SYMBOL INDEX (1795 symbols across 175 files)

FILE: src/benchmark/pretrain_ssl/datasets/SSL4EO/ssl4eo_dataset.py
  function normalize (line 36) | def normalize(img, mean, std):
  class SSL4EO (line 45) | class SSL4EO(torch.utils.data.Dataset):
    method __init__ (line 47) | def __init__(self,root, normalize=False, mode=['s1','s2a','s2c'], dtyp...
    method __getitem__ (line 56) | def __getitem__(self,index):
    method __len__ (line 75) | def __len__(self):
    method get_array (line 78) | def get_array(self, patch_id, mode):
  class Subset (line 127) | class Subset(Dataset):
    method __init__ (line 129) | def __init__(self, dataset, indices):
    method __getitem__ (line 133) | def __getitem__(self, idx):
    method __len__ (line 136) | def __len__(self):
    method __getattr__ (line 139) | def __getattr__(self, name):
  function random_subset (line 143) | def random_subset(dataset, frac, seed=None):
  class _RepeatSampler (line 149) | class _RepeatSampler(object):
    method __init__ (line 156) | def __init__(self, sampler):
    method __iter__ (line 159) | def __iter__(self):
  class InfiniteDataLoader (line 164) | class InfiniteDataLoader(DataLoader):
    method __init__ (line 170) | def __init__(self, *args, **kwargs):
    method __len__ (line 175) | def __len__(self):
    method __iter__ (line 178) | def __iter__(self):
  function make_lmdb (line 183) | def make_lmdb(dataset, lmdb_file, num_workers=6,mode=['s1','s2a','s2c']):

FILE: src/benchmark/pretrain_ssl/datasets/SSL4EO/ssl4eo_dataset_lmdb.py
  function normalize (line 23) | def normalize(img, mean, std):
  class Subset (line 32) | class Subset(Dataset):
    method __init__ (line 34) | def __init__(self, dataset, indices):
    method __getitem__ (line 38) | def __getitem__(self, idx):
    method __len__ (line 41) | def __len__(self):
    method __getattr__ (line 44) | def __getattr__(self, name):
  function random_subset (line 48) | def random_subset(dataset, frac, seed=None):
  class _RepeatSampler (line 54) | class _RepeatSampler(object):
    method __init__ (line 61) | def __init__(self, sampler):
    method __iter__ (line 64) | def __iter__(self):
  class InfiniteDataLoader (line 69) | class InfiniteDataLoader(DataLoader):
    method __init__ (line 75) | def __init__(self, *args, **kwargs):
    method __len__ (line 80) | def __len__(self):
    method __iter__ (line 83) | def __iter__(self):
  function make_lmdb (line 88) | def make_lmdb(dataset, lmdb_file, num_workers=6,mode=['s1','s2a','s2c']):
  class LMDBDataset (line 121) | class LMDBDataset(Dataset):
    method __init__ (line 123) | def __init__(self, lmdb_file, is_slurm_job=False, s1_transform=None, s...
    method _init_db (line 146) | def _init_db(self):
    method __getitem__ (line 152) | def __getitem__(self, index):
    method __len__ (line 228) | def __len__(self):
  class TwoCropsTransform (line 247) | class TwoCropsTransform:
    method __init__ (line 250) | def __init__(self, base_transform, season='fixed'):
    method __call__ (line 254) | def __call__(self, x):
  class GaussianBlur (line 276) | class GaussianBlur(object):
    method __init__ (line 279) | def __init__(self, sigma=[.1, 2.]):
    method __call__ (line 282) | def __call__(self, x):
  class RandomBrightness (line 289) | class RandomBrightness(object):
    method __init__ (line 292) | def __init__(self, brightness=0.4):
    method __call__ (line 295) | def __call__(self, sample):
  class RandomContrast (line 301) | class RandomContrast(object):
    method __init__ (line 304) | def __init__(self, contrast=0.4):
    method __call__ (line 307) | def __call__(self, sample):
  class ToGray (line 313) | class ToGray(object):
    method __init__ (line 314) | def __init__(self, out_channels):
    method __call__ (line 316) | def __call__(self,sample):
  class RandomChannelDrop (line 323) | class RandomChannelDrop(object):
    method __init__ (line 326) | def __init__(self, min_n_drop=1, max_n_drop=8):
    method __call__ (line 330) | def __call__(self, sample):

FILE: src/benchmark/pretrain_ssl/models/data2vec/dall_e/__init__.py
  function load_model (line 9) | def load_model(path: str, device: torch.device = None) -> nn.Module:

FILE: src/benchmark/pretrain_ssl/models/data2vec/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: src/benchmark/pretrain_ssl/models/data2vec/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: src/benchmark/pretrain_ssl/models/data2vec/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: src/benchmark/pretrain_ssl/models/data2vec/dataset_folder.py
  function has_file_allowed_extension (line 21) | def has_file_allowed_extension(filename: str, extensions: Tuple[str, ......
  function is_image_file (line 34) | def is_image_file(filename: str) -> bool:
  function make_dataset (line 46) | def make_dataset(
  class DatasetFolder (line 76) | class DatasetFolder(VisionDataset):
    method __init__ (line 108) | def __init__(
    method _find_classes (line 138) | def _find_classes(self, dir: str) -> Tuple[List[str], Dict[str, int]]:
    method __getitem__ (line 156) | def __getitem__(self, index: int) -> Tuple[Any, Any]:
    method __len__ (line 180) | def __len__(self) -> int:
  function pil_loader (line 187) | def pil_loader(path: str) -> Image.Image:
  function accimage_loader (line 195) | def accimage_loader(path: str) -> Any:
  function default_loader (line 204) | def default_loader(path: str) -> Any:
  class ImageFolder (line 234) | class ImageFolder(DatasetFolder):
    method __init__ (line 261) | def __init__(

FILE: src/benchmark/pretrain_ssl/models/data2vec/datasets.py
  class SeasonTransform (line 35) | class SeasonTransform:
    method __init__ (line 37) | def __init__(self, base_transform, season='fixed'):
    method __call__ (line 41) | def __call__(self, x):
  class DataAugmentations (line 64) | class DataAugmentations(object):
    method __init__ (line 65) | def __init__(self, args):
    method __call__ (line 130) | def __call__(self, image):
    method __repr__ (line 141) | def __repr__(self):
  function build_beit_pretraining_dataset (line 152) | def build_beit_pretraining_dataset(args):
  function build_transform (line 167) | def build_transform(is_train, args):

FILE: src/benchmark/pretrain_ssl/models/data2vec/engine_for_cyclical.py
  function train_one_epoch (line 23) | def train_one_epoch(model: torch.nn.Module, model_ema: torch.nn.Module, ...

FILE: src/benchmark/pretrain_ssl/models/data2vec/engine_for_cyclical_joint.py
  function train_one_epoch (line 24) | def train_one_epoch(model: torch.nn.Module, model_ema: torch.nn.Module, ...

FILE: src/benchmark/pretrain_ssl/models/data2vec/engine_for_finetuning.py
  function train_class_batch (line 27) | def train_class_batch(model, samples, target, criterion, bool_masked_pos...
  function get_loss_scale_for_deepspeed (line 33) | def get_loss_scale_for_deepspeed(model):
  function train_one_epoch (line 38) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
  function evaluate (line 173) | def evaluate(data_loader, model, device, metric='acc', padd=False, oneho...

FILE: src/benchmark/pretrain_ssl/models/data2vec/engine_for_pretraining.py
  function train_one_epoch (line 22) | def train_one_epoch(model: torch.nn.Module, d_vae: torch.nn.Module,

FILE: src/benchmark/pretrain_ssl/models/data2vec/masking_generator.py
  class MaskingGenerator (line 29) | class MaskingGenerator:
    method __init__ (line 30) | def __init__(
    method __repr__ (line 46) | def __repr__(self):
    method get_shape (line 52) | def get_shape(self):
    method _mask (line 55) | def _mask(self, mask, max_mask_patches):
    method __call__ (line 79) | def __call__(self):

FILE: src/benchmark/pretrain_ssl/models/data2vec/modeling_cyclical.py
  function trunc_normal_ (line 22) | def trunc_normal_(tensor, mean=0.0, std=1.0):
  class VisionTransformerForCyclicalTraining (line 32) | class VisionTransformerForCyclicalTraining(nn.Module):
    method __init__ (line 33) | def __init__(
    method fix_init_weight (line 127) | def fix_init_weight(self):
    method _init_weights (line 135) | def _init_weights(self, m):
    method no_weight_decay (line 149) | def no_weight_decay(self):
    method get_num_layers (line 152) | def get_num_layers(self):
    method forward_features (line 155) | def forward_features(self, x, bool_masked_pos, layer_results):
    method forward (line 194) | def forward(self, x, bool_masked_pos, return_all_tokens=False, layer_r...
  function beit_base_patch16_224 (line 214) | def beit_base_patch16_224(pretrained=False, **kwargs):
  function beit_small_patch16_224 (line 234) | def beit_small_patch16_224(pretrained=False, **kwargs):
  function beit_large_patch16_224 (line 255) | def beit_large_patch16_224(pretrained=False, **kwargs):
  function beit_huge_patch16_224 (line 275) | def beit_huge_patch16_224(pretrained=False, **kwargs):

FILE: src/benchmark/pretrain_ssl/models/data2vec/modeling_cyclical_joint.py
  function trunc_normal_ (line 22) | def trunc_normal_(tensor, mean=0.0, std=1.0):
  class VisionTransformerForCyclicalJointTraining (line 32) | class VisionTransformerForCyclicalJointTraining(nn.Module):
    method __init__ (line 33) | def __init__(
    method fix_init_weight (line 126) | def fix_init_weight(self):
    method _init_weights (line 134) | def _init_weights(self, m):
    method no_weight_decay (line 148) | def no_weight_decay(self):
    method get_num_layers (line 151) | def get_num_layers(self):
    method forward_features (line 154) | def forward_features(self, x, bool_masked_pos, layer_results):
    method forward (line 183) | def forward(self, x, bool_masked_pos, return_all_tokens=False, layer_r...
  function beit_base_joint_patch16_224 (line 203) | def beit_base_joint_patch16_224(pretrained=False, **kwargs):

FILE: src/benchmark/pretrain_ssl/models/data2vec/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 64) | class ResBlock(nn.Module):
    method __init__ (line 65) | def __init__(self, chan_in, hidden_size, chan_out):
    method forward (line 75) | def forward(self, x):
  class DiscreteVAE (line 79) | class DiscreteVAE(BasicVAE):
    method __init__ (line 80) | def __init__(
    method get_image_size (line 127) | def get_image_size(self):
    method get_image_tokens_size (line 130) | def get_image_tokens_size(self):
    method get_codebook_indices (line 135) | def get_codebook_indices(self, images):
    method get_codebook_probs (line 142) | def get_codebook_probs(self, images):
    method decode (line 146) | def decode(
    method forward (line 158) | def forward(
  class Dalle_VAE (line 206) | class Dalle_VAE(BasicVAE):
    method __init__ (line 207) | def __init__(self, image_size):
    method load_model (line 213) | def load_model(self, model_dir, device):
    method decode (line 217) | def decode(self, img_seq):
    method get_codebook_indices (line 223) | def get_codebook_indices(self, images):
    method get_codebook_probs (line 227) | def get_codebook_probs(self, images):
    method forward (line 231) | def forward(self, img_seq_prob, no_process=False):

FILE: src/benchmark/pretrain_ssl/models/data2vec/modeling_finetune.py
  function _cfg (line 23) | def _cfg(url='', **kwargs):
  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):
    method extra_repr (line 43) | def extra_repr(self) -> str:
  class Mlp (line 47) | class Mlp(nn.Module):
    method __init__ (line 48) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 57) | def forward(self, x):
  class Attention (line 67) | class Attention(nn.Module):
    method __init__ (line 68) | def __init__(
    method forward (line 121) | def forward(self, x, rel_pos_bias=None):
  class Block (line 154) | class Block(nn.Module):
    method __init__ (line 156) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 177) | def forward(self, x, rel_pos_bias=None):
  class PatchEmbed (line 189) | class PatchEmbed(nn.Module):
    method __init__ (line 192) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 204) | def forward(self, x, **kwargs):
  class RelativePositionBias (line 213) | class RelativePositionBias(nn.Module):
    method __init__ (line 215) | def __init__(self, window_size, num_heads):
    method forward (line 244) | def forward(self):
  class VisionTransformer (line 252) | class VisionTransformer(nn.Module):
    method __init__ (line 255) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
    method fix_init_weight (line 315) | def fix_init_weight(self):
    method _init_weights (line 323) | def _init_weights(self, m):
    method get_num_layers (line 334) | def get_num_layers(self):
    method no_weight_decay (line 338) | def no_weight_decay(self):
    method get_classifier (line 341) | def get_classifier(self):
    method reset_classifier (line 344) | def reset_classifier(self, num_classes, global_pool=''):
    method forward_features (line 348) | def forward_features(self, x, bool_masked_pos=None):
    method forward (line 391) | def forward(self, x, bool_masked_pos=None):
  function beit_small_patch16_224 (line 398) | def beit_small_patch16_224(pretrained=False, **kwargs):
  function beit_base_patch16_224 (line 407) | def beit_base_patch16_224(pretrained=False, **kwargs):
  function beit_base_patch16_384 (line 416) | def beit_base_patch16_384(pretrained=False, **kwargs):
  function beit_large_patch16_224 (line 425) | def beit_large_patch16_224(pretrained=False, **kwargs):
  function beit_large_patch16_384 (line 434) | def beit_large_patch16_384(pretrained=False, **kwargs):
  function beit_large_patch16_512 (line 443) | def beit_large_patch16_512(pretrained=False, **kwargs):

FILE: src/benchmark/pretrain_ssl/models/data2vec/modeling_pretrain.py
  function trunc_normal_ (line 22) | def trunc_normal_(tensor, mean=0., std=1.):
  class VisionTransformerForMaskedImageModeling (line 32) | class VisionTransformerForMaskedImageModeling(nn.Module):
    method __init__ (line 33) | def __init__(self, img_size=224, patch_size=16, in_chans=3, vocab_size...
    method fix_init_weight (line 79) | def fix_init_weight(self):
    method _init_weights (line 87) | def _init_weights(self, m):
    method no_weight_decay (line 101) | def no_weight_decay(self):
    method get_num_layers (line 104) | def get_num_layers(self):
    method forward_features (line 107) | def forward_features(self, x, bool_masked_pos):
    method forward (line 129) | def forward(self, x, bool_masked_pos, return_all_tokens=False):
  function beit_base_patch16_224_8k_vocab (line 140) | def beit_base_patch16_224_8k_vocab(pretrained=False, **kwargs):
  function beit_large_patch16_224_8k_vocab (line 155) | def beit_large_patch16_224_8k_vocab(pretrained=False, **kwargs):

FILE: src/benchmark/pretrain_ssl/models/data2vec/models.py
  function _cfg (line 32) | def _cfg(url='', **kwargs):
  class Attention (line 71) | class Attention(nn.Module):
    method __init__ (line 72) | def __init__(
    method forward (line 127) | def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
  class Block (line 157) | class Block(nn.Module):
    method __init__ (line 159) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=...
    method forward (line 179) | def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
  class RelativePositionBias (line 189) | class RelativePositionBias(nn.Module):
    method __init__ (line 191) | def __init__(self, window_size, num_heads):
    method forward (line 220) | def forward(self):
  class Beit (line 228) | class Beit(nn.Module):
    method __init__ (line 232) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
    method fix_init_weight (line 281) | def fix_init_weight(self):
    method _init_weights (line 289) | def _init_weights(self, m):
    method get_num_layers (line 298) | def get_num_layers(self):
    method no_weight_decay (line 302) | def no_weight_decay(self):
    method get_classifier (line 305) | def get_classifier(self):
    method reset_classifier (line 308) | def reset_classifier(self, num_classes, global_pool=''):
    method forward_features (line 312) | def forward_features(self, x):
    method forward (line 333) | def forward(self, x):
  function _create_beit (line 339) | def _create_beit(variant, pretrained=False, default_cfg=None, **kwargs):
  function beit_base_patch16_224 (line 354) | def beit_base_patch16_224(pretrained=False, **kwargs):
  function beit_base_patch16_384 (line 366) | def beit_base_patch16_384(pretrained=False, **kwargs):
  function beit_base_patch16_224_in22k (line 375) | def beit_base_patch16_224_in22k(pretrained=False, **kwargs):
  function beit_large_patch16_224 (line 384) | def beit_large_patch16_224(pretrained=False, **kwargs):
  function beit_large_patch16_384 (line 393) | def beit_large_patch16_384(pretrained=False, **kwargs):
  function beit_large_patch16_512 (line 402) | def beit_large_patch16_512(pretrained=False, **kwargs):
  function beit_large_patch16_224_in22k (line 411) | def beit_large_patch16_224_in22k(pretrained=False, **kwargs):

FILE: src/benchmark/pretrain_ssl/models/data2vec/optim_factory.py
  function get_num_layer_for_vit (line 33) | def get_num_layer_for_vit(var_name, num_max_layer):
  class LayerDecayValueAssigner (line 47) | class LayerDecayValueAssigner(object):
    method __init__ (line 48) | def __init__(self, values):
    method get_scale (line 51) | def get_scale(self, layer_id):
    method get_layer_id (line 54) | def get_layer_id(self, var_name):
  function get_parameter_groups (line 58) | def get_parameter_groups(model, weight_decay=1e-5, skip_list=(), get_num...
  function create_optimizer (line 100) | def create_optimizer(args, model, get_num_layer=None, get_layer_scale=No...

