R2n2 (#1124)

Summary:
1. Fix https://github.com/facebookresearch/pytorch3d/issues/1115 Change the type annatations of three arguments in the initializer of `R2N2` class.
2. Fix https://github.com/facebookresearch/pytorch3d/issues/1118 Override two functions in `BlenderCamera` class reruired by subclassing `CamerasBase` class.

Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1124

Reviewed By: nikhilaravi

Differential Revision: D34890900

Pulled By: bottler

fbshipit-source-id: 65c385369a5964ecbb17ab28f279d5284614b487

pytorch3d/datasets/r2n2/r2n2.py

@@ -55,9 +55,9 @@ class R2N2(ShapeNetBase):  # pragma: no cover
     def __init__(
         self,
         split: str,
-        shapenet_dir,
-        r2n2_dir,
-        splits_file,
+        shapenet_dir: str,
+        r2n2_dir: str,
+        splits_file: str,
         return_all_views: bool = True,
         return_voxels: bool = False,
         views_rel_path: str = "ShapeNetRendering",
@@ -70,9 +70,9 @@ class R2N2(ShapeNetBase):  # pragma: no cover
 
         Args:
             split (str): One of (train, val, test).
-            shapenet_dir (path): Path to ShapeNet core v1.
-            r2n2_dir (path): Path to the R2N2 dataset.
-            splits_file (path): File containing the train/val/test splits.
+            shapenet_dir (str): Path to ShapeNet core v1.
+            r2n2_dir (str): Path to the R2N2 dataset.
+            splits_file (str): File containing the train/val/test splits.
             return_all_views (bool): Indicator of whether or not to load all the views in
                 the split. If set to False, one of the views in the split will be randomly
                 selected and loaded.

pytorch3d/datasets/r2n2/utils.py

@@ -459,6 +459,12 @@ class BlenderCamera(CamerasBase):  # pragma: no cover
         transform._matrix = self.K.transpose(1, 2).contiguous()
         return transform
 
+    def is_perspective(self):
+        return False
+
+    def in_ndc(self):
+        return True
+
 
 def render_cubified_voxels(
     voxels: torch.Tensor, shader_type=HardPhongShader, device: Device = "cpu", **kwargs