mindspore.dataset.vision.CutOut
- class mindspore.dataset.vision.CutOut(length, num_patches=1, is_hwc=True)[source]
Randomly cut (mask) out a given number of square patches from the input image array.
- Parameters
length (int) – The side length of each square patch, must be larger than 0.
num_patches (int, optional) – Number of patches to be cut out of an image, must be larger than 0. Default:
1
.is_hwc (bool, optional) – Whether the input image is in HWC format.
True
- HWC format,False
- CHW format. Default:True
.
- Raises
TypeError – If length is not of type integer.
TypeError – If is_hwc is not of type bool.
TypeError – If num_patches is not of type integer.
ValueError – If length is less than or equal 0.
ValueError – If num_patches is less than or equal 0.
RuntimeError – If given tensor shape is not <H, W, C>.
- Supported Platforms:
CPU
Examples
>>> import numpy as np >>> import mindspore.dataset as ds >>> import mindspore.dataset.vision as vision >>> >>> # Use the transform in dataset pipeline mode >>> data = np.random.randint(0, 255, size=(1, 100, 100, 3)).astype(np.uint8) >>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"]) >>> transforms_list = [vision.CutOut(80, num_patches=10)] >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms_list, input_columns=["image"]) >>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True): ... print(item["image"].shape, item["image"].dtype) ... break (100, 100, 3) uint8 >>> >>> # Use the transform in eager mode >>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.uint8) >>> output = vision.CutOut(20)(data) >>> print(output.shape, output.dtype) (100, 100, 3) uint8
- Tutorial Examples: