Fix to allow cameras in the renderer forward pass

Summary: Fix to resolve GitHub issue #796 - the cameras were being passed in the renderer forward pass instead of at initialization. The rasterizer was correctly using the cameras passed in the `kwargs` for the projection, but the `cameras` are still part of the `kwargs` for the `get_screen_to_ndc_transform` and `get_ndc_to_screen_transform` functions which is causing issues about duplicate arguments.

Reviewed By: bottler

Differential Revision: D30175679

fbshipit-source-id: 547e88d8439456e728fa2772722df5fa0fe4584d

pytorch3d/renderer/cameras.py

@@ -262,7 +262,10 @@ def get_ndc_camera_transform(self, **kwargs) -> Transform3d:
             # We don't flip xy because we assume that world points are in
             # PyTorch3D coordinates, and thus conversion from screen to ndc
             # is a mere scaling from image to [-1, 1] scale.
-            return get_screen_to_ndc_transform(self, with_xyflip=False, **kwargs)
+            image_size = kwargs.get("image_size", self.get_image_size())
+            return get_screen_to_ndc_transform(
+                self, with_xyflip=False, image_size=image_size
+            )
 
     def transform_points_ndc(
         self, points, eps: Optional[float] = None, **kwargs
@@ -318,8 +321,9 @@ def transform_points_screen(
             new_points: transformed points with the same shape as the input.
         """
         points_ndc = self.transform_points_ndc(points, eps=eps, **kwargs)
+        image_size = kwargs.get("image_size", self.get_image_size())
         return get_ndc_to_screen_transform(
-            self, with_xyflip=True, **kwargs
+            self, with_xyflip=True, image_size=image_size
         ).transform_points(points_ndc, eps=eps)
 
     def clone(self):
@@ -923,7 +927,7 @@ def __init__(
             K: (optional) A calibration matrix of shape (N, 4, 4)
                 If provided, don't need focal_length, principal_point
             image_size: (height, width) of image size.
-                A tensor of shape (N, 2). Required for screen cameras.
+                A tensor of shape (N, 2) or a list/tuple. Required for screen cameras.
             device: torch.device or string
         """
         # The initializer formats all inputs to torch tensors and broadcasts
@@ -1044,8 +1048,9 @@ def get_ndc_camera_transform(self, **kwargs) -> Transform3d:
             pr_point_fix_transform = Transform3d(
                 matrix=pr_point_fix.transpose(1, 2).contiguous(), device=self.device
             )
+            image_size = kwargs.get("image_size", self.get_image_size())
             screen_to_ndc_transform = get_screen_to_ndc_transform(
-                self, with_xyflip=False, **kwargs
+                self, with_xyflip=False, image_size=image_size
             )
             ndc_transform = pr_point_fix_transform.compose(screen_to_ndc_transform)
 
@@ -1105,7 +1110,7 @@ def __init__(
         K: Optional[torch.Tensor] = None,
         device: Device = "cpu",
         in_ndc: bool = True,
-        image_size: Optional[torch.Tensor] = None,
+        image_size: Optional[Union[List, Tuple, torch.Tensor]] = None,
     ) -> None:
         """
 
@@ -1123,7 +1128,7 @@ def __init__(
             K: (optional) A calibration matrix of shape (N, 4, 4)
                 If provided, don't need focal_length, principal_point, image_size
             image_size: (height, width) of image size.
-                A tensor of shape (N, 2). Required for screen cameras.
+                A tensor of shape (N, 2) or list/tuple. Required for screen cameras.
             device: torch.device or string
         """
         # The initializer formats all inputs to torch tensors and broadcasts
@@ -1241,8 +1246,9 @@ def get_ndc_camera_transform(self, **kwargs) -> Transform3d:
             pr_point_fix_transform = Transform3d(
                 matrix=pr_point_fix.transpose(1, 2).contiguous(), device=self.device
             )
+            image_size = kwargs.get("image_size", self.get_image_size())
             screen_to_ndc_transform = get_screen_to_ndc_transform(
-                self, with_xyflip=False, **kwargs
+                self, with_xyflip=False, image_size=image_size
             )
             ndc_transform = pr_point_fix_transform.compose(screen_to_ndc_transform)
 
