103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
# 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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