SYMBOL INDEX (4344 symbols across 607 files) FILE: classification/clip/clip.py function _download (line 39) | def _download(url: str, root: str = os.path.expanduser("~/.cache/clip")): function _transform (line 71) | def _transform(n_px): function available_models (line 81) | def available_models() -> List[str]: function load (line 86) | def load(name: str, device: Union[str, torch.device] = "cuda" if torch.c... function tokenize (line 185) | def tokenize(texts: Union[str, List[str]], context_length: int = 77, tru... FILE: classification/clip/model.py class Bottleneck (line 10) | class Bottleneck(nn.Module): method __init__ (line 13) | def __init__(self, inplanes, planes, stride=1): method forward (line 40) | def forward(self, x: torch.Tensor): class AttentionPool2d (line 56) | class AttentionPool2d(nn.Module): method __init__ (line 57) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o... method forward (line 66) | def forward(self, x): class ModifiedResNet (line 93) | class ModifiedResNet(nn.Module): method __init__ (line 101) | def __init__(self, layers, output_dim, heads, input_resolution=224, wi... method _make_layer (line 126) | def _make_layer(self, planes, blocks, stride=1): method forward (line 135) | def forward(self, x): class LayerNorm (line 153) | class LayerNorm(nn.LayerNorm): method forward (line 156) | def forward(self, x: torch.Tensor): class QuickGELU (line 162) | class QuickGELU(nn.Module): method forward (line 163) | def forward(self, x: torch.Tensor): class ResidualAttentionBlock (line 167) | class ResidualAttentionBlock(nn.Module): method __init__ (line 168) | def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor ... method attention (line 181) | def attention(self, x: torch.Tensor): method forward (line 185) | def forward(self, x: torch.Tensor): class Transformer (line 191) | class Transformer(nn.Module): method __init__ (line 192) | def __init__(self, width: int, layers: int, heads: int, attn_mask: tor... method forward (line 198) | def forward(self, x: torch.Tensor): class VisionTransformer (line 202) | class VisionTransformer(nn.Module): method __init__ (line 203) | def __init__(self, input_resolution: int, patch_size: int, width: int,... method forward (line 219) | def forward(self, x: torch.Tensor): class CLIP (line 239) | class CLIP(nn.Module): method __init__ (line 240) | def __init__(self, method initialize_parameters (line 295) | def initialize_parameters(self): method build_attention_mask (line 324) | def build_attention_mask(self): method dtype (line 333) | def dtype(self): method encode_image (line 336) | def encode_image(self, image): method encode_text (line 339) | def encode_text(self, text): method forward (line 354) | def forward(self, image, text): function convert_weights (line 371) | def convert_weights(model: nn.Module): function build_model (line 395) | def build_model(state_dict: dict): FILE: classification/clip/simple_tokenizer.py function default_bpe (line 11) | def default_bpe(): function bytes_to_unicode (line 16) | def bytes_to_unicode(): function get_pairs (line 38) | def get_pairs(word): function basic_clean (line 50) | def basic_clean(text): function whitespace_clean (line 56) | def whitespace_clean(text): class SimpleTokenizer (line 62) | class SimpleTokenizer(object): method __init__ (line 63) | def __init__(self, bpe_path: str = default_bpe()): method bpe (line 80) | def bpe(self, token): method encode (line 121) | def encode(self, text): method decode (line 129) | def decode(self, tokens): FILE: classification/datasets/MSTAR_EOC1.py class MSTAR_EOC1 (line 12) | class MSTAR_EOC1(DatasetBase): method __init__ (line 16) | def __init__(self, cfg): method read_data (line 56) | def read_data(self, image_dir): FILE: classification/datasets/MSTAR_EOC2C.py class MSTAR_EOC2C (line 12) | class MSTAR_EOC2C(DatasetBase): method __init__ (line 16) | def __init__(self, cfg): method read_data (line 56) | def read_data(self, image_dir): FILE: classification/datasets/MSTAR_EOC2V.py class MSTAR_EOC2V (line 12) | class MSTAR_EOC2V(DatasetBase): method __init__ (line 16) | def __init__(self, cfg): method read_data (line 56) | def read_data(self, image_dir): FILE: classification/datasets/MSTAR_SOC.py class MSTAR_SOC (line 12) | class MSTAR_SOC(DatasetBase): method __init__ (line 16) | def __init__(self, cfg): method read_data (line 56) | def read_data(self, image_dir): FILE: classification/datasets/MyDataset.py class MyDataset (line 12) | class MyDataset(DatasetBase): method __init__ (line 16) | def __init__(self, cfg): method read_data (line 56) | def read_data(self, image_dir): FILE: classification/datasets/New_FUSAR.py class New_FUSAR (line 12) | class New_FUSAR(DatasetBase): method __init__ (line 16) | def __init__(self, cfg): method read_data (line 56) | def read_data(self, image_dir): FILE: classification/datasets/SAR_ACD.py class SAR_ACD (line 12) | class SAR_ACD(DatasetBase): method __init__ (line 15) | def __init__(self, cfg): method read_data (line 55) | def read_data(self, image_dir): FILE: classification/datasets/caltech101.py class Caltech101 (line 20) | class Caltech101(DatasetBase): method __init__ (line 24) | def __init__(self, cfg): FILE: classification/datasets/dtd.py class DescribableTextures (line 12) | class DescribableTextures(DatasetBase): method __init__ (line 16) | def __init__(self, cfg): method read_and_split_data (line 54) | def read_and_split_data(image_dir, p_trn=0.5, p_val=0.2, ignored=[], n... FILE: classification/datasets/eurosat.py class EuroSAT (line 25) | class EuroSAT(DatasetBase): method __init__ (line 29) | def __init__(self, cfg): method update_classname (line 66) | def update_classname(self, dataset_old): FILE: classification/datasets/fgvc_aircraft.py class FGVCAircraft (line 11) | class FGVCAircraft(DatasetBase): method __init__ (line 15) | def __init__(self, cfg): method read_data (line 56) | def read_data(self, cname2lab, split_file): FILE: classification/datasets/food101.py class Food101 (line 12) | class Food101(DatasetBase): method __init__ (line 16) | def __init__(self, cfg): FILE: classification/datasets/imagenet.py class ImageNet (line 12) | class ImageNet(DatasetBase): method __init__ (line 16) | def __init__(self, cfg): method read_classnames (line 64) | def read_classnames(text_file): method read_data (line 78) | def read_data(self, classnames, split_dir): FILE: classification/datasets/imagenet_a.py class ImageNetA (line 12) | class ImageNetA(DatasetBase): method __init__ (line 20) | def __init__(self, cfg): method read_data (line 32) | def read_data(self, classnames): FILE: classification/datasets/imagenet_r.py class ImageNetR (line 12) | class ImageNetR(DatasetBase): method __init__ (line 20) | def __init__(self, cfg): method read_data (line 32) | def read_data(self, classnames): FILE: classification/datasets/imagenet_sketch.py class ImageNetSketch (line 10) | class ImageNetSketch(DatasetBase): method __init__ (line 18) | def __init__(self, cfg): method read_data (line 30) | def read_data(self, classnames): FILE: classification/datasets/imagenetv2.py class ImageNetV2 (line 10) | class ImageNetV2(DatasetBase): method __init__ (line 18) | def __init__(self, cfg): method read_data (line 31) | def read_data(self, classnames): FILE: classification/datasets/oxford_flowers.py class OxfordFlowers (line 14) | class OxfordFlowers(DatasetBase): method __init__ (line 18) | def __init__(self, cfg): method read_data (line 57) | def read_data(self): FILE: classification/datasets/oxford_pets.py class OxfordPets (line 12) | class OxfordPets(DatasetBase): method __init__ (line 16) | def __init__(self, cfg): method read_data (line 56) | def read_data(self, split_file): method split_trainval (line 77) | def split_trainval(trainval, p_val=0.2): method save_split (line 100) | def save_split(train, val, test, filepath, path_prefix): method read_split (line 123) | def read_split(filepath, path_prefix): method subsample_classes (line 141) | def subsample_classes(*args, subsample="all"): FILE: classification/datasets/stanford_cars.py class StanfordCars (line 12) | class StanfordCars(DatasetBase): method __init__ (line 16) | def __init__(self, cfg): method read_data (line 57) | def read_data(self, image_dir, anno_file, meta_file): FILE: classification/datasets/sun397.py class SUN397 (line 11) | class SUN397(DatasetBase): method __init__ (line 15) | def __init__(self, cfg): method read_data (line 61) | def read_data(self, cname2lab, text_file): FILE: classification/datasets/ucf101.py class UCF101 (line 12) | class UCF101(DatasetBase): method __init__ (line 16) | def __init__(self, cfg): method read_data (line 64) | def read_data(self, cname2lab, text_file): FILE: classification/interpret_prompt.py function load_clip_to_cpu (line 10) | def load_clip_to_cpu(backbone_name="RN50"): FILE: classification/lpclip/feat_extractor.py function print_args (line 34) | def print_args(args, cfg): function reset_cfg (line 48) | def reset_cfg(cfg, args): function extend_cfg (line 65) | def extend_cfg(cfg): function setup_cfg (line 85) | def setup_cfg(args): function main (line 105) | def main(args): FILE: classification/lpclip/linear_probe.py function binary_search (line 69) | def binary_search(c_left, c_right, seed, step, test_acc_step_list): FILE: classification/model/Model.py class AlexNet (line 5) | class AlexNet(nn.Module): method __init__ (line 6) | def __init__(self, num_classes: int = 10, dropout: float = 0.5) -> None: method forward (line 35) | def forward(self, x: torch.Tensor) -> torch.Tensor: class A_ConvNet (line 42) | class A_ConvNet(nn.Module): method __init__ (line 43) | def __init__(self, in_ch=1, num_classes=10): method forward (line 57) | def forward(self, x): class A_ConvNet_BN (line 73) | class A_ConvNet_BN(nn.Module): method __init__ (line 74) | def __init__(self, in_ch=1, num_classes=10): method forward (line 88) | def forward(self, x): class ChannelAttention (line 104) | class ChannelAttention(nn.Module): method __init__ (line 105) | def __init__(self, in_planes): method forward (line 113) | def forward(self, x): class SpatialAttention (line 120) | class SpatialAttention(nn.Module): method __init__ (line 121) | def __init__(self, kernel_size=7): method forward (line 130) | def forward(self, x): function atten (line 138) | def atten(x, con, ca ,sa): class AMCNN (line 145) | class AMCNN(torch.nn.Module): method __init__ (line 146) | def __init__(self, num_classes=3): method forward (line 182) | def forward(self, x): class BNACNN (line 199) | class BNACNN(torch.nn.Module): method __init__ (line 200) | def __init__(self, num_classes=3): method forward (line 220) | def forward(self, x): class MVGGNet (line 237) | class MVGGNet(torch.nn.Module): method __init__ (line 238) | def __init__(self, num_classes=3): method forward (line 250) | def forward(self, x): class ResNet_18 (line 257) | class ResNet_18(torch.nn.Module): method __init__ (line 258) | def __init__(self, num_classes=3): method forward (line 266) | def forward(self, x): class ResNet_34 (line 272) | class ResNet_34(torch.nn.Module): method __init__ (line 273) | def __init__(self, num_classes=3): method forward (line 281) | def forward(self, x): class ResNet_50 (line 287) | class ResNet_50(torch.nn.Module): method __init__ (line 288) | def __init__(self, num_classes=3): method forward (line 295) | def forward(self, x): class convnext_1 (line 301) | class convnext_1(torch.nn.Module): method __init__ (line 302) | def __init__(self, num_classes=3): method forward (line 310) | def forward(self, x): class efficientnet_b0 (line 316) | class efficientnet_b0(torch.nn.Module): method __init__ (line 317) | def __init__(self, num_classes=3): method forward (line 326) | def forward(self, x): class efficientnet_b1 (line 332) | class efficientnet_b1(torch.nn.Module): method __init__ (line 333) | def __init__(self, feature_extract=True, num_classes=3): method forward (line 342) | def forward(self, x): function crop (line 358) | def crop(x1, x2): class BN_Conv2d (line 373) | class BN_Conv2d(nn.Module): method __init__ (line 378) | def __init__(self, in_channels: object, out_channels: object, kernel_s... method forward (line 388) | def forward(self, x): class DoubleConv (line 393) | class DoubleConv(nn.Module): method __init__ (line 394) | def __init__(self, in_ch, out_ch, padding=2, bias=False): method forward (line 401) | def forward(self, input): function squash (line 405) | def squash(x, dim=-1): class PrimaryCaps (line 411) | class PrimaryCaps(nn.Module): method __init__ (line 414) | def __init__(self, num_conv_units, in_channels, out_channels, kernel_s... method forward (line 425) | def forward(self, x): class DigitCaps (line 434) | class DigitCaps(nn.Module): method __init__ (line 437) | def __init__(self, in_dim, in_caps, num_caps, dim_caps, num_routing): method forward (line 457) | def forward(self, x): function MLP (line 499) | def MLP(dim, projection_size, hidden_size=64): function SimSiamMLP (line 507) | def SimSiamMLP(dim, projection_size, hidden_size=64): class Unet (line 520) | class Unet(nn.Module): method __init__ (line 521) | def __init__(self, in_ch=1, out_ch=1, num_classes=3): method forward (line 552) | def forward(self, x): FILE: classification/model/cmt.py function _cfg (line 23) | def _cfg(url='', **kwargs): class SwishImplementation (line 35) | class SwishImplementation(torch.autograd.Function): method forward (line 37) | def forward(ctx, i): method backward (line 43) | def backward(ctx, grad_output): class MemoryEfficientSwish (line 49) | class MemoryEfficientSwish(nn.Module): method forward (line 50) | def forward(self, x): class Mlp (line 54) | class Mlp(nn.Module): method __init__ (line 55) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 73) | def forward(self, x, H, W): class Attention (line 87) | class Attention(nn.Module): method __init__ (line 88) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, method forward (line 111) | def forward(self, x, H, W, relative_pos): class Block (line 133) | class Block(nn.Module): method __init__ (line 134) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 148) | def forward(self, x, H, W, relative_pos): class PatchEmbed (line 158) | class PatchEmbed(nn.Module): method __init__ (line 161) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=... method forward (line 177) | def forward(self, x): class CMT (line 189) | class CMT(nn.Module): method __init__ (line 190) | def __init__(self, img_size=128, in_chans=1, num_classes=10, embed_dim... method _init_weights (line 278) | def _init_weights(self, m): method update_temperature (line 294) | def update_temperature(self): method no_weight_decay (line 300) | def no_weight_decay(self): method get_classifier (line 303) | def get_classifier(self): method reset_classifier (line 306) | def reset_classifier(self, num_classes, global_pool=''): method forward_features (line 310) | def forward_features(self, x): method forward (line 352) | def forward(self, x): function resize_pos_embed (line 358) | def resize_pos_embed(posemb, posemb_new): function checkpoint_filter_fn (line 378) | def checkpoint_filter_fn(state_dict, model): function _create_cmt_model (line 396) | def _create_cmt_model(pretrained=False, distilled=False, **kwargs): function cmt_ti (line 421) | def cmt_ti(pretrained=False, **kwargs): function cmt_xs (line 430) | def cmt_xs(pretrained=False, **kwargs): function cmt_s (line 441) | def cmt_s(pretrained=False, **kwargs): function cmt_b (line 452) | def cmt_b(pretrained=False, **kwargs): FILE: classification/model/conformer.py class Mlp (line 8) | class Mlp(nn.Module): method __init__ (line 9) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 18) | def forward(self, x): class Attention (line 27) | class Attention(nn.Module): method __init__ (line 28) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 40) | def forward(self, x): class Block (line 55) | class Block(nn.Module): method __init__ (line 57) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 69) | def forward(self, x): class ConvBlock (line 75) | class ConvBlock(nn.Module): method __init__ (line 77) | def __init__(self, inplanes, outplanes, stride=1, res_conv=False, act_... method zero_init_last_bn (line 104) | def zero_init_last_bn(self): method forward (line 107) | def forward(self, x, x_t=None, return_x_2=True): class FCUDown (line 143) | class FCUDown(nn.Module): method __init__ (line 147) | def __init__(self, inplanes, outplanes, dw_stride, act_layer=nn.GELU, method forward (line 158) | def forward(self, x, x_t): class FCUUp (line 170) | class FCUUp(nn.Module): method __init__ (line 174) | def __init__(self, inplanes, outplanes, up_stride, act_layer=nn.ReLU, method forward (line 183) | def forward(self, x, H, W): class Med_ConvBlock (line 192) | class Med_ConvBlock(nn.Module): method __init__ (line 195) | def __init__(self, inplanes, act_layer=nn.ReLU, groups=1, norm_layer=p... method zero_init_last_bn (line 218) | def zero_init_last_bn(self): method forward (line 221) | def forward(self, x): class ConvTransBlock (line 250) | class ConvTransBlock(nn.Module): method __init__ (line 255) | def __init__(self, inplanes, outplanes, res_conv, stride, dw_stride, e... method forward (line 287) | def forward(self, x, x_t): class Conformer (line 306) | class Conformer(nn.Module): method __init__ (line 308) | def __init__(self, patch_size=16, in_chans=3, num_classes=1000, base_c... method _init_weights (line 396) | def _init_weights(self, m): method no_weight_decay (line 414) | def no_weight_decay(self): method forward (line 418) | def forward(self, x): FILE: classification/model/cotnet.py function _cfg (line 21) | def _cfg(url='', **kwargs): class CotLayer (line 36) | class CotLayer(nn.Module): method __init__ (line 37) | def __init__(self, dim, kernel_size): method forward (line 79) | def forward(self, x): class CoXtLayer (line 106) | class CoXtLayer(nn.Module): method __init__ (line 107) | def __init__(self, dim, kernel_size): method forward (line 150) | def forward(self, x): class Bottleneck (line 181) | class Bottleneck(nn.Module): method __init__ (line 184) | def __init__(self, inplanes, planes, stride=1, downsample=None, cardin... method zero_init_last_bn (line 225) | def zero_init_last_bn(self): method forward (line 228) | def forward(self, x): function _create_cotnet (line 266) | def _create_cotnet(variant, pretrained=False, **kwargs): function cotnet50 (line 271) | def cotnet50(pretrained=False, **kwargs): function cotnext50_2x48d (line 276) | def cotnext50_2x48d(pretrained=False, **kwargs): function cotnet101 (line 281) | def cotnet101(pretrained=False, **kwargs): function cotnext101_2x48d (line 286) | def cotnext101_2x48d(pretrained=False, **kwargs): FILE: classification/model/cotnet_hybrid.py function _cfg (line 23) | def _cfg(url='', **kwargs): class CoTLayer (line 48) | class CoTLayer(nn.Module): method __init__ (line 49) | def __init__(self, dim, kernel_size): method forward (line 91) | def forward(self, x): class CoTBottleneck (line 118) | class CoTBottleneck(nn.Module): method __init__ (line 121) | def __init__(self, block_idx, inplanes, planes, stride=1, downsample=N... method zero_init_last_bn (line 169) | def zero_init_last_bn(self): method forward (line 172) | def forward(self, x): function get_padding (line 204) | def get_padding(kernel_size, stride, dilation=1): function downsample_conv (line 208) | def downsample_conv( function downsample_avg (line 221) | def downsample_avg( function drop_blocks (line 237) | def drop_blocks(drop_block_rate=0.): function make_blocks (line 243) | def make_blocks( function make_blocks_arr (line 288) | def make_blocks_arr( class CoTHybridNet (line 338) | class CoTHybridNet(nn.Module): method __init__ (line 339) | def __init__(self, block, layers, num_classes=1000, in_chans=3, method get_classifier (line 420) | def get_classifier(self): method reset_classifier (line 423) | def reset_classifier(self, num_classes, global_pool='avg'): method forward_features (line 427) | def forward_features(self, x): method forward (line 439) | def forward(self, x): function _create_se_cotnetd (line 448) | def _create_se_cotnetd(variant, pretrained=False, **kwargs): function se_cotnetd_50 (line 453) | def se_cotnetd_50(pretrained=False, **kwargs): function se_cotnetd_101 (line 461) | def se_cotnetd_101(pretrained=False, **kwargs): function se_cotnetd_152 (line 469) | def se_cotnetd_152(pretrained=False, **kwargs): function se_cotnetd_152_L (line 477) | def se_cotnetd_152_L(pretrained=False, **kwargs): function se_cotnetd_200 (line 485) | def se_cotnetd_200(pretrained=False, **kwargs): function se_cotnetd_270 (line 493) | def se_cotnetd_270(pretrained=False, **kwargs): FILE: classification/model/features.py class FeatureInfo (line 20) | class FeatureInfo: method __init__ (line 22) | def __init__(self, feature_info: List[Dict], out_indices: Tuple[int]): method from_other (line 33) | def from_other(self, out_indices: Tuple[int]): method get (line 36) | def get(self, key, idx=None): method get_dicts (line 49) | def get_dicts(self, keys=None, idx=None): method channels (line 62) | def channels(self, idx=None): method reduction (line 67) | def reduction(self, idx=None): method module_name (line 72) | def module_name(self, idx=None): method __getitem__ (line 77) | def __getitem__(self, item): method __len__ (line 80) | def __len__(self): class FeatureHooks (line 84) | class FeatureHooks: method __init__ (line 92) | def __init__(self, hooks, named_modules, out_map=None, default_hook_ty... method _collect_output_hook (line 109) | def _collect_output_hook(self, hook_id, *args): method get_output (line 115) | def get_output(self, device) -> Dict[str, torch.tensor]: function _module_list (line 121) | def _module_list(module, flatten_sequential=False): function _get_feature_info (line 135) | def _get_feature_info(net, out_indices): function _get_return_layers (line 145) | def _get_return_layers(feature_info, out_map): class FeatureDictNet (line 153) | class FeatureDictNet(nn.ModuleDict): method __init__ (line 177) | def __init__( method _collect (line 200) | def _collect(self, x) -> (Dict[str, torch.Tensor]): method forward (line 214) | def forward(self, x) -> Dict[str, torch.Tensor]: class FeatureListNet (line 218) | class FeatureListNet(FeatureDictNet): method __init__ (line 224) | def __init__( method forward (line 231) | def forward(self, x) -> (List[torch.Tensor]): class FeatureHookNet (line 235) | class FeatureHookNet(nn.ModuleDict): method __init__ (line 248) | def __init__( method forward (line 280) | def forward(self, x): FILE: classification/model/helpers.py function load_state_dict (line 23) | def load_state_dict(checkpoint_path, use_ema=False): function load_checkpoint (line 46) | def load_checkpoint(model, checkpoint_path, use_ema=False, strict=True): function resume_checkpoint (line 51) | def resume_checkpoint(model, checkpoint_path, optimizer=None, loss_scale... function load_pretrained (line 91) | def load_pretrained(model, cfg=None, num_classes=1000, in_chans=3, filte... function extract_layer (line 156) | def extract_layer(model, layer): function set_layer (line 174) | def set_layer(model, layer, val): function adapt_model_from_string (line 198) | def adapt_model_from_string(parent_module, model_string): function adapt_model_from_file (line 248) | def adapt_model_from_file(parent_module, model_variant): function default_cfg_for_features (line 253) | def default_cfg_for_features(default_cfg): function overlay_external_default_cfg (line 261) | def overlay_external_default_cfg(default_cfg, kwargs): function model_parameters (line 270) | def model_parameters(model, exclude_head=False): function named_apply (line 277) | def named_apply(fn: Callable, module: nn.Module, name='', depth_first=Tr... function adapt_input_conv (line 287) | def adapt_input_conv(in_chans, conv_weight): function build_model_with_cfg (line 311) | def build_model_with_cfg( FILE: classification/model/hivit.py class Attention (line 9) | class Attention(nn.Module): method __init__ (line 10) | def __init__(self, input_size, dim, num_heads, qkv_bias=True, qk_scale... method forward (line 43) | def forward(self, x, rpe_index=None, mask=None): class BlockWithRPE (line 69) | class BlockWithRPE(nn.Module): method __init__ (line 70) | def __init__(self, input_size, dim, num_heads=0., mlp_ratio=4., qkv_bi... method forward (line 91) | def forward(self, x, rpe_index=None, mask=None): class PatchEmbed (line 98) | class PatchEmbed(nn.Module): method __init__ (line 99) | def __init__( method forward (line 124) | def forward(self, x): class PatchMerge (line 138) | class PatchMerge(nn.Module): method __init__ (line 139) | def __init__(self, dim, norm_layer): method forward (line 144) | def forward(self, x, *args, **kwargs): class HiViT (line 167) | class HiViT(nn.Module): method __init__ (line 168) | def __init__(self, img_size=224, patch_size=16, inner_patches=4, in_ch... method _init_weights (line 242) | def _init_weights(self, m): method no_weight_decay (line 252) | def no_weight_decay(self): method no_weight_decay_keywords (line 256) | def no_weight_decay_keywords(self): method forward_features (line 259) | def forward_features(self, x, ids_keep=None, mask=None): method forward (line 297) | def forward(self, x): function hivit_base (line 304) | def hivit_base(**kwargs): FILE: classification/model/inception_transformer.py function _cfg (line 42) | def _cfg(url='', **kwargs): function _no_grad_trunc_normal_ (line 72) | def _no_grad_trunc_normal_(tensor, mean, std, a, b): function trunc_normal_ (line 108) | def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): function variance_scaling_ (line 130) | def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='no... function lecun_normal_ (line 153) | def lecun_normal_(tensor): class PatchEmbed (line 156) | class PatchEmbed(nn.Module): method __init__ (line 159) | def __init__(self, img_size=224, kernel_size=16, stride=16, padding=0... method forward (line 169) | def forward(self, x): class FirstPatchEmbed (line 176) | class FirstPatchEmbed(nn.Module): method __init__ (line 179) | def __init__(self, kernel_size=3, stride=2, padding=1, in_chans=3, em... method forward (line 188) | def forward(self, x): class HighMixer (line 198) | class HighMixer(nn.Module): method __init__ (line 199) | def __init__(self, dim, kernel_size=3, stride=1, padding=1, method forward (line 217) | def forward(self, x): class LowMixer (line 233) | class LowMixer(nn.Module): method __init__ (line 234) | def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., poo... method att_fun (line 249) | def att_fun(self, q, k, v, B, N, C): method forward (line 257) | def forward(self, x): class Mixer (line 271) | class Mixer(nn.Module): method __init__ (line 272) | def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., pro... method forward (line 289) | def forward(self, x): class Block (line 306) | class Block(nn.Module): method __init__ (line 308) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=... method forward (line 330) | def forward(self, x): class InceptionTransformer (line 339) | class InceptionTransformer(nn.Module): method __init__ (line 340) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe... method init_weights (line 415) | def init_weights(self, mode=''): method _init_weights (line 423) | def _init_weights(self, m): method no_weight_decay (line 429) | def no_weight_decay(self): method get_classifier (line 432) | def get_classifier(self): method reset_classifier (line 438) | def reset_classifier(self, num_classes, global_pool=''): method _get_pos_embed (line 444) | def _get_pos_embed(self, pos_embed, num_patches_def, H, W): method forward_features (line 452) | def forward_features(self, x): method forward (line 480) | def forward(self, x): function _init_vit_weights (line 485) | def _init_vit_weights(module: nn.Module, name: str = '', head_bias: floa... function iformer_small (line 512) | def iformer_small(pretrained=False, **kwargs): function iformer_small_384 (line 536) | def iformer_small_384(pretrained=False, **kwargs): function iformer_base (line 560) | def iformer_base(pretrained=False, **kwargs): function iformer_base_384 (line 584) | def iformer_base_384(pretrained=False, **kwargs): function iformer_large (line 606) | def iformer_large(pretrained=False, **kwargs): function iformer_large_384 (line 630) | def iformer_large_384(pretrained=False, **kwargs): FILE: classification/model/layers/activations.py function swish (line 14) | def swish(x, inplace: bool = False): class Swish (line 20) | class Swish(nn.Module): method __init__ (line 21) | def __init__(self, inplace: bool = False): method forward (line 25) | def forward(self, x): function mish (line 29) | def mish(x, inplace: bool = False): class Mish (line 36) | class Mish(nn.Module): method __init__ (line 39) | def __init__(self, inplace: bool = False): method forward (line 42) | def forward(self, x): function sigmoid (line 46) | def sigmoid(x, inplace: bool = False): class Sigmoid (line 51) | class Sigmoid(nn.Module): method __init__ (line 52) | def __init__(self, inplace: bool = False): method forward (line 56) | def forward(self, x): function tanh (line 60) | def tanh(x, inplace: bool = False): class Tanh (line 65) | class Tanh(nn.Module): method __init__ (line 66) | def __init__(self, inplace: bool = False): method forward (line 70) | def forward(self, x): function hard_swish (line 74) | def hard_swish(x, inplace: bool = False): class HardSwish (line 79) | class HardSwish(nn.Module): method __init__ (line 80) | def __init__(self, inplace: bool = False): method forward (line 84) | def forward(self, x): function hard_sigmoid (line 88) | def hard_sigmoid(x, inplace: bool = False): class HardSigmoid (line 95) | class HardSigmoid(nn.Module): method __init__ (line 96) | def __init__(self, inplace: bool = False): method forward (line 100) | def forward(self, x): function hard_mish (line 104) | def hard_mish(x, inplace: bool = False): class HardMish (line 115) | class HardMish(nn.Module): method __init__ (line 116) | def __init__(self, inplace: bool = False): method forward (line 120) | def forward(self, x): FILE: classification/model/layers/activations_jit.py function swish_jit (line 19) | def swish_jit(x, inplace: bool = False): function mish_jit (line 26) | def mish_jit(x, _inplace: bool = False): class SwishJit (line 32) | class SwishJit(nn.Module): method __init__ (line 33) | def __init__(self, inplace: bool = False): method forward (line 36) | def forward(self, x): class MishJit (line 40) | class MishJit(nn.Module): method __init__ (line 41) | def __init__(self, inplace: bool = False): method forward (line 44) | def forward(self, x): function hard_sigmoid_jit (line 49) | def hard_sigmoid_jit(x, inplace: bool = False): class HardSigmoidJit (line 54) | class HardSigmoidJit(nn.Module): method __init__ (line 55) | def __init__(self, inplace: bool = False): method forward (line 58) | def forward(self, x): function hard_swish_jit (line 63) | def hard_swish_jit(x, inplace: bool = False): class HardSwishJit (line 68) | class HardSwishJit(nn.Module): method __init__ (line 69) | def __init__(self, inplace: bool = False): method forward (line 72) | def forward(self, x): function hard_mish_jit (line 77) | def hard_mish_jit(x, inplace: bool = False): class HardMishJit (line 85) | class HardMishJit(nn.Module): method __init__ (line 86) | def __init__(self, inplace: bool = False): method forward (line 89) | def forward(self, x): FILE: classification/model/layers/activations_me.py function swish_jit_fwd (line 18) | def swish_jit_fwd(x): function swish_jit_bwd (line 23) | def swish_jit_bwd(x, grad_output): class SwishJitAutoFn (line 28) | class SwishJitAutoFn(torch.autograd.Function): method forward (line 35) | def forward(ctx, x): method backward (line 40) | def backward(ctx, grad_output): function swish_me (line 45) | def swish_me(x, inplace=False): class SwishMe (line 49) | class SwishMe(nn.Module): method __init__ (line 50) | def __init__(self, inplace: bool = False): method forward (line 53) | def forward(self, x): function mish_jit_fwd (line 58) | def mish_jit_fwd(x): function mish_jit_bwd (line 63) | def mish_jit_bwd(x, grad_output): class MishJitAutoFn (line 69) | class MishJitAutoFn(torch.autograd.Function): method forward (line 74) | def forward(ctx, x): method backward (line 79) | def backward(ctx, grad_output): function mish_me (line 84) | def mish_me(x, inplace=False): class MishMe (line 88) | class MishMe(nn.Module): method __init__ (line 89) | def __init__(self, inplace: bool = False): method forward (line 92) | def forward(self, x): function hard_sigmoid_jit_fwd (line 97) | def hard_sigmoid_jit_fwd(x, inplace: bool = False): function hard_sigmoid_jit_bwd (line 102) | def hard_sigmoid_jit_bwd(x, grad_output): class HardSigmoidJitAutoFn (line 107) | class HardSigmoidJitAutoFn(torch.autograd.Function): method forward (line 109) | def forward(ctx, x): method backward (line 114) | def backward(ctx, grad_output): function hard_sigmoid_me (line 119) | def hard_sigmoid_me(x, inplace: bool = False): class HardSigmoidMe (line 123) | class HardSigmoidMe(nn.Module): method __init__ (line 124) | def __init__(self, inplace: bool = False): method forward (line 127) | def forward(self, x): function hard_swish_jit_fwd (line 132) | def hard_swish_jit_fwd(x): function hard_swish_jit_bwd (line 137) | def hard_swish_jit_bwd(x, grad_output): class HardSwishJitAutoFn (line 143) | class HardSwishJitAutoFn(torch.autograd.Function): method forward (line 146) | def forward(ctx, x): method backward (line 151) | def backward(ctx, grad_output): function hard_swish_me (line 156) | def hard_swish_me(x, inplace=False): class HardSwishMe (line 160) | class HardSwishMe(nn.Module): method __init__ (line 161) | def __init__(self, inplace: bool = False): method forward (line 164) | def forward(self, x): function hard_mish_jit_fwd (line 169) | def hard_mish_jit_fwd(x): function hard_mish_jit_bwd (line 174) | def hard_mish_jit_bwd(x, grad_output): class HardMishJitAutoFn (line 180) | class HardMishJitAutoFn(torch.autograd.Function): method forward (line 186) | def forward(ctx, x): method backward (line 191) | def backward(ctx, grad_output): function hard_mish_me (line 196) | def hard_mish_me(x, inplace: bool = False): class HardMishMe (line 200) | class HardMishMe(nn.Module): method __init__ (line 201) | def __init__(self, inplace: bool = False): method forward (line 204) | def forward(self, x): FILE: classification/model/layers/adaptive_avgmax_pool.py function adaptive_pool_feat_mult (line 17) | def adaptive_pool_feat_mult(pool_type='avg'): function adaptive_avgmax_pool2d (line 24) | def adaptive_avgmax_pool2d(x, output_size=1): function adaptive_catavgmax_pool2d (line 30) | def adaptive_catavgmax_pool2d(x, output_size=1): function select_adaptive_pool2d (line 36) | def select_adaptive_pool2d(x, pool_type='avg', output_size=1): class FastAdaptiveAvgPool2d (line 52) | class FastAdaptiveAvgPool2d(nn.Module): method __init__ (line 53) | def __init__(self, flatten=False): method forward (line 57) | def forward(self, x): class AdaptiveAvgMaxPool2d (line 61) | class AdaptiveAvgMaxPool2d(nn.Module): method __init__ (line 62) | def __init__(self, output_size=1): method forward (line 66) | def forward(self, x): class AdaptiveCatAvgMaxPool2d (line 70) | class AdaptiveCatAvgMaxPool2d(nn.Module): method __init__ (line 71) | def __init__(self, output_size=1): method forward (line 75) | def forward(self, x): class SelectAdaptivePool2d (line 79) | class SelectAdaptivePool2d(nn.Module): method __init__ (line 82) | def __init__(self, output_size=1, pool_type='fast', flatten=False): method is_identity (line 103) | def is_identity(self): method forward (line 106) | def forward(self, x): method feat_mult (line 112) | def feat_mult(self): method __repr__ (line 115) | def __repr__(self): FILE: classification/model/layers/aggregation_zeropad.py function GET_BLOCKS (line 17) | def GET_BLOCKS(N): class AggregationZeropad (line 112) | class AggregationZeropad(Function): method forward (line 114) | def forward(ctx, input, weight, kernel_size, stride, padding, dilation): method backward (line 148) | def backward(ctx, grad_output): function aggregation_zeropad (line 188) | def aggregation_zeropad(input, weight, kernel_size=3, stride=1, padding=... class LocalConvolution (line 199) | class LocalConvolution(torch.nn.Module): method __init__ (line 200) | def __init__( method forward (line 219) | def forward(self, input: Tensor, weight: Tensor): function test_aggregation_zeropad (line 238) | def test_aggregation_zeropad(): function test_aggregation_zeropad_ks1 (line 266) | def test_aggregation_zeropad_ks1(): FILE: classification/model/layers/anti_aliasing.py class AntiAliasDownsampleLayer (line 7) | class AntiAliasDownsampleLayer(nn.Module): method __init__ (line 8) | def __init__(self, channels: int = 0, filt_size: int = 3, stride: int ... method forward (line 17) | def forward(self, x): class DownsampleJIT (line 22) | class DownsampleJIT(object): method __init__ (line 23) | def __init__(self, channels: int = 0, filt_size: int = 3, stride: int ... method _create_filter (line 31) | def _create_filter(self, like: torch.Tensor): method __call__ (line 37) | def __call__(self, input: torch.Tensor): class Downsample (line 43) | class Downsample(nn.Module): method __init__ (line 44) | def __init__(self, channels=None, filt_size=3, stride=2): method forward (line 58) | def forward(self, input): FILE: classification/model/layers/blur_pool.py class BlurPool2d (line 19) | class BlurPool2d(nn.Module): method __init__ (line 34) | def __init__(self, channels, filt_size=3, stride=2) -> None: method _create_filter (line 45) | def _create_filter(self, like: torch.Tensor): method _apply (line 49) | def _apply(self, fn): method forward (line 54) | def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: FILE: classification/model/layers/cbam.py class ChannelAttn (line 17) | class ChannelAttn(nn.Module): method __init__ (line 20) | def __init__(self, channels, reduction=16, act_layer=nn.ReLU): method forward (line 26) | def forward(self, x): class LightChannelAttn (line 35) | class LightChannelAttn(ChannelAttn): method __init__ (line 38) | def __init__(self, channels, reduction=16): method forward (line 41) | def forward(self, x): class SpatialAttn (line 47) | class SpatialAttn(nn.Module): method __init__ (line 50) | def __init__(self, kernel_size=7): method forward (line 54) | def forward(self, x): class LightSpatialAttn (line 62) | class LightSpatialAttn(nn.Module): method __init__ (line 65) | def __init__(self, kernel_size=7): method forward (line 69) | def forward(self, x): class CbamModule (line 77) | class CbamModule(nn.Module): method __init__ (line 78) | def __init__(self, channels, spatial_kernel_size=7): method forward (line 83) | def forward(self, x): class LightCbamModule (line 89) | class LightCbamModule(nn.Module): method __init__ (line 90) | def __init__(self, channels, spatial_kernel_size=7): method forward (line 95) | def forward(self, x): FILE: classification/model/layers/classifier.py function create_classifier (line 11) | def create_classifier(num_features, num_classes, pool_type='avg', use_co... class ClassifierHead (line 28) | class ClassifierHead(nn.Module): method __init__ (line 31) | def __init__(self, in_chs, num_classes, pool_type='avg', drop_rate=0.): method forward (line 36) | def forward(self, x): FILE: classification/model/layers/cond_conv2d.py function get_condconv_initializer (line 21) | def get_condconv_initializer(initializer, num_experts, expert_shape): class CondConv2d (line 34) | class CondConv2d(nn.Module): method __init__ (line 43) | def __init__(self, in_channels, out_channels, kernel_size=3, method reset_parameters (line 73) | def reset_parameters(self): method forward (line 84) | def forward(self, x, routing_weights): FILE: classification/model/layers/config.py function is_no_jit (line 25) | def is_no_jit(): class set_no_jit (line 29) | class set_no_jit: method __init__ (line 30) | def __init__(self, mode: bool) -> None: method __enter__ (line 35) | def __enter__(self) -> None: method __exit__ (line 38) | def __exit__(self, *args: Any) -> bool: function is_exportable (line 44) | def is_exportable(): class set_exportable (line 48) | class set_exportable: method __init__ (line 49) | def __init__(self, mode: bool) -> None: method __enter__ (line 54) | def __enter__(self) -> None: method __exit__ (line 57) | def __exit__(self, *args: Any) -> bool: function is_scriptable (line 63) | def is_scriptable(): class set_scriptable (line 67) | class set_scriptable: method __init__ (line 68) | def __init__(self, mode: bool) -> None: method __enter__ (line 73) | def __enter__(self) -> None: method __exit__ (line 76) | def __exit__(self, *args: Any) -> bool: class set_layer_config (line 82) | class set_layer_config: method __init__ (line 86) | def __init__( method __enter__ (line 106) | def __enter__(self) -> None: method __exit__ (line 109) | def __exit__(self, *args: Any) -> bool: FILE: classification/model/layers/conv2d_same.py function conv2d_same (line 13) | def conv2d_same( class Conv2dSame (line 20) | class Conv2dSame(nn.Conv2d): method __init__ (line 24) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, method forward (line 29) | def forward(self, x): function create_conv2d_pad (line 33) | def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs): FILE: classification/model/layers/conv_bn_act.py class ConvBnAct (line 11) | class ConvBnAct(nn.Module): method __init__ (line 12) | def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,... method in_channels (line 28) | def in_channels(self): method out_channels (line 32) | def out_channels(self): method forward (line 35) | def forward(self, x): FILE: classification/model/layers/create_act.py function get_act_fn (line 89) | def get_act_fn(name='relu'): function get_act_layer (line 107) | def get_act_layer(name='relu'): function create_act_layer (line 123) | def create_act_layer(name, inplace=False, **kwargs): FILE: classification/model/layers/create_attn.py function create_attn (line 11) | def create_attn(attn_type, channels, **kwargs): FILE: classification/model/layers/create_conv2d.py function create_conv2d (line 11) | def create_conv2d(in_channels, out_channels, kernel_size, **kwargs): FILE: classification/model/layers/create_norm_act.py function get_norm_act_layer (line 22) | def get_norm_act_layer(layer_class): function create_norm_act (line 39) | def create_norm_act(layer_type, num_features, apply_act=True, jit=False,... function convert_norm_act_type (line 50) | def convert_norm_act_type(norm_layer, act_layer, norm_kwargs=None): FILE: classification/model/layers/drop.py function drop_block_2d (line 22) | def drop_block_2d( function drop_block_fast_2d (line 70) | def drop_block_fast_2d( class DropBlock2d (line 109) | class DropBlock2d(nn.Module): method __init__ (line 112) | def __init__(self, method forward (line 129) | def forward(self, x): function drop_path (line 140) | def drop_path(x, drop_prob: float = 0., training: bool = False): class DropPath (line 160) | class DropPath(nn.Module): method __init__ (line 163) | def __init__(self, drop_prob=None): method forward (line 167) | def forward(self, x): FILE: classification/model/layers/eca.py class EcaModule (line 41) | class EcaModule(nn.Module): method __init__ (line 52) | def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1): method forward (line 61) | def forward(self, x): class CecaModule (line 68) | class CecaModule(nn.Module): method __init__ (line 88) | def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1): method forward (line 101) | def forward(self, x): FILE: classification/model/layers/evo_norm.py class EvoNormBatch2d (line 16) | class EvoNormBatch2d(nn.Module): method __init__ (line 17) | def __init__(self, num_features, apply_act=True, momentum=0.1, eps=1e-... method reset_parameters (line 30) | def reset_parameters(self): method forward (line 36) | def forward(self, x): class EvoNormSample2d (line 55) | class EvoNormSample2d(nn.Module): method __init__ (line 56) | def __init__(self, num_features, apply_act=True, groups=8, eps=1e-5, d... method reset_parameters (line 68) | def reset_parameters(self): method forward (line 74) | def forward(self, x): FILE: classification/model/layers/helpers.py function _ntuple (line 10) | def _ntuple(n): FILE: classification/model/layers/inplace_abn.py function inplace_abn (line 10) | def inplace_abn(x, weight, bias, running_mean, running_var, function inplace_abn_sync (line 15) | def inplace_abn_sync(**kwargs): class InplaceAbn (line 19) | class InplaceAbn(nn.Module): method __init__ (line 40) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, ... method reset_parameters (line 74) | def reset_parameters(self): method forward (line 81) | def forward(self, x): FILE: classification/model/layers/kerv2d.py class Kerv2d (line 9) | class Kerv2d(nn.Conv2d): method __init__ (line 10) | def __init__( method forward (line 35) | def forward(self, input): FILE: classification/model/layers/median_pool.py class MedianPool2d (line 9) | class MedianPool2d(nn.Module): method __init__ (line 18) | def __init__(self, kernel_size=3, stride=1, padding=0, same=False): method _padding (line 25) | def _padding(self, x): method forward (line 45) | def forward(self, x): FILE: classification/model/layers/mixed_conv2d.py function _split_channels (line 14) | def _split_channels(num_chan, num_groups): class MixedConv2d (line 20) | class MixedConv2d(nn.ModuleDict): method __init__ (line 26) | def __init__(self, in_channels, out_channels, kernel_size=3, method forward (line 47) | def forward(self, x): FILE: classification/model/layers/mlp.py class Mlp (line 8) | class Mlp(nn.Module): method __init__ (line 11) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 20) | def forward(self, x): class GluMlp (line 29) | class GluMlp(nn.Module): method __init__ (line 33) | def __init__(self, in_features, hidden_features=None, out_features=Non... method init_weights (line 43) | def init_weights(self): method forward (line 49) | def forward(self, x): class GatedMlp (line 59) | class GatedMlp(nn.Module): method __init__ (line 62) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 78) | def forward(self, x): class ConvMlp (line 88) | class ConvMlp(nn.Module): method __init__ (line 91) | def __init__( method forward (line 102) | def forward(self, x): FILE: classification/model/layers/norm_act.py class BatchNormAct2d (line 10) | class BatchNormAct2d(nn.BatchNorm2d): method __init__ (line 17) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, ... method _forward_jit (line 29) | def _forward_jit(self, x): method _forward_python (line 56) | def _forward_python(self, x): method forward (line 59) | def forward(self, x): class GroupNormAct (line 70) | class GroupNormAct(nn.GroupNorm): method __init__ (line 72) | def __init__(self, num_groups, num_channels, eps=1e-5, affine=True, method forward (line 82) | def forward(self, x): FILE: classification/model/layers/padding.py function get_padding (line 12) | def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **... function get_same_padding (line 18) | def get_same_padding(x: int, k: int, s: int, d: int): function is_static_pad (line 23) | def is_static_pad(kernel_size: int, stride: int = 1, dilation: int = 1, ... function pad_same (line 28) | def pad_same(x, k: List[int], s: List[int], d: List[int] = (1, 1), value... function get_padding_value (line 36) | def get_padding_value(padding, kernel_size, **kwargs) -> Tuple[Tuple, bo... FILE: classification/model/layers/patch_embed.py class PatchEmbed (line 15) | class PatchEmbed(nn.Module): method __init__ (line 18) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=... method forward (line 31) | def forward(self, x): FILE: classification/model/layers/pool2d_same.py function avg_pool2d_same (line 14) | def avg_pool2d_same(x, kernel_size: List[int], stride: List[int], paddin... class AvgPool2dSame (line 21) | class AvgPool2dSame(nn.AvgPool2d): method __init__ (line 24) | def __init__(self, kernel_size: int, stride=None, padding=0, ceil_mode... method forward (line 29) | def forward(self, x): function max_pool2d_same (line 34) | def max_pool2d_same( class MaxPool2dSame (line 41) | class MaxPool2dSame(nn.MaxPool2d): method __init__ (line 44) | def __init__(self, kernel_size: int, stride=None, padding=0, dilation=... method forward (line 50) | def forward(self, x): function create_pool2d (line 54) | def create_pool2d(pool_type, kernel_size, stride=None, **kwargs): FILE: classification/model/layers/se.py class SEModule (line 5) | class SEModule(nn.Module): method __init__ (line 7) | def __init__(self, channels, reduction=16, act_layer=nn.ReLU, min_chan... method forward (line 16) | def forward(self, x): class EffectiveSEModule (line 24) | class EffectiveSEModule(nn.Module): method __init__ (line 28) | def __init__(self, channels, gate_layer='hard_sigmoid'): method forward (line 33) | def forward(self, x): FILE: classification/model/layers/selective_kernel.py function _kernel_valid (line 13) | def _kernel_valid(k): class SelectiveKernelAttn (line 20) | class SelectiveKernelAttn(nn.Module): method __init__ (line 21) | def __init__(self, channels, num_paths=2, attn_channels=32, method forward (line 35) | def forward(self, x): class SelectiveKernelConv (line 48) | class SelectiveKernelConv(nn.Module): method __init__ (line 50) | def __init__(self, in_channels, out_channels, kernel_size=None, stride... method forward (line 108) | def forward(self, x): FILE: classification/model/layers/separable_conv.py class SeparableConvBnAct (line 14) | class SeparableConvBnAct(nn.Module): method __init__ (line 17) | def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,... method in_channels (line 34) | def in_channels(self): method out_channels (line 38) | def out_channels(self): method forward (line 41) | def forward(self, x): class SeparableConv2d (line 49) | class SeparableConv2d(nn.Module): method __init__ (line 52) | def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,... method in_channels (line 64) | def in_channels(self): method out_channels (line 68) | def out_channels(self): method forward (line 71) | def forward(self, x): FILE: classification/model/layers/shiftlution.py class Shiftlution (line 5) | class Shiftlution(nn.Module): method __init__ (line 6) | def __init__(self, channels, kernel_h, kernel_w, max_height = 400, max... method forward (line 49) | def forward(self, x): FILE: classification/model/layers/space_to_depth.py class SpaceToDepth (line 5) | class SpaceToDepth(nn.Module): method __init__ (line 6) | def __init__(self, block_size=4): method forward (line 11) | def forward(self, x): class SpaceToDepthJit (line 20) | class SpaceToDepthJit(object): method __call__ (line 21) | def __call__(self, x: torch.Tensor): class SpaceToDepthModule (line 30) | class SpaceToDepthModule(nn.Module): method __init__ (line 31) | def __init__(self, no_jit=False): method forward (line 38) | def forward(self, x): class DepthToSpace (line 42) | class DepthToSpace(nn.Module): method __init__ (line 44) | def __init__(self, block_size): method forward (line 48) | def forward(self, x): FILE: classification/model/layers/split_attn.py class RadixSoftmax (line 14) | class RadixSoftmax(nn.Module): method __init__ (line 15) | def __init__(self, radix, cardinality): method forward (line 20) | def forward(self, x): class SplitAttnConv2d (line 31) | class SplitAttnConv2d(nn.Module): method __init__ (line 34) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, p... method in_channels (line 55) | def in_channels(self): method out_channels (line 59) | def out_channels(self): method forward (line 62) | def forward(self, x): FILE: classification/model/layers/split_batchnorm.py class SplitBatchNorm2d (line 18) | class SplitBatchNorm2d(torch.nn.BatchNorm2d): method __init__ (line 20) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, method forward (line 28) | def forward(self, input: torch.Tensor): function convert_splitbn_model (line 41) | def convert_splitbn_model(module, num_splits=2): FILE: classification/model/layers/std_conv.py function get_weight (line 8) | def get_weight(module): class StdConv2d (line 14) | class StdConv2d(nn.Conv2d): method __init__ (line 19) | def __init__( method get_weight (line 29) | def get_weight(self): method forward (line 34) | def forward(self, x): class StdConv2dSame (line 39) | class StdConv2dSame(nn.Conv2d): method __init__ (line 44) | def __init__( method get_weight (line 54) | def get_weight(self): method forward (line 59) | def forward(self, x): class ScaledStdConv2d (line 66) | class ScaledStdConv2d(nn.Conv2d): method __init__ (line 73) | def __init__( method get_weight (line 86) | def get_weight(self): method forward (line 94) | def forward(self, x): class ScaledStdConv2dSame (line 98) | class ScaledStdConv2dSame(nn.Conv2d): method __init__ (line 105) | def __init__( method get_weight (line 126) | def get_weight(self): method forward (line 134) | def forward(self, x): FILE: classification/model/layers/tbconv.py class TBConv (line 9) | class TBConv(nn.Module): method __init__ (line 10) | def __init__( method reset_parameters (line 68) | def reset_parameters(self) -> None: method extra_repr (line 75) | def extra_repr(self): method __setstate__ (line 94) | def __setstate__(self, state): method forward (line 99) | def forward(self, input: Tensor, weight=None) -> Tensor: FILE: classification/model/layers/test_time_pool.py class TestTimePoolHead (line 16) | class TestTimePoolHead(nn.Module): method __init__ (line 17) | def __init__(self, base, original_pool=7): method forward (line 31) | def forward(self, x): function apply_test_time_pool (line 39) | def apply_test_time_pool(model, config): FILE: classification/model/layers/utils.py function Dtype (line 8) | def Dtype(t): function load_kernel (line 15) | def load_kernel(kernel_name, code, **kwargs): FILE: classification/model/layers/weight_init.py function _no_grad_trunc_normal_ (line 8) | def _no_grad_trunc_normal_(tensor, mean, std, a, b): function trunc_normal_ (line 44) | def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): function variance_scaling_ (line 65) | def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='no... function lecun_normal_ (line 88) | def lecun_normal_(tensor): FILE: classification/model/nextvit.py class ConvBNReLU (line 15) | class ConvBNReLU(nn.Module): method __init__ (line 16) | def __init__( method forward (line 29) | def forward(self, x): function _make_divisible (line 36) | def _make_divisible(v, divisor, min_value=None): class PatchEmbed (line 46) | class PatchEmbed(nn.Module): method __init__ (line 47) | def __init__(self, method forward (line 66) | def forward(self, x): class MHCA (line 70) | class MHCA(nn.Module): method __init__ (line 74) | def __init__(self, out_channels, head_dim): method forward (line 83) | def forward(self, x): class Mlp (line 91) | class Mlp(nn.Module): method __init__ (line 92) | def __init__(self, in_features, out_features=None, mlp_ratio=None, dro... method merge_bn (line 101) | def merge_bn(self, pre_norm): method forward (line 104) | def forward(self, x): class NCB (line 113) | class NCB(nn.Module): method __init__ (line 117) | def __init__(self, in_channels, out_channels, stride=1, path_dropout=0, method merge_bn (line 134) | def merge_bn(self): method forward (line 139) | def forward(self, x): class E_MHSA (line 150) | class E_MHSA(nn.Module): method __init__ (line 154) | def __init__(self, dim, out_dim=None, head_dim=32, qkv_bias=True, qk_s... method merge_bn (line 175) | def merge_bn(self, pre_bn): method forward (line 185) | def forward(self, x): class NTB (line 216) | class NTB(nn.Module): method __init__ (line 220) | def __init__( method merge_bn (line 249) | def merge_bn(self): method forward (line 255) | def forward(self, x): class NextViT (line 278) | class NextViT(nn.Module): method __init__ (line 279) | def __init__(self, stem_chs, depths, path_dropout, attn_drop=0, drop=0... method merge_bn (line 342) | def merge_bn(self): method _initialize_weights (line 348) | def _initialize_weights(self): method forward (line 362) | def forward(self, x): function nextvit_small (line 377) | def nextvit_small(pretrained=False, pretrained_cfg=None, **kwargs): function nextvit_base (line 383) | def nextvit_base(pretrained=False, pretrained_cfg=None, **kwargs): function nextvit_large (line 389) | def nextvit_large(pretrained=False, pretrained_cfg=None, **kwargs): FILE: classification/model/registry.py function register_model (line 18) | def register_model(fn): function _natural_key (line 45) | def _natural_key(string_): function list_models (line 49) | def list_models(filter='', module='', pretrained=False, exclude_filters=... function is_model (line 80) | def is_model(model_name): function model_entrypoint (line 86) | def model_entrypoint(model_name): function list_modules (line 92) | def list_modules(): function is_model_in_modules (line 99) | def is_model_in_modules(model_name, module_names): FILE: classification/model/res2net.py class Bottle2neck (line 19) | class Bottle2neck(nn.Module): method __init__ (line 22) | def __init__(self, inplanes, planes, stride=1, downsample=None, baseWi... method forward (line 62) | def forward(self, x): class Res2Net (line 99) | class Res2Net(nn.Module): method __init__ (line 101) | def __init__(self, block, layers, baseWidth=26, scale=4, num_classes=10): method _make_layer (line 125) | def _make_layer(self, block, planes, blocks, stride=1): method forward (line 143) | def forward(self, x): function res2net50 (line 161) | def res2net50(pretrained=False, **kwargs): function res2net50_26w_4s (line 173) | def res2net50_26w_4s(pretrained=False, **kwargs): function res2net101_26w_4s (line 184) | def res2net101_26w_4s(pretrained=False, **kwargs): function res2net50_26w_6s (line 195) | def res2net50_26w_6s(pretrained=False, **kwargs): function res2net50_26w_8s (line 206) | def res2net50_26w_8s(pretrained=False, **kwargs): function res2net50_48w_2s (line 217) | def res2net50_48w_2s(pretrained=False, **kwargs): function res2net50_14w_8s (line 228) | def res2net50_14w_8s(pretrained=False, **kwargs): FILE: classification/model/resnet.py function _cfg (line 24) | def _cfg(url='', **kwargs): function get_padding (line 217) | def get_padding(kernel_size, stride, dilation=1): class BasicBlock (line 222) | class BasicBlock(nn.Module): method __init__ (line 225) | def __init__(self, inplanes, planes, stride=1, downsample=None, cardin... method zero_init_last_bn (line 257) | def zero_init_last_bn(self): method forward (line 260) | def forward(self, x): class Bottleneck (line 290) | class Bottleneck(nn.Module): method __init__ (line 293) | def __init__(self, inplanes, planes, stride=1, downsample=None, cardin... method zero_init_last_bn (line 327) | def zero_init_last_bn(self): method forward (line 330) | def forward(self, x): function downsample_conv (line 366) | def downsample_conv( function downsample_avg (line 380) | def downsample_avg( function drop_blocks (line 397) | def drop_blocks(drop_block_rate=0.): function make_blocks (line 404) | def make_blocks( class ResNet (line 448) | class ResNet(nn.Module): method __init__ (line 519) | def __init__(self, block, layers, num_classes=1000, in_chans=1, method get_classifier (line 587) | def get_classifier(self): method reset_classifier (line 590) | def reset_classifier(self, num_classes, global_pool='avg'): method forward_features (line 594) | def forward_features(self, x): method forward (line 606) | def forward(self, x): function _create_resnet (line 615) | def _create_resnet(variant, pretrained=False, **kwargs): function resnet18 (line 621) | def resnet18(pretrained=False, **kwargs): function resnet18d (line 629) | def resnet18d(pretrained=False, **kwargs): function resnet34 (line 638) | def resnet34(pretrained=False, **kwargs): function resnet34d (line 646) | def resnet34d(pretrained=False, **kwargs): function resnet26 (line 655) | def resnet26(pretrained=False, **kwargs): function resnet26d (line 663) | def resnet26d(pretrained=False, **kwargs): function resnet50 (line 671) | def resnet50(pretrained=False, **kwargs): function resnet50d (line 679) | def resnet50d(pretrained=False, **kwargs): function resnet66d (line 688) | def resnet66d(pretrained=False, **kwargs): function resnet101 (line 696) | def resnet101(pretrained=False, **kwargs): function resnet101d (line 704) | def resnet101d(pretrained=False, **kwargs): function resnet152 (line 712) | def resnet152(pretrained=False, **kwargs): function resnet152d (line 720) | def resnet152d(pretrained=False, **kwargs): function resnet200 (line 729) | def resnet200(pretrained=False, **kwargs): function resnet200d (line 737) | def resnet200d(pretrained=False, **kwargs): function tv_resnet34 (line 746) | def tv_resnet34(pretrained=False, **kwargs): function tv_resnet50 (line 754) | def tv_resnet50(pretrained=False, **kwargs): function tv_resnet101 (line 762) | def tv_resnet101(pretrained=False, **kwargs): function tv_resnet152 (line 770) | def tv_resnet152(pretrained=False, **kwargs): function wide_resnet50_2 (line 778) | def wide_resnet50_2(pretrained=False, **kwargs): function wide_resnet101_2 (line 790) | def wide_resnet101_2(pretrained=False, **kwargs): function resnext50_32x4d (line 801) | def resnext50_32x4d(pretrained=False, **kwargs): function resnext26_32x4d (line 808) | def resnext26_32x4d(pretrained=False, **kwargs): function resnext50d_32x4d (line 815) | def resnext50d_32x4d(pretrained=False, **kwargs): function resnext101_32x4d (line 825) | def resnext101_32x4d(pretrained=False, **kwargs): function resnext101_32x8d (line 833) | def resnext101_32x8d(pretrained=False, **kwargs): function resnext101_64x4d (line 841) | def resnext101_64x4d(pretrained=False, **kwargs): function tv_resnext50_32x4d (line 849) | def tv_resnext50_32x4d(pretrained=False, **kwargs): function ig_resnext101_32x8d (line 857) | def ig_resnext101_32x8d(pretrained=True, **kwargs): function ig_resnext101_32x16d (line 868) | def ig_resnext101_32x16d(pretrained=True, **kwargs): function ig_resnext101_32x32d (line 879) | def ig_resnext101_32x32d(pretrained=True, **kwargs): function ig_resnext101_32x48d (line 890) | def ig_resnext101_32x48d(pretrained=True, **kwargs): function ssl_resnet18 (line 901) | def ssl_resnet18(pretrained=True, **kwargs): function ssl_resnet50 (line 911) | def ssl_resnet50(pretrained=True, **kwargs): function ssl_resnext50_32x4d (line 921) | def ssl_resnext50_32x4d(pretrained=True, **kwargs): function ssl_resnext101_32x4d (line 931) | def ssl_resnext101_32x4d(pretrained=True, **kwargs): function ssl_resnext101_32x8d (line 941) | def ssl_resnext101_32x8d(pretrained=True, **kwargs): function ssl_resnext101_32x16d (line 951) | def ssl_resnext101_32x16d(pretrained=True, **kwargs): function swsl_resnet18 (line 961) | def swsl_resnet18(pretrained=True, **kwargs): function swsl_resnet50 (line 972) | def swsl_resnet50(pretrained=True, **kwargs): function swsl_resnext50_32x4d (line 983) | def swsl_resnext50_32x4d(pretrained=True, **kwargs): function swsl_resnext101_32x4d (line 994) | def swsl_resnext101_32x4d(pretrained=True, **kwargs): function swsl_resnext101_32x8d (line 1005) | def swsl_resnext101_32x8d(pretrained=True, **kwargs): function swsl_resnext101_32x16d (line 1016) | def swsl_resnext101_32x16d(pretrained=True, **kwargs): function ecaresnet18 (line 1027) | def ecaresnet18(pretrained=False, **kwargs): function ecaresnet50 (line 1035) | def ecaresnet50(pretrained=False, **kwargs): function ecaresnet50d (line 1043) | def ecaresnet50d(pretrained=False, **kwargs): function ecaresnet50d_pruned (line 1053) | def ecaresnet50d_pruned(pretrained=False, **kwargs): function ecaresnetlight (line 1064) | def ecaresnetlight(pretrained=False, **kwargs): function ecaresnet101d (line 1074) | def ecaresnet101d(pretrained=False, **kwargs): function ecaresnet101d_pruned (line 1084) | def ecaresnet101d_pruned(pretrained=False, **kwargs): function ecaresnext26tn_32x4d (line 1095) | def ecaresnext26tn_32x4d(pretrained=False, **kwargs): function resnetblur18 (line 1108) | def resnetblur18(pretrained=False, **kwargs): function resnetblur50 (line 1116) | def resnetblur50(pretrained=False, **kwargs): function seresnet18 (line 1124) | def seresnet18(pretrained=False, **kwargs): function seresnet34 (line 1130) | def seresnet34(pretrained=False, **kwargs): function seresnet50 (line 1136) | def seresnet50(pretrained=False, **kwargs): function seresnet50tn (line 1142) | def seresnet50tn(pretrained=False, **kwargs): function seresnet101 (line 1150) | def seresnet101(pretrained=False, **kwargs): function seresnet152 (line 1156) | def seresnet152(pretrained=False, **kwargs): function seresnext26_32x4d (line 1162) | def seresnext26_32x4d(pretrained=False, **kwargs): function seresnext26d_32x4d (line 1170) | def seresnext26d_32x4d(pretrained=False, **kwargs): function seresnext26t_32x4d (line 1182) | def seresnext26t_32x4d(pretrained=False, **kwargs): function seresnext26tn_32x4d (line 1194) | def seresnext26tn_32x4d(pretrained=False, **kwargs): function seresnext50_32x4d (line 1206) | def seresnext50_32x4d(pretrained=False, **kwargs): function seresnext101_32x4d (line 1214) | def seresnext101_32x4d(pretrained=False, **kwargs): function seresnext101_32x8d (line 1222) | def seresnext101_32x8d(pretrained=False, **kwargs): function senet154 (line 1230) | def senet154(pretrained=False, **kwargs): FILE: classification/model/utils.py class SmoothedValue (line 18) | class SmoothedValue(object): method __init__ (line 23) | def __init__(self, window_size=20, fmt=None): method update (line 31) | def update(self, value, n=1): method synchronize_between_processes (line 36) | def synchronize_between_processes(self): method median (line 50) | def median(self): method avg (line 55) | def avg(self): method global_avg (line 60) | def global_avg(self): method max (line 64) | def max(self): method value (line 68) | def value(self): method __str__ (line 71) | def __str__(self): class MetricLogger (line 80) | class MetricLogger(object): method __init__ (line 81) | def __init__(self, delimiter="\t"): method update (line 85) | def update(self, **kwargs): method __getattr__ (line 92) | def __getattr__(self, attr): method __str__ (line 100) | def __str__(self): method synchronize_between_processes (line 108) | def synchronize_between_processes(self): method add_meter (line 112) | def add_meter(self, name, meter): method log_every (line 115) | def log_every(self, iterable, print_freq, header=None): function _load_checkpoint_for_ema (line 162) | def _load_checkpoint_for_ema(model_ema, checkpoint): function setup_for_distributed (line 172) | 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): function merge_pre_bn (line 241) | def merge_pre_bn(module, pre_bn_1, pre_bn_2=None): function cal_flops_params_with_fvcore (line 283) | def cal_flops_params_with_fvcore(model, inputs): FILE: classification/model/vgg.py class VGG (line 33) | class VGG(nn.Module): method __init__ (line 34) | def __init__( method forward (line 63) | def forward(self, x: torch.Tensor) -> torch.Tensor: function make_layers (line 71) | def make_layers(cfg: List[Union[str, int]], batch_norm: bool = False) ->... function _vgg (line 98) | def _vgg(arch: str, cfg: str, batch_norm: bool, pretrained: bool, progre... function vgg11 (line 108) | def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any... function vgg11_bn (line 120) | def vgg11_bn(pretrained: bool = False, progress: bool = True, **kwargs: ... function vgg13 (line 132) | def vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any... function vgg13_bn (line 144) | def vgg13_bn(pretrained: bool = False, progress: bool = True, **kwargs: ... function vgg16 (line 156) | def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any... function vgg16_bn (line 168) | def vgg16_bn(pretrained: bool = False, progress: bool = True, **kwargs: ... function vgg19 (line 180) | def vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any... function vgg19_bn (line 192) | def vgg19_bn(pretrained: bool = False, progress: bool = True, **kwargs: ... class MVGGNet (line 203) | class MVGGNet(torch.nn.Module): method __init__ (line 204) | def __init__(self, num_classes=3): method forward (line 215) | def forward(self, x): FILE: classification/parse_test_res.py function compute_ci95 (line 60) | def compute_ci95(res): function parse_function (line 63) | def parse_function(*metrics, directory="", args=None, end_signal=None): function main (line 125) | def main(args, end_signal): FILE: classification/train.py function print_args (line 37) | def print_args(args, cfg): function reset_cfg (line 51) | def reset_cfg(cfg, args): function extend_cfg (line 83) | def extend_cfg(cfg): function setup_cfg (line 111) | def setup_cfg(args): function main (line 134) | def main(args): FILE: classification/trainers/MIM_linear.py function interpolate_pos_embed (line 28) | def interpolate_pos_embed(model, checkpoint_model): class CustomCLIP (line 51) | class CustomCLIP(nn.Module): method __init__ (line 53) | def __init__(self, cfg, classnames): method forward (line 123) | def forward(self, image): class MIM_linear (line 130) | class MIM_linear(TrainerX): method build_model (line 133) | def build_model(self): method forward_backward (line 164) | def forward_backward(self, batch): method parse_batch_train (line 182) | def parse_batch_train(self, batch): method load_model (line 189) | def load_model(self, directory, epoch=None): method test (line 231) | def test(self, split=None): method after_epoch (line 261) | def after_epoch(self): FILE: detection/demo/create_result_gif.py function parse_args (line 17) | def parse_args(): function _generate_batch_data (line 32) | def _generate_batch_data(sampler, batch_size): function create_gif (line 43) | def create_gif(frames, gif_name, duration=2): function create_frame_by_matplotlib (line 58) | def create_frame_by_matplotlib(image_dir, function main (line 156) | def main(): FILE: detection/demo/image_demo.py function parse_args (line 9) | def parse_args(): function main (line 32) | def main(args): function async_main (line 47) | async def async_main(args): FILE: detection/demo/video_demo.py function parse_args (line 10) | def parse_args(): function main (line 30) | def main(): FILE: detection/demo/webcam_demo.py function parse_args (line 10) | def parse_args(): function main (line 24) | def main(): FILE: detection/docs/en/conf.py function get_version (line 29) | def get_version(): function builder_inited_handler (line 108) | def builder_inited_handler(app): function setup (line 112) | def setup(app): FILE: detection/docs/zh_cn/conf.py function get_version (line 29) | def get_version(): function builder_inited_handler (line 110) | def builder_inited_handler(app): function setup (line 114) | def setup(app): FILE: detection/huge_image_infer.py class LargeImageDetector (line 10) | class LargeImageDetector: method __init__ (line 11) | def __init__(self, config_file, checkpoint_file, device='cuda:0'): method sliding_window_inference (line 24) | def sliding_window_inference(self, method nms (line 89) | def nms(self, detections, iou_threshold): method calculate_iou (line 122) | def calculate_iou(self, box1, box2): method visualize_results (line 158) | def visualize_results(self, image, results, output_path=None): function main (line 203) | def main(): FILE: detection/mmcv_custom/checkpoint.py function _get_mmcv_home (line 30) | def _get_mmcv_home(): function load_state_dict (line 41) | def load_state_dict(module, state_dict, strict=False, logger=None): function load_url_dist (line 109) | def load_url_dist(url, model_dir=None): function load_pavimodel_dist (line 123) | def load_pavimodel_dist(model_path, map_location=None): function load_fileclient_dist (line 151) | def load_fileclient_dist(filename, backend, map_location): function get_torchvision_models (line 172) | def get_torchvision_models(): function get_external_models (line 184) | def get_external_models(): function get_mmcls_models (line 198) | def get_mmcls_models(): function get_deprecated_model_names (line 205) | def get_deprecated_model_names(): function _process_mmcls_checkpoint (line 214) | def _process_mmcls_checkpoint(checkpoint): function _load_checkpoint (line 225) | def _load_checkpoint(filename, map_location=None): function load_checkpoint (line 286) | def load_checkpoint(model, function weights_to_cpu (line 359) | def weights_to_cpu(state_dict): function _save_to_state_dict (line 374) | def _save_to_state_dict(module, destination, prefix, keep_vars): function get_state_dict (line 394) | def get_state_dict(module, destination=None, prefix='', keep_vars=False): function save_checkpoint (line 438) | def save_checkpoint(model, filename, optimizer=None, meta=None): FILE: detection/mmcv_custom/hivit_layer_decay_optimizer_constructor.py function get_hivit_layer_id (line 19) | def get_hivit_layer_id(name, num_layers, main_block): class HiViTLayerDecayOptimizerConstructor (line 38) | class HiViTLayerDecayOptimizerConstructor(DefaultOptimizerConstructor): method _validate_cfg (line 39) | def _validate_cfg(self): method add_params (line 42) | def add_params(self, params, module, **kwargs): class HiViT2LayerDecayOptimizerConstructor (line 114) | class HiViT2LayerDecayOptimizerConstructor(DefaultOptimizerConstructor): method _validate_cfg (line 115) | def _validate_cfg(self): method add_params (line 118) | def add_params(self, params, module, **kwargs): FILE: detection/mmcv_custom/layer_decay_optimizer_constructor.py function get_num_layer_for_vit (line 17) | def get_num_layer_for_vit(var_name, num_max_layer): class LayerDecayOptimizerConstructor (line 30) | class LayerDecayOptimizerConstructor(DefaultOptimizerConstructor): method _validate_cfg (line 31) | def _validate_cfg(self): method add_params (line 50) | def add_params(self, params, module, prefix='', is_dcn_module=None): FILE: detection/mmcv_custom/runner/checkpoint.py function save_checkpoint (line 19) | def save_checkpoint(model, filename, optimizer=None, meta=None): FILE: detection/mmcv_custom/runner/epoch_based_runner.py class EpochBasedRunnerAmp (line 20) | class EpochBasedRunnerAmp(EpochBasedRunner): method save_checkpoint (line 26) | def save_checkpoint(self, method resume (line 70) | def resume(self, FILE: detection/mmdet/.mim/demo/create_result_gif.py function parse_args (line 17) | def parse_args(): function _generate_batch_data (line 32) | def _generate_batch_data(sampler, batch_size): function create_gif (line 43) | def create_gif(frames, gif_name, duration=2): function create_frame_by_matplotlib (line 58) | def create_frame_by_matplotlib(image_dir, function main (line 156) | def main(): FILE: detection/mmdet/.mim/demo/image_demo.py function parse_args (line 9) | def parse_args(): function main (line 32) | def main(args): function async_main (line 47) | async def async_main(args): FILE: detection/mmdet/.mim/demo/video_demo.py function parse_args (line 10) | def parse_args(): function main (line 30) | def main(): FILE: detection/mmdet/.mim/demo/webcam_demo.py function parse_args (line 10) | def parse_args(): function main (line 24) | def main(): FILE: detection/mmdet/.mim/tools/analysis_tools/analyze_logs.py function cal_train_time (line 11) | def cal_train_time(log_dicts, args): function plot_curve (line 38) | def plot_curve(log_dicts, args): function add_plot_parser (line 106) | def add_plot_parser(subparsers): function add_time_parser (line 144) | def add_time_parser(subparsers): function parse_args (line 160) | def parse_args(): function load_json_logs (line 170) | def load_json_logs(json_logs): function main (line 193) | def main(): FILE: detection/mmdet/.mim/tools/analysis_tools/analyze_results.py function bbox_map_eval (line 15) | def bbox_map_eval(det_result, annotation): class ResultVisualizer (line 50) | class ResultVisualizer: method __init__ (line 60) | def __init__(self, show=False, wait_time=0, score_thr=0): method _save_image_gts_results (line 65) | def _save_image_gts_results(self, dataset, results, mAPs, out_dir=None): method evaluate_and_show (line 97) | def evaluate_and_show(self, function parse_args (line 145) | def parse_args(): function main (line 184) | def main(): FILE: detection/mmdet/.mim/tools/analysis_tools/benchmark.py function parse_args (line 19) | def parse_args(): function measure_inference_speed (line 59) | def measure_inference_speed(cfg, checkpoint, max_iter, log_interval, function repeat_measure_inference_speed (line 136) | def repeat_measure_inference_speed(cfg, function main (line 170) | def main(): FILE: detection/mmdet/.mim/tools/analysis_tools/coco_error_analysis.py function makeplot (line 13) | def makeplot(rs, ps, outDir, class_name, iou_type): function autolabel (line 54) | def autolabel(ax, rects): function makebarplot (line 73) | def makebarplot(rs, ps, outDir, class_name, iou_type): function get_gt_area_group_numbers (line 108) | def get_gt_area_group_numbers(cocoEval): function make_gt_area_group_numbers_plot (line 123) | def make_gt_area_group_numbers_plot(cocoEval, outDir, verbose=True): function make_gt_area_histogram_plot (line 152) | def make_gt_area_histogram_plot(cocoEval, outDir): function analyze_individual_category (line 174) | def analyze_individual_category(k, function analyze_results (line 235) | def analyze_results(res_file, function main (line 308) | def main(): FILE: detection/mmdet/.mim/tools/analysis_tools/confusion_matrix.py function parse_args (line 16) | def parse_args(): function calculate_confusion_matrix (line 60) | def calculate_confusion_matrix(dataset, function analyze_per_img_dets (line 96) | def analyze_per_img_dets(confusion_matrix, function plot_confusion_matrix (line 146) | def plot_confusion_matrix(confusion_matrix, function main (line 230) | def main(): FILE: detection/mmdet/.mim/tools/analysis_tools/eval_metric.py function parse_args (line 11) | def parse_args(): function main (line 48) | def main(): FILE: detection/mmdet/.mim/tools/analysis_tools/get_flops.py function parse_args (line 16) | def parse_args(): function main (line 45) | def main(): FILE: detection/mmdet/.mim/tools/analysis_tools/optimize_anchors.py function parse_args (line 35) | def parse_args(): class BaseAnchorOptimizer (line 66) | class BaseAnchorOptimizer: method __init__ (line 80) | def __init__(self, method get_whs_and_shapes (line 97) | def get_whs_and_shapes(self): method get_zero_center_bbox_tensor (line 124) | def get_zero_center_bbox_tensor(self): method optimize (line 137) | def optimize(self): method save_result (line 140) | def save_result(self, anchors, path=None): class YOLOKMeansAnchorOptimizer (line 151) | class YOLOKMeansAnchorOptimizer(BaseAnchorOptimizer): method __init__ (line 160) | def __init__(self, num_anchors, iters, **kwargs): method optimize (line 166) | def optimize(self): method kmeans_anchors (line 170) | def kmeans_anchors(self): method kmeans_maximization (line 206) | def kmeans_maximization(self, bboxes, assignments, centers): method kmeans_expectation (line 215) | def kmeans_expectation(self, bboxes, assignments, centers): class YOLODEAnchorOptimizer (line 223) | class YOLODEAnchorOptimizer(BaseAnchorOptimizer): method __init__ (line 258) | def __init__(self, method optimize (line 278) | def optimize(self): method differential_evolution (line 282) | def differential_evolution(self): method avg_iou_cost (line 308) | def avg_iou_cost(anchor_params, bboxes): function main (line 322) | def main(): FILE: detection/mmdet/.mim/tools/analysis_tools/robustness_eval.py function print_coco_results (line 9) | def print_coco_results(results): function get_coco_style_results (line 35) | def get_coco_style_results(filename, function get_voc_style_results (line 112) | def get_voc_style_results(filename, prints='mPC', aggregate='benchmark'): function get_results (line 156) | def get_results(filename, function get_distortions_from_file (line 184) | def get_distortions_from_file(filename): function get_distortions_from_results (line 191) | def get_distortions_from_results(eval_output): function main (line 198) | def main(): FILE: detection/mmdet/.mim/tools/analysis_tools/test_robustness.py function coco_eval_with_return (line 24) | def coco_eval_with_return(result_files, function voc_eval_with_return (line 66) | def voc_eval_with_return(result_file, function parse_args (line 93) | def parse_args(): function main (line 181) | def main(): FILE: detection/mmdet/.mim/tools/dataset_converters/cityscapes.py function collect_files (line 12) | def collect_files(img_dir, gt_dir): function collect_annotations (line 29) | def collect_annotations(files, nproc=1): function load_img_info (line 40) | def load_img_info(files): function cvt_annotations (line 85) | def cvt_annotations(image_infos, out_json_name): function parse_args (line 114) | def parse_args(): function main (line 127) | def main(): FILE: detection/mmdet/.mim/tools/dataset_converters/images2coco.py function parse_args (line 9) | def parse_args(): function collect_image_infos (line 30) | def collect_image_infos(path, exclude_extensions=None): function cvt_to_coco_json (line 49) | def cvt_to_coco_json(img_infos, classes): function main (line 80) | def main(): FILE: detection/mmdet/.mim/tools/dataset_converters/pascal_voc.py function parse_xml (line 14) | def parse_xml(args): function cvt_annotations (line 68) | def cvt_annotations(devkit_path, years, split, out_file): function cvt_to_coco_json (line 96) | def cvt_to_coco_json(annotations): function