diff --git a/projects/implicitron_trainer/experiment.py b/projects/implicitron_trainer/experiment.py
index fa109af5..4574916e 100755
--- a/projects/implicitron_trainer/experiment.py
+++ b/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"
 
diff --git a/projects/implicitron_trainer/impl/model_factory.py b/projects/implicitron_trainer/impl/model_factory.py
index 3a0e58ae..1a5b78de 100644
--- a/projects/implicitron_trainer/impl/model_factory.py
+++ b/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
diff --git a/projects/implicitron_trainer/impl/optimizer_factory.py b/projects/implicitron_trainer/impl/optimizer_factory.py
index 2305979e..a05ad32d 100644
--- a/projects/implicitron_trainer/impl/optimizer_factory.py
+++ b/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]
diff --git a/projects/implicitron_trainer/impl/training_loop.py b/projects/implicitron_trainer/impl/training_loop.py
index 28884a41..f00b5fec 100644
--- a/projects/implicitron_trainer/impl/training_loop.py
+++ b/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
diff --git a/pytorch3d/common/compat.py b/pytorch3d/common/compat.py
index 02a64cef..1b4a4373 100644
--- a/pytorch3d/common/compat.py
+++ b/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__
     ):
diff --git a/pytorch3d/implicitron/dataset/data_source.py b/pytorch3d/implicitron/dataset/data_source.py
index ffccee3c..8a90d05b 100644
--- a/pytorch3d/implicitron/dataset/data_source.py
+++ b/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,)
 
diff --git a/pytorch3d/implicitron/dataset/frame_data.py b/pytorch3d/implicitron/dataset/frame_data.py
index 5ec6214e..d8e20004 100644
--- a/pytorch3d/implicitron/dataset/frame_data.py
+++ b/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:]),
diff --git a/pytorch3d/implicitron/dataset/json_index_dataset.py b/pytorch3d/implicitron/dataset/json_index_dataset.py
index 2ea4b091..2ef882b9 100644
--- a/pytorch3d/implicitron/dataset/json_index_dataset.py
+++ b/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)
diff --git a/pytorch3d/implicitron/dataset/json_index_dataset_map_provider.py b/pytorch3d/implicitron/dataset/json_index_dataset_map_provider.py
index da09e04d..168bda2c 100644
--- a/pytorch3d/implicitron/dataset/json_index_dataset_map_provider.py
+++ b/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"
 
diff --git a/pytorch3d/implicitron/dataset/json_index_dataset_map_provider_v2.py b/pytorch3d/implicitron/dataset/json_index_dataset_map_provider_v2.py
index cf1d9859..59bb62b8 100644
--- a/pytorch3d/implicitron/dataset/json_index_dataset_map_provider_v2.py
+++ b/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"
 
diff --git a/pytorch3d/implicitron/dataset/rendered_mesh_dataset_map_provider.py b/pytorch3d/implicitron/dataset/rendered_mesh_dataset_map_provider.py
index 3797d7e5..8413acbb 100644
--- a/pytorch3d/implicitron/dataset/rendered_mesh_dataset_map_provider.py
+++ b/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(  # pyre-ignore[28]
+        self.train_dataset = SingleSceneDataset(
             # pyrefly: ignore [unexpected-keyword]
             object_name="cow",
             # pyrefly: ignore [unexpected-keyword]
diff --git a/pytorch3d/implicitron/dataset/single_sequence_dataset.py b/pytorch3d/implicitron/dataset/single_sequence_dataset.py
index 058db29b..0e9bdf88 100644
--- a/pytorch3d/implicitron/dataset/single_sequence_dataset.py
+++ b/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]
diff --git a/pytorch3d/implicitron/dataset/sql_dataset.py b/pytorch3d/implicitron/dataset/sql_dataset.py
index 84a488b0..481888e4 100644
--- a/pytorch3d/implicitron/dataset/sql_dataset.py
+++ b/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:
diff --git a/pytorch3d/implicitron/dataset/sql_dataset_provider.py b/pytorch3d/implicitron/dataset/sql_dataset_provider.py
index 38e74f9e..4382be4f 100644
--- a/pytorch3d/implicitron/dataset/sql_dataset_provider.py
+++ b/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):
diff --git a/pytorch3d/implicitron/dataset/types.py b/pytorch3d/implicitron/dataset/types.py
index 2ddeb3be..bad3713d 100644
--- a/pytorch3d/implicitron/dataset/types.py
+++ b/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:
diff --git a/pytorch3d/implicitron/dataset/utils.py b/pytorch3d/implicitron/dataset/utils.py
index c8c9726b..caa81a32 100644
--- a/pytorch3d/implicitron/dataset/utils.py
+++ b/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]  # pyre-ignore[16]
+    camera.principal_point = principal_point_scaled[None]
 
 
 # NOTE this cache is per-worker; they are implemented as processes.
diff --git a/pytorch3d/implicitron/dataset/visualize.py b/pytorch3d/implicitron/dataset/visualize.py
index 557f7b43..377905f1 100644
--- a/pytorch3d/implicitron/dataset/visualize.py
+++ b/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
         ]
diff --git a/pytorch3d/implicitron/evaluation/evaluate_new_view_synthesis.py b/pytorch3d/implicitron/evaluation/evaluate_new_view_synthesis.py
index f17b472d..211ff515 100644
--- a/pytorch3d/implicitron/evaluation/evaluate_new_view_synthesis.py
+++ b/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,
diff --git a/pytorch3d/implicitron/models/feature_extractor/resnet_feature_extractor.py b/pytorch3d/implicitron/models/feature_extractor/resnet_feature_extractor.py
index d90b8234..0675ea76 100644
--- a/pytorch3d/implicitron/models/feature_extractor/resnet_feature_extractor.py
+++ b/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
diff --git a/pytorch3d/implicitron/models/generic_model.py b/pytorch3d/implicitron/models/generic_model.py
index 7f7d0a1c..c8696832 100644
--- a/pytorch3d/implicitron/models/generic_model.py
+++ b/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()
diff --git a/pytorch3d/implicitron/models/global_encoder/global_encoder.py b/pytorch3d/implicitron/models/global_encoder/global_encoder.py
index c31dbfc1..8b1f38f6 100644
--- a/pytorch3d/implicitron/models/global_encoder/global_encoder.py
+++ b/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):
diff --git a/pytorch3d/implicitron/models/implicit_function/decoding_functions.py b/pytorch3d/implicitron/models/implicit_function/decoding_functions.py
index 5073d493..3a78f3f9 100644
--- a/pytorch3d/implicitron/models/implicit_function/decoding_functions.py
+++ b/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),
diff --git a/pytorch3d/implicitron/models/implicit_function/idr_feature_field.py b/pytorch3d/implicitron/models/implicit_function/idr_feature_field.py
index 385effbf..caa59896 100644
--- a/pytorch3d/implicitron/models/implicit_function/idr_feature_field.py
+++ b/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.
diff --git a/pytorch3d/implicitron/models/implicit_function/neural_radiance_field.py b/pytorch3d/implicitron/models/implicit_function/neural_radiance_field.py
index 7ba577e0..86ca7029 100644
--- a/pytorch3d/implicitron/models/implicit_function/neural_radiance_field.py
+++ b/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:
diff --git a/pytorch3d/implicitron/models/implicit_function/scene_representation_networks.py b/pytorch3d/implicitron/models/implicit_function/scene_representation_networks.py
index 8c170776..aef908a2 100644
--- a/pytorch3d/implicitron/models/implicit_function/scene_representation_networks.py
+++ b/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):
diff --git a/pytorch3d/implicitron/models/implicit_function/voxel_grid.py b/pytorch3d/implicitron/models/implicit_function/voxel_grid.py
index 26b64f50..beb3422e 100644
--- a/pytorch3d/implicitron/models/implicit_function/voxel_grid.py
+++ b/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)
diff --git a/pytorch3d/implicitron/models/implicit_function/voxel_grid_implicit_function.py b/pytorch3d/implicitron/models/implicit_function/voxel_grid_implicit_function.py
index ecd84684..e5a13f1d 100644
--- a/pytorch3d/implicitron/models/implicit_function/voxel_grid_implicit_function.py
+++ b/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
 
