文章目录
- 前言
- [一、class CVRP_Decoder(nn.Module):init(self, **model_params)](#一、class CVRP_Decoder(nn.Module):init(self, **model_params))
- [二、class CVRP_Decoder(nn.Module):set_kv(self, encoded_nodes)](#二、class CVRP_Decoder(nn.Module):set_kv(self, encoded_nodes))
- [三、class CVRP_Decoder(nn.Module):set_q1(self, encoded_q1)](#三、class CVRP_Decoder(nn.Module):set_q1(self, encoded_q1))
- [四、class CVRP_Decoder(nn.Module):set_q2(self, encoded_q2)](#四、class CVRP_Decoder(nn.Module):set_q2(self, encoded_q2))
- [五、class CVRP_Decoder(nn.Module):forward(self, encoded_last_node, load, ninf_mask)](#五、class CVRP_Decoder(nn.Module):forward(self, encoded_last_node, load, ninf_mask))
- 附录
-
- [class CVRP_Decoder代码（全）](#class CVRP_Decoder代码（全）)
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
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。
函数代码
python
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
python
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)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。
函数代码
python
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)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
python
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)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。
函数代码
python
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附录
class CVRP_Decoder代码（全）
python
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