SYMBOL INDEX (626 symbols across 143 files)

FILE: args.py
  function str2bool (line 9) | def str2bool(v):
  class Args (line 19) | class Args:
    method __init__ (line 23) | def __init__(self):
    method update (line 31) | def update(self):
    method _get_parser (line 38) | def _get_parser():
    method get_args (line 57) | def get_args(self):
    method get_name (line 60) | def get_name(self):
    method get_instance (line 64) | def get_instance() -> (argparse.Namespace, Any):

FILE: augmentation/post.py
  class ClipSTD (line 5) | class ClipSTD(nn.Module):
    method forward (line 7) | def forward(self, x: torch.tensor, inflate: float = 1., per_sample: bo...
  class Clip (line 14) | class Clip(nn.Module):
    method forward (line 16) | def forward(self, x: torch.tensor) -> torch.tensor:
  class LInfClip (line 20) | class LInfClip(nn.Module):
    method __init__ (line 21) | def __init__(self, original: torch.tensor, eps: float = 16 / 255):
    method forward (line 27) | def forward(self, x: torch.tensor) -> torch.tensor:
  class L2Clip (line 31) | class L2Clip(nn.Module):
    method __init__ (line 32) | def __init__(self, original: torch.tensor, eps: float = 16 / 255):
    method forward (line 38) | def forward(self, x: torch.tensor) -> torch.tensor:

FILE: augmentation/pre.py
  class Gray4D (line 6) | class Gray4D(nn.Module):
    method __init__ (line 7) | def __init__(self, n_channels: int = 3):
    method forward (line 11) | def forward(self, x: torch.tensor) -> torch.tensor:
  class Layered (line 16) | class Layered(nn.Module):
    method __init__ (line 17) | def __init__(self, x: torch.tensor):
    method forward (line 21) | def forward(self, x: torch.tensor) -> torch.tensor:
  class Jitter (line 25) | class Jitter(nn.Module):
    method __init__ (line 26) | def __init__(self, lim: int = 32):
    method forward (line 30) | def forward(self, x: torch.tensor) -> torch.tensor:
  class ColorJitter (line 36) | class ColorJitter(nn.Module):
    method __init__ (line 37) | def __init__(self, batch_size: int, shuffle_every: bool = False, mean:...
    method shuffle (line 44) | def shuffle(self):
    method forward (line 48) | def forward(self, img: torch.tensor) -> torch.tensor:
  class GaussianNoise (line 54) | class GaussianNoise(nn.Module):
    method __init__ (line 55) | def __init__(self, batch_size: int, shuffle_every: bool = False, std: ...
    method shuffle (line 63) | def shuffle(self):
    method forward (line 67) | def forward(self, img: torch.tensor) -> torch.tensor:
  class ColorJitterR (line 73) | class ColorJitterR(ColorJitter):
    method forward (line 74) | def forward(self, img: torch.tensor) -> torch.tensor:
  class Centering (line 80) | class Centering(nn.Module):
    method __init__ (line 81) | def __init__(self, size: int, std: float):
    method forward (line 86) | def forward(self, img: torch.tensor) -> torch.tensor:
  class Zoom (line 94) | class Zoom(nn.Module):
    method __init__ (line 95) | def __init__(self, out_size: int = 384):
    method forward (line 99) | def forward(self, img: torch.tensor) -> torch.tensor:
  class Tile (line 103) | class Tile(nn.Module):
    method __init__ (line 104) | def __init__(self, rep: int = 384 // 16):
    method forward (line 108) | def forward(self, x: torch.tensor) -> torch.tensor:
  class RepeatBatch (line 118) | class RepeatBatch(nn.Module):
    method __init__ (line 119) | def __init__(self, repeat: int = 32):
    method forward (line 123) | def forward(self, img: torch.tensor):
  class MaskBatch (line 127) | class MaskBatch(nn.Module):
    method forward (line 128) | def forward(self, x: torch.tensor) -> torch.tensor:
    method __init__ (line 131) | def __init__(self, count: int = -1):
  class Flip (line 136) | class Flip(nn.Module):
    method __init__ (line 137) | def __init__(self, p: float = 0.5):
    method forward (line 141) | def forward(self, x: torch.tensor) -> torch.tensor:

FILE: cam/activation_helper.py
  class ActivationHelper (line 7) | class ActivationHelper:
    method __init__ (line 8) | def __init__(self, how_many_patches: int):
    method __call__ (line 12) | def __call__(self, score: torch.tensor) -> (int, int):
    method most_salient (line 23) | def most_salient(self, x: torch.tensor, model, patch_size: int) -> tor...
    method least_salient_pos (line 30) | def least_salient_pos(self, x: torch.tensor, model, patch_size: int) -...

FILE: cam/base.py
  class BatchFeed (line 10) | class BatchFeed:
    method __init__ (line 11) | def __init__(self, model: ViTPatchFeatWrapper):
    method _batch_gen (line 16) | def _batch_gen(x: torch.tensor, batch_size) -> torch.tensor:
    method __call__ (line 21) | def __call__(self, x: torch.tensor, batch_size: int = 32, reduction: s...
  class Stitcher (line 27) | class Stitcher:
    method __init__ (line 30) | def __init__(self, model: ViTPatchFeatWrapper, patch_size: int, image_...
    method max_score (line 36) | def max_score(self, ims: Union[torch.tensor, list], score: float, orig...
    method __call__ (line 45) | def __call__(self, original: torch.tensor, score: float, x: torch.tens...
    method select_images (line 51) | def select_images(self, original, patch) -> list:
    method select_patch (line 62) | def select_patch(self, x) -> torch.tensor:
  class SniperStitcher (line 68) | class SniperStitcher(Stitcher):
    method select_images (line 70) | def select_images(self, original, patch) -> list:

FILE: cam/model.py
  function project_back (line 7) | def project_back(act: torch.tensor) -> torch.tensor:
  class ViTPatchFeatWrapper (line 25) | class ViTPatchFeatWrapper:
    method __init__ (line 26) | def __init__(self, hook: ViTAttHookHolder, key: str, feature: int):
    method __call__ (line 30) | def __call__(self, x: torch.tensor):

FILE: datasets/base.py
  class EasyDataset (line 6) | class EasyDataset:
    method __init__ (line 7) | def __init__(self, mean, std):
    method eval (line 12) | def eval(self) -> VisionDataset:
    method train (line 15) | def train(self) -> VisionDataset:

FILE: datasets/cifar.py
  class Cifar (line 8) | class Cifar(EasyDataset):
    method __init__ (line 9) | def __init__(self, mean: List, std: List, data_class: Any):
    method eval (line 18) | def eval(self) -> VisionDataset:
    method train (line 21) | def train(self) -> VisionDataset:

