# Copyright (c) 2020 PaddlePaddle 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.
import os
import gzip
import struct
import numpy as np
from PIL import Image
import paddle
from paddle.io import Dataset
from paddle.dataset.common import _check_exists_and_download
__all__ = []
class MNIST(Dataset):
"""
Implementation of `MNIST `_ dataset.
Args:
image_path (str, optional): Path to image file, can be set None if
:attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/mnist.
label_path (str, optional): Path to label file, can be set None if
:attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/mnist.
mode (str, optional): Either train or test mode. Default 'train'.
transform (Callable, optional): Transform to perform on image, None for no transform. Default: None.
download (bool, optional): Download dataset automatically if
:attr:`image_path` :attr:`label_path` is not set. Default: True.
backend (str, optional): Specifies which type of image to be returned:
PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend `,
default backend is 'pil'. Default: None.
Returns:
:ref:`api_paddle_io_Dataset`. An instance of MNIST dataset.
Examples:
.. code-block:: python
import itertools
import paddle.vision.transforms as T
from paddle.vision.datasets import MNIST
mnist = MNIST()
print(len(mnist))
# 60000
for i in range(5): # only show first 5 images
img, label = mnist[i]
# do something with img and label
print(type(img), img.size, label)
# (28, 28) [5]
transform = T.Compose(
[
T.ToTensor(),
T.Normalize(
mean=[127.5],
std=[127.5],
),
]
)
mnist_test = MNIST(
mode="test",
transform=transform, # apply transform to every image
backend="cv2", # use OpenCV as image transform backend
)
print(len(mnist_test))
# 10000
for img, label in itertools.islice(iter(mnist_test), 5): # only show first 5 images
# do something with img and label
print(type(img), img.shape, label)
# [1, 28, 28] [7]
"""
NAME = 'mnist'
URL_PREFIX = 'https://dataset.bj.bcebos.com/mnist/'
TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
TEST_IMAGE_MD5 = '9fb629c4189551a2d022fa330f9573f3'
TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
TEST_LABEL_MD5 = 'ec29112dd5afa0611ce80d1b7f02629c'
TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz'
TRAIN_IMAGE_MD5 = 'f68b3c2dcbeaaa9fbdd348bbdeb94873'
TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz'
TRAIN_LABEL_MD5 = 'd53e105ee54ea40749a09fcbcd1e9432'
def __init__(self,
image_path=None,
label_path=None,
mode='train',
transform=None,
download=True,
backend=None):
assert mode.lower() in ['train', 'test'], \
"mode should be 'train' or 'test', but got {}".format(mode)
if backend is None:
backend = paddle.vision.get_image_backend()
if backend not in ['pil', 'cv2']:
raise ValueError(
"Expected backend are one of ['pil', 'cv2'], but got {}".format(
backend))
self.backend = backend
self.mode = mode.lower()
self.image_path = image_path
if self.image_path is None:
assert download, "image_path is not set and downloading automatically is disabled"
image_url = self.TRAIN_IMAGE_URL if mode == 'train' else self.TEST_IMAGE_URL
image_md5 = self.TRAIN_IMAGE_MD5 if mode == 'train' else self.TEST_IMAGE_MD5
self.image_path = _check_exists_and_download(
image_path, image_url, image_md5, self.NAME, download)
self.label_path = label_path
if self.label_path is None:
assert download, "label_path is not set and downloading automatically is disabled"
label_url = self.TRAIN_LABEL_URL if self.mode == 'train' else self.TEST_LABEL_URL
label_md5 = self.TRAIN_LABEL_MD5 if self.mode == 'train' else self.TEST_LABEL_MD5
self.label_path = _check_exists_and_download(
label_path, label_url, label_md5, self.NAME, download)
self.transform = transform
# read dataset into memory
self._parse_dataset()
self.dtype = paddle.get_default_dtype()
def _parse_dataset(self, buffer_size=100):
self.images = []
self.labels = []
with gzip.GzipFile(self.image_path, 'rb') as image_file:
img_buf = image_file.read()
with gzip.GzipFile(self.label_path, 'rb') as label_file:
lab_buf = label_file.read()
step_label = 0
offset_img = 0
# read from Big-endian
# get file info from magic byte
# image file : 16B
magic_byte_img = '>IIII'
magic_img, image_num, rows, cols = struct.unpack_from(
magic_byte_img, img_buf, offset_img)
offset_img += struct.calcsize(magic_byte_img)
offset_lab = 0
# label file : 8B
magic_byte_lab = '>II'
magic_lab, label_num = struct.unpack_from(
magic_byte_lab, lab_buf, offset_lab)
offset_lab += struct.calcsize(magic_byte_lab)
while True:
if step_label >= label_num:
break
fmt_label = '>' + str(buffer_size) + 'B'
labels = struct.unpack_from(fmt_label, lab_buf, offset_lab)
offset_lab += struct.calcsize(fmt_label)
step_label += buffer_size
fmt_images = '>' + str(buffer_size * rows * cols) + 'B'
images_temp = struct.unpack_from(fmt_images, img_buf,
offset_img)
images = np.reshape(
images_temp,
(buffer_size, rows * cols)).astype('float32')
offset_img += struct.calcsize(fmt_images)
for i in range(buffer_size):
self.images.append(images[i, :])
self.labels.append(
np.array([labels[i]]).astype('int64'))
def __getitem__(self, idx):
image, label = self.images[idx], self.labels[idx]
image = np.reshape(image, [28, 28])
if self.backend == 'pil':
image = Image.fromarray(image.astype('uint8'), mode='L')
if self.transform is not None:
image = self.transform(image)
if self.backend == 'pil':
return image, label.astype('int64')
return image.astype(self.dtype), label.astype('int64')
def __len__(self):
return len(self.labels)
class FashionMNIST(MNIST):
"""
Implementation of `Fashion-MNIST `_ dataset.
Args:
image_path (str, optional): Path to image file, can be set None if
:attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/fashion-mnist.
label_path (str, optional): Path to label file, can be set None if
:attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/fashion-mnist.
mode (str, optional): Either train or test mode. Default 'train'.
transform (Callable, optional): Transform to perform on image, None for no transform. Default: None.
download (bool, optional): Whether to download dataset automatically if
:attr:`image_path` :attr:`label_path` is not set. Default: True.
backend (str, optional): Specifies which type of image to be returned:
PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend `,
default backend is 'pil'. Default: None.
Returns:
:ref:`api_paddle_io_Dataset`. An instance of FashionMNIST dataset.
Examples:
.. code-block:: python
import itertools
import paddle.vision.transforms as T
from paddle.vision.datasets import FashionMNIST
fashion_mnist = FashionMNIST()
print(len(fashion_mnist))
# 60000
for i in range(5): # only show first 5 images
img, label = fashion_mnist[i]
# do something with img and label
print(type(img), img.size, label)
# (28, 28) [9]
transform = T.Compose(
[
T.ToTensor(),
T.Normalize(
mean=[127.5],
std=[127.5],
),
]
)
fashion_mnist_test = FashionMNIST(
mode="test",
transform=transform, # apply transform to every image
backend="cv2", # use OpenCV as image transform backend
)
print(len(fashion_mnist_test))
# 10000
for img, label in itertools.islice(iter(fashion_mnist_test), 5): # only show first 5 images
# do something with img and label
print(type(img), img.shape, label)
# [1, 28, 28] [9]
"""
NAME = 'fashion-mnist'
URL_PREFIX = 'https://dataset.bj.bcebos.com/fashion_mnist/'
TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
TEST_IMAGE_MD5 = 'bef4ecab320f06d8554ea6380940ec79'
TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
TEST_LABEL_MD5 = 'bb300cfdad3c16e7a12a480ee83cd310'
TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz'
TRAIN_IMAGE_MD5 = '8d4fb7e6c68d591d4c3dfef9ec88bf0d'
TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz'
TRAIN_LABEL_MD5 = '25c81989df183df01b3e8a0aad5dffbe'