Remove unused type error suppressions

Summary:
This diff was automatically generated by the Pyre per-target upgrade tool.

It removes `# pyre-fixme` or `pyrefly: ignore` comments that are no longer needed because the underlying type errors have been resolved.
Note that it will also aim to ensure type checking runs cleanly, and will add suppressions to existing type errors.

#pyreupgrade

Differential Revision: D116557086

fbshipit-source-id: 1337613d4fb3ab79bd3a733f3a4c9a499a72f971

projects/implicitron_trainer/experiment.py

@@ -117,16 +117,12 @@ class Experiment(Configurable):
             will be saved here.
     """
 
-    # pyre-fixme[13]: Attribute `data_source` is never initialized.
     data_source: DataSourceBase
     data_source_class_type: str = "ImplicitronDataSource"
-    # pyre-fixme[13]: Attribute `model_factory` is never initialized.
     model_factory: ModelFactoryBase
     model_factory_class_type: str = "ImplicitronModelFactory"
-    # pyre-fixme[13]: Attribute `optimizer_factory` is never initialized.
     optimizer_factory: OptimizerFactoryBase
     optimizer_factory_class_type: str = "ImplicitronOptimizerFactory"
-    # pyre-fixme[13]: Attribute `training_loop` is never initialized.
     training_loop: TrainingLoopBase
     training_loop_class_type: str = "ImplicitronTrainingLoop"
 

projects/implicitron_trainer/impl/model_factory.py

@@ -59,7 +59,6 @@ class ImplicitronModelFactory(ModelFactoryBase):
 
     """
 
-    # pyre-fixme[13]: Attribute `model` is never initialized.
     model: ImplicitronModelBase
     model_class_type: str = "GenericModel"
     resume: bool = True

projects/implicitron_trainer/impl/optimizer_factory.py

@@ -169,7 +169,6 @@ class ImplicitronOptimizerFactory(OptimizerFactoryBase):
                 gamma=self.gamma,
             )
         elif self.lr_policy.casefold() == "Exponential".casefold():
-            # pyre-fixme[28]: Unexpected keyword argument `verbose`.
             scheduler = torch.optim.lr_scheduler.LambdaLR(
                 optimizer,
                 lambda epoch: self.gamma ** (epoch / self.exponential_lr_step_size),
@@ -190,9 +189,7 @@ class ImplicitronOptimizerFactory(OptimizerFactoryBase):
                     gamma = self.gamma ** (epoch_rest / self.exponential_lr_step_size)
                 return gamma
 
-            # pyre-fixme[28]: Unexpected keyword argument `verbose`.
             scheduler = torch.optim.lr_scheduler.LambdaLR(
-                # pyrefly: ignore [unexpected-keyword]
                 optimizer,
                 _get_lr,
                 # pyrefly: ignore [unexpected-keyword]

projects/implicitron_trainer/impl/training_loop.py

@@ -36,7 +36,6 @@ class TrainingLoopBase(ReplaceableBase):
         evaluator: An EvaluatorBase instance, used to evaluate training results.
     """
 
-    # pyre-fixme[13]: Attribute `evaluator` is never initialized.
     evaluator: Optional[EvaluatorBase]
     evaluator_class_type: Optional[str] = "ImplicitronEvaluator"
 
@@ -380,7 +379,6 @@ class ImplicitronTrainingLoop(TrainingLoopBase):
 
             # update the stats logger
             stats.update(preds, time_start=t_start, stat_set=trainmode)
-            # pyre-ignore [16]
             assert stats.it[trainmode] == it, "inconsistent stat iteration number!"
 
             # print textual status update

pytorch3d/common/compat.py

@@ -23,7 +23,6 @@ def meshgrid_ij(
     Like torch.meshgrid was before PyTorch 1.10.0, i.e. with indexing set to ij
     """
     if (
-        # pyre-fixme[16]: Callable `meshgrid` has no attribute `__kwdefaults__`.
         torch.meshgrid.__kwdefaults__ is not None
         and "indexing" in torch.meshgrid.__kwdefaults__
     ):

pytorch3d/implicitron/dataset/data_source.py

@@ -52,11 +52,8 @@ class ImplicitronDataSource(DataSourceBase):
         data_loader_map_provider_class_type: identifies type for data_loader_map_provider.
     """
 
-    # pyre-fixme[13]: Attribute `dataset_map_provider` is never initialized.
     dataset_map_provider: DatasetMapProviderBase
-    # pyre-fixme[13]: Attribute `dataset_map_provider_class_type` is never initialized.
     dataset_map_provider_class_type: str
-    # pyre-fixme[13]: Attribute `data_loader_map_provider` is never initialized.
     data_loader_map_provider: DataLoaderMapProviderBase
     data_loader_map_provider_class_type: str = "SequenceDataLoaderMapProvider"
 
@@ -78,7 +75,7 @@ class ImplicitronDataSource(DataSourceBase):
             )
 
             try:
-                from .sql_dataset_provider import (  # noqa: F401  # pyre-ignore
+                from .sql_dataset_provider import (  # noqa: F401
                     SqlIndexDatasetMapProvider,
                 )
             except ModuleNotFoundError:
@@ -100,7 +97,7 @@ class ImplicitronDataSource(DataSourceBase):
         """
         DEPRECATED! The property will be removed in future versions.
         """
-        if self._all_train_cameras_cache is None:  # pyre-ignore[16]
+        if self._all_train_cameras_cache is None:
             all_train_cameras = self.dataset_map_provider.get_all_train_cameras()
             self._all_train_cameras_cache = (all_train_cameras,)
 

pytorch3d/implicitron/dataset/frame_data.py

@@ -297,7 +297,6 @@ class FrameData(Mapping[str, Any]):
         depth_map = self.depth_map
         if depth_map is not None:
             clamp_bbox_xyxy_depth = rescale_bbox(
-                # pyrefly: ignore [bad-argument-type]
                 clamp_bbox_xyxy,
                 # pyrefly: ignore [bad-argument-type]
                 tuple(depth_map.shape[-2:]),
@@ -312,7 +311,6 @@ class FrameData(Mapping[str, Any]):
         depth_mask = self.depth_mask
         if depth_mask is not None:
             clamp_bbox_xyxy_depth = rescale_bbox(
-                # pyrefly: ignore [bad-argument-type]
                 clamp_bbox_xyxy,
                 # pyrefly: ignore [bad-argument-type]
                 tuple(depth_mask.shape[-2:]),

pytorch3d/implicitron/dataset/json_index_dataset.py

@@ -175,7 +175,6 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
         self._filter_db()  # also computes sequence indices
         self._extract_and_set_eval_batches()
 
-        # pyre-ignore
         self._frame_data_builder = FrameDataBuilder(
             dataset_root=self.dataset_root,
             load_images=self.load_images,
@@ -220,7 +219,6 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
             raise ValueError("This function can only join a list of JsonIndexDataset")
         # pyre-ignore[16]
         self.frame_annots.extend([fa for d in other_datasets for fa in d.frame_annots])
-        # pyre-ignore[16]
         self.seq_annots.update(
             # https://gist.github.com/treyhunner/f35292e676efa0be1728
             functools.reduce(
@@ -301,7 +299,6 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
                 self.frame_annots[idx]["frame_annotation"].frame_number: idx
                 for idx in seq_idx
             }
-            # pyre-ignore[16]
             for seq, seq_idx in self._seq_to_idx.items()
         }
 
@@ -374,7 +371,7 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
 
         # Deep copy the whole dataset except frame_annots, which are large so we
         # deep copy only the requested subset of frame_annots.
-        memo = {id(self.frame_annots): None}  # pyre-ignore[16]
+        memo = {id(self.frame_annots): None}
         dataset_new = copy.deepcopy(self, memo)
         dataset_new.frame_annots = copy.deepcopy(
             [self.frame_annots[i] for i in valid_dataset_indices]
@@ -402,11 +399,9 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
         return dataset_new
 
     def __str__(self) -> str:
-        # pyre-ignore[16]
         return f"JsonIndexDataset #frames={len(self.frame_annots)}"
 
     def __len__(self) -> int:
-        # pyre-ignore[16]
         return len(self.frame_annots)
 
     def _get_frame_type(self, entry: FrameAnnotsEntry) -> Optional[str]:
@@ -418,7 +413,6 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
         """
         logger.info("Loading all train cameras.")
         cameras = []
-        # pyre-ignore[16]
         for frame_idx, frame_annot in enumerate(tqdm(self.frame_annots)):
             frame_type = self._get_frame_type(frame_annot)
             if frame_type is None:
@@ -429,16 +423,13 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
         return join_cameras_as_batch(cameras)
 
     def __getitem__(self, index) -> FrameData:
-        # pyre-ignore[16]
         if index >= len(self.frame_annots):
             raise IndexError(f"index {index} out of range {len(self.frame_annots)}")
 
         entry = self.frame_annots[index]["frame_annotation"]
 
-        # pyre-ignore
         frame_data = self._frame_data_builder.build(
             entry,
-            # pyre-ignore
             self.seq_annots[entry.sequence_name],
         )
         # Optional field
@@ -483,7 +474,6 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
             for subset, frames in subset_to_seq_frame.items()
             for _, _, path in frames
         }
-        # pyre-ignore[16]
         for frame in self.frame_annots:
             frame["subset"] = frame_path_to_subset.get(
                 frame["frame_annotation"].image.path, None
@@ -496,7 +486,6 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
 
     def _sort_frames(self) -> None:
         # Sort frames to have them grouped by sequence, ordered by timestamp
-        # pyre-ignore[16]
         self.frame_annots = sorted(
             self.frame_annots,
             key=lambda f: (
@@ -508,7 +497,6 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
     def _filter_db(self) -> None:
         if self.remove_empty_masks:
             logger.info("Removing images with empty masks.")
-            # pyre-ignore[16]
             old_len = len(self.frame_annots)
 
             msg = "remove_empty_masks needs every MaskAnnotation.mass to be set."
@@ -549,7 +537,6 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
 
         if len(self.limit_category_to) > 0:
             logger.info(f"Limiting dataset to categories: {self.limit_category_to}")
-            # pyre-ignore[16]
             self.seq_annots = {
                 name: entry
                 for name, entry in self.seq_annots.items()
@@ -587,7 +574,6 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
         if self.n_frames_per_sequence > 0:
             logger.info(f"Taking max {self.n_frames_per_sequence} per sequence.")
             keep_idx = []
-            # pyre-ignore[16]
             for seq, seq_indices in self._seq_to_idx.items():
                 # infer the seed from the sequence name, this is reproducible
                 # and makes the selection differ for different sequences
@@ -617,7 +603,6 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
         self._invalidate_seq_to_idx()
 
         if filter_seq_annots:
-            # pyre-ignore[16]
             self.seq_annots = {
                 k: v
                 for k, v in self.seq_annots.items()
@@ -627,7 +612,6 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
 
     def _invalidate_seq_to_idx(self) -> None:
         seq_to_idx = defaultdict(list)
-        # pyre-ignore[16]
         for idx, entry in enumerate(self.frame_annots):
             seq_to_idx[entry["frame_annotation"].sequence_name].append(idx)
         # pyre-ignore[16]
@@ -658,7 +642,6 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
 
     def category_to_sequence_names(self) -> Dict[str, List[str]]:
         c2seq = defaultdict(list)
-        # pyre-ignore
         for sequence_name, sa in self.seq_annots.items():
             c2seq[sa.category].append(sequence_name)
         return dict(c2seq)

pytorch3d/implicitron/dataset/json_index_dataset_map_provider.py

@@ -94,7 +94,6 @@ class JsonIndexDatasetMapProvider(DatasetMapProviderBase):
         path_manager_factory_class_type: The class type of `path_manager_factory`.
     """
 
-    # pyre-fixme[13]: Attribute `category` is never initialized.
     category: str
     task_str: str = "singlesequence"
     dataset_root: str = _CO3D_DATASET_ROOT
@@ -104,10 +103,8 @@ class JsonIndexDatasetMapProvider(DatasetMapProviderBase):
     test_restrict_sequence_id: int = -1
     assert_single_seq: bool = False
     only_test_set: bool = False
-    # pyre-fixme[13]: Attribute `dataset` is never initialized.
     dataset: JsonIndexDataset
     dataset_class_type: str = "JsonIndexDataset"
-    # pyre-fixme[13]: Attribute `path_manager_factory` is never initialized.
     path_manager_factory: PathManagerFactory
     path_manager_factory_class_type: str = "PathManagerFactory"
 

pytorch3d/implicitron/dataset/json_index_dataset_map_provider_v2.py

@@ -169,9 +169,7 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):
         path_manager_factory_class_type: The class type of `path_manager_factory`.
     """
 
-    # pyre-fixme[13]: Attribute `category` is never initialized.
     category: str
-    # pyre-fixme[13]: Attribute `subset_name` is never initialized.
     subset_name: str
     dataset_root: str = _CO3DV2_DATASET_ROOT
 
@@ -183,10 +181,8 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):
     n_known_frames_for_test: int = 0
 
     dataset_class_type: str = "JsonIndexDataset"
-    # pyre-fixme[13]: Attribute `dataset` is never initialized.
     dataset: JsonIndexDataset
 
-    # pyre-fixme[13]: Attribute `path_manager_factory` is never initialized.
     path_manager_factory: PathManagerFactory
     path_manager_factory_class_type: str = "PathManagerFactory"
 

pytorch3d/implicitron/dataset/rendered_mesh_dataset_map_provider.py

@@ -76,12 +76,10 @@ class RenderedMeshDatasetMapProvider(DatasetMapProviderBase):
     resolution: int = 128
     use_point_light: bool = True
     gpu_idx: Optional[int] = 0
-    # pyre-fixme[13]: Attribute `path_manager_factory` is never initialized.
     path_manager_factory: PathManagerFactory
     path_manager_factory_class_type: str = "PathManagerFactory"
 
     def get_dataset_map(self) -> DatasetMap:
-        # pyre-ignore[16]
         return DatasetMap(train=self.train_dataset, val=None, test=None)
 
     def get_all_train_cameras(self) -> CamerasBase:
@@ -117,10 +115,8 @@ class RenderedMeshDatasetMapProvider(DatasetMapProviderBase):
             device=device,
             use_point_light=self.use_point_light,
         )
-        # pyre-ignore[16]
         self.poses = poses.cpu()
-        # pyre-ignore[16]
+        self.train_dataset = SingleSceneDataset(
-        self.train_dataset = SingleSceneDataset(  # pyre-ignore[28]
             # pyrefly: ignore [unexpected-keyword]
             object_name="cow",
             # pyrefly: ignore [unexpected-keyword]

pytorch3d/implicitron/dataset/single_sequence_dataset.py

@@ -99,11 +99,8 @@ class SingleSceneDatasetMapProviderBase(DatasetMapProviderBase):
             testing frame.
     """
 
