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Merged
merged 15 commits into from
Sep 4, 2023
Merged

port AA tests #7927

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749f817
port AA tests
pmeier Sep 1, 2023
93cd592
random seed based on parametrization
pmeier Sep 1, 2023
fe1d477
adapt fill
pmeier Sep 1, 2023
1d654a0
expand correctness tests
pmeier Sep 1, 2023
6726e3e
fix
pmeier Sep 1, 2023
95cd2b3
reorder
pmeier Sep 4, 2023
2e3ba76
refactor forward tests
pmeier Sep 4, 2023
2707869
remove all old tests
pmeier Sep 4, 2023
6608a7e
add error tests
pmeier Sep 4, 2023
f7f908f
Merge branch 'main' into port/aa
pmeier Sep 4, 2023
d26eb67
fix check_transform
pmeier Sep 4, 2023
027bb7a
Update test/test_transforms_v2_refactored.py
pmeier Sep 4, 2023
574cdc0
lint
pmeier Sep 4, 2023
26984bf
fix test name
pmeier Sep 4, 2023
a4de6f0
address comments
pmeier Sep 4, 2023
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275 changes: 0 additions & 275 deletions test/test_transforms_v2_consistency.py
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Original file line number Diff line number Diff line change
Expand Up @@ -705,281 +705,6 @@ def test_to_tensor(self):
assert_equal(prototype_transform(image_numpy), legacy_transform(image_numpy))


class TestAATransforms:
@pytest.mark.parametrize(
"inpt",
[
torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8),
PIL.Image.new("RGB", (256, 256), 123),
tv_tensors.Image(torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8)),
],
)
@pytest.mark.parametrize(
"interpolation",
[
v2_transforms.InterpolationMode.NEAREST,
v2_transforms.InterpolationMode.BILINEAR,
PIL.Image.NEAREST,
],
)
def test_randaug(self, inpt, interpolation, mocker):
t_ref = legacy_transforms.RandAugment(interpolation=interpolation, num_ops=1)
t = v2_transforms.RandAugment(interpolation=interpolation, num_ops=1)

le = len(t._AUGMENTATION_SPACE)
keys = list(t._AUGMENTATION_SPACE.keys())
randint_values = []
for i in range(le):
# Stable API, op_index random call
randint_values.append(i)
# Stable API, if signed there is another random call
if t._AUGMENTATION_SPACE[keys[i]][1]:
randint_values.append(0)
# New API, _get_random_item
randint_values.append(i)
randint_values = iter(randint_values)

mocker.patch("torch.randint", side_effect=lambda *arg, **kwargs: torch.tensor(next(randint_values)))
mocker.patch("torch.rand", return_value=1.0)

for i in range(le):
expected_output = t_ref(inpt)
output = t(inpt)

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

@pytest.mark.parametrize(
"interpolation",
[
v2_transforms.InterpolationMode.NEAREST,
v2_transforms.InterpolationMode.BILINEAR,
],
)
@pytest.mark.parametrize("fill", [None, 85, (10, -10, 10), 0.7, [0.0, 0.0, 0.0], [1], 1])
def test_randaug_jit(self, interpolation, fill):
inpt = torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8)
t_ref = legacy_transforms.RandAugment(interpolation=interpolation, num_ops=1, fill=fill)
t = v2_transforms.RandAugment(interpolation=interpolation, num_ops=1, fill=fill)

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",
[
torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8),
PIL.Image.new("RGB", (256, 256), 123),
tv_tensors.Image(torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8)),
],
)
@pytest.mark.parametrize(
"interpolation",
[
v2_transforms.InterpolationMode.NEAREST,
v2_transforms.InterpolationMode.BILINEAR,
PIL.Image.NEAREST,
],
)
def test_trivial_aug(self, inpt, interpolation, mocker):
t_ref = legacy_transforms.TrivialAugmentWide(interpolation=interpolation)
t = v2_transforms.TrivialAugmentWide(interpolation=interpolation)

le = len(t._AUGMENTATION_SPACE)
keys = list(t._AUGMENTATION_SPACE.keys())
randint_values = []
for i in range(le):
# Stable API, op_index random call
randint_values.append(i)
key = keys[i]
# Stable API, random magnitude
aug_op = t._AUGMENTATION_SPACE[key]
magnitudes = aug_op[0](2, 0, 0)
if magnitudes is not None:
randint_values.append(5)
# Stable API, if signed there is another random call
if aug_op[1]:
randint_values.append(0)
# New API, _get_random_item
randint_values.append(i)
# New API, random magnitude
if magnitudes is not None:
randint_values.append(5)

randint_values = iter(randint_values)

mocker.patch("torch.randint", side_effect=lambda *arg, **kwargs: torch.tensor(next(randint_values)))
mocker.patch("torch.rand", return_value=1.0)

for _ in range(le):
expected_output = t_ref(inpt)
output = t(inpt)

