SYMBOL INDEX (221 symbols across 14 files)

FILE: make_patch.py
  function train (line 125) | def train(epoch, patch, patch_shape):
  function test (line 184) | def test(epoch, patch, patch_shape):
  function attack (line 234) | def attack(x, patch, mask):

FILE: pretrained_models_pytorch/pretrainedmodels/bninception.py
  class BNInception (line 25) | class BNInception(nn.Module):
    method __init__ (line 27) | def __init__(self, num_classes=1000):
    method features (line 252) | def features(self, input):
    method classif (line 485) | def classif(self, features):
    method forward (line 489) | def forward(self, input):
  function bninception (line 494) | def bninception(num_classes=1000, pretrained='imagenet'):

FILE: pretrained_models_pytorch/pretrainedmodels/fbresnet.py
  function conv3x3 (line 25) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 31) | class BasicBlock(nn.Module):
    method __init__ (line 34) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 44) | def forward(self, x):
  class Bottleneck (line 63) | class Bottleneck(nn.Module):
    method __init__ (line 66) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 79) | def forward(self, x):
  class FBResNet (line 101) | class FBResNet(nn.Module):
    method __init__ (line 103) | def __init__(self, block, layers, num_classes=1000):
    method _make_layer (line 132) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 149) | def forward(self, x):
  function fbresnet18 (line 168) | def fbresnet18(num_classes=1000):
  function fbresnet34 (line 178) | def fbresnet34(num_classes=1000):
  function fbresnet50 (line 188) | def fbresnet50(num_classes=1000):
  function fbresnet101 (line 198) | def fbresnet101(num_classes=1000):
  function fbresnet152 (line 208) | def fbresnet152(num_classes=1000, pretrained='imagenet'):

FILE: pretrained_models_pytorch/pretrainedmodels/fbresnet/resnet152_load.py
  function conv3x3 (line 19) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 25) | class BasicBlock(nn.Module):
    method __init__ (line 28) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 38) | def forward(self, x):
  class Bottleneck (line 57) | class Bottleneck(nn.Module):
    method __init__ (line 60) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 73) | def forward(self, x):
  class ResNet (line 97) | class ResNet(nn.Module):
    method __init__ (line 99) | def __init__(self, block, layers, num_classes=1000):
    method _make_layer (line 123) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 140) | def forward(self, x):
  function resnet18 (line 159) | def resnet18(pretrained=False, **kwargs):
  function resnet34 (line 171) | def resnet34(pretrained=False, **kwargs):
  function resnet50 (line 183) | def resnet50(pretrained=False, **kwargs):
  function resnet101 (line 195) | def resnet101(pretrained=False, **kwargs):
  function resnet152 (line 207) | def resnet152(pretrained=False, **kwargs):

FILE: pretrained_models_pytorch/pretrainedmodels/inceptionresnetv2.py
  class BasicConv2d (line 33) | class BasicConv2d(nn.Module):
    method __init__ (line 35) | def __init__(self, in_planes, out_planes, kernel_size, stride, padding...
    method forward (line 46) | def forward(self, x):
  class Mixed_5b (line 53) | class Mixed_5b(nn.Module):
    method __init__ (line 55) | def __init__(self):
    method forward (line 76) | def forward(self, x):
  class Block35 (line 85) | class Block35(nn.Module):
    method __init__ (line 87) | def __init__(self, scale=1.0):
    method forward (line 108) | def forward(self, x):
  class Mixed_6a (line 119) | class Mixed_6a(nn.Module):
    method __init__ (line 121) | def __init__(self):
    method forward (line 134) | def forward(self, x):
  class Block17 (line 142) | class Block17(nn.Module):
    method __init__ (line 144) | def __init__(self, scale=1.0):
    method forward (line 160) | def forward(self, x):
  class Mixed_7a (line 170) | class Mixed_7a(nn.Module):
    method __init__ (line 172) | def __init__(self):
    method forward (line 193) | def forward(self, x):
  class Block8 (line 202) | class Block8(nn.Module):
    method __init__ (line 204) | def __init__(self, scale=1.0, noReLU=False):
    method forward (line 222) | def forward(self, x):
  class InceptionResNetV2 (line 233) | class InceptionResNetV2(nn.Module):
    method __init__ (line 235) | def __init__(self, num_classes=1001):
    method forward (line 303) | def forward(self, x):
  function inceptionresnetv2 (line 324) | def inceptionresnetv2(num_classes=1001, pretrained='imagenet'):