parse_args (line 183) | def parse_args(): function main (line 197) | def main(): FILE: detection/mmdet/.mim/tools/deployment/mmdet2torchserve.py function mmdet2torchserve (line 15) | def mmdet2torchserve( function parse_args (line 70) | def parse_args(): FILE: detection/mmdet/.mim/tools/deployment/mmdet_handler.py class MMdetHandler (line 12) | class MMdetHandler(BaseHandler): method initialize (line 15) | def initialize(self, context): method preprocess (line 31) | def preprocess(self, data): method inference (line 43) | def inference(self, data, *args, **kwargs): method postprocess (line 47) | def postprocess(self, data): FILE: detection/mmdet/.mim/tools/deployment/onnx2tensorrt.py function get_GiB (line 19) | def get_GiB(x: int): function onnx2tensorrt (line 24) | def onnx2tensorrt(onnx_file, function parse_normalize_cfg (line 106) | def parse_normalize_cfg(test_pipeline): function parse_args (line 119) | def parse_args(): function parse_shape (line 208) | def parse_shape(shape): FILE: detection/mmdet/.mim/tools/deployment/pytorch2onnx.py function pytorch2onnx (line 16) | def pytorch2onnx(model, function parse_normalize_cfg (line 210) | def parse_normalize_cfg(test_pipeline): function parse_args (line 223) | def parse_args(): FILE: detection/mmdet/.mim/tools/deployment/test.py function parse_args (line 15) | def parse_args(): function main (line 67) | def main(): FILE: detection/mmdet/.mim/tools/deployment/test_torchserver.py function parse_args (line 10) | def parse_args(): function parse_result (line 28) | def parse_result(input, model_class): function main (line 42) | def main(args): FILE: detection/mmdet/.mim/tools/misc/browse_dataset.py function parse_args (line 17) | def parse_args(): function retrieve_data_cfg (line 51) | def retrieve_data_cfg(config_path, skip_type, cfg_options): function main (line 78) | def main(): FILE: detection/mmdet/.mim/tools/misc/download_dataset.py function parse_args (line 11) | def parse_args(): function download (line 35) | def download(url, dir, unzip=True, delete=False, threads=1): function main (line 65) | def main(): FILE: detection/mmdet/.mim/tools/misc/gen_coco_panoptic_test_info.py function parse_args (line 7) | def parse_args(): function main (line 17) | def main(): FILE: detection/mmdet/.mim/tools/misc/get_image_metas.py function parse_args (line 19) | def parse_args(): function get_metas_from_csv_style_ann_file (line 36) | def get_metas_from_csv_style_ann_file(ann_file): function get_metas_from_txt_style_ann_file (line 52) | def get_metas_from_txt_style_ann_file(ann_file): function get_image_metas (line 65) | def get_image_metas(data_info, img_prefix): function main (line 79) | def main(): FILE: detection/mmdet/.mim/tools/misc/print_config.py function parse_args (line 10) | def parse_args(): function main (line 43) | def main(): FILE: detection/mmdet/.mim/tools/misc/split_coco.py function parse_args (line 15) | def parse_args(): function split_coco (line 42) | def split_coco(data_root, out_dir, percent, fold): function multi_wrapper (line 100) | def multi_wrapper(args): FILE: detection/mmdet/.mim/tools/model_converters/detectron2pytorch.py function convert_bn (line 11) | def convert_bn(blobs, state_dict, caffe_name, torch_name, converted_names): function convert_conv_fc (line 24) | def convert_conv_fc(blobs, state_dict, caffe_name, torch_name, function convert (line 35) | def convert(src, dst, depth): function main (line 73) | def main(): FILE: detection/mmdet/.mim/tools/model_converters/publish_model.py function parse_args (line 8) | def parse_args(): function process_checkpoint (line 17) | def process_checkpoint(in_file, out_file): function main (line 37) | def main(): FILE: detection/mmdet/.mim/tools/model_converters/regnet2mmdet.py function convert_stem (line 8) | def convert_stem(model_key, model_weight, state_dict, converted_names): function convert_head (line 16) | def convert_head(model_key, model_weight, state_dict, converted_names): function convert_reslayer (line 23) | def convert_reslayer(model_key, model_weight, state_dict, converted_names): function convert (line 55) | def convert(src, dst): function main (line 81) | def main(): FILE: detection/mmdet/.mim/tools/model_converters/selfsup2mmdet.py function moco_convert (line 8) | def moco_convert(src, dst): function main (line 28) | def main(): FILE: detection/mmdet/.mim/tools/model_converters/upgrade_model_version.py function is_head (line 11) | def is_head(key): function parse_config (line 19) | def parse_config(config_strings): function reorder_cls_channel (line 46) | def reorder_cls_channel(val, num_classes=81): function truncate_cls_channel (line 68) | def truncate_cls_channel(val, num_classes=81): function truncate_reg_channel (line 90) | def truncate_reg_channel(val, num_classes=81): function convert (line 115) | def convert(in_file, out_file, num_classes): function main (line 196) | def main(): FILE: detection/mmdet/.mim/tools/model_converters/upgrade_ssd_version.py function parse_config (line 10) | def parse_config(config_strings): function convert (line 22) | def convert(in_file, out_file): function main (line 48) | def main(): FILE: detection/mmdet/.mim/tools/test.py function parse_args (line 23) | def parse_args(): function main (line 120) | def main(): FILE: detection/mmdet/.mim/tools/train.py function parse_args (line 24) | def parse_args(): function main (line 107) | def main(): FILE: detection/mmdet/__init__.py function digit_version (line 7) | def digit_version(version_str): FILE: detection/mmdet/apis/inference.py function init_detector (line 18) | def init_detector(config, checkpoint=None, device='cuda:0', cfg_options=... class LoadImage (line 60) | class LoadImage: method __call__ (line 66) | def __call__(self, results): function inference_detector (line 93) | def inference_detector(model, imgs): function async_inference_detector (line 159) | async def async_inference_detector(model, imgs): function show_result_pyplot (line 216) | def show_result_pyplot(model, FILE: detection/mmdet/apis/test.py function single_gpu_test (line 17) | def single_gpu_test(model, function multi_gpu_test (line 83) | def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False): function collect_results_cpu (line 138) | def collect_results_cpu(result_part, size, tmpdir=None): function collect_results_gpu (line 181) | def collect_results_gpu(result_part, size): FILE: detection/mmdet/apis/train.py function init_random_seed (line 24) | def init_random_seed(seed=None, device='cuda'): function set_random_seed (line 57) | def set_random_seed(seed, deterministic=False): function auto_scale_lr (line 76) | def auto_scale_lr(cfg, distributed, logger): function train_detector (line 122) | def train_detector(model, FILE: detection/mmdet/core/anchor/anchor_generator.py class AnchorGenerator (line 13) | class AnchorGenerator: method __init__ (line 61) | def __init__(self, method num_base_anchors (line 116) | def num_base_anchors(self): method num_base_priors (line 121) | def num_base_priors(self): method num_levels (line 127) | def num_levels(self): method gen_base_anchors (line 131) | def gen_base_anchors(self): method gen_single_level_base_anchors (line 151) | def gen_single_level_base_anchors(self, method _meshgrid (line 196) | def _meshgrid(self, x, y, row_major=True): method grid_priors (line 216) | def grid_priors(self, featmap_sizes, dtype=torch.float32, device='cuda'): method single_level_grid_priors (line 241) | def single_level_grid_priors(self, method sparse_priors (line 283) | def sparse_priors(self, method grid_anchors (line 318) | def grid_anchors(self, featmap_sizes, device='cuda'): method single_level_grid_anchors (line 347) | def single_level_grid_anchors(self, method valid_flags (line 392) | def valid_flags(self, featmap_sizes, pad_shape, device='cuda'): method single_level_valid_flags (line 419) | def single_level_valid_flags(self, method __repr__ (line 451) | def __repr__(self): class SSDAnchorGenerator (line 471) | class SSDAnchorGenerator(AnchorGenerator): method __init__ (line 492) | def __init__(self, method gen_base_anchors (line 571) | def gen_base_anchors(self): method __repr__ (line 592) | def __repr__(self): class LegacyAnchorGenerator (line 610) | class LegacyAnchorGenerator(AnchorGenerator): method gen_single_level_base_anchors (line 658) | def gen_single_level_base_anchors(self, class LegacySSDAnchorGenerator (line 709) | class LegacySSDAnchorGenerator(SSDAnchorGenerator, LegacyAnchorGenerator): method __init__ (line 716) | def __init__(self, class YOLOAnchorGenerator (line 734) | class YOLOAnchorGenerator(AnchorGenerator): method __init__ (line 744) | def __init__(self, strides, base_sizes): method num_levels (line 757) | def num_levels(self): method gen_base_anchors (line 761) | def gen_base_anchors(self): method gen_single_level_base_anchors (line 778) | def gen_single_level_base_anchors(self, base_sizes_per_level, center=N... method responsible_flags (line 806) | def responsible_flags(self, featmap_sizes, gt_bboxes, device='cuda'): method single_level_responsible_flags (line 831) | def single_level_responsible_flags(self, FILE: detection/mmdet/core/anchor/builder.py function build_prior_generator (line 11) | def build_prior_generator(cfg, default_args=None): function build_anchor_generator (line 15) | def build_anchor_generator(cfg, default_args=None): FILE: detection/mmdet/core/anchor/point_generator.py class PointGenerator (line 10) | class PointGenerator: method _meshgrid (line 12) | def _meshgrid(self, x, y, row_major=True): method grid_points (line 20) | def grid_points(self, featmap_size, stride=16, device='cuda'): method valid_flags (line 30) | def valid_flags(self, featmap_size, valid_size, device='cuda'): class MlvlPointGenerator (line 44) | class MlvlPointGenerator: method __init__ (line 55) | def __init__(self, strides, offset=0.5): method num_levels (line 60) | def num_levels(self): method num_base_priors (line 65) | def num_base_priors(self): method _meshgrid (line 70) | def _meshgrid(self, x, y, row_major=True): method grid_priors (line 80) | def grid_priors(self, method single_level_grid_priors (line 119) | def single_level_grid_priors(self, method valid_flags (line 177) | def valid_flags(self, featmap_sizes, pad_shape, device='cuda'): method single_level_valid_flags (line 205) | def single_level_valid_flags(self, method sparse_priors (line 234) | def sparse_priors(self, FILE: detection/mmdet/core/anchor/utils.py function images_to_levels (line 5) | def images_to_levels(target, num_levels): function anchor_inside_flags (line 21) | def anchor_inside_flags(flat_anchors, function calc_region (line 50) | def calc_region(bbox, ratio, featmap_size=None): FILE: detection/mmdet/core/bbox/assigners/approx_max_iou_assigner.py class ApproxMaxIoUAssigner (line 10) | class ApproxMaxIoUAssigner(MaxIoUAssigner): method __init__ (line 40) | def __init__(self, method assign (line 60) | def assign(self, FILE: detection/mmdet/core/bbox/assigners/assign_result.py class AssignResult (line 7) | class AssignResult(util_mixins.NiceRepr): method __init__ (line 43) | def __init__(self, num_gts, gt_inds, max_overlaps, labels=None): method num_preds (line 52) | def num_preds(self): method set_extra_property (line 56) | def set_extra_property(self, key, value): method get_extra_property (line 61) | def get_extra_property(self, key): method info (line 66) | def info(self): method __nice__ (line 78) | def __nice__(self): method random (line 98) | def random(cls, **kwargs): method add_gt_ (line 192) | def add_gt_(self, gt_labels): FILE: detection/mmdet/core/bbox/assigners/atss_assigner.py class ATSSAssigner (line 11) | class ATSSAssigner(BaseAssigner): method __init__ (line 24) | def __init__(self, method assign (line 34) | def assign(self, FILE: detection/mmdet/core/bbox/assigners/base_assigner.py class BaseAssigner (line 5) | class BaseAssigner(metaclass=ABCMeta): method assign (line 9) | def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=N... FILE: detection/mmdet/core/bbox/assigners/center_region_assigner.py function scale_boxes (line 10) | def scale_boxes(bboxes, scale): function is_located_in (line 37) | def is_located_in(points, bboxes): function bboxes_area (line 55) | def bboxes_area(bboxes): class CenterRegionAssigner (line 72) | class CenterRegionAssigner(BaseAssigner): method __init__ (line 94) | def __init__(self, method get_gt_priorities (line 108) | def get_gt_priorities(self, gt_bboxes): method assign (line 126) | def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=N... method assign_one_hot_gt_indices (line 257) | def assign_one_hot_gt_indices(self, FILE: detection/mmdet/core/bbox/assigners/grid_assigner.py class GridAssigner (line 11) | class GridAssigner(BaseAssigner): method __init__ (line 31) | def __init__(self, method assign (line 43) | def assign(self, bboxes, box_responsible_flags, gt_bboxes, gt_labels=N... FILE: detection/mmdet/core/bbox/assigners/hungarian_assigner.py class HungarianAssigner (line 17) | class HungarianAssigner(BaseAssigner): method __init__ (line 45) | def __init__(self, method assign (line 53) | def assign(self, FILE: detection/mmdet/core/bbox/assigners/mask_hungarian_assigner.py class MaskHungarianAssigner (line 16) | class MaskHungarianAssigner(BaseAssigner): method __init__ (line 37) | def __init__(self, method assign (line 46) | def assign(self, FILE: detection/mmdet/core/bbox/assigners/max_iou_assigner.py class MaxIoUAssigner (line 11) | class MaxIoUAssigner(BaseAssigner): method __init__ (line 46) | def __init__(self, method assign (line 66) | def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=N... method assign_wrt_overlaps (line 133) | def assign_wrt_overlaps(self, overlaps, gt_labels=None): FILE: detection/mmdet/core/bbox/assigners/point_assigner.py class PointAssigner (line 10) | class PointAssigner(BaseAssigner): method __init__ (line 20) | def __init__(self, scale=4, pos_num=3): method assign (line 24) | def assign(self, points, gt_bboxes, gt_bboxes_ignore=None, gt_labels=N... FILE: detection/mmdet/core/bbox/assigners/region_assigner.py function calc_region (line 10) | def calc_region(bbox, ratio, stride, featmap_size=None): function anchor_ctr_inside_region_flags (line 27) | def anchor_ctr_inside_region_flags(anchors, stride, region): class RegionAssigner (line 38) | class RegionAssigner(BaseAssigner): method __init__ (line 54) | def __init__(self, center_ratio=0.2, ignore_ratio=0.5): method assign (line 58) | def assign(self, FILE: detection/mmdet/core/bbox/assigners/sim_ota_assigner.py class SimOTAAssigner (line 14) | class SimOTAAssigner(BaseAssigner): method __init__ (line 28) | def __init__(self, method assign (line 38) | def assign(self, method _assign (line 95) | def _assign(self, method get_in_gt_and_in_center_info (line 186) | def get_in_gt_and_in_center_info(self, priors, gt_bboxes): method dynamic_k_matching (line 230) | def dynamic_k_matching(self, cost, pairwise_ious, num_gt, valid_mask): FILE: detection/mmdet/core/bbox/assigners/task_aligned_assigner.py class TaskAlignedAssigner (line 13) | class TaskAlignedAssigner(BaseAssigner): method __init__ (line 31) | def __init__(self, topk, iou_calculator=dict(type='BboxOverlaps2D')): method assign (line 36) | def assign(self, FILE: detection/mmdet/core/bbox/assigners/uniform_assigner.py class UniformAssigner (line 12) | class UniformAssigner(BaseAssigner): method __init__ (line 25) | def __init__(self, method assign (line 35) | def assign(self, FILE: detection/mmdet/core/bbox/builder.py function build_assigner (line 9) | def build_assigner(cfg, **default_args): function build_sampler (line 14) | def build_sampler(cfg, **default_args): function build_bbox_coder (line 19) | def build_bbox_coder(cfg, **default_args): FILE: detection/mmdet/core/bbox/coder/base_bbox_coder.py class BaseBBoxCoder (line 5) | class BaseBBoxCoder(metaclass=ABCMeta): method __init__ (line 8) | def __init__(self, **kwargs): method encode (line 12) | def encode(self, bboxes, gt_bboxes): method decode (line 16) | def decode(self, bboxes, bboxes_pred): FILE: detection/mmdet/core/bbox/coder/bucketing_bbox_coder.py class BucketingBBoxCoder (line 13) | class BucketingBBoxCoder(BaseBBoxCoder): method __init__ (line 35) | def __init__(self, method encode (line 50) | def encode(self, bboxes, gt_bboxes): method decode (line 72) | def decode(self, bboxes, pred_bboxes, max_shape=None): function generat_buckets (line 96) | def generat_buckets(proposals, num_buckets, scale_factor=1.0): function bbox2bucket (line 145) | def bbox2bucket(proposals, function bucket2bbox (line 269) | def bucket2bbox(proposals, FILE: detection/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py class DeltaXYWHBBoxCoder (line 13) | class DeltaXYWHBBoxCoder(BaseBBoxCoder): method __init__ (line 34) | def __init__(self, method encode (line 47) | def encode(self, bboxes, gt_bboxes): method decode (line 65) | def decode(self, function bbox2delta (line 118) | def bbox2delta(proposals, gt, means=(0., 0., 0., 0.), stds=(1., 1., 1., ... function delta2bbox (line 164) | def delta2bbox(rois, function onnx_delta2bbox (line 263) | def onnx_delta2bbox(rois, FILE: detection/mmdet/core/bbox/coder/distance_point_bbox_coder.py class DistancePointBBoxCoder (line 8) | class DistancePointBBoxCoder(BaseBBoxCoder): method __init__ (line 19) | def __init__(self, clip_border=True): method encode (line 23) | def encode(self, points, gt_bboxes, max_dis=None, eps=0.1): method decode (line 41) | def decode(self, points, pred_bboxes, max_shape=None): FILE: detection/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py class LegacyDeltaXYWHBBoxCoder (line 11) | class LegacyDeltaXYWHBBoxCoder(BaseBBoxCoder): method __init__ (line 34) | def __init__(self, method encode (line 41) | def encode(self, bboxes, gt_bboxes): method decode (line 59) | def decode(self, function legacy_bbox2delta (line 85) | def legacy_bbox2delta(proposals, function legacy_delta2bbox (line 134) | def legacy_delta2bbox(rois, FILE: detection/mmdet/core/bbox/coder/pseudo_bbox_coder.py class PseudoBBoxCoder (line 7) | class PseudoBBoxCoder(BaseBBoxCoder): method __init__ (line 10) | def __init__(self, **kwargs): method encode (line 13) | def encode(self, bboxes, gt_bboxes): method decode (line 17) | def decode(self, bboxes, pred_bboxes): FILE: detection/mmdet/core/bbox/coder/tblr_bbox_coder.py class TBLRBBoxCoder (line 10) | class TBLRBBoxCoder(BaseBBoxCoder): method __init__ (line 26) | def __init__(self, normalizer=4.0, clip_border=True): method encode (line 31) | def encode(self, bboxes, gt_bboxes): method decode (line 50) | def decode(self, bboxes, pred_bboxes, max_shape=None): function bboxes2tblr (line 77) | def bboxes2tblr(priors, gts, normalizer=4.0, normalize_by_wh=True): function tblr2bboxes (line 124) | def tblr2bboxes(priors, FILE: detection/mmdet/core/bbox/coder/yolo_bbox_coder.py class YOLOBBoxCoder (line 10) | class YOLOBBoxCoder(BaseBBoxCoder): method __init__ (line 22) | def __init__(self, eps=1e-6): method encode (line 27) | def encode(self, bboxes, gt_bboxes, stride): method decode (line 62) | def decode(self, bboxes, pred_bboxes, stride): FILE: detection/mmdet/core/bbox/demodata.py function random_boxes (line 8) | def random_boxes(num=1, scale=1, rng=None): FILE: detection/mmdet/core/bbox/iou_calculators/builder.py function build_iou_calculator (line 7) | def build_iou_calculator(cfg, default_args=None): FILE: detection/mmdet/core/bbox/iou_calculators/iou2d_calculator.py function cast_tensor_type (line 7) | def cast_tensor_type(x, scale=1., dtype=None): function fp16_clamp (line 14) | def fp16_clamp(x, min=None, max=None): class BboxOverlaps2D (line 23) | class BboxOverlaps2D: method __init__ (line 26) | def __init__(self, scale=1., dtype=None): method __call__ (line 30) | def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False): method __repr__ (line 68) | def __repr__(self): function bbox_overlaps (line 75) | def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False, eps=1e... FILE: detection/mmdet/core/bbox/match_costs/builder.py function build_match_cost (line 7) | def build_match_cost(cfg, default_args=None): FILE: detection/mmdet/core/bbox/match_costs/match_cost.py class BBoxL1Cost (line 11) | class BBoxL1Cost: method __init__ (line 29) | def __init__(self, weight=1., box_format='xyxy'): method __call__ (line 34) | def __call__(self, bbox_pred, gt_bboxes): class FocalLossCost (line 55) | class FocalLossCost: method __init__ (line 80) | def __init__(self, method _focal_loss_cost (line 92) | def _focal_loss_cost(self, cls_pred, gt_labels): method _mask_focal_loss_cost (line 111) | def _mask_focal_loss_cost(self, cls_pred, gt_labels): method __call__ (line 136) | def __call__(self, cls_pred, gt_labels): class ClassificationCost (line 153) | class ClassificationCost: method __init__ (line 174) | def __init__(self, weight=1.): method __call__ (line 177) | def __call__(self, cls_pred, gt_labels): class IoUCost (line 197) | class IoUCost: method __init__ (line 215) | def __init__(self, iou_mode='giou', weight=1.): method __call__ (line 219) | def __call__(self, bboxes, gt_bboxes): class DiceCost (line 239) | class DiceCost: method __init__ (line 254) | def __init__(self, weight=1., pred_act=False, eps=1e-3, naive_dice=True): method binary_mask_dice_loss (line 260) | def binary_mask_dice_loss(self, mask_preds, gt_masks): method __call__ (line 283) | def __call__(self, mask_preds, gt_masks): class CrossEntropyLossCost (line 299) | class CrossEntropyLossCost: method __init__ (line 315) | def __init__(self, weight=1., use_sigmoid=True): method _binary_cross_entropy (line 320) | def _binary_cross_entropy(self, cls_pred, gt_labels): method __call__ (line 344) | def __call__(self, cls_pred, gt_labels): FILE: detection/mmdet/core/bbox/samplers/base_sampler.py class BaseSampler (line 9) | class BaseSampler(metaclass=ABCMeta): method __init__ (line 12) | def __init__(self, method _sample_pos (line 26) | def _sample_pos(self, assign_result, num_expected, **kwargs): method _sample_neg (line 31) | def _sample_neg(self, assign_result, num_expected, **kwargs): method sample (line 35) | def sample(self, FILE: detection/mmdet/core/bbox/samplers/combined_sampler.py class CombinedSampler (line 7) | class CombinedSampler(BaseSampler): method __init__ (line 10) | def __init__(self, pos_sampler, neg_sampler, **kwargs): method _sample_pos (line 15) | def _sample_pos(self, **kwargs): method _sample_neg (line 19) | def _sample_neg(self, **kwargs): FILE: detection/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py class InstanceBalancedPosSampler (line 10) | class InstanceBalancedPosSampler(RandomSampler): method _sample_pos (line 14) | def _sample_pos(self, assign_result, num_expected, **kwargs): FILE: detection/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py class IoUBalancedNegSampler (line 10) | class IoUBalancedNegSampler(RandomSampler): method __init__ (line 30) | def __init__(self, method sample_via_interval (line 47) | def sample_via_interval(self, max_overlaps, full_set, num_expected): method _sample_neg (line 89) | def _sample_neg(self, assign_result, num_expected, **kwargs): FILE: detection/mmdet/core/bbox/samplers/mask_pseudo_sampler.py class MaskPseudoSampler (line 13) | class MaskPseudoSampler(BaseSampler): method __init__ (line 16) | def __init__(self, **kwargs): method _sample_pos (line 19) | def _sample_pos(self, **kwargs): method _sample_neg (line 23) | def _sample_neg(self, **kwargs): method sample (line 27) | def sample(self, assign_result, masks, gt_masks, **kwargs): FILE: detection/mmdet/core/bbox/samplers/mask_sampling_result.py class MaskSamplingResult (line 10) | class MaskSamplingResult(SamplingResult): method __init__ (line 13) | def __init__(self, pos_inds, neg_inds, masks, gt_masks, assign_result, method masks (line 37) | def masks(self): method __nice__ (line 41) | def __nice__(self): method info (line 50) | def info(self): FILE: detection/mmdet/core/bbox/samplers/ohem_sampler.py class OHEMSampler (line 10) | class OHEMSampler(BaseSampler): method __init__ (line 16) | def __init__(self, method hard_mining (line 34) | def hard_mining(self, inds, num_expected, bboxes, labels, feats): method _sample_pos (line 55) | def _sample_pos(self, method _sample_neg (line 83) | def _sample_neg(self, FILE: detection/mmdet/core/bbox/samplers/pseudo_sampler.py class PseudoSampler (line 10) | class PseudoSampler(BaseSampler): method __init__ (line 13) | def __init__(self, **kwargs): method _sample_pos (line 16) | def _sample_pos(self, **kwargs): method _sample_neg (line 20) | def _sample_neg(self, **kwargs): method sample (line 24) | def sample(self, assign_result, bboxes, gt_bboxes, *args, **kwargs): FILE: detection/mmdet/core/bbox/samplers/random_sampler.py class RandomSampler (line 9) | class RandomSampler(BaseSampler): method __init__ (line 21) | def __init__(self, method random_choice (line 32) | def random_choice(self, gallery, num): method _sample_pos (line 64) | def _sample_pos(self, assign_result, num_expected, **kwargs): method _sample_neg (line 74) | def _sample_neg(self, assign_result, num_expected, **kwargs): FILE: detection/mmdet/core/bbox/samplers/sampling_result.py class SamplingResult (line 7) | class SamplingResult(util_mixins.NiceRepr): method __init__ (line 26) | def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result, method bboxes (line 53) | def bboxes(self): method to (line 57) | def to(self, device): method __nice__ (line 72) | def __nice__(self): method info (line 81) | def info(self): method random (line 94) | def random(cls, rng=None, **kwargs): FILE: detection/mmdet/core/bbox/samplers/score_hlr_sampler.py class ScoreHLRSampler (line 12) | class ScoreHLRSampler(BaseSampler): method __init__ (line 33) | def __init__(self, method random_choice (line 63) | def random_choice(gallery, num): method _sample_pos (line 92) | def _sample_pos(self, assign_result, num_expected, **kwargs): method _sample_neg (line 100) | def _sample_neg(self, method sample (line 216) | def sample(self, FILE: detection/mmdet/core/bbox/transforms.py function find_inside_bboxes (line 6) | def find_inside_bboxes(bboxes, img_h, img_w): function bbox_flip (line 22) | def bbox_flip(bboxes, img_shape, direction='horizontal'): function bbox_mapping (line 51) | def bbox_mapping(bboxes, function bbox_mapping_back (line 63) | def bbox_mapping_back(bboxes, function bbox2roi (line 75) | def bbox2roi(bbox_list): function roi2bbox (line 97) | def roi2bbox(rois): function bbox2result (line 116) | def bbox2result(bboxes, labels, num_classes): function distance2bbox (line 136) | def distance2bbox(points, distance, max_shape=None): function bbox2distance (line 189) | def bbox2distance(points, bbox, max_dis=None, eps=0.1): function bbox_rescale (line 213) | def bbox_rescale(bboxes, scale_factor=1.0): function bbox_cxcywh_to_xyxy (line 245) | def bbox_cxcywh_to_xyxy(bbox): function bbox_xyxy_to_cxcywh (line 259) | def bbox_xyxy_to_cxcywh(bbox): FILE: detection/mmdet/core/data_structures/general_data.py class GeneralData (line 10) | class GeneralData(NiceRepr): method __init__ (line 87) | def __init__(self, meta_info=None, data=None): method set_meta_info (line 97) | def set_meta_info(self, meta_info): method set_data (line 129) | def set_data(self, data): method new (line 141) | def new(self, meta_info=None, data=None): method keys (line 159) | def keys(self): method meta_info_keys (line 166) | def meta_info_keys(self): method values (line 173) | def values(self): method meta_info_values (line 180) | def meta_info_values(self): method items (line 187) | def items(self): method meta_info_items (line 191) | def meta_info_items(self): method __setattr__ (line 195) | def __setattr__(self, name, val): method __delattr__ (line 211) | def __delattr__(self, item): method __getitem__ (line 228) | def __getitem__(self, name): method get (line 231) | def get(self, *args): method pop (line 235) | def pop(self, *args): method __contains__ (line 252) | def __contains__(self, item): method to (line 257) | def to(self, *args, **kwargs): method cpu (line 267) | def cpu(self): method mlu (line 277) | def mlu(self): method cuda (line 287) | def cuda(self): method detach (line 297) | def detach(self): method numpy (line 307) | def numpy(self): method __nice__ (line 316) | def __nice__(self): FILE: detection/mmdet/core/data_structures/instance_data.py class InstanceData (line 10) | class InstanceData(GeneralData): method __setattr__ (line 66) | def __setattr__(self, name, value): method __getitem__ (line 90) | def __getitem__(self, item): method cat (line 151) | def cat(instances_list): method __len__ (line 183) | def __len__(self): FILE: detection/mmdet/core/evaluation/bbox_overlaps.py function bbox_overlaps (line 5) | def bbox_overlaps(bboxes1, FILE: detection/mmdet/core/evaluation/class_names.py function wider_face_classes (line 5) | def wider_face_classes(): function voc_classes (line 9) | def voc_classes(): function imagenet_det_classes (line 17) | def imagenet_det_classes(): function imagenet_vid_classes (line 58) | def imagenet_vid_classes(): function coco_classes (line 68) | def coco_classes(): function cityscapes_classes (line 86) | def cityscapes_classes(): function oid_challenge_classes (line 93) | def oid_challenge_classes(): function oid_v6_classes (line 188) | def oid_v6_classes(): function get_classes (line 318) | def get_classes(dataset): FILE: detection/mmdet/core/evaluation/eval_hooks.py function _calc_dynamic_intervals (line 12) | def _calc_dynamic_intervals(start_interval, dynamic_interval_list): class EvalHook (line 24) | class EvalHook(BaseEvalHook): method __init__ (line 26) | def __init__(self, *args, dynamic_intervals=None, **kwargs): method _decide_interval (line 34) | def _decide_interval(self, runner): method before_train_epoch (line 41) | def before_train_epoch(self, runner): method before_train_iter (line 46) | def before_train_iter(self, runner): method _do_evaluate (line 50) | def _do_evaluate(self, runner): class DistEvalHook (line 68) | class DistEvalHook(BaseDistEvalHook): method __init__ (line 70) | def __init__(self, *args, dynamic_intervals=None, **kwargs): method _decide_interval (line 78) | def _decide_interval(self, runner): method before_train_epoch (line 85) | def before_train_epoch(self, runner): method before_train_iter (line 90) | def before_train_iter(self, runner): method _do_evaluate (line 94) | def _do_evaluate(self, runner): FILE: detection/mmdet/core/evaluation/mean_ap.py function average_precision (line 13) | def average_precision(recalls, precisions, mode='area'): function tpfp_imagenet (line 60) | def tpfp_imagenet(det_bboxes, function tpfp_default (line 168) | def tpfp_default(det_bboxes, function tpfp_openimages (line 270) | def tpfp_openimages(det_bboxes, function get_cls_results (line 474) | def get_cls_results(det_results, annotations, class_id): function get_cls_group_ofs (line 501) | def get_cls_group_ofs(annotations, class_id): function eval_map (line 522) | def eval_map(det_results, function print_map_summary (line 688) | def print_map_summary(mean_ap, FILE: detection/mmdet/core/evaluation/recall.py function _recalls (line 11) | def _recalls(all_ious, proposal_nums, thrs): function set_recall_param (line 44) | def set_recall_param(proposal_nums, iou_thrs): function eval_recalls (line 65) | def eval_recalls(gts, function print_recall_summary (line 117) | def print_recall_summary(recalls, function plot_num_recall (line 150) | def plot_num_recall(recalls, proposal_nums): function plot_iou_recall (line 175) | def plot_iou_recall(recalls, iou_thrs): FILE: detection/mmdet/core/export/model_wrappers.py class DeployBaseDetector (line 12) | class DeployBaseDetector(BaseDetector): method __init__ (line 15) | def __init__(self, class_names, device_id): method simple_test (line 20) | def simple_test(self, img, img_metas, **kwargs): method aug_test (line 23) | def aug_test(self, imgs, img_metas, **kwargs): method extract_feat (line 26) | def extract_feat(self, imgs): method forward_train (line 29) | def forward_train(self, imgs, img_metas, **kwargs): method val_step (line 32) | def val_step(self, data, optimizer): method train_step (line 35) | def train_step(self, data, optimizer): method forward_test (line 38) | def forward_test(self, *, img, img_metas, **kwargs): method async_simple_test (line 41) | def async_simple_test(self, img, img_metas, **kwargs): method forward (line 44) | def forward(self, img, img_metas, return_loss=True, **kwargs): class ONNXRuntimeDetector (line 96) | class ONNXRuntimeDetector(DeployBaseDetector): method __init__ (line 99) | def __init__(self, onnx_file, class_names, device_id): method forward_test (line 130) | def forward_test(self, imgs, img_metas, **kwargs): class TensorRTDetector (line 152) | class TensorRTDetector(DeployBaseDetector): method __init__ (line 155) | def __init__(self, engine_file, class_names, device_id, output_names=N... method forward_test (line 177) | def forward_test(self, imgs, img_metas, **kwargs): FILE: detection/mmdet/core/export/onnx_helper.py function dynamic_clip_for_onnx (line 7) | def dynamic_clip_for_onnx(x1, y1, x2, y2, max_shape): function get_k_for_topk (line 46) | def get_k_for_topk(k, size): function add_dummy_nms_for_onnx (line 82) | def add_dummy_nms_for_onnx(boxes, class DummyONNXNMSop (line 201) | class DummyONNXNMSop(torch.autograd.Function): method forward (line 208) | def forward(ctx, boxes, scores, max_output_boxes_per_class, iou_thresh... method symbolic (line 214) | def symbolic(g, boxes, scores, max_output_boxes_per_class, iou_threshold, FILE: detection/mmdet/core/export/pytorch2onnx.py function generate_inputs_and_wrap_model (line 10) | def generate_inputs_and_wrap_model(config_path, function build_model_from_cfg (line 65) | def build_model_from_cfg(config_path, checkpoint_path, cfg_options=None): function preprocess_example_input (line 102) | def preprocess_example_input(input_config): FILE: detection/mmdet/core/hook/checkloss_hook.py class CheckInvalidLossHook (line 7) | class CheckInvalidLossHook(Hook): method __init__ (line 18) | def __init__(self, interval=50): method after_train_iter (line 21) | def after_train_iter(self, runner): FILE: detection/mmdet/core/hook/ema.py class BaseEMAHook (line 8) | class BaseEMAHook(Hook): method __init__ (line 32) | def __init__(self, method before_run (line 45) | def before_run(self, runner): method get_momentum (line 67) | def get_momentum(self, runner): method after_train_iter (line 71) | def after_train_iter(self, runner): method after_train_epoch (line 84) | def after_train_epoch(self, runner): method before_train_epoch (line 89) | def before_train_epoch(self, runner): method _swap_ema_parameters (line 94) | def _swap_ema_parameters(self): class ExpMomentumEMAHook (line 104) | class ExpMomentumEMAHook(BaseEMAHook): method __init__ (line 112) | def __init__(self, total_iter=2000, **kwargs): class LinearMomentumEMAHook (line 119) | class LinearMomentumEMAHook(BaseEMAHook): method __init__ (line 127) | def __init__(self, warm_up=100, **kwargs): FILE: detection/mmdet/core/hook/memory_profiler_hook.py class MemoryProfilerHook (line 6) | class MemoryProfilerHook(Hook): method __init__ (line 15) | def __init__(self, interval=50): method after_iter (line 34) | def after_iter(self, runner): FILE: detection/mmdet/core/hook/set_epoch_info_hook.py class SetEpochInfoHook (line 7) | class SetEpochInfoHook(Hook): method before_train_epoch (line 10) | def before_train_epoch(self, runner): FILE: detection/mmdet/core/hook/sync_norm_hook.py function get_norm_states (line 11) | def get_norm_states(module): class SyncNormHook (line 21) | class SyncNormHook(Hook): method __init__ (line 30) | def __init__(self, num_last_epochs=15, interval=1): method before_train_epoch (line 34) | def before_train_epoch(self, runner): method after_train_epoch (line 40) | def after_train_epoch(self, runner): FILE: detection/mmdet/core/hook/sync_random_size_hook.py class SyncRandomSizeHook (line 12) | class SyncRandomSizeHook(Hook): method __init__ (line 33) | def __init__(self, method after_train_epoch (line 52) | def after_train_epoch(self, runner): FILE: detection/mmdet/core/hook/yolox_lrupdater_hook.py class YOLOXLrUpdaterHook (line 8) | class YOLOXLrUpdaterHook(CosineAnnealingLrUpdaterHook): method __init__ (line 23) | def __init__(self, num_last_epochs, **kwargs): method get_warmup_lr (line 27) | def get_warmup_lr(self, cur_iters): method get_lr (line 44) | def get_lr(self, runner, base_lr): FILE: detection/mmdet/core/hook/yolox_mode_switch_hook.py class YOLOXModeSwitchHook (line 7) | class YOLOXModeSwitchHook(Hook): method __init__ (line 21) | def __init__(self, method before_train_epoch (line 28) | def before_train_epoch(self, runner): FILE: detection/mmdet/core/mask/mask_target.py function mask_target (line 7) | def mask_target(pos_proposals_list, pos_assigned_gt_inds_list, gt_masks_... function mask_target_single (line 67) | def mask_target_single(pos_proposals, pos_assigned_gt_inds, gt_masks, cfg): FILE: detection/mmdet/core/mask/structures.py class BaseInstanceMasks (line 12) | class BaseInstanceMasks(metaclass=ABCMeta): method rescale (line 16) | def rescale(self, scale, interpolation='nearest'): method resize (line 29) | def resize(self, out_shape, interpolation='nearest'): method flip (line 41) | def flip(self, flip_direction='horizontal'): method pad (line 52) | def pad(self, out_shape, pad_val): method crop (line 64) | def crop(self, bbox): method crop_and_resize (line 75) | def crop_and_resize(self, method expand (line 104) | def expand(self, expanded_h, expanded_w, top, left): method areas (line 109) | def areas(self): method to_ndarray (line 113) | def to_ndarray(self): method to_tensor (line 121) | def to_tensor(self, dtype, device): method translate (line 133) | def translate(self, method shear (line 153) | def shear(self, method rotate (line 175) | def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0): class BitmapMasks (line 193) | class BitmapMasks(BaseInstanceMasks): method __init__ (line 222) | def __init__(self, masks, height, width): method __getitem__ (line 239) | def __getitem__(self, index): method __iter__ (line 251) | def __iter__(self): method __repr__ (line 254) | def __repr__(self): method __len__ (line 261) | def __len__(self): method rescale (line 265) | def rescale(self, scale, interpolation='nearest'): method resize (line 278) | def resize(self, out_shape, interpolation='nearest'): method flip (line 290) | def flip(self, flip_direction='horizontal'): method pad (line 303) | def pad(self, out_shape, pad_val=0): method crop (line 314) | def crop(self, bbox): method crop_and_resize (line 333) | def crop_and_resize(self, method expand (line 369) | def expand(self, expanded_h, expanded_w, top, left): method translate (line 381) | def translate(self, method shear (line 431) | def shear(self, method rotate (line 466) | def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0): method areas (line 499) | def areas(self): method to_ndarray (line 503) | def to_ndarray(self): method to_tensor (line 507) | def to_tensor(self, dtype, device): method random (line 512) | def random(cls, method get_bboxes (line 532) | def get_bboxes(self): class PolygonMasks (line 548) | class PolygonMasks(BaseInstanceMasks): method __init__ (line 588) | def __init__(self, masks, height, width): method __getitem__ (line 598) | def __getitem__(self, index): method __iter__ (line 621) | def __iter__(self): method __repr__ (line 624) | def __repr__(self): method __len__ (line 631) | def __len__(self): method rescale (line 635) | def rescale(self, scale, interpolation=None): method resize (line 644) | def resize(self, out_shape, interpolation=None): method flip (line 663) | def flip(self, flip_direction='horizontal'): method crop (line 687) | def crop(self, bbox): method pad (line 716) | def pad(self, out_shape, pad_val=0): method expand (line 720) | def expand(self, *args, **kwargs): method crop_and_resize (line 724) | def crop_and_resize(self, method translate (line 765) | def translate(self, method shear (line 799) | def shear(self, method rotate (line 831) | def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0): method to_bitmap (line 860) | def to_bitmap(self): method areas (line 866) | def areas(self): method _polygon_area (line 884) | def _polygon_area(self, x, y): method to_ndarray (line 900) | def to_ndarray(self): method to_tensor (line 910) | def to_tensor(self, dtype, device): method random (line 920) | def random(cls, method get_bboxes (line 1039) | def get_bboxes(self): function polygon_to_bitmap (line 1058) | def polygon_to_bitmap(polygons, height, width): function bitmap_to_polygon (line 1075) | def bitmap_to_polygon(bitmap): FILE: detection/mmdet/core/mask/utils.py function split_combined_polys (line 8) | def split_combined_polys(polys, poly_lens, polys_per_mask): function encode_mask_results (line 38) | def encode_mask_results(mask_results): function mask2bbox (line 68) | def mask2bbox(masks): FILE: detection/mmdet/core/post_processing/bbox_nms.py function multiclass_nms (line 8) | def multiclass_nms(multi_bboxes, function fast_nms (line 98) | def fast_nms(multi_bboxes, FILE: detection/mmdet/core/post_processing/matrix_nms.py function mask_matrix_nms (line 5) | def mask_matrix_nms(masks, FILE: detection/mmdet/core/post_processing/merge_augs.py function merge_aug_proposals (line 13) | def merge_aug_proposals(aug_proposals, img_metas, cfg): function merge_aug_bboxes (line 84) | def merge_aug_bboxes(aug_bboxes, aug_scores, img_metas, rcnn_test_cfg): function merge_aug_scores (line 113) | def merge_aug_scores(aug_scores): function merge_aug_masks (line 121) | def merge_aug_masks(aug_masks, img_metas, rcnn_test_cfg, weights=None): FILE: detection/mmdet/core/utils/dist_utils.py function _allreduce_coalesced (line 15) | def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1): function allreduce_grads (line 37) | def allreduce_grads(params, coalesce=True, bucket_size_mb=-1): class DistOptimizerHook (line 59) | class DistOptimizerHook(OptimizerHook): method __init__ (line 62) | def __init__(self, *args, **kwargs): function reduce_mean (line 68) | def reduce_mean(tensor): function obj2tensor (line 77) | def obj2tensor(pyobj, device='cuda'): function tensor2obj (line 83) | def tensor2obj(tensor): function _get_global_gloo_group (line 89) | def _get_global_gloo_group(): function all_reduce_dict (line 98) | def all_reduce_dict(py_dict, op='sum', group=None, to_float=True): function sync_random_seed (line 157) | def sync_random_seed(seed=None, device='cuda'): FILE: detection/mmdet/core/utils/misc.py function multi_apply (line 11) | def multi_apply(func, *args, **kwargs): function unmap (line 33) | def unmap(data, count, inds, fill=0): function mask2ndarray (line 46) | def mask2ndarray(mask): function flip_tensor (line 65) | def flip_tensor(src_tensor, flip_direction): function select_single_mlvl (line 88) | def select_single_mlvl(mlvl_tensors, batch_id, detach=True): function filter_scores_and_topk (line 119) | def filter_scores_and_topk(scores, score_thr, topk, results=None): function center_of_mass (line 168) | def center_of_mass(mask, esp=1e-6): function generate_coordinate (line 190) | def generate_coordinate(featmap_sizes, device='cuda'): FILE: detection/mmdet/core/visualization/image.py function color_val_matplotlib (line 23) | def color_val_matplotlib(color): function _get_adaptive_scales (line 38) | def _get_adaptive_scales(areas, min_area=800, max_area=30000): function _get_bias_color (line 61) | def _get_bias_color(base, max_dist=30): function draw_bboxes (line 79) | def draw_bboxes(ax, bboxes, color='g', alpha=0.8, thickness=2): function draw_labels (line 112) | def draw_labels(ax, function draw_masks (line 164) | def draw_masks(ax, img, masks, color=None, with_edge=True, alpha=0.8): function imshow_det_bboxes (line 206) | def imshow_det_bboxes(img, function imshow_gt_det_bboxes (line 376) | def imshow_gt_det_bboxes(img, FILE: detection/mmdet/core/visualization/palette.py function palette_val (line 6) | def palette_val(palette): function get_palette (line 22) | def get_palette(palette, num_classes): FILE: detection/mmdet/datasets/api_wrappers/coco_api.py class COCO (line 11) | class COCO(_COCO): method __init__ (line 18) | def __init__(self, annotation_file=None): method get_ann_ids (line 27) | def get_ann_ids(self, img_ids=[], cat_ids=[], area_rng=[], iscrowd=None): method get_cat_ids (line 30) | def get_cat_ids(self, cat_names=[], sup_names=[], cat_ids=[]): method get_img_ids (line 33) | def get_img_ids(self, img_ids=[], cat_ids=[]): method load_anns (line 36) | def load_anns(self, ids): method load_cats (line 39) | def load_cats(self, ids): method load_imgs (line 42) | def load_imgs(self, ids): FILE: detection/mmdet/datasets/api_wrappers/panoptic_evaluation.py function pq_compute_single_core (line 23) | def pq_compute_single_core(proc_id, function pq_compute_multi_core (line 169) | def pq_compute_multi_core(matched_annotations_list, FILE: detection/mmdet/datasets/builder.py function _concat_dataset (line 32) | def _concat_dataset(cfg, default_args=None): function build_dataset (line 59) | def build_dataset(cfg, default_args=None): function build_dataloader (line 87) | def build_dataloader(dataset, function worker_init_fn (line 209) | def worker_init_fn(worker_id, num_workers, rank, seed): FILE: detection/mmdet/datasets/cityscapes.py class CityscapesDataset (line 21) | class CityscapesDataset(CocoDataset): method _filter_imgs (line 29) | def _filter_imgs(self, min_size=32): method _parse_ann_info (line 57) | def _parse_ann_info(self, img_info, ann_info): method results2txt (line 111) | def results2txt(self, results, outfile_prefix): method format_results (line 177) | def format_results(self, results, txtfile_prefix=None): method evaluate (line 211) | def evaluate(self, method _evaluate_cityscapes (line 278) | def _evaluate_cityscapes(self, results, txtfile_prefix, logger): FILE: detection/mmdet/datasets/coco.py class CocoDataset (line 23) | class CocoDataset(CustomDataset): method load_annotations (line 74) | def load_annotations(self, ann_file): method get_ann_info (line 103) | def get_ann_info(self, idx): method get_cat_ids (line 118) | def get_cat_ids(self, idx): method _filter_imgs (line 133) | def _filter_imgs(self, min_size=32): method _parse_ann_info (line 157) | def _parse_ann_info(self, img_info, ann_info): method xyxy2xywh (line 216) | def xyxy2xywh(self, bbox): method _proposal2json (line 236) | def _proposal2json(self, results): method _det2json (line 251) | def _det2json(self, results): method _segm2json (line 268) | def _segm2json(self, results): method results2json (line 306) | def results2json(self, results, outfile_prefix): method fast_eval_recall (line 346) | def fast_eval_recall(self, results, proposal_nums, iou_thrs, logger=No... method format_results (line 370) | def format_results(self, results, jsonfile_prefix=None, **kwargs): method evaluate_det_segm (line 398) | def evaluate_det_segm(self, method evaluate (line 604) | def evaluate(self, FILE: detection/mmdet/datasets/coco_panoptic.py class COCOPanoptic (line 28) | class COCOPanoptic(COCO): method __init__ (line 37) | def __init__(self, annotation_file=None): method createIndex (line 46) | def createIndex(self): method load_anns (line 89) | def load_anns(self, ids=[]): class CocoPanopticDataset (line 114) | class CocoPanopticDataset(CocoDataset): method __init__ (line 259) | def __init__(self, method load_annotations (line 284) | def load_annotations(self, ann_file): method get_ann_info (line 306) | def get_ann_info(self, idx): method _parse_ann_info (line 322) | def _parse_ann_info(self, img_info, ann_info): method _filter_imgs (line 385) | def _filter_imgs(self, min_size=32): method _pan2json (line 410) | def _pan2json(self, results, outfile_prefix): method results2json (line 452) | def results2json(self, results, outfile_prefix): method evaluate_pan_json (line 503) | def evaluate_pan_json(self, method evaluate (line 568) | def evaluate(self, function parse_pq_results (line 641) | def parse_pq_results(pq_results): function print_panoptic_table (line 656) | def print_panoptic_table(pq_results, classwise_results=None, logger=None): FILE: detection/mmdet/datasets/custom.py class CustomDataset (line 18) | class CustomDataset(Dataset): method __init__ (line 59) | def __init__(self, method __len__ (line 131) | def __len__(self): method load_annotations (line 135) | def load_annotations(self, ann_file): method load_proposals (line 139) | def load_proposals(self, proposal_file): method get_ann_info (line 143) | def get_ann_info(self, idx): method get_cat_ids (line 155) | def get_cat_ids(self, idx): method pre_pipeline (line 167) | def pre_pipeline(self, results): method _filter_imgs (line 176) | def _filter_imgs(self, min_size=32): method _set_group_flag (line 187) | def _set_group_flag(self): method _rand_another (line 199) | def _rand_another(self, idx): method __getitem__ (line 204) | def __getitem__(self, idx): method prepare_train_img (line 224) | def prepare_train_img(self, idx): method prepare_test_img (line 243) | def prepare_test_img(self, idx): method get_classes (line 262) | def get_classes(cls, classes=None): method get_cat2imgs (line 288) | def get_cat2imgs(self): method format_results (line 307) | def format_results(self, results, **kwargs): method evaluate (line 310) | def evaluate(self, method __repr__ (line 369) | def __repr__(self): FILE: detection/mmdet/datasets/dataset_wrappers.py class ConcatDataset (line 17) | class ConcatDataset(_ConcatDataset): method __init__ (line 30) | def __init__(self, datasets, separate_eval=True): method get_cat_ids (line 50) | def get_cat_ids(self, idx): method get_ann_info (line 72) | def get_ann_info(self, idx): method evaluate (line 94) | def evaluate(self, results, logger=None, **kwargs): class RepeatDataset (line 154) | class RepeatDataset: method __init__ (line 167) | def __init__(self, dataset, times): method __getitem__ (line 177) | def __getitem__(self, idx): method get_cat_ids (line 180) | def get_cat_ids(self, idx): method get_ann_info (line 192) | def get_ann_info(self, idx): method __len__ (line 204) | def __len__(self): class ClassBalancedDataset (line 211) | class ClassBalancedDataset: method __init__ (line 247) | def __init__(self, dataset, oversample_thr, filter_empty_gt=True): method _get_repeat_factors (line 267) | def _get_repeat_factors(self, dataset, repeat_thr): method __getitem__ (line 316) | def __getitem__(self, idx): method get_ann_info (line 320) | def get_ann_info(self, idx): method __len__ (line 332) | def __len__(self): class MultiImageMixDataset (line 338) | class MultiImageMixDataset: method __init__ (line 362) | def __init__(self, method __len__ (line 398) | def __len__(self): method __getitem__ (line 401) | def __getitem__(self, idx): method update_skip_type_keys (line 446) | def update_skip_type_keys(self, skip_type_keys): FILE: detection/mmdet/datasets/deepfashion.py class DeepFashionDataset (line 7) | class DeepFashionDataset(CocoDataset): FILE: detection/mmdet/datasets/lvis.py class LVISV05Dataset (line 18) | class LVISV05Dataset(CocoDataset): method load_annotations (line 272) | def load_annotations(self, ann_file): method evaluate (line 311) | def evaluate(self, class LVISV1Dataset (line 475) | class LVISV1Dataset(LVISDataset): method load_annotations (line 717) | def load_annotations(self, ann_file): FILE: detection/mmdet/datasets/openimages.py class OpenImagesDataset (line 21) | class OpenImagesDataset(CustomDataset): method __init__ (line 63) | def __init__(self, method get_classes_from_csv (line 127) | def get_classes_from_csv(self, label_file): method load_annotations (line 150) | def load_annotations(self, ann_file): method get_ann_info (line 220) | def get_ann_info(self, idx): method get_meta_from_file (line 291) | def get_meta_from_file(self, meta_file=''): method get_meta_from_pipeline (line 308) | def get_meta_from_pipeline(self, results): method get_img_shape (line 319) | def get_img_shape(self, metas): method prepare_test_img (line 332) | def prepare_test_img(self, idx): method _filter_imgs (line 344) | def _filter_imgs(self, min_size=32): method _set_group_flag (line 352) | def _set_group_flag(self): method get_relation_matrix (line 357) | def get_relation_matrix(self, hierarchy_file): method _convert_hierarchy_tree (line 380) | def _convert_hierarchy_tree(self, method add_supercategory_ann (line 426) | def add_supercategory_ann(self, annotations): method process_results (line 457) | def process_results(self, det_results, annotations, method load_image_label_from_csv (line 503) | def load_image_label_from_csv(self, image_level_ann_file): method get_image_level_ann (line 538) | def get_image_level_ann(self, image_level_ann_file): method denormalize_gt_bboxes (line 591) | def denormalize_gt_bboxes(self, annotations): method get_cat_ids (line 604) | def get_cat_ids(self, idx): method evaluate (line 615) | def evaluate(self, class OpenImagesChallengeDataset (line 727) | class OpenImagesChallengeDataset(OpenImagesDataset): method __init__ (line 730) | def __init__(self, ann_file, **kwargs): method get_classes_from_csv (line 735) | def get_classes_from_csv(self, label_file): method load_annotations (line 765) | def load_annotations(self, ann_file): method prepare_train_img (line 805) | def prepare_train_img(self, idx): method prepare_test_img (line 817) | def prepare_test_img(self, idx): method get_relation_matrix (line 830) | def get_relation_matrix(self, hierarchy_file): method get_ann_info (line 844) | def get_ann_info(self, idx): method load_image_label_from_csv (line 857) | def load_image_label_from_csv(self, image_level_ann_file): FILE: detection/mmdet/datasets/pipelines/auto_augment.py function level_to_value (line 14) | def level_to_value(level, max_value): function enhance_level_to_value (line 19) | def enhance_level_to_value(level, a=1.8, b=0.1): function random_negative (line 24) | def random_negative(value, random_negative_prob): function bbox2fields (line 29) | def bbox2fields(): class AutoAugment (line 47) | class AutoAugment: method __init__ (line 90) | def __init__(self, policies): method __call__ (line 104) | def __call__(self, results): method __repr__ (line 108) | def __repr__(self): class Shear (line 113) | class Shear: method __init__ (line 136) | def __init__(self, method _shear_img (line 180) | def _shear_img(self, method _shear_bboxes (line 205) | def _shear_bboxes(self, results, magnitude): method _shear_masks (line 235) | def _shear_masks(self, method _shear_seg (line 251) | def _shear_seg(self, method _filter_invalid (line 267) | def _filter_invalid(self, results, min_bbox_size=0): method __call__ (line 286) | def __call__(self, results): method __repr__ (line 317) | def __repr__(self): class Rotate (line 331) | class Rotate: method __init__ (line 357) | def __init__(self, method _rotate_img (line 408) | def _rotate_img(self, results, angle, center=None, scale=1.0): method _rotate_bboxes (line 427) | def _rotate_bboxes(self, results, rotate_matrix): method _rotate_masks (line 462) | def _rotate_masks(self, method _rotate_seg (line 474) | def _rotate_seg(self, method _filter_invalid (line 487) | def _filter_invalid(self, results, min_bbox_size=0): method __call__ (line 506) | def __call__(self, results): method __repr__ (line 532) | def __repr__(self): class Translate (line 546) | class Translate: method __init__ (line 572) | def __init__(self, method _translate_img (line 613) | def _translate_img(self, results, offset, direction='horizontal'): method _translate_bboxes (line 628) | def _translate_bboxes(self, results, offset): method _translate_masks (line 646) | def _translate_masks(self, method _translate_seg (line 657) | def _translate_seg(self, method _filter_invalid (line 668) | def _filter_invalid(self, results, min_size=0): method __call__ (line 687) | def __call__(self, results): class ColorTransform (line 713) | class ColorTransform: method __init__ (line 722) | def __init__(self, level, prob=0.5): method _adjust_color_img (line 733) | def _adjust_color_img(self, results, factor=1.0): method __call__ (line 740) | def __call__(self, results): method __repr__ (line 754) | def __repr__(self): class EqualizeTransform (line 762) | class EqualizeTransform: method __init__ (line 770) | def __init__(self, prob=0.5): method _imequalize (line 775) | def _imequalize(self, results): method __call__ (line 781) | def __call__(self, results): method __repr__ (line 795) | def __repr__(self): class BrightnessTransform (line 801) | class BrightnessTransform: method __init__ (line 810) | def __init__(self, level, prob=0.5): method _adjust_brightness_img (line 821) | def _adjust_brightness_img(self, results, factor=1.0): method __call__ (line 828) | def __call__(self, results): method __repr__ (line 842) | def __repr__(self): class ContrastTransform (line 850) | class ContrastTransform: method __init__ (line 859) | def __init__(self, level, prob=0.5): method _adjust_contrast_img (line 870) | def _adjust_contrast_img(self, results, factor=1.0): method __call__ (line 876) | def __call__(self, results): method __repr__ (line 890) | def __repr__(self): FILE: detection/mmdet/datasets/pipelines/compose.py class Compose (line 10) | class Compose: method __init__ (line 18) | def __init__(self, transforms): method __call__ (line 30) | def __call__(self, data): method __repr__ (line 46) | def __repr__(self): FILE: detection/mmdet/datasets/pipelines/formatting.py function to_tensor (line 12) | def to_tensor(data): class ToTensor (line 38) | class ToTensor: method __init__ (line 45) | def __init__(self, keys): method __call__ (line 48) | def __call__(self, results): method __repr__ (line 62) | def __repr__(self): class ImageToTensor (line 67) | class ImageToTensor: method __init__ (line 78) | def __init__(self, keys): method __call__ (line 81) | def __call__(self, results): method __repr__ (line 99) | def __repr__(self): class Transpose (line 104) | class Transpose: method __init__ (line 112) | def __init__(self, keys, order): method __call__ (line 116) | def __call__(self, results): method __repr__ (line 130) | def __repr__(self): class ToDataContainer (line 136) | class ToDataContainer: method __init__ (line 147) | def __init__(self, method __call__ (line 152) | def __call__(self, results): method __repr__ (line 170) | def __repr__(self): class DefaultFormatBundle (line 175) | class DefaultFormatBundle: method __init__ (line 200) | def __init__(self, method __call__ (line 206) | def __call__(self, results): method _add_default_meta_keys (line 248) | def _add_default_meta_keys(self, results): method __repr__ (line 273) | def __repr__(self): class Collect (line 279) | class Collect: method __init__ (line 318) | def __init__(self, method __call__ (line 326) | def __call__(self, results): method __repr__ (line 349) | def __repr__(self): class WrapFieldsToLists (line 355) | class WrapFieldsToLists: method __call__ (line 375) | def __call__(self, results): method __repr__ (line 391) | def __repr__(self): FILE: detection/mmdet/datasets/pipelines/instaboost.py class InstaBoost (line 8) | class InstaBoost: method __init__ (line 35) | def __init__(self, method _load_anns (line 56) | def _load_anns(self, results): method _parse_anns (line 78) | def _parse_anns(self, results, anns, img): method __call__ (line 99) | def __call__(self, results): method __repr__ (line 115) | def __repr__(self): FILE: detection/mmdet/datasets/pipelines/loading.py class LoadImageFromFile (line 18) | class LoadImageFromFile: method __init__ (line 37) | def __init__(self, method __call__ (line 48) | def __call__(self, results): method __repr__ (line 81) | def __repr__(self): class LoadImageFromWebcam (line 91) | class LoadImageFromWebcam(LoadImageFromFile): method __call__ (line 98) | def __call__(self, results): class LoadMultiChannelImageFromFiles (line 123) | class LoadMultiChannelImageFromFiles: method __init__ (line 143) | def __init__(self, method __call__ (line 152) | def __call__(self, results): method __repr__ (line 197) | def __repr__(self): class LoadAnnotations (line 206) | class LoadAnnotations: method __init__ (line 228) | def __init__(self, method _load_bboxes (line 245) | def _load_bboxes(self, results): method _load_labels (line 277) | def _load_labels(self, results): method _poly2mask (line 290) | def _poly2mask(self, mask_ann, img_h, img_w): method process_polygons (line 317) | def process_polygons(self, polygons): method _load_masks (line 334) | def _load_masks(self, results): method _load_semantic_seg (line 359) | def _load_semantic_seg(self, results): method __call__ (line 380) | def __call__(self, results): method __repr__ (line 403) | def __repr__(self): class LoadPanopticAnnotations (line 415) | class LoadPanopticAnnotations(LoadAnnotations): method __init__ (line 432) | def __init__(self, method _load_masks_and_semantic_segs (line 453) | def _load_masks_and_semantic_segs(self, results): method __call__ (line 500) | def __call__(self, results): class LoadProposals (line 526) | class LoadProposals: method __init__ (line 536) | def __init__(self, num_max_proposals=None): method __call__ (line 539) | def __call__(self, results): method __repr__ (line 565) | def __repr__(self): class FilterAnnotations (line 571) | class FilterAnnotations: method __init__ (line 581) | def __init__(self, min_gt_bbox_wh, keep_empty=True): method __call__ (line 586) | def __call__(self, results): method __repr__ (line 606) | def __repr__(self): FILE: detection/mmdet/datasets/pipelines/test_time_aug.py class MultiScaleFlipAug (line 11) | class MultiScaleFlipAug: method __init__ (line 54) | def __init__(self, method __call__ (line 85) | def __call__(self, results): method __repr__ (line 116) | def __repr__(self): FILE: detection/mmdet/datasets/pipelines/transforms.py class Resize (line 31) | class Resize: method __init__ (line 76) | def __init__(self, method random_select (line 111) | def random_select(img_scales): method random_sample (line 129) | def random_sample(img_scales): method random_sample_ratio (line 156) | def random_sample_ratio(img_scale, ratio_range): method _random_scale (line 182) | def _random_scale(self, results): method _resize_img (line 215) | def _resize_img(self, results): method _resize_bboxes (line 248) | def _resize_bboxes(self, results): method _resize_masks (line 258) | def _resize_masks(self, results): method _resize_seg (line 268) | def _resize_seg(self, results): method __call__ (line 285) | def __call__(self, results): method __repr__ (line 322) | def __repr__(self): class RandomFlip (line 333) | class RandomFlip: method __init__ (line 370) | def __init__(self, flip_ratio=None, direction='horizontal'): method bbox_flip (line 396) | def bbox_flip(self, bboxes, img_shape, direction): method __call__ (line 430) | def __call__(self, results): method __repr__ (line 485) | def __repr__(self): class RandomShift (line 490) | class RandomShift: method __init__ (line 501) | def __init__(self, shift_ratio=0.5, max_shift_px=32, filter_thr_px=1): method __call__ (line 513) | def __call__(self, results): method __repr__ (line 573) | def __repr__(self): class Pad (line 580) | class Pad: method __init__ (line 596) | def __init__(self, method _pad_img (line 622) | def _pad_img(self, results): method _pad_masks (line 640) | def _pad_masks(self, results): method _pad_seg (line 647) | def _pad_seg(self, results): method __call__ (line 655) | def __call__(self, results): method __repr__ (line 669) | def __repr__(self): class Normalize (line 679) | class Normalize: method __init__ (line 691) | def __init__(self, mean, std, to_rgb=True): method __call__ (line 696) | def __call__(self, results): method __repr__ (line 713) | def __repr__(self): class RandomCrop (line 720) | class RandomCrop: method __init__ (line 756) | def __init__(self, method _crop_data (line 787) | def _crop_data(self, results, crop_size, allow_negative_crop): method _get_crop_size (line 854) | def _get_crop_size(self, image_size): method __call__ (line 884) | def __call__(self, results): method __repr__ (line 900) | def __repr__(self): class SegRescale (line 910) | class SegRescale: method __init__ (line 920) | def __init__(self, scale_factor=1, backend='cv2'): method __call__ (line 924) | def __call__(self, results): method __repr__ (line 943) | def __repr__(self): class PhotoMetricDistortion (line 948) | class PhotoMetricDistortion: method __init__ (line 969) | def __init__(self, method __call__ (line 979) | def __call__(self, results): method __repr__ (line 1040) | def __repr__(self): class Expand (line 1052) | class Expand: method __init__ (line 1065) | def __init__(self, method __call__ (line 1081) | def __call__(self, results): method __repr__ (line 1135) | def __repr__(self): class MinIoURandomCrop (line 1144) | class MinIoURandomCrop: method __init__ (line 1163) | def __init__(self, method __call__ (line 1181) | def __call__(self, results): method __repr__ (line 1275) | def __repr__(self): class Corrupt (line 1284) | class Corrupt: method __init__ (line 1295) | def __init__(self, corruption, severity=1): method __call__ (line 1299) | def __call__(self, results): method __repr__ (line 1320) | def __repr__(self): class Albu (line 1328) | class Albu: method __init__ (line 1370) | def __init__(self, method albu_builder (line 1413) | def albu_builder(self, cfg): method mapper (line 1448) | def mapper(d, keymap): method __call__ (line 1464) | def __call__(self, results): method __repr__ (line 1526) | def __repr__(self): class RandomCenterCropPad (line 1532) | class RandomCenterCropPad: method __init__ (line 1620) | def __init__(self, method _get_border (line 1666) | def _get_border(self, border, size): method _filter_boxes (line 1685) | def _filter_boxes(self, patch, boxes): method _crop_image_and_paste (line 1701) | def _crop_image_and_paste(self, image, center, size): method _train_aug (line 1752) | def _train_aug(self, results): method _test_aug (line 1820) | def _test_aug(self, results): method __call__ (line 1854) | def __call__(self, results): method __repr__ (line 1866) | def __repr__(self): class CutOut (line 1881) | class CutOut: method __init__ (line 1904) | def __init__(self, method __call__ (line 1925) | def __call__(self, results): method __repr__ (line 1945) | def __repr__(self): class Mosaic (line 1955) | class Mosaic: method __init__ (line 2008) | def __init__(self, method __call__ (line 2029) | def __call__(self, results): method get_indexes (line 2045) | def get_indexes(self, dataset): method _mosaic_transform (line 2058) | def _mosaic_transform(self, results): method _mosaic_combine (line 2155) | def _mosaic_combine(self, loc, center_position_xy, img_shape_wh): method _filter_box_candidates (line 2216) | def _filter_box_candidates(self, bboxes, labels): method __repr__ (line 2225) | def __repr__(self): class MixUp (line 2236) | class MixUp: method __init__ (line 2292) | def __init__(self, method __call__ (line 2316) | def __call__(self, results): method get_indexes (line 2329) | def get_indexes(self, dataset): method _mixup_transform (line 2347) | def _mixup_transform(self, results): method _filter_box_candidates (line 2470) | def _filter_box_candidates(self, bbox1, bbox2): method __repr__ (line 2485) | def __repr__(self): class RandomAffine (line 2500) | class RandomAffine: method __init__ (line 2539) | def __init__(self, method __call__ (line 2566) | def __call__(self, results): method filter_gt_bboxes (line 2649) | def filter_gt_bboxes(self, origin_bboxes, wrapped_bboxes): method __repr__ (line 2664) | def __repr__(self): method _get_rotation_matrix (line 2679) | def _get_rotation_matrix(rotate_degrees): method _get_scaling_matrix (line 2688) | def _get_scaling_matrix(scale_ratio): method _get_share_matrix (line 2695) | def _get_share_matrix(scale_ratio): method _get_shear_matrix (line 2702) | def _get_shear_matrix(x_shear_degrees, y_shear_degrees): method _get_translation_matrix (line 2711) | def _get_translation_matrix(x, y): class YOLOXHSVRandomAug (line 2718) | class YOLOXHSVRandomAug: method __init__ (line 2729) | def __init__(self, hue_delta=5, saturation_delta=30, value_delta=30): method __call__ (line 2734) | def __call__(self, results): method __repr__ (line 2753) | def __repr__(self): class CopyPaste (line 2762) | class CopyPaste: method __init__ (line 2794) | def __init__( method get_indexes (line 2806) | def get_indexes(self, dataset): method __call__ (line 2816) | def __call__(self, results): method _select_object (line 2835) | def _select_object(self, results): method _copy_paste (line 2854) | def _copy_paste(self, dst_results, src_results): method get_updated_masks (line 2907) | def get_updated_masks(self, masks, composed_mask): method __repr__ (line 2913) | def __repr__(self): FILE: detection/mmdet/datasets/samplers/class_aware_sampler.py class ClassAwareSampler (line 11) | class ClassAwareSampler(Sampler): method __init__ (line 40) | def __init__(self, method __iter__ (line 85) | def __iter__(self): method __len__ (line 131) | def __len__(self): method set_epoch (line 134) | def set_epoch(self, epoch): class RandomCycleIter (line 138) | class RandomCycleIter: method __init__ (line 156) | def __init__(self, data, generator=None): method __iter__ (line 163) | def __iter__(self): method __len__ (line 166) | def __len__(self): method __next__ (line 169) | def __next__(self): FILE: detection/mmdet/datasets/samplers/distributed_sampler.py class DistributedSampler (line 11) | class DistributedSampler(_DistributedSampler): method __init__ (line 13) | def __init__(self, method __iter__ (line 31) | def __iter__(self): FILE: detection/mmdet/datasets/samplers/group_sampler.py class GroupSampler (line 10) | class GroupSampler(Sampler): method __init__ (line 12) | def __init__(self, dataset, samples_per_gpu=1): method __iter__ (line 23) | def __iter__(self): method __len__ (line 47) | def __len__(self): class DistributedGroupSampler (line 51) | class DistributedGroupSampler(Sampler): method __init__ (line 72) | def __init__(self, method __iter__ (line 101) | def __iter__(self): method __len__ (line 144) | def __len__(self): method set_epoch (line 147) | def set_epoch(self, epoch): FILE: detection/mmdet/datasets/samplers/infinite_sampler.py class InfiniteGroupBatchSampler (line 12) | class InfiniteGroupBatchSampler(Sampler): method __init__ (line 37) | def __init__(self, method _infinite_indices (line 71) | def _infinite_indices(self): method _indices_of_rank (line 82) | def _indices_of_rank(self): method __iter__ (line 87) | def __iter__(self): method __len__ (line 97) | def __len__(self): method set_epoch (line 101) | def set_epoch(self, epoch): class InfiniteBatchSampler (line 106) | class InfiniteBatchSampler(Sampler): method __init__ (line 128) | def __init__(self, method _infinite_indices (line 155) | def _infinite_indices(self): method _indices_of_rank (line 166) | def _indices_of_rank(self): method __iter__ (line 171) | def __iter__(self): method __len__ (line 180) | def __len__(self): method set_epoch (line 184) | def set_epoch(self, epoch): FILE: detection/mmdet/datasets/utils.py function replace_ImageToTensor (line 15) | def replace_ImageToTensor(pipelines): function get_loading_pipeline (line 75) | def get_loading_pipeline(pipeline): class NumClassCheckHook (line 118) | class NumClassCheckHook(Hook): method _check_head (line 120) | def _check_head(self, runner): method before_train_epoch (line 152) | def before_train_epoch(self, runner): method before_val_epoch (line 160) | def before_val_epoch(self, runner): FILE: detection/mmdet/datasets/voc.py class VOCDataset (line 12) | class VOCDataset(XMLDataset): method __init__ (line 25) | def __init__(self, **kwargs): method evaluate (line 34) | def evaluate(self, FILE: detection/mmdet/datasets/wider_face.py class WIDERFaceDataset (line 12) | class WIDERFaceDataset(XMLDataset): method __init__ (line 22) | def __init__(self, **kwargs): method load_annotations (line 25) | def load_annotations(self, ann_file): FILE: detection/mmdet/datasets/xml_style.py class XMLDataset (line 14) | class XMLDataset(CustomDataset): method __init__ (line 25) | def __init__(self, method load_annotations (line 38) | def load_annotations(self, ann_file): method _filter_imgs (line 69) | def _filter_imgs(self, min_size=32): method get_ann_info (line 90) | def get_ann_info(self, idx): method get_cat_ids (line 156) | def get_cat_ids(self, idx): FILE: detection/mmdet/models/backbones/csp_darknet.py class Focus (line 14) | class Focus(nn.Module): method __init__ (line 30) | def __init__(self, method forward (line 49) | def forward(self, x): class SPPBottleneck (line 67) | class SPPBottleneck(BaseModule): method __init__ (line 85) | def __init__(self, method forward (line 116) | def forward(self, x): class CSPDarknet (line 124) | class CSPDarknet(BaseModule): method __init__ (line 177) | def __init__(self, method _freeze_stages (line 261) | def _freeze_stages(self): method train (line 269) | def train(self, mode=True): method forward (line 277) | def forward(self, x): FILE: detection/mmdet/models/backbones/darknet.py class ResBlock (line 14) | class ResBlock(BaseModule): method __init__ (line 33) | def __init__(self, method forward (line 50) | def forward(self, x): class Darknet (line 60) | class Darknet(BaseModule): method __init__ (line 101) | def __init__(self, method forward (line 153) | def forward(self, x): method _freeze_stages (line 163) | def _freeze_stages(self): method train (line 171) | def train(self, mode=True): method make_conv_res_block (line 180) | def make_conv_res_block(in_channels, FILE: detection/mmdet/models/backbones/detectors_resnet.py class Bottleneck (line 16) | class Bottleneck(_Bottleneck): method __init__ (line 36) | def __init__(self, method rfp_forward (line 73) | def rfp_forward(self, x, rfp_feat): class ResLayer (line 120) | class ResLayer(Sequential): method __init__ (line 146) | def __init__(self, class DetectoRS_ResNet (line 212) | class DetectoRS_ResNet(ResNet): method __init__ (line 234) | def __init__(self, method init_weights (line 296) | def init_weights(self): method make_res_layer (line 325) | def make_res_layer(self, **kwargs): method forward (line 329) | def forward(self, x): method rfp_forward (line 336) | def rfp_forward(self, x, rfp_feats): FILE: detection/mmdet/models/backbones/detectors_resnext.py class Bottleneck (line 11) | class Bottleneck(_Bottleneck): method __init__ (line 14) | def __init__(self, class DetectoRS_ResNeXt (line 99) | class DetectoRS_ResNeXt(DetectoRS_ResNet): method __init__ (line 113) | def __init__(self, groups=1, base_width=4, **kwargs): method make_res_layer (line 118) | def make_res_layer(self, **kwargs): FILE: detection/mmdet/models/backbones/efficientnet.py class EdgeResidual (line 16) | class EdgeResidual(BaseModule): method __init__ (line 41) | def __init__(self, method forward (line 91) | def forward(self, x): function model_scaling (line 115) | def model_scaling(layer_setting, arch_setting): class EfficientNet (line 159) | class EfficientNet(BaseModule): method __init__ (line 254) | def __init__(self, method make_layer (line 327) | def make_layer(self): method forward (line 395) | def forward(self, x): method _freeze_stages (line 404) | def _freeze_stages(self): method train (line 411) | def train(self, mode=True): FILE: detection/mmdet/models/backbones/hivit.py function window_partition (line 21) | def window_partition(x, window_size): function window_unpartition (line 45) | def window_unpartition(windows, window_size, pad_hw, hw): class LayerNorm2d (line 68) | class LayerNorm2d(nn.LayerNorm): method forward (line 69) | def forward(self, x): class HiViT (line 77) | class HiViT(models.HiViT): method __init__ (line 78) | def __init__(self, method init_weights (line 150) | def init_weights(self): method _freeze_stages (line 166) | def _freeze_stages(self): method interpolate_pos_encoding (line 179) | def interpolate_pos_encoding(self, x, h, w): method prepare_tokens (line 200) | def prepare_tokens(self, x, mask=None): method forward (line 225) | def forward(self, x): FILE: detection/mmdet/models/backbones/hourglass.py class HourglassModule (line 12) | class HourglassModule(BaseModule): method __init__ (line 30) | def __init__(self, method forward (line 80) | def forward(self, x): class HourglassNet (line 97) | class HourglassNet(BaseModule): method __init__ (line 131) | def __init__(self, method init_weights (line 195) | def init_weights(self): method forward (line 203) | def forward(self, x): FILE: detection/mmdet/models/backbones/hrnet.py class HRModule (line 13) | class HRModule(BaseModule): method __init__ (line 20) | def __init__(self, method _check_branches (line 49) | def _check_branches(self, num_branches, num_blocks, in_channels, method _make_one_branch (line 66) | def _make_one_branch(self, method _make_branches (line 112) | def _make_branches(self, num_branches, block, num_blocks, num_channels): method _make_fuse_layers (line 121) | def _make_fuse_layers(self): method forward (line 183) | def forward(self, x): class HRNet (line 204) | class HRNet(BaseModule): method __init__ (line 281) | def __init__(self, method norm1 (line 403) | def norm1(self): method norm2 (line 408) | def norm2(self): method _make_transition_layer (line 412) | def _make_transition_layer(self, num_channels_pre_layer, method _make_layer (line 458) | def _make_layer(self, block, inplanes, planes, blocks, stride=1): method _make_stage (line 505) | def _make_stage(self, layer_config, in_channels, multiscale_output=True): method forward (line 545) | def forward(self, x): method train (line 581) | def train(self, mode=True): FILE: detection/mmdet/models/backbones/mobilenet_v2.py class MobileNetV2 (line 14) | class MobileNetV2(BaseModule): method __init__ (line 46) | def __init__(self, method make_layer (line 138) | def make_layer(self, out_channels, num_blocks, stride, expand_ratio): method _freeze_stages (line 167) | def _freeze_stages(self): method forward (line 177) | def forward(self, x): method train (line 188) | def train(self, mode=True): FILE: detection/mmdet/models/backbones/pvt.py class MixFFN (line 23) | class MixFFN(BaseModule): method __init__ (line 47) | def __init__(self, method forward (line 93) | def forward(self, x, hw_shape, identity=None): class SpatialReductionAttention (line 102) | class SpatialReductionAttention(MultiheadAttention): method __init__ (line 129) | def __init__(self, method forward (line 169) | def forward(self, x, hw_shape, identity=None): method legacy_forward (line 200) | def legacy_forward(self, x, hw_shape, identity=None): class PVTEncoderLayer (line 219) | class PVTEncoderLayer(BaseModule): method __init__ (line 245) | def __init__(self, method forward (line 284) | def forward(self, x, hw_shape): class AbsolutePositionEmbedding (line 291) | class AbsolutePositionEmbedding(BaseModule): method __init__ (line 301) | def __init__(self, pos_shape, pos_dim, drop_rate=0., init_cfg=None): method init_weights (line 319) | def init_weights(self): method resize_pos_embed (line 322) | def resize_pos_embed(self, pos_embed, input_shape, mode='bilinear'): method forward (line 351) | def forward(self, x, hw_shape, mode='bilinear'): class PyramidVisionTransformer (line 357) | class PyramidVisionTransformer(BaseModule): method __init__ (line 410) | def __init__(self, method init_weights (line 523) | def init_weights(self): method forward (line 563) | def forward(self, x): class PyramidVisionTransformerV2 (line 580) | class PyramidVisionTransformerV2(PyramidVisionTransformer): method __init__ (line 584) | def __init__(self, **kwargs): FILE: detection/mmdet/models/backbones/regnet.py class RegNet (line 14) | class RegNet(ResNet): method __init__ (line 90) | def __init__(self, method _make_stem_layer (line 238) | def _make_stem_layer(self, in_channels, base_channels): method generate_regnet (line 252) | def generate_regnet(self, method quantize_float (line 285) | def quantize_float(number, divisor): method adjust_width_group (line 297) | def adjust_width_group(self, widths, bottleneck_ratio, groups): method get_stages_from_blocks (line 322) | def get_stages_from_blocks(self, widths): method forward (line 344) | def forward(self, x): FILE: detection/mmdet/models/backbones/res2net.py class Bottle2neck (line 15) | class Bottle2neck(_Bottleneck): method __init__ (line 18) | def __init__(self, method forward (line 106) | def forward(self, x): class Res2Layer (line 163) | class Res2Layer(Sequential): method __init__ (line 182) | def __init__(self, class Res2Net (line 243) | class Res2Net(ResNet): method __init__ (line 303) | def __init__(self, method make_res_layer (line 322) | def make_res_layer(self, **kwargs): FILE: detection/mmdet/models/backbones/resnest.py class RSoftmax (line 17) | class RSoftmax(nn.Module): method __init__ (line 25) | def __init__(self, radix, groups): method forward (line 30) | def forward(self, x): class SplitAttentionConv2d (line 41) | class SplitAttentionConv2d(BaseModule): method __init__ (line 64) | def __init__(self, method norm0 (line 116) | def norm0(self): method norm1 (line 121) | def norm1(self): method forward (line 125) | def forward(self, x): class Bottleneck (line 154) | class Bottleneck(_Bottleneck): method __init__ (line 173) | def __init__(self, method forward (line 234) | def forward(self, x): class ResNeSt (line 278) | class ResNeSt(ResNetV1d): method __init__ (line 299) | def __init__(self, method make_res_layer (line 313) | def make_res_layer(self, **kwargs): FILE: detection/mmdet/models/backbones/resnet.py class BasicBlock (line 14) | class BasicBlock(BaseModule): method __init__ (line 17) | def __init__(self, method norm1 (line 58) | def norm1(self): method norm2 (line 63) | def norm2(self): method forward (line 67) | def forward(self, x): class Bottleneck (line 97) | class Bottleneck(BaseModule): method __init__ (line 100) | def __init__(self, method make_block_plugins (line 219) | def make_block_plugins(self, in_channels, plugins): method forward_plugin (line 242) | def forward_plugin(self, x, plugin_names): method norm1 (line 249) | def norm1(self): method norm2 (line 254) | def norm2(self): method norm3 (line 259) | def norm3(self): method forward (line 263) | def forward(self, x): class ResNet (line 306) | class ResNet(BaseModule): method __init__ (line 369) | def __init__(self, method make_stage_plugins (line 494) | def make_stage_plugins(self, plugins, stage_idx): method make_res_layer (line 556) | def make_res_layer(self, **kwargs): method norm1 (line 561) | def norm1(self): method _make_stem_layer (line 565) | def _make_stem_layer(self, in_channels, stem_channels): method _freeze_stages (line 613) | def _freeze_stages(self): method forward (line 631) | def forward(self, x): method train (line 648) | def train(self, mode=True): class ResNetV1d (line 661) | class ResNetV1d(ResNet): method __init__ (line 670) | def __init__(self, **kwargs): FILE: detection/mmdet/models/backbones/resnext.py class Bottleneck (line 12) | class Bottleneck(_Bottleneck): method __init__ (line 15) | def __init__(self, method _del_block_plugins (line 98) | def _del_block_plugins(self, plugin_names): class ResNeXt (line 110) | class ResNeXt(ResNet): method __init__ (line 143) | def __init__(self, groups=1, base_width=4, **kwargs): method make_res_layer (line 148) | def make_res_layer(self, **kwargs): FILE: detection/mmdet/models/backbones/ssd_vgg.py class SSDVGG (line 13) | class SSDVGG(VGG, BaseModule): method __init__ (line 50) | def __init__(self, method init_weights (line 105) | def init_weights(self, pretrained=None): method forward (line 108) | def forward(self, x): class L2Norm (line 122) | class L2Norm(ssd_neck.L2Norm): method __init__ (line 124) | def __init__(self, **kwargs): FILE: detection/mmdet/models/backbones/swin.py class WindowMSA (line 22) | class WindowMSA(BaseModule): method __init__ (line 41) | def __init__(self, method init_weights (line 78) | def init_weights(self): method forward (line 81) | def forward(self, x, mask=None): method double_step_seq (line 122) | def double_step_seq(step1, len1, step2, len2): class ShiftWindowMSA (line 128) | class ShiftWindowMSA(BaseModule): method __init__ (line 151) | def __init__(self, method forward (line 180) | def forward(self, query, hw_shape): method window_reverse (line 256) | def window_reverse(self, windows, H, W): method window_partition (line 272) | def window_partition(self, x): class SwinBlock (line 288) | class SwinBlock(BaseModule): method __init__ (line 313) | def __init__(self, method forward (line 358) | def forward(self, x, hw_shape): class SwinBlockSequence (line 381) | class SwinBlockSequence(BaseModule): method __init__ (line 410) | def __init__(self, method forward (line 455) | def forward(self, x, hw_shape): class SwinTransformer (line 467) | class SwinTransformer(BaseModule): method __init__ (line 524) | def __init__(self, method train (line 643) | def train(self, mode=True): method _freeze_stages (line 648) | def _freeze_stages(self): method init_weights (line 670) | def init_weights(self): method forward (line 745) | def forward(self, x): FILE: detection/mmdet/models/backbones/trident_resnet.py class TridentConv (line 14) | class TridentConv(BaseModule): method __init__ (line 33) | def __init__(self, method extra_repr (line 61) | def extra_repr(self): method forward (line 73) | def forward(self, inputs): class TridentBottleneck (line 93) | class TridentBottleneck(Bottleneck): method __init__ (line 106) | def __init__(self, trident_dilations, test_branch_idx, concat_output, method forward (line 128) | def forward(self, x): function make_trident_res_layer (line 182) | def make_trident_res_layer(block, class TridentResNet (line 235) | class TridentResNet(ResNet): method __init__ (line 256) | def __init__(self, depth, num_branch, test_branch_idx, trident_dilations, FILE: detection/mmdet/models/backbones/vit.py class PatchEmbed (line 19) | class PatchEmbed(nn.Module): method __init__ (line 22) | def __init__(self, img_size=[224, 224], patch_size=16, in_chans=3, emb... method forward (line 34) | def forward(self, x, mask=None): class VisionTransformer (line 40) | class VisionTransformer(VisionTransformer): method __init__ (line 41) | def __init__(self, method train (line 96) | def train(self, mode=True): method _freeze_stages (line 101) | def _freeze_stages(self): method init_weights (line 122) | def init_weights(self): method forward_encoder (line 137) | def forward_encoder(self, x): method forward (line 142) | def forward(self, x): FILE: detection/mmdet/models/builder.py function build_backbone (line 18) | def build_backbone(cfg): function build_neck (line 23) | def build_neck(cfg): function build_roi_extractor (line 28) | def build_roi_extractor(cfg): function build_shared_head (line 33) | def build_shared_head(cfg): function build_head (line 38) | def build_head(cfg): function build_loss (line 43) | def build_loss(cfg): function build_detector (line 48) | def build_detector(cfg, train_cfg=None, test_cfg=None): FILE: detection/mmdet/models/dense_heads/anchor_free_head.py class AnchorFreeHead (line 18) | class AnchorFreeHead(BaseDenseHead, BBoxTestMixin): method __init__ (line 46) | def __init__(self, method _init_layers (line 107) | def _init_layers(self): method _init_cls_convs (line 113) | def _init_cls_convs(self): method _init_reg_convs (line 133) | def _init_reg_convs(self): method _init_predictor (line 153) | def _init_predictor(self): method _load_from_state_dict (line 159) | def _load_from_state_dict(self, state_dict, prefix, local_metadata, st... method forward (line 197) | def forward(self, feats): method forward_single (line 215) | def forward_single(self, x): method loss (line 240) | def loss(self, method get_targets (line 268) | def get_targets(self, points, gt_bboxes_list, gt_labels_list): method _get_points_single (line 282) | def _get_points_single(self, method get_points (line 310) | def get_points(self, featmap_sizes, dtype, device, flatten=False): method aug_test (line 334) | def aug_test(self, feats, img_metas, rescale=False): FILE: detection/mmdet/models/dense_heads/anchor_head.py class AnchorHead (line 17) | class AnchorHead(BaseDenseHead, BBoxTestMixin): method __init__ (line 39) | def __init__(self, method num_anchors (line 115) | def num_anchors(self): method anchor_generator (line 122) | def anchor_generator(self): method _init_layers (line 127) | def _init_layers(self): method forward_single (line 135) | def forward_single(self, x): method forward (line 152) | def forward(self, feats): method get_anchors (line 171) | def get_anchors(self, featmap_sizes, img_metas, device='cuda'): method _get_targets_single (line 201) | def _get_targets_single(self, method get_targets (line 299) | def get_targets(self, method loss_single (line 402) | def loss_single(self, cls_score, bbox_pred, anchors, labels, label_wei... method loss (line 453) | def loss(self, method aug_test (line 522) | def aug_test(self, feats, img_metas, rescale=False): FILE: detection/mmdet/models/dense_heads/atss_head.py class ATSSHead (line 14) | class ATSSHead(AnchorHead): method __init__ (line 24) | def __init__(self, method _init_layers (line 65) | def _init_layers(self): method forward (line 109) | def forward(self, feats): method forward_single (line 127) | def forward_single(self, x, scale): method loss_single (line 156) | def loss_single(self, anchors, cls_score, bbox_pred, centerness, labels, method loss (line 230) | def loss(self, method centerness_target (line 305) | def centerness_target(self, anchors, gts): method get_targets (line 322) | def get_targets(self, method _get_target_single (line 386) | def _get_target_single(self, method get_num_level_anchors_inside (line 496) | def get_num_level_anchors_inside(self, num_level_anchors, inside_flags): FILE: detection/mmdet/models/dense_heads/autoassign_head.py class CenterPrior (line 21) | class CenterPrior(nn.Module): method __init__ (line 37) | def __init__(self, method forward (line 49) | def forward(self, anchor_points_list, gt_bboxes, labels, class AutoAssignHead (line 128) | class AutoAssignHead(FCOSHead): method __init__ (line 148) | def __init__(self, method init_weights (line 167) | def init_weights(self): method forward_single (line 179) | def forward_single(self, x, scale, stride): method get_pos_loss_single (line 207) | def get_pos_loss_single(self, cls_score, objectness, reg_loss, gt_labels, method get_neg_loss_single (line 251) | def get_neg_loss_single(self, cls_score, objectness, gt_labels, ious, method loss (line 307) | def loss(self, method get_targets (line 439) | def get_targets(self, points, gt_bboxes_list): method _get_target_single (line 468) | def _get_target_single(self, gt_bboxes, points): method _get_points_single (line 506) | def _get_points_single(self, FILE: detection/mmdet/models/dense_heads/base_dense_head.py class BaseDenseHead (line 12) | class BaseDenseHead(BaseModule, metaclass=ABCMeta): method __init__ (line 15) | def __init__(self, init_cfg=None): method init_weights (line 18) | def init_weights(self): method loss (line 27) | def loss(self, **kwargs): method get_bboxes (line 32) | def get_bboxes(self, method _get_bboxes_single (line 109) | def _get_bboxes_single(self, method _bbox_post_process (line 226) | def _bbox_post_process(self, method forward_train (line 303) | def forward_train(self, method simple_test (line 343) | def simple_test(self, feats, img_metas, rescale=False): method onnx_export (line 363) | def onnx_export(self, FILE: detection/mmdet/models/dense_heads/base_mask_head.py class BaseMaskHead (line 7) | class BaseMaskHead(BaseModule, metaclass=ABCMeta): method __init__ (line 10) | def __init__(self, init_cfg): method loss (line 14) | def loss(self, **kwargs): method get_results (line 18) | def get_results(self, **kwargs): method forward_train (line 22) | def forward_train(self, method simple_test (line 73) | def simple_test(self, method onnx_export (line 114) | def onnx_export(self, img, img_metas): FILE: detection/mmdet/models/dense_heads/cascade_rpn_head.py class AdaptiveConv (line 20) | class AdaptiveConv(BaseModule): method __init__ (line 42) | def __init__(self, method forward (line 79) | def forward(self, x, offset): class StageCascadeRPNHead (line 96) | class StageCascadeRPNHead(RPNHead): method __init__ (line 112) | def __init__(self, method _init_layers (line 153) | def _init_layers(self): method forward_single (line 164) | def forward_single(self, x, offset): method forward (line 174) | def forward(self, feats, offset_list=None): method _region_targets_single (line 180) | def _region_targets_single(self, method region_targets (line 236) | def region_targets(self, method get_targets (line 286) | def get_targets(self, method anchor_offset (line 328) | def anchor_offset(self, anchor_list, anchor_strides, featmap_sizes): method loss_single (line 406) | def loss_single(self, cls_score, bbox_pred, anchors, labels, label_wei... method loss (line 436) | def loss(self, method get_bboxes (line 505) | def get_bboxes(self, method _get_bboxes_single (line 547) | def _get_bboxes_single(self, method refine_bboxes (line 672) | def refine_bboxes(self, anchor_list, bbox_preds, img_metas): class CascadeRPNHead (line 690) | class CascadeRPNHead(BaseDenseHead): method __init__ (line 705) | def __init__(self, num_stages, stages, train_cfg, test_cfg, init_cfg=N... method loss (line 720) | def loss(self): method get_bboxes (line 724) | def get_bboxes(self): method forward_train (line 728) | def forward_train(self, method simple_test_rpn (line 773) | def simple_test_rpn(self, x, img_metas): method aug_test_rpn (line 798) | def aug_test_rpn(self, x, img_metas): FILE: detection/mmdet/models/dense_heads/centernet_head.py class CenterNetHead (line 18) | class CenterNetHead(BaseDenseHead, BBoxTestMixin): method __init__ (line 38) | def __init__(self, method _build_head (line 64) | def _build_head(self, in_channel, feat_channel, out_channel): method init_weights (line 72) | def init_weights(self): method forward (line 81) | def forward(self, feats): method forward_single (line 98) | def forward_single(self, feat): method loss (line 116) | def loss(self, method get_targets (line 181) | def get_targets(self, gt_bboxes, gt_labels, feat_shape, img_shape): method get_bboxes (line 252) | def get_bboxes(self, method _get_bboxes_single (line 297) | def _get_bboxes_single(self, method decode_heatmap (line 351) | def decode_heatmap(self, method _bboxes_nms (line 402) | def _bboxes_nms(self, bboxes, labels, cfg): FILE: detection/mmdet/models/dense_heads/centripetal_head.py class CentripetalHead (line 13) | class CentripetalHead(CornerHead): method __init__ (line 48) | def __init__(self, method _init_centripetal_layers (line 73) | def _init_centripetal_layers(self): method _init_layers (line 133) | def _init_layers(self): method init_weights (line 141) | def init_weights(self): method forward_single (line 157) | def forward_single(self, x, lvl_ind): method loss (line 208) | def loss(self, method loss_single (line 284) | def loss_single(self, tl_hmp, br_hmp, tl_off, br_off, tl_guiding_shift, method get_bboxes (line 367) | def get_bboxes(self, FILE: detection/mmdet/models/dense_heads/corner_head.py class BiCornerPool (line 21) | class BiCornerPool(BaseModule): method __init__ (line 34) | def __init__(self, method forward (line 64) | def forward(self, x): class CornerHead (line 85) | class CornerHead(BaseDenseHead, BBoxTestMixin): method __init__ (line 118) | def __init__(self, method _make_layers (line 158) | def _make_layers(self, out_channels, in_channels=256, feat_channels=256): method _init_corner_kpt_layers (line 165) | def _init_corner_kpt_layers(self): method _init_corner_emb_layers (line 203) | def _init_corner_emb_layers(self): method _init_layers (line 221) | def _init_layers(self): method init_weights (line 230) | def init_weights(self): method forward (line 248) | def forward(self, feats): method forward_single (line 280) | def forward_single(self, x, lvl_ind, return_pool=False): method get_targets (line 325) | def get_targets(self, method loss (line 514) | def loss(self, method loss_single (line 576) | def loss_single(self, tl_hmp, br_hmp, tl_emb, br_emb, tl_off, br_off, method get_bboxes (line 655) | def get_bboxes(self, method _get_bboxes_single (line 704) | def _get_bboxes_single(self, method _bboxes_nms (line 777) | def _bboxes_nms(self, bboxes, labels, cfg): method decode_heatmap (line 795) | def decode_heatmap(self, method onnx_export (line 1031) | def onnx_export(self, FILE: detection/mmdet/models/dense_heads/deformable_detr_head.py class DeformableDETRHead (line 17) | class DeformableDETRHead(DETRHead): method __init__ (line 36) | def __init__(self, method _init_layers (line 50) | def _init_layers(self): method init_weights (line 83) | def init_weights(self): method forward (line 97) | def forward(self, mlvl_feats, img_metas): method loss (line 184) | def loss(self, method get_bboxes (line 269) | def get_bboxes(self, FILE: detection/mmdet/models/dense_heads/dense_test_mixins.py class BBoxTestMixin (line 14) | class BBoxTestMixin(object): method simple_test_bboxes (line 17) | def simple_test_bboxes(self, feats, img_metas, rescale=False): method aug_test_bboxes (line 41) | def aug_test_bboxes(self, feats, img_metas, rescale=False): method simple_test_rpn (line 116) | def simple_test_rpn(self, x, img_metas): method aug_test_rpn (line 133) | def aug_test_rpn(self, feats, img_metas): method async_simple_test_rpn (line 169) | async def async_simple_test_rpn(self, x, img_metas): method merge_aug_bboxes (line 179) | def merge_aug_bboxes(self, aug_bboxes, aug_scores, img_metas): FILE: detection/mmdet/models/dense_heads/detr_head.py class DETRHead (line 18) | class DETRHead(AnchorFreeHead): method __init__ (line 52) | def __init__(self, method _init_layers (line 152) | def _init_layers(self): method init_weights (line 167) | def init_weights(self): method _load_from_state_dict (line 172) | def _load_from_state_dict(self, state_dict, prefix, local_metadata, st... method forward (line 202) | def forward(self, feats, img_metas): method forward_single (line 226) | def forward_single(self, x, img_metas): method loss (line 268) | def loss(self, method loss_single (line 334) | def loss_single(self, method get_targets (line 419) | def get_targets(self, method _get_target_single (line 476) | def _get_target_single(self, method forward_train (line 547) | def forward_train(self, method get_bboxes (line 583) | def get_bboxes(self, method _get_bboxes_single (line 628) | def _get_bboxes_single(self, method simple_test_bboxes (line 685) | def simple_test_bboxes(self, feats, img_metas, rescale=False): method forward_onnx (line 707) | def forward_onnx(self, feats, img_metas): method forward_single_onnx (line 735) | def forward_single_onnx(self, x, img_metas): method onnx_export (line 775) | def onnx_export(self, all_cls_scores_list, all_bbox_preds_list, img_me... FILE: detection/mmdet/models/dense_heads/embedding_rpn_head.py class EmbeddingRPNHead (line 11) | class EmbeddingRPNHead(BaseModule): method __init__ (line 26) | def __init__(self, method _init_layers (line 38) | def _init_layers(self): method init_weights (line 44) | def init_weights(self): method _decode_init_proposals (line 54) | def _decode_init_proposals(self, imgs, img_metas): method forward_dummy (line 95) | def forward_dummy(self, img, img_metas): method forward_train (line 102) | def forward_train(self, img, img_metas): method simple_test_rpn (line 106) | def simple_test_rpn(self, img, img_metas): method simple_test (line 110) | def simple_test(self, img, img_metas): method aug_test_rpn (line 114) | def aug_test_rpn(self, feats, img_metas): FILE: detection/mmdet/models/dense_heads/fcos_head.py class FCOSHead (line 17) | class FCOSHead(AnchorFreeHead): method __init__ (line 60) | def __init__(self, method _init_layers (line 106) | def _init_layers(self): method forward (line 112) | def forward(self, feats): method forward_single (line 133) | def forward_single(self, x, scale, stride): method loss (line 168) | def loss(self, method get_targets (line 270) | def get_targets(self, points, gt_bboxes_list, gt_labels_list): method _get_target_single (line 331) | def _get_target_single(self, gt_bboxes, gt_labels, points, regress_ran... method centerness_target (line 415) | def centerness_target(self, pos_bbox_targets): method _get_points_single (line 436) | def _get_points_single(self, FILE: detection/mmdet/models/dense_heads/fovea_head.py class FeatureAlign (line 18) | class FeatureAlign(BaseModule): method __init__ (line 20) | def __init__(self, method forward (line 43) | def forward(self, x, shape): class FoveaHead (line 50) | class FoveaHead(AnchorFreeHead): method __init__ (line 55) | def __init__(self, method _init_layers (line 81) | def _init_layers(self): method forward_single (line 121) | def forward_single(self, x): method loss (line 134) | def loss(self, method get_targets (line 186) | def get_targets(self, gt_bbox_list, gt_label_list, featmap_sizes, poin... method _get_target_single (line 208) | def _get_target_single(self, method _get_bboxes_single (line 272) | def _get_bboxes_single(self, method _bbox_decode (line 358) | def _bbox_decode(self, priors, bbox_pred, base_len, max_shape): method _get_points_single (line 374) | def _get_points_single(self, *args, **kwargs): FILE: detection/mmdet/models/dense_heads/free_anchor_retina_head.py class FreeAnchorRetinaHead (line 13) | class FreeAnchorRetinaHead(RetinaHead): method __init__ (line 34) | def __init__(self, method loss (line 54) | def loss(self, method positive_bag_loss (line 221) | def positive_bag_loss(self, matched_cls_prob, matched_box_prob): method negative_bag_loss (line 248) | def negative_bag_loss(self, cls_prob, box_prob): FILE: detection/mmdet/models/dense_heads/fsaf_head.py class FSAFHead (line 15) | class FSAFHead(RetinaHead): method __init__ (line 43) | def __init__(self, *args, score_threshold=None, init_cfg=None, **kwargs): method forward_single (line 63) | def forward_single(self, x): method _get_targets_single (line 80) | def _get_targets_single(self, method loss (line 188) | def loss(self, method calculate_pos_recall (line 317) | def calculate_pos_recall(self, cls_scores, labels_list, pos_inds): method collect_loss_level_single (line 351) | def collect_loss_level_single(self, cls_loss, reg_loss, assigned_gt_inds, method reweight_loss_single (line 382) | def reweight_loss_single(self, cls_loss, reg_loss, assigned_gt_inds, FILE: detection/mmdet/models/dense_heads/ga_retina_head.py class GARetinaHead (line 11) | class GARetinaHead(GuidedAnchorHead): method __init__ (line 14) | def __init__(self, method _init_layers (line 45) | def _init_layers(self): method forward_single (line 92) | def forward_single(self, x): FILE: detection/mmdet/models/dense_heads/ga_rpn_head.py class GARPNHead (line 16) | class GARPNHead(GuidedAnchorHead): method __init__ (line 19) | def __init__(self, method _init_layers (line 34) | def _init_layers(self): method forward_single (line 40) | def forward_single(self, x): method loss (line 49) | def loss(self, method _get_bboxes_single (line 72) | def _get_bboxes_single(self, FILE: detection/mmdet/models/dense_heads/gfl_head.py class Integral (line 16) | class Integral(nn.Module): method __init__ (line 29) | def __init__(self, reg_max=16): method forward (line 35) | def forward(self, x): class GFLHead (line 53) | class GFLHead(AnchorHead): method __init__ (line 88) | def __init__(self, method _init_layers (line 128) | def _init_layers(self): method forward (line 161) | def forward(self, feats): method forward_single (line 179) | def forward_single(self, x, scale): method anchor_center (line 205) | def anchor_center(self, anchors): method loss_single (line 218) | def loss_single(self, anchors, cls_score, bbox_pred, labels, label_wei... method loss (line 307) | def loss(self, method _get_bboxes_single (line 380) | def _get_bboxes_single(self, method get_targets (line 473) | def get_targets(self, method _get_target_single (line 537) | def _get_target_single(self, method get_num_level_anchors_inside (line 643) | def get_num_level_anchors_inside(self, num_level_anchors, inside_flags): FILE: detection/mmdet/models/dense_heads/guided_anchor_head.py class FeatureAdaption (line 16) | class FeatureAdaption(BaseModule): method __init__ (line 31) | def __init__(self, method forward (line 54) | def forward(self, x, shape): class GuidedAnchorHead (line 61) | class GuidedAnchorHead(AnchorHead): method __init__ (line 97) | def __init__( method num_anchors (line 212) | def num_anchors(self): method _init_layers (line 217) | def _init_layers(self): method forward_single (line 233) | def forward_single(self, x): method forward (line 246) | def forward(self, feats): method get_sampled_approxs (line 249) | def get_sampled_approxs(self, featmap_sizes, img_metas, device='cuda'): method get_anchors (line 299) | def get_anchors(self, method _get_guided_anchors_single (line 350) | def _get_guided_anchors_single(self, method ga_loc_targets (line 384) | def ga_loc_targets(self, gt_bboxes_list, featmap_sizes): method _ga_shape_target_single (line 486) | def _ga_shape_target_single(self, method ga_shape_targets (line 549) | def ga_shape_targets(self, method loss_shape_single (line 615) | def loss_shape_single(self, shape_pred, bbox_anchors, bbox_gts, method loss_loc_single (line 639) | def loss_loc_single(self, loc_pred, loc_target, loc_weight, method loss (line 650) | def loss(self, method get_bboxes (line 756) | def get_bboxes(self, method _get_bboxes_single (line 800) | def _get_bboxes_single(self, FILE: detection/mmdet/models/dense_heads/lad_head.py class LADHead (line 11) | class LADHead(PAAHead): method get_label_assignment (line 16) | def get_label_assignment(self, method forward_train (line 121) | def forward_train(self, method loss (line 160) | def loss(self, FILE: detection/mmdet/models/dense_heads/ld_head.py class LDHead (line 11) | class LDHead(GFLHead): method __init__ (line 26) | def __init__(self, method loss_single (line 38) | def loss_single(self, anchors, cls_score, bbox_pred, labels, label_wei... method forward_train (line 142) | def forward_train(self, method loss (line 185) | def loss(self, FILE: detection/mmdet/models/dense_heads/mask2former_head.py class Mask2FormerHead (line 21) | class Mask2FormerHead(MaskFormerHead): method __init__ (line 58) | def __init__(self, method init_weights (line 137) | def init_weights(self): method _get_target_single (line 148) | def _get_target_single(self, cls_score, mask_pred, gt_labels, gt_masks, method loss_single (line 218) | def loss_single(self, cls_scores, mask_preds, gt_labels_list, method forward_head (line 309) | def forward_head(self, decoder_out, mask_feature, attn_mask_target_size): method forward (line 351) | def forward(self, feats, img_metas): FILE: detection/mmdet/models/dense_heads/maskformer_head.py class MaskFormerHead (line 17) | class MaskFormerHead(AnchorFreeHead): method __init__ (line 53) | def __init__(self, method init_weights (line 127) | def init_weights(self): method preprocess_gt (line 137) | def preprocess_gt(self, gt_labels_list, gt_masks_list, gt_semantic_segs): method get_targets (line 171) | def get_targets(self, cls_scores_list, mask_preds_list, gt_labels_list, method _get_target_single (line 215) | def _get_target_single(self, cls_score, mask_pred, gt_labels, gt_masks, method loss (line 276) | def loss(self, all_cls_scores, all_mask_preds, gt_labels_list, method loss_single (line 320) | def loss_single(self, cls_scores, mask_preds, gt_labels_list, method forward (line 412) | def forward(self, feats, img_metas): method forward_train (line 477) | def forward_train(self, method simple_test (line 524) | def simple_test(self, feats, img_metas, **kwargs): FILE: detection/mmdet/models/dense_heads/nasfcos_head.py class NASFCOSHead (line 12) | class NASFCOSHead(FCOSHead): method __init__ (line 20) | def __init__(self, *args, init_cfg=None, **kwargs): method _init_layers (line 39) | def _init_layers(self): FILE: detection/mmdet/models/dense_heads/paa_head.py function levels_to_images (line 18) | def levels_to_images(mlvl_tensor): class PAAHead (line 46) | class PAAHead(ATSSHead): method __init__ (line 74) | def __init__(self, method loss (line 87) | def loss(self, method get_pos_loss (line 202) | def get_pos_loss(self, anchors, cls_score, bbox_pred, label, label_wei... method paa_reassign (line 258) | def paa_reassign(self, pos_losses, label, label_weight, bbox_weight, method gmm_separation_scheme (line 367) | def gmm_separation_scheme(self, gmm_assignment, scores, pos_inds_gmm): method get_targets (line 402) | def get_targets( method _get_targets_single (line 494) | def _get_targets_single(self, method get_bboxes (line 521) | def get_bboxes(self, method _get_bboxes_single (line 537) | def _get_bboxes_single(self, method _bbox_post_process (line 620) | def _bbox_post_process(self, method score_voting (line 693) | def score_voting(self, det_bboxes, det_labels, mlvl_bboxes, FILE: detection/mmdet/models/dense_heads/pisa_retinanet_head.py class PISARetinaHead (line 12) | class PISARetinaHead(RetinaHead): method loss (line 23) | def loss(self, FILE: detection/mmdet/models/dense_heads/pisa_ssd_head.py class PISASSDHead (line 12) | class PISASSDHead(SSDHead): method loss (line 14) | def loss(self, FILE: detection/mmdet/models/dense_heads/reppoints_head.py class RepPointsHead (line 17) | class RepPointsHead(AnchorFreeHead): method __init__ (line 36) | def __init__(self, method _init_layers (line 126) | def _init_layers(self): method points2bbox (line 170) | def points2bbox(self, pts, y_first=True): method gen_grid_from_reg (line 221) | def gen_grid_from_reg(self, reg, previous_boxes): method forward (line 255) | def forward(self, feats): method forward_single (line 258) | def forward_single(self, x): method get_points (line 304) | def get_points(self, featmap_sizes, img_metas, device): method centers_to_bboxes (line 332) | def centers_to_bboxes(self, point_list): method offset_to_pts (line 350) | def offset_to_pts(self, center_list, pred_list): method _point_target_single (line 371) | def _point_target_single(self, method get_targets (line 442) | def get_targets(self, method loss_single (line 525) | def loss_single(self, cls_score, pts_pred_init, pts_pred_refine, labels, method loss (line 563) | def loss(self, method _get_bboxes_single (line 659) | def _get_bboxes_single(self, method _bbox_decode (line 756) | def _bbox_decode(self, points, bbox_pred, stride, max_shape): FILE: detection/mmdet/models/dense_heads/retina_head.py class RetinaHead (line 10) | class RetinaHead(AnchorHead): method __init__ (line 29) | def __init__(self, method _init_layers (line 61) | def _init_layers(self): method forward_single (line 94) | def forward_single(self, x): FILE: detection/mmdet/models/dense_heads/retina_sepbn_head.py class RetinaSepBNHead (line 10) | class RetinaSepBNHead(AnchorHead): method __init__ (line 18) | def __init__(self, method _init_layers (line 36) | def _init_layers(self): method init_weights (line 78) | def init_weights(self): method forward (line 89) | def forward(self, feats): FILE: detection/mmdet/models/dense_heads/rpn_head.py class RPNHead (line 15) | class RPNHead(AnchorHead): method __init__ (line 24) | def __init__(self, method _init_layers (line 33) | def _init_layers(self): method forward_single (line 62) | def forward_single(self, x): method loss (line 70) | def loss(self, method _get_bboxes_single (line 103) | def _get_bboxes_single(self, method _bbox_post_process (line 189) | def _bbox_post_process(self, mlvl_scores, mlvl_bboxes, mlvl_valid_anch... method onnx_export (line 237) | def onnx_export(self, x, img_metas): FILE: detection/mmdet/models/dense_heads/sabl_retina_head.py class SABLRetinaHead (line 21) | class SABLRetinaHead(BaseDenseHead, BBoxTestMixin): method __init__ (line 54) | def __init__(self, method num_anchors (line 158) | def num_anchors(self): method _init_layers (line 163) | def _init_layers(self): method forward_single (line 194) | def forward_single(self, x): method forward (line 207) | def forward(self, feats): method get_anchors (line 210) | def get_anchors(self, featmap_sizes, img_metas, device='cuda'): method get_target (line 231) | def get_target(self, method _get_target_single (line 331) | def _get_target_single(self, method loss_single (line 448) | def loss_single(self, cls_score, bbox_pred, labels, label_weights, method loss (line 481) | def loss(self, method get_bboxes (line 535) | def get_bboxes(self, method _get_bboxes_single (line 567) | def _get_bboxes_single(self, FILE: detection/mmdet/models/dense_heads/solo_head.py class SOLOHead (line 16) | class SOLOHead(BaseMaskHead): method __init__ (line 49) | def __init__( method _init_layers (line 102) | def _init_layers(self): method resize_feats (line 132) | def resize_feats(self, feats): method forward (line 149) | def forward(self, feats): method loss (line 195) | def loss(self, method _get_targets_single (line 285) | def _get_targets_single(self, method get_results (line 419) | def get_results(self, mlvl_mask_preds, mlvl_cls_scores, img_metas, method _get_results_single (line 468) | def _get_results_single(self, cls_scores, mask_preds, img_meta, cfg=No... class DecoupledSOLOHead (line 570) | class DecoupledSOLOHead(SOLOHead): method __init__ (line 579) | def __init__(self, method _init_layers (line 603) | def _init_layers(self): method forward (line 647) | def forward(self, feats): method loss (line 708) | def loss(self, method _get_targets_single (line 811) | def _get_targets_single(self, method get_results (line 854) | def get_results(self, method _get_results_single (line 919) | def _get_results_single(self, cls_scores, mask_preds_x, mask_preds_y, class DecoupledSOLOLightHead (line 1043) | class DecoupledSOLOLightHead(DecoupledSOLOHead): method __init__ (line 1053) | def __init__(self, method _init_layers (line 1080) | def _init_layers(self): method forward (line 1123) | def forward(self, feats): FILE: detection/mmdet/models/dense_heads/ssd_head.py class SSDHead (line 19) | class SSDHead(AnchorHead): method __init__ (line 50) | def __init__(self, method num_anchors (line 116) | def num_anchors(self): method _init_layers (line 125) | def _init_layers(self): method forward (line 196) | def forward(self, feats): method loss_single (line 220) | def loss_single(self, cls_score, bbox_pred, anchor, labels, label_weig... method loss (line 279) | def loss(self, FILE: detection/mmdet/models/dense_heads/tood_head.py class TaskDecomposition (line 17) | class TaskDecomposition(nn.Module): method __init__ (line 28) | def __init__(self, method init_weights (line 58) | def init_weights(self): method forward (line 64) | def forward(self, feat, avg_feat=None): class TOODHead (line 90) | class TOODHead(ATSSHead): method __init__ (line 113) | def __init__(self, method _init_layers (line 142) | def _init_layers(self): method init_weights (line 191) | def init_weights(self): method forward (line 210) | def forward(self, feats): method deform_sampling (line 284) | def deform_sampling(self, feat, offset): method anchor_center (line 297) | def anchor_center(self, anchors): method loss_single (line 310) | def loss_single(self, anchors, cls_score, bbox_pred, labels, label_wei... method loss (line 383) | def loss(self, method _get_bboxes_single (line 462) | def _get_bboxes_single(self, method get_targets (line 549) | def get_targets(self, method _get_target_single (line 664) | def _get_target_single(self, FILE: detection/mmdet/models/dense_heads/vfnet_head.py class VFNetHead (line 22) | class VFNetHead(ATSSHead, FCOSHead): method __init__ (line 68) | def __init__(self, method num_anchors (line 176) | def num_anchors(self): method anchor_generator (line 186) | def anchor_generator(self): method _init_layers (line 191) | def _init_layers(self): method forward (line 226) | def forward(self, feats): method forward_single (line 248) | def forward_single(self, x, scale, scale_refine, stride, reg_denom): method star_dcn_offset (line 309) | def star_dcn_offset(self, bbox_pred, gradient_mul, stride): method loss (line 351) | def loss(self, method get_targets (line 500) | def get_targets(self, cls_scores, mlvl_points, gt_bboxes, gt_labels, method _get_target_single (line 535) | def _get_target_single(self, *args, **kwargs): method get_fcos_targets (line 542) | def get_fcos_targets(self, points, gt_bboxes_list, gt_labels_list): method get_anchors (line 568) | def get_anchors(self, featmap_sizes, img_metas, device='cuda'): method get_atss_targets (line 598) | def get_atss_targets(self, method transform_bbox_targets (line 677) | def transform_bbox_targets(self, decoded_bboxes, mlvl_points, num_imgs): method _load_from_state_dict (line 703) | def _load_from_state_dict(self, state_dict, prefix, local_metadata, st... method _get_points_single (line 709) | def _get_points_single(self, FILE: detection/mmdet/models/dense_heads/yolact_head.py class YOLACTHead (line 16) | class YOLACTHead(AnchorHead): method __init__ (line 44) | def __init__(self, method _init_layers (line 89) | def _init_layers(self): method forward_single (line 117) | def forward_single(self, x): method loss (line 140) | def loss(self, method loss_single_OHEM (line 261) | def loss_single_OHEM(self, cls_score, bbox_pred, anchors, labels, method get_bboxes (line 297) | def get_bboxes(self, method _get_bboxes_single (line 358) | def _get_bboxes_single(self, class YOLACTSegmHead (line 457) | class YOLACTSegmHead(BaseModule): method __init__ (line 472) | def __init__(self, method _init_layers (line 490) | def _init_layers(self): method forward (line 495) | def forward(self, x): method loss (line 509) | def loss(self, segm_pred, gt_masks, gt_labels): method get_targets (line 542) | def get_targets(self, segm_pred, gt_masks, gt_labels): method simple_test (line 572) | def simple_test(self, feats, img_metas, rescale=False): class YOLACTProtonet (line 580) | class YOLACTProtonet(BaseModule): method __init__ (line 599) | def __init__(self, method _init_layers (line 625) | def _init_layers(self): method forward_dummy (line 662) | def forward_dummy(self, x): method forward (line 666) | def forward(self, x, coeff_pred, bboxes, img_meta, sampling_results=No... method loss (line 743) | def loss(self, mask_pred, gt_masks, gt_bboxes, img_meta, sampling_resu... method get_targets (line 816) | def get_targets(self, mask_pred, gt_masks, pos_assigned_gt_inds): method get_seg_masks (line 841) | def get_seg_masks(self, mask_pred, label_pred, img_meta, rescale): method crop (line 876) | def crop(self, masks, boxes, padding=1): method sanitize_coordinates (line 910) | def sanitize_coordinates(self, x1, x2, img_size, padding=0, cast=True): method simple_test (line 941) | def simple_test(self, class InterpolateModule (line 1004) | class InterpolateModule(BaseModule): method __init__ (line 1010) | def __init__(self, *args, init_cfg=None, **kwargs): method forward (line 1016) | def forward(self, x): FILE: detection/mmdet/models/dense_heads/yolo_head.py class YOLOV3Head (line 23) | class YOLOV3Head(BaseDenseHead, BBoxTestMixin): method __init__ (line 51) | def __init__(self, method anchor_generator (line 125) | def anchor_generator(self): method num_anchors (line 132) | def num_anchors(self): method num_levels (line 142) | def num_levels(self): method num_attrib (line 146) | def num_attrib(self): method _init_layers (line 152) | def _init_layers(self): method init_weights (line 170) | def init_weights(self): method forward (line 186) | def forward(self, feats): method get_bboxes (line 209) | def get_bboxes(self, method loss (line 301) | def loss(self, method loss_single (line 352) | def loss_single(self, pred_map, target_map, neg_map): method get_targets (line 397) | def get_targets(self, anchor_list, responsible_flag_list, gt_bboxes_list, method _get_targets_single (line 433) | def _get_targets_single(self, anchors, responsible_flags, gt_bboxes, method aug_test (line 494) | def aug_test(self, feats, img_metas, rescale=False): method onnx_export (line 513) | def onnx_export(self, pred_maps, img_metas, with_nms=True): FILE: detection/mmdet/models/dense_heads/yolof_head.py function levels_to_images (line 15) | def levels_to_images(mlvl_tensor): class YOLOFHead (line 43) | class YOLOFHead(AnchorHead): method __init__ (line 56) | def __init__(self, method _init_layers (line 68) | def _init_layers(self): method init_weights (line 108) | def init_weights(self): method forward_single (line 119) | def forward_single(self, feature): method loss (line 137) | def loss(self, method get_targets (line 224) | def get_targets(self, method _get_targets_single (line 311) | def _get_targets_single(self, FILE: detection/mmdet/models/dense_heads/yolox_head.py class YOLOXHead (line 22) | class YOLOXHead(BaseDenseHead, BBoxTestMixin): method __init__ (line 53) | def __init__(self, method _init_layers (line 131) | def _init_layers(self): method _build_stacked_convs (line 145) | def _build_stacked_convs(self): method _build_predictor (line 169) | def _build_predictor(self): method init_weights (line 176) | def init_weights(self): method forward_single (line 185) | def forward_single(self, x, cls_convs, reg_convs, conv_cls, conv_reg, method forward (line 198) | def forward(self, feats): method get_bboxes (line 217) | def get_bboxes(self, method _bbox_decode (line 301) | def _bbox_decode(self, priors, bbox_preds): method _bboxes_nms (line 313) | def _bboxes_nms(self, cls_scores, bboxes, score_factor, cfg): method loss (line 328) | def loss(self, method _get_target_single (line 426) | def _get_target_single(self, cls_preds, objectness, priors, decoded_bb... method _get_l1_target (line 488) | def _get_l1_target(self, l1_target, gt_bboxes, priors, eps=1e-8): FILE: detection/mmdet/models/detectors/atss.py class ATSS (line 7) | class ATSS(SingleStageDetector): method __init__ (line 10) | def __init__(self, FILE: detection/mmdet/models/detectors/autoassign.py class AutoAssign (line 7) | class AutoAssign(SingleStageDetector): method __init__ (line 11) | def __init__(self, FILE: detection/mmdet/models/detectors/base.py class BaseDetector (line 14) | class BaseDetector(BaseModule, metaclass=ABCMeta): method __init__ (line 17) | def __init__(self, init_cfg=None): method