SYMBOL INDEX (1281 symbols across 136 files)

FILE: dall_e/__init__.py
  function load_model (line 9) | def load_model(path: str, device: torch.device = None) -> nn.Module:

FILE: dall_e/decoder.py
  class DecoderBlock (line 13) | class DecoderBlock(nn.Module):
    method __attrs_post_init__ (line 21) | def __attrs_post_init__(self) -> None:
    method forward (line 38) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class Decoder (line 42) | class Decoder(nn.Module):
    method __attrs_post_init__ (line 54) | def __attrs_post_init__(self) -> None:
    method forward (line 86) | def forward(self, x: torch.Tensor) -> torch.Tensor:

FILE: dall_e/encoder.py
  class EncoderBlock (line 13) | class EncoderBlock(nn.Module):
    method __attrs_post_init__ (line 21) | def __attrs_post_init__(self) -> None:
    method forward (line 38) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class Encoder (line 42) | class Encoder(nn.Module):
    method __attrs_post_init__ (line 53) | def __attrs_post_init__(self) -> None:
    method forward (line 85) | def forward(self, x: torch.Tensor) -> torch.Tensor:

FILE: dall_e/utils.py
  class Conv2d (line 11) | class Conv2d(nn.Module):
    method __attrs_post_init__ (line 20) | def __attrs_post_init__(self) -> None:
    method forward (line 31) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  function map_pixels (line 45) | def map_pixels(x: torch.Tensor) -> torch.Tensor:
  function unmap_pixels (line 51) | def unmap_pixels(x: torch.Tensor) -> torch.Tensor:

FILE: downstream_tasks/detection/evaluation/object_detection/mmcv_custom/checkpoint.py
  function _get_mmcv_home (line 43) | def _get_mmcv_home():
  function load_state_dict (line 54) | def load_state_dict(module, state_dict, strict=False, logger=None):
  function load_url_dist (line 122) | def load_url_dist(url, model_dir=None, map_location="cpu"):
  function load_pavimodel_dist (line 136) | def load_pavimodel_dist(model_path, map_location=None):
  function load_fileclient_dist (line 164) | def load_fileclient_dist(filename, backend, map_location):
  function get_torchvision_models (line 185) | def get_torchvision_models():
  function get_external_models (line 197) | def get_external_models():
  function get_mmcls_models (line 211) | def get_mmcls_models():
  function get_deprecated_model_names (line 218) | def get_deprecated_model_names():
  function _process_mmcls_checkpoint (line 227) | def _process_mmcls_checkpoint(checkpoint):
  function _load_checkpoint (line 238) | def _load_checkpoint(filename, map_location=None):
  function cosine_scheduler (line 299) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
  function load_checkpoint (line 319) | def load_checkpoint(model,
  function weights_to_cpu (line 597) | def weights_to_cpu(state_dict):
  function _save_to_state_dict (line 612) | def _save_to_state_dict(module, destination, prefix, keep_vars):
  function get_state_dict (line 632) | def get_state_dict(module, destination=None, prefix='', keep_vars=False):
  function save_checkpoint (line 676) | def save_checkpoint(model, filename, optimizer=None, meta=None):

FILE: downstream_tasks/detection/evaluation/object_detection/mmcv_custom/layer_decay_optimizer_constructor.py
  function get_num_layer_for_vit (line 17) | def get_num_layer_for_vit(var_name, num_max_layer):
  class LayerDecayOptimizerConstructor (line 30) | class LayerDecayOptimizerConstructor(DefaultOptimizerConstructor):
    method add_params (line 31) | def add_params(self, params, module, prefix='', is_dcn_module=None):

FILE: downstream_tasks/detection/evaluation/object_detection/mmcv_custom/prepare_rpe.py
  function rpe_index (line 9) | def rpe_index(window_size):
  function prepare_rpe (line 31) | def prepare_rpe(rel_pos_bias, src_patch_shape, dst_patch_shape):

FILE: downstream_tasks/detection/evaluation/object_detection/mmcv_custom/register_backbone.py
  class PatchEmbed (line 19) | class PatchEmbed(nn.Module):
    method __init__ (line 22) | def __init__(self, img_size=[224, 224], patch_size=16, in_chans=3, emb...
    method forward (line 34) | def forward(self, x, mask=None):
  class VisionTransformer (line 39) | class VisionTransformer(VisionTransformer):
    method __init__ (line 40) | def __init__(self,
    method build_2d_sincos_position_embedding (line 115) | def build_2d_sincos_position_embedding(self, embed_dim=768, temperatur...
    method train (line 133) | def train(self, mode=True):
    method _freeze_stages (line 144) | def _freeze_stages(self):
    method init_weights (line 167) | def init_weights(self, pretrained=None):
    method interpolate_pos_encoding (line 186) | def interpolate_pos_encoding(self, x, w, h):
    method forward (line 210) | def forward(self, x):

FILE: downstream_tasks/detection/evaluation/object_detection/mmcv_custom/runner/checkpoint.py
  function save_checkpoint (line 26) | def save_checkpoint(model, filename, optimizer=None, meta=None):

FILE: downstream_tasks/detection/evaluation/object_detection/mmcv_custom/runner/epoch_based_runner.py
  class EpochBasedRunnerAmp (line 27) | class EpochBasedRunnerAmp(EpochBasedRunner):
    method save_checkpoint (line 32) | def save_checkpoint(self,
    method resume (line 75) | def resume(self,

FILE: downstream_tasks/detection/evaluation/object_detection/test.py
  function parse_args (line 28) | def parse_args():
  function main (line 110) | def main():

FILE: downstream_tasks/detection/evaluation/object_detection/train.py
  function parse_args (line 30) | def parse_args():
  function main (line 95) | def main():

FILE: downstream_tasks/detection/loader.py
  class ImageFolderInstance (line 13) | class ImageFolderInstance(ImageFolder):
    method __getitem__ (line 14) | def __getitem__(self, index):
  class ImageFolderMask (line 18) | class ImageFolderMask(ImageFolder):
    method __init__ (line 19) | def __init__(self, *args, patch_size, pred_ratio, pred_ratio_var, pred...
    method get_pred_ratio (line 33) | def get_pred_ratio(self):
    method set_epoch (line 51) | def set_epoch(self, epoch):
    method __getitem__ (line 54) | def __getitem__(self, index):

FILE: downstream_tasks/detection/models/head.py
  class CSyncBatchNorm (line 13) | class CSyncBatchNorm(nn.SyncBatchNorm):
    method __init__ (line 14) | def __init__(self,
    method forward (line 21) | def forward(self, x):
  class PSyncBatchNorm (line 32) | class PSyncBatchNorm(nn.SyncBatchNorm):
    method __init__ (line 33) | def __init__(self,
  class CustomSequential (line 51) | class CustomSequential(nn.Sequential):
    method forward (line 54) | def forward(self, input):
  class DINOHead (line 65) | class DINOHead(nn.Module):
    method __init__ (line 66) | def __init__(self, in_dim, out_dim, norm=None, act='gelu', last_norm=N...
    method _init_weights (line 106) | def _init_weights(self, m):
    method forward (line 112) | def forward(self, x):
    method _build_norm (line 121) | def _build_norm(self, norm, hidden_dim, **kwargs):
    method _build_act (line 136) | def _build_act(self, act):
  class iBOTHead (line 145) | class iBOTHead(DINOHead):
    method __init__ (line 147) | def __init__(self, *args, patch_out_dim=8192, norm=None, act='gelu', l...
    method forward (line 181) | def forward(self, x):

FILE: downstream_tasks/detection/models/swin_transformer.py
  class Mlp (line 26) | class Mlp(nn.Module):
    method __init__ (line 27) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 36) | def forward(self, x):
  function window_partition (line 45) | def window_partition(x, window_size):
  function window_reverse (line 60) | def window_reverse(windows, window_size, H, W):
  class WindowAttention (line 77) | class WindowAttention(nn.Module):
    method __init__ (line 91) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scal...
    method forward (line 125) | def forward(self, x, mask=None):
    method extra_repr (line 159) | def extra_repr(self) -> str:
    method flops (line 162) | def flops(self, N):
    method compute_macs (line 176) | def compute_macs(module, input, output):
  class SwinTransformerBlock (line 182) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 201) | def __init__(self, dim, input_resolution, num_heads, window_size=7, sh...
    method create_attn_mask (line 233) | def create_attn_mask(self, H, W):
    method forward (line 259) | def forward(self, x):
    method extra_repr (line 318) | def extra_repr(self) -> str:
    method flops (line 322) | def flops(self):
  class PatchMerging (line 337) | class PatchMerging(nn.Module):
    method __init__ (line 346) | def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
    method forward (line 353) | def forward(self, x):
    method extra_repr (line 383) | def extra_repr(self) -> str:
    method flops (line 386) | def flops(self):
  class BasicLayer (line 393) | class BasicLayer(nn.Module):
    method __init__ (line 412) | def __init__(self, dim, input_resolution, depth, num_heads, window_size,
    method forward (line 436) | def forward(self, x):
    method forward_with_features (line 443) | def forward_with_features(self, x):
    method forward_with_attention (line 452) | def forward_with_attention(self, x):
    method extra_repr (line 462) | def extra_repr(self) -> str:
    method flops (line 465) | def flops(self):
  class PatchEmbed (line 474) | class PatchEmbed(nn.Module):
    method __init__ (line 478) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 497) | def forward(self, x):
    method flops (line 511) | def flops(self):
  class SwinTransformer (line 519) | class SwinTransformer(nn.Module):
    method __init__ (line 544) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes...
    method _init_weights (line 604) | def _init_weights(self, m):
    method no_weight_decay (line 614) | def no_weight_decay(self):
    method no_weight_decay_keywords (line 618) | def no_weight_decay_keywords(self):
    method forward (line 622) | def forward(self, x, return_all_tokens=None, mask=None):
    method get_selfattention (line 647) | def get_selfattention(self, x, n=1):
    method get_last_selfattention (line 660) | def get_last_selfattention(self, x):
    method get_all_selfattention (line 669) | def get_all_selfattention(self, x):
    method get_intermediate_layers (line 678) | def get_intermediate_layers(self, x, n=1, return_patch_avgpool=False):
    method flops (line 724) | def flops(self):
    method init_weights (line 735) | def init_weights(self, pretrained='', pretrained_layers=[], verbose=Tr...
    method freeze_pretrained_layers (line 802) | def freeze_pretrained_layers(self, frozen_layers=[]):
    method get_num_layers (line 828) | def get_num_layers(self):
    method mask_model (line 832) | def mask_model(self, x, mask):
  function swin_tiny (line 844) | def swin_tiny(window_size=7, **kwargs):
  function swin_small (line 851) | def swin_small(window_size=7, **kwargs):
  function swin_base (line 858) | def swin_base(window_size=7, **kwargs):
  function swin_large (line 865) | def swin_large(window_size=7, **kwargs):

