Avoid raysampler dict

Summary:
A significant speedup (e.g. >2% of a forward pass).

Move NDCMultinomialRaysampler parts of AbstractMaskRaySampler to members instead of living in a dict. The dict was hiding them from the nn.Module system so their _xy_grid members were remaining on the CPU. Therefore they were being copied to the GPU in every forward pass.

(We couldn't easily use a ModuleDict here because the enum keys are not strs.)

Reviewed By: shapovalov

Differential Revision: D39668589

fbshipit-source-id: 719b88e4a08fd7263a284e0ab38189e666bd7e3a

pytorch3d/implicitron/models/renderer/ray_sampler.py

@@ -100,34 +100,32 @@ class AbstractMaskRaySampler(RaySamplerBase, torch.nn.Module):
             ),
         }
 
-        self._raysamplers = {
-            EvaluationMode.TRAINING: NDCMultinomialRaysampler(
-                image_width=self.image_width,
-                image_height=self.image_height,
-                n_pts_per_ray=self.n_pts_per_ray_training,
-                min_depth=0.0,
-                max_depth=0.0,
-                n_rays_per_image=self.n_rays_per_image_sampled_from_mask
-                if self._sampling_mode[EvaluationMode.TRAINING]
-                == RenderSamplingMode.MASK_SAMPLE
-                else None,
-                unit_directions=True,
-                stratified_sampling=self.stratified_point_sampling_training,
-            ),
-            EvaluationMode.EVALUATION: NDCMultinomialRaysampler(
-                image_width=self.image_width,
-                image_height=self.image_height,
-                n_pts_per_ray=self.n_pts_per_ray_evaluation,
-                min_depth=0.0,
-                max_depth=0.0,
-                n_rays_per_image=self.n_rays_per_image_sampled_from_mask
-                if self._sampling_mode[EvaluationMode.EVALUATION]
-                == RenderSamplingMode.MASK_SAMPLE
-                else None,
-                unit_directions=True,
-                stratified_sampling=self.stratified_point_sampling_evaluation,
-            ),
-        }
+        self._training_raysampler = NDCMultinomialRaysampler(
+            image_width=self.image_width,
+            image_height=self.image_height,
+            n_pts_per_ray=self.n_pts_per_ray_training,
+            min_depth=0.0,
+            max_depth=0.0,
+            n_rays_per_image=self.n_rays_per_image_sampled_from_mask
+            if self._sampling_mode[EvaluationMode.TRAINING]
+            == RenderSamplingMode.MASK_SAMPLE
+            else None,
+            unit_directions=True,
+            stratified_sampling=self.stratified_point_sampling_training,
+        )
+        self._evaluation_raysampler = NDCMultinomialRaysampler(
+            image_width=self.image_width,
+            image_height=self.image_height,
+            n_pts_per_ray=self.n_pts_per_ray_evaluation,
+            min_depth=0.0,
+            max_depth=0.0,
+            n_rays_per_image=self.n_rays_per_image_sampled_from_mask
+            if self._sampling_mode[EvaluationMode.EVALUATION]
+            == RenderSamplingMode.MASK_SAMPLE
+            else None,
+            unit_directions=True,
+            stratified_sampling=self.stratified_point_sampling_evaluation,
+        )
 
     def _get_min_max_depth_bounds(self, cameras: CamerasBase) -> Tuple[float, float]:
         raise NotImplementedError()
@@ -169,11 +167,13 @@ class AbstractMaskRaySampler(RaySamplerBase, torch.nn.Module):
 
         min_depth, max_depth = self._get_min_max_depth_bounds(cameras)
 
+        raysampler = {
+            EvaluationMode.TRAINING: self._training_raysampler,
+            EvaluationMode.EVALUATION: self._evaluation_raysampler,
+        }[evaluation_mode]
+
         # pyre-fixme[29]:
-        #  `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self,
-        #  torch.Tensor), Named(item, typing.Any)], typing.Any], torch.Tensor],
-        #  torch.Tensor, torch.nn.Module]` is not a function.
-        ray_bundle = self._raysamplers[evaluation_mode](
+        ray_bundle = raysampler(
             cameras=cameras,
             mask=sample_mask,
             min_depth=min_depth,