code regulation

Author: mxh1999

embodiedscan/datasets/transforms/multiview.py

@@ -117,6 +117,7 @@ class AggregateMultiViewPoints(BaseTransform):
         save_slices (int): Whether to save the index range of
             the points in the current point cloud.
     """
+
     def __init__(self,
                  coord_type: str = 'DEPTH',
                  save_slices: bool = False) -> None:
@@ -170,6 +171,7 @@ class ConstructMultiSweeps(BaseTransform):
     points, gt_bboxes_3d, gt_labels_3d,
     visible_instance_masks, visible_occupancy_masks
     """
+
     def __init__(self):
         super().__init__()
 

embodiedscan/models/dense_heads/fcaf3d_head.py

@@ -868,7 +868,10 @@ def __init__(self,
                  pts_center_threshold: int,
                  center_loss: dict = dict(type='mmdet.CrossEntropyLoss',
                                           use_sigmoid=True),
-                 bbox_loss: dict = dict(type='BBoxCDLoss', mode='l1', loss_weight=1.0, group='g8'),
+                 bbox_loss: dict = dict(type='BBoxCDLoss',
+                                        mode='l1',
+                                        loss_weight=1.0,
+                                        group='g8'),
                  cls_loss: dict = dict(type='mmdet.FocalLoss'),
                  decouple_bbox_loss: bool = False,
                  decouple_groups: int = 3,
@@ -1232,22 +1235,22 @@ def _loss_by_feat_single(self, center_preds: List[Tensor],
                                 (bbox_pred_center, bbox_targ_size,
                                  bbox_targ_euler),
                                 dim=-1),
-                                                 pos_bbox_targets,
-                                                 reduction_override='none')
+                                                pos_bbox_targets,
+                                                reduction_override='none')
                         corner_bbox_loss += self.decouple_weights[
                             1] * self.bbox_loss(torch.concat(
                                 (bbox_targ_center, bbox_pred_size,
                                  bbox_targ_euler),
                                 dim=-1),
-                                                 pos_bbox_targets,
-                                                 reduction_override='none')
+                                                pos_bbox_targets,
+                                                reduction_override='none')
                         corner_bbox_loss += self.decouple_weights[
                             2] * self.bbox_loss(torch.concat(
                                 (bbox_targ_center, bbox_targ_size,
                                  bbox_pred_euler),
                                 dim=-1),
-                                                 pos_bbox_targets,
-                                                 reduction_override='none')
+                                                pos_bbox_targets,
+                                                reduction_override='none')
                         bbox_sizes = bbox_targ_size.norm(
                             dim=-1)[:, None].clamp(min=0.1)
                         corner_bbox_loss = (corner_bbox_loss /
@@ -1272,11 +1275,11 @@ def _loss_by_feat_single(self, center_preds: List[Tensor],
                     if self.decouple_groups == 4:
                         corner_bbox_loss += self.decouple_weights[
                             3] * self.bbox_loss(decode_bbox_preds,
-                                                 pos_bbox_targets)
+                                                pos_bbox_targets)
 
                 else:
-                    corner_bbox_loss += self.bbox_loss(
-                        decode_bbox_preds, pos_bbox_targets)
+                    corner_bbox_loss += self.bbox_loss(decode_bbox_preds,
+                                                       pos_bbox_targets)
 
                 bbox_loss = corner_bbox_loss
 

embodiedscan/models/detectors/embodied_det3d.py

@@ -34,7 +34,8 @@ class Embodied3DDetector(BaseModel):
         bbox_head (dict): Config dict of box head.
         neck (dict, optional): Config dict of neck. Defaults to None.
         neck_3d (dict, optional): Config dict of 3D neck. Defaults to None.
-        neck_lidar (dict, optional): Config dict of lidar neck. Defaults to None.
+        neck_lidar (dict, optional): Config dict of lidar neck.
+            Defaults to None.
         coord_type (str): Type of Box coordinates. Default to CAMERA.
         train_cfg (dict, optional): Config dict of training hyper-parameters.
             Defaults to None.

embodiedscan/models/detectors/sparse_featfusion_single_stage.py

@@ -35,7 +35,8 @@ class SparseFeatureFusionSingleStage3DDetector(BaseModel):
         bbox_head (dict): Config dict of box head.
         neck (dict, optional): Config dict of neck. Defaults to None.
         neck_3d (dict, optional): Config dict of 3D neck. Defaults to None.
-        neck_lidar (dict, optional): Config dict of lidar neck. Defaults to None.
+        neck_lidar (dict, optional): Config dict of lidar neck.
+            Defaults to None.
         coord_type (str): Type of Box coordinates. Default to CAMERA.
         train_cfg (dict, optional): Config dict of training hyper-parameters.
             Defaults to None.

install.py

@@ -109,4 +109,4 @@ def install_requires(fname):
     if args.mode == 'run' or args.mode == 'all':
         install_requires('requirements/run.txt')
 
-    run_subprocess([sys.executable, '-m', 'pip', 'install', '-e', '.'])
+    run_subprocess([sys.executable, '-m', 'pip', 'install', '-e', '.'])