SYMBOL INDEX (643 symbols across 53 files)

FILE: benchmark.py
  function get_benckmark_arg_parser (line 27) | def get_benckmark_arg_parser():
  function measure_average_inference_time (line 37) | def measure_average_inference_time(model, inputs, num_iters=100, warm_it...
  function benchmark (line 51) | def benchmark():

FILE: datasets/__init__.py
  function get_coco_api_from_dataset (line 21) | def get_coco_api_from_dataset(dataset):
  function build_dataset (line 31) | def build_dataset(image_set, args):

FILE: datasets/coco.py
  class CocoDetection (line 28) | class CocoDetection(TvCocoDetection):
    method __init__ (line 29) | def __init__(self, img_folder, ann_file, transforms, return_masks, cac...
    method __getitem__ (line 35) | def __getitem__(self, idx):
  function convert_coco_poly_to_mask (line 45) | def convert_coco_poly_to_mask(segmentations, height, width):
  class ConvertCocoPolysToMask (line 62) | class ConvertCocoPolysToMask(object):
    method __init__ (line 63) | def __init__(self, return_masks=False):
    method __call__ (line 66) | def __call__(self, image, target):
  function make_coco_transforms (line 127) | def make_coco_transforms(image_set):
  function build (line 159) | def build(image_set, args):

FILE: datasets/coco_eval.py
  class CocoEvaluator (line 32) | class CocoEvaluator(object):
    method __init__ (line 33) | def __init__(self, coco_gt, iou_types):
    method update (line 46) | def update(self, predictions):
    method synchronize_between_processes (line 66) | def synchronize_between_processes(self):
    method accumulate (line 71) | def accumulate(self):
    method summarize (line 75) | def summarize(self):
    method prepare (line 80) | def prepare(self, predictions, iou_type):
    method prepare_for_coco_detection (line 90) | def prepare_for_coco_detection(self, predictions):
    method prepare_for_coco_segmentation (line 114) | def prepare_for_coco_segmentation(self, predictions):
    method prepare_for_coco_keypoint (line 149) | def prepare_for_coco_keypoint(self, predictions):
  function convert_to_xywh (line 176) | def convert_to_xywh(boxes):
  function merge (line 181) | def merge(img_ids, eval_imgs):
  function create_common_coco_eval (line 203) | def create_common_coco_eval(coco_eval, img_ids, eval_imgs):
  function evaluate (line 219) | def evaluate(self):

FILE: datasets/coco_panoptic.py
  class CocoPanoptic (line 25) | class CocoPanoptic:
    method __init__ (line 26) | def __init__(self, img_folder, ann_folder, ann_file, transforms=None, ...
    method __getitem__ (line 44) | def __getitem__(self, idx):
    method __len__ (line 80) | def __len__(self):
    method get_height_and_width (line 83) | def get_height_and_width(self, idx):
  function build (line 90) | def build(image_set, args):

FILE: datasets/dance.py
  class DetMOTDetection (line 31) | class DetMOTDetection:
    method __init__ (line 32) | def __init__(self, args, data_txt_path: str, seqs_folder, dataset2tran...
    method set_epoch (line 75) | def set_epoch(self, epoch):
    method step_epoch (line 87) | def step_epoch(self):
    method _targets_to_instances (line 93) | def _targets_to_instances(targets: dict, img_shape) -> Instances:
    method load_crowd (line 101) | def load_crowd(self):
    method _pre_single_frame (line 122) | def _pre_single_frame(self, vid, idx: int):
    method _get_sample_range (line 154) | def _get_sample_range(self, start_idx):
    method pre_continuous_frames (line 165) | def pre_continuous_frames(self, vid, indices):
    method sample_indices (line 168) | def sample_indices(self, vid, f_index):
    method __getitem__ (line 175) | def __getitem__(self, idx):
    method __len__ (line 192) | def __len__(self):
  class DetMOTDetectionValidation (line 196) | class DetMOTDetectionValidation(DetMOTDetection):
    method __init__ (line 197) | def __init__(self, args, seqs_folder, dataset2transform):
  function make_transforms_for_mot17 (line 202) | def make_transforms_for_mot17(image_set, args=None):
  function build_dataset2transform (line 233) | def build_dataset2transform(args, image_set):
  function build (line 247) | def build(image_set, args):

FILE: datasets/data_path/gen_bdd100k_mot.py
  function convert (line 9) | def convert(img_dir, split, label_dir, save_label_dir, filter_crowd=Fals...
  function generate_txt (line 73) | def generate_txt(img_dir,label_dir,txt_path='bdd100k.train',split='train'):

FILE: datasets/data_path/gen_labels_15.py
  function mkdirs (line 7) | def mkdirs(d):

FILE: datasets/data_path/gen_labels_16.py
  function mkdirs (line 4) | def mkdirs(d):

FILE: datasets/data_path/prepare.py
  function solve_MOT_train (line 6) | def solve_MOT_train(root, year):
  function solve_CUHK (line 27) | def solve_CUHK(root):
  function solve_ETHZ (line 40) | def solve_ETHZ(root):
  function solve_PRW (line 59) | def solve_PRW(root):
  function solve (line 82) | def solve(dataset_list: List[str], root, save_path):

FILE: datasets/data_prefetcher.py
  function to_cuda (line 16) | def to_cuda(samples, targets, device):
  function tensor_to_cuda (line 22) | def tensor_to_cuda(tensor: torch.Tensor, device):
  function is_tensor_or_instances (line 26) | def is_tensor_or_instances(data):
  function data_apply (line 30) | def data_apply(data, check_func, apply_func):
  function data_dict_to_cuda (line 52) | def data_dict_to_cuda(data_dict, device):
  class data_prefetcher (line 56) | class data_prefetcher():
    method __init__ (line 57) | def __init__(self, loader, device, prefetch=True):
    method preload (line 65) | def preload(self):
    method next (line 93) | def next(self):

FILE: datasets/detmot.py
  class DetMOTDetection (line 26) | class DetMOTDetection:
    method __init__ (line 27) | def __init__(self, args, data_txt_path: str, seqs_folder, transforms):
    method _register_videos (line 63) | def _register_videos(self):
    method set_epoch (line 71) | def set_epoch(self, epoch):
    method step_epoch (line 83) | def step_epoch(self):
    method _targets_to_instances (line 89) | def _targets_to_instances(targets: dict, img_shape) -> Instances:
    method _pre_single_frame (line 97) | def _pre_single_frame(self, idx: int):
    method _get_sample_range (line 142) | def _get_sample_range(self, start_idx):
    method pre_continuous_frames (line 153) | def pre_continuous_frames(self, start, end, interval=1):
    method __getitem__ (line 162) | def __getitem__(self, idx):
    method __len__ (line 180) | def __len__(self):
  class DetMOTDetectionValidation (line 184) | class DetMOTDetectionValidation(DetMOTDetection):
    method __init__ (line 185) | def __init__(self, args, seqs_folder, transforms):
  function make_detmot_transforms (line 190) | def make_detmot_transforms(image_set, args=None):
  function build (line 217) | def build(image_set, args):