-    # pyre-fixme[13]: Attribute `base_dir` is never initialized.
     base_dir: str
-    # pyre-fixme[13]: Attribute `object_name` is never initialized.
     object_name: str
-    # pyre-fixme[13]: Attribute `path_manager_factory` is never initialized.
     path_manager_factory: PathManagerFactory
     path_manager_factory_class_type: str = "PathManagerFactory"
     n_known_frames_for_test: Optional[int] = None
@@ -149,7 +146,6 @@ class SingleSceneDatasetMapProviderBase(DatasetMapProviderBase):
             split = np.concatenate([split, train_split])
             frame_types.extend([DATASET_TYPE_KNOWN] * len(train_split))
 
-        # pyre-ignore[28]
         return SingleSceneDataset(
             object_name=self.object_name,
             # pyre-ignore[16]

pytorch3d/implicitron/dataset/sql_dataset.py

@@ -205,7 +205,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
         logger.info(str(self))
 
         if self.scoped_session:
-            self._session_factory = sessionmaker(bind=self._sql_engine)  # pyre-ignore
+            self._session_factory = sessionmaker(bind=self._sql_engine)
 
         if self.precompute_seq_to_idx:
             # This is deprecated and will be removed in the future.
@@ -215,7 +215,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
             )
             self._index["rowid"] = np.arange(len(self._index))
             groupby = self._index.groupby("sequence_name", sort=False)["rowid"]
-            self._seq_to_indices = dict(groupby.apply(list))  # pyre-ignore
+            self._seq_to_indices = dict(groupby.apply(list))
             del self._index["rowid"]
 
     def __len__(self) -> int:
@@ -280,7 +280,6 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
             self.sequence_annotations_type.sequence_name == seq
         )
         if self.scoped_session:
-            # pyre-ignore
             with scoped_session(self._session_factory)() as session:
                 entry = session.scalars(stmt).one()
                 seq_metadata = session.scalars(seq_stmt).one()
@@ -404,7 +403,6 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
         only dataset indices.
         """
         if self.precompute_seq_to_idx and subset_filter is None:
-            # pyre-ignore
             yield from self._seq_to_indices[seq_name]
         else:
             for _, _, idx in self.sequence_frames_in_order(seq_name, subset_filter):
@@ -836,7 +834,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
 
         if self.scoped_session:
             stmt_text = str(stmt.compile(compile_kwargs={"literal_binds": True}))
-            with scoped_session(self._session_factory)() as session:  # pyre-ignore
+            with scoped_session(self._session_factory)() as session:
                 frame_no_ts = pd.read_sql_query(stmt_text, session.connection())
         else:
             with self._sql_engine.connect() as connection:

pytorch3d/implicitron/dataset/sql_dataset_provider.py

@@ -193,9 +193,9 @@ class SqlIndexDatasetMapProvider(DatasetMapProviderBase):
 
     # this is a mould that is never constructed, used to build self._dataset_map values
     dataset_class_type: str = "SqlIndexDataset"
-    dataset: SqlIndexDataset  # pyre-ignore [13]
+    dataset: SqlIndexDataset
 
-    path_manager_factory: PathManagerFactory  # pyre-ignore [13]
+    path_manager_factory: PathManagerFactory
     path_manager_factory_class_type: str = "PathManagerFactory"
 
     def __post_init__(self):

pytorch3d/implicitron/dataset/types.py

@@ -306,7 +306,6 @@ def _unwrap_type(tp):
 
 def _get_dataclass_field_default(field: Field) -> Any:
     if field.default_factory is not MISSING:
-        # pyre-fixme[29]: `Union[dataclasses._MISSING_TYPE,
         #  dataclasses._DefaultFactory[typing.Any]]` is not a function.
         return field.default_factory()
     elif field.default is not MISSING:

pytorch3d/implicitron/dataset/utils.py

@@ -192,7 +192,6 @@ def rescale_bbox(
     assert bbox is not None
     assert np.prod(orig_res) > 1e-8
     # average ratio of dimensions
-    # pyre-ignore
     rel_size = (new_res[0] / orig_res[0] + new_res[1] / orig_res[1]) / 2.0
     return bbox * rel_size
 
@@ -368,7 +367,6 @@ 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]
 
 
@@ -397,8 +395,7 @@ 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]
-    camera.principal_point = principal_point_scaled[None]  # pyre-ignore[16]
 
 
 # NOTE this cache is per-worker; they are implemented as processes.

pytorch3d/implicitron/dataset/visualize.py

@@ -46,7 +46,6 @@ def get_implicitron_sequence_pointcloud(
         sequence_entries = [
             ei
             for ei in sequence_entries
-            # pyre-ignore[16]
             if dataset.frame_annots[ei]["frame_annotation"].sequence_name
             == sequence_name
         ]

pytorch3d/implicitron/evaluation/evaluate_new_view_synthesis.py

@@ -321,7 +321,6 @@ def eval_batch(
             # only record depth metrics for the foreground
             _, abs_ = eval_depth(
                 cloned_render["depth_render"],
-                # pyre-fixme[6]: For 2nd param expected `Tensor` but got
                 #  `Optional[Tensor]`.
                 frame_data.depth_map,
                 get_best_scale=True,

pytorch3d/implicitron/models/feature_extractor/resnet_feature_extractor.py

@@ -220,6 +220,5 @@ class ResNetFeatureExtractor(FeatureExtractorBase):
         if self.feature_rescale != 1.0:
             out_feats = {k: self.feature_rescale * f for k, f in out_feats.items()}
 
-        # pyre-fixme[7]: Incompatible return type, expected `Dict[typing.Any, Tensor]`
         # but got `Dict[typing.Any, float]`
         return out_feats

pytorch3d/implicitron/models/generic_model.py

@@ -222,42 +222,34 @@ class GenericModel(ImplicitronModelBase):
 
     # ---- global encoder settings
     global_encoder_class_type: Optional[str] = None
-    # pyre-fixme[13]: Attribute `global_encoder` is never initialized.
     global_encoder: Optional[GlobalEncoderBase]
 
     # ---- raysampler
     raysampler_class_type: str = "AdaptiveRaySampler"
-    # pyre-fixme[13]: Attribute `raysampler` is never initialized.
     raysampler: RaySamplerBase
 
     # ---- renderer configs
     renderer_class_type: str = "MultiPassEmissionAbsorptionRenderer"
-    # pyre-fixme[13]: Attribute `renderer` is never initialized.
     renderer: BaseRenderer
 
     # ---- image feature extractor settings
     # (This is only created if view_pooler is enabled)
-    # pyre-fixme[13]: Attribute `image_feature_extractor` is never initialized.
     image_feature_extractor: Optional[FeatureExtractorBase]
     image_feature_extractor_class_type: Optional[str] = None
     # ---- view pooler settings
     view_pooler_enabled: bool = False
-    # pyre-fixme[13]: Attribute `view_pooler` is never initialized.
     view_pooler: Optional[ViewPooler]
 
     # ---- implicit function settings
     implicit_function_class_type: str = "NeuralRadianceFieldImplicitFunction"
     # This is just a model, never constructed.
     # The actual implicit functions live in self._implicit_functions
-    # pyre-fixme[13]: Attribute `implicit_function` is never initialized.
     implicit_function: ImplicitFunctionBase
 
     # ----- metrics
-    # pyre-fixme[13]: Attribute `view_metrics` is never initialized.
     view_metrics: ViewMetricsBase
     view_metrics_class_type: str = "ViewMetrics"
 
-    # pyre-fixme[13]: Attribute `regularization_metrics` is never initialized.
     regularization_metrics: RegularizationMetricsBase
     regularization_metrics_class_type: str = "RegularizationMetrics"
 
