Prepare for "Fix type-safety of torch.nn.Module instances": wave 2

Summary: See D52890934

Reviewed By: malfet, r-barnes

Differential Revision: D66245100

fbshipit-source-id: 019058106ac7eaacf29c1c55912922ea55894d23

projects/implicitron_trainer/impl/optimizer_factory.py

@@ -123,6 +123,7 @@ def __call__(
         """
         # Get the parameters to optimize
         if hasattr(model, "_get_param_groups"):  # use the model function
+            # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
             p_groups = model._get_param_groups(self.lr, wd=self.weight_decay)
         else:
             p_groups = [

projects/implicitron_trainer/impl/training_loop.py

@@ -395,6 +395,7 @@ def _training_or_validation_epoch(
             ):
                 prefix = f"e{stats.epoch}_it{stats.it[trainmode]}"
                 if hasattr(model, "visualize"):
+                    # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
                     model.visualize(
                         viz,
                         visdom_env_imgs,

pytorch3d/implicitron/dataset/utils.py

@@ -329,6 +329,7 @@ def adjust_camera_to_bbox_crop_(
 
     focal_length_px, principal_point_px = _convert_ndc_to_pixels(
         camera.focal_length[0],
+        # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[Any, A...
         camera.principal_point[0],
         image_size_wh,
     )
@@ -341,6 +342,7 @@ def adjust_camera_to_bbox_crop_(
     )
 
     camera.focal_length = focal_length[None]
+    # pyre-fixme[16]: `PerspectiveCameras` has no attribute `principal_point`.
     camera.principal_point = principal_point_cropped[None]
 
 
@@ -352,6 +354,7 @@ def adjust_camera_to_image_scale_(
 ) -> PerspectiveCameras:
     focal_length_px, principal_point_px = _convert_ndc_to_pixels(
         camera.focal_length[0],
+        # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[Any, A...
         camera.principal_point[0],
         original_size_wh,
     )
@@ -368,6 +371,7 @@ def adjust_camera_to_image_scale_(
         image_size_wh_output,
     )
     camera.focal_length = focal_length_scaled[None]
+    # pyre-fixme[16]: `PerspectiveCameras` has no attribute `principal_point`.
     camera.principal_point = principal_point_scaled[None]
 
 

pytorch3d/implicitron/models/feature_extractor/resnet_feature_extractor.py

@@ -142,9 +142,15 @@ def _get_resnet_stage_feature_name(self, stage) -> str:
         return f"res_layer_{stage + 1}"
 
     def _resnet_normalize_image(self, img: torch.Tensor) -> torch.Tensor:
+        # pyre-fixme[58]: `-` is not supported for operand types `Tensor` and
+        #  `Union[Tensor, Module]`.
+        # pyre-fixme[58]: `/` is not supported for operand types `Tensor` and
+        #  `Union[Tensor, Module]`.
         return (img - self._resnet_mean) / self._resnet_std
 
     def get_feat_dims(self) -> int:
+        # pyre-fixme[29]: `Union[(self: TensorBase) -> Tensor, Tensor, Module]` is
+        #  not a function.
         return sum(self._feat_dim.values())
 
     def forward(
@@ -183,7 +189,12 @@ def forward(
             else:
                 imgs_normed = imgs_resized
             #  is not a function.
+            # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
             feats = self.stem(imgs_normed)
+            # pyre-fixme[6]: For 1st argument expected `Iterable[_T1]` but got
+            #  `Union[Tensor, Module]`.
+            # pyre-fixme[6]: For 2nd argument expected `Iterable[_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

@@ -478,6 +478,8 @@ def curried_viewpooler(pts):
             )
         custom_args["global_code"] = global_code
 
+        # pyre-fixme[29]: `Union[(self: Tensor) -> Any, Tensor, Module]` is not a
+        #  function.
         for func in self._implicit_functions:
             func.bind_args(**custom_args)
 
@@ -500,6 +502,8 @@ def curried_viewpooler(pts):
         # 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[(self: Tensor) -> Any, Tensor, Module]` is not a
+        #  function.
         for func in self._implicit_functions:
             func.unbind_args()
 

pytorch3d/implicitron/models/global_encoder/autodecoder.py

@@ -71,6 +71,7 @@ def _build_key_map(
         return key_map
 
     def calculate_squared_encoding_norm(self) -> Optional[torch.Tensor]:
+        # pyre-fixme[16]: Item `Tensor` of `Tensor | Module` has no attribute `weight`.
         return (self._autodecoder_codes.weight**2).mean()
 
     def get_encoding_dim(self) -> int:
@@ -95,13 +96,15 @@ def forward(self, x: Union[torch.LongTensor, List[str]]) -> Optional[torch.Tenso
                 # pyre-fixme[9]: x has type `Union[List[str], LongTensor]`; used as
                 #  `Tensor`.
                 x = torch.tensor(
+                    # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, ...
                     [self._key_map[elem] for elem in x],
                     dtype=torch.long,
                     device=next(self.parameters()).device,
                 )
             except StopIteration:
                 raise ValueError("Not enough n_instances in the autodecoder") from None
 
