Normalize a tensor image with mean and standard deviation

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]

transform_normalize(img, mean, std, inplace = FALSE)

Arguments

img

A magick-image, array or torch_tensor.

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.

Note

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

See also

Other transforms: transform_adjust_brightness(), transform_adjust_contrast(), transform_adjust_gamma(), transform_adjust_hue(), transform_adjust_saturation(), transform_affine(), transform_center_crop(), transform_color_jitter(), transform_convert_image_dtype(), transform_crop(), transform_five_crop(), transform_grayscale(), transform_hflip(), transform_linear_transformation(), transform_pad(), transform_perspective(), transform_random_affine(), transform_random_apply(), transform_random_choice(), transform_random_crop(), transform_random_erasing(), transform_random_grayscale(), transform_random_horizontal_flip(), transform_random_order(), transform_random_perspective(), transform_random_resized_crop(), transform_random_rotation(), transform_random_vertical_flip(), transform_resize(), transform_resized_crop(), transform_rgb_to_grayscale(), transform_rotate(), transform_ten_crop(), transform_to_tensor(), transform_vflip()