Functions — github.com/ChenhongyiYang/QueryDet-PyTorch

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Functions164 in github.com/ChenhongyiYang/QueryDet-PyTorch

⨍ Functions 164◇ Types & classes 24

↓ 1 callersMethodtrain_forward

(self, batched_inputs, just_forward=False)

models/querydet/detector.py:169

↓ 1 callersMethodvisualize_training

(self, batched_inputs, results)

models/retinanet/retinanet.py:141

↓ 1 callersFunctionvoc_ap

(rec, prec)

visdrone_eval/viseval/calc_accuracy.py:67

Method__call__

(self, boxes, scores, class_idxs)

utils/soft_nms.py:146

Method__call__

(self, gt_boxes, anchors)

utils/loop_matcher.py:36

Method__call__

Args: dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. Returns: dict: a format that

utils/val_mapper_with_ann.py:29

Method__call__

(self, dataset_dict)

visdrone/mapper.py:50

Method__init__

(self, distributed, unit, out_file=None)

utils/time_evaluator.py:10

Method__init__

(self, method, gaussian_sigma, linear_threshold, prune_threshold)

utils/soft_nms.py:140

Method__init__

( self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False )

utils/loop_matcher.py:7

Method__init__

(self, out_json, distributed=True, class_add_1=True)

utils/json_evaluator.py:18

Method__init__

(self, *args, stats_mode="", **kwargs)

utils/merged_sync_bn.py:55

Method__init__

(self, cfg)

utils/val_mapper_with_ann.py:19

Method__init__

Args: cfg (CfgNode):

train_tools/visdrone_train.py:64

Method__init__

Args: cfg (CfgNode):

train_tools/coco_train.py:60

Method__init__

(self, gt_json, det_json, out_dir)

visdrone/json_to_txt.py:11

Method__init__

(self, cfg, is_train=True)

visdrone/mapper.py:36

Method__init__

(self, cfg)

models/querydet/detector.py:73

Method__init__

(self, cfg, in_channels, conv_channels, num_convs, num_anchors)

models/querydet/det_head.py:28

Method__init__

(self, cfg, in_channels, conv_channels, num_convs, num_anchors)

models/querydet/det_head.py:133

Method__init__

(self, in_channels, conv_channels, num_convs, pred_channels, pred_prior=None)

models/querydet/det_head.py:267

Method__init__

(self, anchor_num, num_classes, score_th=0.12, context=2)

models/querydet/qinfer.py:25

Method__init__

(self, cfg, input_shape: List[ShapeSpec])

models/retinanet/retinanet.py:438

Methodbackward

(ctx, *args)

utils/gradient_checkpoint.py:79

Methodbackward

(ctx, grad_output)

utils/merged_sync_bn.py:23

Functionbatched_soft_nms_rotated

Performs soft non-maximum suppression in a batched fashion on rotated bounding boxes. Each index value correspond to a category, and NMS

utils/soft_nms.py:150

Methodbuild_evaluator

(cls, cfg, dataset_name, output_folder=None)

train_tools/coco_infer.py:58

Methodbuild_evaluator

(cls, cfg, dataset_name, output_folder=None)

train_tools/coco_train.py:105

Methodbuild_test_loader

(cls, cfg, dataset_name)

train_tools/coco_train.py:114

Functioncheckpoint_sequential

r"""A helper function for checkpointing sequential models. Sequential models execute a list of modules/functions in order (sequentially). The

utils/gradient_checkpoint.py:167

Methodconvert_gt_cls

(logits, gt_class, f_idxs)

models/querydet/detector.py:275

Methodconvert_gt_cls

(logits, gt_class, f_idxs)

models/retinanet/retinanet.py:274

Methoddevice

(self)

models/querydet/detector.py:160

Methodevaluate

(self)

utils/time_evaluator.py:28

Methodevaluate

(self)

utils/json_evaluator.py:45

Functionforward

(input)

utils/gradient_checkpoint.py:213

Methodforward

(self, features)

utils/anchor_gen.py:25

Methodforward

(ctx, run_function, preserve_rng_state, *args)

utils/gradient_checkpoint.py:58

Methodforward

(ctx, input)

utils/merged_sync_bn.py:15

Methodforward

(self, inputs)

utils/merged_sync_bn.py:71

Methodforward

(self, batched_inputs, just_forward=False)

models/querydet/detector.py:163

Methodforward

(self, features)

models/querydet/det_head.py:66

Methodforward

(self, features)

models/querydet/det_head.py:115

Methodforward

(self, features, lvl_start)

models/querydet/det_head.py:180

Methodforward

(self, features)

models/querydet/det_head.py:298

Methodforward

(self, batched_inputs)

models/retinanet/retinanet.py:171

Methodforward

Arguments: features (list[Tensor]): FPN feature map tensors in high to low resolution. Each tensor in the list co

models/retinanet/retinanet.py:483

Methodget_params

(self)

models/querydet/det_head.py:82

Methodget_params

(self)

models/querydet/det_head.py:253

Methodget_params

(self)

models/querydet/det_head.py:338

Functionpermute_all_cls_and_box_to_N_HWA_K_and_concat

(box_cls, box_delta, num_classes=80)

models/querydet/detector.py:54

Functionpermute_all_cls_and_box_to_N_HWA_K_and_concat

Rearrange the tensor layout from the network output, i.e.: list[Tensor]: #lvl tensors of shape (N, A x K, Hi, Wi) to per-image prediction

models/retinanet/retinanet.py:44

Functionpermute_to_N_HWA_K

(tensor, K)

models/querydet/qinfer.py:7

Methodprocess

Args: inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). It is a list of dict. Each dict corresponds to

utils/coco_eval_fpn.py:64

Methodprocess

(self, inputs, outputs)

utils/time_evaluator.py:22

Methodprocess

(self, inputs, outputs)

utils/json_evaluator.py:34

Functionread_all_labels

models/querydet/qinfer.py:15

Functionstart_train

(args)

train_tools/visdrone_infer.py:162

Functionstart_train

(args)

train_tools/visdrone_train.py:236

Functionstart_train

(args)

train_tools/coco_infer.py:139

Functionstart_train

(args)

train_tools/coco_train.py:189

Functiontrivial_batch_collator

(batch)

visdrone/dataloader.py:151

Functionworker_init_reset_seed

(worker_id)

visdrone/dataloader.py:147

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