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| import math | |
| import torch | |
| import torch.nn as nn | |
| class SelfMHANodeEncoder(nn.Module): | |
| def __init__(self, coord_dim, node_dim, emb_dim, num_heads, num_mha_layers, dropout): | |
| super().__init__() | |
| self.coord_dim = coord_dim | |
| self.node_dim = node_dim | |
| self.emb_dim = emb_dim | |
| self.num_mha_layers = num_mha_layers | |
| # initial embedding | |
| self.init_linear_nodes = nn.Linear(node_dim, emb_dim) | |
| self.init_linear_depot = nn.Linear(coord_dim, emb_dim) | |
| # MHA Encoder (w/o positional encoding) | |
| mha_layer = nn.TransformerEncoderLayer(d_model=emb_dim, | |
| nhead=num_heads, | |
| dim_feedforward=emb_dim, | |
| dropout=dropout, | |
| batch_first=True) | |
| self.mha = nn.TransformerEncoder(mha_layer, num_layers=num_mha_layers) | |
| # Initializing weights | |
| 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 | |
| node_feat: torch.FloatTensor [batch_size x num_nodes x node_dim] | |
| Returns | |
| ------- | |
| node_emb: torch.tensor [batch_size x num_nodes x emb_dim] | |
| node embeddings | |
| """ | |
| #---------------- | |
| # input features | |
| #---------------- | |
| node_feat = inputs["node_feats"] | |
| #------------------------------------------------------------------------ | |
| # initial linear projection for adjusting dimensions of locs & the depot | |
| #------------------------------------------------------------------------ | |
| # node_feat = self.dropout(node_feat) | |
| loc_emb = self.init_linear_nodes(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] | |
| node_emb = torch.cat((depot_emb, loc_emb), 1) # [batch_size x num_nodes x emb_dim] | |
| #-------------- | |
| # MLP encoding | |
| #-------------- | |
| node_emb = self.mha(node_emb) # [batch_size x num_nodes x emb_dim] | |
| return node_emb |