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test_fastconv.py 2.0KB

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  1. from icosagon.fastconv import _sparse_diag_cat, \
  2. _cat
  3. from icosagon.data import _equal
  4. import torch
  5. def test_sparse_diag_cat_01():
  6. matrices = [ torch.rand(5, 10).round() for _ in range(7) ]
  7. ground_truth = torch.zeros(35, 70)
  8. ground_truth[0:5, 0:10] = matrices[0]
  9. ground_truth[5:10, 10:20] = matrices[1]
  10. ground_truth[10:15, 20:30] = matrices[2]
  11. ground_truth[15:20, 30:40] = matrices[3]
  12. ground_truth[20:25, 40:50] = matrices[4]
  13. ground_truth[25:30, 50:60] = matrices[5]
  14. ground_truth[30:35, 60:70] = matrices[6]
  15. res = _sparse_diag_cat([ m.to_sparse() for m in matrices ])
  16. res = res.to_dense()
  17. assert torch.all(res == ground_truth)
  18. def test_sparse_diag_cat_02():
  19. x = [ torch.rand(5, 10).round() for _ in range(7) ]
  20. a = [ m.to_sparse() for m in x ]
  21. a = _sparse_diag_cat(a)
  22. b = torch.rand(70, 64)
  23. res = torch.sparse.mm(a, b)
  24. ground_truth = torch.zeros(35, 64)
  25. ground_truth[0:5, :] = torch.mm(x[0], b[0:10])
  26. ground_truth[5:10, :] = torch.mm(x[1], b[10:20])
  27. ground_truth[10:15, :] = torch.mm(x[2], b[20:30])
  28. ground_truth[15:20, :] = torch.mm(x[3], b[30:40])
  29. ground_truth[20:25, :] = torch.mm(x[4], b[40:50])
  30. ground_truth[25:30, :] = torch.mm(x[5], b[50:60])
  31. ground_truth[30:35, :] = torch.mm(x[6], b[60:70])
  32. assert torch.all(res == ground_truth)
  33. def test_cat_01():
  34. matrices = [ torch.rand(5, 10) for _ in range(7) ]
  35. res = _cat(matrices)
  36. assert res.shape == (35, 10)
  37. assert not res.is_sparse
  38. ground_truth = torch.zeros(35, 10)
  39. for i in range(7):
  40. ground_truth[i*5:(i+1)*5, :] = matrices[i]
  41. assert torch.all(res == ground_truth)
  42. def test_cat_02():
  43. matrices = [ torch.rand(5, 10) for _ in range(7) ]
  44. ground_truth = torch.zeros(35, 10)
  45. for i in range(7):
  46. ground_truth[i*5:(i+1)*5, :] = matrices[i]
  47. res = _cat([ m.to_sparse() for m in matrices ])
  48. assert res.shape == (35, 10)
  49. assert res.is_sparse
  50. assert torch.all(res.to_dense() == ground_truth)