[Fix] Centerpoint head nested list transpose (#879)

* FIX Transpose nested lists without Numpy

* Removed unused Numpy import

mmdet3d/models/dense_heads/centerpoint_head.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import copy
-import numpy as np
 import torch
 from mmcv.cnn import ConvModule, build_conv_layer
 from mmcv.runner import BaseModule, force_fp32
@@ -386,6 +385,17 @@ class CenterHead(BaseModule):
     def get_targets(self, gt_bboxes_3d, gt_labels_3d):
         """Generate targets.
 
+        How each output is transformed:
+
+            Each nested list is transposed so that all same-index elements in
+            each sub-list (1, ..., N) become the new sub-lists.
+                [ [a0, a1, a2, ... ], [b0, b1, b2, ... ], ... ]
+                ==> [ [a0, b0, ... ], [a1, b1, ... ], [a2, b2, ... ] ]
+
+            The new transposed nested list is converted into a list of N
+            tensors generated by concatenating tensors in the new sub-lists.
+                [ tensor0, tensor1, tensor2, ... ]
+
         Args:
             gt_bboxes_3d (list[:obj:`LiDARInstance3DBoxes`]): Ground
                 truth gt boxes.
@@ -405,18 +415,17 @@ class CenterHead(BaseModule):
         """
         heatmaps, anno_boxes, inds, masks = multi_apply(
             self.get_targets_single, gt_bboxes_3d, gt_labels_3d)
-        # transpose heatmaps, because the dimension of tensors in each task is
-        # different, we have to use numpy instead of torch to do the transpose.
-        heatmaps = np.array(heatmaps).transpose(1, 0).tolist()
+        # Transpose heatmaps
+        heatmaps = list(map(list, zip(*heatmaps)))
         heatmaps = [torch.stack(hms_) for hms_ in heatmaps]
-        # transpose anno_boxes
-        anno_boxes = np.array(anno_boxes).transpose(1, 0).tolist()
+        # Transpose anno_boxes
+        anno_boxes = list(map(list, zip(*anno_boxes)))
         anno_boxes = [torch.stack(anno_boxes_) for anno_boxes_ in anno_boxes]
-        # transpose inds
-        inds = np.array(inds).transpose(1, 0).tolist()
+        # Transpose inds
+        inds = list(map(list, zip(*inds)))
         inds = [torch.stack(inds_) for inds_ in inds]
-        # transpose inds
-        masks = np.array(masks).transpose(1, 0).tolist()
+        # Transpose inds
+        masks = list(map(list, zip(*masks)))
         masks = [torch.stack(masks_) for masks_ in masks]
         return heatmaps, anno_boxes, inds, masks