modified bbox3d

Author: lhcstation

.vscode/launch.json

@@ -0,0 +1,49 @@
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Debug Training",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/tools/train.py",
+            "args": [
+                "./projects/configs/stage1_track_map/base_track_map.py", 
+                "--gpus",
+                "1",
+                "--deterministic",
+                "--work-dir",
+                "${workspaceFolder}/work_dirs/train1"
+            ],
+            "env": {
+                "MASTER_ADDR": "127.0.0.1",
+                "MASTER_PORT": "28596",
+                "PYTHONPATH": "${workspaceFolder}/..:${env:PYTHONPATH}"
+            },
+            "console": "integratedTerminal"
+        }
+    ]
+}
+
+
+
+// {
+//     "version": "0.2.0",
+//     "configurations": [
+//         {
+//             "name": "Debug Evaluating",
+//             "type": "python",
+//             "request": "launch",
+//             "program": "${workspaceFolder}/tools/test.py",
+//             "args": [
+//                 "./projects/configs/stage1_track_map/base_track_map.py", 
+//                 "./ckpts/uniad_base_track_map.pth",
+//                 "--show-dir",
+//                 "${workspaceFolder}/work_dirs/eval1"
+//             ],
+//             "env": {
+//                 "PYTHONPATH": "${workspaceFolder}/..:${env:PYTHONPATH}"
+//             },
+//             "console": "integratedTerminal"
+//         }
+//     ]
+// }

projects/mmdet3d_plugin/datasets/data_utils/data_utils.py

@@ -28,8 +28,8 @@ def output_to_nusc_box(detection):
     box_yaw = box3d.yaw.numpy()
     # TODO: check whether this is necessary
     # with dir_offset & dir_limit in the head
-    # TODO(box3d): convert bbox_yaw and bbox_dims to mmdet3d v1.0.0rc6 format.
-    box_yaw = -box_yaw - np.pi / 2
+    # TODO(box3d): convert bbox_yaw and bbox_dims to mmdet3d v1.0.0rc6 format. [DONE]
+    # box_yaw = -box_yaw - np.pi / 2
 
     box_list = []
     for i in range(len(box3d)):
@@ -78,8 +78,8 @@ def output_to_nusc_box_det(detection):
     box_yaw = box3d.yaw.numpy()
     # TODO: check whether this is necessary
     # with dir_offset & dir_limit in the head
-    # TODO(box3d): convert bbox_yaw and bbox_dims to mmdet3d v1.0.0rc6 format.
-    box_yaw = -box_yaw - np.pi / 2
+    # TODO(box3d): convert bbox_yaw and bbox_dims to mmdet3d v1.0.0rc6 format. [DONE]
+    # box_yaw = -box_yaw - np.pi / 2
 
     box_list = []
     for i in range(len(box3d)):

projects/mmdet3d_plugin/datasets/data_utils/trajectory_api.py

@@ -148,8 +148,8 @@ def prepare_sdc_vel_info(self):
 
     def generate_sdc_info(self, sdc_vel, as_lidar_instance3d_box=False):
         # sdc dim from https://forum.nuscenes.org/t/dimensions-of-the-ego-vehicle-used-to-gather-data/550
-        # TODO(box3d): we have changed yaw to mmdet3d 1.0.0rc6 format, maybe we should change this.
-        psudo_sdc_bbox = np.array([0.0, 0.0, 0.0, 1.73, 4.08, 1.56, -np.pi])
+        # TODO(box3d): we have changed yaw to mmdet3d 1.0.0rc6 format, wlh->lwh -pi->0.5pi
+        psudo_sdc_bbox = np.array([0.0, 0.0, 0.0, 4.08, 1.73, 1.56, 0.5*np.pi])
         if self.with_velocity:
             psudo_sdc_bbox = np.concatenate([psudo_sdc_bbox, sdc_vel], axis=-1)
         gt_bboxes_3d = np.array([psudo_sdc_bbox]).astype(np.float32)

projects/mmdet3d_plugin/uniad/dense_heads/motion_head_plugin/motion_utils.py

@@ -34,9 +34,9 @@ def nonlinear_smoother(gt_bboxes_3d, gt_fut_traj, gt_fut_traj_mask, bbox_tensor)
     gt_fut_traj_xy_diff = np.diff(gt_fut_traj_xy_diff, axis=1)
     gt_fut_traj_yaw = np.arctan2(
         gt_fut_traj_xy_diff[:, :, 1], gt_fut_traj_xy_diff[:, :, 0])
-    # TODO(box3d): we have changed yaw to mmdet3d 1.0.0rc6 format, maybe we should change this.
+    # TODO(box3d): we have changed yaw to mmdet3d 1.0.0rc6 format, maybe we should change this. [DONE]
     gt_fut_traj_yaw = np.concatenate(
-        [-np.pi/2 - gt_bboxes_3d[:, None, 6:7], gt_fut_traj_yaw[:, :, None]], axis=1)
+        [gt_bboxes_3d[:, None, 6:7], gt_fut_traj_yaw[:, :, None]], axis=1)
     gt_fut_traj = np.concatenate(
         [gt_bboxes_3d[:, None, :2], gt_fut_traj], axis=1)
 
