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| import math | |
| import torch | |
| import torch.nn as nn | |
| class ConcatEdgeEncoder(nn.Module): | |
| def __init__(self, state_dim, emb_dim, dropout): | |
| super().__init__() | |
| self.state_dim = state_dim | |
| self.emb_dim = emb_dim | |
| self.norm_factor = 1 / math.sqrt(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((2 + 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 | |
| curr_node_id: torch.LongTensor [batch_size] | |
| next_node_id: torch.LongTensor [batch_size] | |
| 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] | |
| edge embeddings | |
| """ | |
| curr_node_id = inputs["curr_node_id"] | |
| next_node_id = inputs["next_node_id"] | |
| state = inputs["state"] | |
| batch_size = curr_node_id.size(0) | |
| #-------------------------------- | |
| # generate queries, keys, values | |
| #-------------------------------- | |
| node_emb = self.dropout(node_emb) | |
| curr_emb = node_emb.gather(1, curr_node_id[:, None, None].expand(batch_size, 1, self.emb_dim)) | |
| next_emb = node_emb.gather(1, next_node_id[:, None, None].expand(batch_size, 1, self.emb_dim)) | |
| if state is not None and self.state_dim > 0: | |
| state_emb = self.init_linear_state(state) # [batch_size x emb_dim] | |
| edge_emb = torch.cat((curr_emb, next_emb, state_emb[:, None, :]), -1) # [batch_size x 1 x (3*emb_dim)] | |
| else: | |
| edge_emb = torch.cat((curr_emb, next_emb), -1) # [batch_size x 1 x (2*emb_dim)] | |
| edge_emb = edge_emb.squeeze(1) # [batch_size x (2*emb_dim)] | |
| return self.out_linear(edge_emb) |