| transform_normalize {torchvision} | R Documentation |
Normalize a tensor image with mean and standard deviation
Description
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]
Usage
transform_normalize(img, mean, std, inplace = FALSE)
Arguments
img |
A |
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()