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_get_boxes_shape(self, device, dtype):
box = Boxes3D(
torch.tensor(
[[[0, 1, 2], [0, 1, 32], [10, 21, 2], [0, 21, 2], [10, 1, 32], [10, 21, 32], [10, 1, 2], [0, 21, 32]]],
device=device,
dtype=dtype,
)
) # 1x8x3
t_boxes = torch.tensor(
[
[[0, 21, 32], [0, 1, 2], [10, 1, 2], [0, 21, 2], [0, 1, 32], [10, 21, 2], [10, 1, 32], [10, 21, 32]],
[[3, 4, 5], [3, 4, 65], [43, 54, 5], [3, 54, 5], [43, 4, 5], [43, 4, 65], [43, 54, 65], [3, 54, 65]],
],
device=device,
dtype=dtype,
) # 2x8x3
boxes = Boxes3D(t_boxes)
boxes_batch = Boxes3D(t_boxes[None]) # (1, 2, 8, 3)
# Single box
d, h, w = box.get_boxes_shape()
assert (d.item(), h.item(), w.item()) == (31.0, 21.0, 11.0)
# Boxes
d, h, w = boxes.get_boxes_shape()
assert h.ndim == 1 and w.ndim == 1
assert len(d) == 2 and len(h) == 2 and len(w) == 2
assert (
(d == torch.as_tensor([31.0, 61.0], device=device)).all()
and (h == torch.as_tensor([21.0, 51.0], device=device)).all()
and (w == torch.as_tensor([11.0, 41.0], device=device)).all()
)
# Box batch
d, h, w = boxes_batch.get_boxes_shape()
assert h.ndim == 2 and w.ndim == 2
assert h.shape == (1, 2) and w.shape == (1, 2)
assert (
(d == torch.as_tensor([[31.0, 61.0]], device=device)).all()
and (h == torch.as_tensor([[21.0, 51.0]], device=device)).all()
and (w == torch.as_tensor([[11.0, 41.0]], device=device)).all()
)
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_smoke(self, device, dtype):
def _create_tensor_box():
# Sample two points of the 3d rect
points = torch.rand(1, 6, device=device, dtype=dtype)
# Fill according missing points
tensor_boxes = torch.zeros(1, 8, 3, device=device, dtype=dtype)
tensor_boxes[0, 0] = points[0][:3]
tensor_boxes[0, 1, 0] = points[0][3]
tensor_boxes[0, 1, 1] = points[0][1]
tensor_boxes[0, 1, 2] = points[0][2]
tensor_boxes[0, 2, 0] = points[0][3]
tensor_boxes[0, 2, 1] = points[0][4]
tensor_boxes[0, 2, 2] = points[0][2]
tensor_boxes[0, 3, 0] = points[0][0]
tensor_boxes[0, 3, 1] = points[0][4]
tensor_boxes[0, 3, 2] = points[0][2]
tensor_boxes[0, 4, 0] = points[0][0]
tensor_boxes[0, 4, 1] = points[0][1]
tensor_boxes[0, 4, 2] = points[0][5]
tensor_boxes[0, 5, 0] = points[0][3]
tensor_boxes[0, 5, 1] = points[0][1]
tensor_boxes[0, 5, 2] = points[0][5]
tensor_boxes[0, 6] = points[0][3:]
tensor_boxes[0, 7, 0] = points[0][0]
tensor_boxes[0, 7, 1] = points[0][4]
tensor_boxes[0, 7, 2] = points[0][5]
return tensor_boxes
# Validate
assert Boxes3D(_create_tensor_box()) # Validate 1 box
# 2 boxes without batching (N, 8, 3) where N=2
two_boxes = torch.cat([_create_tensor_box(), _create_tensor_box()])
assert Boxes3D(two_boxes)
# 2 boxes in batch (B, 1, 8, 3) where B=2
batched_bbox = torch.stack([_create_tensor_box(), _create_tensor_box()])
assert Boxes3D(batched_bbox)
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
)
def test_boxes_to_tensor(self, shape: Tuple[int], device, dtype):
