423A35C7/pytorch3d
1
0
Fork 0
mirror of https://github.com/facebookresearch/pytorch3d.git synced 2025-12-20 06:10:34 +08:00
Code Issues Packages Projects Releases Wiki Activity

fix pts scale, save ply

Browse Source 
Summary:
Fix:
* Scaling of point clouds for scalars
* save_ply compatible cat

Reviewed By: nikhilaravi

Differential Revision: D22298609

fbshipit-source-id: abe94a5b64baf325587202d20adfc36912cc1478
This commit is contained in:
Georgia Gkioxari
2020-07-03 10:19:58 -07:00
committed by Facebook GitHub Bot
parent 275ddade66
commit 2f0fd60186
4 changed files with 41 additions and 27 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
tests/test_meshes.py
View File

@@ -510,8 +510,8 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
N = 5
for test in ["tensor", "scalar"]:
mesh = TestMeshes.init_mesh(N, 10, 100)
for force in [0, 1]:
for force in (False, True):
mesh = TestMeshes.init_mesh(N, 10, 100)
if force:
# force mesh to have computed attributes
mesh.verts_packed()

53
tests/test_pointclouds.py
View File

@@ -460,7 +460,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
def test_scale(self):
def naive_scale(cloud, scale):
if not torch.is_tensor(scale):
scale = torch.full(len(cloud), scale)
scale = torch.full((len(cloud),), scale, device=cloud.device)
new_points_list = [
scale[i] * points.clone()
for (i, points) in enumerate(cloud.points_list())
@@ -470,26 +470,37 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
)
N = 5
clouds = self.init_cloud(N, 100, 10)
for force in (False, True):
if force:
clouds._compute_packed(refresh=True)
clouds._compute_padded()
clouds.padded_to_packed_idx()
scales = torch.rand(N)
new_clouds_naive = naive_scale(clouds, scales)
new_clouds = clouds.scale(scales)
for i in range(N):
self.assertClose(
scales[i] * clouds.points_list()[i], new_clouds.points_list()[i]
)
self.assertClose(
clouds.normals_list()[i], new_clouds_naive.normals_list()[i]
)
self.assertClose(
clouds.features_list()[i], new_clouds_naive.features_list()[i]
)
self.assertCloudsEqual(new_clouds, new_clouds_naive)
for test in ["tensor", "scalar"]:
for force in (False, True):
clouds = self.init_cloud(N, 100, 10)
if force:
clouds._compute_packed(refresh=True)
clouds._compute_padded()
clouds.padded_to_packed_idx()
if test == "tensor":
scales = torch.rand(N)
elif test == "scalar":
scales = torch.rand(1)[0].item()
new_clouds_naive = naive_scale(clouds, scales)
new_clouds = clouds.scale(scales)
for i in range(N):
if test == "tensor":
self.assertClose(
scales[i] * clouds.points_list()[i],
new_clouds.points_list()[i],
)
else:
self.assertClose(
scales * clouds.points_list()[i],
new_clouds.points_list()[i],
)
self.assertClose(
clouds.normals_list()[i], new_clouds_naive.normals_list()[i]
)
self.assertClose(
clouds.features_list()[i], new_clouds_naive.features_list()[i]
)
self.assertCloudsEqual(new_clouds, new_clouds_naive)
def test_extend_list(self):
N = 10