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

* FIX Transpose nested lists without Numpy

* Removed unused Numpy import

mmdet3d/models/dense_heads/centerpoint_head.py

@@ -1,6 +1,5 @@
 # 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 @@ def _gather_feat(self, feat, ind, mask=None):
  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 @@ def get_targets(self, gt_bboxes_3d, gt_labels_3d):
  """
  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