FILE: datasets/imagenet.py
  class ImageNet (line 8) | class ImageNet(EasyDataset):
    method __init__ (line 11) | def __init__(self):
    method eval (line 19) | def eval(self) -> VisionDataset:
    method train (line 22) | def train(self) -> VisionDataset:

FILE: datasets/imagenet_boxes.py
  class BackgroundForegroundImageNet (line 7) | class BackgroundForegroundImageNet(ImageFolder):
    method __init__ (line 11) | def __init__(self, root: str = './data/imagenet/train/', download=True...
    method __len__ (line 31) | def __len__(self):
    method __getitem__ (line 34) | def __getitem__(self, item):

FILE: datasets/inits.py
  class GreatInit (line 8) | class GreatInit(Dataset):
    method __init__ (line 9) | def __init__(self, root: str = 'data/inits/images', subset: str = 'inv...
    method __len__ (line 15) | def __len__(self):
    method __getitem__ (line 18) | def __getitem__(self, item: int) -> torch.tensor:

FILE: datasets/normalizer.py
  class Normalizer (line 5) | class Normalizer(nn.Module):
    method __init__ (line 6) | def __init__(self, mean, std):
    method forward (line 11) | def forward(self, t: torch.tensor) -> torch.tensor:
    method get_normal (line 14) | def get_normal(self, t: torch.Tensor) -> torch.Tensor:
    method get_unit (line 17) | def get_unit(self, t: torch.Tensor) -> torch.Tensor:

FILE: datasets/vit_imagenet.py
  class ImageNet (line 8) | class ImageNet(EasyDataset):
    method __init__ (line 11) | def __init__(self, img_size: int = 384):
    method eval (line 20) | def eval(self) -> VisionDataset:
    method train (line 23) | def train(self) -> VisionDataset:

FILE: experiments/it0/resnet18example.py
  function main (line 16) | def main():

FILE: experiments/it1/bn.py
  function main (line 16) | def main():

FILE: experiments/it10/param_tune.py
  function main (line 14) | def main():

FILE: experiments/it11/clip_best_eval.py
  function main (line 11) | def main():

FILE: experiments/it11/clip_mask.py
  function main (line 15) | def main():

FILE: experiments/it11/costum_imagenet.py
  class BackgroundForegroundImageNet (line 7) | class BackgroundForegroundImageNet(ImageFolder):
    method __init__ (line 11) | def __init__(self, root: str = '.', download=True, boxes: str = None, ...
    method __len__ (line 30) | def __len__(self):
    method __getitem__ (line 33) | def __getitem__(self, item):

FILE: experiments/it11/get_indices.py
  function main (line 5) | def main():

FILE: experiments/it11/read_xml.py
  function translate_obj (line 8) | def translate_obj(cls: str, obj) -> list:
  function objects (line 18) | def objects(expected_class: str, path: str) -> list:
  function get_path (line 25) | def get_path(xml_path: str) -> str:
  function translate_folder (line 29) | def translate_folder(xml_folder: str, root: str) -> {}:
  function translate_dataset (line 34) | def translate_dataset(root: str, classes: list):
  function main (line 39) | def main():

FILE: experiments/it11/test_dataset.py
  function main (line 13) | def main():

FILE: experiments/it11/test_foreground.py
  function test1 (line 8) | def test1():
  function test2 (line 15) | def test2():

FILE: experiments/it11/vis_clip.py
  function main (line 14) | def main():

FILE: experiments/it12/lr_rate.py
  function main (line 14) | def main():

FILE: experiments/it12/vis38.py
  function main (line 14) | def main():

FILE: experiments/it13/fore_back.py
  function main (line 22) | def main():

FILE: experiments/it13/plot_rotate.py
  function main (line 19) | def main():

FILE: experiments/it13/plot_shift.py
  function main (line 17) | def main():

FILE: experiments/it13/rotate.py
  class Rotation (line 14) | class Rotation(nn.Module):
    method __init__ (line 15) | def __init__(self, count: int = 360 // 30):
    method forward (line 20) | def forward(self, x: torch.tensor) -> torch.tensor:
  function main (line 29) | def main():

FILE: experiments/it13/runner/adversarial.py
  class Modifier (line 6) | class Modifier:
    method __call__ (line 7) | def __call__(self, *inputs) -> torch.tensor:
    method attack (line 10) | def attack(self, *inputs) -> torch.tensor:

FILE: experiments/it13/runner/correct.py
  class CorrectClassLoss (line 12) | class CorrectClassLoss(nn.Module):
    method forward (line 13) | def forward(self, output: torch.tensor, y: torch.tensor):
  class CorrectTrainer (line 17) | class CorrectTrainer:
    method __init__ (line 18) | def __init__(self, model: nn.Module, loader: DataLoader = None, loss: ...
    method _method_for_one_epoch (line 30) | def _method_for_one_epoch(self) -> float:
    method train (line 69) | def train(self) -> float:

FILE: experiments/it13/runner/free.py
  class FreeTrainer (line 11) | class FreeTrainer:
    method __init__ (line 12) | def __init__(self, model: nn.Module, loader: DataLoader = None, loss: ...
    method _method_for_one_epoch (line 24) | def _method_for_one_epoch(self) -> float:
    method train (line 53) | def train(self) -> float:

FILE: experiments/it13/runner/general_runner.py
  class BaseRunner (line 12) | class BaseRunner:
    method __init__ (line 13) | def __init__(self, model: nn.Module, loader: DataLoader = None, loss: ...
    method _method_for_one_epoch (line 22) | def _method_for_one_epoch(self) -> (float, float):
  class Trainer (line 48) | class Trainer(BaseRunner):
    method __init__ (line 49) | def __init__(self, model: nn.Module, loader: DataLoader = None, loss: ...
    method train (line 53) | def train(self) -> (float, float):
  class Evaluator (line 59) | class Evaluator(BaseRunner):
    method __init__ (line 60) | def __init__(self, model: torch.nn.Module, loader: DataLoader = None, ...
    method eval (line 65) | def eval(self) -> (float, float):

FILE: experiments/it13/runner/status.py
  class ItemIterator (line 5) | class ItemIterator:
    method iterator_item (line 7) | def iterator_item(self):
    method __iter__ (line 10) | def __iter__(self):
    method __getitem__ (line 13) | def __getitem__(self, item):
    method __len__ (line 16) | def __len__(self):
  class Status (line 20) | class Status(ItemIterator):
    method iterator_item (line 22) | def iterator_item(self):
    method __init__ (line 25) | def __init__(self, loader: DataLoader, num_elements: int, template: st...
    method print (line 29) | def print(self, *values):
  class Meter (line 34) | class Meter:
    method __init__ (line 35) | def __init__(self):
    method reset (line 39) | def reset(self):
    method __add__ (line 42) | def __add__(self, other: (float, float)):
    method value (line 48) | def value(self) -> float:

FILE: experiments/it13/shift.py
  class Shift (line 14) | class Shift(nn.Module):
    method __init__ (line 15) | def __init__(self, count_per_dim: int = 4, shift_per_dim: int = 4):
    method forward (line 19) | def forward(self, x: torch.tensor) -> torch.tensor:
  function main (line 30) | def main():

FILE: experiments/it14/every_and_cls.py
  class ViTInput (line 21) | class ViTInput(nn.Module):
    method __init__ (line 22) | def __init__(self, vit: Union[ViT, AugmentedModel]):
    method forward (line 29) | def forward(self, x: torch.tensor) -> torch.tensor:
  class ViTPre (line 37) | class ViTPre(nn.Sequential):
    method __init__ (line 38) | def __init__(self, vit: AugmentedModel):
  class ViTTransformer (line 42) | class ViTTransformer(nn.Module):
    method __init__ (line 43) | def __init__(self, vit: Union[ViT, AugmentedModel], sl: slice = None):
    method forward (line 49) | def forward(self, x: torch.tensor, mask: torch.tensor = None) -> torch...
  class ViTHead (line 55) | class ViTHead(nn.Module):
    method __init__ (line 56) | def __init__(self, vit: Union[ViT, AugmentedModel], sl: slice = None):
    method forward (line 63) | def forward(self, x: torch.tensor) -> torch.tensor:
  function main (line 69) | def main():

FILE: experiments/it14/every_except_cls_accuracy.py
  class ViTInput (line 21) | class ViTInput(nn.Module):
    method __init__ (line 22) | def __init__(self, vit: Union[ViT, AugmentedModel]):
    method forward (line 29) | def forward(self, x: torch.tensor) -> torch.tensor:
  class ViTPre (line 37) | class ViTPre(nn.Sequential):
    method __init__ (line 38) | def __init__(self, vit: AugmentedModel):
  class ViTTransformer (line 42) | class ViTTransformer(nn.Module):
    method __init__ (line 43) | def __init__(self, vit: Union[ViT, AugmentedModel], sl: slice = None):
    method forward (line 49) | def forward(self, x: torch.tensor, mask: torch.tensor = None) -> torch...
  class ViTHead (line 55) | class ViTHead(nn.Module):
    method __init__ (line 56) | def __init__(self, vit: Union[ViT, AugmentedModel], sl: slice = None):
    method forward (line 63) | def forward(self, x: torch.tensor) -> torch.tensor:
  function main (line 69) | def main():

FILE: experiments/it14/layer_accuracy.py
  class ViTInput (line 21) | class ViTInput(nn.Module):
    method __init__ (line 22) | def __init__(self, vit: Union[ViT, AugmentedModel]):
    method forward (line 29) | def forward(self, x: torch.tensor) -> torch.tensor:
  class ViTPre (line 37) | class ViTPre(nn.Sequential):
    method __init__ (line 38) | def __init__(self, vit: AugmentedModel):
  class ViTTransformer (line 42) | class ViTTransformer(nn.Module):
    method __init__ (line 43) | def __init__(self, vit: Union[ViT, AugmentedModel], sl: slice = None):
    method forward (line 49) | def forward(self, x: torch.tensor, mask: torch.tensor = None) -> torch...
  class ViTHead (line 55) | class ViTHead(nn.Module):
    method __init__ (line 56) | def __init__(self, vit: Union[ViT, AugmentedModel], sl: slice = None):
    method forward (line 63) | def forward(self, x: torch.tensor) -> torch.tensor:
  function main (line 69) | def main():

FILE: experiments/it14/lr_clip.py
  function main (line 14) | def main():

FILE: experiments/it14/lr_rate.py
  function main (line 14) | def main():

FILE: experiments/it14/random_patch_accuracy.py
  class ViTInput (line 21) | class ViTInput(nn.Module):
    method __init__ (line 22) | def __init__(self, vit: Union[ViT, AugmentedModel]):
    method forward (line 29) | def forward(self, x: torch.tensor) -> torch.tensor:
  class ViTPre (line 37) | class ViTPre(nn.Sequential):
    method __init__ (line 38) | def __init__(self, vit: AugmentedModel):
  class ViTTransformer (line 42) | class ViTTransformer(nn.Module):
    method __init__ (line 43) | def __init__(self, vit: Union[ViT, AugmentedModel], sl: slice = None):
    method forward (line 49) | def forward(self, x: torch.tensor, mask: torch.tensor = None) -> torch...
  class ViTHead (line 55) | class ViTHead(nn.Module):
    method __init__ (line 56) | def __init__(self, vit: Union[ViT, AugmentedModel], sl: slice = None):
    method forward (line 63) | def forward(self, x: torch.tensor) -> torch.tensor:
  function main (line 69) | def main():

FILE: experiments/it15/vis35.py
  function main (line 14) | def main():

FILE: experiments/it15/vis98.py
  function main (line 14) | def main():

FILE: experiments/it16/best35.py
  function main (line 10) | def main():

FILE: experiments/it16/best98.py
  function main (line 11) | def main():

FILE: experiments/it16/layer_accuracy.py
  class ViTInput (line 21) | class ViTInput(nn.Module):
    method __init__ (line 22) | def __init__(self, vit: Union[ViT, AugmentedModel]):
    method forward (line 29) | def forward(self, x: torch.tensor) -> torch.tensor:
  class ViTPre (line 37) | class ViTPre(nn.Sequential):
    method __init__ (line 38) | def __init__(self, vit: AugmentedModel):
  class ViTTransformer (line 42) | class ViTTransformer(nn.Module):
    method __init__ (line 43) | def __init__(self, vit: Union[ViT, AugmentedModel], sl: slice = None):
    method forward (line 49) | def forward(self, x: torch.tensor, mask: torch.tensor = None) -> torch...
  class ViTHead (line 55) | class ViTHead(nn.Module):
    method __init__ (line 56) | def __init__(self, vit: Union[ViT, AugmentedModel], sl: slice = None):
    method forward (line 63) | def forward(self, x: torch.tensor) -> torch.tensor:
  class ViTFeatSelector (line 68) | class ViTFeatSelector(nn.Module):
    method __init__ (line 69) | def __init__(self, sl: slice = None):
    method forward (line 73) | def forward(self, x: torch.tensor) -> torch.tensor:
  class ViTCLSLayer (line 78) | class ViTCLSLayer(nn.Sequential):
    method __init__ (line 79) | def __init__(self, vit: AugmentedModel, layer: int):
  function main (line 85) | def main():

FILE: experiments/it16/mask35.py
  function main (line 15) | def main():

FILE: experiments/it16/mask98.py
  function main (line 15) | def main():

FILE: experiments/it16/reconstruct.py
  function main (line 15) | def main():

FILE: experiments/it17/cls.py
  class ViTInput (line 21) | class ViTInput(nn.Module):
    method __init__ (line 22) | def __init__(self, vit: Union[ViT, AugmentedModel]):
    method forward (line 29) | def forward(self, x: torch.tensor) -> torch.tensor:
  class ViTPre (line 37) | class ViTPre(nn.Sequential):
    method __init__ (line 38) | def __init__(self, vit: AugmentedModel):
  class ViTTransformer (line 42) | class ViTTransformer(nn.Module):
    method __init__ (line 43) | def __init__(self, vit: Union[ViT, AugmentedModel], sl: slice = None):
    method forward (line 49) | def forward(self, x: torch.tensor, mask: torch.tensor = None) -> torch...
  class ViTHead (line 55) | class ViTHead(nn.Module):
    method __init__ (line 56) | def __init__(self, vit: Union[ViT, AugmentedModel], sl: slice = None):
    method forward (line 63) | def forward(self, x: torch.tensor) -> torch.tensor:
  class ViTFeatSelector (line 68) | class ViTFeatSelector(nn.Module):
    method __init__ (line 69) | def __init__(self, sl: slice = None):
    method forward (line 73) | def forward(self, x: torch.tensor) -> torch.tensor:
  class ViTCLSLayer (line 78) | class ViTCLSLayer(nn.Module):
    method __init__ (line 79) | def __init__(self, vit: AugmentedModel, layer: int):
    method forward (line 88) | def forward(self, x: torch.tensor) -> torch.tensor:
  function main (line 100) | def main():

FILE: experiments/it17/convert.py
  function convert (line 19) | def convert(old_file: str, new_file: str):
  function main (line 28) | def main():

FILE: experiments/it17/main_cherry_clip.py
  function gen (line 89) | def gen(arr):
  function generate (line 95) | def generate(indices, per_row: int = 7, per_page: int = 4, tvs: list = N...
  function generate2 (line 140) | def generate2(indices, per_row: int = 7, per_page: int = 6, folder: str ...
  function print35 (line 172) | def print35():
  function print98 (line 188) | def print98():
  function fake_arr (line 204) | def fake_arr(arr):

FILE: experiments/it18/best99.py
  function main (line 13) | def main():

FILE: experiments/it18/fb_example.py
  function main (line 12) | def main():

FILE: experiments/it18/mask.py
  function main (line 15) | def main():

FILE: experiments/it2/layered.py
  function main (line 23) | def main():

FILE: experiments/it2/layered2.py
  function main (line 23) | def main():

FILE: experiments/it2/layered3.py
  function main (line 16) | def main():

FILE: experiments/it2/try.py
  function main (line 8) | def main():

FILE: experiments/it2/vis.py
  function main (line 20) | def main():

FILE: experiments/it3/check_hooks_correctness.py
  function to_numpy (line 15) | def to_numpy(x):
  function main (line 30) | def main():

FILE: experiments/it3/make_heap.py
  class FeatHeap (line 27) | class FeatHeap:
    method __init__ (line 28) | def __init__(self, limit: int = 24 * 24, feat_count: int = 10):
    method convert (line 33) | def convert(self, act: torch.tensor, start: int, images: torch.tensor)...
    method __add__ (line 52) | def __add__(self, tup: tuple):
  function main (line 67) | def main():

FILE: experiments/it3/predict_checkpoint_size.py
  function main (line 9) | def main():

FILE: experiments/it4/debug.py
  function main (line 13) | def main():

FILE: experiments/it4/tile.py
  function main (line 13) | def main():

FILE: experiments/it4/visualize.py
  class FeatHeap (line 27) | class FeatHeap:
    method __init__ (line 28) | def __init__(self, limit: int = 24 * 24, feat_count: int = 10):
    method convert (line 34) | def convert(self, act: torch.tensor, start: int, images: torch.tensor)...
    method __add__ (line 57) | def __add__(self, tup: tuple):
  function main (line 74) | def main():

FILE: experiments/it5/best.py
  function main (line 14) | def main():

FILE: experiments/it5/stitch.py
  function get_patches (line 13) | def get_patches(image_size: int, patch_size: int):
  function main (line 24) | def main():

FILE: experiments/it6/best.py
  function main (line 14) | def main():

FILE: experiments/it6/class.py
  function main (line 13) | def main():

FILE: experiments/it6/head.py
  function main (line 14) | def main():

FILE: experiments/it6/lr_rate.py
  function main (line 14) | def main():

FILE: experiments/it6/score.py
  function main (line 14) | def main():

FILE: experiments/it7/lh_pass.py
  function roll_n (line 14) | def roll_n(X, axis, n):
  function fftshift (line 22) | def fftshift(X):
  function ifftshift (line 33) | def ifftshift(X):
  function h_pass (line 45) | def h_pass(img: torch.tensor, filter_rate: float = 0.95):
  function l_pass (line 61) | def l_pass(img: torch.tensor, filter_rate: float = 0.95) -> torch.tensor:
  class FreqPass (line 65) | class FreqPass(nn.Module):
    method __init__ (line 66) | def __init__(self, high: bool = True, rate: float = 0.95):
    method forward (line 70) | def forward(self, x: torch.tensor) -> torch.tensor:
  function main (line 75) | def main():

FILE: experiments/it7/no_pos.py
  class PatchShuffle (line 13) | class PatchShuffle(nn.Module):
    method __init__ (line 14) | def __init__(self, image_size: int = 384, patch_size=16):
    method forward (line 18) | def forward(self, x: torch.tensor) -> torch.tensor:
  function main (line 28) | def main():

FILE: experiments/it8/cj_std.py
  function main (line 14) | def main():

FILE: experiments/it8/gaus.py
  function main (line 14) | def main():

FILE: experiments/it8/high.py
  function main (line 14) | def main():

FILE: experiments/it8/lr_rate.py
  function main (line 14) | def main():

FILE: experiments/it8/score.py
  function main (line 14) | def main():

FILE: experiments/it9/best_eval.py
  function main (line 11) | def main():

FILE: experiments/it9/best_train.py
  function main (line 11) | def main():

FILE: experiments/it9/get_images.py
  function main (line 14) | def main():

FILE: experiments/it9/get_mask.py
  function main (line 15) | def main():

FILE: experiments/it9/vis.py
  function main (line 14) | def main():

FILE: hooks/base.py
  class BasicHook (line 5) | class BasicHook:
    method __init__ (line 6) | def __init__(self, module: nn.Module):
    method close (line 10) | def close(self):
    method base_hook_fn (line 13) | def base_hook_fn(self, model: nn.Module, input_t: torch.tensor, output...
    method hook_fn (line 18) | def hook_fn(self, model: nn.Module, x: torch.tensor):

FILE: hooks/hook_holder.py
  class HookHolder (line 5) | class HookHolder(ItemIterator):
    method __init__ (line 6) | def __init__(self, classifier: nn.Module, hook_class, layer_class):
    method iterator_item (line 10) | def iterator_item(self):
    method check_for_attr (line 13) | def check_for_attr(self, attr: str, hook_class):
    method _broadcast (line 18) | def _broadcast(self, func_name: str, *to_propagate):
    method _gather (line 23) | def _gather(self, attr: str) -> list:
    method close (line 26) | def close(self):

FILE: hooks/layer.py
  class LayerHook (line 5) | class LayerHook:
    method __init__ (line 6) | def __init__(self, classifier: nn.Module, layer_class, layer_depth: in...
    method __call__ (line 10) | def __call__(self) -> torch.tensor:

FILE: hooks/timed_hook.py
  class TimedHookHolder (line 4) | class TimedHookHolder(HookHolder):
    method get_activations (line 5) | def get_activations(self):
    method get_layer (line 11) | def get_layer(self, item):
    method set_seed (line 15) | def set_seed(self, seed: int):
    method set_target (line 18) | def set_target(self, target: list):
    method reset (line 21) | def reset(self):

FILE: hooks/transformer/vit.py
  class ViTHook (line 11) | class ViTHook(BasicHook):
    method __init__ (line 12) | def __init__(self, module: nn.Module, return_output: bool, name: str):
    method base_hook_fn (line 17) | def base_hook_fn(self, model: nn.Module, input_t: torch.tensor, output...
    method hook_fn (line 22) | def hook_fn(self, model: nn.Module, x: torch.tensor):
  class FakeHookWrapper (line 26) | class FakeHookWrapper:
    method __init__ (line 27) | def __init__(self, value):
  class ViTAbsHookHolder (line 31) | class ViTAbsHookHolder(nn.Module):
  class ViTAttHookHolder (line 35) | class ViTAttHookHolder(ViTAbsHookHolder):
    method __init__ (line 36) | def __init__(self, classifier: nn.Module, in_feat: bool = False, keys:...
    method scores (line 53) | def scores(self):
    method forward (line 59) | def forward(self, x: torch.tensor) -> ({}, torch.tensor):
  class ViTGeLUHook (line 71) | class ViTGeLUHook(ViTAbsHookHolder):
    method __init__ (line 72) | def __init__(self, classifier: nn.Module, sl: slice = None):
    method forward (line 80) | def forward(self, x: torch.tensor) -> ({}, torch.tensor):
  class ClipGeLUHook (line 88) | class ClipGeLUHook(ViTAbsHookHolder):
    method __init__ (line 89) | def __init__(self, classifier: nn.Module, sl: slice = None):
    method forward (line 97) | def forward(self, x: torch.tensor) -> ({}, torch.tensor):
  class SimpleViTGeLUHook (line 106) | class SimpleViTGeLUHook(ViTGeLUHook):
    method forward (line 108) | def forward(self, x: torch.tensor) -> torch.tensor:
  class SimpleClipGeLUHook (line 115) | class SimpleClipGeLUHook(ClipGeLUHook):
    method forward (line 117) | def forward(self, x: torch.tensor) -> torch.tensor:
  class SaliencyViTGeLUHook (line 125) | class SaliencyViTGeLUHook(ViTGeLUHook):
    method forward (line 127) | def forward(self, x: torch.tensor, l: int, f: int) -> torch.tensor:
  class SaliencyClipGeLUHook (line 133) | class SaliencyClipGeLUHook(ClipGeLUHook):
    method forward (line 135) | def forward(self, x: torch.tensor, l: int, f: int) -> torch.tensor:
  class ReconstructionViTGeLUHook (line 141) | class ReconstructionViTGeLUHook(ViTGeLUHook):
    method forward (line 142) | def forward(self, x: torch.tensor) -> torch.tensor:
  class ReconstructionClipGeLUHook (line 148) | class ReconstructionClipGeLUHook(ClipGeLUHook):
    method forward (line 149) | def forward(self, x: torch.tensor) -> torch.tensor:

FILE: inversion/imagenet_invertor.py
  class ImageNetVisualizer (line 12) | class ImageNetVisualizer:
    method __init__ (line 13) | def __init__(self, loss_array: LossArray, saver: AbstractSaver = None,...
    method __call__ (line 27) | def __call__(self, img: torch.tensor = None, optimizer: optim.Optimize...

FILE: inversion/utils.py
  function new_init (line 4) | def new_init(size: int, batch_size: int = 1, last: torch.nn = None, padd...

FILE: loss/base.py
  function _abbreviation (line 6) | def _abbreviation(name: str) -> str:
  function _round (line 13) | def _round(num: float) -> str:
  class InvLoss (line 21) | class InvLoss:
    method __init__ (line 22) | def __init__(self, coefficient: float = 1.0):
    method __call__ (line 27) | def __call__(self, x: torch.tensor) -> torch.tensor:
    method loss (line 32) | def loss(self, x: torch.tensor):
    method __str__ (line 35) | def __str__(self):
    method reset (line 38) | def reset(self) -> torch.tensor:
  class LossArray (line 42) | class LossArray:
    method __init__ (line 43) | def __init__(self):
    method __add__ (line 47) | def __add__(self, other: InvLoss):
    method __call__ (line 51) | def __call__(self, x: torch.tensor):
    method header (line 56) | def header(self) -> str:
    method __str__ (line 60) | def __str__(self):
    method reset (line 64) | def reset(self):

