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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() | |