def test_img_normalize(): dummy_tensor = torch.rand(3, 64, 64).add_(-0.5).div_(0.5) normalized_tensor = img_normalize(dummy_tensor) assert normalized_tensor.min() == 0 assert normalized_tensor.max() == 1 dummy_tensor = torch.randn(3, 8, 8) normalized_tensor = img_normalize(dummy_tensor, val_range=(-2, 2)) assert normalized_tensor.min() < 0 assert normalized_tensor.max() > 1 dummy_zero_tensor = torch.zeros(8, 8) img_normalize(dummy_zero_tensor)
def test_img_normalize(): dummy_img_tensor = torch.rand(3, 64, 64) transformed_tensor = dummy_img_tensor.add_(-0.5).div_(0.5) normalized_tensor = img_normalize(transformed_tensor) assert normalized_tensor.min() == 0 assert normalized_tensor.max() == 1 normalized_var = img_normalize(to_var(dummy_img_tensor)) assert normalized_var.data.min() == 0 assert normalized_var.data.max() == 1 dummy_tensor = torch.LongTensor(3, 8, 8).random_(-2, to=2).float() normalized_tensor = img_normalize(dummy_tensor, img_range=(-2, 2)) assert normalized_tensor.min() >= 0 assert normalized_tensor.max() <= 1 dummy_zero_tensor = torch.zeros(8, 8) img_normalize(dummy_zero_tensor)
def image(self, kw_images, epoch, prefix): ''' Args: kw_images: 4-D tensor [batch, channel, height, width] epoch: step for TensorBoard logging prefix: prefix string for tag ''' [ self.writer.add_image( f'{prefix}{tag}/{self._tag_base_counter + i}', img_normalize(image), epoch) for tag, images in kw_images.items() for i, image in enumerate(images) ]
def test_psnr(): dummy_output = to_var(img_normalize(torch.rand(4, 3, 64, 64))) dummy_target = to_var(img_normalize(torch.rand(4, 3, 64, 64))) psnr = onegan.metrics.psnr(dummy_output, dummy_target) assert psnr
def normalize(t): return img_normalize(t, img_range=img_range)