Remove unused `pyre-ignore` or `pyre-fixme`

Reviewed By: bottler

Differential Revision: D47223471

fbshipit-source-id: 8bdabf2a69dd7aec7202141122a9c69220ba7ef1

pytorch3d/implicitron/dataset/utils.py

@@ -316,7 +316,7 @@ def adjust_camera_to_bbox_crop_(
 
     focal_length_px, principal_point_px = _convert_ndc_to_pixels(
         camera.focal_length[0],
-        camera.principal_point[0],  # pyre-ignore
+        camera.principal_point[0],
         image_size_wh,
     )
     principal_point_px_cropped = principal_point_px - clamp_bbox_xywh[:2]
@@ -328,7 +328,7 @@ def adjust_camera_to_bbox_crop_(
     )
 
     camera.focal_length = focal_length[None]
-    camera.principal_point = principal_point_cropped[None]  # pyre-ignore
+    camera.principal_point = principal_point_cropped[None]
 
 
 def adjust_camera_to_image_scale_(
@@ -338,7 +338,7 @@ def adjust_camera_to_image_scale_(
 ) -> PerspectiveCameras:
     focal_length_px, principal_point_px = _convert_ndc_to_pixels(
         camera.focal_length[0],
-        camera.principal_point[0],  # pyre-ignore
+        camera.principal_point[0],
         original_size_wh,
     )
 

pytorch3d/implicitron/models/feature_extractor/resnet_feature_extractor.py

@@ -143,7 +143,6 @@ class ResNetFeatureExtractor(FeatureExtractorBase):
         return (img - self._resnet_mean) / self._resnet_std
 
     def get_feat_dims(self) -> int:
-        # pyre-fixme[29]
         return sum(self._feat_dim.values())
 
     def forward(
@@ -181,13 +180,8 @@ class ResNetFeatureExtractor(FeatureExtractorBase):
                 imgs_normed = self._resnet_normalize_image(imgs_resized)
             else:
                 imgs_normed = imgs_resized
-            # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.modules.module.Module]`
             #  is not a function.
             feats = self.stem(imgs_normed)
-            # pyre-fixme[6]: For 1st param expected `Iterable[Variable[_T1]]` but
-            #  got `Union[Tensor, Module]`.
-            # pyre-fixme[6]: For 2nd param expected `Iterable[Variable[_T2]]` but
-            #  got `Union[Tensor, Module]`.
             for stage, (layer, proj) in enumerate(zip(self.layers, self.proj_layers)):
                 feats = layer(feats)
                 # just a sanity check below

pytorch3d/implicitron/models/generic_model.py

@@ -463,10 +463,6 @@ class GenericModel(ImplicitronModelBase):  # pyre-ignore: 13
             )
         custom_args["global_code"] = global_code
 
-        # pyre-fixme[29]:
-        #  `Union[BoundMethod[typing.Callable(torch.Tensor.__iter__)[[Named(self,
-        #  torch.Tensor)], typing.Iterator[typing.Any]], torch.Tensor], torch.Tensor,
-        #  torch.nn.Module]` is not a function.
         for func in self._implicit_functions:
             func.bind_args(**custom_args)
 
@@ -489,10 +485,6 @@ class GenericModel(ImplicitronModelBase):  # pyre-ignore: 13
         # Unbind the custom arguments to prevent pytorch from storing
         # large buffers of intermediate results due to points in the
         # bound arguments.
-        # pyre-fixme[29]:
-        #  `Union[BoundMethod[typing.Callable(torch.Tensor.__iter__)[[Named(self,
-        #  torch.Tensor)], typing.Iterator[typing.Any]], torch.Tensor], torch.Tensor,
-        #  torch.nn.Module]` is not a function.
         for func in self._implicit_functions:
             func.unbind_args()
 

pytorch3d/implicitron/models/global_encoder/autodecoder.py

@@ -69,7 +69,7 @@ class Autodecoder(Configurable, torch.nn.Module):
         return key_map
 
     def calculate_squared_encoding_norm(self) -> Optional[torch.Tensor]:
-        return (self._autodecoder_codes.weight**2).mean()  # pyre-ignore[16]
+        return (self._autodecoder_codes.weight**2).mean()
 
     def get_encoding_dim(self) -> int:
         return self.encoding_dim
@@ -93,7 +93,6 @@ class Autodecoder(Configurable, torch.nn.Module):
                 # pyre-fixme[9]: x has type `Union[List[str], LongTensor]`; used as
                 #  `Tensor`.
                 x = torch.tensor(
-                    # pyre-ignore[29]
                     [self._key_map[elem] for elem in x],
                     dtype=torch.long,
                     device=next(self.parameters()).device,
@@ -101,7 +100,6 @@ class Autodecoder(Configurable, torch.nn.Module):
             except StopIteration:
                 raise ValueError("Not enough n_instances in the autodecoder") from None
 
-        # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
         return self._autodecoder_codes(x)
 
     def _load_key_map_hook(

pytorch3d/implicitron/models/global_encoder/global_encoder.py

@@ -119,7 +119,7 @@ class HarmonicTimeEncoder(GlobalEncoderBase, torch.nn.Module):
         if frame_timestamp.shape[-1] != 1:
             raise ValueError("Frame timestamp's last dimensions should be one.")
         time = frame_timestamp / self.time_divisor
-        return self._harmonic_embedding(time)  # pyre-ignore: 29
+        return self._harmonic_embedding(time)
 
     def calculate_squared_encoding_norm(self) -> Optional[torch.Tensor]:
         return None

pytorch3d/implicitron/models/implicit_function/decoding_functions.py

@@ -230,14 +230,9 @@ class MLPWithInputSkips(Configurable, torch.nn.Module):
             # 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)

pytorch3d/implicitron/models/implicit_function/idr_feature_field.py

@@ -141,16 +141,11 @@ class IdrFeatureField(ImplicitFunctionBase, torch.nn.Module):
             self.embed_fn is None and fun_viewpool is None and global_code is None
         ):
             return torch.tensor(
-                [],
-                device=rays_points_world.device,
-                dtype=rays_points_world.dtype
-                # pyre-fixme[6]: For 2nd param expected `int` but got `Union[Module,
-                #  Tensor]`.
+                [], device=rays_points_world.device, dtype=rays_points_world.dtype
             ).view(0, self.out_dim)
 
         embeddings = []
         if self.embed_fn is not None:
-            # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
             embeddings.append(self.embed_fn(rays_points_world))
 
         if fun_viewpool is not None:
@@ -169,17 +164,13 @@ class IdrFeatureField(ImplicitFunctionBase, torch.nn.Module):
 
         embedding = torch.cat(embeddings, dim=-1)
         x = embedding
-        # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C._TensorBase.__s...
         for layer_idx in range(self.num_layers - 1):
             if layer_idx in self.skip_in:
                 x = torch.cat([x, embedding], dim=-1) / 2**0.5
 
-            # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
             x = self.linear_layers[layer_idx](x)
 
-            # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C._TensorBase...
             if layer_idx < self.num_layers - 2:
-                # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
                 x = self.softplus(x)
 
         return x

pytorch3d/implicitron/models/implicit_function/neural_radiance_field.py

@@ -113,10 +113,8 @@ class NeuralRadianceFieldBase(ImplicitFunctionBase, torch.nn.Module):
         # Normalize the ray_directions to unit l2 norm.
         rays_directions_normed = torch.nn.functional.normalize(rays_directions, dim=-1)
         # Obtain the harmonic embedding of the normalized ray directions.
-        # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
         rays_embedding = self.harmonic_embedding_dir(rays_directions_normed)
 
-        # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
         return self.color_layer((self.intermediate_linear(features), rays_embedding))
 
     @staticmethod
@@ -171,7 +169,6 @@ class NeuralRadianceFieldBase(ImplicitFunctionBase, torch.nn.Module):
 
         embeds = create_embeddings_for_implicit_function(
             xyz_world=rays_points_world,
-            # pyre-fixme[6]: Expected `Optional[typing.Callable[..., typing.Any]]`
             #  for 2nd param but got `Union[None, torch.Tensor, torch.nn.Module]`.
             xyz_embedding_function=self.harmonic_embedding_xyz
             if self.input_xyz
@@ -183,14 +180,12 @@ class NeuralRadianceFieldBase(ImplicitFunctionBase, torch.nn.Module):
         )
 
