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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BayesianRelevance | BayesianRelevance-master/src/lrp_rules_robustness_main.py | import os
import argparse
import numpy as np
import torch
import torchvision
from torch import nn
import torch.nn.functional as nnf
import torch.optim as torchopt
import torch.nn.functional as F
from utils.data import *
from utils.networks import *
from utils.savedir import *
from utils.seeding import *
from network... | 16,465 | 43.382749 | 120 | py |
BayesianRelevance | BayesianRelevance-master/src/full_test_cifar_resnet.py | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import numpy as np
from tqdm import tqdm
i... | 16,798 | 26.271104 | 90 | py |
BayesianRelevance | BayesianRelevance-master/src/train_networks.py | import argparse
import numpy as np
import os
import torch
import attacks.deeprobust as deeprobust
import attacks.gradient_based as grad_based
from utils import savedir
from utils.data import *
from utils.seeding import *
from networks.advNN import *
from networks.baseNN import *
from networks.fullBNN import *
parse... | 4,377 | 38.441441 | 118 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp_rules_robustness.py | import os
import argparse
import numpy as np
import torch
import torchvision
from torch import nn
import torch.nn.functional as nnf
import torch.optim as torchopt
import torch.nn.functional as F
from utils.data import *
from utils.model_settings import *
from utils.savedir import *
from utils.seeding import *
from n... | 12,112 | 39.784512 | 118 | py |
BayesianRelevance | BayesianRelevance-master/src/full_test_cifar_bayesian_resnet.py | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import numpy as np
from tqdm import tqdm
i... | 23,122 | 28.012547 | 145 | py |
BayesianRelevance | BayesianRelevance-master/src/full_test_cifar_adversarial_resnet.py | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import numpy as np
from tqdm import tqdm
i... | 17,052 | 26.328526 | 90 | py |
BayesianRelevance | BayesianRelevance-master/src/deterministic_atk_vs_bayesian_net.py | import os
import torch
import argparse
import numpy as np
from utils.data import *
from utils import savedir
from utils.seeding import *
from attacks.gradient_based import *
from networks.baseNN import *
from networks.fullBNN import *
from networks.redBNN import *
parser = argparse.ArgumentParser()
parser.add_argume... | 4,760 | 41.132743 | 112 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp_rules_robustness_cifar.py | import os
import argparse
import numpy as np
import torch
import torchvision
from torch import nn
import torch.nn.functional as nnf
import torch.optim as torchopt
import torch.nn.functional as F
from utils.data import *
from utils.networks import *
from utils.savedir import *
from utils.seeding import *
from network... | 8,959 | 44.025126 | 122 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp_heatmaps_det_vs_bay.py | import os
import argparse
import numpy as np
import torch
import torchvision
from torch import nn
import torch.nn.functional as nnf
import torch.optim as torchopt
import torch.nn.functional as F
from utils.data import *
from utils.networks import *
from utils.savedir import *
from utils.seeding import *
from net... | 6,989 | 46.22973 | 120 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp_robustness_distributions.py | import os
import argparse
import numpy as np
import torch
import torchvision
from torch import nn
import torch.nn.functional as nnf
import torch.optim as torchopt
import torch.nn.functional as F
from utils.data import *
from utils.networks import *
from utils.savedir import *
from utils.seeding import *
from network... | 16,260 | 41.346354 | 118 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp_heatmaps_layers.py | import os
import argparse
import numpy as np
import torch
import torchvision
from torch import nn
import torch.nn.functional as nnf
import torch.optim as torchopt
import torch.nn.functional as F
from utils.data import *
from utils.networks import *
from utils.savedir import *
from utils.seeding import *
from network... | 7,009 | 43.367089 | 122 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp_layers_mode_robustness.py | import os
import argparse
import numpy as np
import torch
import torchvision
from torch import nn
import torch.nn.functional as nnf
import torch.optim as torchopt
import torch.nn.functional as F
from utils.data import *
from utils.networks import *
from utils.savedir import *
from utils.seeding import *
from network... | 7,923 | 37.466019 | 116 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp_robustness_diff.py | import os
import argparse
import numpy as np
import torch
import torchvision
from torch import nn
import torch.nn.functional as nnf
import torch.optim as torchopt
import torch.nn.functional as F
from utils.data import *
from utils.networks import *
from utils.savedir import *
from utils.seeding import *
from network... | 12,050 | 38 | 119 | py |
BayesianRelevance | BayesianRelevance-master/src/compute_lrp.py | import argparse
import numpy as np
import torch
