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  1. from decagon_pytorch.layer import InputLayer, \
  2. OneHotInputLayer, \
  3. DecagonLayer
  4. from decagon_pytorch.data import Data
  5. import torch
  6. import pytest
  7. def _some_data():
  8. d = Data()
  9. d.add_node_type('Gene', 1000)
  10. d.add_node_type('Drug', 100)
  11. d.add_relation_type('Target', 1, 0, None)
  12. d.add_relation_type('Interaction', 0, 0, None)
  13. d.add_relation_type('Side Effect: Nausea', 1, 1, None)
  14. d.add_relation_type('Side Effect: Infertility', 1, 1, None)
  15. d.add_relation_type('Side Effect: Death', 1, 1, None)
  16. return d
  17. def _some_data_with_interactions():
  18. d = Data()
  19. d.add_node_type('Gene', 1000)
  20. d.add_node_type('Drug', 100)
  21. d.add_relation_type('Target', 1, 0,
  22. torch.rand((100, 1000), dtype=torch.float32).round())
  23. d.add_relation_type('Interaction', 0, 0,
  24. torch.rand((1000, 1000), dtype=torch.float32).round())
  25. d.add_relation_type('Side Effect: Nausea', 1, 1,
  26. torch.rand((100, 100), dtype=torch.float32).round())
  27. d.add_relation_type('Side Effect: Infertility', 1, 1,
  28. torch.rand((100, 100), dtype=torch.float32).round())
  29. d.add_relation_type('Side Effect: Death', 1, 1,
  30. torch.rand((100, 100), dtype=torch.float32).round())
  31. return d
  32. def test_input_layer_01():
  33. d = _some_data()
  34. for output_dim in [32, 64, 128]:
  35. layer = InputLayer(d, output_dim)
  36. assert layer.output_dim[0] == output_dim
  37. assert len(layer.node_reps) == 2
  38. assert layer.node_reps[0].shape == (1000, output_dim)
  39. assert layer.node_reps[1].shape == (100, output_dim)
  40. assert layer.data == d
  41. def test_input_layer_02():
  42. d = _some_data()
  43. layer = InputLayer(d, 32)
  44. res = layer()
  45. assert isinstance(res[0], torch.Tensor)
  46. assert isinstance(res[1], torch.Tensor)
  47. assert res[0].shape == (1000, 32)
  48. assert res[1].shape == (100, 32)
  49. assert torch.all(res[0] == layer.node_reps[0])
  50. assert torch.all(res[1] == layer.node_reps[1])
  51. def test_input_layer_03():
  52. if torch.cuda.device_count() == 0:
  53. pytest.skip('No CUDA devices on this host')
  54. d = _some_data()
  55. layer = InputLayer(d, 32)
  56. device = torch.device('cuda:0')
  57. layer = layer.to(device)
  58. print(list(layer.parameters()))
  59. # assert layer.device.type == 'cuda:0'
  60. assert layer.node_reps[0].device == device
  61. assert layer.node_reps[1].device == device
  62. def test_decagon_layer_01():
  63. d = _some_data_with_interactions()
  64. in_layer = InputLayer(d)
  65. d_layer = DecagonLayer(d, in_layer, output_dim=32)
  66. def test_decagon_layer_02():
  67. d = _some_data_with_interactions()
  68. in_layer = OneHotInputLayer(d)
  69. d_layer = DecagonLayer(d, in_layer, output_dim=32)
  70. _ = d_layer() # dummy call