SYMBOL INDEX (298 symbols across 17 files)

FILE: data/cub2011.py
  class Cub2011 (line 9) | class Cub2011(VisionDataset):
    method __init__ (line 29) | def __init__(self, root, train=True, transform=None, target_transform=...
    method _load_metadata (line 40) | def _load_metadata(self):
    method _check_integrity (line 59) | def _check_integrity(self):
    method _download (line 72) | def _download(self):
    method __len__ (line 84) | def __len__(self):
    method __getitem__ (line 87) | def __getitem__(self, idx):

FILE: data/dataset_factory.py
  function _search_split (line 48) | def _search_split(root, split):
  function create_dataset (line 68) | def create_dataset(

FILE: data/loader.py
  function fast_collate (line 23) | def fast_collate(batch):
  function expand_to_chs (line 58) | def expand_to_chs(x, n):
  class PrefetchLoader (line 68) | class PrefetchLoader:
    method __init__ (line 70) | def __init__(
    method __iter__ (line 99) | def __iter__(self):
    method __len__ (line 125) | def __len__(self):
    method sampler (line 129) | def sampler(self):
    method dataset (line 133) | def dataset(self):
    method mixup_enabled (line 137) | def mixup_enabled(self):
    method mixup_enabled (line 144) | def mixup_enabled(self, x):
  function _worker_init (line 149) | def _worker_init(worker_id, worker_seeding='all'):
  function create_loader (line 165) | def create_loader(
  class MultiEpochsDataLoader (line 283) | class MultiEpochsDataLoader(torch.utils.data.DataLoader):
    method __init__ (line 285) | def __init__(self, *args, **kwargs):
    method __len__ (line 292) | def __len__(self):
    method __iter__ (line 295) | def __iter__(self):
  class _RepeatSampler (line 300) | class _RepeatSampler(object):
    method __init__ (line 307) | def __init__(self, sampler):
    method __iter__ (line 310) | def __iter__(self):

FILE: data/nabirds.py
  class NABirds (line 19) | class NABirds(Dataset):
    method __init__ (line 35) | def __init__(self, root, train=True, transform=None):
    method __len__ (line 62) | def __len__(self):
    method __getitem__ (line 65) | def __getitem__(self, idx):
  function get_continuous_class_map (line 75) | def get_continuous_class_map(class_labels):
  function load_class_names (line 79) | def load_class_names(dataset_path=''):
  function load_hierarchy (line 90) | def load_hierarchy(dataset_path=''):

FILE: data/stanford_dogs.py
  class dogs (line 12) | class dogs(data.Dataset):
    method __init__ (line 31) | def __init__(self,
    method __len__ (line 190) | def __len__(self):
    method __getitem__ (line 193) | def __getitem__(self, index):
    method download (line 215) | def download(self):
    method get_boxes (line 233) | def get_boxes(path):
    method load_split (line 244) | def load_split(self):
    method stats (line 256) | def stats(self):

FILE: data/transforms_factory.py
  function transforms_direct_resize (line 17) | def transforms_direct_resize(
  function transforms_simpleaug_train (line 43) | def transforms_simpleaug_train(
  function transforms_imagenet_train (line 72) | def transforms_imagenet_train(
  function transforms_imagenet_eval (line 158) | def transforms_imagenet_eval(
  function create_transform (line 199) | def create_transform(

FILE: data/vtab.py
  class VTAB (line 4) | class VTAB(ImageFolder):
    method __init__ (line 5) | def __init__(self, root, train=True, transform=None, target_transform=...

FILE: models/as_mlp.py
  function _cfg (line 30) | def _cfg(url='', file='', **kwargs):
  class Mlp (line 48) | class Mlp(nn.Module):
    method __init__ (line 49) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 65) | def forward(self, x):
  class AxialShift (line 82) | class AxialShift(nn.Module):
    method __init__ (line 92) | def __init__(self, dim, shift_size, as_bias=True, proj_drop=0., tuning...
    method forward (line 117) | def forward(self, x):
    method extra_repr (line 166) | def extra_repr(self) -> str:
    method flops (line 169) | def flops(self, N):
  class AxialShiftedBlock (line 187) | class AxialShiftedBlock(nn.Module):
    method __init__ (line 202) | def __init__(self, dim, input_resolution, shift_size=7,
    method forward (line 227) | def forward(self, x):
    method extra_repr (line 248) | def extra_repr(self) -> str:
    method flops (line 252) | def flops(self):
  class PatchMerging (line 266) | class PatchMerging(nn.Module):
    method __init__ (line 275) | def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm, tun...
    method forward (line 286) | def forward(self, x):
    method extra_repr (line 310) | def extra_repr(self) -> str:
    method flops (line 313) | def flops(self):
  class BasicLayer (line 320) | class BasicLayer(nn.Module):
    method __init__ (line 340) | def __init__(self, dim, input_resolution, depth, shift_size,
    method forward (line 367) | def forward(self, x):
    method extra_repr (line 377) | def extra_repr(self) -> str:
    method flops (line 380) | def flops(self):
  class PatchEmbed (line 389) | class PatchEmbed(nn.Module):
    method __init__ (line 400) | def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=9...
    method forward (line 429) | def forward(self, x):
    method flops (line 447) | def flops(self):
  function MyNorm (line 455) | def MyNorm(dim):
  function init_ssf_scale_shift (line 459) | def init_ssf_scale_shift(dim):
  function ssf_ada (line 469) | def ssf_ada(x, scale, shift):
  class AS_MLP (line 479) | class AS_MLP(nn.Module):
    method __init__ (line 501) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes...
    method no_weight_decay (line 559) | def no_weight_decay(self):
    method no_weight_decay_keywords (line 563) | def no_weight_decay_keywords(self):
    method get_classifier (line 566) | def get_classifier(self):
    method reset_classifier (line 569) | def reset_classifier(self, num_classes, global_pool=''):
    method forward_features (line 574) | def forward_features(self, x):
    method forward (line 589) | def forward(self, x):
    method flops (line 594) | def flops(self):
  function _create_as_mlp (line 604) | def _create_as_mlp(variant, pretrained=False, **kwargs):
  function as_base_patch4_window7_224 (line 614) | def as_base_patch4_window7_224(pretrained=False, **kwargs):

FILE: models/convnext.py
  function _cfg (line 32) | def _cfg(url='', **kwargs):
  function _is_contiguous (line 78) | def _is_contiguous(tensor: torch.Tensor) -> bool:
  class LayerNorm2d (line 90) | class LayerNorm2d(nn.LayerNorm):
    method __init__ (line 94) | def __init__(self, normalized_shape, eps=1e-6):
    method forward (line 97) | def forward(self, x) -> torch.Tensor:
  class Mlp (line 110) | class Mlp(nn.Module):
    method __init__ (line 113) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 132) | def forward(self, x):
  class ConvNeXtBlock (line 149) | class ConvNeXtBlock(nn.Module):
    method __init__ (line 165) | def __init__(self, dim, drop_path=0., ls_init_value=1e-6, conv_mlp=Fal...
    method forward (line 187) | def forward(self, x):
  class Downsample (line 211) | class Downsample(nn.Module):
    method __init__ (line 214) | def __init__(self, dim, out_dim, kernel_size, stride, norm_layer=None,...
    method forward (line 226) | def forward(self, x):
  class ConvNeXtStage (line 238) | class ConvNeXtStage(nn.Module):
    method __init__ (line 240) | def __init__(
    method forward (line 258) | def forward(self, x):
  class PatchEmbed (line 270) | class PatchEmbed(nn.Module):
    method __init__ (line 273) | def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=...
    method forward (line 284) | def forward(self, x):
  function init_ssf_scale_shift (line 295) | def init_ssf_scale_shift(dim):
  function ssf_ada (line 305) | def ssf_ada(x, scale, shift):
  class ConvNeXt (line 315) | class ConvNeXt(nn.Module):
    method __init__ (line 330) | def __init__(
    method set_grad_checkpointing (line 398) | def set_grad_checkpointing(self, enable=True):
    method get_classifier (line 402) | def get_classifier(self):
    method reset_classifier (line 405) | def reset_classifier(self, num_classes=0, global_pool='avg'):
    method forward_features (line 420) | def forward_features(self, x):
    method forward (line 430) | def forward(self, x):
  function _init_weights (line 436) | def _init_weights(module, name=None, head_init_scale=1.0):
  function checkpoint_filter_fn (line 448) | def checkpoint_filter_fn(state_dict, model):
  function _create_convnext (line 478) | def _create_convnext(variant, pretrained=False, **kwargs):
  function convnext_tiny (line 489) | def convnext_tiny(pretrained=False, **kwargs):
  function convnext_tiny_hnf (line 496) | def convnext_tiny_hnf(pretrained=False, **kwargs):
  function convnext_small (line 503) | def convnext_small(pretrained=False, **kwargs):
  function convnext_base (line 510) | def convnext_base(pretrained=False, **kwargs):
  function convnext_large (line 517) | def convnext_large(pretrained=False, **kwargs):
  function convnext_base_in22ft1k (line 524) | def convnext_base_in22ft1k(pretrained=False, **kwargs):
  function convnext_large_in22ft1k (line 531) | def convnext_large_in22ft1k(pretrained=False, **kwargs):
  function convnext_xlarge_in22ft1k (line 538) | def convnext_xlarge_in22ft1k(pretrained=False, **kwargs):
  function convnext_base_384_in22ft1k (line 545) | def convnext_base_384_in22ft1k(pretrained=False, **kwargs):
  function convnext_large_384_in22ft1k (line 552) | def convnext_large_384_in22ft1k(pretrained=False, **kwargs):
  function convnext_xlarge_384_in22ft1k (line 559) | def convnext_xlarge_384_in22ft1k(pretrained=False, **kwargs):
  function convnext_base_in22k (line 566) | def convnext_base_in22k(pretrained=False, **kwargs):
  function convnext_large_in22k (line 573) | def convnext_large_in22k(pretrained=False, **kwargs):
  function convnext_xlarge_in22k (line 580) | def convnext_xlarge_in22k(pretrained=False, **kwargs):