import torch.nn.functional as F
import torch.nn.functional as nnf
import torch.optim as torchopt
import torchvision
from networks.advNN import *
from networks.baseNN import *
from networks.fullBNN import *
from utils.data import *
from utils.model_settings import *
from... | 5,683 | 45.211382 | 130 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp_robustness_scatterplot.py | import os
import argparse
import numpy as np
import torch
import torchvision
from torch import nn
import torch.nn.functional as nnf
import torch.optim as torchopt
import torch.nn.functional as F
from utils.data import *
from utils.networks import *
from utils.savedir import *
from utils.seeding import *
from network... | 10,729 | 38.304029 | 119 | py |
BayesianRelevance | BayesianRelevance-master/src/attack_explanations.py | import argparse
import numpy as np
import os
import torch
from attacks.gradient_based import evaluate_attack
from attacks.run_attacks import *
from networks.advNN import *
from networks.baseNN import *
from networks.fullBNN import *
from utils import savedir
from utils.data import *
from utils.seeding import *
parser... | 4,775 | 40.530435 | 120 | py |
BayesianRelevance | BayesianRelevance-master/src/networks/redBNN.py | """
Neural network with one bayesian layer.
"""
import argparse
import copy
import numpy as np
import os
import pandas as pd
from collections import OrderedDict
import torch
import torch.distributions.constraints as constraints
import torch.nn.functional as nnf
import torch.optim as torchopt
from torch import nn
s... | 14,109 | 35.840731 | 112 | py |
BayesianRelevance | BayesianRelevance-master/src/networks/advNN.py | """
Deterministic Neural Network model with adversarial training.
"""
import argparse
import numpy as np
import os
import torch
import torch.nn.functional as F
import torch.nn.functional as nnf
import torch.optim as torchopt
from torch import nn
from tqdm import tqdm
from utils.data import *
from utils.model_setting... | 3,593 | 35.30303 | 115 | py |
BayesianRelevance | BayesianRelevance-master/src/networks/baseNN.py | """
Deterministic Neural Network model.
Last layer is separated from the others.
"""
import argparse
import numpy as np
import os
import torch
import torch.nn.functional as F
import torch.nn.functional as nnf
import torch.optim as torchopt
from torch import nn
from utils.data import *
from utils.model_settings impor... | 10,062 | 34.558304 | 113 | py |
BayesianRelevance | BayesianRelevance-master/src/networks/fullBNN.py | """
Bayesian Neural Network model
"""
import argparse
import copy
import keras
import numpy as np
import os
import pandas as pd
from collections import OrderedDict
import torch
import torch.distributions.constraints as constraints
import torch.nn.functional as nnf
import torch.optim as torchopt
from torch import n... | 13,948 | 37.008174 | 106 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deepfool.py | ### CODE TAKEN FROM: https://github.com/aminul-huq/DeepFool/tree/master
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data_utils
import torchvision
import torchvision.transforms as transforms
import torchvision.models as models
import numpy a... | 2,081 | 26.394737 | 72 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/beta.py | import copy
import math
import numpy as np
import os
import torch
import torch.nn as nn
import torch.nn.functional as nnf
import torch.optim as optim
import torch.utils.data as data_utils
import torchvision
import torchvision.models as models
import torchvision.transforms as transforms
from utils.networks import chan... | 2,868 | 29.849462 | 140 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/topk.py | import torch
import torch.nn.functional as nnf
from utils.lrp import select_informative_pixels
def Topk(image, model, epsilon, lrp_rule, iters, k=20, step_size=0.5, lr=0.01):
x_orig = image.clone().detach()
x_orig.requires_grad=True
probs = nnf.softmax(model.forward(x_orig, explain=True, rule=lrp_rule), dim=-1)
... | 1,175 | 27 | 80 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/run_attacks.py | import copy
import numpy as np
import torch
from torch import autograd
from tqdm import tqdm
# from attacks.robustness_measures import softmax_robustness
from plot.attacks import plot_grid_attacks
from torch.autograd.gradcheck import zero_gradients
from utils.data import *
from utils.savedir import *
from utils.seedi... | 6,699 | 35.216216 | 118 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/gradient_based.py | """
FGSM and PGD classic & bayesian adversarial attacks
"""
import os
import sys
import copy
import torch
import numpy as np
from tqdm import tqdm
import torch.nn.functional as nnf
from torch.utils.data import DataLoader
from utils.data import *
from utils.seeding import *
from utils.savedir import *
from utils.netw... | 8,049 | 34.307018 | 108 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/robustness_measures.py | import torch
import torch.nn.functional as nnf
DEBUG=False
def softmax_difference(original_predictions, adversarial_predictions):
"""
Compute the difference between predictions and adversarial
predictions.
"""
# original_predictions = nnf.softmax(original_predictions, dim=-1)
# adversarial_p... | 1,637 | 33.125 | 92 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/region.py | import torch
import torch.nn.functional as nnf
def TargetRegion(image, model, epsilon, lrp_rule, iters, target_pxls, step_size=0.5, lr=0.01):
x_adv = image.clone().detach()
x_adv.requires_grad = True
for i in range(iters):
probs = nnf.softmax(model.forward(x_adv, explain=True, rule=lrp_rule), d... | 872 | 27.16129 | 94 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deeprobust/pgd.py | import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import torch.nn.functional as F
from attacks.deeprobust.base_attack import BaseAttack
class PGD(BaseAttack):
"""
This is the multi-step version of FGSM attack.
"""
def __init__(self,... | 4,514 | 29.924658 | 145 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deeprobust/evaluation_attack.py | import requests
import torch
from torchvision import datasets,models,transforms
import torch.nn.functional as F
import os
import numpy as np
import argparse
import matplotlib.pyplot as plt
import random
from attacks.deeprobust.image import utils
def run_attack(attackmethod, batch_size, batch_num, device, test_loader... | 9,853 | 42.409692 | 158 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deeprobust/deepfool.py | import numpy as np
from torch.autograd import Variable
import torch as torch
import copy
from torch.autograd.gradcheck import zero_gradients
from attacks.deeprobust.base_attack import BaseAttack
class DeepFool(BaseAttack):
"""DeepFool attack.
"""
def __init__(self, model, device = 'cuda' ):
super... | 3,969 | 27.561151 | 90 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deeprobust/base_attack.py | from abc import ABCMeta
import torch
class BaseAttack(object):
"""
Attack base class.
"""
__metaclass__ = ABCMeta
def __init__(self, model, device = 'cuda'):
self.model = model
self.device = device
def generate(self, image, label, **kwargs):
"""
Overide this f... | 2,341 | 25.314607 | 84 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deeprobust/cw.py | import torch
from torch import optim
import torch.nn as nn
import numpy as np
import logging
from attacks.deeprobust.base_attack import BaseAttack
from attacks.deeprobust.utils import onehot_like
from attacks.deeprobust.optimizer import AdamOptimizer
class CarliniWagner(BaseAttack):
"""
C&W attack is an effec... | 9,404 | 31.884615 | 145 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deeprobust/fgsm.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from numpy import linalg as LA
from attacks.deeprobust.base_attack import BaseAttack
class FGSM(BaseAttack):
"""
FGSM attack is an one step gradient descent method.
"""
def __init__(self... | 3,309 | 25.269841 | 80 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deeprobust/utils.py | import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
import urllib.request
import os
def create_train_dataset(batch_size = 128, root = '../data'):
"""
Create different training dataset
"""
transform_train = transforms.Compose([
transforms.ToTensor(),
... | 6,457 | 29.462264 | 114 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deeprobust/optimizer.py | """
This module include the following optimizer:
1. differential_evolution:
The differential evolution global optimization algorithm
https://github.com/scipy/scipy/blob/70e61dee181de23fdd8d893eaa9491100e2218d7/scipy/optimize/_differentialevolution.py
modified by:
https://github.com/DebangLi/one-pixel-attack-pytorch/b... | 38,893 | 41.460699 | 117 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deeprobust/other/YOPOpgd.py | import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import torch.nn.functional as F
from attacks.deeprobust.base_attack import BaseAttack
class FASTPGD(BaseAttack):
'''
This module is the adversarial example gererated algorithm in YOPO.
... | 4,234 | 36.149123 | 133 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deeprobust/other/lbfgs.py | import torch
import torch.nn as nn
import scipy.optimize as so
import numpy as np
import torch.nn.functional as F #233
from attacks.deeprobust.base_attack import BaseAttack
class LBFGS(BaseAttack):
"""
LBFGS is the first adversarial generating algorithm.
"""
def __init__(self, model, label, devi... | 6,194 | 28.221698 | 117 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deeprobust/other/Universal.py | """
https://github.com/ferjad/Universal_Adversarial_Perturbation_pytorch
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
"""
from attacks.deeprobust.attack import deepfool
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import nu... | 4,136 | 27.93007 | 117 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deeprobust/other/Nattack.py | import torch
from torch import optim
import numpy as np
import logging
from attacks.deeprobust.base_attack import BaseAttack
from attacks.deeprobust.utils import onehot_like, arctanh
class NATTACK(BaseAttack):
"""
Nattack is a black box attack algorithm.
"""
def __init__(self, model, device = 'cud... | 6,107 | 31.663102 | 130 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deeprobust/other/BPDA.py | """
https://github.com/lordwarlock/Pytorch-BPDA/blob/master/bpda.py
"""
import torch
import torch.nn as nn
import torchvision.models as models
import numpy as np
def normalize(image, mean, std):
return (image - mean)/std
def preprocess(image):
image = image / 255
image = np.transpose(image, (2, 0, 1))
... | 3,177 | 28.981132 | 117 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deeprobust/other/onepixel.py | import numpy as np
import argparse
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
from torch.autograd import Variable
from attacks.deeprobust.optimizer import different... | 5,935 | 30.743316 | 149 | py |
BayesianRelevance | BayesianRelevance-master/src/attacks/deeprobust/other/l2_attack.py | import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
class CarliniL2:
def __init__(self, model, device):
self.model = model
self.device = device
def parse_params(self, gan, confidence=0, targeted=False, learning_rate=1e-1,
binary_search_steps... | 6,741 | 37.747126 | 97 | py |
BayesianRelevance | BayesianRelevance-master/src/plot/lrp_heatmaps.py | import os
import lrp
import copy
import torch
import numpy as np
from tqdm import tqdm
import matplotlib
import pandas as pd
import seaborn as sns
import matplotlib.colors as colors
import matplotlib.pyplot as plt
from matplotlib.pyplot import cm
from utils.savedir import *
from utils.seeding import set_seed
from uti... | 17,425 | 41.502439 | 123 | py |
BayesianRelevance | BayesianRelevance-master/src/plot/lrp_distributions.py | import os
import lrp
import copy
import torch
import matplotlib
import numpy as np
import pandas as pd
import seaborn as sns
from tqdm import tqdm
from scipy import stats
import matplotlib.colors as colors
import matplotlib.pyplot as plt
from matplotlib.pyplot import cm
from utils.savedir import *
from utils.seeding ... | 39,083 | 43.718535 | 126 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp/conv_cifar.py | import torch
import torch.nn.functional as F
from lrp.functional.conv_cifar import conv2d_cifar
class Conv2d(torch.nn.Conv2d):
def _conv_forward_explain(self, input, weight, conv2d_fn, **kwargs):
if self.padding_mode != 'zeros':
return conv2d_fn(F.pad(input, self._reversed_padding_repeated_twi... | 1,390 | 42.46875 | 168 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp/patterns.py | import torch
import torch.nn.functional as F
from .functional.utils import safe_divide
from tqdm import tqdm
__all__ = [
'fit_patternnet',
'fit_patternnet_positive',
]
"""
This implementation is based on the implementation from
https://github.com/albermax/innvestigate/blob/master/innvestigate/a... | 4,582 | 29.758389 | 96 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp/maxpool.py | import torch
from lrp.functional import maxpool2d
class MaxPool2d(torch.nn.MaxPool2d):
def forward(self, input, explain=False, rule="epsilon", **kwargs):
if not explain: return super(MaxPool2d, self).forward(input)
return maxpool2d[rule](input, self.kernel_size, self.stride, self.padding)
| 311 | 38 | 82 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp/sequential.py | import torch
from lrp.linear import Linear
from lrp.conv import Conv2d
from lrp.maxpool import MaxPool2d
from lrp.functional.utils import normalize
def grad_decorator_fn(module):
"""
Currently not used but can be used for debugging purposes.
