SYMBOL INDEX (431 symbols across 41 files)

FILE: collect_results.py
  function load_results (line 28) | def load_results(targt_exp, model_name):

FILE: dataset.py
  class DatasetGenerator (line 89) | class DatasetGenerator():
    method __init__ (line 90) | def __init__(self, train_batch_size=128, eval_batch_size=256, num_of_w...
    method getDataLoader (line 273) | def getDataLoader(self, train_shuffle=True, train_drop_last=True):
    method _split_validation_set (line 288) | def _split_validation_set(self, train_portion, train_shuffle=True, tra...
  function patch_noise_extend_to_img (line 354) | def patch_noise_extend_to_img(noise, image_size=[32, 32, 3], patch_locat...
  class PoisonCIFAR10 (line 376) | class PoisonCIFAR10(datasets.CIFAR10):
    method __init__ (line 377) | def __init__(self, root, train=True, transform=None, target_transform=...
  class PoisonCIFAR100 (line 441) | class PoisonCIFAR100(datasets.CIFAR100):
    method __init__ (line 442) | def __init__(self, root, train=True, transform=None, target_transform=...
  class PoisonCIFAR101 (line 496) | class PoisonCIFAR101(datasets.VisionDataset):
    method __init__ (line 497) | def __init__(self, root, split='poison_train', transform=None, target_...
    method __len__ (line 525) | def __len__(self):
    method __getitem__ (line 528) | def __getitem__(self, index):
  class PoisonSVHN (line 536) | class PoisonSVHN(datasets.SVHN):
    method __init__ (line 537) | def __init__(self, root, split='train', transform=None, target_transfo...
  class ImageNetMini (line 591) | class ImageNetMini(datasets.ImageNet):
    method __init__ (line 592) | def __init__(self, root, split='train', **kwargs):
  class PoisonImageNetMini (line 608) | class PoisonImageNetMini(ImageNetMini):
    method __init__ (line 609) | def __init__(self, root, split, poison_rate=1.0, seed=0,
    method __getitem__ (line 628) | def __getitem__(self, index):
  class Augmentation (line 648) | class Augmentation(object):
    method __init__ (line 649) | def __init__(self, policies):
    method __call__ (line 652) | def __call__(self, img):
  class CatDogDataset (line 662) | class CatDogDataset(datasets.VisionDataset):
    method __init__ (line 663) | def __init__(self, root, split='train', transform=None, target_transfo...
    method __len__ (line 670) | def __len__(self):
    method __getitem__ (line 673) | def __getitem__(self, index):
  class CelebAMini (line 695) | class CelebAMini(datasets.CelebA):
    method __init__ (line 696) | def __init__(self, root, split="train", target_type="attr", transform=...
    method __len__ (line 721) | def __len__(self):
    method __getitem__ (line 724) | def __getitem__(self, index):
  class Cutout (line 733) | class Cutout(object):
    method __init__ (line 734) | def __init__(self, length):
    method __call__ (line 737) | def __call__(self, img):
  class CutMix (line 755) | class CutMix(Dataset):
    method __init__ (line 756) | def __init__(self, dataset, num_class, num_mix=2, beta=1.0, prob=0.5):
    method __getitem__ (line 763) | def __getitem__(self, index):
    method __len__ (line 786) | def __len__(self):
  class MixUp (line 790) | class MixUp(Dataset):
    method __init__ (line 791) | def __init__(self, dataset, num_class, num_mix=2, beta=1.0, prob=0.5):
    method __getitem__ (line 798) | def __getitem__(self, index):
    method __len__ (line 819) | def __len__(self):

FILE: evaluator.py
  class Evaluator (line 15) | class Evaluator():
    method __init__ (line 16) | def __init__(self, data_loader, logger, config):
    method _reset_stats (line 30) | def _reset_stats(self):
    method eval (line 37) | def eval(self, epoch, model):
    method eval_batch (line 52) | def eval_batch(self, images, labels, model):
    method _pgd_whitebox (line 73) | def _pgd_whitebox(self, model, X, y, random_start=True, epsilon=0.031,...

FILE: fast_autoaugment/FastAutoAugment/archive.py
  function arsaug_policy (line 11) | def arsaug_policy():
  function autoaug2arsaug (line 59) | def autoaug2arsaug(f):
  function autoaug_paper_cifar10 (line 91) | def autoaug_paper_cifar10():
  function autoaug_policy (line 122) | def autoaug_policy():
  function float_parameter (line 248) | def float_parameter(level, maxval):
  function int_parameter (line 252) | def int_parameter(level, maxval):
  function no_duplicates (line 256) | def no_duplicates(f):
  function remove_deplicates (line 264) | def remove_deplicates(policies):
  function fa_reduced_cifar10 (line 281) | def fa_reduced_cifar10():
  function fa_resnet50_rimagenet (line 286) | def fa_resnet50_rimagenet():
  function fa_reduced_svhn (line 291) | def fa_reduced_svhn():
  function policy_decoder (line 296) | def policy_decoder(augment, num_policy, num_op):

FILE: fast_autoaugment/FastAutoAugment/aug_mixup.py
  function mixup (line 13) | def mixup(data, targets, alpha):
  class CrossEntropyMixUpLabelSmooth (line 26) | class CrossEntropyMixUpLabelSmooth(torch.nn.Module):
    method __init__ (line 27) | def __init__(self, num_classes, epsilon, reduction='mean'):
    method forward (line 31) | def forward(self, input, target1, target2, lam):  # pylint: disable=re...

FILE: fast_autoaugment/FastAutoAugment/augmentations.py
  function ShearX (line 13) | def ShearX(img, v):  # [-0.3, 0.3]
  function ShearY (line 20) | def ShearY(img, v):  # [-0.3, 0.3]
  function TranslateX (line 27) | def TranslateX(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]
  function TranslateY (line 35) | def TranslateY(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]
  function TranslateXAbs (line 43) | def TranslateXAbs(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]
  function TranslateYAbs (line 50) | def TranslateYAbs(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]
  function Rotate (line 57) | def Rotate(img, v):  # [-30, 30]
  function AutoContrast (line 64) | def AutoContrast(img, _):
  function Invert (line 68) | def Invert(img, _):
  function Equalize (line 72) | def Equalize(img, _):
  function Flip (line 76) | def Flip(img, _):  # not from the paper
  function Solarize (line 80) | def Solarize(img, v):  # [0, 256]
  function Posterize (line 85) | def Posterize(img, v):  # [4, 8]
  function Posterize2 (line 91) | def Posterize2(img, v):  # [0, 4]
  function Contrast (line 97) | def Contrast(img, v):  # [0.1,1.9]
  function Color (line 102) | def Color(img, v):  # [0.1,1.9]
  function Brightness (line 107) | def Brightness(img, v):  # [0.1,1.9]
  function Sharpness (line 112) | def Sharpness(img, v):  # [0.1,1.9]
  function Cutout (line 117) | def Cutout(img, v):  # [0, 60] => percentage: [0, 0.2]
  function CutoutAbs (line 126) | def CutoutAbs(img, v):  # [0, 60] => percentage: [0, 0.2]
  function SamplePairing (line 147) | def SamplePairing(imgs):  # [0, 0.4]
  function augment_list (line 156) | def augment_list(for_autoaug=True):  # 16 oeprations and their ranges
  function get_augment (line 188) | def get_augment(name):
  function apply_augment (line 192) | def apply_augment(img, name, level):
  class Lighting (line 197) | class Lighting(object):
    method __init__ (line 200) | def __init__(self, alphastd, eigval, eigvec):
    method __call__ (line 205) | def __call__(self, img):

FILE: fast_autoaugment/FastAutoAugment/common.py
  function get_logger (line 10) | def get_logger(name, level=logging.DEBUG):
  function add_filehandler (line 21) | def add_filehandler(logger, filepath, level=logging.DEBUG):
  class EMA (line 28) | class EMA:
    method __init__ (line 29) | def __init__(self, mu):
    method state_dict (line 33) | def state_dict(self):
    method __len__ (line 36) | def __len__(self):
    method __call__ (line 39) | def __call__(self, module, step=None):