FILE: downstream_tasks/detection/models/vision_transformer.py
  function drop_path (line 22) | def drop_path(x, drop_prob: float = 0., training: bool = False):
  class DropPath (line 33) | class DropPath(nn.Module):
    method __init__ (line 36) | def __init__(self, drop_prob=None):
    method forward (line 40) | def forward(self, x):
  class Mlp (line 44) | class Mlp(nn.Module):
    method __init__ (line 45) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 54) | def forward(self, x):
  class Attention (line 63) | class Attention(nn.Module):
    method __init__ (line 64) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at...
    method forward (line 87) | def forward(self, x, x_rel_pos_bias = None):
  class Block (line 121) | class Block(nn.Module):
    method __init__ (line 122) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 141) | def forward(self, x, x_rel_pos_bias=None, return_attention=False):
  class PatchEmbed (line 172) | class PatchEmbed(nn.Module):
    method __init__ (line 175) | def __init__(self, img_size=[224, 224], patch_size=16, in_chans=3, emb...
    method forward (line 188) | def forward(self, x, mask=None):
  class VisionTransformer (line 192) | class VisionTransformer(nn.Module):
    method __init__ (line 194) | def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_clas...
    method _init_weights (line 258) | def _init_weights(self, m):
    method interpolate_pos_encoding (line 267) | def interpolate_pos_encoding(self, x, w, h):
    method prepare_tokens (line 290) | def prepare_tokens(self, x, mask=None):
    method forward (line 310) | def forward(self, x, return_all_tokens=None, mask=None):
    method get_last_selfattention (line 331) | def get_last_selfattention(self, x):
    method get_intermediate_layers (line 340) | def get_intermediate_layers(self, x, n=1):
    method get_num_layers (line 350) | def get_num_layers(self):
    method mask_model (line 353) | def mask_model(self, x, mask):
  function vit_tiny (line 357) | def vit_tiny(patch_size=16, **kwargs):
  function vit_small (line 363) | def vit_small(patch_size=16, **kwargs):
  function vit_base (line 369) | def vit_base(patch_size=16, **kwargs):
  function vit_large (line 375) | def vit_large(patch_size=16, **kwargs):

FILE: downstream_tasks/detection/utils.py
  class GaussianBlur (line 29) | class GaussianBlur(object):
    method __init__ (line 33) | def __init__(self, p=0.5, radius_min=0.1, radius_max=2.):
    method __call__ (line 38) | def __call__(self, img):
  class Solarization (line 50) | class Solarization(object):
    method __init__ (line 54) | def __init__(self, p):
    method __call__ (line 57) | def __call__(self, img):
  class PermutePatch (line 64) | class PermutePatch(object):
    method __init__ (line 68) | def __init__(self, psz):
    method __call__ (line 71) | def __call__(self, img):
  class HideAndSeek (line 87) | class HideAndSeek(object):
    method __init__ (line 91) | def __init__(self, ratio, psz):
    method __call__ (line 95) | def __call__(self, img):
  function load_pretrained_weights (line 111) | def load_pretrained_weights(model, pretrained_weights, checkpoint_key, m...
  function clip_gradients (line 154) | def clip_gradients(model, clip):
  function cancel_gradients_last_layer (line 166) | def cancel_gradients_last_layer(epoch, model, freeze_last_layer):
  function restart_from_checkpoint (line 174) | def restart_from_checkpoint(ckp_path, run_variables=None, **kwargs):
  function cosine_scheduler (line 209) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
  function bool_flag (line 223) | def bool_flag(s):
  function fix_random_seeds (line 237) | def fix_random_seeds(seed=31):
  class SmoothedValue (line 248) | class SmoothedValue(object):
    method __init__ (line 253) | def __init__(self, window_size=20, fmt=None):
    method update (line 261) | def update(self, value, n=1):
    method synchronize_between_processes (line 266) | def synchronize_between_processes(self):
    method median (line 280) | def median(self):
    method avg (line 285) | def avg(self):
    method global_avg (line 290) | def global_avg(self):
    method max (line 294) | def max(self):
    method value (line 298) | def value(self):
    method __str__ (line 301) | def __str__(self):
  function reduce_dict (line 310) | def reduce_dict(input_dict, average=True):
  class MetricLogger (line 337) | class MetricLogger(object):
    method __init__ (line 338) | def __init__(self, delimiter="\t"):
    method update (line 342) | def update(self, **kwargs):
    method __getattr__ (line 349) | def __getattr__(self, attr):
    method __str__ (line 357) | def __str__(self):
    method synchronize_between_processes (line 365) | def synchronize_between_processes(self):
    method add_meter (line 369) | def add_meter(self, name, meter):
    method log_every (line 372) | def log_every(self, iterable, print_freq, header=None):
  function get_sha (line 427) | def get_sha():
  function is_dist_avail_and_initialized (line 447) | def is_dist_avail_and_initialized():
  function get_world_size (line 455) | def get_world_size():
  function get_rank (line 461) | def get_rank():
  function is_main_process (line 467) | def is_main_process():
  function save_on_master (line 471) | def save_on_master(*args, **kwargs):
  function setup_for_distributed (line 476) | def setup_for_distributed(is_master):
  function init_distributed_mode (line 491) | def init_distributed_mode(args):
  function accuracy (line 526) | def accuracy(output, target, topk=(1,)):
  function _no_grad_trunc_normal_ (line 536) | def _no_grad_trunc_normal_(tensor, mean, std, a, b):
  function trunc_normal_ (line 572) | def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
  class LARS (line 577) | class LARS(torch.optim.Optimizer):
    method __init__ (line 581) | def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, eta=0.001,
    method step (line 589) | def step(self):
  function create_ds_config (line 617) | def create_ds_config(args):
  class MultiCropWrapper (line 648) | class MultiCropWrapper(nn.Module):
    method __init__ (line 657) | def __init__(self, backbone, head=None):
    method forward (line 667) | def forward(self, x, mask=None, return_backbone_feat=False,
  function get_params_groups (line 698) | def get_params_groups(model):
  function has_batchnorms (line 712) | def has_batchnorms(model):
  function concat_all_gather (line 720) | def concat_all_gather(tensor):
  class PCA (line 733) | class PCA():
    method __init__ (line 737) | def __init__(self, dim=256, whit=0.5):
    method train_pca (line 742) | def train_pca(self, cov):
    method apply (line 768) | def apply(self, x):
  function compute_ap (line 787) | def compute_ap(ranks, nres):
  function compute_map (line 822) | def compute_map(ranks, gnd, kappas=[]):

FILE: downstream_tasks/semantic_segmentation/backbone/beit.py
  class DropPath (line 29) | class DropPath(nn.Module):
    method __init__ (line 32) | def __init__(self, drop_prob=None):
    method forward (line 36) | def forward(self, x):
    method extra_repr (line 39) | def extra_repr(self) -> str:
  class Mlp (line 43) | class Mlp(nn.Module):
    method __init__ (line 44) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 53) | def forward(self, x):
  class Attention (line 63) | class Attention(nn.Module):
    method __init__ (line 64) | def __init__(
    method forward (line 120) | def forward(self, x, rel_pos_bias=None):
  class Block (line 153) | class Block(nn.Module):
    method __init__ (line 155) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 175) | def forward(self, x, rel_pos_bias=None):
  class PatchEmbed (line 185) | class PatchEmbed(nn.Module):
    method __init__ (line 188) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 200) | def forward(self, x, **kwargs):
  class HybridEmbed (line 212) | class HybridEmbed(nn.Module):
    method __init__ (line 216) | def __init__(self, backbone, img_size=224, feature_size=None, in_chans...
    method forward (line 240) | def forward(self, x):
  class RelativePositionBias (line 247) | class RelativePositionBias(nn.Module):
    method __init__ (line 249) | def __init__(self, window_size, num_heads):
    method forward (line 278) | def forward(self):
  function get_sinusoid_encoding_table (line 285) | def get_sinusoid_encoding_table(n_position, d_hid, token=False):
  class BEiT (line 301) | class BEiT(nn.Module):
    method __init__ (line 304) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
    method build_2d_sincos_position_embedding (line 398) | def build_2d_sincos_position_embedding(self, embed_dim=768, temperatur...
    method fix_init_weight (line 416) | def fix_init_weight(self):
    method _init_weights (line 424) | def _init_weights(self, m):
    method init_weights (line 433) | def init_weights(self, pretrained=None):
    method get_num_layers (line 459) | def get_num_layers(self):
    method no_weight_decay (line 463) | def no_weight_decay(self):
    method forward_features (line 466) | def forward_features(self, x):
    method forward (line 502) | def forward(self, x):

FILE: downstream_tasks/semantic_segmentation/backbone/beit_fapn.py
  class DropPath (line 28) | class DropPath(nn.Module):
    method __init__ (line 31) | def __init__(self, drop_prob=None):
    method forward (line 35) | def forward(self, x):
    method extra_repr (line 38) | def extra_repr(self) -> str:
  class Mlp (line 42) | class Mlp(nn.Module):
    method __init__ (line 43) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 52) | def forward(self, x):
  class Attention (line 62) | class Attention(nn.Module):
    method __init__ (line 63) | def __init__(
    method forward (line 119) | def forward(self, x, rel_pos_bias=None):
  class Block (line 152) | class Block(nn.Module):
    method __init__ (line 154) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 174) | def forward(self, x, rel_pos_bias=None):
  class PatchEmbed (line 184) | class PatchEmbed(nn.Module):
    method __init__ (line 187) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 199) | def forward(self, x, **kwargs):
  class HybridEmbed (line 211) | class HybridEmbed(nn.Module):
    method __init__ (line 215) | def __init__(self, backbone, img_size=224, feature_size=None, in_chans...
    method forward (line 239) | def forward(self, x):
  class RelativePositionBias (line 246) | class RelativePositionBias(nn.Module):
    method __init__ (line 248) | def __init__(self, window_size, num_heads):
    method forward (line 277) | def forward(self):
  class FeatureSelectionModule (line 284) | class FeatureSelectionModule(nn.Module):
    method __init__ (line 285) | def __init__(self, in_c, out_c, norm="GM"):
    method forward (line 300) | def forward(self, x):
  class FeatureAlign (line 307) | class FeatureAlign(nn.Module):
    method __init__ (line 308) | def __init__(self, in_c, out_c, norm=None):
    method forward (line 314) | def forward(self, feat_l, feat_s):
  class BEiT_FaPN (line 328) | class BEiT_FaPN(nn.Module):
    method __init__ (line 331) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
    method fix_init_weight (line 415) | def fix_init_weight(self):
    method _init_weights (line 423) | def _init_weights(self, m):
    method init_weights (line 432) | def init_weights(self, pretrained=None):
    method get_num_layers (line 458) | def get_num_layers(self):
    method no_weight_decay (line 462) | def no_weight_decay(self):
    method forward_features (line 465) | def forward_features(self, x):
    method forward (line 499) | def forward(self, x):

FILE: downstream_tasks/semantic_segmentation/backbone/cae.py
  class DropPath (line 29) | class DropPath(nn.Module):
    method __init__ (line 32) | def __init__(self, drop_prob=None):
    method forward (line 36) | def forward(self, x):
    method extra_repr (line 39) | def extra_repr(self) -> str:
  class Mlp (line 43) | class Mlp(nn.Module):
    method __init__ (line 44) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 53) | def forward(self, x):
  class Attention (line 63) | class Attention(nn.Module):
    method __init__ (line 64) | def __init__(
    method forward (line 120) | def forward(self, x, rel_pos_bias=None):
  class Block (line 153) | class Block(nn.Module):
    method __init__ (line 155) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 175) | def forward(self, x, rel_pos_bias=None):
  class PatchEmbed (line 185) | class PatchEmbed(nn.Module):
    method __init__ (line 188) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 200) | def forward(self, x, **kwargs):
  class HybridEmbed (line 212) | class HybridEmbed(nn.Module):
    method __init__ (line 216) | def __init__(self, backbone, img_size=224, feature_size=None, in_chans...
    method forward (line 240) | def forward(self, x):
  class RelativePositionBias (line 247) | class RelativePositionBias(nn.Module):
    method __init__ (line 249) | def __init__(self, window_size, num_heads):
    method forward (line 278) | def forward(self):
  function get_sinusoid_encoding_table (line 285) | def get_sinusoid_encoding_table(n_position, d_hid, token=False):
  class CAE (line 301) | class CAE(nn.Module):
    method __init__ (line 304) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
    method build_2d_sincos_position_embedding (line 398) | def build_2d_sincos_position_embedding(self, embed_dim=768, temperatur...
    method fix_init_weight (line 416) | def fix_init_weight(self):
    method _init_weights (line 424) | def _init_weights(self, m):
    method init_weights (line 433) | def init_weights(self, pretrained=None):
    method get_num_layers (line 459) | def get_num_layers(self):
    method no_weight_decay (line 463) | def no_weight_decay(self):
    method forward_features (line 466) | def forward_features(self, x):
    method forward (line 502) | def forward(self, x):

