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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
from copy import deepcopy
from mmengine import DictAction
from mmdeploy.apis import build_task_processor
from mmdeploy.utils.config_utils import load_config
from mmdeploy.utils.timer import TimeCounter
def parse_args():
parser = argparse.ArgumentParser(
description='MMDeploy test (and eval) a backend.')
parser.add_argument('deploy_cfg', help='Deploy config path')
parser.add_argument('model_cfg', help='Model config path')
parser.add_argument(
'--model', type=str, nargs='+', help='Input model files.')
parser.add_argument(
'--device', help='device used for conversion', default='cpu')
parser.add_argument(
'--work-dir',
default='./work_dir',
help='the directory to save the file containing evaluation metrics')
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('--show', action='store_true', help='show results')
parser.add_argument(
'--show-dir', help='directory where painted images will be saved')
parser.add_argument(
'--interval',
type=int,
default=1,
help='visualize per interval samples.')
parser.add_argument(
'--wait-time',
type=float,
default=2,
help='display time of every window. (second)')
parser.add_argument(
'--log2file',
type=str,
help='log evaluation results and speed to file',
default=None)
parser.add_argument(
'--speed-test', action='store_true', help='activate speed test')
parser.add_argument(
'--warmup',
type=int,
help='warmup before counting inference elapse, require setting '
'speed-test first',
default=10)
parser.add_argument(
'--log-interval',
type=int,
help='the interval between each log, require setting '
'speed-test first',
default=100)
parser.add_argument(
'--batch-size',
type=int,
default=1,
help='the batch size for test, would override `samples_per_gpu`'
'in data config.')
parser.add_argument(
'--uri',
action='store_true',
default='192.168.1.1:60000',
help='Remote ipv4:port or ipv6:port for inference on edge device.')
args = parser.parse_args()
return args
def main():
args = parse_args()
deploy_cfg_path = args.deploy_cfg
model_cfg_path = args.model_cfg
# load deploy_cfg
deploy_cfg, model_cfg = load_config(deploy_cfg_path, model_cfg_path)
# 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
work_dir = args.work_dir
elif model_cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
# merge options for model cfg
if args.cfg_options is not None:
model_cfg.merge_from_dict(args.cfg_options)
task_processor = build_task_processor(model_cfg, deploy_cfg, args.device)
# prepare the dataset loader
test_dataloader = deepcopy(model_cfg['test_dataloader'])
if isinstance(test_dataloader, list):
dataset = []
for loader in test_dataloader:
ds = task_processor.build_dataset(loader['dataset'])
dataset.append(ds)
loader['dataset'] = ds
loader['batch_size'] = args.batch_size
loader = task_processor.build_dataloader(loader)
dataloader = test_dataloader
else:
test_dataloader['batch_size'] = args.batch_size
dataset = task_processor.build_dataset(test_dataloader['dataset'])
test_dataloader['dataset'] = dataset
dataloader = task_processor.build_dataloader(test_dataloader)
# load the model of the backend
model = task_processor.build_backend_model(
args.model,
data_preprocessor_updater=task_processor.update_data_preprocessor)
destroy_model = model.destroy
is_device_cpu = (args.device == 'cpu')
runner = task_processor.build_test_runner(
model,
work_dir,
log_file=args.log2file,
show=args.show,
show_dir=args.show_dir,
wait_time=args.wait_time,
interval=args.interval,
dataloader=dataloader)
if args.speed_test:
with_sync = not is_device_cpu
with TimeCounter.activate(
warmup=args.warmup,
log_interval=args.log_interval,
with_sync=with_sync,
file=args.log2file,
batch_size=args.batch_size):
runner.test()
else:
runner.test()
# only effective when the backend requires explicit clean-up (e.g. Ascend)
destroy_model()
if __name__ == '__main__':
main()
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