SYMBOL INDEX (546 symbols across 79 files)

FILE: diffusion/models/diffusion_head.py
  function exists (line 21) | def exists(x):
  function default (line 25) | def default(val, d):
  function extract (line 31) | def extract(a, t, x_shape):
  function cosine_beta_schedule (line 38) | def cosine_beta_schedule(timesteps, s=0.008):
  class DiffusionHead (line 50) | class DiffusionHead(nn.Module):
    method __init__ (line 55) | def __init__(self,
    method predict_noise_from_start (line 155) | def predict_noise_from_start(self, x_t, t, x0):
    method model_predictions (line 161) | def model_predictions(self, backbone_feats,images_whwh,x,t,lost_featur...
    method new_ddim_sample (line 189) | def new_ddim_sample(self,backbone_feats,images_whwh,ref_targets=None,d...
    method q_sample (line 354) | def q_sample(self, x_start, t, noise=None):
    method forward (line 363) | def forward(self,features,mate_info,targets=None):
    method prepare_diffusion_repeat (line 395) | def prepare_diffusion_repeat(self,gt_boxes,t,ref_repeat_tensor=None):
    method prepare_diffusion_concat (line 436) | def prepare_diffusion_concat(self,gt_boxes,t,ref_mask=None):
    method prepare_targets (line 489) | def prepare_targets(self,targets,images_whwh):

FILE: diffusion/models/diffusion_losses.py
  class SetCriterionDynamicK (line 11) | class SetCriterionDynamicK(nn.Module):
    method __init__ (line 17) | def __init__(self,num_classes, matcher, weight_dict, eos_coef, losses,...
    method get_fed_loss_classes (line 53) | def get_fed_loss_classes(self, gt_classes, num_fed_loss_classes, num_c...
    method loss_labels (line 80) | def loss_labels(self, outputs, targets, indices, num_boxes, log=False):
    method loss_boxes (line 153) | def loss_boxes(self, outputs, targets, indices, num_boxes):
    method _get_src_permutation_idx (line 203) | def _get_src_permutation_idx(self, indices):
    method _get_tgt_permutation_idx (line 209) | def _get_tgt_permutation_idx(self, indices):
    method get_loss (line 215) | def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs):
    method forward (line 223) | def forward(self, outputs, targets):
  class HungarianMatcherDynamicK (line 266) | class HungarianMatcherDynamicK(nn.Module):
    method __init__ (line 272) | def __init__(self,  cost_class: float = 1, cost_bbox: float = 1, cost_...
    method forward (line 291) | def forward(self, outputs, targets):
    method get_in_boxes_info (line 399) | def get_in_boxes_info(self, boxes, target_gts, expanded_strides):
    method dynamic_k_matching (line 430) | def dynamic_k_matching(self, cost, pair_wise_ious, num_gt):

FILE: diffusion/models/diffusion_models.py
  function exists (line 14) | def exists(val):
  class SinusoidalPositionEmbeddings (line 24) | class SinusoidalPositionEmbeddings(nn.Module):
    method __init__ (line 25) | def __init__(self, dim):
    method forward (line 29) | def forward(self, time):
  class GaussianFourierProjection (line 39) | class GaussianFourierProjection(nn.Module):
    method __init__ (line 42) | def __init__(self, embed_dim, scale=30.):
    method forward (line 48) | def forward(self, x):
  class Dense (line 53) | class Dense(nn.Module):
    method __init__ (line 56) | def __init__(self, input_dim, output_dim):
    method forward (line 60) | def forward(self, x):
  class DynamicHead (line 64) | class DynamicHead(nn.Module):
    method __init__ (line 66) | def __init__(self,
    method _reset_parameters (line 112) | def _reset_parameters(self):
    method _init_box_pooler (line 124) | def _init_box_pooler(pooler_resolution,strides,in_channels):
    method forward (line 143) | def forward(self,features,init_bboxes,t,lost_features=None,fix_ref_box...
  class RCNNHead (line 168) | class RCNNHead(nn.Module):
    method __init__ (line 170) | def __init__(self,d_model, num_classes, pooler_resolution,dim_feedforw...
    method forward (line 248) | def forward(self, features,bboxes,pro_features,pooler,time_emb,lost_fe...
    method apply_deltas (line 385) | def apply_deltas(self, deltas, boxes):
  class SFT (line 426) | class SFT(nn.Module):
    method __init__ (line 428) | def __init__(self, hidden_dim, pooler_resolution,dim_dynamic=2*64,num_...
    method forward (line 448) | def forward(self,roi_features,pro_features):
  class PerceiverAttention (line 477) | class PerceiverAttention(nn.Module):
    method __init__ (line 478) | def __init__(self, *, dim, dim_head=64, heads=8):
    method forward (line 491) | def forward(self, x, latents):
  function FeedForward (line 520) | def FeedForward(dim, mult=4):
  class WindowAttention (line 573) | class WindowAttention(nn.Module):
    method __init__ (line 586) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scal...
    method forward (line 606) | def forward(self,q,k,v,H,W):
  function _get_clones (line 657) | def _get_clones(module, N):
  function _get_activation_fn (line 661) | def _get_activation_fn(activation):
  function window_partition (line 671) | def window_partition(x, window_size):
  function window_reverse (line 685) | def window_reverse(windows, window_size, H, W):

FILE: diffusion/models/diffusionnet.py
  class DiffusionNet (line 13) | class DiffusionNet(nn.Module):
    method __init__ (line 18) | def __init__(self, backbone=None, head=None, act="silu"):
    method forward (line 34) | def forward(self, x, targets=(None,None),random_flip=False,input_size=...

FILE: exps/default/nano.py
  class Exp (line 11) | class Exp(MyExp):
    method __init__ (line 12) | def __init__(self):
    method get_model (line 22) | def get_model(self, sublinear=False):

FILE: exps/default/yolov3.py
  class Exp (line 12) | class Exp(MyExp):
    method __init__ (line 13) | def __init__(self):
    method get_model (line 19) | def get_model(self, sublinear=False):
    method get_data_loader (line 35) | def get_data_loader(self, batch_size, is_distributed, no_aug=False):

FILE: exps/default/yolox_l.py
  class Exp (line 10) | class Exp(MyExp):
    method __init__ (line 11) | def __init__(self):

FILE: exps/default/yolox_m.py
  class Exp (line 10) | class Exp(MyExp):
    method __init__ (line 11) | def __init__(self):

FILE: exps/default/yolox_s.py
  class Exp (line 10) | class Exp(MyExp):
    method __init__ (line 11) | def __init__(self):

FILE: exps/default/yolox_tiny.py
  class Exp (line 10) | class Exp(MyExp):
    method __init__ (line 11) | def __init__(self):

FILE: exps/default/yolox_x.py
  class Exp (line 10) | class Exp(MyExp):
    method __init__ (line 11) | def __init__(self):

FILE: exps/example/mot/yolox_x_diffusion_det_dancetrack.py
  class Exp (line 11) | class Exp(MyExp):
    method __init__ (line 12) | def __init__(self):
    method get_data_loader (line 38) | def get_data_loader(self, batch_size, is_distributed, no_aug=False):
    method get_eval_loader (line 100) | def get_eval_loader(self, batch_size, is_distributed, testdev=False):
    method get_evaluator (line 133) | def get_evaluator(self, batch_size, is_distributed, testdev=False):
    method get_model (line 149) | def get_model(self):
    method get_optimizer (line 171) | def get_optimizer(self, batch_size):

FILE: exps/example/mot/yolox_x_diffusion_det_mot17.py
  class Exp (line 11) | class Exp(MyExp):
    method __init__ (line 12) | def __init__(self):
    method get_data_loader (line 38) | def get_data_loader(self, batch_size, is_distributed, no_aug=False):
    method get_eval_loader (line 100) | def get_eval_loader(self, batch_size, is_distributed, testdev=False):
    method get_evaluator (line 133) | def get_evaluator(self, batch_size, is_distributed, testdev=False):
    method get_model (line 149) | def get_model(self):
    method get_optimizer (line 171) | def get_optimizer(self, batch_size):

FILE: exps/example/mot/yolox_x_diffusion_det_mot17_ablation.py
  class Exp (line 11) | class Exp(MyExp):
    method __init__ (line 12) | def __init__(self):
    method get_data_loader (line 38) | def get_data_loader(self, batch_size, is_distributed, no_aug=False):
    method get_eval_loader (line 100) | def get_eval_loader(self, batch_size, is_distributed, testdev=False):
    method get_evaluator (line 133) | def get_evaluator(self, batch_size, is_distributed, testdev=False):
    method get_model (line 149) | def get_model(self):
    method get_optimizer (line 171) | def get_optimizer(self, batch_size):

FILE: exps/example/mot/yolox_x_diffusion_det_mot20.py
  class Exp (line 11) | class Exp(MyExp):
    method __init__ (line 12) | def __init__(self):
    method get_data_loader (line 38) | def get_data_loader(self, batch_size, is_distributed, no_aug=False):
    method get_eval_loader (line 100) | def get_eval_loader(self, batch_size, is_distributed, testdev=False):
    method get_evaluator (line 133) | def get_evaluator(self, batch_size, is_distributed, testdev=False):
    method get_model (line 149) | def get_model(self):
    method get_optimizer (line 171) | def get_optimizer(self, batch_size):

FILE: exps/example/mot/yolox_x_diffusion_track_dancetrack.py
  class Exp (line 11) | class Exp(MyExp):
    method __init__ (line 12) | def __init__(self):
    method get_data_loader (line 37) | def get_data_loader(self, batch_size, is_distributed, no_aug=False):
    method get_eval_loader (line 101) | def get_eval_loader(self, batch_size, is_distributed, testdev=False):
    method get_evaluator (line 134) | def get_evaluator(self, batch_size, is_distributed, testdev=False):
    method get_model (line 150) | def get_model(self):
    method get_optimizer (line 172) | def get_optimizer(self, batch_size):

FILE: exps/example/mot/yolox_x_diffusion_track_dancetrack_baseline.py
  class Exp (line 11) | class Exp(MyExp):
    method __init__ (line 12) | def __init__(self):
    method get_data_loader (line 37) | def get_data_loader(self, batch_size, is_distributed, no_aug=False):
    method get_eval_loader (line 101) | def get_eval_loader(self, batch_size, is_distributed, testdev=False):
    method get_evaluator (line 134) | def get_evaluator(self, batch_size, is_distributed, testdev=False):
    method get_model (line 150) | def get_model(self):
    method get_optimizer (line 172) | def get_optimizer(self, batch_size):

FILE: exps/example/mot/yolox_x_diffusion_track_mot17.py
  class Exp (line 11) | class Exp(MyExp):
    method __init__ (line 12) | def __init__(self):
    method get_data_loader (line 37) | def get_data_loader(self, batch_size, is_distributed, no_aug=False):
    method get_eval_loader (line 101) | def get_eval_loader(self, batch_size, is_distributed, testdev=False):
    method get_evaluator (line 134) | def get_evaluator(self, batch_size, is_distributed, testdev=False):
    method get_model (line 150) | def get_model(self):
    method get_optimizer (line 172) | def get_optimizer(self, batch_size):

FILE: exps/example/mot/yolox_x_diffusion_track_mot17_ablation.py
  class Exp (line 11) | class Exp(MyExp):
    method __init__ (line 12) | def __init__(self):
    method get_data_loader (line 38) | def get_data_loader(self, batch_size, is_distributed, no_aug=False):
    method get_eval_loader (line 102) | def get_eval_loader(self, batch_size, is_distributed, testdev=False):
    method get_evaluator (line 135) | def get_evaluator(self, batch_size, is_distributed, testdev=False):
    method get_model (line 151) | def get_model(self):
    method get_optimizer (line 173) | def get_optimizer(self, batch_size):

FILE: exps/example/mot/yolox_x_diffusion_track_mot17_baseline.py
  class Exp (line 11) | class Exp(MyExp):
    method __init__ (line 12) | def __init__(self):
    method get_data_loader (line 37) | def get_data_loader(self, batch_size, is_distributed, no_aug=False):
    method get_eval_loader (line 101) | def get_eval_loader(self, batch_size, is_distributed, testdev=False):
    method get_evaluator (line 134) | def get_evaluator(self, batch_size, is_distributed, testdev=False):
    method get_model (line 150) | def get_model(self):
    method get_optimizer (line 172) | def get_optimizer(self, batch_size):

FILE: exps/example/mot/yolox_x_diffusion_track_mot20.py
  class Exp (line 11) | class Exp(MyExp):
    method __init__ (line 12) | def __init__(self):
    method get_data_loader (line 37) | def get_data_loader(self, batch_size, is_distributed, no_aug=False):
    method get_eval_loader (line 101) | def get_eval_loader(self, batch_size, is_distributed, testdev=False):
    method get_evaluator (line 134) | def get_evaluator(self, batch_size, is_distributed, testdev=False):
    method get_model (line 150) | def get_model(self):
    method get_optimizer (line 172) | def get_optimizer(self, batch_size):

FILE: exps/example/mot/yolox_x_diffusion_track_mot20_baseline.py
  class Exp (line 11) | class Exp(MyExp):
    method __init__ (line 12) | def __init__(self):
    method get_data_loader (line 37) | def get_data_loader(self, batch_size, is_distributed, no_aug=False):
    method get_eval_loader (line 101) | def get_eval_loader(self, batch_size, is_distributed, testdev=False):
    method get_evaluator (line 134) | def get_evaluator(self, batch_size, is_distributed, testdev=False):
    method get_model (line 150) | def get_model(self):
    method get_optimizer (line 172) | def get_optimizer(self, batch_size):

FILE: setup.py
  function get_extensions (line 15) | def get_extensions():

FILE: tools/convert_bdd100k_to_coco.py
  function read_tid_num_per_video (line 41) | def read_tid_num_per_video(video_ann_dir):

FILE: tools/convert_cityperson_to_coco.py
  function load_paths (line 10) | def load_paths(data_path):

FILE: tools/convert_crowdhuman_to_coco.py
  function load_func (line 11) | def load_func(fpath):

FILE: tools/convert_ethz_to_coco.py
  function load_paths (line 10) | def load_paths(data_path):

FILE: tools/convert_kitti_to_coco.py
  function project_to_image (line 42) | def project_to_image(pts_3d, P):
  function read_clib (line 52) | def read_clib(calib_path):
  function _bbox_to_coco_bbox (line 60) | def _bbox_to_coco_bbox(bbox):

FILE: tools/convert_video.py
  function convert_video (line 3) | def convert_video(video_path):

FILE: tools/mota.py
  function compare_dataframes (line 31) | def compare_dataframes(gts, ts):

FILE: tools/track.py
  function make_parser (line 33) | def make_parser():
  function compare_dataframes (line 127) | def compare_dataframes(gts, ts):
  function main (line 142) | def main(exp, args, num_gpu):

FILE: tools/train.py
  function make_parser (line 24) | def make_parser():
  function main (line 95) | def main(exp, args):

FILE: tools/txt2video.py
  function colormap (line 9) | def colormap(rgb=False):
  function txt2img (line 99) | def txt2img(visual_path="visual_val_gt"):
  function img2video (line 189) | def img2video(visual_path="visual_val_gt"):

FILE: yolox/core/launch.py
  function _find_free_port (line 25) | def _find_free_port():
  function launch (line 40) | def launch(
  function launch_by_subprocess (line 93) | def launch_by_subprocess(
  function _distributed_worker (line 168) | def _distributed_worker(

FILE: yolox/core/trainer.py
  class Trainer (line 32) | class Trainer:
    method __init__ (line 33) | def __init__(self, exp, args):
    method train (line 70) | def train(self):
    method train_in_epoch (line 79) | def train_in_epoch(self):
    method train_in_iter (line 85) | def train_in_iter(self):
    method train_one_iter (line 91) | def train_one_iter(self):
    method before_train (line 128) | def before_train(self):
    method after_train (line 184) | def after_train(self):
    method before_epoch (line 191) | def before_epoch(self):
    method after_epoch (line 208) | def after_epoch(self):
    method before_iter (line 219) | def before_iter(self):
    method after_iter (line 222) | def after_iter(self):
    method progress_in_iter (line 268) | def progress_in_iter(self):
    method resume_train (line 271) | def resume_train(self, model):
    method evaluate_and_save_model (line 304) | def evaluate_and_save_model(self):
    method save_ckpt (line 320) | def save_ckpt(self, ckpt_name, update_best_ckpt=False):