         # embeds.shape = [minibatch x n_src x n_rays x n_pts x self.n_harmonic_functions*6+3]
-        # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
         features = self.xyz_encoder(embeds)
         # features.shape = [minibatch x ... x self.n_hidden_neurons_xyz]
         # NNs operate on the flattenned rays; reshaping to the correct spatial size
         # TODO: maybe make the transformer work on non-flattened tensors to avoid this reshape
         features = features.reshape(*rays_points_world.shape[:-1], -1)
 
-        # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
         raw_densities = self.density_layer(features)
         # raw_densities.shape = [minibatch x ... x 1] in [0-1]
 

pytorch3d/implicitron/models/implicit_function/scene_representation_networks.py

@@ -101,8 +101,6 @@ class SRNRaymarchFunction(Configurable, torch.nn.Module):
 
         embeds = create_embeddings_for_implicit_function(
             xyz_world=rays_points_world,
-            # pyre-fixme[6]: Expected `Optional[typing.Callable[..., typing.Any]]`
-            #  for 2nd param but got `Union[torch.Tensor, torch.nn.Module]`.
             xyz_embedding_function=self._harmonic_embedding,
             global_code=global_code,
             fun_viewpool=fun_viewpool,
@@ -112,7 +110,6 @@ class SRNRaymarchFunction(Configurable, torch.nn.Module):
 
         # Before running the network, we have to resize embeds to ndims=3,
         # otherwise the SRN layers consume huge amounts of memory.
-        # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
         raymarch_features = self._net(
             embeds.view(embeds.shape[0], -1, embeds.shape[-1])
         )
@@ -167,9 +164,7 @@ class SRNPixelGenerator(Configurable, torch.nn.Module):
         # Normalize the ray_directions to unit l2 norm.
         rays_directions_normed = torch.nn.functional.normalize(rays_directions, dim=-1)
         # Obtain the harmonic embedding of the normalized ray directions.
-        # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
         rays_embedding = self._harmonic_embedding(rays_directions_normed)
-        # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
         return self._color_layer((features, rays_embedding))
 
     def forward(
@@ -198,7 +193,6 @@ class SRNPixelGenerator(Configurable, torch.nn.Module):
                 denoting the color of each ray point.
         """
         # raymarch_features.shape = [minibatch x ... x pts_per_ray x 3]
-        # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
         features = self._net(raymarch_features)
         # features.shape = [minibatch x ... x self.n_hidden_units]
 
@@ -213,7 +207,6 @@ class SRNPixelGenerator(Configurable, torch.nn.Module):
         # NNs operate on the flattenned rays; reshaping to the correct spatial size
         features = features.reshape(*raymarch_features.shape[:-1], -1)
 
-        # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
         raw_densities = self._density_layer(features)
 
         rays_colors = self._get_colors(features, directions)
@@ -274,7 +267,6 @@ class SRNRaymarchHyperNet(Configurable, torch.nn.Module):
         srn_raymarch_function.
         """
 
-        # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
         net = self._hypernet(global_code)
 
         # use the hyper-net generated network to instantiate the raymarch module
@@ -310,7 +302,6 @@ class SRNRaymarchHyperNet(Configurable, torch.nn.Module):
         # across LSTM iterations for the same global_code.
         if self.cached_srn_raymarch_function is None:
             # generate the raymarching network from the hypernet
-            # pyre-fixme[16]: `SRNRaymarchHyperNet` has no attribute
             self.cached_srn_raymarch_function = self._run_hypernet(global_code)
         (srn_raymarch_function,) = cast(
             Tuple[SRNRaymarchFunction], self.cached_srn_raymarch_function
@@ -337,7 +328,6 @@ class SRNImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
     def create_raymarch_function(self) -> None:
         self.raymarch_function = SRNRaymarchFunction(
             latent_dim=self.latent_dim,
-            # pyre-ignore[32]
             **self.raymarch_function_args,
         )
 