@@ -71,9 +71,9 @@ def _check_ade(traj_pert, traj_ref, thres):
     perturb_used_count = 0
     for i in range(gt_fut_traj.shape[0]):
         ts = ts_limit[i]
-        # TODO(box3d): we have changed yaw to mmdet3d 1.0.0rc6 format, maybe we should change this.
-        x_curr = [bbox_tensor[i, 0], bbox_tensor[i, 1], -
-                  np.pi/2 - yaw_preds[i], speed_preds[i]]
+        # TODO(box3d): we have changed yaw to mmdet3d 1.0.0rc6 format, maybe we should change this. [DONE]
+        x_curr = [bbox_tensor[i, 0], bbox_tensor[i, 1], 
+                  yaw_preds[i], speed_preds[i]]
         reference_trajectory = np.concatenate(
             [gt_fut_traj[i], gt_fut_traj_yaw[i]], axis=-1)
         if ts > 1 and _is_dynamic(gt_fut_traj[i], int(ts), 2) and _check_diff(x_curr, reference_trajectory):

projects/mmdet3d_plugin/uniad/dense_heads/seg_head_plugin/seg_assigner.py

@@ -266,7 +266,7 @@ def assign(self,
         cost = cls_cost + reg_cost + iou_cost 
 
         # 3. do Hungarian matching on CPU using linear_sum_assignment
-        cost = cost.detach().cpu()
+        cost = cost.detach()
 
         assigned_gt_inds[:] = 0
         #index_set = []
@@ -276,14 +276,15 @@ def assign(self,
                               'to install scipy first.')
         result=None
         for i in range(min(self.max_pos, 300//num_gts)):
+            cost = cost.cpu()
             matched_row_inds, matched_col_inds = linear_sum_assignment(cost)
 
             matched_row_inds = torch.from_numpy(matched_row_inds).to(
                 bbox_pred.device)
             matched_col_inds = torch.from_numpy(matched_col_inds).to(
                 bbox_pred.device)     
             #print(matched_row_inds)
-                
+            cost = cost.to(bbox_pred.device)
             cost[matched_row_inds,:] = INF   
             #index_set.(matched_row_inds)
             #print('this mathed row inds ', len(matched_row_inds), i)

tools/analysis_tools/visualize/utils.py

@@ -102,8 +102,8 @@ def __init__(self,
         self.pred_label = pred_label
         self.pred_center = pred_center
         self.pred_dim = pred_dim
-        # TODO(box3d): we have changed yaw to mmdet3d 1.0.0rc6 format, maybe we should change this.
-        self.pred_yaw = -pred_yaw-np.pi/2
+        # TODO(box3d): we have changed yaw to mmdet3d 1.0.0rc6 format, maybe we should change this. [DONE]
+        self.pred_yaw = pred_yaw
         self.pred_vel = pred_vel
         self.pred_traj = pred_traj
         self.pred_traj_score = pred_traj_score

tools/data_converter/uniad_nuscenes_converter.py

@@ -335,8 +335,8 @@ def _fill_trainval_infos(nusc,
                     names[i] = NuScenesDataset.NameMapping[names[i]]
             names = np.array(names)
             # instance_inds = [nusc.getind('instance', ann['instance_token']) for ann in annotations]
-            # TODO(box3d): convert gt_boxes to mmdet3d 1.0.0rc6 LiDARInstance3DBoxes format.
-            gt_boxes = np.concatenate([locs, dims, -rots - np.pi / 2], axis=1)
+            # TODO(box3d): convert gt_boxes to mmdet3d 1.0.0rc6 LiDARInstance3DBoxes format. [DONE]
+            gt_boxes = np.concatenate([locs, dims, rots], axis=1)
             # gt_boxes = np.concatenate([locs, dims[:, [1, 0, 2]], rots], axis=1)
             assert len(gt_boxes) == len(
                 annotations), f'{len(gt_boxes)}, {len(annotations)}'