wehub-resource-sync
340 lines
13 KiB
Python
340 lines
13 KiB
Python
# 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.
|
|
from __future__ import annotations
|
|
|
|
from typing import TYPE_CHECKING, Any, Literal
|
|
|
|
if TYPE_CHECKING:
|
|
import numpy.typing as npt
|
|
|
|
from paddle._typing.dtype_like import _DTypeLiteral
|
|
from paddle.vision.transforms.transforms import _Transform
|
|
|
|
from ..image import _ImageBackend, _ImageDataType
|
|
|
|
_DatasetMode = Literal["train", "test"]
|
|
|
|
import gzip
|
|
import struct
|
|
|
|
import numpy as np
|
|
from PIL import Image
|
|
|
|
import paddle
|
|
from paddle.dataset.common import _check_exists_and_download
|
|
from paddle.io import Dataset
|
|
|
|
__all__ = []
|
|
|
|
|
|
class MNIST(Dataset[tuple["_ImageDataType", "npt.NDArray[np.int64]"]]):
|
|
"""
|
|
Implementation of `MNIST <http://yann.lecun.com/exdb/mnist/>`_ dataset.
|
|
|
|
Args:
|
|
image_path (str|None, 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|None, 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|None, 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|None, 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 <api_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:: pycon
|
|
|
|
>>> import itertools
|
|
>>> import paddle
|
|
>>> 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)
|
|
... # <class 'PIL.Image.Image'> (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
|
|
... assert isinstance(img, paddle.Tensor)
|
|
... print(type(img), img.shape, label)
|
|
... # <class 'paddle.Tensor'> [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'
|
|
|
|
mode: _DatasetMode
|
|
image_path: str | None
|
|
label_path: str | None
|
|
transform: _Transform[Any, Any] | None
|
|
backend: _ImageBackend
|
|
dtype: _DTypeLiteral
|
|
labels: list
|
|
images: list
|
|
|
|
def __init__(
|
|
self,
|
|
image_path: str | None = None,
|
|
label_path: str | None = None,
|
|
mode: _DatasetMode = 'train',
|
|
transform: _Transform[Any, Any] | None = None,
|
|
download: bool = True,
|
|
backend: _ImageBackend | None = None,
|
|
) -> None:
|
|
assert mode.lower() in [
|
|
'train',
|
|
'test',
|
|
], f"mode should be 'train' or 'test', but got {mode}"
|
|
|
|
if backend is None:
|
|
backend = paddle.vision.get_image_backend()
|
|
if backend not in ['pil', 'cv2']:
|
|
raise ValueError(
|
|
f"Expected backend are one of ['pil', 'cv2'], but got {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: int
|
|
) -> tuple[_ImageDataType, npt.NDArray[np.int64]]:
|
|
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) -> int:
|
|
return len(self.labels)
|
|
|
|
|
|
class FashionMNIST(MNIST):
|
|
"""
|
|
Implementation of `Fashion-MNIST <https://github.com/zalandoresearch/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 <api_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:: pycon
|
|
|
|
>>> import itertools
|
|
>>> import paddle
|
|
>>> 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)
|
|
... # <class 'PIL.Image.Image'> (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
|
|
... assert isinstance(img, paddle.Tensor)
|
|
... print(type(img), img.shape, label)
|
|
... # <class 'paddle.Tensor'> [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'
|