SYMBOL INDEX (485 symbols across 28 files)

FILE: examples/imagenet_eval.py
  function main (line 64) | def main():
  function train (line 162) | def train(train_loader, model, criterion, optimizer, epoch):
  function validate (line 211) | def validate(val_loader, model, criterion):
  function save_checkpoint (line 255) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
  class AverageMeter (line 261) | class AverageMeter(object):
    method __init__ (line 264) | def __init__(self):
    method reset (line 267) | def reset(self):
    method update (line 273) | def update(self, val, n=1):
  function adjust_learning_rate (line 280) | def adjust_learning_rate(optimizer, epoch):
  function accuracy (line 287) | def accuracy(output, target, topk=(1,)):

FILE: examples/imagenet_logits.py
  function main (line 27) | def main():

FILE: examples/visu_arch.py
  function print_info (line 75) | def print_info(self, input, output):
  function save_activation (line 177) | def save_activation(self, input, output):

FILE: examples/voc2007_extract.py
  function extract_features_targets (line 27) | def extract_features_targets(model, features_size, loader, path_data, cu...
  function train_multilabel (line 59) | def train_multilabel(features, targets, classes, train_split, test_split...
  function main (line 135) | def main ():

FILE: pretrainedmodels/datasets/utils.py
  function load_imagenet_classes (line 9) | def load_imagenet_classes(path_synsets='data/imagenet_synsets.txt',
  class Warp (line 32) | class Warp(object):
    method __init__ (line 33) | def __init__(self, size, interpolation=Image.BILINEAR):
    method __call__ (line 37) | def __call__(self, img):
    method __str__ (line 40) | def __str__(self):
  function download_url (line 45) | def download_url(url, destination=None, progress_bar=True):
  class AveragePrecisionMeter (line 86) | class AveragePrecisionMeter(object):
    method __init__ (line 100) | def __init__(self, difficult_examples=False):
    method reset (line 105) | def reset(self):
    method add (line 110) | def add(self, output, target):
    method value (line 158) | def value(self):
    method average_precision (line 180) | def average_precision(output, target, difficult_examples=True):

FILE: pretrainedmodels/datasets/voc.py
  function read_image_label (line 29) | def read_image_label(file):
  function read_object_labels (line 43) | def read_object_labels(root, dataset, set):
  function write_object_labels_csv (line 64) | def write_object_labels_csv(file, labeled_data):
  function read_object_labels_csv (line 82) | def read_object_labels_csv(file, header=True):
  function find_images_classification (line 104) | def find_images_classification(root, dataset, set):
  function download_voc2007 (line 114) | def download_voc2007(root):
  class Voc2007Classification (line 215) | class Voc2007Classification(data.Dataset):
    method __init__ (line 217) | def __init__(self, root, set, transform=None, target_transform=None):
    method __getitem__ (line 248) | def __getitem__(self, index):
    method __len__ (line 257) | def __len__(self):
    method get_number_classes (line 260) | def get_number_classes(self):

FILE: pretrainedmodels/models/bninception.py
  class BNInception (line 27) | class BNInception(nn.Module):
    method __init__ (line 29) | def __init__(self, num_classes=1000):
    method features (line 253) | def features(self, input):
    method logits (line 485) | def logits(self, features):
    method forward (line 492) | def forward(self, input):
  function bninception (line 497) | def bninception(num_classes=1000, pretrained='imagenet'):

FILE: pretrainedmodels/models/cafferesnet.py
  function conv3x3 (line 23) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 29) | class BasicBlock(nn.Module):
    method __init__ (line 32) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 42) | def forward(self, x):
  class Bottleneck (line 61) | class Bottleneck(nn.Module):
    method __init__ (line 64) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 77) | def forward(self, x):
  class ResNet (line 100) | class ResNet(nn.Module):
    method __init__ (line 102) | def __init__(self, block, layers, num_classes=1000):
    method _make_layer (line 127) | def _make_layer(self, block, planes, blocks, stride=1):
    method features (line 144) | def features(self, x):
    method logits (line 156) | def logits(self, x):
    method forward (line 162) | def forward(self, x):
  function cafferesnet101 (line 168) | def cafferesnet101(num_classes=1000, pretrained='imagenet'):

FILE: pretrainedmodels/models/dpn.py
  function dpn68 (line 98) | def dpn68(num_classes=1000, pretrained='imagenet'):
  function dpn68b (line 116) | def dpn68b(num_classes=1000, pretrained='imagenet+5k'):
  function dpn92 (line 134) | def dpn92(num_classes=1000, pretrained='imagenet+5k'):
  function dpn98 (line 152) | def dpn98(num_classes=1000, pretrained='imagenet'):
  function dpn131 (line 170) | def dpn131(num_classes=1000, pretrained='imagenet'):
  function dpn107 (line 188) | def dpn107(num_classes=1000, pretrained='imagenet+5k'):
  class CatBnAct (line 207) | class CatBnAct(nn.Module):
    method __init__ (line 208) | def __init__(self, in_chs, activation_fn=nn.ReLU(inplace=True)):
    method forward (line 213) | def forward(self, x):
  class BnActConv2d (line 218) | class BnActConv2d(nn.Module):
    method __init__ (line 219) | def __init__(self, in_chs, out_chs, kernel_size, stride,
    method forward (line 226) | def forward(self, x):
  class InputBlock (line 230) | class InputBlock(nn.Module):
    method __init__ (line 231) | def __init__(self, num_init_features, kernel_size=7,
    method forward (line 240) | def forward(self, x):
  class DualPathBlock (line 248) | class DualPathBlock(nn.Module):
    method __init__ (line 249) | def __init__(
    method forward (line 285) | def forward(self, x):
  class DPN (line 312) | class DPN(nn.Module):
    method __init__ (line 313) | def __init__(self, small=False, num_init_features=64, k_r=96, groups=32,
    method logits (line 375) | def logits(self, features):
    method forward (line 386) | def forward(self, input):
  function pooling_factor (line 403) | def pooling_factor(pool_type='avg'):
  function adaptive_avgmax_pool2d (line 407) | def adaptive_avgmax_pool2d(x, pool_type='avg', padding=0, count_include_...
  class AdaptiveAvgMaxPool2d (line 431) | class AdaptiveAvgMaxPool2d(torch.nn.Module):
    method __init__ (line 434) | def __init__(self, output_size=1, pool_type='avg'):
    method forward (line 447) | def forward(self, x):
    method factor (line 456) | def factor(self):
    method __repr__ (line 459) | def __repr__(self):

FILE: pretrainedmodels/models/fbresnet.py
  function conv3x3 (line 27) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 33) | class BasicBlock(nn.Module):
    method __init__ (line 36) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 46) | def forward(self, x):
  class Bottleneck (line 65) | class Bottleneck(nn.Module):
    method __init__ (line 68) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 81) | def forward(self, x):
  class FBResNet (line 103) | class FBResNet(nn.Module):
    method __init__ (line 105) | def __init__(self, block, layers, num_classes=1000):
    method _make_layer (line 133) | def _make_layer(self, block, planes, blocks, stride=1):
    method features (line 150) | def features(self, input):
    method logits (line 163) | def logits(self, features):
    method forward (line 170) | def forward(self, input):
  function fbresnet18 (line 176) | def fbresnet18(num_classes=1000):
  function fbresnet34 (line 186) | def fbresnet34(num_classes=1000):
  function fbresnet50 (line 196) | def fbresnet50(num_classes=1000):
  function fbresnet101 (line 206) | def fbresnet101(num_classes=1000):
  function fbresnet152 (line 216) | def fbresnet152(num_classes=1000, pretrained='imagenet'):

FILE: pretrainedmodels/models/fbresnet/resnet152_load.py
  function conv3x3 (line 20) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 26) | class BasicBlock(nn.Module):
    method __init__ (line 29) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 39) | def forward(self, x):
  class Bottleneck (line 58) | class Bottleneck(nn.Module):
    method __init__ (line 61) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 74) | def forward(self, x):
  class ResNet (line 98) | class ResNet(nn.Module):
    method __init__ (line 100) | def __init__(self, block, layers, num_classes=1000):
    method _make_layer (line 124) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 141) | def forward(self, x):
  function resnet18 (line 160) | def resnet18(pretrained=False, **kwargs):
  function resnet34 (line 172) | def resnet34(pretrained=False, **kwargs):
  function resnet50 (line 184) | def resnet50(pretrained=False, **kwargs):
  function resnet101 (line 196) | def resnet101(pretrained=False, **kwargs):
  function resnet152 (line 208) | def resnet152(pretrained=False, **kwargs):

