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  1. import torch
  2. from .convolve import DropoutGraphConvActivation
  3. from .data import Data
  4. from .trainprep import PreparedData
  5. from typing import List, \
  6. Union, \
  7. Callable
  8. from collections import defaultdict
  9. from dataclasses import dataclass
  10. class Convolutions(torch.nn.Module):
  11. node_type_column: int
  12. convolutions: torch.nn.ModuleList # [DropoutGraphConvActivation]
  13. def __init__(self, node_type_column: int,
  14. convolutions: torch.nn.ModuleList, **kwargs):
  15. super().__init__(**kwargs)
  16. self.node_type_column = node_type_column
  17. self.convolutions = convolutions
  18. class DecagonLayer(torch.nn.Module):
  19. def __init__(self,
  20. input_dim: List[int],
  21. output_dim: List[int],
  22. data: Union[Data, PreparedData],
  23. keep_prob: float = 1.,
  24. rel_activation: Callable[[torch.Tensor], torch.Tensor] = lambda x: x,
  25. layer_activation: Callable[[torch.Tensor], torch.Tensor] = torch.nn.functional.relu,
  26. **kwargs):
  27. super().__init__(**kwargs)
  28. if not isinstance(input_dim, list):
  29. raise ValueError('input_dim must be a list')
  30. if not output_dim:
  31. raise ValueError('output_dim must be specified')
  32. if not isinstance(output_dim, list):
  33. output_dim = [output_dim] * len(data.node_types)
  34. if not isinstance(data, Data) and not isinstance(data, PreparedData):
  35. raise ValueError('data must be of type Data or PreparedData')
  36. self.input_dim = input_dim
  37. self.output_dim = output_dim
  38. self.data = data
  39. self.keep_prob = float(keep_prob)
  40. self.rel_activation = rel_activation
  41. self.layer_activation = layer_activation
  42. self.is_sparse = False
  43. self.next_layer_repr = None
  44. self.build()
  45. def build_fam_one_node_type(self, fam):
  46. convolutions = torch.nn.ModuleList()
  47. for r in fam.relation_types:
  48. conv = DropoutGraphConvActivation(self.input_dim[fam.node_type_column],
  49. self.output_dim[fam.node_type_row], r.adjacency_matrix,
  50. self.keep_prob, self.rel_activation)
  51. convolutions.append(conv)
  52. self.next_layer_repr[fam.node_type_row].append(
  53. Convolutions(fam.node_type_column, convolutions))
  54. def build_fam_two_node_types(self, fam) -> None:
  55. convolutions_row = torch.nn.ModuleList()
  56. convolutions_column = torch.nn.ModuleList()
  57. for r in fam.relation_types:
  58. if r.adjacency_matrix is not None:
  59. conv = DropoutGraphConvActivation(self.input_dim[fam.node_type_column],
  60. self.output_dim[fam.node_type_row], r.adjacency_matrix,
  61. self.keep_prob, self.rel_activation)
  62. convolutions_row.append(conv)
  63. if r.adjacency_matrix_backward is not None:
  64. conv = DropoutGraphConvActivation(self.input_dim[fam.node_type_row],
  65. self.output_dim[fam.node_type_column], r.adjacency_matrix_backward,
  66. self.keep_prob, self.rel_activation)
  67. convolutions_column.append(conv)
  68. self.next_layer_repr[fam.node_type_row].append(
  69. Convolutions(fam.node_type_column, convolutions_row))
  70. self.next_layer_repr[fam.node_type_column].append(
  71. Convolutions(fam.node_type_row, convolutions_column))
  72. def build_family(self, fam) -> None:
  73. if fam.node_type_row == fam.node_type_column:
  74. self.build_fam_one_node_type(fam)
  75. else:
  76. self.build_fam_two_node_types(fam)
  77. def build(self):
  78. self.next_layer_repr = torch.nn.ModuleList([
  79. torch.nn.ModuleList() for _ in range(len(self.data.node_types)) ])
  80. for fam in self.data.relation_families:
  81. self.build_family(fam)
  82. def __call__(self, prev_layer_repr):
  83. next_layer_repr = [ [] for _ in range(len(self.data.node_types)) ]
  84. n = len(self.data.node_types)
  85. for node_type_row in range(n):
  86. for convolutions in self.next_layer_repr[node_type_row]:
  87. repr_ = [ conv(prev_layer_repr[convolutions.node_type_column]) \
  88. for conv in convolutions.convolutions ]
  89. repr_ = sum(repr_)
  90. repr_ = torch.nn.functional.normalize(repr_, p=2, dim=1)
  91. next_layer_repr[node_type_row].append(repr_)
  92. if len(next_layer_repr[node_type_row]) == 0:
  93. next_layer_repr[node_type_row] = torch.zeros(self.output_dim[node_type_row])
  94. else:
  95. next_layer_repr[node_type_row] = sum(next_layer_repr[node_type_row])
  96. next_layer_repr[node_type_row] = self.layer_activation(next_layer_repr[node_type_row])
  97. return next_layer_repr