More type annotations

Summary: More type annotations: device, shaders, pluggable I/O, stats in NeRF project, cameras, textures, etc...

Reviewed By: nikhilaravi

Differential Revision: D29327396

fbshipit-source-id: cdf0ceaaa010e22423088752688c8dd81f1acc3c

projects/nerf/nerf/dataset.py

@@ -136,7 +136,7 @@ def download_data(
     dataset_names: Optional[List[str]] = None,
     data_root: str = DEFAULT_DATA_ROOT,
     url_root: str = DEFAULT_URL_ROOT,
-):
+) -> None:
     """
     Downloads the relevant dataset files.
 

projects/nerf/nerf/stats.py

@@ -29,7 +29,7 @@ class AverageMeter:
         self.history = []
         self.reset()
 
-    def reset(self):
+    def reset(self) -> None:
         """
         Reset the running average meter.
         """
@@ -38,7 +38,7 @@ class AverageMeter:
         self.sum = 0
         self.count = 0
 
-    def update(self, val: float, n: int = 1, epoch: int = 0):
+    def update(self, val: float, n: int = 1, epoch: int = 0) -> None:
         """
         Updates the average meter with a value `val`.
 
@@ -123,7 +123,7 @@ class Stats:
         self.plot_file = plot_file
         self.hard_reset(epoch=epoch)
 
-    def reset(self):
+    def reset(self) -> None:
         """
         Called before an epoch to clear current epoch buffers.
         """
@@ -138,7 +138,7 @@ class Stats:
         # Set a new timestamp.
         self._epoch_start = time.time()
 
-    def hard_reset(self, epoch: int = -1):
+    def hard_reset(self, epoch: int = -1) -> None:
         """
         Erases all logged data.
         """
@@ -149,7 +149,7 @@ class Stats:
         self.stats = {}
         self.reset()
 
-    def new_epoch(self):
+    def new_epoch(self) -> None:
         """
         Initializes a new epoch.
         """
@@ -166,7 +166,7 @@ class Stats:
             val = float(val.sum())
         return val
 
-    def update(self, preds: dict, stat_set: str = "train"):
+    def update(self, preds: dict, stat_set: str = "train") -> None:
         """
         Update the internal logs with metrics of a training step.
 
@@ -211,7 +211,7 @@ class Stats:
             if val is not None:
                 self.stats[stat_set][stat].update(val, epoch=epoch, n=1)
 
-    def print(self, max_it: Optional[int] = None, stat_set: str = "train"):
+    def print(self, max_it: Optional[int] = None, stat_set: str = "train") -> None:
         """
         Print the current values of all stored stats.
 
@@ -247,7 +247,7 @@ class Stats:
         viz: Visdom = None,
         visdom_env: Optional[str] = None,
         plot_file: Optional[str] = None,
-    ):
+    ) -> None:
         """
         Plot the line charts of the history of the stats.
 

pytorch3d/io/experimental_gltf_io.py

@@ -42,14 +42,13 @@ from base64 import b64decode
 from collections import deque
 from enum import IntEnum
 from io import BytesIO
-from pathlib import Path
 from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union, cast
 
 import numpy as np
 import torch
 from iopath.common.file_io import PathManager
 from PIL import Image
-from pytorch3d.io.utils import _open_file
+from pytorch3d.io.utils import PathOrStr, _open_file
 from pytorch3d.renderer.mesh import TexturesBase, TexturesUV, TexturesVertex
 from pytorch3d.structures import Meshes, join_meshes_as_scene
 from pytorch3d.transforms import Transform3d, quaternion_to_matrix
@@ -498,7 +497,7 @@ class _GLTFLoader:
 
 
 def load_meshes(
-    path: Union[str, Path],
+    path: PathOrStr,
     path_manager: PathManager,
     include_textures: bool = True,
 ) -> List[Tuple[Optional[str], Meshes]]:
@@ -544,7 +543,7 @@ class MeshGlbFormat(MeshFormatInterpreter):
 
     def read(
         self,
-        path: Union[str, Path],
+        path: PathOrStr,
         include_textures: bool,
         device,
         path_manager: PathManager,
@@ -566,7 +565,7 @@ class MeshGlbFormat(MeshFormatInterpreter):
     def save(
         self,
         data: Meshes,
-        path: Union[str, Path],
+        path: PathOrStr,
         path_manager: PathManager,
         binary: Optional[bool],
         **kwargs,

pytorch3d/io/obj_io.py

@@ -18,7 +18,12 @@ from iopath.common.file_io import PathManager
 from PIL import Image
 from pytorch3d.common.types import Device
 from pytorch3d.io.mtl_io import load_mtl, make_mesh_texture_atlas
-from pytorch3d.io.utils import _check_faces_indices, _make_tensor, _open_file
+from pytorch3d.io.utils import (
+    PathOrStr,
+    _check_faces_indices,
+    _make_tensor,
+    _open_file,
+)
 from pytorch3d.renderer import TexturesAtlas, TexturesUV
 from pytorch3d.structures import Meshes, join_meshes_as_batch
 