FILE: downstream_tasks/semantic_segmentation/backbone/fapn.py
  class FeatureSelectionModule (line 1) | class FeatureSelectionModule(nn.Module):
    method __init__ (line 2) | def __init__(self, in_c, out_c, norm="GM"):
  class FeatureAlign (line 24) | class FeatureAlign(nn.Module):
    method __init__ (line 25) | def __init__(self, in_c, out_c, norm=None):
    method forward (line 31) | def forward(self, teat_l, feat_s):

FILE: downstream_tasks/semantic_segmentation/backbone/mae.py
  class DropPath (line 29) | class DropPath(nn.Module):
    method __init__ (line 32) | def __init__(self, drop_prob=None):
    method forward (line 36) | def forward(self, x):
    method extra_repr (line 39) | def extra_repr(self) -> str:
  class Mlp (line 43) | class Mlp(nn.Module):
    method __init__ (line 44) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 53) | def forward(self, x):
  class Attention (line 63) | class Attention(nn.Module):
    method __init__ (line 64) | def __init__(
    method forward (line 120) | def forward(self, x, rel_pos_bias=None):
  class Block (line 153) | class Block(nn.Module):
    method __init__ (line 155) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 175) | def forward(self, x, rel_pos_bias=None):
  class PatchEmbed (line 185) | class PatchEmbed(nn.Module):
    method __init__ (line 188) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 200) | def forward(self, x, **kwargs):
  class HybridEmbed (line 212) | class HybridEmbed(nn.Module):
    method __init__ (line 216) | def __init__(self, backbone, img_size=224, feature_size=None, in_chans...
    method forward (line 240) | def forward(self, x):
  class RelativePositionBias (line 247) | class RelativePositionBias(nn.Module):
    method __init__ (line 249) | def __init__(self, window_size, num_heads):
    method forward (line 278) | def forward(self):
  function get_sinusoid_encoding_table (line 285) | def get_sinusoid_encoding_table(n_position, d_hid, token=False):
  class MAE (line 301) | class MAE(nn.Module):
    method __init__ (line 304) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
    method build_2d_sincos_position_embedding (line 389) | def build_2d_sincos_position_embedding(self, embed_dim=768, temperatur...
    method fix_init_weight (line 407) | def fix_init_weight(self):
    method _init_weights (line 415) | def _init_weights(self, m):
    method init_weights (line 424) | def init_weights(self, pretrained=None):
    method get_num_layers (line 450) | def get_num_layers(self):
    method no_weight_decay (line 454) | def no_weight_decay(self):
    method forward_features (line 457) | def forward_features(self, x):
    method forward (line 492) | def forward(self, x):

FILE: downstream_tasks/semantic_segmentation/mmcv_custom/apex_runner/apex_iter_based_runner.py
  class IterBasedRunnerAmp (line 20) | class IterBasedRunnerAmp(IterBasedRunner):
    method save_checkpoint (line 26) | def save_checkpoint(self,
    method resume (line 68) | def resume(self,

FILE: downstream_tasks/semantic_segmentation/mmcv_custom/apex_runner/checkpoint.py
  function save_checkpoint (line 19) | def save_checkpoint(model, filename, optimizer=None, meta=None):

FILE: downstream_tasks/semantic_segmentation/mmcv_custom/apex_runner/optimizer.py
  class DistOptimizerHook (line 9) | class DistOptimizerHook(OptimizerHook):
    method __init__ (line 12) | def __init__(self, update_interval=1, grad_clip=None, coalesce=True, b...
    method before_run (line 19) | def before_run(self, runner):
    method after_train_iter (line 22) | def after_train_iter(self, runner):

FILE: downstream_tasks/semantic_segmentation/mmcv_custom/checkpoint.py
  function _get_mmcv_home (line 34) | def _get_mmcv_home():
  function load_state_dict (line 45) | def load_state_dict(module, state_dict, strict=False, logger=None):
  function load_url_dist (line 113) | def load_url_dist(url, model_dir=None, map_location="cpu"):
  function load_pavimodel_dist (line 127) | def load_pavimodel_dist(model_path, map_location=None):
  function load_fileclient_dist (line 155) | def load_fileclient_dist(filename, backend, map_location):
  function get_torchvision_models (line 176) | def get_torchvision_models():
  function get_external_models (line 188) | def get_external_models():
  function get_mmcls_models (line 202) | def get_mmcls_models():
  function get_deprecated_model_names (line 209) | def get_deprecated_model_names():
  function _process_mmcls_checkpoint (line 218) | def _process_mmcls_checkpoint(checkpoint):
  function _load_checkpoint (line 229) | def _load_checkpoint(filename, map_location=None):
  function cosine_scheduler (line 290) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
  function load_checkpoint (line 310) | def load_checkpoint(model,
  function weights_to_cpu (line 534) | def weights_to_cpu(state_dict):
  function _save_to_state_dict (line 549) | def _save_to_state_dict(module, destination, prefix, keep_vars):
  function get_state_dict (line 569) | def get_state_dict(module, destination=None, prefix='', keep_vars=False):
  function save_checkpoint (line 613) | def save_checkpoint(model, filename, optimizer=None, meta=None):

FILE: downstream_tasks/semantic_segmentation/mmcv_custom/checkpoint_beit.py
  function _get_mmcv_home (line 34) | def _get_mmcv_home():
  function load_state_dict (line 45) | def load_state_dict(module, state_dict, strict=False, logger=None):
  function load_url_dist (line 113) | def load_url_dist(url, model_dir=None, map_location="cpu"):
  function load_pavimodel_dist (line 127) | def load_pavimodel_dist(model_path, map_location=None):
  function load_fileclient_dist (line 155) | def load_fileclient_dist(filename, backend, map_location):
  function get_torchvision_models (line 176) | def get_torchvision_models():
  function get_external_models (line 188) | def get_external_models():
  function get_mmcls_models (line 202) | def get_mmcls_models():
  function get_deprecated_model_names (line 209) | def get_deprecated_model_names():
  function _process_mmcls_checkpoint (line 218) | def _process_mmcls_checkpoint(checkpoint):
  function _load_checkpoint (line 229) | def _load_checkpoint(filename, map_location=None):
  function cosine_scheduler (line 290) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
  function load_checkpoint (line 310) | def load_checkpoint(model,
  function weights_to_cpu (line 515) | def weights_to_cpu(state_dict):
  function _save_to_state_dict (line 530) | def _save_to_state_dict(module, destination, prefix, keep_vars):
  function get_state_dict (line 550) | def get_state_dict(module, destination=None, prefix='', keep_vars=False):
  function save_checkpoint (line 594) | def save_checkpoint(model, filename, optimizer=None, meta=None):

FILE: downstream_tasks/semantic_segmentation/mmcv_custom/layer_decay_optimizer_constructor.py
  function get_num_layer_for_vit (line 6) | def get_num_layer_for_vit(var_name, num_max_layer):
  class LayerDecayOptimizerConstructor (line 19) | class LayerDecayOptimizerConstructor(DefaultOptimizerConstructor):
    method add_params (line 20) | def add_params(self, params, module, prefix='', is_dcn_module=None):

FILE: downstream_tasks/semantic_segmentation/mmcv_custom/resize_transform.py
  class SETR_Resize (line 8) | class SETR_Resize(object):
    method __init__ (line 35) | def __init__(self,
    method random_select (line 66) | def random_select(img_scales):
    method random_sample (line 84) | def random_sample(img_scales):
    method random_sample_ratio (line 111) | def random_sample_ratio(img_scale, ratio_range):
    method _random_scale (line 137) | def _random_scale(self, results):
    method _resize_img (line 170) | def _resize_img(self, results):
    method _resize_seg (line 206) | def _resize_seg(self, results):
    method __call__ (line 217) | def __call__(self, results):
    method __repr__ (line 235) | def __repr__(self):

FILE: downstream_tasks/semantic_segmentation/mmcv_custom/train_api.py
  function set_random_seed (line 18) | def set_random_seed(seed, deterministic=False):
  function train_segmentor (line 37) | def train_segmentor(model,

FILE: downstream_tasks/semantic_segmentation/mmseg/__init__.py
  function digit_version (line 9) | def digit_version(version_str):

FILE: downstream_tasks/semantic_segmentation/mmseg/apis/inference.py
  function init_segmentor (line 11) | def init_segmentor(config, checkpoint=None, device='cuda:0'):
  class LoadImage (line 42) | class LoadImage:
    method __call__ (line 45) | def __call__(self, results):
  function inference_segmentor (line 69) | def inference_segmentor(model, img):
  function show_result_pyplot (line 101) | def show_result_pyplot(model, img, result, palette=None, fig_size=(15, 1...

FILE: downstream_tasks/semantic_segmentation/mmseg/apis/test.py
  function np2tmp (line 14) | def np2tmp(array, temp_file_name=None):
  function single_gpu_test (line 34) | def single_gpu_test(model,
  function multi_gpu_test (line 102) | def multi_gpu_test(model,
  function collect_results_cpu (line 160) | def collect_results_cpu(result_part, size, tmpdir=None):
  function collect_results_gpu (line 203) | def collect_results_gpu(result_part, size):

FILE: downstream_tasks/semantic_segmentation/mmseg/apis/train.py
  function set_random_seed (line 14) | def set_random_seed(seed, deterministic=False):
  function train_segmentor (line 33) | def train_segmentor(model,

FILE: downstream_tasks/semantic_segmentation/mmseg/core/evaluation/class_names.py
  function cityscapes_classes (line 4) | def cityscapes_classes():
  function ade_classes (line 14) | def ade_classes():
  function voc_classes (line 44) | def voc_classes():
  function cityscapes_palette (line 54) | def cityscapes_palette():
  function ade_palette (line 63) | def ade_palette():
  function voc_palette (line 105) | def voc_palette():
  function get_classes (line 121) | def get_classes(dataset):
  function get_palette (line 138) | def get_palette(dataset):

FILE: downstream_tasks/semantic_segmentation/mmseg/core/evaluation/eval_hooks.py
  class EvalHook (line 7) | class EvalHook(Hook):
    method __init__ (line 15) | def __init__(self, dataloader, interval=1, by_epoch=False, **eval_kwar...
    method after_train_iter (line 24) | def after_train_iter(self, runner):
    method after_train_epoch (line 33) | def after_train_epoch(self, runner):
    method evaluate (line 42) | def evaluate(self, runner, results):
  class DistEvalHook (line 51) | class DistEvalHook(EvalHook):
    method __init__ (line 63) | def __init__(self,
    method after_train_iter (line 79) | def after_train_iter(self, runner):
    method after_train_epoch (line 94) | def after_train_epoch(self, runner):

FILE: downstream_tasks/semantic_segmentation/mmseg/core/evaluation/metrics.py
  function intersect_and_union (line 5) | def intersect_and_union(pred_label,
  function total_intersect_and_union (line 62) | def total_intersect_and_union(results,
  function mean_iou (line 105) | def mean_iou(results,
  function mean_dice (line 142) | def mean_dice(results,
  function eval_metrics (line 179) | def eval_metrics(results,

FILE: downstream_tasks/semantic_segmentation/mmseg/core/seg/builder.py
  function build_pixel_sampler (line 6) | def build_pixel_sampler(cfg, **default_args):

FILE: downstream_tasks/semantic_segmentation/mmseg/core/seg/sampler/base_pixel_sampler.py
  class BasePixelSampler (line 4) | class BasePixelSampler(metaclass=ABCMeta):
    method __init__ (line 7) | def __init__(self, **kwargs):
    method sample (line 11) | def sample(self, seg_logit, seg_label):