FILE: fast_autoaugment/FastAutoAugment/data.py
  function get_dataloaders (line 37) | def get_dataloaders(dataset, batch, dataroot, split=0.15, split_idx=0, m...
  class CutoutDefault (line 228) | class CutoutDefault(object):
    method __init__ (line 232) | def __init__(self, length):
    method __call__ (line 235) | def __call__(self, img):
  class Augmentation (line 253) | class Augmentation(object):
    method __init__ (line 254) | def __init__(self, policies):
    method __call__ (line 257) | def __call__(self, img):
  class EfficientNetRandomCrop (line 267) | class EfficientNetRandomCrop:
    method __init__ (line 268) | def __init__(self, imgsize, min_covered=0.1, aspect_ratio_range=(3./4,...
    method __call__ (line 280) | def __call__(self, img):
  class EfficientNetCenterCrop (line 323) | class EfficientNetCenterCrop:
    method __init__ (line 324) | def __init__(self, imgsize):
    method __call__ (line 327) | def __call__(self, img):
  class SubsetSampler (line 348) | class SubsetSampler(Sampler):
    method __init__ (line 355) | def __init__(self, indices):
    method __iter__ (line 358) | def __iter__(self):
    method __len__ (line 361) | def __len__(self):

FILE: fast_autoaugment/FastAutoAugment/imagenet.py
  class ImageNet (line 28) | class ImageNet(torchvision.datasets.ImageFolder):
    method __init__ (line 52) | def __init__(self, root, split='train', download=False, **kwargs):
    method download (line 102) | def download(self):
    method meta_file (line 134) | def meta_file(self):
    method _load_meta_file (line 137) | def _load_meta_file(self):
    method _save_meta_file (line 143) | def _save_meta_file(self, wnid_to_class, val_wnids):
    method _verify_split (line 146) | def _verify_split(self, split):
    method valid_splits (line 154) | def valid_splits(self):
    method split_folder (line 158) | def split_folder(self):
    method extra_repr (line 161) | def extra_repr(self):
  function extract_tar (line 165) | def extract_tar(src, dest=None, gzip=None, delete=False):
  function download_and_extract_tar (line 181) | def download_and_extract_tar(url, download_root, extract_root=None, file...
  function parse_devkit (line 195) | def parse_devkit(root):
  function parse_meta (line 202) | def parse_meta(devkit_root, path='data', filename='meta.mat'):
  function parse_val_groundtruth (line 217) | def parse_val_groundtruth(devkit_root, path='data',
  function prepare_train_folder (line 224) | def prepare_train_folder(folder):
  function prepare_val_folder (line 229) | def prepare_val_folder(folder, wnids):
  function _splitexts (line 239) | def _splitexts(root):

FILE: fast_autoaugment/FastAutoAugment/lr_scheduler.py
  function adjust_learning_rate_resnet (line 6) | def adjust_learning_rate_resnet(optimizer):
  class MultiStepLR_HotFix (line 20) | class MultiStepLR_HotFix(MultiStepLR):
    method __init__ (line 21) | def __init__(self, optimizer, milestones, gamma=0.1, last_epoch=-1):

FILE: fast_autoaugment/FastAutoAugment/metrics.py
  function accuracy (line 10) | def accuracy(output, target, topk=(1,)):
  class CrossEntropyLabelSmooth (line 26) | class CrossEntropyLabelSmooth(torch.nn.Module):
    method __init__ (line 27) | def __init__(self, num_classes, epsilon, reduction='mean'):
    method forward (line 34) | def forward(self, input, target):  # pylint: disable=redefined-builtin
  class Accumulator (line 49) | class Accumulator:
    method __init__ (line 50) | def __init__(self):
    method add (line 53) | def add(self, key, value):
    method add_dict (line 56) | def add_dict(self, dict):
    method __getitem__ (line 60) | def __getitem__(self, item):
    method __setitem__ (line 63) | def __setitem__(self, key, value):
    method get_dict (line 66) | def get_dict(self):
    method items (line 69) | def items(self):
    method __str__ (line 72) | def __str__(self):
    method __truediv__ (line 75) | def __truediv__(self, other):
  class SummaryWriterDummy (line 88) | class SummaryWriterDummy:
    method __init__ (line 89) | def __init__(self, log_dir):
    method add_scalar (line 92) | def add_scalar(self, *args, **kwargs):

FILE: fast_autoaugment/FastAutoAugment/networks/__init__.py
  function get_model (line 19) | def get_model(conf, num_class=10, local_rank=-1):
  function num_class (line 93) | def num_class(dataset):

