Data API

KonfAI datasets are built from group definitions, transforms, augmentations, and optional patching strategies.

Dataset configuration objects

class konfai.data.data_manager.DataTrain(dataset_filenames=['default|./Dataset:mha'], groups_src={'default|Labels': {'default|Labels': {'transforms': [], 'patch_transforms': []}}}, augmentations={'DataAugmentation_0': <konfai.data.augmentation.DataAugmentationsList object>}, inline_augmentations=False, patch=<konfai.data.patching.DatasetPatch object>, use_cache=True, subset=<konfai.data.data_manager.TrainSubset object>, batch_size=1, validation=0.2, num_workers=None, pin_memory=False, prefetch_factor=None, persistent_workers=None)

Bases: Data

Dataset configuration used by the training workflow.

class konfai.data.data_manager.DataPrediction(dataset_filenames=['default|./Dataset'], groups_src={'default': {'default|Labels': {'transforms': [], 'patch_transforms': []}}}, augmentations={'DataAugmentation_0': <konfai.data.augmentation.DataAugmentationsList object>}, patch=<konfai.data.patching.DatasetPatch object>, subset=<konfai.data.data_manager.PredictionSubset object>, batch_size=1, num_workers=None, pin_memory=False, prefetch_factor=None, persistent_workers=None)

Bases: Data

Dataset configuration used by the prediction workflow.

class konfai.data.data_manager.DataMetric(dataset_filenames=['default|./Dataset:mha'], groups_src={'default': {'default|group_dest': {'transforms': [], 'patch_transforms': []}}}, subset=<konfai.data.data_manager.PredictionSubset object>, validation=None, num_workers=None, pin_memory=False, prefetch_factor=None, persistent_workers=None)

Bases: Data

Dataset configuration used by the evaluation workflow.

class konfai.data.data_manager.DatasetIter(rank, data, mapping, groups_src, inline_augmentations, data_augmentations_list, patch_size, overlap, buffer_size, use_cache=True)

Bases: Dataset

Torch dataset view over KonfAI dataset managers and patch mappings.

Patching

class konfai.data.patching.DatasetPatch(patch_size=[128, 128, 128], overlap=None, pad_value=None, extend_slice=0)

Bases: Patch

Patch definition applied when sampling data from datasets.

class konfai.data.patching.ModelPatch(patch_size=[128, 128, 128], overlap=None, patch_combine=None, pad_value=None, extend_slice=0)

Bases: Patch

Patch definition applied inside model graphs during prediction or training.

class konfai.data.patching.DatasetManager(index, group_src, group_dest, name, dataset, patch, transforms, data_augmentations_list)

Bases: object

Cache-backed manager for one dataset case and one source/destination group.

class konfai.data.patching.Accumulator(patch_slices, patch_size, patch_combine=None, batch=True)

Bases: object

Accumulate patch predictions and reassemble them into a full tensor.

Transforms and metadata

class konfai.data.transform.Transform

Bases: NeedDevice, ABC

Base class for transforms operating on tensors and cached attributes.

class konfai.data.transform.TransformInverse(inverse)

Bases: Transform, ABC

Base class for transforms that can also invert their effect.

class konfai.data.transform.TransformLoader

Bases: object

Resolve and instantiate transform classes from KonfAI configuration.

class konfai.data.transform.Clip(min_value=-1024, max_value=1024, save_clip_min=False, save_clip_max=False, mask=None)

Bases: Transform

Clip tensor intensities to a fixed or data-dependent value range.

class konfai.data.transform.Normalize(lazy=False, channels=None, min_value=-1, max_value=1, inverse=True)

Bases: TransformInverse

Map intensities to a target min/max interval and optionally invert it.

class konfai.data.transform.Standardize(lazy=False, mean=None, std=None, mask=None, inverse=True)

Bases: TransformInverse

Standardize tensors using cached or computed mean and standard deviation.

Dataset utilities

class konfai.utils.dataset.Attribute(attributes=None)

Bases: dict[str, Any]

Metadata container storing repeated values with a stack-like naming scheme.

pop(key, default=None)

If the key is not found, return the default if given; otherwise, raise a KeyError.

Return type:

Any

class konfai.utils.dataset.Dataset(filename, file_format)

Bases: object

Filesystem or HDF5-backed dataset abstraction used across KonfAI.

class AbstractFile

Bases: ABC

class H5File(filename, read)

Bases: AbstractFile

class SitkFile(filename, read, file_format)

Bases: AbstractFile

konfai.utils.dataset.data_to_image(data, attributes)

Convert a NumPy array and KonfAI attributes into a SimpleITK image.

Return type:

Image

konfai.utils.dataset.image_to_data(image)

Convert a SimpleITK image into a channel-first NumPy array and attributes.

Return type:

tuple[ndarray, Attribute]

konfai.utils.dataset.get_infos(filename)

Read shape and metadata from an image file without loading its full pixel data.

Return type:

tuple[list[int], Attribute]

See also

• Datasets and groups

• Segmentation example

• Synthesis example