Remove unused type error suppressions (landing group 42)

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 changes only type-error suppression comments: it removes suppressions that are no longer needed and adds `# pyrefly: ignore[...]` comments for remaining errors. It does not change Python runtime behavior or application logic.

Landing group: `74a406ba3ec1f23501f4841695298046d3c8db50f864040e2c4b64506cdb66bd` (attempt 1, 20 batches, 391 files)
Merged patch SHA-256: `c3e986b54ae5248e9810275f66372a9b2047a8e0aa14b3d3301ff8783f4e7e0e`

#pyreupgrade

Differential Revision: D126124958

fbshipit-source-id: f89b7d78ab77074fd80a15de7cef6f1f9fdb4237

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/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: