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Functions239 in github.com/CAMMA-public/SurgLatentGraph

⨍ Functions 239◇ Types & classes 63
↓ 1 callersMethodmap_roi_levels
Map rois to corresponding feature levels by scales. - scale < finest_scale * 2: level 0 - finest_scale * 2 <= scale < finest_scale *
model/roi_extractors/sg_single_level_roi_extractor.py:31
↓ 1 callersMethodparse_data_info
(self, raw_data_info: dict)
datasets/custom_loading.py:101
↓ 1 callersMethodpredict
(self, batch_inputs: Tensor, batch_data_samples: SampleList)
model/deepcvs_temporal.py:111
↓ 1 callersMethodpredict
(self, graph: BaseDataElement, feats: BaseDataElement)
model/predictor_heads/ds.py:121
↓ 1 callersMethodpredict
(self, graph: BaseDataElement, feats: BaseDataElement)
model/predictor_heads/ds.py:434
↓ 1 callersMethodprepare_data
Get date processed by ``self.pipeline``. Note that ``idx`` is a video index in default since the base element of video dataset is a vi
datasets/custom_loading.py:146
↓ 1 callersMethodreset
(self)
evaluator/CocoMetricRGD.py:469
↓ 1 callersMethodreshape_as_clip
(self, feats: BaseDataElement, graphs: BaseDataElement, results: SampleList, B: int, T: int)
model/sv2lstg.py:180
↓ 1 callersMethodresults2json
(self, results: Sequence[dict], outfile_prefix: str, gts: Sequence[dict] = None)
evaluator/CocoMetricRGD.py:371
↓ 1 callersMethodsample_indices
(self, matched_indices, unmatched_indices, N, R)
model/predictor_heads/graph.py:665
↓ 1 callersMethodsampling_frames
(self, video_infos: dict)
datasets/custom_loading.py:351
↓ 1 callersMethodshortcut
(self, x, seg)
model/predictor_heads/modules/decoder.py:124
Method__call__
(self, graph: BaseDataElement)
model/predictor_heads/modules/gnn.py:47
Method__init__
(self, detector: ConfigType, num_classes: int, viz_feat_size: int, semantic_feat_size: int, sem_fe
model/lg.py:41
Method__init__
(self, clip_size: int, temporal_arch: str = 'transformer', causal: bool = False, per_video: bool =
model/deepcvs_temporal.py:22
Method__init__
(self, detector: ConfigType, num_classes: int, detector_num_classes: int,
model/deepcvs.py:27
Method__init__
(self, lg_detector: BaseDetector, ds_head: ConfigType, clip_size: int, viz_feat_size: int, semanti
model/sv2lstg.py:22
Method__init__
(self, backbone: ConfigType, loss: ConfigType, num_classes: int,
model/simple_classifier.py:14
Method__init__
(self, edges_per_node: int, viz_feat_size: int, roi_extractor: BaseRoIExtractor, num_edge_classes:
model/predictor_heads/graph.py:33
Method__init__
(self, num_temp_frames: int, graph_pooling_window: int = 1, use_temporal_model: bool = False, temp
model/predictor_heads/ds.py:394
Method__init__
(self, decoder_cfg: ConfigType, aspect_ratio: Union[Tuple, List], viz_feat_size: int, semantic_fea
model/predictor_heads/reconstruction.py:27
Method__init__
(self, l1_weight: float, deep_loss_weight: float, ssim_weight: float, perceptual_weight: float, bo
model/predictor_heads/modules/loss.py:14
Method__init__
(self, fin, fout, seg_nc, src_nc, spade_config_str='spadebatch3x3', spectral=True)
model/predictor_heads/modules/decoder.py:78
Method__init__
(self, channels, normalization='batch', activation='relu', padding='same', kernel
model/predictor_heads/modules/layers.py:88
Method__init__
(self, d_model, dropout=0.1, max_len=5000, batch_first=False, return_enc_only=False)
model/predictor_heads/modules/layers.py:254
Method__init__
(self, squeeze_index)
model/predictor_heads/modules/utils.py:28
Method__init__
(self)
model/predictor_heads/modules/utils.py:37
Method__init__
(self, num_layers: int, arch: str, add_self_loops: bool, use_reverse_edges: bool, norm: str, skip_
