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# Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import tempfile
from unittest import TestCase
import mmengine
import numpy as np
import pytest
import torch
from mmengine.utils import is_list_of
from mmdet3d.apis import LidarSeg3DInferencer
from mmdet3d.structures import Det3DDataSample
class TestLiDARSeg3DInferencer(TestCase):
def setUp(self):
# init from alias
self.inferencer = LidarSeg3DInferencer('pointnet2-ssg_s3dis-seg')
def test_init(self):
# init from metafile
LidarSeg3DInferencer('pointnet2-ssg_s3dis-seg')
# init from cfg
LidarSeg3DInferencer(
'configs/pointnet2/pointnet2_ssg_2xb16-cosine-50e_s3dis-seg.py',
'https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class/pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class_20210514_144205-995d0119.pth' # noqa
)
def assert_predictions_equal(self, preds1, preds2):
for pred1, pred2 in zip(preds1, preds2):
self.assertTrue(
np.allclose(pred1['pts_semantic_mask'],
pred2['pts_semantic_mask']))
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
@pytest.mark.skipif(
'DISPLAY' not in os.environ, reason='requires DISPLAY device')
def test_call(self):
# single point cloud
inputs = dict(points='tests/data/s3dis/points/Area_1_office_2.bin')
torch.manual_seed(0)
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, 6)
inputs = dict(points=points)
torch.manual_seed(0)
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 point clouds
inputs = [
dict(points='tests/data/s3dis/points/Area_1_office_2.bin'),
dict(points='tests/data/s3dis/points/Area_1_office_2.bin')
]
torch.manual_seed(0)
res_path = self.inferencer(inputs, return_vis=True)
# list of ndarray
all_points = []
for p in inputs:
pts_bytes = mmengine.fileio.get(p['points'])
points = np.frombuffer(pts_bytes, dtype=np.float32)
points = points.reshape(-1, 6)
all_points.append(dict(points=points))
torch.manual_seed(0)
res_ndarray = self.inferencer(all_points, return_vis=True)
self.assert_predictions_equal(res_path['predictions'],
res_ndarray['predictions'])
self.assertIn('visualization', res_path)
self.assertIn('visualization', res_ndarray)
# point cloud dir, test different batch sizes
pc_dir = dict(points='tests/data/s3dis/points/')
res_bs2 = self.inferencer(pc_dir, batch_size=2, return_vis=True)
self.assertIn('visualization', res_bs2)
self.assertIn('predictions', res_bs2)
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
@pytest.mark.skipif(
'DISPLAY' not in os.environ, reason='requires DISPLAY device')
def test_visualizer(self):
inputs = dict(points='tests/data/s3dis/points/Area_1_office_2.bin')
# img_out_dir
with tempfile.TemporaryDirectory() as tmp_dir:
self.inferencer(inputs, out_dir=tmp_dir)
def test_post_processor(self):
if not torch.cuda.is_available():
return
# return_datasample
inputs = dict(points='tests/data/s3dis/points/Area_1_office_2.bin')
res = self.inferencer(inputs, return_datasamples=True)
self.assertTrue(is_list_of(res['predictions'], Det3DDataSample))
# pred_out_dir
with tempfile.TemporaryDirectory() as tmp_dir:
res = self.inferencer(inputs, print_result=True, out_dir=tmp_dir)
dumped_res = mmengine.load(
osp.join(tmp_dir, 'preds', 'Area_1_office_2.json'))
self.assertEqual(res['predictions'][0], dumped_res)
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