | py |
attackbox | attackbox-master/sign_sgd/OPT_attack_sign_SGD.py | from utils import mulvt
import time
import numpy as np
from numpy import linalg as LA
import torch
import scipy.spatial
from scipy.linalg import qr
#from qpsolvers import solve_qp
import random
start_learning_rate = 1.0
def quad_solver(Q, b):
"""
Solve min_a 0.5*aQa + b^T a s.t. a>=0
"""
K = Q.shape... | 24,899 | 37.90625 | 127 | py |
attackbox | attackbox-master/sign_sgd/OPT_attack.py | from utils import mulvt
import time, torch
import numpy as np
from numpy import linalg as LA
class OPT_attack(object):
def __init__(self, model, train_dataset=None):
self.model = model
self.train_dataset = train_dataset
def attack_untargeted(self, x0, y0, alpha = 0.2, beta = 0.001, iterations... | 16,305 | 38.57767 | 117 | py |
attackbox | attackbox-master/sign_sgd/models.py | import torch
import numpy as np
from torch.autograd import Variable
class PytorchModel(object):
def __init__(self,model, bounds, num_classes):
self.model = model
self.model.eval()
self.bounds = bounds
self.num_classes = num_classes
def predict(self,image):
image = t... | 1,761 | 34.959184 | 84 | py |
attackbox | attackbox-master/sign_sgd/OPT_attack_sign_SGD_batch.py | from utils import mulvt
import time
import numpy as np
from numpy import linalg as LA
import torch
import scipy.spatial
from scipy.linalg import qr
#from qpsolvers import solve_qp
import random
start_learning_rate = 1.0
stopping = 0.0005
def quad_solver(Q, b):
"""
Solve min_a 0.5*aQa + b^T a s.t. a>=0
"... | 24,009 | 37.851133 | 127 | py |
attackbox | attackbox-master/sign_sgd/OPT_attack_sign_SGD_lf.py | from utils import mulvt
import time
import numpy as np
from numpy import linalg as LA
import torch
import scipy.spatial
from scipy.linalg import qr
#from qpsolvers import solve_qp
import random
start_learning_rate = 1.0
def quad_solver(Q, b):
"""
Solve min_a 0.5*aQa + b^T a s.t. a>=0
"""
K = Q.shape... | 24,271 | 37.588235 | 127 | py |
attackbox | attackbox-master/sign_sgd/foolbox/models/mxnet.py | from __future__ import absolute_import
import numpy as np
from .base import DifferentiableModel
class MXNetModel(DifferentiableModel):
"""Creates a :class:`Model` instance from existing `MXNet` symbols and weights.
Parameters
----------
data : `mxnet.symbol.Variable`
The input to the model.... | 5,871 | 33.339181 | 83 | py |
attackbox | attackbox-master/sign_sgd/foolbox/models/keras.py | from __future__ import absolute_import
import numpy as np
import logging
from .base import DifferentiableModel
class KerasModel(DifferentiableModel):
"""Creates a :class:`Model` instance from a `Keras` model.
Parameters
----------
model : `keras.models.Model`
The `Keras` model that should be... | 6,557 | 36.474286 | 131 | py |
attackbox | attackbox-master/sign_sgd/foolbox/models/pytorch.py | import numpy as np
import warnings
from .base import DifferentiableModel
class PyTorchModel(DifferentiableModel):
"""Creates a :class:`Model` instance from a `PyTorch` module.
Parameters
----------
model : `torch.nn.Module`
The PyTorch model that should be attacked.
bounds : tuple
... | 5,943 | 30.449735 | 76 | py |
attackbox | attackbox-master/sign_sgd/foolbox/models/__init__.py | """
Provides classes to wrap existing models in different framworks so
that they provide a unified API to the attacks.
