Flowers

class paddle.vision.datasets. Flowers ( data_file=None, label_file=None, setid_file=None, mode='train', transform=None, download=True, backend=None ) [source]

Implementation of Flowers102 dataset.

Parameters

  • data_file (str, optional) – Path to data file, can be set None if download is True. Default: None, default data path: ~/.cache/paddle/dataset/flowers/.

  • label_file (str, optional) – Path to label file, can be set None if download is True. Default: None, default data path: ~/.cache/paddle/dataset/flowers/.

  • setid_file (str, optional) – Path to subset index file, can be set None if download is True. Default: None, default data path: ~/.cache/paddle/dataset/flowers/.

  • 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 data_file is None. 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 paddle.vision.get_image_backend, default backend is ‘pil’. Default: None.

Returns

Dataset. An instance of Flowers dataset.

Examples

>>> 
>>> import itertools
>>> import paddle.vision.transforms as T
>>> from paddle.vision.datasets import Flowers

>>> flowers = Flowers()
>>> print(len(flowers))
6149

>>> for i in range(5):  # only show first 5 images
...     img, label = flowers[i]
...     # do something with img and label
...     print(type(img), img.size, label)
...     # <class 'PIL.JpegImagePlugin.JpegImageFile'> (523, 500) [1]

>>> 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,
...         ),
...     ]
... )
>>> flowers_test = Flowers(
...     mode="test",
...     transform=transform,  # apply transform to every image
...     backend="cv2",  # use OpenCV as image transform backend
... )
>>> print(len(flowers_test))
1020

>>> for img, label in itertools.islice(iter(flowers_test), 5):  # only show first 5 images
...     # do something with img and label
...     print(type(img), img.shape, label)
...     # <class 'paddle.Tensor'> [3, 64, 96] [1]