suppress errors in vision/fair/pytorch3d

Reviewed By: kjchalup

Differential Revision: D39198333

fbshipit-source-id: 3f4ebcf625215f21d165073837578ff69b05f72d

Author: Pyre Bot Jr

projects/implicitron_trainer/experiment.py

@@ -207,6 +207,7 @@ def run(self) -> None:
                 val_loader,
             ) = accelerator.prepare(model, optimizer, train_loader, val_loader)
 
+        # pyre-fixme[16]: Optional type has no attribute `is_multisequence`.
         if not self.training_loop.evaluator.is_multisequence:
             all_train_cameras = self.data_source.all_train_cameras
         else:

projects/implicitron_trainer/impl/training_loop.py

@@ -29,6 +29,7 @@
 logger = logging.getLogger(__name__)
 
 
+# pyre-fixme[13]: Attribute `evaluator` is never initialized.
 class TrainingLoopBase(ReplaceableBase):
     """
     Members:
@@ -62,7 +63,7 @@ def load_stats(
 
 
 @registry.register
-class ImplicitronTrainingLoop(TrainingLoopBase):  # pyre-ignore [13]
+class ImplicitronTrainingLoop(TrainingLoopBase):
     """
     Members:
         eval_only: If True, only run evaluation using the test dataloader.
@@ -137,6 +138,7 @@ def run(
         # only run evaluation on the test dataloader
         if self.eval_only:
             if test_loader is not None:
+                # pyre-fixme[16]: `Optional` has no attribute `run`.
                 self.evaluator.run(
                     all_train_cameras=all_train_cameras,
                     dataloader=test_loader,

pytorch3d/implicitron/models/implicit_function/decoding_functions.py

@@ -158,9 +158,14 @@ def forward(self, x: torch.Tensor, z: Optional[torch.Tensor] = None):
             # if the skip tensor is None, we use `x` instead.
             z = x
         skipi = 0
+        # pyre-fixme[6]: For 1st param expected `Iterable[Variable[_T]]` but got
+        #  `Union[Tensor, Module]`.
         for li, layer in enumerate(self.mlp):
+            # pyre-fixme[58]: `in` is not supported for right operand type
+            #  `Union[torch._tensor.Tensor, torch.nn.modules.module.Module]`.
             if li in self._input_skips:
                 if self._skip_affine_trans:
+                    # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C._Te...
                     y = self._apply_affine_layer(self.skip_affines[skipi], y, z)
                 else:
                     y = torch.cat((y, z), dim=-1)
@@ -170,6 +175,7 @@ def forward(self, x: torch.Tensor, z: Optional[torch.Tensor] = None):
 
 
 @registry.register
+# pyre-fixme[13]: Attribute `network` is never initialized.
 class MLPDecoder(DecoderFunctionBase):
     """
     Decoding function which uses `MLPWithIputSkips` to convert the embedding to output.

pytorch3d/implicitron/models/implicit_function/voxel_grid.py

@@ -89,7 +89,6 @@ def evaluate_world(
             torch.Tensor: shape (n_grids, n_points, n_features)
         """
         points_local = locator.world_to_local_coords(points)
-        # pyre-ignore[29]
         return self.evaluate_local(points_local, grid_values)
 
     def evaluate_local(
@@ -139,6 +138,8 @@ class FullResolutionVoxelGrid(VoxelGridBase):
     # the type of grid_values argument needed to run evaluate_local()
     values_type: ClassVar[Type[VoxelGridValuesBase]] = FullResolutionVoxelGridValues
 
+    # pyre-fixme[14]: `evaluate_local` overrides method defined in `VoxelGridBase`
+    #  inconsistently.
     def evaluate_local(
         self, points: torch.Tensor, grid_values: FullResolutionVoxelGridValues
     ) -> torch.Tensor:
@@ -213,6 +214,8 @@ class CPFactorizedVoxelGrid(VoxelGridBase):
     n_components: int = 24
     matrix_reduction: bool = True
 
+    # pyre-fixme[14]: `evaluate_local` overrides method defined in `VoxelGridBase`
+    #  inconsistently.
     def evaluate_local(
         self, points: torch.Tensor, grid_values: CPFactorizedVoxelGridValues
     ) -> torch.Tensor:
@@ -318,6 +321,8 @@ class VMFactorizedVoxelGrid(VoxelGridBase):
     distribution_of_components: Optional[Tuple[int, int, int]] = None
     matrix_reduction: bool = True
 
+    # pyre-fixme[14]: `evaluate_local` overrides method defined in `VoxelGridBase`
+    #  inconsistently.
     def evaluate_local(
         self, points: torch.Tensor, grid_values: VMFactorizedVoxelGridValues
     ) -> torch.Tensor:
@@ -392,9 +397,11 @@ def get_shapes(self) -> Dict[str, Tuple]:
         if self.distribution_of_components is None and self.n_components % 3 != 0:
             raise ValueError("n_components must be divisible by 3")
         if self.distribution_of_components is None:
-            # pyre-ignore[58]
             calculated_distribution_of_components = [
-                self.n_components // 3 for _ in range(3)
+                # pyre-fixme[58]: `//` is not supported for operand types
+                #  `Optional[int]` and `int`.
+                self.n_components // 3
+                for _ in range(3)
             ]
         else:
             calculated_distribution_of_components = self.distribution_of_components
@@ -437,6 +444,7 @@ def get_shapes(self) -> Dict[str, Tuple]:
         return shape_dict
 
 
+# pyre-fixme[13]: Attribute `voxel_grid` is never initialized.
 class VoxelGridModule(Configurable, torch.nn.Module):
     """
     A wrapper torch.nn.Module for the VoxelGrid classes, which
@@ -459,6 +467,7 @@ class VoxelGridModule(Configurable, torch.nn.Module):
     voxel_grid_class_type: str = "FullResolutionVoxelGrid"
     voxel_grid: VoxelGridBase
 
+    # pyre-fixme[8]: Attribute has type `Tuple[float, float, float]`; used as `float`.
     extents: Tuple[float, float, float] = 1.0
     translation: Tuple[float, float, float] = (0.0, 0.0, 0.0)
 
@@ -505,8 +514,11 @@ def forward(self, points: torch.Tensor) -> torch.Tensor:
             # voxel size and translation.
             voxel_size=self.extents,
             volume_translation=self.translation,
+            # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C._TensorBase...
             device=next(self.params.values()).device,
         )
+        # pyre-fixme[29]: `Union[torch._tensor.Tensor,
+        #  torch.nn.modules.module.Module]` is not a function.
         grid_values = self.voxel_grid.values_type(**self.params)
         # voxel grids operate with extra n_grids dimension, which we fix to one
         return self.voxel_grid.evaluate_world(points[None], grid_values, locator)[0]

pytorch3d/implicitron/tools/eval_video_trajectory.py

@@ -185,11 +185,14 @@ def _remove_outlier_cameras(
     keep_indices = utils.get_inlier_indicators(
         cameras.get_camera_center(), dim=0, outlier_rate=outlier_rate
     )
+    # pyre-fixme[6]: For 1st param expected `Union[List[int], int, BoolTensor,
+    #  LongTensor]` but got `Tensor`.
     clean_cameras = cameras[keep_indices]
     logger.info(
         "Filtered outlier cameras when estimating the trajectory: "
         f"{len(cameras)} → {len(clean_cameras)}"
     )
+    # pyre-fixme[7]: Expected `PerspectiveCameras` but got `CamerasBase`.
     return clean_cameras
 
 

pytorch3d/renderer/cameras.py

@@ -401,7 +401,9 @@ def __getitem__(
         kwargs = {}
 
         tensor_types = {
+            # pyre-fixme[16]: Module `cuda` has no attribute `BoolTensor`.
             "bool": (torch.BoolTensor, torch.cuda.BoolTensor),
+            # pyre-fixme[16]: Module `cuda` has no attribute `LongTensor`.
             "long": (torch.LongTensor, torch.cuda.LongTensor),
         }
         if not isinstance(
@@ -419,8 +421,14 @@ def __getitem__(
             index = [index]
 
         if isinstance(index, tensor_types["bool"]):
+            # pyre-fixme[16]: Item `List` of `Union[List[int], BoolTensor,
+            #  LongTensor]` has no attribute `ndim`.
+            # pyre-fixme[16]: Item `List` of `Union[List[int], BoolTensor,
+            #  LongTensor]` has no attribute `shape`.
             if index.ndim != 1 or index.shape[0] != len(self):
                 raise ValueError(
+                    # pyre-fixme[16]: Item `List` of `Union[List[int], BoolTensor,
+                    #  LongTensor]` has no attribute `shape`.
                     f"Boolean index of shape {index.shape} does not match cameras"
                 )
         elif max(index) >= len(self):

pytorch3d/renderer/mesh/rasterizer.py

@@ -204,6 +204,7 @@ def transform(self, meshes_world, **kwargs) -> torch.Tensor:
         to_ndc_transform = cameras.get_ndc_camera_transform(**kwargs)
         projection_transform = try_get_projection_transform(cameras, kwargs)
         if projection_transform is not None:
+            # pyre-fixme[16]: Anonymous callable has no attribute `compose`.
             projection_transform = projection_transform.compose(to_ndc_transform)
             verts_ndc = projection_transform.transform_points(verts_view, eps=eps)
         else:

pytorch3d/renderer/points/rasterizer.py

@@ -107,6 +107,7 @@ def transform(self, point_clouds, **kwargs) -> Pointclouds:
         to_ndc_transform = cameras.get_ndc_camera_transform(**kwargs)
         projection_transform = try_get_projection_transform(cameras, kwargs)
         if projection_transform is not None:
+            # pyre-fixme[16]: Anonymous callable has no attribute `compose`.
             projection_transform = projection_transform.compose(to_ndc_transform)
             pts_ndc = projection_transform.transform_points(pts_view, eps=eps)
         else:

pytorch3d/structures/volumes.py

@@ -981,6 +981,9 @@ def _copy_transform_and_sizes(
         device = device if device is not None else self.device
         other._grid_sizes = self._grid_sizes[index].to(device)
         other._local_to_world_transform = self.get_local_to_world_coords_transform()[
+            # pyre-fixme[6]: For 1st param expected `Union[List[int], int, slice,
+            #  BoolTensor, LongTensor]` but got `Union[None, List[int], Tuple[int],
+            #  int, slice, Tensor]`.
             index
         ].to(device)