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Add rotated bounding box formats #8841

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Add rotated bounding box formats #8841

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182 changes: 180 additions & 2 deletions test/test_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1288,6 +1288,38 @@ def test_bbox_same(self):
assert_equal(ops.box_convert(box_tensor, in_fmt="xywh", out_fmt="xywh"), exp_xyxy)
assert_equal(ops.box_convert(box_tensor, in_fmt="cxcywh", out_fmt="cxcywh"), exp_xyxy)

def test_rotated_bbox_same(self):
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I think this kind of check is already taken care of in

vision/test/test_transforms_v2.py

Lines 3521 to 3531 in 06a925c

@pytest.mark.parametrize("format", list(tv_tensors.BoundingBoxFormat))
@pytest.mark.parametrize("inplace", [False, True])
def test_kernel_noop(self, format, inplace):
input = make_bounding_boxes(format=format).as_subclass(torch.Tensor)
input_version = input._version
output = F.convert_bounding_box_format(input, old_format=format, new_format=format, inplace=inplace)
assert output is input
assert output.data_ptr() == input.data_ptr()
assert output._version == input_version

box_tensor = torch.tensor(
[
[0, 0, 100, 100, 0],
[0, 0, 0, 0, 0],
[10, 15, 30, 35, 0],
[23, 35, 93, 95, 0],
],
dtype=torch.float,
)

exp_xyxyr = torch.tensor(
[
[0, 0, 100, 100, 0],
[0, 0, 0, 0, 0],
[10, 15, 30, 35, 0],
[23, 35, 93, 95, 0],
],
dtype=torch.float,
)

assert exp_xyxyr.size() == torch.Size([4, 5])
assert_equal(
ops.box_convert(box_tensor, in_fmt="xyxyr", out_fmt="xyxyr"), exp_xyxyr
)
assert_equal(
ops.box_convert(box_tensor, in_fmt="xywhr", out_fmt="xywhr"), exp_xyxyr
)
assert_equal(
ops.box_convert(box_tensor, in_fmt="cxcywhr", out_fmt="cxcywhr"), exp_xyxyr
)

def test_bbox_xyxy_xywh(self):
# Simple test convert boxes to xywh and back. Make sure they are same.
# box_tensor is in x1 y1 x2 y2 format.
Expand Down Expand Up @@ -1339,8 +1371,154 @@ def test_bbox_xywh_cxcywh(self):
box_xywh = ops.box_convert(box_cxcywh, in_fmt="cxcywh", out_fmt="xywh")
assert_equal(box_xywh, box_tensor)

@pytest.mark.parametrize("inv_infmt", ["xwyh", "cxwyh"])
@pytest.mark.parametrize("inv_outfmt", ["xwcx", "xhwcy"])
def test_bbox_xyxy_to_cxcywhr(self):
box_tensor = torch.tensor(
[[0, 0, 100, 100], [0, 0, 0, 0], [10, 15, 30, 35], [23, 35, 93, 95]],
dtype=torch.float,
)
exp_cxcywhr = torch.tensor(
[
[50, 50, 100, 100, 0],
[0, 0, 0, 0, 0],
[20, 25, 20, 20, 0],
[58, 65, 70, 60, 0],
],
dtype=torch.float,
)

assert exp_cxcywhr.size() == torch.Size([4, 5])
box_cxcywhr = ops.box_convert(box_tensor, in_fmt="xyxy", out_fmt="cxcywhr")
assert_equal(box_cxcywhr, exp_cxcywhr)

def test_bbox_xyxyr_xywhr(self):
# Simple test convert boxes to xywh and back. Make sure they are same.
# box_tensor is in x1 y1 x2 y2 format.
box_tensor = torch.tensor(
[
[0, 0, 100, 100, 0],
[0, 0, 0, 0, 0],
[10, 15, 30, 35, 0],
[23, 35, 93, 95, 0],
[3, 2, 7, 4, 0],
[3, 2, 5, -2, 90],
],
dtype=torch.float,
)
exp_xywhr = torch.tensor(
[
[0, 0, 100, 100, 0],
[0, 0, 0, 0, 0],
[10, 15, 20, 20, 0],
[23, 35, 70, 60, 0],
[3, 2, 4, 2, 0],
[3, 2, 4, 2, 90],
],
dtype=torch.float,
)

assert exp_xywhr.size() == torch.Size([6, 5])
box_xywhr = ops.box_convert(box_tensor, in_fmt="xyxyr", out_fmt="xywhr")
assert torch.allclose(box_xywhr, exp_xywhr)
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Let's use torch.testing.assert_close() instead of torch.allclose, as it's more robust and provides better messages.

That being said, could we use assert_equal() here, since we're expecting integer-valued tensors? Is it because of the r column?