FILE: loss/hooks/activation.py
  class AbsActivationHook (line 7) | class AbsActivationHook(BasicHook):
    method __init__ (line 8) | def __init__(self, module: nn.Module, feature: int = 0, targets: list ...
    method hook_fn (line 14) | def hook_fn(self, model: nn.Module, x: torch.tensor):
    method reset (line 17) | def reset(self):
    method set_feature (line 24) | def set_feature(self, feature: int):
    method set_target (line 27) | def set_target(self, target: list):
    method __call__ (line 30) | def __call__(self) -> torch.tensor:
  class ActivationHook (line 36) | class ActivationHook(AbsActivationHook):
    method hook_fn (line 37) | def hook_fn(self, model: nn.Module, input_t: torch.Tensor):
  class ActivationReluHook (line 44) | class ActivationReluHook(AbsActivationHook):
    method hook_fn (line 45) | def hook_fn(self, model: nn.Module, input_t: torch.Tensor):
  class TargetActivationHook (line 53) | class TargetActivationHook(AbsActivationHook):
    method hook_fn (line 54) | def hook_fn(self, model: nn.Module, input_t: torch.Tensor):
  class ContrastiveActivationHook (line 61) | class ContrastiveActivationHook(AbsActivationHook):
    method hook_fn (line 62) | def hook_fn(self, model: nn.Module, input_t: torch.Tensor):
  class ViTCLSActivationHook (line 71) | class ViTCLSActivationHook(AbsActivationHook):
    method hook_fn (line 72) | def hook_fn(self, model: nn.Module, input_t: torch.Tensor):
  class ViTMeanActivationHook (line 82) | class ViTMeanActivationHook(AbsActivationHook):
    method hook_fn (line 83) | def hook_fn(self, model: nn.Module, input_t: torch.Tensor):

FILE: loss/hooks/batch_norm.py
  class BatchNormHookHookAbs (line 9) | class BatchNormHookHookAbs(AbsActivationHook):
    method hook_fn (line 10) | def hook_fn(self, model: nn.Module, x: torch.tensor):
    method get_mean_var (line 14) | def get_mean_var(x: torch.tensor) -> (torch.tensor, torch.tensor):
    method normalize_eval (line 19) | def normalize_eval(model: nn.Module, x: torch.tensor) -> torch.tensor:
  class MatchModelBNStatsHook (line 26) | class MatchModelBNStatsHook(BatchNormHookHookAbs):
    method hook_fn (line 27) | def hook_fn(self, model: nn.Module, input_t: torch.Tensor):

FILE: loss/image_net.py
  class MatchBatchNorm (line 15) | class MatchBatchNorm(InvLoss):
    method __init__ (line 16) | def __init__(self, bn: BaseFakeBN, coefficient: float = 1.):
    method loss (line 20) | def loss(self, x: torch.tensor) -> torch.tensor:
  class TotalVariation (line 24) | class TotalVariation(InvLoss):
    method loss (line 25) | def loss(self, x: torch.tensor):
    method __init__ (line 28) | def __init__(self, p: int = 2, size: int = 224, coefficient: float = 1.):
  class NormalVariation (line 34) | class NormalVariation(InvLoss):
    method loss (line 35) | def loss(self, x: torch.tensor):
    method __init__ (line 38) | def __init__(self, p: int = 2, size: int = 224, coefficient: float = 1.):
  class ColorVariation (line 44) | class ColorVariation(InvLoss):
    method loss (line 45) | def loss(self, x: torch.tensor):
    method __init__ (line 48) | def __init__(self, p: int = 2, size: int = 224, coefficient: float = 1.):
  class CrossEntropyLoss (line 54) | class CrossEntropyLoss(InvLoss):
    method loss (line 55) | def loss(self, x: torch.tensor):
    method __init__ (line 58) | def __init__(self, model: torch.nn.Module, label: torch.tensor, coeffi...
  class BatchAugment (line 65) | class BatchAugment(InvLoss):
    method loss (line 66) | def loss(self, x: torch.tensor):
    method __init__ (line 71) | def __init__(self, other: InvLoss, aug: torch.tensor = None):
  class NetworkPass (line 77) | class NetworkPass(InvLoss):
    method __init__ (line 78) | def __init__(self, model: torch.nn.Module):
    method loss (line 82) | def loss(self, x: torch.tensor):
  class BatchNorm1stLayer (line 87) | class BatchNorm1stLayer(InvLoss):
    method loss (line 88) | def loss(self, x: torch.tensor) -> torch.tensor:
    method reset (line 91) | def reset(self) -> torch.tensor:
    method __init__ (line 94) | def __init__(self, bn_hook: TimedHookHolder, layer: int = 0, coefficie...
  class ViTFeatHook (line 100) | class ViTFeatHook(InvLoss):
    method __init__ (line 101) | def __init__(self, hook: ViTAbsHookHolder, key: str, coefficient: floa...
    method loss (line 106) | def loss(self, x: torch.tensor):
  class ViTEnsFeatHook (line 113) | class ViTEnsFeatHook(ViTFeatHook):
    method __init__ (line 114) | def __init__(self, hook: ViTAbsHookHolder, key: str, feat: int = 0, co...
    method loss (line 118) | def loss(self, x: torch.tensor):
  class ReconstructionLoss (line 125) | class ReconstructionLoss(ViTFeatHook):
    method __init__ (line 126) | def __init__(self, hook: ViTAbsHookHolder, x: torch.tensor, key: str, ...
    method loss (line 132) | def loss(self, x: torch.tensor):
  class ViTHeadHook (line 136) | class ViTHeadHook(ViTEnsFeatHook):
    method loss (line 137) | def loss(self, x: torch.tensor):
  class ViTScoreHook (line 143) | class ViTScoreHook(ViTEnsFeatHook):
    method loss (line 144) | def loss(self, x: torch.tensor):
  class LayerActivationNorm (line 153) | class LayerActivationNorm(InvLoss):
    method __init__ (line 154) | def __init__(self, hook: LayerHook, model: torch.nn.Module, coefficien...
    method loss (line 158) | def loss(self, x: torch.tensor) -> torch.tensor:
  class ActivationNorm (line 163) | class ActivationNorm(InvLoss):
    method loss (line 164) | def loss(self, x: torch.tensor):
    method __init__ (line 167) | def __init__(self, activation_hook: TimedHookHolder, layer: int, coeff...
    method reset (line 172) | def reset(self) -> torch.tensor:
  class ColorDistribution (line 176) | class ColorDistribution(InvLoss):
    method loss (line 177) | def loss(self, x: torch.tensor):
    method __init__ (line 180) | def __init__(self, normalizer: Normalizer, coefficient: float = 1.):

FILE: loss/regularizers/batch_norm.py
  class FakeBatchNorm (line 6) | class FakeBatchNorm(nn.Module):
    method __init__ (line 7) | def __init__(self, resnet_function, normalizer: Normalizer):
    method forward (line 13) | def forward(self, x: torch.tensor) -> torch.tensor:

FILE: loss/regularizers/image.py
  class TotalVariation (line 8) | class TotalVariation(nn.Module):
    method __init__ (line 9) | def __init__(self, p: int = 2):
    method forward (line 13) | def forward(self, x: torch.tensor) -> torch.tensor:
  class NormalVariation (line 22) | class NormalVariation(TotalVariation):
    method forward (line 23) | def forward(self, x: torch.tensor, per_sample: bool = True) -> torch.t...
  class ColorVariation (line 29) | class ColorVariation(TotalVariation):
    method forward (line 30) | def forward(self, x: torch.tensor) -> torch.tensor:
  class L1Norm (line 35) | class L1Norm(nn.Module):
    method forward (line 36) | def forward(self, x: torch.tensor) -> torch.tensor:
  class L2Norm (line 40) | class L2Norm(nn.Module):
    method forward (line 41) | def forward(self, x: torch.tensor) -> torch.tensor:
  class FakeColorDistribution (line 45) | class FakeColorDistribution(nn.Module):
    method __init__ (line 46) | def __init__(self, normalizer: Normalizer):
    method forward (line 50) | def forward(self, x: torch.tensor) -> torch.tensor:

FILE: model/augmented.py
  class AugmentedModel (line 5) | class AugmentedModel(nn.Module):
    method __init__ (line 6) | def __init__(self, classifier: nn.Module, augmentations: nn.Module = N...
    method forward (line 11) | def forward(self, *inputs: [torch.Tensor]) -> [torch.Tensor]:
  class BNModel (line 16) | class BNModel(nn.Module):
    method __init__ (line 17) | def __init__(self, conv: nn.Module, bn: nn.Module):
    method forward (line 22) | def forward(self, x: torch.tensor):

FILE: model/library/base.py
  class InvertModel (line 8) | class InvertModel(ItemIterator):
    method iterator_item (line 12) | def iterator_item(self):
    method __init__ (line 15) | def __init__(self, name: str, constructor, constructor_args: dict, ima...
    method __call__ (line 22) | def __call__(self, normalize: bool = True, grad: bool = False) -> (tor...
  class TorchVisionModel (line 34) | class TorchVisionModel(InvertModel):
    method __init__ (line 35) | def __init__(self, constructor, batch_size: int):
  class ModelLibrary (line 43) | class ModelLibrary(ItemIterator):
    method iterator_item (line 45) | def iterator_item(self):
    method __init__ (line 48) | def __init__(self, other_models: list):
    method names (line 52) | def names(self) -> dict:
    method __str__ (line 55) | def __str__(self):
    method _fw_score (line 64) | def _fw_score(q: str, reference: str) -> int:
    method _amin_score (line 74) | def _amin_score(q: str, reference: str) -> float:
    method search (line 78) | def search(self, query: str, top_k=5):

FILE: model/library/cnns/alexnet.py
  class AlexNet (line 6) | class AlexNet(TorchVisionModel):

FILE: model/library/cnns/densenet.py
  class DensNet (line 6) | class DensNet(TorchVisionModel):

FILE: model/library/cnns/googlenet.py
  class GoogleNet (line 6) | class GoogleNet(TorchVisionModel):

FILE: model/library/cnns/inception.py
  class Inception (line 6) | class Inception(TorchVisionModel):

FILE: model/library/cnns/mnasnet.py
  class MNasNet (line 6) | class MNasNet(TorchVisionModel):

FILE: model/library/cnns/mobilenet.py
  class MobileNet (line 6) | class MobileNet(TorchVisionModel):

FILE: model/library/cnns/resnet.py
  class ResNet (line 7) | class ResNet(TorchVisionModel):

FILE: model/library/cnns/robust.py
  function robust_resnet50 (line 9) | def robust_resnet50():
  class RobustResNet50 (line 17) | class RobustResNet50(InvertModel):
    method __init__ (line 18) | def __init__(self, constructor, batch_size: int):

FILE: model/library/cnns/shufflenet.py
  class ShuffleNet (line 6) | class ShuffleNet(TorchVisionModel):

FILE: model/library/cnns/squeezenet.py
  class SqueezeNet (line 6) | class SqueezeNet(TorchVisionModel):

FILE: model/library/cnns/vggs.py
  class VGG (line 6) | class VGG(TorchVisionModel):

FILE: model/library/transformers/base.py
  class TransformerModel (line 9) | class TransformerModel(InvertModel):
    method __call__ (line 12) | def __call__(self, normalize: bool = True, grad: bool = False):
  class TimmModel (line 19) | class TimmModel(TransformerModel):
    method get_size_based_on_name (line 22) | def get_size_based_on_name(self, name: str) -> int:
    method __init__ (line 25) | def __init__(self, o: int, batch_size: int):

FILE: model/library/transformers/clip.py
  class ClipWrapper (line 10) | class ClipWrapper(nn.Module):
    method __init__ (line 11) | def __init__(self, clip):
    method forward (line 15) | def forward(self, x: torch.tensor) -> torch.tensor:
  class CLIP (line 19) | class CLIP(TransformerModel):
    method __init__ (line 20) | def __init__(self, o: int, batch_size: int):

FILE: model/library/transformers/coat.py
  class CoaT (line 5) | class CoaT(TimmModel):

FILE: model/library/transformers/convit.py
  class ConViT (line 5) | class ConViT(TimmModel):

FILE: model/library/transformers/deit.py
  class DeiT (line 8) | class DeiT(TransformerModel):
    method __init__ (line 9) | def __init__(self, o: int, batch_size: int):

FILE: model/library/transformers/levit.py
  class LeViT (line 5) | class LeViT(TimmModel):
    method get_size_based_on_name (line 7) | def get_size_based_on_name(self, name:str) -> int:

FILE: model/library/transformers/mixer.py
  class Mixer (line 5) | class Mixer(TimmModel):

FILE: model/library/transformers/pit.py
  class PiT (line 5) | class PiT(TimmModel):

FILE: model/library/transformers/resmpl.py
  class ResMLP (line 5) | class ResMLP(TimmModel):

FILE: model/library/transformers/swin.py
  class Swin (line 5) | class Swin(TimmModel):

FILE: model/library/transformers/twin.py
  class Twin (line 5) | class Twin(TimmModel):

FILE: model/library/transformers/vit.py
  class ViT (line 9) | class ViT(TransformerModel):
    method __init__ (line 10) | def __init__(self, o: int, batch_size: int):

FILE: model/utils.py
  function default_bn (line 9) | def default_bn():

FILE: saliency_tools/guided.py
  class GuidedBackProp (line 4) | class GuidedBackProp:
    method __init__ (line 9) | def __init__(self, model):
    method update_relus (line 16) | def update_relus(self):
  function generate_gradients (line 50) | def generate_gradients(self, input_image, target_class):

FILE: saliency_tools/vanilla.py
  class VanillaBackProp (line 5) | class VanillaBackProp:
    method __init__ (line 10) | def __init__(self, model):
    method generate_gradients (line 14) | def generate_gradients(self, input_image, target_class):

