[fix] fix bug in projects/PETR (#2212)

projects/PETR/petr/petr.py

@@ -8,14 +8,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # ------------------------------------------------------------------------
 
-import numpy as np
 import torch
 from mmengine.structures import InstanceData
 
-import mmdet3d
 from mmdet3d.models.detectors.mvx_two_stage import MVXTwoStageDetector
 from mmdet3d.registry import MODELS
-from mmdet3d.structures.bbox_3d import LiDARInstance3DBoxes, limit_period
 from mmdet3d.structures.ops import bbox3d2result
 from .grid_mask import GridMask
 
@@ -174,8 +171,6 @@ class PETR(MVXTwoStageDetector):
         gt_labels_3d = [gt.labels_3d for gt in batch_gt_instances_3d]
         gt_bboxes_ignore = None
 
-        gt_bboxes_3d = self.LidarBox3dVersionTransfrom(gt_bboxes_3d)
-
         batch_img_metas = self.add_lidar2img(img, batch_img_metas)
 
         img_feats = self.extract_feat(img=img, img_metas=batch_img_metas)
@@ -265,8 +260,8 @@ class PETR(MVXTwoStageDetector):
         Returns:
             batch_input_metas (list[dict]): Meta info with lidar2img added
         """
-        lidar2img_rts = []
         for meta in batch_input_metas:
+            lidar2img_rts = []
             # obtain lidar to image transformation matrix
             for i in range(len(meta['cam2img'])):
                 lidar2cam_rt = torch.tensor(meta['lidar2cam'][i]).double()
@@ -281,19 +276,7 @@ class PETR(MVXTwoStageDetector):
                 # and LoadMultiViewImageFromMultiSweepsFiles.
                 lidar2img_rts.append(lidar2img_rt)
             meta['lidar2img'] = lidar2img_rts
-            meta['img_shape'] = [i.shape for i in img[0]]
-        return batch_input_metas
+            img_shape = meta['img_shape'][:3]
+            meta['img_shape'] = [img_shape] * len(img[0])
 
-    def LidarBox3dVersionTransfrom(self, gt_bboxes_3d):
-        if int(mmdet3d.__version__[0]) >= 1:
-            # Begin hack adaptation to mmdet3d v1.0 ####
-            gt_bboxes_3d = gt_bboxes_3d[0].tensor
-
-            gt_bboxes_3d[:, [3, 4]] = gt_bboxes_3d[:, [4, 3]]
-            gt_bboxes_3d[:, 6] = -gt_bboxes_3d[:, 6] - np.pi / 2
-            gt_bboxes_3d[:, 6] = limit_period(
-                gt_bboxes_3d[:, 6], period=np.pi * 2)
-
-            gt_bboxes_3d = LiDARInstance3DBoxes(gt_bboxes_3d, box_dim=9)
-            gt_bboxes_3d = [gt_bboxes_3d]
-        return gt_bboxes_3d
+        return batch_input_metas

projects/PETR/petr/transforms_3d.py

@@ -185,8 +185,8 @@ class GlobalRotScaleTransImage(BaseTransform):
         num_view = len(results['lidar2cam'])
         for view in range(num_view):
             results['lidar2cam'][view] = (
-                torch.tensor(np.array(results['lidar2cam'][view])).float()
-                @ rot_mat_inv).numpy()
+                torch.tensor(np.array(results['lidar2cam'][view]).T).float()
+                @ rot_mat_inv).T.numpy()
 
         return
 
@@ -203,5 +203,7 @@ class GlobalRotScaleTransImage(BaseTransform):
         num_view = len(results['lidar2cam'])
         for view in range(num_view):
             results['lidar2cam'][view] = (torch.tensor(
-                rot_mat_inv.T @ results['lidar2cam'][view]).float()).numpy()
+                rot_mat_inv.T
+                @ results['lidar2cam'][view].T).float()).T.numpy()
+
         return

projects/PETR/petr/utils.py

@@ -2,7 +2,6 @@
 import numpy as np
 import torch
 
-import mmdet3d
 from mmdet3d.structures.bbox_3d.utils import limit_period
 
 
@@ -11,19 +10,21 @@ def normalize_bbox(bboxes, pc_range):
     cx = bboxes[..., 0:1]
     cy = bboxes[..., 1:2]
     cz = bboxes[..., 2:3]
-    w = bboxes[..., 3:4].log()
-    length = bboxes[..., 4:5].log()
-    h = bboxes[..., 5:6].log()
+    length = bboxes[..., 3:4].log()
+    width = bboxes[..., 4:5].log()
+    height = bboxes[..., 5:6].log()
 
-    rot = bboxes[..., 6:7]
+    rot = -bboxes[..., 6:7] - np.pi / 2
+    rot = limit_period(rot, period=np.pi * 2)
     if bboxes.size(-1) > 7:
         vx = bboxes[..., 7:8]
         vy = bboxes[..., 8:9]
         normalized_bboxes = torch.cat(
-            (cx, cy, w, length, cz, h, rot.sin(), rot.cos(), vx, vy), dim=-1)
+            (cx, cy, length, width, cz, height, rot.sin(), rot.cos(), vx, vy),
+            dim=-1)
     else:
         normalized_bboxes = torch.cat(
-            (cx, cy, w, length, cz, h, rot.sin(), rot.cos()), dim=-1)
+            (cx, cy, length, width, cz, height, rot.sin(), rot.cos()), dim=-1)
     return normalized_bboxes
 
 
@@ -33,6 +34,8 @@ def denormalize_bbox(normalized_bboxes, pc_range):
 
     rot_cosine = normalized_bboxes[..., 7:8]
     rot = torch.atan2(rot_sine, rot_cosine)
+    rot = -rot - np.pi / 2
+    rot = limit_period(rot, period=np.pi * 2)
 
     # center in the bev
     cx = normalized_bboxes[..., 0:1]
@@ -40,30 +43,21 @@ def denormalize_bbox(normalized_bboxes, pc_range):
     cz = normalized_bboxes[..., 4:5]
 
     # size
-    w = normalized_bboxes[..., 2:3]
-    length = normalized_bboxes[..., 3:4]
-    h = normalized_bboxes[..., 5:6]
+    length = normalized_bboxes[..., 2:3]
+    width = normalized_bboxes[..., 3:4]
+    height = normalized_bboxes[..., 5:6]
 
-    w = w.exp()
+    width = width.exp()
     length = length.exp()
-    h = h.exp()
+    height = height.exp()
     if normalized_bboxes.size(-1) > 8:
         # velocity
         vx = normalized_bboxes[:, 8:9]
         vy = normalized_bboxes[:, 9:10]
         denormalized_bboxes = torch.cat(
-            [cx, cy, cz, w, length, h, rot, vx, vy], dim=-1)
+            [cx, cy, cz, length, width, height, rot, vx, vy], dim=-1)
     else:
-        denormalized_bboxes = torch.cat([cx, cy, cz, w, length, h, rot],
-                                        dim=-1)
+        denormalized_bboxes = torch.cat(
+            [cx, cy, cz, length, width, height, rot], dim=-1)
 
-    if int(mmdet3d.__version__[0]) >= 1:
-        denormalized_bboxes_clone = denormalized_bboxes.clone()
-        denormalized_bboxes[:, 3] = denormalized_bboxes_clone[:, 4]
-        denormalized_bboxes[:, 4] = denormalized_bboxes_clone[:, 3]
-        # change yaw
-        denormalized_bboxes[:,
-                            6] = -denormalized_bboxes_clone[:, 6] - np.pi / 2
-        denormalized_bboxes[:, 6] = limit_period(
-            denormalized_bboxes[:, 6], period=np.pi * 2)
     return denormalized_bboxes