SYMBOL INDEX (316 symbols across 28 files)

FILE: config.py
  function _update_config_from_file (line 268) | def _update_config_from_file(config, cfg_file):
  function update_config (line 283) | def update_config(config, args):
  function get_config (line 352) | def get_config(args):

FILE: data/__init__.py
  function build_loader (line 6) | def build_loader(config, simmim=False, is_pretrain=False):

FILE: data/build.py
  function _pil_interp (line 25) | def _pil_interp(method):
  function build_loader (line 44) | def build_loader(config):
  function build_dataset (line 98) | def build_dataset(is_train, config):
  function build_transform (line 125) | def build_transform(is_train, config):

FILE: data/cached_image_folder.py
  function has_file_allowed_extension (line 18) | def has_file_allowed_extension(filename, extensions):
  function find_classes (line 29) | def find_classes(dir):
  function make_dataset (line 36) | def make_dataset(dir, class_to_idx, extensions):
  function make_dataset_with_ann (line 54) | def make_dataset_with_ann(ann_file, img_prefix, extensions):
  class DatasetFolder (line 71) | class DatasetFolder(data.Dataset):
    method __init__ (line 92) | def __init__(self, root, loader, extensions, ann_file='', img_prefix='...
    method init_cache (line 123) | def init_cache(self):
    method __getitem__ (line 145) | def __getitem__(self, index):
    method __len__ (line 161) | def __len__(self):
    method __repr__ (line 164) | def __repr__(self):
  function pil_loader (line 178) | def pil_loader(path):
  function accimage_loader (line 192) | def accimage_loader(path):
  function default_img_loader (line 201) | def default_img_loader(path):
  class CachedImageFolder (line 209) | class CachedImageFolder(DatasetFolder):
    method __init__ (line 228) | def __init__(self, root, ann_file='', img_prefix='', transform=None, t...
    method __getitem__ (line 236) | def __getitem__(self, index):

FILE: data/data_simmim_ft.py
  function build_loader_finetune (line 18) | def build_loader_finetune(config):
  function build_dataset (line 61) | def build_dataset(is_train, config):
  function build_transform (line 75) | def build_transform(is_train, config):

FILE: data/data_simmim_pt.py
  class MaskGenerator (line 21) | class MaskGenerator:
    method __init__ (line 22) | def __init__(self, input_size=192, mask_patch_size=32, model_patch_siz...
    method __call__ (line 37) | def __call__(self):
  class SimMIMTransform (line 48) | class SimMIMTransform:
    method __init__ (line 49) | def __init__(self, config):
    method __call__ (line 70) | def __call__(self, img):
  function collate_fn (line 77) | def collate_fn(batch):
  function build_loader_simmim (line 92) | def build_loader_simmim(config):

FILE: data/imagenet22k_dataset.py
  class IN22KDATASET (line 12) | class IN22KDATASET(data.Dataset):
    method __init__ (line 13) | def __init__(self, root, ann_file='', transform=None, target_transform...
    method _load_image (line 24) | def _load_image(self, path):
    method __getitem__ (line 33) | def __getitem__(self, index):
    method __len__ (line 54) | def __len__(self):

FILE: data/samplers.py
  class SubsetRandomSampler (line 11) | class SubsetRandomSampler(torch.utils.data.Sampler):
    method __init__ (line 18) | def __init__(self, indices):
    method __iter__ (line 22) | def __iter__(self):
    method __len__ (line 25) | def __len__(self):
    method set_epoch (line 28) | def set_epoch(self, epoch):

FILE: data/zipreader.py
  function is_zip_path (line 18) | def is_zip_path(img_or_path):
  class ZipReader (line 23) | class ZipReader(object):
    method __init__ (line 27) | def __init__(self):
    method get_zipfile (line 31) | def get_zipfile(path):
    method split_zip_style_path (line 39) | def split_zip_style_path(path):
    method list_folder (line 49) | def list_folder(path):
    method list_files (line 67) | def list_files(path, extension=None):
    method read (line 86) | def read(path):
    method imread (line 93) | def imread(path):

