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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmengine.config import Config, ConfigDict, DictAction
from mmengine.registry import RUNNERS
from mmengine.runner import Runner
from mmdet3d.utils import replace_ceph_backend
# TODO: support fuse_conv_bn and format_only
def parse_args():
parser = argparse.ArgumentParser(
description='MMDet3D test (and eval) a model')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument(
'--work-dir',
help='the directory to save the file containing evaluation metrics')
parser.add_argument(
'--ceph', action='store_true', help='Use ceph as data storage backend')
parser.add_argument(
'--show', action='store_true', help='show prediction results')
parser.add_argument(
'--show-dir',
help='directory where painted images will be saved. '
'If specified, it will be automatically saved '
'to the work_dir/timestamp/show_dir')
parser.add_argument(
'--score-thr', type=float, default=0.1, help='bbox score threshold')
parser.add_argument(
'--task',
type=str,
choices=[
'mono_det', 'multi-view_det', 'lidar_det', 'lidar_seg',
'multi-modality_det'
],
help='Determine the visualization method depending on the task.')
parser.add_argument(
'--wait-time', type=float, default=2, help='the interval of show (s)')
parser.add_argument(
'--cfg-options',
nargs='+',
action=DictAction,
help='override some settings in the used config, the key-value pair '
'in xxx=yyy format will be merged into config file. If the value to '
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
'Note that the quotation marks are necessary and that no white space '
'is allowed.')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
parser.add_argument(
'--tta', action='store_true', help='Test time augmentation')
# When using PyTorch version >= 2.0.0, the `torch.distributed.launch`
# will pass the `--local-rank` parameter to `tools/test.py` instead
# of `--local_rank`.
parser.add_argument('--local_rank', '--local-rank', type=int, default=0)
args = parser.parse_args()
if 'LOCAL_RANK' not in os.environ:
os.environ['LOCAL_RANK'] = str(args.local_rank)
return args
def trigger_visualization_hook(cfg, args):
default_hooks = cfg.default_hooks
if 'visualization' in default_hooks:
visualization_hook = default_hooks['visualization']
# Turn on visualization
visualization_hook['draw'] = True
if args.show:
visualization_hook['show'] = True
visualization_hook['wait_time'] = args.wait_time
if args.show_dir:
visualization_hook['test_out_dir'] = args.show_dir
all_task_choices = [
'mono_det', 'multi-view_det', 'lidar_det', 'lidar_seg',
'multi-modality_det'
]
assert args.task in all_task_choices, 'You must set '\
f"'--task' in {all_task_choices} in the command " \
'if you want to use visualization hook'
visualization_hook['vis_task'] = args.task
visualization_hook['score_thr'] = args.score_thr
else:
raise RuntimeError(
'VisualizationHook must be included in default_hooks.'
'refer to usage '
'"visualization=dict(type=\'VisualizationHook\')"')
return cfg
def main():
args = parse_args()
# load config
cfg = Config.fromfile(args.config)
# TODO: We will unify the ceph support approach with other OpenMMLab repos
if args.ceph:
cfg = replace_ceph_backend(cfg)
cfg.launcher = args.launcher
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
# work_dir is determined in this priority: CLI > segment in file > filename
if args.work_dir is not None:
# update configs according to CLI args if args.work_dir is not None
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
cfg.load_from = args.checkpoint
if args.show or args.show_dir:
cfg = trigger_visualization_hook(cfg, args)
if args.tta:
# Currently, we only support tta for 3D segmentation
# TODO: Support tta for 3D detection
assert 'tta_model' in cfg, 'Cannot find ``tta_model`` in config.'
assert 'tta_pipeline' in cfg, 'Cannot find ``tta_pipeline`` in config.'
cfg.test_dataloader.dataset.pipeline = cfg.tta_pipeline
cfg.model = ConfigDict(**cfg.tta_model, module=cfg.model)
# build the runner from config
if 'runner_type' not in cfg:
# build the default runner
runner = Runner.from_cfg(cfg)
else:
# build customized runner from the registry
# if 'runner_type' is set in the cfg
runner = RUNNERS.build(cfg)
# start testing
runner.test()
if __name__ == '__main__':
main()
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