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| import math | |
| import torch | |
| import torch.nn as nn | |
| class MeanReadout(nn.Module): | |
| def __init__(self, state_dim, emb_dim, dropout): | |
| super().__init__() | |
| self.state_dim = state_dim | |
| self.emb_dim = emb_dim | |
| # initial embedding for state | |
| if state_dim > 0: | |
| self.init_linear_state = nn.Linear(state_dim, emb_dim) | |
| # out linear layer | |
| self.out_linear = nn.Linear((1 + int(state_dim > 0))*emb_dim, emb_dim) | |
| # Dropout | |
| self.dropout = nn.Dropout(dropout) | |
| self.reset_parameters() | |
| def reset_parameters(self): | |
| for param in self.parameters(): | |
| stdv = 1. / math.sqrt(param.size(-1)) | |
| param.data.uniform_(-stdv, stdv) | |
| def forward(self, inputs, node_emb): | |
| """ | |
| Paramters | |
| --------- | |
| inputs: dict | |
| mask: torch.LongTensor [batch_size x num_nodes] | |
| state: torch.FloatTensor [batch_size x state_dim] | |
| node_emb: torch.tensor [batch_size x num_nodes x emb_dim] | |
| node embeddings obtained from the node encoder | |
| Returns | |
| ------- | |
| h: torch.tensor [batch_size x emb_dim] | |
| graph embeddings | |
| """ | |
| mask = inputs["mask"] | |
| state = inputs["state"] | |
| node_emb = self.dropout(node_emb) | |
| # pooling with a mask | |
| mask = mask.unsqueeze(-1).expand_as(node_emb) | |
| node_emb = node_emb * mask | |
| h = torch.mean(node_emb, dim=1) # [batch_size x emb_dim] | |
| # out linear layer | |
| if state is not None and self.state_dim > 0: | |
| state_emb = self.init_linear_state(state) # [batch_size x emb_dim] | |
| h = torch.cat((h, state_emb), -1) # [batch_size x (2*emb_dim)] | |
| return self.out_linear(h) |