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Functions415 in github.com/SJTU-LuHe/TransVOD

⨍ Functions 415◇ Types & classes 92

(self, img, target)

datasets/transforms_single.py:246

(self, image, target=None)

datasets/transforms_single.py:255

(self, image, target)

datasets/transforms_single.py:273

(self, image, target)

datasets/vid_multi.py:149

(self, image, target)

datasets/coco.py:64

Method__getattr__

util/misc.py:206

Method__getattr__

util/misc_multi.py:206

Method__getitem__

Args: index (int): Index Returns: tuple: Tuple (image, target). target is the object returned by ``coco.l

datasets/vid_single.py:34

Method__getitem__

Args: index (int): Index Returns: tuple: Tuple (image, target). target is the object returned by ``coco.l

datasets/vid_multi.py:44

Method__getitem__

datasets/coco_panoptic.py:42

Method__getitem__

Args: index (int): Index Returns: tuple: Tuple (image, target). target is the object returned by ``coco.l

datasets/torchvision_datasets/coco.py:63

(self, img_folder, ann_file, transforms, return_masks, cache_mode=False, local_rank=0, local_size=1)

datasets/vid_single.py:28

(self, return_masks=False)

datasets/vid_single.py:79

datasets/transforms_multi.py:196

(self, min_size: int, max_size: int)

datasets/transforms_multi.py:205

datasets/transforms_multi.py:217

(self, min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3)

datasets/transforms_multi.py:229

(self, lower=0.5, upper=1.5)

datasets/transforms_multi.py:285

(self, delta=32)

datasets/transforms_multi.py:298

(self, lower=0.5, upper=1.5)

datasets/transforms_multi.py:309

(self, delta=18.0)

datasets/transforms_multi.py:321

datasets/transforms_multi.py:333

(self, current='BGR', transform='HSV')

datasets/transforms_multi.py:345

datasets/transforms_multi.py:359

datasets/transforms_multi.py:366

datasets/transforms_multi.py:394

datasets/transforms_multi.py:423

datasets/transforms_multi.py:432

(self, sizes, max_size=None)

datasets/transforms_multi.py:442

(self, max_pad)

datasets/transforms_multi.py:453

(self, transforms1, transforms2, p=0.5)

datasets/transforms_multi.py:467

(self, *args, **kwargs)

datasets/transforms_multi.py:488

(self, mean, std)

datasets/transforms_multi.py:496

(self, transforms)

datasets/transforms_multi.py:517

(self, dataset, num_replicas=None, rank=None, local_rank=None, local_size=None, shuffle=True)

datasets/samplers.py:31

(self, dataset, num_replicas=None, rank=None, local_rank=None, local_size=None, shuffle=True)

datasets/samplers.py:90

(self, annotation_file=None, load_img_as_vid=False)

datasets/coco_video_parser.py:15

(self, coco_gt, iou_types)

datasets/coco_eval.py:31

(self, ann_file, ann_folder, output_dir="panoptic_eval")

datasets/panoptic_eval.py:22

datasets/transforms_single.py:157

(self, min_size: int, max_size: int)

datasets/transforms_single.py:166

datasets/transforms_single.py:178

datasets/transforms_single.py:190

(self, sizes, max_size=None)

datasets/transforms_single.py:200

(self, max_pad)

datasets/transforms_single.py:211

(self, transforms1, transforms2, p=0.5)

datasets/transforms_single.py:225

(self, *args, **kwargs)

datasets/transforms_single.py:243

(self, mean, std)

datasets/transforms_single.py:251

(self, transforms)

datasets/transforms_single.py:270

(self, img_folder, ann_file, transforms, return_masks, interval1, interval2, num_ref_frames= 3, is_tr

datasets/vid_multi.py:29

(self, return_masks=False)

datasets/vid_multi.py:146

(self, img_folder, ann_folder, ann_file, transforms=None, return_masks=True)

datasets/coco_panoptic.py:24

(self, img_folder, ann_file, transforms, return_masks, cache_mode=False, local_rank=0, local_size=1)

datasets/coco.py:27

(self, return_masks=False)

datasets/coco.py:61

(self, loader, device, prefetch=True)