FILE: datasets/joint.py
  class DetMOTDetection (line 26) | class DetMOTDetection:
    method __init__ (line 27) | def __init__(self, args, data_txt_path: str, seqs_folder, dataset2tran...
    method _register_videos (line 64) | def _register_videos(self):
    method set_epoch (line 72) | def set_epoch(self, epoch):
    method step_epoch (line 84) | def step_epoch(self):
    method _targets_to_instances (line 90) | def _targets_to_instances(targets: dict, img_shape) -> Instances:
    method _pre_single_frame (line 98) | def _pre_single_frame(self, idx: int):
    method _get_sample_range (line 149) | def _get_sample_range(self, start_idx):
    method pre_continuous_frames (line 160) | def pre_continuous_frames(self, start, end, interval=1):
    method __getitem__ (line 169) | def __getitem__(self, idx):
    method __len__ (line 189) | def __len__(self):
  class DetMOTDetectionValidation (line 193) | class DetMOTDetectionValidation(DetMOTDetection):
    method __init__ (line 194) | def __init__(self, args, seqs_folder, dataset2transform):
  function make_transforms_for_mot17 (line 200) | def make_transforms_for_mot17(image_set, args=None):
  function make_transforms_for_crowdhuman (line 231) | def make_transforms_for_crowdhuman(image_set, args=None):
  function build_dataset2transform (line 264) | def build_dataset2transform(args, image_set):
  function build (line 279) | def build(image_set, args):

FILE: datasets/panoptic_eval.py
  class PanopticEvaluator (line 23) | class PanopticEvaluator(object):
    method __init__ (line 24) | def __init__(self, ann_file, ann_folder, output_dir="panoptic_eval"):
    method update (line 33) | def update(self, predictions):
    method synchronize_between_processes (line 40) | def synchronize_between_processes(self):
    method summarize (line 47) | def summarize(self):

FILE: datasets/samplers.py
  class DistributedSampler (line 19) | class DistributedSampler(Sampler):
    method __init__ (line 34) | def __init__(self, dataset, num_replicas=None, rank=None, local_rank=N...
    method __iter__ (line 51) | def __iter__(self):
    method __len__ (line 71) | def __len__(self):
    method set_epoch (line 74) | def set_epoch(self, epoch):
  class NodeDistributedSampler (line 78) | class NodeDistributedSampler(Sampler):
    method __init__ (line 93) | def __init__(self, dataset, num_replicas=None, rank=None, local_rank=N...
    method __iter__ (line 118) | def __iter__(self):
    method __len__ (line 138) | def __len__(self):
    method set_epoch (line 141) | def set_epoch(self, epoch):

FILE: datasets/static_detmot.py
  class DetMOTDetection (line 26) | class DetMOTDetection:
    method __init__ (line 27) | def __init__(self, args, data_txt_path: str, seqs_folder, transforms):
    method _register_videos (line 63) | def _register_videos(self):
    method set_epoch (line 71) | def set_epoch(self, epoch):
    method step_epoch (line 83) | def step_epoch(self):
    method _targets_to_instances (line 89) | def _targets_to_instances(targets: dict, img_shape) -> Instances:
    method _pre_single_frame (line 97) | def _pre_single_frame(self, idx: int):
    method _get_sample_range (line 142) | def _get_sample_range(self, start_idx):
    method pre_continuous_frames (line 153) | def pre_continuous_frames(self, idx):
    method __getitem__ (line 163) | def __getitem__(self, idx):
    method __len__ (line 180) | def __len__(self):
  class DetMOTDetectionValidation (line 184) | class DetMOTDetectionValidation(DetMOTDetection):
    method __init__ (line 185) | def __init__(self, args, seqs_folder, transforms):
  function make_detmot_transforms (line 191) | def make_detmot_transforms(image_set, args=None):
  function build (line 238) | def build(image_set, args):

FILE: datasets/torchvision_datasets/coco.py
  class CocoDetection (line 23) | class CocoDetection(VisionDataset):
    method __init__ (line 36) | def __init__(self, root, annFile, transform=None, target_transform=Non...
    method cache_images (line 49) | def cache_images(self):
    method get_image (line 58) | def get_image(self, path):
    method __getitem__ (line 66) | def __getitem__(self, index):
    method __len__ (line 86) | def __len__(self):

FILE: datasets/transforms.py
  function crop_mot (line 28) | def crop_mot(image, target, region):
  function random_shift (line 69) | def random_shift(image, target, region, sizes):
  function crop (line 114) | def crop(image, target, region):
  function hflip (line 160) | def hflip(image, target):
  function resize (line 177) | def resize(image, target, size, max_size=None):
  function pad (line 236) | def pad(image, target, padding):
  class RandomCrop (line 249) | class RandomCrop(object):
    method __init__ (line 250) | def __init__(self, size):
    method __call__ (line 253) | def __call__(self, img, target):
  class MotRandomCrop (line 258) | class MotRandomCrop(RandomCrop):
    method __call__ (line 259) | def __call__(self, imgs: list, targets: list):
  class FixedMotRandomCrop (line 269) | class FixedMotRandomCrop(object):
    method __init__ (line 270) | def __init__(self, min_size: int, max_size: int):
    method __call__ (line 274) | def __call__(self, imgs: list, targets: list):
  class MotRandomShift (line 286) | class MotRandomShift(object):
    method __init__ (line 287) | def __init__(self, bs=1):
    method __call__ (line 290) | def __call__(self, imgs: list, targets: list):
  class FixedMotRandomShift (line 313) | class FixedMotRandomShift(object):
    method __init__ (line 314) | def __init__(self, bs=1, padding=50):
    method __call__ (line 318) | def __call__(self, imgs: list, targets: list):
  class RandomSizeCrop (line 345) | class RandomSizeCrop(object):
    method __init__ (line 346) | def __init__(self, min_size: int, max_size: int):
    method __call__ (line 350) | def __call__(self, img: PIL.Image.Image, target: dict):
  class MotRandomSizeCrop (line 357) | class MotRandomSizeCrop(RandomSizeCrop):
    method __call__ (line 358) | def __call__(self, imgs, targets):
  class CenterCrop (line 371) | class CenterCrop(object):
    method __init__ (line 372) | def __init__(self, size):
    method __call__ (line 375) | def __call__(self, img, target):
  class MotCenterCrop (line 383) | class MotCenterCrop(CenterCrop):
    method __call__ (line 384) | def __call__(self, imgs, targets):
  class RandomHorizontalFlip (line 398) | class RandomHorizontalFlip(object):
    method __init__ (line 399) | def __init__(self, p=0.5):
    method __call__ (line 402) | def __call__(self, img, target):
  class MotRandomHorizontalFlip (line 408) | class MotRandomHorizontalFlip(RandomHorizontalFlip):
    method __call__ (line 409) | def __call__(self, imgs, targets):
  class RandomResize (line 421) | class RandomResize(object):
    method __init__ (line 422) | def __init__(self, sizes, max_size=None):
    method __call__ (line 427) | def __call__(self, img, target=None):
  class MotRandomResize (line 432) | class MotRandomResize(RandomResize):
    method __call__ (line 433) | def __call__(self, imgs, targets):
  class RandomPad (line 444) | class RandomPad(object):
    method __init__ (line 445) | def __init__(self, max_pad):
    method __call__ (line 448) | def __call__(self, img, target):
  class MotRandomPad (line 454) | class MotRandomPad(RandomPad):
    method __call__ (line 455) | def __call__(self, imgs, targets):
  class RandomSelect (line 467) | class RandomSelect(object):
    method __init__ (line 472) | def __init__(self, transforms1, transforms2, p=0.5):
    method __call__ (line 477) | def __call__(self, img, target):
  class MotRandomSelect (line 483) | class MotRandomSelect(RandomSelect):
    method __call__ (line 488) | def __call__(self, imgs, targets):
  class ToTensor (line 494) | class ToTensor(object):
    method __call__ (line 495) | def __call__(self, img, target):
  class MotToTensor (line 499) | class MotToTensor(ToTensor):
    method __call__ (line 500) | def __call__(self, imgs, targets):
  class RandomErasing (line 507) | class RandomErasing(object):
    method __init__ (line 509) | def __init__(self, *args, **kwargs):
    method __call__ (line 512) | def __call__(self, img, target):
  class MotRandomErasing (line 516) | class MotRandomErasing(RandomErasing):
    method __call__ (line 517) | def __call__(self, imgs, targets):
  class MoTColorJitter (line 525) | class MoTColorJitter(T.ColorJitter):
    method __call__ (line 526) | def __call__(self, imgs, targets):
  class Normalize (line 535) | class Normalize(object):
    method __init__ (line 536) | def __init__(self, mean, std):
    method __call__ (line 540) | def __call__(self, image, target=None):
  class MotNormalize (line 556) | class MotNormalize(Normalize):
    method __call__ (line 557) | def __call__(self, imgs, targets=None):
  class Compose (line 569) | class Compose(object):
    method __init__ (line 570) | def __init__(self, transforms):
    method __call__ (line 573) | def __call__(self, image, target):
    method __repr__ (line 578) | def __repr__(self):
  class MotCompose (line 587) | class MotCompose(Compose):
    method __call__ (line 588) | def __call__(self, imgs, targets):

FILE: demo.py
  function plot_one_box (line 59) | def plot_one_box(x, img, color=None, label=None, score=None, line_thickn...
  function draw_bboxes (line 84) | def draw_bboxes(ori_img, bbox, identities=None, offset=(0, 0), cvt_color...
  function draw_points (line 107) | def draw_points(img: np.ndarray, points: np.ndarray, color=(255, 255, 25...
  class LoadVideo (line 115) | class LoadVideo:  # for inference
    method __init__ (line 116) | def __init__(self, path, img_size=(1536, 800)):
    method __iter__ (line 135) | def __iter__(self):
    method __next__ (line 139) | def __next__(self):
    method init_img (line 151) | def init_img(self, img):
    method __len__ (line 164) | def __len__(self):
  class MOTR (line 167) | class MOTR(object):
    method update (line 168) | def update(self, dt_instances: Instances):
  class Detector (line 180) | class Detector(object):
    method __init__ (line 181) | def __init__(self, args):
    method filter_dt_by_score (line 206) | def filter_dt_by_score(dt_instances: Instances, prob_threshold: float)...
    method filter_dt_by_area (line 211) | def filter_dt_by_area(dt_instances: Instances, area_threshold: float) ...
    method write_results (line 218) | def write_results(txt_path, frame_id, bbox_xyxy, identities):
    method visualize_img_with_bbox (line 230) | def visualize_img_with_bbox(img_path, img, dt_instances: Instances, re...
    method run (line 243) | def run(self, prob_threshold=0.7, area_threshold=100, vis=True, dump=T...

FILE: engine.py
  function train_one_epoch (line 33) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
  function train_one_epoch_mot (line 91) | def train_one_epoch_mot(model: torch.nn.Module, criterion: torch.nn.Module,
  function evaluate (line 150) | def evaluate(model, criterion, postprocessors, data_loader, base_ds, dev...

FILE: eval.py
  function plot_one_box (line 71) | def plot_one_box(x, img, color=None, label=None, score=None, line_thickn...
  function draw_bboxes (line 96) | def draw_bboxes(ori_img, bbox, identities=None, offset=(0, 0), cvt_color...
  function draw_points (line 119) | def draw_points(img: np.ndarray, points: np.ndarray, color=(255, 255, 25...
  function tensor_to_numpy (line 128) | def tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:
  class Track (line 132) | class Track(object):
    method __init__ (line 135) | def __init__(self, box):
    method miss_one_frame (line 142) | def miss_one_frame(self):
    method clear_miss (line 145) | def clear_miss(self):
    method update (line 148) | def update(self, box):
  class MOTR (line 153) | class MOTR(object):
    method __init__ (line 154) | def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
    method update (line 157) | def update(self, dt_instances: Instances):
  function load_label (line 171) | def load_label(label_path: str, img_size: tuple) -> dict:
  function filter_pub_det (line 198) | def filter_pub_det(res_file, pub_det_file, filter_iou=False):
  class Detector (line 264) | class Detector(object):
    method __init__ (line 265) | def __init__(self, args, model=None, seq_num=2):
    method load_img_from_file (line 295) | def load_img_from_file(self, f_path):
    method init_img (line 302) | def init_img(self, img):
    method filter_dt_by_score (line 316) | def filter_dt_by_score(dt_instances: Instances, prob_threshold: float)...
    method filter_dt_by_area (line 321) | def filter_dt_by_area(dt_instances: Instances, area_threshold: float) ...
    method write_results (line 328) | def write_results(txt_path, frame_id, bbox_xyxy, identities):
    method eval_seq (line 339) | def eval_seq(self):
    method visualize_img_with_bbox (line 347) | def visualize_img_with_bbox(img_path, img, dt_instances: Instances, re...
    method detect (line 359) | def detect(self, prob_threshold=0.7, area_threshold=100, vis=False):

