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[proto] Fixed xfailed geom functional segm mask tests #6546

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Merged
merged 2 commits into from
Sep 9, 2022
Merged

[proto] Fixed xfailed geom functional segm mask tests #6546

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c4134a7
[proto] Fixed xfailed geom functional segm mask tests
vfdev-5 Sep 8, 2022
94b438a
Merge branch 'main' into proto-fix-tests-xfail-segm-mask
vfdev-5 Sep 9, 2022
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62 changes: 23 additions & 39 deletions test/test_prototype_transforms_functional.py
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Original file line number Diff line number Diff line change
Expand Up @@ -163,7 +163,7 @@ def make_segmentation_masks(
yield make_segmentation_mask(size=size, dtype=dtype, extra_dims=extra_dims_)

for dtype, extra_dims_, num_objects_ in itertools.product(dtypes, extra_dims, num_objects):
yield make_segmentation_mask(num_objects=num_objects_, dtype=dtype, extra_dims=extra_dims_)
yield make_segmentation_mask(size=sizes[0], num_objects=num_objects_, dtype=dtype, extra_dims=extra_dims_)


class SampleInput:
Expand Down Expand Up @@ -904,21 +904,14 @@ def test_correctness_affine_bounding_box_on_fixed_input(device):
torch.testing.assert_close(output_boxes.tolist(), expected_bboxes)


incorrect_expected_segmentation_mask_setup = pytest.mark.xfail(
reason="This test fails because the expected result computation is wrong. Fix ASAP.",
strict=False,
)


@incorrect_expected_segmentation_mask_setup
@pytest.mark.parametrize("angle", [-54, 56])
@pytest.mark.parametrize("translate", [-7, 8])
@pytest.mark.parametrize("scale", [0.89, 1.12])
@pytest.mark.parametrize("shear", [4])
@pytest.mark.parametrize("center", [None, (12, 14)])
def test_correctness_affine_segmentation_mask(angle, translate, scale, shear, center):
def _compute_expected_mask(mask, angle_, translate_, scale_, shear_, center_):
assert mask.ndim == 3 and mask.shape[0] == 1
assert mask.ndim == 3
affine_matrix = _compute_affine_matrix(angle_, translate_, scale_, shear_, center_)
inv_affine_matrix = np.linalg.inv(affine_matrix)
inv_affine_matrix = inv_affine_matrix[:2, :]
Expand All @@ -927,10 +920,11 @@ def _compute_expected_mask(mask, angle_, translate_, scale_, shear_, center_):
for out_y in range(expected_mask.shape[1]):
for out_x in range(expected_mask.shape[2]):
output_pt = np.array([out_x + 0.5, out_y + 0.5, 1.0])
input_pt = np.floor(np.dot(inv_affine_matrix, output_pt)).astype(np.int32)
input_pt = np.floor(np.dot(inv_affine_matrix, output_pt)).astype("int")
in_x, in_y = input_pt[:2]
if 0 <= in_x < mask.shape[2] and 0 <= in_y < mask.shape[1]:
expected_mask[0, out_y, out_x] = mask[0, in_y, in_x]
for i in range(expected_mask.shape[0]):
expected_mask[i, out_y, out_x] = mask[i, in_y, in_x]
return expected_mask.to(mask.device)

for mask in make_segmentation_masks(extra_dims=((), (4,))):
Expand Down Expand Up @@ -1128,13 +1122,12 @@ def test_correctness_rotate_bounding_box_on_fixed_input(device, expand):
torch.testing.assert_close(output_boxes.tolist(), expected_bboxes)


@incorrect_expected_segmentation_mask_setup
@pytest.mark.parametrize("angle", range(-90, 90, 37))
@pytest.mark.parametrize("angle", range(-89, 90, 37))
@pytest.mark.parametrize("expand, center", [(True, None), (False, None), (False, (12, 14))])
def test_correctness_rotate_segmentation_mask(angle, expand, center):
def _compute_expected_mask(mask, angle_, expand_, center_):
assert mask.ndim == 3 and mask.shape[0] == 1
image_size = mask.shape[-2:]
assert mask.ndim == 3
c, *image_size = mask.shape
affine_matrix = _compute_affine_matrix(angle_, [0.0, 0.0], 1.0, [0.0, 0.0], center_)
inv_affine_matrix = np.linalg.inv(affine_matrix)

