repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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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/attacks.py | import os
import copy
import numpy as np
import matplotlib.pyplot as plt
def plot_grid_attacks(original_images, perturbed_images, filename, savedir):
fig, axes = plt.subplots(2, len(original_images), figsize = (12,4))
for i in range(0, len(original_images)):
original_image = original_images[i].permu... | 729 | 32.181818 | 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/__init__.py | from .conv import conv2d
from .linear import linear
from .maxpool import maxpool2d
__all__ = [
'maxpool2d',
'conv2d',
'linear',
]
| 170 | 16.1 | 32 | 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/model_settings.py | """ Architectures and parameters """
baseNN_settings = {"model_0":{"dataset":"mnist", "hidden_size":512, "activation":"leaky",
"architecture":"conv", "epochs":5, "lr":0.001},
"model_1":{"dataset":"fashion_mnist", "hidden_size":1024, "activation":"leaky",
... | 2,311 | 78.724138 | 123 | 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/__init__.py | 0 | 0 | 0 | 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/__init__.py | 0 | 0 | 0 | py | |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/models/flipout/resnet.py | 0 | 0 | 0 | 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/flipout/__init__.py | 0 | 0 | 0 | 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/deterministic/__init__.py | 0 | 0 | 0 | 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/__init__.py | 0 | 0 | 0 | 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/__init__.py | from .flipout_layers import *
from .variational_layers import *
from .base_variational_layer import *
from .batchnorm import *
from .dropout import *
from .relu import *
| 170 | 23.428571 | 37 | 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/__init__.py | from .linear_variational import *
from .conv_variational import *
from .rnn_variational import *
| 97 | 23.5 | 33 | 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/__init__.py | from .conv_flipout import *
from .linear_flipout import *
from .rnn_flipout import *
| 85 | 20.5 | 29 | 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 |
BayesianRelevance | BayesianRelevance-master/src/bayesian_torch/bayesian_torch/utils/__init__.py | 0 | 0 | 0 | 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/server.py | from flask import Flask
from flask import request, jsonify, abort, make_response
from flask_cors import CORS
import nltk
nltk.download('punkt')
from nltk import tokenize
from typing import List
import argparse
from summarizer import Summarizer, TransformerSummarizer
app = Flask(__name__)
CORS(app)
class Parser(obje... | 4,393 | 32.8 | 113 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/examples/summarize.py | from summarizer import Summarizer
import argparse
def run():
parser = argparse.ArgumentParser(description='Process and summarize lectures')
parser.add_argument('-path', dest='path', default=None, help='File path of lecture')
parser.add_argument('-model', dest='model', default='bert-large-uncased', help=''... | 1,036 | 31.40625 | 128 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/summarizer/bert.py | from functools import partial
from typing import List, Optional, Union
from transformers import (AlbertModel, AlbertTokenizer, BartModel, BigBirdModel, BigBirdTokenizer,
BartTokenizer, BertModel, BertTokenizer,
CamembertModel, CamembertTokenizer, CTRLModel,
... | 7,082 | 47.183673 | 120 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/summarizer/summary_processor.py | from typing import Callable, List, Optional, Tuple, Union
import numpy as np
from summarizer.cluster_features import ClusterFeatures
from summarizer.text_processors.sentence_handler import SentenceHandler
from summarizer.util import AGGREGATE_MAP
class SummaryProcessor:
"""General Summarizer Parent for all clus... | 8,794 | 36.109705 | 110 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/summarizer/sbert.py | from summarizer.summary_processor import SummaryProcessor
from summarizer.text_processors.sentence_handler import SentenceHandler
from summarizer.transformer_embeddings.sbert_embedding import SBertEmbedding
class SBertSummarizer(SummaryProcessor):
"""
The SBert Summarizer.
This is based on the Sentence B... | 992 | 32.1 | 110 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/summarizer/util.py | import numpy as np
AGGREGATE_MAP = {
'mean': np.mean,
'min': np.min,
'median': np.median,
'max': np.max,
}
| 124 | 12.888889 | 24 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/summarizer/__init__.py | from summarizer.bert import Summarizer, TransformerSummarizer
__all__ = ["Summarizer", "TransformerSummarizer"]
| 113 | 27.5 | 61 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/summarizer/cluster_features.py | from typing import Dict, List, Union
import numpy as np
from numpy import ndarray
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.mixture import GaussianMixture
class ClusterFeatures:
"""Basic handling of clustering features."""
def __init__(
self,
featu... | 4,938 | 28.753012 | 100 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/summarizer/text_processors/sentence_abc.py | from typing import List
from spacy.language import Language
class SentenceABC:
"""Parent Class for sentence processing."""
def __init__(self, nlp: Language, is_spacy_3: bool):
"""
Base Sentence Handler with Spacy support.
:param nlp: NLP Pipeline.