with_neck (line 22) | def with_neck(self): method with_shared_head (line 29) | def with_shared_head(self): method with_bbox (line 34) | def with_bbox(self): method with_mask (line 40) | def with_mask(self): method extract_feat (line 46) | def extract_feat(self, imgs): method extract_feats (line 50) | def extract_feats(self, imgs): method forward_train (line 63) | def forward_train(self, imgs, img_metas, **kwargs): method async_simple_test (line 82) | async def async_simple_test(self, img, img_metas, **kwargs): method simple_test (line 86) | def simple_test(self, img, img_metas, **kwargs): method aug_test (line 90) | def aug_test(self, imgs, img_metas, **kwargs): method aforward_test (line 94) | async def aforward_test(self, *, img, img_metas, **kwargs): method forward_test (line 112) | def forward_test(self, imgs, img_metas, **kwargs): method forward (line 157) | def forward(self, img, img_metas, return_loss=True, **kwargs): method _parse_losses (line 176) | def _parse_losses(self, losses): method train_step (line 221) | def train_step(self, data, optimizer): method val_step (line 256) | def val_step(self, data, optimizer=None): method show_result (line 271) | def show_result(self, method onnx_export (line 358) | def onnx_export(self, img, img_metas): FILE: detection/mmdet/models/detectors/cascade_rcnn.py class CascadeRCNN (line 7) | class CascadeRCNN(TwoStageDetector): method __init__ (line 11) | def __init__(self, method show_result (line 30) | def show_result(self, data, result, **kwargs): FILE: detection/mmdet/models/detectors/centernet.py class CenterNet (line 11) | class CenterNet(SingleStageDetector): method __init__ (line 17) | def __init__(self, method merge_aug_results (line 28) | def merge_aug_results(self, aug_results, with_nms): method aug_test (line 54) | def aug_test(self, imgs, img_metas, rescale=True): FILE: detection/mmdet/models/detectors/cornernet.py class CornerNet (line 10) | class CornerNet(SingleStageDetector): method __init__ (line 17) | def __init__(self, method merge_aug_results (line 28) | def merge_aug_results(self, aug_results, img_metas): method aug_test (line 62) | def aug_test(self, imgs, img_metas, rescale=False): FILE: detection/mmdet/models/detectors/deformable_detr.py class DeformableDETR (line 7) | class DeformableDETR(DETR): method __init__ (line 9) | def __init__(self, *args, **kwargs): FILE: detection/mmdet/models/detectors/detr.py class DETR (line 11) | class DETR(SingleStageDetector): method __init__ (line 15) | def __init__(self, method forward_dummy (line 27) | def forward_dummy(self, img): method onnx_export (line 50) | def onnx_export(self, img, img_metas): FILE: detection/mmdet/models/detectors/fast_rcnn.py class FastRCNN (line 7) | class FastRCNN(TwoStageDetector): method __init__ (line 10) | def __init__(self, method forward_test (line 27) | def forward_test(self, imgs, img_metas, proposals, **kwargs): FILE: detection/mmdet/models/detectors/faster_rcnn.py class FasterRCNN (line 7) | class FasterRCNN(TwoStageDetector): method __init__ (line 10) | def __init__(self, FILE: detection/mmdet/models/detectors/fcos.py class FCOS (line 7) | class FCOS(SingleStageDetector): method __init__ (line 10) | def __init__(self, FILE: detection/mmdet/models/detectors/fovea.py class FOVEA (line 7) | class FOVEA(SingleStageDetector): method __init__ (line 10) | def __init__(self, FILE: detection/mmdet/models/detectors/fsaf.py class FSAF (line 7) | class FSAF(SingleStageDetector): method __init__ (line 10) | def __init__(self, FILE: detection/mmdet/models/detectors/gfl.py class GFL (line 7) | class GFL(SingleStageDetector): method __init__ (line 9) | def __init__(self, FILE: detection/mmdet/models/detectors/grid_rcnn.py class GridRCNN (line 7) | class GridRCNN(TwoStageDetector): method __init__ (line 15) | def __init__(self, FILE: detection/mmdet/models/detectors/htc.py class HybridTaskCascade (line 7) | class HybridTaskCascade(CascadeRCNN): method __init__ (line 10) | def __init__(self, **kwargs): method with_semantic (line 14) | def with_semantic(self): FILE: detection/mmdet/models/detectors/kd_one_stage.py class KnowledgeDistillationSingleStageDetector (line 14) | class KnowledgeDistillationSingleStageDetector(SingleStageDetector): method __init__ (line 25) | def __init__(self, method forward_train (line 46) | def forward_train(self, method cuda (line 78) | def cuda(self, device=None): method train (line 84) | def train(self, mode=True): method __setattr__ (line 92) | def __setattr__(self, name, value): FILE: detection/mmdet/models/detectors/lad.py class LAD (line 11) | class LAD(KnowledgeDistillationSingleStageDetector): method __init__ (line 14) | def __init__(self, method with_teacher_neck (line 42) | def with_teacher_neck(self): method extract_teacher_feat (line 47) | def extract_teacher_feat(self, img): method forward_train (line 54) | def forward_train(self, FILE: detection/mmdet/models/detectors/mask2former.py class Mask2Former (line 7) | class Mask2Former(MaskFormer): method __init__ (line 12) | def __init__(self, FILE: detection/mmdet/models/detectors/mask_rcnn.py class MaskRCNN (line 7) | class MaskRCNN(TwoStageDetector): method __init__ (line 10) | def __init__(self, FILE: detection/mmdet/models/detectors/mask_scoring_rcnn.py class MaskScoringRCNN (line 7) | class MaskScoringRCNN(TwoStageDetector): method __init__ (line 13) | def __init__(self, FILE: detection/mmdet/models/detectors/maskformer.py class MaskFormer (line 12) | class MaskFormer(SingleStageDetector): method __init__ (line 17) | def __init__(self, method forward_dummy (line 46) | def forward_dummy(self, img, img_metas): method forward_train (line 64) | def forward_train(self, method simple_test (line 106) | def simple_test(self, imgs, img_metas, **kwargs): method aug_test (line 156) | def aug_test(self, imgs, img_metas, **kwargs): method onnx_export (line 159) | def onnx_export(self, img, img_metas): method show_result (line 162) | def show_result(self, FILE: detection/mmdet/models/detectors/nasfcos.py class NASFCOS (line 7) | class NASFCOS(SingleStageDetector): method __init__ (line 13) | def __init__(self, FILE: detection/mmdet/models/detectors/paa.py class PAA (line 7) | class PAA(SingleStageDetector): method __init__ (line 10) | def __init__(self, FILE: detection/mmdet/models/detectors/panoptic_fpn.py class PanopticFPN (line 7) | class PanopticFPN(TwoStagePanopticSegmentor): method __init__ (line 11) | def __init__( FILE: detection/mmdet/models/detectors/panoptic_two_stage_segmentor.py class TwoStagePanopticSegmentor (line 14) | class TwoStagePanopticSegmentor(TwoStageDetector): method __init__ (line 21) | def __init__( method with_semantic_head (line 52) | def with_semantic_head(self): method with_panoptic_fusion_head (line 57) | def with_panoptic_fusion_head(self): method forward_dummy (line 61) | def forward_dummy(self, img): method forward_train (line 69) | def forward_train(self, method simple_test_mask (line 108) | def simple_test_mask(self, method simple_test (line 169) | def simple_test(self, img, img_metas, proposals=None, rescale=False): method show_result (line 208) | def show_result(self, FILE: detection/mmdet/models/detectors/point_rend.py class PointRend (line 7) | class PointRend(TwoStageDetector): method __init__ (line 15) | def __init__(self, FILE: detection/mmdet/models/detectors/queryinst.py class QueryInst (line 7) | class QueryInst(SparseRCNN): method __init__ (line 11) | def __init__(self, FILE: detection/mmdet/models/detectors/reppoints_detector.py class RepPointsDetector (line 7) | class RepPointsDetector(SingleStageDetector): method __init__ (line 14) | def __init__(self, FILE: detection/mmdet/models/detectors/retinanet.py class RetinaNet (line 7) | class RetinaNet(SingleStageDetector): method __init__ (line 10) | def __init__(self, FILE: detection/mmdet/models/detectors/rpn.py class RPN (line 14) | class RPN(BaseDetector): method __init__ (line 17) | def __init__(self, method extract_feat (line 39) | def extract_feat(self, img): method forward_dummy (line 54) | def forward_dummy(self, img): method forward_train (line 60) | def forward_train(self, method simple_test (line 91) | def simple_test(self, img, img_metas, rescale=False): method aug_test (line 117) | def aug_test(self, imgs, img_metas, rescale=False): method show_result (line 142) | def show_result(self, data, result, top_k=20, **kwargs): FILE: detection/mmdet/models/detectors/scnet.py class SCNet (line 7) | class SCNet(CascadeRCNN): method __init__ (line 10) | def __init__(self, **kwargs): FILE: detection/mmdet/models/detectors/single_stage.py class SingleStageDetector (line 12) | class SingleStageDetector(BaseDetector): method __init__ (line 19) | def __init__(self, method extract_feat (line 41) | def extract_feat(self, img): method forward_dummy (line 48) | def forward_dummy(self, img): method forward_train (line 57) | def forward_train(self, method simple_test (line 87) | def simple_test(self, img, img_metas, rescale=False): method aug_test (line 110) | def aug_test(self, imgs, img_metas, rescale=False): method onnx_export (line 141) | def onnx_export(self, img, img_metas, with_nms=True): FILE: detection/mmdet/models/detectors/single_stage_instance_seg.py class SingleStageInstanceSegmentor (line 17) | class SingleStageInstanceSegmentor(BaseDetector): method __init__ (line 20) | def __init__(self, method extract_feat (line 56) | def extract_feat(self, img): method forward_dummy (line 63) | def forward_dummy(self, img): method forward_train (line 71) | def forward_train(self, method simple_test (line 142) | def simple_test(self, img, img_metas, rescale=False): method format_results (line 184) | def format_results(self, results): method aug_test (line 252) | def aug_test(self, imgs, img_metas, rescale=False): method show_result (line 255) | def show_result(self, FILE: detection/mmdet/models/detectors/solo.py class SOLO (line 7) | class SOLO(SingleStageInstanceSegmentor): method __init__ (line 13) | def __init__(self, FILE: detection/mmdet/models/detectors/sparse_rcnn.py class SparseRCNN (line 7) | class SparseRCNN(TwoStageDetector): method __init__ (line 11) | def __init__(self, *args, **kwargs): method forward_train (line 16) | def forward_train(self, method simple_test (line 67) | def simple_test(self, img, img_metas, rescale=False): method forward_dummy (line 93) | def forward_dummy(self, img): FILE: detection/mmdet/models/detectors/tood.py class TOOD (line 7) | class TOOD(SingleStageDetector): method __init__ (line 11) | def __init__(self, method set_epoch (line 22) | def set_epoch(self, epoch): FILE: detection/mmdet/models/detectors/trident_faster_rcnn.py class TridentFasterRCNN (line 7) | class TridentFasterRCNN(FasterRCNN): method __init__ (line 10) | def __init__(self, method simple_test (line 34) | def simple_test(self, img, img_metas, proposals=None, rescale=False): method aug_test (line 49) | def aug_test(self, imgs, img_metas, rescale=False): method forward_train (line 62) | def forward_train(self, img, img_metas, gt_bboxes, gt_labels, **kwargs): FILE: detection/mmdet/models/detectors/two_stage.py class TwoStageDetector (line 11) | class TwoStageDetector(BaseDetector): method __init__ (line 18) | def __init__(self, method with_rpn (line 56) | def with_rpn(self): method with_roi_head (line 61) | def with_roi_head(self): method extract_feat (line 65) | def extract_feat(self, img): method forward_dummy (line 72) | def forward_dummy(self, img): method forward_train (line 90) | def forward_train(self, method async_simple_test (line 155) | async def async_simple_test(self, method simple_test (line 173) | def simple_test(self, img, img_metas, proposals=None, rescale=False): method aug_test (line 186) | def aug_test(self, imgs, img_metas, rescale=False): method onnx_export (line 197) | def onnx_export(self, img, img_metas): FILE: detection/mmdet/models/detectors/vfnet.py class VFNet (line 7) | class VFNet(SingleStageDetector): method __init__ (line 11) | def __init__(self, FILE: detection/mmdet/models/detectors/yolact.py class YOLACT (line 10) | class YOLACT(SingleStageDetector): method __init__ (line 13) | def __init__(self, method forward_dummy (line 28) | def forward_dummy(self, img): method forward_train (line 38) | def forward_train(self, method simple_test (line 97) | def simple_test(self, img, img_metas, rescale=False): method aug_test (line 117) | def aug_test(self, imgs, img_metas, rescale=False): FILE: detection/mmdet/models/detectors/yolo.py class YOLOV3 (line 10) | class YOLOV3(SingleStageDetector): method __init__ (line 12) | def __init__(self, method onnx_export (line 23) | def onnx_export(self, img, img_metas): FILE: detection/mmdet/models/detectors/yolof.py class YOLOF (line 7) | class YOLOF(SingleStageDetector): method __init__ (line 11) | def __init__(self, FILE: detection/mmdet/models/detectors/yolox.py class YOLOX (line 15) | class YOLOX(SingleStageDetector): method __init__ (line 46) | def __init__(self, method forward_train (line 69) | def forward_train(self, method _preprocess (line 105) | def _preprocess(self, img, gt_bboxes): method _random_resize (line 119) | def _random_resize(self): FILE: detection/mmdet/models/losses/accuracy.py function accuracy (line 7) | def accuracy(pred, target, topk=1, thresh=None): class Accuracy (line 54) | class Accuracy(nn.Module): method __init__ (line 56) | def __init__(self, topk=(1, ), thresh=None): method forward (line 69) | def forward(self, pred, target): FILE: detection/mmdet/models/losses/ae_loss.py function ae_loss_per_image (line 11) | def ae_loss_per_image(tl_preds, br_preds, match): class AssociativeEmbeddingLoss (line 75) | class AssociativeEmbeddingLoss(nn.Module): method __init__ (line 88) | def __init__(self, pull_weight=0.25, push_weight=0.25): method forward (line 93) | def forward(self, pred, target, match): FILE: detection/mmdet/models/losses/balanced_l1_loss.py function balanced_l1_loss (line 13) | def balanced_l1_loss(pred, class BalancedL1Loss (line 57) | class BalancedL1Loss(nn.Module): method __init__ (line 74) | def __init__(self, method forward (line 87) | def forward(self, FILE: detection/mmdet/models/losses/cross_entropy_loss.py function cross_entropy (line 12) | def cross_entropy(pred, function _expand_onehot_labels (line 64) | def _expand_onehot_labels(labels, label_weights, label_channels, ignore_... function binary_cross_entropy (line 85) | def binary_cross_entropy(pred, function mask_cross_entropy (line 148) | def mask_cross_entropy(pred, class CrossEntropyLoss (line 201) | class CrossEntropyLoss(nn.Module): method __init__ (line 203) | def __init__(self, method extra_repr (line 252) | def extra_repr(self): method forward (line 257) | def forward(self, FILE: detection/mmdet/models/losses/dice_loss.py function dice_loss (line 9) | def dice_loss(pred, class DiceLoss (line 66) | class DiceLoss(nn.Module): method __init__ (line 68) | def __init__(self, method forward (line 103) | def forward(self, FILE: detection/mmdet/models/losses/focal_loss.py function py_sigmoid_focal_loss (line 12) | def py_sigmoid_focal_loss(pred, function py_focal_loss_with_prob (line 60) | def py_focal_loss_with_prob(pred, function sigmoid_focal_loss (line 113) | def sigmoid_focal_loss(pred, class FocalLoss (line 160) | class FocalLoss(nn.Module): method __init__ (line 162) | def __init__(self, method forward (line 196) | def forward(self, FILE: detection/mmdet/models/losses/gaussian_focal_loss.py function gaussian_focal_loss (line 11) | def gaussian_focal_loss(pred, gaussian_target, alpha=2.0, gamma=4.0): class GaussianFocalLoss (line 33) | class GaussianFocalLoss(nn.Module): method __init__ (line 50) | def __init__(self, method forward (line 61) | def forward(self, FILE: detection/mmdet/models/losses/gfocal_loss.py function quality_focal_loss (line 12) | def quality_focal_loss(pred, target, beta=2.0): function quality_focal_loss_with_prob (line 56) | def quality_focal_loss_with_prob(pred, target, beta=2.0): function distribution_focal_loss (line 103) | def distribution_focal_loss(pred, label): class QualityFocalLoss (line 128) | class QualityFocalLoss(nn.Module): method __init__ (line 146) | def __init__(self, method forward (line 160) | def forward(self, class DistributionFocalLoss (line 203) | class DistributionFocalLoss(nn.Module): method __init__ (line 213) | def __init__(self, reduction='mean', loss_weight=1.0): method forward (line 218) | def forward(self, FILE: detection/mmdet/models/losses/ghm_loss.py function _expand_onehot_labels (line 10) | def _expand_onehot_labels(labels, label_weights, label_channels): class GHMC (line 23) | class GHMC(nn.Module): method __init__ (line 39) | def __init__(self, method forward (line 60) | def forward(self, class GHMR (line 122) | class GHMR(nn.Module): method __init__ (line 138) | def __init__(self, method forward (line 158) | def forward(self, FILE: detection/mmdet/models/losses/iou_loss.py function iou_loss (line 16) | def iou_loss(pred, target, linear=False, mode='log', eps=1e-6): function bounded_iou_loss (line 55) | def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3): function giou_loss (line 102) | def giou_loss(pred, target, eps=1e-7): function diou_loss (line 122) | def diou_loss(pred, target, eps=1e-7): function ciou_loss (line 177) | def ciou_loss(pred, target, eps=1e-7): class IoULoss (line 241) | class IoULoss(nn.Module): method __init__ (line 256) | def __init__(self, method forward (line 275) | def forward(self, class BoundedIoULoss (line 322) | class BoundedIoULoss(nn.Module): method __init__ (line 324) | def __init__(self, beta=0.2, eps=1e-3, reduction='mean', loss_weight=1... method forward (line 331) | def forward(self, class GIoULoss (line 358) | class GIoULoss(nn.Module): method __init__ (line 360) | def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0): method forward (line 366) | def forward(self, class DIoULoss (line 398) | class DIoULoss(nn.Module): method __init__ (line 400) | def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0): method forward (line 406) | def forward(self, class CIoULoss (line 438) | class CIoULoss(nn.Module): method __init__ (line 440) | def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0): method forward (line 446) | def forward(self, FILE: detection/mmdet/models/losses/kd_loss.py function knowledge_distillation_kl_div_loss (line 12) | def knowledge_distillation_kl_div_loss(pred, class KnowledgeDistillationKLDivLoss (line 40) | class KnowledgeDistillationKLDivLoss(nn.Module): method __init__ (line 49) | def __init__(self, reduction='mean', loss_weight=1.0, T=10): method forward (line 56) | def forward(self, FILE: detection/mmdet/models/losses/mse_loss.py function mse_loss (line 10) | def mse_loss(pred, target): class MSELoss (line 16) | class MSELoss(nn.Module): method __init__ (line 25) | def __init__(self, reduction='mean', loss_weight=1.0): method forward (line 30) | def forward(self, FILE: detection/mmdet/models/losses/pisa_loss.py function isr_p (line 9) | def isr_p(cls_score, function carl_loss (line 123) | def carl_loss(cls_score, FILE: detection/mmdet/models/losses/seesaw_loss.py function seesaw_ce_loss (line 12) | def seesaw_ce_loss(cls_score, class SeesawLoss (line 80) | class SeesawLoss(nn.Module): method __init__ (line 103) | def __init__(self, method _split_cls_score (line 138) | def _split_cls_score(self, cls_score): method get_cls_channels (line 145) | def get_cls_channels(self, num_classes): method get_activation (line 157) | def get_activation(self, cls_score): method get_accuracy (line 177) | def get_accuracy(self, cls_score, labels): method forward (line 199) | def forward(self, FILE: detection/mmdet/models/losses/smooth_l1_loss.py function smooth_l1_loss (line 12) | def smooth_l1_loss(pred, target, beta=1.0): function l1_loss (line 37) | def l1_loss(pred, target): class SmoothL1Loss (line 56) | class SmoothL1Loss(nn.Module): method __init__ (line 67) | def __init__(self, beta=1.0, reduction='mean', loss_weight=1.0): method forward (line 73) | def forward(self, class L1Loss (line 108) | class L1Loss(nn.Module): method __init__ (line 117) | def __init__(self, reduction='mean', loss_weight=1.0): method forward (line 122) | def forward(self, FILE: detection/mmdet/models/losses/utils.py function reduce_loss (line 9) | def reduce_loss(loss, reduction): function weight_reduce_loss (line 30) | def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=N... function weighted_loss (line 62) | def weighted_loss(loss_func): FILE: detection/mmdet/models/losses/varifocal_loss.py function varifocal_loss (line 11) | def varifocal_loss(pred, class VarifocalLoss (line 60) | class VarifocalLoss(nn.Module): method __init__ (line 62) | def __init__(self, method forward (line 97) | def forward(self, FILE: detection/mmdet/models/necks/bfp.py class BFP (line 11) | class BFP(BaseModule): method __init__ (line 33) | def __init__(self, method forward (line 70) | def forward(self, inputs): FILE: detection/mmdet/models/necks/channel_mapper.py class ChannelMapper (line 10) | class ChannelMapper(BaseModule): method __init__ (line 46) | def __init__(self, method forward (line 90) | def forward(self, inputs): FILE: detection/mmdet/models/necks/ct_resnet_neck.py class CTResNetNeck (line 12) | class CTResNetNeck(BaseModule): method __init__ (line 24) | def __init__(self, method _make_deconv_layer (line 38) | def _make_deconv_layer(self, num_deconv_filters, num_deconv_kernels): method init_weights (line 64) | def init_weights(self): method forward (line 91) | def forward(self, inputs): FILE: detection/mmdet/models/necks/dilated_encoder.py class Bottleneck (line 10) | class Bottleneck(nn.Module): method __init__ (line 24) | def __init__(self, method forward (line 42) | def forward(self, x): class DilatedEncoder (line 52) | class DilatedEncoder(nn.Module): method __init__ (line 67) | def __init__(self, in_channels, out_channels, block_mid_channels, method _init_layers (line 77) | def _init_layers(self): method init_weights (line 94) | def init_weights(self): method forward (line 105) | def forward(self, feature): FILE: detection/mmdet/models/necks/dyhead.py class DyDCNv2 (line 17) | class DyDCNv2(nn.Module): method __init__ (line 32) | def __init__(self, method forward (line 45) | def forward(self, x, offset, mask): class DyHeadBlock (line 53) | class DyHeadBlock(nn.Module): method __init__ (line 69) | def __init__(self, method _init_weights (line 91) | def _init_weights(self): method forward (line 98) | def forward(self, x): class DyHead (line 130) | class DyHead(BaseModule): method __init__ (line 146) | def __init__(self, method forward (line 170) | def forward(self, inputs): FILE: detection/mmdet/models/necks/fpg.py class Transition (line 10) | class Transition(BaseModule): method __init__ (line 18) | def __init__(self, in_channels, out_channels, init_cfg=None): method forward (line 23) | def forward(x): class UpInterpolationConv (line 27) | class UpInterpolationConv(Transition): method __init__ (line 43) | def __init__(self, method forward (line 63) | def forward(self, x): class LastConv (line 73) | class LastConv(Transition): method __init__ (line 83) | def __init__(self, method forward (line 99) | def forward(self, inputs): class FPG (line 105) | class FPG(BaseModule): method __init__ (line 150) | def __init__(self, method build_trans (line 317) | def build_trans(self, cfg, in_channels, out_channels, **extra_args): method fuse (line 323) | def fuse(self, fuse_dict): method forward (line 333) | def forward(self, inputs): FILE: detection/mmdet/models/necks/fpn.py class FPN (line 11) | class FPN(BaseModule): method __init__ (line 62) | def __init__(self, method forward (line 152) | def forward(self, inputs): FILE: detection/mmdet/models/necks/fpn_carafe.py class FPN_CARAFE (line 11) | class FPN_CARAFE(BaseModule): method __init__ (line 37) | def __init__(self, method init_weights (line 209) | def init_weights(self): method slice_as (line 219) | def slice_as(self, src, dst): method tensor_add (line 239) | def tensor_add(self, a, b): method forward (line 247) | def forward(self, inputs): FILE: detection/mmdet/models/necks/hrfpn.py class HRFPN (line 13) | class HRFPN(BaseModule): method __init__ (line 33) | def __init__(self, method forward (line 77) | def forward(self, inputs): FILE: detection/mmdet/models/necks/nas_fpn.py class NASFPN (line 11) | class NASFPN(BaseModule): method __init__ (line 33) | def __init__(self, method forward (line 127) | def forward(self, inputs): FILE: detection/mmdet/models/necks/nasfcos_fpn.py class NASFCOS_FPN (line 12) | class NASFCOS_FPN(BaseModule): method __init__ (line 35) | def __init__(self, method forward (line 123) | def forward(self, inputs): method init_weights (line 157) | def init_weights(self): FILE: detection/mmdet/models/necks/pafpn.py class PAFPN (line 12) | class PAFPN(FPN): method __init__ (line 46) | def __init__(self, method forward (line 100) | def forward(self, inputs): FILE: detection/mmdet/models/necks/rfp.py class ASPP (line 12) | class ASPP(BaseModule): method __init__ (line 26) | def __init__(self, method forward (line 48) | def forward(self, x): class RFP (line 60) | class RFP(FPN): method __init__ (line 78) | def __init__(self, method init_weights (line 105) | def init_weights(self): method forward (line 117) | def forward(self, inputs): FILE: detection/mmdet/models/necks/ssd_neck.py class SSDNeck (line 11) | class SSDNeck(BaseModule): method __init__ (line 33) | def __init__(self, method forward (line 93) | def forward(self, inputs): class L2Norm (line 106) | class L2Norm(nn.Module): method __init__ (line 108) | def __init__(self, n_dims, scale=20., eps=1e-10): method forward (line 123) | def forward(self, x): FILE: detection/mmdet/models/necks/yolo_neck.py class DetectionBlock (line 12) | class DetectionBlock(BaseModule): method __init__ (line 35) | def __init__(self, method forward (line 55) | def forward(self, x): class YOLOV3Neck (line 65) | class YOLOV3Neck(BaseModule): method __init__ (line 92) | def __init__(self, method forward (line 120) | def forward(self, feats): FILE: detection/mmdet/models/necks/yolox_pafpn.py class YOLOXPAFPN (line 14) | class YOLOXPAFPN(BaseModule): method __init__ (line 35) | def __init__(self, method forward (line 117) | def forward(self, inputs): FILE: detection/mmdet/models/plugins/dropblock.py class DropBlock (line 11) | class DropBlock(nn.Module): method __init__ (line 25) | def __init__(self, drop_prob, block_size, warmup_iters=2000, **kwargs): method forward (line 35) | def forward(self, x): method _compute_gamma (line 62) | def _compute_gamma(self, feat_size): method extra_repr (line 83) | def extra_repr(self): FILE: detection/mmdet/models/plugins/msdeformattn_pixel_decoder.py class MSDeformAttnPixelDecoder (line 16) | class MSDeformAttnPixelDecoder(BaseModule): method __init__ (line 40) | def __init__(self, method init_weights (line 135) | def init_weights(self): method forward (line 161) | def forward(self, feats): FILE: detection/mmdet/models/plugins/pixel_decoder.py class PixelDecoder (line 12) | class PixelDecoder(BaseModule): method __init__ (line 34) | def __init__(self, method init_weights (line 79) | def init_weights(self): method forward (line 88) | def forward(self, feats, img_metas): class TransformerEncoderPixelDecoder (line 116) | class TransformerEncoderPixelDecoder(PixelDecoder): method __init__ (line 138) | def __init__(self, method init_weights (line 178) | def init_weights(self): method forward (line 192) | def forward(self, feats, img_metas): FILE: detection/mmdet/models/roi_heads/base_roi_head.py class BaseRoIHead (line 9) | class BaseRoIHead(BaseModule, metaclass=ABCMeta): method __init__ (line 12) | def __init__(self, method with_bbox (line 38) | def with_bbox(self): method with_mask (line 43) | def with_mask(self): method with_shared_head (line 48) | def with_shared_head(self): method init_bbox_head (line 53) | def init_bbox_head(self): method init_mask_head (line 58) | def init_mask_head(self): method init_assigner_sampler (line 63) | def init_assigner_sampler(self): method forward_train (line 68) | def forward_train(self, method async_simple_test (line 79) | async def async_simple_test(self, method simple_test (line 89) | def simple_test(self, method aug_test (line 98) | def aug_test(self, x, proposal_list, img_metas, rescale=False, **kwargs): FILE: detection/mmdet/models/roi_heads/bbox_heads/bbox_head.py class BBoxHead (line 15) | class BBoxHead(BaseModule): method __init__ (line 19) | def __init__(self, method custom_cls_channels (line 97) | def custom_cls_channels(self): method custom_activation (line 101) | def custom_activation(self): method custom_accuracy (line 105) | def custom_accuracy(self): method forward (line 109) | def forward(self, x): method _get_target_single (line 122) | def _get_target_single(self, pos_bboxes, neg_bboxes, pos_gt_bboxes, method get_targets (line 189) | def get_targets(self, method loss (line 257) | def loss(self, method get_bboxes (line 316) | def get_bboxes(self, method refine_bboxes (line 381) | def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas): method regress_by_class (line 460) | def regress_by_class(self, rois, label, bbox_pred, img_meta): method onnx_export (line 499) | def onnx_export(self, FILE: detection/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py class ConvFCBBoxHead (line 11) | class ConvFCBBoxHead(BBoxHead): method __init__ (line 22) | def __init__(self, method _add_conv_fc_branch (line 118) | def _add_conv_fc_branch(self, method forward (line 159) | def forward(self, x): class Shared2FCBBoxHead (line 201) | class Shared2FCBBoxHead(ConvFCBBoxHead): method __init__ (line 203) | def __init__(self, fc_out_channels=1024, *args, **kwargs): class Shared4Conv1FCBBoxHead (line 217) | class Shared4Conv1FCBBoxHead(ConvFCBBoxHead): method __init__ (line 219) | def __init__(self, fc_out_channels=1024, *args, **kwargs): FILE: detection/mmdet/models/roi_heads/bbox_heads/dii_head.py class DIIHead (line 18) | class DIIHead(BBoxHead): method __init__ (line 46) | def __init__(self, method init_weights (line 125) | def init_weights(self): method forward (line 141) | def forward(self, roi_feat, proposal_feat): method loss (line 201) | def loss(self, method _get_target_single (line 285) | def _get_target_single(self, pos_inds, neg_inds, pos_bboxes, neg_bboxes, method get_targets (line 359) | def get_targets(self, FILE: detection/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py class BasicResBlock (line 11) | class BasicResBlock(BaseModule): method __init__ (line 26) | def __init__(self, method forward (line 63) | def forward(self, x): class DoubleConvFCBBoxHead (line 77) | class DoubleConvFCBBoxHead(BBoxHead): method __init__ (line 91) | def __init__(self, method _add_conv_branch (line 136) | def _add_conv_branch(self): method _add_fc_branch (line 148) | def _add_fc_branch(self): method forward (line 158) | def forward(self, x_cls, x_reg): FILE: detection/mmdet/models/roi_heads/bbox_heads/sabl_head.py class SABLHead (line 15) | class SABLHead(BaseModule): method __init__ (line 58) | def __init__(self, method custom_cls_channels (line 210) | def custom_cls_channels(self): method custom_activation (line 214) | def custom_activation(self): method custom_accuracy (line 218) | def custom_accuracy(self): method _add_fc_branch (line 221) | def _add_fc_branch(self, num_branch_fcs, in_channels, roi_feat_size, method cls_forward (line 230) | def cls_forward(self, cls_x): method attention_pool (line 237) | def attention_pool(self, reg_x): method side_aware_feature_extractor (line 250) | def side_aware_feature_extractor(self, reg_x): method reg_pred (line 271) | def reg_pred(self, x, offset_fcs, cls_fcs): method side_aware_split (line 289) | def side_aware_split(self, feat): method bbox_pred_split (line 301) | def bbox_pred_split(self, bbox_pred, num_proposals_per_img): method reg_forward (line 310) | def reg_forward(self, reg_x): method forward (line 329) | def forward(self, x): method get_targets (line 336) | def get_targets(self, sampling_results, gt_bboxes, gt_labels, method bucket_target (line 351) | def bucket_target(self, method _bucket_target_single (line 377) | def _bucket_target_single(self, pos_proposals, neg_proposals, method loss (line 440) | def loss(self, method get_bboxes (line 485) | def get_bboxes(self, method refine_bboxes (line 527) | def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas): method regress_by_class (line 573) | def regress_by_class(self, rois, label, bbox_pred, img_meta): FILE: detection/mmdet/models/roi_heads/bbox_heads/scnet_bbox_head.py class SCNetBBoxHead (line 7) | class SCNetBBoxHead(ConvFCBBoxHead): method _forward_shared (line 14) | def _forward_shared(self, x): method _forward_cls_reg (line 31) | def _forward_cls_reg(self, x): method forward (line 59) | def forward(self, x, return_shared_feat=False): FILE: detection/mmdet/models/roi_heads/cascade_roi_head.py class CascadeRoIHead (line 16) | class CascadeRoIHead(BaseRoIHead, BBoxTestMixin, MaskTestMixin): method __init__ (line 22) | def __init__(self, method init_bbox_head (line 52) | def init_bbox_head(self, bbox_roi_extractor, bbox_head): method init_mask_head (line 72) | def init_mask_head(self, mask_roi_extractor, mask_head): method init_assigner_sampler (line 100) | def init_assigner_sampler(self): method forward_dummy (line 112) | def forward_dummy(self, x, proposals): method _bbox_forward (line 130) | def _bbox_forward(self, stage, x, rois): method _bbox_forward_train (line 143) | def _bbox_forward_train(self, stage, x, sampling_results, gt_bboxes, method _mask_forward (line 158) | def _mask_forward(self, stage, x, rois): method _mask_forward_train (line 170) | def _mask_forward_train(self, method forward_train (line 191) | def forward_train(self, method simple_test (line 288) | def simple_test(self, x, proposal_list, img_metas, rescale=False): method aug_test (line 458) | def aug_test(self, features, proposal_list, img_metas, rescale=False): method onnx_export (line 561) | def onnx_export(self, x, proposals, img_metas): FILE: detection/mmdet/models/roi_heads/double_roi_head.py class DoubleHeadRoIHead (line 7) | class DoubleHeadRoIHead(StandardRoIHead): method __init__ (line 13) | def __init__(self, reg_roi_scale_factor, **kwargs): method _bbox_forward (line 17) | def _bbox_forward(self, x, rois): FILE: detection/mmdet/models/roi_heads/dynamic_roi_head.py class DynamicRoIHead (line 14) | class DynamicRoIHead(StandardRoIHead): method __init__ (line 17) | def __init__(self, **kwargs): method forward_train (line 25) | def forward_train(self, method _bbox_forward_train (line 109) | def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels, method update_hyperparameters (line 134) | def update_hyperparameters(self): FILE: detection/mmdet/models/roi_heads/grid_roi_head.py class GridRoIHead (line 11) | class GridRoIHead(StandardRoIHead): method __init__ (line 17) | def __init__(self, grid_roi_extractor, grid_head, **kwargs): method _random_jitter (line 28) | def _random_jitter(self, sampling_results, img_metas, amplitude=0.15): method forward_dummy (line 53) | def forward_dummy(self, x, proposals): method _bbox_forward_train (line 79) | def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels, method simple_test (line 117) | def simple_test(self, FILE: detection/mmdet/models/roi_heads/htc_roi_head.py class HybridTaskCascadeRoIHead (line 14) | class HybridTaskCascadeRoIHead(CascadeRoIHead): method __init__ (line 20) | def __init__(self, method with_semantic (line 44) | def with_semantic(self): method forward_dummy (line 51) | def forward_dummy(self, x, proposals): method _bbox_forward_train (line 86) | def _bbox_forward_train(self, method _mask_forward_train (line 113) | def _mask_forward_train(self, method _bbox_forward (line 158) | def _bbox_forward(self, stage, x, rois, semantic_feat=None): method _mask_forward_test (line 176) | def _mask_forward_test(self, stage, x, bboxes, semantic_feat=None): method forward_train (line 205) | def forward_train(self, method simple_test (line 330) | def simple_test(self, x, proposal_list, img_metas, rescale=False): method aug_test (line 505) | def aug_test(self, img_feats, proposal_list, img_metas, rescale=False): FILE: detection/mmdet/models/roi_heads/mask_heads/coarse_mask_head.py class CoarseMaskHead (line 10) | class CoarseMaskHead(FCNMaskHead): method __init__ (line 26) | def __init__(self, method init_weights (line 84) | def init_weights(self): method forward (line 88) | def forward(self, x): FILE: detection/mmdet/models/roi_heads/mask_heads/dynamic_mask_head.py class DynamicMaskHead (line 14) | class DynamicMaskHead(FCNMaskHead): method __init__ (line 42) | def __init__(self, method init_weights (line 83) | def init_weights(self): method forward (line 92) | def forward(self, roi_feat, proposal_feat): method loss (line 125) | def loss(self, mask_pred, mask_targets, labels): method get_targets (line 139) | def get_targets(self, sampling_results, gt_masks, rcnn_train_cfg): FILE: detection/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py class FCNMaskHead (line 23) | class FCNMaskHead(BaseModule): method __init__ (line 25) | def __init__(self, method init_weights (line 115) | def init_weights(self): method forward (line 128) | def forward(self, x): method get_targets (line 138) | def get_targets(self, sampling_results, gt_masks, rcnn_train_cfg): method loss (line 148) | def loss(self, mask_pred, mask_targets, labels): method get_seg_masks (line 179) | def get_seg_masks(self, mask_pred, det_bboxes, det_labels, rcnn_test_cfg, method onnx_export (line 312) | def onnx_export(self, mask_pred, det_bboxes, det_labels, rcnn_test_cfg, function _do_paste_mask (line 344) | def _do_paste_mask(masks, boxes, img_h, img_w, skip_empty=True): FILE: detection/mmdet/models/roi_heads/mask_heads/feature_relay_head.py class FeatureRelayHead (line 9) | class FeatureRelayHead(BaseModule): method __init__ (line 22) | def __init__(self, method forward (line 43) | def forward(self, x): FILE: detection/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py class FusedSemanticHead (line 13) | class FusedSemanticHead(BaseModule): method __init__ (line 29) | def __init__(self, method forward (line 97) | def forward(self, feats): method loss (line 114) | def loss(self, mask_pred, labels): FILE: detection/mmdet/models/roi_heads/mask_heads/global_context_head.py class GlobalContextHead (line 11) | class GlobalContextHead(BaseModule): method __init__ (line 29) | def __init__(self, method forward (line 80) | def forward(self, feats): method loss (line 94) | def loss(self, pred, labels): FILE: detection/mmdet/models/roi_heads/mask_heads/grid_head.py class GridHead (line 13) | class GridHead(BaseModule): method __init__ (line 15) | def __init__(self, method forward (line 155) | def forward(self, x): method calc_sub_regions (line 193) | def calc_sub_regions(self): method get_targets (line 224) | def get_targets(self, sampling_results, rcnn_train_cfg): method loss (line 292) | def loss(self, grid_pred, grid_targets): method get_bboxes (line 298) | def get_bboxes(self, det_bboxes, grid_pred, img_metas): FILE: detection/mmdet/models/roi_heads/mask_heads/htc_mask_head.py class HTCMaskHead (line 9) | class HTCMaskHead(FCNMaskHead): method __init__ (line 11) | def __init__(self, with_conv_res=True, *args, **kwargs): method forward (line 22) | def forward(self, x, res_feat=None, return_logits=True, return_feat=Tr... FILE: detection/mmdet/models/roi_heads/mask_heads/mask_point_head.py class MaskPointHead (line 16) | class MaskPointHead(BaseModule): method __init__ (line 42) | def __init__(self, method forward (line 88) | def forward(self, fine_grained_feats, coarse_feats): method get_targets (line 109) | def get_targets(self, rois, rel_roi_points, sampling_results, gt_masks, method _get_target_single (line 149) | def _get_target_single(self, rois, rel_roi_points, pos_assigned_gt_inds, method loss (line 167) | def loss(self, point_pred, point_targets, labels): method get_roi_rel_points_train (line 190) | def get_roi_rel_points_train(self, mask_pred, labels, cfg): method get_roi_rel_points_test (line 215) | def get_roi_rel_points_test(self, mask_pred, pred_label, cfg): FILE: detection/mmdet/models/roi_heads/mask_heads/maskiou_head.py class MaskIoUHead (line 13) | class MaskIoUHead(BaseModule): method __init__ (line 19) | def __init__(self, method forward (line 73) | def forward(self, mask_feat, mask_pred): method loss (line 88) | def loss(self, mask_iou_pred, mask_iou_targets): method get_targets (line 98) | def get_targets(self, sampling_results, gt_masks, mask_pred, mask_targ... method _get_area_ratio (line 147) | def _get_area_ratio(self, pos_proposals, pos_assigned_gt_inds, gt_masks): method get_mask_scores (line 174) | def get_mask_scores(self, mask_iou_pred, det_bboxes, det_labels): FILE: detection/mmdet/models/roi_heads/mask_heads/scnet_mask_head.py class SCNetMaskHead (line 8) | class SCNetMaskHead(FCNMaskHead): method __init__ (line 16) | def __init__(self, conv_to_res=True, **kwargs): FILE: detection/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py class SCNetSemanticHead (line 8) | class SCNetSemanticHead(FusedSemanticHead): method __init__ (line 16) | def __init__(self, conv_to_res=True, **kwargs): FILE: detection/mmdet/models/roi_heads/mask_scoring_roi_head.py class MaskScoringRoIHead (line 10) | class MaskScoringRoIHead(StandardRoIHead): method __init__ (line 16) | def __init__(self, mask_iou_head, **kwargs): method _mask_forward_train (line 21) | def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks, method simple_test_mask (line 49) | def simple_test_mask(self, FILE: detection/mmdet/models/roi_heads/pisa_roi_head.py class PISARoIHead (line 9) | class PISARoIHead(StandardRoIHead): method forward_train (line 13) | def forward_train(self, method _bbox_forward (line 87) | def _bbox_forward(self, x, rois): method _bbox_forward_train (line 100) | def _bbox_forward_train(self, FILE: detection/mmdet/models/roi_heads/point_rend_roi_head.py class PointRendRoIHead (line 18) | class PointRendRoIHead(StandardRoIHead): method __init__ (line 21) | def __init__(self, point_head, *args, **kwargs): method init_point_head (line 26) | def init_point_head(self, point_head): method _mask_forward_train (line 30) | def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks, method _mask_point_forward_train (line 45) | def _mask_point_forward_train(self, x, sampling_results, mask_pred, method _get_fine_grained_point_feats (line 66) | def _get_fine_grained_point_feats(self, x, rois, rel_roi_points, method _mask_point_forward_test (line 104) | def _mask_point_forward_test(self, x, rois, label_pred, mask_pred, method simple_test_mask (line 155) | def simple_test_mask(self, method aug_test_mask (line 217) | def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels): method _onnx_get_fine_grained_point_feats (line 250) | def _onnx_get_fine_grained_point_feats(self, x, rois, rel_roi_points): method _mask_point_onnx_export (line 284) | def _mask_point_onnx_export(self, x, rois, label_pred, mask_pred): method mask_onnx_export (line 351) | def mask_onnx_export(self, x, img_metas, det_bboxes, det_labels, **kwa... FILE: detection/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py class BaseRoIExtractor (line 10) | class BaseRoIExtractor(BaseModule, metaclass=ABCMeta): method __init__ (line 21) | def __init__(self, method num_inputs (line 33) | def num_inputs(self): method build_roi_layers (line 37) | def build_roi_layers(self, layer_cfg, featmap_strides): method roi_rescale (line 62) | def roi_rescale(self, rois, scale_factor): method forward (line 87) | def forward(self, feats, rois, roi_scale_factor=None): FILE: detection/mmdet/models/roi_heads/roi_extractors/generic_roi_extractor.py class GenericRoIExtractor (line 10) | class GenericRoIExtractor(BaseRoIExtractor): method __init__ (line 25) | def __init__(self, method forward (line 44) | def forward(self, feats, rois, roi_scale_factor=None): FILE: detection/mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py class SingleRoIExtractor (line 10) | class SingleRoIExtractor(BaseRoIExtractor): method __init__ (line 26) | def __init__(self, method map_roi_levels (line 36) | def map_roi_levels(self, rois, num_levels): method forward (line 58) | def forward(self, feats, rois, roi_scale_factor=None): FILE: detection/mmdet/models/roi_heads/scnet_roi_head.py class SCNetRoIHead (line 14) | class SCNetRoIHead(CascadeRoIHead): method __init__ (line 26) | def __init__(self, method init_mask_head (line 50) | def init_mask_head(self, mask_roi_extractor, mask_head): method with_semantic (line 57) | def with_semantic(self): method with_feat_relay (line 63) | def with_feat_relay(self): method with_glbctx (line 69) | def with_glbctx(self): method _fuse_glbctx (line 73) | def _fuse_glbctx(self, roi_feats, glbctx_feat, rois): method _slice_pos_feats (line 83) | def _slice_pos_feats(self, feats, sampling_results): method _bbox_forward (line 96) | def _bbox_forward(self, method _mask_forward (line 125) | def _mask_forward(self, method _bbox_forward_train (line 150) | def _bbox_forward_train(self, method _mask_forward_train (line 179) | def _mask_forward_train(self, method forward_train (line 206) | def forward_train(self, method simple_test (line 314) | def simple_test(self, x, proposal_list, img_metas, rescale=False): method aug_test (line 487) | def aug_test(self, img_feats, proposal_list, img_metas, rescale=False): FILE: detection/mmdet/models/roi_heads/shared_heads/res_layer.py class ResLayer (line 13) | class ResLayer(BaseModule): method __init__ (line 15) | def __init__(self, method forward (line 70) | def forward(self, x): method train (line 75) | def train(self, mode=True): FILE: detection/mmdet/models/roi_heads/sparse_roi_head.py class SparseRoIHead (line 12) | class SparseRoIHead(CascadeRoIHead): method __init__ (line 37) | def __init__(self, method _bbox_forward (line 88) | def _bbox_forward(self, stage, x, rois, object_feats, img_metas): method _mask_forward (line 151) | def _mask_forward(self, stage, x, rois, attn_feats): method _mask_forward_train (line 163) | def _mask_forward_train(self, stage, x, attn_feats, sampling_results, method forward_train (line 184) | def forward_train(self, method simple_test (line 276) | def simple_test(self, method aug_test (line 400) | def aug_test(self, features, proposal_list, img_metas, rescale=False): method forward_dummy (line 404) | def forward_dummy(self, x, proposal_boxes, proposal_features, img_metas): FILE: detection/mmdet/models/roi_heads/standard_roi_head.py class StandardRoIHead (line 11) | class StandardRoIHead(BaseRoIHead, BBoxTestMixin, MaskTestMixin): method init_assigner_sampler (line 14) | def init_assigner_sampler(self): method init_bbox_head (line 23) | def init_bbox_head(self, bbox_roi_extractor, bbox_head): method init_mask_head (line 28) | def init_mask_head(self, mask_roi_extractor, mask_head): method forward_dummy (line 38) | def forward_dummy(self, x, proposals): method forward_train (line 54) | def forward_train(self, method _bbox_forward (line 118) | def _bbox_forward(self, x, rois): method _bbox_forward_train (line 131) | def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels, method _mask_forward_train (line 146) | def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks, method _mask_forward (line 181) | def _mask_forward(self, x, rois=None, pos_inds=None, bbox_feats=None): method async_simple_test (line 198) | async def async_simple_test(self, method simple_test (line 223) | def simple_test(self, method aug_test (line 269) | def aug_test(self, x, proposal_list, img_metas, rescale=False): method onnx_export (line 295) | def onnx_export(self, x, proposals, img_metas, rescale=False): method mask_onnx_export (line 308) | def mask_onnx_export(self, x, img_metas, det_bboxes, det_labels, **kwa... method bbox_onnx_export (line 350) | def bbox_onnx_export(self, x, img_metas, proposals, rcnn_test_cfg, FILE: detection/mmdet/models/roi_heads/test_mixins.py class BBoxTestMixin (line 15) | class BBoxTestMixin: method async_test_bboxes (line 19) | async def async_test_bboxes(self, method simple_test_bboxes (line 51) | def simple_test_bboxes(self, method aug_test_bboxes (line 138) | def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_c... class MaskTestMixin (line 179) | class MaskTestMixin: method async_test_mask (line 183) | async def async_test_mask(self, method simple_test_mask (line 224) | def simple_test_mask(self, method aug_test_mask (line 281) | def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels): FILE: detection/mmdet/models/roi_heads/trident_roi_head.py class TridentRoIHead (line 12) | class TridentRoIHead(StandardRoIHead): method __init__ (line 22) | def __init__(self, num_branch, test_branch_idx, **kwargs): method merge_trident_bboxes (line 27) | def merge_trident_bboxes(self, trident_det_bboxes, trident_det_labels): method simple_test (line 46) | def simple_test(self, method aug_test_bboxes (line 83) | def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_c... FILE: detection/mmdet/models/seg_heads/base_semantic_head.py class BaseSemanticHead (line 11) | class BaseSemanticHead(BaseModule, metaclass=ABCMeta): method __init__ (line 20) | def __init__(self, method loss (line 32) | def loss(self, seg_preds, gt_semantic_seg): method forward (line 55) | def forward(self, x): method forward_train (line 65) | def forward_train(self, x, gt_semantic_seg): method simple_test (line 70) | def simple_test(self, x, img_metas, rescale=False): FILE: detection/mmdet/models/seg_heads/panoptic_fpn_head.py class PanopticFPNHead (line 14) | class PanopticFPNHead(BaseSemanticHead): method __init__ (line 52) | def __init__(self, method _set_things_to_void (line 108) | def _set_things_to_void(self, gt_semantic_seg): method loss (line 129) | def loss(self, seg_preds, gt_semantic_seg): method init_weights (line 137) | def init_weights(self): method forward (line 142) | def forward(self, x): FILE: detection/mmdet/models/seg_heads/panoptic_fusion_heads/base_panoptic_fusion_head.py class BasePanopticFusionHead (line 9) | class BasePanopticFusionHead(BaseModule, metaclass=ABCMeta): method __init__ (line 12) | def __init__(self, method with_loss (line 31) | def with_loss(self): method forward_train (line 36) | def forward_train(self, gt_masks=None, gt_semantic_seg=None, **kwargs): method simple_test (line 40) | def simple_test(self, FILE: detection/mmdet/models/seg_heads/panoptic_fusion_heads/heuristic_fusion_head.py class HeuristicFusionHead (line 10) | class HeuristicFusionHead(BasePanopticFusionHead): method __init__ (line 13) | def __init__(self, method forward_train (line 23) | def forward_train(self, gt_masks=None, gt_semantic_seg=None, **kwargs): method _lay_masks (line 27) | def _lay_masks(self, bboxes, labels, masks, overlap_thr=0.5): method simple_test (line 82) | def simple_test(self, det_bboxes, det_labels, mask_preds, seg_preds, FILE: detection/mmdet/models/seg_heads/panoptic_fusion_heads/maskformer_fusion_head.py class MaskFormerFusionHead (line 12) | class MaskFormerFusionHead(BasePanopticFusionHead): method __init__ (line 14) | def __init__(self, method forward_train (line 24) | def forward_train(self, **kwargs): method panoptic_postprocess (line 28) | def panoptic_postprocess(self, mask_cls, mask_pred): method semantic_postprocess (line 94) | def semantic_postprocess(self, mask_cls, mask_pred): method instance_postprocess (line 112) | def instance_postprocess(self, mask_cls, mask_pred): method simple_test (line 164) | def simple_test(self, FILE: detection/mmdet/models/utils/brick_wrappers.py function adaptive_avg_pool2d (line 15) | def adaptive_avg_pool2d(input, output_size): class AdaptiveAvgPool2d (line 32) | class AdaptiveAvgPool2d(nn.AdaptiveAvgPool2d): method forward (line 35) | def forward(self, x): FILE: detection/mmdet/models/utils/builder.py function build_transformer (line 9) | def build_transformer(cfg, default_args=None): function build_linear_layer (line 17) | def build_linear_layer(cfg, *args, **kwargs): FILE: detection/mmdet/models/utils/ckpt_convert.py function pvt_convert (line 12) | def pvt_convert(ckpt): function swin_converter (line 85) | def swin_converter(ckpt): FILE: detection/mmdet/models/utils/conv_upsample.py class ConvUpsample (line 7) | class ConvUpsample(BaseModule): method __init__ (line 28) | def __init__(self, method forward (line 59) | def forward(self, x): FILE: detection/mmdet/models/utils/csp_layer.py class DarknetBottleneck (line 8) | class DarknetBottleneck(BaseModule): method __init__ (line 32) | def __init__(self, method forward (line 64) | def forward(self, x): class CSPLayer (line 75) | class CSPLayer(BaseModule): method __init__ (line 96) | def __init__(self, method forward (line 143) | def forward(self, x): FILE: detection/mmdet/models/utils/gaussian_target.py function gaussian2D (line 8) | def gaussian2D(radius, sigma=1, dtype=torch.float32, device='cpu'): function gen_gaussian_target (line 32) | def gen_gaussian_target(heatmap, center, radius, k=1): function gaussian_radius (line 68) | def gaussian_radius(det_size, min_overlap): function get_local_maximum (line 190) | def get_local_maximum(heat, kernel=3): function get_topk_from_heatmap (line 207) | def get_topk_from_heatmap(scores, k=20): function gather_feat (line 234) | def gather_feat(feat, ind, mask=None): function transpose_and_gather_feat (line 255) | def transpose_and_gather_feat(feat, ind): FILE: detection/mmdet/models/utils/inverted_residual.py class InvertedResidual (line 11) | class InvertedResidual(BaseModule): method __init__ (line 42) | def __init__(self, method forward (line 105) | def forward(self, x): FILE: detection/mmdet/models/utils/make_divisible.py function make_divisible (line 2) | def make_divisible(value, divisor, min_value=None, min_ratio=0.9): FILE: detection/mmdet/models/utils/misc.py class SigmoidGeometricMean (line 6) | class SigmoidGeometricMean(Function): method forward (line 17) | def forward(ctx, x, y): method backward (line 25) | def backward(ctx, grad_output): function interpolate_as (line 35) | def interpolate_as(source, target, mode='bilinear', align_corners=False): FILE: detection/mmdet/models/utils/normed_predictor.py class NormedLinear (line 11) | class NormedLinear(nn.Linear): method __init__ (line 21) | def __init__(self, *args, tempearture=20, power=1.0, eps=1e-6, **kwargs): method init_weights (line 28) | def init_weights(self): method forward (line 33) | def forward(self, x): class NormedConv2d (line 43) | class NormedConv2d(nn.Conv2d): method __init__ (line 55) | def __init__(self, method forward (line 68) | def forward(self, x): FILE: detection/mmdet/models/utils/panoptic_gt_processing.py function preprocess_panoptic_gt (line 5) | def preprocess_panoptic_gt(gt_labels, gt_masks, gt_semantic_seg, num_thi... FILE: detection/mmdet/models/utils/point_sample.py function get_uncertainty (line 6) | def get_uncertainty(mask_pred, labels): function get_uncertain_point_coords_with_randomness (line 32) | def get_uncertain_point_coords_with_randomness(mask_pred, labels, num_po... FILE: detection/mmdet/models/utils/positional_encoding.py class SinePositionalEncoding (line 11) | class SinePositionalEncoding(BaseModule): method __init__ (line 36) | def __init__(self, method forward (line 56) | def forward(self, mask): method __repr__ (line 95) | def __repr__(self): class LearnedPositionalEncoding (line 107) | class LearnedPositionalEncoding(BaseModule): method __init__ (line 121) | def __init__(self, method forward (line 133) | def forward(self, mask): method __repr__ (line 157) | def __repr__(self): FILE: detection/mmdet/models/utils/res_layer.py class ResLayer (line 7) | class ResLayer(Sequential): method __init__ (line 26) | def __init__(self, class SimplifiedBasicBlock (line 107) | class SimplifiedBasicBlock(BaseModule): method __init__ (line 116) | def __init__(self, method norm1 (line 162) | def norm1(self): method norm2 (line 167) | def norm2(self): method forward (line 171) | def forward(self, x): FILE: detection/mmdet/models/utils/se_layer.py class SELayer (line 9) | class SELayer(BaseModule): method __init__ (line 28) | def __init__(self, method forward (line 55) | def forward(self, x): class DyReLU (line 62) | class DyReLU(BaseModule): method __init__ (line 88) | def __init__(self, method forward (line 118) | def forward(self, x): FILE: detection/mmdet/models/utils/transformer.py function nlc_to_nchw (line 32) | def nlc_to_nchw(x, hw_shape): function nchw_to_nlc (line 49) | def nchw_to_nlc(x): class AdaptivePadding (line 62) | class AdaptivePadding(nn.Module): method __init__ (line 93) | def __init__(self, kernel_size=1, stride=1, dilation=1, padding='corne... method get_pad_shape (line 109) | def get_pad_shape(self, input_shape): method forward (line 121) | def forward(self, x): class PatchEmbed (line 134) | class PatchEmbed(BaseModule): method __init__ (line 162) | def __init__( method forward (line 236) | def forward(self, x): class PatchMerging (line 260) | class PatchMerging(BaseModule): method __init__ (line 291) | def __init__(self, method forward (line 340) | def forward(self, x, input_size): function inverse_sigmoid (line 388) | def inverse_sigmoid(x, eps=1e-5): class DetrTransformerDecoderLayer (line 408) | class DetrTransformerDecoderLayer(BaseTransformerLayer): method __init__ (line 430) | def __init__(self, class DetrTransformerEncoder (line 454) | class DetrTransformerEncoder(TransformerLayerSequence): method __init__ (line 462) | def __init__(self, *args, post_norm_cfg=dict(type='LN'), **kwargs): method forward (line 473) | def forward(self, *args, **kwargs): class DetrTransformerDecoder (line 486) | class DetrTransformerDecoder(TransformerLayerSequence): method __init__ (line 495) | def __init__(self, method forward (line 509) | def forward(self, query, *args, **kwargs): class Transformer (line 539) | class Transformer(BaseModule): method __init__ (line 561) | def __init__(self, encoder=None, decoder=None, init_cfg=None): method init_weights (line 567) | def init_weights(self): method forward (line 574) | def forward(self, x, mask, query_embed, pos_embed): class DeformableDetrTransformerDecoder (line 625) | class DeformableDetrTransformerDecoder(TransformerLayerSequence): method __init__ (line 634) | def __init__(self, *args, return_intermediate=False, **kwargs): method forward (line 639) | def forward(self, class DeformableDetrTransformer (line 713) | class DeformableDetrTransformer(Transformer): method __init__ (line 725) | def __init__(self, method init_layers (line 737) | def init_layers(self): method init_weights (line 751) | def init_weights(self): method gen_encoder_output_proposals (line 763) | def gen_encoder_output_proposals(self, memory, memory_padding_mask, method get_reference_points (line 831) | def get_reference_points(spatial_shapes, valid_ratios, device): method get_valid_ratio (line 865) | def get_valid_ratio(self, mask): method get_proposal_pos_embed (line 875) | def get_proposal_pos_embed(self, method forward (line 893) | def forward(self, class DynamicConv (line 1063) | class DynamicConv(BaseModule): method __init__ (line 1090) | def __init__(self, method forward (line 1124) | def forward(self, param_feature, input_feature): FILE: detection/mmdet/utils/collect_env.py function collect_env (line 8) | def collect_env(): FILE: detection/mmdet/utils/compat_config.py function compat_cfg (line 8) | def compat_cfg(cfg): function compat_runner_args (line 22) | def compat_runner_args(cfg): function compat_imgs_per_gpu (line 37) | def compat_imgs_per_gpu(cfg): function compat_loader_args (line 54) | def compat_loader_args(cfg): FILE: detection/mmdet/utils/contextmanagers.py function completed (line 17) | async def completed(trace_name='', function concurrent (line 92) | async def concurrent(streamqueue: asyncio.Queue, FILE: detection/mmdet/utils/logger.py function get_root_logger (line 8) | def get_root_logger(log_file=None, log_level=logging.INFO): function get_caller_name (line 24) | def get_caller_name(): function log_img_scale (line 37) | def log_img_scale(img_scale, shape_order='hw', skip_square=False): FILE: detection/mmdet/utils/misc.py function find_latest_checkpoint (line 11) | def find_latest_checkpoint(path, suffix='pth'): function update_data_root (line 45) | def update_data_root(cfg, logger=None): FILE: detection/mmdet/utils/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: detection/mmdet/utils/profiling.py function profile_time (line 11) | def profile_time(trace_name, FILE: detection/mmdet/utils/setup_env.py function setup_multi_processes (line 10) | def setup_multi_processes(cfg): FILE: detection/mmdet/utils/split_batch.py function split_batch (line 5) | def split_batch(img, img_metas, kwargs): FILE: detection/mmdet/utils/util_distribution.py function build_dp (line 10) | def build_dp(model, device='cuda', dim=0, *args, **kwargs): function build_ddp (line 34) | def build_ddp(model, device='cuda', *args, **kwargs): function is_mlu_available (line 62) | def is_mlu_available(): function get_device (line 67) | def get_device(): FILE: detection/mmdet/utils/util_mixins.py class NiceRepr (line 42) | class NiceRepr: method __nice__ (line 76) | def __nice__(self): method __repr__ (line 87) | def __repr__(self): method __str__ (line 97) | def __str__(self): FILE: detection/mmdet/utils/util_random.py function ensure_rng (line 6) | def ensure_rng(rng=None): FILE: detection/mmdet/version.py function parse_version_info (line 7) | def parse_version_info(version_str): FILE: detection/models/models_hivit.py class Attention (line 11) | class Attention(nn.Module): method __init__ (line 12) | def __init__(self, input_size, dim, num_heads, qkv_bias=True, qk_scale... method forward (line 34) | def forward(self, x, rpe_index=None, mask=None): class BlockWithRPE (line 61) | class BlockWithRPE(nn.Module): method __init__ (line 62) | def __init__(self, input_size, dim, num_heads=0., mlp_ratio=4., qkv_bi... method forward (line 83) | def forward(self, x, rpe_index=None, mask=None): class PatchEmbed (line 90) | class PatchEmbed(nn.Module): method __init__ (line 91) | def __init__(self, img_size=224, patch_size=16, inner_patches=4, in_ch... method forward (line 112) | def forward(self, x): class PatchMerge (line 126) | class PatchMerge(nn.Module): method __init__ (line 127) | def __init__(self, dim, norm_layer): method forward (line 132) | def forward(self, x): class HiViT (line 144) | class HiViT(nn.Module): method __init__ (line 145) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe... method _init_weights (line 221) | def _init_weights(self, m): method no_weight_decay (line 231) | def no_weight_decay(self): method no_weight_decay_keywords (line 235) | def no_weight_decay_keywords(self): method forward_features (line 238) | def forward_features(self, x, ids_keep=None, mask=None): method forward (line 275) | def forward(self, x): function hivit_base (line 283) | def hivit_base(**kwargs): FILE: detection/models/models_vit.py function drop_path (line 21) | def drop_path(x, drop_prob: float = 0., training: bool = False): class DropPath (line 32) | class DropPath(nn.Module): method __init__ (line 35) | def __init__(self, drop_prob=None): method forward (line 39) | def forward(self, x): class Mlp (line 43) | class Mlp(nn.Module): method __init__ (line 44) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 53) | def forward(self, x): class Attention (line 62) | class Attention(nn.Module): method __init__ (line 63) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 74) | def forward(self, x): class Block (line 90) | class Block(nn.Module): method __init__ (line 91) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 108) | def forward(self, x, return_attention=False): class PatchEmbed (line 120) | class PatchEmbed(nn.Module): method __init__ (line 123) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=... method forward (line 132) | def forward(self, x): class VisionTransformer (line 136) | class VisionTransformer(nn.Module): method __init__ (line 138) | def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_clas... method _init_weights (line 171) | def _init_weights(self, m): method interpolate_pos_encoding (line 180) | def interpolate_pos_encoding(self, x, w, h): method prepare_tokens (line 202) | def prepare_tokens(self, x, mask=None): method forward (line 221) | def forward(self, x, return_all_tokens=None): FILE: detection/setup.py function get_version (line 19) | def get_version(): function make_cuda_ext (line 25) | def make_cuda_ext(name, module, sources, sources_cuda=[]): function parse_requirements (line 50) | def parse_requirements(fname='requirements.txt', with_version=True): function add_mim_extension (line 128) | def add_mim_extension(): FILE: detection/tests/test_data/test_datasets/test_coco_dataset.py function _create_ids_error_coco_json (line 11) | def _create_ids_error_coco_json(json_name): function test_coco_annotation_ids_unique (line 51) | def test_coco_annotation_ids_unique(): FILE: detection/tests/test_data/test_datasets/test_common.py function _create_dummy_coco_json (line 20) | def _create_dummy_coco_json(json_name): function _create_dummy_custom_pkl (line 80) | def _create_dummy_custom_pkl(pkl_name): function _create_dummy_results (line 96) | def _create_dummy_results(): function test_dataset_init (line 106) | def test_dataset_init(config_path, monkeypatch): function test_dataset_evaluation (line 119) | def test_dataset_evaluation(): function test_evaluation_hook (line 222) | def test_evaluation_hook(EvalHookParam): function _build_demo_runner (line 318) | def _build_demo_runner(): function test_allow_empty_images (line 346) | def test_allow_empty_images(classes, expected_length): FILE: detection/tests/test_data/test_datasets/test_custom_dataset.py function test_custom_classes_override_default (line 21) | def test_custom_classes_override_default(dataset): class CustomDatasetTests (line 94) | class CustomDatasetTests(unittest.TestCase): method setUp (line 96) | def setUp(self): method test_data_infos__default_db_directories (line 102) | def test_data_infos__default_db_directories(self): method test_data_infos__overridden_db_subdirectories (line 120) | def test_data_infos__overridden_db_subdirectories(self): FILE: detection/tests/test_data/test_datasets/test_dataset_wrapper.py function test_dataset_wrapper (line 14) | def test_dataset_wrapper(): FILE: detection/tests/test_data/test_datasets/test_openimages_dataset.py function _create_ids_error_oid_csv (line 12) | def _create_ids_error_oid_csv( function _create_oid_style_ann (line 44) | def _create_oid_style_ann(label_file, csv_file, label_level_file): function _create_hierarchy_json (line 85) | def _create_hierarchy_json(hierarchy_name): function _create_hierarchy_np (line 112) | def _create_hierarchy_np(hierarchy_name): function _create_dummy_results (line 120) | def _create_dummy_results(): function _creat_oid_challenge_style_ann (line 132) | def _creat_oid_challenge_style_ann(txt_file, label_file, label_level_file): function _create_metas (line 166) | def _create_metas(meta_file): function test_oid_annotation_ids_unique (line 175) | def test_oid_annotation_ids_unique(): function test_openimages_dataset (line 189) | def test_openimages_dataset(): function test_openimages_challenge_dataset (line 320) | def test_openimages_challenge_dataset(): FILE: detection/tests/test_data/test_datasets/test_panoptic_dataset.py function _create_panoptic_style_json (line 18) | def _create_panoptic_style_json(json_name): function test_load_panoptic_style_json (line 128) | def test_load_panoptic_style_json(): function _create_panoptic_gt_annotations (line 154) | def _create_panoptic_gt_annotations(ann_file): function test_panoptic_evaluation (line 233) | def test_panoptic_evaluation(): function _create_instance_segmentation_gt_annotations (line 343) | def _create_instance_segmentation_gt_annotations(ann_file): function test_instance_segmentation_evaluation (line 411) | def test_instance_segmentation_evaluation(): FILE: detection/tests/test_data/test_datasets/test_xml_dataset.py function test_xml_dataset (line 7) | def test_xml_dataset(): FILE: detection/tests/test_data/test_pipelines/test_formatting.py function test_default_format_bundle (line 9) | def test_default_format_bundle(): FILE: detection/tests/test_data/test_pipelines/test_loading.py class TestLoading (line 12) | class TestLoading: method setup_class (line 15) | def setup_class(cls): method test_load_img (line 18) | def test_load_img(self): method test_load_multi_channel_img (line 60) | def test_load_multi_channel_img(self): method test_load_webcam_img (line 81) | def test_load_webcam_img(self): FILE: detection/tests/test_data/test_pipelines/test_sampler.py function test_random_sampler (line 9) | def test_random_sampler(): function test_random_sampler_empty_gt (line 45) | def test_random_sampler_empty_gt(): function test_random_sampler_empty_pred (line 71) | def test_random_sampler_empty_pred(): function _context_for_ohem (line 95) | def _context_for_ohem(): function test_ohem_sampler (line 110) | def test_ohem_sampler(): function test_ohem_sampler_empty_gt (line 155) | def test_ohem_sampler_empty_gt(): function test_ohem_sampler_empty_pred (line 196) | def test_ohem_sampler_empty_pred(): function test_random_sample_result (line 236) | def test_random_sample_result(): function test_score_hlr_sampler_empty_pred (line 250) | def test_score_hlr_sampler_empty_pred(): FILE: detection/tests/test_data/test_pipelines/test_transform/test_img_augment.py function test_adjust_color (line 13) | def test_adjust_color(): function test_imequalize (line 44) | def test_imequalize(nb_rand_test=100): function test_adjust_brightness (line 79) | def test_adjust_brightness(nb_rand_test=100): function test_adjust_contrast (line 124) | def test_adjust_contrast(nb_rand_test=100): FILE: detection/tests/test_data/test_pipelines/test_transform/test_models_aug_test.py function model_aug_test_template (line 13) | def model_aug_test_template(cfg_file): function test_aug_test_size (line 46) | def test_aug_test_size(): function test_cascade_rcnn_aug_test (line 71) | def test_cascade_rcnn_aug_test(): function test_mask_rcnn_aug_test (line 77) | def test_mask_rcnn_aug_test(): function test_htc_aug_test (line 85) | def test_htc_aug_test(): function test_scnet_aug_test (line 92) | def test_scnet_aug_test(): function test_cornernet_aug_test (line 100) | def test_cornernet_aug_test(): FILE: detection/tests/test_data/test_pipelines/test_transform/test_rotate.py function test_rotate (line 13) | def test_rotate(): FILE: detection/tests/test_data/test_pipelines/test_transform/test_shear.py function test_shear (line 13) | def test_shear(): FILE: detection/tests/test_data/test_pipelines/test_transform/test_transform.py function test_resize (line 16) | def test_resize(): function test_flip (line 107) | def test_flip(): function test_random_crop (line 208) | def test_random_crop(): function test_min_iou_random_crop (line 337) | def test_min_iou_random_crop(): function test_pad (line 384) | def test_pad(): function test_normalize (line 446) | def test_normalize(): function test_albu_transform (line 475) | def test_albu_transform(): function test_random_center_crop_pad (line 499) | def test_random_center_crop_pad(): function test_multi_scale_flip_aug (line 639) | def test_multi_scale_flip_aug(): function test_cutout (line 733) | def test_cutout(): function test_random_shift (line 799) | def test_random_shift(): function test_random_affine (line 834) | def test_random_affine(): function test_mosaic (line 897) | def test_mosaic(): function test_mixup (line 937) | def test_mixup(): function test_photo_metric_distortion (line 1003) | def test_photo_metric_distortion(): function test_copypaste (line 1030) | def test_copypaste(): FILE: detection/tests/test_data/test_pipelines/test_transform/test_translate.py function _check_keys (line 13) | def _check_keys(results, results_translated): function _pad (line 20) | def _pad(h, w, c, pad_val, axis=-1, dtype=np.float32): function _construct_img (line 30) | def _construct_img(results): function _construct_ann_info (line 40) | def _construct_ann_info(h=427, w=640, c=3): function _load_bboxes (line 76) | def _load_bboxes(results): function _load_labels (line 86) | def _load_labels(results): function _poly2mask (line 90) | def _poly2mask(mask_ann, img_h, img_w): function _process_polygons (line 106) | def _process_polygons(polygons): function _load_masks (line 115) | def _load_masks(results, poly2mask=True): function _construct_semantic_seg (line 128) | def _construct_semantic_seg(results): function construct_toy_data (line 135) | def construct_toy_data(poly2mask=True): function test_translate (line 149) | def test_translate(): FILE: detection/tests/test_data/test_pipelines/test_transform/utils.py function _check_fields (line 7) | def _check_fields(results, pipeline_results, keys): function check_result_same (line 18) | def check_result_same(results, pipeline_results): function construct_toy_data (line 42) | def construct_toy_data(poly2mask=True): function create_random_bboxes (line 72) | def create_random_bboxes(num_bboxes, img_w, img_h): function create_full_masks (line 81) | def create_full_masks(gt_bboxes, img_w, img_h): FILE: detection/tests/test_data/test_utils.py function test_replace_ImageToTensor (line 7) | def test_replace_ImageToTensor(): function test_get_loading_pipeline (line 65) | def test_get_loading_pipeline(): FILE: detection/tests/test_downstream/test_mmtrack.py function test_vid_fgfa_style_forward (line 14) | def test_vid_fgfa_style_forward(cfg_file): function test_tracktor_forward (line 125) | def test_tracktor_forward(cfg_file): function _demo_mm_inputs (line 152) | def _demo_mm_inputs( FILE: detection/tests/test_metrics/test_box_overlap.py function test_bbox_overlaps_2d (line 11) | def test_bbox_overlaps_2d(eps=1e-7): function test_voc_recall_overlaps (line 111) | def test_voc_recall_overlaps(): FILE: detection/tests/test_metrics/test_losses.py function test_ce_loss (line 8) | def test_ce_loss(): function test_varifocal_loss (line 35) | def test_varifocal_loss(): function test_kd_loss (line 82) | def test_kd_loss(): function test_seesaw_loss (line 113) | def test_seesaw_loss(): function test_accuracy (line 186) | def test_accuracy(): FILE: detection/tests/test_metrics/test_mean_ap.py function test_tpfp_imagenet (line 15) | def test_tpfp_imagenet(): function test_tpfp_default (line 42) | def test_tpfp_default(): function test_eval_map (line 70) | def test_eval_map(): function test_tpfp_openimages (line 91) | def test_tpfp_openimages(): FILE: detection/tests/test_metrics/test_recall.py function test_eval_recalls (line 14) | def test_eval_recalls(): FILE: detection/tests/test_models/test_backbones/test_csp_darknet.py function test_csp_darknet_backbone (line 10) | def test_csp_darknet_backbone(): FILE: detection/tests/test_models/test_backbones/test_detectors_resnet.py function test_detectorrs_resnet_backbone (line 7) | def test_detectorrs_resnet_backbone(): FILE: detection/tests/test_models/test_backbones/test_efficientnet.py function test_efficientnet_backbone (line 7) | def test_efficientnet_backbone(): FILE: detection/tests/test_models/test_backbones/test_hourglass.py function test_hourglass_backbone (line 8) | def test_hourglass_backbone(): FILE: detection/tests/test_models/test_backbones/test_hrnet.py function test_hrmodule (line 10) | def test_hrmodule(block): function test_hrnet_backbone (line 54) | def test_hrnet_backbone(): FILE: detection/tests/test_models/test_backbones/test_mobilenet_v2.py function test_mobilenetv2_backbone (line 11) | def test_mobilenetv2_backbone(): FILE: detection/tests/test_models/test_backbones/test_pvt.py function test_pvt_block (line 9) | def test_pvt_block(): function test_pvt (line 21) | def test_pvt(): function test_pvtv2 (line 67) | def test_pvtv2(): FILE: detection/tests/test_models/test_backbones/test_regnet.py function test_regnet_backbone (line 36) | def test_regnet_backbone(arch_name, arch, out_channels): FILE: detection/tests/test_models/test_backbones/test_renext.py function test_renext_bottleneck (line 10) | def test_renext_bottleneck(): function test_resnext_backbone (line 59) | def test_resnext_backbone(): FILE: detection/tests/test_models/test_backbones/test_res2net.py function test_res2net_bottle2neck (line 10) | def test_res2net_bottle2neck(): function test_res2net_backbone (line 44) | def test_res2net_backbone(): FILE: detection/tests/test_models/test_backbones/test_resnest.py function test_resnest_bottleneck (line 9) | def test_resnest_bottleneck(): function test_resnest_backbone (line 27) | def test_resnest_backbone(): FILE: detection/tests/test_models/test_backbones/test_resnet.py function test_resnet_basic_block (line 15) | def test_resnet_basic_block(): function test_resnet_bottleneck (line 64) | def test_resnet_bottleneck(): function test_simplied_basic_block (line 198) | def test_simplied_basic_block(): function test_resnet_res_layer (line 251) | def test_resnet_res_layer(): function test_resnest_stem (line 307) | def test_resnest_stem(): function test_resnet_backbone (line 347) | def test_resnet_backbone(): FILE: detection/tests/test_models/test_backbones/test_swin.py function test_swin_block (line 7) | def test_swin_block(): function test_swin_transformer (line 26) | def test_swin_transformer(): FILE: detection/tests/test_models/test_backbones/test_trident_resnet.py function test_trident_resnet_bottleneck (line 9) | def test_trident_resnet_bottleneck(): function test_trident_resnet_backbone (line 156) | def test_trident_resnet_backbone(): FILE: detection/tests/test_models/test_backbones/utils.py function is_block (line 11) | def is_block(modules): function is_norm (line 19) | def is_norm(modules): function check_norm_state (line 26) | def check_norm_state(modules, train_state): FILE: detection/tests/test_models/test_dense_heads/test_anchor_head.py function test_anchor_head_loss (line 8) | def test_anchor_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_atss_head.py function test_atss_head_loss (line 8) | def test_atss_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_autoassign_head.py function test_autoassign_head_loss (line 9) | def test_autoassign_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_centernet_head.py function test_center_head_loss (line 9) | def test_center_head_loss(): function test_centernet_head_get_bboxes (line 55) | def test_centernet_head_get_bboxes(): FILE: detection/tests/test_models/test_dense_heads/test_corner_head.py function test_corner_head_loss (line 8) | def test_corner_head_loss(): function test_corner_head_encode_and_decode_heatmap (line 96) | def test_corner_head_encode_and_decode_heatmap(): FILE: detection/tests/test_models/test_dense_heads/test_dense_heads_attr.py function test_dense_heads_test_attr (line 10) | def test_dense_heads_test_attr(): FILE: detection/tests/test_models/test_dense_heads/test_detr_head.py function test_detr_head_loss (line 8) | def test_detr_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_fcos_head.py function test_fcos_head_loss (line 8) | def test_fcos_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_fsaf_head.py function test_fsaf_head_loss (line 8) | def test_fsaf_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_ga_anchor_head.py function test_ga_anchor_head_loss (line 8) | def test_ga_anchor_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_gfl_head.py function test_gfl_head_loss (line 8) | def test_gfl_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_lad_head.py function test_lad_head_loss (line 10) | def test_lad_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_ld_head.py function test_ld_head_loss (line 8) | def test_ld_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_mask2former_head.py function test_mask2former_head_loss (line 9) | def test_mask2former_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_maskformer_head.py function test_maskformer_head_loss (line 9) | def test_maskformer_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_paa_head.py function test_paa_head_loss (line 10) | def test_paa_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_pisa_head.py function test_pisa_retinanet_head_loss (line 9) | def test_pisa_retinanet_head_loss(): function test_pisa_ssd_head_loss (line 76) | def test_pisa_ssd_head_loss(): function test_pisa_roi_head_loss (line 151) | def test_pisa_roi_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_sabl_retina_head.py function test_sabl_retina_head_loss (line 8) | def test_sabl_retina_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_solo_head.py function test_solo_head_loss (line 8) | def test_solo_head_loss(): function test_desolo_head_loss (line 97) | def test_desolo_head_loss(): function test_desolo_light_head_loss (line 194) | def test_desolo_light_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_tood_head.py function test_tood_head_loss (line 8) | def test_tood_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_vfnet_head.py function test_vfnet_head_loss (line 8) | def test_vfnet_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_yolact_head.py function test_yolact_head_loss (line 8) | def test_yolact_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_yolof_head.py function test_yolof_head_loss (line 8) | def test_yolof_head_loss(): FILE: detection/tests/test_models/test_dense_heads/test_yolox_head.py function test_yolox_head_loss (line 