def test_from_invalid_tensor(self, shape: Tuple[int], device, dtype):
box_xyzxyz = torch.as_tensor([[1, 2, 3, 4, -5, 6]], device=device, dtype=dtype).view(*shape)
box_xyzxyz_plus = torch.as_tensor([[1, 2, 3, 0, 6, 4]], device=device, dtype=dtype).view(*shape)
try:
Boxes3D.from_tensor(box_xyzxyz, mode='xyzxyz')
assert False, "Boxes3D.from_tensor should have raised any exception"
except ValueError:
pass
try:
Boxes3D.from_tensor(box_xyzxyz_plus, mode='xyzxyz_plus')
assert False, "Boxes3D.from_tensor should have raised any exception"
except ValueError:
pass
def test_transform_multiple_boxes(self, device, dtype):
# Define boxes in XYZXYZ format for simplicity.
boxes_xyzxyz = torch.tensor(
[
[139.2640, 103.0150, 283.162, 398.3120, 411.5225, 454.185],
[1.0240, 80.5547, 469.50, 513.0000, 513.0000, 513.0],
[165.2053, 262.1440, 42.98, 511.6347, 509.9280, 785.443],
[119.8080, 144.2067, 234.21, 258.0240, 411.1292, 387.14],
],
device=device,
dtype=dtype,
).repeat(
2, 1, 1
) # 2 x 4 x 4 two images 4 boxes each
expected_boxes_xyzxyz = torch.tensor(
[
[
[372.7360, 103.0150, 567.324, 115.6880, 411.5225, 908.37],
[510.9760, 80.5547, 940.0, 1.0000, 513.0000, 1026.0],
[346.7947, 262.1440, 86.96, 2.3653, 509.9280, 1570.886],
[392.1920, 144.2067, 469.42, 255.9760, 411.1292, 774.28],
],
[
[139.2640, 103.0150, 283.162, 398.3120, 411.5225, 454.185],
[1.0240, 80.5547, 469.50, 513.0000, 513.0000, 513.0],
[165.2053, 262.1440, 42.98, 511.6347, 509.9280, 785.443],
[119.8080, 144.2067, 234.21, 258.0240, 411.1292, 387.14],
],
],
device=device,
dtype=dtype,
)
trans_mat = torch.tensor(
[
[[-1.0, 0.0, 0.0, 512.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 2.0, 1.0], [0.0, 0.0, 0.0, 1.0]],
[[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]],
],
device=device,
dtype=dtype,
)
boxes = Boxes3D.from_tensor(boxes_xyzxyz)
expected_boxes = Boxes3D.from_tensor(expected_boxes_xyzxyz, validate_boxes=False)
out = boxes.transform_boxes(trans_mat)
assert_allclose(out.data, expected_boxes.data, atol=1e-4, rtol=1e-4)
def test_gradcheck(self, device, dtype):
# Define boxes in XYZXYZ format for simplicity.
boxes_xyzxyz = torch.tensor(
[
[139.2640, 103.0150, 283.162, 397.3120, 410.5225, 453.185],
[1.0240, 80.5547, 469.50, 512.0000, 512.0000, 512.0],
[165.2053, 262.1440, 42.98, 510.6347, 508.9280, 784.443],
[119.8080, 144.2067, 234.21, 257.0240, 410.1292, 386.14],
],
device=device,
dtype=dtype,
)
boxes = Boxes3D.from_tensor(boxes_xyzxyz)
trans_mat = torch.tensor(
[[[-1.0, 0.0, 0.0, 512.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 2.0, 1.0], [0.0, 0.0, 0.0, 1.0]]],
device=device,
dtype=dtype,
)
trans_mat = utils.tensor_to_gradcheck_var(trans_mat)
t_boxes = utils.tensor_to_gradcheck_var(boxes.data)
def _wrapper_transform_boxes(hexahedrons, M):
boxes = Boxes3D(hexahedrons)
boxes = boxes.transform_boxes(M)
return boxes.data
assert gradcheck(_wrapper_transform_boxes, (t_boxes, trans_mat), raise_exception=True)
def test_to(self, device, dtype):
boxes = Boxes3D.from_tensor(torch.as_tensor([[1, 2, 3, 4, 5, 6]], device='cpu', dtype=torch.float32))
assert boxes.to(device=device).data.device == device
assert boxes.to(dtype=dtype).data.dtype == dtype
boxes_moved = boxes.to(device, dtype)
assert boxes_moved is boxes # to is an inplace op.
assert boxes_moved.data.device == device, boxes_moved.data.dtype == dtype
def test_transform_boxes_(self, device, dtype):
# Define boxes in XYZXYZ format for simplicity.
boxes_xyzxyz = torch.tensor(
[[139.2640, 103.0150, 283.162, 398.3120, 411.5225, 454.185]], device=device, dtype=dtype
)
expected_boxes_xyzxyz = torch.tensor(
[[372.7360, 103.0150, 567.324, 115.6880, 411.5225, 908.37]], device=device, dtype=dtype
)
boxes = Boxes3D.from_tensor(boxes_xyzxyz)
expected_boxes = Boxes3D.from_tensor(expected_boxes_xyzxyz, validate_boxes=False)
trans_mat = torch.tensor(
[[[-1.0, 0.0, 0.0, 512.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 2.0, 1.0], [0.0, 0.0, 0.0, 1.0]]],
device=device,
dtype=dtype,
)
transformed_boxes = boxes.transform_boxes_(trans_mat)
assert_allclose(transformed_boxes.data, expected_boxes.data, atol=1e-4, rtol=1e-4)
# inplace check
assert transformed_boxes is boxes
def test_from_tensor(self, shape: Tuple[int], device, dtype):
box_xyzxyz = torch.as_tensor([[1, 2, 3, 4, 5, 6]], device=device, dtype=dtype).view(*shape)
box_xyzxyz_plus = torch.as_tensor([[1, 2, 3, 3, 4, 5]], device=device, dtype=dtype).view(*shape)
box_xyzwhd = torch.as_tensor([[1, 2, 3, 3, 3, 3]], device=device, dtype=dtype).view(*shape)
expected_box = torch.as_tensor(
[[[1, 2, 3], [3, 2, 3], [3, 4, 3], [1, 4, 3], [1, 2, 5], [3, 2, 5], [3, 4, 5], [1, 4, 5]]], # Front # Back
device=device,
dtype=dtype,
).view(*shape[:-1], 8, 3)
kornia_xyzxyz = Boxes3D.from_tensor(box_xyzxyz, mode='xyzxyz').data
kornia_xyzxyz_plus = Boxes3D.from_tensor(box_xyzxyz_plus, mode='xyzxyz_plus').data
kornia_xyzwhd = Boxes3D.from_tensor(box_xyzwhd, mode='xyzwhd').data
assert kornia_xyzxyz.shape == expected_box.shape
assert_allclose(kornia_xyzxyz, expected_box)
assert kornia_xyzxyz_plus.shape == expected_box.shape
assert_allclose(kornia_xyzxyz_plus, expected_box)
assert kornia_xyzwhd.shape == expected_box.shape
assert_allclose(kornia_xyzwhd, expected_box)
def test_gradcheck(self, device, dtype):
# Uncomment when enabling gradient checks
# def apply_boxes_method(tensor: torch.Tensor, method: str, **kwargs):
# boxes = Boxes3D(tensor)
# result = getattr(boxes, method)(**kwargs)
# return result.data if isinstance(result, Boxes3D) else result
t_boxes1 = torch.tensor(
[[[0, 1, 2], [10, 1, 2], [10, 21, 2], [0, 21, 2], [0, 1, 32], [10, 1, 32], [10, 21, 32], [0, 21, 32]]],
device=device,
dtype=dtype,
)
t_boxes1 = utils.tensor_to_gradcheck_var(t_boxes1)
# Uncomment when enabling gradient checks
# t_boxes2 = utils.tensor_to_gradcheck_var(t_boxes1.detach().clone())
# t_boxes3 = utils.tensor_to_gradcheck_var(t_boxes1.detach().clone())
# t_boxes4 = utils.tensor_to_gradcheck_var(t_boxes1.detach().clone())
t_boxes_xyzxyz = utils.tensor_to_gradcheck_var(torch.tensor([[1.0, 3.0, 8.0, 5.0, 6.0, 12.0]]))
t_boxes_xyzxyz1 = utils.tensor_to_gradcheck_var(t_boxes_xyzxyz.detach().clone())
# Gradient checks for Boxes3D.to_tensor (and Boxes3D.get_boxes_shape) are disable since the is a bug
# in their gradient. See https://github.com/kornia/kornia/issues/1396.
# assert gradcheck(partial(apply_boxes_method, method='to_tensor'), (t_boxes2,), raise_exception=True)
# assert gradcheck(
# partial(apply_boxes_method, method='to_tensor', mode='xyzxyz_plus'), (t_boxes3,), raise_exception=True
# )
# assert gradcheck(
# partial(apply_boxes_method, method='to_tensor', mode='vertices_plus'), (t_boxes4,), raise_exception=True
# )
# assert gradcheck(partial(apply_boxes_method, method='get_boxes_shape'), (t_boxes1,), raise_exception=True)
assert gradcheck(
lambda x: Boxes3D.from_tensor(x, mode='xyzxyz_plus').data, (t_boxes_xyzxyz,), raise_exception=True
)
assert gradcheck(
lambda x: Boxes3D.from_tensor(x, mode='xyzwhd').data, (t_boxes_xyzxyz1,), raise_exception=True
)