:param is_spacy_3: Whether ... | 2,106 | 30.447761 | 84 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/summarizer/text_processors/sentence_handler.py | from typing import List
from spacy.lang.en import English
from spacy.language import Language
from summarizer.text_processors.sentence_abc import SentenceABC
class SentenceHandler(SentenceABC):
"""Basic Sentence Handler."""
def __init__(self, language: Language = English):
"""
Base Sentence ... | 1,266 | 28.465116 | 71 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/summarizer/text_processors/__init__.py | 0 | 0 | 0 | py | |
bert-extractive-summarizer | bert-extractive-summarizer-master/summarizer/text_processors/coreference_handler.py | # removed previous import and related functionality since it's just a blank language model,
# while neuralcoref requires passing pretrained language model via spacy.load()
from typing import List
import neuralcoref
import spacy
from summarizer.text_processors.sentence_abc import SentenceABC
class CoreferenceHandl... | 1,430 | 33.071429 | 91 | 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/summarizer/transformer_embeddings/__init__.py | 0 | 0 | 0 | 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 |
bert-extractive-summarizer | bert-extractive-summarizer-master/tests/test_sentence_handler.py | import pytest
from summarizer.text_processors.sentence_handler import SentenceHandler
@pytest.fixture()
def sentence_handler():
return SentenceHandler()
@pytest.fixture()
def passage():
return '''
The Chrysler Building, the famous art deco New York skyscraper, will be sold for a small fraction of its p... | 3,186 | 73.116279 | 383 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/tests/test_sbert.py | import pytest
from summarizer.sbert import SBertSummarizer
from summarizer.text_processors.sentence_handler import SentenceHandler
@pytest.fixture()
def passage():
return '''
The Chrysler Building, the famous art deco New York skyscraper, will be sold for a small fraction of its previous sales price.
The... | 5,169 | 55.813187 | 545 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/tests/test_coreference.py | import pytest
from summarizer.text_processors.coreference_handler import CoreferenceHandler
@pytest.fixture()
def coreference_handler():
return CoreferenceHandler()
def test_coreference_handler(coreference_handler):
orig = '''My sister has a dog. She loves him.'''
resolved = '''My sister has a dog. My ... | 444 | 26.8125 | 77 | py |
bert-extractive-summarizer | bert-extractive-summarizer-master/tests/__init__.py | 0 | 0 | 0 | py | |
FreeSolv | FreeSolv-master/scripts/generate-tripos-mol2files.py | """
Generate Tripos mol2 files with AM1-BCC charges from canonical isomeric SMILES strings.
Molecules will be named after database key.
"""
import os
from openeye import oechem
from openeye import oeiupac
from openeye import oeomega
from openeye import oequacpac
import utils
def generate_molecule_from_smiles(smil... | 4,251 | 28.943662 | 143 | py |
FreeSolv | FreeSolv-master/scripts/make_supporting_files.py | #!/bin/env python
import pickle
import utils
file = open('../database.pickle', 'rb')
database = pickle.load(file, encoding='latin1')
file.close()
utils.convert_to_json('../database.pickle', '../database.json')
#Put it in a nice table for easy parsing. Use semicolons to separate fields, making sure each individual fi... | 3,112 | 39.960526 | 393 | py |
FreeSolv | FreeSolv-master/scripts/make_v0.32.py | #!/bin/env python
"""Make edits to database for v0.32 release - specifically, fixing some issues relating to two nitro compounds which had incorrect SMILES."""
#Load database
import pickle
file = open('../database.pickle', 'r')
database = pickle.load(file)
file.close()
#Fix SMILES for mobley_3802803
database['mobley... | 943 | 28.5 | 141 | py |
FreeSolv | FreeSolv-master/scripts/utils.py | """
Shared utilities.
"""
#import cPickle as pickle
import pickle
from openeye.oechem import *
def read_database():
"""Read the database from a pickle file and return it"""
database_filename = 'database.pickle'
with open(database_filename, 'rb') as database_file:
database = pickle.load(database_f... | 1,907 | 25.5 | 91 | py |
FreeSolv | FreeSolv-master/scripts/rebuild_freesolv.py | #!/usr/bin/env python
"""
Use the openmoltools wrappers of OpenEye and Antechamber to rebuild input files
for the FreeSolv database.
Looks for freesolve database using environment variable FREESOLV_PATH
Outputs two LOCAL directories of files: ./tripos_mol2/ and ./mol2files_gaff/
"""
import os
import openmoltools
impor... | 2,555 | 43.842105 | 181 | py |
FreeSolv | FreeSolv-master/scripts/extract-primary-data.py | """
Extract the primary data from the original database pickle file.
Primary data is defined as:
- canonical isomeric SMILES all match
- experimental data:
+ experimental value
+ experiemntal uncertainty
+ citation for experimental data
- notes field
- nickname field
Example entry:
{'smiles': 'CCc1cccc2c1cccc2... | 1,665 | 36.022222 | 370 | py |
FreeSolv | FreeSolv-master/scripts/make_v0.52.py | #!/bin/env python
"""This will update the current v0.51 database to v0.52 to reflect the following changes:
- Update DOI for all calculated values to 2017 J Chem Eng Data paper associated with v0.51 (10.1021/acs.jced.7b00104)
- Remove duplicate compound mobley_4689084, which was a SAMPL1 compound that was already pres... | 1,663 | 45.222222 | 565 | py |
FreeSolv | FreeSolv-master/scripts/hComponents.py | #!/usr/bin/env python
import os, pickle, glob, sys
import numpy as np
from pymbar.timeseries import statisticalInefficiency
def doStatistics( filename ):
array = np.genfromtxt( filename, skip_header = 100 , usecols = 1, dtype = float)
return np.mean(array), np.std(array) / np.sqrt(len(array)/statisticalIneffic... | 2,365 | 35.4 | 111 | 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/__init__.py | 0 | 0 | 0 | py | |
probdet | probdet-master/src/train_net.py | """
Probabilistic Detectron Training Script following Detectron2 training script found at detectron2/tools.
"""
import core
import os
import sys
# This is very ugly. Essential for now but should be fixed.
sys.path.append(os.path.join(core.top_dir(), 'src', 'detr'))
# Detectron imports
import detectron2.utils.comm as ... | 3,264 | 27.391304 | 103 | py |
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