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True，data_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'或'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_file是None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。
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