FILE: pretrainedmodels/models/inceptionresnetv2.py
  class BasicConv2d (line 34) | class BasicConv2d(nn.Module):
    method __init__ (line 36) | def __init__(self, in_planes, out_planes, kernel_size, stride, padding...
    method forward (line 47) | def forward(self, x):
  class Mixed_5b (line 54) | class Mixed_5b(nn.Module):
    method __init__ (line 56) | def __init__(self):
    method forward (line 77) | def forward(self, x):
  class Block35 (line 86) | class Block35(nn.Module):
    method __init__ (line 88) | def __init__(self, scale=1.0):
    method forward (line 109) | def forward(self, x):
  class Mixed_6a (line 120) | class Mixed_6a(nn.Module):
    method __init__ (line 122) | def __init__(self):
    method forward (line 135) | def forward(self, x):
  class Block17 (line 143) | class Block17(nn.Module):
    method __init__ (line 145) | def __init__(self, scale=1.0):
    method forward (line 161) | def forward(self, x):
  class Mixed_7a (line 171) | class Mixed_7a(nn.Module):
    method __init__ (line 173) | def __init__(self):
    method forward (line 194) | def forward(self, x):
  class Block8 (line 203) | class Block8(nn.Module):
    method __init__ (line 205) | def __init__(self, scale=1.0, noReLU=False):
    method forward (line 223) | def forward(self, x):
  class InceptionResNetV2 (line 234) | class InceptionResNetV2(nn.Module):
    method __init__ (line 236) | def __init__(self, num_classes=1001):
    method features (line 304) | def features(self, input):
    method logits (line 322) | def logits(self, features):
    method forward (line 328) | def forward(self, input):
  function inceptionresnetv2 (line 333) | def inceptionresnetv2(num_classes=1000, pretrained='imagenet'):

FILE: pretrainedmodels/models/inceptionv4.py
  class BasicConv2d (line 35) | class BasicConv2d(nn.Module):
    method __init__ (line 37) | def __init__(self, in_planes, out_planes, kernel_size, stride, padding...
    method forward (line 48) | def forward(self, x):
  class Mixed_3a (line 55) | class Mixed_3a(nn.Module):
    method __init__ (line 57) | def __init__(self):
    method forward (line 62) | def forward(self, x):
  class Mixed_4a (line 69) | class Mixed_4a(nn.Module):
    method __init__ (line 71) | def __init__(self):
    method forward (line 86) | def forward(self, x):
  class Mixed_5a (line 93) | class Mixed_5a(nn.Module):
    method __init__ (line 95) | def __init__(self):
    method forward (line 100) | def forward(self, x):
  class Inception_A (line 107) | class Inception_A(nn.Module):
    method __init__ (line 109) | def __init__(self):
    method forward (line 129) | def forward(self, x):
  class Reduction_A (line 138) | class Reduction_A(nn.Module):
    method __init__ (line 140) | def __init__(self):
    method forward (line 152) | def forward(self, x):
  class Inception_B (line 160) | class Inception_B(nn.Module):
    method __init__ (line 162) | def __init__(self):
    method forward (line 185) | def forward(self, x):
  class Reduction_B (line 194) | class Reduction_B(nn.Module):
    method __init__ (line 196) | def __init__(self):
    method forward (line 213) | def forward(self, x):
  class Inception_C (line 221) | class Inception_C(nn.Module):
    method __init__ (line 223) | def __init__(self):
    method forward (line 243) | def forward(self, x):
  class InceptionV4 (line 264) | class InceptionV4(nn.Module):
    method __init__ (line 266) | def __init__(self, num_classes=1001):
    method logits (line 300) | def logits(self, features):
    method forward (line 308) | def forward(self, input):
  function inceptionv4 (line 314) | def inceptionv4(num_classes=1000, pretrained='imagenet'):