FILE: kernels/window_process/swin_window_process.cpp
  function roll_and_window_partition_forward (line 70) | at::Tensor roll_and_window_partition_forward(
  function roll_and_window_partition_backward (line 84) | at::Tensor roll_and_window_partition_backward(
  function window_merge_and_roll_forward (line 98) | at::Tensor window_merge_and_roll_forward(
  function window_merge_and_roll_backward (line 112) | at::Tensor window_merge_and_roll_backward(
  function PYBIND11_MODULE (line 127) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

FILE: kernels/window_process/unit_test.py
  class WindowProcess (line 14) | class WindowProcess(torch.autograd.Function):
    method forward (line 16) | def forward(ctx, input, B, H, W, C, shift_size, window_size):
    method backward (line 28) | def backward(ctx, grad_in):
  class WindowProcessReverse (line 40) | class WindowProcessReverse(torch.autograd.Function):
    method forward (line 42) | def forward(ctx, input, B, H, W, C, shift_size, window_size):
    method backward (line 55) | def backward(ctx, grad_in):
  function window_partition (line 67) | def window_partition(x, window_size):
  function window_reverse (line 80) | def window_reverse(windows, window_size, H, W):
  function pyt_forward (line 96) | def pyt_forward(x, shift_size, window_size):
  function reverse_pyt_forward (line 108) | def reverse_pyt_forward(attn_windows, shift_size, window_size, H, W):
  function copy_one_tensor (line 118) | def copy_one_tensor(input, requires_grad=True):
  class Test_WindowProcess (line 122) | class Test_WindowProcess(unittest.TestCase):
    method setUp (line 123) | def setUp(self):
    method test_roll_and_window_partition_forward (line 133) | def test_roll_and_window_partition_forward(self, dtype=torch.float32):
    method test_roll_and_window_partition_backward (line 148) | def test_roll_and_window_partition_backward(self, dtype=torch.float32):
    method test_window_merge_and_roll_forward (line 165) | def test_window_merge_and_roll_forward(self, dtype=torch.float32):
    method test_window_merge_and_roll_backward (line 181) | def test_window_merge_and_roll_backward(self, dtype=torch.float32):
    method test_forward_backward_speed (line 198) | def test_forward_backward_speed(self, dtype=torch.float32, times=1000):
    method test_roll_and_window_partition_forward_fp16 (line 231) | def test_roll_and_window_partition_forward_fp16(self, dtype=torch.floa...
    method test_roll_and_window_partition_backward_fp16 (line 234) | def test_roll_and_window_partition_backward_fp16(self, dtype=torch.flo...
    method test_window_merge_and_roll_forward_fp16 (line 237) | def test_window_merge_and_roll_forward_fp16(self, dtype=torch.float16):
    method test_window_merge_and_roll_backward_fp16 (line 240) | def test_window_merge_and_roll_backward_fp16(self, dtype=torch.float16):
    method test_forward_backward_speed_fp16 (line 243) | def test_forward_backward_speed_fp16(self, dtype=torch.float16, times=...

FILE: kernels/window_process/window_process.py
  class WindowProcess (line 11) | class WindowProcess(torch.autograd.Function):
    method forward (line 13) | def forward(ctx, input, B, H, W, C, shift_size, window_size):
    method backward (line 25) | def backward(ctx, grad_in):
  class WindowProcessReverse (line 37) | class WindowProcessReverse(torch.autograd.Function):
    method forward (line 39) | def forward(ctx, input, B, H, W, C, shift_size, window_size):
    method backward (line 52) | def backward(ctx, grad_in):

FILE: logger.py
  function create_logger (line 16) | def create_logger(output_dir, dist_rank=0, name=''):

