Normalize

class torchvision.transforms.Normalize(mean, std, inplace=False)

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:

Tensor transforms and JIT

Optical Flow: Predicting movement with the RAFT model

forward(tensor: torch.Tensor) → torch.Tensor

Parameters

tensor (Tensor) – Tensor image to be normalized.

Returns

Normalized Tensor image.

Return type

Tensor