def test_clone(self):
tex = TexturesAtlas(atlas=torch.rand(size=(1, 10, 2, 2, 3)))
tex.atlas_list()
tex_cloned = tex.clone()
self.assertSeparate(tex._atlas_padded, tex_cloned._atlas_padded)
self.assertClose(tex._atlas_padded, tex_cloned._atlas_padded)
self.assertSeparate(tex.valid, tex_cloned.valid)
self.assertTrue(tex.valid.eq(tex_cloned.valid).all())
for i in range(tex._N):
self.assertSeparate(tex._atlas_list[i], tex_cloned._atlas_list[i])
self.assertClose(tex._atlas_list[i], tex_cloned._atlas_list[i])
def test_padded_to_packed(self):
# Case where each face in the mesh has 3 unique uv vertex indices
# - i.e. even if a vertex is shared between multiple faces it will
# have a unique uv coordinate for each face.
R = 2
N = 20
num_faces_per_mesh = torch.randint(size=(N, ), low=0, high=30)
atlas_list = [torch.rand(f, R, R, 3) for f in num_faces_per_mesh]
tex = TexturesAtlas(atlas=atlas_list)
# This is set inside Meshes when textures is passed as an input.
# Here we set _num_faces_per_mesh explicity.
tex1 = tex.clone()
tex1._num_faces_per_mesh = num_faces_per_mesh.tolist()
atlas_packed = tex1.atlas_packed()
atlas_list_new = tex1.atlas_list()
atlas_padded = tex1.atlas_padded()
for f1, f2 in zip(atlas_list_new, atlas_list):
self.assertTrue((f1 == f2).all().item())
sum_F = num_faces_per_mesh.sum()
max_F = num_faces_per_mesh.max().item()
self.assertTrue(atlas_packed.shape == (sum_F, R, R, 3))
self.assertTrue(atlas_padded.shape == (N, max_F, R, R, 3))
# Case where num_faces_per_mesh is not set and textures
# are initialized with a padded tensor.
atlas_list_padded = _list_to_padded_wrapper(atlas_list)
tex2 = TexturesAtlas(atlas=atlas_list_padded)
atlas_packed = tex2.atlas_packed()
atlas_list_new = tex2.atlas_list()
# Packed is just flattened padded as num_faces_per_mesh
# has not been provided.
self.assertTrue(atlas_packed.shape == (N * max_F, R, R, 3))
for i, (f1, f2) in enumerate(zip(atlas_list_new, atlas_list)):
n = num_faces_per_mesh[i]
self.assertTrue((f1[:n] == f2).all().item())
def test_clone(self):
tex = TexturesAtlas(atlas=torch.rand(size=(1, 10, 2, 2, 3)))
tex_cloned = tex.clone()
self.assertSeparate(tex._atlas_padded, tex_cloned._atlas_padded)
self.assertSeparate(tex.valid, tex_cloned.valid)