FILE: pretrainedmodels/models/nasnet.py
  class MaxPoolPad (line 32) | class MaxPoolPad(nn.Module):
    method __init__ (line 34) | def __init__(self):
    method forward (line 39) | def forward(self, x):
  class AvgPoolPad (line 46) | class AvgPoolPad(nn.Module):
    method __init__ (line 48) | def __init__(self, stride=2, padding=1):
    method forward (line 53) | def forward(self, x):
  class SeparableConv2d (line 60) | class SeparableConv2d(nn.Module):
    method __init__ (line 62) | def __init__(self, in_channels, out_channels, dw_kernel, dw_stride, dw...
    method forward (line 71) | def forward(self, x):
  class BranchSeparables (line 77) | class BranchSeparables(nn.Module):
    method __init__ (line 79) | def __init__(self, in_channels, out_channels, kernel_size, stride, pad...
    method forward (line 88) | def forward(self, x):
  class BranchSeparablesStem (line 98) | class BranchSeparablesStem(nn.Module):
    method __init__ (line 100) | def __init__(self, in_channels, out_channels, kernel_size, stride, pad...
    method forward (line 109) | def forward(self, x):
  class BranchSeparablesReduction (line 119) | class BranchSeparablesReduction(BranchSeparables):
    method __init__ (line 121) | def __init__(self, in_channels, out_channels, kernel_size, stride, pad...
    method forward (line 125) | def forward(self, x):
  class CellStem0 (line 137) | class CellStem0(nn.Module):
    method __init__ (line 138) | def __init__(self, stem_filters, num_filters=42):
    method forward (line 161) | def forward(self, x):
  class CellStem1 (line 187) | class CellStem1(nn.Module):
    method __init__ (line 189) | def __init__(self, stem_filters, num_filters):
    method forward (line 223) | def forward(self, x_conv0, x_stem_0):
  class FirstCell (line 260) | class FirstCell(nn.Module):
    method __init__ (line 262) | def __init__(self, in_channels_left, out_channels_left, in_channels_ri...
    method forward (line 293) | def forward(self, x, x_prev):
  class NormalCell (line 329) | class NormalCell(nn.Module):
    method __init__ (line 331) | def __init__(self, in_channels_left, out_channels_left, in_channels_ri...
    method forward (line 356) | def forward(self, x, x_prev):
  class ReductionCell0 (line 382) | class ReductionCell0(nn.Module):
    method __init__ (line 384) | def __init__(self, in_channels_left, out_channels_left, in_channels_ri...
    method forward (line 410) | def forward(self, x, x_prev):
  class ReductionCell1 (line 437) | class ReductionCell1(nn.Module):
    method __init__ (line 439) | def __init__(self, in_channels_left, out_channels_left, in_channels_ri...
    method forward (line 465) | def forward(self, x, x_prev):
  class NASNetALarge (line 492) | class NASNetALarge(nn.Module):
    method __init__ (line 495) | def __init__(self, num_classes=1001, stem_filters=96, penultimate_filt...
    method features (line 563) | def features(self, input):
    method logits (line 594) | def logits(self, features):
    method forward (line 602) | def forward(self, input):
  function nasnetalarge (line 608) | def nasnetalarge(num_classes=1001, pretrained='imagenet'):

FILE: pretrainedmodels/models/nasnet_mobile.py
  class MaxPoolPad (line 48) | class MaxPoolPad(nn.Module):
    method __init__ (line 50) | def __init__(self):
    method forward (line 55) | def forward(self, x):
  class AvgPoolPad (line 62) | class AvgPoolPad(nn.Module):
    method __init__ (line 64) | def __init__(self, stride=2, padding=1):
    method forward (line 69) | def forward(self, x):
  class SeparableConv2d (line 76) | class SeparableConv2d(nn.Module):
    method __init__ (line 78) | def __init__(self, in_channels, out_channels, dw_kernel, dw_stride, dw...
    method forward (line 87) | def forward(self, x):
  class BranchSeparables (line 93) | class BranchSeparables(nn.Module):
    method __init__ (line 95) | def __init__(self, in_channels, out_channels, kernel_size, stride, pad...
    method forward (line 105) | def forward(self, x):
  class BranchSeparablesStem (line 120) | class BranchSeparablesStem(nn.Module):
    method __init__ (line 122) | def __init__(self, in_channels, out_channels, kernel_size, stride, pad...
    method forward (line 131) | def forward(self, x):
  class BranchSeparablesReduction (line 141) | class BranchSeparablesReduction(BranchSeparables):
    method __init__ (line 143) | def __init__(self, in_channels, out_channels, kernel_size, stride, pad...
    method forward (line 147) | def forward(self, x):
  class CellStem0 (line 159) | class CellStem0(nn.Module):
    method __init__ (line 160) | def __init__(self, stem_filters, num_filters=42):
    method forward (line 183) | def forward(self, x):
  class CellStem1 (line 209) | class CellStem1(nn.Module):
    method __init__ (line 211) | def __init__(self, stem_filters, num_filters):
    method forward (line 248) | def forward(self, x_conv0, x_stem_0):
  class FirstCell (line 285) | class FirstCell(nn.Module):
    method __init__ (line 287) | def __init__(self, in_channels_left, out_channels_left, in_channels_ri...
    method forward (line 318) | def forward(self, x, x_prev):
  class NormalCell (line 354) | class NormalCell(nn.Module):
    method __init__ (line 356) | def __init__(self, in_channels_left, out_channels_left, in_channels_ri...
    method forward (line 381) | def forward(self, x, x_prev):
  class ReductionCell0 (line 407) | class ReductionCell0(nn.Module):
    method __init__ (line 409) | def __init__(self, in_channels_left, out_channels_left, in_channels_ri...
    method forward (line 435) | def forward(self, x, x_prev):
  class ReductionCell1 (line 462) | class ReductionCell1(nn.Module):
    method __init__ (line 464) | def __init__(self, in_channels_left, out_channels_left, in_channels_ri...
    method forward (line 493) | def forward(self, x, x_prev):
  class NASNetAMobile (line 520) | class NASNetAMobile(nn.Module):
    method __init__ (line 523) | def __init__(self, num_classes=1000, stem_filters=32, penultimate_filt...
    method features (line 579) | def features(self, input):
    method logits (line 604) | def logits(self, features):
    method forward (line 612) | def forward(self, input):
  function nasnetamobile (line 618) | def nasnetamobile(num_classes=1000, pretrained='imagenet'):

