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Functions1,090 in github.com/HealthX-Lab/MedCLIP-SAMv2

⨍ Functions 1,090◇ Types & classes 206↳ Endpoints 7
↓ 1 callersMethodmodify_seg_fn
(self, seg: np.ndarray, plans_manager: PlansManager, dataset_json: dict, configuration_m
weak_segmentation/nnunetv2/preprocessing/preprocessors/default_preprocessor.py:263
↓ 1 callersFunctionmultiprocessing_plot_overlay
(list_of_image_files, list_of_seg_files, image_reader_writer, list_of_output_
weak_segmentation/nnunetv2/utilities/overlay_plots.py:168
↓ 1 callersFunctionmultiprocessing_plot_overlay_preprocessed
(list_of_case_files, list_of_output_files, overlay_intensity, nu
weak_segmentation/nnunetv2/utilities/overlay_plots.py:179
↓ 1 callersFunctionmy_iterator
(list_of_input_arrs, list_of_input_props)
weak_segmentation/nnunetv2/inference/examples.py:89
↓ 1 callersFunctionneighbour_exchange
(from_rank, to_rank, tensor, group=None)
biomedclip_finetuning/open_clip/src/open_clip/loss.py:270
↓ 1 callersFunctionneighbour_exchange_bidir
(left_rank, right_rank, tensor_to_left, tensor_to_right, group=None)
biomedclip_finetuning/open_clip/src/open_clip/loss.py:290
↓ 1 callersFunctionneighbour_exchange_bidir_with_grad
(left_rank, right_rank, tensor_to_left, tensor_to_right, group=None)
biomedclip_finetuning/open_clip/src/open_clip/loss.py:354
↓ 1 callersMethodon_train_end
(self)
weak_segmentation/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py:833
↓ 1 callersMethodon_train_end
(self)
weak_segmentation/nnunetv2/training/nnUNetTrainer/variants/benchmarking/nnUNetTrainerBenchmark_5epochs.py:31
↓ 1 callersFunctionpad_shape
pads shape so that it is divisible by must_be_divisible_by :param shape: :param must_be_divisible_by: :return:
weak_segmentation/nnunetv2/experiment_planning/experiment_planners/network_topology.py:9
↓ 1 callersFunctionplan_and_preprocess_entry
()
weak_segmentation/nnunetv2/experiment_planning/plan_and_preprocess_entrypoints.py:109
↓ 1 callersFunctionplan_experiment_dataset
overwrite_target_spacing ONLY applies to 3d_fullres and 3d_cascade fullres!
weak_segmentation/nnunetv2/experiment_planning/plan_and_preprocess_api.py:50
↓ 1 callersMethodplot_network_architecture
(self)
weak_segmentation/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py:472
↓ 1 callersMethodplot_progress_png
(self, output_folder)
weak_segmentation/nnunetv2/training/logging/nnunet_logger.py:54
↓ 1 callersFunctionplot_text_with_colors
(ax, tokens, rgba_colors, max_width, max_height, fontsize=12)
saliency_maps/scripts/plot.py:19
↓ 1 callersFunctionpostprocess_crf
(args)
postprocessing/postprocess_saliency_maps.py:14
↓ 1 callersFunctionpostprocess_kmeans
(args)
postprocessing/postprocess_saliency_maps.py:103
↓ 1 callersMethodpostprocess_small_regions
Removes small disconnected regions and holes in masks, then reruns box NMS to remove any new duplicates. Edits mask_data
segment-anything/segment_anything/automatic_mask_generator.py:324
↓ 1 callersFunctionpostprocess_thresholding
(args)
postprocessing/postprocess_saliency_maps.py:69
↓ 1 callersMethodpredict
Predict masks for the given input prompts, using the currently set image. Arguments: point_coords (np.ndarray or None)
segment-anything/segment_anything/predictor.py:92
↓ 1 callersFunctionpredict_from_folder
(dataset, fold:int , indir , outdir , rule = 'both')
weak_segmentation/nnunetv2/nnunet/predict_from_folder.py:22
↓ 1 callersFunctionpredict_from_folder_entry