FILE: main.py
  function get_args_parser (line 34) | def get_args_parser():
  function main (line 183) | def main(args):

FILE: models/__init__.py
  function build_model (line 14) | def build_model(args):

FILE: models/backbone.py
  class FrozenBatchNorm2d (line 27) | class FrozenBatchNorm2d(torch.nn.Module):
    method __init__ (line 36) | def __init__(self, n, eps=1e-5):
    method _load_from_state_dict (line 44) | def _load_from_state_dict(self, state_dict, prefix, local_metadata, st...
    method forward (line 54) | def forward(self, x):
  class BackboneBase (line 67) | class BackboneBase(nn.Module):
    method __init__ (line 69) | def __init__(self, backbone: nn.Module, train_backbone: bool, return_i...
    method forward (line 85) | def forward(self, tensor_list: NestedTensor):
  class Backbone (line 96) | class Backbone(BackboneBase):
    method __init__ (line 98) | def __init__(self, name: str,
  class Joiner (line 112) | class Joiner(nn.Sequential):
    method __init__ (line 113) | def __init__(self, backbone, position_embedding):
    method forward (line 118) | def forward(self, tensor_list: NestedTensor):
  function build_backbone (line 132) | def build_backbone(args):

FILE: models/deformable_detr.py
  function _get_clones (line 33) | def _get_clones(module, N):
  class DeformableDETR (line 37) | class DeformableDETR(nn.Module):
    method __init__ (line 39) | def __init__(self, backbone, transformer, num_classes, num_queries, nu...
    method _get_valid_ratio (line 118) | def _get_valid_ratio(mask):
    method forward (line 127) | def forward(self, samples: NestedTensor):
    method _set_aux_loss (line 212) | def _set_aux_loss(self, outputs_class, outputs_coord):
  class SetCriterion (line 220) | class SetCriterion(nn.Module):
    method __init__ (line 226) | def __init__(self, num_classes, matcher, weight_dict, losses, focal_al...
    method loss_labels (line 242) | def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
    method loss_cardinality (line 269) | def loss_cardinality(self, outputs, targets, indices, num_boxes):
    method loss_boxes (line 282) | def loss_boxes(self, outputs, targets, indices, num_boxes):
    method loss_masks (line 303) | def loss_masks(self, outputs, targets, indices, num_boxes):
    method _get_src_permutation_idx (line 332) | def _get_src_permutation_idx(self, indices):
    method _get_tgt_permutation_idx (line 338) | def _get_tgt_permutation_idx(self, indices):
    method get_loss (line 344) | def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs):
    method forward (line 354) | def forward(self, outputs, targets):
  class PostProcess (line 416) | class PostProcess(nn.Module):
    method forward (line 420) | def forward(self, outputs, target_sizes):
  class MLP (line 451) | class MLP(nn.Module):
    method __init__ (line 454) | def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
    method forward (line 460) | def forward(self, x):
  function build (line 466) | def build(args):

FILE: models/deformable_transformer.py
  class DeformableTransformer (line 28) | class DeformableTransformer(nn.Module):
    method __init__ (line 29) | def __init__(self, d_model=256, nhead=8,
    method _reset_parameters (line 67) | def _reset_parameters(self):
    method get_proposal_pos_embed (line 79) | def get_proposal_pos_embed(self, proposals):
    method gen_encoder_output_proposals (line 94) | def gen_encoder_output_proposals(self, memory, memory_padding_mask, sp...
    method get_valid_ratio (line 126) | def get_valid_ratio(self, mask):
    method forward (line 135) | def forward(self, srcs, masks, pos_embeds, query_embed=None, ref_pts=N...
  class DeformableTransformerEncoderLayer (line 202) | class DeformableTransformerEncoderLayer(nn.Module):
    method __init__ (line 203) | def __init__(self,
    method with_pos_embed (line 223) | def with_pos_embed(tensor, pos):
    method forward_ffn (line 226) | def forward_ffn(self, src):
    method forward (line 232) | def forward(self, src, pos, reference_points, spatial_shapes, level_st...
  class DeformableTransformerEncoder (line 244) | class DeformableTransformerEncoder(nn.Module):
    method __init__ (line 245) | def __init__(self, encoder_layer, num_layers):
    method get_reference_points (line 251) | def get_reference_points(spatial_shapes, valid_ratios, device):
    method forward (line 265) | def forward(self, src, spatial_shapes, level_start_index, valid_ratios...
  class DeformableTransformerDecoderLayer (line 274) | class DeformableTransformerDecoderLayer(nn.Module):
    method __init__ (line 275) | def __init__(self, d_model=256, d_ffn=1024,
    method with_pos_embed (line 302) | def with_pos_embed(tensor, pos):
    method forward_ffn (line 305) | def forward_ffn(self, tgt):
    method _forward_self_attn (line 311) | def _forward_self_attn(self, tgt, query_pos, attn_mask=None):
    method _forward_self_cross (line 321) | def _forward_self_cross(self, tgt, query_pos, reference_points, src, s...
    method _forward_cross_self (line 338) | def _forward_cross_self(self, tgt, query_pos, reference_points, src, s...
    method forward (line 353) | def forward(self, tgt, query_pos, reference_points, src, src_spatial_s...
  class DeformableTransformerDecoder (line 362) | class DeformableTransformerDecoder(nn.Module):
    method __init__ (line 363) | def __init__(self, decoder_layer, num_layers, return_intermediate=False):
    method forward (line 372) | def forward(self, tgt, reference_points, src, src_spatial_shapes, src_...
  function _get_clones (line 410) | def _get_clones(module, N):
  function _get_activation_fn (line 414) | def _get_activation_fn(activation):
  function build_deforamble_transformer (line 425) | def build_deforamble_transformer(args):

