(breaking) image_size-agnostic GridRaySampler

Summary:
As suggested in #802. By not persisting the _xy_grid buffer, we can allow (in some cases) a model with one image_size to be loaded from a saved model which was trained at a different resolution.

Also avoid persisting _frequencies in HarmonicEmbedding for similar reasons.

BC-break: This will cause load_state_dict, in strict mode, to complain if you try to load an old model with the new code.

Reviewed By: patricklabatut

Differential Revision: D30349234

fbshipit-source-id: d6061d1e51c9f79a78d61a9f732c9a5dfadbbb47

projects/nerf/nerf/harmonic_embedding.py

@@ -14,7 +14,7 @@ class HarmonicEmbedding(torch.nn.Module):
         omega0: float = 1.0,
         logspace: bool = True,
         include_input: bool = True,
-    ):
+    ) -> None:
         """
         Given an input tensor `x` of shape [minibatch, ... , dim],
         the harmonic embedding layer converts each feature
@@ -69,10 +69,10 @@ class HarmonicEmbedding(torch.nn.Module):
                 dtype=torch.float32,
             )
 
-        self.register_buffer("_frequencies", omega0 * frequencies)
+        self.register_buffer("_frequencies", omega0 * frequencies, persistent=False)
         self.include_input = include_input
 
-    def forward(self, x: torch.Tensor):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         """
         Args:
             x: tensor of shape [..., dim]

pytorch3d/renderer/implicit/raysampling.py

@@ -96,7 +96,7 @@ class GridRaysampler(torch.nn.Module):
             ),
             dim=-1,
         )
-        self.register_buffer("_xy_grid", _xy_grid)
+        self.register_buffer("_xy_grid", _xy_grid, persistent=False)
 
     def forward(self, cameras: CamerasBase, **kwargs) -> RayBundle:
         """

tests/test_raysampling.py

@@ -425,3 +425,23 @@ class TestRaysampling(TestCaseMixin, unittest.TestCase):
             ray_bundle_camera_fix_seed.directions.view(batch_size, -1, 3),
             atol=1e-5,
         )
+
+    def test_load_state(self):
+        # check that we can load the state of one ray sampler into
+        # another with different image size.
+        module1 = NDCGridRaysampler(
+            image_width=20,
+            image_height=30,
+            n_pts_per_ray=40,
+            min_depth=1.2,
+            max_depth=2.3,
+        )
+        module2 = NDCGridRaysampler(
+            image_width=22,
+            image_height=32,
+            n_pts_per_ray=42,
+            min_depth=1.2,
+            max_depth=2.3,
+        )
+        state = module1.state_dict()
+        module2.load_state_dict(state)