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