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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TaNP | TaNP-main/TaNP/utils/loader.py | import json
import random
import torch
import numpy as np
import pickle
import codecs
import re
import os
import datetime
import tqdm
import pandas as pd
#convert userids to userdict key-id(int), val:onehot_vector(tensor)
#element in list is str type.
def to_onehot_dict(list):
dict={}
length = len(list)
fo... | 10,229 | 45.712329 | 128 | py |
crfasrnn | crfasrnn-master/python-scripts/crfasrnn_demo.py | # -*- coding: utf-8 -*-
"""
This package contains code for the "CRF-RNN" semantic image segmentation method, published in the
ICCV 2015 paper Conditional Random Fields as Recurrent Neural Networks. Our software is built on
top of the Caffe deep learning library.
Contact:
Shuai Zheng (szheng@robots.ox.ac.uk), Sadeep Ja... | 6,612 | 31.101942 | 133 | py |
Nested-UNet | Nested-UNet-master/model_logic.py | '''
'''
import keras
import tensorflow as tf
from keras.models import Model
from keras import backend as K
from keras.layers import Input, merge, Conv2D, ZeroPadding2D, UpSampling2D, Dense, concatenate, Conv2DTranspose
from keras.layers.pooling import MaxPooling2D, GlobalAveragePooling2D, MaxPooling2D
from keras.lay... | 12,054 | 44.149813 | 186 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/__version__.py | VERSION = (0, 1, 2)
__version__ = '.'.join(map(str, VERSION)) | 62 | 20 | 41 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/utils.py | """ Utility functions for segmentation models """
from functools import wraps
import numpy as np
def get_layer_number(model, layer_name):
"""
Help find layer in Keras model by name
Args:
model: Keras `Model`
layer_name: str, name of layer
Returns:
index of layer
Raises:
... | 2,175 | 23.449438 | 90 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/__init__.py | name = "segmentation_models"
from .unet import Unet
from .nestnet import Nestnet
from .xnet import Xnet
from .fpn import FPN
#from .linknet import Linknet
from .pspnet import PSPNet | 182 | 21.875 | 29 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/xnet/model.py | from .builder import build_xnet
from ..utils import freeze_model
from ..backbones import get_backbone
DEFAULT_SKIP_CONNECTIONS = {
'vgg16': ('block5_conv3', 'block4_conv3', 'block3_conv3', 'block2_conv2', 'block1_conv2',
'block5_pool', 'block4_pool', 'block3_pool', 'block2_pool... | 5,117 | 46.388889 | 115 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/xnet/__init__.py | from .model import Xnet
| 24 | 11.5 | 23 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/xnet/builder.py | from keras.layers import Conv2D
from keras.layers import Activation
from keras.models import Model
from .blocks import Transpose2D_block
from .blocks import Upsample2D_block
from ..utils import get_layer_number, to_tuple
import copy
def build_xnet(backbone, classes, skip_connection_layers,
decoder_fi... | 7,554 | 41.926136 | 112 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/xnet/blocks.py | from keras.layers import Conv2DTranspose
from keras.layers import UpSampling2D
from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Concatenate
def handle_block_names(stage, cols):
conv_name = 'decoder_stage{}-{}_conv'.format(stag... | 3,112 | 38.910256 | 106 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/common/functions.py | import numpy as np
import tensorflow as tf
def transpose_shape(shape, target_format, spatial_axes):
"""Converts a tuple or a list to the correct `data_format`.
It does so by switching the positions of its elements.
# Arguments
shape: Tuple or list, often representing shape,
correspondi... | 3,882 | 32.188034 | 88 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/common/layers.py | from keras.engine import Layer
from keras.engine import InputSpec
from keras.utils import conv_utils
from keras.legacy import interfaces
from keras.utils.generic_utils import get_custom_objects
from .functions import resize_images
class ResizeImage(Layer):
"""ResizeImage layer for 2D inputs.