FILE: saver/base.py
  class AbstractSaver (line 10) | class AbstractSaver:
    method __init__ (line 11) | def __init__(self, extension: str, folder: str = None, save_id: bool =...
    method _get_mkdir_path (line 18) | def _get_mkdir_path(self, *path):
    method save (line 26) | def save(self, result: torch.Tensor, *path):
    method save_function (line 30) | def save_function(self, result: Any, path: str):
  class ImageSaver (line 34) | class ImageSaver(AbstractSaver):
    method save_function (line 35) | def save_function(self, result: Any, path: str):
    method get_nrow (line 40) | def get_nrow() -> torch.tensor:
    method __init__ (line 49) | def __init__(self, folder: str, save_id: bool = False):
  class TensorSaver (line 54) | class TensorSaver(AbstractSaver):
    method save_function (line 55) | def save_function(self, result: Any, path: str):
    method __init__ (line 58) | def __init__(self, folder: str, save_id: bool = False):
  class ExperimentSaver (line 62) | class ExperimentSaver(AbstractSaver):
    method save_function (line 63) | def save_function(self, result: Any, path: str):
    method save (line 66) | def save(self, result: torch.Tensor, *path):
    method __init__ (line 71) | def __init__(self, folder: str, save_id: bool = False, disk_saver: boo...

FILE: train_cifar100.py
  function get_cifar100_loaders (line 20) | def get_cifar100_loaders(batch_size=128, train=None):
  function main (line 34) | def main():

FILE: utils/bn.py
  class Accumulator (line 7) | class Accumulator:
    method __init__ (line 8) | def __init__(self):
    method __add__ (line 11) | def __add__(self, other):
    method value (line 17) | def value(self) -> np.ndarray:
    method save (line 21) | def save(self, name: str):
  function get_len (line 25) | def get_len(model: torch.nn.Module):
  function get_mean (line 30) | def get_mean(model: torch.nn.Module) -> np.ndarray:
  function get_var (line 34) | def get_var(model: torch.nn.Module) -> np.ndarray:
  function get_stat_params (line 38) | def get_stat_params(model: torch.nn.Module, get_all: bool = False) -> Li...
  function get_error (line 50) | def get_error(first: np.ndarray, other: np.ndarray) -> float:
  function replace_bn (line 56) | def replace_bn(model: torch.nn.Module, mean: np.ndarray, var: np.ndarray...
  function replace_bn_with_multiplicative_inverse (line 67) | def replace_bn_with_multiplicative_inverse(model: torch.nn.Module):
  function random_model (line 74) | def random_model(model: torch.nn.Module):
  function shirt_on_model (line 82) | def shirt_on_model(model: torch.nn.Module, mean, var):
  function get_var_params (line 93) | def get_var_params(model: torch.nn.Module) -> torch.Tensor:

FILE: utils/classification.py
  function get_acc (line 10) | def get_acc(output: torch.Tensor, y: torch.Tensor) -> (float, int):
  function get_acc5 (line 16) | def get_acc5(output: torch.Tensor, y: torch.Tensor) -> (float, int):
  function print_acc_stats (line 23) | def print_acc_stats(pred: np.ndarray, y: np.ndarray):
  function get_confusion (line 35) | def get_confusion(e_pred: np.ndarray, e_label: np.ndarray) -> np.ndarray:
  function tqdm_stat (line 44) | def tqdm_stat(tqdm_obj: tqdm, template: str, values: List[Any]):

FILE: utils/device.py
  function to_device (line 6) | def to_device(x: Union[Tensor, nn.Module, list, tuple, dict], device: st...
  function to_cuda (line 21) | def to_cuda(x: Union[Tensor, list, tuple, dict, nn.Module]) -> \

FILE: utils/experiment.py
  function _load (line 10) | def _load(file_address: str) -> int:
  function _save (line 21) | def _save(file_address: str, cur: int):
  function get_exp_no (line 26) | def get_exp_no() -> int:
  function _two_digits (line 33) | def _two_digits(cur: str) -> str:
  function _time (line 37) | def _time():
  function _get_exp_name (line 43) | def _get_exp_name() -> str:
  function _fancy_print (line 56) | def _fancy_print():
  function _fix_random_seed (line 60) | def _fix_random_seed(seed: int = 6247423):
  function exp_starter_pack (line 70) | def exp_starter_pack() -> (str, Namespace, dict):

FILE: utils/iterators.py
  class ItemIterator (line 1) | class ItemIterator:
    method iterator_item (line 3) | def iterator_item(self):
    method __iter__ (line 6) | def __iter__(self):
    method __getitem__ (line 9) | def __getitem__(self, item):
    method __len__ (line 12) | def __len__(self):

FILE: utils/jasper/metrics.py
  function __levenshtein (line 4) | def __levenshtein(a: List, b: List) -> int:
  function word_error_rate (line 26) | def word_error_rate(hypotheses: List[str], references: List[str]) -> (fl...

FILE: utils/jasper/translator.py
  class Translator (line 7) | class Translator:
    method __init__ (line 8) | def __init__(self, converter: Manifest):
    method __call__ (line 12) | def __call__(self, prediction: torch.Tensor, length: torch.Tensor) -> ...

FILE: utils/jasper_iterator.py
  function layer_range (line 6) | def layer_range(stop=-1):
  function feature_range (line 13) | def feature_range(layer: int, stop=-1):

FILE: utils/log.py
  function _convert_form (line 4) | def _convert_form(f):
  function j_print (line 11) | def j_print(*args, file=None):
  function j_header (line 16) | def j_header(*args, file=None):

FILE: utils/pickle.py
  function load_pickle (line 4) | def load_pickle(file_name: str) -> list:

FILE: utils/regularizers.py
  function get_tv (line 4) | def get_tv(t: torch.Tensor) -> torch.Tensor:
  function get_shadow_tv (line 13) | def get_shadow_tv(t: torch.Tensor) -> torch.Tensor:

FILE: utils/statistics.py
  class Meter (line 1) | class Meter:
    method __init__ (line 2) | def __init__(self):
    method reset (line 6) | def reset(self):
    method __add__ (line 9) | def __add__(self, other: (float, float)):
    method value (line 15) | def value(self) -> float:

FILE: utils/training.py
  function save_model (line 5) | def save_model(model: torch.nn.Module, exp_name: str, epoch: int, root: ...
  function load_model (line 12) | def load_model(model: torch.nn.Module, checkpoint: str, root='checkpoint...

FILE: visualize.py
  function visualize_feature (line 16) | def visualize_feature(layer, feature):