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前言

• class CVRP_Encoder
• class EncoderLayer(nn.Module)

以上是CVRPModel.py的类

/home/tang/RL_exa/NCO_code-main/single_objective/LCH-Regret/Regret-POMO/CVRP/POMO/CVRPModel.py

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一、class CVRP_Encoder(nn.Module):init(self, **model_params)

函数功能

该代码片段是 CVRP_Encoder 类的 init 构造函数。其主要功能是 ==初始化模型的参数 ==和 构建神经网络的层，用于解决 容量限制的车辆路径问题（CVRP）。

执行流程图链接

函数代码

    def __init__(self, **model_params):super().__init__()self.model_params = model_paramsembedding_dim = self.model_params['embedding_dim']encoder_layer_num = self.model_params['encoder_layer_num']self.embedding_depot = nn.Linear(2, embedding_dim)self.embedding_node = nn.Linear(3, embedding_dim)self.layers = nn.ModuleList([EncoderLayer(**model_params) for _ in range(encoder_layer_num)])

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二、class CVRP_Encoder(nn.Module):forward(self, depot_xy, node_xy_demand)

函数功能

forward 方法是 CVRP_Encoder 类的一部分，主要功能是将仓库（depot）和客户节点（node）的位置和需求信息进行嵌入（embedding）并通过编码器进行处理。
它返回处理后的节点和仓库的嵌入表示，用于后续的模型推理。

执行流程图链接

函数代码

    def forward(self, depot_xy, node_xy_demand):# depot_xy.shape: (batch, 1, 2)# node_xy_demand.shape: (batch, problem, 3)embedded_depot = self.embedding_depot(depot_xy)# shape: (batch, 1, embedding)embedded_node = self.embedding_node(node_xy_demand)# shape: (batch, problem, embedding)out = torch.cat((embedded_depot, embedded_node), dim=1)# shape: (batch, problem+1, embedding)for layer in self.layers:out = layer(out)return out# shape: (batch, problem+1, embedding)

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三、class EncoderLayer(nn.Module):init(self, **model_params)

函数功能

init 方法是 EncoderLayer 类的构造函数，主要功能是初始化编码器层的各个子组件。
设置多头注意力机制所需的权重矩阵和相关的正则化层、前馈网络等，以便在 forward 方法中执行实际的操作。

执行流程图链接

函数代码

    def __init__(self, **model_params):super().__init__()self.model_params = model_paramsembedding_dim = self.model_params['embedding_dim']head_num = self.model_params['head_num']qkv_dim = self.model_params['qkv_dim']self.Wq = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)self.Wk = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)self.Wv = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)self.multi_head_combine = nn.Linear(head_num * qkv_dim, embedding_dim)self.add_n_normalization_1 = AddAndInstanceNormalization(**model_params)self.feed_forward = FeedForward(**model_params)self.add_n_normalization_2 = AddAndInstanceNormalization(**model_params)

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四、class EncoderLayer(nn.Module):forward(self, input1)

函数功能

forward 方法是 EncoderLayer 类中的前向传播函数，主要功能是执行 多头自注意力机制 和 前馈神经网络，并进行相应的 残差连接和归一化。
执行流程图链接

函数代码

    def forward(self, input1):# input1.shape: (batch, problem+1, embedding)head_num = self.model_params['head_num']q = reshape_by_heads(self.Wq(input1), head_num=head_num)k = reshape_by_heads(self.Wk(input1), head_num=head_num)v = reshape_by_heads(self.Wv(input1), head_num=head_num)# qkv shape: (batch, head_num, problem, qkv_dim)out_concat = multi_head_attention(q, k, v)# shape: (batch, problem, head_num*qkv_dim)multi_head_out = self.multi_head_combine(out_concat)# shape: (batch, problem, embedding)out1 = self.add_n_normalization_1(input1, multi_head_out)out2 = self.feed_forward(out1)out3 = self.add_n_normalization_2(out1, out2)return out3# shape: (batch, problem, embedding)

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附录

代码（全）

########################################
# ENCODER
########################################class CVRP_Encoder(nn.Module):def __init__(self, **model_params):super().__init__()self.model_params = model_paramsembedding_dim = self.model_params['embedding_dim']encoder_layer_num = self.model_params['encoder_layer_num']self.embedding_depot = nn.Linear(2, embedding_dim)self.embedding_node = nn.Linear(3, embedding_dim)self.layers = nn.ModuleList([EncoderLayer(**model_params) for _ in range(encoder_layer_num)])def forward(self, depot_xy, node_xy_demand):# depot_xy.shape: (batch, 1, 2)# node_xy_demand.shape: (batch, problem, 3)embedded_depot = self.embedding_depot(depot_xy)# shape: (batch, 1, embedding)embedded_node = self.embedding_node(node_xy_demand)# shape: (batch, problem, embedding)out = torch.cat((embedded_depot, embedded_node), dim=1)# shape: (batch, problem+1, embedding)for layer in self.layers:out = layer(out)return out# shape: (batch, problem+1, embedding)class EncoderLayer(nn.Module):def __init__(self, **model_params):super().__init__()self.model_params = model_paramsembedding_dim = self.model_params['embedding_dim']head_num = self.model_params['head_num']qkv_dim = self.model_params['qkv_dim']self.Wq = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)self.Wk = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)self.Wv = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)self.multi_head_combine = nn.Linear(head_num * qkv_dim, embedding_dim)self.add_n_normalization_1 = AddAndInstanceNormalization(**model_params)self.feed_forward = FeedForward(**model_params)self.add_n_normalization_2 = AddAndInstanceNormalization(**model_params)def forward(self, input1):# input1.shape: (batch, problem+1, embedding) 364thead_num = self.model_params['head_num']q = reshape_by_heads(self.Wq(input1), head_num=head_num)k = reshape_by_heads(self.Wk(input1), head_num=head_num)v = reshape_by_heads(self.Wv(input1), head_num=head_num)# qkv shape: (batch, head_num, problem, qkv_dim)out_concat = multi_head_attention(q, k, v)# shape: (batch, problem, head_num*qkv_dim)multi_head_out = self.multi_head_combine(out_concat)# shape: (batch, problem, embedding)out1 = self.add_n_normalization_1(input1, multi_head_out)out2 = self.feed_forward(out1)out3 = self.add_n_normalization_2(out1, out2)return out3# shape: (batch, problem, embedding)