ci(pre-commit): autoupdate

.pre-commit-config.yaml

@@ -19,7 +19,7 @@ repos:
         args: [--markdown-linebreak-ext=md]
 
   - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 25.9.0
+    rev: 26.1.0
     hooks:
       - id: black
         language_version: python3.11

projects/BEVFusion/bevfusion/ops/voxel/scatter_points.py

@@ -24,15 +24,15 @@ def forward(ctx, feats, coors, reduce_type="max"):
             coordinates: [M, ndim] int tensor, voxel coordinates.
         """
         results = dynamic_point_to_voxel_forward(feats, coors, reduce_type)
-        (voxel_feats, voxel_coors, point2voxel_map, voxel_points_count) = results
+        voxel_feats, voxel_coors, point2voxel_map, voxel_points_count = results
         ctx.reduce_type = reduce_type
         ctx.save_for_backward(feats, voxel_feats, point2voxel_map, voxel_points_count)
         ctx.mark_non_differentiable(voxel_coors)
         return voxel_feats, voxel_coors
 
     @staticmethod
     def backward(ctx, grad_voxel_feats, grad_voxel_coors=None):
-        (feats, voxel_feats, point2voxel_map, voxel_points_count) = ctx.saved_tensors
+        feats, voxel_feats, point2voxel_map, voxel_points_count = ctx.saved_tensors
         grad_feats = torch.zeros_like(feats)
         # TODO: whether to use index put or use cuda_backward
         # To use index put, need point to voxel index

projects/PTv3/engines/defaults.py

@@ -60,8 +60,7 @@ def worker_init_fn(worker_id, num_workers, rank, seed):
 
 def default_argument_parser(epilog=None):
     parser = argparse.ArgumentParser(
-        epilog=epilog
-        or f"""
+        epilog=epilog or f"""
     Examples:
     Run on single machine:
         $ {sys.argv[0]} --num-gpus 8 --config-file cfg.yaml

projects/PTv3/models/utils/serialization/hilbert.py

@@ -118,23 +118,17 @@ def encode(locs, num_dims, num_bits):
     bitpack_mask_rev = bitpack_mask.flip(-1)
 
     if orig_shape[-1] != num_dims:
-        raise ValueError(
-            """
+        raise ValueError("""
       The shape of locs was surprising in that the last dimension was of size
       %d, but num_dims=%d.  These need to be equal.
-      """
-            % (orig_shape[-1], num_dims)
-        )
+      """ % (orig_shape[-1], num_dims))
 
     if num_dims * num_bits > 63:
-        raise ValueError(
-            """
+        raise ValueError("""
       num_dims=%d and num_bits=%d for %d bits total, which can't be encoded
       into a int64.  Are you sure you need that many points on your Hilbert
       curve?
-      """
-            % (num_dims, num_bits, num_dims * num_bits)
-        )
+      """ % (num_dims, num_bits, num_dims * num_bits))
 
     # Treat the location integers as 64-bit unsigned and then split them up into
     # a sequence of uint8s.  Preserve the association by dimension.
@@ -206,14 +200,11 @@ def decode(hilberts, num_dims, num_bits):
     """
 
     if num_dims * num_bits > 64:
-        raise ValueError(
-            """
+        raise ValueError("""
       num_dims=%d and num_bits=%d for %d bits total, which can't be encoded
       into a uint64.  Are you sure you need that many points on your Hilbert
       curve?
-      """
-            % (num_dims, num_bits)
-        )
+      """ % (num_dims, num_bits))
 
     # Handle the case where we got handed a naked integer.
     hilberts = torch.atleast_1d(hilberts)

projects/StreamPETR/stream_petr/models/dense_heads/petr_head_dn.py

@@ -615,7 +615,7 @@ def get_targets(
         num_imgs = len(cls_scores_list)
         gt_bboxes_ignore_list = [gt_bboxes_ignore_list for _ in range(num_imgs)]
 
-        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, pos_inds_list, neg_inds_list) = (
+        labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, pos_inds_list, neg_inds_list = (
             multi_apply(
                 self._get_target_single,
                 cls_scores_list,
@@ -654,7 +654,7 @@ def loss_single(self, cls_scores, bbox_preds, gt_bboxes_list, gt_labels_list, gt
         cls_reg_targets = self.get_targets(
             cls_scores_list, bbox_preds_list, gt_bboxes_list, gt_labels_list, gt_bboxes_ignore_list
         )
-        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg) = (
+        labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg = (
             cls_reg_targets
         )
         labels = torch.cat(labels_list, 0)

projects/StreamPETR/stream_petr/models/dense_heads/sparse_head.py

@@ -676,7 +676,7 @@ def get_targets(
         num_imgs = len(cls_scores_list)
         gt_bboxes_ignore_list = [gt_bboxes_ignore_list for _ in range(num_imgs)]
 