Repeats the rows... | 3,623 | 42.662651 | 102 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/common/__init__.py | from .blocks import Conv2DBlock
from .layers import ResizeImage | 63 | 31 | 31 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/common/blocks.py | from keras.layers import Conv2D
from keras.layers import Activation
from keras.layers import BatchNormalization
def Conv2DBlock(n_filters, kernel_size,
activation='relu',
use_batchnorm=True,
name='conv_block',
**kwargs):
"""Extension of Conv2D layer ... | 682 | 31.52381 | 71 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/pspnet/model.py | from .builder import build_psp
from ..utils import freeze_model
from ..backbones import get_backbone
PSP_BASE_LAYERS = {
'vgg16': ('block5_conv3', 'block4_conv3', 'block3_conv3'),
'vgg19': ('block5_conv4', 'block4_conv4', 'block3_conv4'),
'resnet18': ('stage4_unit... | 4,891 | 39.429752 | 95 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/pspnet/__init__.py | from .model import PSPNet | 25 | 25 | 25 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/pspnet/builder.py | """
Code is constructed based on following repositories:
https://github.com/ykamikawa/PSPNet/
https://github.com/hujh14/PSPNet-Keras/
https://github.com/Vladkryvoruchko/PSPNet-Keras-tensorflow/
And original paper of PSPNet:
https://arxiv.org/pdf/1612.01105.pdf
"""
from keras.layers import Conv2D
from ... | 1,860 | 28.078125 | 92 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/pspnet/blocks.py | import numpy as np
from keras.layers import MaxPool2D
from keras.layers import AveragePooling2D
from keras.layers import Concatenate
from keras.layers import Permute
from keras.layers import Reshape
from keras.backend import int_shape
from ..common import Conv2DBlock
from ..common import ResizeImage
def InterpBlock(... | 3,539 | 32.396226 | 104 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/nestnet/model.py | from .builder import build_nestnet
from ..utils import freeze_model
from ..backbones import get_backbone
DEFAULT_SKIP_CONNECTIONS = {
'vgg16': ('block5_conv3', 'block4_conv3', 'block3_conv3', 'block2_conv2', 'block1_conv2',
'block5_pool', 'block4_pool', 'block3_pool', 'block2_p... | 5,129 | 46.5 | 115 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/nestnet/__init__.py | from .model import Nestnet
| 27 | 13 | 26 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/nestnet/builder.py | from keras.layers import Conv2D
from keras.layers import Activation
from keras.models import Model
from .blocks import Transpose2D_block
from .blocks import Upsample2D_block
from ..utils import get_layer_number, to_tuple
import copy
def build_nestnet(backbone, classes, skip_connection_layers,
decoder... | 7,484 | 41.771429 | 112 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/nestnet/blocks.py | from keras.layers import Conv2DTranspose
from keras.layers import UpSampling2D
from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Concatenate
def handle_block_names(stage, cols):
conv_name = 'decoder_stage{}-{}_conv'.format(stag... | 2,735 | 38.085714 | 106 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/linknet/model.py | from .builder import build_linknet
from ..utils import freeze_model
from ..backbones import get_backbone
DEFAULT_SKIP_CONNECTIONS = {
'vgg16': ('block5_conv3', 'block4_conv3', 'block3_conv3', 'block2_conv2'),
'vgg19': ('block5_conv4', 'block4_conv4', 'block3_conv4', 'block2_conv2... | 4,257 | 46.311111 | 115 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/linknet/__init__.py | from .model import Linknet
| 27 | 13 | 26 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/linknet/builder.py | from keras.layers import Conv2D
from keras.layers import Activation
from keras.models import Model
from .blocks import DecoderBlock
from ..utils import get_layer_number, to_tuple
def build_linknet(backbone,
classes,
skip_connection_layers,
decoder_filters=(None, ... | 1,663 | 32.28 | 86 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/linknet/blocks.py | import keras.backend as K
from keras.layers import Conv2DTranspose as Transpose
from keras.layers import UpSampling2D
from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Add
def handle_block_names(stage):
conv_name = 'decoder_sta... | 4,938 | 28.753012 | 86 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/unet/model.py | from .builder import build_unet
from ..utils import freeze_model
from ..backbones import get_backbone
DEFAULT_SKIP_CONNECTIONS = {
'vgg16': ('block5_conv3', 'block4_conv3', 'block3_conv3', 'block2_conv2', 'block1_conv2'),
'vgg19': ('block5_conv4', 'block4_conv4', 'block3_conv4', 'block2_... | 3,928 | 42.655556 | 118 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/unet/__init__.py | from .model import Unet
| 24 | 11.5 | 23 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/unet/builder.py | from keras.layers import Conv2D
from keras.layers import Activation
from keras.models import Model
from .blocks import Transpose2D_block
from .blocks import Upsample2D_block
from ..utils import get_layer_number, to_tuple
def build_unet(backbone, classes, skip_connection_layers,
decoder_filters=(256,12... | 1,491 | 30.083333 | 86 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/unet/blocks.py | from keras.layers import Conv2DTranspose
from keras.layers import UpSampling2D
from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Concatenate
def handle_block_names(stage):
conv_name = 'decoder_stage{}_conv'.format(stage)
bn... | 2,552 | 36 | 106 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/preprocessing.py | """
Image pre-processing functions.
