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import argparse
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from operator import itemgetter
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import torch
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from mmengine import Config, DictAction
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from mmaction.apis import inference_recognizer, init_recognizer
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def parse_args():
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parser = argparse.ArgumentParser(description='MMAction2 demo')
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parser.add_argument('config', help='test config file path')
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parser.add_argument('checkpoint', help='checkpoint file/url')
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parser.add_argument('audio', help='audio file')
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parser.add_argument('label', help='label file')
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parser.add_argument(
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'--cfg-options',
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nargs='+',
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action=DictAction,
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default={},
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help='override some settings in the used config, the key-value pair '
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'in xxx=yyy format will be merged into config file. For example, '
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"'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'")
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parser.add_argument(
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'--device', type=str, default='cuda:0', help='CPU/CUDA device option')
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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device = torch.device(args.device)
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cfg = Config.fromfile(args.config)
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cfg.merge_from_dict(args.cfg_options)
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model = init_recognizer(cfg, args.checkpoint, device=device)
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if not args.audio.endswith('.npy'):
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raise NotImplementedError('Demo works on extracted audio features')
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pred_result = inference_recognizer(model, args.audio)
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pred_scores = pred_result.pred_score.tolist()
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score_tuples = tuple(zip(range(len(pred_scores)), pred_scores))
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score_sorted = sorted(score_tuples, key=itemgetter(1), reverse=True)
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top5_label = score_sorted[:5]
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labels = open(args.label).readlines()
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labels = [x.strip() for x in labels]
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results = [(labels[k[0]], k[1]) for k in top5_label]
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print('The top-5 labels with corresponding scores are:')
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for result in results:
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print(f'{result[0]}: ', result[1])
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if __name__ == '__main__':
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main()
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