suppress errors in `vision/fair/pytorch3d`

Differential Revision: D25781183

fbshipit-source-id: e27808a4c2b94bba205756001cb909827182b592

pytorch3d/ops/points_to_volumes.py

@@ -184,6 +184,7 @@ def add_points_features_to_volume_densities_features(
 
     if volume_features is None:
         # initialize features if not passed in
+        # pyre-fixme[16]: `Tensor` has no attribute `new_zeros`.
         volume_features_flatten = volume_densities.new_zeros(ba, feature_dim, n_voxels)
     else:
         # otherwise just flatten
@@ -227,6 +228,7 @@ def _check_points_to_volumes_inputs(
     mask: Optional[torch.Tensor] = None,
 ):
 
+    # pyre-fixme[16]: `Tuple` has no attribute `values`.
     max_grid_size = grid_sizes.max(dim=0).values
     if torch.prod(max_grid_size) > volume_densities.shape[1]:
         raise ValueError(
@@ -321,9 +323,11 @@ def splat_points_to_volumes(
     rXYZ = points_3d_indices - XYZ.type_as(points_3d)  # remainder of floor
 
     # split into separate coordinate vectors
+    # pyre-fixme[16]: `LongTensor` has no attribute `split`.
     X, Y, Z = XYZ.split(1, dim=2)
     # rX = remainder after floor = 1-"the weight of each vote into
     #      the X coordinate of the 8-neighborhood"
+    # pyre-fixme[16]: `Tensor` has no attribute `split`.
     rX, rY, rZ = rXYZ.split(1, dim=2)
 
     # get random indices for the purpose of adding out-of-bounds values
@@ -344,6 +348,7 @@ def splat_points_to_volumes(
                 w = wX * wY * wZ
 
                 # valid - binary indicators of votes that fall into the volume
+                # pyre-fixme[16]: `int` has no attribute `long`.
                 valid = (
                     (0 <= X_)
                     * (X_ < grid_sizes_xyz[:, None, 0:1])
@@ -362,20 +367,25 @@ def splat_points_to_volumes(
                 idx_valid = idx * valid + rand_idx * (1 - valid)
                 w_valid = w * valid.type_as(w)
                 if mask is not None:
+                    # pyre-fixme[6]: Expected `_OtherTensor` for 1st param but got
+                    #  `int`.
                     w_valid = w_valid * mask.type_as(w)[:, :, None]
 
                 # scatter add casts the votes into the weight accumulator
                 # and the feature accumulator
+                # pyre-fixme[16]: `Tensor` has no attribute `scatter_add_`.
                 volume_densities.scatter_add_(1, idx_valid, w_valid)
 
                 # reshape idx_valid -> (minibatch, feature_dim, n_points)
                 idx_valid = idx_valid.view(ba, 1, n_points).expand_as(points_features)
+                # pyre-fixme[16]: `int` has no attribute `view`.
                 w_valid = w_valid.view(ba, 1, n_points)
 
                 # volume_features of shape (minibatch, feature_dim, n_voxels)
                 volume_features.scatter_add_(2, idx_valid, w_valid * points_features)
 
     # divide each feature by the total weight of the votes
+    # pyre-fixme[20]: Argument `max` expected.
     volume_features = volume_features / volume_densities.view(ba, 1, n_voxels).clamp(
         min_weight
     )
@@ -453,6 +463,7 @@ def round_points_to_volumes(
 
     # valid - binary indicators of votes that fall into the volume
     grid_sizes = grid_sizes.type_as(XYZ)
+    # pyre-fixme[16]: `int` has no attribute `long`.
     valid = (
         (0 <= X)
         * (X < grid_sizes_xyz[:, None, 0:1])
@@ -474,6 +485,7 @@ def round_points_to_volumes(
 
     # scatter add casts the votes into the weight accumulator
     # and the feature accumulator
+    # pyre-fixme[16]: `Tensor` has no attribute `scatter_add_`.
     volume_densities.scatter_add_(1, idx_valid, w_valid)
 
     # reshape idx_valid -> (minibatch, feature_dim, n_points)
@@ -484,6 +496,7 @@ def round_points_to_volumes(
     volume_features.scatter_add_(2, idx_valid, w_valid * points_features)
 
     # divide each feature by the total weight of the votes
+    # pyre-fixme[20]: Argument `max` expected.
     volume_features = volume_features / volume_densities.view(ba, 1, n_voxels).clamp(
         1.0
     )

pytorch3d/renderer/implicit/raymarching.py

@@ -153,6 +153,7 @@ class AbsorptionOnlyRaymarcher(torch.nn.Module):
         _check_density_bounds(rays_densities)
         total_transmission = torch.prod(1 - rays_densities, dim=-1, keepdim=True)
         opacities = 1.0 - total_transmission
+        # pyre-fixme[7]: Expected `Optional[torch.Tensor]` but got `float`.
         return opacities
 
 
@@ -176,6 +177,7 @@ def _check_density_bounds(
     Checks whether the elements of `rays_densities` range within `bounds`.
     If not issues a warning.
     """
+    # pyre-fixme[16]: `ByteTensor` has no attribute `any`.
     if ((rays_densities > bounds[1]) | (rays_densities < bounds[0])).any():
         warnings.warn(
             "One or more elements of rays_densities are outside of valid"
@@ -213,6 +215,7 @@ def _check_raymarcher_inputs(
 
     rays_shape = rays_densities.shape[:-1]
 
+    # pyre-fixme[16]: `Optional` has no attribute `shape`.
     if not z_can_be_none and rays_z.shape != rays_shape:
         raise ValueError("rays_z have to be of the same shape as rays_densities.")
 

pytorch3d/structures/volumes.py

@@ -473,6 +473,7 @@ class Volumes(object):
         )
 
     def __len__(self) -> int:
+        # pyre-fixme[16]: `List` has no attribute `shape`.
         return self._densities.shape[0]
 
     def __getitem__(
@@ -497,6 +498,7 @@ class Volumes(object):
             raise IndexError(index)
 
         new = self.__class__(
+            # pyre-fixme[16]: `Optional` has no attribute `__getitem__`.
             features=self.features()[index] if self._features is not None else None,
             densities=self.densities()[index],
         )
@@ -521,6 +523,8 @@ class Volumes(object):
         Returns:
             **densities**: The tensor of volume densities.
         """
+        # pyre-fixme[7]: Expected `Tensor` but got `Union[List[torch.Tensor],
+        #  torch.Tensor]`.
         return self._densities
 
     def densities_list(self) -> List[torch.Tensor]:
@@ -542,6 +546,7 @@ class Volumes(object):
         """
         if self._features is None:
             # No features provided so return None
+            # pyre-fixme[7]: Expected `List[torch.Tensor]` but got `None`.
             return None
         return self._features_densities_list(self.features())
 
@@ -687,8 +692,10 @@ class Volumes(object):
         other = self.clone()
         if self.device != device:
             other.device = device
+            # pyre-fixme[16]: `List` has no attribute `to`.
             other._densities = self._densities.to(device)
             if self._features is not None:
+                # pyre-fixme[16]: `Optional` has no attribute `to`.
                 other._features = self.features().to(device)
             other._local_to_world_transform = (
                 self.get_local_to_world_coords_transform().to(device)