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