252 lines
9.3 KiB
Python
252 lines
9.3 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 PatchEmbedding, ReprogrammingLayer
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import torch.nn.functional as F
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class DynamicGraphEnhancer(nn.Module):
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"""
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动态图增强器，基于节点嵌入自动生成图结构
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参考DDGCRN的设计，使用节点嵌入和特征信息动态计算邻接矩阵
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"""
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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
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self.embed_dim = embed_dim
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# 节点嵌入参数
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self.node_embeddings = nn.Parameter(
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torch.randn(num_nodes, embed_dim), requires_grad=True
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)
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# 特征转换层，用于生成动态调整的嵌入
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self.feature_transform = nn.Sequential(
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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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# 注册单位矩阵作为固定的支持矩阵
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self.register_buffer("eye", torch.eye(num_nodes))
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def get_laplacian(self, graph, I, normalize=True):
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"""
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计算归一化拉普拉斯矩阵
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"""
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# 计算度矩阵的逆平方根
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D_inv = torch.diag_embed(torch.sum(graph, -1) ** (-0.5))
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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)
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else:
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return torch.matmul(torch.matmul(D_inv, graph + I), D_inv)
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def forward(self, X):
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"""
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X: 输入特征 [B, N, D]
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返回: 动态生成的归一化拉普拉斯矩阵 [B, N, N]
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"""
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batch_size = X.size(0)
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laplacians = []
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# 获取单位矩阵
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I = self.eye.to(X.device)
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for b in range(batch_size):
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# 使用特征转换层生成动态嵌入调整因子
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filt = self.feature_transform(X[b]) # [N, embed_dim]
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# 计算节点嵌入向量
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nodevec = torch.tanh(self.node_embeddings * filt)
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# 通过节点嵌入的点积计算邻接矩阵
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adj = F.relu(torch.matmul(nodevec, nodevec.transpose(0, 1)))
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# 计算归一化拉普拉斯矩阵
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laplacian = self.get_laplacian(adj, I)
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laplacians.append(laplacian)
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return torch.stack(laplacians, dim=0)
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class GraphEnhancedEncoder(nn.Module):
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"""
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基于Chebyshev多项式和动态拉普拉斯矩阵的图增强编码器
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用于将动态图结构信息整合到特征编码中
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"""
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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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super().__init__()
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self.K = K # Chebyshev多项式阶数
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self.in_dim = in_dim
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self.hidden_dim = hidden_dim
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self.device = device
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# 动态图增强器
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self.graph_enhancer = DynamicGraphEnhancer(num_nodes, in_dim, embed_dim)
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# 谱系数 α_k (可学习参数)
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self.alpha = nn.Parameter(torch.randn(K + 1, 1))
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# 传播权重 W_k (可学习参数)
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self.W = nn.ParameterList([
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nn.Parameter(torch.randn(in_dim, hidden_dim)) for _ in range(K + 1)
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])
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self.to(device)
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def chebyshev_polynomials(self, L_tilde, X):
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"""递归计算 [T_0(L_tilde)X, ..., T_K(L_tilde)X]"""
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T_k_list = [X]
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if self.K >= 1:
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T_k_list.append(torch.matmul(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])
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return T_k_list
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def forward(self, X):
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"""
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X: 输入特征 [B, N, D]
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返回: 增强后的特征 [B, N, hidden_dim*(K+1)]
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"""
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batch_size, num_nodes, _ = X.shape
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enhanced_features = []
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# 动态生成拉普拉斯矩阵
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laplacians = self.graph_enhancer(X)
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for b in range(batch_size):
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L = laplacians[b]
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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
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# 避免除零问题
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if lambda_max < 1e-6:
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lambda_max = 1.0
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L_tilde = (2.0 / lambda_max) * L - torch.eye(L.size(0), device=L.device)
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except:
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# 如果计算特征值失败，使用单位矩阵
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L_tilde = torch.eye(num_nodes, device=X.device)
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# 计算Chebyshev多项式展开
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T_k_list = self.chebyshev_polynomials(L_tilde, X[b])
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H_list = []
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# 应用传播权重
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for k in range(self.K + 1):
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H_k = torch.matmul(T_k_list[k], self.W[k])
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H_list.append(H_k)
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# 拼接所有尺度的特征
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X_enhanced = torch.cat(H_list, dim=-1) # [N, hidden_dim*(K+1)]
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enhanced_features.append(X_enhanced)
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return torch.stack(enhanced_features, dim=0)
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class AEPSA(nn.Module):
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def __init__(self, configs):
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super(AEPSA, self).__init__()
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self.device = configs['device']
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self.pred_len = configs['pred_len']
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self.seq_len = configs['seq_len']
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self.patch_len = configs['patch_len']
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self.input_dim = configs['input_dim']
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self.stride = configs['stride']
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self.dropout = configs['dropout']
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self.gpt_layers = configs['gpt_layers']
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self.d_ff = configs['d_ff']
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self.gpt_path = configs['gpt_path']
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self.num_nodes = configs.get('num_nodes', 325) # 添加节点数量配置
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self.output_dim = configs.get('output_dim', 1)
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self.word_choice = GumbelSoftmax(configs['word_num'])
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self.d_model = configs['d_model']
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self.n_heads = configs['n_heads']
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self.d_keys = None
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self.d_llm = 768
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self.patch_nums = int((self.seq_len - self.patch_len) / self.stride + 2)
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self.head_nf = self.d_ff * self.patch_nums
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# 词嵌入
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self.patch_embedding = PatchEmbedding(self.d_model, self.patch_len, self.stride, self.dropout, self.patch_nums, self.input_dim, self.output_dim)
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# GPT2初始化
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self.gpts = GPT2Model.from_pretrained(self.gpt_path, output_attentions=True, output_hidden_states=True)
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self.gpts.h = self.gpts.h[:self.gpt_layers]
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self.gpts.apply(self.reset_parameters)
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self.word_embeddings = self.gpts.get_input_embeddings().weight.to(self.device)
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self.vocab_size = self.word_embeddings.shape[0]
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self.mapping_layer = nn.Linear(self.vocab_size, 1)
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self.reprogramming_layer = ReprogrammingLayer(self.d_model, self.n_heads, self.d_keys, self.d_llm)
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# 添加动态图增强编码器
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self.graph_encoder = GraphEnhancedEncoder(
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K=configs.get('chebyshev_order', 3),
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in_dim=self.d_model,
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hidden_dim=configs.get('graph_hidden_dim', 32),
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num_nodes=self.num_nodes,
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embed_dim=configs.get('graph_embed_dim', 10),
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device=self.device
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)
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# 特征融合层
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self.feature_fusion = nn.Linear(
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self.d_model + configs.get('graph_hidden_dim', 32) * (configs.get('chebyshev_order', 3) + 1),
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self.d_model
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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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for i, (name, param) in enumerate(self.gpts.named_parameters()):
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if 'wpe' in name:
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param.requires_grad = True
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else:
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param.requires_grad = False
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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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"""
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x: 输入数据 [B, T, N, C]
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自动生成图结构，无需外部提供邻接矩阵
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"""
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x = x[..., :self.input_dim]
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x_enc = rearrange(x, 'b t n c -> b n c t')
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# 原版Patch
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enc_out, n_vars = self.patch_embedding(x_enc) # (B, N, C)
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# 应用图增强编码器（自动生成图结构）
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graph_enhanced = self.graph_encoder(enc_out)
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# 特征融合 - 现在两个张量都是三维的 [B, N, d_model]
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enc_out = torch.cat([enc_out, graph_enhanced], dim=-1)
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enc_out = self.feature_fusion(enc_out)
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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))
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source_embeddings = self.word_embeddings[masks==1]
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enc_out = self.reprogramming_layer(enc_out, source_embeddings, source_embeddings)
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enc_out = self.gpts(inputs_embeds=enc_out).last_hidden_state
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dec_out = self.out_mlp(enc_out)
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outputs = dec_out.unsqueeze(dim=-1)
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outputs = outputs.repeat(1, 1, 1, n_vars)
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outputs = outputs.permute(0,2,1,3)
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return outputs
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