VOC2012

class paddle.vision.datasets. VOC2012 ( data_file=None, mode='train', transform=None, download=True, backend=None ) [源代码]

VOC2012 数据集的实现。

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/voc2012

  • 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,VOC2012 数据集实例。

代码示例

import itertools
import paddle.vision.transforms as T
from paddle.vision.datasets import VOC2012


voc2012 = VOC2012()
print(len(voc2012))
# 2913

for i in range(5):  # only show first 5 images
    img, label = voc2012[i]
    # do something with img and label
    print(type(img), img.size)
    # <class 'PIL.JpegImagePlugin.JpegImageFile'> (500, 281)
    print(type(label), label.size)
    # <class 'PIL.PngImagePlugin.PngImageFile'> (500, 281)


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

voc2012_test = VOC2012(
    mode="test",
    transform=transform,  # apply transform to every image
    backend="cv2",  # use OpenCV as image transform backend
)
print(len(voc2012_test))
# 1464

for img, label in itertools.islice(iter(voc2012_test), 5):  # only show first 5 images
    # do something with img and label
    print(type(img), img.shape)
    # <class 'paddle.Tensor'> [3, 281, 500]
    print(type(label), label.shape)
    # <class 'numpy.ndarray'> (281, 500)