[Enhance] Support nuscenes demo (#353)

* support nuscenes dataset in demo

* add convert_SyncBN in __init__

* fix meshlab visualization bug

* modify meshlab unittest

* add docstring

* add empty line in docstring

Co-authored-by: xiliu8006 <xiliu800@gmail.com>

mmdet3d/apis/__init__.py

@@ -1,7 +1,8 @@
-from .inference import inference_detector, init_detector, show_result_meshlab
+from .inference import (convert_SyncBN, inference_detector, init_detector,
+ show_result_meshlab)
 from .test import single_gpu_test
 
 __all__ = [
  'inference_detector', 'init_detector', 'single_gpu_test',
- 'show_result_meshlab'
+ 'show_result_meshlab', 'convert_SyncBN'
 ]

mmdet3d/apis/inference.py

@@ -11,6 +11,22 @@
 from mmdet3d.models import build_detector
 
 
+def convert_SyncBN(config):
+ """Convert config's naiveSyncBN to BN.
+
+ Args:
+ config (str or :obj:`mmcv.Config`): Config file path or the config
+ object.
+ """
+ if isinstance(config, dict):
+ for item in config:
+ if item == 'norm_cfg':
+ config[item]['type'] = config[item]['type']. \
+ replace('naiveSyncBN', 'BN')
+ else:
+ convert_SyncBN(config[item])
+
+
 def init_detector(config, checkpoint=None, device='cuda:0'):
  """Initialize a detector from config file.
 
@@ -30,6 +46,7 @@ def init_detector(config, checkpoint=None, device='cuda:0'):
  raise TypeError('config must be a filename or Config object, '
  f'but got {type(config)}')
  config.model.pretrained = None
+ convert_SyncBN(config.model)
  config.model.train_cfg = None
  model = build_detector(config.model, test_cfg=config.get('test_cfg'))
  if checkpoint is not None:
@@ -64,6 +81,9 @@ def inference_detector(model, pcd):
  pts_filename=pcd,
  box_type_3d=box_type_3d,
  box_mode_3d=box_mode_3d,
+ sweeps=[],
+ # set timestamp = 0
+ timestamp=[0],
  img_fields=[],
  bbox3d_fields=[],
  pts_mask_fields=[],
@@ -100,15 +120,16 @@ def show_result_meshlab(data, result, out_dir):
 
  assert out_dir is not None, 'Expect out_dir, got none.'
 
- pred_bboxes = result[0]['boxes_3d'].tensor.numpy()
+ if 'pts_bbox' in result[0].keys():
+ pred_bboxes = result[0]['pts_bbox']['boxes_3d'].tensor.numpy()
+ else:
+ pred_bboxes = result[0]['boxes_3d'].tensor.numpy()
  # for now we convert points into depth mode
  if data['img_metas'][0][0]['box_mode_3d'] != Box3DMode.DEPTH:
  points = points[..., [1, 0, 2]]
  points[..., 0] *= -1
  pred_bboxes = Box3DMode.convert(pred_bboxes,
  data['img_metas'][0][0]['box_mode_3d'],
  Box3DMode.DEPTH)
- pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
- else:
- pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
- show_result(points, None, pred_bboxes, out_dir, file_name)
+ show_result(points, None, pred_bboxes, out_dir, file_name, show=False)
+ return out_dir, file_name

mmdet3d/core/visualizer/show_result.py

@@ -79,9 +79,9 @@ def show_result(points, gt_bboxes, pred_bboxes, out_dir, filename, show=True):
  filename (str): Filename of the current frame.
  show (bool): Visualize the results online.
  """
- from .open3d_vis import Visualizer
-
  if show:
+ from .open3d_vis import Visualizer
+
  vis = Visualizer(points)
  if pred_bboxes is not None:
  vis.add_bboxes(bbox3d=pred_bboxes)

tests/test_runtime/test_apis.py

@@ -1,9 +1,15 @@
+import numpy as np
+import os
 import pytest
+import tempfile
 import torch
 from mmcv.parallel import MMDataParallel
 from os.path import dirname, exists, join
 
-from mmdet3d.apis import inference_detector, init_detector, single_gpu_test
+from mmdet3d.apis import (convert_SyncBN, inference_detector, init_detector,
+ show_result_meshlab, single_gpu_test)
+from mmdet3d.core import Box3DMode
+from mmdet3d.core.bbox import LiDARInstance3DBoxes
 from mmdet3d.datasets import build_dataloader, build_dataset
 from mmdet3d.models import build_detector
 
@@ -32,6 +38,48 @@ def _get_config_module(fname):
  return config_mod
 
 
+def test_convert_SyncBN():
+ cfg = _get_config_module(
+ 'pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py')
+ model_cfg = cfg.model
+ convert_SyncBN(model_cfg)
+ assert model_cfg['pts_voxel_encoder']['norm_cfg']['type'] == 'BN1d'
+ assert model_cfg['pts_backbone']['norm_cfg']['type'] == 'BN2d'
+ assert model_cfg['pts_neck']['norm_cfg']['type'] == 'BN2d'
+
+
+def test_show_result_meshlab():
+ pcd = 'tests/data/nuscenes/samples/LIDAR_TOP/n015-2018-08-02-17-16-37+' \
+ '0800__LIDAR_TOP__1533201470948018.pcd.bin'
+ box_3d = LiDARInstance3DBoxes(
+ torch.tensor(
+ [[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]]))
+ labels_3d = torch.tensor([0])
+ scores_3d = torch.tensor([0.5])
+ points = np.random.rand(100, 4)
+ img_meta = dict(
+ pts_filename=pcd, boxes_3d=box_3d, box_mode_3d=Box3DMode.LIDAR)
+ data = dict(points=[[torch.tensor(points)]], img_metas=[[img_meta]])
+ result = [
+ dict(
+ pts_bbox=dict(
+ boxes_3d=box_3d, labels_3d=labels_3d, scores_3d=scores_3d))
+ ]
+ temp_out_dir = tempfile.mkdtemp()
+ out_dir, file_name = show_result_meshlab(data, result, temp_out_dir)
+ expected_outfile_ply = file_name + '_pred.ply'
+ expected_outfile_obj = file_name + '_points.obj'
+ expected_outfile_ply_path = os.path.join(out_dir, file_name,
+ expected_outfile_ply)
+ expected_outfile_obj_path = os.path.join(out_dir, file_name,
+ expected_outfile_obj)
+ assert os.path.exists(expected_outfile_ply_path)
+ assert os.path.exists(expected_outfile_obj_path)
+ os.remove(expected_outfile_obj_path)
+ os.remove(expected_outfile_ply_path)
+ os.removedirs(os.path.join(temp_out_dir, file_name))
+
+
 def test_inference_detector():
  pcd = 'tests/data/kitti/training/velodyne_reduced/000000.bin'
  detector_cfg = 'configs/pointpillars/hv_pointpillars_secfpn_' \