otx.core.data#
Module for data related objects, such as OTXDataset, OTXDataModule, and Transforms.
Classes
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LightningDataModule extension for OTX pipeline. |
Factory class for OTXDataset. |
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Factory class for transform. |
- class otx.core.data.OTXDataModule(task: OTXTaskType, data_format: str, data_root: str, train_subset: SubsetConfig, val_subset: SubsetConfig, test_subset: SubsetConfig, unlabeled_subset: UnlabeledDataConfig = UnlabeledDataConfig(batch_size=0, subset_name='unlabeled', transforms={}, transform_lib_type=<TransformLibType.TORCHVISION: 'TORCHVISION'>, num_workers=2, sampler=SamplerConfig(class_path='torch.utils.data.RandomSampler', init_args={}), to_tv_image=True, input_size=None, data_root=None, data_format='image_dir'), tile_config: TileConfig = TileConfig(enable_tiler=False, enable_adaptive_tiling=True, tile_size=(400, 400), overlap=0.2, iou_threshold=0.45, max_num_instances=1500, object_tile_ratio=0.03, sampling_ratio=1.0, with_full_img=False), vpm_config: VisualPromptingConfig = VisualPromptingConfig(use_bbox=False, use_point=False), mem_cache_size: str = '1GB', mem_cache_img_max_size: tuple[int, int] | None = None, image_color_channel: ImageColorChannel = ImageColorChannel.RGB, stack_images: bool = True, include_polygons: bool = False, ignore_index: int = 255, unannotated_items_ratio: float = 0.0, auto_num_workers: bool = False, device: DeviceType = DeviceType.auto, input_size: int | tuple[int, int] | None = None)[source]#
Bases:
LightningDataModule
LightningDataModule extension for OTX pipeline.
Constructor.
- unlabeled_dataloader() dict[str, DataLoader] | None [source]#
Returns a dictionary of unlabeled dataloaders.
The method creates and returns dataloaders for unlabeled datasets based on the configuration settings. If the data root is not specified in the configuration, it returns None.
- property hparams_initial: AttributeDict#
The collection of hyperparameters saved with save_hyperparameters(). It is read-only.
The reason why we override is that we have some custom resolvers for DictConfig. Some resolved Python objects has not a primitive type, so that is not loggable without errors. Therefore, we need to unresolve it this time.
- class otx.core.data.OTXDatasetFactory[source]#
Bases:
object
Factory class for OTXDataset.
- classmethod create(task: OTXTaskType, dm_subset: DmDataset, cfg_subset: SubsetConfig, mem_cache_handler: MemCacheHandlerBase, mem_cache_img_max_size: tuple[int, int] | None = None, image_color_channel: ImageColorChannel = ImageColorChannel.RGB, stack_images: bool = True, include_polygons: bool = False, ignore_index: int = 255, vpm_config: VisualPromptingConfig = VisualPromptingConfig(use_bbox=False, use_point=False)) OTXDataset [source]#
Create OTXDataset.