FILE: downstream_tasks/semantic_segmentation/mmseg/core/seg/sampler/ohem_pixel_sampler.py
  class OHEMPixelSampler (line 9) | class OHEMPixelSampler(BasePixelSampler):
    method __init__ (line 23) | def __init__(self, context, thresh=None, min_kept=100000):
    method sample (line 30) | def sample(self, seg_logit, seg_label):

FILE: downstream_tasks/semantic_segmentation/mmseg/core/utils/misc.py
  function add_prefix (line 1) | def add_prefix(inputs, prefix):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/ade.py
  class ADE20KDataset (line 6) | class ADE20KDataset(CustomDataset):
    method __init__ (line 79) | def __init__(self, **kwargs):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/builder.py
  function _concat_dataset (line 25) | def _concat_dataset(cfg, default_args=None):
  function build_dataset (line 61) | def build_dataset(cfg, default_args=None):
  function build_dataloader (line 78) | def build_dataloader(dataset,
  function worker_init_fn (line 155) | def worker_init_fn(worker_id, num_workers, rank, seed):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/chase_db1.py
  class ChaseDB1Dataset (line 8) | class ChaseDB1Dataset(CustomDataset):
    method __init__ (line 21) | def __init__(self, **kwargs):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/cityscapes.py
  class CityscapesDataset (line 14) | class CityscapesDataset(CustomDataset):
    method __init__ (line 32) | def __init__(self, **kwargs):
    method _convert_to_label_id (line 39) | def _convert_to_label_id(result):
    method results2img (line 50) | def results2img(self, results, imgfile_prefix, to_label_id):
    method format_results (line 91) | def format_results(self, results, imgfile_prefix=None, to_label_id=True):
    method evaluate (line 124) | def evaluate(self,
    method _evaluate_cityscapes (line 164) | def _evaluate_cityscapes(self, results, logger, imgfile_prefix):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/coco_stuff.py
  class COCOStuffDataset (line 6) | class COCOStuffDataset(CustomDataset):
    method __init__ (line 90) | def __init__(self, **kwargs):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/custom.py
  class CustomDataset (line 18) | class CustomDataset(Dataset):
    method __init__ (line 74) | def __init__(self,
    method __len__ (line 115) | def __len__(self):
    method load_annotations (line 119) | def load_annotations(self, img_dir, img_suffix, ann_dir, seg_map_suffix,
    method get_ann_info (line 157) | def get_ann_info(self, idx):
    method pre_pipeline (line 169) | def pre_pipeline(self, results):
    method __getitem__ (line 177) | def __getitem__(self, idx):
    method prepare_train_img (line 193) | def prepare_train_img(self, idx):
    method prepare_test_img (line 210) | def prepare_test_img(self, idx):
    method format_results (line 226) | def format_results(self, results, **kwargs):
    method get_gt_seg_maps (line 230) | def get_gt_seg_maps(self, efficient_test=False):
    method get_classes_and_palette (line 243) | def get_classes_and_palette(self, classes=None, palette=None):
    method get_palette_for_custom_classes (line 287) | def get_palette_for_custom_classes(self, class_names, palette=None):
    method evaluate (line 306) | def evaluate(self,

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/dataset_wrappers.py
  class ConcatDataset (line 7) | class ConcatDataset(_ConcatDataset):
    method __init__ (line 17) | def __init__(self, datasets):
  class RepeatDataset (line 24) | class RepeatDataset(object):
    method __init__ (line 37) | def __init__(self, dataset, times):
    method __getitem__ (line 44) | def __getitem__(self, idx):
    method __len__ (line 48) | def __len__(self):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/drive.py
  class DRIVEDataset (line 8) | class DRIVEDataset(CustomDataset):
    method __init__ (line 21) | def __init__(self, **kwargs):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/hrf.py
  class HRFDataset (line 8) | class HRFDataset(CustomDataset):
    method __init__ (line 21) | def __init__(self, **kwargs):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/pascal_context.py
  class PascalContextDataset (line 8) | class PascalContextDataset(CustomDataset):
    method __init__ (line 47) | def __init__(self, split, **kwargs):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/pipelines/compose.py
  class Compose (line 9) | class Compose(object):
    method __init__ (line 17) | def __init__(self, transforms):
    method __call__ (line 29) | def __call__(self, data):
    method __repr__ (line 45) | def __repr__(self):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/pipelines/formating.py
  function to_tensor (line 11) | def to_tensor(data):
  class ToTensor (line 37) | class ToTensor(object):
    method __init__ (line 44) | def __init__(self, keys):
    method __call__ (line 47) | def __call__(self, results):
    method __repr__ (line 62) | def __repr__(self):
  class ImageToTensor (line 67) | class ImageToTensor(object):
    method __init__ (line 78) | def __init__(self, keys):
    method __call__ (line 81) | def __call__(self, results):
    method __repr__ (line 100) | def __repr__(self):
  class Transpose (line 105) | class Transpose(object):
    method __init__ (line 113) | def __init__(self, keys, order):
    method __call__ (line 117) | def __call__(self, results):
    method __repr__ (line 133) | def __repr__(self):
  class ToDataContainer (line 139) | class ToDataContainer(object):
    method __init__ (line 150) | def __init__(self,
    method __call__ (line 155) | def __call__(self, results):
    method __repr__ (line 173) | def __repr__(self):
  class DefaultFormatBundle (line 178) | class DefaultFormatBundle(object):
    method __call__ (line 189) | def __call__(self, results):
    method __repr__ (line 214) | def __repr__(self):
  class Collect (line 219) | class Collect(object):
    method __init__ (line 256) | def __init__(self,
    method __call__ (line 264) | def __call__(self, results):
    method __repr__ (line 286) | def __repr__(self):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/pipelines/loading.py
  class LoadImageFromFile (line 10) | class LoadImageFromFile(object):
    method __init__ (line 31) | def __init__(self,
    method __call__ (line 42) | def __call__(self, results):
    method __repr__ (line 81) | def __repr__(self):
  class LoadAnnotations (line 90) | class LoadAnnotations(object):
    method __init__ (line 104) | def __init__(self,
    method __call__ (line 113) | def __call__(self, results):
    method __repr__ (line 149) | def __repr__(self):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/pipelines/test_time_aug.py
  class MultiScaleFlipAug (line 10) | class MultiScaleFlipAug(object):
    method __init__ (line 53) | def __init__(self,
    method __call__ (line 93) | def __call__(self, results):
    method __repr__ (line 128) | def __repr__(self):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/pipelines/transforms.py
  class Resize (line 10) | class Resize(object):
    method __init__ (line 41) | def __init__(self,
    method random_select (line 68) | def random_select(img_scales):
    method random_sample (line 86) | def random_sample(img_scales):
    method random_sample_ratio (line 113) | def random_sample_ratio(img_scale, ratio_range):
    method _random_scale (line 139) | def _random_scale(self, results):
    method _resize_img (line 177) | def _resize_img(self, results):
    method _resize_seg (line 199) | def _resize_seg(self, results):
    method __call__ (line 210) | def __call__(self, results):
    method __repr__ (line 228) | def __repr__(self):
  class RandomFlip (line 238) | class RandomFlip(object):
    method __init__ (line 252) | def __init__(self, prob=None, direction='horizontal'):
    method __call__ (line 259) | def __call__(self, results):
    method __repr__ (line 288) | def __repr__(self):
  class Pad (line 293) | class Pad(object):
    method __init__ (line 308) | def __init__(self,
    method _pad_img (line 321) | def _pad_img(self, results):
    method _pad_seg (line 334) | def _pad_seg(self, results):
    method __call__ (line 342) | def __call__(self, results):
    method __repr__ (line 356) | def __repr__(self):
  class Normalize (line 364) | class Normalize(object):
    method __init__ (line 376) | def __init__(self, mean, std, to_rgb=True):
    method __call__ (line 381) | def __call__(self, results):
    method __repr__ (line 398) | def __repr__(self):
  class Rerange (line 406) | class Rerange(object):
    method __init__ (line 416) | def __init__(self, min_value=0, max_value=255):
    method __call__ (line 423) | def __call__(self, results):
    method __repr__ (line 445) | def __repr__(self):
  class CLAHE (line 452) | class CLAHE(object):
    method __init__ (line 465) | def __init__(self, clip_limit=40.0, tile_grid_size=(8, 8)):
    method __call__ (line 472) | def __call__(self, results):
    method __repr__ (line 489) | def __repr__(self):
  class RandomCrop (line 497) | class RandomCrop(object):
    method __init__ (line 506) | def __init__(self, crop_size, cat_max_ratio=1., ignore_index=255):
    method get_crop_bbox (line 512) | def get_crop_bbox(self, img):
    method crop (line 523) | def crop(self, img, crop_bbox):
    method __call__ (line 529) | def __call__(self, results):
    method __repr__ (line 565) | def __repr__(self):
  class RandomRotate (line 570) | class RandomRotate(object):
    method __init__ (line 588) | def __init__(self,
    method __call__ (line 609) | def __call__(self, results):
    method __repr__ (line 641) | def __repr__(self):
  class RGB2Gray (line 653) | class RGB2Gray(object):
    method __init__ (line 668) | def __init__(self, out_channels=None, weights=(0.299, 0.587, 0.114)):
    method __call__ (line 676) | def __call__(self, results):
    method __repr__ (line 700) | def __repr__(self):
  class AdjustGamma (line 708) | class AdjustGamma(object):
    method __init__ (line 716) | def __init__(self, gamma=1.0):
    method __call__ (line 724) | def __call__(self, results):
    method __repr__ (line 739) | def __repr__(self):
  class SegRescale (line 744) | class SegRescale(object):
    method __init__ (line 751) | def __init__(self, scale_factor=1):
    method __call__ (line 754) | def __call__(self, results):
    method __repr__ (line 769) | def __repr__(self):
  class PhotoMetricDistortion (line 774) | class PhotoMetricDistortion(object):
    method __init__ (line 795) | def __init__(self,
    method convert (line 805) | def convert(self, img, alpha=1, beta=0):
    method brightness (line 811) | def brightness(self, img):
    method contrast (line 820) | def contrast(self, img):
    method saturation (line 828) | def saturation(self, img):
    method hue (line 839) | def hue(self, img):
    method __call__ (line 849) | def __call__(self, results):
    method __repr__ (line 882) | def __repr__(self):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/stare.py
  class STAREDataset (line 8) | class STAREDataset(CustomDataset):
    method __init__ (line 21) | def __init__(self, **kwargs):

