def test_get_perspective_transform3d(self, batch_size, device, dtype):
# generate input data
d_max, h_max, w_max = 16, 64, 32 # height, width
d = torch.ceil(d_max * torch.rand(batch_size, device=device, dtype=dtype))
h = torch.ceil(h_max * torch.rand(batch_size, device=device, dtype=dtype))
w = torch.ceil(w_max * torch.rand(batch_size, device=device, dtype=dtype))
norm = torch.rand(batch_size, 8, 3, device=device, dtype=dtype)
points_src = torch.rand_like(norm, device=device, dtype=dtype)
points_dst = points_src + norm
# compute transform from source to target
dst_homo_src = kornia.get_perspective_transform3d(points_src, points_dst)
# TODO: get_perspective_transform3d seems to be correct since it would result in the
# expected output for cropping volumes. Not sure what is going on here.
assert_allclose(
kornia.transform_points(dst_homo_src, points_src), points_dst, rtol=1e-4, atol=1e-4)
# compute gradient check
points_src = utils.tensor_to_gradcheck_var(points_src) # to var
points_dst = utils.tensor_to_gradcheck_var(points_dst) # to var
assert gradcheck(
kornia.get_perspective_transform3d, (
points_src,
points_dst,
),
raise_exception=True)
def test_get_perspective_transform3d_2(self, batch_size, device, dtype):
torch.manual_seed(0)
src = kornia.bbox_generator3d(
torch.randint_like(torch.ones(batch_size), 0, 50, dtype=dtype),
torch.randint_like(torch.ones(batch_size), 0, 50, dtype=dtype),
torch.randint_like(torch.ones(batch_size), 0, 50, dtype=dtype),
torch.randint(0, 50, (1, ), dtype=dtype).repeat(batch_size),
torch.randint(0, 50, (1, ), dtype=dtype).repeat(batch_size),
torch.randint(0, 50, (1, ), dtype=dtype).repeat(batch_size),
).to(device=device, dtype=dtype)
dst = kornia.bbox_generator3d(
torch.randint_like(torch.ones(batch_size), 0, 50, dtype=dtype),
torch.randint_like(torch.ones(batch_size), 0, 50, dtype=dtype),
torch.randint_like(torch.ones(batch_size), 0, 50, dtype=dtype),
torch.randint(0, 50, (1, ), dtype=dtype).repeat(batch_size),
torch.randint(0, 50, (1, ), dtype=dtype).repeat(batch_size),
torch.randint(0, 50, (1, ), dtype=dtype).repeat(batch_size),
).to(device=device, dtype=dtype)
out = kornia.get_perspective_transform3d(src, dst)
if batch_size == 1:
expected = torch.tensor(
[[
[3.3000, 0.0000, 0.0000, -118.2000],
[0.0000, 0.0769, 0.0000, 0.0000],
[0.0000, 0.0000, 0.5517, 28.7930],
[0.0000, 0.0000, 0.0000, 1.0000],
]],
device=device,
dtype=dtype,
)
if batch_size == 2:
expected = torch.tensor(
[
[
[0.9630, 0.0000, 0.0000, -9.3702],
[0.0000, 2.0000, 0.0000, -49.9999],
[0.0000, 0.0000, 0.3830, 44.0213],
[0.0000, 0.0000, 0.0000, 1.0000],
],
[
[0.9630, 0.0000, 0.0000, -36.5555],
[0.0000, 2.0000, 0.0000, -14.0000],
[0.0000, 0.0000, 0.3830, 16.8940],
[0.0000, 0.0000, 0.0000, 1.0000],
],
],
device=device,
dtype=dtype,
)
assert_close(out, expected, rtol=1e-4, atol=1e-4)
# compute gradient check
points_src = utils.tensor_to_gradcheck_var(src) # to var
points_dst = utils.tensor_to_gradcheck_var(dst) # to var
assert gradcheck(kornia.get_perspective_transform3d,
(points_src, points_dst),
raise_exception=True)