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remove obsolete transforms tests #7678

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Merged
pmeier merged 2 commits into from
Jun 19, 2023
Merged

remove obsolete transforms tests #7678

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70e340b
remove obsolete transforms tests
pmeier Jun 19, 2023
965a5c5
revert unrelated formatting
pmeier Jun 19, 2023
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44 changes: 0 additions & 44 deletions test/test_functional_tensor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,7 +67,6 @@ def test_scale_channel():


class TestRotate:

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@NicolasHug NicolasHug Jun 19, 2023

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Is that a new black thing??

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@pmeier pmeier Jun 19, 2023

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We are pinning the version

vision/.pre-commit-config.yaml

Line 18 in 8324c48

- black == 22.3.0

so let's hope this was just a fluke on my side. I'll revert these.

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@pmeier pmeier Jun 19, 2023

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It indeed comes from a newer black version that I have on my system. Since the removed blank lines don't conflict with the "old" style, it was not automatically reverted by running the pre-commit hook. I opened #7680 to upgrade our linters.

ALL_DTYPES = [None, torch.float32, torch.float64, torch.float16]
scripted_rotate = torch.jit.script(F.rotate)
IMG_W = 26
Expand Down Expand Up @@ -153,7 +152,6 @@ def test_rotate_interpolation_type(self):


class TestAffine:

ALL_DTYPES = [None, torch.float32, torch.float64, torch.float16]
scripted_affine = torch.jit.script(F.affine)

Expand Down Expand Up @@ -407,7 +405,6 @@ def _get_data_dims_and_points_for_perspective():
)
@pytest.mark.parametrize("fn", [F.perspective, torch.jit.script(F.perspective)])
def test_perspective_pil_vs_tensor(device, dims_and_points, dt, fill, fn):

if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
Expand Down Expand Up @@ -439,7 +436,6 @@ def test_perspective_pil_vs_tensor(device, dims_and_points, dt, fill, fn):
@pytest.mark.parametrize("dims_and_points", _get_data_dims_and_points_for_perspective())
@pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
def test_perspective_batch(device, dims_and_points, dt):

if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
Expand Down Expand Up @@ -491,7 +487,6 @@ def test_perspective_interpolation_type():
@pytest.mark.parametrize("max_size", [None, 34, 40, 1000])
@pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC, NEAREST, NEAREST_EXACT])
def test_resize(device, dt, size, max_size, interpolation):

if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
Expand Down Expand Up @@ -541,7 +536,6 @@ def test_resize(device, dt, size, max_size, interpolation):

@pytest.mark.parametrize("device", cpu_and_gpu())
def test_resize_asserts(device):

tensor, pil_img = _create_data(26, 36, device=device)

res1 = F.resize(tensor, size=32, interpolation=PIL.Image.BILINEAR)
Expand All @@ -561,7 +555,6 @@ def test_resize_asserts(device):
@pytest.mark.parametrize("size", [[96, 72], [96, 420], [420, 72]])
@pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC])
def test_resize_antialias(device, dt, size, interpolation):

if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
Expand Down Expand Up @@ -609,23 +602,7 @@ def test_resize_antialias(device, dt, size, interpolation):
assert_equal(resized_tensor, resize_result)


@needs_cuda
@pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC])
def test_assert_resize_antialias(interpolation):

# Checks implementation on very large scales
# and catch TORCH_CHECK inside PyTorch implementation
torch.manual_seed(12)
tensor, _ = _create_data(1000, 1000, device="cuda")

# Error message is not yet updated in pytorch nightly
# with pytest.raises(RuntimeError, match=r"Provided interpolation parameters can not be handled"):
with pytest.raises(RuntimeError, match=r"Too much shared memory required"):
F.resize(tensor, size=(5, 5), interpolation=interpolation, antialias=True)


def test_resize_antialias_default_warning():

img = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8)

match = "The default value of the antialias"
Expand All @@ -641,29 +618,9 @@ def test_resize_antialias_default_warning():
F.resized_crop(img, 0, 0, 10, 10, size=(20, 20), interpolation=NEAREST)


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dt", [torch.float32, torch.float64, torch.float16])
@pytest.mark.parametrize("size", [[10, 7], [10, 42], [42, 7]])
@pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC])
def test_interpolate_antialias_backward(device, dt, size, interpolation):

if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return

torch.manual_seed(12)
x = (torch.rand(1, 32, 29, 3, dtype=torch.double, device=device).permute(0, 3, 1, 2).requires_grad_(True),)
resize = partial(F.resize, size=size, interpolation=interpolation, antialias=True)
assert torch.autograd.gradcheck(resize, x, eps=1e-8, atol=1e-6, rtol=1e-6, fast_mode=False)

x = (torch.rand(1, 3, 32, 29, dtype=torch.double, device=device, requires_grad=True),)
assert torch.autograd.gradcheck(resize, x, eps=1e-8, atol=1e-6, rtol=1e-6, fast_mode=False)


def check_functional_vs_PIL_vs_scripted(
fn, fn_pil, fn_t, config, device, dtype, channels=3, tol=2.0 + 1e-10, agg_method="max"
):

script_fn = torch.jit.script(fn)
torch.manual_seed(15)
tensor, pil_img = _create_data(26, 34, channels=channels, device=device)
Expand Down Expand Up @@ -1100,7 +1057,6 @@ def test_crop(device, top, left, height, width):
@pytest.mark.parametrize("sigma", [[0.5, 0.5], (0.5, 0.5), (0.8, 0.8), (1.7, 1.7)])
@pytest.mark.parametrize("fn", [F.gaussian_blur, torch.jit.script(F.gaussian_blur)])
def test_gaussian_blur(device, image_size, dt, ksize, sigma, fn):

# true_cv2_results = {
# # np_img = np.arange(3 * 10 * 12, dtype="uint8").reshape((10, 12, 3))
# # cv2.GaussianBlur(np_img, ksize=(3, 3), sigmaX=0.8)
Expand Down