def test_gradcheck(self, device, dtype):
def apply_boxes_method(tensor: torch.Tensor, method: str, **kwargs):
boxes = Boxes(tensor)
result = getattr(boxes, method)(**kwargs)
return result.data if isinstance(result, Boxes) else result
t_boxes1 = torch.tensor([[[1.0, 1.0], [3.0, 1.0], [3.0, 2.0], [1.0, 2.0]]], device=device, dtype=dtype)
t_boxes1 = utils.tensor_to_gradcheck_var(t_boxes1)
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_xyxy = utils.tensor_to_gradcheck_var(torch.tensor([[1.0, 3.0, 5.0, 6.0]]))
t_boxes_xyxy1 = utils.tensor_to_gradcheck_var(t_boxes_xyxy.detach().clone())
assert gradcheck(partial(apply_boxes_method, method='to_tensor'), (t_boxes2,), raise_exception=True)
assert gradcheck(
partial(apply_boxes_method, method='to_tensor', mode='xyxy_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: Boxes.from_tensor(x, mode='xyxy_plus').data, (t_boxes_xyxy,), raise_exception=True
)
assert gradcheck(
lambda x: Boxes.from_tensor(x, mode='xywh').data, (t_boxes_xyxy1,), raise_exception=True
)
def test_smoke(self, device, dtype):
def _create_tensor_box():
# Sample two points of the rectangle
points = torch.rand(1, 4, device=device, dtype=dtype)
# Fill according missing points
tensor_boxes = torch.zeros(1, 4, 2, device=device, dtype=dtype)
tensor_boxes[0, 0] = points[0][:2]
tensor_boxes[0, 1, 0] = points[0][2]
tensor_boxes[0, 1, 1] = points[0][1]
tensor_boxes[0, 2] = points[0][2:]
tensor_boxes[0, 3, 0] = points[0][0]
tensor_boxes[0, 3, 1] = points[0][3]
return tensor_boxes
# Validate
assert Boxes(_create_tensor_box()) # Validate 1 box
# 2 boxes without batching (N, 4, 2) where N=2
two_boxes = torch.cat([_create_tensor_box(), _create_tensor_box()])
assert Boxes(two_boxes)
# 2 boxes in batch (B, 1, 4, 2) where B=2
batched_bbox = torch.stack([_create_tensor_box(), _create_tensor_box()])
assert Boxes(batched_bbox)
def test_get_boxes_shape(self, device, dtype):
box = Boxes(torch.tensor([[[1.0, 1.0], [3.0, 2.0], [1.0, 2.0], [3.0, 1.0]]], device=device, dtype=dtype))
t_boxes = torch.tensor(
[[[1.0, 1.0], [3.0, 1.0], [1.0, 2.0], [3.0, 2.0]], [[5.0, 4.0], [2.0, 2.0], [5.0, 2.0], [2.0, 4.0]]],
device=device,
dtype=dtype,
) # (2, 4, 2)
boxes = Boxes(t_boxes)
boxes_batch = Boxes(t_boxes[None]) # (1, 2, 4, 2)
# Single box
h, w = box.get_boxes_shape()
assert (h.item(), w.item()) == (2, 3)
# Boxes
h, w = boxes.get_boxes_shape()
assert h.ndim == 1 and w.ndim == 1
assert len(h) == 2 and len(w) == 2
assert (h == torch.as_tensor([2.0, 3.0], device=device)).all() and (
w == torch.as_tensor([3.0, 4.0], device=device)
).all()
# Box batch
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 (h == torch.as_tensor([[2.0, 3.0]], device=device)).all() and (
w == torch.as_tensor([[3.0, 4.0]], device=device)
).all()
def test_get_boxes_shape_batch(self, device, dtype):
t_box1 = torch.tensor([[[1.0, 1.0], [3.0, 2.0], [3.0, 1.0], [1.0, 2.0]]], device=device, dtype=dtype)
t_box2 = torch.tensor([[[5.0, 2.0], [2.0, 2.0], [5.0, 4.0], [2.0, 4.0]]], device=device, dtype=dtype)
batched_boxes = Boxes(torch.stack([t_box1, t_box2]))
h, w = batched_boxes.get_boxes_shape()
assert h.ndim == 2 and w.ndim == 2
assert h.shape == (2, 1) and w.shape == (2, 1)
assert (h == torch.as_tensor([[2], [3]], device=device)).all() and (
w == torch.as_tensor([[3], [4]], device=device)
).all()
def test_transform_boxes_(self, device, dtype):
# Define boxes in XYXY format for simplicity.
boxes_xyxy = torch.tensor([[139.2640, 103.0150, 398.3120, 411.5225]], device=device, dtype=dtype)
expected_boxes_xyxy = torch.tensor([[372.7360, 103.0150, 115.6880, 411.5225]], device=device, dtype=dtype)
boxes = Boxes.from_tensor(boxes_xyxy)
expected_boxes = Boxes.from_tensor(expected_boxes_xyxy, validate_boxes=False)
trans_mat = torch.tensor([[[-1.0, 0.0, 512.0], [0.0, 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_invalid_tensor(self, shape: Tuple[int], device, dtype):
box_xyxy = torch.as_tensor([[1, 2, -3, 4]], device=device, dtype=dtype).view(*shape) # Invalid width
box_xyxy_plus = torch.as_tensor([[1, 2, 0, 3]], device=device, dtype=dtype).view(*shape) # Invalid height
try:
Boxes.from_tensor(box_xyxy, mode='xyxy')
assert False, "Boxes.from_tensor should have raised any exception"
except ValueError:
pass
try:
Boxes.from_tensor(box_xyxy_plus, mode='xyxy_plus')
assert False, "Boxes.from_tensor should have raised any exception"
except ValueError:
pass
def test_transform_multiple_boxes(self, device, dtype):
# Define boxes in XYXY format for simplicity.
boxes_xyxy = torch.tensor(
[
[139.2640, 103.0150, 398.3120, 411.5225],
[1.0240, 80.5547, 513.0000, 513.0000],
[165.2053, 262.1440, 511.6347, 509.9280],
[119.8080, 144.2067, 258.0240, 411.1292],
],
device=device,
dtype=dtype,
).repeat(
2, 1, 1
) # 2 x 4 x 4 two images 4 boxes each
expected_boxes_xyxy = torch.tensor(
[
[
[372.7360, 103.0150, 115.6880, 411.5225],
[510.9760, 80.5547, 1.0000, 513.0000],
[346.7947, 262.1440, 2.3653, 509.9280],
[392.1920, 144.2067, 255.9760, 411.1292],
],
[
[139.2640, 103.0150, 398.3120, 411.5225],
[1.0240, 80.5547, 513.0000, 513.0000],
[165.2053, 262.1440, 511.6347, 509.9280],
[119.8080, 144.2067, 258.0240, 411.1292],
],
],
device=device,
dtype=dtype,
)
trans_mat = torch.tensor(
[
[[-1.0, 0.0, 512.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]],
[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]],
],
device=device,
dtype=dtype,
)
boxes = Boxes.from_tensor(boxes_xyxy)
expected_boxes = Boxes.from_tensor(expected_boxes_xyxy, 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 XYXY format for simplicity.
boxes_xyxy = torch.tensor(
[
[139.2640, 103.0150, 258.0480, 307.5075],
[1.0240, 80.5547, 510.9760, 431.4453],
[165.2053, 262.1440, 345.4293, 546.7840],
[119.8080, 144.2067, 137.2160, 265.9225],
],
device=device,
dtype=dtype,
)
boxes = Boxes.from_tensor(boxes_xyxy)
trans_mat = torch.tensor([[[-1.0, 0.0, 512.0], [0.0, 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(quadrilaterals, M):
boxes = Boxes(quadrilaterals)
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 = Boxes.from_tensor(torch.as_tensor([[1, 2, 3, 4]], 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_boxes_to_tensor(self, shape: Tuple[int], device, dtype):
# quadrilateral with randomized vertices to reflect possible transforms.
box = Boxes(torch.as_tensor([[[2, 2], [2, 3], [1, 3], [1, 2]]], device=device, dtype=dtype).view(*shape, 2))
expected_box_xyxy = torch.as_tensor([[1, 2, 3, 4]], device=device, dtype=dtype).view(*shape)
expected_box_xyxy_plus = torch.as_tensor([[1, 2, 2, 3]], device=device, dtype=dtype).view(*shape)
expected_box_xywh = torch.as_tensor([[1, 2, 2, 2]], device=device, dtype=dtype).view(*shape)
expected_vertices = torch.as_tensor([[[1, 2], [3, 2], [3, 4], [1, 4]]], device=device, dtype=dtype).view(
*shape, 2
)
expected_vertices_plus = torch.as_tensor([[[1, 2], [2, 2], [2, 3], [1, 3]]], device=device, dtype=dtype).view(
*shape, 2
)
boxes_xyxy = box.to_tensor(mode='xyxy')
boxes_xyxy_plus = box.to_tensor(mode='xyxy_plus')
boxes_xywh = box.to_tensor(mode='xywh')
boxes_vertices = box.to_tensor(mode='vertices')
boxes_vertices_plus = box.to_tensor(mode='vertices_plus')
assert boxes_xyxy.shape == expected_box_xyxy.shape # type: ignore
assert_allclose(boxes_xyxy, expected_box_xyxy)
assert boxes_xyxy_plus.shape == expected_box_xyxy_plus.shape # type: ignore
assert_allclose(boxes_xyxy_plus, expected_box_xyxy_plus)
assert boxes_xywh.shape == expected_box_xywh.shape # type: ignore
assert_allclose(boxes_xywh, expected_box_xywh)
assert boxes_vertices.shape == expected_vertices.shape # type: ignore
assert_allclose(boxes_vertices, expected_vertices)
assert boxes_vertices_plus.shape == expected_vertices_plus.shape # type: ignore
assert_allclose(boxes_vertices_plus, expected_vertices_plus)
def test_from_tensor(self, shape: Tuple[int], device, dtype):
box_xyxy = torch.as_tensor([[1, 2, 3, 4]], device=device, dtype=dtype).view(*shape)
box_xyxy_plus = torch.as_tensor([[1, 2, 2, 3]], device=device, dtype=dtype).view(*shape)
box_xywh = torch.as_tensor([[1, 2, 2, 2]], device=device, dtype=dtype).view(*shape)
expected_box = torch.as_tensor([[[1, 2], [2, 2], [2, 3], [1, 3]]], device=device, dtype=dtype).view(*shape, 2)
boxes_xyxy = Boxes.from_tensor(box_xyxy, mode='xyxy').data
boxes_xyxy_plus = Boxes.from_tensor(box_xyxy_plus, mode='xyxy_plus').data
boxes_xywh = Boxes.from_tensor(box_xywh, mode='xywh').data
assert boxes_xyxy.shape == expected_box.shape
assert_allclose(boxes_xyxy, expected_box)
assert boxes_xyxy_plus.shape == expected_box.shape
assert_allclose(boxes_xyxy_plus, expected_box)
assert boxes_xywh.shape == expected_box.shape
assert_allclose(boxes_xywh, expected_box)
def test_boxes_list_to_tensor_list(self, mode, device, dtype):
src_1 = [
torch.as_tensor([[[2, 2], [2, 3], [1, 3], [1, 2]]], device=device, dtype=dtype),
torch.as_tensor([
[[2, 2], [2, 3], [1, 3], [1, 2]], [[2, 2], [2, 3], [1, 3], [1, 2]]
], device=device, dtype=dtype)
]
src_2 = [
torch.as_tensor([[1, 1, 5, 5]], device=device, dtype=dtype),
torch.as_tensor([[1, 1, 5, 5], [1, 1, 5, 5]], device=device, dtype=dtype)
]
src = src_1 if mode in ['vertices', 'vertices_plus'] else src_2
box = Boxes.from_tensor(src, mode=mode)
out = box.to_tensor(mode)
assert out[0].shape == src[0].shape
assert out[1].shape == src[1].shape
def _arguments_preproc(self, *args: Tensor, data_keys: List[DataKey]):
inp: List[Any] = []
for arg, dcate in zip(args, data_keys):
if DataKey.get(dcate) in [
DataKey.INPUT, DataKey.MASK, DataKey.KEYPOINTS
]:
inp.append(arg)
elif DataKey.get(dcate) in [
DataKey.BBOX, DataKey.BBOX_XYXY, DataKey.BBOX_XYWH
]:
if DataKey.get(dcate) in [DataKey.BBOX]:
mode = "vertices_plus"
elif DataKey.get(dcate) in [DataKey.BBOX_XYXY]:
mode = "xyxy"
elif DataKey.get(dcate) in [DataKey.BBOX_XYWH]:
mode = "xywh"
else:
raise ValueError(
f"Unsupported mode `{DataKey.get(dcate).name}`.")
inp.append(Boxes.from_tensor(arg, mode=mode)) # type: ignore
else:
raise NotImplementedError(
f"input type of {dcate} is not implemented.")
return inp
def _wrapper_transform_boxes(quadrilaterals, M):
boxes = Boxes(quadrilaterals)
boxes = boxes.transform_boxes(M)
return boxes.data
def apply_boxes_method(tensor: torch.Tensor, method: str, **kwargs):
boxes = Boxes(tensor)
result = getattr(boxes, method)(**kwargs)
return result.data if isinstance(result, Boxes) else result
def test_boxes_to_mask(self, device, dtype):
t_box1 = torch.tensor(
[[[1.0, 1.0], [3.0, 1.0], [3.0, 2.0], [1.0, 2.0]]], device=device, dtype=dtype
) # (1, 4, 2)
t_box2 = torch.tensor(
[[[2.0, 2.0], [4.0, 2.0], [4.0, 5.0], [2.0, 4.0]]], device=device, dtype=dtype
) # (1, 4, 2)
box1, box2 = Boxes(t_box1), Boxes(t_box2)
two_boxes = Boxes(torch.cat([t_box1, t_box2])) # (2, 4, 2)
batched_boxes = Boxes(torch.stack([t_box1, t_box2])) # (2, 1, 4, 2)
height, width = 7, 5
expected_mask1 = torch.tensor(
[
[
[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(
[
[
[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],
]
],
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(height, width)
mask2 = box2.to_mask(height, width)
two_masks = two_boxes.to_mask(height, width)
batched_masks = batched_boxes.to_mask(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)
