mindspore.dataset.vision.ToTensor

class mindspore.dataset.vision.ToTensor(output_type=np.float32)[source]

Convert the input PIL Image or numpy.ndarray to numpy.ndarray of the desired dtype, rescale the pixel value range from [0, 255] to [0.0, 1.0] and change the shape from (H, W, C) to (C, H, W).

Parameters

output_type (Union[mindspore.dtype, numpy.dtype], optional) – The desired dtype of the output image. Default: numpy.float32.

Raises

TypeError – If the input image is not of type PIL.Image.Image or numpy.ndarray.
TypeError – If dimension of the input image is not 2 or 3.

Supported Platforms:: CPU

Examples

>>> from mindspore.dataset.transforms import Compose
>>>
>>> # create a list of transformations to be applied to the "image" column of each data row
>>> transforms_list = Compose([vision.Decode(to_pil=True)),
...                            vision.RandomHorizontalFlip(0.5),
...                            vision.ToTensor()])
>>> # apply the transform to dataset through map function
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
...                                                 input_columns="image")