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Showing content from https://docs.pytorch.org/vision/stable/generated/torchvision.transforms.Normalize.html below:

Normalize — Torchvision 0.23 documentation

class torchvision.transforms.Normalize(mean, std, inplace=False)[source]¶

Normalize a tensor image 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:

forward(tensor: Tensor) → Tensor[source]¶

Parameters:

tensor (Tensor) – Tensor image to be normalized.

Returns:

Normalized Tensor image.

Return type:

Tensor

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