@@ -213,7 +218,7 @@ def load_obj(
               None.
     """
     data_dir = "./"
-    if isinstance(f, (str, bytes, os.PathLike)):
+    if isinstance(f, (str, bytes, Path)):
         data_dir = os.path.dirname(f)
     if path_manager is None:
         path_manager = PathManager()
@@ -297,7 +302,7 @@ class MeshObjFormat(MeshFormatInterpreter):
 
     def read(
         self,
-        path: Union[str, Path],
+        path: PathOrStr,
         include_textures: bool,
         device: Device,
         path_manager: PathManager,
@@ -322,7 +327,7 @@ class MeshObjFormat(MeshFormatInterpreter):
     def save(
         self,
         data: Meshes,
-        path: Union[str, Path],
+        path: PathOrStr,
         path_manager: PathManager,
         binary: Optional[bool],
         decimal_places: Optional[int] = None,
@@ -650,7 +655,7 @@ def _load_obj(
 
 
 def save_obj(
-    f: Union[str, os.PathLike],
+    f: PathOrStr,
     verts,
     faces,
     decimal_places: Optional[int] = None,

pytorch3d/io/off_io.py

@@ -13,13 +13,12 @@ This format is introduced, for example, at
 http://www.geomview.org/docs/html/OFF.html .
 """
 import warnings
-from pathlib import Path
 from typing import Optional, Tuple, Union, cast
 
 import numpy as np
 import torch
 from iopath.common.file_io import PathManager
-from pytorch3d.io.utils import _check_faces_indices, _open_file
+from pytorch3d.io.utils import PathOrStr, _check_faces_indices, _open_file
 from pytorch3d.renderer import TexturesAtlas, TexturesVertex
 from pytorch3d.structures import Meshes
 
@@ -424,7 +423,7 @@ class MeshOffFormat(MeshFormatInterpreter):
 
     def read(
         self,
-        path: Union[str, Path],
+        path: PathOrStr,
         include_textures: bool,
         device,
         path_manager: PathManager,
@@ -460,7 +459,7 @@ class MeshOffFormat(MeshFormatInterpreter):
     def save(
         self,
         data: Meshes,
-        path: Union[str, Path],
+        path: PathOrStr,
         path_manager: PathManager,
         binary: Optional[bool],
         decimal_places: Optional[int] = None,

pytorch3d/io/pluggable_formats.py

@@ -5,10 +5,12 @@
 # LICENSE file in the root directory of this source tree.
 
 
-from pathlib import Path
+import pathlib
 from typing import Optional, Tuple, Union
 
 from iopath.common.file_io import PathManager
+from pytorch3d.common.types import Device
+from pytorch3d.io.utils import PathOrStr
 from pytorch3d.structures import Meshes, Pointclouds
 
 
@@ -20,14 +22,14 @@ its load_* and save_* functions.
 """
 
 
-def endswith(path, suffixes: Tuple[str, ...]) -> bool:
+def endswith(path: PathOrStr, suffixes: Tuple[str, ...]) -> bool:
     """
     Returns whether the path ends with one of the given suffixes.
     If `path` is not actually a path, returns True. This is useful
     for allowing interpreters to bypass inappropriate paths, but
     always accepting streams.
     """
-    if isinstance(path, Path):
+    if isinstance(path, pathlib.Path):
         return path.suffix.lower() in suffixes
     if isinstance(path, str):
         return path.lower().endswith(suffixes)
@@ -42,9 +44,9 @@ class MeshFormatInterpreter:
 
     def read(
         self,
-        path: Union[str, Path],
+        path: PathOrStr,
         include_textures: bool,
-        device,
+        device: Device,
         path_manager: PathManager,
         **kwargs,
     ) -> Optional[Meshes]:
@@ -68,7 +70,7 @@ class MeshFormatInterpreter:
     def save(
         self,
         data: Meshes,
-        path: Union[str, Path],
+        path: PathOrStr,
         path_manager: PathManager,
         binary: Optional[bool],
         **kwargs,
@@ -96,7 +98,7 @@ class PointcloudFormatInterpreter:
     """
 
     def read(
-        self, path: Union[str, Path], device, path_manager: PathManager, **kwargs
+        self, path: PathOrStr, device: Device, path_manager: PathManager, **kwargs
     ) -> Optional[Pointclouds]:
         """
         Read the data from the specified file and return it as
@@ -117,7 +119,7 @@ class PointcloudFormatInterpreter:
     def save(
         self,
         data: Pointclouds,
-        path: Union[str, Path],
+        path: PathOrStr,
         path_manager: PathManager,
         binary: Optional[bool],
         **kwargs,

