209 lines
10 KiB
Python
209 lines
10 KiB
Python
import torch
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import torch.nn as nn
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from transformers.models.gpt2.modeling_gpt2 import GPT2Model
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from einops import rearrange
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from model.AEPSA.normalizer import GumbelSoftmax
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from model.AEPSA.reprogramming import ReprogrammingLayer
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import torch.nn.functional as F
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# 基于动态图增强的时空序列预测模型实现
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class DynamicGraphEnhancer(nn.Module):
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"""动态图增强编码器"""
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def __init__(self, num_nodes, in_dim, embed_dim=10):
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super().__init__()
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self.num_nodes = num_nodes # 节点个数
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self.embed_dim = embed_dim # 节点嵌入维度
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self.node_embeddings = nn.Parameter(torch.randn(num_nodes, embed_dim), requires_grad=True) # 节点嵌入参数
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self.feature_transform = nn.Sequential( # 特征转换网络
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nn.Linear(in_dim, 16),
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nn.Sigmoid(),
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nn.Linear(16, 2),
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nn.Sigmoid(),
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nn.Linear(2, embed_dim)
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)
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self.register_buffer("eye", torch.eye(num_nodes)) # 注册单位矩阵
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def get_laplacian(self, graph, I, normalize=True):
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D_inv = torch.diag_embed(torch.sum(graph, -1) ** (-0.5)) # 度矩阵的逆平方根
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D_inv[torch.isinf(D_inv)] = 0.0 # 处理零除问题
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if normalize:
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return torch.matmul(torch.matmul(D_inv, graph), D_inv) # 归一化拉普拉斯矩阵
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else:
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return torch.matmul(torch.matmul(D_inv, graph + I), D_inv) # 带自环的归一化拉普拉斯矩阵
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def forward(self, X):
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"""生成动态拉普拉斯矩阵"""
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batch_size = X.size(0) # 批次大小
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laplacians = [] # 存储各批次的拉普拉斯矩阵
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I = self.eye.to(X.device) # 移动单位矩阵到目标设备
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for b in range(batch_size):
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filt = self.feature_transform(X[b]) # 特征转换
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nodevec = torch.tanh(self.node_embeddings * filt) # 计算节点嵌入
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adj = F.relu(torch.matmul(nodevec, nodevec.transpose(0, 1))) # 计算邻接矩阵
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laplacian = self.get_laplacian(adj, I) # 计算拉普拉斯矩阵
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laplacians.append(laplacian)
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return torch.stack(laplacians, dim=0) # 堆叠并返回
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class GraphEnhancedEncoder(nn.Module):
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"""图增强编码器"""
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def __init__(self, K=3, in_dim=64, hidden_dim=32, num_nodes=325, embed_dim=10, device='cpu',
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temporal_dim=12, num_features=1):
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super().__init__()
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self.K = K # Chebyshev多项式阶数
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self.in_dim = in_dim # 输入特征维度
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self.hidden_dim = hidden_dim # 隐藏层维度
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self.device = device # 运行设备
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self.temporal_dim = temporal_dim # 时间序列长度
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self.num_features = num_features # 特征通道数量
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self.input_projection = nn.Sequential( # 输入投影层
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nn.Conv2d(num_features, 16, kernel_size=(1, 3), padding=(0, 1)),
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nn.ReLU(),
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nn.Conv2d(16, in_dim, kernel_size=(1, temporal_dim)),
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nn.ReLU()
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)
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self.graph_enhancer = DynamicGraphEnhancer(num_nodes, in_dim, embed_dim) # 动态图增强器
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self.alpha = nn.Parameter(torch.randn(K + 1, 1)) # 谱系数
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self.W = nn.ParameterList([nn.Parameter(torch.randn(in_dim, hidden_dim)) for _ in range(K + 1)]) # 传播权重
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self.to(device) # 移动到指定设备
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def chebyshev_polynomials(self, L_tilde, X):
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"""计算Chebyshev多项式展开"""
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T_k_list = [X] # T_0(X) = X
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if self.K >= 1:
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T_k_list.append(torch.matmul(L_tilde, X)) # T_1(X) = L_tilde * X
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for k in range(2, self.K + 1):
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T_k_list.append(2 * torch.matmul(L_tilde, T_k_list[-1]) - T_k_list[-2]) # 递推计算
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return T_k_list # 返回多项式列表
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def forward(self, X):
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"""输入特征[B,N,C,T]，返回增强特征[B,N,hidden_dim*(K+1)]"""
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batch_size = X.size(0) # 批次大小
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num_nodes = X.size(1) # 节点数量
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x = X.permute(0, 2, 1, 3) # [B,C,N,T]
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x_proj = self.input_projection(x).squeeze(-1) # [B,in_dim,N]
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x_proj = x_proj.permute(0, 2, 1) # [B,N,in_dim]
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enhanced_features = [] # 存储增强特征
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laplacians = self.graph_enhancer(x_proj) # 生成动态拉普拉斯矩阵
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for b in range(batch_size):
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L = laplacians[b] # 当前批次的拉普拉斯矩阵
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# 特征值缩放
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try:
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lambda_max = torch.linalg.eigvalsh(L).max().real # 最大特征值
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lambda_max = 1.0 if lambda_max < 1e-6 else lambda_max # 防止除零
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L_tilde = (2.0 / lambda_max) * L - torch.eye(L.size(0), device=L.device) # 归一化拉普拉斯
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except:
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L_tilde = torch.eye(num_nodes, device=X.device) # 异常处理
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# 计算展开并应用权重
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T_k_list = self.chebyshev_polynomials(L_tilde, x_proj[b]) # 计算Chebyshev多项式
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H_list = [torch.matmul(T_k_list[k], self.W[k]) for k in range(self.K + 1)] # 应用权重
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X_enhanced = torch.cat(H_list, dim=-1) # 拼接特征
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enhanced_features.append(X_enhanced)
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return torch.stack(enhanced_features, dim=0) # 堆叠返回[B,N,hidden_dim*(K+1)]，每个节点在每个k阶下的切比雪夫特征
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class AEPSA(nn.Module):
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"""自适应特征投影时空自注意力模型"""
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def __init__(self, configs):
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super(AEPSA, self).__init__()
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self.device = configs['device'] # 运行设备
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self.pred_len = configs['pred_len'] # 预测序列长度
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self.seq_len = configs['seq_len'] # 输入序列长度
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self.patch_len = configs['patch_len'] # 补丁长度
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self.input_dim = configs['input_dim'] # 输入特征维度
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self.stride = configs['stride'] # 步长
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self.dropout = configs['dropout'] # Dropout概率
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self.gpt_layers = configs['gpt_layers'] # 使用的GPT2层数
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self.d_ff = configs['d_ff'] # 前馈网络隐藏层维度
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self.gpt_path = configs['gpt_path'] # 预训练GPT2模型路径
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self.num_nodes = configs.get('num_nodes', 325) # 节点数量
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self.word_choice = GumbelSoftmax(configs['word_num']) # 词汇选择层
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self.d_model = configs['d_model'] # 模型维度
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self.n_heads = configs['n_heads'] # 注意力头数量
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self.d_keys = None # 键维度
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self.d_llm = 768 # GPT2隐藏层维度
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self.patch_nums = int((self.seq_len - self.patch_len) / self.stride + 2) # 补丁数量
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self.head_nf = self.d_ff * self.patch_nums # 头特征维度
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# 初始化GPT2模型
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self.gpts = GPT2Model.from_pretrained(self.gpt_path, output_attentions=True, output_hidden_states=True) # GPT2模型
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self.gpts.h = self.gpts.h[:self.gpt_layers] # 截取指定层数
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self.gpts.apply(self.reset_parameters) # 重置参数
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self.word_embeddings = self.gpts.get_input_embeddings().weight.to(self.device) # 词嵌入权重
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self.vocab_size = self.word_embeddings.shape[0] # 词汇表大小
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self.mapping_layer = nn.Linear(self.vocab_size, 1) # 映射层
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self.reprogramming_layer = ReprogrammingLayer(self.d_model + configs.get('graph_hidden_dim', 32) * (configs.get('chebyshev_order', 3) + 1), self.n_heads, self.d_keys, self.d_llm) # 重编程层
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# 初始化图增强编码器
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self.graph_encoder = GraphEnhancedEncoder(
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K=configs.get('chebyshev_order', 3), # Chebyshev多项式阶数
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in_dim=self.d_model, # 输入特征维度
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hidden_dim=configs.get('graph_hidden_dim', 32), # 隐藏层维度
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num_nodes=self.num_nodes, # 节点数量
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embed_dim=configs.get('graph_embed_dim', 10), # 节点嵌入维度
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device=self.device, # 运行设备
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temporal_dim=self.seq_len, # 时间序列长度
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num_features=self.input_dim # 特征通道数
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)
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self.graph_projection = nn.Linear( # 图特征投影层，每一k阶的切比雪夫权重映射到隐藏维度
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configs.get('graph_hidden_dim', 32) * (configs.get('chebyshev_order', 3) + 1), # 输入维度
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self.d_model # 输出维度
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)
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self.out_mlp = nn.Sequential(
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nn.Linear(self.d_llm, 128),
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nn.ReLU(),
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nn.Linear(128, self.pred_len)
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)
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# 设置参数可训练性 wps=word position embeddings
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for name, param in self.gpts.named_parameters():
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param.requires_grad = 'wpe' in name
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def reset_parameters(self, module):
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if hasattr(module, 'weight') and module.weight is not None:
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torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
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if hasattr(module, 'bias') and module.bias is not None:
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torch.nn.init.zeros_(module.bias)
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def forward(self, x):
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# 数据处理
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x = x[..., :1] # [B,T,N,1]
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x_enc = rearrange(x, 'b t n c -> b n c t') # [B,N,1,T]
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# 图编码
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H_t = self.graph_encoder(x_enc) # [B,N,1,T] -> [B, N, hidden_dim*(K+1)]
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X_t_1 = self.graph_projection(H_t) # [B,N,d_model]
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X_enc = torch.cat([H_t, X_t_1], dim = -1) # [B, N, d_model + hidden_dim*(K+1)]
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# 词嵌入处理
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self.mapping_layer(self.word_embeddings.permute(1, 0)).permute(1, 0)
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masks = self.word_choice(self.mapping_layer.weight.data.permute(1,0)) # [d_llm,1]
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source_embeddings = self.word_embeddings[masks==1] # [selected_words,d_llm]
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# 重编程与预测
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X_enc = self.reprogramming_layer(X_enc, source_embeddings, source_embeddings)
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X_enc = self.gpts(inputs_embeds=X_enc).last_hidden_state # [B,N,d_llm]
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dec_out = self.out_mlp(X_enc) # [B,N,pred_len]
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# 维度调整
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outputs = dec_out.unsqueeze(dim=-1) # [B,N,pred_len,1]
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outputs = outputs.permute(0, 2, 1, 3) # [B,pred_len,N,1]
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return outputs |