mnist.py

# coding=utf-8
# Copyright 2021 The OneFlow Authors. All rights reserved.
#
# 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
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# 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.
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from typing import Callable, Optional

import oneflow as flow
from flowvision import datasets

from libai.data.structures import DistTensorData, Instance


class MNISTDataset(datasets.MNIST):
    r"""`MNIST <http://yann.lecun.com/exdb/mnist/>`_ Dataset in LiBai.

    Args:
        root (string): Root directory of dataset where ``MNIST/processed/training.pt``
            and  ``MNIST/processed/test.pt`` exist.
        train (bool, optional): If True, creates dataset from ``training.pt``,
            otherwise from ``test.pt``.
        download (bool, optional): If true, downloads the dataset from the internet and
            puts it in root directory. If the dataset is already downloaded, it will not be
            downloaded again.
        transform (callable, optional): A function/transform that  takes in a PIL image
            and returns a transformed version. E.g, ``transforms.RandomCrop``
        dataset_name (str, optional): Name for the dataset as an identifier. E.g, ``mnist``
    """

    def __init__(
        self,
        root: str,
        train: bool = True,
        transform: Optional[Callable] = None,
        download: bool = False,
        **kwargs
    ):
        super(MNISTDataset, self).__init__(
            root=root, train=train, transform=transform, download=download, **kwargs
        )

    def __getitem__(self, index: int):
        img, target = super().__getitem__(index)
        data_sample = Instance(
            images=DistTensorData(img, placement_idx=0),
            labels=DistTensorData(flow.tensor(target, dtype=flow.long), placement_idx=-1),
        )
        return data_sample