Data

Data subpackage has data preprocessers and dataloader abstractions.

Scripts

You can use scripts typing catalyst-data in your terminal. For example:

$ catalyst-data tag2label --help

Augmentor

class catalyst.data.augmentor.Augmentor(dict_key: str, augment_fn: Callable, input_key: str = None, output_key: str = None, **kwargs)

Augmentation abstraction to use with data dictionaries.

__init__(dict_key: str, augment_fn: Callable, input_key: str = None, output_key: str = None, **kwargs)

Parameters:

• dict_key (str) – key to transform
• augment_fn (Callable) – augmentation function to use
• input_key (str) – augment_fn input key
• output_key (str) – augment_fn output key
• **kwargs – default kwargs for augmentations function

class catalyst.data.augmentor.AugmentorCompose(key2augment_fn: Dict[str, Callable])

Compose augmentors.

__init__(key2augment_fn: Dict[str, Callable])

Parameters:key2augment_fn (Dict[str, Callable]) – mapping from input key to augmentation function to apply

class catalyst.data.augmentor.AugmentorKeys(dict2fn_dict: Union[Dict[str, str], List[str]], augment_fn: Callable)

Augmentation abstraction to match input and augmentations keys.

__init__(dict2fn_dict: Union[Dict[str, str], List[str]], augment_fn: Callable)

Parameters:

• dict2fn_dict (Dict[str, str]) – keys matching dict {input_key: augment_fn_key}. For example: {"image": "image", "mask": "mask"}
• augment_fn – augmentation function

Collate Functions

Dataset

Reader

Readers are the abstraction for your dataset. They can open an elem from the dataset and transform it to data, needed by your network. For example open image by path, or read string and tokenize it.

Sampler