FILE: fast_autoaugment/FastAutoAugment/networks/efficientnet_pytorch/condconv.py
  function _ntuple (line 13) | def _ntuple(n):
  function _is_static_pad (line 27) | def _is_static_pad(kernel_size, stride=1, dilation=1, **_):
  function _get_padding (line 31) | def _get_padding(kernel_size, stride=1, dilation=1, **_):
  function _calc_same_pad (line 36) | def _calc_same_pad(i: int, k: int, s: int, d: int):
  function conv2d_same (line 40) | def conv2d_same(
  function get_padding_value (line 52) | def get_padding_value(padding, kernel_size, **kwargs):
  function get_condconv_initializer (line 75) | def get_condconv_initializer(initializer, num_experts, expert_shape):
  class CondConv2d (line 86) | class CondConv2d(nn.Module):
    method __init__ (line 94) | def __init__(self, in_channels, out_channels, kernel_size=3,
    method reset_parameters (line 128) | def reset_parameters(self):
    method forward (line 145) | def forward(self, x, routing_weights):
    method forward_legacy (line 175) | def forward_legacy(self, x, routing_weights):

FILE: fast_autoaugment/FastAutoAugment/networks/efficientnet_pytorch/model.py
  class RoutingFn (line 18) | class RoutingFn(nn.Linear):
  class MBConvBlock (line 22) | class MBConvBlock(nn.Module):
    method __init__ (line 34) | def __init__(self, block_args, global_params, norm_layer=None):
    method _is_condconv (line 79) | def _is_condconv(self):
    method forward (line 82) | def forward(self, inputs, drop_connect_rate=None):
    method set_swish (line 124) | def set_swish(self):
  class EfficientNet (line 129) | class EfficientNet(nn.Module):
    method __init__ (line 142) | def __init__(self, blocks_args=None, global_params=None, norm_layer=No...
    method set_swish (line 193) | def set_swish(self):
    method extract_features (line 199) | def extract_features(self, inputs):
    method forward (line 217) | def forward(self, inputs):
    method from_name (line 231) | def from_name(cls, model_name, override_params=None, norm_layer=None, ...
    method from_pretrained (line 237) | def from_pretrained(cls, model_name, num_classes=1000):
    method get_image_size (line 244) | def get_image_size(cls, model_name):
    method _check_model_name_is_valid (line 250) | def _check_model_name_is_valid(cls, model_name, also_need_pretrained_w...

FILE: fast_autoaugment/FastAutoAugment/networks/efficientnet_pytorch/utils.py
  class SwishImplementation (line 38) | class SwishImplementation(torch.autograd.Function):
    method forward (line 40) | def forward(ctx, i):
    method backward (line 46) | def backward(ctx, grad_output):
  class MemoryEfficientSwish (line 52) | class MemoryEfficientSwish(nn.Module):
    method forward (line 53) | def forward(self, x):
  function round_filters (line 57) | def round_filters(filters, global_params):
  function round_repeats (line 72) | def round_repeats(repeats, global_params):
  function drop_connect (line 80) | def drop_connect(inputs, drop_p, training):
  function get_same_padding_conv2d (line 101) | def get_same_padding_conv2d(image_size=None, condconv_num_expert=1):
  class Conv2dDynamicSamePadding (line 112) | class Conv2dDynamicSamePadding(nn.Conv2d):
    method __init__ (line 115) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, d...
    method forward (line 119) | def forward(self, x):
  class Conv2dStaticSamePadding (line 131) | class Conv2dStaticSamePadding(nn.Conv2d):
    method __init__ (line 134) | def __init__(self, in_channels, out_channels, kernel_size, image_size=...
    method forward (line 151) | def forward(self, x):
  class Identity (line 157) | class Identity(nn.Module):
    method __init__ (line 158) | def __init__(self, ):
    method forward (line 161) | def forward(self, input):
  function efficientnet_params (line 170) | def efficientnet_params(model_name):
  class BlockDecoder (line 186) | class BlockDecoder(object):
    method _decode_block_string (line 190) | def _decode_block_string(block_string):
    method _encode_block_string (line 219) | def _encode_block_string(block):
    method decode (line 236) | def decode(string_list):
    method encode (line 250) | def encode(blocks_args):
  function efficientnet (line 263) | def efficientnet(width_coefficient=None, depth_coefficient=None, dropout...
  function get_model_params (line 298) | def get_model_params(model_name, override_params, condconv_num_expert=1):
  function load_pretrained_weights (line 325) | def load_pretrained_weights(model, model_name, load_fc=True):