FILE: yolox/data/data_augment.py
  function augment_hsv (line 23) | def augment_hsv(img, hgain=0.015, sgain=0.7, vgain=0.4):
  function box_candidates (line 39) | def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.2):
  function random_perspective (line 54) | def random_perspective(
  function _distort (line 150) | def _distort(image):
  function _mirror (line 180) | def _mirror(image, boxes):
  function preproc (line 189) | def preproc(image, input_size, mean, std, swap=(2, 0, 1)):
  class TrainTransform (line 214) | class TrainTransform:
    method __init__ (line 215) | def __init__(self, p=0.5, rgb_means=None, std=None, max_labels=100):
    method __call__ (line 221) | def __call__(self, image, targets, input_dim):
  class DiffusionValTransform (line 272) | class DiffusionValTransform:
    method __init__ (line 273) | def __init__(self, p=0.5, rgb_means=None, std=None, max_labels=100):
    method __call__ (line 279) | def __call__(self, image, targets, input_dim):
  class DiffusionTrainTransform (line 313) | class DiffusionTrainTransform:
    method __init__ (line 314) | def __init__(self, p=0.5, rgb_means=None, std=None, max_labels=100):
    method __call__ (line 320) | def __call__(self, ref_image, ref_targets, track_image, track_targets,...
  class ValTransform (line 390) | class ValTransform:
    method __init__ (line 408) | def __init__(self, rgb_means=None, std=None, swap=(2, 0, 1)):
    method __call__ (line 414) | def __call__(self, img, res, input_size):

FILE: yolox/data/data_prefetcher.py
  class DataPrefetcher (line 13) | class DataPrefetcher:
    method __init__ (line 21) | def __init__(self, loader,task):
    method preload (line 28) | def preload(self):
    method next (line 50) | def next(self):
    method _record_stream_for_image (line 73) | def _record_stream_for_image(input):
  function random_resize (line 77) | def random_resize(data_loader, exp, epoch, rank, is_distributed):

FILE: yolox/data/dataloading.py
  function get_yolox_datadir (line 15) | def get_yolox_datadir():
  class DataLoader (line 29) | class DataLoader(torchDataLoader):
    method __init__ (line 72) | def __init__(self, *args, **kwargs):
    method close_mosaic (line 120) | def close_mosaic(self):
    method change_input_dim (line 123) | def change_input_dim(self, multiple=32, random_range=(10, 19)):
  function list_collate (line 164) | def list_collate(batch):

FILE: yolox/data/samplers.py
  class YoloBatchSampler (line 14) | class YoloBatchSampler(torchBatchSampler):
    method __init__ (line 21) | def __init__(self, *args, input_dimension=None, mosaic=True, **kwargs):
    method __iter__ (line 27) | def __iter__(self):
    method __set_input_dim (line 33) | def __set_input_dim(self):
  class InfiniteSampler (line 40) | class InfiniteSampler(Sampler):
    method __init__ (line 51) | def __init__(
    method __iter__ (line 79) | def __iter__(self):
    method _infinite_indices (line 85) | def _infinite_indices(self):
    method __len__ (line 94) | def __len__(self):

FILE: yolox/evaluators/coco_evaluator.py
  class COCOEvaluator (line 28) | class COCOEvaluator:
    method __init__ (line 34) | def __init__(
    method evaluate (line 55) | def evaluate(
    method convert_to_coco_format (line 142) | def convert_to_coco_format(self, outputs, info_imgs, ids):
    method evaluate_prediction (line 174) | def evaluate_prediction(self, data_dict, statistics):

FILE: yolox/evaluators/diffusion_mot_evaluator.py
  function write_results (line 27) | def write_results(filename, results):
  function write_results_no_score (line 40) | def write_results_no_score(filename, results):
  class DiffusionMOTEvaluator (line 53) | class DiffusionMOTEvaluator:
    method __init__ (line 59) | def __init__(
    method evaluate (line 80) | def evaluate(
    method convert_to_coco_format (line 253) | def convert_to_coco_format(self, output, info_imgs, ids):
    method evaluate_prediction (line 283) | def evaluate_prediction(self, data_dict, statistics):

FILE: yolox/evaluators/diffusion_mot_evaluator_kl.py
  function write_results (line 29) | def write_results(filename, results):
  function write_results_no_score (line 42) | def write_results_no_score(filename, results):
  class DiffusionMOTEvaluatorKL (line 55) | class DiffusionMOTEvaluatorKL:
    method __init__ (line 61) | def __init__(
    method evaluate (line 82) | def evaluate(
    method convert_to_coco_format (line 224) | def convert_to_coco_format(self, output, info_imgs, ids):
    method evaluate_prediction (line 254) | def evaluate_prediction(self, data_dict, statistics):

FILE: yolox/evaluators/evaluation.py
  class Evaluator (line 8) | class Evaluator(object):
    method __init__ (line 10) | def __init__(self, data_root, seq_name, data_type):
    method load_annotations (line 18) | def load_annotations(self):
    method reset_accumulator (line 25) | def reset_accumulator(self):
    method eval_frame (line 28) | def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
    method eval_file (line 76) | def eval_file(self, filename):
    method get_summary (line 90) | def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', '...
    method save_summary (line 107) | def save_summary(summary, filename):
  function read_results (line 117) | def read_results(filename, data_type: str, is_gt=False, is_ignore=False):
  function read_mot_results (line 144) | def read_mot_results(filename, is_gt, is_ignore):
  function unzip_objs (line 193) | def unzip_objs(objs):

FILE: yolox/exp/base_exp.py
  class BaseExp (line 17) | class BaseExp(metaclass=ABCMeta):
    method __init__ (line 20) | def __init__(self):
    method get_model (line 27) | def get_model(self) -> Module:
    method get_data_loader (line 31) | def get_data_loader(
    method get_optimizer (line 37) | def get_optimizer(self, batch_size: int) -> torch.optim.Optimizer:
    method get_lr_scheduler (line 41) | def get_lr_scheduler(
    method get_evaluator (line 47) | def get_evaluator(self):
    method eval (line 51) | def eval(self, model, evaluator, weights):
    method __repr__ (line 54) | def __repr__(self):
    method merge (line 63) | def merge(self, cfg_list):

FILE: yolox/exp/build.py
  function get_exp_by_file (line 10) | def get_exp_by_file(exp_file):
  function get_exp_by_name (line 20) | def get_exp_by_name(exp_name):
  function get_exp (line 38) | def get_exp(exp_file, exp_name):

FILE: yolox/exp/yolox_base.py
  class Exp (line 15) | class Exp(BaseExp):
    method __init__ (line 16) | def __init__(self):
    method get_model (line 63) | def get_model(self):
    method get_data_loader (line 82) | def get_data_loader(self, batch_size, is_distributed, no_aug=False):
    method random_resize (line 141) | def random_resize(self, data_loader, epoch, rank, is_distributed):
    method get_optimizer (line 160) | def get_optimizer(self, batch_size):
    method get_lr_scheduler (line 188) | def get_lr_scheduler(self, lr, iters_per_epoch):
    method get_eval_loader (line 203) | def get_eval_loader(self, batch_size, is_distributed, testdev=False):
    method get_evaluator (line 234) | def get_evaluator(self, batch_size, is_distributed, testdev=False):
    method eval (line 248) | def eval(self, model, evaluator, is_distributed, half=False):

FILE: yolox/layers/csrc/cocoeval/cocoeval.cpp
  type COCOeval (line 10) | namespace COCOeval {
    function SortInstancesByDetectionScore (line 16) | void SortInstancesByDetectionScore(
    function SortInstancesByIgnore (line 32) | void SortInstancesByIgnore(
    function MatchDetectionsToGroundTruth (line 59) | void MatchDetectionsToGroundTruth(
    function EvaluateImages (line 140) | std::vector<ImageEvaluation> EvaluateImages(
    function list_to_vec (line 201) | std::vector<T> list_to_vec(const py::list& l) {
    function BuildSortedDetectionList (line 221) | int BuildSortedDetectionList(
    function ComputePrecisionRecallCurve (line 282) | void ComputePrecisionRecallCurve(
    function Accumulate (line 370) | py::dict Accumulate(

FILE: yolox/layers/csrc/cocoeval/cocoeval.h
  function namespace (line 12) | namespace COCOeval {

FILE: yolox/layers/csrc/vision.cpp
  function PYBIND11_MODULE (line 3) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

FILE: yolox/layers/fast_coco_eval_api.py
  class COCOeval_opt (line 19) | class COCOeval_opt(COCOeval):
    method evaluate (line 25) | def evaluate(self):
    method accumulate (line 124) | def accumulate(self):

FILE: yolox/models/darknet.py
  class Darknet (line 10) | class Darknet(nn.Module):
    method __init__ (line 14) | def __init__(
    method diffusion_freeze (line 60) | def diffusion_freeze(self):
    method make_group_layer (line 66) | def make_group_layer(self, in_channels: int, num_blocks: int, stride: ...
    method make_spp_block (line 73) | def make_spp_block(self, filters_list, in_filters):
    method forward (line 89) | def forward(self, x):
  class CSPDarknet (line 104) | class CSPDarknet(nn.Module):
    method __init__ (line 105) | def __init__(
    method forward (line 174) | def forward(self, x):

FILE: yolox/models/losses.py
  class IOUloss (line 10) | class IOUloss(nn.Module):
    method __init__ (line 11) | def __init__(self, reduction="none", loss_type="iou"):
    method forward (line 16) | def forward(self, pred, target):
  function sigmoid_focal_loss (line 56) | def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, ...

FILE: yolox/models/network_blocks.py
  class SiLU (line 9) | class SiLU(nn.Module):
    method forward (line 13) | def forward(x):
  function get_activation (line 17) | def get_activation(name="silu", inplace=True):
  class BaseConv (line 29) | class BaseConv(nn.Module):
    method __init__ (line 32) | def __init__(
    method forward (line 50) | def forward(self, x):
    method fuseforward (line 53) | def fuseforward(self, x):
  class DWConv (line 57) | class DWConv(nn.Module):
    method __init__ (line 60) | def __init__(self, in_channels, out_channels, ksize, stride=1, act="si...
    method forward (line 74) | def forward(self, x):
  class Bottleneck (line 79) | class Bottleneck(nn.Module):
    method __init__ (line 81) | def __init__(
    method forward (line 97) | def forward(self, x):
  class ResLayer (line 104) | class ResLayer(nn.Module):
    method __init__ (line 107) | def __init__(self, in_channels: int):
    method forward (line 117) | def forward(self, x):
  class SPPBottleneck (line 122) | class SPPBottleneck(nn.Module):
    method __init__ (line 125) | def __init__(
    method forward (line 140) | def forward(self, x):
  class CSPLayer (line 147) | class CSPLayer(nn.Module):
    method __init__ (line 150) | def __init__(
    method forward (line 180) | def forward(self, x):
  class Focus (line 188) | class Focus(nn.Module):
    method __init__ (line 191) | def __init__(self, in_channels, out_channels, ksize=1, stride=1, act="...
    method forward (line 195) | def forward(self, x):

FILE: yolox/models/yolo_fpn.py
  class YOLOFPN (line 12) | class YOLOFPN(nn.Module):
    method __init__ (line 17) | def __init__(
    method _make_cbl (line 38) | def _make_cbl(self, _in, _out, ks):
    method _make_embedding (line 41) | def _make_embedding(self, filters_list, in_filters):
    method load_pretrained_model (line 53) | def load_pretrained_model(self, filename="./weights/darknet53.mix.pth"):
    method forward (line 59) | def forward(self, inputs):

