[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

bayes3d/colmap/colmap_loader.py

@@ -196,9 +196,9 @@ def read_intrinsics_text(path):
                 elems = line.split()
                 camera_id = int(elems[0])
                 model = elems[1]
-                assert (
-                    model == "PINHOLE"
-                ), "While the loader support other types, the rest of the code assumes PINHOLE"
+                assert model == "PINHOLE", (
+                    "While the loader support other types, the rest of the code assumes PINHOLE"
+                )
                 width = int(elems[2])
                 height = int(elems[3])
                 params = np.array(tuple(map(float, elems[4:])))

bayes3d/colmap/dataset_loader.py

@@ -95,7 +95,9 @@ def readColmapCameras(cam_extrinsics, cam_intrinsics, images_folder):
             FovY = focal2fov(focal_length_y, height)
             FovX = focal2fov(focal_length_x, width)
         else:
-            assert False, "Colmap camera model not handled: only undistorted datasets (PINHOLE or SIMPLE_PINHOLE cameras) supported!"
+            assert False, (
+                "Colmap camera model not handled: only undistorted datasets (PINHOLE or SIMPLE_PINHOLE cameras) supported!"
+            )
 
         image_path = os.path.join(images_folder, os.path.basename(extr.name))
         image_name = os.path.basename(image_path).split(".")[0]

bayes3d/genjax/model.py

@@ -129,11 +129,11 @@ def get_far_plane(trace):
 def add_object(trace, key, obj_id, parent, face_parent, face_child):
     N = b.get_indices(trace).shape[0] + 1
     choices = trace.get_choices()
-    choices[f"parent_{N-1}"] = parent
-    choices[f"id_{N-1}"] = obj_id
-    choices[f"face_parent_{N-1}"] = face_parent
-    choices[f"face_child_{N-1}"] = face_child
-    choices[f"contact_params_{N-1}"] = jnp.zeros(3)
+    choices[f"parent_{N - 1}"] = parent
+    choices[f"id_{N - 1}"] = obj_id
+    choices[f"face_parent_{N - 1}"] = face_parent
+    choices[f"face_child_{N - 1}"] = face_child
+    choices[f"contact_params_{N - 1}"] = jnp.zeros(3)
     return model.importance(key, choices, (jnp.arange(N), *trace.get_args()[1:]))[1]
 
 

bayes3d/neural/cosypose_baseline/cosypose_utils.py

@@ -206,7 +206,7 @@ def cosypose_interface(rgb_imgs, camera_k):
     all_scores = []
     for i, rgb_img in enumerate(rgb_imgs):
         pred = COSYPOSE_MODEL.inference(rgb_img, camera_k)
-        print(f"{i+1}/{num_imgs} inference done")
+        print(f"{i + 1}/{num_imgs} inference done")
 
         pred_poses = np.asarray(pred.poses.cpu())
         pred_ids = [

bayes3d/neural/dino.py

@@ -189,9 +189,9 @@ def patch_vit_resolution(model: nn.Module, stride: int) -> nn.Module:
             return model
 
         stride = nn_utils._pair(stride)
-        assert all(
-            [(patch_size // s_) * s_ == patch_size for s_ in stride]
-        ), f"stride {stride} should divide patch_size {patch_size}"
+        assert all([(patch_size // s_) * s_ == patch_size for s_ in stride]), (
+            f"stride {stride} should divide patch_size {patch_size}"
+        )
 
         # fix the stride
         model.patch_embed.proj.stride = stride
@@ -415,7 +415,9 @@ def extract_descriptors(
         if not include_cls:
             x = x[:, :, 1:, :]  # remove cls token
         else:
-            assert not bin, "bin = True and include_cls = True are not supported together, set one of them False."
+            assert not bin, (
+                "bin = True and include_cls = True are not supported together, set one of them False."
+            )
         if not bin:
             desc = (
                 x.permute(0, 2, 3, 1).flatten(start_dim=-2, end_dim=-1).unsqueeze(dim=1)
@@ -431,9 +433,9 @@ def extract_saliency_maps(self, batch: torch.Tensor) -> torch.Tensor:
         :param batch: batch to extract saliency maps for. Has shape BxCxHxW.
         :return: a tensor of saliency maps. has shape Bxt-1
         """
-        assert (
-            self.model_type == "dino_vits8"
-        ), "saliency maps are supported only for dino_vits model_type."
+        assert self.model_type == "dino_vits8", (
+            "saliency maps are supported only for dino_vits model_type."
+        )
         self._extract_features(batch, [11], "attn")
         head_idxs = [0, 2, 4, 5]
         curr_feats = self._feats[0]  # Bxhxtxt

