Method test_kernel_image

test/test_transforms_v2.py:727–758

(self, size, interpolation, use_max_size, antialias, dtype, device)

    @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_kernel_image(self, size, interpolation, use_max_size, antialias, dtype, device):
        if not (max_size_kwarg := self._make_max_size_kwarg(use_max_size=use_max_size, size=size)):
            return

        if interpolation is transforms.InterpolationMode.LANCZOS and str(device) == "cuda":
            pytest.skip("Lanczos is not supported on CUDA")

        if interpolation is transforms.InterpolationMode.LANCZOS and not antialias:
            pytest.skip("Lanczos requires antialias=True")

        # In contrast to CPU, there is no native `InterpolationMode.BICUBIC` implementation for uint8 images on CUDA.
        # Internally, it uses the float path. Thus, we need to test with an enormous tolerance here to account for that.
        atol = (
            30
            if (
                interpolation in (transforms.InterpolationMode.BICUBIC, transforms.InterpolationMode.LANCZOS)
                and dtype is torch.uint8
            )
            else 1
        )
        check_cuda_vs_cpu_tolerances = dict(rtol=0, atol=atol / 255 if dtype.is_floating_point else atol)

        check_kernel(
            F.resize_image,
            make_image(self.INPUT_SIZE, dtype=dtype, device=device),
            size=size,
            interpolation=interpolation,
            **max_size_kwarg,
            antialias=antialias,
            check_cuda_vs_cpu=check_cuda_vs_cpu_tolerances,
            check_scripted_vs_eager=not isinstance(size, int),
        )

    @pytest.mark.parametrize("format", list(tv_tensors.BoundingBoxFormat))
    @pytest.mark.parametrize("size", OUTPUT_SIZES)