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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmdet.engine.hooks.utils import trigger_visualization_hook
from mmengine.config import Config, ConfigDict, DictAction
from mmengine.evaluator import DumpResults
from mmengine.runner import Runner
from mmyolo.registry import RUNNERS
from mmyolo.utils import is_metainfo_lower
# TODO: support fuse_conv_bn
def parse_args():
parser = argparse.ArgumentParser(
description='MMYOLO 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(
'--out',
type=str,
help='output result file (must be a .pkl file) in pickle format')
parser.add_argument(
'--json-prefix',
type=str,
help='the prefix of the output json file without perform evaluation, '
'which is useful when you want to format the result to a specific '
'format and submit it to the test server')
parser.add_argument(
'--tta',
action='store_true',
help='Whether to use test time augmentation')
parser.add_argument(
'--show', action='store_true', help='show prediction results')
parser.add_argument(
'--deploy',
action='store_true',
help='Switch model to deployment mode')
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(
'--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('--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 main():
args = parse_args()
# load config
cfg = Config.fromfile(args.config)
# replace the ${key} with the value of cfg.key
# cfg = replace_cfg_vals(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.deploy:
cfg.custom_hooks.append(dict(type='SwitchToDeployHook'))
# add `format_only` and `outfile_prefix` into cfg
if args.json_prefix is not None:
cfg_json = {
'test_evaluator.format_only': True,
'test_evaluator.outfile_prefix': args.json_prefix
}
cfg.merge_from_dict(cfg_json)
# Determine whether the custom metainfo fields are all lowercase
is_metainfo_lower(cfg)
if args.tta:
assert 'tta_model' in cfg, 'Cannot find ``tta_model`` in config.' \
" Can't use tta !"
assert 'tta_pipeline' in cfg, 'Cannot find ``tta_pipeline`` ' \
"in config. Can't use tta !"
cfg.model = ConfigDict(**cfg.tta_model, module=cfg.model)
test_data_cfg = cfg.test_dataloader.dataset
while 'dataset' in test_data_cfg:
test_data_cfg = test_data_cfg['dataset']
# batch_shapes_cfg will force control the size of the output image,
# it is not compatible with tta.
if 'batch_shapes_cfg' in test_data_cfg:
test_data_cfg.batch_shapes_cfg = None
test_data_cfg.pipeline = cfg.tta_pipeline
# 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)
# add `DumpResults` dummy metric
if args.out is not None:
assert args.out.endswith(('.pkl', '.pickle')), \
'The dump file must be a pkl file.'
runner.test_evaluator.metrics.append(
DumpResults(out_file_path=args.out))
# start testing
runner.test()
if __name__ == '__main__':
main()
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