FILE: src/benchmark/pretrain_ssl/models/data2vec/run_beit_pretraining.py
  function get_args (line 34) | def get_args():
  function get_model (line 139) | def get_model(args):
  function main (line 154) | def main(args):

FILE: src/benchmark/pretrain_ssl/models/data2vec/run_class_finetuning.py
  function get_args (line 39) | def get_args():
  function main (line 241) | def main(args, ds_init):

FILE: src/benchmark/pretrain_ssl/models/data2vec/run_cyclical.py
  function get_args (line 37) | def get_args():
  function get_model (line 280) | def get_model(args):
  function main (line 304) | def main(args):

FILE: src/benchmark/pretrain_ssl/models/data2vec/run_cyclical_joint.py
  function get_args (line 36) | def get_args():
  function get_model (line 264) | def get_model(args):
  function main (line 279) | def main(args):

FILE: src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/backbone/beit.py
  class DropPath (line 26) | class DropPath(nn.Module):
    method __init__ (line 29) | def __init__(self, drop_prob=None):
    method forward (line 33) | def forward(self, x):
    method extra_repr (line 36) | def extra_repr(self) -> str:
  class Mlp (line 40) | class Mlp(nn.Module):
    method __init__ (line 41) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 50) | def forward(self, x):
  class Attention (line 60) | class Attention(nn.Module):
    method __init__ (line 61) | def __init__(
    method forward (line 117) | def forward(self, x, rel_pos_bias=None):
  class Block (line 150) | class Block(nn.Module):
    method __init__ (line 152) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 172) | def forward(self, x, rel_pos_bias=None):
  class PatchEmbed (line 182) | class PatchEmbed(nn.Module):
    method __init__ (line 185) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 197) | def forward(self, x, **kwargs):
  class HybridEmbed (line 209) | class HybridEmbed(nn.Module):
    method __init__ (line 213) | def __init__(self, backbone, img_size=224, feature_size=None, in_chans...
    method forward (line 237) | def forward(self, x):
  class RelativePositionBias (line 244) | class RelativePositionBias(nn.Module):
    method __init__ (line 246) | def __init__(self, window_size, num_heads):
    method forward (line 275) | def forward(self):
  class BEiT (line 284) | class BEiT(nn.Module):
    method __init__ (line 287) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
    method fix_init_weight (line 366) | def fix_init_weight(self):
    method _init_weights (line 374) | def _init_weights(self, m):
    method init_weights (line 383) | def init_weights(self, pretrained=None):
    method get_num_layers (line 409) | def get_num_layers(self):
    method no_weight_decay (line 413) | def no_weight_decay(self):
    method forward_features (line 416) | def forward_features(self, x):
    method forward (line 441) | def forward(self, x):

FILE: src/benchmark/pretrain_ssl/models/data2vec/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: src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/apex_runner/checkpoint.py
  function save_checkpoint (line 19) | def save_checkpoint(model, filename, optimizer=None, meta=None):

FILE: src/benchmark/pretrain_ssl/models/data2vec/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: src/benchmark/pretrain_ssl/models/data2vec/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 476) | def weights_to_cpu(state_dict):
  function _save_to_state_dict (line 491) | def _save_to_state_dict(module, destination, prefix, keep_vars):
  function get_state_dict (line 511) | def get_state_dict(module, destination=None, prefix='', keep_vars=False):
  function save_checkpoint (line 555) | def save_checkpoint(model, filename, optimizer=None, meta=None):

FILE: src/benchmark/pretrain_ssl/models/data2vec/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: src/benchmark/pretrain_ssl/models/data2vec/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: src/benchmark/pretrain_ssl/models/data2vec/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: src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/tools/test.py
  function parse_args (line 17) | def parse_args():
  function main (line 67) | def main():

FILE: src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/tools/train.py
  function parse_args (line 23) | def parse_args():
  function main (line 67) | def main():

FILE: src/benchmark/pretrain_ssl/models/data2vec/transforms.py
  class ToNumpy (line 20) | class ToNumpy:
    method __call__ (line 22) | def __call__(self, pil_img):
  class ToTensor (line 30) | class ToTensor:
    method __init__ (line 32) | def __init__(self, dtype=torch.float32):
    method __call__ (line 35) | def __call__(self, pil_img):
  function _pil_interp (line 53) | def _pil_interp(method):
  class RandomResizedCropAndInterpolationWithTwoPic (line 68) | class RandomResizedCropAndInterpolationWithTwoPic:
    method __init__ (line 83) | def __init__(self, size, second_size=None, scale=(0.08, 1.0), ratio=(3...
    method get_params (line 109) | def get_params(img, scale, ratio):
    method __call__ (line 151) | def __call__(self, img):
    method __repr__ (line 170) | def __repr__(self):

FILE: src/benchmark/pretrain_ssl/models/data2vec/utils.py
  class SmoothedValue (line 33) | class SmoothedValue(object):
    method __init__ (line 38) | def __init__(self, window_size=20, fmt=None):
    method update (line 46) | def update(self, value, n=1):
    method synchronize_between_processes (line 51) | def synchronize_between_processes(self):
    method median (line 65) | def median(self):
    method avg (line 70) | def avg(self):
    method global_avg (line 75) | def global_avg(self):
    method max (line 79) | def max(self):
    method value (line 83) | def value(self):
    method __str__ (line 86) | def __str__(self):
  class MetricLogger (line 95) | class MetricLogger(object):
    method __init__ (line 96) | def __init__(self, delimiter="\t"):
    method update (line 100) | def update(self, **kwargs):
    method __getattr__ (line 109) | def __getattr__(self, attr):
    method __str__ (line 117) | def __str__(self):
    method synchronize_between_processes (line 125) | def synchronize_between_processes(self):
    method add_meter (line 129) | def add_meter(self, name, meter):
    method log_every (line 132) | def log_every(self, iterable, print_freq, header=None):
  class TensorboardLogger (line 179) | class TensorboardLogger(object):
    method __init__ (line 180) | def __init__(self, log_dir):
    method set_step (line 184) | def set_step(self, step=None):
    method update (line 190) | def update(self, head='scalar', step=None, **kwargs):
    method flush (line 199) | def flush(self):
  function _load_checkpoint_for_ema (line 203) | def _load_checkpoint_for_ema(model_ema, checkpoint):
  function setup_for_distributed (line 217) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 232) | def is_dist_avail_and_initialized():
  function get_world_size (line 240) | def get_world_size():
  function get_rank (line 246) | def get_rank():
  function is_main_process (line 252) | def is_main_process():
  function save_on_master (line 256) | def save_on_master(*args, **kwargs):
  function init_distributed_mode (line 261) | def init_distributed_mode(args):
  function load_state_dict (line 314) | def load_state_dict(model, state_dict, prefix='', ignore_missing="relati...
  class NativeScalerWithGradNormCount (line 363) | class NativeScalerWithGradNormCount:
    method __init__ (line 366) | def __init__(self):
    method __call__ (line 369) | def __call__(self, loss, optimizer, clip_grad=None, parameters=None, c...
    method state_dict (line 385) | def state_dict(self):
    method load_state_dict (line 388) | def load_state_dict(self, state_dict):
  function get_grad_norm_ (line 392) | def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
  function cosine_scheduler (line 407) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
  function tri_phase_scheduler (line 426) | def tri_phase_scheduler(base_value, final_value, epochs, niter_per_ep, w...
  function save_model (line 461) | def save_model(args, epoch, model, model_without_ddp, optimizer, loss_sc...
  function auto_load_model (line 486) | def auto_load_model(args, model, model_without_ddp, optimizer, loss_scal...
  function create_d_vae (line 541) | def create_d_vae(weight_path, d_vae_type, image_size, device):
  function get_dalle_vae (line 550) | def get_dalle_vae(weight_path, image_size, device):
  function get_d_vae (line 556) | def get_d_vae(weight_path, image_size, device):
  function create_ds_config (line 576) | def create_ds_config(args):

FILE: src/benchmark/pretrain_ssl/models/dino/utils.py
  class GaussianBlur (line 37) | class GaussianBlur(object):
    method __init__ (line 41) | def __init__(self, p=0.5, radius_min=0.1, radius_max=2.):
    method __call__ (line 46) | def __call__(self, img):
  class cvGaussianBlur (line 58) | class cvGaussianBlur(object):
    method __init__ (line 61) | def __init__(self, p=0.5, sigma=[.1, 2.]):
    method __call__ (line 65) | def __call__(self, x):
  class Solarization (line 78) | class Solarization(object):
    method __init__ (line 82) | def __init__(self, p):
    method __call__ (line 85) | def __call__(self, img):
  function load_pretrained_weights (line 92) | def load_pretrained_weights(model, pretrained_weights, checkpoint_key, m...
  function load_pretrained_linear_weights (line 133) | def load_pretrained_linear_weights(linear_classifier, model_name, patch_...
  function clip_gradients (line 153) | def clip_gradients(model, clip):
  function cancel_gradients_last_layer (line 165) | def cancel_gradients_last_layer(epoch, model, freeze_last_layer):
  function restart_from_checkpoint (line 173) | def restart_from_checkpoint(ckp_path, run_variables=None, **kwargs):
  function cosine_scheduler (line 208) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
  function bool_flag (line 222) | def bool_flag(s):
  function fix_random_seeds (line 236) | def fix_random_seeds(seed=31):
  class SmoothedValue (line 245) | class SmoothedValue(object):
    method __init__ (line 250) | def __init__(self, window_size=20, fmt=None):
    method update (line 258) | def update(self, value, n=1):
    method synchronize_between_processes (line 263) | def synchronize_between_processes(self):
    method median (line 277) | def median(self):
    method avg (line 282) | def avg(self):
    method global_avg (line 287) | def global_avg(self):
    method max (line 291) | def max(self):
    method value (line 295) | def value(self):
    method __str__ (line 298) | def __str__(self):
  function reduce_dict (line 307) | def reduce_dict(input_dict, average=True):
  class MetricLogger (line 334) | class MetricLogger(object):
    method __init__ (line 335) | def __init__(self, delimiter="\t"):
    method update (line 339) | def update(self, **kwargs):
    method __getattr__ (line 346) | def __getattr__(self, attr):
    method __str__ (line 354) | def __str__(self):
    method synchronize_between_processes (line 362) | def synchronize_between_processes(self):
    method add_meter (line 366) | def add_meter(self, name, meter):
    method log_every (line 369) | def log_every(self, iterable, print_freq, header=None):
  function get_sha (line 424) | def get_sha():
  function is_dist_avail_and_initialized (line 444) | def is_dist_avail_and_initialized():
  function get_world_size (line 452) | def get_world_size():
  function get_rank (line 458) | def get_rank():
  function is_main_process (line 464) | def is_main_process():
  function save_on_master (line 468) | def save_on_master(*args, **kwargs):
  function setup_for_distributed (line 473) | def setup_for_distributed(is_master):
  function init_distributed_mode (line 488) | 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):
  class MultiCropWrapper (line 618) | class MultiCropWrapper(nn.Module):
    method __init__ (line 627) | def __init__(self, backbone, head):
    method forward (line 634) | def forward(self, x):
  function get_params_groups (line 656) | def get_params_groups(model):
  function has_batchnorms (line 670) | def has_batchnorms(model):
  class PCA (line 678) | class PCA():
    method __init__ (line 682) | def __init__(self, dim=256, whit=0.5):
    method train_pca (line 687) | def train_pca(self, cov):
    method apply (line 713) | def apply(self, x):
  function compute_ap (line 732) | def compute_ap(ranks, nres):
  function compute_map (line 767) | def compute_map(ranks, gnd, kappas=[]):
  function multi_scale (line 839) | def multi_scale(samples, model):

FILE: src/benchmark/pretrain_ssl/models/dino/vision_transformer.py
  function drop_path (line 27) | def drop_path(x, drop_prob: float = 0., training: bool = False):
  class DropPath (line 38) | class DropPath(nn.Module):
    method __init__ (line 41) | def __init__(self, drop_prob=None):
    method forward (line 45) | def forward(self, x):
  class Mlp (line 49) | class Mlp(nn.Module):
    method __init__ (line 50) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 59) | def forward(self, x):
  class Attention (line 68) | class Attention(nn.Module):
    method __init__ (line 69) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at...
    method forward (line 80) | def forward(self, x):
  class Block (line 95) | class Block(nn.Module):
    method __init__ (line 96) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 107) | def forward(self, x, return_attention=False):
  class PatchEmbed (line 116) | class PatchEmbed(nn.Module):
    method __init__ (line 119) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 128) | def forward(self, x):
  class VisionTransformer (line 134) | class VisionTransformer(nn.Module):
    method __init__ (line 136) | def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_clas...
    method _init_weights (line 165) | def _init_weights(self, m):
    method interpolate_pos_encoding (line 174) | def interpolate_pos_encoding(self, x, w, h):
    method prepare_tokens (line 196) | def prepare_tokens(self, x):
    method forward (line 209) | def forward(self, x):
    method get_last_selfattention (line 216) | def get_last_selfattention(self, x):
    method get_intermediate_layers (line 225) | def get_intermediate_layers(self, x, n=1):
  function vit_tiny (line 236) | def vit_tiny(patch_size=16, **kwargs):
  function vit_small (line 243) | def vit_small(patch_size=16, **kwargs):
  function vit_base (line 250) | def vit_base(patch_size=16, **kwargs):
  class DINOHead (line 257) | class DINOHead(nn.Module):
    method __init__ (line 258) | def __init__(self, in_dim, out_dim, use_bn=False, norm_last_layer=True...
    method _init_weights (line 281) | def _init_weights(self, m):
    method forward (line 287) | def forward(self, x):

FILE: src/benchmark/pretrain_ssl/models/mae/engine_finetune.py
  function train_one_epoch (line 25) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
  function evaluate (line 99) | def evaluate(data_loader, model, device):

FILE: src/benchmark/pretrain_ssl/models/mae/engine_finetune_BE.py
  function train_one_epoch (line 29) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
  function evaluate (line 107) | def evaluate(data_loader, model, device, criterion):

FILE: src/benchmark/pretrain_ssl/models/mae/engine_finetune_EU.py
  function train_one_epoch (line 29) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
  function evaluate (line 104) | def evaluate(data_loader, model, device, criterion):

FILE: src/benchmark/pretrain_ssl/models/mae/engine_finetune_SS.py
  function train_one_epoch (line 29) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
  function evaluate (line 104) | def evaluate(data_loader, model, device, criterion):