FILE: downstream_tasks/semantic_segmentation/mmseg/datasets/voc.py
  class PascalVOCDataset (line 8) | class PascalVOCDataset(CustomDataset):
    method __init__ (line 26) | def __init__(self, split, **kwargs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/backbones/cgnet.py
  class GlobalContextExtractor (line 13) | class GlobalContextExtractor(nn.Module):
    method __init__ (line 26) | def __init__(self, channel, reduction=16, with_cp=False):
    method forward (line 37) | def forward(self, x):
  class ContextGuidedBlock (line 53) | class ContextGuidedBlock(nn.Module):
    method __init__ (line 78) | def __init__(self,
    method forward (line 142) | def forward(self, x):
  class InputInjection (line 170) | class InputInjection(nn.Module):
    method __init__ (line 173) | def __init__(self, num_downsampling):
    method forward (line 179) | def forward(self, x):
  class CGNet (line 186) | class CGNet(nn.Module):
    method __init__ (line 215) | def __init__(self,
    method forward (line 309) | def forward(self, x):
    method init_weights (line 338) | def init_weights(self, pretrained=None):
    method train (line 359) | def train(self, mode=True):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/backbones/fast_scnn.py
  class LearningToDownsample (line 13) | class LearningToDownsample(nn.Module):
    method __init__ (line 29) | def __init__(self,
    method forward (line 66) | def forward(self, x):
  class GlobalFeatureExtractor (line 73) | class GlobalFeatureExtractor(nn.Module):
    method __init__ (line 106) | def __init__(self,
    method _make_layer (line 148) | def _make_layer(self,
    method forward (line 172) | def forward(self, x):
  class FeatureFusionModule (line 181) | class FeatureFusionModule(nn.Module):
    method __init__ (line 199) | def __init__(self,
    method forward (line 235) | def forward(self, higher_res_feature, lower_res_feature):
  class FastSCNN (line 250) | class FastSCNN(nn.Module):
    method __init__ (line 296) | def __init__(self,
    method init_weights (line 360) | def init_weights(self, pretrained=None):
    method forward (line 367) | def forward(self, x):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/backbones/hrnet.py
  class HRModule (line 13) | class HRModule(nn.Module):
    method __init__ (line 20) | def __init__(self,
    method _check_branches (line 46) | def _check_branches(self, num_branches, num_blocks, in_channels,
    method _make_one_branch (line 64) | def _make_one_branch(self,
    method _make_branches (line 109) | def _make_branches(self, num_branches, block, num_blocks, num_channels):
    method _make_fuse_layers (line 119) | def _make_fuse_layers(self):
    method forward (line 185) | def forward(self, x):
  class HRNet (line 212) | class HRNet(nn.Module):
    method __init__ (line 273) | def __init__(self,
    method norm1 (line 362) | def norm1(self):
    method norm2 (line 367) | def norm2(self):
    method _make_transition_layer (line 371) | def _make_transition_layer(self, num_channels_pre_layer,
    method _make_layer (line 418) | def _make_layer(self, block, inplanes, planes, blocks, stride=1):
    method _make_stage (line 454) | def _make_stage(self, layer_config, in_channels, multiscale_output=True):
    method init_weights (line 484) | def init_weights(self, pretrained=None):
    method forward (line 510) | def forward(self, x):
    method train (line 547) | def train(self, mode=True):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/backbones/mobilenet_v2.py
  class MobileNetV2 (line 13) | class MobileNetV2(nn.Module):
    method __init__ (line 45) | def __init__(self,
    method make_layer (line 107) | def make_layer(self, out_channels, num_blocks, stride, dilation,
    method init_weights (line 136) | def init_weights(self, pretrained=None):
    method forward (line 149) | def forward(self, x):
    method _freeze_stages (line 164) | def _freeze_stages(self):
    method train (line 174) | def train(self, mode=True):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/backbones/mobilenet_v3.py
  class MobileNetV3 (line 15) | class MobileNetV3(nn.Module):
    method __init__ (line 70) | def __init__(self,
    method _make_layer (line 104) | def _make_layer(self):
    method init_weights (line 220) | def init_weights(self, pretrained=None):
    method forward (line 233) | def forward(self, x):
    method _freeze_stages (line 242) | def _freeze_stages(self):
    method train (line 249) | def train(self, mode=True):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/backbones/resnest.py
  class RSoftmax (line 15) | class RSoftmax(nn.Module):
    method __init__ (line 23) | def __init__(self, radix, groups):
    method forward (line 28) | def forward(self, x):
  class SplitAttentionConv2d (line 39) | class SplitAttentionConv2d(nn.Module):
    method __init__ (line 58) | def __init__(self,
    method norm0 (line 108) | def norm0(self):
    method norm1 (line 113) | def norm1(self):
    method forward (line 117) | def forward(self, x):
  class Bottleneck (line 146) | class Bottleneck(_Bottleneck):
    method __init__ (line 165) | def __init__(self,
    method forward (line 226) | def forward(self, x):
  class ResNeSt (line 270) | class ResNeSt(ResNetV1d):
    method __init__ (line 291) | def __init__(self,
    method make_res_layer (line 305) | def make_res_layer(self, **kwargs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/backbones/resnet.py
  class BasicBlock (line 13) | class BasicBlock(nn.Module):
    method __init__ (line 18) | def __init__(self,
    method norm1 (line 58) | def norm1(self):
    method norm2 (line 63) | def norm2(self):
    method forward (line 67) | def forward(self, x):
  class Bottleneck (line 97) | class Bottleneck(nn.Module):
    method __init__ (line 106) | def __init__(self,
    method make_block_plugins (line 219) | def make_block_plugins(self, in_channels, plugins):
    method forward_plugin (line 242) | def forward_plugin(self, x, plugin_names):
    method norm1 (line 250) | def norm1(self):
    method norm2 (line 255) | def norm2(self):
    method norm3 (line 260) | def norm3(self):
    method forward (line 264) | def forward(self, x):
  class ResNet (line 308) | class ResNet(nn.Module):
    method __init__ (line 373) | def __init__(self,
    method make_stage_plugins (line 470) | def make_stage_plugins(self, plugins, stage_idx):
    method make_res_layer (line 523) | def make_res_layer(self, **kwargs):
    method norm1 (line 528) | def norm1(self):
    method _make_stem_layer (line 532) | def _make_stem_layer(self, in_channels, stem_channels):
    method _freeze_stages (line 581) | def _freeze_stages(self):
    method init_weights (line 600) | def init_weights(self, pretrained=None):
    method forward (line 632) | def forward(self, x):
    method train (line 649) | def train(self, mode=True):
  class ResNetV1c (line 662) | class ResNetV1c(ResNet):
    method __init__ (line 672) | def __init__(self, **kwargs):
  class ResNetV1d (line 678) | class ResNetV1d(ResNet):
    method __init__ (line 686) | def __init__(self, **kwargs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/backbones/resnext.py
  class Bottleneck (line 11) | class Bottleneck(_Bottleneck):
    method __init__ (line 18) | def __init__(self,
  class ResNeXt (line 87) | class ResNeXt(ResNet):
    method __init__ (line 134) | def __init__(self, groups=1, base_width=4, **kwargs):
    method make_res_layer (line 139) | def make_res_layer(self, **kwargs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/backbones/unet.py
  class BasicConvBlock (line 13) | class BasicConvBlock(nn.Module):
    method __init__ (line 43) | def __init__(self,
    method forward (line 76) | def forward(self, x):
  class DeconvModule (line 87) | class DeconvModule(nn.Module):
    method __init__ (line 105) | def __init__(self,
    method forward (line 137) | def forward(self, x):
  class InterpConv (line 148) | class InterpConv(nn.Module):
    method __init__ (line 179) | def __init__(self,
    method forward (line 211) | def forward(self, x):
  class UNet (line 222) | class UNet(nn.Module):
    method __init__ (line 277) | def __init__(self,
    method forward (line 375) | def forward(self, x):
    method train (line 388) | def train(self, mode=True):
    method _check_input_devisible (line 398) | def _check_input_devisible(self, x):
    method init_weights (line 411) | def init_weights(self, pretrained=None):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/builder.py
  function build (line 13) | def build(cfg, registry, default_args=None):
  function build_backbone (line 36) | def build_backbone(cfg):
  function build_neck (line 41) | def build_neck(cfg):
  function build_head (line 46) | def build_head(cfg):
  function build_loss (line 51) | def build_loss(cfg):
  function build_segmentor (line 56) | def build_segmentor(cfg, train_cfg=None, test_cfg=None):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/ann_head.py
  class PPMConcat (line 10) | class PPMConcat(nn.ModuleList):
    method __init__ (line 18) | def __init__(self, pool_scales=(1, 3, 6, 8)):
    method forward (line 22) | def forward(self, feats):
  class SelfAttentionBlock (line 32) | class SelfAttentionBlock(_SelfAttentionBlock):
    method __init__ (line 52) | def __init__(self, low_in_channels, high_in_channels, channels,
  class AFNB (line 79) | class AFNB(nn.Module):
    method __init__ (line 99) | def __init__(self, low_in_channels, high_in_channels, channels,
    method forward (line 125) | def forward(self, low_feats, high_feats):
  class APNB (line 133) | class APNB(nn.Module):
    method __init__ (line 150) | def __init__(self, in_channels, channels, out_channels, query_scales,
    method forward (line 175) | def forward(self, feats):
  class ANNHead (line 184) | class ANNHead(BaseDecodeHead):
    method __init__ (line 198) | def __init__(self,
    method forward (line 236) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/apc_head.py
  class ACM (line 11) | class ACM(nn.Module):
    method __init__ (line 25) | def __init__(self, pool_scale, fusion, in_channels, channels, conv_cfg,
    method forward (line 78) | def forward(self, x):
  class APCHead (line 110) | class APCHead(BaseDecodeHead):
    method __init__ (line 124) | def __init__(self, pool_scales=(1, 2, 3, 6), fusion=True, **kwargs):
    method forward (line 149) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/aspp_head.py
  class ASPPModule (line 10) | class ASPPModule(nn.ModuleList):
    method __init__ (line 22) | def __init__(self, dilations, in_channels, channels, conv_cfg, norm_cfg,
    method forward (line 43) | def forward(self, x):
  class ASPPHead (line 53) | class ASPPHead(BaseDecodeHead):
    method __init__ (line 64) | def __init__(self, dilations=(1, 6, 12, 18), **kwargs):
    method forward (line 93) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/cascade_decode_head.py
  class BaseCascadeDecodeHead (line 6) | class BaseCascadeDecodeHead(BaseDecodeHead, metaclass=ABCMeta):
    method __init__ (line 10) | def __init__(self, *args, **kwargs):
    method forward (line 14) | def forward(self, inputs, prev_output):
    method forward_train (line 18) | def forward_train(self, inputs, prev_output, img_metas, gt_semantic_seg,
    method forward_test (line 41) | def forward_test(self, inputs, prev_output, img_metas, test_cfg):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/cc_head.py
  class CCHead (line 13) | class CCHead(FCNHead):
    method __init__ (line 24) | def __init__(self, recurrence=2, **kwargs):
    method forward (line 32) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/da_head.py
  class PAM (line 12) | class PAM(_SelfAttentionBlock):
    method __init__ (line 20) | def __init__(self, in_channels, channels):
    method forward (line 41) | def forward(self, x):
  class CAM (line 49) | class CAM(nn.Module):
    method __init__ (line 52) | def __init__(self):
    method forward (line 56) | def forward(self, x):
  class DAHead (line 75) | class DAHead(BaseDecodeHead):
    method __init__ (line 85) | def __init__(self, pam_channels, **kwargs):
    method pam_cls_seg (line 128) | def pam_cls_seg(self, feat):
    method cam_cls_seg (line 135) | def cam_cls_seg(self, feat):
    method forward (line 142) | def forward(self, inputs):
    method forward_test (line 160) | def forward_test(self, inputs, img_metas, test_cfg):
    method losses (line 164) | def losses(self, seg_logit, seg_label):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/decode_head.py
  class BaseDecodeHead (line 14) | class BaseDecodeHead(nn.Module, metaclass=ABCMeta):
    method __init__ (line 46) | def __init__(self,
    method extra_repr (line 88) | def extra_repr(self):
    method _init_inputs (line 95) | def _init_inputs(self, in_channels, in_index, input_transform):
    method init_weights (line 133) | def init_weights(self):
    method _transform_inputs (line 137) | def _transform_inputs(self, inputs):
    method forward (line 166) | def forward(self, inputs):
    method forward_train (line 170) | def forward_train(self, inputs, img_metas, gt_semantic_seg, train_cfg):
    method forward_test (line 190) | def forward_test(self, inputs, img_metas, test_cfg):
    method cls_seg (line 207) | def cls_seg(self, feat):
    method losses (line 215) | def losses(self, seg_logit, seg_label):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/dm_head.py
  class DCM (line 10) | class DCM(nn.Module):
    method __init__ (line 24) | def __init__(self, filter_size, fusion, in_channels, channels, conv_cfg,
    method forward (line 60) | def forward(self, x):
  class DMHead (line 92) | class DMHead(BaseDecodeHead):
    method __init__ (line 106) | def __init__(self, filter_sizes=(1, 3, 5, 7), fusion=False, **kwargs):
    method forward (line 131) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/dnl_head.py
  class DisentangledNonLocal2d (line 9) | class DisentangledNonLocal2d(NonLocal2d):
    method __init__ (line 16) | def __init__(self, *arg, temperature, **kwargs):
    method embedded_gaussian (line 21) | def embedded_gaussian(self, theta_x, phi_x):
    method forward (line 33) | def forward(self, x):
  class DNLHead (line 87) | class DNLHead(FCNHead):
    method __init__ (line 102) | def __init__(self,
    method forward (line 122) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/ema_head.py
  function reduce_mean (line 13) | def reduce_mean(tensor):
  class EMAModule (line 22) | class EMAModule(nn.Module):
    method __init__ (line 31) | def __init__(self, channels, num_bases, num_stages, momentum):
    method forward (line 44) | def forward(self, feats):
  class EMAHead (line 79) | class EMAHead(BaseDecodeHead):
    method __init__ (line 94) | def __init__(self,
    method forward (line 154) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/enc_head.py
  class EncModule (line 11) | class EncModule(nn.Module):
    method __init__ (line 22) | def __init__(self, in_channels, num_codes, conv_cfg, norm_cfg, act_cfg):
    method forward (line 50) | def forward(self, x):
  class EncHead (line 62) | class EncHead(BaseDecodeHead):
    method __init__ (line 78) | def __init__(self,
    method forward (line 129) | def forward(self, inputs):
    method forward_test (line 151) | def forward_test(self, inputs, img_metas, test_cfg):
    method _convert_to_onehot_labels (line 159) | def _convert_to_onehot_labels(seg_label, num_classes):
    method losses (line 178) | def losses(self, seg_logit, seg_label):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/fcn_head.py
  class FCNHead (line 10) | class FCNHead(BaseDecodeHead):
    method __init__ (line 22) | def __init__(self,
    method forward (line 69) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/fpn_head.py
  class FPNHead (line 11) | class FPNHead(BaseDecodeHead):
    method __init__ (line 23) | def __init__(self, feature_strides, **kwargs):
    method forward (line 54) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/gc_head.py
  class GCHead (line 9) | class GCHead(FCNHead):
    method __init__ (line 23) | def __init__(self,
    method forward (line 38) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/lraspp_head.py
  class LRASPPHead (line 12) | class LRASPPHead(BaseDecodeHead):
    method __init__ (line 23) | def __init__(self, branch_channels=(32, 64), **kwargs):
    method forward (line 68) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/nl_head.py
  class NLHead (line 9) | class NLHead(FCNHead):
    method __init__ (line 23) | def __init__(self,
    method forward (line 40) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/ocr_head.py
  class SpatialGatherModule (line 12) | class SpatialGatherModule(nn.Module):
    method __init__ (line 19) | def __init__(self, scale):
    method forward (line 23) | def forward(self, feats, probs):
  class ObjectAttentionBlock (line 39) | class ObjectAttentionBlock(_SelfAttentionBlock):
    method __init__ (line 42) | def __init__(self, in_channels, channels, scale, conv_cfg, norm_cfg,
    method forward (line 73) | def forward(self, query_feats, key_feats):
  class OCRHead (line 85) | class OCRHead(BaseCascadeDecodeHead):
    method __init__ (line 97) | def __init__(self, ocr_channels, scale=1, **kwargs):
    method forward (line 119) | def forward(self, inputs, prev_output):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/point_head.py
  function calculate_uncertainty (line 14) | def calculate_uncertainty(seg_logits):
  class PointHead (line 35) | class PointHead(BaseCascadeDecodeHead):
    method __init__ (line 60) | def __init__(self,
    method init_weights (line 104) | def init_weights(self):
    method cls_seg (line 108) | def cls_seg(self, feat):
    method forward (line 115) | def forward(self, fine_grained_point_feats, coarse_point_feats):
    method _get_fine_grained_point_feats (line 123) | def _get_fine_grained_point_feats(self, x, points):
    method _get_coarse_point_feats (line 147) | def _get_coarse_point_feats(self, prev_output, points):
    method forward_train (line 165) | def forward_train(self, inputs, prev_output, img_metas, gt_semantic_seg,
    method forward_test (line 203) | def forward_test(self, inputs, prev_output, img_metas, test_cfg):
    method losses (line 248) | def losses(self, point_logits, point_label):
    method get_points_train (line 256) | def get_points_train(self, seg_logits, uncertainty_func, cfg):
    method get_points_test (line 310) | def get_points_test(self, seg_logits, uncertainty_func, cfg):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/psa_head.py
  class PSAHead (line 17) | class PSAHead(BaseDecodeHead):
    method __init__ (line 35) | def __init__(self,
    method forward (line 113) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/psp_head.py
  class PPM (line 10) | class PPM(nn.ModuleList):
    method __init__ (line 24) | def __init__(self, pool_scales, in_channels, channels, conv_cfg, norm_...
    method forward (line 46) | def forward(self, x):
  class PSPHead (line 61) | class PSPHead(BaseDecodeHead):
    method __init__ (line 72) | def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
    method forward (line 93) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/sep_aspp_head.py
  class DepthwiseSeparableASPPModule (line 10) | class DepthwiseSeparableASPPModule(ASPPModule):
    method __init__ (line 14) | def __init__(self, **kwargs):
  class DepthwiseSeparableASPPHead (line 29) | class DepthwiseSeparableASPPHead(ASPPHead):
    method __init__ (line 42) | def __init__(self, c1_in_channels, c1_channels, **kwargs):
    method forward (line 78) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/sep_fcn_head.py
  class DepthwiseSeparableFCNHead (line 8) | class DepthwiseSeparableFCNHead(FCNHead):
    method __init__ (line 29) | def __init__(self, **kwargs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/decode_heads/uper_head.py
  class UPerHead (line 12) | class UPerHead(BaseDecodeHead):
    method __init__ (line 23) | def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
    method psp_forward (line 76) | def psp_forward(self, inputs):
    method forward (line 86) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/losses/accuracy.py
  function accuracy (line 4) | def accuracy(pred, target, topk=1, thresh=None):
  class Accuracy (line 52) | class Accuracy(nn.Module):
    method __init__ (line 55) | def __init__(self, topk=(1, ), thresh=None):
    method forward (line 68) | def forward(self, pred, target):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/losses/cross_entropy_loss.py
  function cross_entropy (line 9) | def cross_entropy(pred,
  function _expand_onehot_labels (line 35) | def _expand_onehot_labels(labels, label_weights, target_shape, ignore_in...
  function binary_cross_entropy (line 57) | def binary_cross_entropy(pred,
  function mask_cross_entropy (line 100) | def mask_cross_entropy(pred,
  class CrossEntropyLoss (line 139) | class CrossEntropyLoss(nn.Module):
    method __init__ (line 154) | def __init__(self,
    method forward (line 175) | def forward(self,