FILE: models/swin_transformer.py
  function _cfg (line 37) | def _cfg(url='', **kwargs):
  class Mlp (line 94) | class Mlp(nn.Module):
    method __init__ (line 97) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 117) | def forward(self, x):
  function window_partition (line 133) | def window_partition(x, window_size: int):
  function window_reverse (line 148) | def window_reverse(windows, window_size: int, H: int, W: int):
  class WindowAttention (line 164) | class WindowAttention(nn.Module):
    method __init__ (line 176) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_dr...
    method forward (line 218) | def forward(self, x, mask: Optional[torch.Tensor] = None):
  class SwinTransformerBlock (line 261) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 278) | def __init__(self, dim, input_resolution, num_heads, window_size=7, sh...
    method forward (line 336) | def forward(self, x):
  class PatchMerging (line 382) | class PatchMerging(nn.Module):
    method __init__ (line 390) | def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm, tun...
    method forward (line 401) | def forward(self, x):
    method extra_repr (line 428) | def extra_repr(self) -> str:
    method flops (line 431) | def flops(self):
  class BasicLayer (line 438) | class BasicLayer(nn.Module):
    method __init__ (line 456) | def __init__(self, dim, input_resolution, depth, num_heads, window_size,
    method forward (line 481) | def forward(self, x):
    method extra_repr (line 491) | def extra_repr(self) -> str:
  class PatchEmbed (line 495) | class PatchEmbed(nn.Module):
    method __init__ (line 498) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 520) | def forward(self, x):
  function init_ssf_scale_shift (line 540) | def init_ssf_scale_shift(dim):
  function ssf_ada (line 550) | def ssf_ada(x, scale, shift):
  class SwinTransformer (line 560) | class SwinTransformer(nn.Module):
    method __init__ (line 584) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes...
    method init_weights (line 653) | def init_weights(self, mode=''):
    method no_weight_decay (line 661) | def no_weight_decay(self):
    method no_weight_decay_keywords (line 665) | def no_weight_decay_keywords(self):
    method get_classifier (line 668) | def get_classifier(self):
    method reset_classifier (line 671) | def reset_classifier(self, num_classes, global_pool=''):
    method forward_features (line 675) | def forward_features(self, x):
    method forward (line 690) | def forward(self, x):
  function _create_swin_transformer (line 696) | def _create_swin_transformer(variant, pretrained=False, **kwargs):
  function swin_base_patch4_window12_384 (line 707) | def swin_base_patch4_window12_384(pretrained=False, **kwargs):
  function swin_base_patch4_window7_224 (line 716) | def swin_base_patch4_window7_224(pretrained=False, **kwargs):
  function swin_large_patch4_window12_384 (line 725) | def swin_large_patch4_window12_384(pretrained=False, **kwargs):
  function swin_large_patch4_window7_224 (line 734) | def swin_large_patch4_window7_224(pretrained=False, **kwargs):
  function swin_small_patch4_window7_224 (line 743) | def swin_small_patch4_window7_224(pretrained=False, **kwargs):
  function swin_tiny_patch4_window7_224 (line 752) | def swin_tiny_patch4_window7_224(pretrained=False, **kwargs):
  function swin_base_patch4_window12_384_in22k (line 761) | def swin_base_patch4_window12_384_in22k(pretrained=False, **kwargs):
  function swin_base_patch4_window7_224_in22k (line 770) | def swin_base_patch4_window7_224_in22k(pretrained=False, **kwargs):
  function swin_large_patch4_window12_384_in22k (line 779) | def swin_large_patch4_window12_384_in22k(pretrained=False, **kwargs):
  function swin_large_patch4_window7_224_in22k (line 788) | def swin_large_patch4_window7_224_in22k(pretrained=False, **kwargs):

