suppress errors in `vision/fair/pytorch3d`

Differential Revision: D27934268

fbshipit-source-id: 51185fa493451012a9b2fd37379897d60596f73b

pytorch3d/datasets/r2n2/utils.py

@@ -443,7 +443,7 @@ class BlenderCamera(CamerasBase):
 
     def get_projection_transform(self, **kwargs) -> Transform3d:
         transform = Transform3d(device=self.device)
-        transform._matrix = self.K.transpose(1, 2).contiguous()  # pyre-ignore[16]
+        transform._matrix = self.K.transpose(1, 2).contiguous()
         return transform
 
 

pytorch3d/ops/cameras_alignment.py

@@ -97,7 +97,6 @@ def corresponding_cameras_alignment(
         cameras_src_aligned: `cameras_src` after applying the alignment transform.
     """
 
-    # pyre-fixme[16]: `CamerasBase` has no attribute `R`.
     if cameras_src.R.shape[0] != cameras_tgt.R.shape[0]:
         raise ValueError(
             "cameras_src and cameras_tgt have to contain the same number of cameras!"
@@ -121,7 +120,6 @@ def corresponding_cameras_alignment(
         torch.bmm(
             align_t_T[:, None].repeat(cameras_src.R.shape[0], 1, 1), cameras_src.R
         )[:, 0]
-        # pyre-fixme[16]: `CamerasBase` has no attribute `T`.
         + cameras_src.T * align_t_s
     )
 
@@ -169,7 +167,6 @@ def _align_camera_extrinsics(
         R_A = (U V^T)^T
         ```
     """
-    # pyre-fixme[16]: `CamerasBase` has no attribute `R`.
     RRcov = torch.bmm(cameras_src.R, cameras_tgt.R.transpose(2, 1)).mean(0)
     U, _, V = torch.svd(RRcov)
     align_t_R = V @ U.t()
@@ -199,8 +196,15 @@ def _align_camera_extrinsics(
         T_A = mean(B) - mean(A) * s_A
         ```
     """
-    # pyre-fixme[16]: `CamerasBase` has no attribute `T`.
+    # pyre-fixme[29]:
+    #  `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self,
+    #  torch.Tensor), Named(item, typing.Any)], typing.Any], torch.Tensor],
+    #  torch.Tensor, torch.nn.Module]` is not a function.
     A = torch.bmm(cameras_src.R, cameras_src.T[:, :, None])[:, :, 0]
+    # pyre-fixme[29]:
+    #  `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self,
+    #  torch.Tensor), Named(item, typing.Any)], typing.Any], torch.Tensor],
+    #  torch.Tensor, torch.nn.Module]` is not a function.
     B = torch.bmm(cameras_src.R, cameras_tgt.T[:, :, None])[:, :, 0]
     Amu = A.mean(0, keepdim=True)
     Bmu = B.mean(0, keepdim=True)

pytorch3d/renderer/cameras.py

@@ -478,17 +478,17 @@ class FoVPerspectiveCameras(CamerasBase):
                     [0,    0,    1,   0],
             ]
         """
-        K = kwargs.get("K", self.K)  # pyre-ignore[16]
+        K = kwargs.get("K", self.K)
         if K is not None:
             if K.shape != (self._N, 4, 4):
                 msg = "Expected K to have shape of (%r, 4, 4)"
                 raise ValueError(msg % (self._N))
         else:
             K = self.compute_projection_matrix(
-                kwargs.get("znear", self.znear),  # pyre-ignore[16]
-                kwargs.get("zfar", self.zfar),  # pyre-ignore[16]
-                kwargs.get("fov", self.fov),  # pyre-ignore[16]
-                kwargs.get("aspect_ratio", self.aspect_ratio),  # pyre-ignore[16]
+                kwargs.get("znear", self.znear),
+                kwargs.get("zfar", self.zfar),
+                kwargs.get("fov", self.fov),
+                kwargs.get("aspect_ratio", self.aspect_ratio),
                 kwargs.get("degrees", self.degrees),
             )
 