FILE: downstream_tasks/semantic_segmentation/mmseg/models/losses/lovasz_loss.py
  function lovasz_grad (line 14) | def lovasz_grad(gt_sorted):
  function flatten_binary_logits (line 29) | def flatten_binary_logits(logits, labels, ignore_index=None):
  function flatten_probs (line 42) | def flatten_probs(probs, labels, ignore_index=None):
  function lovasz_hinge_flat (line 59) | def lovasz_hinge_flat(logits, labels):
  function lovasz_hinge (line 83) | def lovasz_hinge(logits,
  function lovasz_softmax_flat (line 128) | def lovasz_softmax_flat(probs, labels, classes='present', class_weight=N...
  function lovasz_softmax (line 171) | def lovasz_softmax(probs,
  class LovaszLoss (line 225) | class LovaszLoss(nn.Module):
    method __init__ (line 248) | def __init__(self,
    method forward (line 274) | def forward(self,

FILE: downstream_tasks/semantic_segmentation/mmseg/models/losses/utils.py
  function reduce_loss (line 6) | def reduce_loss(loss, reduction):
  function weight_reduce_loss (line 26) | def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=N...
  function weighted_loss (line 58) | def weighted_loss(loss_func):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/necks/fpn.py
  class FPN (line 9) | class FPN(nn.Module):
    method __init__ (line 63) | def __init__(self,
    method init_weights (line 157) | def init_weights(self):
    method forward (line 162) | def forward(self, inputs):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/segmentors/base.py
  class BaseSegmentor (line 14) | class BaseSegmentor(nn.Module):
    method __init__ (line 19) | def __init__(self):
    method with_neck (line 24) | def with_neck(self):
    method with_auxiliary_head (line 29) | def with_auxiliary_head(self):
    method with_decode_head (line 35) | def with_decode_head(self):
    method extract_feat (line 40) | def extract_feat(self, imgs):
    method encode_decode (line 45) | def encode_decode(self, img, img_metas):
    method forward_train (line 51) | def forward_train(self, imgs, img_metas, **kwargs):
    method simple_test (line 56) | def simple_test(self, img, img_meta, **kwargs):
    method aug_test (line 61) | def aug_test(self, imgs, img_metas, **kwargs):
    method init_weights (line 65) | def init_weights(self, pretrained=None):
    method forward_test (line 76) | def forward_test(self, imgs, img_metas, **kwargs):
    method forward (line 111) | def forward(self, img, img_metas, return_loss=True, **kwargs):
    method train_step (line 126) | def train_step(self, data_batch, optimizer, **kwargs):
    method val_step (line 162) | def val_step(self, data_batch, **kwargs):
    method _parse_losses (line 173) | def _parse_losses(losses):
    method show_result (line 208) | def show_result(self,

FILE: downstream_tasks/semantic_segmentation/mmseg/models/segmentors/cascade_encoder_decoder.py
  class CascadeEncoderDecoder (line 11) | class CascadeEncoderDecoder(EncoderDecoder):
    method __init__ (line 19) | def __init__(self,
    method _init_decode_head (line 38) | def _init_decode_head(self, decode_head):
    method init_weights (line 48) | def init_weights(self, pretrained=None):
    method encode_decode (line 65) | def encode_decode(self, img, img_metas):
    method _decode_head_forward_train (line 80) | def _decode_head_forward_train(self, x, img_metas, gt_semantic_seg):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/segmentors/encoder_decoder.py
  class EncoderDecoder (line 13) | class EncoderDecoder(BaseSegmentor):
    method __init__ (line 21) | def __init__(self,
    method _init_decode_head (line 43) | def _init_decode_head(self, decode_head):
    method _init_auxiliary_head (line 49) | def _init_auxiliary_head(self, auxiliary_head):
    method init_weights (line 59) | def init_weights(self, pretrained=None):
    method extract_feat (line 77) | def extract_feat(self, img):
    method encode_decode (line 84) | def encode_decode(self, img, img_metas):
    method _decode_head_forward_train (line 96) | def _decode_head_forward_train(self, x, img_metas, gt_semantic_seg):
    method _decode_head_forward_test (line 107) | def _decode_head_forward_test(self, x, img_metas):
    method _auxiliary_head_forward_train (line 113) | def _auxiliary_head_forward_train(self, x, img_metas, gt_semantic_seg):
    method forward_dummy (line 130) | def forward_dummy(self, img):
    method forward_train (line 136) | def forward_train(self, img, img_metas, gt_semantic_seg):
    method slide_inference (line 169) | def slide_inference(self, img, img_meta, rescale):
    method whole_inference (line 214) | def whole_inference(self, img, img_meta, rescale):
    method inference (line 228) | def inference(self, img, img_meta, rescale):
    method simple_test (line 263) | def simple_test(self, img, img_meta, rescale=True):
    method aug_test (line 276) | def aug_test(self, imgs, img_metas, rescale=True):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/utils/inverted_residual.py
  class InvertedResidual (line 8) | class InvertedResidual(nn.Module):
    method __init__ (line 31) | def __init__(self,
    method forward (line 81) | def forward(self, x):
  class InvertedResidualV3 (line 97) | class InvertedResidualV3(nn.Module):
    method __init__ (line 124) | def __init__(self,
    method forward (line 183) | def forward(self, x):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/utils/make_divisible.py
  function make_divisible (line 1) | def make_divisible(value, divisor, min_value=None, min_ratio=0.9):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/utils/res_layer.py
  class ResLayer (line 5) | class ResLayer(nn.Sequential):
    method __init__ (line 26) | def __init__(self,

