o
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)print_functionN)Image)Dataset)_check_exists_and_downloadc @ sx e Zd ZdZdZdZed ZdZed ZdZ ed Z
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dddZdddZdd Zdd Zd S )MNISTa
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]
Zmnistz$https://dataset.bj.bcebos.com/mnist/t10k-images-idx3-ubyte.gzZ 9fb629c4189551a2d022fa330f9573f3t10k-labels-idx1-ubyte.gzZ ec29112dd5afa0611ce80d1b7f02629ctrain-images-idx3-ubyte.gzZ f68b3c2dcbeaaa9fbdd348bbdeb94873train-labels-idx1-ubyte.gzZ d53e105ee54ea40749a09fcbcd1e9432NtrainTc C s" | dv s
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image_pathTRAIN_IMAGE_URLTEST_IMAGE_URLTRAIN_IMAGE_MD5TEST_IMAGE_MD5r NAME
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"zMNIST._parse_datasetc C s | j | | j| }}t|ddg}| jdkr"tj|ddd}| jd ur,| |}| jdkr8||dfS || j |dfS )N r
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FashionMNISTaU
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]
z
fashion-mnistz,https://dataset.bj.bcebos.com/fashion_mnist/r Z bef4ecab320f06d8554ea6380940ec79r Z bb300cfdad3c16e7a12a480ee83cd310r Z 8d4fb7e6c68d591d4c3dfef9ec88bf0dr
Z 25c81989df183df01b3e8a0aad5dffbeN)rG rH rI rJ r rK r r r r r r r r r$ r$ r$ r% rL s @rL )
__future__r osr. r1 numpyr5 ZPILr r Z paddle.ior Zpaddle.dataset.commonr __all__r rL r$ r$ r$ r% s 7