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SkeletonAgent

Video Classification
PyTorch
skeleton-based-action-recognition
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  1. ntu120_xset/b_1/20250503_153832.log +0 -0
  2. ntu120_xset/b_1/best_pred.pkl +3 -0
  3. ntu120_xset/b_1/best_top1_acc_epoch_150.pth +3 -0
  4. ntu120_xset/b_2/20250623_102101.log +0 -0
  5. ntu120_xset/b_2/best_pred.pkl +3 -0
  6. ntu120_xset/b_2/best_top1_acc_epoch_147.pth +3 -0
  7. ntu120_xset/bm/20250623_102341.log +0 -0
  8. ntu120_xset/bm/best_pred.pkl +3 -0
  9. ntu120_xset/bm/best_top1_acc_epoch_143.pth +3 -0
  10. ntu120_xset/j_1/20250627_105326.log +0 -0
  11. ntu120_xset/j_1/best_pred.pkl +3 -0
  12. ntu120_xset/j_1/best_top1_acc_epoch_149.pth +3 -0
  13. ntu120_xset/j_2/20250627_105246.log +0 -0
  14. ntu120_xset/j_2/best_pred.pkl +3 -0
  15. ntu120_xset/j_2/best_top1_acc_epoch_149.pth +3 -0
  16. ntu120_xset/jm/20250504_151214.log +0 -0
  17. ntu120_xset/jm/best_pred.pkl +3 -0
  18. ntu120_xset/jm/best_top1_acc_epoch_148.pth +3 -0
  19. ntu120_xset/k_1/20250627_105613.log +0 -0
  20. ntu120_xset/k_1/best_pred.pkl +3 -0
  21. ntu120_xset/k_1/best_top1_acc_epoch_150.pth +3 -0
  22. ntu120_xset/k_2/20250627_105452.log +0 -0
  23. ntu120_xset/k_2/best_pred.pkl +3 -0
  24. ntu120_xset/k_2/best_top1_acc_epoch_149.pth +3 -0
  25. ntu120_xset/km/20250627_105655.log +0 -0
  26. ntu120_xset/km/best_pred.pkl +3 -0
  27. ntu120_xset/km/best_top1_acc_epoch_149.pth +3 -0
  28. ntu120_xset/ntu120_xset_ensemble.py +65 -0
  29. ntu120_xsub/b_1/20250518_151602.log +0 -0
  30. ntu120_xsub/b_1/best_pred.pkl +3 -0
  31. ntu120_xsub/b_1/best_top1_acc_epoch_150.pth +3 -0
  32. ntu120_xsub/b_2/20250609_122316.log +0 -0
  33. ntu120_xsub/b_2/best_pred.pkl +3 -0
  34. ntu120_xsub/b_2/best_top1_acc_epoch_149.pth +3 -0
  35. ntu120_xsub/bm/20250618_162718.log +0 -0
  36. ntu120_xsub/bm/best_pred.pkl +3 -0
  37. ntu120_xsub/bm/best_top1_acc_epoch_150.pth +3 -0
  38. ntu120_xsub/j_1/20250609_122419.log +0 -0
  39. ntu120_xsub/j_1/best_pred.pkl +3 -0
  40. ntu120_xsub/j_1/best_top1_acc_epoch_146.pth +3 -0
  41. ntu120_xsub/j_2/20250609_122545.log +0 -0
  42. ntu120_xsub/j_2/best_pred.pkl +3 -0
  43. ntu120_xsub/j_2/best_top1_acc_epoch_149.pth +3 -0
  44. ntu120_xsub/jm/20250503_153658.log +0 -0
  45. ntu120_xsub/jm/best_pred.pkl +3 -0
  46. ntu120_xsub/jm/best_top1_acc_epoch_149.pth +3 -0
  47. ntu120_xsub/k_1/20250623_101900.log +0 -0
  48. ntu120_xsub/k_1/best_pred.pkl +3 -0
  49. ntu120_xsub/k_1/best_top1_acc_epoch_148.pth +3 -0
  50. ntu120_xsub/k_2/20250623_101718.log +0 -0
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1
+ from mmcv import load
2
+ import sys
3
+ # Note: please adjust the relative path according to the actual situation.
4
+ sys.path.append('../..')
5
+ from skeletonagent.smp import *
6
+
7
+
8
+ j_1 = load('j_1/best_pred.pkl')
9
+ b_1 = load('b_1/best_pred.pkl')
10
+ k_1 = load('k_1/best_pred.pkl')
11
+ j_2 = load('j_2/best_pred.pkl')
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+ b_2 = load('b_2/best_pred.pkl')
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+ k_2 = load('k_2/best_pred.pkl')
14
+ jm = load('jm/best_pred.pkl')
15
+ bm = load('bm/best_pred.pkl')
16
+ km = load('km/best_pred.pkl')
17
+ label = load_label('/data/nturgbd/ntu120_3danno.pkl', 'xset_val')
18
+
19
+
20
+ """
21
+ ***************
22
+ InfoGCN v0:
23
+ j jm b bm k km
24
+ 2S: 92.43
25
+ 4S: 92.60
26
+ 6S: 92.73
27
+ ***************
28
+ """
29
+ print('InfoGCN v0:')
30
+ print('j jm b bm k km')
31
+ print('2S')
32
+ fused = comb([j_1, b_1], [1, 1])
33
+ print('Top-1', top1(fused, label))
34
+
35
+ print('4S')
36
+ fused = comb([j_1, b_1, jm, bm], [8, 8, 3, 3])
37
+ print('Top-1', top1(fused, label))
38
+
39
+ print('6S')
40
+ fused = comb([j_1, b_1, k_1, jm, bm, km], [8, 8, 8, 3, 3, 3])
41
+ print('Top-1', top1(fused, label))
42
+
43
+
44
+ """
45
+ ***************
46
+ InfoGCN v1:
47
+ j j b b k k
48
+ 2S: 92.43
49
+ 4S: 92.97
50
+ 6S: 93.05
51
+ ***************
52
+ """
53
+ print('InfoGCN v1:')
54
+ print('j j b b k k')
55
+ print('2S')
56
+ fused = comb([j_1, b_1], [1, 1])
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+ print('Top-1', top1(fused, label))
58
+
59
+ print('4S')
60
+ fused = comb([j_1, j_2, b_1, b_2], [7, 7, 9, 9])
61
+ print('Top-1', top1(fused, label))
62
+
63
+ print('6S')
64
+ fused = comb([j_1, j_2, b_1, b_2, k_1, k_2], [7, 7, 9, 9, 2, 2])
65
+ print('Top-1', top1(fused, label))
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