FILE: yolox/models/yolo_head.py
  class YOLOXHead (line 19) | class YOLOXHead(nn.Module):
    method __init__ (line 20) | def __init__(
    method initialize_biases (line 134) | def initialize_biases(self, prior_prob):
    method forward (line 145) | def forward(self, xin, labels=None, imgs=None):
    method get_output_and_grid (line 218) | def get_output_and_grid(self, output, k, stride, dtype):
    method decode_outputs (line 238) | def decode_outputs(self, outputs, dtype):
    method get_losses (line 255) | def get_losses(
    method get_l1_target (line 429) | def get_l1_target(self, l1_target, gt, stride, x_shifts, y_shifts, eps...
    method get_assignments (line 437) | def get_assignments(
    method get_in_boxes_info (line 536) | def get_in_boxes_info(
    method dynamic_k_matching (line 627) | def dynamic_k_matching(self, cost, pair_wise_ious, gt_classes, num_gt,...

FILE: yolox/models/yolo_pafpn.py
  class YOLOPAFPN (line 12) | class YOLOPAFPN(nn.Module):
    method __init__ (line 17) | def __init__(
    method forward (line 84) | def forward(self, input):

FILE: yolox/models/yolox.py
  class YOLOX (line 10) | class YOLOX(nn.Module):
    method __init__ (line 17) | def __init__(self, backbone=None, head=None):
    method forward (line 27) | def forward(self, x, targets=None):

FILE: yolox/tracker/basetrack.py
  class TrackState (line 5) | class TrackState(object):
  class BaseTrack (line 12) | class BaseTrack(object):
    method end_frame (line 31) | def end_frame(self):
    method next_id (line 35) | def next_id():
    method activate (line 39) | def activate(self, *args):
    method predict (line 42) | def predict(self):
    method update (line 45) | def update(self, *args, **kwargs):
    method mark_lost (line 48) | def mark_lost(self):
    method mark_removed (line 51) | def mark_removed(self):

FILE: yolox/tracker/diffusion_tracker.py
  class DiffusionTracker (line 17) | class DiffusionTracker(object):
    method __init__ (line 18) | def __init__(self,model,tensor_type,conf_thresh=0.7,det_thresh=0.6,nms...
    method update (line 49) | def update(self,cur_image):
    method get_results (line 181) | def get_results(self):
    method extract_feature (line 191) | def extract_feature(self,cur_image):
    method extract_mean_track_t (line 199) | def extract_mean_track_t(self,pre_box,cur_box):
    method diffusion_postprocess (line 209) | def diffusion_postprocess(self,diffusion_outputs,conf_scores,nms_thre=...
    method diffusion_track_filt (line 257) | def diffusion_track_filt(self,ref_detections,track_detections,conf_thr...
    method diffusion_det_filt (line 274) | def diffusion_det_filt(self,diffusion_detections,conf_thre=0.6,nms_thr...
    method diffusion_matching (line 290) | def diffusion_matching(self,ref_bboxes,ref_track_ids,diffusion_pre_tra...
    method proposal_schedule (line 300) | def proposal_schedule(self,num_ref_bboxes):
    method sampling_steps_schedule (line 304) | def sampling_steps_schedule(self,num_ref_bboxes):
    method vote_to_remove_candidate (line 313) | def vote_to_remove_candidate(self,track_ids,detections,vote_iou_thres=...
    method prepare_input (line 349) | def prepare_input(self,pre_features,cur_features):
    method get_targets_from_tracklet_db (line 357) | def get_targets_from_tracklet_db(self):
  function joint_stracks (line 364) | def joint_stracks(tlista, tlistb):
  function sub_stracks (line 378) | def sub_stracks(tlista, tlistb):

FILE: yolox/tracker/diffusion_tracker_kl.py
  class STrack (line 19) | class STrack(BaseTrack):
    method __init__ (line 21) | def __init__(self, tlwh, score):
    method predict (line 32) | def predict(self):
    method multi_predict (line 39) | def multi_predict(stracks):
    method activate (line 51) | def activate(self, kalman_filter, frame_id):
    method re_activate (line 65) | def re_activate(self, new_track, frame_id, new_id=False):
    method update (line 78) | def update(self, new_track, frame_id):
    method tlwh (line 100) | def tlwh(self):
    method tlbr (line 113) | def tlbr(self):
    method tlwh_to_xyah (line 123) | def tlwh_to_xyah(tlwh):
    method to_xyah (line 132) | def to_xyah(self):
    method tlbr_to_tlwh (line 137) | def tlbr_to_tlwh(tlbr):
    method tlwh_to_tlbr (line 144) | def tlwh_to_tlbr(tlwh):
    method __repr__ (line 149) | def __repr__(self):
  class DiffusionTracker (line 152) | class DiffusionTracker(object):
    method __init__ (line 153) | def __init__(self,model,tensor_type,conf_thresh=0.7,det_thresh=0.6,nms...
    method update (line 187) | def update(self,cur_image):
    method extract_feature (line 321) | def extract_feature(self,cur_image):
    method extract_mean_track_t (line 329) | def extract_mean_track_t(self,pre_box,cur_box):
    method diffusion_postprocess (line 342) | def diffusion_postprocess(self,diffusion_outputs,conf_scores,nms_thre=...
    method diffusion_track_filt (line 390) | def diffusion_track_filt(self,ref_detections,track_detections,conf_thr...
    method diffusion_det_filt (line 407) | def diffusion_det_filt(self,diffusion_detections,conf_thre=0.6,nms_thr...
    method proposal_schedule (line 423) | def proposal_schedule(self,num_ref_bboxes):
    method sampling_steps_schedule (line 427) | def sampling_steps_schedule(self,num_ref_bboxes):
    method vote_to_remove_candidate (line 436) | def vote_to_remove_candidate(self,track_ids,detections,vote_iou_thres=...
    method prepare_input (line 472) | def prepare_input(self,pre_features,cur_features):
  function joint_stracks (line 487) | def joint_stracks(tlista, tlistb):
  function sub_stracks (line 501) | def sub_stracks(tlista, tlistb):
  function remove_duplicate_stracks (line 512) | def remove_duplicate_stracks(stracksa, stracksb):

FILE: yolox/tracker/kalman_filter.py
  class KalmanFilter (line 23) | class KalmanFilter(object):
    method __init__ (line 40) | def __init__(self):
    method initiate (line 55) | def initiate(self, measurement):
    method predict (line 88) | def predict(self, mean, covariance):
    method project (line 126) | def project(self, mean, covariance):
    method multi_predict (line 155) | def multi_predict(self, mean, covariance):
    method update (line 194) | def update(self, mean, covariance, measurement):
    method gating_distance (line 228) | def gating_distance(self, mean, covariance, measurements,

FILE: yolox/tracker/matching.py
  function merge_matches (line 10) | def merge_matches(m1, m2, shape):
  function _indices_to_matches (line 27) | def _indices_to_matches(cost_matrix, indices, thresh):
  function linear_assignment (line 38) | def linear_assignment(cost_matrix, thresh):
  function ious (line 52) | def ious(atlbrs, btlbrs):
  function iou_distance (line 72) | def iou_distance(atracks, btracks):
  function v_iou_distance (line 92) | def v_iou_distance(atracks, btracks):
  function embedding_distance (line 112) | def embedding_distance(tracks, detections, metric='cosine'):
  function fuse_iou (line 158) | def fuse_iou(cost_matrix, tracks, detections):
  function fuse_score (line 172) | def fuse_score(cost_matrix, scores):

FILE: yolox/tracking_utils/evaluation.py
  class Evaluator (line 10) | class Evaluator(object):
    method __init__ (line 12) | def __init__(self, data_root, seq_name, data_type):
    method load_annotations (line 20) | def load_annotations(self):
    method reset_accumulator (line 27) | def reset_accumulator(self):
    method eval_frame (line 30) | def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
    method eval_file (line 78) | def eval_file(self, filename):
    method get_summary (line 92) | def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', '...
    method save_summary (line 109) | def save_summary(summary, filename):

FILE: yolox/tracking_utils/io.py
  function write_results (line 6) | def write_results(filename, results_dict: Dict, data_type: str):
  function read_results (line 33) | def read_results(filename, data_type: str, is_gt=False, is_ignore=False):
  function read_mot_results (line 60) | def read_mot_results(filename, is_gt, is_ignore):
  function unzip_objs (line 109) | def unzip_objs(objs):

FILE: yolox/tracking_utils/timer.py
  class Timer (line 4) | class Timer(object):
    method __init__ (line 6) | def __init__(self):
    method tic (line 15) | def tic(self):
    method toc (line 20) | def toc(self, average=True):
    method clear (line 31) | def clear(self):

FILE: yolox/utils/allreduce_norm.py
  function get_async_norm_states (line 32) | def get_async_norm_states(module):
  function pyobj2tensor (line 41) | def pyobj2tensor(pyobj, device="cuda"):
  function tensor2pyobj (line 47) | def tensor2pyobj(tensor):
  function _get_reduce_op (line 52) | def _get_reduce_op(op_name):
  function all_reduce (line 59) | def all_reduce(py_dict, op="sum", group=None):
  function all_reduce_norm (line 97) | def all_reduce_norm(module):

FILE: yolox/utils/box_ops.py
  function box_cxcywh_to_xyxy (line 9) | def box_cxcywh_to_xyxy(x):
  function box_xyxy_to_cxcywh (line 16) | def box_xyxy_to_cxcywh(x):
  function box_iou (line 24) | def box_iou(boxes1, boxes2):
  function generalized_box_iou (line 40) | def generalized_box_iou(boxes1,boxes2,boxes3,boxes4):
  function masks_to_boxes (line 80) | def masks_to_boxes(masks):

FILE: yolox/utils/boxes.py
  function filter_box (line 24) | def filter_box(output, scale_range):
  function postprocess (line 35) | def postprocess(prediction, num_classes, conf_thre=0.7, nms_thre=0.45):
  function diffusion_postprocess (line 76) | def diffusion_postprocess(pre_prediction,cur_prediction,conf_scores,conf...
  function bboxes_iou (line 127) | def bboxes_iou(bboxes_a, bboxes_b, xyxy=True):
  function matrix_iou (line 153) | def matrix_iou(a, b):
  function adjust_box_anns (line 166) | def adjust_box_anns(bbox, scale_ratio, padw, padh, w_max, h_max):
  function xyxy2xywh (line 174) | def xyxy2xywh(bboxes):
  function xyxy2cxcywh (line 180) | def xyxy2cxcywh(bboxes):

FILE: yolox/utils/checkpoint.py
  function load_ckpt (line 12) | def load_ckpt(model, ckpt):
  function save_checkpoint (line 37) | def save_checkpoint(state, is_best, save_dir, model_name=""):

FILE: yolox/utils/cluster_nms.py
  function intersect (line 4) | def intersect(box_a, box_b):
  function garea (line 25) | def garea(box_a, box_b):
  function get_box_area (line 46) | def get_box_area(box):
  function giou_3d (line 49) | def giou_3d(box_a,box_b,box_c,box_d):
  function cluster_nms (line 86) | def cluster_nms(boxes_a,boxes_c,scores,iou_threshold:float=0.5, top_k:in...

