20250226-代码笔记05-classCVRP_Decoder

文章目录 前言一、class CVRP_Decoder(nn.Module):__init__(self, **model_params)函数功能函数代码 二、class CVRP_Decoder(nn.Module):set_kv(self, encoded_nodes)函数功能函数代码 三、class CVRP_Decoder(nn.Module):set_q1(self, encoded_q1)函数功能函数代码 四、class CVRP_Decoder(nn.Module):set_q2(self, encoded_q2)函数功能函数代码 五、class CVRP_Decoder(nn.Module):forward(self, encoded_last_node, load, ninf_mask)函数功能函数代码 附录class CVRP_Decoder代码（全）

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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_params embedding_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 attention self.v = None # saved value, for multi-head_attention self.single_head_key = None # saved, for single-head attention # self.q1 = None # saved q1, for multi-head attention self.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。 执行流程图链接

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 pomo head_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 pomo head_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。

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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_mask probs = 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_params embedding_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 attention self.v = None # saved value, for multi-head_attention self.single_head_key = None # saved, for single-head attention # self.q1 = None # saved q1, for multi-head attention self.q2 = None # saved q2, for multi-head attention 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) def set_q1(self, encoded_q1): # encoded_q.shape: (batch, n, embedding) # n can be 1 or pomo head_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 pomo head_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_mask probs = F.softmax(score_masked, dim=2) # shape: (batch, pomo, problem) return probs