@@ -395,7 +385,6 @@ class SRNHyperNetImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
         self.hypernet = SRNRaymarchHyperNet(
             latent_dim=self.latent_dim,
             latent_dim_hypernet=self.latent_dim_hypernet,
-            # pyre-ignore[32]
             **self.hypernet_args,
         )
 

pytorch3d/implicitron/models/implicit_function/voxel_grid.py

@@ -267,7 +267,6 @@ class VoxelGridBase(ReplaceableBase, torch.nn.Module):
                 for name, tensor in vars(grid_values_with_wanted_resolution).items()
             }
 
-        # pyre-ignore[29]
         return self.values_type(**params), True
 
     def get_resolution_change_epochs(self) -> Tuple[int, ...]:
@@ -881,8 +880,6 @@ class VoxelGridModule(Configurable, torch.nn.Module):
             torch.Tensor of shape (..., n_features)
         """
         locator = self._get_volume_locator()
-        # 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]
@@ -896,8 +893,6 @@ class VoxelGridModule(Configurable, torch.nn.Module):
                 replace current parameters
         """
         if self.hold_voxel_grid_as_parameters:
-            # pyre-ignore [16]
-            # Nones are converted to empty tensors by Parameter()
             self.params = torch.nn.ParameterDict(
                 {
                     k: torch.nn.Parameter(val)
@@ -948,7 +943,6 @@ class VoxelGridModule(Configurable, torch.nn.Module):
         Returns:
             True if parameter change has happened else False.
         """
-        # pyre-ignore[29]
         grid_values = self.voxel_grid.values_type(**self.params)
         grid_values, change = self.voxel_grid.change_resolution(
             grid_values, epoch=epoch
@@ -996,19 +990,16 @@ class VoxelGridModule(Configurable, torch.nn.Module):
         """
         '''
         new_params = {}
-        # pyre-ignore[29]
         for name in self.params:
             key = prefix + "params." + name
             if key in state_dict:
                 new_params[name] = torch.zeros_like(state_dict[key])
-        # pyre-ignore[29]
         self.set_voxel_grid_parameters(self.voxel_grid.values_type(**new_params))
 
     def get_device(self) -> torch.device:
         """
         Returns torch.device on which module parameters are located
         """
-        # pyre-ignore[29]
         return next(val for val in self.params.values() if val is not None).device
 
     def crop_self(self, min_point: torch.Tensor, max_point: torch.Tensor) -> None:
@@ -1024,7 +1015,6 @@ class VoxelGridModule(Configurable, torch.nn.Module):
             nothing
         """
         locator = self._get_volume_locator()
-        # pyre-fixme[29]: `Union[torch._tensor.Tensor,
         #  torch.nn.modules.module.Module]` is not a function.
         old_grid_values = self.voxel_grid.values_type(**self.params)
         new_grid_values = self.voxel_grid.crop_world(
@@ -1033,7 +1023,6 @@ class VoxelGridModule(Configurable, torch.nn.Module):
         grid_values, _ = self.voxel_grid.change_resolution(
             new_grid_values, grid_values_with_wanted_resolution=old_grid_values
         )
-        # pyre-ignore [16]
         self.params = torch.nn.ParameterDict(
             {
                 k: torch.nn.Parameter(val)

pytorch3d/implicitron/models/implicit_function/voxel_grid_implicit_function.py

@@ -187,21 +187,16 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
 
     def __post_init__(self) -> None:
         run_auto_creation(self)
-        # pyre-ignore[16]
         self.voxel_grid_scaffold = self._create_voxel_grid_scaffold()
-        # pyre-ignore[16]
         self.harmonic_embedder_xyz_density = HarmonicEmbedding(
             **self.harmonic_embedder_xyz_density_args
         )
-        # pyre-ignore[16]
         self.harmonic_embedder_xyz_color = HarmonicEmbedding(
             **self.harmonic_embedder_xyz_color_args
         )
-        # pyre-ignore[16]
         self.harmonic_embedder_dir_color = HarmonicEmbedding(
             **self.harmonic_embedder_dir_color_args
         )
-        # pyre-ignore[16]
         self._scaffold_ready = False
 
     def forward(
@@ -252,7 +247,6 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
         # ########## filter the points using the scaffold ########## #
         if self._scaffold_ready and self.scaffold_filter_points:
             with torch.no_grad():
-                # pyre-ignore[29]
                 non_empty_points = self.voxel_grid_scaffold(points)[..., 0] > 0
             points = points[non_empty_points]
             if len(points) == 0:
@@ -364,7 +358,6 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
                 feature dimensionality which `decoder_density` returns
         """
         embeds_density = self.voxel_grid_density(points)
-        # pyre-ignore[29]
         harmonic_embedding_density = self.harmonic_embedder_xyz_density(embeds_density)
         # shape = [..., density_dim]
         return self.decoder_density(harmonic_embedding_density)
@@ -407,13 +400,11 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
 
         # ########## embed with the harmonic function ########## #
         # Obtain the harmonic embedding of the voxel grid output.
-        # pyre-ignore[29]
         harmonic_embedding_color = self.harmonic_embedder_xyz_color(embeds_color)
 
         # Normalize the ray_directions to unit l2 norm.
         rays_directions_normed = torch.nn.functional.normalize(directions, dim=-1)
         # Obtain the harmonic embedding of the normalized ray directions.
-        # pyre-ignore[29]
         harmonic_embedding_dir = self.harmonic_embedder_dir_color(
             rays_directions_normed
         )
@@ -482,10 +473,8 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
             an object inside, else False.
         """
         # find bounding box
-        # pyre-ignore[16]
         points = self.voxel_grid_scaffold.get_grid_points(epoch=epoch)
         assert self._scaffold_ready, "Scaffold has to be calculated before cropping."
-        # pyre-ignore[29]
         occupancy = self.voxel_grid_scaffold(points)[..., 0] > 0
         non_zero_idxs = torch.nonzero(occupancy)
         if len(non_zero_idxs) == 0:
@@ -517,7 +506,6 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
         """
 
         planes = []
-        # pyre-ignore[16]
         points = self.voxel_grid_scaffold.get_grid_points(epoch=epoch)
 
         chunk_size = (
@@ -537,9 +525,7 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
             stride=1,
         )
         occupancy_cube = density_cube > self.scaffold_empty_space_threshold
-        # pyre-ignore[16]
         self.voxel_grid_scaffold.params["voxel_grid"] = occupancy_cube.float()
-        # pyre-ignore[16]
         self._scaffold_ready = True
 
         return False
@@ -556,7 +542,6 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
         decoding function to this value.
         """
         grid_args = self.voxel_grid_density_args
-        # pyre-ignore[6]
         grid_output_dim = VoxelGridModule.get_output_dim(grid_args)
 
         embedder_args = self.harmonic_embedder_xyz_density_args
@@ -585,7 +570,6 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
         decoding function to this value.
         """
         grid_args = self.voxel_grid_color_args
-        # pyre-ignore[6]
         grid_output_dim = VoxelGridModule.get_output_dim(grid_args)
 
         embedder_args = self.harmonic_embedder_xyz_color_args
@@ -619,9 +603,7 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
                     `self.voxel_grid_density`
         """
         return VoxelGridModule(
-            # pyre-ignore[29]
             extents=self.voxel_grid_density_args["extents"],
-            # pyre-ignore[29]
             translation=self.voxel_grid_density_args["translation"],
             voxel_grid_class_type="FullResolutionVoxelGrid",
             hold_voxel_grid_as_parameters=False,

pytorch3d/implicitron/models/renderer/lstm_renderer.py

@@ -134,7 +134,6 @@ class LSTMRenderer(BaseRenderer, torch.nn.Module):
                 break
 
