mindspore.dataset.vision.py_transforms.Cutout
- class mindspore.dataset.vision.py_transforms.Cutout(length, num_patches=1)[source]
Randomly apply a given number of square patches of zeros to a location within the input numpy.ndarray image of shape (C, H, W).
See Terrance DeVries and Graham W. Taylor ‘Improved Regularization of Convolutional Neural Networks with Cutout’ 2017 on https://arxiv.org/pdf/1708.04552.pdf
- Parameters
- Raises
TypeError – If length is not of type integer.
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
>>> from mindspore.dataset.transforms.py_transforms import Compose >>> transforms_list = Compose([py_vision.Decode(), ... py_vision.ToTensor(), ... py_vision.Cutout(80)]) >>> # apply the transform to dataset through map function >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list, ... input_columns="image")