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Fixed issue with jitted AA transforms in v2 and added tests #7839

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merged 3 commits into from
Aug 22, 2023
Merged

Fixed issue with jitted AA transforms in v2 and added tests #7839

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151a0d2
Fixed issue with jitted AA and added tests
vfdev-5 Aug 16, 2023
1e4111a
Merge branch 'main' into v2-vs-v1-fixes
vfdev-5 Aug 16, 2023
0fe1c54
Merge branch 'main' into v2-vs-v1-fixes
vfdev-5 Aug 22, 2023
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94 changes: 94 additions & 0 deletions test/test_transforms_v2_consistency.py
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Original file line number Diff line number Diff line change
Expand Up @@ -927,6 +927,29 @@ def test_randaug(self, inpt, interpolation, mocker):

assert_close(expected_output, output, atol=1, rtol=0.1)

@pytest.mark.parametrize(
"interpolation",
[
v2_transforms.InterpolationMode.NEAREST,
v2_transforms.InterpolationMode.BILINEAR,
],
)
def test_randaug_jit(self, interpolation):
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@vfdev-5 @pmeier instead of adding new tests here, shouldn't we be writing the new ones in test_transforms_v2_refactored.py?

We're going to delete this file, right?

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@NicolasHug can we merge this PR and address your comment later ?

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We're going to delete this file, right?

Yes. Indeed, all new tests should go into test_transforms_v2_refactored.py. No strong opinion of doing this now or later.

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OK, let's do it later

inpt = torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8)
t_ref = legacy_transforms.RandAugment(interpolation=interpolation, num_ops=1)
t = v2_transforms.RandAugment(interpolation=interpolation, num_ops=1)

tt_ref = torch.jit.script(t_ref)
tt = torch.jit.script(t)

torch.manual_seed(12)
expected_output = tt_ref(inpt)

torch.manual_seed(12)
scripted_output = tt(inpt)

assert_equal(scripted_output, expected_output)

@pytest.mark.parametrize(
"inpt",
[
Expand Down Expand Up @@ -979,6 +1002,29 @@ def test_trivial_aug(self, inpt, interpolation, mocker):

assert_close(expected_output, output, atol=1, rtol=0.1)

@pytest.mark.parametrize(
"interpolation",
[
v2_transforms.InterpolationMode.NEAREST,
v2_transforms.InterpolationMode.BILINEAR,
],
)
def test_trivial_aug_jit(self, interpolation):
inpt = torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8)
t_ref = legacy_transforms.TrivialAugmentWide(interpolation=interpolation)
t = v2_transforms.TrivialAugmentWide(interpolation=interpolation)

tt_ref = torch.jit.script(t_ref)
tt = torch.jit.script(t)

torch.manual_seed(12)
expected_output = tt_ref(inpt)

torch.manual_seed(12)
scripted_output = tt(inpt)

assert_equal(scripted_output, expected_output)

@pytest.mark.parametrize(
"inpt",
[
Expand Down Expand Up @@ -1032,6 +1078,30 @@ def test_augmix(self, inpt, interpolation, mocker):

assert_equal(expected_output, output)

@pytest.mark.parametrize(
"interpolation",
[
v2_transforms.InterpolationMode.NEAREST,
v2_transforms.InterpolationMode.BILINEAR,
],
)
def test_augmix_jit(self, interpolation):
inpt = torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8)

t_ref = legacy_transforms.AugMix(interpolation=interpolation, mixture_width=1, chain_depth=1)
t = v2_transforms.AugMix(interpolation=interpolation, mixture_width=1, chain_depth=1)

tt_ref = torch.jit.script(t_ref)
tt = torch.jit.script(t)

torch.manual_seed(12)
expected_output = tt_ref(inpt)

torch.manual_seed(12)
scripted_output = tt(inpt)

assert_equal(scripted_output, expected_output)

@pytest.mark.parametrize(
"inpt",
[
Expand Down Expand Up @@ -1061,6 +1131,30 @@ def test_aa(self, inpt, interpolation):

assert_equal(expected_output, output)

@pytest.mark.parametrize(
"interpolation",
[
v2_transforms.InterpolationMode.NEAREST,
v2_transforms.InterpolationMode.BILINEAR,
],
)
def test_aa_jit(self, interpolation):
inpt = torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8)
aa_policy = legacy_transforms.AutoAugmentPolicy("imagenet")
t_ref = legacy_transforms.AutoAugment(aa_policy, interpolation=interpolation)
t = v2_transforms.AutoAugment(aa_policy, interpolation=interpolation)

tt_ref = torch.jit.script(t_ref)
tt = torch.jit.script(t)

torch.manual_seed(12)
expected_output = tt_ref(inpt)

torch.manual_seed(12)
scripted_output = tt(inpt)

assert_equal(scripted_output, expected_output)


def import_transforms_from_references(reference):
HERE = Path(__file__).parent
Expand Down
19 changes: 14 additions & 5 deletions torchvision/transforms/v2/_auto_augment.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,16 @@ def __init__(
) -> None:
super().__init__()
self.interpolation = _check_interpolation(interpolation)
self.fill = _setup_fill_arg(fill)
self.fill = fill
self._fill = _setup_fill_arg(fill)

def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
params = super()._extract_params_for_v1_transform()

if not (params["fill"] is None or isinstance(params["fill"], (int, float))):
raise ValueError(f"{type(self).__name__}() can only be scripted for a scalar `fill`, but got {self.fill}.")

return params

def _get_random_item(self, dct: Dict[str, Tuple[Callable, bool]]) -> Tuple[str, Tuple[Callable, bool]]:
keys = tuple(dct.keys())
Expand Down Expand Up @@ -335,7 +344,7 @@ def forward(self, *inputs: Any) -> Any:
magnitude = 0.0

image_or_video = self._apply_image_or_video_transform(
image_or_video, transform_id, magnitude, interpolation=self.interpolation, fill=self.fill
image_or_video, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill
)

return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, image_or_video)
Expand Down Expand Up @@ -419,7 +428,7 @@ def forward(self, *inputs: Any) -> Any:
else:
magnitude = 0.0
image_or_video = self._apply_image_or_video_transform(
image_or_video, transform_id, magnitude, interpolation=self.interpolation, fill=self.fill
image_or_video, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill
)

return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, image_or_video)
Expand Down Expand Up @@ -491,7 +500,7 @@ def forward(self, *inputs: Any) -> Any:
magnitude = 0.0

image_or_video = self._apply_image_or_video_transform(
image_or_video, transform_id, magnitude, interpolation=self.interpolation, fill=self.fill
image_or_video, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill
)
return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, image_or_video)

Expand Down Expand Up @@ -614,7 +623,7 @@ def forward(self, *inputs: Any) -> Any:
magnitude = 0.0

aug = self._apply_image_or_video_transform(
aug, transform_id, magnitude, interpolation=self.interpolation, fill=self.fill
aug, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill
)
mix.add_(combined_weights[:, i].reshape(batch_dims) * aug)
mix = mix.reshape(orig_dims).to(dtype=image_or_video.dtype)
Expand Down