def test_mesh_renderer_to(self):
"""
Test moving all the tensors in the mesh renderer to a new device.
"""
device1 = torch.device("cpu")
R, T = look_at_view_transform(1500, 0.0, 0.0)
# Init shader settings
materials = Materials(device=device1)
lights = PointLights(device=device1)
lights.location = torch.tensor([0.0, 0.0, +1000.0], device=device1)[None]
raster_settings = RasterizationSettings(
image_size=256, blur_radius=0.0, faces_per_pixel=1
)
cameras = FoVPerspectiveCameras(
device=device1, R=R, T=T, aspect_ratio=1.0, fov=60.0, zfar=100
)
rasterizer = MeshRasterizer(cameras=cameras, raster_settings=raster_settings)
blend_params = BlendParams(
1e-4,
1e-4,
background_color=torch.zeros(3, dtype=torch.float32, device=device1),
)
shader = SoftPhongShader(
lights=lights,
cameras=cameras,
materials=materials,
blend_params=blend_params,
)
renderer = MeshRenderer(rasterizer=rasterizer, shader=shader)
mesh = ico_sphere(2, device1)
verts_padded = mesh.verts_padded()
textures = TexturesVertex(
verts_features=torch.ones_like(verts_padded, device=device1)
)
mesh.textures = textures
self._check_mesh_renderer_props_on_device(renderer, device1)
# Test rendering on cpu
output_images = renderer(mesh)
self.assertEqual(output_images.device, device1)
# Move renderer and mesh to another device and re render
# This also tests that background_color is correctly moved to
# the new device
device2 = torch.device("cuda:0")
renderer = renderer.to(device2)
mesh = mesh.to(device2)
self._check_mesh_renderer_props_on_device(renderer, device2)
output_images = renderer(mesh)
self.assertEqual(output_images.device, device2)
plt.imsave('./data/2.png', images[0, ..., :3].cpu().numpy())
plt.grid("off")
plt.axis("off")
# ## 4. Move the light behind the object and re-render
#
# We can pass arbirary keyword arguments to the `rasterizer`/`shader` via the call to the `renderer` so the renderer does not need to be reinitialized if any of the settings change/
#
# In this case, we can simply update the location of the lights and pass them into the call to the renderer.
#
# The image is now dark as there is only ambient lighting, and there are no specular highlights.
# In[6]:
# Now move the light so it is on the +Z axis which will be behind the cow.
lights.location = torch.tensor([0.0, 0.0, +1.0], device=device)[None]
images = renderer(mesh, lights=lights)
# In[7]:
plt.figure(figsize=(10, 10))
print("saving figure 3!")
plt.imsave('./data/3.png', images[0, ..., :3].cpu().numpy())
plt.grid("off")
plt.axis("off")
# ## 5. Rotate the object, modify the material properties or light properties
#
# We can also change many other settings in the rendering pipeline. Here we:
#
# - change the **viewing angle** of the camera