Images are assumed to be read in uint8 format (range 0-255).
"""
from keras.applications import vgg16
from keras.applications import vgg19
from keras.applications import densenet
from keras.applications import inception_v3
from keras.applications import inception_resnet_v2
identica... | 1,019 | 28.142857 | 62 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/inception_v3.py | # -*- coding: utf-8 -*-
"""Inception V3 model for Keras.
Note that the input image format for this model is different than for
the VGG16 and ResNet models (299x299 instead of 224x224),
and that the input preprocessing function is also different (same as Xception).
# Reference
- [Rethinking the Inception Architecture fo... | 15,272 | 36.898263 | 152 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/__init__.py | from .classification_models.classification_models import *
from .inception_resnet_v2 import InceptionResNetV2
from .inception_v3 import InceptionV3
from .backbones import get_backbone
from .preprocessing import get_preprocessing | 229 | 37.333333 | 58 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/inception_resnet_v2.py | # -*- coding: utf-8 -*-
"""Inception-ResNet V2 model for Keras.
Model naming and structure follows TF-slim implementation (which has some additional
layers and different number of filters from the original arXiv paper):
https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py
Pre-train... | 16,002 | 42.134771 | 92 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/backbones.py |
from .classification_models.classification_models import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
from .classification_models.classification_models import ResNeXt50, ResNeXt101
from .inception_resnet_v2 import InceptionResNetV2
from .inception_v3 import InceptionV3
from keras.applications import DenseNet12... | 930 | 28.09375 | 107 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/__init__.py | 1 | 0 | 0 | py | |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/weights.py | weights_collection = [
# ResNet18
{
'model': 'resnet18',
'dataset': 'imagenet',
'classes': 1000,
'include_top': True,
'url': 'https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet18_imagenet_1000.h5',
'name': 'resnet18_imagenet_1000.h5',
... | 6,485 | 32.43299 | 127 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/utils.py | from keras.utils import get_file
def find_weights(weights_collection, model_name, dataset, include_top):
w = list(filter(lambda x: x['model'] == model_name, weights_collection))
w = list(filter(lambda x: x['dataset'] == dataset, w))
w = list(filter(lambda x: x['include_top'] == include_top, w))
return... | 1,263 | 38.5 | 91 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/__init__.py | from .resnet.models import ResNet18
from .resnet.models import ResNet34
from .resnet.models import ResNet50
from .resnet.models import ResNet101
from .resnet.models import ResNet152
from .resnext.models import ResNeXt50
from .resnext.models import ResNeXt101
__all__ = ['ResNet18', 'ResNet34', 'ResNet50', 'ResNet101', ... | 370 | 36.1 | 72 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnext/preprocessing.py | import numpy as np
from skimage.transform import resize
def preprocess_input(x, size=None):
"""input standardizing function
Args:
x: numpy.ndarray with shape (H, W, C)
size: tuple (H_new, W_new), resized input shape
Return:
x: numpy.ndarray
"""
if size:
x = resize(x,... | 346 | 22.133333 | 55 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnext/models.py | from .builder import build_resnext
from ..utils import load_model_weights
from ..weights import weights_collection
def ResNeXt50(input_shape, input_tensor=None, weights=None, classes=1000, include_top=True):
model = build_resnext(input_tensor=input_tensor,
input_shape=input_shape,
... | 1,197 | 35.30303 | 93 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnext/__init__.py | from .preprocessing import preprocess_input | 43 | 43 | 43 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnext/params.py | # default parameters for convolution and batchnorm layers of ResNet models
# parameters are obtained from MXNet converted model
def get_conv_params(**params):
default_conv_params = {
'kernel_initializer': 'glorot_uniform',
'use_bias': False,
'padding': 'valid',
}
default_conv_param... | 614 | 23.6 | 74 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnext/builder.py | import keras.backend as K
from keras.layers import Input
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import GlobalAveragePooling2D
from keras.layers import ZeroPadding2D
from keras.layers import D... | 3,364 | 31.355769 | 92 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnext/blocks.py | from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Add
from keras.layers import Lambda
from keras.layers import Concatenate
from keras.layers import ZeroPadding2D
from .params import get_conv_params
from .params import get_bn_params
... | 4,292 | 36.657895 | 107 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnet/preprocessing.py | import numpy as np
from skimage.transform import resize
def preprocess_input(x, size=None, BGRTranspose=True):
"""input standardizing function
Args:
x: numpy.ndarray with shape (H, W, C)
size: tuple (H_new, W_new), resized input shape
Return:
x: numpy.ndarray
"""
if size:
... | 412 | 21.944444 | 55 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnet/models.py | from .builder import build_resnet
from ..utils import load_model_weights
from ..weights import weights_collection
def ResNet18(input_shape, input_tensor=None, weights=None, classes=1000, include_top=True):
model = build_resnet(input_tensor=input_tensor,
input_shape=input_shape,
... | 2,640 | 36.197183 | 92 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnet/__init__.py | from .preprocessing import preprocess_input
__all__ = ['preprocess_input'] | 75 | 24.333333 | 43 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnet/params.py | # default parameters for convolution and batchnorm layers of ResNet models
# parameters are obtained from MXNet converted model
def get_conv_params(**params):
default_conv_params = {
'kernel_initializer': 'glorot_uniform',
'use_bias': False,
'padding': 'valid',
}
default_conv_param... | 614 | 23.6 | 74 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnet/builder.py | import keras.backend as K
from keras.layers import Input
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import GlobalAveragePooling2D
from keras.layers import ZeroPadding2D
from keras.layers import D... | 3,750 | 32.491071 | 92 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnet/blocks.py | from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Add
from keras.layers import ZeroPadding2D
from .params import get_conv_params
from .params import get_bn_params
def handle_block_names(stage, block):
name_base = 'stage{}_unit... | 6,363 | 37.569697 | 100 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/tests/test_imagenet.py | import numpy as np
from skimage.io import imread
from keras.applications.imagenet_utils import decode_predictions
import sys
sys.path.insert(0, '..')
from classification_models import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
from classification_models import ResNeXt50, ResNeXt101
from classification_models ... | 4,964 | 29.838509 | 135 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/fpn/model.py | from .builder import build_fpn
from ..backbones import get_backbone
from ..utils import freeze_model
DEFAULT_FEATURE_PYRAMID_LAYERS = {
'vgg16': ('block5_conv3', 'block4_conv3', 'block3_conv3'),
'vgg19': ('block5_conv4', 'block4_conv4', 'block3_conv4'),
'resnet18': ('stage4_u... | 3,960 | 42.054348 | 107 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/fpn/__init__.py | from .model import FPN
| 24 | 7.333333 | 22 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/fpn/builder.py | import numpy as np
from keras.layers import Conv2D
from keras.layers import Concatenate
from keras.layers import Activation
from keras.layers import SpatialDropout2D
from keras.models import Model
from .blocks import pyramid_block
from ..common import ResizeImage
from ..common import Conv2DBlock
from ..utils import ex... | 3,418 | 34.614583 | 95 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/fpn/blocks.py | from keras.layers import Add
from ..common import Conv2DBlock
from ..common import ResizeImage
from ..utils import to_tuple
def pyramid_block(pyramid_filters=256, segmentation_filters=128, upsample_rate=2,
use_batchnorm=False, stage=0):
"""
Pyramid block according to:
http://present... | 1,750 | 33.333333 | 83 | py |
hackathon-ci-2020 | hackathon-ci-2020-master/solutions/unaccountable_penguis/pix2pix_cloudtop.py | import numpy as np
import tensorflow as tf
import os
import time
#_____________________
#Loading and preprocessing the data
#_____________________
my_path = '/home/harder/imagery'
CloudTop = np.load(my_path + '/X_train_CI20.npy')
TrueColor = np.load(my_path + '/Y_train_CI20.npy')
#sort out dark pictures (just quick ... | 8,978 | 39.813636 | 127 | py |
BayesianRelevance | BayesianRelevance-master/src/attack_networks.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... | 6,374 | 45.195652 | 127 | py |
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/__init__.py | 0 | 0 | 0 | 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/__init__.py | 0 | 0 | 0 | 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/__init__.py | #__init__.py
import logging
from deeprobust.image.attack import base_attack
from deeprobust.image.attack import pgd
from deeprobust.image.attack import deepfool
from deeprobust.image.attack import fgsm
from deeprobust.image.attack import lbfgs
from deeprobust.image.attack import cw
from deeprobust.image.attack import... | 458 | 27.6875 | 78 | 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 |
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