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test_layer_input.py 3.7KB

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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. from decagon_pytorch.convolve import SparseDropoutGraphConvActivation, \
  8. SparseMultiDGCA, \
  9. DropoutGraphConvActivation
  10. def _some_data():
  11. d = Data()
  12. d.add_node_type('Gene', 1000)
  13. d.add_node_type('Drug', 100)
  14. d.add_relation_type('Target', 1, 0, None)
  15. d.add_relation_type('Interaction', 0, 0, None)
  16. d.add_relation_type('Side Effect: Nausea', 1, 1, None)
  17. d.add_relation_type('Side Effect: Infertility', 1, 1, None)
  18. d.add_relation_type('Side Effect: Death', 1, 1, None)
  19. return d
  20. def _some_data_with_interactions():
  21. d = Data()
  22. d.add_node_type('Gene', 1000)
  23. d.add_node_type('Drug', 100)
  24. d.add_relation_type('Target', 1, 0,
  25. torch.rand((100, 1000), dtype=torch.float32).round())
  26. d.add_relation_type('Interaction', 0, 0,
  27. torch.rand((1000, 1000), dtype=torch.float32).round())
  28. d.add_relation_type('Side Effect: Nausea', 1, 1,
  29. torch.rand((100, 100), dtype=torch.float32).round())
  30. d.add_relation_type('Side Effect: Infertility', 1, 1,
  31. torch.rand((100, 100), dtype=torch.float32).round())
  32. d.add_relation_type('Side Effect: Death', 1, 1,
  33. torch.rand((100, 100), dtype=torch.float32).round())
  34. return d
  35. def test_input_layer_01():
  36. d = _some_data()
  37. for output_dim in [32, 64, 128]:
  38. layer = InputLayer(d, output_dim)
  39. assert layer.output_dim[0] == output_dim
  40. assert len(layer.node_reps) == 2
  41. assert layer.node_reps[0].shape == (1000, output_dim)
  42. assert layer.node_reps[1].shape == (100, output_dim)
  43. assert layer.data == d
  44. def test_input_layer_02():
  45. d = _some_data()
  46. layer = InputLayer(d, 32)
  47. res = layer()
  48. assert isinstance(res[0], torch.Tensor)
  49. assert isinstance(res[1], torch.Tensor)
  50. assert res[0].shape == (1000, 32)
  51. assert res[1].shape == (100, 32)
  52. assert torch.all(res[0] == layer.node_reps[0])
  53. assert torch.all(res[1] == layer.node_reps[1])
  54. def test_input_layer_03():
  55. if torch.cuda.device_count() == 0:
  56. pytest.skip('No CUDA devices on this host')
  57. d = _some_data()
  58. layer = InputLayer(d, 32)
  59. device = torch.device('cuda:0')
  60. layer = layer.to(device)
  61. print(list(layer.parameters()))
  62. # assert layer.device.type == 'cuda:0'
  63. assert layer.node_reps[0].device == device
  64. assert layer.node_reps[1].device == device
  65. def test_one_hot_input_layer_01():
  66. d = _some_data()
  67. layer = OneHotInputLayer(d)
  68. assert layer.output_dim == [1000, 100]
  69. assert len(layer.node_reps) == 2
  70. assert layer.node_reps[0].shape == (1000, 1000)
  71. assert layer.node_reps[1].shape == (100, 100)
  72. assert layer.data == d
  73. assert layer.is_sparse
  74. def test_one_hot_input_layer_02():
  75. d = _some_data()
  76. layer = OneHotInputLayer(d)
  77. res = layer()
  78. assert isinstance(res[0], torch.Tensor)
  79. assert isinstance(res[1], torch.Tensor)
  80. assert res[0].shape == (1000, 1000)
  81. assert res[1].shape == (100, 100)
  82. assert torch.all(res[0].to_dense() == layer.node_reps[0].to_dense())
  83. assert torch.all(res[1].to_dense() == layer.node_reps[1].to_dense())
  84. def test_one_hot_input_layer_03():
  85. if torch.cuda.device_count() == 0:
  86. pytest.skip('No CUDA devices on this host')
  87. d = _some_data()
  88. layer = OneHotInputLayer(d)
  89. device = torch.device('cuda:0')
  90. layer = layer.to(device)
  91. print(list(layer.parameters()))
  92. # assert layer.device.type == 'cuda:0'
  93. assert layer.node_reps[0].device == device
  94. assert layer.node_reps[1].device == device