model/predictor_heads/modules/gnn.py:29
Method__init__
(self, num_layers, num_f_maps, dim, num_cl
model/predictor_heads/modules/mstcn.py:69
Method__init__
(self, dilation, in_channels, out_channels,
model/predictor_heads/modules/mstcn.py:97
Method__init__
(self, causal_conv=True)
model/predictor_heads/modules/mstcn.py:133
Method__init__
(self, norm_type, hidden_dim=64)
model/predictor_heads/modules/norm.py:5
Method__init__
(self, input_dim_obj, input_dim_pred, output_dim=None, output_dim_pred=None, hidden_dim=512, pooli
model/predictor_heads/modules/gnn_models.py:32
Method__init__
(self, input_dim_obj, input_dim_pred, output_dim=None, output_dim_pred=None, num_layers=5, hidden_
model/predictor_heads/modules/gnn_models.py:200
Method__init__
(self, in_channels, out_channels)
model/predictor_heads/modules/gnn_models.py:390
Method__init__
(self, in_channels, hidden_channels, out_channels, num_layers, dropout, num_groups=2)
model/predictor_heads/modules/gnn_models.py:406
Method__init__
(self)
model/predictor_heads/modules/SPADE/base_network.py:11
Method__init__
(self, requires_grad=False)
model/predictor_heads/modules/SPADE/architectures.py:16
Method__init__
(self, config_text, norm_nc, label_nc)
model/predictor_heads/modules/SPADE/normalization.py:67
Method__init__
(self, roi_layer, out_channels, featmap_strides,
model/roi_extractors/sg_single_level_roi_extractor.py:22
Method__init__
(self, sam_type: str = 'vit_h', sam_weights_path: str = None, num_classes: int = 7, num_nodes: int
model/modified_detectors/sam_detector.py:26
Method__init__
(self, data_root: str, data_prefix: str, use_pred_boxes_recon: bool, num_classes: int, additional_
evaluator/CocoMetricRGD.py:25
Method__init__
( self, dataset: Sized, seed: Optional[int] = None, load_video: bool = False,
datasets/custom_loading.py:209
Method__init__
(self, sampling_ratio: int = 1, collect_video_keys: List[str] = ['video_id', 'video_length'])
datasets/custom_loading.py:347
Method__init__
(self, name: str, prefix: str = 'endoscapes', draw: bool = False, **kwargs)
visualizer/SAMQueryVisualizer.py:13
Method__init__
(self, name: str, dataset: str = 'endoscapes', detector: str = 'faster_rcnn', results_dir: str = '
visualizer/LatentGraphVisualizer.py:13
Method__init__
(self, freeze_detector: bool = True, freeze_graph_head: bool = False, freeze_projectors: bool = Fa
hooks/custom_hooks.py:8
Method__init__
(self, finetune_backbone=False)
hooks/custom_hooks.py:65
Method__init__
(self, temporal=False)
hooks/custom_hooks.py:89
Method__repr__
(self)
model/predictor_heads/modules/layers.py:67
Method__repr__
(self)
model/predictor_heads/modules/layers.py:78
Method_forward
(self, batch_inputs: Tensor, batch_data_samples: OptSampleList = None)
model/lg.py:589
Method_forward
(self, batch_inputs: Tensor, batch_data_samples: OptSampleList = None)
model/sv2lstg.py:769
Method_forward
(self, batch_inputs: Tensor, batch_data_samples: SampleList = None)
model/simple_classifier.py:59
Method_forward
(self, batch_inputs: Tensor, batch_data_samples: OptSampleList = None)
model/modified_detectors/sam_detector.py:290
Function_init_weights
(module)
model/predictor_heads/modules/gnn_models.py:26
Method_load_masks
(self, results: dict)
datasets/custom_loading.py:24
Method_predict_by_feat_single
(self, cls_score: Tensor, bbox_pred: Tensor, img_meta: dict, r
model/modified_detectors/def_detr_with_queries.py:40
Methodadd_datasample
(self, name: str, image: np.ndarray, data_sample: Optional['DetDataSample'] = None, ou
visualizer/SAMQueryVisualizer.py:18
Methodadd_datasample
(self, name: str, image: np.ndarray, pred_score_thr: float, data_sample: Optional['DetDataSample']
visualizer/LatentGraphVisualizer.py:144
Methodadd_model_specific_args
(parser)
model/predictor_heads/modules/mstcn.py:48
Functionadd_norm_layer
(layer)
model/predictor_heads/modules/SPADE/normalization.py:24
Methodafter_test_iter
(self, runner, **kwargs)
hooks/custom_hooks.py:142
Methodafter_train_iter
(self, runner, **kwargs)
hooks/custom_hooks.py:136
Methodafter_val_iter
(self, runner, **kwargs)
hooks/custom_hooks.py:139
Methodbefore_train
(self, runner)
hooks/custom_hooks.py:48
Methodbefore_train
(self, runner)
hooks/custom_hooks.py:92
Methodbefore_train_iter
(self, runner, **kwargs)
hooks/custom_hooks.py:20
Methodbefore_train_iter
(self, runner, **kwargs)
hooks/custom_hooks.py:68
Functionbox_union
(boxes1: torch.Tensor, boxes2: torch.Tensor)
model/predictor_heads/modules/utils.py:9
Functionbuild_cnn
Build a CNN from an architecture string, which is a list of layer specification strings. The overall architecture can be given as a list or a
model/predictor_heads/modules/layers.py:125
Functionbuild_mask_net
(dim, mask_size, scale_factor=2, output_dim=1, batch_norm=True)
model/predictor_heads/modules/layers.py:237
Methodcompute_metrics
(self, results: list)
evaluator/CocoMetricRGD.py:126
Methodconvert_img
(self, img)
model/predictor_heads/modules/loss.py:82
Methoddetach_results
(self, results: SampleList)
model/lg.py:352
Methodextract_feat
(self, batch_inputs: Tensor, batch_data_samples: SampleList, compute_instance_feats: bool = False,
model/modified_detectors/sam_detector.py:67
Methodforward
(self, data: dict, training: bool = False)
model/saved_lg_preprocessor.py:7
Methodforward
Output will have same size as layout
model/predictor_heads/reconstruction.py:352
Methodforward
(self, reconstructed_imgs, orig_imgs, boxes=None)
model/predictor_heads/modules/loss.py:90
Methodforward
(self, layout, feats)
model/predictor_heads/modules/decoder.py:53
Methodforward
(self, seg_, x)
model/predictor_heads/modules/decoder.py:106
Methodforward
(self, x)
model/predictor_heads/modules/layers.py:64
Methodforward
(self, x)
model/predictor_heads/modules/layers.py:75
Methodforward
(self, x)
model/predictor_heads/modules/layers.py:83
Methodforward
(self, x)
model/predictor_heads/modules/layers.py:109
Methodforward
(self, x)
model/predictor_heads/modules/layers.py:276
Methodforward
(self, inputs)
model/predictor_heads/modules/utils.py:24
Methodforward
(self, inputs)
model/predictor_heads/modules/utils.py:33
Methodforward
(self, inputs)
model/predictor_heads/modules/utils.py:41
Methodforward
(self, input)
model/predictor_heads/modules/utils.py:45
Methodforward
(self, x)
model/predictor_heads/modules/mstcn.py:38
Methodforward
(self, x)
model/predictor_heads/modules/mstcn.py:88
Methodforward
(self, x)
model/predictor_heads/modules/mstcn.py:123
Methodforward
(self, x)
model/predictor_heads/modules/mstcn.py:165
Methodforward
(self, tensor, nodes_per_img=None, print_=False)
model/predictor_heads/modules/norm.py:23
Methodforward
Inputs: - obj_vecs: FloatTensor of shape (num_objs, D) giving vectors for all objects - pred_vecs: FloatTensor of shape (num_
model/predictor_heads/modules/gnn_models.py:87
Methodforward
(self, node_features, edge_features, edge_flats, graph=None)
model/predictor_heads/modules/gnn_models.py:233
Methodforward
(self, node_features, edge_features, edge_flats, graph)
model/predictor_heads/modules/gnn_models.py:325
Methodforward
(self, x, edge_index, dropout_mask=None)
model/predictor_heads/modules/gnn_models.py:399
Methodforward
(self, x, edge_index)
model/predictor_heads/modules/gnn_models.py:432
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