58 lines
2.1 KiB
Python
58 lines
2.1 KiB
Python
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import warnings
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import math
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import numbers
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.nn import init, Parameter
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class GraphMultiTypeAttention(nn.Module):
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def __init__(self, in_features, hidden_features, out_features, bias=True, types=1):
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super(GraphMultiTypeAttention, self).__init__()
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self.types = types
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self.in_features = in_features
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self.out_features = out_features
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self.node_self_loop_weight = Parameter(torch.Tensor(hidden_features, in_features[0]))
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self.weight_per_type = nn.ParameterList()
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for i in range(types):
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self.weight_per_type.append(Parameter(torch.Tensor(hidden_features, in_features[i])))
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if bias:
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self.bias = Parameter(torch.Tensor(hidden_features))
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else:
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self.register_parameter('bias', None)
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self.linear_to_out = nn.Linear(hidden_features, out_features, bias=bias)
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self.reset_parameters()
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def reset_parameters(self):
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for weight in self.weight_per_type:
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bound = 1 / math.sqrt(weight.size(1))
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init.uniform_(weight, -bound, bound)
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bound = 1 / math.sqrt(self.node_self_loop_weight.size(1))
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init.uniform_(self.node_self_loop_weight, -bound, bound)
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if self.bias is not None:
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init.uniform_(self.bias, -bound, bound)
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def forward(self, inputs, types, edge_weights):
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weight_list = list()
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for i, type in enumerate(types):
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weight_list.append((edge_weights[i] / len(edge_weights)) * self.weight_per_type[type].T)
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weight_list.append(self.node_self_loop_weight.T)
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weight = torch.cat(weight_list, dim=0)
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stacked_input = torch.cat(inputs, dim=-1)
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output = stacked_input.matmul(weight)
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output = output
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if self.bias is not None:
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output += self.bias
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return torch.relu(self.linear_to_out(torch.relu(output)))
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def extra_repr(self):
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return 'in_features={}, hidden_features={},, out_features={}, types={}, bias={}'.format(
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self.in_features, self.hidden_features, self.out_features, self.types, self.bias is not None
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)
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