Normalize¶

classtorchvision.transforms.v2.Normalize(mean:Sequence[float], std:Sequence[float], inplace:bool=False)[source]¶

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

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.

Examples using Normalize:

👁 Image

How to write your own v2 transforms

How to write your own v2 transforms

👁 Image

How to use CutMix and MixUp

How to use CutMix and MixUp

👁 Image

Getting started with transforms v2

Getting started with transforms v2

transform(inpt:Any, params:dict[str,Any]) → Any[source]¶

Method to override for custom transforms.

See How to write your own v2 transforms