bayes3d/renderer.py

@@ -126,7 +126,7 @@ def add_mesh(self, mesh, mesh_name=None, scaling_factor=1.0, center_mesh=True):
         bounding_box_dims, bounding_box_pose = bayes3d.utils.aabb(mesh.vertices)
         if center_mesh:
             if not jnp.isclose(bounding_box_pose[:3, 3], 0.0).all():
-                print(f"Centering mesh with translation {bounding_box_pose[:3,3]}")
+                print(f"Centering mesh with translation {bounding_box_pose[:3, 3]}")
             mesh.vertices = mesh.vertices - bounding_box_pose[:3, 3]
 
         self.meshes.append(mesh)

bayes3d/rendering/nvdiffrast_jax/renderer_matching_pytorch.py

@@ -178,7 +178,7 @@ def xfm_points(points, matrix):
         pos_grads.min().item(),
         pos_grads.max().item(),
     )
-    print(f"JAX rasterization (eval + grad): {(end_time - start_time)*1000} ms")
+    print(f"JAX rasterization (eval + grad): {(end_time - start_time) * 1000} ms")
 
     # save viz
     b.viz.get_depth_image(rast_out[0][:, :, 2]).save("img_jax.png")
@@ -229,7 +229,7 @@ def xfm_points(points, matrix):
         pos_grads.min().item(),
         pos_grads.max().item(),
     )
-    print(f"Torch rasterization (eval + grad): {(end_time - start_time)*1000} ms")
+    print(f"Torch rasterization (eval + grad): {(end_time - start_time) * 1000} ms")
 
     # save viz
     b.viz.get_depth_image(jnp.array(rast_out[0][:, :, 2].cpu())).save("img_torch.png")
@@ -278,7 +278,7 @@ def xfm_points(points, matrix):
     print(
         f"JAX BWD (sum, min, max): g_attr={g_attr.sum().item(), g_attr.min().item(), g_attr.max().item()}\ng_rast={g_rast.sum().item(), g_rast.min().item(), g_rast.max().item()}"
     )
-    print(f"JAX interpolation: {(end_time - start_time)*1000} ms")
+    print(f"JAX interpolation: {(end_time - start_time) * 1000} ms")
 
     # save viz
     b.viz.get_depth_image(gb_pos[0][:, :, 2]).save("interpolate_jax.png")
@@ -316,7 +316,7 @@ def xfm_points(points, matrix):
     print(
         f"TORCH BWD (sum, min, max): g_attr={g_attr.sum().item(), g_attr.min().item(), g_attr.max().item()}\ng_rast={g_rast.sum().item(), g_rast.min().item(), g_rast.max().item()}"
     )
-    print(f"Torch interpolation: {(end_time - start_time)*1000} ms")
+    print(f"Torch interpolation: {(end_time - start_time) * 1000} ms")
 
     # save viz
     b.viz.get_depth_image(jnp.array(gb_pos[0][:, :, 2].cpu())).save(

scripts/_mkl/notebooks/00a - Types.ipynb

@@ -6,7 +6,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#|default_exp types"
+    "# |default_exp types"
    ]
   },
   {
@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#|export\n",
+    "# |export\n",
     "from typing import Any, NamedTuple\n",
     "import numpy as np\n",
     "import jax\n",
@@ -29,18 +29,18 @@
     "Int = Array\n",
     "FaceIndex = int\n",
     "FaceIndices = Array\n",
-    "ArrayN      = Array\n",
-    "Array3      = Array\n",
-    "Array2      = Array\n",
-    "ArrayNx2    = Array\n",
-    "ArrayNx3    = Array\n",
-    "Matrix      = jaxlib.xla_extension.ArrayImpl\n",
-    "PrecisionMatrix  = Matrix\n",
+    "ArrayN = Array\n",
+    "Array3 = Array\n",
+    "Array2 = Array\n",
+    "ArrayNx2 = Array\n",
+    "ArrayNx3 = Array\n",
+    "Matrix = jaxlib.xla_extension.ArrayImpl\n",
+    "PrecisionMatrix = Matrix\n",
     "CovarianceMatrix = Matrix\n",
-    "CholeskyMatrix   = Matrix\n",
-    "SquareMatrix     = Matrix\n",
-    "Vector     = Array\n",
-    "Direction  = Vector\n",
+    "CholeskyMatrix = Matrix\n",
+    "SquareMatrix = Matrix\n",
+    "Vector = Array\n",
+    "Direction = Vector\n",
     "BaseVector = Vector"
    ]
   },