"""
from .base import Model # noqa: F401
from .base import DifferentiableModel # noqa: F401
from .wrappers import ModelWrapper # noqa: F401
from .wrappers import GradientLess # noqa: F401
from .... | 650 | 31.55 | 66 | py |
attackbox | attackbox-master/sign_sgd/foolbox/tests/test_attacks_lbfgs.py | import numpy as np
from foolbox.attacks import LBFGSAttack as Attack
def test_attack(bn_adversarial):
adv = bn_adversarial
attack = Attack()
attack(adv, num_random_targets=2)
assert adv.image is not None
assert adv.distance.value < np.inf
def test_targeted_attack(bn_targeted_adversarial):
a... | 1,094 | 23.886364 | 66 | py |
attackbox | attackbox-master/sign_sgd/foolbox/tests/conftest.py | # the different frameworks interfer with each other and
# sometimes cause segfaults or similar problems;
# choosing the right import order seems to be a
# workaround; given the current test order,
# first import tensorflow, then pytorch and then
# according to test order seems to solve it
import tensorflow
print(tensor... | 7,060 | 22.935593 | 93 | py |
attackbox | attackbox-master/sign_sgd/foolbox/tests/test_models_pytorch.py | import pytest
import numpy as np
from foolbox.models import PyTorchModel
@pytest.mark.parametrize('num_classes', [10, 1000])
def test_pytorch_model(num_classes):
import torch
import torch.nn as nn
bounds = (0, 255)
channels = num_classes
class Net(nn.Module):
def __init__(self):
... | 5,120 | 23.73913 | 70 | py |
attackbox | attackbox-master/sign_sgd/foolbox/tests/test_models_mxnet.py | import pytest
import mxnet as mx
import numpy as np
from foolbox.models import MXNetModel
@pytest.mark.parametrize('num_classes', [10, 1000])
def test_model(num_classes):
bounds = (0, 255)
channels = num_classes
def mean_brightness_net(images):
logits = mx.symbol.mean(images, axis=(2, 3))
... | 3,490 | 26.062016 | 70 | py |
attackbox | attackbox-master/sign_sgd/foolbox/tests/test_models_keras.py | import pytest
import warnings
import numpy as np
from keras.layers import GlobalAveragePooling2D
from keras.layers import Activation
from keras.layers import Input
from keras.activations import softmax
from keras.models import Model
from keras.models import Sequential
from foolbox.models import KerasModel
@pytest.m... | 6,915 | 28.682403 | 79 | py |
attackbox | attackbox-master/sign_sgd/experimental/OPT_attack_polar_GD.py | from utils import mulvt
import time
import numpy as np
from numpy import linalg as LA
import torch
import scipy
learning_rate = 0.01
class OPT_attack_polar_GD(object):
def __init__(self,model):
self.model = model
def attack_untargeted(self, x0, y0, alpha = 0.2, beta = 0.001, iterations = 1000):
... | 8,443 | 37.381818 | 125 | py |
attackbox | attackbox-master/sign_sgd/experimental/OPT_attack_polar.py | from utils import mulvt
import time
import numpy as np
from numpy import linalg as LA
import torch
import scipy
class OPT_attack_polar(object):
def __init__(self, model, gradient_bias=False):
self.gradient_bias = gradient_bias
self.model = model
def attack_untargeted(self, x0, y0, alpha = 0.2... | 14,307 | 39.304225 | 124 | py |
attackbox | attackbox-master/sign_sgd/experimental/OPT_attack_lsq.py | from utils import mulvt
import time
import numpy as np
from numpy import linalg as LA
import torch
import scipy
#from scipy import spacial
import scipy.spatial
learning_rate = 0.05
prev_contribution = 0
stopping = 0.001
class OPT_attack_lsq(object):
def __init__(self,model):
self.model = model
s... | 9,107 | 38.6 | 124 | py |
attackbox | attackbox-master/sign_sgd/experimental/OPT_attack_polar_lsq.py | from utils import mulvt
import time
import numpy as np
from numpy import linalg as LA
import torch
import scipy
import scipy.spatial
learning_rate = 0.01
prev_contribution = 0
class OPT_attack_polar_lsq(object):
def __init__(self,model):
self.model = model
self.prevX = None
self.prevF = N... | 10,612 | 38.600746 | 125 | py |
attackbox | attackbox-master/sign_sgd/experimental/OPT_attack_GD.py | from utils import mulvt
import time
import numpy as np
from numpy import linalg as LA
import torch
learning_rate = 0.05
stopping = 0.001
class OPT_attack_GD(object):
def __init__(self,model):
self.model = model
def attack_untargeted(self, x0, y0, alpha = 0.2, beta = 0.001, iterations = 1000, distort... | 6,514 | 37.779762 | 118 | py |
attackbox | attackbox-master/attack/Sign_OPT_lf.py | from os import linesep
from pickle import NONE
from matplotlib import lines
from matplotlib.pyplot import errorbar, fignum_exists
import numpy as np
from numpy.core.numeric import indices
import torch
import scipy.spatial
import random, logging, time
#from qpsolvers import solve_qp
from numpy import linalg as LA
from ... | 12,685 | 37.442424 | 127 | py |
attackbox | attackbox-master/attack/SimBA.py | import torch
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.models as models
import numpy as np
import utils
import math
import random
import torch.nn.functional as F
import argparse
import os
import pdb
from scipy.fftpack import dct, idct
# parser = argparse.Argument... | 13,359 | 45.550523 | 184 | py |
attackbox | attackbox-master/attack/FGSM.py | import torch
from torch.autograd import Variable
import torch.optim as optim
import torch.nn as nn
class FGSM(object):
def __init__(self,model):
self.model = model
def get_loss(self,xi,label_or_target,TARGETED):
criterion = nn.CrossEntropyLoss()
output = self.model.predict(xi)
... | 2,154 | 32.153846 | 107 | py |
attackbox | attackbox-master/attack/Evolutionary.py | import torch
import scipy
import numpy as np
import scipy.misc
import scipy.ndimage
import PIL
class Evolutionary(object):
def __init__(self,model, train_dataset=None):
self.model = model
def predict(self, x):
return self.model.predict_label(torch.tensor(x.reshape(3, 32, 32), dtype=torch.float... | 4,161 | 32.031746 | 128 | py |
attackbox | attackbox-master/attack/Sign_OPT_v2.py | import time
import numpy as np
from numpy import linalg as LA
import torch
import scipy.spatial
from scipy.linalg import qr
#from qpsolvers import solve_qp
import random
start_learning_rate = 1.0
def quad_solver(Q, b):
"""
Solve min_a 0.5*aQa + b^T a s.t. a>=0
"""
K = Q.shape[0]
alpha = np.zeros... | 24,908 | 37.981221 | 127 | py |
attackbox | attackbox-master/attack/OPT_attack_lf.py | import time
import numpy as np
from numpy import linalg as LA
import random
class OPT_attack_lf(object):
def __init__(self,model):
self.model = model
def attack_untargeted(self, x0, y0, alpha = 0.2, beta = 0.01, iterations = 1000):
""" Attack the original image and return adversarial example
... | 16,366 | 38.533816 | 180 | py |
attackbox | attackbox-master/attack/NATTACK.py | import torch
import numpy as np
class NATTACK(object):
def __init__(self, model, lr=0.01, delta=0.001, npop=300):
self.model=model
self.lr = lr
self.delta = delta
self.npop = npop
def predict(self,x_in):
x_t = torch.tensor(x_in).float().cuda()
prob = self.model.p... | 3,439 | 40.445783 | 106 | py |
attackbox | attackbox-master/attack/Bandit.py | import time
import numpy as np
import random
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import torch.nn as nn
from torch.nn.modules import Upsample
def norm(t):
assert len(t.shape) == 4
norm_vec = torch.sqrt(t.pow(2).sum(dim=[1,2,3])).view(-1, 1... | 4,385 | 33 | 103 | py |
attackbox | attackbox-master/attack/HSJA.py | from __future__ import absolute_import, division, print_function
import numpy as np
import torch
class HSJA(object):
def __init__(self,model,constraint='l2',num_iterations=40,gamma=1.0,stepsize_search='geometric_progression',max_num_evals=1e4,init_num_evals=100, verbose=True):
self.model = model
se... | 12,431 | 42.017301 | 164 | py |
attackbox | attackbox-master/attack/Sign_OPT.py | import time
import numpy as np
from numpy import linalg as LA
import torch
import scipy.spatial
from scipy.linalg import qr
#from qpsolvers import solve_qp
import random
start_learning_rate = 1.0
MAX_ITER = 1000
def quad_solver(Q, b):
"""
Solve min_a 0.5*aQa + b^T a s.t. a>=0
"""
K = Q.shape[0]
... | 14,639 | 37.832891 | 132 | py |
attackbox | attackbox-master/attack/Sign_SGD.py | import torch
class Sign_SGD(object):
def __init__(self,model):
self.model=model
def sign_grad_est(self,x, y, net, sigma=1e-3, q = 10):
g = torch.zeros(x.size()).cuda()
g = g.view(x.size()[0],-1)
y = y.view(-1,1)
out2 = net.predict_prob(x)
out2 = torch.gather(out... | 2,934 | 34.361446 | 104 | py |
attackbox | attackbox-master/attack/OPT_attack.py | import time, torch
import numpy as np
from numpy import linalg as LA
MAX_ITER = 1000
class OPT_attack(object):
def __init__(self,model):
self.model = model
self.log = torch.ones(MAX_ITER,2)
def get_log(self):
return self.log
def attack_untargeted(self, x0, y0, alpha ... | 8,141 | 36.520737 | 180 | py |
attackbox | attackbox-master/attack/ZOO.py | import torch
from torch.autograd import Variable
import torch.optim as optim
import numpy as np
class ZOO(object):
def __init__(self,model):
self.model = model
def get_loss(self,xi,label_onehot_v, c, modifier, TARGETED):