FILE: models/deformable_transformer_plus.py
  class DeformableTransformer (line 28) | class DeformableTransformer(nn.Module):
    method __init__ (line 29) | def __init__(self, d_model=256, nhead=8,
    method _reset_parameters (line 67) | def _reset_parameters(self):
    method get_proposal_pos_embed (line 79) | def get_proposal_pos_embed(self, proposals):
    method gen_encoder_output_proposals (line 94) | def gen_encoder_output_proposals(self, memory, memory_padding_mask, sp...
    method get_valid_ratio (line 126) | def get_valid_ratio(self, mask):
    method forward (line 135) | def forward(self, srcs, masks, pos_embeds, query_embed=None, ref_pts=N...
  class DeformableTransformerEncoderLayer (line 200) | class DeformableTransformerEncoderLayer(nn.Module):
    method __init__ (line 201) | def __init__(self,
    method with_pos_embed (line 221) | def with_pos_embed(tensor, pos):
    method forward_ffn (line 224) | def forward_ffn(self, src):
    method forward (line 230) | def forward(self, src, pos, reference_points, spatial_shapes, level_st...
  class DeformableTransformerEncoder (line 241) | class DeformableTransformerEncoder(nn.Module):
    method __init__ (line 242) | def __init__(self, encoder_layer, num_layers):
    method get_reference_points (line 248) | def get_reference_points(spatial_shapes, valid_ratios, device):
    method forward (line 262) | def forward(self, src, spatial_shapes, level_start_index, valid_ratios...
  class DeformableTransformerDecoderLayer (line 271) | class DeformableTransformerDecoderLayer(nn.Module):
    method __init__ (line 272) | def __init__(self, d_model=256, d_ffn=1024,
    method with_pos_embed (line 311) | def with_pos_embed(tensor, pos):
    method forward_ffn (line 314) | def forward_ffn(self, tgt):
    method _forward_self_attn (line 320) | def _forward_self_attn(self, tgt, query_pos, attn_mask=None):
    method _forward_track_attn (line 333) | def _forward_track_attn(self, tgt, query_pos):
    method _forward_self_cross (line 342) | def _forward_self_cross(self, tgt, query_pos, reference_points, src, s...
    method _forward_cross_self (line 359) | def _forward_cross_self(self, tgt, query_pos, reference_points, src, s...
    method forward (line 374) | def forward(self, tgt, query_pos, reference_points, src, src_spatial_s...
  class DeformableTransformerDecoder (line 383) | class DeformableTransformerDecoder(nn.Module):
    method __init__ (line 384) | def __init__(self, decoder_layer, num_layers, return_intermediate=False):
    method forward (line 393) | def forward(self, tgt, reference_points, src, src_spatial_shapes, src_...
  function _get_clones (line 431) | def _get_clones(module, N):
  function _get_activation_fn (line 435) | def _get_activation_fn(activation):
  function build_deforamble_transformer (line 447) | def build_deforamble_transformer(args):

FILE: models/matcher.py
  class HungarianMatcher (line 23) | class HungarianMatcher(nn.Module):
    method __init__ (line 31) | def __init__(self,
    method forward (line 48) | def forward(self, outputs, targets, use_focal=True):
  function build_matcher (line 119) | def build_matcher(args):

FILE: models/memory_bank.py
  class MemoryBank (line 14) | class MemoryBank(nn.Module):
    method __init__ (line 15) | def __init__(self, args, dim_in, hidden_dim, dim_out):
    method _build_layers (line 22) | def _build_layers(self, args, dim_in, hidden_dim, dim_out):
    method update (line 47) | def update(self, track_instances):
    method _forward_spatial_attn (line 70) | def _forward_spatial_attn(self, track_instances):
    method _forward_track_cls (line 90) | def _forward_track_cls(self, track_instances):
    method _forward_temporal_attn (line 94) | def _forward_temporal_attn(self, track_instances):
    method forward_temporal_attn (line 122) | def forward_temporal_attn(self, track_instances):
    method forward (line 125) | def forward(self, track_instances: Instances, update_bank=True) -> Ins...
  function build_memory_bank (line 136) | def build_memory_bank(args, dim_in, hidden_dim, dim_out):

FILE: models/motr.py
  class ClipMatcher (line 38) | class ClipMatcher(SetCriterion):
    method __init__ (line 39) | def __init__(self, num_classes,
    method initialize_for_single_clip (line 60) | def initialize_for_single_clip(self, gt_instances: List[Instances]):
    method _step (line 67) | def _step(self):
    method calc_loss_for_track_scores (line 70) | def calc_loss_for_track_scores(self, track_instances: Instances):
    method get_num_boxes (line 91) | def get_num_boxes(self, num_samples):
    method get_loss (line 98) | def get_loss(self, loss, outputs, gt_instances, indices, num_boxes, **...
    method loss_boxes (line 107) | def loss_boxes(self, outputs, gt_instances: List[Instances], indices: ...
    method loss_labels (line 138) | def loss_labels(self, outputs, gt_instances: List[Instances], indices,...
    method match_for_single_frame (line 175) | def match_for_single_frame(self, outputs: dict):
    method forward (line 294) | def forward(self, outputs, input_data: dict):
  class RuntimeTrackerBase (line 303) | class RuntimeTrackerBase(object):
    method __init__ (line 304) | def __init__(self, score_thresh=0.7, filter_score_thresh=0.6, miss_tol...
    method clear (line 310) | def clear(self):
    method update (line 313) | def update(self, track_instances: Instances):
  class TrackerPostProcess (line 328) | class TrackerPostProcess(nn.Module):
    method __init__ (line 330) | def __init__(self):
    method forward (line 334) | def forward(self, track_instances: Instances, target_size) -> Instances:
  function _get_clones (line 364) | def _get_clones(module, N):
  class MOTR (line 368) | class MOTR(nn.Module):
    method __init__ (line 369) | def __init__(self, backbone, transformer, num_classes, num_queries, nu...
    method _generate_empty_tracks (line 454) | def _generate_empty_tracks(self):
    method clear (line 477) | def clear(self):
    method _set_aux_loss (line 481) | def _set_aux_loss(self, outputs_class, outputs_coord):
    method _forward_single_image (line 488) | def _forward_single_image(self, samples, track_instances: Instances):
    method _post_process_single_image (line 545) | def _post_process_single_image(self, frame_res, track_instances, is_la...
    method inference_single_image (line 580) | def inference_single_image(self, img, ori_img_size, track_instances=No...
    method forward (line 600) | def forward(self, data: dict):
  function build (line 657) | def build(args):

FILE: models/ops/functions/ms_deform_attn_func.py
  class MSDeformAttnFunction (line 24) | class MSDeformAttnFunction(Function):
    method forward (line 26) | def forward(ctx, value, value_spatial_shapes, value_level_start_index,...
    method backward (line 35) | def backward(ctx, grad_output):
  function ms_deform_attn_core_pytorch (line 44) | def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_lo...