()
weak_segmentation/nnunetv2/nnunet/predict_from_folder.py:134
↓ 1 callersMethodpredict_from_list_of_npy_arrays
(self, image_or_list_of_images: Union[np.ndarray, List[np.ndarray]],
weak_segmentation/nnunetv2/inference/predict_from_raw_data.py:313
↓ 1 callersFunctionprepare_inputs_for_generation
(input_ids, image_inputs, past=None, **kwargs)
biomedclip_finetuning/open_clip/src/open_clip/coca_model.py:479
↓ 1 callersFunctionpreprocess_dataset
(dataset_id: int, plans_identifier: str = 'nnUNetPlans', configu
weak_segmentation/nnunetv2/experiment_planning/plan_and_preprocess_api.py:86
↓ 1 callersFunctionpreprocessing_iterator_fromfiles
(list_of_lists: List[List[str]], list_of_segs_from_prev_stage_files: Unio
weak_segmentation/nnunetv2/inference/data_iterators.py:60
↓ 1 callersFunctionpreprocessing_iterator_fromnpy
(list_of_images: List[np.ndarray], list_of_segs_from_prev_stage: Union[List
weak_segmentation/nnunetv2/inference/data_iterators.py:258
↓ 1 callersFunctionprint_inference_instructions
(inference_info_dict: dict, instructions_file: str = None)
weak_segmentation/nnunetv2/evaluation/find_best_configuration.py:214
↓ 1 callersMethodprint_plans
(self)
weak_segmentation/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py:457
↓ 1 callersFunctionprofile_fvcore
( model, image_input_size=(3, 224, 224), text_input_size=(77,), batch_size=1,
biomedclip_finetuning/open_clip/src/open_clip_train/profiler.py:23
↓ 1 callersFunctionprofile_fvcore_image
( model, image_input_size=(3, 224, 224), batch_size=1, detailed=False,
biomedclip_finetuning/open_clip/src/open_clip_train/profiler.py:63
↓ 1 callersFunctionprofile_fvcore_text
( model, text_input_size=(77,), batch_size=1, detailed=False, force_cp
biomedclip_finetuning/open_clip/src/open_clip_train/profiler.py:44
↓ 1 callersFunctionprofile_model
(model_name, batch_size=1, profiler='torch', device="cuda")
biomedclip_finetuning/open_clip/src/open_clip_train/profiler.py:128
↓ 1 callersFunctionprofile_torch
Profile the full model using torch.utils.flop_counter
biomedclip_finetuning/open_clip/src/open_clip_train/profiler.py:110
↓ 1 callersFunctionprofile_torch_image
Profile the image encoder using torch.utils.flop_counter
biomedclip_finetuning/open_clip/src/open_clip_train/profiler.py:82
↓ 1 callersFunctionprofile_torch_text
Profile the text encoder using torch.utils.flop_counter
biomedclip_finetuning/open_clip/src/open_clip_train/profiler.py:96
↓ 1 callersFunctionpt_load
(file_path, map_location=None)
biomedclip_finetuning/open_clip/src/open_clip_train/file_utils.py:69
↓ 1 callersFunctionpush_pretrained_to_hf_hub
( model_name, pretrained: str, repo_id: str, precision: str = 'fp32', image_mean: Optional
biomedclip_finetuning/open_clip/src/open_clip/push_to_hf_hub.py:156
↓ 1 callersFunctionpush_to_hf_hub
( model, tokenizer, model_config: Optional[dict], repo_id: str, commit_message: str = 'Add
biomedclip_finetuning/open_clip/src/open_clip/push_to_hf_hub.py:85
↓ 1 callersFunctionrandom_image_batch
(batch_size, size)
biomedclip_finetuning/open_clip/tests/util_test.py:55
↓ 1 callersFunctionrandom_text_batch
(batch_size, min_length = 75, max_length = 75)
biomedclip_finetuning/open_clip/tests/util_test.py:60
↓ 1 callersMethodread_images
(self, image_fnames: Union[List[str], Tuple[str, ...]])
weak_segmentation/nnunetv2/imageio/nibabel_reader_writer.py:115
↓ 1 callersMethodread_images
(self, image_fnames: Union[List[str], Tuple[str, ...]])
