IF YOU WOULD LIKE TO GET AN ACCOUNT, please write an email to s dot adaszewski at gmail dot com. User accounts are meant only to report issues and/or generate pull requests. This is a purpose-specific Git hosting for ADARED projects. Thank you for your understanding!
Vous ne pouvez pas sélectionner plus de 25 sujets Les noms de sujets doivent commencer par une lettre ou un nombre, peuvent contenir des tirets ('-') et peuvent comporter jusqu'à 35 caractères.

decode.py 4.6KB

il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
il y a 4 ans
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123
  1. #
  2. # Copyright (C) Stanislaw Adaszewski, 2020
  3. # License: GPLv3
  4. #
  5. import torch
  6. from .weights import init_glorot
  7. from .dropout import dropout
  8. class DEDICOMDecoder(torch.nn.Module):
  9. """DEDICOM Tensor Factorization Decoder model layer for link prediction."""
  10. def __init__(self, input_dim, num_relation_types, keep_prob=1.,
  11. activation=torch.sigmoid, **kwargs):
  12. super().__init__(**kwargs)
  13. self.input_dim = input_dim
  14. self.num_relation_types = num_relation_types
  15. self.keep_prob = keep_prob
  16. self.activation = activation
  17. self.global_interaction = torch.nn.Parameter(init_glorot(input_dim, input_dim))
  18. self.local_variation = torch.nn.ParameterList([
  19. torch.nn.Parameter(torch.flatten(init_glorot(input_dim, 1))) \
  20. for _ in range(num_relation_types)
  21. ])
  22. def forward(self, inputs_row, inputs_col, relation_index):
  23. inputs_row = dropout(inputs_row, self.keep_prob)
  24. inputs_col = dropout(inputs_col, self.keep_prob)
  25. relation = torch.diag(self.local_variation[relation_index])
  26. product1 = torch.mm(inputs_row, relation)
  27. product2 = torch.mm(product1, self.global_interaction)
  28. product3 = torch.mm(product2, relation)
  29. rec = torch.bmm(product3.view(product3.shape[0], 1, product3.shape[1]),
  30. inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
  31. rec = torch.flatten(rec)
  32. return self.activation(rec)
  33. class DistMultDecoder(torch.nn.Module):
  34. """DEDICOM Tensor Factorization Decoder model layer for link prediction."""
  35. def __init__(self, input_dim, num_relation_types, keep_prob=1.,
  36. activation=torch.sigmoid, **kwargs):
  37. super().__init__(**kwargs)
  38. self.input_dim = input_dim
  39. self.num_relation_types = num_relation_types
  40. self.keep_prob = keep_prob
  41. self.activation = activation
  42. self.relation = torch.nn.ParameterList([
  43. torch.nn.Parameter(torch.flatten(init_glorot(input_dim, 1))) \
  44. for _ in range(num_relation_types)
  45. ])
  46. def forward(self, inputs_row, inputs_col, relation_index):
  47. inputs_row = dropout(inputs_row, self.keep_prob)
  48. inputs_col = dropout(inputs_col, self.keep_prob)
  49. relation = torch.diag(self.relation[relation_index])
  50. intermediate_product = torch.mm(inputs_row, relation)
  51. rec = torch.bmm(intermediate_product.view(intermediate_product.shape[0], 1, intermediate_product.shape[1]),
  52. inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
  53. rec = torch.flatten(rec)
  54. return self.activation(rec)
  55. class BilinearDecoder(torch.nn.Module):
  56. """DEDICOM Tensor Factorization Decoder model layer for link prediction."""
  57. def __init__(self, input_dim, num_relation_types, keep_prob=1.,
  58. activation=torch.sigmoid, **kwargs):
  59. super().__init__(**kwargs)
  60. self.input_dim = input_dim
  61. self.num_relation_types = num_relation_types
  62. self.keep_prob = keep_prob
  63. self.activation = activation
  64. self.relation = torch.nn.ParameterList([
  65. torch.nn.Parameter(init_glorot(input_dim, input_dim)) \
  66. for _ in range(num_relation_types)
  67. ])
  68. def forward(self, inputs_row, inputs_col, relation_index):
  69. inputs_row = dropout(inputs_row, self.keep_prob)
  70. inputs_col = dropout(inputs_col, self.keep_prob)
  71. intermediate_product = torch.mm(inputs_row, self.relation[relation_index])
  72. rec = torch.bmm(intermediate_product.view(intermediate_product.shape[0], 1, intermediate_product.shape[1]),
  73. inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
  74. rec = torch.flatten(rec)
  75. return self.activation(rec)
  76. class InnerProductDecoder(torch.nn.Module):
  77. """DEDICOM Tensor Factorization Decoder model layer for link prediction."""
  78. def __init__(self, input_dim, num_relation_types, keep_prob=1.,
  79. activation=torch.sigmoid, **kwargs):
  80. super().__init__(**kwargs)
  81. self.input_dim = input_dim
  82. self.num_relation_types = num_relation_types
  83. self.keep_prob = keep_prob
  84. self.activation = activation
  85. def forward(self, inputs_row, inputs_col, _):
  86. inputs_row = dropout(inputs_row, self.keep_prob)
  87. inputs_col = dropout(inputs_col, self.keep_prob)
  88. rec = torch.bmm(inputs_row.view(inputs_row.shape[0], 1, inputs_row.shape[1]),
  89. inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
  90. rec = torch.flatten(rec)
  91. return self.activation(rec)