pytorch3d/io/ply_io.py

@@ -15,13 +15,17 @@ import sys
 import warnings
 from collections import namedtuple
 from io import BytesIO, TextIOBase
-from pathlib import Path
 from typing import List, Optional, Tuple, Union, cast
 
 import numpy as np
 import torch
 from iopath.common.file_io import PathManager
-from pytorch3d.io.utils import _check_faces_indices, _make_tensor, _open_file
+from pytorch3d.io.utils import (
+    PathOrStr,
+    _check_faces_indices,
+    _make_tensor,
+    _open_file,
+)
 from pytorch3d.renderer import TexturesVertex
 from pytorch3d.structures import Meshes, Pointclouds
 
@@ -1237,7 +1241,7 @@ class MeshPlyFormat(MeshFormatInterpreter):
 
     def read(
         self,
-        path: Union[str, Path],
+        path: PathOrStr,
         include_textures: bool,
         device,
         path_manager: PathManager,
@@ -1269,7 +1273,7 @@ class MeshPlyFormat(MeshFormatInterpreter):
     def save(
         self,
         data: Meshes,
-        path: Union[str, Path],
+        path: PathOrStr,
         path_manager: PathManager,
         binary: Optional[bool],
         decimal_places: Optional[int] = None,
@@ -1318,7 +1322,7 @@ class PointcloudPlyFormat(PointcloudFormatInterpreter):
 
     def read(
         self,
-        path: Union[str, Path],
+        path: PathOrStr,
         device,
         path_manager: PathManager,
         **kwargs,
@@ -1339,7 +1343,7 @@ class PointcloudPlyFormat(PointcloudFormatInterpreter):
     def save(
         self,
         data: Pointclouds,
-        path: Union[str, Path],
+        path: PathOrStr,
         path_manager: PathManager,
         binary: Optional[bool],
         decimal_places: Optional[int] = None,

pytorch3d/io/utils.py

@@ -7,7 +7,7 @@
 import contextlib
 import pathlib
 import warnings
-from typing import IO, ContextManager, Optional
+from typing import IO, ContextManager, Optional, Union
 
 import numpy as np
 import torch
@@ -25,6 +25,9 @@ def nullcontext(x):
     yield x
 
 
+PathOrStr = Union[pathlib.Path, str]
+
+
 def _open_file(f, path_manager: PathManager, mode="r") -> ContextManager[IO]:
     if isinstance(f, str):
         f = path_manager.open(f, mode)

pytorch3d/renderer/blending.py

@@ -20,10 +20,12 @@ from pytorch3d import _C
 class BlendParams(NamedTuple):
     sigma: float = 1e-4
     gamma: float = 1e-4
-    background_color: Sequence = (1.0, 1.0, 1.0)
+    background_color: Union[torch.Tensor, Sequence[float]] = (1.0, 1.0, 1.0)
 
 
-def hard_rgb_blend(colors, fragments, blend_params) -> torch.Tensor:
+def hard_rgb_blend(
+    colors: torch.Tensor, fragments, blend_params: BlendParams
+) -> torch.Tensor:
     """
     Naive blending of top K faces to return an RGBA image
       - **RGB** - choose color of the closest point i.e. K=0
@@ -47,10 +49,11 @@ def hard_rgb_blend(colors, fragments, blend_params) -> torch.Tensor:
     # Mask for the background.
     is_background = fragments.pix_to_face[..., 0] < 0  # (N, H, W)
 
-    if torch.is_tensor(blend_params.background_color):
+    background_color_ = blend_params.background_color
-        background_color = blend_params.background_color.to(device)
+    if isinstance(background_color_, torch.Tensor):
+        background_color = background_color_.to(device)
     else:
-        background_color = colors.new_tensor(blend_params.background_color)  # (3)
+        background_color = colors.new_tensor(background_color_)  # pyre-fixme[16]
 
     # Find out how much background_color needs to be expanded to be used for masked_scatter.
     num_background_pixels = is_background.sum()
@@ -90,7 +93,7 @@ class _SigmoidAlphaBlend(torch.autograd.Function):
 _sigmoid_alpha = _SigmoidAlphaBlend.apply
 
 
-def sigmoid_alpha_blend(colors, fragments, blend_params) -> torch.Tensor:
+def sigmoid_alpha_blend(colors, fragments, blend_params: BlendParams) -> torch.Tensor:
     """
     Silhouette blending to return an RGBA image
       - **RGB** - choose color of the closest point.
@@ -121,9 +124,9 @@ def sigmoid_alpha_blend(colors, fragments, blend_params) -> torch.Tensor:
 
 
 def softmax_rgb_blend(
-    colors,
+    colors: torch.Tensor,
     fragments,
-    blend_params,
+    blend_params: BlendParams,
     znear: Union[float, torch.Tensor] = 1.0,
     zfar: Union[float, torch.Tensor] = 100,
 ) -> torch.Tensor:
@@ -167,11 +170,11 @@ def softmax_rgb_blend(
     N, H, W, K = fragments.pix_to_face.shape
     device = fragments.pix_to_face.device
     pixel_colors = torch.ones((N, H, W, 4), dtype=colors.dtype, device=colors.device)
-    background = blend_params.background_color
+    background_ = blend_params.background_color
-    if not torch.is_tensor(background):
+    if not isinstance(background_, torch.Tensor):
-        background = torch.tensor(background, dtype=torch.float32, device=device)
+        background = torch.tensor(background_, dtype=torch.float32, device=device)
     else:
-        background = background.to(device)
+        background = background_.to(device)
 
     # Weight for background color
     eps = 1e-10

pytorch3d/renderer/cameras.py

@@ -172,9 +172,11 @@ class CamerasBase(TensorProperties):
             A Transform3d object which represents a batch of transforms
             of shape (N, 3, 3)
         """
-        self.R = kwargs.get("R", self.R)  # pyre-ignore[16]
+        R: torch.Tensor = kwargs.get("R", self.R)
-        self.T = kwargs.get("T", self.T)  # pyre-ignore[16]
+        T: torch.Tensor = kwargs.get("T", self.T)
-        world_to_view_transform = get_world_to_view_transform(R=self.R, T=self.T)
+        self.R = R  # pyre-ignore[16]
+        self.T = T  # pyre-ignore[16]
+        world_to_view_transform = get_world_to_view_transform(R=R, T=T)
         return world_to_view_transform
 
     def get_full_projection_transform(self, **kwargs) -> Transform3d:
@@ -195,8 +197,8 @@ class CamerasBase(TensorProperties):
             a Transform3d object which represents a batch of transforms
             of shape (N, 3, 3)
         """
-        self.R = kwargs.get("R", self.R)  # pyre-ignore[16]
+        self.R: torch.Tensor = kwargs.get("R", self.R)  # pyre-ignore[16]
-        self.T = kwargs.get("T", self.T)  # pyre-ignore[16]
+        self.T: torch.Tensor = kwargs.get("T", self.T)  # pyre-ignore[16]
         world_to_view_transform = self.get_world_to_view_transform(R=self.R, T=self.T)
         view_to_ndc_transform = self.get_projection_transform(**kwargs)
         return world_to_view_transform.compose(view_to_ndc_transform)
@@ -293,10 +295,10 @@ def OpenGLPerspectiveCameras(
     aspect_ratio=1.0,
     fov=60.0,
     degrees: bool = True,
-    R=_R,
+    R: torch.Tensor = _R,
-    T=_T,
+    T: torch.Tensor = _T,
     device: Device = "cpu",
-):
+) -> "FoVPerspectiveCameras":
     """
     OpenGLPerspectiveCameras has been DEPRECATED. Use FoVPerspectiveCameras instead.
     Preserving OpenGLPerspectiveCameras for backward compatibility.
@@ -360,9 +362,9 @@ class FoVPerspectiveCameras(CamerasBase):
         aspect_ratio=1.0,
         fov=60.0,
         degrees: bool = True,
-        R=_R,
+        R: torch.Tensor = _R,
-        T=_T,
+        T: torch.Tensor = _T,
-        K=None,
+        K: Optional[torch.Tensor] = None,
         device: Device = "cpu",
     ) -> None:
         """
@@ -397,7 +399,7 @@ class FoVPerspectiveCameras(CamerasBase):
         self.degrees = degrees
 
     def compute_projection_matrix(
-        self, znear, zfar, fov, aspect_ratio, degrees
+        self, znear, zfar, fov, aspect_ratio, degrees: bool
     ) -> torch.Tensor:
         """
         Compute the calibration matrix K of shape (N, 4, 4)
@@ -559,10 +561,10 @@ def OpenGLOrthographicCameras(
     left=-1.0,
     right=1.0,
     scale_xyz=((1.0, 1.0, 1.0),),  # (1, 3)
-    R=_R,
+    R: torch.Tensor = _R,
-    T=_T,
+    T: torch.Tensor = _T,
-    device="cpu",
+    device: Device = "cpu",
-):
+) -> "FoVOrthographicCameras":
     """
     OpenGLOrthographicCameras has been DEPRECATED. Use FoVOrthographicCameras instead.
     Preserving OpenGLOrthographicCameras for backward compatibility.
@@ -605,10 +607,10 @@ class FoVOrthographicCameras(CamerasBase):
         max_x=1.0,
         min_x=-1.0,
         scale_xyz=((1.0, 1.0, 1.0),),  # (1, 3)
-        R=_R,
+        R: torch.Tensor = _R,
-        T=_T,
+        T: torch.Tensor = _T,
-        K=None,
+        K: Optional[torch.Tensor] = None,
-        device="cpu",
+        device: Device = "cpu",
     ):
         """
 