#print(c.size(),modifier.size())
loss1 = c*torch.sum(modi... | 4,077 | 41.479167 | 96 | py |
attackbox | attackbox-master/attack/CW.py | import torch
from torch.autograd import Variable
import torch.optim as optim
class CW(object):
def __init__(self,model):
self.model = model
def get_loss(self,xi,label_onehot_v, c, modifier, TARGETED):
#print(c.size(),modifier.size())
loss1 = c*torch.sum(modifier*modifier)
#outp... | 2,800 | 35.855263 | 90 | py |
attackbox | attackbox-master/attack/OPT_genattack.py | import time
import numpy as np
from numpy import linalg as LA
from models import PytorchModel
import torch, random
from allmodels import MNIST, load_model, load_mnist_data, load_cifar10_data, CIFAR10
class OPT_genattack(object):
def __init__(self,model):
self.model = model
def genattack_untargete... | 5,630 | 37.047297 | 101 | py |
attackbox | attackbox-master/attack/NES.py | import torch
class NES(object):
def __init__(self,model):
self.model=model
def nes_grad_est(self,x, y, net, sigma=1e-3, n = 10):
g = torch.zeros(x.size()).cuda()
g = g.view(x.size()[0],-1)
y = y.view(-1,1)
for _ in range(n):
u = torch.randn(x.size()).cuda()
... | 1,718 | 34.8125 | 103 | py |
attackbox | attackbox-master/attack/PGD.py | from __future__ import division
from __future__ import print_function
import time
import numpy as np
import random
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import torch.nn as nn
class PGD(object):
def __init__(self,model):
self.model = mod... | 2,101 | 32.903226 | 94 | py |
attackbox | attackbox-master/layers/conv2d.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from .weight_noise import noise_fn
class RandConv2d(nn.Module):
def __init__(self, sigma_0, N, init_s, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
... | 3,287 | 45.309859 | 137 | py |
attackbox | attackbox-master/layers/batchnorm2d.py | import math
import torch
import torch.nn as nn
from torch.nn import Parameter
import torch.nn.functional as F
from .weight_noise import noise_fn
class RandBatchNorm2d(nn.Module):
def __init__(self, sigma_0, N, init_s, num_features, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True):
super(RandB... | 4,818 | 46.245098 | 170 | py |
attackbox | attackbox-master/layers/linear.py | import math
import torch
import torch.nn as nn
from torch.nn import Parameter
import torch.nn.functional as F
from .weight_noise import noise_fn
class RandLinear(nn.Module):
def __init__(self, sigma_0, N, init_s, in_features, out_features, bias=True):
super(RandLinear, self).__init__()
self.sigma_0... | 2,594 | 44.526316 | 132 | py |
attackbox | attackbox-master/layers/weight_noise.py | import torch
import torch.nn.functional as F
from torch.autograd import Function
class NoiseFn(Function):
@staticmethod
def forward(ctx, mu, sigma, eps, sigma_0, N):
eps.normal_()
ctx.save_for_backward(mu, sigma, eps)
ctx.sigma_0 = sigma_0
ctx.N = N
return mu + torch.exp... | 1,096 | 29.472222 | 82 | py |
attackbox | attackbox-master/layers/feat_noise.py | import torch
import torch.nn as nn
class Noise(nn.Module):
def __init__(self, std):
super(Noise, self).__init__()
self.std = std
self.buffer = None
def forward(self, x):
if self.std > 0:
if self.buffer is None:
self.buffer = torch.Tensor(x.size()).no... | 488 | 26.166667 | 80 | py |
mae | mae-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.
# --------------------------------------------------------
# References:
# DeiT: https://github.com/facebookresearch/deit
#... | 8,860 | 38.914414 | 129 | py |
mae | mae-main/models_mae.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.
# --------------------------------------------------------
# References:
# timm: https://github.com/rwightman/pytorch-image... | 9,742 | 37.816733 | 145 | py |
mae | mae-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.
# --------------------------------------------------------
# References:
# DeiT: https://github.com/facebookresearch/deit
#... | 4,778 | 35.761538 | 114 | py |
mae | mae-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.
# --------------------------------------------------------
# References:
# DeiT: https://github.com/facebookresearch/deit
#... | 3,000 | 35.597561 | 108 | py |
mae | mae-main/models_vit.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.
# --------------------------------------------------------
# References:
# timm: https://github.com/rwightman/pytorch-image... | 2,383 | 31.216216 | 101 | py |
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