FILE: lr_scheduler.py
  function build_scheduler (line 16) | def build_scheduler(config, optimizer, n_iter_per_epoch):
  class LinearLRScheduler (line 66) | class LinearLRScheduler(Scheduler):
    method __init__ (line 67) | def __init__(self,
    method _get_lr (line 96) | def _get_lr(self, t):
    method get_epoch_values (line 105) | def get_epoch_values(self, epoch: int):
    method get_update_values (line 111) | def get_update_values(self, num_updates: int):
  class MultiStepLRScheduler (line 118) | class MultiStepLRScheduler(Scheduler):
    method __init__ (line 119) | def __init__(self, optimizer: torch.optim.Optimizer, milestones, gamma...
    method _get_lr (line 135) | def _get_lr(self, t):
    method get_epoch_values (line 142) | def get_epoch_values(self, epoch: int):
    method get_update_values (line 148) | def get_update_values(self, num_updates: int):

FILE: main.py
  function parse_option (line 36) | def parse_option():
  function main (line 90) | def main(config):
  function train_one_epoch (line 174) | def train_one_epoch(config, model, criterion, data_loader, optimizer, ep...
  function validate (line 235) | def validate(config, data_loader, model):
  function throughput (line 283) | def throughput(data_loader, model, logger):

FILE: main_moe.py
  function parse_option (line 40) | def parse_option():
  function main (line 86) | def main(config):
  function train_one_epoch (line 184) | def train_one_epoch(config, model, criterion, data_loader, optimizer, ep...
  function validate (line 252) | def validate(config, data_loader, model):
  function throughput (line 302) | def throughput(data_loader, model, logger):

FILE: main_simmim_ft.py
  function parse_option (line 36) | def parse_option():
  function main (line 76) | def main(config):
  function train_one_epoch (line 155) | def train_one_epoch(config, model, criterion, data_loader, optimizer, ep...
  function validate (line 230) | def validate(config, data_loader, model):
  function throughput (line 277) | def throughput(data_loader, model, logger):

FILE: main_simmim_pt.py
  function parse_option (line 33) | def parse_option():
  function main (line 70) | def main(config):
  function train_one_epoch (line 120) | def train_one_epoch(config, model, data_loader, optimizer, epoch, lr_sch...

FILE: models/build.py
  function build_model (line 15) | def build_model(config, is_pretrain=False):

FILE: models/simmim.py
  function norm_targets (line 21) | def norm_targets(targets, patch_size):
  class SwinTransformerForSimMIM (line 41) | class SwinTransformerForSimMIM(SwinTransformer):
    method __init__ (line 42) | def __init__(self, **kwargs):
    method forward (line 50) | def forward(self, x, mask):
    method no_weight_decay (line 75) | def no_weight_decay(self):
  class SwinTransformerV2ForSimMIM (line 79) | class SwinTransformerV2ForSimMIM(SwinTransformerV2):
    method __init__ (line 80) | def __init__(self, **kwargs):
    method forward (line 88) | def forward(self, x, mask):
    method no_weight_decay (line 113) | def no_weight_decay(self):
  class SimMIM (line 117) | class SimMIM(nn.Module):
    method __init__ (line 118) | def __init__(self, config, encoder, encoder_stride, in_chans, patch_si...
    method forward (line 134) | def forward(self, x, mask):
    method no_weight_decay (line 149) | def no_weight_decay(self):
    method no_weight_decay_keywords (line 155) | def no_weight_decay_keywords(self):
  function build_simmim (line 161) | def build_simmim(config):

FILE: models/swin_mlp.py
  class Mlp (line 15) | class Mlp(nn.Module):
    method __init__ (line 16) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 25) | def forward(self, x):
  function window_partition (line 34) | def window_partition(x, window_size):
  function window_reverse (line 49) | def window_reverse(windows, window_size, H, W):
  class SwinMLPBlock (line 66) | class SwinMLPBlock(nn.Module):
    method __init__ (line 82) | def __init__(self, dim, input_resolution, num_heads, window_size=7, sh...
    method forward (line 113) | def forward(self, x):
    method extra_repr (line 162) | def extra_repr(self) -> str:
    method flops (line 166) | def flops(self):
  class PatchMerging (line 185) | class PatchMerging(nn.Module):
    method __init__ (line 194) | def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
    method forward (line 201) | def forward(self, x):
    method extra_repr (line 224) | def extra_repr(self) -> str:
    method flops (line 227) | def flops(self):
  class BasicLayer (line 234) | class BasicLayer(nn.Module):
    method __init__ (line 251) | def __init__(self, dim, input_resolution, depth, num_heads, window_size,
    method forward (line 278) | def forward(self, x):
    method extra_repr (line 288) | def extra_repr(self) -> str:
    method flops (line 291) | def flops(self):
  class PatchEmbed (line 300) | class PatchEmbed(nn.Module):
    method __init__ (line 311) | def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=9...
    method forward (line 330) | def forward(self, x):
    method flops (line 340) | def flops(self):
  class SwinMLP (line 348) | class SwinMLP(nn.Module):
    method __init__ (line 369) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes...
    method _init_weights (line 425) | def _init_weights(self, m):
    method no_weight_decay (line 435) | def no_weight_decay(self):
    method no_weight_decay_keywords (line 439) | def no_weight_decay_keywords(self):
    method forward_features (line 442) | def forward_features(self, x):
    method forward (line 456) | def forward(self, x):
    method flops (line 461) | def flops(self):

FILE: models/swin_transformer.py
  class Mlp (line 26) | class Mlp(nn.Module):
    method __init__ (line 27) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 36) | def forward(self, x):
  function window_partition (line 45) | def window_partition(x, window_size):
  function window_reverse (line 60) | def window_reverse(windows, window_size, H, W):
  class WindowAttention (line 77) | class WindowAttention(nn.Module):
    method __init__ (line 91) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scal...
    method forward (line 125) | def forward(self, x, mask=None):
    method extra_repr (line 158) | def extra_repr(self) -> str:
    method flops (line 161) | def flops(self, N):
  class SwinTransformerBlock (line 175) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 195) | def __init__(self, dim, input_resolution, num_heads, window_size=7, sh...
    method forward (line 248) | def forward(self, x):
    method extra_repr (line 296) | def extra_repr(self) -> str:
    method flops (line 300) | def flops(self):
  class PatchMerging (line 315) | class PatchMerging(nn.Module):
    method __init__ (line 324) | def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
    method forward (line 331) | def forward(self, x):
    method extra_repr (line 354) | def extra_repr(self) -> str:
    method flops (line 357) | def flops(self):
  class BasicLayer (line 364) | class BasicLayer(nn.Module):
    method __init__ (line 385) | def __init__(self, dim, input_resolution, depth, num_heads, window_size,
    method forward (line 415) | def forward(self, x):
    method extra_repr (line 425) | def extra_repr(self) -> str:
    method flops (line 428) | def flops(self):
  class PatchEmbed (line 437) | class PatchEmbed(nn.Module):
    method __init__ (line 448) | def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=9...
    method forward (line 467) | def forward(self, x):
    method flops (line 477) | def flops(self):
  class SwinTransformer (line 485) | class SwinTransformer(nn.Module):
    method __init__ (line 512) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes...
    method _init_weights (line 571) | def _init_weights(self, m):
    method no_weight_decay (line 581) | def no_weight_decay(self):
    method no_weight_decay_keywords (line 585) | def no_weight_decay_keywords(self):
    method forward_features (line 588) | def forward_features(self, x):
    method forward (line 602) | def forward(self, x):
    method flops (line 607) | def flops(self):

FILE: models/swin_transformer_moe.py
  class Mlp (line 23) | class Mlp(nn.Module):
    method __init__ (line 24) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 34) | def forward(self, x):
  class MoEMlp (line 43) | class MoEMlp(nn.Module):
    method __init__ (line 44) | def __init__(self, in_features, hidden_features, num_local_experts, to...
    method forward (line 86) | def forward(self, x):
    method extra_repr (line 90) | def extra_repr(self) -> str:
    method _init_weights (line 96) | def _init_weights(self):
  function window_partition (line 104) | def window_partition(x, window_size):
  function window_reverse (line 119) | def window_reverse(windows, window_size, H, W):
  class WindowAttention (line 136) | class WindowAttention(nn.Module):
    method __init__ (line 151) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scal...
    method forward (line 205) | def forward(self, x, mask=None):
    method extra_repr (line 239) | def extra_repr(self) -> str:
    method flops (line 243) | def flops(self, N):
  class SwinTransformerBlock (line 257) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 292) | def __init__(self, dim, input_resolution, num_heads, window_size=7, sh...
    method forward (line 369) | def forward(self, x):
    method extra_repr (line 414) | def extra_repr(self) -> str:
    method flops (line 418) | def flops(self):
  class PatchMerging (line 436) | class PatchMerging(nn.Module):
    method __init__ (line 445) | def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
    method forward (line 452) | def forward(self, x):
    method extra_repr (line 475) | def extra_repr(self) -> str:
    method flops (line 478) | def flops(self):
  class BasicLayer (line 485) | class BasicLayer(nn.Module):
    method __init__ (line 521) | def __init__(self, dim, input_resolution, depth, num_heads, window_size,
    method forward (line 569) | def forward(self, x):
    method extra_repr (line 587) | def extra_repr(self) -> str:
    method flops (line 590) | def flops(self):
  class PatchEmbed (line 599) | class PatchEmbed(nn.Module):
    method __init__ (line 610) | def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=9...
    method forward (line 629) | def forward(self, x):
    method flops (line 639) | def flops(self):
  class SwinTransformerMoE (line 647) | class SwinTransformerMoE(nn.Module):
    method __init__ (line 690) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes...
    method _init_weights (line 774) | def _init_weights(self, m):
    method no_weight_decay (line 786) | def no_weight_decay(self):
    method no_weight_decay_keywords (line 790) | def no_weight_decay_keywords(self):
    method forward_features (line 794) | def forward_features(self, x):
    method forward (line 809) | def forward(self, x):
    method add_param_to_skip_allreduce (line 814) | def add_param_to_skip_allreduce(self, param_name):
    method flops (line 817) | def flops(self):

FILE: models/swin_transformer_v2.py
  class Mlp (line 16) | class Mlp(nn.Module):
    method __init__ (line 17) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 26) | def forward(self, x):
  function window_partition (line 35) | def window_partition(x, window_size):
  function window_reverse (line 50) | def window_reverse(windows, window_size, H, W):
  class WindowAttention (line 67) | class WindowAttention(nn.Module):
    method __init__ (line 81) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_dr...
    method forward (line 140) | def forward(self, x, mask=None):
    method extra_repr (line 181) | def extra_repr(self) -> str:
    method flops (line 185) | def flops(self, N):
  class SwinTransformerBlock (line 199) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 218) | def __init__(self, dim, input_resolution, num_heads, window_size=7, sh...
    method forward (line 270) | def forward(self, x):
    method extra_repr (line 308) | def extra_repr(self) -> str:
    method flops (line 312) | def flops(self):
  class PatchMerging (line 327) | class PatchMerging(nn.Module):
    method __init__ (line 336) | def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
    method forward (line 343) | def forward(self, x):
    method extra_repr (line 366) | def extra_repr(self) -> str:
    method flops (line 369) | def flops(self):
  class BasicLayer (line 376) | class BasicLayer(nn.Module):
    method __init__ (line 396) | def __init__(self, dim, input_resolution, depth, num_heads, window_size,
    method forward (line 426) | def forward(self, x):
    method extra_repr (line 436) | def extra_repr(self) -> str:
    method flops (line 439) | def flops(self):
    method _init_respostnorm (line 447) | def _init_respostnorm(self):
  class PatchEmbed (line 455) | class PatchEmbed(nn.Module):
    method __init__ (line 466) | def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=9...
    method forward (line 485) | def forward(self, x):
    method flops (line 495) | def flops(self):
  class SwinTransformerV2 (line 503) | class SwinTransformerV2(nn.Module):
    method __init__ (line 529) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes...
    method _init_weights (line 590) | def _init_weights(self, m):
    method no_weight_decay (line 600) | def no_weight_decay(self):
    method no_weight_decay_keywords (line 604) | def no_weight_decay_keywords(self):
    method forward_features (line 607) | def forward_features(self, x):
    method forward (line 621) | def forward(self, x):
    method flops (line 626) | def flops(self):

FILE: optimizer.py
  function build_optimizer (line 19) | def build_optimizer(config, model, simmim=False, is_pretrain=False):
  function set_weight_decay (line 59) | def set_weight_decay(model, skip_list=(), skip_keywords=()):
  function check_keywords_in_name (line 76) | def check_keywords_in_name(name, keywords=()):
  function get_pretrain_param_groups (line 84) | def get_pretrain_param_groups(model, skip_list=(), skip_keywords=()):
  function get_swin_layer (line 104) | def get_swin_layer(name, num_layers, depths):
  function get_finetune_param_groups (line 120) | def get_finetune_param_groups(model, lr, weight_decay, get_layer_func, s...

FILE: utils.py
  function load_checkpoint (line 18) | def load_checkpoint(config, model, optimizer, lr_scheduler, loss_scaler,...
  function load_pretrained (line 45) | def load_pretrained(config, model, logger):
  function save_checkpoint (line 135) | def save_checkpoint(config, epoch, model, max_accuracy, optimizer, lr_sc...
  function get_grad_norm (line 150) | def get_grad_norm(parameters, norm_type=2):
  function auto_resume_helper (line 163) | def auto_resume_helper(output_dir):
  function reduce_tensor (line 176) | def reduce_tensor(tensor):
  function ampscaler_get_grad_norm (line 183) | def ampscaler_get_grad_norm(parameters, norm_type: float = 2.0) -> torch...
  class NativeScalerWithGradNormCount (line 199) | class NativeScalerWithGradNormCount:
    method __init__ (line 202) | def __init__(self):
    method __call__ (line 205) | def __call__(self, loss, optimizer, clip_grad=None, parameters=None, c...
    method state_dict (line 221) | def state_dict(self):
    method load_state_dict (line 224) | def load_state_dict(self, state_dict):

FILE: utils_moe.py
  function split_moe_model_state_dict (line 13) | def split_moe_model_state_dict(moe_keys, model_state_dict):
  function merge_moe_model_state_dict (line 24) | def merge_moe_model_state_dict(moe_model_state_dict, non_moe_model_state...
  function load_checkpoint (line 31) | def load_checkpoint(config, model, optimizer, lr_scheduler, loss_scaler,...
  function load_pretrained (line 64) | def load_pretrained(config, model, logger):
  function save_checkpoint (line 175) | def save_checkpoint(config, epoch, model, max_accuracy, optimizer, lr_sc...
  function auto_resume_helper (line 222) | def auto_resume_helper(output_dir, save_master=False):
  function hook_scale_grad (line 240) | def hook_scale_grad(scale, tensor):

FILE: utils_simmim.py
  function load_checkpoint (line 16) | def load_checkpoint(config, model, optimizer, lr_scheduler, scaler, logg...
  function save_checkpoint (line 53) | def save_checkpoint(config, epoch, model, max_accuracy, optimizer, lr_sc...
  function get_grad_norm (line 68) | def get_grad_norm(parameters, norm_type=2):
  function auto_resume_helper (line 81) | def auto_resume_helper(output_dir, logger):
  function reduce_tensor (line 94) | def reduce_tensor(tensor):
  function load_pretrained (line 101) | def load_pretrained(config, model, logger):
  function remap_pretrained_keys_swin (line 126) | def remap_pretrained_keys_swin(model, checkpoint_model, logger):