Functions — github.com/Nota-NetsPresso/netspresso-trainer

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Functions1,065 in github.com/Nota-NetsPresso/netspresso-trainer

⨍ Functions 1,065◇ Types & classes 251

↓ 1 callersMethodvisualize

(self, image)

tools/device_runtime/visualizers/detection_visualizer.py:77

↓ 1 callersMethodweighted_bce

(bd_pre, target)

src/netspresso_trainer/losses/segmentation/pidnet.py:128

↓ 1 callersMethodwith_pos_embed

(tensor, pos_embed)

src/netspresso_trainer/models/necks/experimental/rtdetr_hybrid_encoder.py:59

↓ 1 callersFunctionyaml_for_logging

(config: DictConfig)

src/netspresso_trainer/utils/logger.py:103

Method__call__

(self, outputs, original_shape)

tools/device_runtime/postprocessors/detection_postprocessor.py:130

Method__call__

(self, img)

tools/device_runtime/preprocessors/preprocessor.py:73

Method__call__

(self, predictions: List[dict], targets: List[dict])

src/netspresso_trainer/metrics/pose_estimation/metric.py:31

Method__call__

(self, predictions: List[dict], targets: List[dict])

src/netspresso_trainer/metrics/detection/metric.py:270

Method__call__

(self, predictions: List[dict], targets: List[dict])

src/netspresso_trainer/metrics/classification/metric.py:41

Method__call__

(self, predictions: List[dict], targets: List[dict])

src/netspresso_trainer/metrics/segmentation/metric.py:59

Method__call__

(self, outputs: ModelOutput, k: Optional[int]=None)

src/netspresso_trainer/postprocessors/classification.py:29

Method__call__

(self, outputs: ModelOutput, original_shape)

src/netspresso_trainer/postprocessors/detection.py:303

Method__call__

(self, outputs: ModelOutput)

src/netspresso_trainer/postprocessors/pose_estimation.py:66

Method__call__

(self, outputs: ModelOutput, original_shape)

src/netspresso_trainer/postprocessors/segmentation.py:28

Method__call__

(self, image, label=None, mask=None, bbox=None, keypoint=None, dataset=None)

src/netspresso_trainer/dataloaders/augmentation/custom/mosaic.py:189

Method__call__

(self, samples, targets)

src/netspresso_trainer/dataloaders/augmentation/custom/mixing.py:53

Method__call__

(self, samples, targets)

src/netspresso_trainer/dataloaders/augmentation/custom/mixing.py:134

Method__call__

(self, samples, targets)

src/netspresso_trainer/dataloaders/augmentation/custom/mixing.py:231

Method__call__

(self, image, label=None, mask=None, bbox=None, keypoint=None, visualize_for_debug=False, dataset=None, **kwar

src/netspresso_trainer/dataloaders/augmentation/custom/image_proc.py:57

Method__call__

(self, image, label=None, mask=None, bbox=None, keypoint=None, dataset=None)

src/netspresso_trainer/dataloaders/augmentation/custom/image_proc.py:109

Method__call__

(self, image, label=None, mask=None, bbox=None, keypoint=None, dataset=None)

src/netspresso_trainer/dataloaders/augmentation/custom/image_proc.py:168

Method__call__

(self, image, label=None, mask=None, bbox=None, keypoint=None, dataset=None)

src/netspresso_trainer/dataloaders/augmentation/custom/image_proc.py:194

Method__call__

(self, image, label=None, mask=None, bbox=None, keypoint=None, dataset=None)

src/netspresso_trainer/dataloaders/augmentation/custom/image_proc.py:229

Method__call__

(self, image, label=None, mask=None, bbox=None, keypoint=None, dataset=None)

src/netspresso_trainer/dataloaders/augmentation/custom/image_proc.py:333

Method__call__

(self, image, label=None, mask=None, bbox=None, keypoint=None, dataset=None)

src/netspresso_trainer/dataloaders/augmentation/custom/image_proc.py:496

Method__call__

(self, image, label=None, mask=None, bbox=None, keypoint=None, dataset=None)

src/netspresso_trainer/dataloaders/augmentation/custom/image_proc.py:573

Method__call__

(self, image, label=None, mask=None, bbox=None, keypoint=None, dataset=None)

src/netspresso_trainer/dataloaders/augmentation/custom/image_proc.py:736

Method__call__

@illian01: Count random resized samples. If one batch completed, randomly reset target size and set counter to 0.

src/netspresso_trainer/dataloaders/augmentation/custom/image_proc.py:777

Method__call__

(self, image, label=None, mask=None, bbox=None, keypoint=None, dataset=None)

src/netspresso_trainer/dataloaders/augmentation/custom/image_proc.py:819

Method__call__

(self, image, label=None, mask=None, bbox=None, keypoint=None, dataset=None)

src/netspresso_trainer/dataloaders/augmentation/custom/image_proc.py:934

Method__call__

(self, image, label=None, mask=None, bbox=None, keypoint=None, dataset=None)

src/netspresso_trainer/dataloaders/augmentation/custom/image_proc.py:984

Method__call__

(self, image, label=None, mask=None, bbox=None, keypoint=None, dataset=None)

src/netspresso_trainer/dataloaders/augmentation/custom/image_proc.py:1000

Method__call__

(self, x, targets=None)

src/netspresso_trainer/models/base.py:153

Method__call__

Perform inference on the input tensor. Args: x (Union[np.ndarray, torch.Tensor]): Input tensor targets: Not

src/netspresso_trainer/models/base.py:214

Method__call__

Matches each target to the most suitable anchor. 1. For each anchor prediction, find the highest suitability targets. 2. Match target

src/netspresso_trainer/losses/detection/yolov9.py:194

Method__call__

( self, prefix: Literal['training', 'validation', 'evaluation', 'inference'], epoch: O

src/netspresso_trainer/loggers/tensorboard.py:118

Method__call__

( self, prefix: Literal['training', 'validation', 'evaluation', 'inference'], epoch: O

src/netspresso_trainer/loggers/image.py:56

Method__call__

( self, prefix: Literal['training', 'validation', 'evaluation', 'inference'], epoch: O

src/netspresso_trainer/loggers/stdout.py:32

Method__call__

(self, original_images: List, pred_or_target: List)

src/netspresso_trainer/loggers/visualizer.py:52

Method__call__

(self, original_images: List, pred_or_target: List)

src/netspresso_trainer/loggers/visualizer.py:99

Method__call__

(self, original_images: List, pred_or_target: List)

src/netspresso_trainer/loggers/visualizer.py:158

Method__call__

(self, results, images=None)

src/netspresso_trainer/loggers/visualizer.py:170

Method__call__

( self, prefix: Literal["training", "validation", "evaluation", "inference"], epoch: O

src/netspresso_trainer/loggers/mlflow.py:163

Method__enter__

(self)

src/netspresso_trainer/schedulers/cosine_warm_restart.py:181

Method__exit__

(self, type, value, traceback)

src/netspresso_trainer/schedulers/cosine_warm_restart.py:185

Method__getitem__

(self, idx)

tools/scheduler_test.py:52

Method__getitem__

(self, index)

src/netspresso_trainer/dataloaders/classification.py:160

Method__getitem__

(self, index)

src/netspresso_trainer/dataloaders/classification.py:223

Method__getitem__

(self, index)

src/netspresso_trainer/dataloaders/detection.py:148

Method__getitem__

(self, index)

src/netspresso_trainer/dataloaders/base.py:49

Method__getitem__

(self, index)

src/netspresso_trainer/dataloaders/base.py:106

Method__getitem__

(self, index)

src/netspresso_trainer/dataloaders/pose_estimation.py:140

Method__getitem__

(self, index)

src/netspresso_trainer/dataloaders/segmentation.py:180

Method__getitem__

(self, index)

src/netspresso_trainer/dataloaders/segmentation.py:275

Method__init__

(self, samples=100)

tools/scheduler_test.py:46

Method__init__

(self, class_map=None)

tools/device_runtime/visualizers/detection_visualizer.py:43

Method__init__

(self, postprocess_conf)

tools/device_runtime/postprocessors/detection_postprocessor.py:126

Method__init__

(self)

tools/device_runtime/dataloaders/cam_loader.py:20

Method__init__

(self, preprocess_conf)

tools/device_runtime/preprocessors/preprocessor.py:63

Method__init__

(self, name: str, fmt=':f')

src/netspresso_trainer/utils/record.py:27

Method__init__

(self, name: str, fmt=':f')

src/netspresso_trainer/utils/record.py:56

Method__init__

(self)

src/netspresso_trainer/utils/record.py:83

Method__init__

(self)

src/netspresso_trainer/utils/record.py:103

Method__init__

(self, model, decay, device=None)

src/netspresso_trainer/utils/model_ema.py:26

Method__init__

(self, model, decay, beta, device=None)

src/netspresso_trainer/utils/model_ema.py:44

Method__init__

(self, metric_name, num_classes, classwise_analysis, **kwargs)

src/netspresso_trainer/metrics/base.py:25

Method__init__

(self, task, metrics, metric_adaptor, classwise_analysis)

src/netspresso_trainer/metrics/base.py:38

Method__init__

(self, metric_names)

src/netspresso_trainer/metrics/pose_estimation/metric.py:28

Method__init__

(self, num_classes, classwise_analysis, **kwargs)

src/netspresso_trainer/metrics/pose_estimation/metric.py:36

Method__init__

(self, metric_names)

src/netspresso_trainer/metrics/detection/metric.py:267

Method__init__

(self, num_classes, classwise_analysis, **kwargs)

src/netspresso_trainer/metrics/detection/metric.py:327

Method__init__

(self, num_classes, classwise_analysis, **kwargs)

src/netspresso_trainer/metrics/detection/metric.py:349

Method__init__

(self, num_classes, classwise_analysis, **kwargs)

src/netspresso_trainer/metrics/detection/metric.py:371

Method__init__

(self, num_classes, classwise_analysis, **kwargs)

src/netspresso_trainer/metrics/detection/metric.py:391

Method__init__

(self, metric_names)

src/netspresso_trainer/metrics/classification/metric.py:38

Method__init__

(self, num_classes, classwise_analysis, **kwargs)

src/netspresso_trainer/metrics/classification/metric.py:69

Method__init__

(self, num_classes, name: str, fmt=':f')

src/netspresso_trainer/metrics/segmentation/metric.py:30

Method__init__

(self, metric_names)

src/netspresso_trainer/metrics/segmentation/metric.py:56

Method__init__

(self, num_classes, classwise_analysis, ignore_index=IGNORE_INDEX_NONE_VALUE, **kwargs)

src/netspresso_trainer/metrics/segmentation/metric.py:110

Method__init__

( self, conf: DictConfig, task: str, task_processor: BaseTaskProcessor,

src/netspresso_trainer/pipelines/train.py:51

Method__init__

( self, conf: DictConfig, task: str, task_processor: BaseTaskProcessor,

src/netspresso_trainer/pipelines/evaluation.py:41

Method__init__

( self, conf: DictConfig, task: str, task_processor: BaseTaskProcessor,

src/netspresso_trainer/pipelines/base.py:30

Method__init__

( self, conf: DictConfig, task: str, task_processor: BaseTaskProcessor,

src/netspresso_trainer/pipelines/inference.py:39

Method__init__

(self, conf, postprocessor, devices, **kwargs)

src/netspresso_trainer/pipelines/task_processors/classification.py:29

Method__init__

(self, conf, postprocessor, devices, **kwargs)

src/netspresso_trainer/pipelines/task_processors/detection.py:29

Method__init__

(self, conf, postprocessor, devices, **kwargs)

src/netspresso_trainer/pipelines/task_processors/base.py:26

Method__init__

(self, conf, postprocessor, devices, **kwargs)

src/netspresso_trainer/pipelines/task_processors/pose_estimation.py:29

Method__init__

(self, conf, postprocessor, devices, **kwargs)

src/netspresso_trainer/pipelines/task_processors/segmentation.py:30

Method__init__

(self, conf_model)

src/netspresso_trainer/postprocessors/classification.py:26

Method__init__

(self, conf_model)

src/netspresso_trainer/postprocessors/detection.py:280

Method__init__

(self, conf_model)

src/netspresso_trainer/postprocessors/pose_estimation.py:29

Method__init__

(self, conf_model)

src/netspresso_trainer/postprocessors/segmentation.py:26

Method__init__

(self, conf_data, train_valid_split_ratio)

src/netspresso_trainer/dataloaders/classification.py:40

Method__init__

(self, conf_data, conf_augmentation, model_name, idx_to_class, split, samples, transform=None

src/netspresso_trainer/dataloaders/classification.py:135

Method__init__

(self, conf_data, conf_augmentation, model_name, idx_to_class, split, samples, transform=None

src/netspresso_trainer/dataloaders/detection.py:102

Method__init__

(self, conf_data, conf_augmentation, model_name, idx_to_class, split, samples, transform, **kwargs)

src/netspresso_trainer/dataloaders/base.py:30

Method__init__

(self, conf_data, train_valid_split_ratio)

src/netspresso_trainer/dataloaders/base.py:135

Method__init__

(self, conf_data, conf_augmentation, model_name, idx_to_class, split, samples, transform=None

src/netspresso_trainer/dataloaders/pose_estimation.py:93

Method__init__

(self, conf_data, train_valid_split_ratio)

src/netspresso_trainer/dataloaders/segmentation.py:39

Method__init__

(self, conf_data, conf_augmentation, model_name, idx_to_class, split, samples, transform=None

src/netspresso_trainer/dataloaders/segmentation.py:146

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