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

@pytest.mark.parametrize(
"interpolation",
[
v2_transforms.InterpolationMode.NEAREST,
v2_transforms.InterpolationMode.BILINEAR,
],
)
@pytest.mark.parametrize("fill", [None, 85, (10, -10, 10), 0.7, [0.0, 0.0, 0.0], [1], 1])
def test_trivial_aug_jit(self, interpolation, fill):
inpt = torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8)
t_ref = legacy_transforms.TrivialAugmentWide(interpolation=interpolation, fill=fill)
t = v2_transforms.TrivialAugmentWide(interpolation=interpolation, fill=fill)

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",
[
torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8),
PIL.Image.new("RGB", (256, 256), 123),
tv_tensors.Image(torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8)),
],
)
@pytest.mark.parametrize(
"interpolation",
[
v2_transforms.InterpolationMode.NEAREST,
v2_transforms.InterpolationMode.BILINEAR,
PIL.Image.NEAREST,
],
)
def test_augmix(self, inpt, interpolation, mocker):
t_ref = legacy_transforms.AugMix(interpolation=interpolation, mixture_width=1, chain_depth=1)
t_ref._sample_dirichlet = lambda t: t.softmax(dim=-1)
t = v2_transforms.AugMix(interpolation=interpolation, mixture_width=1, chain_depth=1)
t._sample_dirichlet = lambda t: t.softmax(dim=-1)

le = len(t._AUGMENTATION_SPACE)
keys = list(t._AUGMENTATION_SPACE.keys())
randint_values = []
for i in range(le):
# Stable API, op_index random call
randint_values.append(i)
key = keys[i]
# Stable API, random magnitude
aug_op = t._AUGMENTATION_SPACE[key]
magnitudes = aug_op[0](2, 0, 0)
if magnitudes is not None:
randint_values.append(5)
# Stable API, if signed there is another random call
if aug_op[1]:
randint_values.append(0)
# New API, _get_random_item
randint_values.append(i)
# New API, random magnitude
if magnitudes is not None:
randint_values.append(5)

randint_values = iter(randint_values)

mocker.patch("torch.randint", side_effect=lambda *arg, **kwargs: torch.tensor(next(randint_values)))
mocker.patch("torch.rand", return_value=1.0)

expected_output = t_ref(inpt)
output = t(inpt)

assert_equal(expected_output, output)

@pytest.mark.parametrize(
"interpolation",
[
v2_transforms.InterpolationMode.NEAREST,
v2_transforms.InterpolationMode.BILINEAR,
],
)
@pytest.mark.parametrize("fill", [None, 85, (10, -10, 10), 0.7, [0.0, 0.0, 0.0], [1], 1])
def test_augmix_jit(self, interpolation, fill):
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, fill=fill)
t = v2_transforms.AugMix(interpolation=interpolation, mixture_width=1, chain_depth=1, fill=fill)

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",
[
torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8),
PIL.Image.new("RGB", (256, 256), 123),
tv_tensors.Image(torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8)),
],
)
@pytest.mark.parametrize(
"interpolation",
[
v2_transforms.InterpolationMode.NEAREST,
v2_transforms.InterpolationMode.BILINEAR,
PIL.Image.NEAREST,
],
)
def test_aa(self, inpt, interpolation):
aa_policy = legacy_transforms.AutoAugmentPolicy("imagenet")
t_ref = legacy_transforms.AutoAugment(aa_policy, interpolation=interpolation)
t = v2_transforms.AutoAugment(aa_policy, interpolation=interpolation)

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

torch.manual_seed(12)
output = t(inpt)

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
PROJECT_ROOT = HERE.parent
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