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port horizontal flip tests #7703

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merged 5 commits into from
Jun 28, 2023
Merged

port horizontal flip tests #7703

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e8da28a
port horizontal flip tests
pmeier Jun 27, 2023
6664940
put reference inputs fn for vertical flip back
pmeier Jun 27, 2023
e1f00fe
simlify
pmeier Jun 27, 2023
5dd4e53
Merge branch 'main' into port/horizontal-flip
pmeier Jun 27, 2023
95c80d2
add transform noop test
pmeier Jun 28, 2023
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53 changes: 0 additions & 53 deletions test/test_transforms_v2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -406,59 +406,6 @@ def was_applied(output, inpt):
assert transform.was_applied(output, input)


@pytest.mark.parametrize("p", [0.0, 1.0])
class TestRandomHorizontalFlip:
def input_expected_image_tensor(self, p, dtype=torch.float32):
input = torch.tensor([[[0, 1], [0, 1]], [[1, 0], [1, 0]]], dtype=dtype)
expected = torch.tensor([[[1, 0], [1, 0]], [[0, 1], [0, 1]]], dtype=dtype)

return input, expected if p == 1 else input

def test_simple_tensor(self, p):
input, expected = self.input_expected_image_tensor(p)
transform = transforms.RandomHorizontalFlip(p=p)

actual = transform(input)

assert_equal(expected, actual)

def test_pil_image(self, p):
input, expected = self.input_expected_image_tensor(p, dtype=torch.uint8)
transform = transforms.RandomHorizontalFlip(p=p)

actual = transform(to_pil_image(input))

assert_equal(expected, pil_to_tensor(actual))

def test_datapoints_image(self, p):
input, expected = self.input_expected_image_tensor(p)
transform = transforms.RandomHorizontalFlip(p=p)

actual = transform(datapoints.Image(input))

assert_equal(datapoints.Image(expected), actual)

def test_datapoints_mask(self, p):
input, expected = self.input_expected_image_tensor(p)
transform = transforms.RandomHorizontalFlip(p=p)

actual = transform(datapoints.Mask(input))

assert_equal(datapoints.Mask(expected), actual)

def test_datapoints_bounding_box(self, p):
input = datapoints.BoundingBox([0, 0, 5, 5], format=datapoints.BoundingBoxFormat.XYXY, spatial_size=(10, 10))
transform = transforms.RandomHorizontalFlip(p=p)

actual = transform(input)

expected_image_tensor = torch.tensor([5, 0, 10, 5]) if p == 1.0 else input
expected = datapoints.BoundingBox.wrap_like(input, expected_image_tensor)
assert_equal(expected, actual)
assert actual.format == expected.format
assert actual.spatial_size == expected.spatial_size


@pytest.mark.parametrize("p", [0.0, 1.0])
class TestRandomVerticalFlip:
def input_expected_image_tensor(self, p, dtype=torch.float32):
Expand Down
160 changes: 140 additions & 20 deletions test/test_transforms_v2_refactored.py
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Original file line number Diff line number Diff line change
Expand Up @@ -295,9 +295,9 @@ def check_transform(transform_cls, input, *args, **kwargs):
_check_transform_v1_compatibility(transform, input)


def transform_cls_to_functional(transform_cls):
def transform_cls_to_functional(transform_cls, **default_kwargs):
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def wrapper(input, *args, **kwargs):
transform = transform_cls(*args, **kwargs)
transform = transform_cls(*args, **{**default_kwargs, **kwargs})
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return transform(input)

wrapper.__name__ = transform_cls.__name__
Expand All @@ -321,14 +321,14 @@ def assert_warns_antialias_default_value():