"""
def fn(x):
return normalize(x)
return fn... | 1,789 | 30.403509 | 91 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp/linear.py | import torch
from lrp.functional import linear
class Linear(torch.nn.Linear):
def forward(self, input, explain=False, rule="epsilon", **kwargs):
if not explain: return super(Linear, self).forward(input)
p = kwargs.get('pattern')
if p is not None: return linear[rule](input, self.weight, sel... | 644 | 32.947368 | 91 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp/converter.py | import torch
from .conv import Conv2d
from .linear import Linear
from .sequential import Sequential
conversion_table = {
'Linear': Linear,
'Conv2d': Conv2d
}
# # # # # Convert torch.models.vggxx to lrp model
def convert_vgg(module, modules=None):
# First time
if modules is None... | 1,436 | 30.23913 | 84 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp/conv.py | import torch
import torch.nn.functional as F
from lrp.functional import conv2d
class Conv2d(torch.nn.Conv2d):
def _conv_forward_explain(self, input, weight, conv2d_fn, **kwargs):
if self.padding_mode != 'zeros':
return conv2d_fn(F.pad(input, self._reversed_padding_repeated_twice, mode=self.pad... | 1,367 | 41.75 | 168 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp/functional/conv_cifar.py | import torch
import torch.nn.functional as F
from torch.autograd import Function
from .utils import identity_fn, gamma_fn, add_epsilon_fn, normalize
def _forward_rho(rho, incr, ctx, input, weight, bias, stride, padding, dilation, groups):
ctx.save_for_backward(input, weight, bias)
ctx.rho = rho
... | 6,354 | 37.98773 | 126 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp/functional/maxpool.py | import torch
import torch.nn.functional as F
from torch.autograd import Function
class MaxPooling2d(Function):
@staticmethod
def forward(ctx, input, kernel_size=2, stride=None, padding=0):
ctx.kernel_size = kernel_size
ctx.stride = stride
ctx.padding = padding
ctx.save... | 1,398 | 36.810811 | 108 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp/functional/utils.py | import torch
# # # rhos
identity_fn = lambda w, b: (w, b)
def gamma_fn(gamma):
def _gamma_fn(w, b):
w = w + w * torch.max(torch.tensor(0., device=w.device), w) * gamma
if b is not None: b = b + b * torch.max(torch.tensor(0., device=b.device), b) * gamma
return w, b
return _gamma_f... | 981 | 24.842105 | 120 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp/functional/linear.py | import torch
import torch.nn.functional as F
from torch.autograd import Function
from .utils import identity_fn, gamma_fn, add_epsilon_fn, normalize
def _forward_rho(rho, incr, ctx, input, weight, bias):
ctx.save_for_backward(input, weight, bias)
ctx.rho = rho
ctx.incr = incr
return F.linear(input, we... | 5,193 | 32.509677 | 167 | py |
BayesianRelevance | BayesianRelevance-master/src/lrp/functional/conv.py | import torch
import torch.nn.functional as F
from torch.autograd import Function
from .utils import identity_fn, gamma_fn, add_epsilon_fn, normalize
def _forward_rho(rho, incr, ctx, input, weight, bias, stride, padding, dilation, groups):
ctx.save_for_backward(input, weight, bias)
ctx.rho = rho
... | 6,341 | 37.907975 | 126 | py |
BayesianRelevance | BayesianRelevance-master/src/utils/data.py | import os
import math
import time
import random
import numpy as np
import pickle as pkl
from utils.savedir import *
import torch
import keras
import tensorflow as tf
from keras import backend as K
from keras.datasets import mnist, fashion_mnist
from sklearn.datasets import make_moons
from pandas import DataFrame
from ... | 12,410 | 34.766571 | 116 | py |
BayesianRelevance | BayesianRelevance-master/src/utils/networks.py | import torch
import torch.nn as nn
def relu_to_softplus(model, beta):
for child_name, child in model.named_children():
if isinstance(child, nn.LeakyReLU):
setattr(model, child_name, nn.Softplus(beta=beta))
else:
relu_to_softplus(child, beta)
return model
def change_beta(model, beta):
for child_name, ch... | 492 | 22.47619 | 53 | py |
BayesianRelevance | BayesianRelevance-master/src/utils/seeding.py | import torch
import numpy as np
import random
import pyro
def set_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
pyro.set_rng_seed(seed)
set_seed(0) | 267 | 14.764706 | 36 | py |
BayesianRelevance | BayesianRelevance-master/src/utils/savedir.py | import os
import sys
import time
DATA = "../data/"
TESTS = "../experiments/"
ATK_DIR = "attacks/"