-        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, pos_inds_list, neg_inds_list) = (
+        labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, pos_inds_list, neg_inds_list = (
             multi_apply(
                 self._get_target_single,
                 cls_scores_list,
@@ -715,7 +715,7 @@ def loss_single(self, cls_scores, bbox_preds, gt_bboxes_list, gt_labels_list, gt
         cls_reg_targets = self.get_targets(
             cls_scores_list, bbox_preds_list, gt_bboxes_list, gt_labels_list, gt_bboxes_ignore_list
         )
-        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg) = (
+        labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg = (
             cls_reg_targets
         )
         labels = torch.cat(labels_list, 0)

projects/StreamPETR/stream_petr/models/dense_heads/streampetr_head.py

@@ -827,7 +827,7 @@ def get_targets(
         num_imgs = len(cls_scores_list)
         gt_bboxes_ignore_list = [gt_bboxes_ignore_list for _ in range(num_imgs)]
 
-        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, pos_inds_list, neg_inds_list) = (
+        labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, pos_inds_list, neg_inds_list = (
             multi_apply(
                 self._get_target_single,
                 cls_scores_list,
@@ -866,7 +866,7 @@ def loss_single(self, cls_scores, bbox_preds, gt_bboxes_list, gt_labels_list, gt
         cls_reg_targets = self.get_targets(
             cls_scores_list, bbox_preds_list, gt_bboxes_list, gt_labels_list, gt_bboxes_ignore_list
         )
-        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg) = (
+        labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg = (
             cls_reg_targets
         )
         labels = torch.cat(labels_list, 0)

projects/StreamPETR/stream_petr/models/dense_heads/yolox_head.py

@@ -310,7 +310,7 @@ def loss(
         gt_labels = [labels2d for i in gt_labels2d_list for labels2d in i]
         centers2d = [center2d for i in centers2d for center2d in i]
 
-        (pos_masks, cls_targets, obj_targets, bbox_targets, l1_targets, centers2d_target, num_fg_imgs) = multi_apply(
+        pos_masks, cls_targets, obj_targets, bbox_targets, l1_targets, centers2d_target, num_fg_imgs = multi_apply(
             self._get_target_single,
             flatten_cls_preds.detach(),
             flatten_objectness.detach(),

tools/detection3d/dataset_converters/kitti_converter.py

@@ -391,7 +391,7 @@ def export_2d_annotation(root_path, info_path, mono3d=True):
 
     for info in mmengine.track_iter_progress(kitti_infos):
         coco_infos = get_2d_boxes(info, occluded=[0, 1, 2, 3], mono3d=mono3d)
-        (height, width, _) = mmcv.imread(osp.join(root_path, info["image"]["image_path"])).shape
+        height, width, _ = mmcv.imread(osp.join(root_path, info["image"]["image_path"])).shape
         coco_2d_dict["images"].append(
             dict(
                 file_name=info["image"]["image_path"],

tools/detection3d/dataset_converters/lyft_converter.py

@@ -62,10 +62,8 @@ def create_lyft_infos(root_path, info_prefix, version="v1.01-train", max_sweeps=
     if test:
         print(f"test scene: {len(train_scenes)}")
     else:
-        print(
-            f"train scene: {len(train_scenes)}, \
-                val scene: {len(val_scenes)}"
-        )
+        print(f"train scene: {len(train_scenes)}, \
+                val scene: {len(val_scenes)}")
     train_lyft_infos, val_lyft_infos = _fill_trainval_infos(
         lyft, train_scenes, val_scenes, test, max_sweeps=max_sweeps
     )
@@ -78,10 +76,8 @@ def create_lyft_infos(root_path, info_prefix, version="v1.01-train", max_sweeps=
         info_path = osp.join(root_path, f"{info_name}.pkl")
         mmengine.dump(data, info_path)
     else:
-        print(
-            f"train sample: {len(train_lyft_infos)}, \
-                val sample: {len(val_lyft_infos)}"
-        )
+        print(f"train sample: {len(train_lyft_infos)}, \
+                val sample: {len(val_lyft_infos)}")
         data = dict(infos=train_lyft_infos, metadata=metadata)
         train_info_name = f"{info_prefix}_infos_train"
         info_path = osp.join(root_path, f"{train_info_name}.pkl")
@@ -234,7 +230,7 @@ def export_2d_annotation(root_path, info_path, version):
         for cam in camera_types:
             cam_info = info["cams"][cam]
             coco_infos = get_2d_boxes(lyft, cam_info["sample_data_token"], visibilities=["", "1", "2", "3", "4"])
-            (height, width, _) = mmcv.imread(cam_info["data_path"]).shape
+            height, width, _ = mmcv.imread(cam_info["data_path"]).shape
             coco_2d_dict["images"].append(
                 dict(file_name=cam_info["data_path"], id=cam_info["sample_data_token"], width=width, height=height)
             )

tools/detection3d/dataset_converters/nuscenes_converter.py

@@ -346,7 +346,7 @@ def export_2d_annotation(root_path, info_path, version, mono3d=True):
             coco_infos = get_2d_boxes(
                 nusc, cam_info["sample_data_token"], visibilities=["", "1", "2", "3", "4"], mono3d=mono3d
             )
-            (height, width, _) = mmcv.imread(cam_info["data_path"]).shape
+            height, width, _ = mmcv.imread(cam_info["data_path"]).shape
             coco_2d_dict["images"].append(
                 dict(
                     file_name=cam_info["data_path"].split("data/nuscenes/")[-1],