OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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102 lines
3.6 KiB
102 lines
3.6 KiB
# Copyright (c) OpenMMLab. All rights reserved. |
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import logging |
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import os.path as osp |
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from argparse import ArgumentParser |
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from mmcv import Config |
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from mmdet.apis import inference_detector, init_detector, show_result_pyplot |
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from mmdet.utils import get_root_logger |
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def parse_args(): |
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parser = ArgumentParser() |
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parser.add_argument('config', help='test config file path') |
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parser.add_argument('checkpoint_root', help='Checkpoint file root path') |
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parser.add_argument('--img', default='demo/demo.jpg', help='Image file') |
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parser.add_argument('--aug', action='store_true', help='aug test') |
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parser.add_argument('--model-name', help='model name to inference') |
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parser.add_argument('--show', action='store_true', help='show results') |
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parser.add_argument( |
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'--wait-time', |
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type=float, |
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default=1, |
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help='the interval of show (s), 0 is block') |
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parser.add_argument( |
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'--device', default='cuda:0', help='Device used for inference') |
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parser.add_argument( |
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'--score-thr', type=float, default=0.3, help='bbox score threshold') |
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args = parser.parse_args() |
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return args |
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def inference_model(config_name, checkpoint, args, logger=None): |
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cfg = Config.fromfile(config_name) |
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if args.aug: |
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if 'flip' in cfg.data.test.pipeline[1]: |
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cfg.data.test.pipeline[1].flip = True |
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else: |
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if logger is not None: |
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logger.error(f'{config_name}: unable to start aug test') |
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else: |
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print(f'{config_name}: unable to start aug test', flush=True) |
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model = init_detector(cfg, checkpoint, device=args.device) |
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# test a single image |
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result = inference_detector(model, args.img) |
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# show the results |
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if args.show: |
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show_result_pyplot( |
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model, |
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args.img, |
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result, |
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score_thr=args.score_thr, |
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wait_time=args.wait_time) |
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return result |
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# Sample test whether the inference code is correct |
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def main(args): |
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config = Config.fromfile(args.config) |
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# test single model |
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if args.model_name: |
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if args.model_name in config: |
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model_infos = config[args.model_name] |
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if not isinstance(model_infos, list): |
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model_infos = [model_infos] |
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model_info = model_infos[0] |
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config_name = model_info['config'].strip() |
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print(f'processing: {config_name}', flush=True) |
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checkpoint = osp.join(args.checkpoint_root, |
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model_info['checkpoint'].strip()) |
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# build the model from a config file and a checkpoint file |
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inference_model(config_name, checkpoint, args) |
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return |
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else: |
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raise RuntimeError('model name input error.') |
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# test all model |
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logger = get_root_logger( |
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log_file='benchmark_test_image.log', log_level=logging.ERROR) |
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for model_key in config: |
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model_infos = config[model_key] |
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if not isinstance(model_infos, list): |
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model_infos = [model_infos] |
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for model_info in model_infos: |
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print('processing: ', model_info['config'], flush=True) |
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config_name = model_info['config'].strip() |
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checkpoint = osp.join(args.checkpoint_root, |
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model_info['checkpoint'].strip()) |
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try: |
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# build the model from a config file and a checkpoint file |
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inference_model(config_name, checkpoint, args, logger) |
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except Exception as e: |
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logger.error(f'{config_name} " : {repr(e)}') |
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if __name__ == '__main__': |
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args = parse_args() |
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main(args)
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