def get_model_savedir(model, dataset, architecture, iters=None, inference=None, baseiters=None,
model_idx=None, layer_idx=None, debug=False, torchvision=False, attack_method=None):
if torchvis... | 2,068 | 28.557143 | 106 | py |
BayesianRelevance | BayesianRelevance-master/src/utils/lrp.py | import os
import lrp
import copy
import torch
import numpy as np
from torch import nn
from tqdm import tqdm
import torch.nn.functional as nnf
from torchvision import transforms
from scipy.stats import wasserstein_distance
from utils.savedir import *
from utils.seeding import set_seed
from utils.data import load_from_p... | 8,089 | 28.418182 | 129 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/examples/main_bayesian_flipout_cifar.py | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
from torch.utils.tensorboard import SummaryWriter
import torchvision.transforms as transforms
import torchvision.datasets as da... | 18,058 | 32.881801 | 111 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/examples/main_deterministic_cifar.py | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
from torch.utils.tensorboard import SummaryWriter
import torchvision.transforms as transforms
import torchvision.datasets as da... | 15,192 | 32.100218 | 78 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/examples/main_bayesian_cifar.py | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
# from torch.utils.tensorboard import SummaryWriter
import torchvision.transforms as transforms
import torchvision.datasets as ... | 24,193 | 33.31773 | 122 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/examples/main_bayesian_imagenet.py | '''
code adapted from PyTorch examples
'''
import argparse
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import tor... | 26,624 | 36.082173 | 110 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/examples/main_bayesian_flipout_imagenet.py | '''
code adapted from PyTorch examples
'''
import argparse
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import tor... | 26,792 | 36.472727 | 114 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/examples/main_deterministic_imagenet.py | '''
code adapted from PyTorch examples
'''
import argparse
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import tor... | 20,770 | 34.264856 | 110 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/examples/main_deterministic_mnist.py | from __future__ import print_function
import os
import argparse
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.optim.lr_scheduler import StepLR
from torch.utils.tensorboard import SummaryWriter
import numpy as np
im... | 7,802 | 36.157143 | 79 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/examples/main_bayesian_mnist.py | from __future__ import print_function
import os
import argparse
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.optim.lr_scheduler import StepLR
from torch.utils.tensorboard import SummaryWriter
import numpy as np
imp... | 9,196 | 35.208661 | 79 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/models/flipout/simple_cnn.py | from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
from bayesian_torch.layers import Conv2dFlipout
from bayesian_torch.layers import LinearFlipout
prior_mu = 0
prior_sigma = 0.05
posterior_mu_init = 0
posterior_rho_init = -7.0 #-6.0
class SCNN(n... | 2,267 | 28.842105 | 71 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/models/deterministic/resnet.py | '''
ResNet for CIFAR10.
Ref:
[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
import torch.nn.init as init
from lrp.linear import Linear
from lrp.conv_cifar import Conv2d
from... | 4,977 | 29.539877 | 76 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/models/deterministic/resnet_large.py | # ResNet for ImageNet
# ResNet architecture ref:
# https://arxiv.org/abs/1512.03385
# Code from torchvision package
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = [
'ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152'
]
model_urls = {
'resnet18': 'http... | 7,104 | 29.625 | 78 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/models/deterministic/simple_cnn.py | from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
class SCNN(nn.Module):
def __init__(self):
super(SCNN, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = ... | 836 | 25.15625 | 44 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/models/bayesian/resnet_flipout.py | '''
Bayesian ResNet with Flipout Monte Carlo estimator for CIFAR10.
