trw.datasets.mnist_cluttered

Module Contents

Functions

_clutter(images, cluttered_size, clutter_window, nb_clutter_windows, normalization_factor)

create_mnist_cluttered_datasset(batch_size: int = 1000, cluttered_size: trw.basic_typing.ShapeX = (64, 64), clutter_window: trw.basic_typing.ShapeX = (6, 6), nb_clutter_windows: int = 16, root: Optional[str] = None, train_transforms: List[trw.transforms.Transform] = None, test_transforms: List[trw.transforms.Transform] = None, nb_workers: int = 5, data_processing_batch_size: int = 200, normalize_0_1: bool = False) → Tuple[trw.basic_typing.Datasets, trw.basic_typing.DatasetsInfo]

param batch_size
trw.datasets.mnist_cluttered._clutter(images, cluttered_size, clutter_window, nb_clutter_windows, normalization_factor)
trw.datasets.mnist_cluttered.create_mnist_cluttered_datasset(batch_size: int = 1000, cluttered_size: trw.basic_typing.ShapeX = (64, 64), clutter_window: trw.basic_typing.ShapeX = (6, 6), nb_clutter_windows: int = 16, root: Optional[str] = None, train_transforms: List[trw.transforms.Transform] = None, test_transforms: List[trw.transforms.Transform] = None, nb_workers: int = 5, data_processing_batch_size: int = 200, normalize_0_1: bool = False) → Tuple[trw.basic_typing.Datasets, trw.basic_typing.DatasetsInfo]
Parameters

  • batch_size –

  • cluttered_size – the size of the final image

  • root –

  • clutter_window – the size of the random windows to create the clutter

  • nb_clutter_windows – the number of clutter windows added to the image

  • train_transforms – the transform function applied on the training batches

  • test_transforms – the transform function applied on the test batches

  • nb_workers – the number of workers to preprocess the dataset

  • data_processing_batch_size – the number of samples each worker process at once

  • normalize_0_1 – if True, the pixels will be in range [0..1]

Returns

datasets