@@ -784,8 +786,12 @@ we assume the parameters are in screen space.
 
 
 def SfMPerspectiveCameras(
-    focal_length=1.0, principal_point=((0.0, 0.0),), R=_R, T=_T, device="cpu"
+    focal_length=1.0,
-):
+    principal_point=((0.0, 0.0),),
+    R: torch.Tensor = _R,
+    T: torch.Tensor = _T,
+    device: Device = "cpu",
+) -> "PerspectiveCameras":
     """
     SfMPerspectiveCameras has been DEPRECATED. Use PerspectiveCameras instead.
     Preserving SfMPerspectiveCameras for backward compatibility.
@@ -843,10 +849,10 @@ class PerspectiveCameras(CamerasBase):
         self,
         focal_length=1.0,
         principal_point=((0.0, 0.0),),
-        R=_R,
+        R: torch.Tensor = _R,
-        T=_T,
+        T: torch.Tensor = _T,
-        K=None,
+        K: Optional[torch.Tensor] = None,
-        device="cpu",
+        device: Device = "cpu",
         image_size=((-1, -1),),
     ) -> None:
         """
@@ -950,8 +956,12 @@ class PerspectiveCameras(CamerasBase):
 
 
 def SfMOrthographicCameras(
-    focal_length=1.0, principal_point=((0.0, 0.0),), R=_R, T=_T, device="cpu"
+    focal_length=1.0,
-):
+    principal_point=((0.0, 0.0),),
+    R: torch.Tensor = _R,
+    T: torch.Tensor = _T,
+    device: Device = "cpu",
+) -> "OrthographicCameras":
     """
     SfMOrthographicCameras has been DEPRECATED. Use OrthographicCameras instead.
     Preserving SfMOrthographicCameras for backward compatibility.
@@ -1008,10 +1018,10 @@ class OrthographicCameras(CamerasBase):
         self,
         focal_length=1.0,
         principal_point=((0.0, 0.0),),
-        R=_R,
+        R: torch.Tensor = _R,
-        T=_T,
+        T: torch.Tensor = _T,
-        K=None,
+        K: Optional[torch.Tensor] = None,
-        device="cpu",
+        device: Device = "cpu",
         image_size=((-1, -1),),
     ) -> None:
         """
@@ -1116,8 +1126,8 @@ class OrthographicCameras(CamerasBase):
 
 
 def _get_sfm_calibration_matrix(
-    N,
+    N: int,
-    device,
+    device: Device,
     focal_length,
     principal_point,
     orthographic: bool = False,
@@ -1216,7 +1226,9 @@ def _get_sfm_calibration_matrix(
 ################################################
 
 
-def get_world_to_view_transform(R=_R, T=_T) -> Transform3d:
+def get_world_to_view_transform(
+    R: torch.Tensor = _R, T: torch.Tensor = _T
+) -> Transform3d:
     """
     This function returns a Transform3d representing the transformation
     matrix to go from world space to view space by applying a rotation and
@@ -1250,13 +1262,17 @@ def get_world_to_view_transform(R=_R, T=_T) -> Transform3d:
         raise ValueError(msg % repr(R.shape))
 
     # Create a Transform3d object
-    T = Translate(T, device=T.device)
+    T_ = Translate(T, device=T.device)
-    R = Rotate(R, device=R.device)
+    R_ = Rotate(R, device=R.device)
-    return R.compose(T)
+    return R_.compose(T_)
 
 
 def camera_position_from_spherical_angles(
-    distance, elevation, azimuth, degrees: bool = True, device: str = "cpu"
+    distance: float,
+    elevation: float,
+    azimuth: float,
+    degrees: bool = True,
+    device: Device = "cpu",
 ) -> torch.Tensor:
     """
     Calculate the location of the camera based on the distance away from
@@ -1294,7 +1310,7 @@ def camera_position_from_spherical_angles(
 
 
 def look_at_rotation(
-    camera_position, at=((0, 0, 0),), up=((0, 1, 0),), device: str = "cpu"
+    camera_position, at=((0, 0, 0),), up=((0, 1, 0),), device: Device = "cpu"
 ) -> torch.Tensor:
     """
     This function takes a vector 'camera_position' which specifies the location
@@ -1351,7 +1367,7 @@ def look_at_view_transform(
     eye: Optional[Sequence] = None,
     at=((0, 0, 0),),  # (1, 3)
     up=((0, 1, 0),),  # (1, 3)
-    device="cpu",
+    device: Device = "cpu",
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     """
     This function returns a rotation and translation matrix

pytorch3d/renderer/mesh/shader.py

@@ -5,11 +5,13 @@
 # LICENSE file in the root directory of this source tree.
 