FILE: fast_autoaugment/FastAutoAugment/networks/pyramidnet.py
  function conv3x3 (line 8) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 15) | class BasicBlock(nn.Module):
    method __init__ (line 18) | def __init__(self, inplanes, planes, stride=1, downsample=None, p_shak...
    method forward (line 30) | 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, p_shak...
    method forward (line 81) | def forward(self, x):
  class PyramidNet (line 120) | class PyramidNet(nn.Module):
    method __init__ (line 122) | def __init__(self, dataset, depth, alpha, num_classes, bottleneck=True):
    method pyramidal_make_layer (line 199) | def pyramidal_make_layer(self, block, block_depth, stride=1):
    method forward (line 216) | def forward(self, x):

FILE: fast_autoaugment/FastAutoAugment/networks/resnet.py
  function conv3x3 (line 7) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 13) | class BasicBlock(nn.Module):
    method __init__ (line 16) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 27) | def forward(self, x):
  class Bottleneck (line 46) | class Bottleneck(nn.Module):
    method __init__ (line 49) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 63) | def forward(self, x):
  class ResNet (line 84) | class ResNet(nn.Module):
    method __init__ (line 85) | def __init__(self, dataset, depth, num_classes, bottleneck=False):
    method _make_layer (line 134) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 151) | def forward(self, x):

FILE: fast_autoaugment/FastAutoAugment/networks/shakedrop.py
  class ShakeDropFunction (line 9) | class ShakeDropFunction(torch.autograd.Function):
    method forward (line 12) | def forward(ctx, x, training=True, p_drop=0.5, alpha_range=[-1, 1]):
    method backward (line 26) | def backward(ctx, grad_output):
  class ShakeDrop (line 37) | class ShakeDrop(nn.Module):
    method __init__ (line 39) | def __init__(self, p_drop=0.5, alpha_range=[-1, 1]):
    method forward (line 44) | def forward(self, x):

FILE: fast_autoaugment/FastAutoAugment/networks/shakeshake/shake_resnet.py
  class ShakeBlock (line 12) | class ShakeBlock(nn.Module):
    method __init__ (line 14) | def __init__(self, in_ch, out_ch, stride=1):
    method forward (line 22) | def forward(self, x):
    method _make_branch (line 29) | def _make_branch(self, in_ch, out_ch, stride=1):
  class ShakeResNet (line 39) | class ShakeResNet(nn.Module):
    method __init__ (line 41) | def __init__(self, depth, w_base, label):
    method forward (line 65) | def forward(self, x):
    method _make_layer (line 76) | def _make_layer(self, n_units, in_ch, out_ch, stride=1):

FILE: fast_autoaugment/FastAutoAugment/networks/shakeshake/shake_resnext.py
  class ShakeBottleNeck (line 12) | class ShakeBottleNeck(nn.Module):
    method __init__ (line 14) | def __init__(self, in_ch, mid_ch, out_ch, cardinary, stride=1):
    method forward (line 22) | def forward(self, x):
    method _make_branch (line 29) | def _make_branch(self, in_ch, mid_ch, out_ch, cardinary, stride=1):
  class ShakeResNeXt (line 41) | class ShakeResNeXt(nn.Module):
    method __init__ (line 43) | def __init__(self, depth, w_base, cardinary, label):
    method forward (line 67) | def forward(self, x):
    method _make_layer (line 78) | def _make_layer(self, n_units, n_ch, w_base, cardinary, stride=1):

FILE: fast_autoaugment/FastAutoAugment/networks/shakeshake/shakeshake.py
  class ShakeShake (line 9) | class ShakeShake(torch.autograd.Function):
    method forward (line 12) | def forward(ctx, x1, x2, training=True):
    method backward (line 21) | def backward(ctx, grad_output):
  class Shortcut (line 29) | class Shortcut(nn.Module):
    method __init__ (line 31) | def __init__(self, in_ch, out_ch, stride):
    method forward (line 38) | def forward(self, x):