FILE: src/benchmark/pretrain_ssl/models/mae/engine_pretrain.py
  function train_one_epoch (line 23) | def train_one_epoch(model: torch.nn.Module,

FILE: src/benchmark/pretrain_ssl/models/mae/main_finetune.py
  function get_args_parser (line 42) | def get_args_parser():
  function main (line 158) | def main(args):

FILE: src/benchmark/pretrain_ssl/models/mae/main_linprobe.py
  function get_args_parser (line 47) | def get_args_parser():
  function main (line 121) | def main(args):

FILE: src/benchmark/pretrain_ssl/models/mae/main_pretrain.py
  class SeasonTransform (line 41) | class SeasonTransform:
    method __init__ (line 43) | def __init__(self, base_transform, season='fixed'):
    method __call__ (line 47) | def __call__(self, x):
  function get_args_parser (line 72) | def get_args_parser():
  function main (line 151) | def main(args):

FILE: src/benchmark/pretrain_ssl/models/mae/models_mae.py
  class MaskedAutoencoderViT (line 22) | class MaskedAutoencoderViT(nn.Module):
    method __init__ (line 25) | def __init__(self, img_size=224, patch_size=16, in_chans=3,
    method initialize_weights (line 67) | def initialize_weights(self):
    method _init_weights (line 87) | def _init_weights(self, m):
    method patchify (line 97) | def patchify(self, imgs):
    method unpatchify (line 111) | def unpatchify(self, x):
    method random_masking (line 125) | def random_masking(self, x, mask_ratio):
    method forward_encoder (line 152) | def forward_encoder(self, x, mask_ratio):
    method forward_decoder (line 174) | def forward_decoder(self, x, ids_restore):
    method forward_loss (line 200) | def forward_loss(self, imgs, pred, mask):
    method forward (line 218) | def forward(self, imgs, mask_ratio=0.75):
  function mae_vit_small_patch16_dec512d8b (line 225) | def mae_vit_small_patch16_dec512d8b(**kwargs):
  function mae_vit_base_patch16_dec512d8b (line 232) | def mae_vit_base_patch16_dec512d8b(**kwargs):
  function mae_vit_large_patch16_dec512d8b (line 240) | def mae_vit_large_patch16_dec512d8b(**kwargs):
  function mae_vit_huge_patch14_dec512d8b (line 248) | def mae_vit_huge_patch14_dec512d8b(**kwargs):

FILE: src/benchmark/pretrain_ssl/models/mae/models_vit.py
  class VisionTransformer (line 20) | class VisionTransformer(timm.models.vision_transformer.VisionTransformer):
    method __init__ (line 23) | def __init__(self, global_pool=False, **kwargs):
    method forward_features (line 34) | def forward_features(self, x):
  function vit_small_patch16 (line 55) | def vit_small_patch16(**kwargs):
  function vit_base_patch16 (line 61) | def vit_base_patch16(**kwargs):
  function vit_large_patch16 (line 68) | def vit_large_patch16(**kwargs):
  function vit_huge_patch14 (line 75) | def vit_huge_patch14(**kwargs):

FILE: src/benchmark/pretrain_ssl/models/mae/submitit_finetune.py
  function parse_args (line 19) | def parse_args():
  function get_shared_folder (line 33) | def get_shared_folder() -> Path:
  function get_init_file (line 42) | def get_init_file():
  class Trainer (line 51) | class Trainer(object):
    method __init__ (line 52) | def __init__(self, args):
    method __call__ (line 55) | def __call__(self):
    method checkpoint (line 61) | def checkpoint(self):
    method _setup_gpu_args (line 73) | def _setup_gpu_args(self):
  function main (line 86) | def main():

FILE: src/benchmark/pretrain_ssl/models/mae/submitit_linprobe.py
  function parse_args (line 19) | def parse_args():
  function get_shared_folder (line 33) | def get_shared_folder() -> Path:
  function get_init_file (line 42) | def get_init_file():
  class Trainer (line 51) | class Trainer(object):
    method __init__ (line 52) | def __init__(self, args):
    method __call__ (line 55) | def __call__(self):
    method checkpoint (line 61) | def checkpoint(self):
    method _setup_gpu_args (line 73) | def _setup_gpu_args(self):
  function main (line 86) | def main():

FILE: src/benchmark/pretrain_ssl/models/mae/submitit_pretrain.py
  function parse_args (line 19) | def parse_args():
  function get_shared_folder (line 33) | def get_shared_folder() -> Path:
  function get_init_file (line 42) | def get_init_file():
  class Trainer (line 51) | class Trainer(object):
    method __init__ (line 52) | def __init__(self, args):
    method __call__ (line 55) | def __call__(self):
    method checkpoint (line 61) | def checkpoint(self):
    method _setup_gpu_args (line 73) | def _setup_gpu_args(self):
  function main (line 86) | def main():

FILE: src/benchmark/pretrain_ssl/models/mae/util/crop.py
  class RandomResizedCrop (line 15) | class RandomResizedCrop(transforms.RandomResizedCrop):
    method get_params (line 23) | def get_params(img, scale, ratio):

FILE: src/benchmark/pretrain_ssl/models/mae/util/datasets.py
  function build_dataset (line 20) | def build_dataset(is_train, args):
  function build_transform (line 31) | def build_transform(is_train, args):

FILE: src/benchmark/pretrain_ssl/models/mae/util/lars.py
  class LARS (line 14) | class LARS(torch.optim.Optimizer):
    method __init__ (line 18) | def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, trust_c...
    method step (line 23) | def step(self):

FILE: src/benchmark/pretrain_ssl/models/mae/util/lr_decay.py
  function param_groups_lrd (line 15) | def param_groups_lrd(model, weight_decay=0.05, no_weight_decay_list=[], ...
  function get_layer_id_for_vit (line 64) | def get_layer_id_for_vit(name, num_layers):

FILE: src/benchmark/pretrain_ssl/models/mae/util/lr_sched.py
  function adjust_learning_rate (line 9) | def adjust_learning_rate(optimizer, epoch, args):

FILE: src/benchmark/pretrain_ssl/models/mae/util/misc.py
  class SmoothedValue (line 24) | class SmoothedValue(object):
    method __init__ (line 29) | def __init__(self, window_size=20, fmt=None):
    method update (line 37) | def update(self, value, n=1):
    method synchronize_between_processes (line 42) | def synchronize_between_processes(self):
    method median (line 56) | def median(self):
    method avg (line 61) | def avg(self):
    method global_avg (line 66) | def global_avg(self):
    method max (line 70) | def max(self):
    method value (line 74) | def value(self):
    method __str__ (line 77) | def __str__(self):
  class MetricLogger (line 86) | class MetricLogger(object):
    method __init__ (line 87) | def __init__(self, delimiter="\t"):
    method update (line 91) | def update(self, **kwargs):
    method __getattr__ (line 100) | def __getattr__(self, attr):
    method __str__ (line 108) | def __str__(self):
    method synchronize_between_processes (line 116) | def synchronize_between_processes(self):
    method add_meter (line 120) | def add_meter(self, name, meter):
    method log_every (line 123) | def log_every(self, iterable, print_freq, header=None):
  function setup_for_distributed (line 170) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 187) | def is_dist_avail_and_initialized():
  function get_world_size (line 195) | def get_world_size():
  function get_rank (line 201) | def get_rank():
  function is_main_process (line 207) | def is_main_process():
  function save_on_master (line 211) | def save_on_master(*args, **kwargs):
  function init_distributed_mode (line 216) | def init_distributed_mode(args):
  class NativeScalerWithGradNormCount (line 253) | class NativeScalerWithGradNormCount:
    method __init__ (line 256) | def __init__(self):
    method __call__ (line 259) | def __call__(self, loss, optimizer, clip_grad=None, parameters=None, c...
    method state_dict (line 275) | def state_dict(self):
    method load_state_dict (line 278) | def load_state_dict(self, state_dict):
  function get_grad_norm_ (line 282) | def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
  function save_model (line 297) | def save_model(args, epoch, model, model_without_ddp, optimizer, loss_sc...
  function load_model (line 317) | def load_model(args, model_without_ddp, optimizer, loss_scaler):
  function all_reduce_mean (line 334) | def all_reduce_mean(x):

FILE: src/benchmark/pretrain_ssl/models/mae/util/pos_embed.py
  function get_2d_sincos_pos_embed (line 20) | def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
  function get_2d_sincos_pos_embed_from_grid (line 38) | def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
  function get_1d_sincos_pos_embed_from_grid (line 49) | def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
  function interpolate_pos_embed (line 75) | def interpolate_pos_embed(model, checkpoint_model):

FILE: src/benchmark/pretrain_ssl/models/moco/builder.py
  class MoCo (line 6) | class MoCo(nn.Module):
    method __init__ (line 11) | def __init__(self, base_encoder, dim=128, K=65536, m=0.999, T=0.07, ml...
    method _momentum_update_key_encoder (line 68) | def _momentum_update_key_encoder(self):
    method _dequeue_and_enqueue (line 76) | def _dequeue_and_enqueue(self, keys):
    method _batch_shuffle_ddp (line 92) | def _batch_shuffle_ddp(self, x):
    method _batch_unshuffle_ddp (line 120) | def _batch_unshuffle_ddp(self, x, idx_unshuffle):
    method forward (line 138) | def forward(self, im_q, im_k):
  function concat_all_gather (line 188) | def concat_all_gather(tensor):

FILE: src/benchmark/pretrain_ssl/models/moco/loader.py
  class TwoCropsTransform (line 11) | class TwoCropsTransform:
    method __init__ (line 14) | def __init__(self, base_transform):
    method __call__ (line 17) | def __call__(self, x):
  class GaussianBlur (line 23) | class GaussianBlur(object):
    method __init__ (line 26) | def __init__(self, sigma=[.1, 2.]):
    method __call__ (line 29) | def __call__(self, x):

FILE: src/benchmark/pretrain_ssl/models/moco_v2/builder.py
  class MoCo (line 6) | class MoCo(nn.Module):
    method __init__ (line 11) | def __init__(self, base_encoder, dim=128, K=65536, m=0.999, T=0.07, ml...
    method _momentum_update_key_encoder (line 57) | def _momentum_update_key_encoder(self):
    method _dequeue_and_enqueue (line 65) | def _dequeue_and_enqueue(self, keys):
    method _batch_shuffle_ddp (line 81) | def _batch_shuffle_ddp(self, x):
    method _batch_unshuffle_ddp (line 109) | def _batch_unshuffle_ddp(self, x, idx_unshuffle):
    method forward (line 127) | def forward(self, im_q, im_k):
  function concat_all_gather (line 177) | def concat_all_gather(tensor):

FILE: src/benchmark/pretrain_ssl/models/moco_v2/detection/train_net.py
  class Res5ROIHeadsExtraNorm (line 15) | class Res5ROIHeadsExtraNorm(Res5ROIHeads):
    method _build_res5_block (line 20) | def _build_res5_block(self, cfg):
  class Trainer (line 28) | class Trainer(DefaultTrainer):
    method build_evaluator (line 30) | def build_evaluator(cls, cfg, dataset_name, output_folder=None):
  function setup (line 40) | def setup(args):
  function main (line 49) | def main(args):

FILE: src/benchmark/pretrain_ssl/models/moco_v2/loader.py
  class TwoCropsTransform (line 11) | class TwoCropsTransform:
    method __init__ (line 14) | def __init__(self, base_transform):
    method __call__ (line 17) | def __call__(self, x):
  class GaussianBlur (line 23) | class GaussianBlur(object):
    method __init__ (line 26) | def __init__(self, sigma=[.1, 2.]):
    method __call__ (line 29) | def __call__(self, x):

FILE: src/benchmark/pretrain_ssl/models/moco_v2/main_lincls.py
  function main (line 86) | def main():
  function main_worker (line 121) | def main_worker(gpu, ngpus_per_node, args):
  function train (line 308) | def train(train_loader, model, criterion, optimizer, epoch, args):
  function validate (line 360) | def validate(val_loader, model, criterion, args):
  function save_checkpoint (line 404) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
  function sanity_check (line 410) | def sanity_check(state_dict, pretrained_weights):
  class AverageMeter (line 434) | class AverageMeter(object):
    method __init__ (line 436) | def __init__(self, name, fmt=':f'):
    method reset (line 441) | def reset(self):
    method update (line 447) | def update(self, val, n=1):
    method __str__ (line 453) | def __str__(self):
  class ProgressMeter (line 458) | class ProgressMeter(object):
    method __init__ (line 459) | def __init__(self, num_batches, meters, prefix=""):
    method display (line 464) | def display(self, batch):
    method _get_batch_fmtstr (line 469) | def _get_batch_fmtstr(self, num_batches):
  function adjust_learning_rate (line 475) | def adjust_learning_rate(optimizer, epoch, args):
  function accuracy (line 484) | def accuracy(output, target, topk=(1,)):

FILE: src/benchmark/pretrain_ssl/models/moco_v3/builder.py
  class MoCo (line 11) | class MoCo(nn.Module):
    method __init__ (line 16) | def __init__(self, base_encoder, dim=256, mlp_dim=4096, T=1.0):
    method _build_mlp (line 36) | def _build_mlp(self, num_layers, input_dim, mlp_dim, output_dim, last_...
    method _build_projector_and_predictor_mlps (line 54) | def _build_projector_and_predictor_mlps(self, dim, mlp_dim):
    method _update_momentum_encoder (line 58) | def _update_momentum_encoder(self, m):
    method contrastive_loss (line 63) | def contrastive_loss(self, q, k):
    method forward (line 75) | def forward(self, x1, x2, m):
  class MoCo_ResNet (line 99) | class MoCo_ResNet(MoCo):
    method _build_projector_and_predictor_mlps (line 100) | def _build_projector_and_predictor_mlps(self, dim, mlp_dim):
  class MoCo_ViT (line 112) | class MoCo_ViT(MoCo):
    method _build_projector_and_predictor_mlps (line 113) | def _build_projector_and_predictor_mlps(self, dim, mlp_dim):
  function concat_all_gather (line 127) | def concat_all_gather(tensor):

FILE: src/benchmark/pretrain_ssl/models/moco_v3/loader.py
  class TwoCropsTransform (line 14) | class TwoCropsTransform:
    method __init__ (line 17) | def __init__(self, base_transform1, base_transform2):
    method __call__ (line 21) | def __call__(self, x):
  class GaussianBlur (line 27) | class GaussianBlur(object):
    method __init__ (line 30) | def __init__(self, sigma=[.1, 2.]):
    method __call__ (line 33) | def __call__(self, x):
  class Solarize (line 40) | class Solarize(object):
    method __call__ (line 43) | def __call__(self, x):
  class cvGaussianBlur (line 50) | class cvGaussianBlur(object):
    method __init__ (line 53) | def __init__(self, p=0.5, sigma=[.1, 2.]):
    method __call__ (line 57) | def __call__(self, x):

FILE: src/benchmark/pretrain_ssl/models/moco_v3/optimizer.py
  class LARS (line 10) | class LARS(torch.optim.Optimizer):
    method __init__ (line 14) | def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, trust_c...
    method step (line 19) | def step(self):

FILE: src/benchmark/pretrain_ssl/models/moco_v3/vits.py
  class VisionTransformerMoCo (line 25) | class VisionTransformerMoCo(VisionTransformer):
    method __init__ (line 26) | def __init__(self, stop_grad_conv1=False, **kwargs):
    method build_2d_sincos_position_embedding (line 53) | def build_2d_sincos_position_embedding(self, temperature=10000.):
  class ConvStem (line 72) | class ConvStem(nn.Module):
    method __init__ (line 76) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 104) | def forward(self, x):
  function vit_small (line 115) | def vit_small(**kwargs):
  function vit_base (line 122) | def vit_base(**kwargs):
  function vit_conv_small (line 129) | def vit_conv_small(**kwargs):
  function vit_conv_base (line 137) | def vit_conv_base(**kwargs):

