How to change a batch RGB images To YCbCr images during training?

what I want do is:

RGB_images = netG(input)   #netG is a pretrained model and not change during training,RGB_images  is a batch of RGB images
YCbCr_images = f(RGB_images)   # YCbCr_images  is a batch of YCbCr mode  images
# do things with YCbCr_images 

Is there any function f in pytorch can achieve what i want?

there isn’t an in-built way to do this.
However, you can simply write this as an autograd function

def rgb_to_ycbcr(input):
  # input is mini-batch N x 3 x H x W of an RGB image
  output = Variable(input.data.new(*input.size()))
  output[:, 0, :, :] = input[:, 0, :, :] * 65.481 + input[:, 1, :, :] * 128.553 + input[:, 2, :, :] * 24.966 + 16
  # similarly write output[:, 1, :, :] and output[:, 2, :, :] using formulas from https://en.wikipedia.org/wiki/YCbCr
  return output
def rgb_to_ycbcr(image: torch.Tensor) -> torch.Tensor:
    r"""Convert an RGB image to YCbCr.

    Args:
        image (torch.Tensor): RGB Image to be converted to YCbCr.

    Returns:
        torch.Tensor: YCbCr version of the image.
    """

    if not torch.is_tensor(image):
        raise TypeError("Input type is not a torch.Tensor. Got {}".format(
            type(image)))

    if len(image.shape) < 3 or image.shape[-3] != 3:
        raise ValueError("Input size must have a shape of (*, 3, H, W). Got {}"
                         .format(image.shape))

    r: torch.Tensor = image[..., 0, :, :]
    g: torch.Tensor = image[..., 1, :, :]
    b: torch.Tensor = image[..., 2, :, :]

    delta = .5
    y: torch.Tensor = .299 * r + .587 * g + .114 * b
    cb: torch.Tensor = (b - y) * .564 + delta
    cr: torch.Tensor = (r - y) * .713 + delta
    return torch.stack((y, cb, cr), -3)