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         return self._autodecoder_codes(x)
 
     def _load_key_map_hook(

pytorch3d/implicitron/models/global_encoder/global_encoder.py

@@ -122,6 +122,7 @@ def forward(
         if frame_timestamp.shape[-1] != 1:
             raise ValueError("Frame timestamp's last dimensions should be one.")
         time = frame_timestamp / self.time_divisor
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         return self._harmonic_embedding(time)
 
     def calculate_squared_encoding_norm(self) -> Optional[torch.Tensor]:

pytorch3d/implicitron/models/implicit_function/decoding_functions.py

@@ -232,9 +232,14 @@ 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 argument expected `Iterable[_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[Tensor, Module]`.
             if li in self._input_skips:
                 if self._skip_affine_trans:
+                    # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, ...
                     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

@@ -141,11 +141,16 @@ def forward(
             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
+                [],
+                device=rays_points_world.device,
+                dtype=rays_points_world.dtype,
+                # pyre-fixme[6]: For 2nd argument expected `Union[int, SymInt]` but got
+                #  `Union[Module, Tensor]`.
             ).view(0, self.out_dim)
 
         embeddings = []
         if self.embed_fn is not None:
+            # pyre-fixme[29]: `Union[Module, Tensor]` is not a function.
             embeddings.append(self.embed_fn(rays_points_world))
 
         if fun_viewpool is not None:
@@ -164,13 +169,19 @@ def forward(
 
         embedding = torch.cat(embeddings, dim=-1)
         x = embedding
+        # pyre-fixme[29]: `Union[(self: TensorBase, other: Union[bool, complex,
+        #  float, int, Tensor]) -> Tensor, Module, Tensor]` is not a function.
         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[(self: TensorBase, indices: Union[None, slice[An...
             x = self.linear_layers[layer_idx](x)
 
+            # pyre-fixme[29]: `Union[(self: TensorBase, other: Union[bool, complex,
+            #  float, int, Tensor]) -> Tensor, Module, Tensor]` is not a function.
             if layer_idx < self.num_layers - 2:
+                # pyre-fixme[29]: `Union[Module, Tensor]` is not a function.
                 x = self.softplus(x)
 
         return x

pytorch3d/implicitron/models/implicit_function/neural_radiance_field.py

@@ -123,8 +123,10 @@ def _get_colors(self, features: torch.Tensor, rays_directions: torch.Tensor):
         # 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[Tensor, Module]` is not a function.
         rays_embedding = self.harmonic_embedding_dir(rays_directions_normed)
 
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         return self.color_layer((self.intermediate_linear(features), rays_embedding))
 
     @staticmethod
@@ -195,6 +197,8 @@ def forward(
         embeds = create_embeddings_for_implicit_function(
             xyz_world=rays_points_world,
             #  for 2nd param but got `Union[None, torch.Tensor, torch.nn.Module]`.
+            # pyre-fixme[6]: For 2nd argument expected `Optional[(...) -> Any]` but
+            #  got `Union[None, Tensor, Module]`.
             xyz_embedding_function=(
                 self.harmonic_embedding_xyz if self.input_xyz else None
             ),
@@ -206,19 +210,23 @@ def forward(
         )
 
         # embeds.shape = [minibatch x n_src x n_rays x n_pts x self.n_harmonic_functions*6+3]
+        # pyre-fixme[29]: `Union[Tensor, 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[Tensor, Module]` is not a function.
         raw_densities = self.density_layer(features)
         # raw_densities.shape = [minibatch x ... x 1] in [0-1]
 
         if self.xyz_ray_dir_in_camera_coords:
             if camera is None:
                 raise ValueError("Camera must be given if xyz_ray_dir_in_camera_coords")
 
+            # pyre-fixme[58]: `@` is not supported for operand types `Tensor` and
+            #  `Union[Tensor, Module]`.
             directions = ray_bundle.directions @ camera.R
         else:
             directions = ray_bundle.directions

pytorch3d/implicitron/models/implicit_function/scene_representation_networks.py

@@ -103,6 +103,8 @@ def forward(
 
         embeds = create_embeddings_for_implicit_function(
             xyz_world=rays_points_world,
+            # pyre-fixme[6]: For 2nd argument expected `Optional[(...) -> Any]` but
+            #  got `Union[Tensor, Module]`.
             xyz_embedding_function=self._harmonic_embedding,
             global_code=global_code,
             fun_viewpool=fun_viewpool,
@@ -112,6 +114,7 @@ def forward(
 