FILE: models/vision_transformer.py
  function _cfg (line 47) | def _cfg(url='', **kwargs):
  class Mlp (line 134) | class Mlp(nn.Module):
    method __init__ (line 137) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 157) | def forward(self, x):
  class Attention (line 175) | class Attention(nn.Module):
    method __init__ (line 176) | def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., pro...
    method forward (line 196) | def forward(self, x):
  class LayerScale (line 216) | class LayerScale(nn.Module):
    method __init__ (line 217) | def __init__(self, dim, init_values=1e-5, inplace=False):
    method forward (line 222) | def forward(self, x):
  class Block (line 226) | class Block(nn.Module):
    method __init__ (line 228) | def __init__(
    method forward (line 252) | def forward(self, x):
  class ResPostBlock (line 262) | class ResPostBlock(nn.Module):
    method __init__ (line 263) | def __init__(
    method init_weights (line 279) | def init_weights(self):
    method forward (line 285) | def forward(self, x):
  class ParallelBlock (line 291) | class ParallelBlock(nn.Module):
    method __init__ (line 293) | def __init__(
    method _forward_jit (line 314) | def _forward_jit(self, x):
    method _forward (line 320) | def _forward(self, x):
    method forward (line 325) | def forward(self, x):
  class PatchEmbed (line 332) | class PatchEmbed(nn.Module):
    method __init__ (line 335) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 358) | def forward(self, x):
  function init_ssf_scale_shift (line 378) | def init_ssf_scale_shift(dim):
  function ssf_ada (line 388) | def ssf_ada(x, scale, shift):
  class VisionTransformer (line 398) | class VisionTransformer(nn.Module):
    method __init__ (line 405) | def __init__(
    method init_weights (line 477) | def init_weights(self, mode=''):
    method _init_weights (line 485) | def _init_weights(self, m):
    method load_pretrained (line 490) | def load_pretrained(self, checkpoint_path, prefix=''):
    method no_weight_decay (line 494) | def no_weight_decay(self):
    method group_matcher (line 498) | def group_matcher(self, coarse=False):
    method set_grad_checkpointing (line 505) | def set_grad_checkpointing(self, enable=True):
    method get_classifier (line 509) | def get_classifier(self):
    method reset_classifier (line 512) | def reset_classifier(self, num_classes: int, global_pool=None):
    method forward_features (line 520) | def forward_features(self, x):
    method forward_head (line 537) | def forward_head(self, x, pre_logits: bool = False):
    method forward (line 543) | def forward(self, x):
  function init_weights_vit_timm (line 550) | def init_weights_vit_timm(module: nn.Module, name: str = ''):
  function init_weights_vit_jax (line 560) | def init_weights_vit_jax(module: nn.Module, name: str = '', head_bias: f...
  function init_weights_vit_moco (line 578) | def init_weights_vit_moco(module: nn.Module, name: str = ''):
  function get_init_weights_vit (line 593) | def get_init_weights_vit(mode='jax', head_bias: float = 0.):
  function _load_weights (line 603) | def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix...
  function resize_pos_embed (line 683) | def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()):
  function checkpoint_filter_fn (line 705) | def checkpoint_filter_fn(state_dict, model):
  function _create_vision_transformer (line 727) | def _create_vision_transformer(variant, pretrained=False, **kwargs):
  function vit_tiny_patch16_224 (line 743) | def vit_tiny_patch16_224(pretrained=False, **kwargs):
  function vit_tiny_patch16_384 (line 752) | def vit_tiny_patch16_384(pretrained=False, **kwargs):
  function vit_small_patch16_224 (line 763) | def vit_small_patch16_224(pretrained=False, **kwargs):
  function vit_small_patch16_384 (line 773) | def vit_small_patch16_384(pretrained=False, **kwargs):
  function vit_base_patch16_224 (line 785) | def vit_base_patch16_224(pretrained=False, **kwargs):
  function vit_base_patch16_384 (line 795) | def vit_base_patch16_384(pretrained=False, **kwargs):
  function vit_large_patch16_224 (line 806) | def vit_large_patch16_224(pretrained=False, **kwargs):
  function vit_large_patch16_384 (line 816) | def vit_large_patch16_384(pretrained=False, **kwargs):
  function vit_tiny_patch16_224_in21k (line 827) | def vit_tiny_patch16_224_in21k(pretrained=False, **kwargs):
  function vit_small_patch16_224_in21k (line 838) | def vit_small_patch16_224_in21k(pretrained=False, **kwargs):
  function vit_base_patch16_224_in21k (line 849) | def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
  function vit_large_patch16_224_in21k (line 860) | def vit_large_patch16_224_in21k(pretrained=False, **kwargs):

