def meshes_collate(batch):
r"""Puts each data field into a tensor with outer dimension batch size"""
elem = batch[0]
elem_type = type(elem)
if isinstance(elem, torch.Tensor):
out = None
if torch.utils.data.get_worker_info() is not None:
# If we're in a background process, concatenate directly into a
# shared memory tensor to avoid an extra copy
numel = sum([x.numel() for x in batch])
storage = elem.storage()._new_shared(numel)
out = elem.new(storage)
return torch.stack(batch, 0, out=out)
elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
and elem_type.__name__ != 'string_':
elem = batch[0]
if elem_type.__name__ == 'ndarray':
# array of string classes and object
if collate.np_str_obj_array_pattern.search(
elem.dtype.str) is not None:
raise TypeError(
collate.default_collate_err_msg_format.format(elem.dtype))
return meshes_collate([torch.as_tensor(b) for b in batch])
elif elem.shape == (): # scalars
return torch.as_tensor(batch)
elif isinstance(elem, float):
return torch.tensor(batch, dtype=torch.float64)
elif isinstance(elem, collate.int_classes):
return torch.tensor(batch)
elif isinstance(elem, collate.string_classes):
return batch
elif isinstance(elem, collate.container_abcs.Mapping):
return {key: meshes_collate([d[key] for d in batch]) for key in elem}
elif isinstance(elem, tuple) and hasattr(elem, '_fields'): # namedtuple
return elem_type(*(meshes_collate(samples) for samples in zip(*batch)))
elif isinstance(elem, collate.container_abcs.Sequence):
transposed = zip(*batch)
return [meshes_collate(samples) for samples in transposed]
elif isinstance(elem, Meshes):
return join_meshes_as_batch(batch)
raise TypeError(collate.default_collate_err_msg_format.format(elem_type))
def test_batch_uvs(self):
"""Test that two random tori with TexturesUV render the same as each individually."""
torch.manual_seed(1)
device = torch.device("cuda:0")
plain_torus = torus(r=1, R=4, sides=10, rings=10, device=device)
[verts] = plain_torus.verts_list()
[faces] = plain_torus.faces_list()
nocolor = torch.zeros((100, 100), device=device)
color_gradient = torch.linspace(0, 1, steps=100, device=device)
color_gradient1 = color_gradient[None].expand_as(nocolor)
color_gradient2 = color_gradient[:, None].expand_as(nocolor)
colors1 = torch.stack([nocolor, color_gradient1, color_gradient2], dim=2)
colors2 = torch.stack([color_gradient1, color_gradient2, nocolor], dim=2)
verts_uvs1 = torch.rand(size=(verts.shape[0], 2), device=device)
verts_uvs2 = torch.rand(size=(verts.shape[0], 2), device=device)
textures1 = TexturesUV(
maps=[colors1], faces_uvs=[faces], verts_uvs=[verts_uvs1]
)
textures2 = TexturesUV(
maps=[colors2], faces_uvs=[faces], verts_uvs=[verts_uvs2]
)
mesh1 = Meshes(verts=[verts], faces=[faces], textures=textures1)
mesh2 = Meshes(verts=[verts], faces=[faces], textures=textures2)
mesh_both = join_meshes_as_batch([mesh1, mesh2])
R, T = look_at_view_transform(10, 10, 0)
cameras = FoVPerspectiveCameras(device=device, R=R, T=T)
raster_settings = RasterizationSettings(
image_size=128, blur_radius=0.0, faces_per_pixel=1
)
# Init shader settings
lights = PointLights(device=device)
lights.location = torch.tensor([0.0, 0.0, 2.0], device=device)[None]
blend_params = BlendParams(
sigma=1e-1,
gamma=1e-4,
background_color=torch.tensor([1.0, 1.0, 1.0], device=device),
)
# Init renderer
renderer = MeshRenderer(
rasterizer=MeshRasterizer(cameras=cameras, raster_settings=raster_settings),
shader=HardPhongShader(
device=device, lights=lights, cameras=cameras, blend_params=blend_params
),
)
outputs = []
for meshes in [mesh_both, mesh1, mesh2]:
outputs.append(renderer(meshes))
if DEBUG:
Image.fromarray(
(outputs[0][0, ..., :3].cpu().numpy() * 255).astype(np.uint8)
).save(DATA_DIR / "test_batch_uvs0.png")
Image.fromarray(
(outputs[1][0, ..., :3].cpu().numpy() * 255).astype(np.uint8)
).save(DATA_DIR / "test_batch_uvs1.png")
Image.fromarray(
(outputs[0][1, ..., :3].cpu().numpy() * 255).astype(np.uint8)
).save(DATA_DIR / "test_batch_uvs2.png")
Image.fromarray(
(outputs[2][0, ..., :3].cpu().numpy() * 255).astype(np.uint8)
).save(DATA_DIR / "test_batch_uvs3.png")
diff = torch.abs(outputs[0][0, ..., :3] - outputs[1][0, ..., :3])
Image.fromarray(((diff > 1e-5).cpu().numpy().astype(np.uint8) * 255)).save(
DATA_DIR / "test_batch_uvs01.png"
)
diff = torch.abs(outputs[0][1, ..., :3] - outputs[2][0, ..., :3])
Image.fromarray(((diff > 1e-5).cpu().numpy().astype(np.uint8) * 255)).save(
DATA_DIR / "test_batch_uvs23.png"
)
self.assertClose(outputs[0][0, ..., :3], outputs[1][0, ..., :3], atol=1e-5)
self.assertClose(outputs[0][1, ..., :3], outputs[2][0, ..., :3], atol=1e-5)