Suppress type errors for Pyrefly upgrade (landing group 135)

Summary:
This Phabricator Diff was generated from Pyrefly type-checker output by the per-target upgrade tool. It was not generated by an AI coding system.

It adds only `# pyrefly: ignore[...]` comments for type errors reported by the upcoming Pyrefly version. It does not change Python runtime behavior or application logic.

If this diff does not land before the Pyrefly upgrade, the associated type-checking tests will fail.

Landing group: `c004445c7bbada027a0d112b6d018972b720eb832b6be71177fcfbbe4758bbd0` (attempt 1, 13 batches, 86 files)
Merged patch SHA-256: `17964792f0a8a521b30c0c5a41641c2b6301018eba369246c7af786200728d97`

#pyreupgrade

Reviewed By: maggiemoss

Differential Revision: D128539582

fbshipit-source-id: 6714747585617e68fa66c240a2c05bab8e2f792f

pytorch3d/common/workaround/symeig3x3.py

@@ -104,7 +104,6 @@ class _SymEig3x3(nn.Module):
         )
 
         p = torch.sqrt(p2 / 6.0)
-        # pyrefly: ignore [unsupported-operation]
         B = (inputs - q[..., None, None] * self._identity) / p[..., None, None]
 
         r = torch.det(B) / 2.0
@@ -189,10 +188,8 @@ class _SymEig3x3(nn.Module):
         """
 
         # Find the eigenvector corresponding to alpha0, its eigenvalue is distinct
-        # pyrefly: ignore [unsupported-operation]
         ev0 = self._get_ev0(inputs - alpha0[..., None, None] * self._identity)
         u, v = self._get_uv(ev0)
-        # pyrefly: ignore [unsupported-operation]
         ev1 = self._get_ev1(inputs - alpha1[..., None, None] * self._identity, u, v)
         # Third eigenvector is computed as the cross-product of the other two
         ev2 = torch.cross(ev0, ev1, dim=-1)
@@ -267,7 +264,6 @@ class _SymEig3x3(nn.Module):
         """
 
         min_idx = w.abs().argmin(dim=-1)
-        # pyrefly: ignore [bad-index]
         rotation_2d = self._rotations_3d[min_idx].to(w)
 
         u = F.normalize((rotation_2d @ w[..., None])[..., 0], dim=-1)

pytorch3d/implicitron/tools/eval_video_trajectory.py

@@ -182,10 +182,12 @@ def generate_eval_video_cameras(
         )
 
     test_cameras = PerspectiveCameras(
+        # pyrefly: ignore [bad-argument-type]
         focal_length=focal_length,
         principal_point=principal_point,
         R=R,
         T=T,
+        # pyrefly: ignore [missing-attribute]
         device=focal_length.device,
     )
 

pytorch3d/io/obj_io.py

@@ -472,26 +472,26 @@ def _parse_obj(f, data_dir: str):
                 materials_idx = len(material_names) - 1
             else:
                 materials_idx = material_names.index(material_name)
-        elif line.startswith("v "):  # Line is a vertex.
+        elif tokens[:1] == ["v"]:  # Line is a vertex.
             vert = [float(x) for x in tokens[1:4]]
             if len(vert) != 3:
                 msg = "Vertex %s does not have 3 values. Line: %s"
                 raise ValueError(msg % (str(vert), str(line)))
             verts.append(vert)
-        elif line.startswith("vt "):  # Line is a texture.
+        elif tokens[:1] == ["vt"]:  # Line is a texture.
             tx = [float(x) for x in tokens[1:3]]
             if len(tx) != 2:
                 raise ValueError(
                     "Texture %s does not have 2 values. Line: %s" % (str(tx), str(line))
                 )
             verts_uvs.append(tx)
-        elif line.startswith("vn "):  # Line is a normal.
+        elif tokens[:1] == ["vn"]:  # Line is a normal.
             norm = [float(x) for x in tokens[1:4]]
             if len(norm) != 3:
                 msg = "Normal %s does not have 3 values. Line: %s"
                 raise ValueError(msg % (str(norm), str(line)))
             normals.append(norm)
-        elif line.startswith("f "):  # Line is a face.
+        elif tokens[:1] == ["f"]:  # Line is a face.
             # Update face properties info.
             _parse_face(
                 line,
@@ -794,7 +794,7 @@ def save_obj(
             mtl_path = str(mtl_path)
 
         # Save texture map to output folder
-        # pyre-fixme[16] # undefined attribute cpu
+        # undefined attribute cpu
         texture_map = texture_map.detach().cpu() * 255.0
         image = Image.fromarray(texture_map.numpy().astype(np.uint8))
         with _open_file(image_path, path_manager, "wb") as im_f:

pytorch3d/ops/points_alignment.py

@@ -340,8 +340,11 @@ def corresponding_points_alignment(
     total_weight = torch.clamp(num_points, 1)
     # special handling for heterogeneous point clouds and/or input weights
     if weights is not None:
+        # pyrefly: ignore [bad-index]
         Xc *= weights[:, :, None]
+        # pyrefly: ignore [bad-index]
         Yc *= weights[:, :, None]
+        # pyrefly: ignore [missing-attribute]
         total_weight = torch.clamp(weights.sum(1), eps)
 
     if (num_points < (dim + 1)).any():

pytorch3d/renderer/cameras.py

@@ -427,9 +427,11 @@ class CamerasBase(TensorProperties):
         if isinstance(index, tensor_types["bool"]):
             #  LongTensor]` has no attribute `ndim`.
             #  LongTensor]` has no attribute `shape`.
+            # pyrefly: ignore [missing-attribute]
             if index.ndim != 1 or index.shape[0] != len(self):
                 raise ValueError(
                     #  LongTensor]` has no attribute `shape`.
+                    # pyrefly: ignore [missing-attribute]
                     f"Boolean index of shape {index.shape} does not match cameras"
                 )
         elif max(index) >= len(self):
@@ -1790,11 +1792,13 @@ def get_ndc_to_screen_transform(
         image_size = torch.tensor(image_size, device=cameras.device)
     # pyrefly: ignore [missing-attribute]
     image_size = image_size.view(-1, 2)  # of shape (1 or B)x2
+    # pyrefly: ignore [missing-attribute]
     height, width = image_size.unbind(1)
 
     # For non square images, we scale the points such that smallest side
     # has range [-1, 1] and the largest side has range [-u, u], with u > 1.
     # This convention is consistent with the PyTorch3D renderer
+    # pyrefly: ignore [missing-attribute]
     scale = (image_size.min(dim=1).values - 0.0) / 2.0
 
     K[:, 0, 0] = scale

pytorch3d/renderer/implicit/harmonic_embedding.py

@@ -136,16 +136,13 @@ class HarmonicEmbedding(torch.nn.Module):
             [..., (n_harmonic_functions * 2 + int(append_input)) * num_points_per_ray]
         """
         # [..., dim, n_harmonic_functions]
-        # pyrefly: ignore [unsupported-operation]
         embed = x[..., None] * self._frequencies
         # [..., 1, dim, n_harmonic_functions] + [2, 1, 1] => [..., 2, dim, n_harmonic_functions]
-        # pyrefly: ignore [bad-index]
         embed = embed[..., None, :, :] + self._zero_half_pi[..., None, None]
         # Use the trig identity cos(x) = sin(x + pi/2)
         # and do one vectorized call to sin([x, x+pi/2]) instead of (sin(x), cos(x)).
         embed = embed.sin()
         if diag_cov is not None:
-            # pyrefly: ignore [no-matching-overload]
             x_var = diag_cov[..., None] * torch.pow(self._frequencies, 2)
             exp_var = torch.exp(-0.5 * x_var)
             # [..., 2, dim, n_harmonic_functions]
@@ -184,7 +181,6 @@ class HarmonicEmbedding(torch.nn.Module):
         """
         return self.get_output_dim_static(
             input_dims,
-            # pyrefly: ignore [bad-argument-type]
             len(self._frequencies),
             self.append_input,
         )

pytorch3d/renderer/mesh/rasterizer.py

@@ -239,12 +239,10 @@ class MeshRasterizer(nn.Module):
         if raster_settings.perspective_correct is not None:
             perspective_correct = raster_settings.perspective_correct
         else:
-            # pyrefly: ignore [missing-attribute]
             perspective_correct = cameras.is_perspective()
         if raster_settings.z_clip_value is not None:
             z_clip = raster_settings.z_clip_value
         else:
-            # pyrefly: ignore [missing-attribute]
             znear = cameras.get_znear()
             if isinstance(znear, torch.Tensor):
                 znear = znear.min().item()

pytorch3d/renderer/mesh/textures.py

@@ -482,6 +482,7 @@ class TexturesAtlas(TexturesBase):
             else self._num_faces_per_mesh
         )
         tex.valid = self.valid.clone()
+        # pyrefly: ignore[bad-assignment]
         tex._num_faces_per_mesh = num_faces
         return tex
 
