def infer_batch(feed_im: torch.Tensor): feed_im = feed_im.to(device) out_im = model.forward(feed_im) draw_im = (denormalize( out_im.permute((0, 2, 3, 1)).detach().to('cpu').numpy()) * 255).astype('uint8') return draw_im
def color_loss(self, con, fake): con = rgb2yuv(denormalize(con)) fake = rgb2yuv(denormalize(fake)) return (self.l1_loss(con[..., 0], fake[..., 0]) + self.huber_loss(con[..., 1], fake[..., 1]) + self.huber_loss(con[..., 2], fake[..., 2]))
def _process(self, x): # NOTE 图像范围为[-1~1],先denormalize到0-1再归一化 return self.vgg_normalize(denormalize(x))
def _process(self, x): # NOTE 图像范围为[-1~1],先denormalize到0-1再归一化 rgb = denormalize(x) * 255 # to 255 bgr = rgb[:, [2, 1, 0], :, :] # rgb to bgr return self.vgg_normalize(bgr) # vgg norm