 import warnings
+from typing import Optional
 
 import torch
 import torch.nn as nn
 
 from ...common.types import Device
+from ...structures.meshes import Meshes
 from ..blending import (
     BlendParams,
     hard_rgb_blend,
@@ -18,6 +20,8 @@ from ..blending import (
 )
 from ..lighting import PointLights
 from ..materials import Materials
+from ..utils import TensorProperties
+from .rasterizer import Fragments
 from .shading import flat_shading, gouraud_shading, phong_shading
 
 
@@ -47,10 +51,10 @@ class HardPhongShader(nn.Module):
     def __init__(
         self,
         device: Device = "cpu",
-        cameras=None,
+        cameras: Optional[TensorProperties] = None,
-        lights=None,
+        lights: Optional[TensorProperties] = None,
-        materials=None,
+        materials: Optional[Materials] = None,
-        blend_params=None,
+        blend_params: Optional[BlendParams] = None,
     ) -> None:
         super().__init__()
         self.lights = lights if lights is not None else PointLights(device=device)
@@ -62,13 +66,14 @@ class HardPhongShader(nn.Module):
 
     def to(self, device: Device):
         # Manually move to device modules which are not subclasses of nn.Module
-        if self.cameras is not None:
+        cameras = self.cameras
-            self.cameras = self.cameras.to(device)
+        if cameras is not None:
+            self.cameras = cameras.to(device)
         self.materials = self.materials.to(device)
         self.lights = self.lights.to(device)
         return self
 
-    def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
+    def forward(self, fragments: Fragments, meshes: Meshes, **kwargs) -> torch.Tensor:
         cameras = kwargs.get("cameras", self.cameras)
         if cameras is None:
             msg = "Cameras must be specified either at initialization \
@@ -108,10 +113,10 @@ class SoftPhongShader(nn.Module):
     def __init__(
         self,
         device: Device = "cpu",
-        cameras=None,
+        cameras: Optional[TensorProperties] = None,
-        lights=None,
+        lights: Optional[TensorProperties] = None,
-        materials=None,
+        materials: Optional[Materials] = None,
-        blend_params=None,
+        blend_params: Optional[BlendParams] = None,
     ) -> None:
         super().__init__()
         self.lights = lights if lights is not None else PointLights(device=device)
@@ -123,13 +128,14 @@ class SoftPhongShader(nn.Module):
 
     def to(self, device: Device):
         # Manually move to device modules which are not subclasses of nn.Module
-        if self.cameras is not None:
+        cameras = self.cameras
-            self.cameras = self.cameras.to(device)
+        if cameras is not None:
+            self.cameras = cameras.to(device)
         self.materials = self.materials.to(device)
         self.lights = self.lights.to(device)
         return self
 
-    def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
+    def forward(self, fragments: Fragments, meshes: Meshes, **kwargs) -> torch.Tensor:
         cameras = kwargs.get("cameras", self.cameras)
         if cameras is None:
             msg = "Cameras must be specified either at initialization \
@@ -174,10 +180,10 @@ class HardGouraudShader(nn.Module):
     def __init__(
         self,
         device: Device = "cpu",
-        cameras=None,
+        cameras: Optional[TensorProperties] = None,
-        lights=None,
+        lights: Optional[TensorProperties] = None,
-        materials=None,
+        materials: Optional[Materials] = None,
-        blend_params=None,
+        blend_params: Optional[BlendParams] = None,
     ) -> None:
         super().__init__()
         self.lights = lights if lights is not None else PointLights(device=device)
@@ -189,13 +195,14 @@ class HardGouraudShader(nn.Module):
 
     def to(self, device: Device):
         # Manually move to device modules which are not subclasses of nn.Module
-        if self.cameras is not None:
+        cameras = self.cameras
-            self.cameras = self.cameras.to(device)
+        if cameras is not None:
+            self.cameras = cameras.to(device)
         self.materials = self.materials.to(device)
         self.lights = self.lights.to(device)
         return self
 
-    def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
+    def forward(self, fragments: Fragments, meshes: Meshes, **kwargs) -> torch.Tensor:
         cameras = kwargs.get("cameras", self.cameras)
         if cameras is None:
             msg = "Cameras must be specified either at initialization \
@@ -239,10 +246,10 @@ class SoftGouraudShader(nn.Module):
     def __init__(
         self,
         device: Device = "cpu",
-        cameras=None,
+        cameras: Optional[TensorProperties] = None,
-        lights=None,
+        lights: Optional[TensorProperties] = None,
-        materials=None,
+        materials: Optional[Materials] = None,
-        blend_params=None,
+        blend_params: Optional[BlendParams] = None,
     ) -> None:
         super().__init__()
         self.lights = lights if lights is not None else PointLights(device=device)
@@ -254,13 +261,14 @@ class SoftGouraudShader(nn.Module):
 
     def to(self, device: Device):
         # Manually move to device modules which are not subclasses of nn.Module
-        if self.cameras is not None:
+        cameras = self.cameras
-            self.cameras = self.cameras.to(device)
+        if cameras is not None:
+            self.cameras = cameras.to(device)
         self.materials = self.materials.to(device)
         self.lights = self.lights.to(device)
         return self
 
-    def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
+    def forward(self, fragments: Fragments, meshes: Meshes, **kwargs) -> torch.Tensor:
         cameras = kwargs.get("cameras", self.cameras)
         if cameras is None:
             msg = "Cameras must be specified either at initialization \
@@ -284,7 +292,11 @@ class SoftGouraudShader(nn.Module):
 
 
 def TexturedSoftPhongShader(
-    device: Device = "cpu", cameras=None, lights=None, materials=None, blend_params=None
+    device: Device = "cpu",
+    cameras: Optional[TensorProperties] = None,
+    lights: Optional[TensorProperties] = None,
+    materials: Optional[Materials] = None,
+    blend_params: Optional[BlendParams] = None,
 ):
     """
     TexturedSoftPhongShader class has been DEPRECATED. Use SoftPhongShader instead.
@@ -321,10 +333,10 @@ class HardFlatShader(nn.Module):
     def __init__(
         self,
         device: Device = "cpu",
-        cameras=None,
+        cameras: Optional[TensorProperties] = None,
-        lights=None,
+        lights: Optional[TensorProperties] = None,
-        materials=None,
+        materials: Optional[Materials] = None,
-        blend_params=None,
+        blend_params: Optional[BlendParams] = None,
     ) -> None:
         super().__init__()
         self.lights = lights if lights is not None else PointLights(device=device)
@@ -336,13 +348,14 @@ class HardFlatShader(nn.Module):
 
     def to(self, device: Device):
         # Manually move to device modules which are not subclasses of nn.Module
-        if self.cameras is not None:
+        cameras = self.cameras
-            self.cameras = self.cameras.to(device)
+        if cameras is not None:
+            self.cameras = cameras.to(device)
         self.materials = self.materials.to(device)
         self.lights = self.lights.to(device)
         return self
 
-    def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
+    def forward(self, fragments: Fragments, meshes: Meshes, **kwargs) -> torch.Tensor:
         cameras = kwargs.get("cameras", self.cameras)
         if cameras is None:
             msg = "Cameras must be specified either at initialization \
@@ -381,11 +394,11 @@ class SoftSilhouetteShader(nn.Module):
         3D Reasoning', ICCV 2019
     """
 
-    def __init__(self, blend_params=None) -> None:
+    def __init__(self, blend_params: Optional[BlendParams] = None) -> None:
         super().__init__()
         self.blend_params = blend_params if blend_params is not None else BlendParams()
 
-    def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
+    def forward(self, fragments: Fragments, meshes: Meshes, **kwargs) -> torch.Tensor:
         """
         Only want to render the silhouette so RGB values can be ones.
         There is no need for lighting or texturing

pytorch3d/renderer/mesh/textures.py

@@ -223,7 +223,7 @@ class TexturesBase:
 
         return new_props
 
-    def sample_textures(self):
+    def sample_textures(self) -> torch.Tensor:
         """
         Different texture classes sample textures in different ways
         e.g. for vertex textures, the values at each vertex
@@ -237,7 +237,7 @@ class TexturesBase:
         """
         raise NotImplementedError()
 
-    def faces_verts_textures_packed(self):
+    def faces_verts_textures_packed(self) -> torch.Tensor:
         """
         Returns the texture for each vertex for each face in the mesh.
         For N meshes, this function returns sum(Fi)x3xC where Fi is the
@@ -248,14 +248,14 @@ class TexturesBase:
         """
         raise NotImplementedError()
 
-    def clone(self):
+    def clone(self) -> "TexturesBase":
         """
         Each texture class should implement a method
         to clone all necessary internal tensors.
         """
         raise NotImplementedError()
 
-    def detach(self):
+    def detach(self) -> "TexturesBase":
         """
         Each texture class should implement a method
         to detach all necessary internal tensors.
@@ -394,7 +394,7 @@ class TexturesAtlas(TexturesBase):
         # refer to the __init__ of Meshes.
         self.valid = torch.ones((self._N,), dtype=torch.bool, device=self.device)
 