FILE: src/benchmark/pretrain_ssl/models/rs_transforms_float32.py
  class RandomBrightness (line 7) | class RandomBrightness(object):
    method __init__ (line 10) | def __init__(self, brightness=0.4):
    method __call__ (line 13) | def __call__(self, sample):
  class RandomContrast (line 19) | class RandomContrast(object):
    method __init__ (line 22) | def __init__(self, contrast=0.4):
    method __call__ (line 25) | def __call__(self, sample):
  class ToGray (line 31) | class ToGray(object):
    method __init__ (line 32) | def __init__(self, out_channels):
    method __call__ (line 34) | def __call__(self,sample):
  class RandomChannelDrop (line 41) | class RandomChannelDrop(object):
    method __init__ (line 44) | def __init__(self, min_n_drop=1, max_n_drop=8):
    method __call__ (line 48) | def __call__(self, sample):

FILE: src/benchmark/pretrain_ssl/models/rs_transforms_uint8.py
  class RandomBrightness (line 7) | class RandomBrightness(object):
    method __init__ (line 10) | def __init__(self, brightness=0.4):
    method __call__ (line 13) | def __call__(self, sample):
  class RandomContrast (line 19) | class RandomContrast(object):
    method __init__ (line 22) | def __init__(self, contrast=0.4):
    method __call__ (line 25) | def __call__(self, sample):
  class ToGray (line 31) | class ToGray(object):
    method __init__ (line 32) | def __init__(self, out_channels):
    method __call__ (line 34) | def __call__(self,sample):
  class RandomChannelDrop (line 41) | class RandomChannelDrop(object):
    method __init__ (line 44) | def __init__(self, min_n_drop=1, max_n_drop=8):
    method __call__ (line 48) | def __call__(self, sample):
  class GaussianBlur (line 57) | class GaussianBlur(object):
    method __init__ (line 60) | def __init__(self, sigma=[.1, 2.]):
    method __call__ (line 63) | def __call__(self, x):
  class Solarize (line 70) | class Solarize(object):
    method __init__ (line 72) | def __init__(self, threshold=0.5):
    method __call__ (line 75) | def __call__(self, x):
  class RandomSensorDrop_S1S2 (line 82) | class RandomSensorDrop_S1S2(object):
    method __init__ (line 85) | def __init__(self):
    method __call__ (line 88) | def __call__(self, sample):
  class SensorDrop_S1S2 (line 98) | class SensorDrop_S1S2(object):
    method __init__ (line 99) | def __init__(self, sensor):
    method __call__ (line 101) | def __call__(self,sample):
  class RandomSensorDrop_RGBD (line 109) | class RandomSensorDrop_RGBD(object):
    method __init__ (line 112) | def __init__(self):
    method __call__ (line 115) | def __call__(self, sample):
  class SensorDrop_RGBD (line 125) | class SensorDrop_RGBD(object):
    method __init__ (line 126) | def __init__(self, sensor):
    method __call__ (line 128) | def __call__(self,sample):

FILE: src/benchmark/pretrain_ssl/pretrain_data2vec.py
  function get_args (line 38) | def get_args():
  function get_model (line 293) | def get_model(args):
  function main (line 317) | def main(args):

FILE: src/benchmark/pretrain_ssl/pretrain_dino_s2c.py
  function get_args_parser (line 54) | def get_args_parser():
  function train_dino (line 158) | def train_dino(args):
  function train_one_epoch (line 369) | def train_one_epoch(student, teacher, teacher_without_ddp, dino_loss, da...
  class DINOLoss (line 434) | class DINOLoss(nn.Module):
    method __init__ (line 435) | def __init__(self, out_dim, ncrops, warmup_teacher_temp, teacher_temp,
    method forward (line 451) | def forward(self, student_output, teacher_output, epoch):
    method update_center (line 478) | def update_center(self, teacher_output):
  class DataAugmentationDINO (line 491) | class DataAugmentationDINO(object):
    method __init__ (line 492) | def __init__(self, global_crops_scale, local_crops_scale, local_crops_...
    method __call__ (line 534) | def __call__(self, image):
  class DataAugmentationDINO_S2 (line 549) | class DataAugmentationDINO_S2(object):
    method __init__ (line 550) | def __init__(self, global_crops_scale, local_crops_scale, local_crops_...
    method __call__ (line 591) | def __call__(self, image):
  class DataAugmentationDINO_S1 (line 618) | class DataAugmentationDINO_S1(object):
    method __init__ (line 619) | def __init__(self, global_crops_scale, local_crops_scale, local_crops_...
    method __call__ (line 657) | def __call__(self, image):

FILE: src/benchmark/pretrain_ssl/pretrain_mae_s2c.py
  class SeasonTransform (line 41) | class SeasonTransform:
    method __init__ (line 43) | def __init__(self, base_transform, season='fixed'):
    method __call__ (line 47) | def __call__(self, x):
  function get_args_parser (line 72) | def get_args_parser():
  function main (line 153) | def main(args):

FILE: src/benchmark/pretrain_ssl/pretrain_moco_v2_s2c.py
  class TwoCropsTransform (line 128) | class TwoCropsTransform:
    method __init__ (line 131) | def __init__(self, base_transform, season='fixed'):
    method __call__ (line 135) | def __call__(self, x):
  function main (line 157) | def main():
  function main_worker (line 213) | def main_worker(gpu, ngpus_per_node, args):
  function train (line 388) | def train(train_loader, model, criterion, optimizer, epoch, args):
  function save_checkpoint (line 442) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
  class AverageMeter (line 448) | class AverageMeter(object):
    method __init__ (line 450) | def __init__(self, name, fmt=':f'):
    method reset (line 455) | def reset(self):
    method update (line 461) | def update(self, val, n=1):
    method __str__ (line 467) | def __str__(self):
  class ProgressMeter (line 472) | class ProgressMeter(object):
    method __init__ (line 473) | def __init__(self, num_batches, meters, prefix=""):
    method display (line 478) | def display(self, batch):
    method _get_batch_fmtstr (line 483) | def _get_batch_fmtstr(self, num_batches):
  function adjust_learning_rate (line 489) | def adjust_learning_rate(optimizer, epoch, args):
  function accuracy (line 501) | def accuracy(output, target, topk=(1,)):

FILE: src/benchmark/pretrain_ssl/pretrain_moco_v2_seco_ms.py
  class SeCoDataset (line 135) | class SeCoDataset(torch.utils.data.Dataset):
    method __init__ (line 137) | def __init__(self,root, normalize=False, dtype='uint8'):
    method __getitem__ (line 145) | def __getitem__(self,index):
    method __len__ (line 152) | def __len__(self):
    method get_array (line 155) | def get_array(self, patch_id, season):
  class LMDBDataset (line 173) | class LMDBDataset(torch.utils.data.Dataset):
    method __init__ (line 175) | def __init__(self, lmdb_file, is_slurm_job=False, s2a_transform=None):
    method _init_db (line 189) | def _init_db(self):
    method __getitem__ (line 195) | def __getitem__(self, index):
    method __len__ (line 212) | def __len__(self):
  class TwoCropsTransform (line 217) | class TwoCropsTransform:
    method __init__ (line 220) | def __init__(self, base_transform):
    method __call__ (line 223) | def __call__(self, x):
  function main (line 234) | def main():
  function main_worker (line 290) | def main_worker(gpu, ngpus_per_node, args):
  function train (line 463) | def train(train_loader, model, criterion, optimizer, epoch, args):
  function save_checkpoint (line 517) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
  class AverageMeter (line 523) | class AverageMeter(object):
    method __init__ (line 525) | def __init__(self, name, fmt=':f'):
    method reset (line 530) | def reset(self):
    method update (line 536) | def update(self, val, n=1):
    method __str__ (line 542) | def __str__(self):
  class ProgressMeter (line 547) | class ProgressMeter(object):
    method __init__ (line 548) | def __init__(self, num_batches, meters, prefix=""):
    method display (line 553) | def display(self, batch):
    method _get_batch_fmtstr (line 558) | def _get_batch_fmtstr(self, num_batches):
  function adjust_learning_rate (line 564) | def adjust_learning_rate(optimizer, epoch, args):
  function accuracy (line 576) | def accuracy(output, target, topk=(1,)):

FILE: src/benchmark/pretrain_ssl/pretrain_moco_v2_sen12ms_ms.py
  class SEN12MSDataset (line 134) | class SEN12MSDataset(torch.utils.data.Dataset):
    method __init__ (line 135) | def __init__(self, root_dir, mode, transform):
    method __getitem__ (line 141) | def __getitem__(self,index):
    method __len__ (line 157) | def __len__(self):
  class LMDBDataset (line 161) | class LMDBDataset(torch.utils.data.Dataset):
    method __init__ (line 163) | def __init__(self, lmdb_file, is_slurm_job=False, s2c_transform=None):
    method _init_db (line 177) | def _init_db(self):
    method __getitem__ (line 183) | def __getitem__(self, index):
    method __len__ (line 200) | def __len__(self):
  class TwoCropsTransform (line 205) | class TwoCropsTransform:
    method __init__ (line 208) | def __init__(self, base_transform):
    method __call__ (line 211) | def __call__(self, x):
  function main (line 222) | def main():
  function main_worker (line 278) | def main_worker(gpu, ngpus_per_node, args):
  function train (line 451) | def train(train_loader, model, criterion, optimizer, epoch, args):
  function save_checkpoint (line 505) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
  class AverageMeter (line 511) | class AverageMeter(object):
    method __init__ (line 513) | def __init__(self, name, fmt=':f'):
    method reset (line 518) | def reset(self):
    method update (line 524) | def update(self, val, n=1):
    method __str__ (line 530) | def __str__(self):
  class ProgressMeter (line 535) | class ProgressMeter(object):
    method __init__ (line 536) | def __init__(self, num_batches, meters, prefix=""):
    method display (line 541) | def display(self, batch):
    method _get_batch_fmtstr (line 546) | def _get_batch_fmtstr(self, num_batches):
  function adjust_learning_rate (line 552) | def adjust_learning_rate(optimizer, epoch, args):
  function accuracy (line 564) | def accuracy(output, target, topk=(1,)):

FILE: src/benchmark/pretrain_ssl/pretrain_moco_v3_s2c.py
  class TwoCropsTransform (line 141) | class TwoCropsTransform:
    method __init__ (line 144) | def __init__(self, base_transform1, base_transform2, season='fixed'):
    method __call__ (line 149) | def __call__(self, x):
  function main (line 172) | def main():
  function main_worker (line 216) | def main_worker(gpu, ngpus_per_node, args):
  function train (line 447) | def train(train_loader, model, optimizer, scaler, summary_writer, epoch,...
  function save_checkpoint (line 498) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
  class AverageMeter (line 504) | class AverageMeter(object):
    method __init__ (line 506) | def __init__(self, name, fmt=':f'):
    method reset (line 511) | def reset(self):
    method update (line 517) | def update(self, val, n=1):
    method __str__ (line 523) | def __str__(self):
  class ProgressMeter (line 528) | class ProgressMeter(object):
    method __init__ (line 529) | def __init__(self, num_batches, meters, prefix=""):
    method display (line 534) | def display(self, batch):
    method _get_batch_fmtstr (line 539) | def _get_batch_fmtstr(self, num_batches):
  function adjust_learning_rate (line 545) | def adjust_learning_rate(optimizer, epoch, args):
  function adjust_moco_momentum (line 556) | def adjust_moco_momentum(epoch, args):

FILE: src/benchmark/transfer_change_detection/datasets/oscd_datamodule.py
  class RandomFlip (line 15) | class RandomFlip:
    method __call__ (line 17) | def __call__(self, *xs):
  class RandomRotation (line 23) | class RandomRotation:
    method __init__ (line 25) | def __init__(self):
    method __call__ (line 28) | def __call__(self, *xs):
  class RandomSwap (line 33) | class RandomSwap:
    method __call__ (line 35) | def __call__(self, x1, x2, y):
  class ToTensor (line 42) | class ToTensor:
    method __call__ (line 44) | def __call__(self, *xs):
  class Compose (line 48) | class Compose:
    method __init__ (line 50) | def __init__(self, transforms):
    method __call__ (line 53) | def __call__(self, *xs):
  class ChangeDetectionDataModule (line 59) | class ChangeDetectionDataModule(LightningDataModule):
    method __init__ (line 61) | def __init__(self, data_dir, RGB_bands=True, BGR_bands=False, S2A_band...
    method setup (line 79) | def setup(self, stage=None):
    method train_dataloader (line 97) | def train_dataloader(self):
    method val_dataloader (line 107) | def val_dataloader(self):

FILE: src/benchmark/transfer_change_detection/datasets/oscd_dataset.py
  function read_image (line 48) | def read_image(path, bands, normalize=True, value_discard=True):
  class ChangeDetectionDataset (line 81) | class ChangeDetectionDataset(Dataset):
    method __init__ (line 83) | def __init__(self, root, split='all', bands=None, transform=None,
    method __getitem__ (line 104) | def __getitem__(self, index):
    method __len__ (line 128) | def __len__(self):

FILE: src/benchmark/transfer_change_detection/main_oscd.py
  function get_args (line 21) | def get_args():
  function dice_loss (line 43) | def dice_loss(out,mask,epsilon=1e-5):
  class SiamSegment (line 49) | class SiamSegment(LightningModule):
    method __init__ (line 51) | def __init__(self, backbone, feature_indices, feature_channels):
    method forward (line 60) | def forward(self, x1, x2):
    method training_step (line 63) | def training_step(self, batch, batch_idx):
    method validation_step (line 83) | def validation_step(self, batch, batch_idx):
    method shared_step (line 103) | def shared_step(self, batch):
    method configure_optimizers (line 114) | def configure_optimizers(self):
  function load_ssl_resnet_encoder (line 122) | def load_ssl_resnet_encoder(net, ckp_path, pf_sdict='module.encoder_q.'):

FILE: src/benchmark/transfer_change_detection/models/segmentation.py
  function get_segmentation_model (line 6) | def get_segmentation_model(backbone, feature_indices, feature_channels):
  class SegmentationEncoder (line 23) | class SegmentationEncoder(torch.nn.Module):
    method __init__ (line 24) | def __init__(self, backbone, feature_indices, diff=False):
    method forward (line 39) | def forward(self, x1, x2):
    method concatenate (line 64) | def concatenate(self, x1, x2):
  class DoubleConv (line 71) | class DoubleConv(nn.Module):
    method __init__ (line 72) | def __init__(self, in_channels, out_channels, mid_channels=None):
    method forward (line 85) | def forward(self, x):
  class Down (line 89) | class Down(nn.Module):
    method __init__ (line 92) | def __init__(self, in_channels, out_channels):
    method forward (line 99) | def forward(self, x):
  class Up (line 103) | class Up(nn.Module):
    method __init__ (line 106) | def __init__(self, in_channels, out_channels, bilinear=True):
    method forward (line 117) | def forward(self, x1, x2):
  class OutConv (line 132) | class OutConv(nn.Module):
    method __init__ (line 133) | def __init__(self, in_channels, out_channels):
    method forward (line 137) | def forward(self, x):
  class UNet (line 165) | class UNet(nn.Module):
    method __init__ (line 166) | def __init__(self, encoder, feature_channels, n_classes, concat_mult=2...
    method forward (line 196) | def forward(self, *in_x):
  class UNetSmall (line 220) | class UNetSmall(nn.Module):
    method __init__ (line 221) | def __init__(self, encoder, feature_channels, n_classes, concat_mult=2...
    method forward (line 245) | def forward(self, *in_x):

FILE: src/benchmark/transfer_change_detection/utils/data.py
  class Subset (line 10) | class Subset(Dataset):
    method __init__ (line 12) | def __init__(self, dataset, indices):
    method __getitem__ (line 16) | def __getitem__(self, idx):
    method __len__ (line 19) | def __len__(self):
    method __getattr__ (line 22) | def __getattr__(self, name):
  function random_subset (line 26) | def random_subset(dataset, frac, seed=None):
  class _RepeatSampler (line 32) | class _RepeatSampler(object):
    method __init__ (line 39) | def __init__(self, sampler):
    method __iter__ (line 42) | def __iter__(self):
  class InfiniteDataLoader (line 47) | class InfiniteDataLoader(DataLoader):
    method __init__ (line 53) | def __init__(self, *args, **kwargs):
    method __len__ (line 58) | def __len__(self):
    method __iter__ (line 61) | def __iter__(self):
  function make_lmdb (line 66) | def make_lmdb(dataset, lmdb_file, num_workers=8):
  class LMDBDataset (line 84) | class LMDBDataset(Dataset):
    method __init__ (line 86) | def __init__(self, lmdb_file, transform=None, target_transform=None):
    method __getitem__ (line 95) | def __getitem__(self, index):
    method __len__ (line 111) | def __len__(self):

FILE: src/benchmark/transfer_change_detection/utils/transforms.py
  class Map (line 2) | class Map:
    method __init__ (line 4) | def __init__(self, transform):
    method __call__ (line 7) | def __call__(self, inputs):
  class ApplyN (line 11) | class ApplyN:
    method __init__ (line 13) | def __init__(self, transform, n):
    method __call__ (line 17) | def __call__(self, input):
  class LeaveOneOut (line 21) | class LeaveOneOut:
    method __init__ (line 23) | def __init__(self, transforms):
    method __call__ (line 26) | def __call__(self, input):
  class AddOne (line 37) | class AddOne:
    method __init__ (line 39) | def __init__(self, transforms):
    method __call__ (line 42) | def __call__(self, input):

FILE: src/benchmark/transfer_change_detection/validate_oscd.py
  function get_args (line 21) | def get_args():
  class SiamSegment (line 44) | class SiamSegment(LightningModule):
    method __init__ (line 46) | def __init__(self, backbone, feature_indices, feature_channels):
    method forward (line 54) | def forward(self, x1, x2):
    method training_step (line 57) | def training_step(self, batch, batch_idx):
    method validation_step (line 77) | def validation_step(self, batch, batch_idx):
    method shared_step (line 97) | def shared_step(self, batch):
    method configure_optimizers (line 107) | def configure_optimizers(self):
  function load_ssl_resnet_encoder (line 115) | def load_ssl_resnet_encoder(net, ckp_path, pf_sdict='module.encoder_q.'):

FILE: src/benchmark/transfer_classification/datasets/BigEarthNet/bigearthnet_dataset_seco.py
  function normalize (line 117) | def normalize(img, mean, std):
  class Bigearthnet (line 125) | class Bigearthnet(Dataset):
    method __init__ (line 138) | def __init__(self, root, split, bands=None, transform=None, target_tra...
    method __getitem__ (line 165) | def __getitem__(self, index):
    method __len__ (line 192) | def __len__(self):
    method get_multihot_old (line 196) | def get_multihot_old(labels):
    method get_multihot_new (line 203) | def get_multihot_new(labels):

FILE: src/benchmark/transfer_classification/datasets/BigEarthNet/bigearthnet_dataset_seco_lmdb_s2_uint8.py
  function normalize (line 17) | def normalize(img, mean, std):
  class Subset (line 24) | class Subset(Dataset):
    method __init__ (line 26) | def __init__(self, dataset, indices):
    method __getitem__ (line 30) | def __getitem__(self, idx):
    method __len__ (line 33) | def __len__(self):
    method __getattr__ (line 36) | def __getattr__(self, name):
  function random_subset (line 40) | def random_subset(dataset, frac, seed=None):
  class _RepeatSampler (line 46) | class _RepeatSampler(object):
    method __init__ (line 53) | def __init__(self, sampler):
    method __iter__ (line 56) | def __iter__(self):
  class InfiniteDataLoader (line 61) | class InfiniteDataLoader(DataLoader):
    method __init__ (line 67) | def __init__(self, *args, **kwargs):
    method __len__ (line 72) | def __len__(self):
    method __iter__ (line 75) | def __iter__(self):
  function make_lmdb (line 80) | def make_lmdb(dataset, lmdb_file, num_workers=6):
  class LMDBDataset (line 98) | class LMDBDataset(Dataset):
    method __init__ (line 100) | def __init__(self, lmdb_file, is_slurm_job=False, transform=None, norm...
    method _init_db (line 122) | def _init_db(self):
    method __getitem__ (line 128) | def __getitem__(self, index):
    method __len__ (line 149) | def __len__(self):

FILE: src/benchmark/transfer_classification/datasets/EuroSat/eurosat_dataset.py
  function is_valid_file (line 51) | def is_valid_file(filename):
  function normalize (line 54) | def normalize(img, mean, std):
  class EurosatDataset (line 61) | class EurosatDataset(Dataset):
    method __init__ (line 63) | def __init__(self, root, bands='B13', transform=None, normalize=False):
    method __getitem__ (line 89) | def __getitem__(self, index):
    method __len__ (line 123) | def __len__(self):
  class Subset (line 127) | class Subset(Dataset):
    method __init__ (line 135) | def __init__(self, dataset, indices, transform=None):
    method __getitem__ (line 140) | def __getitem__(self, idx):
    method __len__ (line 146) | def __len__(self):

FILE: src/benchmark/transfer_classification/datasets/So2Sat/so2sat_lcz42_dataset.py
  class Subset (line 52) | class Subset(Dataset):
    method __init__ (line 54) | def __init__(self, dataset, indices):
    method __getitem__ (line 58) | def __getitem__(self, idx):
    method __len__ (line 61) | def __len__(self):
    method __getattr__ (line 64) | def __getattr__(self, name):
  function random_subset (line 68) | def random_subset(dataset, frac, seed=None):
  function normalize (line 90) | def normalize(img, mean, std):
  class So2SatDataset (line 100) | class So2SatDataset(Dataset):
    method __init__ (line 101) | def __init__(self,path,bands=None,quantile=None,transform=None, normal...
    method __getitem__ (line 113) | def __getitem__(self, index):
    method __len__ (line 161) | def __len__(self):

FILE: src/benchmark/transfer_classification/linear_BE_data2vec.py
  function build_dataset_be (line 41) | def build_dataset_be(is_train, args):
  function get_args (line 83) | def get_args():
  function main (line 292) | def main(args, ds_init):

FILE: src/benchmark/transfer_classification/linear_BE_dino.py
  function eval_linear (line 41) | def eval_linear(args):
  function train (line 212) | def train(model, linear_classifier, optimizer, loader, epoch, n, avgpool):
  function validate_network (line 259) | def validate_network(val_loader, model, linear_classifier, n, avgpool):
  class LinearClassifier (line 311) | class LinearClassifier(nn.Module):
    method __init__ (line 313) | def __init__(self, dim, num_labels=1000):
    method forward (line 320) | def forward(self, x):

FILE: src/benchmark/transfer_classification/linear_BE_mae.py
  function get_args_parser (line 47) | def get_args_parser():
  function main (line 127) | def main(args):

FILE: src/benchmark/transfer_classification/linear_BE_moco.py
  function init_distributed_mode (line 69) | def init_distributed_mode(args):
  function fix_random_seeds (line 98) | def fix_random_seeds(seed=42):
  function adjust_learning_rate (line 106) | def adjust_learning_rate(optimizer, epoch, args):
  function main (line 118) | def main():

FILE: src/benchmark/transfer_classification/linear_BE_moco_v3.py
  function main (line 113) | def main():
  function main_worker (line 157) | def main_worker(gpu, ngpus_per_node, args):
  function train (line 407) | def train(train_loader, model, criterion, optimizer, epoch, args):
  function validate (line 471) | def validate(val_loader, model, criterion, args):
  function save_checkpoint (line 526) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
  function sanity_check (line 532) | def sanity_check(state_dict, pretrained_weights, linear_keyword):
  class AverageMeter (line 556) | class AverageMeter(object):
    method __init__ (line 558) | def __init__(self, name, fmt=':f'):
    method reset (line 563) | def reset(self):
    method update (line 569) | def update(self, val, n=1):
    method __str__ (line 575) | def __str__(self):
  class ProgressMeter (line 580) | class ProgressMeter(object):
    method __init__ (line 581) | def __init__(self, num_batches, meters, prefix=""):
    method display (line 586) | def display(self, batch):
    method _get_batch_fmtstr (line 591) | def _get_batch_fmtstr(self, num_batches):
  function adjust_learning_rate (line 597) | def adjust_learning_rate(optimizer, epoch, args):
  function accuracy (line 610) | def accuracy(output, target, topk=(1,)):

FILE: src/benchmark/transfer_classification/linear_BE_sup.py
  function init_distributed_mode (line 71) | def init_distributed_mode(args):
  function fix_random_seeds (line 100) | def fix_random_seeds(seed=42):
  function adjust_learning_rate (line 108) | def adjust_learning_rate(optimizer, epoch, args):
  function main (line 120) | def main():

FILE: src/benchmark/transfer_classification/linear_EU_data2vec.py
  function build_dataset_eu (line 44) | def build_dataset_eu(is_train, args):
  function get_args (line 76) | def get_args():
  function main (line 285) | def main(args, ds_init):

FILE: src/benchmark/transfer_classification/linear_EU_dino.py
  function eval_linear (line 43) | def eval_linear(args):
  function train (line 199) | def train(model, linear_classifier, optimizer, loader, epoch, n, avgpool):
  function validate_network (line 243) | def validate_network(val_loader, model, linear_classifier, n, avgpool):
  class LinearClassifier (line 293) | class LinearClassifier(nn.Module):
    method __init__ (line 295) | def __init__(self, dim, num_labels=1000):
    method forward (line 302) | def forward(self, x):

FILE: src/benchmark/transfer_classification/linear_EU_mae.py
  function get_args_parser (line 48) | def get_args_parser():
  function main (line 127) | def main(args):

FILE: src/benchmark/transfer_classification/linear_EU_moco.py
  function init_distributed_mode (line 57) | def init_distributed_mode(args):
  function fix_random_seeds (line 86) | def fix_random_seeds(seed=42):
  function adjust_learning_rate (line 94) | def adjust_learning_rate(optimizer, epoch, args):
  function main (line 106) | def main():

FILE: src/benchmark/transfer_classification/linear_EU_moco_v3.py
  function init_distributed_mode (line 60) | def init_distributed_mode(args):
  function fix_random_seeds (line 89) | def fix_random_seeds(seed=42):
  function adjust_learning_rate (line 97) | def adjust_learning_rate(optimizer, epoch, args):
  function main (line 109) | def main():

FILE: src/benchmark/transfer_classification/linear_SS_data2vec.py
  function build_dataset_ss (line 42) | def build_dataset_ss(is_train, args):
  function get_args (line 76) | def get_args():
  function main (line 287) | def main(args, ds_init):

FILE: src/benchmark/transfer_classification/linear_SS_dino.py
  function eval_linear (line 39) | def eval_linear(args):
  function train (line 201) | def train(model, linear_classifier, optimizer, loader, epoch, n, avgpool):
  function validate_network (line 245) | def validate_network(val_loader, model, linear_classifier, n, avgpool):
  class LinearClassifier (line 295) | class LinearClassifier(nn.Module):
    method __init__ (line 297) | def __init__(self, dim, num_labels=1000):
    method forward (line 304) | def forward(self, x):

FILE: src/benchmark/transfer_classification/linear_SS_mae.py
  function get_args_parser (line 46) | def get_args_parser():
  function main (line 125) | def main(args):

FILE: src/benchmark/transfer_classification/linear_SS_moco.py
  function init_distributed_mode (line 53) | def init_distributed_mode(args):
  function fix_random_seeds (line 82) | def fix_random_seeds(seed=42):
  function adjust_learning_rate (line 90) | def adjust_learning_rate(optimizer, epoch, args):
  function main (line 102) | def main():

FILE: src/benchmark/transfer_classification/linear_SS_moco_v3.py
  function init_distributed_mode (line 60) | def init_distributed_mode(args):
  function fix_random_seeds (line 89) | def fix_random_seeds(seed=42):
  function adjust_learning_rate (line 97) | def adjust_learning_rate(optimizer, epoch, args):
  function main (line 109) | def main():

FILE: src/benchmark/transfer_classification/models/data2vec/dall_e/__init__.py
  function load_model (line 9) | def load_model(path: str, device: torch.device = None) -> nn.Module:

FILE: src/benchmark/transfer_classification/models/data2vec/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: src/benchmark/transfer_classification/models/data2vec/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: src/benchmark/transfer_classification/models/data2vec/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: src/benchmark/transfer_classification/models/data2vec/dataset_folder.py
  function has_file_allowed_extension (line 21) | def has_file_allowed_extension(filename: str, extensions: Tuple[str, ......
  function is_image_file (line 34) | def is_image_file(filename: str) -> bool:
  function make_dataset (line 46) | def make_dataset(
  class DatasetFolder (line 76) | class DatasetFolder(VisionDataset):
    method __init__ (line 108) | def __init__(
    method _find_classes (line 138) | def _find_classes(self, dir: str) -> Tuple[List[str], Dict[str, int]]:
    method __getitem__ (line 156) | def __getitem__(self, index: int) -> Tuple[Any, Any]:
    method __len__ (line 180) | def __len__(self) -> int:
  function pil_loader (line 187) | def pil_loader(path: str) -> Image.Image:
  function accimage_loader (line 195) | def accimage_loader(path: str) -> Any:
  function default_loader (line 204) | def default_loader(path: str) -> Any:
  class ImageFolder (line 234) | class ImageFolder(DatasetFolder):
    method __init__ (line 261) | def __init__(

FILE: src/benchmark/transfer_classification/models/data2vec/datasets.py
  class SeasonTransform (line 35) | class SeasonTransform:
    method __init__ (line 37) | def __init__(self, base_transform, season='fixed'):
    method __call__ (line 41) | def __call__(self, x):
  class DataAugmentations (line 64) | class DataAugmentations(object):
    method __init__ (line 65) | def __init__(self, args):
    method __call__ (line 130) | def __call__(self, image):
    method __repr__ (line 141) | def __repr__(self):
  function build_beit_pretraining_dataset (line 152) | def build_beit_pretraining_dataset(args):
  function build_transform (line 167) | def build_transform(is_train, args):

FILE: src/benchmark/transfer_classification/models/data2vec/engine_for_cyclical.py
  function train_one_epoch (line 23) | def train_one_epoch(model: torch.nn.Module, model_ema: torch.nn.Module, ...

FILE: src/benchmark/transfer_classification/models/data2vec/engine_for_cyclical_joint.py
  function train_one_epoch (line 24) | def train_one_epoch(model: torch.nn.Module, model_ema: torch.nn.Module, ...

FILE: src/benchmark/transfer_classification/models/data2vec/engine_for_finetuning.py
  function train_class_batch (line 27) | def train_class_batch(model, samples, target, criterion, bool_masked_pos...
  function get_loss_scale_for_deepspeed (line 33) | def get_loss_scale_for_deepspeed(model):
  function train_one_epoch (line 38) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
  function evaluate (line 173) | def evaluate(data_loader, model, device, metric='acc', padd=False, oneho...

FILE: src/benchmark/transfer_classification/models/data2vec/engine_for_pretraining.py
  function train_one_epoch (line 22) | def train_one_epoch(model: torch.nn.Module, d_vae: torch.nn.Module,

FILE: src/benchmark/transfer_classification/models/data2vec/masking_generator.py
  class MaskingGenerator (line 29) | class MaskingGenerator:
    method __init__ (line 30) | def __init__(
    method __repr__ (line 46) | def __repr__(self):
    method get_shape (line 52) | def get_shape(self):
    method _mask (line 55) | def _mask(self, mask, max_mask_patches):
    method __call__ (line 79) | def __call__(self):

FILE: src/benchmark/transfer_classification/models/data2vec/modeling_cyclical.py
  function trunc_normal_ (line 22) | def trunc_normal_(tensor, mean=0.0, std=1.0):
  class VisionTransformerForCyclicalTraining (line 32) | class VisionTransformerForCyclicalTraining(nn.Module):
    method __init__ (line 33) | def __init__(
    method fix_init_weight (line 127) | def fix_init_weight(self):
    method _init_weights (line 135) | def _init_weights(self, m):
    method no_weight_decay (line 149) | def no_weight_decay(self):
    method get_num_layers (line 152) | def get_num_layers(self):
    method forward_features (line 155) | def forward_features(self, x, bool_masked_pos, layer_results):
    method forward (line 194) | def forward(self, x, bool_masked_pos, return_all_tokens=False, layer_r...
  function beit_base_patch16_224 (line 214) | def beit_base_patch16_224(pretrained=False, **kwargs):
  function beit_small_patch16_224 (line 234) | def beit_small_patch16_224(pretrained=False, **kwargs):
  function beit_large_patch16_224 (line 255) | def beit_large_patch16_224(pretrained=False, **kwargs):
  function beit_huge_patch16_224 (line 275) | def beit_huge_patch16_224(pretrained=False, **kwargs):

FILE: src/benchmark/transfer_classification/models/data2vec/modeling_cyclical_joint.py
  function trunc_normal_ (line 22) | def trunc_normal_(tensor, mean=0.0, std=1.0):
  class VisionTransformerForCyclicalJointTraining (line 32) | class VisionTransformerForCyclicalJointTraining(nn.Module):
    method __init__ (line 33) | def __init__(
    method fix_init_weight (line 126) | def fix_init_weight(self):
    method _init_weights (line 134) | def _init_weights(self, m):
    method no_weight_decay (line 148) | def no_weight_decay(self):
    method get_num_layers (line 151) | def get_num_layers(self):
    method forward_features (line 154) | def forward_features(self, x, bool_masked_pos, layer_results):
    method forward (line 183) | def forward(self, x, bool_masked_pos, return_all_tokens=False, layer_r...
  function beit_base_joint_patch16_224 (line 203) | def beit_base_joint_patch16_224(pretrained=False, **kwargs):

FILE: src/benchmark/transfer_classification/models/data2vec/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 64) | class ResBlock(nn.Module):
    method __init__ (line 65) | def __init__(self, chan_in, hidden_size, chan_out):
    method forward (line 75) | def forward(self, x):
  class DiscreteVAE (line 79) | class DiscreteVAE(BasicVAE):
    method __init__ (line 80) | def __init__(
    method get_image_size (line 127) | def get_image_size(self):
    method get_image_tokens_size (line 130) | def get_image_tokens_size(self):
    method get_codebook_indices (line 135) | def get_codebook_indices(self, images):
    method get_codebook_probs (line 142) | def get_codebook_probs(self, images):
    method decode (line 146) | def decode(
    method forward (line 158) | def forward(
  class Dalle_VAE (line 206) | class Dalle_VAE(BasicVAE):
    method __init__ (line 207) | def __init__(self, image_size):
    method load_model (line 213) | def load_model(self, model_dir, device):
    method decode (line 217) | def decode(self, img_seq):
    method get_codebook_indices (line 223) | def get_codebook_indices(self, images):
    method get_codebook_probs (line 227) | def get_codebook_probs(self, images):
    method forward (line 231) | def forward(self, img_seq_prob, no_process=False):

FILE: src/benchmark/transfer_classification/models/data2vec/modeling_finetune.py
  function _cfg (line 23) | def _cfg(url='', **kwargs):
  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):
    method extra_repr (line 43) | def extra_repr(self) -> str:
  class Mlp (line 47) | class Mlp(nn.Module):
    method __init__ (line 48) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 57) | def forward(self, x):
  class Attention (line 67) | class Attention(nn.Module):
    method __init__ (line 68) | def __init__(
    method forward (line 121) | def forward(self, x, rel_pos_bias=None):
  class Block (line 154) | class Block(nn.Module):
    method __init__ (line 156) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 177) | def forward(self, x, rel_pos_bias=None):
  class PatchEmbed (line 189) | class PatchEmbed(nn.Module):
    method __init__ (line 192) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 204) | def forward(self, x, **kwargs):
  class RelativePositionBias (line 213) | class RelativePositionBias(nn.Module):
    method __init__ (line 215) | def __init__(self, window_size, num_heads):
    method forward (line 244) | def forward(self):
  class VisionTransformer (line 252) | class VisionTransformer(nn.Module):
    method __init__ (line 255) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
    method fix_init_weight (line 315) | def fix_init_weight(self):
    method _init_weights (line 323) | def _init_weights(self, m):
    method get_num_layers (line 334) | def get_num_layers(self):
    method no_weight_decay (line 338) | def no_weight_decay(self):
    method get_classifier (line 341) | def get_classifier(self):
    method reset_classifier (line 344) | def reset_classifier(self, num_classes, global_pool=''):
    method forward_features (line 348) | def forward_features(self, x, bool_masked_pos=None):
    method forward (line 391) | def forward(self, x, bool_masked_pos=None):
  function beit_small_patch16_224 (line 398) | def beit_small_patch16_224(pretrained=False, **kwargs):
  function beit_base_patch16_224 (line 407) | def beit_base_patch16_224(pretrained=False, **kwargs):
  function beit_base_patch16_384 (line 416) | def beit_base_patch16_384(pretrained=False, **kwargs):
  function beit_large_patch16_224 (line 425) | def beit_large_patch16_224(pretrained=False, **kwargs):
  function beit_large_patch16_384 (line 434) | def beit_large_patch16_384(pretrained=False, **kwargs):
  function beit_large_patch16_512 (line 443) | def beit_large_patch16_512(pretrained=False, **kwargs):

FILE: src/benchmark/transfer_classification/models/data2vec/modeling_pretrain.py
  function trunc_normal_ (line 22) | def trunc_normal_(tensor, mean=0., std=1.):
  class VisionTransformerForMaskedImageModeling (line 32) | class VisionTransformerForMaskedImageModeling(nn.Module):
    method __init__ (line 33) | def __init__(self, img_size=224, patch_size=16, in_chans=3, vocab_size...
    method fix_init_weight (line 79) | def fix_init_weight(self):
    method _init_weights (line 87) | def _init_weights(self, m):
    method no_weight_decay (line 101) | def no_weight_decay(self):
    method get_num_layers (line 104) | def get_num_layers(self):
    method forward_features (line 107) | def forward_features(self, x, bool_masked_pos):
    method forward (line 129) | def forward(self, x, bool_masked_pos, return_all_tokens=False):
  function beit_base_patch16_224_8k_vocab (line 140) | def beit_base_patch16_224_8k_vocab(pretrained=False, **kwargs):
  function beit_large_patch16_224_8k_vocab (line 155) | def beit_large_patch16_224_8k_vocab(pretrained=False, **kwargs):

FILE: src/benchmark/transfer_classification/models/data2vec/models.py
  function _cfg (line 32) | def _cfg(url='', **kwargs):
  class Attention (line 71) | class Attention(nn.Module):
    method __init__ (line 72) | def __init__(
    method forward (line 127) | def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
  class Block (line 157) | class Block(nn.Module):
    method __init__ (line 159) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=...
    method forward (line 179) | def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
  class RelativePositionBias (line 189) | class RelativePositionBias(nn.Module):
    method __init__ (line 191) | def __init__(self, window_size, num_heads):
    method forward (line 220) | def forward(self):
  class Beit (line 228) | class Beit(nn.Module):
    method __init__ (line 232) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
    method fix_init_weight (line 281) | def fix_init_weight(self):
    method _init_weights (line 289) | def _init_weights(self, m):
    method get_num_layers (line 298) | def get_num_layers(self):
    method no_weight_decay (line 302) | def no_weight_decay(self):
    method get_classifier (line 305) | def get_classifier(self):
    method reset_classifier (line 308) | def reset_classifier(self, num_classes, global_pool=''):
    method forward_features (line 312) | def forward_features(self, x):
    method forward (line 333) | def forward(self, x):
  function _create_beit (line 339) | def _create_beit(variant, pretrained=False, default_cfg=None, **kwargs):
  function beit_base_patch16_224 (line 354) | def beit_base_patch16_224(pretrained=False, **kwargs):
  function beit_base_patch16_384 (line 366) | def beit_base_patch16_384(pretrained=False, **kwargs):
  function beit_base_patch16_224_in22k (line 375) | def beit_base_patch16_224_in22k(pretrained=False, **kwargs):
  function beit_large_patch16_224 (line 384) | def beit_large_patch16_224(pretrained=False, **kwargs):
  function beit_large_patch16_384 (line 393) | def beit_large_patch16_384(pretrained=False, **kwargs):
  function beit_large_patch16_512 (line 402) | def beit_large_patch16_512(pretrained=False, **kwargs):
  function beit_large_patch16_224_in22k (line 411) | def beit_large_patch16_224_in22k(pretrained=False, **kwargs):

FILE: src/benchmark/transfer_classification/models/data2vec/optim_factory.py
  function get_num_layer_for_vit (line 33) | def get_num_layer_for_vit(var_name, num_max_layer):
  class LayerDecayValueAssigner (line 47) | class LayerDecayValueAssigner(object):
    method __init__ (line 48) | def __init__(self, values):
    method get_scale (line 51) | def get_scale(self, layer_id):
    method get_layer_id (line 54) | def get_layer_id(self, var_name):
  function get_parameter_groups (line 58) | def get_parameter_groups(model, weight_decay=1e-5, skip_list=(), get_num...
  function create_optimizer (line 100) | def create_optimizer(args, model, get_num_layer=None, get_layer_scale=No...

FILE: src/benchmark/transfer_classification/models/data2vec/run_beit_pretraining.py
  function get_args (line 34) | def get_args():
  function get_model (line 139) | def get_model(args):
  function main (line 154) | def main(args):

FILE: src/benchmark/transfer_classification/models/data2vec/run_class_finetuning.py
  function get_args (line 39) | def get_args():
  function main (line 241) | def main(args, ds_init):

FILE: src/benchmark/transfer_classification/models/data2vec/run_cyclical.py
  function get_args (line 37) | def get_args():
  function get_model (line 280) | def get_model(args):
  function main (line 304) | def main(args):

FILE: src/benchmark/transfer_classification/models/data2vec/run_cyclical_joint.py
  function get_args (line 36) | def get_args():
  function get_model (line 264) | def get_model(args):
  function main (line 279) | def main(args):

FILE: src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/backbone/beit.py
  class DropPath (line 26) | class DropPath(nn.Module):
    method __init__ (line 29) | def __init__(self, drop_prob=None):
    method forward (line 33) | def forward(self, x):
    method extra_repr (line 36) | def extra_repr(self) -> str:
  class Mlp (line 40) | class Mlp(nn.Module):
    method __init__ (line 41) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 50) | def forward(self, x):
  class Attention (line 60) | class Attention(nn.Module):
    method __init__ (line 61) | def __init__(
    method forward (line 117) | def forward(self, x, rel_pos_bias=None):
  class Block (line 150) | class Block(nn.Module):
    method __init__ (line 152) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 172) | def forward(self, x, rel_pos_bias=None):
  class PatchEmbed (line 182) | class PatchEmbed(nn.Module):
    method __init__ (line 185) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 197) | def forward(self, x, **kwargs):
  class HybridEmbed (line 209) | class HybridEmbed(nn.Module):
    method __init__ (line 213) | def __init__(self, backbone, img_size=224, feature_size=None, in_chans...
    method forward (line 237) | def forward(self, x):
  class RelativePositionBias (line 244) | class RelativePositionBias(nn.Module):
    method __init__ (line 246) | def __init__(self, window_size, num_heads):
    method forward (line 275) | def forward(self):
  class BEiT (line 284) | class BEiT(nn.Module):
    method __init__ (line 287) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
    method fix_init_weight (line 366) | def fix_init_weight(self):
    method _init_weights (line 374) | def _init_weights(self, m):
    method init_weights (line 383) | def init_weights(self, pretrained=None):
    method get_num_layers (line 409) | def get_num_layers(self):
    method no_weight_decay (line 413) | def no_weight_decay(self):
    method forward_features (line 416) | def forward_features(self, x):
    method forward (line 441) | def forward(self, x):

FILE: src/benchmark/transfer_classification/models/data2vec/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: src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/mmcv_custom/apex_runner/checkpoint.py
  function save_checkpoint (line 19) | def save_checkpoint(model, filename, optimizer=None, meta=None):

FILE: src/benchmark/transfer_classification/models/data2vec/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: src/benchmark/transfer_classification/models/data2vec/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 476) | def weights_to_cpu(state_dict):
  function _save_to_state_dict (line 491) | def _save_to_state_dict(module, destination, prefix, keep_vars):
  function get_state_dict (line 511) | def get_state_dict(module, destination=None, prefix='', keep_vars=False):
  function save_checkpoint (line 555) | def save_checkpoint(model, filename, optimizer=None, meta=None):

FILE: src/benchmark/transfer_classification/models/data2vec/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: src/benchmark/transfer_classification/models/data2vec/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: src/benchmark/transfer_classification/models/data2vec/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: src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/tools/test.py
  function parse_args (line 17) | def parse_args():
  function main (line 67) | def main():

FILE: src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/tools/train.py
  function parse_args (line 23) | def parse_args():
  function main (line 67) | def main():

FILE: src/benchmark/transfer_classification/models/data2vec/transforms.py
  class ToNumpy (line 20) | class ToNumpy:
    method __call__ (line 22) | def __call__(self, pil_img):
  class ToTensor (line 30) | class ToTensor:
    method __init__ (line 32) | def __init__(self, dtype=torch.float32):
    method __call__ (line 35) | def __call__(self, pil_img):
  function _pil_interp (line 53) | def _pil_interp(method):
  class RandomResizedCropAndInterpolationWithTwoPic (line 68) | class RandomResizedCropAndInterpolationWithTwoPic:
    method __init__ (line 83) | def __init__(self, size, second_size=None, scale=(0.08, 1.0), ratio=(3...
    method get_params (line 109) | def get_params(img, scale, ratio):
    method __call__ (line 151) | def __call__(self, img):
    method __repr__ (line 170) | def __repr__(self):

FILE: src/benchmark/transfer_classification/models/data2vec/utils.py
  class SmoothedValue (line 33) | class SmoothedValue(object):
    method __init__ (line 38) | def __init__(self, window_size=20, fmt=None):
    method update (line 46) | def update(self, value, n=1):
    method synchronize_between_processes (line 51) | def synchronize_between_processes(self):
    method median (line 65) | def median(self):
    method avg (line 70) | def avg(self):
    method global_avg (line 75) | def global_avg(self):
    method max (line 79) | def max(self):
    method value (line 83) | def value(self):
    method __str__ (line 86) | def __str__(self):
  class MetricLogger (line 95) | class MetricLogger(object):
    method __init__ (line 96) | def __init__(self, delimiter="\t"):
    method update (line 100) | def update(self, **kwargs):
    method __getattr__ (line 109) | def __getattr__(self, attr):
    method __str__ (line 117) | def __str__(self):
    method synchronize_between_processes (line 125) | def synchronize_between_processes(self):
    method add_meter (line 129) | def add_meter(self, name, meter):
    method log_every (line 132) | def log_every(self, iterable, print_freq, header=None):
  class TensorboardLogger (line 179) | class TensorboardLogger(object):
    method __init__ (line 180) | def __init__(self, log_dir):
    method set_step (line 184) | def set_step(self, step=None):
    method update (line 190) | def update(self, head='scalar', step=None, **kwargs):
    method flush (line 199) | def flush(self):
  function _load_checkpoint_for_ema (line 203) | def _load_checkpoint_for_ema(model_ema, checkpoint):
  function setup_for_distributed (line 217) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 232) | def is_dist_avail_and_initialized():
  function get_world_size (line 240) | def get_world_size():
  function get_rank (line 246) | def get_rank():
  function is_main_process (line 252) | def is_main_process():
  function save_on_master (line 256) | def save_on_master(*args, **kwargs):
  function init_distributed_mode (line 261) | def init_distributed_mode(args):
  function load_state_dict (line 314) | def load_state_dict(model, state_dict, prefix='', ignore_missing="relati...
  class NativeScalerWithGradNormCount (line 363) | class NativeScalerWithGradNormCount:
    method __init__ (line 366) | def __init__(self):
    method __call__ (line 369) | def __call__(self, loss, optimizer, clip_grad=None, parameters=None, c...
    method state_dict (line 385) | def state_dict(self):
    method load_state_dict (line 388) | def load_state_dict(self, state_dict):
  function get_grad_norm_ (line 392) | def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
  function cosine_scheduler (line 407) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
  function tri_phase_scheduler (line 426) | def tri_phase_scheduler(base_value, final_value, epochs, niter_per_ep, w...
  function save_model (line 461) | def save_model(args, epoch, model, model_without_ddp, optimizer, loss_sc...
  function auto_load_model (line 486) | def auto_load_model(args, model, model_without_ddp, optimizer, loss_scal...
  function create_d_vae (line 541) | def create_d_vae(weight_path, d_vae_type, image_size, device):
  function get_dalle_vae (line 550) | def get_dalle_vae(weight_path, image_size, device):
  function get_d_vae (line 556) | def get_d_vae(weight_path, image_size, device):
  function create_ds_config (line 576) | def create_ds_config(args):

