def test_unscaled(self):
D = 5
P = 1000
B, C, H, W = 2, 3, D, D
densities = torch.zeros(B, 1, D, H, W)
features = torch.zeros(B, C, D, H, W)
volumes = Volumes(densities=densities, features=features)
points = torch.rand(B, 1000, 3) * (D - 1) - ((D - 1) * 0.5)
point_features = torch.rand(B, 1000, C)
pointclouds = Pointclouds(points=points, features=point_features)
volumes2 = add_pointclouds_to_volumes(pointclouds,
volumes,
rescale_features=False)
self.assertConstant(volumes2.densities().sum([2, 3, 4]) / P,
1,
atol=1e-5)
self.assertConstant(volumes2.features().sum([2, 3, 4]) / P,
0.5,
atol=0.03)
def test_from_point_cloud(self, interp_mode="trilinear"):
"""
Generates a volume from a random point cloud sampled from faces
of a 3D cube. Since each side of the cube is homogenously colored with
a different color, this should result in a volume with a
predefined homogenous color of the cells along its borders
and black interior. The test is run for both cube and non-cube shaped
volumes.
"""
# batch_size = 4 sides of the cube
batch_size = 4
for volume_size in ([25, 25, 25], [30, 25, 15]):
for interp_mode in ("trilinear", "nearest"):
(pointclouds, initial_volumes) = init_volume_boundary_pointcloud(
volume_size=volume_size,
n_points=int(1e5),
interp_mode=interp_mode,
batch_size=batch_size,
require_grad=True,
)
volumes = add_pointclouds_to_volumes(
pointclouds, initial_volumes, mode=interp_mode
)
V_color, V_density = volumes.features(), volumes.densities()
# expected colors of different cube sides
clr_sides = torch.tensor(
[
[[1.0, 1.0, 1.0], [1.0, 0.0, 1.0]],
[[1.0, 0.0, 0.0], [1.0, 1.0, 0.0]],
[[1.0, 0.0, 1.0], [1.0, 1.0, 1.0]],
[[1.0, 1.0, 0.0], [1.0, 0.0, 0.0]],
],
dtype=V_color.dtype,
device=V_color.device,
)
clr_ambient = torch.tensor(
[0.0, 0.0, 0.0], dtype=V_color.dtype, device=V_color.device
)
clr_top_bot = torch.tensor(
[[0.0, 1.0, 0.0], [0.0, 1.0, 1.0]],
dtype=V_color.dtype,
device=V_color.device,
)
if DEBUG:
outdir = tempfile.gettempdir() + "/test_points_to_volumes"
os.makedirs(outdir, exist_ok=True)
for slice_dim in (1, 2):
for vidx in range(V_color.shape[0]):
vim = V_color.detach()[vidx].split(1, dim=slice_dim)
vim = torch.stack([v.squeeze() for v in vim])
vim = TestPointsToVolumes.stack_4d_tensor_to_3d(vim.cpu())
im = Image.fromarray(
(vim.numpy() * 255.0)
.astype(np.uint8)
.transpose(1, 2, 0)
)
outfile = (
outdir
+ f"/rgb_{interp_mode}"
+ f"_{str(volume_size).replace(' ','')}"
+ f"_{vidx:003d}_sldim{slice_dim}.png"
)
im.save(outfile)
print("exported %s" % outfile)
# check the density V_density
# first binarize the density
V_density_bin = (V_density > 1e-4).type_as(V_density)
d_one = V_density.new_ones(1)
d_zero = V_density.new_zeros(1)
for vidx in range(V_color.shape[0]):
# the first/last depth-wise slice has to be filled with 1.0
self._check_volume_slice_color_density(
V_density_bin[vidx], 1, interp_mode, d_one, "first"
)
self._check_volume_slice_color_density(
V_density_bin[vidx], 1, interp_mode, d_one, "last"
)
# the middle depth-wise slices have to be empty
self._check_volume_slice_color_density(
V_density_bin[vidx], 1, interp_mode, d_zero, "middle"
)
# the top/bottom slices have to be filled with 1.0
self._check_volume_slice_color_density(
V_density_bin[vidx], 2, interp_mode, d_one, "first"
)
self._check_volume_slice_color_density(
V_density_bin[vidx], 2, interp_mode, d_one, "last"
)
# check the colors
for vidx in range(V_color.shape[0]):
self._check_volume_slice_color_density(
V_color[vidx], 1, interp_mode, clr_sides[vidx][0], "first"
)
self._check_volume_slice_color_density(
V_color[vidx], 1, interp_mode, clr_sides[vidx][1], "last"
)
self._check_volume_slice_color_density(
V_color[vidx], 1, interp_mode, clr_ambient, "middle"
)
self._check_volume_slice_color_density(
V_color[vidx], 2, interp_mode, clr_top_bot[0], "first"
)
self._check_volume_slice_color_density(
V_color[vidx], 2, interp_mode, clr_top_bot[1], "last"
)
# check differentiability
loss = V_color.mean() + V_density.mean()
loss.backward()
rgb = pointclouds.features_padded()
xyz = pointclouds.points_padded()
for field in (xyz, rgb):
if interp_mode == "nearest" and (field is xyz):
# this does not produce grads w.r.t. xyz
self.assertIsNone(field.grad)
else:
self.assertTrue(field.grad.data.isfinite().all())
def _add_points_to_volumes():
add_pointclouds_to_volumes(pointclouds, initial_volumes, mode=interp_mode)
def _add_points_to_volumes():
add_pointclouds_to_volumes(pointclouds,
initial_volumes,
mode=interp_mode)
torch.cuda.synchronize()