9) | def test_yolox_head_loss(): FILE: detection/tests/test_models/test_forward.py function _get_config_directory (line 11) | def _get_config_directory(): function _get_config_module (line 26) | def _get_config_module(fname): function _get_detector_cfg (line 35) | def _get_detector_cfg(fname): function _replace_r50_with_r18 (line 46) | def _replace_r50_with_r18(model): function test_sparse_rcnn_forward (line 56) | def test_sparse_rcnn_forward(): function test_rpn_forward (line 122) | def test_rpn_forward(): function test_single_stage_forward_gpu (line 166) | def test_single_stage_forward_gpu(cfg_file): function test_faster_rcnn_ohem_forward (line 208) | def test_faster_rcnn_ohem_forward(): function test_two_stage_forward (line 273) | def test_two_stage_forward(cfg_file): function test_single_stage_forward_cpu (line 404) | def test_single_stage_forward_cpu(cfg_file): function _demo_mm_inputs (line 440) | def _demo_mm_inputs(input_shape=(1, 3, 300, 300), function test_yolact_forward (line 518) | def test_yolact_forward(): function test_detr_forward (line 561) | def test_detr_forward(): function test_inference_detector (line 618) | def test_inference_detector(): function test_yolox_random_size (line 678) | def test_yolox_random_size(): function test_maskformer_forward (line 705) | def test_maskformer_forward(): function test_mask2former_forward (line 816) | def test_mask2former_forward(): FILE: detection/tests/test_models/test_loss.py function test_iou_type_loss_zeros_weight (line 19) | def test_iou_type_loss_zeros_weight(loss_class): function test_loss_with_reduction_override (line 34) | def test_loss_with_reduction_override(loss_class): function test_regression_losses (line 52) | def test_regression_losses(loss_class, input_shape): function test_classification_losses (line 89) | def test_classification_losses(loss_class, input_shape): function test_GHMR_loss (line 127) | def test_GHMR_loss(loss_class, input_shape): function test_loss_with_ignore_index (line 140) | def test_loss_with_ignore_index(use_sigmoid, reduction, avg_non_ignore): function test_dice_loss (line 182) | def test_dice_loss(naive_dice): FILE: detection/tests/test_models/test_loss_compatibility.py function _get_config_directory (line 11) | def _get_config_directory(): function _get_config_module (line 26) | def _get_config_module(fname): function _get_detector_cfg (line 35) | def _get_detector_cfg(fname): function test_bbox_loss_compatibility (line 58) | def test_bbox_loss_compatibility(loss_bbox): function test_cls_loss_compatibility (line 98) | def test_cls_loss_compatibility(loss_cls): function _demo_mm_inputs (line 126) | def _demo_mm_inputs(input_shape=(1, 3, 300, 300), FILE: detection/tests/test_models/test_necks.py function test_fpn (line 11) | def test_fpn(): function test_channel_mapper (line 196) | def test_channel_mapper(): function test_dilated_encoder (line 233) | def test_dilated_encoder(): function test_ct_resnet_neck (line 243) | def test_ct_resnet_neck(): function test_yolov3_neck (line 285) | def test_yolov3_neck(): function test_ssd_neck (line 334) | def test_ssd_neck(): function test_yolox_pafpn (line 401) | def test_yolox_pafpn(): function test_dyhead (line 431) | def test_dyhead(): function test_fpg (line 454) | def test_fpg(): function test_fpn_carafe (line 571) | def test_fpn_carafe(): function test_nas_fpn (line 604) | def test_nas_fpn(): function test_nasfcos_fpn (line 641) | def test_nasfcos_fpn(): FILE: detection/tests/test_models/test_plugins.py function test_dropblock (line 10) | def test_dropblock(): function test_pixel_decoder (line 34) | def test_pixel_decoder(): function test_transformer_encoder_pixel_decoder (line 56) | def test_transformer_encoder_pixel_decoder(): function test_msdeformattn_pixel_decoder (line 114) | def test_msdeformattn_pixel_decoder(): FILE: detection/tests/test_models/test_roi_heads/test_bbox_head.py function test_bbox_head_loss (line 12) | def test_bbox_head_loss(): function test_bbox_head_get_bboxes (line 71) | def test_bbox_head_get_bboxes(num_sample): function test_refine_boxes (line 89) | def test_refine_boxes(): function _demodata_refine_boxes (line 212) | def _demodata_refine_boxes(n_roi, n_img, rng=0): FILE: detection/tests/test_models/test_roi_heads/test_mask_head.py function test_mask_head_loss (line 10) | def test_mask_head_loss(): FILE: detection/tests/test_models/test_roi_heads/test_roi_extractor.py function test_groie (line 8) | def test_groie(): FILE: detection/tests/test_models/test_roi_heads/test_sabl_bbox_head.py function test_sabl_bbox_head_loss (line 10) | def test_sabl_bbox_head_loss(): FILE: detection/tests/test_models/test_roi_heads/utils.py function _dummy_bbox_sampling (line 7) | def _dummy_bbox_sampling(proposal_list, gt_bboxes, gt_labels): FILE: detection/tests/test_models/test_seg_heads/test_maskformer_fusion_head.py function test_maskformer_fusion_head (line 8) | def test_maskformer_fusion_head(): FILE: detection/tests/test_models/test_utils/test_brick_wrappers.py function test_adaptive_avg_pool2d (line 16) | def test_adaptive_avg_pool2d(): function test_AdaptiveAvgPool2d (line 41) | def test_AdaptiveAvgPool2d(): FILE: detection/tests/test_models/test_utils/test_conv_upsample.py function test_conv_upsample (line 9) | def test_conv_upsample(num_layers): FILE: detection/tests/test_models/test_utils/test_inverted_residual.py function test_inverted_residual (line 10) | def test_inverted_residual(): FILE: detection/tests/test_models/test_utils/test_model_misc.py function test_interpolate_as (line 9) | def test_interpolate_as(): function test_sigmoid_geometric_mean (line 31) | def test_sigmoid_geometric_mean(): FILE: detection/tests/test_models/test_utils/test_position_encoding.py function test_sine_positional_encoding (line 9) | def test_sine_positional_encoding(num_feats=16, batch_size=2): function test_learned_positional_encoding (line 29) | def test_learned_positional_encoding(num_feats=16, FILE: detection/tests/test_models/test_utils/test_se_layer.py function test_se_layer (line 10) | def test_se_layer(): function test_dyrelu (line 29) | def test_dyrelu(): FILE: detection/tests/test_models/test_utils/test_transformer.py function test_adaptive_padding (line 12) | def test_adaptive_padding(): function test_patch_embed (line 104) | def test_patch_embed(): function test_patch_merging (line 318) | def test_patch_merging(): function test_detr_transformer_dencoder_encoder_layer (line 468) | def test_detr_transformer_dencoder_encoder_layer(): function test_transformer (line 534) | def test_transformer(): FILE: detection/tests/test_onnx/test_head.py function test_cascade_onnx_export (line 25) | def test_cascade_onnx_export(): function test_faster_onnx_export (line 60) | def test_faster_onnx_export(): function retinanet_config (line 95) | def retinanet_config(): function test_retina_head_forward_single (line 126) | def test_retina_head_forward_single(): function test_retina_head_forward (line 135) | def test_retina_head_forward(): function test_retinanet_head_onnx_export (line 148) | def test_retinanet_head_onnx_export(): function yolo_config (line 176) | def yolo_config(): function test_yolov3_head_forward (line 208) | def test_yolov3_head_forward(): function test_yolov3_head_onnx_export (line 220) | def test_yolov3_head_onnx_export(): function fcos_config (line 243) | def fcos_config(): function test_fcos_head_forward_single (line 259) | def test_fcos_head_forward_single(): function test_fcos_head_forward (line 271) | def test_fcos_head_forward(): function test_fcos_head_onnx_export (line 282) | def test_fcos_head_onnx_export(): function fsaf_config (line 311) | def fsaf_config(): function test_fsaf_head_forward_single (line 334) | def test_fsaf_head_forward_single(): function test_fsaf_head_forward (line 342) | def test_fsaf_head_forward(): function test_fsaf_head_onnx_export (line 354) | def test_fsaf_head_onnx_export(): function ssd_config (line 382) | def ssd_config(): function test_ssd_head_forward (line 415) | def test_ssd_head_forward(): function test_ssd_head_onnx_export (line 428) | def test_ssd_head_onnx_export(): FILE: detection/tests/test_onnx/test_neck.py function fpn_neck_config (line 35) | def fpn_neck_config(test_step_name): function yolo_neck_config (line 103) | def yolo_neck_config(test_step_name): function test_fpn_normal (line 121) | def test_fpn_normal(): function test_fpn_wo_extra_convs (line 126) | def test_fpn_wo_extra_convs(): function test_fpn_lateral_bns (line 131) | def test_fpn_lateral_bns(): function test_fpn_bilinear_upsample (line 136) | def test_fpn_bilinear_upsample(): function test_fpn_scale_factor (line 141) | def test_fpn_scale_factor(): function test_fpn_extra_convs_inputs (line 146) | def test_fpn_extra_convs_inputs(): function test_fpn_extra_convs_laterals (line 151) | def test_fpn_extra_convs_laterals(): function test_fpn_extra_convs_outputs (line 156) | def test_fpn_extra_convs_outputs(): function test_yolo_normal (line 161) | def test_yolo_normal(): FILE: detection/tests/test_onnx/utils.py class WrapFunction (line 21) | class WrapFunction(nn.Module): method __init__ (line 24) | def __init__(self, wrapped_function): method forward (line 28) | def forward(self, *args, **kwargs): function ort_validate (line 32) | def ort_validate(model, feats, onnx_io='tmp.onnx'): function get_ort_model_output (line 88) | def get_ort_model_output(feat, onnx_io='tmp.onnx'): function convert_result_list (line 118) | def convert_result_list(outputs): FILE: detection/tests/test_runtime/async_benchmark.py function main (line 16) | async def main(): FILE: detection/tests/test_runtime/test_apis.py function test_init_detector (line 9) | def test_init_detector(): FILE: detection/tests/test_runtime/test_async.py class AsyncTestCase (line 18) | class AsyncTestCase(asynctest.TestCase): method _run_test_method (line 24) | def _run_test_method(self, method): class MaskRCNNDetector (line 31) | class MaskRCNNDetector: method __init__ (line 33) | def __init__(self, method init (line 46) | async def init(self): method apredict (line 54) | async def apredict(self, img): class AsyncInferenceTestCase (line 62) | class AsyncInferenceTestCase(AsyncTestCase): method test_simple_inference (line 66) | async def test_simple_inference(self): FILE: detection/tests/test_runtime/test_config.py function _get_config_directory (line 12) | def _get_config_directory(): function _check_numclasscheckhook (line 28) | def _check_numclasscheckhook(detector, config_mod): function _check_roi_head (line 63) | def _check_roi_head(config, head): function _check_roi_extractor (line 116) | def _check_roi_extractor(config, roi_extractor, prev_roi_extractor=None): function _check_mask_head (line 145) | def _check_mask_head(mask_cfg, mask_head): function _check_bbox_head (line 168) | def _check_bbox_head(bbox_cfg, bbox_head): function _check_anchorhead (line 219) | def _check_anchorhead(config, head): function test_config_data_pipeline (line 256) | def test_config_data_pipeline(config_rpath): FILE: detection/tests/test_runtime/test_eval_hook.py class ExampleDataset (line 18) | class ExampleDataset(Dataset): method __init__ (line 20) | def __init__(self): method __getitem__ (line 24) | def __getitem__(self, idx): method __len__ (line 28) | def __len__(self): method evaluate (line 32) | def evaluate(self, results, logger=None): class EvalDataset (line 36) | class EvalDataset(ExampleDataset): method evaluate (line 38) | def evaluate(self, results, logger=None): class ExampleModel (line 45) | class ExampleModel(nn.Module): method __init__ (line 47) | def __init__(self): method forward (line 52) | def forward(self, imgs, rescale=False, return_loss=False): method train_step (line 55) | def train_step(self, data_batch, optimizer, **kwargs): function test_eval_hook (line 71) | def test_eval_hook(EvalHookCls): FILE: detection/tests/test_runtime/test_fp16.py function test_cast_tensor_type (line 10) | def test_cast_tensor_type(): function test_auto_fp16 (line 52) | def test_auto_fp16(): function test_force_fp32 (line 178) | def test_force_fp32(): FILE: detection/tests/test_utils/test_anchor.py function test_standard_points_generator (line 12) | def test_standard_points_generator(): function test_sparse_prior (line 107) | def test_sparse_prior(): function test_standard_anchor_generator (line 273) | def test_standard_anchor_generator(): function test_strides (line 288) | def test_strides(): function test_ssd_anchor_generator (line 310) | def test_ssd_anchor_generator(): function test_anchor_generator_with_tuples (line 472) | def test_anchor_generator_with_tuples(): function test_yolo_anchor_generator (line 507) | def test_yolo_anchor_generator(): function test_retina_anchor (line 550) | def test_retina_anchor(): function test_guided_anchor (line 649) | def test_guided_anchor(): FILE: detection/tests/test_utils/test_assigner.py function test_max_iou_assigner (line 18) | def test_max_iou_assigner(): function test_max_iou_assigner_with_ignore (line 42) | def test_max_iou_assigner_with_ignore(): function test_max_iou_assigner_with_empty_gt (line 69) | def test_max_iou_assigner_with_empty_gt(): function test_max_iou_assigner_with_empty_boxes (line 88) | def test_max_iou_assigner_with_empty_boxes(): function test_max_iou_assigner_with_empty_boxes_and_ignore (line 112) | def test_max_iou_assigner_with_empty_boxes_and_ignore(): function test_max_iou_assigner_with_empty_boxes_and_gt (line 146) | def test_max_iou_assigner_with_empty_boxes_and_gt(): function test_point_assigner (line 158) | def test_point_assigner(): function test_point_assigner_with_empty_gt (line 175) | def test_point_assigner_with_empty_gt(): function test_point_assigner_with_empty_boxes_and_gt (line 191) | def test_point_assigner_with_empty_boxes_and_gt(): function test_approx_iou_assigner (line 200) | def test_approx_iou_assigner(): function test_approx_iou_assigner_with_empty_gt (line 225) | def test_approx_iou_assigner_with_empty_gt(): function test_approx_iou_assigner_with_empty_boxes (line 248) | def test_approx_iou_assigner_with_empty_boxes(): function test_approx_iou_assigner_with_empty_boxes_and_gt (line 267) | def test_approx_iou_assigner_with_empty_boxes_and_gt(): function test_random_assign_result (line 283) | def test_random_assign_result(): function test_center_region_assigner (line 297) | def test_center_region_assigner(): function test_center_region_assigner_with_ignore (line 323) | def test_center_region_assigner_with_ignore(): function test_center_region_assigner_with_empty_bboxes (line 352) | def test_center_region_assigner_with_empty_bboxes(): function test_center_region_assigner_with_empty_gts (line 368) | def test_center_region_assigner_with_empty_gts(): function test_hungarian_match_assigner (line 385) | def test_hungarian_match_assigner(): function test_uniform_assigner (line 431) | def test_uniform_assigner(): function test_uniform_assigner_with_empty_gt (line 459) | def test_uniform_assigner_with_empty_gt(): function test_uniform_assigner_with_empty_boxes (line 481) | def test_uniform_assigner_with_empty_boxes(): function test_sim_ota_assigner (line 503) | def test_sim_ota_assigner(): function test_task_aligned_assigner (line 518) | def test_task_aligned_assigner(): function test_mask_hungarian_match_assigner (line 560) | def test_mask_hungarian_match_assigner(): FILE: detection/tests/test_utils/test_coder.py function test_yolo_bbox_coder (line 9) | def test_yolo_bbox_coder(): function test_delta_bbox_coder (line 27) | def test_delta_bbox_coder(): function test_tblr_bbox_coder (line 78) | def test_tblr_bbox_coder(): function test_distance_point_bbox_coder (line 113) | def test_distance_point_bbox_coder(): FILE: detection/tests/test_utils/test_compat_config.py function test_compat_runner_args (line 8) | def test_compat_runner_args(): function test_compat_loader_args (line 19) | def test_compat_loader_args(): function test_compat_imgs_per_gpu (line 105) | def test_compat_imgs_per_gpu(): FILE: detection/tests/test_utils/test_general_data.py function _equal (line 10) | def _equal(a, b): function test_general_data (line 17) | def test_general_data(): function test_instance_data (line 413) | def test_instance_data(): FILE: detection/tests/test_utils/test_hook.py function _build_demo_runner_without_hook (line 22) | def _build_demo_runner_without_hook(runner_type='EpochBasedRunner', function _build_demo_runner (line 68) | def _build_demo_runner(runner_type='EpochBasedRunner', function test_yolox_lrupdater_hook (line 86) | def test_yolox_lrupdater_hook(multi_optimziers): function test_ema_hook (line 158) | def test_ema_hook(): function test_sync_norm_hook (line 242) | def test_sync_norm_hook(): function test_sync_random_size_hook (line 253) | def test_sync_random_size_hook(): function test_check_invalid_loss_hook (line 288) | def test_check_invalid_loss_hook(set_loss): function test_set_epoch_info_hook (line 328) | def test_set_epoch_info_hook(): function test_memory_profiler_hook (line 357) | def test_memory_profiler_hook(): FILE: detection/tests/test_utils/test_logger.py function callee_func (line 7) | def callee_func(): class CallerClassForTest (line 12) | class CallerClassForTest: method __init__ (line 14) | def __init__(self): function test_get_caller_name (line 18) | def test_get_caller_name(): function test_log_img_scale (line 28) | def test_log_img_scale(): FILE: detection/tests/test_utils/test_masks.py function dummy_raw_bitmap_masks (line 9) | def dummy_raw_bitmap_masks(size): function dummy_raw_polygon_masks (line 20) | def dummy_raw_polygon_masks(size): function dummy_bboxes (line 36) | def dummy_bboxes(num, max_height, max_width): function test_bitmap_mask_init (line 43) | def test_bitmap_mask_init(): function test_bitmap_mask_rescale (line 78) | def test_bitmap_mask_rescale(): function test_bitmap_mask_resize (line 99) | def test_bitmap_mask_resize(): function test_bitmap_mask_get_bboxes (line 133) | def test_bitmap_mask_get_bboxes(): function test_bitmap_mask_flip (line 162) | def test_bitmap_mask_flip(): function test_bitmap_mask_pad (line 203) | def test_bitmap_mask_pad(): function test_bitmap_mask_crop (line 222) | def test_bitmap_mask_crop(): function test_bitmap_mask_crop_and_resize (line 248) | def test_bitmap_mask_crop_and_resize(): function test_bitmap_mask_expand (line 271) | def test_bitmap_mask_expand(): function test_bitmap_mask_area (line 291) | def test_bitmap_mask_area(): function test_bitmap_mask_to_ndarray (line 305) | def test_bitmap_mask_to_ndarray(): function test_bitmap_mask_to_tensor (line 322) | def test_bitmap_mask_to_tensor(): function test_bitmap_mask_index (line 339) | def test_bitmap_mask_index(): function test_bitmap_mask_iter (line 346) | def test_bitmap_mask_iter(): function test_polygon_mask_init (line 354) | def test_polygon_mask_init(): function test_polygon_mask_rescale (line 382) | def test_polygon_mask_rescale(): function test_polygon_mask_resize (line 410) | def test_polygon_mask_resize(): function test_polygon_mask_flip (line 483) | def test_polygon_mask_flip(): function test_polygon_mask_crop (line 531) | def test_polygon_mask_crop(): function test_polygon_mask_pad (line 560) | def test_polygon_mask_pad(): function test_polygon_mask_expand (line 581) | def test_polygon_mask_expand(): function test_polygon_mask_crop_and_resize (line 588) | def test_polygon_mask_crop_and_resize(): function test_polygon_mask_area (line 613) | def test_polygon_mask_area(): function test_polygon_mask_to_bitmap (line 630) | def test_polygon_mask_to_bitmap(): function test_polygon_mask_to_ndarray (line 638) | def test_polygon_mask_to_ndarray(): function test_polygon_to_tensor (line 654) | def test_polygon_to_tensor(): function test_polygon_mask_index (line 671) | def test_polygon_mask_index(): function test_polygon_mask_iter (line 685) | def test_polygon_mask_iter(): function test_mask2bbox (line 692) | def test_mask2bbox(): FILE: detection/tests/test_utils/test_misc.py function dummy_raw_polygon_masks (line 16) | def dummy_raw_polygon_masks(size): function test_mask2ndarray (line 32) | def test_mask2ndarray(): function test_distance2bbox (line 57) | def test_distance2bbox(): function test_center_of_mass (line 107) | def test_center_of_mass(mask): function test_flip_tensor (line 118) | def test_flip_tensor(): function test_select_single_mlvl (line 143) | def test_select_single_mlvl(): function test_filter_scores_and_topk (line 149) | def test_filter_scores_and_topk(): function test_find_latest_checkpoint (line 169) | def test_find_latest_checkpoint(): FILE: detection/tests/test_utils/test_nms.py function _create_mask (line 7) | def _create_mask(N, h, w): function test_nms_input_errors (line 14) | def test_nms_input_errors(): FILE: detection/tests/test_utils/test_setup_env.py function test_setup_multi_processes (line 12) | def test_setup_multi_processes(): FILE: detection/tests/test_utils/test_split_batch.py function test_split_batch (line 12) | def test_split_batch(): FILE: detection/tests/test_utils/test_version.py function test_version_check (line 5) | def test_version_check(): FILE: detection/tests/test_utils/test_visualization.py function test_color (line 16) | def test_color(): function test_imshow_det_bboxes (line 33) | def test_imshow_det_bboxes(): function test_imshow_gt_det_bboxes (line 82) | def test_imshow_gt_det_bboxes(): function test_palette (line 132) | def test_palette(): FILE: detection/tools/analysis_tools/analyze_logs.py function cal_train_time (line 11) | def cal_train_time(log_dicts, args): function plot_curve (line 38) | def plot_curve(log_dicts, args): function add_plot_parser (line 106) | def add_plot_parser(subparsers): function add_time_parser (line 144) | def add_time_parser(subparsers): function parse_args (line 160) | def parse_args(): function load_json_logs (line 170) | def load_json_logs(json_logs): function main (line 193) | def main(): FILE: detection/tools/analysis_tools/analyze_results.py function bbox_map_eval (line 15) | def bbox_map_eval(det_result, annotation): class ResultVisualizer (line 50) | class ResultVisualizer: method __init__ (line 60) | def __init__(self, show=False, wait_time=0, score_thr=0): method _save_image_gts_results (line 65) | def _save_image_gts_results(self, dataset, results, mAPs, out_dir=None): method evaluate_and_show (line 97) | def evaluate_and_show(self, function parse_args (line 145) | def parse_args(): function main (line 184) | def main(): FILE: detection/tools/analysis_tools/benchmark.py function parse_args (line 19) | def parse_args(): function measure_inference_speed (line 59) | def measure_inference_speed(cfg, checkpoint, max_iter, log_interval, function repeat_measure_inference_speed (line 136) | def repeat_measure_inference_speed(cfg, function main (line 170) | def main(): FILE: detection/tools/analysis_tools/coco_error_analysis.py function makeplot (line 13) | def makeplot(rs, ps, outDir, class_name, iou_type): function autolabel (line 54) | def autolabel(ax, rects): function makebarplot (line 73) | def makebarplot(rs, ps, outDir, class_name, iou_type): function get_gt_area_group_numbers (line 108) | def get_gt_area_group_numbers(cocoEval): function make_gt_area_group_numbers_plot (line 123) | def make_gt_area_group_numbers_plot(cocoEval, outDir, verbose=True): function make_gt_area_histogram_plot (line 152) | def make_gt_area_histogram_plot(cocoEval, outDir): function analyze_individual_category (line 174) | def analyze_individual_category(k, function analyze_results (line 235) | def analyze_results(res_file, function main (line 308) | def main(): FILE: detection/tools/analysis_tools/confusion_matrix.py function parse_args (line 16) | def parse_args(): function calculate_confusion_matrix (line 60) | def calculate_confusion_matrix(dataset, function analyze_per_img_dets (line 96) | def analyze_per_img_dets(confusion_matrix, function plot_confusion_matrix (line 146) | def plot_confusion_matrix(confusion_matrix, function main (line 230) | def main(): FILE: detection/tools/analysis_tools/eval_metric.py function parse_args (line 11) | def parse_args(): function main (line 48) | def main(): FILE: detection/tools/analysis_tools/get_flops.py function parse_args (line 16) | def parse_args(): function main (line 45) | def main(): FILE: detection/tools/analysis_tools/optimize_anchors.py function parse_args (line 35) | def parse_args(): class BaseAnchorOptimizer (line 66) | class BaseAnchorOptimizer: method __init__ (line 80) | def __init__(self, method get_whs_and_shapes (line 97) | def get_whs_and_shapes(self): method get_zero_center_bbox_tensor (line 124) | def get_zero_center_bbox_tensor(self): method optimize (line 137) | def optimize(self): method save_result (line 140) | def save_result(self, anchors, path=None): class YOLOKMeansAnchorOptimizer (line 151) | class YOLOKMeansAnchorOptimizer(BaseAnchorOptimizer): method __init__ (line 160) | def __init__(self, num_anchors, iters, **kwargs): method optimize (line 166) | def optimize(self): method kmeans_anchors (line 170) | def kmeans_anchors(self): method kmeans_maximization (line 206) | def kmeans_maximization(self, bboxes, assignments, centers): method kmeans_expectation (line 215) | def kmeans_expectation(self, bboxes, assignments, centers): class YOLODEAnchorOptimizer (line 223) | class YOLODEAnchorOptimizer(BaseAnchorOptimizer): method __init__ (line 258) | def __init__(self, method optimize (line 278) | def optimize(self): method differential_evolution (line 282) | def differential_evolution(self): method avg_iou_cost (line 308) | def avg_iou_cost(anchor_params, bboxes): function main (line 322) | def main(): FILE: detection/tools/analysis_tools/robustness_eval.py function print_coco_results (line 9) | def print_coco_results(results): function get_coco_style_results (line 35) | def get_coco_style_results(filename, function get_voc_style_results (line 112) | def get_voc_style_results(filename, prints='mPC', aggregate='benchmark'): function get_results (line 156) | def get_results(filename, function get_distortions_from_file (line 184) | def get_distortions_from_file(filename): function get_distortions_from_results (line 191) | def get_distortions_from_results(eval_output): function main (line 198) | def main(): FILE: detection/tools/analysis_tools/test_robustness.py function coco_eval_with_return (line 24) | def coco_eval_with_return(result_files, function voc_eval_with_return (line 66) | def voc_eval_with_return(result_file, function parse_args (line 93) | def parse_args(): function main (line 181) | def main(): FILE: detection/tools/dataset_converters/cityscapes.py function collect_files (line 12) | def collect_files(img_dir, gt_dir): function collect_annotations (line 29) | def collect_annotations(files, nproc=1): function load_img_info (line 40) | def load_img_info(files): function cvt_annotations (line 85) | def cvt_annotations(image_infos, out_json_name): function parse_args (line 114) | def parse_args(): function main (line 127) | def main(): FILE: detection/tools/dataset_converters/images2coco.py function parse_args (line 9) | def parse_args(): function collect_image_infos (line 30) | def collect_image_infos(path, exclude_extensions=None): function cvt_to_coco_json (line 49) | def cvt_to_coco_json(img_infos, classes): function main (line 80) | def main(): FILE: detection/tools/dataset_converters/pascal_voc.py function parse_xml (line 14) | def parse_xml(args): function cvt_annotations (line 68) | def cvt_annotations(devkit_path, years, split, out_file): function cvt_to_coco_json (line 96) | def cvt_to_coco_json(annotations): function parse_args (line 183) | def parse_args(): function main (line 197) | def main(): FILE: detection/tools/deployment/mmdet2torchserve.py function mmdet2torchserve (line 15) | def mmdet2torchserve( function parse_args (line 70) | def parse_args(): FILE: detection/tools/deployment/mmdet_handler.py class MMdetHandler (line 12) | class MMdetHandler(BaseHandler): method initialize (line 15) | def initialize(self, context): method preprocess (line 31) | def preprocess(self, data): method inference (line 43) | def inference(self, data, *args, **kwargs): method postprocess (line 47) | def postprocess(self, data): FILE: detection/tools/deployment/onnx2tensorrt.py function get_GiB (line 19) | def get_GiB(x: int): function onnx2tensorrt (line 24) | def onnx2tensorrt(onnx_file, function parse_normalize_cfg (line 106) | def parse_normalize_cfg(test_pipeline): function parse_args (line 119) | def parse_args(): function parse_shape (line 208) | def parse_shape(shape): FILE: detection/tools/deployment/pytorch2onnx.py function pytorch2onnx (line 16) | def pytorch2onnx(model, function parse_normalize_cfg (line 210) | def parse_normalize_cfg(test_pipeline): function parse_args (line 223) | def parse_args(): FILE: detection/tools/deployment/test.py function parse_args (line 15) | def parse_args(): function main (line 67) | def main(): FILE: detection/tools/deployment/test_torchserver.py function parse_args (line 10) | def parse_args(): function parse_result (line 28) | def parse_result(input, model_class): function main (line 42) | def main(args): FILE: detection/tools/misc/browse_dataset.py function parse_args (line 17) | def parse_args(): function retrieve_data_cfg (line 51) | def retrieve_data_cfg(config_path, skip_type, cfg_options): function main (line 78) | def main(): FILE: detection/tools/misc/download_dataset.py function parse_args (line 11) | def parse_args(): function download (line 35) | def download(url, dir, unzip=True, delete=False, threads=1): function main (line 65) | def main(): FILE: detection/tools/misc/gen_coco_panoptic_test_info.py function parse_args (line 7) | def parse_args(): function main (line 17) | def main(): FILE: detection/tools/misc/get_image_metas.py function parse_args (line 19) | def parse_args(): function get_metas_from_csv_style_ann_file (line 36) | def get_metas_from_csv_style_ann_file(ann_file): function get_metas_from_txt_style_ann_file (line 52) | def get_metas_from_txt_style_ann_file(ann_file): function get_image_metas (line 65) | def get_image_metas(data_info, img_prefix): function main (line 79) | def main(): FILE: detection/tools/misc/print_config.py function parse_args (line 10) | def parse_args(): function main (line 43) | def main(): FILE: detection/tools/misc/split_coco.py function parse_args (line 15) | def parse_args(): function split_coco (line 42) | def split_coco(data_root, out_dir, percent, fold): function multi_wrapper (line 100) | def multi_wrapper(args): FILE: detection/tools/model_converters/detectron2pytorch.py function convert_bn (line 11) | def convert_bn(blobs, state_dict, caffe_name, torch_name, converted_names): function convert_conv_fc (line 24) | def convert_conv_fc(blobs, state_dict, caffe_name, torch_name, function convert (line 35) | def convert(src, dst, depth): function main (line 73) | def main(): FILE: detection/tools/model_converters/publish_model.py function parse_args (line 8) | def parse_args(): function process_checkpoint (line 17) | def process_checkpoint(in_file, out_file): function main (line 37) | def main(): FILE: detection/tools/model_converters/regnet2mmdet.py function convert_stem (line 8) | def convert_stem(model_key, model_weight, state_dict, converted_names): function convert_head (line 16) | def convert_head(model_key, model_weight, state_dict, converted_names): function convert_reslayer (line 23) | def convert_reslayer(model_key, model_weight, state_dict, converted_names): function convert (line 55) | def convert(src, dst): function main (line 81) | def main(): FILE: detection/tools/model_converters/selfsup2mmdet.py function moco_convert (line 8) | def moco_convert(src, dst): function main (line 28) | def main(): FILE: detection/tools/model_converters/upgrade_model_version.py function is_head (line 11) | def is_head(key): function parse_config (line 19) | def parse_config(config_strings): function reorder_cls_channel (line 46) | def reorder_cls_channel(val, num_classes=81): function truncate_cls_channel (line 68) | def truncate_cls_channel(val, num_classes=81): function truncate_reg_channel (line 90) | def truncate_reg_channel(val, num_classes=81): function convert (line 115) | def convert(in_file, out_file, num_classes): function main (line 196) | def main(): FILE: detection/tools/model_converters/upgrade_ssd_version.py function parse_config (line 10) | def parse_config(config_strings): function convert (line 22) | def convert(in_file, out_file): function main (line 48) | def main(): FILE: detection/tools/test.py function parse_args (line 23) | def parse_args(): function main (line 120) | def main(): FILE: detection/tools/train.py function parse_args (line 24) | def parse_args(): function main (line 107) | def main(): FILE: pre-training/engine_finetune.py function train_one_epoch (line 14) | def train_one_epoch(model: torch.nn.Module, model_ema, criterion: torch.... function evaluate (line 90) | def evaluate(data_loader, model, device): FILE: pre-training/engine_pretrain.py function train_one_epoch (line 11) | def train_one_epoch(model: torch.nn.Module, FILE: pre-training/main_finetune.py function get_args_parser (line 30) | def get_args_parser(): function main (line 156) | def main(args): FILE: pre-training/main_pretrain.py function get_args_parser (line 36) | def get_args_parser(): function main (line 107) | def main(args): FILE: pre-training/models/masked_autoencoder.py class MaskedAutoencoder (line 15) | class MaskedAutoencoder(nn.Module): method __init__ (line 16) | def __init__(self): method patchify (line 20) | def patchify(self, imgs): method unpatchify (line 34) | def unpatchify(self, x): method masking_id (line 48) | def masking_id(self, batch_size, mask_ratio): method random_masking (line 68) | def random_masking(self, x, ids_keep): method forward_encoder (line 73) | def forward_encoder(self, x, mask_ratio): method forward_decoder (line 76) | def forward_decoder(self, x, ids_restore): method forward_loss (line 79) | def forward_loss(self, imgs, cls_pred, pred, mask): method forward (line 97) | def forward(self, imgs, mask_ratio=0.75): FILE: pre-training/models/models_hivit.py class Attention (line 10) | class Attention(nn.Module): method __init__ (line 11) | def __init__(self, input_size, dim, num_heads, qkv_bias=True, qk_scale... method forward (line 30) | def forward(self, x, rpe_index=None, mask=None): class BlockWithRPE (line 56) | class BlockWithRPE(nn.Module): method __init__ (line 57) | def __init__(self, input_size, dim, num_heads=0., mlp_ratio=4., qkv_bi... method forward (line 78) | def forward(self, x, rpe_index=None, mask=None): class PatchEmbed (line 85) | class PatchEmbed(nn.Module): method __init__ (line 86) | def __init__(self, img_size=224, patch_size=16, inner_patches=4, in_ch... method forward (line 107) | def forward(self, x): class PatchMerge (line 121) | class PatchMerge(nn.Module): method __init__ (line 122) | def __init__(self, dim, norm_layer): method forward (line 127) | def forward(self, x): class HiViT (line 139) | class HiViT(nn.Module): method __init__ (line 140) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe... method _init_weights (line 216) | def _init_weights(self, m): method no_weight_decay (line 226) | def no_weight_decay(self): method no_weight_decay_keywords (line 230) | def no_weight_decay_keywords(self): method forward_features (line 233) | def forward_features(self, x, ids_keep=None, mask=None, return_hifeat=... method forward (line 274) | def forward(self, x): function hivit_base (line 283) | def hivit_base(**kwargs): FILE: pre-training/models/models_hivit_mae.py class GF (line 13) | class GF(nn.Module): method __init__ (line 14) | def __init__(self, nbins=9, pool=7, kensize=5, img_size=224, patch_siz... method forward (line 46) | def forward(self, x): class HiViTMaskedAutoencoder (line 106) | class HiViTMaskedAutoencoder(MaskedAutoencoder, HiViT): method __init__ (line 107) | def __init__(self, img_size=224, patch_size=16, in_chans=3, method masking_id (line 229) | def masking_id(self, batch_size, mask_ratio): method forward_encoder (line 249) | def forward_encoder(self, x, mask_ratio): method forward_decoder (line 263) | def forward_decoder(self, x, ids_restore): method forward_loss (line 284) | def forward_loss(self, imgs, cls_pred, pred, mask): method forward (line 304) | def forward(self, imgs, mask_ratio=0.75): function mae_hivit_base_dec512d6b (line 311) | def mae_hivit_base_dec512d6b(**kwargs): FILE: pre-training/models/models_vit.py class VisionTransformer (line 20) | class VisionTransformer(timm.models.vision_transformer.VisionTransformer): method __init__ (line 23) | def __init__(self, stem_layer=PatchEmbed, stem_configs=dict(), global_... method forward_features (line 40) | def forward_features(self, x): function vit_small_patch16 (line 63) | def vit_small_patch16(**kwargs): function vit_base_patch16 (line 71) | def vit_base_patch16(**kwargs): function vit_large_patch16 (line 79) | def vit_large_patch16(**kwargs): function vit_huge_patch14 (line 87) | def vit_huge_patch14(**kwargs): FILE: pre-training/models/models_vit_mae.py class SAR_HOGLayerC (line 27) | class SAR_HOGLayerC(nn.Module): method __init__ (line 28) | def __init__(self, nbins=9, pool=7, kensize=5, img_size=224, patch_siz... method forward (line 63) | def forward(self, x): class MaskedAutoencoderViT (line 122) | class MaskedAutoencoderViT(nn.Module): method __init__ (line 125) | def __init__(self, img_size=224, patch_size=16, in_chans=3, method initialize_weights (line 183) | def initialize_weights(self): method _init_weights (line 213) | def _init_weights(self, m): method patchify (line 223) | def patchify(self, imgs): method unpatchify (line 237) | def unpatchify(self, x): method masking_id (line 251) | def masking_id(self, batch_size, mask_ratio): method random_masking (line 271) | def random_masking(self, x, ids_keep): method forward_encoder (line 276) | def forward_encoder(self, x, mask_ratio): method forward_decoder (line 300) | def forward_decoder(self, x, ids_restore): method forward_loss (line 324) | def forward_loss(self, imgs, cls_pred, pred, mask): method forward (line 344) | def forward(self, imgs, mask_ratio=0.75): function mae_vit_small_patch16_dec192d6b (line 351) | def mae_vit_small_patch16_dec192d6b(**kwargs): function mae_vit_base_patch16_dec512d8b (line 359) | def mae_vit_base_patch16_dec512d8b(**kwargs): function mae_vit_base_patch16_dec512d8b_cls10crop10 (line 367) | def mae_vit_base_patch16_dec512d8b_cls10crop10(**kwargs): function mae_vit_large_patch16_dec512d8b (line 376) | def mae_vit_large_patch16_dec512d8b(**kwargs): function mae_vit_huge_patch14_dec512d8b (line 384) | def mae_vit_huge_patch14_dec512d8b(**kwargs): FILE: pre-training/util/crop.py class RandomResizedCrop (line 15) | class RandomResizedCrop(transforms.RandomResizedCrop): method get_params (line 23) | def get_params(img, scale, ratio): FILE: pre-training/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): class MyDataSet (line 71) | class MyDataSet(Dataset): method __init__ (line 72) | def __init__(self, image_list, label_list, transform=None): method __len__ (line 77) | def __len__(self): method __getitem__ (line 80) | def __getitem__(self, index): function load_data (line 95) | def load_data(file_dir, transform): FILE: pre-training/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: pre-training/util/lr_decay.py function param_groups_lrd (line 16) | def param_groups_lrd(model, weight_decay=0.05, no_weight_decay_list=[], ... function get_layer_id (line 68) | def get_layer_id(name, max_layer, main_block): FILE: pre-training/util/lr_sched.py function adjust_learning_rate (line 9) | def adjust_learning_rate(optimizer, epoch, args): FILE: pre-training/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 main_process (line 211) | def main_process(func): function save_on_master (line 218) | def save_on_master(*args, **kwargs): function init_distributed_mode (line 223) | def init_distributed_mode(args): class NativeScalerWithGradNormCount (line 258) | class NativeScalerWithGradNormCount: method __init__ (line 261) | def __init__(self): method __call__ (line 264) | def __call__(self, loss, optimizer, clip_grad=None, parameters=None, c... method state_dict (line 280) | def state_dict(self): method load_state_dict (line 283) | def load_state_dict(self, state_dict): function get_grad_norm_ (line 287) | def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor: function save_model (line 302) | def save_model(args, epoch, model, model_without_ddp, optimizer, loss_sc... function load_model (line 324) | def load_model(args, model_without_ddp, optimizer, loss_scaler, model_em... function all_reduce_mean (line 343) | def all_reduce_mean(x): FILE: pre-training/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):