Expand All @@ -1155,22 +1148,23 @@ def _compute_expected_mask(mask, angle_, expand_, center_):
max_vals = np.max(new_points, axis=0)[:2]
cmax = np.ceil(np.trunc(max_vals * 1e4) * 1e-4)
cmin = np.floor(np.trunc((min_vals + 1e-8) * 1e4) * 1e-4)
new_width, new_height = (cmax - cmin).astype("int32").tolist()
new_width, new_height = (cmax - cmin).astype("int").tolist()
tr = np.array([-(new_width - width) / 2.0, -(new_height - height) / 2.0, 1.0]) @ inv_affine_matrix.T

inv_affine_matrix[:2, 2] = tr[:2]
image_size = [new_height, new_width]

inv_affine_matrix = inv_affine_matrix[:2, :]
expected_mask = torch.zeros(1, *image_size, dtype=mask.dtype)
expected_mask = torch.zeros(c, *image_size, dtype=mask.dtype)

for out_y in range(expected_mask.shape[1]):
for out_x in range(expected_mask.shape[2]):
output_pt = np.array([out_x + 0.5, out_y + 0.5, 1.0])
input_pt = np.floor(np.dot(inv_affine_matrix, output_pt)).astype(np.int32)
input_pt = np.floor(np.dot(inv_affine_matrix, output_pt)).astype("int")
in_x, in_y = input_pt[:2]
if 0 <= in_x < mask.shape[2] and 0 <= in_y < mask.shape[1]:
expected_mask[0, out_y, out_x] = mask[0, in_y, in_x]
for i in range(expected_mask.shape[0]):
expected_mask[i, out_y, out_x] = mask[i, in_y, in_x]
return expected_mask.to(mask.device)

for mask in make_segmentation_masks(extra_dims=((), (4,))):
Expand Down Expand Up @@ -1617,7 +1611,6 @@ def _compute_expected_bbox(bbox, pcoeffs_):
torch.testing.assert_close(output_bboxes, expected_bboxes, rtol=1e-5, atol=1e-5)


@incorrect_expected_segmentation_mask_setup
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize(
"startpoints, endpoints",
Expand All @@ -1629,19 +1622,9 @@ def _compute_expected_bbox(bbox, pcoeffs_):
)
def test_correctness_perspective_segmentation_mask(device, startpoints, endpoints):
def _compute_expected_mask(mask, pcoeffs_):
assert mask.ndim == 3 and mask.shape[0] == 1
m1 = np.array(
[
[pcoeffs_[0], pcoeffs_[1], pcoeffs_[2]],
[pcoeffs_[3], pcoeffs_[4], pcoeffs_[5]],
]
)
m2 = np.array(
[
[pcoeffs_[6], pcoeffs_[7], 1.0],
[pcoeffs_[6], pcoeffs_[7], 1.0],
]
)
assert mask.ndim == 3
m1 = np.array([[pcoeffs_[0], pcoeffs_[1], pcoeffs_[2]], [pcoeffs_[3], pcoeffs_[4], pcoeffs_[5]]])
m2 = np.array([[pcoeffs_[6], pcoeffs_[7], 1.0], [pcoeffs_[6], pcoeffs_[7], 1.0]])

expected_mask = torch.zeros_like(mask.cpu())
for out_y in range(expected_mask.shape[1]):
Expand All @@ -1654,7 +1637,8 @@ def _compute_expected_mask(mask, pcoeffs_):

in_x, in_y = input_pt[:2]
if 0 <= in_x < mask.shape[2] and 0 <= in_y < mask.shape[1]:
expected_mask[0, out_y, out_x] = mask[0, in_y, in_x]
for i in range(expected_mask.shape[0]):
expected_mask[i, out_y, out_x] = mask[i, in_y, in_x]
return expected_mask.to(mask.device)

pcoeffs = _get_perspective_coeffs(startpoints, endpoints)
Expand Down Expand Up @@ -1819,7 +1803,6 @@ def test_correctness_gaussian_blur_image_tensor(device, image_size, dt, ksize, s
torch.testing.assert_close(out, true_out, rtol=0.0, atol=1.0, msg=f"{ksize}, {sigma}")


@incorrect_expected_segmentation_mask_setup
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize(
"fn, make_samples", [(F.elastic_image_tensor, make_images), (F.elastic_segmentation_mask, make_segmentation_masks)]
Expand All @@ -1829,10 +1812,11 @@ def test_correctness_elastic_image_or_mask_tensor(device, fn, make_samples):
for sample in make_samples(sizes=((64, 76),), extra_dims=((), (4,))):
c, h, w = sample.shape[-3:]
# Setup a dummy image with 4 points
sample[..., in_box[1], in_box[0]] = torch.tensor([12, 34, 96, 112])[:c]
sample[..., in_box[3] - 1, in_box[0]] = torch.tensor([12, 34, 96, 112])[:c]
sample[..., in_box[3] - 1, in_box[2] - 1] = torch.tensor([12, 34, 96, 112])[:c]
sample[..., in_box[1], in_box[2] - 1] = torch.tensor([12, 34, 96, 112])[:c]
print(sample.shape)
sample[..., in_box[1], in_box[0]] = torch.arange(10, 10 + c)
sample[..., in_box[3] - 1, in_box[0]] = torch.arange(20, 20 + c)
sample[..., in_box[3] - 1, in_box[2] - 1] = torch.arange(30, 30 + c)
sample[..., in_box[1], in_box[2] - 1] = torch.arange(40, 40 + c)
sample = sample.to(device)

if fn == F.elastic_image_tensor:
Expand Down