mnist.py

"""
(C) Copyright 2021 IBM Corp.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Created on June 30, 2021

"""
from typing import Optional, Sequence

from torchvision import datasets, transforms

from fuse.data.datasets.dataset_default import DatasetDefault
from fuse.data.datasets.dataset_wrap_seq_to_dict import DatasetWrapSeqToDict


class MNIST:
    """
    FuseMedML style of MNIST dataset: http://yann.lecun.com/exdb/mnist/
    """

    # bump whenever the static pipeline modified
    MNIST_DATASET_VER = 0

    @staticmethod
    def dataset(
        cache_dir: Optional[str] = None, train: bool = True, sample_ids: Sequence = None
    ) -> DatasetDefault:
        """
        Get mnist dataset - each sample includes: 'data.image', 'data.label' and 'data.sample_id'
        :param cache_dir: optional - destination to cache mnist
        :param train: If True, creates dataset from ``train-images-idx3-ubyte``,
            otherwise from ``t10k-images-idx3-ubyte``.
        :param sample_ids: Optional list of sample ids. If None, then all data is used.
        """

        transform = transforms.Compose(
            [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
        )

        # Create dataset
        torch_dataset = datasets.MNIST(
            cache_dir, download=cache_dir is not None, train=train, transform=transform
        )

        # wrapping torch dataset
        name_str = "train" if train else "test"
        wrapped_dataset = DatasetWrapSeqToDict(
            name=f"mnist-{name_str}",
            dataset=torch_dataset,
            sample_keys=("data.image", "data.label"),
            sample_ids=sample_ids,
        )

        wrapped_dataset.create()
        return wrapped_dataset