# 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
#
# 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.
from typing import Callable, Optional
import oneflow as flow
from flowvision import datasets
from libai.data.structures import DistTensorData, Instance
[docs]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