             # run the lstm marcher
-            # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
             state_h, state_c = self._lstm(
                 raymarch_features.view(-1, raymarch_features.shape[-1]),
                 states[-1],
@@ -142,7 +141,6 @@ class LSTMRenderer(BaseRenderer, torch.nn.Module):
             if state_h.requires_grad:
                 state_h.register_hook(lambda x: x.clamp(min=-10, max=10))
             # predict the next step size
-            # pyre-fixme[29]: `Union[torch.Tensor, torch.nn.Module]` is not a function.
             signed_distance = self._out_layer(state_h).view(ray_bundle_t.lengths.shape)
             # log the lstm states
             states.append((state_h, state_c))

pytorch3d/implicitron/models/renderer/ray_sampler.py

@@ -171,7 +171,6 @@ class AbstractMaskRaySampler(RaySamplerBase, torch.nn.Module):
         """
         sample_mask = None
         if (
-            # pyre-fixme[29]
             self._sampling_mode[evaluation_mode] == RenderSamplingMode.MASK_SAMPLE
             and mask is not None
         ):
@@ -188,7 +187,6 @@ class AbstractMaskRaySampler(RaySamplerBase, torch.nn.Module):
             EvaluationMode.EVALUATION: self._evaluation_raysampler,
         }[evaluation_mode]
 
-        # pyre-fixme[29]:
         ray_bundle = raysampler(
             cameras=cameras,
             mask=sample_mask,

pytorch3d/implicitron/models/renderer/raymarcher.py

@@ -170,9 +170,9 @@ class AccumulativeRaymarcherBase(RaymarcherBase, torch.nn.Module):
             rays_densities = torch.relu(rays_densities)
 
         weighted_densities = deltas * rays_densities
-        capped_densities = self._capping_function(weighted_densities)  # pyre-ignore: 29
+        capped_densities = self._capping_function(weighted_densities)
 
-        rays_opacities = self._capping_function(  # pyre-ignore: 29
+        rays_opacities = self._capping_function(
             torch.cumsum(weighted_densities, dim=-1)
         )
         opacities = rays_opacities[..., -1:]
@@ -181,9 +181,7 @@ class AccumulativeRaymarcherBase(RaymarcherBase, torch.nn.Module):
         )
         absorption_shifted[..., : self.surface_thickness] = 1.0
 
-        weights = self._weight_function(  # pyre-ignore: 29
-            capped_densities, absorption_shifted
-        )
+        weights = self._weight_function(capped_densities, absorption_shifted)
         features = (weights[..., None] * rays_features).sum(dim=-2)
         depth = (weights * ray_lengths)[..., None].sum(dim=-2)
 

pytorch3d/implicitron/models/renderer/sdf_renderer.py

@@ -58,7 +58,6 @@ class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module):  # pyre-ign
 
     def create_ray_tracer(self) -> None:
         self.ray_tracer = RayTracing(
-            # pyre-ignore[32]
             **self.ray_tracer_args,
             object_bounding_sphere=self.object_bounding_sphere,
         )
@@ -146,7 +145,6 @@ class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module):  # pyre-ign
             eikonal_points = torch.empty(
                 n_eik_points,
                 3,
-                # pyre-fixme[6]: For 3rd param expected `Union[None, str, device]`
                 #  but got `Union[device, Tensor, Module]`.
                 device=self._bg_color.device,
             ).uniform_(-eik_bounding_box, eik_bounding_box)
@@ -205,7 +203,7 @@ class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module):  # pyre-ign
             normals_full.view(-1, 3)[surface_mask] = normals
             render_full.view(-1, self.render_features_dimensions)[
                 surface_mask
-            ] = self._rgb_network(  # pyre-fixme[29]:
+            ] = self._rgb_network(
                 features,
                 differentiable_surface_points[None],
                 normals,

pytorch3d/implicitron/models/view_pooler/feature_aggregator.py

@@ -530,11 +530,6 @@ def _get_ray_dir_dot_prods(camera: CamerasBase, pts: torch.Tensor):
 
     # does not produce nans randomly unlike get_camera_center() below
     cam_centers_rep = -torch.bmm(
-        # 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.modules.module.Module]` is not a function.
-        # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch.Tensor.permute)[[N...
         camera_rep.T[:, None],
         camera_rep.R.permute(0, 2, 1),
     ).reshape(-1, *([1] * (pts.ndim - 2)), 3)

pytorch3d/ops/cameras_alignment.py

@@ -120,16 +120,12 @@ def corresponding_cameras_alignment(
 
     # create a new cameras object and set the R and T accordingly
     cameras_src_aligned = cameras_src.clone()
-    # pyre-fixme[6]: For 2nd param expected `Tensor` but got `Union[Tensor, Module]`.
     cameras_src_aligned.R = torch.bmm(align_t_R.expand_as(cameras_src.R), cameras_src.R)
     cameras_src_aligned.T = (
         torch.bmm(
             align_t_T[:, None].repeat(cameras_src.R.shape[0], 1, 1),
-            # pyre-fixme[6]: For 2nd param expected `Tensor` but got `Union[Tensor,
-            #  Module]`.
             cameras_src.R,
         )[:, 0]
-        # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C._TensorBase.__m...
         + cameras_src.T * align_t_s
     )
 
@@ -177,7 +173,6 @@ def _align_camera_extrinsics(
         R_A = (U V^T)^T
         ```
     """
-    # pyre-fixme[6]: For 1st param expected `Tensor` but got `Union[Tensor, Module]`.
     RRcov = torch.bmm(cameras_src.R, cameras_tgt.R.transpose(2, 1)).mean(0)
     U, _, V = torch.svd(RRcov)
     align_t_R = V @ U.t()
@@ -207,17 +202,7 @@ def _align_camera_extrinsics(
         T_A = mean(B) - mean(A) * s_A
         ```
     """
-    # 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.
-    # pyre-fixme[6]: For 1st param expected `Tensor` but got `Union[Tensor, Module]`.
     A = torch.bmm(cameras_src.R, cameras_src.T[:, :, None])[:, :, 0]
-    # 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.
-    # pyre-fixme[6]: For 1st param expected `Tensor` but got `Union[Tensor, Module]`.
     B = torch.bmm(cameras_src.R, cameras_tgt.T[:, :, None])[:, :, 0]
     Amu = A.mean(0, keepdim=True)
     Bmu = B.mean(0, keepdim=True)

pytorch3d/renderer/camera_conversions.py

@@ -63,8 +63,8 @@ def _opencv_from_cameras_projection(
     cameras: PerspectiveCameras,
     image_size: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    R_pytorch3d = cameras.R.clone()  # pyre-ignore
-    T_pytorch3d = cameras.T.clone()  # pyre-ignore
+    R_pytorch3d = cameras.R.clone()
+    T_pytorch3d = cameras.T.clone()
     focal_pytorch3d = cameras.focal_length
     p0_pytorch3d = cameras.principal_point
     T_pytorch3d[:, :2] *= -1

pytorch3d/renderer/cameras.py

@@ -201,8 +201,8 @@ class CamerasBase(TensorProperties):
         """
         R: torch.Tensor = kwargs.get("R", self.R)
         T: torch.Tensor = kwargs.get("T", self.T)
-        self.R = R  # pyre-ignore[16]
-        self.T = T  # pyre-ignore[16]
+        self.R = R
+        self.T = T
         world_to_view_transform = get_world_to_view_transform(R=R, T=T)
         return world_to_view_transform
 
@@ -226,8 +226,8 @@ class CamerasBase(TensorProperties):
             a Transform3d object which represents a batch of transforms
             of shape (N, 3, 3)
         """
-        self.R: torch.Tensor = kwargs.get("R", self.R)  # pyre-ignore[16]
-        self.T: torch.Tensor = kwargs.get("T", self.T)  # pyre-ignore[16]
+        self.R: torch.Tensor = kwargs.get("R", self.R)
+        self.T: torch.Tensor = kwargs.get("T", self.T)
         world_to_view_transform = self.get_world_to_view_transform(R=self.R, T=self.T)
         view_to_proj_transform = self.get_projection_transform(**kwargs)
         return world_to_view_transform.compose(view_to_proj_transform)

pytorch3d/renderer/lighting.py

@@ -264,9 +264,7 @@ class PointLights(TensorProperties):
         shape (P, 3) or (N, H, W, K, 3).
         """
         if self.location.ndim == points.ndim:
-            # pyre-fixme[7]
             return self.location
-        # pyre-fixme[29]
         return self.location[:, None, None, None, :]
 
     def diffuse(self, normals, points) -> torch.Tensor: