upgrade pyre version in fbcode/vision - batch 2

Reviewed By: bottler

Differential Revision: D60992234

fbshipit-source-id: 899db6ed590ef966ff651c11027819e59b8401a3

projects/implicitron_trainer/experiment.py

@@ -99,7 +99,7 @@ except ModuleNotFoundError:
 no_accelerate = os.environ.get("PYTORCH3D_NO_ACCELERATE") is not None
 
 
-class Experiment(Configurable):  # pyre-ignore: 13
+class Experiment(Configurable):
     """
     This class is at the top level of Implicitron's config hierarchy. Its
     members are high-level components necessary for training an implicit rende-
@@ -120,12 +120,16 @@ class Experiment(Configurable):  # pyre-ignore: 13
             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

@@ -45,7 +45,7 @@ class ModelFactoryBase(ReplaceableBase):
 
 
 @registry.register
-class ImplicitronModelFactory(ModelFactoryBase):  # pyre-ignore [13]
+class ImplicitronModelFactory(ModelFactoryBase):
     """
     A factory class that initializes an implicit rendering model.
 
@@ -61,6 +61,7 @@ class ImplicitronModelFactory(ModelFactoryBase):  # pyre-ignore [13]
 
     """
 
+    # pyre-fixme[13]: Attribute `model` is never initialized.
     model: ImplicitronModelBase
     model_class_type: str = "GenericModel"
     resume: bool = True

projects/implicitron_trainer/impl/training_loop.py

@@ -30,13 +30,13 @@ from .utils import seed_all_random_engines
 logger = logging.getLogger(__name__)
 
 
-# pyre-fixme[13]: Attribute `evaluator` is never initialized.
 class TrainingLoopBase(ReplaceableBase):
     """
     Members:
         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"
 

pytorch3d/implicitron/dataset/data_source.py

@@ -41,7 +41,7 @@ class DataSourceBase(ReplaceableBase):
 
 
 @registry.register
-class ImplicitronDataSource(DataSourceBase):  # pyre-ignore[13]
+class ImplicitronDataSource(DataSourceBase):
     """
     Represents the data used in Implicitron. This is the only implementation
     of DataSourceBase provided.
@@ -52,8 +52,11 @@ class ImplicitronDataSource(DataSourceBase):  # pyre-ignore[13]
         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"
 

pytorch3d/implicitron/dataset/json_index_dataset_map_provider.py

@@ -66,7 +66,7 @@ _NEED_CONTROL: Tuple[str, ...] = (
 
 
 @registry.register
-class JsonIndexDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
+class JsonIndexDatasetMapProvider(DatasetMapProviderBase):
     """
     Generates the training / validation and testing dataset objects for
     a dataset laid out on disk like Co3D, with annotations in json files.
@@ -95,6 +95,7 @@ class JsonIndexDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
         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
@@ -104,8 +105,10 @@ class JsonIndexDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
     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

@@ -56,7 +56,7 @@ logger = logging.getLogger(__name__)
 
 
 @registry.register
-class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
+class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):
     """
     Generates the training, validation, and testing dataset objects for
     a dataset laid out on disk like CO3Dv2, with annotations in gzipped json files.
@@ -171,7 +171,9 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
         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
 
@@ -183,8 +185,10 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
     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

@@ -32,7 +32,7 @@ from .utils import DATASET_TYPE_KNOWN
 
 
 @registry.register
-class RenderedMeshDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
+class RenderedMeshDatasetMapProvider(DatasetMapProviderBase):
     """
     A simple single-scene dataset based on PyTorch3D renders of a mesh.
     Provides `num_views` renders of the mesh as train, with no val
@@ -76,6 +76,7 @@ class RenderedMeshDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13
     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"
 

pytorch3d/implicitron/dataset/single_sequence_dataset.py

@@ -83,7 +83,6 @@ class SingleSceneDataset(DatasetBase, Configurable):
         return self.eval_batches
 
 
-# pyre-fixme[13]: Uninitialized attribute
 class SingleSceneDatasetMapProviderBase(DatasetMapProviderBase):
     """
     Base for provider of data for one scene from LLFF or blender datasets.
@@ -100,8 +99,11 @@ 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

pytorch3d/implicitron/dataset/utils.py

@@ -348,6 +348,7 @@ def adjust_camera_to_image_scale_(
     camera: PerspectiveCameras,
     original_size_wh: torch.Tensor,
     new_size_wh: torch.LongTensor,
+    # pyre-fixme[7]: Expected `PerspectiveCameras` but got implicit return value of `None`.
 ) -> PerspectiveCameras:
     focal_length_px, principal_point_px = _convert_ndc_to_pixels(
         camera.focal_length[0],
@@ -367,7 +368,7 @@ def adjust_camera_to_image_scale_(
         image_size_wh_output,
     )
     camera.focal_length = focal_length_scaled[None]
-    camera.principal_point = principal_point_scaled[None]  # pyre-ignore
+    camera.principal_point = principal_point_scaled[None]
 
 
 # NOTE this cache is per-worker; they are implemented as processes.

pytorch3d/implicitron/models/generic_model.py

@@ -65,7 +65,7 @@ logger = logging.getLogger(__name__)
 
 
 @registry.register
-class GenericModel(ImplicitronModelBase):  # pyre-ignore: 13
+class GenericModel(ImplicitronModelBase):
     """
     GenericModel is a wrapper for the neural implicit
     rendering and reconstruction pipeline which consists
@@ -226,34 +226,42 @@ class GenericModel(ImplicitronModelBase):  # pyre-ignore: 13
 
     # ---- 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"
 

pytorch3d/implicitron/models/global_encoder/global_encoder.py

@@ -59,12 +59,13 @@ class GlobalEncoderBase(ReplaceableBase):
 
 # TODO: probabilistic embeddings?
 @registry.register
-class SequenceAutodecoder(GlobalEncoderBase, torch.nn.Module):  # pyre-ignore: 13
+class SequenceAutodecoder(GlobalEncoderBase, torch.nn.Module):
     """
     A global encoder implementation which provides an autodecoder encoding
     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

@@ -244,7 +244,6 @@ class MLPWithInputSkips(Configurable, torch.nn.Module):
 
 
 @registry.register
-# pyre-fixme[13]: Attribute `network` is never initialized.
 class MLPDecoder(DecoderFunctionBase):
     """
     Decoding function which uses `MLPWithIputSkips` to convert the embedding to output.
@@ -272,6 +271,7 @@ 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):

pytorch3d/implicitron/models/implicit_function/scene_representation_networks.py

@@ -318,10 +318,11 @@ class SRNRaymarchHyperNet(Configurable, torch.nn.Module):
 
 
 @registry.register
-# pyre-fixme[13]: Uninitialized attribute
 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):
@@ -366,7 +367,6 @@ class SRNImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
 
 
 @registry.register
-# pyre-fixme[13]: Uninitialized attribute
 class SRNHyperNetImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
     """
     This implicit function uses a hypernetwork to generate the
@@ -377,7 +377,9 @@ 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

@@ -805,7 +805,6 @@ class VMFactorizedVoxelGrid(VoxelGridBase):
         )
 
 
-# pyre-fixme[13]: Attribute `voxel_grid` is never initialized.
 class VoxelGridModule(Configurable, torch.nn.Module):
     """
     A wrapper torch.nn.Module for the VoxelGrid classes, which
@@ -845,6 +844,7 @@ 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)

pytorch3d/implicitron/models/implicit_function/voxel_grid_implicit_function.py

@@ -39,7 +39,6 @@ enable_get_default_args(HarmonicEmbedding)
 
 
 @registry.register
-# pyre-ignore[13]
 class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
     """
     This implicit function consists of two streams, one for the density calculation and one
@@ -145,9 +144,11 @@ 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
@@ -163,10 +164,12 @@ 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

pytorch3d/implicitron/models/overfit_model.py

@@ -69,7 +69,7 @@ IMPLICIT_FUNCTION_ARGS_TO_REMOVE: List[str] = [
 
 
 @registry.register
-class OverfitModel(ImplicitronModelBase):  # pyre-ignore: 13
+class OverfitModel(ImplicitronModelBase):
     """
     OverfitModel is a wrapper for the neural implicit
     rendering and reconstruction pipeline which consists
@@ -198,27 +198,34 @@ class OverfitModel(ImplicitronModelBase):  # pyre-ignore: 13
 
     # ---- 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"
 

pytorch3d/implicitron/models/renderer/multipass_ea.py

@@ -18,9 +18,7 @@ from .raymarcher import RaymarcherBase
 
 
 @registry.register
-class MultiPassEmissionAbsorptionRenderer(  # pyre-ignore: 13
-    BaseRenderer, torch.nn.Module
-):
+class MultiPassEmissionAbsorptionRenderer(BaseRenderer, torch.nn.Module):
     """
     Implements the multi-pass rendering function, in particular,
     with emission-absorption ray marching used in NeRF [1]. First, it evaluates
@@ -86,6 +84,7 @@ class MultiPassEmissionAbsorptionRenderer(  # pyre-ignore: 13
     """
 