FILE: pretrainedmodels/models/pnasnet.py
  class MaxPool (line 33) | class MaxPool(nn.Module):
    method __init__ (line 35) | def __init__(self, kernel_size, stride=1, padding=1, zero_pad=False):
    method forward (line 40) | def forward(self, x):
  class SeparableConv2d (line 49) | class SeparableConv2d(nn.Module):
    method __init__ (line 51) | def __init__(self, in_channels, out_channels, dw_kernel_size, dw_stride,
    method forward (line 61) | def forward(self, x):
  class BranchSeparables (line 67) | class BranchSeparables(nn.Module):
    method __init__ (line 69) | def __init__(self, in_channels, out_channels, kernel_size, stride=1,
    method forward (line 86) | def forward(self, x):
  class ReluConvBn (line 100) | class ReluConvBn(nn.Module):
    method __init__ (line 102) | def __init__(self, in_channels, out_channels, kernel_size, stride=1):
    method forward (line 110) | def forward(self, x):
  class FactorizedReduction (line 117) | class FactorizedReduction(nn.Module):
    method __init__ (line 119) | def __init__(self, in_channels, out_channels):
    method forward (line 135) | def forward(self, x):
  class CellBase (line 149) | class CellBase(nn.Module):
    method cell_forward (line 151) | def cell_forward(self, x_left, x_right):
  class CellStem0 (line 181) | class CellStem0(CellBase):
    method __init__ (line 183) | def __init__(self, in_channels_left, out_channels_left, in_channels_ri...
    method forward (line 220) | def forward(self, x_left):
  class Cell (line 226) | class Cell(CellBase):
    method __init__ (line 228) | def __init__(self, in_channels_left, out_channels_left, in_channels_ri...
    method forward (line 284) | def forward(self, x_left, x_right):
  class PNASNet5Large (line 291) | class PNASNet5Large(nn.Module):
    method __init__ (line 292) | def __init__(self, num_classes=1001):
    method features (line 340) | def features(self, x):
    method logits (line 358) | def logits(self, features):
    method forward (line 366) | def forward(self, input):
  function pnasnet5large (line 372) | def pnasnet5large(num_classes=1001, pretrained='imagenet'):