FILE: pretrained_models_pytorch/pretrainedmodels/inceptionv4.py
  class BasicConv2d (line 33) | class BasicConv2d(nn.Module):
    method __init__ (line 35) | def __init__(self, in_planes, out_planes, kernel_size, stride, padding...
    method forward (line 46) | def forward(self, x):
  class Mixed_3a (line 53) | class Mixed_3a(nn.Module):
    method __init__ (line 55) | def __init__(self):
    method forward (line 60) | def forward(self, x):
  class Mixed_4a (line 67) | class Mixed_4a(nn.Module):
    method __init__ (line 69) | def __init__(self):
    method forward (line 84) | def forward(self, x):
  class Mixed_5a (line 91) | class Mixed_5a(nn.Module):
    method __init__ (line 93) | def __init__(self):
    method forward (line 98) | def forward(self, x):
  class Inception_A (line 105) | class Inception_A(nn.Module):
    method __init__ (line 107) | def __init__(self):
    method forward (line 127) | def forward(self, x):
  class Reduction_A (line 136) | class Reduction_A(nn.Module):
    method __init__ (line 138) | def __init__(self):
    method forward (line 150) | def forward(self, x):
  class Inception_B (line 158) | class Inception_B(nn.Module):
    method __init__ (line 160) | def __init__(self):
    method forward (line 183) | def forward(self, x):
  class Reduction_B (line 192) | class Reduction_B(nn.Module):
    method __init__ (line 194) | def __init__(self):
    method forward (line 211) | def forward(self, x):
  class Inception_C (line 219) | class Inception_C(nn.Module):
    method __init__ (line 221) | def __init__(self):
    method forward (line 241) | def forward(self, x):
  class InceptionV4 (line 262) | class InceptionV4(nn.Module):
    method __init__ (line 264) | def __init__(self, num_classes=1001):
    method forward (line 299) | def forward(self, x):
  function inceptionv4 (line 306) | def inceptionv4(num_classes=1001, pretrained='imagenet'):

FILE: pretrained_models_pytorch/pretrainedmodels/nasnet.py
  class MaxPoolPad (line 30) | class MaxPoolPad(nn.Module):
    method __init__ (line 32) | def __init__(self):
    method forward (line 37) | def forward(self, x):
  class AvgPoolPad (line 44) | class AvgPoolPad(nn.Module):
    method __init__ (line 46) | def __init__(self, stride=2, padding=1):
    method forward (line 51) | def forward(self, x):
  class SeparableConv2d (line 58) | class SeparableConv2d(nn.Module):
    method __init__ (line 60) | def __init__(self, in_channels, out_channels, dw_kernel, dw_stride, dw...
    method forward (line 69) | def forward(self, x):
  class BranchSeparables (line 75) | class BranchSeparables(nn.Module):
    method __init__ (line 77) | def __init__(self, in_channels, out_channels, kernel_size, stride, pad...
    method forward (line 86) | def forward(self, x):
  class BranchSeparablesStem (line 96) | class BranchSeparablesStem(nn.Module):
    method __init__ (line 98) | def __init__(self, in_channels, out_channels, kernel_size, stride, pad...
    method forward (line 107) | def forward(self, x):
  class BranchSeparablesReduction (line 117) | class BranchSeparablesReduction(BranchSeparables):
    method __init__ (line 119) | def __init__(self, in_channels, out_channels, kernel_size, stride, pad...
    method forward (line 123) | def forward(self, x):
  class CellStem0 (line 135) | class CellStem0(nn.Module):
    method __init__ (line 137) | def __init__(self):
    method forward (line 158) | def forward(self, x):
  class CellStem1 (line 184) | class CellStem1(nn.Module):
    method __init__ (line 186) | def __init__(self):
    method forward (line 218) | def forward(self, x_conv0, x_stem_0):
  class FirstCell (line 255) | class FirstCell(nn.Module):
    method __init__ (line 257) | def __init__(self, in_channels_left, out_channels_left, in_channels_ri...
    method forward (line 288) | def forward(self, x, x_prev):
  class NormalCell (line 324) | class NormalCell(nn.Module):
    method __init__ (line 326) | def __init__(self, in_channels_left, out_channels_left, in_channels_ri...
    method forward (line 351) | def forward(self, x, x_prev):
  class ReductionCell0 (line 377) | class ReductionCell0(nn.Module):
    method __init__ (line 379) | def __init__(self, in_channels_left, out_channels_left, in_channels_ri...
    method forward (line 405) | def forward(self, x, x_prev):
  class ReductionCell1 (line 432) | class ReductionCell1(nn.Module):
    method __init__ (line 434) | def __init__(self, in_channels_left, out_channels_left, in_channels_ri...
    method forward (line 460) | def forward(self, x, x_prev):
  class NASNetALarge (line 487) | class NASNetALarge(nn.Module):
    method __init__ (line 489) | def __init__(self, num_classes=1001):
    method features (line 551) | def features(self, x):
    method classifier (line 583) | def classifier(self, x):
    method forward (line 591) | def forward(self, x):
  function nasnetalarge (line 597) | def nasnetalarge(num_classes=1001, pretrained='imagenet'):

FILE: pretrained_models_pytorch/pretrainedmodels/resnext.py
  class ResNeXt101_32x4d (line 38) | class ResNeXt101_32x4d(nn.Module):
    method __init__ (line 40) | def __init__(self, nb_classes=1000):
    method forward (line 46) | def forward(self, input):
  class ResNeXt101_64x4d (line 54) | class ResNeXt101_64x4d(nn.Module):
    method __init__ (line 56) | def __init__(self, nb_classes=1000):
    method forward (line 62) | def forward(self, input):
  function resnext101_32x4d (line 70) | def resnext101_32x4d(num_classes=1000, pretrained='imagenet'):
  function resnext101_64x4d (line 98) | def resnext101_64x4d(num_classes=1000, pretrained='imagenet'):

FILE: pretrained_models_pytorch/pretrainedmodels/resnext_features/resnext101_32x4d_features.py
  class LambdaBase (line 6) | class LambdaBase(nn.Sequential):
    method __init__ (line 7) | def __init__(self, fn, *args):
    method forward_prepare (line 11) | def forward_prepare(self, input):
  class Lambda (line 17) | class Lambda(LambdaBase):
    method forward (line 18) | def forward(self, input):
  class LambdaMap (line 21) | class LambdaMap(LambdaBase):
    method forward (line 22) | def forward(self, input):
  class LambdaReduce (line 25) | class LambdaReduce(LambdaBase):
    method forward (line 26) | def forward(self, input):

FILE: pretrained_models_pytorch/pretrainedmodels/resnext_features/resnext101_64x4d_features.py
  class LambdaBase (line 6) | class LambdaBase(nn.Sequential):
    method __init__ (line 7) | def __init__(self, fn, *args):
    method forward_prepare (line 11) | def forward_prepare(self, input):
  class Lambda (line 17) | class Lambda(LambdaBase):
    method forward (line 18) | def forward(self, input):
  class LambdaMap (line 21) | class LambdaMap(LambdaBase):
    method forward (line 22) | def forward(self, input):
  class LambdaReduce (line 25) | class LambdaReduce(LambdaBase):
    method forward (line 26) | def forward(self, input):

FILE: pretrained_models_pytorch/pretrainedmodels/torchvision.py
  function load_pretrained (line 80) | def load_pretrained(model, num_classes, settings):
  function alexnet (line 92) | def alexnet(num_classes=1000, pretrained='imagenet'):
  function densenet121 (line 103) | def densenet121(num_classes=1000, pretrained='imagenet'):
  function densenet169 (line 114) | def densenet169(num_classes=1000, pretrained='imagenet'):
  function densenet201 (line 125) | def densenet201(num_classes=1000, pretrained='imagenet'):
  function densenet161 (line 136) | def densenet161(num_classes=1000, pretrained='imagenet'):
  function inceptionv3 (line 147) | def inceptionv3(num_classes=1000, pretrained='imagenet'):
  function resnet18 (line 158) | def resnet18(num_classes=1000, pretrained='imagenet'):
  function resnet34 (line 167) | def resnet34(num_classes=1000, pretrained='imagenet'):
  function resnet50 (line 176) | def resnet50(num_classes=1000, pretrained='imagenet'):
  function resnet101 (line 185) | def resnet101(num_classes=1000, pretrained='imagenet'):
  function resnet152 (line 194) | def resnet152(num_classes=1000, pretrained='imagenet'):
  function squeezenet1_0 (line 204) | def squeezenet1_0(num_classes=1000, pretrained='imagenet'):
  function squeezenet1_1 (line 215) | def squeezenet1_1(num_classes=1000, pretrained='imagenet'):
  function vgg11 (line 228) | def vgg11(num_classes=1000, pretrained='imagenet'):
  function vgg11_bn (line 238) | def vgg11_bn(num_classes=1000, pretrained='imagenet'):
  function vgg13 (line 248) | def vgg13(num_classes=1000, pretrained='imagenet'):
  function vgg13_bn (line 258) | def vgg13_bn(num_classes=1000, pretrained='imagenet'):
  function vgg16 (line 268) | def vgg16(num_classes=1000, pretrained='imagenet'):
  function vgg16_bn (line 278) | def vgg16_bn(num_classes=1000, pretrained='imagenet'):
  function vgg19 (line 288) | def vgg19(num_classes=1000, pretrained='imagenet'):
  function vgg19_bn (line 298) | def vgg19_bn(num_classes=1000, pretrained='imagenet'):

FILE: pretrained_models_pytorch/pretrainedmodels/wideresnet.py
  function define_model (line 14) | def define_model(params):
  class WideResNet (line 57) | class WideResNet(nn.Module):
    method __init__ (line 59) | def __init__(self, pooling):
    method forward (line 64) | def forward(self, x):
  function wideresnet50 (line 69) | def wideresnet50(pooling):

FILE: pretrained_models_pytorch/test/imagenet.py
  class ToSpaceBGR (line 62) | class ToSpaceBGR(object):
    method __init__ (line 64) | def __init__(self, is_bgr):
    method __call__ (line 67) | def __call__(self, tensor):
  class ToRange255 (line 75) | class ToRange255(object):
    method __init__ (line 77) | def __init__(self, is_255):
    method __call__ (line 80) | def __call__(self, tensor):
  function main (line 85) | def main():
  function train (line 182) | def train(train_loader, model, criterion, optimizer, epoch):
  function validate (line 231) | def validate(val_loader, model, criterion):
  function save_checkpoint (line 275) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
  class AverageMeter (line 281) | class AverageMeter(object):
    method __init__ (line 283) | def __init__(self):
    method reset (line 286) | def reset(self):
    method update (line 292) | def update(self, val, n=1):
  function adjust_learning_rate (line 299) | def adjust_learning_rate(optimizer, epoch):
  function accuracy (line 306) | def accuracy(output, target, topk=(1,)):

FILE: utils.py
  function progress_bar (line 20) | def progress_bar(current, total, msg=None):
  function format_time (line 63) | def format_time(seconds):
  function submatrix (line 96) | def submatrix(arr):
  class ToSpaceBGR (line 104) | class ToSpaceBGR(object):
    method __init__ (line 105) | def __init__(self, is_bgr):
    method __call__ (line 107) | def __call__(self, tensor):
  class ToRange255 (line 116) | class ToRange255(object):
    method __init__ (line 117) | def __init__(self, is_255):
    method __call__ (line 119) | def __call__(self, tensor):
  function init_patch_circle (line 125) | def init_patch_circle(image_size, patch_size):
  function circle_transform (line 142) | def circle_transform(patch, data_shape, patch_shape, image_size):
  function init_patch_square (line 177) | def init_patch_square(image_size, patch_size):
  function square_transform (line 186) | def square_transform(patch, data_shape, patch_shape, image_size):