neodroidvision.data.classification.mnist.MNISTDataset

class neodroidvision.data.classification.mnist.MNISTDataset(data_dir: Path, split: SplitEnum = SplitEnum.training)[source]

Bases: SupervisedDataset

description

__init__(data_dir: Path, split: SplitEnum = SplitEnum.training)[source]

Methods

__init__(data_dir[, split])

get_test_loader(data_dir, batch_size, *[, ...])

Test datalaoder.

get_train_valid_loader(data_dir, *, ...[, ...])

Train and validation data loaders.

plot_images(images, label)

param images

sample()

description

Attributes

inverse_transform

predictor_shape

return: :rtype:

response_shape

return: :rtype:

split_names

return: :rtype:

trans

static get_test_loader(data_dir: Path, batch_size: int, *, num_workers: int = 0, pin_memory: bool = False, using_cuda: bool = True) → DataLoader[source]

Test datalaoder.

If using CUDA, num_workers should be set to 1 and pin_memory to True.

Args: data_dir: path directory to the dataset. batch_size: how many samples per batch to load. num_workers: number of subprocesses to use when loading the dataset. pin_memory: whether to copy tensors into CUDA pinned memory. Set it to

True if using GPU.

Parameters

  • data_dir –

  • batch_size –

  • num_workers –

  • pin_memory –

  • using_cuda –

static get_train_valid_loader(data_dir: Path, *, batch_size: int, random_seed: int, valid_size: float = 0.1, shuffle: bool = True, num_workers: int = 0, pin_memory: bool = False, using_cuda: bool = True) → Tuple[DataLoader, DataLoader][source]

Train and validation data loaders.

If using CUDA, num_workers should be set to 1 and pin_memory to True.

Args: data_dir: path directory to the dataset. batch_size: how many samples per batch to load. random_seed: fix seed for reproducibility. valid_size: percentage split of the training set used for

the validation set. Should be a float in the range [0, 1]. In the paper, this number is set to 0.1.

shuffle: whether to shuffle the train/validation indices. show_sample: plot 9x9 sample grid of the dataset. num_workers: number of subprocesses to use when loading the dataset. pin_memory: whether to copy tensors into CUDA pinned memory. Set it to

True if using GPU. :param data_dir: :type data_dir: :param batch_size: :type batch_size: :param random_seed: :type random_seed: :param valid_size: :type valid_size: :param shuffle: :type shuffle: :param num_workers: :type num_workers: :param pin_memory: :type pin_memory: :param using_cuda: :type using_cuda:

static plot_images(images: ndarray, label: Sequence) → None[source]
Parameters

  • images –

  • label –

property predictor_shape: Tuple[int, ...]

return: :rtype:

property response_shape: Tuple[int, ...]

return: :rtype:

sample() → None[source]

description

property split_names: Dict[SplitEnum, str]

return: :rtype: