Normalize

class paddle.vision.transforms. Normalize ( mean=0.0, std=1.0, data_format='CHW', to_rgb=False, keys=None ) [source]

Normalize the input data with mean and standard deviation. Given mean: (M1,...,Mn) and std: (S1,..,Sn) for n channels, this transform will normalize each channel of the input data. output[channel] = (input[channel] - mean[channel]) / std[channel]

Parameters

  • mean (int|float|list|tuple, optional) – Sequence of means for each channel.

  • std (int|float|list|tuple, optional) – Sequence of standard deviations for each channel.

  • data_format (str, optional) – Data format of img, should be ‘HWC’ or ‘CHW’. Default: ‘CHW’.

  • to_rgb (bool, optional) – Whether to convert to rgb. Default: False.

  • keys (list[str]|tuple[str], optional) – Same as BaseTransform. Default: None.

Shape:

  • img(PIL.Image|np.ndarray|Paddle.Tensor): The input image with shape (H x W x C).

  • output(PIL.Image|np.ndarray|Paddle.Tensor): A normalized array or tensor.

Returns

A callable object of Normalize.

Examples

  normalize = Normalize(mean=[127.5, 127.5, 127.5],
                        std=[127.5, 127.5, 127.5],
                        data_format='HWC')

  fake_img = paddle.rand([300,320,3]).numpy() * 255.

  fake_img = normalize(fake_img)
  print(fake_img.shape)
  # (300, 320, 3)
  print(fake_img.max(), fake_img.min())
  # 0.99999905 -0.999974