@@ -496,6 +497,7 @@ class TexturesAtlas(TexturesBase):
             else self._num_faces_per_mesh
         )
         tex.valid = self.valid.detach()
+        # pyrefly: ignore[bad-assignment]
         tex._num_faces_per_mesh = num_faces
         return tex
 
@@ -982,6 +984,7 @@ class TexturesUV(TexturesBase):
             if torch.is_tensor(self._num_faces_per_mesh)
             else self._num_faces_per_mesh
         )
+        # pyrefly: ignore[bad-assignment]
         tex._num_faces_per_mesh = num_faces
         tex.valid = self.valid.clone()
         return tex
@@ -1014,6 +1017,7 @@ class TexturesUV(TexturesBase):
             if torch.is_tensor(self._num_faces_per_mesh)
             else self._num_faces_per_mesh
         )
+        # pyrefly: ignore[bad-assignment]
         tex._num_faces_per_mesh = num_faces
         tex.valid = self.valid.detach()
         return tex

pytorch3d/structures/meshes.py

@@ -510,7 +510,9 @@ class Meshes:
                 index = index.squeeze(1) if index.numel() > 0 else index
                 # pyrefly: ignore [missing-attribute]
                 index = index.tolist()
+            # pyrefly: ignore [not-iterable]
             verts = [self.verts_list()[i] for i in index]
+            # pyrefly: ignore [not-iterable]
             faces = [self.faces_list()[i] for i in index]
         else:
             raise IndexError(index)

pytorch3d/structures/pointclouds.py

@@ -411,10 +411,13 @@ class Pointclouds:
                 index = index.squeeze(1) if index.numel() > 0 else index
                 # pyrefly: ignore [missing-attribute]
                 index = index.tolist()
+            # pyrefly: ignore [not-iterable]
             points = [self.points_list()[i] for i in index]
             if normals_list is not None:
+                # pyrefly: ignore [not-iterable]
                 normals = [normals_list[i] for i in index]
             if features_list is not None:
+                # pyrefly: ignore [not-iterable]
                 features = [features_list[i] for i in index]
         else:
             raise IndexError(index)

tests/test_io_obj.py

@@ -319,6 +319,29 @@ class TestMeshObjIO(TestCaseMixin, unittest.TestCase):
             self.assertTrue(materials is None)
             self.assertTrue(tex_maps is None)
 
+    def test_load_obj_tab_separated(self):
+        # Tabs are whitespace too, including between the keyword and the values.
+        obj_file = "\n".join(
+            [
+                "v\t0.1\t0.2\t0.3",
+                "v 0.2 0.3 0.4",
+                "v\t0.3 0.4 0.5",
+                "vt\t0.5\t0.5",
+                "vn\t0.0\t0.0\t1.0",
+                "f\t1/1/1\t2/1/1\t3/1/1",
+            ]
+        )
+        verts, faces, aux = load_obj(StringIO(obj_file))
+        expected_verts = torch.tensor(
+            [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4], [0.3, 0.4, 0.5]], dtype=torch.float32
+        )
+        self.assertClose(verts, expected_verts)
+        self.assertClose(faces.verts_idx, torch.tensor([[0, 1, 2]]))
+        self.assertClose(faces.textures_idx, torch.tensor([[0, 0, 0]]))
+        self.assertClose(faces.normals_idx, torch.tensor([[0, 0, 0]]))
+        self.assertClose(aux.verts_uvs, torch.tensor([[0.5, 0.5]]))
+        self.assertClose(aux.normals, torch.tensor([[0.0, 0.0, 1.0]]))
+
     def test_load_obj_error_textures(self):
         obj_file = "\n".join(["vt 0.1"])
         with NamedTemporaryFile(mode="w", suffix=".obj") as f: