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前言

class CVRP_Decoder是CVRP_Model.py里的类。

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

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

函数功能

init 方法是 CVRP_Decoder 类中的构造函数，主要功能是初始化该类所需的所有网络层、权重矩阵和参数。
该方法设置了用于多头注意力机制的权重、一个用于表示"遗憾"的参数、以及其他必要的操作用于计算注意力权重。

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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_1 = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)self.Wq_2 = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)self.Wq_last = nn.Linear(embedding_dim+1, 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.regret_embedding = nn.Parameter(torch.Tensor(embedding_dim))self.regret_embedding.data.uniform_(-1, 1)self.multi_head_combine = nn.Linear(head_num * qkv_dim, embedding_dim)self.k = None  # saved key, for multi-head attentionself.v = None  # saved value, for multi-head_attentionself.single_head_key = None  # saved, for single-head attention# self.q1 = None  # saved q1, for multi-head attentionself.q2 = None  # saved q2, for multi-head attention

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二、class CVRP_Decoder(nn.Module):set_kv(self, encoded_nodes)

函数功能

set_kv 方法的功能是将 encoded_nodes 中的节点嵌入转换为多头注意力机制所需的 键（K） 和 值（V），并将它们分别保存为类的属性。
这个方法将输入的节点嵌入通过权重矩阵进行线性变换，得到键和值的表示，并为后续的多头注意力计算做好准备。
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函数代码

    def set_kv(self, encoded_nodes):# encoded_nodes.shape: (batch, problem+1, embedding)head_num = self.model_params['head_num']self.k = reshape_by_heads(self.Wk(encoded_nodes), head_num=head_num)self.v = reshape_by_heads(self.Wv(encoded_nodes), head_num=head_num)# shape: (batch, head_num, problem+1, qkv_dim)self.single_head_key = encoded_nodes.transpose(1, 2)# shape: (batch, embedding, problem+1)

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三、class CVRP_Decoder(nn.Module):set_q1(self, encoded_q1)

函数功能

set_q1 方法的主要功能是 计算查询（Q） 并将其转换为适用于多头注意力机制的形状。
该方法接受输入的查询张量 encoded_q1，通过线性层 self.Wq_1 映射到一个新的维度，并使用 reshape_by_heads 函数将其调整为适合多头注意力机制计算的形状。计算出的查询会被保存为类的属性 q1，供后续使用。

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函数代码

    def set_q1(self, encoded_q1):# encoded_q.shape: (batch, n, embedding)  # n can be 1 or pomohead_num = self.model_params['head_num']self.q1 = reshape_by_heads(self.Wq_1(encoded_q1), head_num=head_num)# shape: (batch, head_num, n, qkv_dim)

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四、class CVRP_Decoder(nn.Module):set_q2(self, encoded_q2)

函数功能

set_q2 方法的主要功能是 计算查询（Q） 并将其转换为适用于多头注意力机制的形状。
该方法接收输入的查询张量 encoded_q2，通过线性层 self.Wq_2 映射到一个新的维度，并使用 reshape_by_heads 函数将其调整为适合多头注意力计算的形状。
执行流程图链接

函数代码

    def set_q2(self, encoded_q2):# encoded_q.shape: (batch, n, embedding)  # n can be 1 or pomohead_num = self.model_params['head_num']self.q2 = reshape_by_heads(self.Wq_2(encoded_q2), head_num=head_num)# shape: (batch, head_num, n, qkv_dim)

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五、class CVRP_Decoder(nn.Module):forward(self, encoded_last_node, load, ninf_mask)

函数功能

forward 方法是 CVRP_Decoder 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 单头注意力计算，并最终输出每个可能节点的选择概率（probs）。
该方法通过多头注意力计算、前馈神经网络处理，以及概率计算来进行节点选择。

执行流程图链接

函数代码

    def forward(self, encoded_last_node, load, ninf_mask):# encoded_last_node.shape: (batch, pomo, embedding)# load.shape: (batch, pomo)# ninf_mask.shape: (batch, pomo, problem)head_num = self.model_params['head_num']#  Multi-Head Attention#######################################################input_cat = torch.cat((encoded_last_node, load[:, :, None]), dim=2)# shape = (batch, group, EMBEDDING_DIM+1)q_last = reshape_by_heads(self.Wq_last(input_cat), head_num=head_num)# shape: (batch, head_num, pomo, qkv_dim)# q = self.q1 + self.q2 + q_last# # shape: (batch, head_num, pomo, qkv_dim)# q = q_last# shape: (batch, head_num, pomo, qkv_dim)q = self.q2 + q_last# # shape: (batch, head_num, pomo, qkv_dim)out_concat = multi_head_attention(q, self.k, self.v, rank3_ninf_mask=ninf_mask)# shape: (batch, pomo, head_num*qkv_dim)mh_atten_out = self.multi_head_combine(out_concat)# shape: (batch, pomo, embedding)#  Single-Head Attention, for probability calculation#######################################################score = torch.matmul(mh_atten_out, self.single_head_key)# shape: (batch, pomo, problem)sqrt_embedding_dim = self.model_params['sqrt_embedding_dim']logit_clipping = self.model_params['logit_clipping']score_scaled = score / sqrt_embedding_dim# shape: (batch, pomo, problem)score_clipped = logit_clipping * torch.tanh(score_scaled)score_masked = score_clipped + ninf_maskprobs = F.softmax(score_masked, dim=2)# shape: (batch, pomo, problem)return probs

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附录

class CVRP_Decoder代码（全）

class CVRP_Decoder(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_1 = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)self.Wq_2 = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)self.Wq_last = nn.Linear(embedding_dim+1, 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.regret_embedding = nn.Parameter(torch.Tensor(embedding_dim))self.regret_embedding.data.uniform_(-1, 1)self.multi_head_combine = nn.Linear(head_num * qkv_dim, embedding_dim)self.k = None  # saved key, for multi-head attentionself.v = None  # saved value, for multi-head_attentionself.single_head_key = None  # saved, for single-head attention# self.q1 = None  # saved q1, for multi-head attentionself.q2 = None  # saved q2, for multi-head attentiondef set_kv(self, encoded_nodes):# encoded_nodes.shape: (batch, problem+1, embedding)head_num = self.model_params['head_num']self.k = reshape_by_heads(self.Wk(encoded_nodes), head_num=head_num)self.v = reshape_by_heads(self.Wv(encoded_nodes), head_num=head_num)# shape: (batch, head_num, problem+1, qkv_dim)self.single_head_key = encoded_nodes.transpose(1, 2)# shape: (batch, embedding, problem+1)def set_q1(self, encoded_q1):# encoded_q.shape: (batch, n, embedding)  # n can be 1 or pomohead_num = self.model_params['head_num']self.q1 = reshape_by_heads(self.Wq_1(encoded_q1), head_num=head_num)# shape: (batch, head_num, n, qkv_dim)def set_q2(self, encoded_q2):# encoded_q.shape: (batch, n, embedding)  # n can be 1 or pomohead_num = self.model_params['head_num']self.q2 = reshape_by_heads(self.Wq_2(encoded_q2), head_num=head_num)# shape: (batch, head_num, n, qkv_dim)def forward(self, encoded_last_node, load, ninf_mask):# encoded_last_node.shape: (batch, pomo, embedding)# load.shape: (batch, pomo)# ninf_mask.shape: (batch, pomo, problem)head_num = self.model_params['head_num']#  Multi-Head Attention#######################################################input_cat = torch.cat((encoded_last_node, load[:, :, None]), dim=2)# shape = (batch, group, EMBEDDING_DIM+1)q_last = reshape_by_heads(self.Wq_last(input_cat), head_num=head_num)# shape: (batch, head_num, pomo, qkv_dim)# q = self.q1 + self.q2 + q_last# # shape: (batch, head_num, pomo, qkv_dim)# q = q_last# shape: (batch, head_num, pomo, qkv_dim)q = self.q2 + q_last# # shape: (batch, head_num, pomo, qkv_dim)out_concat = multi_head_attention(q, self.k, self.v, rank3_ninf_mask=ninf_mask)# shape: (batch, pomo, head_num*qkv_dim)mh_atten_out = self.multi_head_combine(out_concat)# shape: (batch, pomo, embedding)#  Single-Head Attention, for probability calculation#######################################################score = torch.matmul(mh_atten_out, self.single_head_key)# shape: (batch, pomo, problem)sqrt_embedding_dim = self.model_params['sqrt_embedding_dim']logit_clipping = self.model_params['logit_clipping']score_scaled = score / sqrt_embedding_dim# shape: (batch, pomo, problem)score_clipped = logit_clipping * torch.tanh(score_scaled)score_masked = score_clipped + ninf_maskprobs = F.softmax(score_masked, dim=2)# shape: (batch, pomo, problem)return probs