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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
import mmcv
import mmengine
import numpy as np
import torch
from mmengine.utils import is_list_of
from mmdet3d.apis import MultiModalityDet3DInferencer
from mmdet3d.structures import Det3DDataSample
class TestMultiModalityDet3DInferencer(TestCase):
def setUp(self):
# init from alias
self.inferencer = MultiModalityDet3DInferencer('mvxnet_kitti-3class')
def test_init(self):
# init from metafile
MultiModalityDet3DInferencer('mvxnet_kitti-3class')
# init from cfg
MultiModalityDet3DInferencer(
'configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py', # noqa
weights= # noqa
'https://download.openmmlab.com/mmdetection3d/v1.0.0_models/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class_20210831_060805-83442923.pth' # noqa
)
def assert_predictions_equal(self, preds1, preds2):
for pred1, pred2 in zip(preds1, preds2):
if 'bboxes_3d' in pred1:
self.assertTrue(
np.allclose(pred1['bboxes_3d'], pred2['bboxes_3d'], 0.1))
if 'scores_3d' in pred1:
self.assertTrue(
np.allclose(pred1['scores_3d'], pred2['scores_3d'], 0.1))
if 'labels_3d' in pred1:
self.assertTrue(
np.allclose(pred1['labels_3d'], pred2['labels_3d']))
def test_call(self):
if not torch.cuda.is_available():
return
infos_path = 'demo/data/kitti/000008.pkl'
points_path = 'demo/data/kitti/000008.bin'
img_path = 'demo/data/kitti/000008.png'
# single img & point cloud
inputs = dict(points=points_path, img=img_path, infos=infos_path)
res_path = self.inferencer(inputs, return_vis=True)
# ndarray
pts_bytes = mmengine.fileio.get(inputs['points'])
points = np.frombuffer(pts_bytes, dtype=np.float32)
points = points.reshape(-1, 4)
points = points[:, :4]
img = mmcv.imread(inputs['img'])
inputs = dict(points=points, img=img, infos=infos_path)
res_ndarray = self.inferencer(inputs, return_vis=True)
self.assert_predictions_equal(res_path['predictions'],
res_ndarray['predictions'])
self.assertIn('visualization', res_path)
self.assertIn('visualization', res_ndarray)
# multiple imgs & point clouds
inputs = [
dict(points=points_path, img=img_path, infos=infos_path),
dict(points=points_path, img=img_path, infos=infos_path)
]
res_path = self.inferencer(inputs, return_vis=True)
# list of ndarray
all_inputs = []
for p in inputs:
pts_bytes = mmengine.fileio.get(p['points'])
points = np.frombuffer(pts_bytes, dtype=np.float32)
points = points.reshape(-1, 4)
img = mmcv.imread(p['img'])
all_inputs.append(dict(points=points, img=img, infos=infos_path))
res_ndarray = self.inferencer(all_inputs, return_vis=True)
self.assert_predictions_equal(res_path['predictions'],
res_ndarray['predictions'])
self.assertIn('visualization', res_path)
self.assertIn('visualization', res_ndarray)
def test_visualize(self):
if not torch.cuda.is_available():
return
inputs = dict(
points='demo/data/kitti/000008.bin',
img='demo/data/kitti/000008.png',
infos='demo/data/kitti/000008.pkl'),
# img_out_dir
with tempfile.TemporaryDirectory() as tmp_dir:
self.inferencer(inputs, out_dir=tmp_dir)
# TODO: For results of LiDAR-based detection, the saved image only
# exists when show=True.
# self.assertTrue(osp.exists(osp.join(tmp_dir, '000000.png')))
def test_postprocess(self):
if not torch.cuda.is_available():
return
# return_datasample
infos_path = 'demo/data/kitti/000008.pkl'
points_path = 'demo/data/kitti/000008.bin'
img_path = 'demo/data/kitti/000008.png'
# single img & point cloud
inputs = dict(points=points_path, img=img_path, infos=infos_path)
res = self.inferencer(inputs, return_datasamples=True)
self.assertTrue(is_list_of(res['predictions'], Det3DDataSample))
# pred_out_dir
with tempfile.TemporaryDirectory() as tmp_dir:
inputs = dict(points=points_path, img=img_path, infos=infos_path)
res = self.inferencer(inputs, print_result=True, out_dir=tmp_dir)
dumped_res = mmengine.load(
osp.join(tmp_dir, 'preds', '000008.json'))
self.assertEqual(res['predictions'][0], dumped_res)
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