FILE: downstream_tasks/semantic_segmentation/mmseg/models/utils/se_layer.py
  class SELayer (line 8) | class SELayer(nn.Module):
    method __init__ (line 26) | def __init__(self,
    method forward (line 53) | def forward(self, x):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/utils/self_attention_block.py
  class SelfAttentionBlock (line 7) | class SelfAttentionBlock(nn.Module):
    method __init__ (line 32) | def __init__(self, key_in_channels, query_in_channels, channels,
    method init_weights (line 93) | def init_weights(self):
    method build_project (line 99) | def build_project(self, in_channels, channels, num_convs, use_conv_mod...
    method forward (line 131) | def forward(self, query_feats, key_feats):

FILE: downstream_tasks/semantic_segmentation/mmseg/models/utils/up_conv_block.py
  class UpConvBlock (line 6) | class UpConvBlock(nn.Module):
    method __init__ (line 44) | def __init__(self,
    method forward (line 94) | def forward(self, skip, x):

FILE: downstream_tasks/semantic_segmentation/mmseg/ops/encoding.py
  class Encoding (line 6) | class Encoding(nn.Module):
    method __init__ (line 17) | def __init__(self, channels, num_codes):
    method scaled_l2 (line 33) | def scaled_l2(x, codewords, scale):
    method aggregate (line 46) | def aggregate(assigment_weights, x, codewords):
    method forward (line 57) | def forward(self, x):
    method __repr__ (line 70) | def __repr__(self):

FILE: downstream_tasks/semantic_segmentation/mmseg/ops/wrappers.py
  function resize (line 8) | def resize(input,
  class Upsample (line 32) | class Upsample(nn.Module):
    method __init__ (line 34) | def __init__(self,
    method forward (line 48) | def forward(self, x):

FILE: downstream_tasks/semantic_segmentation/mmseg/utils/collect_env.py
  function collect_env (line 7) | def collect_env():

FILE: downstream_tasks/semantic_segmentation/mmseg/utils/logger.py
  function get_root_logger (line 6) | def get_root_logger(log_file=None, log_level=logging.INFO):

FILE: downstream_tasks/semantic_segmentation/mmseg/version.py
  function parse_version_info (line 6) | def parse_version_info(version_str):

FILE: downstream_tasks/semantic_segmentation/tools/test.py
  function parse_args (line 17) | def parse_args():
  function main (line 67) | def main():

FILE: downstream_tasks/semantic_segmentation/tools/train.py
  function parse_args (line 26) | def parse_args():
  function main (line 70) | def main():

FILE: furnace/dataset_folder.py
  function has_file_allowed_extension (line 11) | def has_file_allowed_extension(filename: str, extensions: Tuple[str, ......
  function is_image_file (line 24) | def is_image_file(filename: str) -> bool:
  function make_dataset (line 36) | def make_dataset(
  class DatasetFolder (line 66) | class DatasetFolder(VisionDataset):
    method __init__ (line 98) | def __init__(
    method _find_classes (line 125) | def _find_classes(self, dir: str) -> Tuple[List[str], Dict[str, int]]:
    method __getitem__ (line 143) | def __getitem__(self, index: int) -> Tuple[Any, Any]:
    method __len__ (line 167) | def __len__(self) -> int:
  function pil_loader (line 174) | def pil_loader(path: str) -> Image.Image:
  function accimage_loader (line 182) | def accimage_loader(path: str) -> Any:
  function default_loader (line 191) | def default_loader(path: str) -> Any:
  class ImageFolder (line 199) | class ImageFolder(DatasetFolder):
    method __init__ (line 226) | def __init__(

FILE: furnace/datasets.py
  function preprocess_vqgan (line 15) | def preprocess_vqgan(x):
  class DataAugmentationForCAE (line 19) | class DataAugmentationForCAE(object):
    method __init__ (line 20) | def __init__(self, args):
    method __call__ (line 85) | def __call__(self, image):
    method __repr__ (line 92) | def __repr__(self):
  function build_cae_pretraining_dataset (line 101) | def build_cae_pretraining_dataset(args):
  function build_dataset (line 107) | def build_dataset(is_train, args):
  function build_transform (line 141) | def build_transform(is_train, args):

FILE: furnace/engine_for_finetuning.py
  function train_class_batch (line 14) | def train_class_batch(model, samples, target, criterion):
  function get_loss_scale_for_deepspeed (line 20) | def get_loss_scale_for_deepspeed(model):
  function train_one_epoch (line 25) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
  function evaluate (line 150) | def evaluate(data_loader, model, device):

FILE: furnace/engine_for_pretraining.py
  function loss_selector (line 13) | def loss_selector(loss_type, pred, target):
  function train_one_epoch (line 19) | def train_one_epoch(model: torch.nn.Module, d_vae: torch.nn.Module,

FILE: furnace/masking_generator.py
  class MaskingGenerator (line 5) | class MaskingGenerator:
    method __init__ (line 6) | def __init__(
    method __repr__ (line 22) | def __repr__(self):
    method get_shape (line 28) | def get_shape(self):
    method _mask (line 31) | def _mask(self, mask, max_mask_patches):
    method __call__ (line 55) | def __call__(self):
  class RandomMaskingGenerator (line 68) | class RandomMaskingGenerator:
    method __init__ (line 69) | def __init__(
    method __repr__ (line 78) | def __repr__(self):
    method __call__ (line 85) | def __call__(self):

FILE: furnace/optim_factory.py
  function get_num_layer_for_vit (line 24) | def get_num_layer_for_vit(var_name, num_max_layer):
  class LayerDecayValueAssigner (line 38) | class LayerDecayValueAssigner(object):
    method __init__ (line 39) | def __init__(self, values):
    method get_scale (line 42) | def get_scale(self, layer_id):
    method get_layer_id (line 45) | def get_layer_id(self, var_name):
  function get_parameter_groups (line 49) | def get_parameter_groups(model, weight_decay=1e-5, skip_list=(), get_num...
  function create_optimizer (line 91) | def create_optimizer(args, model, get_num_layer=None, get_layer_scale=No...

FILE: furnace/transforms.py
  class ToNumpy (line 10) | class ToNumpy:
    method __call__ (line 12) | def __call__(self, pil_img):
  class ToTensor (line 20) | class ToTensor:
    method __init__ (line 22) | def __init__(self, dtype=torch.float32):
    method __call__ (line 25) | def __call__(self, pil_img):
  function _pil_interp (line 43) | def _pil_interp(method):
  class RandomResizedCropAndInterpolationWithTwoPic (line 58) | class RandomResizedCropAndInterpolationWithTwoPic:
    method __init__ (line 73) | def __init__(self, size, second_size=None, scale=(0.08, 1.0), ratio=(3...
    method get_params (line 98) | def get_params(img, scale, ratio):
    method __call__ (line 140) | def __call__(self, img):
    method __repr__ (line 159) | def __repr__(self):

FILE: furnace/utils.py
  class SmoothedValue (line 34) | class SmoothedValue(object):
    method __init__ (line 39) | def __init__(self, window_size=20, fmt=None):
    method update (line 47) | def update(self, value, n=1):
    method synchronize_between_processes (line 52) | def synchronize_between_processes(self):
    method median (line 66) | def median(self):
    method avg (line 71) | def avg(self):
    method global_avg (line 76) | def global_avg(self):
    method max (line 80) | def max(self):
    method value (line 84) | def value(self):
    method __str__ (line 87) | def __str__(self):
  class MetricLogger (line 96) | class MetricLogger(object):
    method __init__ (line 97) | def __init__(self, delimiter="\t"):
    method update (line 101) | def update(self, **kwargs):
    method __getattr__ (line 110) | def __getattr__(self, attr):
    method __str__ (line 118) | def __str__(self):
    method synchronize_between_processes (line 126) | def synchronize_between_processes(self):
    method add_meter (line 130) | def add_meter(self, name, meter):
    method log_every (line 133) | def log_every(self, iterable, print_freq, header=None):
  class TensorboardLogger (line 180) | class TensorboardLogger(object):
    method __init__ (line 181) | def __init__(self, log_dir):
    method set_step (line 185) | def set_step(self, step=None):
    method update (line 191) | def update(self, head='scalar', step=None, **kwargs):
    method flush (line 200) | def flush(self):
  function _load_checkpoint_for_ema (line 204) | def _load_checkpoint_for_ema(model_ema, checkpoint):
  function setup_for_distributed_each_gpu (line 213) | def setup_for_distributed_each_gpu(rank):
  function setup_for_distributed (line 225) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 242) | def is_dist_avail_and_initialized():
  function get_world_size (line 250) | def get_world_size():
  function get_rank (line 256) | def get_rank():
  function is_main_process (line 262) | def is_main_process():
  function save_on_master (line 266) | def save_on_master(*args, **kwargs):
  function init_distributed_mode (line 271) | def init_distributed_mode(args):
  function load_state_dict (line 308) | def load_state_dict(model, state_dict, prefix='', ignore_missing="relati...
  class NativeScalerWithGradNormCount (line 357) | class NativeScalerWithGradNormCount:
    method __init__ (line 360) | def __init__(self):
    method __call__ (line 363) | def __call__(self, loss, optimizer, clip_grad=None, parameters=None, c...
    method state_dict (line 379) | def state_dict(self):
    method load_state_dict (line 382) | def load_state_dict(self, state_dict):
  function get_grad_norm_ (line 386) | def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
  function cosine_scheduler (line 401) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
  function save_model (line 421) | def save_model(args, epoch, model, model_without_ddp, optimizer, loss_sc...
  function auto_load_model (line 459) | def auto_load_model(args, model, model_without_ddp, optimizer, loss_scal...
  function create_d_vae (line 529) | def create_d_vae(weight_path, d_vae_type, image_size, device, args=None):
  function get_vqgan_gumbel_f8_8192 (line 541) | def get_vqgan_gumbel_f8_8192(weight_path, image_size, device):
  function get_dalle_vae (line 549) | def get_dalle_vae(weight_path, image_size, device):
  function get_d_vae (line 555) | def get_d_vae(weight_path, image_size, device, args):
  function create_ds_config (line 575) | def create_ds_config(args):
  class LP_BatchNorm (line 607) | class LP_BatchNorm(_NormBase):
    method __init__ (line 614) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
    method _check_input_dim (line 619) | def _check_input_dim(self, input):
    method forward (line 624) | def forward(self, input, is_train):

FILE: linear_util/crop.py
  class RandomResizedCrop (line 9) | class RandomResizedCrop(transforms.RandomResizedCrop):
    method get_params (line 17) | def get_params(img, scale, ratio):

FILE: linear_util/datasets.py
  class DataAugmentationMySelf (line 10) | class DataAugmentationMySelf(object):
    method __init__ (line 11) | def __init__(self, args):
    method __call__ (line 29) | def __call__(self, image):
    method __repr__ (line 32) | def __repr__(self):
  function build_dataset (line 39) | def build_dataset(is_train, args):
  function build_dataset_finetune (line 49) | def build_dataset_finetune(is_train, args):
  function build_transform_finetune (line 59) | def build_transform_finetune(is_train, args):
  function build_transform (line 95) | def build_transform(is_train, args):

FILE: linear_util/engine_finetune.py
  function train_one_epoch (line 14) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
  function evaluate (line 88) | def evaluate(data_loader, model, device):

FILE: linear_util/lars.py
  class LARS (line 4) | class LARS(torch.optim.Optimizer):
    method __init__ (line 8) | def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, trust_c...
    method step (line 13) | def step(self):

FILE: linear_util/lr_decay.py
  function param_groups_lrd (line 4) | def param_groups_lrd(model, weight_decay=0.05, no_weight_decay_list=[], ...
  function get_layer_id_for_vit (line 49) | def get_layer_id_for_vit(name, num_layers):