         # 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[Tensor, Module]` is not a function.
         raymarch_features = self._net(
             embeds.view(embeds.shape[0], -1, embeds.shape[-1])
         )
@@ -166,7 +169,9 @@ def _get_colors(self, features: torch.Tensor, rays_directions: torch.Tensor):
         # 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[Tensor, Module]` is not a function.
         rays_embedding = self._harmonic_embedding(rays_directions_normed)
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         return self._color_layer((features, rays_embedding))
 
     def forward(
@@ -195,20 +200,24 @@ def forward(
                 denoting the color of each ray point.
         """
         # raymarch_features.shape = [minibatch x ... x pts_per_ray x 3]
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         features = self._net(raymarch_features)
         # features.shape = [minibatch x ... x self.n_hidden_units]
 
         if self.ray_dir_in_camera_coords:
             if camera is None:
                 raise ValueError("Camera must be given if xyz_ray_dir_in_camera_coords")
 
+            # pyre-fixme[58]: `@` is not supported for operand types `Tensor` and
+            #  `Union[Tensor, Module]`.
             directions = ray_bundle.directions @ camera.R
         else:
             directions = ray_bundle.directions
 
         # NNs operate on the flattenned rays; reshaping to the correct spatial size
         features = features.reshape(*raymarch_features.shape[:-1], -1)
 
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         raw_densities = self._density_layer(features)
 
         rays_colors = self._get_colors(features, directions)
@@ -269,6 +278,7 @@ def _run_hypernet(self, global_code: torch.Tensor) -> Tuple[SRNRaymarchFunction]
         srn_raymarch_function.
         """
 
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         net = self._hypernet(global_code)
 
         # use the hyper-net generated network to instantiate the raymarch module
@@ -304,6 +314,8 @@ def forward(
         # 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
+            #  `cached_srn_raymarch_function`.
             self.cached_srn_raymarch_function = self._run_hypernet(global_code)
         (srn_raymarch_function,) = cast(
             Tuple[SRNRaymarchFunction], self.cached_srn_raymarch_function
@@ -331,6 +343,7 @@ def __post_init__(self):
     def create_raymarch_function(self) -> None:
         self.raymarch_function = SRNRaymarchFunction(
             latent_dim=self.latent_dim,
+            # pyre-fixme[32]: Keyword argument must be a mapping with string keys.
             **self.raymarch_function_args,
         )
 
@@ -389,6 +402,7 @@ def create_hypernet(self) -> None:
         self.hypernet = SRNRaymarchHyperNet(
             latent_dim=self.latent_dim,
             latent_dim_hypernet=self.latent_dim_hypernet,
+            # pyre-fixme[32]: Keyword argument must be a mapping with string keys.
             **self.hypernet_args,
         )
 

pytorch3d/implicitron/models/implicit_function/voxel_grid.py

@@ -269,6 +269,7 @@ def change_individual_resolution(tensor, wanted_resolution):
                 for name, tensor in vars(grid_values_with_wanted_resolution).items()
             }
 
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         return self.values_type(**params), True
 
     def get_resolution_change_epochs(self) -> Tuple[int, ...]:
@@ -882,6 +883,7 @@ def forward(self, points: torch.Tensor) -> torch.Tensor:
             torch.Tensor of shape (..., n_features)
         """
         locator = self._get_volume_locator()
+        # pyre-fixme[29]: `Union[Tensor, 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]
@@ -895,6 +897,7 @@ def set_voxel_grid_parameters(self, params: VoxelGridValuesBase) -> None:
                 replace current parameters
         """
         if self.hold_voxel_grid_as_parameters:
+            # pyre-fixme[16]: `VoxelGridModule` has no attribute `params`.
             self.params = torch.nn.ParameterDict(
                 {
                     k: torch.nn.Parameter(val)
@@ -945,6 +948,7 @@ def _apply_epochs(self, epoch: int) -> bool:
         Returns:
             True if parameter change has happened else False.
         """
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         grid_values = self.voxel_grid.values_type(**self.params)
         grid_values, change = self.voxel_grid.change_resolution(
             grid_values, epoch=epoch
@@ -992,16 +996,21 @@ def _create_parameters_with_new_size(
         """
         '''
         new_params = {}
+        # pyre-fixme[29]: `Union[(self: Tensor) -> Any, Tensor, Module]` is not a
+        #  function.
         for name in self.params:
             key = prefix + "params." + name
             if key in state_dict:
                 new_params[name] = torch.zeros_like(state_dict[key])
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         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-fixme[29]: `Union[(self: TensorBase) -> Tensor, Tensor, Module]` is
+        #  not a function.
         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:
@@ -1018,13 +1027,15 @@ def crop_self(self, min_point: torch.Tensor, max_point: torch.Tensor) -> None:
         """
         locator = self._get_volume_locator()
         #  torch.nn.modules.module.Module]` is not a function.
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         old_grid_values = self.voxel_grid.values_type(**self.params)
         new_grid_values = self.voxel_grid.crop_world(
             min_point, max_point, old_grid_values, locator
         )
         grid_values, _ = self.voxel_grid.change_resolution(
             new_grid_values, grid_values_with_wanted_resolution=old_grid_values
         )
+        # pyre-fixme[16]: `VoxelGridModule` has no attribute `params`.
         self.params = torch.nn.ParameterDict(
             {
                 k: torch.nn.Parameter(val)