def main():
parser = ArgumentParser()
parser.add_argument('pcd', help='Point cloud file')
parser.add_argument('config', help='Config file')
parser.add_argument('checkpoint', help='Checkpoint file')
parser.add_argument('--device',
default='cuda:0',
help='Device used for inference')
parser.add_argument('--out-dir',
type=str,
default='demo',
help='dir to save results')
parser.add_argument('--show',
action='store_true',
help='show online visuliaztion results')
parser.add_argument('--snapshot',
action='store_true',
help='whether to save online visuliaztion results')
args = parser.parse_args()
# build the model from a config file and a checkpoint file
model = init_model(args.config, args.checkpoint, device=args.device)
# test a single image
result, data = inference_segmentor(model, args.pcd)
# show the results
show_result_meshlab(data,
result,
args.out_dir,
show=args.show,
snapshot=args.snapshot,
task='seg',
palette=model.PALETTE)
def test_inference_segmentor():
# PN2 only has GPU implementations
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
pcd = 'tests/data/scannet/points/scene0000_00.bin'
segmentor_cfg = 'configs/pointnet2/pointnet2_ssg_' \
'16x2_cosine_200e_scannet_seg-3d-20class.py'
segmentor = init_model(segmentor_cfg, device='cuda:0')
results = inference_segmentor(segmentor, pcd)
seg_3d = results[0][0]['semantic_mask']
assert seg_3d.shape == torch.Size([100])
assert seg_3d.min() >= 0
assert seg_3d.max() <= 19