@@ -1537,7 +1543,9 @@ def look_at_view_transform(
 
 
 def get_ndc_to_screen_transform(
-    cameras, with_xyflip: bool = False, **kwargs
+    cameras,
+    with_xyflip: bool = False,
+    image_size: Optional[Union[List, Tuple, torch.Tensor]] = None,
 ) -> Transform3d:
     """
     PyTorch3D NDC to screen conversion.
@@ -1563,7 +1571,6 @@ def get_ndc_to_screen_transform(
 
     """
     # We require the image size, which is necessary for the transform
-    image_size = kwargs.get("image_size", cameras.get_image_size())
     if image_size is None:
         msg = "For NDC to screen conversion, image_size=(height, width) needs to be specified."
         raise ValueError(msg)
@@ -1605,7 +1612,9 @@ def get_ndc_to_screen_transform(
 
 
 def get_screen_to_ndc_transform(
-    cameras, with_xyflip: bool = False, **kwargs
+    cameras,
+    with_xyflip: bool = False,
+    image_size: Optional[Union[List, Tuple, torch.Tensor]] = None,
 ) -> Transform3d:
     """
     Screen to PyTorch3D NDC conversion.
@@ -1631,6 +1640,8 @@ def get_screen_to_ndc_transform(
 
     """
     transform = get_ndc_to_screen_transform(
-        cameras, with_xyflip=with_xyflip, **kwargs
+        cameras,
+        with_xyflip=with_xyflip,
+        image_size=image_size,
     ).inverse()
     return transform

tests/test_render_meshes.py

@@ -1146,3 +1146,54 @@ def test_simple_sphere_outside_zfar(self):
                 )
 
             self.assertClose(rgb, image_ref, atol=0.05)
+
+    def test_cameras_kwarg(self):
+        """
+        Test that when cameras are passed in as a kwarg the rendering
+        works as expected
+        """
+        device = torch.device("cuda:0")
+
+        # Init mesh
+        sphere_mesh = ico_sphere(5, device)
+        verts_padded = sphere_mesh.verts_padded()
+        faces_padded = sphere_mesh.faces_padded()
+        feats = torch.ones_like(verts_padded, device=device)
+        textures = TexturesVertex(verts_features=feats)
+        sphere_mesh = Meshes(verts=verts_padded, faces=faces_padded, textures=textures)
+
+        # No elevation or azimuth rotation
+        R, T = look_at_view_transform(2.7, 0.0, 0.0)
+        for cam_type in (
+            FoVPerspectiveCameras,
+            FoVOrthographicCameras,
+            PerspectiveCameras,
+            OrthographicCameras,
+        ):
+            cameras = cam_type(device=device, R=R, T=T)
+
+            # Init shader settings
+            materials = Materials(device=device)
+            lights = PointLights(device=device)
+            lights.location = torch.tensor([0.0, 0.0, +2.0], device=device)[None]
+
+            raster_settings = RasterizationSettings(
+                image_size=512, blur_radius=0.0, faces_per_pixel=1
+            )
+            rasterizer = MeshRasterizer(raster_settings=raster_settings)
+            blend_params = BlendParams(1e-4, 1e-4, (0, 0, 0))
+
+            shader = HardPhongShader(
+                lights=lights,
+                materials=materials,
+                blend_params=blend_params,
+            )
+            renderer = MeshRenderer(rasterizer=rasterizer, shader=shader)
+
+            # Cameras can be passed into the renderer in the forward pass
+            images = renderer(sphere_mesh, cameras=cameras)
+            rgb = images.squeeze()[..., :3].cpu().numpy()
+            image_ref = load_rgb_image(
+                "test_simple_sphere_light_phong_%s.png" % cam_type.__name__, DATA_DIR
+            )
+            self.assertClose(rgb, image_ref, atol=0.05)