     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

@@ -16,8 +16,6 @@ from pytorch3d.renderer.implicit.sample_pdf import sample_pdf
 
 
 @expand_args_fields
-# pyre-fixme[13]: Attribute `n_pts_per_ray` is never initialized.
-# pyre-fixme[13]: Attribute `random_sampling` is never initialized.
 class RayPointRefiner(Configurable, torch.nn.Module):
     """
     Implements the importance sampling of points along rays.
@@ -45,7 +43,9 @@ 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/sdf_renderer.py

@@ -24,9 +24,10 @@ from .rgb_net import RayNormalColoringNetwork
 
 
 @registry.register
-class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module):  # pyre-ignore[13]
+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

pytorch3d/implicitron/models/view_pooler/view_pooler.py

@@ -16,7 +16,6 @@ from .feature_aggregator import FeatureAggregatorBase
 from .view_sampler import ViewSampler
 
 
-# pyre-ignore: 13
 class ViewPooler(Configurable, torch.nn.Module):
     """
     Implements sampling of image-based features at the 2d projections of a set
@@ -35,8 +34,10 @@ 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/point_cloud_utils.py

@@ -156,7 +156,6 @@ def render_point_cloud_pytorch3d(
     cumprod = torch.cat((torch.ones_like(cumprod[..., :1]), cumprod[..., :-1]), dim=-1)
     depths = (weights * cumprod * fragments.zbuf).sum(dim=-1)
     # add the rendering mask
-    # pyre-fixme[6]: For 1st param expected `Tensor` but got `float`.
     render_mask = -torch.prod(1.0 - weights, dim=-1) + 1.0
 
     # cat depths and render mask

pytorch3d/io/mtl_io.py

@@ -409,6 +409,7 @@ def _parse_mtl(
     texture_files = {}
     material_name = ""
 
+    # pyre-fixme[9]: f has type `str`; used as `IO[typing.Any]`.
     with _open_file(f, path_manager, "r") as f:
         for line in f:
             tokens = line.strip().split()

pytorch3d/io/obj_io.py

@@ -756,10 +756,13 @@ def save_obj(
     output_path = Path(f)
 
     # Save the .obj file
+    # pyre-fixme[9]: f has type `Union[Path, str]`; used as `IO[typing.Any]`.
     with _open_file(f, path_manager, "w") as f:
         if save_texture:
             # Add the header required for the texture info to be loaded correctly
             obj_header = "\nmtllib {0}.mtl\nusemtl mesh\n\n".format(output_path.stem)
+            # pyre-fixme[16]: Item `Path` of `Union[Path, str]` has no attribute
+            #  `write`.
             f.write(obj_header)
         _save(
             f,

pytorch3d/ops/points_to_volumes.py

@@ -617,6 +617,7 @@ def _splat_points_to_volumes(
                 w = wX * wY * wZ
 
                 # valid - binary indicators of votes that fall into the volume
+                # pyre-fixme[16]: `int` has no attribute `long`.
                 valid = (
                     (0 <= X_)
                     * (X_ < grid_sizes_xyz[:, None, 0:1])
@@ -635,14 +636,19 @@ def _splat_points_to_volumes(
                 idx_valid = idx * valid + rand_idx * (1 - valid)
                 w_valid = w * valid.type_as(w)
                 if mask is not None:
+                    # pyre-fixme[6]: For 1st argument expected `Tensor` but got `int`.
                     w_valid = w_valid * mask.type_as(w)[:, :, None]
 
                 # scatter add casts the votes into the weight accumulator
                 # and the feature accumulator
+                # pyre-fixme[6]: For 3rd argument expected `Tensor` but got
+                #  `Union[int, Tensor]`.
                 volume_densities.scatter_add_(1, idx_valid, w_valid)
 
                 # reshape idx_valid -> (minibatch, feature_dim, n_points)
                 idx_valid = idx_valid.view(ba, 1, n_points).expand_as(points_features)
+                # pyre-fixme[16]: Item `int` of `Union[int, Tensor]` has no
+                #  attribute `view`.
                 w_valid = w_valid.view(ba, 1, n_points)
 
                 # volume_features of shape (minibatch, feature_dim, n_voxels)
@@ -724,6 +730,7 @@ def _round_points_to_volumes(
     # valid - binary indicators of votes that fall into the volume
     # pyre-fixme[9]: grid_sizes has type `LongTensor`; used as `Tensor`.
     grid_sizes = grid_sizes.type_as(XYZ)
+    # pyre-fixme[16]: `int` has no attribute `long`.
     valid = (
         (0 <= X)
         * (X < grid_sizes_xyz[:, None, 0:1])

pytorch3d/renderer/mesh/clip.py

@@ -497,6 +497,7 @@ def clip_faces(
     faces_case3 = face_verts_unclipped[case3_unclipped_idx]
 
     # index (0, 1, or 2) of the vertex in front of the clipping plane
+    # pyre-fixme[61]: `faces_clipped_verts` is undefined, or not always defined.
     p1_face_ind = torch.where(~faces_clipped_verts[case3_unclipped_idx])[1]
 
     # Solve for the points p4, p5 that intersect the clipping plane
@@ -540,6 +541,7 @@ 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

pytorch3d/vis/plotly_vis.py

@@ -369,6 +369,7 @@ def plot_scene(
         # update camera viewpoint if provided
         if viewpoints_eye_at_up_world is not None:
             # Use camera params for batch index or the first camera if only one provided.
+            # pyre-fixme[61]: `n_viewpoint_cameras` is undefined, or not always defined.
             viewpoint_idx = min(n_viewpoint_cameras - 1, subplot_idx)
 
             eye, at, up = (i[viewpoint_idx] for i in viewpoints_eye_at_up_world)
@@ -627,7 +628,7 @@ def _add_struct_from_batch(
 
 
 def _add_mesh_trace(
-    fig: go.Figure,  # pyre-ignore[11]
+    fig: go.Figure,
     meshes: Meshes,
     trace_name: str,
     subplot_idx: int,
@@ -673,6 +674,7 @@ def _add_mesh_trace(
     verts[~verts_used] = verts_center
 
     row, col = subplot_idx // ncols + 1, subplot_idx % ncols + 1
+    # pyre-fixme[16]: `Figure` has no attribute `add_trace`.
     fig.add_trace(
         go.Mesh3d(
             x=verts[:, 0],
@@ -739,6 +741,7 @@ def _add_pointcloud_trace(
 
     row = subplot_idx // ncols + 1
     col = subplot_idx % ncols + 1
+    # pyre-fixme[16]: `Figure` has no attribute `add_trace`.
     fig.add_trace(
         go.Scatter3d(
             x=verts[:, 0],
@@ -800,6 +803,7 @@ def _add_camera_trace(
     x, y, z = all_cam_wires.detach().cpu().numpy().T.astype(float)
 
     row, col = subplot_idx // ncols + 1, subplot_idx % ncols + 1
+    # pyre-fixme[16]: `Figure` has no attribute `add_trace`.
     fig.add_trace(
         go.Scatter3d(x=x, y=y, z=z, marker={"size": 1}, name=trace_name),
         row=row,
@@ -894,6 +898,7 @@ def _add_ray_bundle_trace(
         ray_lines = torch.cat((ray_lines, nan_tensor, ray_line))
     x, y, z = ray_lines.detach().cpu().numpy().T.astype(float)
     row, col = subplot_idx // ncols + 1, subplot_idx % ncols + 1
+    # pyre-fixme[16]: `Figure` has no attribute `add_trace`.
     fig.add_trace(
         go.Scatter3d(
             x=x,
@@ -988,7 +993,7 @@ def _gen_fig_with_subplots(
 def _update_axes_bounds(
     verts_center: torch.Tensor,
     max_expand: float,
-    current_layout: go.Scene,  # pyre-ignore[11]
+    current_layout: go.Scene,
 ) -> None:  # pragma: no cover
     """
     Takes in the vertices' center point and max spread, and the current plotly figure
@@ -1005,6 +1010,7 @@ def _update_axes_bounds(
 
     # Ensure that within a subplot, the bounds capture all traces
     old_xrange, old_yrange, old_zrange = (
+        # pyre-fixme[16]: `Scene` has no attribute `__getitem__`.
         current_layout["xaxis"]["range"],
         current_layout["yaxis"]["range"],
         current_layout["zaxis"]["range"],
@@ -1023,6 +1029,7 @@ def _update_axes_bounds(
     xaxis = {"range": x_range}
     yaxis = {"range": y_range}
     zaxis = {"range": z_range}
+    # pyre-fixme[16]: `Scene` has no attribute `update`.
     current_layout.update({"xaxis": xaxis, "yaxis": yaxis, "zaxis": zaxis})