FILE: models/ops/modules/ms_deform_attn.py
  function _is_power_of_2 (line 27) | def _is_power_of_2(n):
  class MSDeformAttn (line 33) | class MSDeformAttn(nn.Module):
    method __init__ (line 34) | def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4, sig...
    method _reset_parameters (line 66) | def _reset_parameters(self):
    method forward (line 82) | def forward(self, query, reference_points, input_flatten, input_spatia...

FILE: models/ops/setup.py
  function get_extensions (line 23) | def get_extensions():

FILE: models/ops/src/cpu/ms_deform_attn_cpu.cpp
  function ms_deform_attn_cpu_forward (line 17) | at::Tensor
  function ms_deform_attn_cpu_backward (line 29) | std::vector<at::Tensor>

FILE: models/ops/src/ms_deform_attn.h
  function im2col_step (line 27) | int im2col_step)

FILE: models/ops/src/vision.cpp
  function PYBIND11_MODULE (line 13) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

FILE: models/ops/test.py
  function check_forward_equal_with_pytorch_double (line 32) | def check_forward_equal_with_pytorch_double():
  function check_forward_equal_with_pytorch_float (line 48) | def check_forward_equal_with_pytorch_float():
  function check_gradient_numerical (line 63) | def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_...

FILE: models/position_encoding.py
  class PositionEmbeddingSine (line 22) | class PositionEmbeddingSine(nn.Module):
    method __init__ (line 27) | def __init__(self, num_pos_feats=64, temperature=10000, normalize=Fals...
    method forward (line 38) | def forward(self, tensor_list: NestedTensor):
  class PositionEmbeddingLearned (line 61) | class PositionEmbeddingLearned(nn.Module):
    method __init__ (line 65) | def __init__(self, num_pos_feats=256):
    method reset_parameters (line 71) | def reset_parameters(self):
    method forward (line 75) | def forward(self, tensor_list: NestedTensor):
  function build_position_encoding (line 89) | def build_position_encoding(args):

FILE: models/qim.py
  function random_drop_tracks (line 16) | def random_drop_tracks(track_instances: Instances, drop_probability: flo...
  class QueryInteractionBase (line 23) | class QueryInteractionBase(nn.Module):
    method __init__ (line 24) | def __init__(self, args, dim_in, hidden_dim, dim_out):
    method _build_layers (line 30) | def _build_layers(self, args, dim_in, hidden_dim, dim_out):
    method _reset_parameters (line 33) | def _reset_parameters(self):
    method _select_active_tracks (line 38) | def _select_active_tracks(self, data: dict) -> Instances:
    method _update_track_embedding (line 41) | def _update_track_embedding(self, track_instances):
  class FFN (line 45) | class FFN(nn.Module):
    method __init__ (line 46) | def __init__(self, d_model, d_ffn, dropout=0):
    method forward (line 55) | def forward(self, tgt):
  class QueryInteractionModule (line 62) | class QueryInteractionModule(QueryInteractionBase):
    method __init__ (line 63) | def __init__(self, args, dim_in, hidden_dim, dim_out):
    method _build_layers (line 69) | def _build_layers(self, args, dim_in, hidden_dim, dim_out):
    method _random_drop_tracks (line 103) | def _random_drop_tracks(self, track_instances: Instances) -> Instances:
    method _add_fp_tracks (line 106) | def _add_fp_tracks(self, track_instances: Instances, active_track_inst...
    method _select_active_tracks (line 133) | def _select_active_tracks(self, data: dict) -> Instances:
    method _update_track_embedding (line 147) | def _update_track_embedding(self, track_instances: Instances) -> Insta...
    method forward (line 179) | def forward(self, data) -> Instances:
  function build (line 187) | def build(args, layer_name, dim_in, hidden_dim, dim_out):

FILE: models/relu_dropout.py
  class ReLUDropout (line 4) | class ReLUDropout(torch.nn.Dropout):
    method forward (line 5) | def forward(self, input):
  function relu_dropout (line 8) | def relu_dropout(x, p=0, inplace=False, training=False):

FILE: models/segmentation.py
  class DETRsegm (line 32) | class DETRsegm(nn.Module):
    method __init__ (line 33) | def __init__(self, detr, freeze_detr=False):
    method forward (line 45) | def forward(self, samples: NestedTensor):
  class MaskHeadSmallConv (line 74) | class MaskHeadSmallConv(nn.Module):
    method __init__ (line 80) | def __init__(self, dim, fpn_dims, context_dim):
    method forward (line 107) | def forward(self, x, bbox_mask, fpns):
  class MHAttentionMap (line 148) | class MHAttentionMap(nn.Module):
    method __init__ (line 151) | def __init__(self, query_dim, hidden_dim, num_heads, dropout=0, bias=T...
    method forward (line 166) | def forward(self, q, k, mask=None):
  function dice_loss (line 180) | def dice_loss(inputs, targets, num_boxes):
  function sigmoid_focal_loss (line 198) | def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, ...
  class PostProcessSegm (line 228) | class PostProcessSegm(nn.Module):
    method __init__ (line 229) | def __init__(self, threshold=0.5):
    method forward (line 234) | def forward(self, results, outputs, orig_target_sizes, max_target_sizes):
  class PostProcessPanoptic (line 251) | class PostProcessPanoptic(nn.Module):
    method __init__ (line 255) | def __init__(self, is_thing_map, threshold=0.85):
    method forward (line 266) | def forward(self, outputs, processed_sizes, target_sizes=None):

FILE: models/structures/boxes.py
  function _maybe_jit_unused (line 14) | def _maybe_jit_unused(x):
  class BoxMode (line 19) | class BoxMode(IntEnum):
    method convert (line 50) | def convert(box: _RawBoxType, from_mode: "BoxMode", to_mode: "BoxMode"...
  class Boxes (line 136) | class Boxes:
    method __init__ (line 148) | def __init__(self, tensor: torch.Tensor):
    method clone (line 163) | def clone(self) -> "Boxes":
    method to (line 173) | def to(self, device: torch.device):
    method area (line 177) | def area(self) -> torch.Tensor:
    method clip (line 188) | def clip(self, box_size: Tuple[int, int]) -> None:
    method nonempty (line 204) | def nonempty(self, threshold: float = 0.0) -> torch.Tensor:
    method __getitem__ (line 220) | def __getitem__(self, item) -> "Boxes":
    method __len__ (line 244) | def __len__(self) -> int:
    method __repr__ (line 247) | def __repr__(self) -> str:
    method inside_box (line 250) | def inside_box(self, box_size: Tuple[int, int], boundary_threshold: in...
    method get_centers (line 269) | def get_centers(self) -> torch.Tensor:
    method scale (line 276) | def scale(self, scale_x: float, scale_y: float) -> None:
    method cat (line 285) | def cat(cls, boxes_list: List["Boxes"]) -> "Boxes":
    method device (line 305) | def device(self) -> device:
    method __iter__ (line 311) | def __iter__(self):
  function pairwise_intersection (line 318) | def pairwise_intersection(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
  function pairwise_iou (line 342) | def pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
  function pairwise_ioa (line 367) | def pairwise_ioa(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
  function matched_boxlist_iou (line 387) | def matched_boxlist_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:

FILE: models/structures/instances.py
  class Instances (line 12) | class Instances:
    method __init__ (line 43) | def __init__(self, image_size: Tuple[int, int], **kwargs: Any):
    method image_size (line 55) | def image_size(self) -> Tuple[int, int]:
    method __setattr__ (line 62) | def __setattr__(self, name: str, val: Any) -> None:
    method __getattr__ (line 68) | def __getattr__(self, name: str) -> Any:
    method set (line 73) | def set(self, name: str, value: Any) -> None:
    method has (line 86) | def has(self, name: str) -> bool:
    method remove (line 93) | def remove(self, name: str) -> None:
    method get (line 99) | def get(self, name: str) -> Any:
    method get_fields (line 105) | def get_fields(self) -> Dict[str, Any]:
    method to (line 115) | def to(self, *args: Any, **kwargs: Any) -> "Instances":
    method numpy (line 127) | def numpy(self):
    method __getitem__ (line 135) | def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "I...
    method __len__ (line 155) | def __len__(self) -> int:
    method __iter__ (line 161) | def __iter__(self):
    method cat (line 165) | def cat(instance_lists: List["Instances"]) -> "Instances":
    method __str__ (line 196) | def __str__(self) -> str:

FILE: submit.py
  function plot_one_box (line 72) | def plot_one_box(x, img, color=None, label=None, score=None, line_thickn...
  function draw_bboxes (line 100) | def draw_bboxes(ori_img, bbox, identities=None, offset=(0, 0), cvt_color...
  function draw_points (line 123) | def draw_points(img: np.ndarray, points: np.ndarray, color=(255, 255, 25...
  function tensor_to_numpy (line 132) | def tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:
  class Track (line 136) | class Track(object):
    method __init__ (line 139) | def __init__(self, box):
    method miss_one_frame (line 146) | def miss_one_frame(self):
    method clear_miss (line 149) | def clear_miss(self):
    method update (line 152) | def update(self, box):
  class MOTR (line 157) | class MOTR(object):
    method __init__ (line 158) | def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
    method _remove_track (line 171) | def _remove_track(self, slot_id):
    method clear_disappeared_track (line 175) | def clear_disappeared_track(self):
    method update (line 178) | def update(self, dt_instances: Instances):
  function load_label (line 240) | def load_label(label_path: str, img_size: tuple) -> dict:
  function filter_pub_det (line 267) | def filter_pub_det(res_file, pub_det_file, filter_iou=False):
  class ListImgDataset (line 332) | class ListImgDataset(Dataset):
    method __init__ (line 333) | def __init__(self, img_list) -> None:
    method load_img_from_file (line 345) | def load_img_from_file(self, f_path):
    method init_img (line 353) | def init_img(self, img):
    method __len__ (line 366) | def __len__(self):
    method __getitem__ (line 369) | def __getitem__(self, index):
  class Detector (line 374) | class Detector(object):
    method __init__ (line 375) | def __init__(self, args, model=None, seq_num=2):
    method filter_dt_by_score (line 398) | def filter_dt_by_score(dt_instances: Instances, prob_threshold: float)...
    method filter_dt_by_area (line 403) | def filter_dt_by_area(dt_instances: Instances, area_threshold: float) ...
    method write_results (line 410) | def write_results(txt_path, frame_id, bbox_xyxy, identities):
    method eval_seq (line 421) | def eval_seq(self):
    method visualize_img_with_bbox (line 429) | def visualize_img_with_bbox(img_path, img, dt_instances: Instances, re...
    method detect (line 440) | def detect(self, prob_threshold=0.7, area_threshold=100):

FILE: submit_dance.py
  function plot_one_box (line 72) | def plot_one_box(x, img, color=None, label=None, score=None, line_thickn...
  function draw_bboxes (line 100) | def draw_bboxes(ori_img, bbox, identities=None, offset=(0, 0), cvt_color...
  function draw_points (line 123) | def draw_points(img: np.ndarray, points: np.ndarray, color=(255, 255, 25...
  function tensor_to_numpy (line 132) | def tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:
  class Track (line 136) | class Track(object):
    method __init__ (line 139) | def __init__(self, box):
    method miss_one_frame (line 146) | def miss_one_frame(self):
    method clear_miss (line 149) | def clear_miss(self):
    method update (line 152) | def update(self, box):
  class MOTR (line 157) | class MOTR(object):
    method __init__ (line 158) | def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
    method _remove_track (line 171) | def _remove_track(self, slot_id):
    method clear_disappeared_track (line 175) | def clear_disappeared_track(self):
    method update (line 178) | def update(self, dt_instances: Instances):
  function load_label (line 240) | def load_label(label_path: str, img_size: tuple) -> dict:
  function filter_pub_det (line 267) | def filter_pub_det(res_file, pub_det_file, filter_iou=False):
  class ListImgDataset (line 332) | class ListImgDataset(Dataset):
    method __init__ (line 333) | def __init__(self, img_list) -> None:
    method load_img_from_file (line 345) | def load_img_from_file(self, f_path):
    method init_img (line 353) | def init_img(self, img):
    method __len__ (line 366) | def __len__(self):
    method __getitem__ (line 369) | def __getitem__(self, index):
  class Detector (line 374) | class Detector(object):
    method __init__ (line 375) | def __init__(self, args, model=None, seq_num=2):
    method filter_dt_by_score (line 398) | def filter_dt_by_score(dt_instances: Instances, prob_threshold: float)...
    method filter_dt_by_area (line 403) | def filter_dt_by_area(dt_instances: Instances, area_threshold: float) ...
    method write_results (line 410) | def write_results(txt_path, frame_id, bbox_xyxy, identities):
    method eval_seq (line 421) | def eval_seq(self):
    method visualize_img_with_bbox (line 429) | def visualize_img_with_bbox(img_path, img, dt_instances: Instances, re...
    method detect (line 440) | def detect(self, prob_threshold=0.7, area_threshold=100, vis=False):

FILE: tools/launch.py
  function parse_args (line 119) | def parse_args():
  function main (line 162) | def main():

FILE: util/box_ops.py
  function box_cxcywh_to_xyxy (line 19) | def box_cxcywh_to_xyxy(x):
  function box_xyxy_to_cxcywh (line 26) | def box_xyxy_to_cxcywh(x):
  function box_iou (line 34) | def box_iou(boxes1, boxes2):
  function generalized_box_iou (line 50) | def generalized_box_iou(boxes1, boxes2):
  function masks_to_boxes (line 74) | def masks_to_boxes(masks):

FILE: util/checkpoint.py
  function detach_variable (line 7) | def detach_variable(inputs):
  function check_backward_validity (line 20) | def check_backward_validity(inputs):
  class CheckpointFunction (line 25) | class CheckpointFunction(torch.autograd.Function):
    method forward (line 27) | def forward(ctx, run_function, length, *args):
    method backward (line 36) | def backward(ctx, *output_grads):

FILE: util/evaluation.py
  function read_results (line 22) | def read_results(filename, data_type: str, is_gt=False, is_ignore=False):
  function read_mot_results (line 59) | def read_mot_results(filename, is_gt, is_ignore):
  function unzip_objs (line 104) | def unzip_objs(objs):
  class Evaluator (line 113) | class Evaluator(object):
    method __init__ (line 114) | def __init__(self, data_root, seq_name, data_type='mot'):
    method load_annotations (line 123) | def load_annotations(self):
    method reset_accumulator (line 130) | def reset_accumulator(self):
    method eval_frame (line 133) | def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
    method eval_file (line 171) | def eval_file(self, filename):
    method get_summary (line 184) | def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', '...
    method save_summary (line 201) | def save_summary(summary, filename):

FILE: util/misc.py
  function _check_size_scale_factor (line 35) | def _check_size_scale_factor(dim, size, scale_factor):
  function _output_size (line 46) | def _output_size(dim, input, size, scale_factor):
  class SmoothedValue (line 64) | class SmoothedValue(object):
    method __init__ (line 69) | def __init__(self, window_size=20, fmt=None):
    method update (line 77) | def update(self, value, n=1):
    method synchronize_between_processes (line 82) | def synchronize_between_processes(self):
    method median (line 96) | def median(self):
    method avg (line 101) | def avg(self):
    method global_avg (line 106) | def global_avg(self):
    method max (line 110) | def max(self):
    method value (line 114) | def value(self):
    method __str__ (line 117) | def __str__(self):
  function all_gather (line 126) | def all_gather(data):
  function reduce_dict (line 169) | def reduce_dict(input_dict, average=True):
  class MetricLogger (line 196) | class MetricLogger(object):
    method __init__ (line 197) | def __init__(self, delimiter="\t"):
    method update (line 201) | def update(self, **kwargs):
    method __getattr__ (line 208) | def __getattr__(self, attr):
    method __str__ (line 216) | def __str__(self):
    method synchronize_between_processes (line 224) | def synchronize_between_processes(self):
    method add_meter (line 228) | def add_meter(self, name, meter):
    method log_every (line 231) | def log_every(self, iterable, print_freq, header=None):
  function get_sha (line 286) | def get_sha():
  function collate_fn (line 306) | def collate_fn(batch):
  function mot_collate_fn (line 312) | def mot_collate_fn(batch: List[dict]) -> dict:
  function _max_by_axis (line 322) | def _max_by_axis(the_list):
  function nested_tensor_from_tensor_list (line 331) | def nested_tensor_from_tensor_list(tensor_list: List[Tensor], size_divis...
  class NestedTensor (line 358) | class NestedTensor(object):
    method __init__ (line 359) | def __init__(self, tensors, mask: Optional[Tensor]):
    method to (line 363) | def to(self, device, non_blocking=False):
    method record_stream (line 374) | def record_stream(self, *args, **kwargs):
    method decompose (line 379) | def decompose(self):
    method __repr__ (line 382) | def __repr__(self):
  function setup_for_distributed (line 386) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 401) | def is_dist_avail_and_initialized():
  function get_world_size (line 409) | def get_world_size():
  function get_rank (line 415) | def get_rank():
  function get_local_size (line 421) | def get_local_size():
  function get_local_rank (line 427) | def get_local_rank():
  function is_main_process (line 433) | def is_main_process():
  function save_on_master (line 437) | def save_on_master(*args, **kwargs):
  function init_distributed_mode (line 442) | def init_distributed_mode(args):
  function accuracy (line 484) | def accuracy(output, target, topk=(1,)):
  function interpolate (line 502) | def interpolate(input, size=None, scale_factor=None, mode="nearest", ali...
  function get_total_grad_norm (line 524) | def get_total_grad_norm(parameters, norm_type=2):
  function inverse_sigmoid (line 532) | def inverse_sigmoid(x, eps=1e-5):

FILE: util/motdet_eval.py
  function ap_per_class (line 16) | def ap_per_class(tp, conf, pred_cls, target_cls):
  function compute_ap (line 69) | def compute_ap(recall, precision):
  function bbox_iou (line 97) | def bbox_iou(box1, box2, x1y1x2y2=False):
  function xyxy2xywh (line 126) | def xyxy2xywh(x):
  function xywh2xyxy (line 136) | def xywh2xyxy(x):
  function motdet_evaluate (line 147) | def motdet_evaluate(model, data_loader, iou_thres=0.5, print_interval=10):
  function init_metrics (line 259) | def init_metrics():
  function detmotdet_evaluate (line 282) | def detmotdet_evaluate(model, data_loader, device, iou_thres=0.5, print_...

FILE: util/plot_utils.py
  function plot_logs (line 27) | def plot_logs(logs, fields=('class_error', 'loss_bbox_unscaled', 'mAP'),...
  function plot_precision_recall (line 83) | def plot_precision_recall(files, naming_scheme='iter'):
  function draw_boxes (line 117) | def draw_boxes(image: Tensor, boxes: Tensor, color=(0, 255, 0), texts=No...
  function draw_ref_pts (line 141) | def draw_ref_pts(image: Tensor, ref_pts: Tensor) -> np.ndarray:
  function image_hwc2chw (line 157) | def image_hwc2chw(image: np.ndarray):

FILE: util/tool.py
  function load_model (line 15) | def load_model(model, model_path, optimizer=None, resume=False,