@@ -702,20 +702,20 @@ class FoVOrthographicCameras(CamerasBase):
                     [0,              0,         0,       1],
             ]
         """
-        K = kwargs.get("K", self.K)  # pyre-ignore[16]
+        K = kwargs.get("K", self.K)
         if K is not None:
             if K.shape != (self._N, 4, 4):
                 msg = "Expected K to have shape of (%r, 4, 4)"
                 raise ValueError(msg % (self._N))
         else:
             K = self.compute_projection_matrix(
-                kwargs.get("znear", self.znear),  # pyre-ignore[16]
-                kwargs.get("zfar", self.zfar),  # pyre-ignore[16]
-                kwargs.get("max_x", self.max_x),  # pyre-ignore[16]
-                kwargs.get("min_x", self.min_x),  # pyre-ignore[16]
-                kwargs.get("max_y", self.max_y),  # pyre-ignore[16]
-                kwargs.get("min_y", self.min_y),  # pyre-ignore[16]
-                kwargs.get("scale_xyz", self.scale_xyz),  # pyre-ignore[16]
+                kwargs.get("znear", self.znear),
+                kwargs.get("zfar", self.zfar),
+                kwargs.get("max_x", self.max_x),
+                kwargs.get("min_x", self.min_x),
+                kwargs.get("max_y", self.max_y),
+                kwargs.get("min_y", self.min_y),
+                kwargs.get("scale_xyz", self.scale_xyz),
             )
 
         transform = Transform3d(device=self.device)
@@ -902,13 +902,12 @@ class PerspectiveCameras(CamerasBase):
                     [0,    0,    1,   0],
             ]
         """
-        K = kwargs.get("K", self.K)  # pyre-ignore[16]
+        K = kwargs.get("K", self.K)
         if K is not None:
             if K.shape != (self._N, 4, 4):
                 msg = "Expected K to have shape of (%r, 4, 4)"
                 raise ValueError(msg % (self._N))
         else:
-            # pyre-ignore[16]
             image_size = kwargs.get("image_size", self.image_size)
             # if imwidth > 0, parameters are in screen space
             image_size = image_size if image_size[0][0] > 0 else None
@@ -916,8 +915,8 @@ class PerspectiveCameras(CamerasBase):
             K = _get_sfm_calibration_matrix(
                 self._N,
                 self.device,
-                kwargs.get("focal_length", self.focal_length),  # pyre-ignore[16]
-                kwargs.get("principal_point", self.principal_point),  # pyre-ignore[16]
+                kwargs.get("focal_length", self.focal_length),
+                kwargs.get("principal_point", self.principal_point),
                 orthographic=False,
                 image_size=image_size,
             )
@@ -1067,13 +1066,12 @@ class OrthographicCameras(CamerasBase):
                     [0,    0,    0,   1],
             ]
         """
-        K = kwargs.get("K", self.K)  # pyre-ignore[16]
+        K = kwargs.get("K", self.K)
         if K is not None:
             if K.shape != (self._N, 4, 4):
                 msg = "Expected K to have shape of (%r, 4, 4)"
                 raise ValueError(msg % (self._N))
         else:
-            # pyre-ignore[16]
             image_size = kwargs.get("image_size", self.image_size)
             # if imwidth > 0, parameters are in screen space
             image_size = image_size if image_size[0][0] > 0 else None
@@ -1081,8 +1079,8 @@ class OrthographicCameras(CamerasBase):
             K = _get_sfm_calibration_matrix(
                 self._N,
                 self.device,
-                kwargs.get("focal_length", self.focal_length),  # pyre-ignore[16]
-                kwargs.get("principal_point", self.principal_point),  # pyre-ignore[16]
+                kwargs.get("focal_length", self.focal_length),
+                kwargs.get("principal_point", self.principal_point),
                 orthographic=True,
                 image_size=image_size,
             )

pytorch3d/renderer/implicit/raysampling.py

@@ -113,7 +113,7 @@ class GridRaysampler(torch.nn.Module):
                 containing the 2D image coordinates of each ray.
         """
 