def reference_affine_bounding_box_helper(bounding_box, *, format, spatial_size, affine_matrix):
def transform(bbox, affine_matrix_, format_, spatial_size_):
def transform(bbox):
# Go to float before converting to prevent precision loss in case of CXCYWH -> XYXY and W or H is 1
in_dtype = bbox.dtype
if not torch.is_floating_point(bbox):
bbox = bbox.float()
bbox_xyxy = F.convert_format_bounding_box(
bbox.as_subclass(torch.Tensor),
old_format=format_,
old_format=format,
new_format=datapoints.BoundingBoxFormat.XYXY,
inplace=True,
)
Expand All @@ -340,7 +340,7 @@ def transform(bbox, affine_matrix_, format_, spatial_size_):
[bbox_xyxy[2].item(), bbox_xyxy[3].item(), 1.0],
]
)
transformed_points = np.matmul(points, affine_matrix_.T)
transformed_points = np.matmul(points, affine_matrix.T)
out_bbox = torch.tensor(
[
np.min(transformed_points[:, 0]).item(),
Expand All @@ -351,23 +351,14 @@ def transform(bbox, affine_matrix_, format_, spatial_size_):
dtype=bbox_xyxy.dtype,
)
out_bbox = F.convert_format_bounding_box(
out_bbox, old_format=datapoints.BoundingBoxFormat.XYXY, new_format=format_, inplace=True
out_bbox, old_format=datapoints.BoundingBoxFormat.XYXY, new_format=format, inplace=True
)
# It is important to clamp before casting, especially for CXCYWH format, dtype=int64
out_bbox = F.clamp_bounding_box(out_bbox, format=format_, spatial_size=spatial_size_)
out_bbox = F.clamp_bounding_box(out_bbox, format=format, spatial_size=spatial_size)
out_bbox = out_bbox.to(dtype=in_dtype)
return out_bbox

if bounding_box.ndim < 2:
bounding_box = [bounding_box]

expected_bboxes = [transform(bbox, affine_matrix, format, spatial_size) for bbox in bounding_box]
if len(expected_bboxes) > 1:
expected_bboxes = torch.stack(expected_bboxes)
else:
expected_bboxes = expected_bboxes[0]

return expected_bboxes
return torch.stack([transform(b) for b in bounding_box.reshape(-1, 4).unbind()]).reshape(bounding_box.shape)
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IMO, the old logic was harder to parse. Basically all we are doing here is to break a batched tensor into its individual boxes, apply the helper to them, and reverse the process.

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bounding_box.reshape(-1, 4)

The only reason we need this reshape is because we may pass non-2D boxes (i.e. a single box as 1D tensor), right?

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Yup. We could factor this out into a decorator that we can put onto reference functions so they only need to handle the unbatched case. For now, we only have the affine bbox helper so I left it as is. Will look into the decorator again if we need it elsewhere.



class TestResize:
Expand Down Expand Up @@ -493,7 +484,7 @@ def test_kernel_video(self):

@pytest.mark.parametrize("size", OUTPUT_SIZES)
@pytest.mark.parametrize(
"input_type_and_kernel",
("input_type", "kernel"),
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Just a QoL improvement that saves us a line in the test below.

[
(torch.Tensor, F.resize_image_tensor),
(PIL.Image.Image, F.resize_image_pil),
Expand All @@ -503,8 +494,7 @@ def test_kernel_video(self):
(datapoints.Video, F.resize_video),
],
)
def test_dispatcher(self, size, input_type_and_kernel):
input_type, kernel = input_type_and_kernel
def test_dispatcher(self, size, input_type, kernel):
check_dispatcher(
F.resize,
kernel,
Expand Down Expand Up @@ -726,3 +716,133 @@ def test_no_regression_5405(self, input_type):
output = F.resize(input, size=size, max_size=max_size, antialias=True)

assert max(F.get_spatial_size(output)) == max_size


class TestHorizontalFlip:
def _make_input(self, input_type, *, dtype=None, device="cpu", spatial_size=(17, 11), **kwargs):
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is this mostly the same as the one in TestResize?

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Yes. If that's ok with you, I'll copy paste it for now until I have a handful transforms ported. If it turns out we never or rarely use something else, I'll factor it out as public function.

if input_type in {torch.Tensor, PIL.Image.Image, datapoints.Image}:
input = make_image(size=spatial_size, dtype=dtype or torch.uint8, device=device, **kwargs)
if input_type is torch.Tensor:
input = input.as_subclass(torch.Tensor)
elif input_type is PIL.Image.Image:
input = F.to_image_pil(input)
elif input_type is datapoints.BoundingBox:
kwargs.setdefault("format", datapoints.BoundingBoxFormat.XYXY)
input = make_bounding_box(
dtype=dtype or torch.float32,
device=device,
spatial_size=spatial_size,
**kwargs,
)
elif input_type is datapoints.Mask:
input = make_segmentation_mask(size=spatial_size, dtype=dtype or torch.uint8, device=device, **kwargs)
elif input_type is datapoints.Video:
input = make_video(size=spatial_size, dtype=dtype or torch.uint8, device=device, **kwargs)

return input

@pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_kernel_image_tensor(self, dtype, device):
check_kernel(F.horizontal_flip_image_tensor, self._make_input(torch.Tensor))

@pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
@pytest.mark.parametrize("dtype", [torch.float32, torch.int64])
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_kernel_bounding_box(self, format, dtype, device):
bounding_box = self._make_input(datapoints.BoundingBox, dtype=dtype, device=device, format=format)
check_kernel(
F.horizontal_flip_bounding_box,
bounding_box,
format=format,
spatial_size=bounding_box.spatial_size,
)

@pytest.mark.parametrize(
"dtype_and_make_mask", [(torch.uint8, make_segmentation_mask), (torch.bool, make_detection_mask)]
)
def test_kernel_mask(self, dtype_and_make_mask):
dtype, make_mask = dtype_and_make_mask
check_kernel(F.horizontal_flip_mask, make_mask(dtype=dtype))

def test_kernel_video(self):
check_kernel(F.horizontal_flip_video, self._make_input(datapoints.Video))

@pytest.mark.parametrize(
("input_type", "kernel"),
[
(torch.Tensor, F.horizontal_flip_image_tensor),
(PIL.Image.Image, F.horizontal_flip_image_pil),
(datapoints.Image, F.horizontal_flip_image_tensor),
(datapoints.BoundingBox, F.horizontal_flip_bounding_box),
(datapoints.Mask, F.horizontal_flip_mask),
(datapoints.Video, F.horizontal_flip_video),
],
)
def test_dispatcher(self, kernel, input_type):
check_dispatcher(F.horizontal_flip, kernel, self._make_input(input_type))

@pytest.mark.parametrize(
("input_type", "kernel"),
[
(torch.Tensor, F.resize_image_tensor),
(PIL.Image.Image, F.resize_image_pil),
(datapoints.Image, F.resize_image_tensor),
(datapoints.BoundingBox, F.resize_bounding_box),
(datapoints.Mask, F.resize_mask),
(datapoints.Video, F.resize_video),
],
)
def test_dispatcher_signature(self, kernel, input_type):
check_dispatcher_signatures_match(F.resize, kernel=kernel, input_type=input_type)

@pytest.mark.parametrize(
"input_type",
[torch.Tensor, PIL.Image.Image, datapoints.Image, datapoints.BoundingBox, datapoints.Mask, datapoints.Video],
)
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_transform(self, input_type, device):
input = self._make_input(input_type, device=device)

check_transform(transforms.RandomHorizontalFlip, input)

@pytest.mark.parametrize(
"fn", [F.horizontal_flip, transform_cls_to_functional(transforms.RandomHorizontalFlip, p=1)]
)
def test_image_correctness(self, fn):
image = self._make_input(torch.Tensor, dtype=torch.uint8, device="cpu")

actual = fn(image)
expected = F.to_image_tensor(F.horizontal_flip(F.to_image_pil(image)))

torch.testing.assert_close(actual, expected)

def _reference_horizontal_flip_bounding_box(self, bounding_box):
affine_matrix = np.array(
[
[-1, 0, bounding_box.spatial_size[1]],
[0, 1, 0],
],
dtype="float64" if bounding_box.dtype == torch.float64 else "float32",
)

expected_bboxes = reference_affine_bounding_box_helper(
bounding_box,
format=bounding_box.format,
spatial_size=bounding_box.spatial_size,
affine_matrix=affine_matrix,
)

return datapoints.BoundingBox.wrap_like(bounding_box, expected_bboxes)

@pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
@pytest.mark.parametrize(
"fn", [F.horizontal_flip, transform_cls_to_functional(transforms.RandomHorizontalFlip, p=1)]
)
def test_bounding_box_correctness(self, format, fn):
bounding_box = self._make_input(datapoints.BoundingBox)

actual = fn(bounding_box)
expected = self._reference_horizontal_flip_bounding_box(bounding_box)

torch.testing.assert_close(actual, expected)
10 changes: 0 additions & 10 deletions test/transforms_v2_dispatcher_infos.py
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Original file line number Diff line number Diff line change
Expand Up @@ -138,16 +138,6 @@ def fill_sequence_needs_broadcast(args_kwargs):


DISPATCHER_INFOS = [
DispatcherInfo(
F.horizontal_flip,
kernels={
datapoints.Image: F.horizontal_flip_image_tensor,
datapoints.Video: F.horizontal_flip_video,
datapoints.BoundingBox: F.horizontal_flip_bounding_box,
datapoints.Mask: F.horizontal_flip_mask,
},
pil_kernel_info=PILKernelInfo(F.horizontal_flip_image_pil, kernel_name="horizontal_flip_image_pil"),
),
DispatcherInfo(
F.affine,
kernels={
Expand Down
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