FILE: yolox/utils/demo_utils.py
  function mkdir (line 12) | def mkdir(path):
  function nms (line 17) | def nms(boxes, scores, nms_thr):
  function multiclass_nms (line 47) | def multiclass_nms(boxes, scores, nms_thr, score_thr):
  function demo_postprocess (line 71) | def demo_postprocess(outputs, img_size, p6=False):

FILE: yolox/utils/dist.py
  function synchronize (line 37) | def synchronize():
  function get_world_size (line 51) | def get_world_size() -> int:
  function get_rank (line 59) | def get_rank() -> int:
  function get_local_rank (line 67) | def get_local_rank() -> int:
  function get_local_size (line 80) | def get_local_size() -> int:
  function is_main_process (line 92) | def is_main_process() -> bool:
  function _get_global_gloo_group (line 97) | def _get_global_gloo_group():
  function _serialize_to_tensor (line 108) | def _serialize_to_tensor(data, group):
  function _pad_to_largest_tensor (line 126) | def _pad_to_largest_tensor(tensor, group):
  function all_gather (line 156) | def all_gather(data, group=None):
  function gather (line 194) | def gather(data, dst=0, group=None):
  function shared_random_seed (line 238) | def shared_random_seed():
  function time_synchronized (line 251) | def time_synchronized():
  function is_dist_avail_and_initialized (line 257) | def is_dist_avail_and_initialized():

FILE: yolox/utils/ema.py
  function is_parallel (line 11) | def is_parallel(model):
  function copy_attr (line 21) | def copy_attr(a, b, include=(), exclude=()):
  class ModelEMA (line 30) | class ModelEMA:
    method __init__ (line 41) | def __init__(self, model, decay=0.9999, updates=0):
    method update (line 56) | def update(self, model):
    method update_attr (line 70) | def update_attr(self, model, include=(), exclude=("process_group", "re...

FILE: yolox/utils/logger.py
  function get_caller_name (line 12) | def get_caller_name(depth=0):
  class StreamToLoguru (line 28) | class StreamToLoguru:
    method __init__ (line 33) | def __init__(self, level="INFO", caller_names=("apex", "pycocotools")):
    method write (line 44) | def write(self, buf):
    method flush (line 54) | def flush(self):
  function redirect_sys_output (line 58) | def redirect_sys_output(log_level="INFO"):
  function setup_logger (line 64) | def setup_logger(save_dir, distributed_rank=0, filename="log.txt", mode=...

FILE: yolox/utils/lr_scheduler.py
  class LRScheduler (line 9) | class LRScheduler:
    method __init__ (line 10) | def __init__(self, name, lr, iters_per_epoch, total_epochs, **kwargs):
    method update_lr (line 33) | def update_lr(self, iters):
    method _get_lr_func (line 36) | def _get_lr_func(self, name):
  function cos_lr (line 97) | def cos_lr(lr, total_iters, iters):
  function warm_cos_lr (line 103) | def warm_cos_lr(lr, total_iters, warmup_total_iters, warmup_lr_start, it...
  function yolox_warm_cos_lr (line 121) | def yolox_warm_cos_lr(
  function yolox_semi_warm_cos_lr (line 151) | def yolox_semi_warm_cos_lr(
  function multistep_lr (line 201) | def multistep_lr(lr, milestones, gamma, iters):

FILE: yolox/utils/metric.py
  function get_total_and_free_memory_in_Mb (line 22) | def get_total_and_free_memory_in_Mb(cuda_device):
  function occupy_mem (line 31) | def occupy_mem(cuda_device, mem_ratio=0.95):
  function gpu_mem_usage (line 43) | def gpu_mem_usage():
  class AverageMeter (line 51) | class AverageMeter:
    method __init__ (line 56) | def __init__(self, window_size=50):
    method update (line 61) | def update(self, value):
    method median (line 67) | def median(self):
    method avg (line 72) | def avg(self):
    method global_avg (line 78) | def global_avg(self):
    method latest (line 82) | def latest(self):
    method total (line 86) | def total(self):
    method reset (line 89) | def reset(self):
    method clear (line 94) | def clear(self):
  class MeterBuffer (line 98) | class MeterBuffer(defaultdict):
    method __init__ (line 101) | def __init__(self, window_size=20):
    method reset (line 105) | def reset(self):
    method get_filtered_meter (line 109) | def get_filtered_meter(self, filter_key="time"):
    method update (line 112) | def update(self, values=None, **kwargs):
    method clear_meters (line 121) | def clear_meters(self):

FILE: yolox/utils/model_utils.py
  function get_model_info (line 19) | def get_model_info(model, tsize):
  function fuse_conv_and_bn (line 31) | def fuse_conv_and_bn(conv, bn):
  function fuse_model (line 66) | def fuse_model(model):
  function replace_module (line 77) | def replace_module(module, replaced_module_type, new_module_type, replac...

FILE: yolox/utils/setup_env.py
  function configure_nccl (line 13) | def configure_nccl():
  function configure_module (line 25) | def configure_module(ulimit_value=8192):

FILE: yolox/utils/visualize.py
  function vis (line 11) | def vis(img, boxes, scores, cls_ids, conf=0.5, class_names=None):
  function get_color (line 45) | def get_color(idx):
  function plot_tracking (line 52) | def plot_tracking(image, tlwhs, obj_ids, scores=None, frame_id=0, fps=0....