FILE: src/benchmark/transfer_classification/models/dino/utils.py
  class GaussianBlur (line 37) | class GaussianBlur(object):
    method __init__ (line 41) | def __init__(self, p=0.5, radius_min=0.1, radius_max=2.):
    method __call__ (line 46) | def __call__(self, img):
  class cvGaussianBlur (line 58) | class cvGaussianBlur(object):
    method __init__ (line 61) | def __init__(self, p=0.5, sigma=[.1, 2.]):
    method __call__ (line 65) | def __call__(self, x):
  class Solarization (line 78) | class Solarization(object):
    method __init__ (line 82) | def __init__(self, p):
    method __call__ (line 85) | def __call__(self, img):
  function load_pretrained_weights (line 92) | def load_pretrained_weights(model, pretrained_weights, checkpoint_key, m...
  function load_pretrained_linear_weights (line 133) | def load_pretrained_linear_weights(linear_classifier, model_name, patch_...
  function clip_gradients (line 153) | def clip_gradients(model, clip):
  function cancel_gradients_last_layer (line 165) | def cancel_gradients_last_layer(epoch, model, freeze_last_layer):
  function restart_from_checkpoint (line 173) | def restart_from_checkpoint(ckp_path, run_variables=None, **kwargs):
  function cosine_scheduler (line 208) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
  function bool_flag (line 222) | def bool_flag(s):
  function fix_random_seeds (line 236) | def fix_random_seeds(seed=31):
  class SmoothedValue (line 245) | class SmoothedValue(object):
    method __init__ (line 250) | def __init__(self, window_size=20, fmt=None):
    method update (line 258) | def update(self, value, n=1):
    method synchronize_between_processes (line 263) | def synchronize_between_processes(self):
    method median (line 277) | def median(self):
    method avg (line 282) | def avg(self):
    method global_avg (line 287) | def global_avg(self):
    method max (line 291) | def max(self):
    method value (line 295) | def value(self):
    method __str__ (line 298) | def __str__(self):
  function reduce_dict (line 307) | def reduce_dict(input_dict, average=True):
  class MetricLogger (line 334) | class MetricLogger(object):
    method __init__ (line 335) | def __init__(self, delimiter="\t"):
    method update (line 339) | def update(self, **kwargs):
    method __getattr__ (line 346) | def __getattr__(self, attr):
    method __str__ (line 354) | def __str__(self):
    method synchronize_between_processes (line 362) | def synchronize_between_processes(self):
    method add_meter (line 366) | def add_meter(self, name, meter):
    method log_every (line 369) | def log_every(self, iterable, print_freq, header=None):
  function get_sha (line 424) | def get_sha():
  function is_dist_avail_and_initialized (line 444) | def is_dist_avail_and_initialized():
  function get_world_size (line 452) | def get_world_size():
  function get_rank (line 458) | def get_rank():
  function is_main_process (line 464) | def is_main_process():
  function save_on_master (line 468) | def save_on_master(*args, **kwargs):
  function setup_for_distributed (line 473) | def setup_for_distributed(is_master):
  function init_distributed_mode (line 488) | 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):
  class MultiCropWrapper (line 618) | class MultiCropWrapper(nn.Module):
    method __init__ (line 627) | def __init__(self, backbone, head):
    method forward (line 634) | def forward(self, x):
  function get_params_groups (line 656) | def get_params_groups(model):
  function has_batchnorms (line 670) | def has_batchnorms(model):
  class PCA (line 678) | class PCA():
    method __init__ (line 682) | def __init__(self, dim=256, whit=0.5):
    method train_pca (line 687) | def train_pca(self, cov):
    method apply (line 713) | def apply(self, x):
  function compute_ap (line 732) | def compute_ap(ranks, nres):
  function compute_map (line 767) | def compute_map(ranks, gnd, kappas=[]):
  function multi_scale (line 839) | def multi_scale(samples, model):

FILE: src/benchmark/transfer_classification/models/dino/vision_transformer.py
  function drop_path (line 27) | def drop_path(x, drop_prob: float = 0., training: bool = False):
  class DropPath (line 38) | class DropPath(nn.Module):
    method __init__ (line 41) | def __init__(self, drop_prob=None):
    method forward (line 45) | def forward(self, x):
  class Mlp (line 49) | class Mlp(nn.Module):
    method __init__ (line 50) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 59) | def forward(self, x):
  class Attention (line 68) | class Attention(nn.Module):
    method __init__ (line 69) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at...
    method forward (line 80) | def forward(self, x):
  class Block (line 95) | class Block(nn.Module):
    method __init__ (line 96) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 107) | def forward(self, x, return_attention=False):
  class PatchEmbed (line 116) | class PatchEmbed(nn.Module):
    method __init__ (line 119) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 128) | def forward(self, x):
  class VisionTransformer (line 134) | class VisionTransformer(nn.Module):
    method __init__ (line 136) | def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_clas...
    method _init_weights (line 165) | def _init_weights(self, m):
    method interpolate_pos_encoding (line 174) | def interpolate_pos_encoding(self, x, w, h):
    method prepare_tokens (line 196) | def prepare_tokens(self, x):
    method forward (line 209) | def forward(self, x):
    method get_last_selfattention (line 216) | def get_last_selfattention(self, x):
    method get_intermediate_layers (line 225) | def get_intermediate_layers(self, x, n=1):
  function vit_tiny (line 236) | def vit_tiny(patch_size=16, **kwargs):
  function vit_small (line 243) | def vit_small(patch_size=16, **kwargs):
  function vit_base (line 250) | def vit_base(patch_size=16, **kwargs):
  class DINOHead (line 257) | class DINOHead(nn.Module):
    method __init__ (line 258) | def __init__(self, in_dim, out_dim, use_bn=False, norm_last_layer=True...
    method _init_weights (line 281) | def _init_weights(self, m):
    method forward (line 287) | def forward(self, x):

FILE: src/benchmark/transfer_classification/models/mae/engine_finetune.py
  function train_one_epoch (line 25) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
  function evaluate (line 99) | def evaluate(data_loader, model, device):

FILE: src/benchmark/transfer_classification/models/mae/engine_finetune_BE.py
  function train_one_epoch (line 29) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
  function evaluate (line 107) | def evaluate(data_loader, model, device, criterion):

FILE: src/benchmark/transfer_classification/models/mae/engine_finetune_EU.py
  function train_one_epoch (line 29) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
  function evaluate (line 104) | def evaluate(data_loader, model, device, criterion):

FILE: src/benchmark/transfer_classification/models/mae/engine_finetune_SS.py
  function train_one_epoch (line 29) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
  function evaluate (line 104) | def evaluate(data_loader, model, device, criterion):

FILE: src/benchmark/transfer_classification/models/mae/engine_pretrain.py
  function train_one_epoch (line 23) | def train_one_epoch(model: torch.nn.Module,

FILE: src/benchmark/transfer_classification/models/mae/main_finetune.py
  function get_args_parser (line 42) | def get_args_parser():
  function main (line 158) | def main(args):

FILE: src/benchmark/transfer_classification/models/mae/main_linprobe.py
  function get_args_parser (line 47) | def get_args_parser():
  function main (line 121) | def main(args):

FILE: src/benchmark/transfer_classification/models/mae/main_pretrain.py
  class SeasonTransform (line 41) | class SeasonTransform:
    method __init__ (line 43) | def __init__(self, base_transform, season='fixed'):
    method __call__ (line 47) | def __call__(self, x):
  function get_args_parser (line 72) | def get_args_parser():
  function main (line 151) | def main(args):

FILE: src/benchmark/transfer_classification/models/mae/models_mae.py
  class MaskedAutoencoderViT (line 22) | class MaskedAutoencoderViT(nn.Module):
    method __init__ (line 25) | def __init__(self, img_size=224, patch_size=16, in_chans=3,
    method initialize_weights (line 67) | def initialize_weights(self):
    method _init_weights (line 87) | def _init_weights(self, m):
    method patchify (line 97) | def patchify(self, imgs):
    method unpatchify (line 111) | def unpatchify(self, x):
    method random_masking (line 125) | def random_masking(self, x, mask_ratio):
    method forward_encoder (line 152) | def forward_encoder(self, x, mask_ratio):
    method forward_decoder (line 174) | def forward_decoder(self, x, ids_restore):
    method forward_loss (line 200) | def forward_loss(self, imgs, pred, mask):
    method forward (line 218) | def forward(self, imgs, mask_ratio=0.75):
  function mae_vit_small_patch16_dec512d8b (line 225) | def mae_vit_small_patch16_dec512d8b(**kwargs):
  function mae_vit_base_patch16_dec512d8b (line 232) | def mae_vit_base_patch16_dec512d8b(**kwargs):
  function mae_vit_large_patch16_dec512d8b (line 240) | def mae_vit_large_patch16_dec512d8b(**kwargs):
  function mae_vit_huge_patch14_dec512d8b (line 248) | def mae_vit_huge_patch14_dec512d8b(**kwargs):

FILE: src/benchmark/transfer_classification/models/mae/models_vit.py
  class VisionTransformer (line 20) | class VisionTransformer(timm.models.vision_transformer.VisionTransformer):
    method __init__ (line 23) | def __init__(self, global_pool=False, **kwargs):
    method forward_features (line 34) | def forward_features(self, x):
  function vit_small_patch16 (line 55) | def vit_small_patch16(**kwargs):
  function vit_base_patch16 (line 61) | def vit_base_patch16(**kwargs):
  function vit_large_patch16 (line 68) | def vit_large_patch16(**kwargs):
  function vit_huge_patch14 (line 75) | def vit_huge_patch14(**kwargs):

FILE: src/benchmark/transfer_classification/models/mae/submitit_finetune.py
  function parse_args (line 19) | def parse_args():
  function get_shared_folder (line 33) | def get_shared_folder() -> Path:
  function get_init_file (line 42) | def get_init_file():
  class Trainer (line 51) | class Trainer(object):
    method __init__ (line 52) | def __init__(self, args):
    method __call__ (line 55) | def __call__(self):
    method checkpoint (line 61) | def checkpoint(self):
    method _setup_gpu_args (line 73) | def _setup_gpu_args(self):
  function main (line 86) | def main():

FILE: src/benchmark/transfer_classification/models/mae/submitit_linprobe.py
  function parse_args (line 19) | def parse_args():
  function get_shared_folder (line 33) | def get_shared_folder() -> Path:
  function get_init_file (line 42) | def get_init_file():
  class Trainer (line 51) | class Trainer(object):
    method __init__ (line 52) | def __init__(self, args):
    method __call__ (line 55) | def __call__(self):
    method checkpoint (line 61) | def checkpoint(self):
    method _setup_gpu_args (line 73) | def _setup_gpu_args(self):
  function main (line 86) | def main():

FILE: src/benchmark/transfer_classification/models/mae/submitit_pretrain.py
  function parse_args (line 19) | def parse_args():
  function get_shared_folder (line 33) | def get_shared_folder() -> Path:
  function get_init_file (line 42) | def get_init_file():
  class Trainer (line 51) | class Trainer(object):
    method __init__ (line 52) | def __init__(self, args):
    method __call__ (line 55) | def __call__(self):
    method checkpoint (line 61) | def checkpoint(self):
    method _setup_gpu_args (line 73) | def _setup_gpu_args(self):
  function main (line 86) | def main():

FILE: src/benchmark/transfer_classification/models/mae/util/crop.py
  class RandomResizedCrop (line 15) | class RandomResizedCrop(transforms.RandomResizedCrop):
    method get_params (line 23) | def get_params(img, scale, ratio):

FILE: src/benchmark/transfer_classification/models/mae/util/datasets.py
  function build_dataset (line 20) | def build_dataset(is_train, args):
  function build_transform (line 31) | def build_transform(is_train, args):

FILE: src/benchmark/transfer_classification/models/mae/util/lars.py
  class LARS (line 14) | class LARS(torch.optim.Optimizer):
    method __init__ (line 18) | def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, trust_c...
    method step (line 23) | def step(self):

FILE: src/benchmark/transfer_classification/models/mae/util/lr_decay.py
  function param_groups_lrd (line 15) | def param_groups_lrd(model, weight_decay=0.05, no_weight_decay_list=[], ...
  function get_layer_id_for_vit (line 64) | def get_layer_id_for_vit(name, num_layers):

FILE: src/benchmark/transfer_classification/models/mae/util/lr_sched.py
  function adjust_learning_rate (line 9) | def adjust_learning_rate(optimizer, epoch, args):

FILE: src/benchmark/transfer_classification/models/mae/util/misc.py
  class SmoothedValue (line 24) | class SmoothedValue(object):
    method __init__ (line 29) | def __init__(self, window_size=20, fmt=None):
    method update (line 37) | def update(self, value, n=1):
    method synchronize_between_processes (line 42) | def synchronize_between_processes(self):
    method median (line 56) | def median(self):
    method avg (line 61) | def avg(self):
    method global_avg (line 66) | def global_avg(self):
    method max (line 70) | def max(self):
    method value (line 74) | def value(self):
    method __str__ (line 77) | def __str__(self):
  class MetricLogger (line 86) | class MetricLogger(object):
    method __init__ (line 87) | def __init__(self, delimiter="\t"):
    method update (line 91) | def update(self, **kwargs):
    method __getattr__ (line 100) | def __getattr__(self, attr):
    method __str__ (line 108) | def __str__(self):
    method synchronize_between_processes (line 116) | def synchronize_between_processes(self):
    method add_meter (line 120) | def add_meter(self, name, meter):
    method log_every (line 123) | def log_every(self, iterable, print_freq, header=None):
  function setup_for_distributed (line 170) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 187) | def is_dist_avail_and_initialized():
  function get_world_size (line 195) | def get_world_size():
  function get_rank (line 201) | def get_rank():
  function is_main_process (line 207) | def is_main_process():
  function save_on_master (line 211) | def save_on_master(*args, **kwargs):
  function init_distributed_mode (line 216) | def init_distributed_mode(args):
  class NativeScalerWithGradNormCount (line 253) | class NativeScalerWithGradNormCount:
    method __init__ (line 256) | def __init__(self):
    method __call__ (line 259) | def __call__(self, loss, optimizer, clip_grad=None, parameters=None, c...
    method state_dict (line 275) | def state_dict(self):
    method load_state_dict (line 278) | def load_state_dict(self, state_dict):
  function get_grad_norm_ (line 282) | def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
  function save_model (line 297) | def save_model(args, epoch, model, model_without_ddp, optimizer, loss_sc...
  function load_model (line 317) | def load_model(args, model_without_ddp, optimizer, loss_scaler):
  function all_reduce_mean (line 334) | def all_reduce_mean(x):

