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| import math | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import numpy as np | |
| class AttentionGraphEncoder(nn.Module): | |
| def __init__(self, coord_dim, node_dim, state_dim, emb_dim, dropout): | |
| super().__init__() | |
| self.coord_dim = coord_dim | |
| self.node_dim = node_dim | |
| self.emb_dim = emb_dim | |
| self.state_dim = state_dim | |
| self.norm_factor = 1 / math.sqrt(emb_dim) | |
| # initial embedding | |
| self.init_linear_node = nn.Linear(node_dim, emb_dim) | |
| self.init_linear_depot = nn.Linear(coord_dim, emb_dim) | |
| if state_dim > 0: | |
| self.init_linear_state = nn.Linear(state_dim, emb_dim) | |
| # An attention layer | |
| self.w_q = nn.Parameter(torch.FloatTensor((2 + int(state_dim > 0)) * emb_dim, emb_dim)) | |
| self.w_k = nn.Parameter(torch.FloatTensor(2 * emb_dim, emb_dim)) | |
| self.w_v = nn.Parameter(torch.FloatTensor(2 * 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): | |
| """ | |
| Paramters | |
| --------- | |
| inputs: dict | |
| curr_node_id: torch.LongTensor [batch_size x 1] | |
| next_node_id: torch.LongTensor [batch_size x 1] | |
| node_feat: torch.FloatTensor [batch_size x num_nodes x node_dim] | |
| mask: torch.LongTensor [batch_size x num_nodes] | |
| state: torch.FloatTensor [batch_size x state_dim] | |
| Returns | |
| ------- | |
| h: torch.tensor [batch_size x emb_dim] | |
| graph embeddings | |
| """ | |
| #---------------- | |
| # input features | |
| #---------------- | |
| curr_node_id = inputs["curr_node_id"] | |
| next_node_id = inputs["next_node_id"] | |
| node_feat = inputs["node_feats"] | |
| mask = inputs["mask"] | |
| state = inputs["state"] | |
| #--------------------------- | |
| # initial linear projection | |
| #--------------------------- | |
| node_emb = self.init_linear_node(node_feat[:, 1:, :]) # [batch_size x num_loc x emb_dim] | |
| depot_emb = self.init_linear_depot(node_feat[:, 0:1, :2]) # [batch_size x 1 x emb_dim] | |
| new_node_feat = torch.cat((depot_emb, node_emb), 1) # [batch_size x num_nodes x emb_dim] | |
| new_node_feat = self.dropout(new_node_feat) | |
| #--------------- | |
| # preprocessing | |
| #--------------- | |
| batch_size = curr_node_id.size(0) | |
| curr_emb = new_node_feat.gather(1, curr_node_id[:, None, None].expand(batch_size, 1, self.emb_dim)) | |
| next_emb = new_node_feat.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] | |
| input_q = torch.cat((curr_emb, next_emb, state_emb[:, None, :]), -1) # [batch_size x 1 x (3*emb_dim)] | |
| else: | |
| input_q = torch.cat((curr_emb, next_emb), -1) # [batch_size x 1 x (2*emb_dim)] | |
| input_kv = torch.cat((curr_emb.expand_as(new_node_feat), new_node_feat), -1) # [batch_size x num_nodes x (2*emb_dim)] | |
| #-------------------- | |
| # An attention layer | |
| #-------------------- | |
| q = torch.matmul(input_q, self.w_q) # [batch_size x 1 x emb_dim] | |
| k = torch.matmul(input_kv, self.w_k) # [batch_size x num_nodes x emb_dim] | |
| v = torch.matmul(input_kv, self.w_v) # [batch_size x num_nodes x emb_dim] | |
| compatibility = self.norm_factor * torch.matmul(q, k.transpose(-2, -1)) # [batch_size x 1 x num_nodes] | |
| compatibility[(~mask).unsqueeze(1).expand_as(compatibility)] = -math.inf | |
| attn = torch.softmax(compatibility, dim=-1) | |
| h = torch.matmul(attn, v) # [batch_size x 1 x emb_dim] | |
| h = h.squeeze(1) # [batch_size x emb_dim] | |
| return h |