Return self in the to method for the renderer classes

Summary: Add `return self` to the `to` function for the renderer classes.

Reviewed By: bottler

Differential Revision: D25534487

fbshipit-source-id: e8dbd35524f0bd40e835439e93184b5a1f1532ca

Author: nikhilaravi

pytorch3d/renderer/mesh/rasterizer.py

@@ -79,6 +79,7 @@ def __init__(self, cameras=None, raster_settings=None):
     def to(self, device):
         # Manually move to device cameras as it is not a subclass of nn.Module
         self.cameras = self.cameras.to(device)
+        return self
 
     def transform(self, meshes_world, **kwargs) -> torch.Tensor:
         """

pytorch3d/renderer/mesh/renderer.py

@@ -37,6 +37,7 @@ def to(self, device):
         # Rasterizer and shader have submodules which are not of type nn.Module
         self.rasterizer.to(device)
         self.shader.to(device)
+        return self
 
     def forward(self, meshes_world, **kwargs) -> torch.Tensor:
         """

pytorch3d/renderer/mesh/shader.py

@@ -55,6 +55,7 @@ def to(self, device):
         self.cameras = self.cameras.to(device)
         self.materials = self.materials.to(device)
         self.lights = self.lights.to(device)
+        return self
 
     def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
         cameras = kwargs.get("cameras", self.cameras)
@@ -109,6 +110,7 @@ def to(self, device):
         self.cameras = self.cameras.to(device)
         self.materials = self.materials.to(device)
         self.lights = self.lights.to(device)
+        return self
 
     def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
         cameras = kwargs.get("cameras", self.cameras)
@@ -168,6 +170,7 @@ def to(self, device):
         self.cameras = self.cameras.to(device)
         self.materials = self.materials.to(device)
         self.lights = self.lights.to(device)
+        return self
 
     def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
         cameras = kwargs.get("cameras", self.cameras)
@@ -226,6 +229,7 @@ def to(self, device):
         self.cameras = self.cameras.to(device)
         self.materials = self.materials.to(device)
         self.lights = self.lights.to(device)
+        return self
 
     def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
         cameras = kwargs.get("cameras", self.cameras)
@@ -301,6 +305,7 @@ def to(self, device):
         self.cameras = self.cameras.to(device)
         self.materials = self.materials.to(device)
         self.lights = self.lights.to(device)
+        return self
 
     def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
         cameras = kwargs.get("cameras", self.cameras)

pytorch3d/renderer/points/rasterizer.py

@@ -99,6 +99,11 @@ def transform(self, point_clouds, **kwargs) -> torch.Tensor:
         point_clouds = point_clouds.update_padded(pts_screen)
         return point_clouds
 
+    def to(self, device):
+        # Manually move to device cameras as it is not a subclass of nn.Module
+        self.cameras = self.cameras.to(device)
+        return self
+
     def forward(self, point_clouds, **kwargs) -> PointFragments:
         """
         Args:

pytorch3d/renderer/points/renderer.py

@@ -32,6 +32,13 @@ def __init__(self, rasterizer, compositor):
         self.rasterizer = rasterizer
         self.compositor = compositor
 
+    def to(self, device):
+        # Manually move to device rasterizer as the cameras
+        # within the class are not of type nn.Module
+        self.rasterizer = self.rasterizer.to(device)
+        self.compositor = self.compositor.to(device)
+        return self
+
     def forward(self, point_clouds, **kwargs) -> torch.Tensor:
         fragments = self.rasterizer(point_clouds, **kwargs)
 

tests/test_render_multigpu.py

@@ -6,18 +6,22 @@
 import torch.nn as nn
 from common_testing import TestCaseMixin, get_random_cuda_device
 from pytorch3d.renderer import (
+    AlphaCompositor,
     BlendParams,
     HardGouraudShader,
     Materials,
     MeshRasterizer,
     MeshRenderer,
     PointLights,
+    PointsRasterizationSettings,
+    PointsRasterizer,
+    PointsRenderer,
     RasterizationSettings,
     SoftPhongShader,
     TexturesVertex,
 )
 from pytorch3d.renderer.cameras import FoVPerspectiveCameras, look_at_view_transform
-from pytorch3d.structures.meshes import Meshes
+from pytorch3d.structures import Meshes, Pointclouds
 from pytorch3d.utils.ico_sphere import ico_sphere
 
 
@@ -27,7 +31,7 @@
 print("GPUs: %s" % ", ".join(GPU_LIST))
 
 
-class TestRenderMultiGPU(TestCaseMixin, unittest.TestCase):
+class TestRenderMeshesMultiGPU(TestCaseMixin, unittest.TestCase):
     def _check_mesh_renderer_props_on_device(self, renderer, device):
         """
         Helper function to check that all the properties of the mesh
@@ -99,7 +103,7 @@ def test_mesh_renderer_to(self):
         # This also tests that background_color is correctly moved to
         # the new device
         device2 = torch.device("cuda:0")
-        renderer.to(device2)
+        renderer = renderer.to(device2)
         mesh = mesh.to(device2)
         self._check_mesh_renderer_props_on_device(renderer, device2)
         output_images = renderer(mesh)
@@ -137,7 +141,7 @@ def init_render(self):
 
             def forward(self, verts, texs):
                 batch_size = verts.size(0)
-                self.renderer.to(verts.device)
+                self.renderer = self.renderer.to(verts.device)
                 tex = TexturesVertex(verts_features=texs)
                 faces = self.faces.expand(batch_size, -1, -1).to(verts.device)
                 mesh = Meshes(verts, faces, tex).to(verts.device)
@@ -157,3 +161,53 @@ def forward(self, verts, texs):
         # Test a few iterations
         for _ in range(100):
             model(verts, texs)
+
+
+class TestRenderPointssMultiGPU(TestCaseMixin, unittest.TestCase):
+    def _check_points_renderer_props_on_device(self, renderer, device):
+        """
+        Helper function to check that all the properties have
+        been moved to the correct device.
+        """
+        # Cameras
+        self.assertEqual(renderer.rasterizer.cameras.device, device)
+        self.assertEqual(renderer.rasterizer.cameras.R.device, device)
+        self.assertEqual(renderer.rasterizer.cameras.T.device, device)
+
+    def test_points_renderer_to(self):
+        """
+        Test moving all the tensors in the points renderer to a new device.
+        """
+
+        device1 = torch.device("cpu")
+
+        R, T = look_at_view_transform(1500, 0.0, 0.0)
+
+        raster_settings = PointsRasterizationSettings(
+            image_size=256, radius=0.001, points_per_pixel=1
+        )
+        cameras = FoVPerspectiveCameras(
+            device=device1, R=R, T=T, aspect_ratio=1.0, fov=60.0, zfar=100
+        )
+        rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings)
+
+        renderer = PointsRenderer(rasterizer=rasterizer, compositor=AlphaCompositor())
+
+        mesh = ico_sphere(2, device1)
+        verts_padded = mesh.verts_padded()
+        pointclouds = Pointclouds(
+            points=verts_padded, features=torch.randn_like(verts_padded)
+        )
+        self._check_points_renderer_props_on_device(renderer, device1)
+
+        # Test rendering on cpu
+        output_images = renderer(pointclouds)
+        self.assertEqual(output_images.device, device1)
+
+        # Move renderer and pointclouds to another device and re render
+        device2 = torch.device("cuda:0")
+        renderer = renderer.to(device2)
+        pointclouds = pointclouds.to(device2)
+        self._check_points_renderer_props_on_device(renderer, device2)
+        output_images = renderer(pointclouds)
+        self.assertEqual(output_images.device, device2)