datasets/data_prefetcher_multi.py:21

(self, loader, device, prefetch=True)

datasets/data_prefetcher_single.py:15

(self, root, annFile, transform=None, target_transform=None, transforms=None, cache_mode=Fal

datasets/torchvision_datasets/coco.py:33

(self, annotation_file=None, load_img_as_vid=False)

datasets/parsers/coco_video_parser.py:17

(self, d_model=256, nhead=8, num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=102

models/deformable_transformer_multi.py:24

(self, d_model = 256, d_ffn = 1024, dropout=0.1, activation="relu", n_heads = 8)

models/deformable_transformer_multi.py:274

(self, d_model = 256, d_ffn=1024, dropout=0.1, activation='relu', num_ref_frames = 3, n_hea

models/deformable_transformer_multi.py:338

(self, d_model=256, d_ffn=1024, dropout=0.1, activation="relu",

models/deformable_transformer_multi.py:389

(self, encoder_layer, num_layers)

models/deformable_transformer_multi.py:431

(self, d_model=256, d_ffn=1024, dropout=0.1, activation="relu", n_levels=4

models/deformable_transformer_multi.py:462

(self, decoder_layer, num_layers, return_intermediate=False)

models/deformable_transformer_multi.py:523

(self, decoder_layer, num_layers, return_intermediate=False)

models/deformable_transformer_multi.py:571

Creates the matcher Params: cost_class: This is the relative weight of the classification error in the matching cost

models/matcher.py:28

Initializes the model. Parameters: backbone: torch module of the backbone to be used. See backbone.py transformer:

models/deformable_detr_single.py:37

Create the criterion. Parameters: num_classes: number of object categories, omitting the special no-object category

models/deformable_detr_single.py:204

(self, d_model=256, d_ffn=1024, dropout=0.1, activation="relu",

models/deformable_transformer_single.py:191

(self, encoder_layer, num_layers)

models/deformable_transformer_single.py:233

(self, d_model=256, d_ffn=1024, dropout=0.1, activation="relu", n_levels=4

models/deformable_transformer_single.py:263

(self, d_model = 256, d_ffn=1024, dropout=0.1, activation='relu', n_frames = 4, h_heads = 8

models/deformable_transformer_single.py:317

(self, decoder_layer, num_layers, return_intermediate=False)

models/deformable_transformer_single.py:370

Initializes the model. Parameters: backbone: torch module of the backbone to be used. See backbone.py transformer:

models/deformable_detr_multi.py:37

Create the criterion. Parameters: num_classes: number of object categories, omitting the special no-object category

models/deformable_detr_multi.py:210

(self, dim, fpn_dims, context_dim)

models/segmentation.py:78

(self, query_dim, hidden_dim, num_heads, dropout=0, bias=True)

models/segmentation.py:149

(self, threshold=0.5)

models/segmentation.py:225

Parameters: is_thing_map: This is a whose keys are the class ids, and the values a boolean indicating whether

models/segmentation.py:251

(self, n, eps=1e-5)

models/backbone.py:36

(self, backbone: nn.Module, train_backbone: bool, return_interm_layers: bool)

models/backbone.py:69

(self, name: str, train_backbone: bool, return_interm_layers: bool,

models/backbone.py:99

(self, num_pos_feats=256)

models/position_encoding.py:63

Multi-Scale Deformable Attention Module :param d_model hidden dimension :param n_levels number of feature levels

models/ops/modules/ms_deform_attn.py:31

(self, window_size=20, fmt=None)

util/misc.py:67

(self, delimiter="\t")

util/misc.py:195

(self, tensors, mask: Optional[Tensor])

util/misc.py:340

(self, window_size=20, fmt=None)

util/misc_multi.py:67

(self, delimiter="\t")

util/misc_multi.py:195

(self, tensors, mask: Optional[Tensor])

util/misc_multi.py:361

datasets/samplers.py:48

datasets/samplers.py:115

datasets/samplers.py:68

datasets/samplers.py:135

datasets/coco_panoptic.py:78

datasets/torchvision_datasets/coco.py:83

datasets/transforms_multi.py:525

datasets/transforms_single.py:278

util/misc.py:363

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