FILE: pretrainedmodels/models/polynet.py
  class BasicConv2d (line 23) | class BasicConv2d(nn.Module):
    method __init__ (line 25) | def __init__(self, in_planes, out_planes, kernel_size, stride=1, paddi...
    method forward (line 33) | def forward(self, x):
  class PolyConv2d (line 41) | class PolyConv2d(nn.Module):
    method __init__ (line 49) | def __init__(self, in_planes, out_planes, kernel_size, num_blocks,
    method forward (line 59) | def forward(self, x, block_index):
  class Stem (line 67) | class Stem(nn.Module):
    method __init__ (line 69) | def __init__(self):
    method forward (line 91) | def forward(self, x):
  class BlockA (line 108) | class BlockA(nn.Module):
    method __init__ (line 111) | def __init__(self):
    method forward (line 125) | def forward(self, x):
  class BlockB (line 134) | class BlockB(nn.Module):
    method __init__ (line 137) | def __init__(self):
    method forward (line 147) | def forward(self, x):
  class BlockC (line 155) | class BlockC(nn.Module):
    method __init__ (line 158) | def __init__(self):
    method forward (line 168) | def forward(self, x):
  class ReductionA (line 176) | class ReductionA(nn.Module):
    method __init__ (line 181) | def __init__(self):
    method forward (line 191) | def forward(self, x):
  class ReductionB (line 199) | class ReductionB(nn.Module):
    method __init__ (line 203) | def __init__(self):
    method forward (line 220) | def forward(self, x):
  class InceptionResNetBPoly (line 229) | class InceptionResNetBPoly(nn.Module):
    method __init__ (line 239) | def __init__(self, scale, num_blocks):
    method forward_block (line 259) | def forward_block(self, x, block_index):
    method forward (line 269) | def forward(self, x):
  class InceptionResNetCPoly (line 279) | class InceptionResNetCPoly(nn.Module):
    method __init__ (line 289) | def __init__(self, scale, num_blocks):
    method forward_block (line 309) | def forward_block(self, x, block_index):
    method forward (line 319) | def forward(self, x):
  class MultiWay (line 329) | class MultiWay(nn.Module):
    method __init__ (line 332) | def __init__(self, scale, block_cls, num_blocks):
    method forward (line 339) | def forward(self, x):
  class InceptionResNetA2Way (line 349) | class InceptionResNetA2Way(MultiWay):
    method __init__ (line 351) | def __init__(self, scale):
  class InceptionResNetB2Way (line 356) | class InceptionResNetB2Way(MultiWay):
    method __init__ (line 358) | def __init__(self, scale):
  class InceptionResNetC2Way (line 363) | class InceptionResNetC2Way(MultiWay):
    method __init__ (line 365) | def __init__(self, scale):
  class InceptionResNetBPoly3 (line 370) | class InceptionResNetBPoly3(InceptionResNetBPoly):
    method __init__ (line 372) | def __init__(self, scale):
  class InceptionResNetCPoly3 (line 376) | class InceptionResNetCPoly3(InceptionResNetCPoly):
    method __init__ (line 378) | def __init__(self, scale):
  class PolyNet (line 382) | class PolyNet(nn.Module):
    method __init__ (line 384) | def __init__(self, num_classes=1000):
    method features (line 439) | def features(self, x):
    method logits (line 448) | def logits(self, x):
    method forward (line 455) | def forward(self, x):
  function polynet (line 461) | def polynet(num_classes=1000, pretrained='imagenet'):

FILE: pretrainedmodels/models/resnext.py
  class ResNeXt101_32x4d (line 37) | class ResNeXt101_32x4d(nn.Module):
    method __init__ (line 39) | def __init__(self, num_classes=1000):
    method logits (line 46) | def logits(self, input):
    method forward (line 52) | def forward(self, input):
  class ResNeXt101_64x4d (line 58) | class ResNeXt101_64x4d(nn.Module):
    method __init__ (line 60) | def __init__(self, num_classes=1000):
    method logits (line 67) | def logits(self, input):
    method forward (line 73) | def forward(self, input):
  function resnext101_32x4d (line 79) | def resnext101_32x4d(num_classes=1000, pretrained='imagenet'):
  function resnext101_64x4d (line 93) | def resnext101_64x4d(num_classes=1000, pretrained='imagenet'):

FILE: pretrainedmodels/models/resnext_features/resnext101_32x4d_features.py
  class LambdaBase (line 7) | class LambdaBase(nn.Sequential):
    method __init__ (line 8) | def __init__(self, *args):
    method forward_prepare (line 11) | def forward_prepare(self, input):
  class Lambda (line 17) | class Lambda(LambdaBase):
    method __init__ (line 18) | def __init__(self, *args):
    method forward (line 22) | def forward(self, input):
  class LambdaMap (line 25) | class LambdaMap(LambdaBase):
    method __init__ (line 26) | def __init__(self, *args):
    method forward (line 30) | def forward(self, input):
  class LambdaReduce (line 33) | class LambdaReduce(LambdaBase):
    method __init__ (line 34) | def __init__(self, *args):
    method forward (line 38) | def forward(self, input):
  function identity (line 41) | def identity(x): return x
  function add (line 43) | def add(x, y): return x + y

