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Merged
merged 2 commits into from
Mar 23, 2022
Merged

cleanup prototype transforms functional test #5668

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cleanup prototype transforms functional test
pmeier Mar 23, 2022
373feaf
Merge branch 'main' into cleanup-functional-test
pmeier Mar 23, 2022
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37 changes: 18 additions & 19 deletions test/test_prototype_transforms_functional.py
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Original file line number Diff line number Diff line change
Expand Up @@ -379,12 +379,15 @@ def _compute_expected_bbox(bbox, angle_, translate_, scale_, shear_, center_):
np.max(transformed_points[:, 1]),
]
out_bbox = features.BoundingBox(
out_bbox, format=features.BoundingBoxFormat.XYXY, image_size=bbox.image_size, dtype=torch.float32
out_bbox,
format=features.BoundingBoxFormat.XYXY,
image_size=bbox.image_size,
dtype=torch.float32,
device=bbox.device,
)
out_bbox = convert_bounding_box_format(
return convert_bounding_box_format(
out_bbox, old_format=features.BoundingBoxFormat.XYXY, new_format=bbox.format, copy=False
)
return out_bbox.to(bbox.device)

image_size = (32, 38)

Expand Down Expand Up @@ -439,8 +442,8 @@ def test_correctness_affine_bounding_box_on_fixed_input(device):
[1, 1, 5, 5],
]
in_boxes = features.BoundingBox(
in_boxes, format=features.BoundingBoxFormat.XYXY, image_size=image_size, dtype=torch.float64
).to(device)
in_boxes, format=features.BoundingBoxFormat.XYXY, image_size=image_size, dtype=torch.float64, device=device
)
# Tested parameters
angle = 63
scale = 0.89
Expand Down Expand Up @@ -473,9 +476,7 @@ def test_correctness_affine_bounding_box_on_fixed_input(device):
shear=(0, 0),
)

assert len(output_boxes) == len(expected_bboxes)
for a_out_box, out_box in zip(expected_bboxes, output_boxes.cpu()):
np.testing.assert_allclose(out_box.cpu().numpy(), a_out_box)
torch.testing.assert_close(output_boxes.tolist(), expected_bboxes)


@pytest.mark.parametrize("angle", [-54, 56])
Expand Down Expand Up @@ -589,12 +590,15 @@ def _compute_expected_bbox(bbox, angle_, expand_, center_):
out_bbox[3] -= tr_y

out_bbox = features.BoundingBox(
out_bbox, format=features.BoundingBoxFormat.XYXY, image_size=image_size, dtype=torch.float32
out_bbox,
format=features.BoundingBoxFormat.XYXY,
image_size=image_size,
dtype=torch.float32,
device=bbox.device,
)
out_bbox = convert_bounding_box_format(
return convert_bounding_box_format(
out_bbox, old_format=features.BoundingBoxFormat.XYXY, new_format=bbox.format, copy=False
)
return out_bbox.to(bbox.device)

image_size = (32, 38)

Expand Down Expand Up @@ -630,9 +634,6 @@ def _compute_expected_bbox(bbox, angle_, expand_, center_):
expected_bboxes = torch.stack(expected_bboxes)
else:
expected_bboxes = expected_bboxes[0]
print("input:", bboxes)
print("output_bboxes:", output_bboxes)
print("expected_bboxes:", expected_bboxes)
torch.testing.assert_close(output_bboxes, expected_bboxes)


Expand All @@ -649,8 +650,8 @@ def test_correctness_rotate_bounding_box_on_fixed_input(device, expand):
[image_size[1] // 2 - 10, image_size[0] // 2 - 10, image_size[1] // 2 + 10, image_size[0] // 2 + 10],
]
in_boxes = features.BoundingBox(
in_boxes, format=features.BoundingBoxFormat.XYXY, image_size=image_size, dtype=torch.float64
).to(device)
in_boxes, format=features.BoundingBoxFormat.XYXY, image_size=image_size, dtype=torch.float64, device=device
)
# Tested parameters
angle = 45
center = None if expand else [12, 23]
Expand Down Expand Up @@ -687,6 +688,4 @@ def test_correctness_rotate_bounding_box_on_fixed_input(device, expand):
center=center,
)

assert len(output_boxes) == len(expected_bboxes)
for a_out_box, out_box in zip(expected_bboxes, output_boxes.cpu()):
np.testing.assert_allclose(out_box.cpu().numpy(), a_out_box)
torch.testing.assert_close(output_boxes.tolist(), expected_bboxes)
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assert_close refuses to compare a list to a tensor?
IIRC numpy testing utils allow to compare array-likes with array-likes right?

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@pmeier pmeier Mar 23, 2022 •
edited
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Yes and yes. numpy testing functions coerce everything into an np.ndarray first and than compare. We consciously opted against that for mainly two reasons:

  1. If you have tests parametrized over operators like PyTorch core does, it is impossible to see if the returned type is correct. For example

    >>> expected = 1.0
>>> np.testing.assert_allclose(np.array(expected), expected)
>>> torch.testing.assert_close(torch.tensor(expected), expected)
TypeError: No comparison pair was able to handle inputs of type <class 'torch.Tensor'> and <class 'float'>.

  2. It allows us to compare sequences of tensors with different shapes elementwise

    >>> shapes = [(2, 3), (2, 4)]
>>> np.testing.assert_allclose(*([np.ones(shape) for shape in shapes],) * 2)
ValueError: could not broadcast input array from shape (2,3) into shape (2,)
>>> torch.testing.assert_close(*([torch.ones(shape) for shape in shapes],) * 2)

    This aligns nicely with the ability of torch.testing.assert_close to compare mappings elementwise, which is not possible with the numpy functions.