weak_segmentation/nnunetv2/imageio/simpleitk_reader_writer.py:29
↓ 1 callersMethodread_seg
(self, seg_fname: str)
weak_segmentation/nnunetv2/imageio/natural_image_reager_writer.py:61
↓ 1 callersFunctionremote_sync_fsspec
(local_dir, remote_dir)
biomedclip_finetuning/open_clip/src/open_clip_train/file_utils.py:20
↓ 1 callersFunctionremote_sync_s3
(local_dir, remote_dir)
biomedclip_finetuning/open_clip/src/open_clip_train/file_utils.py:10
↓ 1 callersFunctionreplace_in
(model: nn.Module, target: nn.Module, replacement: nn.Module)
saliency_maps/scripts/utils.py:29
↓ 1 callersFunctionreplace_linear
(model, linear_replacement, include_modules=['c_fc', 'c_proj'], copy_weights=True)
biomedclip_finetuning/open_clip/src/open_clip/utils.py:65
↓ 1 callersMethodresampling_fn_data
(self)
weak_segmentation/nnunetv2/utilities/plans_handling/plans_handler.py:126
↓ 1 callersMethodresampling_fn_seg
(self)
weak_segmentation/nnunetv2/utilities/plans_handling/plans_handler.py:152
↓ 1 callersMethodresize_longest_image_size
( input_image_size: torch.Tensor, longest_side: int )
segment-anything/segment_anything/utils/onnx.py:42
↓ 1 callersFunctionresize_pos_embed
(state_dict, model, interpolation: str = 'bicubic', antialias: bool = True)
biomedclip_finetuning/open_clip/src/open_clip/model.py:525
↓ 1 callersFunctionresize_text_pos_embed
(state_dict, model, interpolation: str = 'linear', antialias: bool = False)
biomedclip_finetuning/open_clip/src/open_clip/model.py:559
↓ 1 callersMethodrevert_cropping_on_probabilities
ONLY USE THIS WITH PROBABILITIES, DO NOT USE LOGITS AND DO NOT USE FOR SEGMENTATION MAPS!!! predicted_probabilities must be (c, x, y
weak_segmentation/nnunetv2/utilities/label_handling/label_handling.py:182
↓ 1 callersMethodrun
data identifier = configuration name in plans. EZ.
weak_segmentation/nnunetv2/preprocessing/preprocessors/default_preprocessor.py:193
↓ 1 callersFunctionrun_export
( model_type: str, checkpoint: str, output: str, opset: int, return_single_mask: bool
segment-anything/scripts/export_onnx_model.py:97
↓ 1 callersFunctionrun_training
(dataset_name_or_id: Union[str, int], configuration: str, fold: Union[int, str],
weak_segmentation/nnunetv2/run/run_training.py:143
↓ 1 callersFunctionrun_training_entry
()
weak_segmentation/nnunetv2/run/run_training.py:217
↓ 1 callersFunctionrun_uncertainty_on_fold
(proba_dir, raw_path,score_type , labels , output_pred_path = False)
weak_segmentation/nnunetv2/nnunet/run_uncertainty_on_fold.py:13
↓ 1 callersFunctionrun_uncertainty_on_fold_entry
()
weak_segmentation/nnunetv2/nnunet/run_uncertainty_on_fold.py:93
↓ 1 callersFunctionsave_config_for_hf
( model, config_path: str, model_config: Optional[dict] )
biomedclip_finetuning/open_clip/src/open_clip/push_to_hf_hub.py:35
↓ 1 callersFunctionsave_for_hf
( model, tokenizer: HFTokenizer, model_config: dict, save_directory: str, safe_serializati
biomedclip_finetuning/open_clip/src/open_clip/push_to_hf_hub.py:58
↓ 1 callersMethodsave_plans
(self, plans)
weak_segmentation/nnunetv2/experiment_planning/experiment_planners/default_experiment_planner.py:502
↓ 1 callersFunctionsave_summary_json
stupid json does not support tuples as keys (why does it have to be so shitty) so we need to convert that shit ourselves
weak_segmentation/nnunetv2/evaluation/evaluate_predictions.py:34
↓ 1 callersFunctionscoremap2bbox
(scoremap, multi_contour_eval=False)
segment-anything/prompt_sam.py:153
↓ 1 callersFunctionseed_all
(seed = 0)
biomedclip_finetuning/open_clip/tests/util_test.py:12
↓ 1 callersMethodselect_masks
( self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int )
segment-anything/segment_anything/utils/onnx.py:92
↓ 1 callersFunctionset_device
(device)
biomedclip_finetuning/open_clip/src/open_clip_train/distributed.py:47
↓ 1 callersMethodset_epoch
(self, epoch)
biomedclip_finetuning/open_clip/src/open_clip_train/data.py:67
↓ 1 callersMethodset_language
(self, src_lang)
biomedclip_finetuning/open_clip/src/open_clip/tokenizer.py:457
↓ 1 callersFunctionset_model_preprocess_cfg