FILE: pretrainedmodels/models/resnext_features/resnext101_64x4d_features.py
  class LambdaBase (line 7) | class LambdaBase(nn.Sequential):
    method __init__ (line 8) | def __init__(self, *args):
    method forward_prepare (line 11) | def forward_prepare(self, input):
  class Lambda (line 17) | class Lambda(LambdaBase):
    method __init__ (line 18) | def __init__(self, *args):
    method forward (line 22) | def forward(self, input):
  class LambdaMap (line 25) | class LambdaMap(LambdaBase):
    method __init__ (line 26) | def __init__(self, *args):
    method forward (line 30) | def forward(self, input):
  class LambdaReduce (line 33) | class LambdaReduce(LambdaBase):
    method __init__ (line 34) | def __init__(self, *args):
    method forward (line 38) | def forward(self, input):
  function identity (line 41) | def identity(x): return x
  function add (line 43) | def add(x, y): return x + y

FILE: pretrainedmodels/models/senet.py
  class SEModule (line 85) | class SEModule(nn.Module):
    method __init__ (line 87) | def __init__(self, channels, reduction):
    method forward (line 97) | def forward(self, x):
  class Bottleneck (line 107) | class Bottleneck(nn.Module):
    method forward (line 111) | def forward(self, x):
  class SEBottleneck (line 134) | class SEBottleneck(Bottleneck):
    method __init__ (line 140) | def __init__(self, inplanes, planes, groups, reduction, stride=1,
  class SEResNetBottleneck (line 158) | class SEResNetBottleneck(Bottleneck):
    method __init__ (line 166) | def __init__(self, inplanes, planes, groups, reduction, stride=1,
  class SEResNeXtBottleneck (line 183) | class SEResNeXtBottleneck(Bottleneck):
    method __init__ (line 189) | def __init__(self, inplanes, planes, groups, reduction, stride=1,
  class SENet (line 207) | class SENet(nn.Module):
    method __init__ (line 209) | def __init__(self, block, layers, groups, reduction, dropout_p=0.2,
    method _make_layer (line 327) | def _make_layer(self, block, planes, blocks, groups, reduction, stride=1,
    method features (line 347) | def features(self, x):
    method logits (line 355) | def logits(self, x):
    method forward (line 363) | def forward(self, x):
  function initialize_pretrained_model (line 369) | def initialize_pretrained_model(model, num_classes, settings):
  function senet154 (line 381) | def senet154(num_classes=1000, pretrained='imagenet'):
  function se_resnet50 (line 390) | def se_resnet50(num_classes=1000, pretrained='imagenet'):
  function se_resnet101 (line 401) | def se_resnet101(num_classes=1000, pretrained='imagenet'):
  function se_resnet152 (line 412) | def se_resnet152(num_classes=1000, pretrained='imagenet'):
  function se_resnext50_32x4d (line 423) | def se_resnext50_32x4d(num_classes=1000, pretrained='imagenet'):
  function se_resnext101_32x4d (line 434) | def se_resnext101_32x4d(num_classes=1000, pretrained='imagenet'):

FILE: pretrainedmodels/models/torchvision_models.py
  function update_state_dict (line 98) | def update_state_dict(state_dict):
  function load_pretrained (line 113) | def load_pretrained(model, num_classes, settings):
  function modify_alexnet (line 129) | def modify_alexnet(model):
  function alexnet (line 168) | def alexnet(num_classes=1000, pretrained='imagenet'):
  function modify_densenets (line 183) | def modify_densenets(model):
  function densenet121 (line 205) | def densenet121(num_classes=1000, pretrained='imagenet'):
  function densenet169 (line 216) | def densenet169(num_classes=1000, pretrained='imagenet'):
  function densenet201 (line 227) | def densenet201(num_classes=1000, pretrained='imagenet'):
  function densenet161 (line 238) | def densenet161(num_classes=1000, pretrained='imagenet'):
  function inceptionv3 (line 252) | def inceptionv3(num_classes=1000, pretrained='imagenet'):
  function modify_resnets (line 314) | def modify_resnets(model):
  function resnet18 (line 348) | def resnet18(num_classes=1000, pretrained='imagenet'):
  function resnet34 (line 358) | def resnet34(num_classes=1000, pretrained='imagenet'):
  function resnet50 (line 368) | def resnet50(num_classes=1000, pretrained='imagenet'):
  function resnet101 (line 378) | def resnet101(num_classes=1000, pretrained='imagenet'):
  function resnet152 (line 388) | def resnet152(num_classes=1000, pretrained='imagenet'):
  function modify_squeezenets (line 401) | def modify_squeezenets(model):
  function squeezenet1_0 (line 428) | def squeezenet1_0(num_classes=1000, pretrained='imagenet'):
  function squeezenet1_1 (line 440) | def squeezenet1_1(num_classes=1000, pretrained='imagenet'):
  function modify_vggs (line 456) | def modify_vggs(model):
  function vgg11 (line 495) | def vgg11(num_classes=1000, pretrained='imagenet'):
  function vgg11_bn (line 505) | def vgg11_bn(num_classes=1000, pretrained='imagenet'):
  function vgg13 (line 515) | def vgg13(num_classes=1000, pretrained='imagenet'):
  function vgg13_bn (line 525) | def vgg13_bn(num_classes=1000, pretrained='imagenet'):
  function vgg16 (line 535) | def vgg16(num_classes=1000, pretrained='imagenet'):
  function vgg16_bn (line 545) | def vgg16_bn(num_classes=1000, pretrained='imagenet'):
  function vgg19 (line 555) | def vgg19(num_classes=1000, pretrained='imagenet'):
  function vgg19_bn (line 565) | def vgg19_bn(num_classes=1000, pretrained='imagenet'):

FILE: pretrainedmodels/models/vggm.py
  class SpatialCrossMapLRN (line 24) | class SpatialCrossMapLRN(nn.Module):
    method __init__ (line 25) | def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1, ACROSS_CHA...
    method forward (line 40) | def forward(self, x):
  class LambdaBase (line 52) | class LambdaBase(nn.Sequential):
    method __init__ (line 53) | def __init__(self, fn, *args):
    method forward_prepare (line 57) | def forward_prepare(self, input):
  class Lambda (line 63) | class Lambda(LambdaBase):
    method forward (line 64) | def forward(self, input):
  class VGGM (line 67) | class VGGM(nn.Module):
    method __init__ (line 69) | def __init__(self, num_classes=1000):
    method forward (line 99) | def forward(self, x):
  function vggm (line 105) | def vggm(num_classes=1000, pretrained='imagenet'):

FILE: pretrainedmodels/models/wideresnet.py
  function define_model (line 15) | def define_model(params):
  class WideResNet (line 58) | class WideResNet(nn.Module):
    method __init__ (line 60) | def __init__(self, pooling):
    method forward (line 65) | def forward(self, x):
  function wideresnet50 (line 70) | def wideresnet50(pooling):

FILE: pretrainedmodels/models/xception.py
  class SeparableConv2d (line 50) | class SeparableConv2d(nn.Module):
    method __init__ (line 51) | def __init__(self,in_channels,out_channels,kernel_size=1,stride=1,padd...
    method forward (line 57) | def forward(self,x):
  class Block (line 63) | class Block(nn.Module):
    method __init__ (line 64) | def __init__(self,in_filters,out_filters,reps,strides=1,start_with_rel...
    method forward (line 101) | def forward(self,inp):
  class Xception (line 114) | class Xception(nn.Module):
    method __init__ (line 119) | def __init__(self, num_classes=1000):
    method features (line 172) | def features(self, input):
    method logits (line 202) | def logits(self, features):
    method forward (line 210) | def forward(self, input):
  function xception (line 216) | def xception(num_classes=1000, pretrained='imagenet'):

FILE: pretrainedmodels/utils.py
  class ToSpaceBGR (line 9) | class ToSpaceBGR(object):
    method __init__ (line 11) | def __init__(self, is_bgr):
    method __call__ (line 14) | def __call__(self, tensor):
  class ToRange255 (line 23) | class ToRange255(object):
    method __init__ (line 25) | def __init__(self, is_255):
    method __call__ (line 28) | def __call__(self, tensor):
  class TransformImage (line 34) | class TransformImage(object):
    method __init__ (line 36) | def __init__(self, opts, scale=0.875, random_crop=False,
    method __call__ (line 79) | def __call__(self, img):
  class LoadImage (line 84) | class LoadImage(object):
    method __init__ (line 86) | def __init__(self, space='RGB'):
    method __call__ (line 89) | def __call__(self, path_img):
  class LoadTransformImage (line 96) | class LoadTransformImage(object):
    method __init__ (line 98) | def __init__(self, model, scale=0.875):
    method __call__ (line 102) | def __call__(self, path_img):
  class Identity (line 108) | class Identity(nn.Module):
    method __init__ (line 110) | def __init__(self):
    method forward (line 113) | def forward(self, x):

FILE: tests/test_pm_imagenet.py
  function equal (line 20) | def equal(x,y):
  function test_pm_imagenet (line 24) | def test_pm_imagenet(model_name, pretrained):

FILE: tests/test_torch_save.py
  function test_torch_save (line 15) | def test_torch_save(model_name, pretrained, tmp_path):