# Reverse conversion
box_xyxyr = ops.box_convert(box_xywhr, in_fmt="xywhr", out_fmt="xyxyr")
assert torch.allclose(box_xyxyr, box_tensor)

def test_bbox_xyxyr_cxcywhr(self):
# Simple test convert boxes to cxcywh and back. Make sure they are same.
# box_tensor is in x1 y1 x2 y2 format.
box_tensor = torch.tensor(
[
[0, 0, 100, 100, 0],
[0, 0, 0, 0, 0],
[10, 15, 30, 35, 0],
[23, 35, 93, 95, 0],
[3, 2, 7, 4, 0],
],
dtype=torch.float,
)
exp_cxcywhr = torch.tensor(
[
[50, 50, 100, 100, 0],
[0, 0, 0, 0, 0],
[20, 25, 20, 20, 0],
[58, 65, 70, 60, 0],
[5, 3, 4, 2, 0],
],
dtype=torch.float,
)

assert exp_cxcywhr.size() == torch.Size([5, 5])
box_cxcywhr = ops.box_convert(box_tensor, in_fmt="xyxyr", out_fmt="cxcywhr")
assert torch.allclose(box_cxcywhr, exp_cxcywhr)

# Reverse conversion
box_xyxyr = ops.box_convert(box_cxcywhr, in_fmt="cxcywhr", out_fmt="xyxyr")
assert torch.allclose(box_xyxyr, box_tensor)

def test_bbox_xywhr_cxcywhr(self):
box_tensor = torch.tensor(
[
[0, 0, 100, 100, 0],
[0, 0, 0, 0, 0],
[10, 15, 20, 20, 0],
[23, 35, 70, 60, 0],
[4.0, 2.0, 4.0, 2.0, 0.0],
[5.0, 5.0, 4.0, 2.0, 90.0],
[8.0, 4.0, 4.0, 2.0, 180.0],
[7.0, 1.0, 4.0, 2.0, -90.0],
],
dtype=torch.float,
)

exp_cxcywhr = torch.tensor(
[
[50, 50, 100, 100, 0],
[0, 0, 0, 0, 0],
[20, 25, 20, 20, 0],
[58, 65, 70, 60, 0],
[6, 3, 4, 2, 0],
[6, 3, 4, 2, 90],
[6, 3, 4, 2, 180],
[6, 3, 4, 2, -90],
],
dtype=torch.float,
)

assert exp_cxcywhr.size() == torch.Size([8, 5])
box_cxcywhr = ops.box_convert(box_tensor, in_fmt="xywhr", out_fmt="cxcywhr")
assert torch.allclose(box_cxcywhr, exp_cxcywhr)

# Reverse conversion
box_xywhr = ops.box_convert(box_cxcywhr, in_fmt="cxcywhr", out_fmt="xywhr")
assert torch.allclose(box_xywhr, box_tensor)

def test_bbox_xyxyr_to_xyxyxyxy(self):
box_tensor = torch.tensor([[4, 5, 6, 1, 90]], dtype=torch.float)
exp_xyxyxyxy = torch.tensor([[4, 5, 4, 1, 6, 1, 6, 5]], dtype=torch.float)

assert exp_xyxyxyxy.size() == torch.Size([1, 8])
box_xyxyxyxy = ops.box_convert(box_tensor, in_fmt="xyxyr", out_fmt="xyxyxyxy")
assert_equal(box_xyxyxyxy, exp_xyxyxyxy)

def test_bbox_cxcywhr_to_xyxyxyxy(self):
box_tensor = torch.tensor([[5, 3, 4, 2, 90]], dtype=torch.float)
exp_xyxyxyxy = torch.tensor([[4, 5, 4, 1, 6, 1, 6, 5]], dtype=torch.float)

assert exp_xyxyxyxy.size() == torch.Size([1, 8])
box_xyxyxyxy = ops.box_convert(box_tensor, in_fmt="cxcywhr", out_fmt="xyxyxyxy")
assert_equal(box_xyxyxyxy, exp_xyxyxyxy)

def test_bbox_xywhr_to_xyxyxyxy(self):
box_tensor = torch.tensor([[4, 5, 4, 2, 90]], dtype=torch.float)
exp_xyxyxyxy = torch.tensor([[4, 5, 4, 1, 6, 1, 6, 5]], dtype=torch.float)

assert exp_xyxyxyxy.size() == torch.Size([1, 8])
box_xyxyxyxy = ops.box_convert(box_tensor, in_fmt="xywhr", out_fmt="xyxyxyxy")
assert_equal(box_xyxyxyxy, exp_xyxyxyxy)

@pytest.mark.parametrize("inv_infmt", ["xwyh", "cxwyh", "xwyhr", "cxwyhr", "xxxxyyyy"])
@pytest.mark.parametrize("inv_outfmt", ["xwcx", "xhwcy", "xwcxr", "xhwcyr", "xyxyxxyy"])
def test_bbox_invalid(self, inv_infmt, inv_outfmt):
box_tensor = torch.tensor(
[[0, 0, 100, 100], [0, 0, 0, 0], [10, 15, 20, 20], [23, 35, 70, 60]], dtype=torch.float
Expand Down
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