Cifar10

class paddle.vision.datasets. Cifar10 ( data_file=None, mode='train', transform=None, download=True, backend=None ) [源代码]

Cifar-10 数据集的实现,包含 10 种类别。

参数

  • data_file (str,可选) - 数据集文件路径,如果 download 参数设置为 Truedata_file 参数可以设置为 None。默认值为 None,默认存放在:~/.cache/paddle/dataset/cifar

  • mode (str,可选) - 'train''test' 模式两者之一,默认值为 'train'

  • transform (Callable,可选) - 图片数据的预处理,若为 None 即为不做预处理。默认值为 None

  • download (bool,可选) - 当 data_fileNone 时,该参数决定是否自动下载数据集文件。默认值为 True

  • backend (str,可选) - 指定要返回的图像类型:PIL.Image 或 numpy.ndarray。必须是 {'pil','cv2'} 中的值。如果未设置此选项,将从 paddle.vision.get_image_backend 获得这个值。默认值为 None

返回

Dataset,Cifar10 数据集实例。

代码示例

import itertools
import paddle.vision.transforms as T
from paddle.vision.datasets import Cifar10


cifar10 = Cifar10()
print(len(cifar10))
# 50000

for i in range(5):  # only show first 5 images
    img, label = cifar10[i]
    # do something with img and label
    print(type(img), img.size, label)
    # <class 'PIL.Image.Image'> (32, 32) 6


transform = T.Compose(
    [
        T.Resize(64),
        T.ToTensor(),
        T.Normalize(
            mean=[0.5, 0.5, 0.5],
            std=[0.5, 0.5, 0.5],
            to_rgb=True,
        ),
    ]
)

cifar10_test = Cifar10(
    mode="test",
    transform=transform,  # apply transform to every image
    backend="cv2",  # use OpenCV as image transform backend
)
print(len(cifar10_test))
# 10000

for img, label in itertools.islice(iter(cifar10_test), 5):  # only show first 5 images
    # do something with img and label
    print(type(img), img.shape, label)
    # <class 'paddle.Tensor'> [3, 64, 64] 3