FILE: fast_autoaugment/FastAutoAugment/networks/wideresnet.py
  function conv3x3 (line 7) | def conv3x3(in_planes, out_planes, stride=1):
  function conv_init (line 11) | def conv_init(m):
  class WideBasic (line 21) | class WideBasic(nn.Module):
    method __init__ (line 22) | def __init__(self, in_planes, planes, dropout_rate, stride=1):
    method forward (line 36) | def forward(self, x):
  class WideResNet (line 44) | class WideResNet(nn.Module):
    method __init__ (line 45) | def __init__(self, depth, widen_factor, dropout_rate, num_classes):
    method _wide_layer (line 64) | def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride):
    method forward (line 74) | def forward(self, x):

FILE: fast_autoaugment/FastAutoAugment/safe_shell_exec.py
  function terminate_executor_shell_and_children (line 29) | def terminate_executor_shell_and_children(pid):
  function forward_stream (line 63) | def forward_stream(src_fd, dst_stream, prefix, index):
  function execute (line 90) | def execute(command, env=None, stdout=None, stderr=None, index=None, eve...

FILE: fast_autoaugment/FastAutoAugment/search.py
  function step_w_log (line 32) | def step_w_log(self):
  function _get_path (line 56) | def _get_path(dataset, model, tag):
  function train_model (line 61) | def train_model(config, dataroot, augment, cv_ratio_test, cv_fold, save_...
  function eval_tta (line 70) | def eval_tta(config, augment, reporter):

FILE: fast_autoaugment/FastAutoAugment/tf_port/rmsprop.py
  class RMSpropTF (line 5) | class RMSpropTF(Optimizer):
    method __init__ (line 30) | def __init__(self, params, lr=1e-2, alpha=0.99, eps=1e-8, momentum=0, ...
    method __setstate__ (line 45) | def __setstate__(self, state):
    method load_state_dict (line 50) | def load_state_dict(self, state_dict):
    method step (line 54) | def step(self, closure=None):

FILE: fast_autoaugment/FastAutoAugment/tf_port/tpu_bn.py
  class TpuBatchNormalization (line 8) | class TpuBatchNormalization(nn.Module):
    method __init__ (line 10) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
    method _reduce_avg (line 24) | def _reduce_avg(self, t):
    method forward (line 28) | def forward(self, input):

FILE: fast_autoaugment/FastAutoAugment/train.py
  function run_epoch (line 35) | def run_epoch(model, loader, loss_fn, optimizer, desc_default='', epoch=...
  function train_and_eval (line 110) | def train_and_eval(tag, dataroot, test_ratio=0.0, cv_fold=0, reporter=No...

FILE: fast_autoaugment/FastAutoAugment/train_dist.py
  function _exec_command (line 15) | def _exec_command(command):
  function execute_function_multithreaded (line 29) | def execute_function_multithreaded(fn,

FILE: fast_autoaugment/archive.py
  function arsaug_policy (line 11) | def arsaug_policy():
  function autoaug2arsaug (line 59) | def autoaug2arsaug(f):
  function autoaug_paper_cifar10 (line 91) | def autoaug_paper_cifar10():
  function autoaug_policy (line 122) | def autoaug_policy():
  function float_parameter (line 248) | def float_parameter(level, maxval):
  function int_parameter (line 252) | def int_parameter(level, maxval):
  function no_duplicates (line 256) | def no_duplicates(f):
  function remove_deplicates (line 264) | def remove_deplicates(policies):
  function fa_reduced_cifar10 (line 281) | def fa_reduced_cifar10():
  function fa_resnet50_rimagenet (line 286) | def fa_resnet50_rimagenet():
  function fa_reduced_svhn (line 291) | def fa_reduced_svhn():
  function policy_decoder (line 296) | def policy_decoder(augment, num_policy, num_op):

FILE: madrys.py
  class MadrysLoss (line 12) | class MadrysLoss(nn.Module):
    method __init__ (line 13) | def __init__(self, step_size=0.007, epsilon=0.031, perturb_steps=10, d...
    method forward (line 21) | def forward(self, model, x_natural, y, optimizer):

FILE: main.py
  function train (line 77) | def train(starting_epoch, model, optimizer, scheduler, criterion, traine...
  function main (line 142) | def main():

FILE: models/DenseNet.py
  class Bottleneck (line 12) | class Bottleneck(nn.Module):
    method __init__ (line 13) | def __init__(self, in_planes, growth_rate):
    method forward (line 20) | def forward(self, x):
  class Transition (line 27) | class Transition(nn.Module):
    method __init__ (line 28) | def __init__(self, in_planes, out_planes):
    method forward (line 33) | def forward(self, x):
  class DenseNet (line 39) | class DenseNet(nn.Module):
    method __init__ (line 40) | def __init__(self, block, nblocks, growth_rate=12, reduction=0.5, num_...
    method _make_dense_layers (line 71) | def _make_dense_layers(self, block, in_planes, nblock):
    method forward (line 78) | def forward(self, x):
  function DenseNet121 (line 90) | def DenseNet121(num_classes=10):
  function DenseNet169 (line 94) | def DenseNet169(num_classes=10):
  function DenseNet201 (line 98) | def DenseNet201(num_classes=10):
  function DenseNet161 (line 102) | def DenseNet161(num_classes=10):
  function densenet_cifar (line 106) | def densenet_cifar():

FILE: models/ResNet.py
  class BasicBlock (line 7) | class BasicBlock(nn.Module):
    method __init__ (line 10) | def __init__(self, in_planes, planes, stride=1):
    method forward (line 24) | def forward(self, x):
  class Bottleneck (line 32) | class Bottleneck(nn.Module):
    method __init__ (line 35) | def __init__(self, in_planes, planes, stride=1):
    method forward (line 51) | def forward(self, x):
  class ResNet (line 60) | class ResNet(nn.Module):
    method __init__ (line 61) | def __init__(self, block, num_blocks, num_classes=10):
    method _make_layer (line 72) | def _make_layer(self, block, planes, num_blocks, stride):
    method forward (line 80) | def forward(self, x):
  function ResNet18 (line 92) | def ResNet18(num_classes=10):
  function ResNet34 (line 96) | def ResNet34(num_classes=10):
  function ResNet50 (line 100) | def ResNet50(num_classes=10):
  function ResNet101 (line 104) | def ResNet101(num_classes=10):
  function ResNet152 (line 108) | def ResNet152(num_classes=10):
  function test (line 112) | def test():
  class BasicConv2d (line 118) | class BasicConv2d(nn.Module):
    method __init__ (line 119) | def __init__(self, in_planes, out_planes, kernel_size, stride, padding...
    method forward (line 134) | def forward(self, x):
  class Block35 (line 141) | class Block35(nn.Module):
    method __init__ (line 143) | def __init__(self, scale=1.0):
    method forward (line 164) | def forward(self, x):
  class Block17 (line 175) | class Block17(nn.Module):
    method __init__ (line 177) | def __init__(self, scale=1.0):
    method forward (line 193) | 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 Mixed_6a (line 234) | class Mixed_6a(nn.Module):
    method __init__ (line 236) | def __init__(self):
    method forward (line 249) | def forward(self, x):
  class Mixed_7a (line 257) | class Mixed_7a(nn.Module):
    method __init__ (line 259) | def __init__(self):
    method forward (line 280) | def forward(self, x):
  class InceptionResnetV1 (line 289) | class InceptionResnetV1(nn.Module):
    method __init__ (line 290) | def __init__(self, num_classes=10575, face_features=512, dropout_prob=...
    method forward (line 337) | def forward(self, x):

FILE: models/ToyModel.py
  class ConvBrunch (line 5) | class ConvBrunch(nn.Module):
    method __init__ (line 6) | def __init__(self, in_planes, out_planes, kernel_size=3):
    method forward (line 14) | def forward(self, x):
  class ToyModel (line 18) | class ToyModel(nn.Module):
    method __init__ (line 19) | def __init__(self, num_classes=10):
    method forward (line 31) | def forward(self, x):

FILE: models/__init__.py
  class FocalLoss (line 39) | class FocalLoss(nn.Module):
    method __init__ (line 40) | def __init__(self, gamma=0, eps=1e-7):
    method forward (line 46) | def forward(self, input, target):
  function cross_entropy (line 53) | def cross_entropy(input, target, size_average=True):
  class CutMixCrossEntropyLoss (line 75) | class CutMixCrossEntropyLoss(torch.nn.Module):
    method __init__ (line 76) | def __init__(self, size_average=True):
    method forward (line 80) | def forward(self, input, target):

FILE: models/download.py
  class tqdm (line 16) | class tqdm(object):  # type: ignore
    method __init__ (line 18) | def __init__(self, total=None, disable=False,
    method update (line 25) | def update(self, n):
    method __enter__ (line 36) | def __enter__(self):
    method __exit__ (line 39) | def __exit__(self, exc_type, exc_val, exc_tb):
  function download_url_to_file (line 46) | def download_url_to_file(url, dst, hash_prefix=None, progress=True):

FILE: models/inception_resnet_v1.py
  class BasicConv2d (line 11) | class BasicConv2d(nn.Module):
    method __init__ (line 12) | def __init__(self, in_planes, out_planes, kernel_size, stride, padding...
    method forward (line 19) | def forward(self, x):
  class Block35 (line 26) | class Block35(nn.Module):
    method __init__ (line 28) | def __init__(self, scale=1.0):
    method forward (line 49) | def forward(self, x):
  class Block17 (line 60) | class Block17(nn.Module):
    method __init__ (line 62) | def __init__(self, scale=1.0):
    method forward (line 78) | def forward(self, x):
  class Block8 (line 88) | class Block8(nn.Module):
    method __init__ (line 90) | def __init__(self, scale=1.0, noReLU=False):
    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 Mixed_7a (line 142) | class Mixed_7a(nn.Module):
    method __init__ (line 144) | def __init__(self):
    method forward (line 165) | def forward(self, x):
  class InceptionResnetV1 (line 174) | class InceptionResnetV1(nn.Module):
    method __init__ (line 190) | def __init__(self, pretrained=None, classify=False, num_classes=None, ...
    method forward (line 252) | def forward(self, x):
  function load_weights (line 283) | def load_weights(mdl, name):
  function get_torch_home (line 309) | def get_torch_home():

FILE: perturbation.py
  function train (line 88) | def train(starting_epoch, model, optimizer, scheduler, criterion, traine...
  function universal_perturbation_eval (line 122) | def universal_perturbation_eval(noise_generator, random_noise, data_load...
  function universal_perturbation (line 143) | def universal_perturbation(noise_generator, trainer, evaluator, model, c...
  function samplewise_perturbation_eval (line 236) | def samplewise_perturbation_eval(random_noise, data_loader, model, eval_...
  function sample_wise_perturbation (line 265) | def sample_wise_perturbation(noise_generator, trainer, evaluator, model,...
  function main (line 395) | def main():

FILE: toolbox.py
  class PerturbationTool (line 11) | class PerturbationTool():
    method __init__ (line 12) | def __init__(self, seed=0, epsilon=0.03137254901, num_steps=20, step_s...
    method random_noise (line 19) | def random_noise(self, noise_shape=[10, 3, 32, 32]):
    method min_min_attack (line 23) | def min_min_attack(self, images, labels, model, optimizer, criterion, ...
    method min_max_attack (line 51) | def min_max_attack(self, images, labels, model, optimizer, criterion, ...
    method _patch_noise_extend_to_img (line 77) | def _patch_noise_extend_to_img(self, noise, image_size=[3, 32, 32], pa...

FILE: trainer.py
  class Trainer (line 12) | class Trainer():
    method __init__ (line 13) | def __init__(self, criterion, data_loader, logger, config, global_step=0,
    method _reset_stats (line 27) | def _reset_stats(self):
    method train (line 32) | def train(self, epoch, model, criterion, optimizer, random_noise=None):
    method train_batch (line 55) | def train_batch(self, images, labels, model, optimizer):

FILE: util.py
  function _patch_noise_extend_to_img (line 16) | def _patch_noise_extend_to_img(noise, image_size=[3, 32, 32], patch_loca...
  function setup_logger (line 38) | def setup_logger(name, log_file, level=logging.INFO):
  function log_display (line 52) | def log_display(epoch, global_step, time_elapse, **kwargs):
  function accuracy (line 64) | def accuracy(output, target, topk=(1,)):
  function save_model (line 79) | def save_model(filename, epoch, model, optimizer, scheduler, save_best=F...
  function load_model (line 96) | def load_model(filename, model, optimizer, scheduler, **kwargs):
  function count_parameters_in_MB (line 108) | def count_parameters_in_MB(model):
  function build_dirs (line 112) | def build_dirs(path):
  class AverageMeter (line 118) | class AverageMeter(object):
    method __init__ (line 121) | def __init__(self):
    method reset (line 124) | def reset(self):
    method update (line 131) | def update(self, val, n=1):
  function onehot (line 139) | def onehot(size, target):
  function rand_bbox (line 145) | def rand_bbox(size, lam):