mindspore.dataset.vision.Normalize

class mindspore.dataset.vision.Normalize(mean, std, is_hwc=True)[source]

Normalize the input image with respect to mean and standard deviation. This operation 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].

  • is_hwc (bool, optional) – Whether the input image is HWC. True - HWC format, False - CHW format. Default: True.

Raises

  • TypeError – If mean is not of type sequence.

  • TypeError – If std is not of type sequence.

  • TypeError – If is_hwc is not of type bool.

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

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

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

Supported Platforms:

CPU

Examples

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.vision as vision
>>>
>>> image_folder_dataset = ds.ImageFolderDataset("/path/to/image_folder_dataset_directory")
>>> decode_op = vision.Decode() ## Decode output is expected to be HWC format
>>> normalize_op = vision.Normalize(mean=[121.0, 115.0, 100.0], std=[70.0, 68.0, 71.0], is_hwc=True)
>>> transforms_list = [decode_op, normalize_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
...                                                 input_columns=["image"])
Tutorial Examples: