Annotate dunder functions

Summary: Annotate the (return type of the) following dunder functions across the codebase: `__init__()`, `__len__()`, `__getitem__()`

Reviewed By: nikhilaravi

Differential Revision: D29001801

fbshipit-source-id: 928d9e1c417ffe01ab8c0445311287786e997c7c

pytorch3d/structures/meshes.py

@@ -219,7 +219,7 @@ class Meshes:
         textures=None,
         *,
         verts_normals=None,
-    ):
+    ) -> None:
         """
         Args:
             verts:
@@ -469,10 +469,10 @@ class Meshes:
         else:
             raise ValueError("verts_normals must be a list or tensor")
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self._N
 
-    def __getitem__(self, index):
+    def __getitem__(self, index) -> "Meshes":
         """
         Args:
             index: Specifying the index of the mesh to retrieve.
@@ -493,7 +493,7 @@ class Meshes:
             # NOTE consider converting index to cpu for efficiency
             if index.dtype == torch.bool:
                 # advanced indexing on a single dimension
-                index = index.nonzero()
+                index = index.nonzero()  # pyre-ignore
                 index = index.squeeze(1) if index.numel() > 0 else index
                 index = index.tolist()
             verts = [self.verts_list()[i] for i in index]

pytorch3d/structures/pointclouds.py

@@ -108,7 +108,7 @@ class Pointclouds:
         "equisized",
     ]
 
-    def __init__(self, points, normals=None, features=None):
+    def __init__(self, points, normals=None, features=None) -> None:
         """
         Args:
             points:
@@ -306,10 +306,10 @@ class Pointclouds:
                     points in a cloud."
             )
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self._N
 
-    def __getitem__(self, index):
+    def __getitem__(self, index) -> "Pointclouds":
         """
         Args:
             index: Specifying the index of the cloud to retrieve.
@@ -343,7 +343,7 @@ class Pointclouds:
             # NOTE consider converting index to cpu for efficiency
             if index.dtype == torch.bool:
                 # advanced indexing on a single dimension
-                index = index.nonzero()
+                index = index.nonzero()  # pyre-ignore
                 index = index.squeeze(1) if index.numel() > 0 else index
                 index = index.tolist()
             points = [self.points_list()[i] for i in index]

pytorch3d/structures/volumes.py

@@ -155,7 +155,7 @@ class Volumes:
         features: Optional[_TensorBatch] = None,
         voxel_size: _VoxelSize = 1.0,
         volume_translation: _Translation = (0.0, 0.0, 0.0),
-    ):
+    ) -> None:
         """
         Args:
             **densities**: Batch of input feature volume occupancies of shape