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223 lines
8.6 KiB
223 lines
8.6 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import os
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import gzip
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import struct
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import numpy as np
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from PIL import Image
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import paddle
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from paddle.io import Dataset
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from paddle.dataset.common import _check_exists_and_download
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__all__ = ["MNIST", "FashionMNIST"]
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class MNIST(Dataset):
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"""
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Implementation of `MNIST <http://yann.lecun.com/exdb/mnist/>`_ dataset
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Args:
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image_path(str): path to image file, can be set None if
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:attr:`download` is True. Default None
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label_path(str): path to label file, can be set None if
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:attr:`download` is True. Default None
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mode(str): 'train' or 'test' mode. Default 'train'.
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download(bool): whether to download dataset automatically if
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:attr:`image_path` :attr:`label_path` is not set. Default True
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backend(str, optional): Specifies which type of image to be returned:
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PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
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If this option is not set, will get backend from ``paddle.vsion.get_image_backend`` ,
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default backend is 'pil'. Default: None.
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Returns:
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Dataset: MNIST Dataset.
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Examples:
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.. code-block:: python
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from paddle.vision.datasets import MNIST
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mnist = MNIST(mode='test')
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for i in range(len(mnist)):
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sample = mnist[i]
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print(sample[0].size, sample[1])
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"""
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NAME = 'mnist'
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URL_PREFIX = 'https://dataset.bj.bcebos.com/mnist/'
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TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
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TEST_IMAGE_MD5 = '9fb629c4189551a2d022fa330f9573f3'
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TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
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TEST_LABEL_MD5 = 'ec29112dd5afa0611ce80d1b7f02629c'
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TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz'
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TRAIN_IMAGE_MD5 = 'f68b3c2dcbeaaa9fbdd348bbdeb94873'
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TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz'
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TRAIN_LABEL_MD5 = 'd53e105ee54ea40749a09fcbcd1e9432'
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def __init__(self,
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image_path=None,
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label_path=None,
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mode='train',
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transform=None,
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download=True,
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backend=None):
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assert mode.lower() in ['train', 'test'], \
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"mode should be 'train' or 'test', but got {}".format(mode)
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if backend is None:
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backend = paddle.vision.get_image_backend()
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if backend not in ['pil', 'cv2']:
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raise ValueError(
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"Expected backend are one of ['pil', 'cv2'], but got {}"
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.format(backend))
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self.backend = backend
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self.mode = mode.lower()
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self.image_path = image_path
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if self.image_path is None:
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assert download, "image_path is not set and downloading automatically is disabled"
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image_url = self.TRAIN_IMAGE_URL if mode == 'train' else self.TEST_IMAGE_URL
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image_md5 = self.TRAIN_IMAGE_MD5 if mode == 'train' else self.TEST_IMAGE_MD5
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self.image_path = _check_exists_and_download(
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image_path, image_url, image_md5, self.NAME, download)
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self.label_path = label_path
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if self.label_path is None:
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assert download, "label_path is not set and downloading automatically is disabled"
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label_url = self.TRAIN_LABEL_URL if self.mode == 'train' else self.TEST_LABEL_URL
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label_md5 = self.TRAIN_LABEL_MD5 if self.mode == 'train' else self.TEST_LABEL_MD5
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self.label_path = _check_exists_and_download(
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label_path, label_url, label_md5, self.NAME, download)
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self.transform = transform
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# read dataset into memory
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self._parse_dataset()
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self.dtype = paddle.get_default_dtype()
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def _parse_dataset(self, buffer_size=100):
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self.images = []
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self.labels = []
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with gzip.GzipFile(self.image_path, 'rb') as image_file:
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img_buf = image_file.read()
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with gzip.GzipFile(self.label_path, 'rb') as label_file:
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lab_buf = label_file.read()
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step_label = 0
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offset_img = 0
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# read from Big-endian
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# get file info from magic byte
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# image file : 16B
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magic_byte_img = '>IIII'
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magic_img, image_num, rows, cols = struct.unpack_from(
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magic_byte_img, img_buf, offset_img)
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offset_img += struct.calcsize(magic_byte_img)
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offset_lab = 0
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# label file : 8B
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magic_byte_lab = '>II'
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magic_lab, label_num = struct.unpack_from(magic_byte_lab,
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lab_buf, offset_lab)
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offset_lab += struct.calcsize(magic_byte_lab)
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while True:
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if step_label >= label_num:
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break
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fmt_label = '>' + str(buffer_size) + 'B'
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labels = struct.unpack_from(fmt_label, lab_buf, offset_lab)
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offset_lab += struct.calcsize(fmt_label)
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step_label += buffer_size
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fmt_images = '>' + str(buffer_size * rows * cols) + 'B'
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images_temp = struct.unpack_from(fmt_images, img_buf,
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offset_img)
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images = np.reshape(images_temp, (buffer_size, rows *
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cols)).astype('float32')
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offset_img += struct.calcsize(fmt_images)
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for i in range(buffer_size):
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self.images.append(images[i, :])
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self.labels.append(
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np.array([labels[i]]).astype('int64'))
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def __getitem__(self, idx):
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image, label = self.images[idx], self.labels[idx]
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image = np.reshape(image, [28, 28])
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if self.backend == 'pil':
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image = Image.fromarray(image, mode='L')
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if self.transform is not None:
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image = self.transform(image)
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if self.backend == 'pil':
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return image, label.astype('int64')
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return image.astype(self.dtype), label.astype('int64')
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def __len__(self):
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return len(self.labels)
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class FashionMNIST(MNIST):
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"""
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Implementation `Fashion-MNIST <https://github.com/zalandoresearch/fashion-mnist>`_ dataset.
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Args:
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image_path(str): path to image file, can be set None if
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:attr:`download` is True. Default None
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label_path(str): path to label file, can be set None if
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:attr:`download` is True. Default None
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mode(str): 'train' or 'test' mode. Default 'train'.
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download(bool): whether to download dataset automatically if
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:attr:`image_path` :attr:`label_path` is not set. Default True
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backend(str, optional): Specifies which type of image to be returned:
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PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
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If this option is not set, will get backend from ``paddle.vsion.get_image_backend`` ,
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default backend is 'pil'. Default: None.
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Returns:
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Dataset: Fashion-MNIST Dataset.
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Examples:
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.. code-block:: python
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from paddle.vision.datasets import FashionMNIST
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mnist = FashionMNIST(mode='test')
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for i in range(len(mnist)):
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sample = mnist[i]
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print(sample[0].size, sample[1])
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"""
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NAME = 'fashion-mnist'
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URL_PREFIX = 'https://dataset.bj.bcebos.com/fashion_mnist/'
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TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
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TEST_IMAGE_MD5 = 'bef4ecab320f06d8554ea6380940ec79'
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TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
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TEST_LABEL_MD5 = 'bb300cfdad3c16e7a12a480ee83cd310'
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TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz'
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TRAIN_IMAGE_MD5 = '8d4fb7e6c68d591d4c3dfef9ec88bf0d'
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TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz'
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TRAIN_LABEL_MD5 = '25c81989df183df01b3e8a0aad5dffbe'
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