Normalize

class torchvision.transforms.v2.Normalize(mean: Sequence[float], std: Sequence[float], inplace: bool = False)

[BETA] Normalize a tensor image or video with mean and standard deviation.

Warning

The Normalize transform is in Beta stage, and while we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes.

This transform does not support PIL Image. Given mean: (mean[1],...,mean[n]) and std: (std[1],..,std[n]) for n channels, this transform will normalize each channel of the input torch.*Tensor i.e., output[channel] = (input[channel] - mean[channel]) / std[channel]

Note

This transform acts out of place, i.e., it does not mutate the input tensor.

Parameters:

• mean (sequence) – Sequence of means for each channel.

• std (sequence) – Sequence of standard deviations for each channel.

• inplace (bool,optional) – Bool to make this operation in-place.