benchmark_test_image.py

import logging
import os.path as osp
from argparse import ArgumentParser

from mmcv import Config

from mmdet.apis import inference_detector, init_detector, show_result_pyplot
from mmdet.utils import get_root_logger


def parse_args():
    parser = ArgumentParser()
    parser.add_argument('config', help='test config file path')
    parser.add_argument('checkpoint_root', help='Checkpoint file root path')
    parser.add_argument('--img', default='demo/demo.jpg', help='Image file')
    parser.add_argument('--aug', action='store_true', help='aug test')
    parser.add_argument('--show', action='store_true', help='show results')
    parser.add_argument(
        '--device', default='cuda:0', help='Device used for inference')
    parser.add_argument(
        '--score-thr', type=float, default=0.3, help='bbox score threshold')
    args = parser.parse_args()
    return args


# Sample test whether the inference code is correct
def main(args):
    config = Config.fromfile(args.config)
    logger = get_root_logger(
        log_file='benchmark_test_image.log', log_level=logging.ERROR)

    for model_key in config:
        model_infos = config[model_key]
        if not isinstance(model_infos, list):
            model_infos = [model_infos]
        for model_info in model_infos:
            print('processing: ', model_info['config'], flush=True)
            config_name = model_info['config'].strip()
            checkpoint = osp.join(args.checkpoint_root,
                                  model_info['checkpoint'].strip())
            try:
                # build the model from a config file and a checkpoint file
                cfg = Config.fromfile(config_name)
                if args.aug:
                    if 'flip' in cfg.data.test.pipeline[1]:
                        cfg.data.test.pipeline[1].flip = True
                    else:
                        logger.error(
                            f'{config_name} " : Unable to start aug test')

                model = init_detector(cfg, checkpoint, device=args.device)
                # test a single image
                result = inference_detector(model, args.img)
                # show the results
                if args.show:
                    show_result_pyplot(
                        model,
                        args.img,
                        result,
                        score_thr=args.score_thr,
                        wait_time=1)
            except Exception as e:
                logger.error(f'{config_name} " : {repr(e)}')


if __name__ == '__main__':
    args = parse_args()
    main(args)