diff --git a/pytorch3d/implicitron/models/overfit_model.py b/pytorch3d/implicitron/models/overfit_model.py
index 1f6af62b..4f0af7e8 100644
--- a/pytorch3d/implicitron/models/overfit_model.py
+++ b/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)
diff --git a/pytorch3d/implicitron/models/renderer/base.py b/pytorch3d/implicitron/models/renderer/base.py
index d82361b7..4d8a2954 100644
--- a/pytorch3d/implicitron/models/renderer/base.py
+++ b/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
diff --git a/pytorch3d/implicitron/models/renderer/lstm_renderer.py b/pytorch3d/implicitron/models/renderer/lstm_renderer.py
index 02a9c7aa..e99ca051 100644
--- a/pytorch3d/implicitron/models/renderer/lstm_renderer.py
+++ b/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],
diff --git a/pytorch3d/implicitron/models/renderer/multipass_ea.py b/pytorch3d/implicitron/models/renderer/multipass_ea.py
index c6367fc8..859182fa 100644
--- a/pytorch3d/implicitron/models/renderer/multipass_ea.py
+++ b/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
diff --git a/pytorch3d/implicitron/models/renderer/ray_point_refiner.py b/pytorch3d/implicitron/models/renderer/ray_point_refiner.py
index df77eec7..4bf88166 100644
--- a/pytorch3d/implicitron/models/renderer/ray_point_refiner.py
+++ b/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
diff --git a/pytorch3d/implicitron/models/renderer/ray_sampler.py b/pytorch3d/implicitron/models/renderer/ray_sampler.py
index 417d0ae6..ca4bcda2 100644
--- a/pytorch3d/implicitron/models/renderer/ray_sampler.py
+++ b/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)
diff --git a/pytorch3d/implicitron/models/renderer/ray_tracing.py b/pytorch3d/implicitron/models/renderer/ray_tracing.py
index 9ad46a83..678b17d6 100644
--- a/pytorch3d/implicitron/models/renderer/ray_tracing.py
+++ b/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)
diff --git a/pytorch3d/implicitron/models/renderer/sdf_renderer.py b/pytorch3d/implicitron/models/renderer/sdf_renderer.py
index c769b5f5..29cac4b2 100644
--- a/pytorch3d/implicitron/models/renderer/sdf_renderer.py
+++ b/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],
diff --git a/pytorch3d/implicitron/models/view_pooler/feature_aggregator.py b/pytorch3d/implicitron/models/view_pooler/feature_aggregator.py
index 5d600b01..8c471a87 100644
--- a/pytorch3d/implicitron/models/view_pooler/feature_aggregator.py
+++ b/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
diff --git a/pytorch3d/implicitron/models/view_pooler/view_pooler.py b/pytorch3d/implicitron/models/view_pooler/view_pooler.py
index 47f7258a..94ba36d1 100644
--- a/pytorch3d/implicitron/models/view_pooler/view_pooler.py
+++ b/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):
diff --git a/pytorch3d/implicitron/tools/config.py b/pytorch3d/implicitron/tools/config.py
index 821155a2..d99e18e8 100644
--- a/pytorch3d/implicitron/tools/config.py
+++ b/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(
diff --git a/pytorch3d/implicitron/tools/depth_cleanup.py b/pytorch3d/implicitron/tools/depth_cleanup.py
index 76edf963..0af0bd47 100644
--- a/pytorch3d/implicitron/tools/depth_cleanup.py
+++ b/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()
diff --git a/pytorch3d/implicitron/tools/metric_utils.py b/pytorch3d/implicitron/tools/metric_utils.py
index bdb8cfaf..88627b96 100644
--- a/pytorch3d/implicitron/tools/metric_utils.py
+++ b/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
diff --git a/pytorch3d/implicitron/tools/model_io.py b/pytorch3d/implicitron/tools/model_io.py
index d7942d0c..1210848f 100644
--- a/pytorch3d/implicitron/tools/model_io.py
+++ b/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
diff --git a/pytorch3d/implicitron/tools/rasterize_mc.py b/pytorch3d/implicitron/tools/rasterize_mc.py
index ff1fd1e6..8e00911f 100644
--- a/pytorch3d/implicitron/tools/rasterize_mc.py
+++ b/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]
diff --git a/pytorch3d/io/obj_io.py b/pytorch3d/io/obj_io.py
index e1862fc0..6a569a30 100644
--- a/pytorch3d/io/obj_io.py
+++ b/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:
diff --git a/pytorch3d/ops/cameras_alignment.py b/pytorch3d/ops/cameras_alignment.py
index 5986206f..0f2bbbe6 100644
--- a/pytorch3d/ops/cameras_alignment.py
+++ b/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)
diff --git a/pytorch3d/ops/laplacian_matrices.py b/pytorch3d/ops/laplacian_matrices.py
index a2c2997b..dfbe215e 100644
--- a/pytorch3d/ops/laplacian_matrices.py
+++ b/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)
diff --git a/pytorch3d/ops/points_normals.py b/pytorch3d/ops/points_normals.py
index baa21c2c..56168c2d 100644
--- a/pytorch3d/ops/points_normals.py
+++ b/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]
diff --git a/pytorch3d/ops/sample_farthest_points.py b/pytorch3d/ops/sample_farthest_points.py
index ddcb75ad..1467bfd0 100644
--- a/pytorch3d/ops/sample_farthest_points.py
+++ b/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])
 
diff --git a/pytorch3d/ops/utils.py b/pytorch3d/ops/utils.py
index 317b9ede..78786b51 100644
--- a/pytorch3d/ops/utils.py
+++ b/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
diff --git a/pytorch3d/renderer/blending.py b/pytorch3d/renderer/blending.py
index ba5793dc..cb86d0b6 100644
--- a/pytorch3d/renderer/blending.py
+++ b/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.
diff --git a/pytorch3d/renderer/camera_conversions.py b/pytorch3d/renderer/camera_conversions.py
index 6c5b2cbc..56d34901 100644
--- a/pytorch3d/renderer/camera_conversions.py
+++ b/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
diff --git a/pytorch3d/renderer/cameras.py b/pytorch3d/renderer/cameras.py
index 821353f9..16e7a496 100644
--- a/pytorch3d/renderer/cameras.py
+++ b/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
         )
diff --git a/pytorch3d/renderer/implicit/harmonic_embedding.py b/pytorch3d/renderer/implicit/harmonic_embedding.py
index ee44f237..47382a0c 100644
--- a/pytorch3d/renderer/implicit/harmonic_embedding.py
+++ b/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),
diff --git a/pytorch3d/renderer/implicit/raysampling.py b/pytorch3d/renderer/implicit/raysampling.py
index 222e0249..ec580d84 100644
--- a/pytorch3d/renderer/implicit/raysampling.py
+++ b/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
diff --git a/pytorch3d/renderer/implicit/renderer.py b/pytorch3d/renderer/implicit/renderer.py
index 963f7d07..f2b00baf 100644
--- a/pytorch3d/renderer/implicit/renderer.py
+++ b/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
         )
diff --git a/pytorch3d/renderer/mesh/clip.py b/pytorch3d/renderer/mesh/clip.py
index 27b23b0f..b0e41cb5 100644
--- a/pytorch3d/renderer/mesh/clip.py
+++ b/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]
diff --git a/pytorch3d/renderer/mesh/textures.py b/pytorch3d/renderer/mesh/textures.py
index df81c7c9..bac350ea 100644
--- a/pytorch3d/renderer/mesh/textures.py
+++ b/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,
diff --git a/pytorch3d/renderer/opengl/rasterizer_opengl.py b/pytorch3d/renderer/opengl/rasterizer_opengl.py
index 0d7ad9a4..8c896216 100644
--- a/pytorch3d/renderer/opengl/rasterizer_opengl.py
+++ b/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,
diff --git a/pytorch3d/structures/meshes.py b/pytorch3d/structures/meshes.py
index 85a1feec..07acece0 100644
--- a/pytorch3d/structures/meshes.py
+++ b/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(),
diff --git a/pytorch3d/vis/plotly_vis.py b/pytorch3d/vis/plotly_vis.py
index b81e2404..762dadd0 100644
--- a/pytorch3d/vis/plotly_vis.py
+++ b/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"]
diff --git a/pytorch3d/vis/texture_vis.py b/pytorch3d/vis/texture_vis.py
index 9f044b2c..fdad1d0d 100644
--- a/pytorch3d/vis/texture_vis.py
+++ b/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))