@@ -475,7 +467,6 @@ class GenericModel(ImplicitronModelBase):
         # pyrefly: ignore [unsupported-operation]
         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)
@@ -499,7 +490,6 @@ class GenericModel(ImplicitronModelBase):
         # 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/global_encoder.py

@@ -65,7 +65,6 @@ class SequenceAutodecoder(GlobalEncoderBase, torch.nn.Module):
     of the frame's sequence identifier.
     """
 
-    # pyre-fixme[13]: Attribute `autodecoder` is never initialized.
     autodecoder: Autodecoder
 
     def __post_init__(self):

pytorch3d/implicitron/models/implicit_function/decoding_functions.py

@@ -229,10 +229,8 @@ 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 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:
@@ -273,7 +271,6 @@ class MLPDecoder(DecoderFunctionBase):
 
     input_dim: int = 3
     param_groups: Dict[str, str] = field(default_factory=lambda: {})
-    # pyre-fixme[13]: Attribute `network` is never initialized.
     network: MLPWithInputSkips
 
     def __post_init__(self):
@@ -351,7 +348,6 @@ class TransformerWithInputSkips(torch.nn.Module):
         self.last = torch.nn.Linear(dimout, output_dim)
         _xavier_init(self.last)
 
-        # pyre-fixme[8]: Attribute has type `Tuple[ModuleList, ModuleList]`; used as
         #  `ModuleList`.
         self.layers_pool, self.layers_ray = (
             torch.nn.ModuleList(layers_pool),

pytorch3d/implicitron/models/implicit_function/idr_feature_field.py

@@ -177,7 +177,6 @@ class IdrFeatureField(ImplicitFunctionBase, torch.nn.Module):
             # 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.

pytorch3d/implicitron/models/implicit_function/neural_radiance_field.py

@@ -125,7 +125,6 @@ class NeuralRadianceFieldBase(ImplicitFunctionBase, torch.nn.Module):
         # 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
@@ -196,7 +195,6 @@ class NeuralRadianceFieldBase(ImplicitFunctionBase, torch.nn.Module):
         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
@@ -224,7 +222,6 @@ class NeuralRadianceFieldBase(ImplicitFunctionBase, torch.nn.Module):
             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:

pytorch3d/implicitron/models/implicit_function/scene_representation_networks.py

@@ -171,7 +171,6 @@ class SRNPixelGenerator(Configurable, torch.nn.Module):
         # 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(
@@ -208,7 +207,6 @@ class SRNPixelGenerator(Configurable, torch.nn.Module):
             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:
@@ -331,9 +329,7 @@ class SRNRaymarchHyperNet(Configurable, torch.nn.Module):
 @registry.register
 class SRNImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
     latent_dim: int = 0
-    # pyre-fixme[13]: Attribute `raymarch_function` is never initialized.
     raymarch_function: SRNRaymarchFunction
-    # pyre-fixme[13]: Attribute `pixel_generator` is never initialized.
     pixel_generator: SRNPixelGenerator
 
     def __post_init__(self):
@@ -389,9 +385,7 @@ class SRNHyperNetImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
 
     latent_dim_hypernet: int = 0
     latent_dim: int = 0
-    # pyre-fixme[13]: Attribute `hypernet` is never initialized.
     hypernet: SRNRaymarchHyperNet
-    # pyre-fixme[13]: Attribute `pixel_generator` is never initialized.
     pixel_generator: SRNPixelGenerator
 
     def __post_init__(self):

pytorch3d/implicitron/models/implicit_function/voxel_grid.py

@@ -844,7 +844,6 @@ class VoxelGridModule(Configurable, torch.nn.Module):
     """
 
     voxel_grid_class_type: str = "FullResolutionVoxelGrid"
-    # pyre-fixme[13]: Attribute `voxel_grid` is never initialized.
     voxel_grid: VoxelGridBase
 
     extents: Tuple[float, float, float] = (2.0, 2.0, 2.0)
@@ -907,7 +906,6 @@ class VoxelGridModule(Configurable, torch.nn.Module):
         else:
             # Torch Module to hold parameters since they can only be registered
             # at object level.
-            # pyrefly: ignore [bad-assignment]
             self.params = _RegistratedBufferDict(vars(params))
 
     @staticmethod
@@ -996,7 +994,6 @@ class VoxelGridModule(Configurable, torch.nn.Module):
         """
         '''
         new_params = {}
-        # pyre-fixme[29]: `Union[(self: Tensor) -> Any, Tensor, Module]` is not a
         #  function.
         for name in self.params:
             key = prefix + "params." + name
@@ -1035,7 +1032,6 @@ 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-fixme[16]: `VoxelGridModule` has no attribute `params`.
         self.params = torch.nn.ParameterDict(
             {
                 k: torch.nn.Parameter(val)

pytorch3d/implicitron/models/implicit_function/voxel_grid_implicit_function.py

@@ -142,11 +142,9 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
     """
 
     # ---- voxel grid for density
-    # pyre-fixme[13]: Attribute `voxel_grid_density` is never initialized.
     voxel_grid_density: VoxelGridModule
 
     # ---- voxel grid for color
-    # pyre-fixme[13]: Attribute `voxel_grid_color` is never initialized.
     voxel_grid_color: VoxelGridModule
 
     # ---- harmonic embeddings density
@@ -162,12 +160,10 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
 
     # ---- decoder function for density
     decoder_density_class_type: str = "MLPDecoder"
-    # pyre-fixme[13]: Attribute `decoder_density` is never initialized.
     decoder_density: DecoderFunctionBase
 
     # ---- decoder function for color
     decoder_color_class_type: str = "MLPDecoder"
-    # pyre-fixme[13]: Attribute `decoder_color` is never initialized.
     decoder_color: DecoderFunctionBase
 
     # ---- cuda streams
@@ -190,25 +186,20 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
 
     def __post_init__(self) -> None:
         run_auto_creation(self)
-        # pyre-fixme[16]: `VoxelGridImplicitFunction` has no attribute
         #  `voxel_grid_scaffold`.
         self.voxel_grid_scaffold = self._create_voxel_grid_scaffold()
-        # pyre-fixme[16]: `VoxelGridImplicitFunction` has no attribute
         #  `harmonic_embedder_xyz_density`.
         self.harmonic_embedder_xyz_density = HarmonicEmbedding(
             **self.harmonic_embedder_xyz_density_args
         )
-        # pyre-fixme[16]: `VoxelGridImplicitFunction` has no attribute
         #  `harmonic_embedder_xyz_color`.
         self.harmonic_embedder_xyz_color = HarmonicEmbedding(
             **self.harmonic_embedder_xyz_color_args
         )
-        # pyre-fixme[16]: `VoxelGridImplicitFunction` has no attribute
         #  `harmonic_embedder_dir_color`.
         self.harmonic_embedder_dir_color = HarmonicEmbedding(
             **self.harmonic_embedder_dir_color_args
         )
-        # pyre-fixme[16]: `VoxelGridImplicitFunction` has no attribute
         #  `_scaffold_ready`.
         self._scaffold_ready = False
 
@@ -372,7 +363,6 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
                 feature dimensionality which `decoder_density` returns
         """
         embeds_density = self.voxel_grid_density(points)
-        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         harmonic_embedding_density = self.harmonic_embedder_xyz_density(embeds_density)
         # shape = [..., density_dim]
         return self.decoder_density(harmonic_embedding_density)
@@ -407,7 +397,6 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
         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 = directions @ camera.R
 
@@ -417,13 +406,11 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
 
         # ########## embed with the harmonic function ########## #
         # Obtain the harmonic embedding of the voxel grid output.
-        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         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-fixme[29]: `Union[Tensor, Module]` is not a function.
         harmonic_embedding_dir = self.harmonic_embedder_dir_color(
             rays_directions_normed
         )
@@ -493,7 +480,6 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
             an object inside, else False.
         """
         # find bounding box
-        # pyre-fixme[16]: Item `Tensor` of `Tensor | Module` has no attribute
         #  `get_grid_points`.
         points = self.voxel_grid_scaffold.get_grid_points(epoch=epoch)
         assert self._scaffold_ready, "Scaffold has to be calculated before cropping."
@@ -529,7 +515,6 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
         """
 
         planes = []
-        # pyre-fixme[16]: Item `Tensor` of `Tensor | Module` has no attribute
         #  `get_grid_points`.
         points = self.voxel_grid_scaffold.get_grid_points(epoch=epoch)
 
@@ -550,9 +535,7 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
             stride=1,
         )
         occupancy_cube = density_cube > self.scaffold_empty_space_threshold
-        # pyre-fixme[16]: Item `Tensor` of `Tensor | Module` has no attribute `params`.
         self.voxel_grid_scaffold.params["voxel_grid"] = occupancy_cube.float()
-        # pyre-fixme[16]: `VoxelGridImplicitFunction` has no attribute
         #  `_scaffold_ready`.
         self._scaffold_ready = True
 

pytorch3d/implicitron/models/overfit_model.py

@@ -195,34 +195,27 @@ class OverfitModel(ImplicitronModelBase):
 
     # ---- global encoder settings
     global_encoder_class_type: Optional[str] = None
-    # pyre-fixme[13]: Attribute `global_encoder` is never initialized.
     global_encoder: Optional[GlobalEncoderBase]
 
     # ---- raysampler
     raysampler_class_type: str = "AdaptiveRaySampler"
-    # pyre-fixme[13]: Attribute `raysampler` is never initialized.
     raysampler: RaySamplerBase
 
     # ---- renderer configs
     renderer_class_type: str = "MultiPassEmissionAbsorptionRenderer"
-    # pyre-fixme[13]: Attribute `renderer` is never initialized.
     renderer: BaseRenderer
 
     # ---- implicit function settings
     share_implicit_function_across_passes: bool = False
     implicit_function_class_type: str = "NeuralRadianceFieldImplicitFunction"
-    # pyre-fixme[13]: Attribute `implicit_function` is never initialized.
     implicit_function: ImplicitFunctionBase
     coarse_implicit_function_class_type: Optional[str] = None
-    # pyre-fixme[13]: Attribute `coarse_implicit_function` is never initialized.
     coarse_implicit_function: Optional[ImplicitFunctionBase]
 
     # ----- metrics
-    # pyre-fixme[13]: Attribute `view_metrics` is never initialized.
     view_metrics: ViewMetricsBase
     view_metrics_class_type: str = "ViewMetrics"
 
-    # pyre-fixme[13]: Attribute `regularization_metrics` is never initialized.
     regularization_metrics: RegularizationMetricsBase
     regularization_metrics_class_type: str = "RegularizationMetrics"
 
@@ -658,7 +651,6 @@ class OverfitModel(ImplicitronModelBase):
 
             implicit_function_type = registry.get(
                 ImplicitFunctionBase,
-                # pyre-ignore: config is None allow to check if this is None.
                 self.coarse_implicit_function_class_type,
             )
             expand_args_fields(implicit_function_type)

pytorch3d/implicitron/models/renderer/base.py

@@ -108,7 +108,6 @@ class ImplicitronRayBundle:
     def lengths(self) -> torch.Tensor:
         if self.bins is not None:
             # equivalent to: 0.5 * (bins[..., 1:] + bins[..., :-1]) but more efficient
-            # pyre-ignore
             return torch.lerp(self.bins[..., :-1], self.bins[..., 1:], 0.5)
         # pyrefly: ignore [bad-return]
         return self._lengths

pytorch3d/implicitron/models/renderer/lstm_renderer.py

@@ -135,7 +135,6 @@ class LSTMRenderer(BaseRenderer, torch.nn.Module):
                 break
 
             # run the lstm marcher
-            # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
             state_h, state_c = self._lstm(
                 raymarch_features.view(-1, raymarch_features.shape[-1]),
                 states[-1],

pytorch3d/implicitron/models/renderer/multipass_ea.py

@@ -84,7 +84,6 @@ class MultiPassEmissionAbsorptionRenderer(BaseRenderer, torch.nn.Module):
     """
 
     raymarcher_class_type: str = "EmissionAbsorptionRaymarcher"
-    # pyre-fixme[13]: Attribute `raymarcher` is never initialized.
     raymarcher: RaymarcherBase
 
     n_pts_per_ray_fine_training: int = 64

pytorch3d/implicitron/models/renderer/ray_point_refiner.py

@@ -42,9 +42,7 @@ class RayPointRefiner(Configurable, torch.nn.Module):
             for Anti-Aliasing Neural Radiance Fields." ICCV 2021.
     """
 
-    # pyre-fixme[13]: Attribute `n_pts_per_ray` is never initialized.
     n_pts_per_ray: int
-    # pyre-fixme[13]: Attribute `random_sampling` is never initialized.
     random_sampling: bool
     add_input_samples: bool = True
     blurpool_weights: bool = False

pytorch3d/implicitron/models/renderer/ray_sampler.py

@@ -207,7 +207,6 @@ class AbstractMaskRaySampler(RaySamplerBase, torch.nn.Module):
         """
         sample_mask = None
         if (
-            # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[An...
             self._sampling_mode[evaluation_mode] == RenderSamplingMode.MASK_SAMPLE
             and mask is not None
         ):
@@ -242,7 +241,6 @@ class AbstractMaskRaySampler(RaySamplerBase, torch.nn.Module):
                 "Heterogeneous ray bundle is not supported for conical frustum computation yet"
             )
         elif self.cast_ray_bundle_as_cone:
-            # pyre-fixme[9]: pixel_hw has type `Tuple[float, float]`; used as
             #  `Tuple[Union[Tensor, Module], Union[Tensor, Module]]`.
             pixel_hw: Tuple[float, float] = (self.pixel_height, self.pixel_width)
             pixel_radii_2d = compute_radii(cameras, ray_bundle.xys[..., :2], pixel_hw)

pytorch3d/implicitron/models/renderer/ray_tracing.py

@@ -571,7 +571,6 @@ def _get_sphere_intersection(
     # cam_loc = cam_loc.unsqueeze(-1)
     # ray_cam_dot = torch.bmm(ray_directions, cam_loc).squeeze()
     ray_cam_dot = (ray_directions * cam_loc).sum(-1)  # n_images x n_rays
-    # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
     under_sqrt = ray_cam_dot**2 - (cam_loc.norm(2, dim=-1) ** 2 - r**2)
 
     under_sqrt = under_sqrt.reshape(-1)

pytorch3d/implicitron/models/renderer/sdf_renderer.py

@@ -27,7 +27,6 @@ from .rgb_net import RayNormalColoringNetwork
 class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module):
     render_features_dimensions: int = 3
     object_bounding_sphere: float = 1.0
-    # pyre-fixme[13]: Attribute `ray_tracer` is never initialized.
     ray_tracer: RayTracing
     ray_normal_coloring_network_args: DictConfig = get_default_args_field(
         RayNormalColoringNetwork
@@ -208,7 +207,6 @@ class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module):
             ]
             normals_full.view(-1, 3)[surface_mask] = normals
             render_full.view(-1, self.render_features_dimensions)[surface_mask] = (
-                # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
                 self._rgb_network(
                     features,
                     differentiable_surface_points[None],

pytorch3d/implicitron/models/view_pooler/feature_aggregator.py

@@ -532,7 +532,6 @@ 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[(self: TensorBase, indices: Union[None, slice[Any, A...
         camera_rep.T[:, None],
         camera_rep.R.permute(0, 2, 1),
     ).reshape(-1, *([1] * (pts.ndim - 2)), 3)
@@ -632,7 +631,6 @@ def _avgmaxstd_reduction_function(
     x_aggr = torch.cat(pooled_features, dim=-1)
 