-    def clone(self):
+    def clone(self) -> "TexturesAtlas":
         tex = self.__class__(atlas=self.atlas_padded().clone())
         if self._atlas_list is not None:
             tex._atlas_list = [atlas.clone() for atlas in self._atlas_list]
@@ -407,7 +407,7 @@ class TexturesAtlas(TexturesBase):
         tex._num_faces_per_mesh = num_faces
         return tex
 
-    def detach(self):
+    def detach(self) -> "TexturesAtlas":
         tex = self.__class__(atlas=self.atlas_padded().detach())
         if self._atlas_list is not None:
             tex._atlas_list = [atlas.detach() for atlas in self._atlas_list]

pytorch3d/renderer/utils.py

@@ -160,7 +160,7 @@ class TensorProperties(nn.Module):
         msg = "Expected index of type int or slice; got %r"
         raise ValueError(msg % type(index))
 
-    def to(self, device: Device = "cpu"):
+    def to(self, device: Device = "cpu") -> "TensorProperties":
         """
         In place operation to move class properties which are tensors to a
         specified device. If self has a property "device", update this as well.
@@ -174,7 +174,7 @@ class TensorProperties(nn.Module):
                 setattr(self, k, v.to(device_))
         return self
 
-    def clone(self, other):
+    def clone(self, other) -> "TensorProperties":
         """
         Update the tensor properties of other with the cloned properties of self.
         """
@@ -189,7 +189,7 @@ class TensorProperties(nn.Module):
             setattr(other, k, v_clone)
         return other
 
-    def gather_props(self, batch_idx):
+    def gather_props(self, batch_idx) -> "TensorProperties":
         """
         This is an in place operation to reformat all tensor class attributes
         based on a set of given indices using torch.gather. This is useful when

pytorch3d/structures/volumes.py

@@ -187,15 +187,15 @@ class Volumes:
         """
 
         # handle densities
-        densities, grid_sizes = self._convert_densities_features_to_tensor(
+        densities_, grid_sizes = self._convert_densities_features_to_tensor(
             densities, "densities"
         )
 
         # take device from densities
-        self.device = densities.device
+        self.device = densities_.device
 
         # assign to the internal buffers
-        self._densities = densities
+        self._densities = densities_
         self._grid_sizes = grid_sizes
 
         # handle features
@@ -497,7 +497,6 @@ class Volumes:
         )
 
     def __len__(self) -> int:
-        # pyre-fixme[16]: `List` has no attribute `shape`.
         return self._densities.shape[0]
 
     def __getitem__(
@@ -547,8 +546,6 @@ class Volumes:
         Returns:
             **densities**: The tensor of volume densities.
         """
-        # pyre-fixme[7]: Expected `Tensor` but got `Union[List[torch.Tensor],
-        #  torch.Tensor]`.
         return self._densities
 
     def densities_list(self) -> List[torch.Tensor]:
@@ -723,7 +720,6 @@ class Volumes:
             return other
 
         other.device = device_
-        # pyre-fixme[16]: `List` has no attribute `to`.
         other._densities = self._densities.to(device_)
         if self._features is not None:
             # pyre-fixme[16]: `Optional` has no attribute `to`.

pytorch3d/transforms/rotation_conversions.py

@@ -10,6 +10,8 @@ from typing import Optional
 import torch
 import torch.nn.functional as F
 
+from ..common.types import Device
+
 
 """
 The transformation matrices returned from the functions in this file assume
@@ -286,7 +288,9 @@ def matrix_to_euler_angles(matrix, convention: str):
     return torch.stack(o, -1)
 
 
-def random_quaternions(n: int, dtype: Optional[torch.dtype] = None, device=None):
+def random_quaternions(
+    n: int, dtype: Optional[torch.dtype] = None, device: Optional[Device] = None
+):
     """
     Generate random quaternions representing rotations,
     i.e. versors with nonnegative real part.
@@ -306,7 +310,9 @@ def random_quaternions(n: int, dtype: Optional[torch.dtype] = None, device=None)
     return o
 
 
-def random_rotations(n: int, dtype: Optional[torch.dtype] = None, device=None):
+def random_rotations(
+    n: int, dtype: Optional[torch.dtype] = None, device: Optional[Device] = None
+):
     """
     Generate random rotations as 3x3 rotation matrices.
 
@@ -323,7 +329,9 @@ def random_rotations(n: int, dtype: Optional[torch.dtype] = None, device=None):
     return quaternion_to_matrix(quaternions)
 
 
-def random_rotation(dtype: Optional[torch.dtype] = None, device=None):
+def random_rotation(
+    dtype: Optional[torch.dtype] = None, device: Optional[Device] = None
+):
     """
     Generate a single random 3x3 rotation matrix.