(model, preprocess_cfg: Dict[str, Any])
biomedclip_finetuning/open_clip/src/open_clip/model.py:608
↓ 1 callersMethodset_torch_image
Calculates the image embeddings for the provided image, allowing masks to be predicted with the 'predict' method. Expects the input
segment-anything/segment_anything/predictor.py:63
↓ 1 callersMethodset_value
(self, epoch)
biomedclip_finetuning/open_clip/src/open_clip_train/data.py:54
↓ 1 callersFunctionsetup_ddp
(rank, world_size)
weak_segmentation/nnunetv2/run/run_training.py:108
↓ 1 callersFunctionsetup_logging
(log_file, level, include_host=False)
biomedclip_finetuning/open_clip/src/open_clip_train/logger.py:4
↓ 1 callersMethodshape
(self)
saliency_maps/scripts/iba.py:44
↓ 1 callersFunctionsparsify_segmentation
(seg: np.ndarray, label_manager: LabelManager, percent_of_slices: float)
weak_segmentation/nnunetv2/dataset_conversion/datasets_for_integration_tests/Dataset996_IntegrationTest_Hippocampus_regions_ignore.py:13
↓ 1 callersFunctionstart_sync_process
(sync_every, local_dir, remote_dir, protocol)
biomedclip_finetuning/open_clip/src/open_clip_train/file_utils.py:59
↓ 1 callersMethodstem
(self, x)
biomedclip_finetuning/open_clip/src/open_clip/modified_resnet.py:166
↓ 1 callersMethodtext_heatmap
(self, text_t, image_t)
saliency_maps/scripts/iba.py:146
↓ 1 callersFunctiontext_heatmap_iba
(text_t, image_t, model, layer_idx, beta, var, lr=1, train_steps=10, progbar=True)
saliency_maps/scripts/methods.py:45
↓ 1 callersFunctionto_numpy
(tensor)
segment-anything/scripts/export_onnx_model.py:170
↓ 1 callersMethodto_numpy
(self)
segment-anything/segment_anything/utils/amg.py:72
↓ 1 callersFunctiontrace_model
(model, batch_size=256, device=torch.device('cpu'))
biomedclip_finetuning/open_clip/src/open_clip/model.py:509
↓ 1 callersFunctiontrain_one_epoch
(model, data, loss, epoch, optimizer, scaler, scheduler, dist_model, args, tb_writer=None)
biomedclip_finetuning/open_clip/src/open_clip_train/train.py:64
↓ 1 callersFunctionuncrop_masks
( masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int )
segment-anything/segment_anything/utils/amg.py:254
↓ 1 callersFunctionuncrop_points
(points: torch.Tensor, crop_box: List[int])
segment-anything/segment_anything/utils/amg.py:245
↓ 1 callersFunctionunwrap_model
(model)
biomedclip_finetuning/open_clip/src/open_clip_train/train.py:50
↓ 1 callersMethodvalidation_step
(self, batch: dict)
weak_segmentation/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py:909
↓ 1 callersMethodvalidation_step
(self, batch: dict)
weak_segmentation/nnunetv2/training/nnUNetTrainer/variants/network_architecture/nnUNetTrainerNoDeepSupervision.py:55
↓ 1 callersMethodvision_heatmap
(self, text_t, image_t)
saliency_maps/scripts/iba.py:152
↓ 1 callersFunctionwindow_partition
Partition into non-overlapping windows with padding if needed. Args: x (tensor): input tokens with [B, H, W, C]. window_s
segment-anything/segment_anything/modeling/image_encoder.py:243
↓ 1 callersFunctionwindow_unpartition
Window unpartition into original sequences and removing padding. Args: windows (tensor): input tokens with [B * num_windows, windo
segment-anything/segment_anything/modeling/image_encoder.py:267
↓ 1 callersFunctionwrite_mask_to_folder
(mask , t_mask, path: str,num_contours)
segment-anything/prompt_sam.py:131
↓ 1 callersFunctionwrite_masks_to_folder
(masks: List[Dict[str, Any]], path: str)
segment-anything/scripts/amg.py:154
↓ 1 callersFunctionzero_shot_eval
(model, data, epoch, args, tokenizer=None)
biomedclip_finetuning/open_clip/src/open_clip_train/zero_shot.py:45
MethodUNet_base_num_features
(self)
weak_segmentation/nnunetv2/utilities/plans_handling/plans_handler.py:97
MethodUNet_class
(self)
weak_segmentation/nnunetv2/utilities/plans_handling/plans_handler.py:85
MethodUNet_class_name
(self)
weak_segmentation/nnunetv2/utilities/plans_handling/plans_handler.py:80
Method__call__
(self, model_output)
saliency_maps/scripts/utils.py:52
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