FILE: src/benchmark/transfer_classification/models/mae/util/pos_embed.py
  function get_2d_sincos_pos_embed (line 20) | def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
  function get_2d_sincos_pos_embed_from_grid (line 38) | def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
  function get_1d_sincos_pos_embed_from_grid (line 49) | def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
  function interpolate_pos_embed (line 75) | def interpolate_pos_embed(model, checkpoint_model):

FILE: src/benchmark/transfer_classification/models/moco/builder.py
  class MoCo (line 6) | class MoCo(nn.Module):
    method __init__ (line 11) | def __init__(self, base_encoder, dim=128, K=65536, m=0.999, T=0.07, ml...
    method _momentum_update_key_encoder (line 68) | def _momentum_update_key_encoder(self):
    method _dequeue_and_enqueue (line 76) | def _dequeue_and_enqueue(self, keys):
    method _batch_shuffle_ddp (line 92) | def _batch_shuffle_ddp(self, x):
    method _batch_unshuffle_ddp (line 120) | def _batch_unshuffle_ddp(self, x, idx_unshuffle):
    method forward (line 138) | def forward(self, im_q, im_k):
  function concat_all_gather (line 188) | def concat_all_gather(tensor):

FILE: src/benchmark/transfer_classification/models/moco/loader.py
  class TwoCropsTransform (line 11) | class TwoCropsTransform:
    method __init__ (line 14) | def __init__(self, base_transform):
    method __call__ (line 17) | def __call__(self, x):
  class GaussianBlur (line 23) | class GaussianBlur(object):
    method __init__ (line 26) | def __init__(self, sigma=[.1, 2.]):
    method __call__ (line 29) | def __call__(self, x):

FILE: src/benchmark/transfer_classification/models/moco_v2/builder.py
  class MoCo (line 6) | class MoCo(nn.Module):
    method __init__ (line 11) | def __init__(self, base_encoder, dim=128, K=65536, m=0.999, T=0.07, ml...
    method _momentum_update_key_encoder (line 57) | def _momentum_update_key_encoder(self):
    method _dequeue_and_enqueue (line 65) | def _dequeue_and_enqueue(self, keys):
    method _batch_shuffle_ddp (line 81) | def _batch_shuffle_ddp(self, x):
    method _batch_unshuffle_ddp (line 109) | def _batch_unshuffle_ddp(self, x, idx_unshuffle):
    method forward (line 127) | def forward(self, im_q, im_k):
  function concat_all_gather (line 177) | def concat_all_gather(tensor):

FILE: src/benchmark/transfer_classification/models/moco_v2/detection/train_net.py
  class Res5ROIHeadsExtraNorm (line 15) | class Res5ROIHeadsExtraNorm(Res5ROIHeads):
    method _build_res5_block (line 20) | def _build_res5_block(self, cfg):
  class Trainer (line 28) | class Trainer(DefaultTrainer):
    method build_evaluator (line 30) | def build_evaluator(cls, cfg, dataset_name, output_folder=None):
  function setup (line 40) | def setup(args):
  function main (line 49) | def main(args):

FILE: src/benchmark/transfer_classification/models/moco_v2/loader.py
  class TwoCropsTransform (line 11) | class TwoCropsTransform:
    method __init__ (line 14) | def __init__(self, base_transform):
    method __call__ (line 17) | def __call__(self, x):
  class GaussianBlur (line 23) | class GaussianBlur(object):
    method __init__ (line 26) | def __init__(self, sigma=[.1, 2.]):
    method __call__ (line 29) | def __call__(self, x):

FILE: src/benchmark/transfer_classification/models/moco_v2/main_lincls.py
  function main (line 86) | def main():
  function main_worker (line 121) | def main_worker(gpu, ngpus_per_node, args):
  function train (line 308) | def train(train_loader, model, criterion, optimizer, epoch, args):
  function validate (line 360) | def validate(val_loader, model, criterion, args):
  function save_checkpoint (line 404) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
  function sanity_check (line 410) | def sanity_check(state_dict, pretrained_weights):
  class AverageMeter (line 434) | class AverageMeter(object):
    method __init__ (line 436) | def __init__(self, name, fmt=':f'):
    method reset (line 441) | def reset(self):
    method update (line 447) | def update(self, val, n=1):
    method __str__ (line 453) | def __str__(self):
  class ProgressMeter (line 458) | class ProgressMeter(object):
    method __init__ (line 459) | def __init__(self, num_batches, meters, prefix=""):
    method display (line 464) | def display(self, batch):
    method _get_batch_fmtstr (line 469) | def _get_batch_fmtstr(self, num_batches):
  function adjust_learning_rate (line 475) | def adjust_learning_rate(optimizer, epoch, args):
  function accuracy (line 484) | def accuracy(output, target, topk=(1,)):

FILE: src/benchmark/transfer_classification/models/moco_v3/builder.py
  class MoCo (line 11) | class MoCo(nn.Module):
    method __init__ (line 16) | def __init__(self, base_encoder, dim=256, mlp_dim=4096, T=1.0):
    method _build_mlp (line 36) | def _build_mlp(self, num_layers, input_dim, mlp_dim, output_dim, last_...
    method _build_projector_and_predictor_mlps (line 54) | def _build_projector_and_predictor_mlps(self, dim, mlp_dim):
    method _update_momentum_encoder (line 58) | def _update_momentum_encoder(self, m):
    method contrastive_loss (line 63) | def contrastive_loss(self, q, k):
    method forward (line 75) | def forward(self, x1, x2, m):
  class MoCo_ResNet (line 99) | class MoCo_ResNet(MoCo):
    method _build_projector_and_predictor_mlps (line 100) | def _build_projector_and_predictor_mlps(self, dim, mlp_dim):
  class MoCo_ViT (line 112) | class MoCo_ViT(MoCo):
    method _build_projector_and_predictor_mlps (line 113) | def _build_projector_and_predictor_mlps(self, dim, mlp_dim):
  function concat_all_gather (line 127) | def concat_all_gather(tensor):

FILE: src/benchmark/transfer_classification/models/moco_v3/loader.py
  class TwoCropsTransform (line 14) | class TwoCropsTransform:
    method __init__ (line 17) | def __init__(self, base_transform1, base_transform2):
    method __call__ (line 21) | def __call__(self, x):
  class GaussianBlur (line 27) | class GaussianBlur(object):
    method __init__ (line 30) | def __init__(self, sigma=[.1, 2.]):
    method __call__ (line 33) | def __call__(self, x):
  class Solarize (line 40) | class Solarize(object):
    method __call__ (line 43) | def __call__(self, x):
  class cvGaussianBlur (line 50) | class cvGaussianBlur(object):
    method __init__ (line 53) | def __init__(self, p=0.5, sigma=[.1, 2.]):
    method __call__ (line 57) | def __call__(self, x):

FILE: src/benchmark/transfer_classification/models/moco_v3/optimizer.py
  class LARS (line 10) | class LARS(torch.optim.Optimizer):
    method __init__ (line 14) | def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, trust_c...
    method step (line 19) | def step(self):

FILE: src/benchmark/transfer_classification/models/moco_v3/vits.py
  class VisionTransformerMoCo (line 25) | class VisionTransformerMoCo(VisionTransformer):
    method __init__ (line 26) | def __init__(self, stop_grad_conv1=False, **kwargs):
    method build_2d_sincos_position_embedding (line 53) | def build_2d_sincos_position_embedding(self, temperature=10000.):
  class ConvStem (line 72) | class ConvStem(nn.Module):
    method __init__ (line 76) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 104) | def forward(self, x):
  function vit_small (line 115) | def vit_small(**kwargs):
  function vit_base (line 122) | def vit_base(**kwargs):
  function vit_conv_small (line 129) | def vit_conv_small(**kwargs):
  function vit_conv_base (line 137) | def vit_conv_base(**kwargs):

FILE: src/benchmark/transfer_classification/models/rs_transforms_float32.py
  class RandomBrightness (line 7) | class RandomBrightness(object):
    method __init__ (line 10) | def __init__(self, brightness=0.4):
    method __call__ (line 13) | def __call__(self, sample):
  class RandomContrast (line 19) | class RandomContrast(object):
    method __init__ (line 22) | def __init__(self, contrast=0.4):
    method __call__ (line 25) | def __call__(self, sample):
  class ToGray (line 31) | class ToGray(object):
    method __init__ (line 32) | def __init__(self, out_channels):
    method __call__ (line 34) | def __call__(self,sample):
  class RandomChannelDrop (line 41) | class RandomChannelDrop(object):
    method __init__ (line 44) | def __init__(self, min_n_drop=1, max_n_drop=8):
    method __call__ (line 48) | def __call__(self, sample):

FILE: src/benchmark/transfer_classification/models/rs_transforms_uint8.py
  class RandomBrightness (line 7) | class RandomBrightness(object):
    method __init__ (line 10) | def __init__(self, brightness=0.4):
    method __call__ (line 13) | def __call__(self, sample):
  class RandomContrast (line 19) | class RandomContrast(object):
    method __init__ (line 22) | def __init__(self, contrast=0.4):
    method __call__ (line 25) | def __call__(self, sample):
  class ToGray (line 31) | class ToGray(object):
    method __init__ (line 32) | def __init__(self, out_channels):
    method __call__ (line 34) | def __call__(self,sample):
  class RandomChannelDrop (line 41) | class RandomChannelDrop(object):
    method __init__ (line 44) | def __init__(self, min_n_drop=1, max_n_drop=8):
    method __call__ (line 48) | def __call__(self, sample):
  class GaussianBlur (line 57) | class GaussianBlur(object):
    method __init__ (line 60) | def __init__(self, sigma=[.1, 2.]):
    method __call__ (line 63) | def __call__(self, x):
  class Solarize (line 70) | class Solarize(object):
    method __init__ (line 72) | def __init__(self, threshold=0.5):
    method __call__ (line 75) | def __call__(self, x):
  class RandomSensorDrop_S1S2 (line 82) | class RandomSensorDrop_S1S2(object):
    method __init__ (line 85) | def __init__(self):
    method __call__ (line 88) | def __call__(self, sample):
  class SensorDrop_S1S2 (line 98) | class SensorDrop_S1S2(object):
    method __init__ (line 99) | def __init__(self, sensor):
    method __call__ (line 101) | def __call__(self,sample):
  class RandomSensorDrop_RGBD (line 109) | class RandomSensorDrop_RGBD(object):
    method __init__ (line 112) | def __init__(self):
    method __call__ (line 115) | def __call__(self, sample):
  class SensorDrop_RGBD (line 125) | class SensorDrop_RGBD(object):
    method __init__ (line 126) | def __init__(self, sensor):
    method __call__ (line 128) | def __call__(self,sample):

FILE: src/download_data/convert_rgb.py
  function normalize (line 73) | def normalize(img,mean,std):
  function get_array (line 82) | def get_array(patch_id, mode, RGB=False, norm=False):

FILE: src/download_data/ssl4eo_downloader.py
  class UniformSampler (line 98) | class UniformSampler:
    method sample_point (line 99) | def sample_point(self) -> List[float]:
  class GaussianSampler (line 105) | class GaussianSampler:
    method __init__ (line 106) | def __init__(
    method sample_point (line 119) | def sample_point(self) -> List[float]:
    method get_world_cities (line 127) | def get_world_cities(download_root: str = "world_cities") -> List[Dict...
    method get_interest_points (line 143) | def get_interest_points(
    method km2deg (line 151) | def km2deg(kms: float, radius: float = 6371) -> float:
    method deg2km (line 155) | def deg2km(deg: float, radius: float = 6371) -> float:
  class BoundedUniformSampler (line 159) | class BoundedUniformSampler:
    method __init__ (line 160) | def __init__(self, boundaries: shape = None) -> None:
    method sample_point (line 166) | def sample_point(self) -> List[float]:
    method get_country_boundaries (line 177) | def get_country_boundaries(
  class OverlapError (line 188) | class OverlapError(Exception):
  function date2str (line 192) | def date2str(date: datetime) -> str:
  function get_period (line 196) | def get_period(date: datetime, days: int = 5) -> Tuple[str, str, str, str]:
  function maskS2clouds (line 212) | def maskS2clouds(args: Any, image: ee.Image) -> ee.Image:
  function get_collection (line 220) | def get_collection(
  function filter_collection (line 230) | def filter_collection(
  function center_crop (line 254) | def center_crop(
  function adjust_coords (line 264) | def adjust_coords(
  function get_properties (line 280) | def get_properties(image: ee.Image) -> Any:
  function get_patch (line 284) | def get_patch(
  function get_random_patches_match (line 329) | def get_random_patches_match(
  function get_random_patches_rtree (line 366) | def get_random_patches_rtree(
  function get_random_patches_grid (line 447) | def get_random_patches_grid(
  function save_geotiff (line 526) | def save_geotiff(
  function save_patch (line 550) | def save_patch(
  class Counter (line 567) | class Counter:
    method __init__ (line 568) | def __init__(self, start: int = 0) -> None:
    method update (line 572) | def update(self, delta: int = 1) -> int:
  function fix_random_seeds (line 578) | def fix_random_seeds(seed: int = 42) -> None:
  function worker (line 756) | def worker(idx: int) -> None:

FILE: src/download_data/ssl4eo_s12_downloader.py
  class GeoSampler (line 59) | class GeoSampler:
    method sample_point (line 61) | def sample_point(self):
  class UniformSampler (line 65) | class UniformSampler(GeoSampler):
    method sample_point (line 67) | def sample_point(self):
  class GaussianSampler (line 74) | class GaussianSampler(GeoSampler):
    method __init__ (line 76) | def __init__(self, interest_points=None, num_cities=1000, std=20):
    method sample_point (line 84) | def sample_point(self,idx):
    method get_world_cities (line 96) | def get_world_cities(download_root=os.path.expanduser('./world_cities/...
    method get_interest_points (line 110) | def get_interest_points(cities, size=10000):
    method km2deg (line 116) | def km2deg(kms, radius=6371):
    method deg2km (line 119) | def deg2km(deg, radius=6371):
  class BoundedUniformSampler (line 123) | class BoundedUniformSampler(GeoSampler):
    method __init__ (line 125) | def __init__(self, boundaries=None):
    method sample_point (line 131) | def sample_point(self):
    method get_country_boundaries (line 143) | def get_country_boundaries(download_root=os.path.expanduser('~/.cache/...
  class OverlapError (line 152) | class OverlapError(Exception):
  function date2str (line 156) | def date2str(date):
  function get_period (line 160) | def get_period(date, days=5):
  function maskS2clouds (line 168) | def maskS2clouds(image):
  function get_collection_s2a (line 179) | def get_collection_s2a(cloud_pct=20):
  function get_collection_s2c (line 185) | def get_collection_s2c(cloud_pct=20):
  function get_collection_s1 (line 191) | def get_collection_s1():
  function filter_collection (line 195) | def filter_collection(collection, coords, period=None):
  function filter_collection_s1 (line 208) | def filter_collection_s1(collection, coords, period=None):
  function center_crop (line 226) | def center_crop(img, out_size):
  function adjust_coords (line 234) | def adjust_coords(coords, old_size, new_size):
  function get_properties (line 245) | def get_properties(image):
  function get_patch_s1 (line 252) | def get_patch_s1(collection, coords, radius, bands=None, crop=None):
  function get_patch_s2 (line 286) | def get_patch_s2(collection, coords, radius, bands=None, crop=None):
  function get_random_patches_grid (line 324) | def get_random_patches_grid(idx, collections, bands, crops, sampler, dat...
  function get_random_patches_rtree (line 382) | def get_random_patches_rtree(idx, collections, bands, crops, sampler, da...
  function get_random_patches_match (line 433) | def get_random_patches_match(idx, collections, bands, crops, sampler, da...
  function save_geotiff (line 471) | def save_geotiff(img, coords, filename):
  function save_patch (line 491) | def save_patch(raster, coords, metadata, path, preview=False):
  class Counter (line 509) | class Counter:
    method __init__ (line 511) | def __init__(self, start=0):
    method update (line 515) | def update(self, delta=1):
  function fix_random_seeds (line 521) | def fix_random_seeds(seed=42):
  function worker (line 639) | def worker(idx):