# Hexahedron with randomized vertices to reflect possible transforms.
box = Boxes3D(
torch.as_tensor(
[[[2, 2, 1], [1, 2, 1], [2, 3, 2], [1, 3, 2], [2, 2, 2], [1, 3, 1], [2, 3, 1], [1, 2, 2]]],
device=device,
dtype=dtype,
).view(*shape[:-1], 8, 3)
)
expected_box_xyzxyz = torch.as_tensor([[1, 2, 1, 3, 4, 3]], device=device, dtype=dtype).view(*shape)
expected_box_xyzxyz_plus = torch.as_tensor([[1, 2, 1, 2, 3, 2]], device=device, dtype=dtype).view(*shape)
expected_box_xyzwhd = torch.as_tensor([[1, 2, 1, 2, 2, 2]], device=device, dtype=dtype).view(*shape)
expected_vertices = torch.as_tensor(
[[[1, 2, 1], [3, 2, 1], [3, 4, 1], [1, 4, 1], [1, 2, 3], [3, 2, 3], [3, 4, 3], [1, 4, 3]]], # Front # Back
device=device,
dtype=dtype,
).view(*shape[:-1], 8, 3)
expected_vertices_plus = torch.as_tensor(
[[[1, 2, 1], [2, 2, 1], [2, 3, 1], [1, 3, 1], [1, 2, 2], [2, 2, 2], [2, 3, 2], [1, 3, 2]]], # Front # Back
device=device,
dtype=dtype,
).view(*shape[:-1], 8, 3)
kornia_xyzxyz = box.to_tensor(mode='xyzxyz')
kornia_xyzxyz_plus = box.to_tensor(mode='xyzxyz_plus')
kornia_xyzwhd = box.to_tensor(mode='xyzwhd')
kornia_vertices = box.to_tensor(mode='vertices')
kornia_vertices_plus = box.to_tensor(mode='vertices_plus')
assert kornia_xyzxyz.shape == expected_box_xyzxyz.shape
assert_allclose(kornia_xyzxyz, expected_box_xyzxyz)
assert kornia_xyzxyz_plus.shape == expected_box_xyzxyz_plus.shape
assert_allclose(kornia_xyzxyz_plus, expected_box_xyzxyz_plus)
assert kornia_xyzwhd.shape == expected_box_xyzwhd.shape
assert_allclose(kornia_xyzwhd, expected_box_xyzwhd)
assert kornia_vertices.shape == expected_vertices.shape
assert_allclose(kornia_vertices, expected_vertices)
assert kornia_vertices_plus.shape == expected_vertices_plus.shape
assert_allclose(kornia_vertices_plus, expected_vertices_plus)
def test_get_boxes_shape_batch(self, device, dtype):
t_box1 = torch.tensor(
[[[0, 1, 2], [0, 1, 32], [10, 21, 2], [0, 21, 2], [10, 1, 32], [10, 21, 32], [10, 1, 2], [0, 21, 32]]],
device=device,
dtype=dtype
)
t_box2 = torch.tensor(
[[[3, 4, 5], [3, 4, 65], [43, 54, 5], [3, 54, 5], [43, 4, 5], [43, 4, 65], [43, 54, 65], [3, 54, 65]]],
device=device,
dtype=dtype
)
batched_boxes = Boxes3D(torch.stack([t_box1, t_box2]))
d, h, w = batched_boxes.get_boxes_shape()
assert d.ndim == 2 and h.ndim == 2 and w.ndim == 2
assert d.shape == (2, 1) and h.shape == (2, 1) and w.shape == (2, 1)
assert (
(d == torch.as_tensor([[31.0], [61.0]], device=device)).all()
and (h == torch.as_tensor([[21.0], [51.0]], device=device)).all()
and (w == torch.as_tensor([[11.0], [41.0]], device=device)).all()
)
def _wrapper_transform_boxes(hexahedrons, M):
boxes = Boxes3D(hexahedrons)
boxes = boxes.transform_boxes(M)
return boxes.data
def test_bbox_to_mask(self, device, dtype):
t_box1 = torch.tensor(
[
[
[1.0, 1.0, 1.0],
[3.0, 1.0, 1.0],
[3.0, 2.0, 1.0],
[1.0, 2.0, 1.0], # Front
[1.0, 1.0, 2.0],
[3.0, 1.0, 2.0],
[3.0, 2.0, 2.0],
[1.0, 2.0, 2.0], # Back
]
],
device=device,
dtype=dtype,
) # (1, 8, 3)
t_box2 = torch.tensor(
[
[
[2.0, 2.0, 1.0],
[4.0, 2.0, 1.0],
[4.0, 5.0, 1.0],
[4.0, 2.0, 1.0], # Front
[2.0, 2.0, 1.0],
[4.0, 2.0, 1.0],
[4.0, 5.0, 1.0],
[4.0, 5.0, 1.0], # Back
]
],
device=device,
dtype=dtype,
) # (1, 8, 3)
box1, box2 = Boxes3D(t_box1), Boxes3D(t_box2)
two_boxes = Boxes3D(torch.cat([t_box1, t_box2])) # (2, 8, 3)
batched_boxes = Boxes3D(torch.stack([t_box1, t_box2])) # (2, 1, 8, 3)
depth, height, width = 3, 7, 5
expected_mask1 = torch.tensor(
[
[
[ # Depth 0
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
],
[ # Depth 1
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
],
[ # Depth 2
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
],
]
],
device=device,
dtype=dtype,
)
expected_mask2 = torch.tensor(
[
[
[ # Depth 0
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
],
[ # Depth 1
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 1, 1],
[0, 0, 1, 1, 1],
[0, 0, 1, 1, 1],
[0, 0, 1, 1, 1],
[0, 0, 0, 0, 0],
],
[ # Depth 2
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
],
]
],
device=device,
dtype=dtype,
)
expected_two_masks = torch.cat([expected_mask1, expected_mask2])
expected_batched_masks = torch.stack([expected_mask1, expected_mask2])
mask1 = box1.to_mask(depth, height, width)
mask2 = box2.to_mask(depth, height, width)
two_masks = two_boxes.to_mask(depth, height, width)
batched_masks = batched_boxes.to_mask(depth, height, width)
assert mask1.shape == expected_mask1.shape
assert_allclose(mask1, expected_mask1)
assert mask2.shape == expected_mask2.shape
assert_allclose(mask2, expected_mask2)
assert two_masks.shape == expected_two_masks.shape
assert_allclose(two_masks, expected_two_masks)
assert batched_masks.shape == expected_batched_masks.shape
assert_allclose(batched_masks, expected_batched_masks)