     # zero out features that were all masked out
-    # pyre-fixme[16]: `bool` has no attribute `type_as`.
     any_active = (w.max(dim=dim, keepdim=True).values > 1e-4).type_as(x_aggr)
     x_aggr = x_aggr * any_active[..., None]
 
@@ -660,7 +658,6 @@ def _std_reduction_function(
 ):
     if mu is None:
         mu = _avg_reduction_function(x, w, dim=dim)
-    # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
     std = wmean((x - mu) ** 2, w, dim=dim, eps=1e-2).clamp(1e-4).sqrt()
     # FIXME: somehow this is extremely heavy in mem?
     return std

pytorch3d/implicitron/models/view_pooler/view_pooler.py

@@ -34,10 +34,8 @@ class ViewPooler(Configurable, torch.nn.Module):
             from a set of source images. FeatureAggregator executes step (4) above.
     """
 
-    # pyre-fixme[13]: Attribute `view_sampler` is never initialized.
     view_sampler: ViewSampler
     feature_aggregator_class_type: str = "AngleWeightedReductionFeatureAggregator"
-    # pyre-fixme[13]: Attribute `feature_aggregator` is never initialized.
     feature_aggregator: FeatureAggregatorBase
 
     def __post_init__(self):

pytorch3d/implicitron/tools/config.py

@@ -312,7 +312,6 @@ class _Registry:
             raise ValueError(
                 f"{name} resolves to {result} which does not subclass {base_class_wanted}"
             )
-        # pyre-ignore[7]
         return result
 
     def get_all(

pytorch3d/implicitron/tools/depth_cleanup.py

@@ -51,7 +51,6 @@ def cleanup_eval_depth(
     # the threshold is a sigma-multiple of the standard deviation of the depth
     mu = wmean(depth.view(ba, -1, 1), mask.view(ba, -1)).view(ba, 1)
     std = (
-        # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
         wmean((depth.view(ba, -1) - mu).view(ba, -1, 1) ** 2, mask.view(ba, -1))
         .clamp(1e-4)
         .sqrt()

pytorch3d/implicitron/tools/metric_utils.py

@@ -79,7 +79,6 @@ def eval_depth(
 
     df = gt - pred
 
-    # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
     mse_depth = (dmask * (df**2)).sum((1, 2, 3)) / dmask_mass
     abs_depth = (dmask * df.abs()).sum((1, 2, 3)) / dmask_mass
 
@@ -115,10 +114,8 @@ def calc_mse(
     Calculates the mean square error between tensors `x` and `y`.
     """
     if mask is None:
-        # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
         return torch.mean((x - y) ** 2)
     else:
-        # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
         return (((x - y) ** 2) * mask).sum() / mask.expand_as(x).sum().clamp(1e-5)
 
 
@@ -146,7 +143,6 @@ def calc_bce(
         mask_bg = (1 - mask_fg) * mask
         weight = mask_fg / mask_fg.sum().clamp(1.0) + mask_bg / mask_bg.sum().clamp(1.0)
         # weight sum should be at this point ~2
-        # pyre-fixme[58]: `/` is not supported for operand types `int` and `Tensor`.
         weight = weight * (weight.numel() / weight.sum().clamp(1.0))
     else:
         weight = torch.ones_like(gt) * mask

pytorch3d/implicitron/tools/model_io.py

@@ -49,7 +49,6 @@ def get_stats_path(fl, eval_results: bool = False) -> str:
                 break
     else:
         flstats = "%s_stats.jgz" % fl
-    # pyre-fixme[61]: `flstats` is undefined, or not always defined.
     return flstats
 
 
@@ -148,15 +147,12 @@ def find_last_checkpoint(
         )
         if len(fls) > 0:
             break
-    # pyre-fixme[61]: `fls` is undefined, or not always defined.
     if len(fls) == 0:
         fl = None
     else:
         if all_checkpoints:
-            # pyre-fixme[61]: `fls` is undefined, or not always defined.
             fl = [f[0 : -len(ext)] + ".pth" for f in fls]
         else:
-            # pyre-fixme[61]: `ext` is undefined, or not always defined.
             fl = fls[-1][0 : -len(ext)] + ".pth"
 
     return fl

pytorch3d/implicitron/tools/rasterize_mc.py

@@ -62,7 +62,6 @@ def rasterize_sparse_ray_bundle(
 
         max_size = torch.max(camera_counts).item()
         features_depth_ras = packed_to_padded(
-            # pyrefly: ignore [bad-argument-type]
             features_depth_ras[:, 0],
             first_idxs,
             # pyrefly: ignore [bad-argument-type]

pytorch3d/io/obj_io.py

@@ -218,7 +218,6 @@ def load_obj(
     """
     data_dir = "./"
     if isinstance(f, (str, bytes, Path)):
-        # pyre-fixme[6]: For 1st argument expected `PathLike[Variable[AnyStr <:
         #  [str, bytes]]]` but got `Union[Path, bytes, str]`.
         data_dir = os.path.dirname(f)
     if path_manager is None:

pytorch3d/ops/cameras_alignment.py

@@ -122,16 +122,13 @@ 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 argument 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 argument expected `Tensor` but got
             #  `Union[Tensor, Module]`.
             cameras_src.R,
         )[:, 0]
-        # pyre-fixme[29]: `Union[(self: TensorBase, other: Union[bool, complex,
         #  float, int, Tensor]) -> Tensor, Tensor, Module]` is not a function.
         + cameras_src.T * align_t_s
     )
@@ -180,8 +177,6 @@ def _align_camera_extrinsics(
         R_A = (U V^T)^T
         ```
     """
-    # pyre-fixme[6]: For 1st argument expected `Tensor` but got `Union[Tensor, Module]`.
-    # pyre-fixme[29]: `Union[(self: TensorBase, dim0: int, dim1: int) -> Tensor,
     #  Tensor, Module]` is not a function.
     RRcov = torch.bmm(cameras_src.R, cameras_tgt.R.transpose(2, 1)).mean(0)
     U, _, V = torch.svd(RRcov)
@@ -212,11 +207,7 @@ def _align_camera_extrinsics(
         T_A = mean(B) - mean(A) * s_A
         ```
     """
-    # pyre-fixme[6]: For 1st argument expected `Tensor` but got `Union[Tensor, Module]`.
-    # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[Any, Any, ...
     A = torch.bmm(cameras_src.R, cameras_src.T[:, :, None])[:, :, 0]
-    # pyre-fixme[6]: For 1st argument expected `Tensor` but got `Union[Tensor, Module]`.
-    # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[Any, Any, ...
     B = torch.bmm(cameras_src.R, cameras_tgt.T[:, :, None])[:, :, 0]
     Amu = A.mean(0, keepdim=True)
     Bmu = B.mean(0, keepdim=True)

pytorch3d/ops/laplacian_matrices.py

@@ -103,7 +103,6 @@ def cot_laplacian(
     s = 0.5 * (A + B + C)
     # note that the area can be negative (close to 0) causing nans after sqrt()
     # we clip it to a small positive value
-    # pyre-fixme[16]: `float` has no attribute `clamp`.
     area = (s * (s - A) * (s - B) * (s - C)).clamp(min=eps).sqrt()
 
     # Compute cotangents of angles, of shape (sum(F_n), 3)

pytorch3d/ops/points_normals.py

@@ -156,7 +156,6 @@ def estimate_pointcloud_local_coord_frames(
     if disambiguate_directions:
         # disambiguate normal
         n = _disambiguate_vector_directions(
-            # pyrefly: ignore [unsupported-operation]
             points_centered,
             knns,
             # pyrefly: ignore [unsupported-operation]
@@ -164,7 +163,6 @@ def estimate_pointcloud_local_coord_frames(
         )
         # disambiguate the main curvature
         z = _disambiguate_vector_directions(
-            # pyrefly: ignore [unsupported-operation]
             points_centered,
             knns,
             # pyrefly: ignore [unsupported-operation]

pytorch3d/ops/sample_farthest_points.py

@@ -168,9 +168,7 @@ def sample_farthest_points_naive(
         sample_idx_batch[0] = selected_idx
 
         # If the pointcloud has fewer than K points then only iterate over the min
-        # pyre-fixme[6]: For 1st param expected `SupportsRichComparisonT` but got
         #  `Tensor`.
-        # pyre-fixme[6]: For 2nd param expected `SupportsRichComparisonT` but got
         #  `Tensor`.
         k_n = min(lengths[n], K[n])
 

pytorch3d/ops/utils.py

@@ -91,7 +91,6 @@ def wmean(
     args = {"dim": dim, "keepdim": keepdim}
 
     if weight is None:
-        # pyre-fixme[6]: For 1st param expected `Optional[dtype]` but got
         #  `Union[Tuple[int], int]`.
         return x.mean(**args)
 
@@ -101,7 +100,6 @@ def wmean(
     ):
         raise ValueError("wmean: weights are not compatible with the tensor")
 
-    # pyre-fixme[6]: For 1st param expected `Optional[dtype]` but got
     #  `Union[Tuple[int], int]`.
     return (x * weight[..., None]).sum(**args) / weight[..., None].sum(**args).clamp(
         eps

pytorch3d/renderer/blending.py

@@ -228,7 +228,6 @@ def softmax_rgb_blend(
 
     # Also apply exp normalize trick for the background color weight.
     # Clamp to ensure delta is never 0.
-    # pyre-fixme[6]: Expected `Tensor` for 1st param but got `float`.
     delta = torch.exp((eps - z_inv_max) / blend_params.gamma).clamp(min=eps)
 
     # Normalize weights.

pytorch3d/renderer/camera_conversions.py

@@ -65,10 +65,8 @@ def _opencv_from_cameras_projection(
     cameras: PerspectiveCameras,
     image_size: torch.Tensor,
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    # pyre-fixme[29]: `Union[(self: TensorBase, memory_format:
     #  Optional[memory_format] = ...) -> Tensor, Tensor, Module]` is not a function.
     R_pytorch3d = cameras.R.clone()
-    # pyre-fixme[29]: `Union[(self: TensorBase, memory_format:
     #  Optional[memory_format] = ...) -> Tensor, Tensor, Module]` is not a function.
     T_pytorch3d = cameras.T.clone()
     focal_pytorch3d = cameras.focal_length

pytorch3d/renderer/cameras.py

@@ -230,9 +230,7 @@ class CamerasBase(TensorProperties):
             a Transform3d object which represents a batch of transforms
             of shape (N, 3, 3)
         """
-        # pyre-fixme[16]: `CamerasBase` has no attribute `R`.
         self.R: torch.Tensor = kwargs.get("R", self.R)
-        # pyre-fixme[16]: `CamerasBase` has no attribute `T`.
         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)
@@ -409,9 +407,7 @@ class CamerasBase(TensorProperties):
         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(
@@ -429,13 +425,10 @@ class CamerasBase(TensorProperties):
             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"
                 )
@@ -1179,7 +1172,6 @@ class PerspectiveCameras(CamerasBase):
 
         unprojection_transform = to_camera_transform.inverse()
         xy_inv_depth = torch.cat(
-            # pyre-fixme[6]: For 1st argument expected `Union[List[Tensor],
             #  tuple[Tensor, ...]]` but got `Tuple[Tensor, float]`.
             (xy_depth[..., :2], torch.reciprocal(xy_depth[..., 2:3])),
             dim=-1,  # type: ignore
@@ -1750,7 +1742,7 @@ def look_at_view_transform(
                 elev,  # pyrefly: ignore [bad-argument-type]
                 azim,  # pyrefly: ignore [bad-argument-type]
                 degrees=degrees,
-                device=device,  # pyrefly: ignore [bad-argument-type]
+                device=device,
             )
             + at
         )

pytorch3d/renderer/implicit/harmonic_embedding.py

@@ -183,7 +183,6 @@ class HarmonicEmbedding(torch.nn.Module):
         so the input might be xyz.
         """
         return self.get_output_dim_static(
-            # pyrefly: ignore [bad-argument-type]
             input_dims,
             # pyrefly: ignore [bad-argument-type]
             len(self._frequencies),

pytorch3d/renderer/implicit/raysampling.py

@@ -236,7 +236,6 @@ class MultinomialRaysampler(torch.nn.Module):
                 # is not batched and does not support partial permutation
                 _, width, height, _ = xy_grid.shape
                 weights = xy_grid.new_ones(batch_size, width * height)
-            # pyre-fixme[6]: For 2nd param expected `int` but got `Union[bool,
             #  float, int]`.
             rays_idx = _safe_multinomial(weights, n_rays_per_image)[..., None].expand(
                 -1, -1, 2

pytorch3d/renderer/implicit/renderer.py

@@ -170,7 +170,6 @@ class ImplicitRenderer(torch.nn.Module):
         # given sampled rays, call the volumetric function that
         # evaluates the densities and features at the locations of the
         # ray points
-        # pyre-fixme[23]: Unable to unpack `object` into 2 values.
         rays_densities, rays_features = volumetric_function(
             ray_bundle=ray_bundle, cameras=cameras, **kwargs
         )

pytorch3d/renderer/mesh/clip.py

@@ -496,7 +496,6 @@ def clip_faces(
 
     # Solve for the points p4, p5 that intersect the clipping plane
     p, p_barycentric = _find_verts_intersecting_clipping_plane(
-        # pyrefly: ignore [bad-argument-type]
         faces_case3,
         p1_face_ind,
         # pyrefly: ignore [bad-argument-type]
@@ -540,12 +539,10 @@ def clip_faces(
     faces_case4 = face_verts_unclipped[case4_unclipped_idx]
 
     # index (0, 1, or 2) of the vertex behind the clipping plane
-    # pyre-fixme[61]: `faces_clipped_verts` is undefined, or not always defined.
     p1_face_ind = torch.where(faces_clipped_verts[case4_unclipped_idx])[1]
 
     # Solve for the points p4, p5 that intersect the clipping plane
     p, p_barycentric = _find_verts_intersecting_clipping_plane(
-        # pyrefly: ignore [bad-argument-type]
         faces_case4,
         p1_face_ind,
         # pyrefly: ignore [bad-argument-type]

pytorch3d/renderer/mesh/textures.py

@@ -453,7 +453,6 @@ class TexturesAtlas(TexturesBase):
                 msg = "Expected atlas to be of shape (N, F, R, R, C); got %r"
                 raise ValueError(msg % repr(atlas.ndim))
             self._atlas_padded = atlas
-            # pyrefly: ignore [bad-assignment]
             self._atlas_list = None
             self.device = atlas.device
 
@@ -537,7 +536,6 @@ class TexturesAtlas(TexturesBase):
                 self._atlas_padded = [
                     torch.empty((0, 0, 0, 3), dtype=torch.float32, device=self.device)
                 ] * self._N
-            # pyrefly: ignore [bad-assignment]
             self._atlas_list = _padded_to_list_wrapper(
                 # pyrefly: ignore [bad-argument-type]
                 self._atlas_padded,
@@ -803,7 +801,6 @@ class TexturesUV(TexturesBase):
                 msg = "Expected faces_uvs to be of shape (N, F, 3); got %r"
                 raise ValueError(msg % repr(faces_uvs.shape))
             self._faces_uvs_padded = faces_uvs
-            # pyrefly: ignore [bad-assignment]
             self._faces_uvs_list = None
             self.device = faces_uvs.device
 
@@ -840,7 +837,6 @@ class TexturesUV(TexturesBase):
                 msg = "Expected verts_uvs to be of shape (N, V, 2); got %r"
                 raise ValueError(msg % repr(verts_uvs.shape))
             self._verts_uvs_padded = verts_uvs
-            # pyrefly: ignore [bad-assignment]
             self._verts_uvs_list = None
 
             if verts_uvs.device != self.device:
@@ -853,7 +849,6 @@ class TexturesUV(TexturesBase):
         if isinstance(maps, (list, tuple)):
             self._maps_list = maps
         else:
-            # pyrefly: ignore [bad-assignment]
             self._maps_list = None
         self._maps_padded = self._format_maps_padded(maps)
 
@@ -1099,7 +1094,6 @@ class TexturesUV(TexturesBase):
                     torch.empty((0, 3), dtype=torch.float32, device=self.device)
                 ] * self._N
             else:
-                # pyrefly: ignore [bad-assignment]
                 self._faces_uvs_list = padded_to_list(
                     # pyrefly: ignore [bad-argument-type]
                     self._faces_uvs_padded,
@@ -1132,7 +1126,7 @@ class TexturesUV(TexturesBase):
                 # The number of vertices in the mesh and in verts_uvs can differ
                 # e.g. if a vertex is shared between 3 faces, it can
                 # have up to 3 different uv coordinates.
-                # pyrefly: ignore [bad-assignment, missing-attribute]
+                # pyrefly: ignore [missing-attribute]
                 self._verts_uvs_list = list(self._verts_uvs_padded.unbind(0))
         # pyrefly: ignore [bad-return]
         return self._verts_uvs_list
@@ -1755,7 +1749,6 @@ class TexturesVertex(TexturesBase):
                 msg = "Expected verts_features to be of shape (N, V, C); got %r"
                 raise ValueError(msg % repr(verts_features.shape))
             self._verts_features_padded = verts_features
-            # pyrefly: ignore [bad-assignment]
             self._verts_features_list = None
             self.device = verts_features.device
 
@@ -1832,7 +1825,6 @@ class TexturesVertex(TexturesBase):
                     torch.empty((0, 3), dtype=torch.float32, device=self.device)
                 ] * self._N
             else:
-                # pyrefly: ignore [bad-assignment]
                 self._verts_features_list = padded_to_list(
                     # pyrefly: ignore [bad-argument-type]
                     self._verts_features_padded,

pytorch3d/renderer/opengl/rasterizer_opengl.py

@@ -288,7 +288,6 @@ class _OpenGLMachinery:
             bary_coords = []
             zbufs = []
 
-            # pyre-ignore Incompatible parameter type [6]
             for mesh_id, mesh in enumerate(meshes_gl_ndc):
                 pix_to_face, bary_coord, zbuf = self._rasterize_mesh(
                     mesh,
@@ -385,13 +384,11 @@ class _OpenGLMachinery:
 
         # Free GL resources.
         gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, self.fbo)
-        # pyre-fixme[16]: Module `GL_3_0` has no attribute `glDeleteFramebuffers`.
         gl.glDeleteFramebuffers(1, [self.fbo])
         gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, 0)
         del self.fbo
 
         gl.glBindBufferBase(gl.GL_SHADER_STORAGE_BUFFER, 0, self.mesh_buffer_object)
-        # pyre-fixme[16]: Module `GL_1_5` has no attribute `glDeleteBuffers`.
         gl.glDeleteBuffers(1, [self.mesh_buffer_object])
         gl.glBindBufferBase(gl.GL_SHADER_STORAGE_BUFFER, 0, 0)
         del self.mesh_buffer_object
@@ -408,7 +405,6 @@ class _OpenGLMachinery:
             projection matrix: A 3x3 float tensor.
         """
         gl.glUseProgram(self.program)
-        # pyre-fixme[16]: Module `GL_2_0` has no attribute `glUniformMatrix4fv`.
         gl.glUniformMatrix4fv(
             self.perspective_projection_uniform,
             1,

pytorch3d/structures/meshes.py

@@ -471,7 +471,6 @@ class Meshes:
             ):
                 raise ValueError("Vertex normals tensor has incorrect dimensions.")
             self._verts_normals_packed = struct_utils.padded_to_packed(
-                # pyrefly: ignore [missing-attribute]
                 verts_normals,
                 # pyrefly: ignore [missing-attribute]
                 split_size=self._num_verts_per_mesh.tolist(),

pytorch3d/vis/plotly_vis.py

@@ -61,7 +61,6 @@ def _is_heterogeneous_ray_bundle(struct: Union[List[Struct], Struct]) -> bool:
         True if something is a HeterogeneousRayBundle or ImplicitronRayBundle
         and cant be reduced to RayBundle else False
     """
-    # pyre-ignore[16]
     return hasattr(struct, "camera_counts") and struct.camera_counts is not None
 
 
@@ -586,15 +585,11 @@ def _add_struct_from_batch(
     if isinstance(batched_struct, CamerasBase):
         # we can't index directly into camera batches
         R, T = batched_struct.R, batched_struct.T
-        # pyre-fixme[6]: For 1st argument expected
         #  `pyre_extensions.PyreReadOnly[Sized]` but got `Union[Tensor, Module]`.
         r_idx = min(scene_num, len(R) - 1)
-        # pyre-fixme[6]: For 1st argument expected
         #  `pyre_extensions.PyreReadOnly[Sized]` but got `Union[Tensor, Module]`.
         t_idx = min(scene_num, len(T) - 1)
-        # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[Any, A...
         R = R[r_idx].unsqueeze(0)
-        # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[Any, A...
         T = T[t_idx].unsqueeze(0)
         struct = CamerasBase(device=batched_struct.device, R=R, T=T)
     elif _is_ray_bundle(batched_struct) and not _is_heterogeneous_ray_bundle(
@@ -616,7 +611,6 @@ def _add_struct_from_batch(
         struct = RayBundle(
             **{
                 attr: getattr(batched_struct, attr)[
-                    # pyre-ignore[16]
                     first_idxs[struct_idx] : first_idxs[struct_idx + 1]
                 ]
                 for attr in ["origins", "directions", "lengths", "xys"]

pytorch3d/vis/texture_vis.py

@@ -59,7 +59,6 @@ def texturesuv_image_matplotlib(
     for i in indices:
         # setting clip_on=False makes it obvious when
         # we have UV coordinates outside the correct range
-        # pyre-fixme[6]: For 1st argument expected `Tuple[float, float]` but got
         #  `ndarray[Any, Any]`.
         ax.add_patch(Circle(centers[i], radius, color=color, clip_on=False))