mindspore.dataset.vision.c_transforms.Normalize

class mindspore.dataset.vision.c_transforms.Normalize(mean, std)[source]

Normalize the input image with respect to mean and standard deviation. This operator will normalize the input image with: output[channel] = (input[channel] - mean[channel]) / std[channel], where channel >= 1.

Note

This operation supports running on Ascend or GPU platforms by Offload.

Parameters

  • mean (sequence) – List or tuple of mean values for each channel, with respect to channel order. The mean values must be in range [0.0, 255.0].

  • std (sequence) – List or tuple of standard deviations for each channel, with respect to channel order. The standard deviation values must be in range (0.0, 255.0].

Raises

  • TypeError – If mean is not of type sequence.

  • TypeError – If std is not of type sequence.

  • ValueError – If mean is not in range [0.0, 255.0].

  • ValueError – If mean is not in range (0.0, 255.0].

  • RuntimeError – If given tensor shape is not <H, W> or <H, W, C>.

Supported Platforms:

CPU Ascend GPU

Examples

>>> decode_op = c_vision.Decode()
>>> normalize_op = c_vision.Normalize(mean=[121.0, 115.0, 100.0], std=[70.0, 68.0, 71.0])
>>> transforms_list = [decode_op, normalize_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
...                                                 input_columns=["image"])