Ref:
ResNet architecture:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
Flipout:
[2] Wen, Yeming, et al. "Flipout: Efficient Pseudo-Independent
Weight Perturbations on Mi... | 5,606 | 28.356021 | 77 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/models/bayesian/resnet_variational.py | '''
Bayesian ResNet for CIFAR10.
ResNet architecture ref:
[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
import torch.nn.init as init
from bayesian_torch.bayesian_torch.laye... | 6,935 | 30.103139 | 116 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/models/bayesian/resnet_flipout_large.py | # Bayesian ResNet for ImageNet
# ResNet architecture ref:
# https://arxiv.org/abs/1512.03385
# Code adapted from torchvision package to build Bayesian model from deterministic model
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch.nn as nn
import torch.nn.functional as F
import ... | 10,869 | 33.507937 | 88 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/models/bayesian/resnet_variational_large.py | # Bayesian ResNet for ImageNet
# ResNet architecture ref:
# https://arxiv.org/abs/1512.03385
# Code adapted from torchvision package to build Bayesian model from deterministic model
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch.nn as nn
import torch.nn.functional as F
import ... | 10,428 | 31.590625 | 88 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/models/bayesian/simple_cnn_variational.py | from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
from bayesian_torch.layers import Conv2dReparameterization
from bayesian_torch.layers import LinearReparameterization
prior_mu = 0.0
prior_sigma = 1.0
posterior_mu_init = 0.0
posterior_rho_init = -... | 2,246 | 27.443038 | 58 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/layers/batchnorm.py | '''
wrapper for Batch Normalization layers
'''
import torch
import torch.nn as nn
from torch.nn import Parameter
import torch.nn.functional as F
class BatchNorm2dLayer(nn.Module):
def __init__(self,
num_features,
eps=1e-5,
momentum=0.1,
affine=Tr... | 7,672 | 37.365 | 78 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/layers/base_variational_layer.py | # Copyright (C) 2021 Intel Labs
#
# BSD-3-Clause License
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the f... | 2,497 | 45.259259 | 97 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/layers/dropout.py | '''
wrapper for Dropout
'''
import torch
import torch.nn as nn
from torch.nn import Parameter
import torch.nn.functional as F
class Dropout(nn.Module):
__constants__ = ['p', 'inplace']
def __init__(self, p=0.5, inplace=False):
super(Dropout, self).__init__()
if p < 0 or p > 1:
r... | 703 | 23.275862 | 76 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/layers/relu.py | '''
wrapper for ReLU
'''
import torch
import torch.nn as nn
from torch.nn import Parameter
import torch.nn.functional as F
class ReLU(nn.Module):
__constants__ = ['inplace']
def __init__(self, inplace=False):
super(ReLU, self).__init__()
self.inplace = inplace
def forward(self, input):
... | 508 | 19.36 | 60 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/layers/variational_layers/linear_variational.py | # Copyright (C) 2021 Intel Labs
#
# BSD-3-Clause License
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the ... | 7,337 | 46.341935 | 148 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/layers/variational_layers/conv_variational.py | # Copyright (C) 2021 Intel Labs
#
# BSD-3-Clause License
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the ... | 38,039 | 44.231867 | 148 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/layers/variational_layers/rnn_variational.py | # Copyright (C) 2021 Intel Labs
#
# BSD-3-Clause License
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the ... | 5,973 | 40.486111 | 121 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/layers/flipout_layers/linear_flipout.py | # Copyright (C) 2021 Intel Labs
#
# BSD-3-Clause License
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the f... | 6,701 | 43.979866 | 148 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/layers/flipout_layers/rnn_flipout.py | # Copyright (C) 2021 Intel Labs
#
# BSD-3-Clause License
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the f... | 6,145 | 42.588652 | 148 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/layers/flipout_layers/conv_flipout.py | # Copyright (C) 2021 Intel Labs
#
# BSD-3-Clause License
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the f... | 39,426 | 42.042576 | 148 | py |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/utils/util.py | # Copyright (C) 2021 Intel Labs
#
# BSD-3-Clause License
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the ... | 5,400 | 41.195313 | 98 | py |
ssl-torch | ssl-torch-main/transform.py | import numpy as np
import torch
from scipy import signal
import math
import cv2
import random
class Transform:
def __init__(self):
pass
def add_noise(self, signal, noise_amount):
"""
adding noise
"""
signal = signal.T
noise = (0.4 ** 0.5) * np.random.normal(... | 9,975 | 34.884892 | 103 | py |
ssl-torch | ssl-torch-main/contrast.py | from net import resnet18, resnet34, resnet50, resnet101, resnet152
import torch
import torch.nn as nn
import numpy as np
# import pandas as pd
import tqdm
import mit_utils as utils
# import analytics
import time
import os, shutil
from mail import mail_it
from sklearn.metrics import confusion_matrix
from sklearn.metric... | 12,884 | 30.274272 | 111 | py |
ssl-torch | ssl-torch-main/net.py | import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.... | 8,532 | 32.073643 | 98 | py |
ssl-torch | ssl-torch-main/mit_utils.py | # -*- coding: utf-8 -*-
"""
Created on Thu Mar 14 23:47:38 2019
@author: Winham
辅助函数
"""
import warnings
import numpy as np
from scipy.signal import resample
# import pywt
from sklearn.preprocessing import scale
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.util... | 4,714 | 29.031847 | 156 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/setup.py | from setuptools import setup
from setuptools import find_packages
setup(name='bert-extractive-summarizer',
version='0.10.1',
description='Extractive Text Summarization with BERT',
keywords=['bert', 'pytorch', 'machine learning',
'deep learning', 'extractive summarization', 'summary'],... | 833 | 42.894737 | 101 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/summarizer/transformer_embeddings/bert_embedding.py | from typing import List, Union
import numpy as np
import torch
from numpy import ndarray
from transformers import (AlbertModel, AlbertTokenizer, BertModel,
BertTokenizer, DistilBertModel, DistilBertTokenizer,
PreTrainedModel, PreTrainedTokenizer, XLMModel,
... | 6,387 | 35.712644 | 114 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/summarizer/transformer_embeddings/sbert_embedding.py | from typing import List
import numpy as np
import torch
from sentence_transformers import SentenceTransformer
class SBertEmbedding:
"""SBert Embedding. This is for the SentenceTransformer Package."""
def __init__(self, model: str):
"""
SBert Parent Handler.
:param model: The model s... | 1,129 | 27.974359 | 82 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/tests/test_summary_items.py | import pytest
import torch
from transformers import AlbertTokenizer, AlbertModel
from summarizer import Summarizer, TransformerSummarizer
@pytest.fixture()
def custom_summarizer():
albert_model = AlbertModel.from_pretrained('albert-base-v2', output_hidden_states=True)
albert_tokenizer = AlbertTokenizer.from_... | 7,139 | 46.6 | 424 | py |
probdet | probdet-master/src/single_image_inference.py | """
Probabilistic Detectron Single Image Inference Script
"""
import core
import cv2
import json
import os
import sys
import torch
import tqdm
# This is very ugly. Essential for now but should be fixed.
sys.path.append(os.path.join(core.top_dir(), 'src', 'detr'))
# Detectron imports
from detectron2.engine import laun... | 4,579 | 34.78125 | 104 | py |
probdet | probdet-master/src/apply_net.py | """
Probabilistic Detectron Inference Script
"""
import core
import json
import os
import sys
import torch
import tqdm
from shutil import copyfile
# This is very ugly. Essential for now but should be fixed.
sys.path.append(os.path.join(core.top_dir(), 'src', 'detr'))
# Detectron imports
from detectron2.engine import ... | 4,133 | 33.45 | 150 | py |
probdet | probdet-master/src/core/setup.py | import numpy as np
import os
import random
import torch
from shutil import copyfile
# Detectron imports
import detectron2.utils.comm as comm
from detectron2.config import get_cfg, CfgNode as CN
from detectron2.engine import default_argument_parser, default_setup
from detectron2.utils.logger import setup_logger
# De... | 8,904 | 33.649805 | 155 | py |
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