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import argparse
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import numpy as np
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from mmengine import load
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from scipy.special import softmax
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from mmaction.evaluation.functional import (get_weighted_score,
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mean_class_accuracy,
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mmit_mean_average_precision,
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top_k_accuracy)
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def parse_args():
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parser = argparse.ArgumentParser(description='Fusing multiple scores')
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parser.add_argument(
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'--preds',
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nargs='+',
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help='list of predict result',
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default=['demo/fuse/joint.pkl', 'demo/fuse/bone.pkl'])
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parser.add_argument(
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'--coefficients',
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nargs='+',
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type=float,
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help='coefficients of each score file',
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default=[1.0, 1.0])
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parser.add_argument('--apply-softmax', action='store_true')
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parser.add_argument(
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'--multi-label',
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action='store_true',
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help='whether the task is multi label classification')
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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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assert len(args.preds) == len(args.coefficients)
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data_sample_list = [load(f) for f in args.preds]
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score_list = []
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for data_samples in data_sample_list:
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scores = [sample['pred_score'].numpy() for sample in data_samples]
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score_list.append(scores)
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if args.multi_label:
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labels = [sample['gt_label'] for sample in data_sample_list[0]]
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else:
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labels = [sample['gt_label'].item() for sample in data_sample_list[0]]
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if args.apply_softmax:
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def apply_softmax(scores):
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return [softmax(score) for score in scores]
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score_list = [apply_softmax(scores) for scores in score_list]
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weighted_scores = get_weighted_score(score_list, args.coefficients)
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if args.multi_label:
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mean_avg_prec = mmit_mean_average_precision(
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np.array(weighted_scores), np.stack([t.numpy() for t in labels]))
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print(f'MMit Average Precision: {mean_avg_prec:.04f}')
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else:
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mean_class_acc = mean_class_accuracy(weighted_scores, labels)
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top_1_acc, top_5_acc = top_k_accuracy(weighted_scores, labels, (1, 5))
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print(f'Mean Class Accuracy: {mean_class_acc:.04f}')
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print(f'Top 1 Accuracy: {top_1_acc:.04f}')
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print(f'Top 5 Accuracy: {top_5_acc:.04f}')
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if __name__ == '__main__':
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main()
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