-        batch_size = cameras.R.shape[0]  # pyre-ignore
+        batch_size = cameras.R.shape[0]
 
         device = cameras.device
 
@@ -229,7 +229,7 @@ class MonteCarloRaysampler(torch.nn.Module):
                 containing the 2D image coordinates of each ray.
         """
 
-        batch_size = cameras.R.shape[0]  # pyre-ignore
+        batch_size = cameras.R.shape[0]
 
         device = cameras.device
 

pytorch3d/renderer/lighting.py

@@ -183,7 +183,6 @@ class DirectionalLights(TensorProperties):
             direction=direction,
         )
         _validate_light_properties(self)
-        # pyre-fixme[16]: `DirectionalLights` has no attribute `direction`.
         if self.direction.shape[-1] != 3:
             msg = "Expected direction to have shape (N, 3); got %r"
             raise ValueError(msg % repr(self.direction.shape))
@@ -198,9 +197,7 @@ class DirectionalLights(TensorProperties):
         # need to know the light type.
         return diffuse(
             normals=normals,
-            # pyre-fixme[16]: `DirectionalLights` has no attribute `diffuse_color`.
             color=self.diffuse_color,
-            # pyre-fixme[16]: `DirectionalLights` has no attribute `direction`.
             direction=self.direction,
         )
 
@@ -208,9 +205,7 @@ class DirectionalLights(TensorProperties):
         return specular(
             points=points,
             normals=normals,
-            # pyre-fixme[16]: `DirectionalLights` has no attribute `specular_color`.
             color=self.specular_color,
-            # pyre-fixme[16]: `DirectionalLights` has no attribute `direction`.
             direction=self.direction,
             camera_position=camera_position,
             shininess=shininess,
@@ -249,7 +244,6 @@ class PointLights(TensorProperties):
             location=location,
         )
         _validate_light_properties(self)
-        # pyre-fixme[16]: `PointLights` has no attribute `location`.
         if self.location.shape[-1] != 3:
             msg = "Expected location to have shape (N, 3); got %r"
             raise ValueError(msg % repr(self.location.shape))
@@ -259,18 +253,14 @@ class PointLights(TensorProperties):
         return super().clone(other)
 
     def diffuse(self, normals, points) -> torch.Tensor:
-        # pyre-fixme[16]: `PointLights` has no attribute `location`.
         direction = self.location - points
-        # pyre-fixme[16]: `PointLights` has no attribute `diffuse_color`.
         return diffuse(normals=normals, color=self.diffuse_color, direction=direction)
 
     def specular(self, normals, points, camera_position, shininess) -> torch.Tensor:
-        # pyre-fixme[16]: `PointLights` has no attribute `location`.
         direction = self.location - points
         return specular(
             points=points,
             normals=normals,
-            # pyre-fixme[16]: `PointLights` has no attribute `specular_color`.
             color=self.specular_color,
             direction=direction,
             camera_position=camera_position,

pytorch3d/renderer/points/pulsar/renderer.py

@@ -632,7 +632,6 @@ class Renderer(torch.nn.Module):
                     enabled.
         """
         # The device tracker is registered as buffer.
-        # pyre-fixme[16]: `Renderer` has no attribute `device_tracker`.
         self._renderer.device_tracker = self.device_tracker
         (
             pos_vec,

pytorch3d/renderer/points/pulsar/unified.py

@@ -189,17 +189,11 @@ class PulsarPointsRenderer(nn.Module):
         if orthogonal_projection:
             focal_length = torch.zeros((1,), dtype=torch.float32)
             if isinstance(cameras, FoVOrthographicCameras):
-                # pyre-fixme[16]: `FoVOrthographicCameras` has no attribute `znear`.
                 znear = kwargs.get("znear", cameras.znear)[cloud_idx]
-                # pyre-fixme[16]: `FoVOrthographicCameras` has no attribute `zfar`.
                 zfar = kwargs.get("zfar", cameras.zfar)[cloud_idx]
-                # pyre-fixme[16]: `FoVOrthographicCameras` has no attribute `max_y`.
                 max_y = kwargs.get("max_y", cameras.max_y)[cloud_idx]
-                # pyre-fixme[16]: `FoVOrthographicCameras` has no attribute `min_y`.
                 min_y = kwargs.get("min_y", cameras.min_y)[cloud_idx]
-                # pyre-fixme[16]: `FoVOrthographicCameras` has no attribute `max_x`.
                 max_x = kwargs.get("max_x", cameras.max_x)[cloud_idx]
-                # pyre-fixme[16]: `FoVOrthographicCameras` has no attribute `min_x`.
                 min_x = kwargs.get("min_x", cameras.min_x)[cloud_idx]
                 if max_y != -min_y:
                     raise ValueError(
@@ -212,9 +206,7 @@ class PulsarPointsRenderer(nn.Module):
                         f"Max is {max_x} and min is {min_x}."
                     )
                 if not torch.all(
-                    # pyre-fixme[16]: `FoVOrthographicCameras` has no attribute `scale_xyz`.
-                    kwargs.get("scale_xyz", cameras.scale_xyz)[cloud_idx]
-                    == 1.0
+                    kwargs.get("scale_xyz", cameras.scale_xyz)[cloud_idx] == 1.0
                 ):
                     raise ValueError(
                         "The orthographic camera scale must be ((1.0, 1.0, 1.0),). "
@@ -297,7 +289,6 @@ class PulsarPointsRenderer(nn.Module):
                     torch.zeros((1,), dtype=torch.float32),
                 )
             else:
-                # pyre-fixme[16]: `PerspectiveCameras` has no attribute `focal_length`.
                 focal_length_conf = kwargs.get("focal_length", cameras.focal_length)[
                     cloud_idx
                 ]
@@ -336,7 +327,6 @@ class PulsarPointsRenderer(nn.Module):
                 )
                 sensor_width = focal_length / focal_length_px * 2.0
                 principal_point_x = (
-                    # pyre-fixme[16]: `PerspectiveCameras` has no attribute `principal_point`.
                     kwargs.get("principal_point", cameras.principal_point)[cloud_idx][0]
                     * 0.5
                     * self.renderer._renderer.width

pytorch3d/vis/plotly_vis.py

@@ -470,10 +470,22 @@ def _add_struct_from_batch(
     struct = None
     if isinstance(batched_struct, CamerasBase):
         # we can't index directly into camera batches
-        R, T = batched_struct.R, batched_struct.T  # pyre-ignore[16]
+        R, T = batched_struct.R, batched_struct.T
+        # pyre-fixme[6]: Expected `Sized` for 1st param but got `Union[torch.Tensor,
+        #  torch.nn.Module]`.
         r_idx = min(scene_num, len(R) - 1)
+        # pyre-fixme[6]: Expected `Sized` for 1st param but got `Union[torch.Tensor,
+        #  torch.nn.Module]`.
         t_idx = min(scene_num, len(T) - 1)
+        # pyre-fixme[29]:
+        #  `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self,
+        #  torch.Tensor), Named(item, typing.Any)], typing.Any], torch.Tensor],
+        #  torch.Tensor, torch.nn.Module]` is not a function.
         R = R[r_idx].unsqueeze(0)
+        # pyre-fixme[29]:
+        #  `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self,
+        #  torch.Tensor), Named(item, typing.Any)], typing.Any], torch.Tensor],
+        #  torch.Tensor, torch.nn.Module]` is not a function.
         T = T[t_idx].unsqueeze(0)
         struct = CamerasBase(device=batched_struct.device, R=R, T=T)
     else:  # batched meshes and pointclouds are indexable