FILE: optim_factory.py
  function param_groups_weight_decay (line 39) | def param_groups_weight_decay(
  function _group (line 61) | def _group(it, size):
  function _layer_map (line 66) | def _layer_map(model, layers_per_group=12, num_groups=None):
  function group_with_matcher (line 94) | def group_with_matcher(
  function group_parameters (line 155) | def group_parameters(
  function group_modules (line 165) | def group_modules(
  function param_groups_layer_decay (line 177) | def param_groups_layer_decay(
  function optimizer_kwargs (line 239) | def optimizer_kwargs(cfg):
  function create_optimizer (line 260) | def create_optimizer(args, model, filter_bias_and_bn=True):
  function create_optimizer_v2 (line 271) | def create_optimizer_v2(

FILE: train.py
  function _parse_args (line 335) | def _parse_args():
  function main (line 352) | def main():
  function train_one_epoch (line 727) | def train_one_epoch(
  function validate (line 835) | def validate(model, loader, loss_fn, args, amp_autocast=suppress, log_su...

FILE: utils/mce_utils.py
  function get_ce_alexnet (line 43) | def get_ce_alexnet():
  function get_mce_from_accuracy (line 65) | def get_mce_from_accuracy(accuracy, error_alexnet):
  class SmoothedValue (line 72) | class SmoothedValue(object):
    method __init__ (line 77) | def __init__(self, window_size=20, fmt=None):
    method update (line 85) | def update(self, value, n=1):
    method synchronize_between_processes (line 90) | def synchronize_between_processes(self):
    method median (line 104) | def median(self):
    method avg (line 109) | def avg(self):
    method global_avg (line 114) | def global_avg(self):
    method max (line 118) | def max(self):
    method value (line 122) | def value(self):
    method __str__ (line 125) | def __str__(self):
  class MetricLogger (line 134) | class MetricLogger(object):
    method __init__ (line 135) | def __init__(self, delimiter="\t"):
    method update (line 139) | def update(self, **kwargs):
    method __getattr__ (line 146) | def __getattr__(self, attr):
    method __str__ (line 154) | def __str__(self):
    method synchronize_between_processes (line 162) | def synchronize_between_processes(self):
    method add_meter (line 166) | def add_meter(self, name, meter):
    method log_every (line 169) | def log_every(self, iterable, print_freq, header=None):
  function _load_checkpoint_for_ema (line 216) | def _load_checkpoint_for_ema(model_ema, checkpoint):
  function setup_for_distributed (line 226) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 241) | def is_dist_avail_and_initialized():
  function get_world_size (line 249) | def get_world_size():
  function get_rank (line 255) | def get_rank():
  function is_main_process (line 261) | def is_main_process():
  function save_on_master (line 265) | def save_on_master(*args, **kwargs):

FILE: utils/scaler.py
  class ApexScaler_SAM (line 10) | class ApexScaler_SAM(ApexScaler):
    method __call__ (line 12) | def __call__(self, loss, optimizer, clip_grad=None, clip_mode='norm', ...

FILE: utils/utils.py
  function resize_pos_embed (line 38) | def resize_pos_embed(posemb, posemb_new): # example: 224:(14x14+1)-> 384...
  function resize_pos_embed_cait (line 56) | def resize_pos_embed_cait(posemb, posemb_new): # example: 224:(14x14+1)-...
  function resize_pos_embed_nocls (line 70) | def resize_pos_embed_nocls(posemb, posemb_new): # example: 224:(14x14+1)...
  function load_state_dict (line 83) | def load_state_dict(checkpoint_path,model, use_ema=False, num_classes=10...
  function load_for_transfer_learning (line 124) | def load_for_transfer_learning(model, checkpoint_path, use_ema=False, st...
  function load_for_probing (line 128) | def load_for_probing(model, checkpoint_path, use_ema=False, strict=False...
  function get_mean_and_std (line 133) | def get_mean_and_std(dataset):
  function init_params (line 147) | def init_params(net):

FILE: validate_ood.py
  function validate (line 147) | def validate(args):
  function main (line 320) | def main():
  function write_results (line 367) | def write_results(results_file, results):