FILE: linear_util/lr_sched.py
  function adjust_learning_rate (line 3) | def adjust_learning_rate(optimizer, epoch, args):

FILE: linear_util/misc.py
  class SmoothedValue (line 13) | class SmoothedValue(object):
    method __init__ (line 18) | def __init__(self, window_size=20, fmt=None):
    method update (line 26) | def update(self, value, n=1):
    method synchronize_between_processes (line 31) | def synchronize_between_processes(self):
    method median (line 45) | def median(self):
    method avg (line 50) | def avg(self):
    method global_avg (line 55) | def global_avg(self):
    method max (line 59) | def max(self):
    method value (line 63) | def value(self):
    method __str__ (line 66) | def __str__(self):
  class MetricLogger (line 75) | class MetricLogger(object):
    method __init__ (line 76) | def __init__(self, delimiter="\t"):
    method update (line 80) | def update(self, **kwargs):
    method __getattr__ (line 89) | def __getattr__(self, attr):
    method __str__ (line 97) | def __str__(self):
    method synchronize_between_processes (line 105) | def synchronize_between_processes(self):
    method add_meter (line 109) | def add_meter(self, name, meter):
    method log_every (line 112) | def log_every(self, iterable, print_freq, header=None):
  function setup_for_distributed (line 159) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 176) | def is_dist_avail_and_initialized():
  function get_world_size (line 184) | def get_world_size():
  function get_rank (line 190) | def get_rank():
  function is_main_process (line 196) | def is_main_process():
  function save_on_master (line 200) | def save_on_master(*args, **kwargs):
  function init_distributed_mode (line 205) | def init_distributed_mode(args):
  class NativeScalerWithGradNormCount (line 240) | class NativeScalerWithGradNormCount:
    method __init__ (line 243) | def __init__(self):
    method __call__ (line 246) | def __call__(self, loss, optimizer, clip_grad=None, parameters=None, c...
    method state_dict (line 262) | def state_dict(self):
    method load_state_dict (line 265) | def load_state_dict(self, state_dict):
  function get_grad_norm_ (line 269) | def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
  function save_model (line 284) | def save_model(args, epoch, model, model_without_ddp, optimizer, loss_sc...
  function load_model (line 311) | def load_model(args, model_without_ddp, optimizer, loss_scaler):
  function all_reduce_mean (line 330) | def all_reduce_mean(x):

FILE: linear_util/pos_embed.py
  function get_2d_sincos_pos_embed (line 5) | def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
  function get_2d_sincos_pos_embed_from_grid (line 23) | def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
  function get_1d_sincos_pos_embed_from_grid (line 34) | def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
  function interpolate_pos_embed (line 60) | def interpolate_pos_embed(model, checkpoint_model):

FILE: models/modeling_cae.py
  function trunc_normal_ (line 12) | def trunc_normal_(tensor, mean=0., std=1.):
  class VisionTransformerForMaskedImageModeling (line 16) | class VisionTransformerForMaskedImageModeling(nn.Module):
    method __init__ (line 17) | def __init__(self, img_size=224, patch_size=16, in_chans=3, vocab_size...
    method _init_teacher (line 62) | def _init_teacher(self):
    method momentum_update (line 69) | def momentum_update(self, base_momentum=0):
    method _init_weights (line 76) | def _init_weights(self, m):
    method forward (line 90) | def forward(self, x, bool_masked_pos, return_all_tokens=None):
  function cae_small_patch16_224_8k_vocab (line 144) | def cae_small_patch16_224_8k_vocab(pretrained=False, **kwargs):
  function cae_base_patch16_224_8k_vocab (line 158) | def cae_base_patch16_224_8k_vocab(pretrained=False, **kwargs):
  function cae_large_patch16_224_8k_vocab (line 172) | def cae_large_patch16_224_8k_vocab(pretrained=False, **kwargs):

FILE: models/modeling_cae_helper.py
  function trunc_normal_ (line 12) | def trunc_normal_(tensor, mean=0., std=1.):
  class Attention (line 15) | class Attention(nn.Module):
    method __init__ (line 16) | def __init__(
    method forward (line 39) | def forward(self, x, bool_masked_pos=None):
  class CrossAttention (line 65) | class CrossAttention(nn.Module):
    method __init__ (line 66) | def __init__(
    method forward (line 93) | def forward(self, x, bool_masked_pos=None, k=None, v=None):
  class Block (line 126) | class Block(nn.Module):
    method __init__ (line 128) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 147) | def forward(self, x, bool_masked_pos=None):
  class RegressorBlock (line 158) | class RegressorBlock(nn.Module):
    method __init__ (line 159) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 183) | def forward(self, x_q, x_kv, pos_q, pos_k, bool_masked_pos):
  class VisionTransformerEncoder (line 195) | class VisionTransformerEncoder(nn.Module):
    method __init__ (line 196) | def __init__(self, img_size=224, patch_size=16, in_chans=3, vocab_size...
    method build_2d_sincos_position_embedding (line 233) | def build_2d_sincos_position_embedding(self, embed_dim=768, temperatur...
    method fix_init_weight (line 254) | def fix_init_weight(self):
    method _init_weights (line 262) | def _init_weights(self, m):
    method no_weight_decay (line 276) | def no_weight_decay(self):
    method get_num_layers (line 279) | def get_num_layers(self):
    method forward_features (line 282) | def forward_features(self, x, bool_masked_pos):
    method forward (line 307) | def forward(self, x, bool_masked_pos, return_all_tokens=False):
  class VisionTransformerNeck (line 314) | class VisionTransformerNeck(nn.Module):
    method __init__ (line 315) | def __init__(self, patch_size=16, num_classes=8192, embed_dim=768, dep...
    method fix_init_weight (line 353) | def fix_init_weight(self):
    method _init_weights (line 365) | def _init_weights(self, m):
    method no_weight_decay (line 379) | def no_weight_decay(self):
    method forward (line 382) | def forward(self, x_masked, x_unmasked, pos_embed_masked, pos_embed_un...

FILE: models/modeling_discrete_vae.py
  function top_k (line 19) | def top_k(logits, thres = 0.5):
  function exists (line 28) | def exists(val):
  function default (line 32) | def default(val, d):
  function eval_decorator (line 36) | def eval_decorator(fn):
  class BasicVAE (line 46) | class BasicVAE(nn.Module):
    method get_codebook_indices (line 48) | def get_codebook_indices(self, images):
    method decode (line 51) | def decode(self, img_seq):
    method get_codebook_probs (line 54) | def get_codebook_probs(self, img_seq):
    method get_image_tokens_size (line 57) | def get_image_tokens_size(self):
    method get_image_size (line 60) | def get_image_size(self):
  class ResBlock (line 65) | class ResBlock(nn.Module):
    method __init__ (line 66) | def __init__(self, chan):
    method forward (line 76) | def forward(self, x):
  class DiscreteVAE (line 82) | class DiscreteVAE(BasicVAE):
    method __init__ (line 83) | def __init__(
    method get_image_size (line 144) | def get_image_size(self):
    method get_image_tokens_size (line 147) | def get_image_tokens_size(self):
    method get_codebook_indices (line 152) | def get_codebook_indices(self, images):
    method get_codebook_probs (line 159) | def get_codebook_probs(self, images, temp):
    method decode (line 163) | def decode(
    method forward (line 175) | def forward(
  class Dalle_VAE (line 220) | class Dalle_VAE(BasicVAE):
    method __init__ (line 221) | def __init__(self, image_size):
    method load_model (line 227) | def load_model(self, model_dir, device):
    method decode (line 231) | def decode(self, img_seq):
    method get_codebook_indices (line 237) | def get_codebook_indices(self, images):
    method get_codebook_probs (line 241) | def get_codebook_probs(self, images):
    method forward (line 245) | def forward(self, img_seq_prob, no_process=False):
  class VGGAN (line 254) | class VGGAN(BasicVAE):
    method __init__ (line 255) | def __init__(self, image_size):
    method load_model (line 261) | def load_model(self, weight_path, device):
    method get_codebook_indices (line 264) | def get_codebook_indices(self, images):

FILE: models/modeling_finetune.py
  function _cfg (line 12) | def _cfg(url='', **kwargs):
  class DropPath (line 22) | class DropPath(nn.Module):
    method __init__ (line 25) | def __init__(self, drop_prob=None):
    method forward (line 29) | def forward(self, x):
    method extra_repr (line 32) | def extra_repr(self) -> str:
  class Mlp (line 36) | class Mlp(nn.Module):
    method __init__ (line 37) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 46) | def forward(self, x):
  class Attention (line 55) | class Attention(nn.Module):
    method __init__ (line 56) | def __init__(
    method forward (line 109) | def forward(self, x, rel_pos_bias=None):
  class CrossAttention (line 141) | class CrossAttention(nn.Module):
    method __init__ (line 142) | def __init__(
    method forward (line 169) | def forward(self, x, bool_masked_pos=None, k=None, v=None):
  class Block (line 201) | class Block(nn.Module):
    method __init__ (line 203) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 223) | def forward(self, x, rel_pos_bias=None):
  class AttentiveBlock (line 232) | class AttentiveBlock(nn.Module):
    method __init__ (line 234) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 249) | def forward(self, x_q, x_kv, pos_q, pos_k, bool_masked_pos, rel_pos_bi...
  class PatchEmbed (line 258) | class PatchEmbed(nn.Module):
    method __init__ (line 261) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 273) | def forward(self, x, **kwargs):
  class RelativePositionBias (line 282) | class RelativePositionBias(nn.Module):
    method __init__ (line 284) | def __init__(self, window_size, num_heads):
    method forward (line 313) | def forward(self):
  function get_sinusoid_encoding_table (line 320) | def get_sinusoid_encoding_table(n_position, d_hid, token=False):
  class VisionTransformer (line 335) | class VisionTransformer(nn.Module):
    method __init__ (line 338) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
    method build_2d_sincos_position_embedding (line 413) | def build_2d_sincos_position_embedding(self, embed_dim=768, temperatur...
    method fix_init_weight (line 434) | def fix_init_weight(self):
    method _init_weights (line 442) | def _init_weights(self, m):
    method get_num_layers (line 451) | def get_num_layers(self):
    method no_weight_decay (line 455) | def no_weight_decay(self):
    method get_classifier (line 458) | def get_classifier(self):
    method reset_classifier (line 461) | def reset_classifier(self, num_classes, global_pool=''):
    method forward_features (line 465) | def forward_features(self, x, is_train=True):
    method forward (line 501) | def forward(self, x, is_train=True):
  function cae_small_patch16_224 (line 508) | def cae_small_patch16_224(pretrained=False, **kwargs):
  function cae_base_patch16_224 (line 516) | def cae_base_patch16_224(pretrained=False, **kwargs):
  function cae_base_patch16_384 (line 525) | def cae_base_patch16_384(pretrained=False, **kwargs):
  function cae_large_patch16_224 (line 534) | def cae_large_patch16_224(pretrained=False, **kwargs):
  function cae_large_patch16_384 (line 543) | def cae_large_patch16_384(pretrained=False, **kwargs):
  function cae_large_patch16_512 (line 552) | def cae_large_patch16_512(pretrained=False, **kwargs):

FILE: tools/run_attentive.py
  function get_args (line 26) | def get_args():
  function main (line 216) | def main(args, ds_init):

FILE: tools/run_class_finetuning.py
  function get_args (line 27) | def get_args():
  function main (line 216) | def main(args, ds_init):

FILE: tools/run_linear.py
  function setup_for_distributed (line 31) | def setup_for_distributed(rank):
  function get_args_parser (line 44) | def get_args_parser():
  function main (line 144) | def main(args):

FILE: tools/run_pretraining.py
  function get_args (line 23) | def get_args():
  function get_model (line 167) | def get_model(args):
  function main (line 182) | def main(args):