171 lines
5.3 KiB
Python
Executable File
171 lines
5.3 KiB
Python
Executable File
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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"""
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基于傅里叶变换优化的双层三明治结构模型
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新增：TemporalFourierBlock 用于全局捕捉时序频域特征，提升预测精度
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第一层：Fourier 时域 -> 空间 -> 时间
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残差连接：层输出 + 层输入
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第二层：同样三明治结构 -> 最终输出
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"""
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class TemporalFourierBlock(nn.Module):
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"""
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时序傅里叶变换块
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输入： x (B, T, N)
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输出：时域重构 (B, T, N)
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"""
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def __init__(self, seq_len):
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super().__init__()
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# 频域系数学习：对每个频率分量应用可学习缩放
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# rfft 输出频率数 = seq_len//2 + 1
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freq_len = seq_len // 2 + 1
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self.scale = nn.Parameter(torch.randn(freq_len), requires_grad=True)
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self.seq_len = seq_len
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def forward(self, x):
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# x: (B, T, N)
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# FFT 到频域
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Xf = torch.fft.rfft(x, dim=1) # (B, F, N), complex
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# 学习缩放：实部和虚部同时缩放
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scale = self.scale.view(1, -1, 1)
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Xf = Xf * scale
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# IFFT 回时域
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x_rec = torch.fft.irfft(Xf, n=self.seq_len, dim=1) # (B, T, N)
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return x_rec
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class DynamicGraphConstructor(nn.Module):
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def __init__(self, node_num, embed_dim):
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super().__init__()
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self.nodevec1 = nn.Parameter(
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torch.randn(node_num, embed_dim), requires_grad=True
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)
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self.nodevec2 = nn.Parameter(
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torch.randn(node_num, embed_dim), requires_grad=True
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)
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def forward(self):
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adj = torch.matmul(self.nodevec1, self.nodevec2.T)
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adj = F.relu(adj)
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adj = F.softmax(adj, dim=-1)
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return adj
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class GraphConvBlock(nn.Module):
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def __init__(self, input_dim, output_dim):
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super().__init__()
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self.theta = nn.Linear(input_dim, output_dim)
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self.residual = input_dim == output_dim
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if not self.residual:
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self.res_proj = nn.Linear(input_dim, output_dim)
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def forward(self, x, adj):
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# x: (B, N, C); adj: (N, N)
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res = x
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x = torch.matmul(adj, x)
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x = self.theta(x)
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x = x + (res if self.residual else self.res_proj(res))
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return F.relu(x)
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class MANBA_Block(nn.Module):
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def __init__(self, input_dim, hidden_dim):
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super().__init__()
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self.attn = nn.MultiheadAttention(
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embed_dim=input_dim, num_heads=4, batch_first=True
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)
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self.ffn = nn.Sequential(
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nn.Linear(input_dim, hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, input_dim),
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)
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self.norm1 = nn.LayerNorm(input_dim)
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self.norm2 = nn.LayerNorm(input_dim)
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def forward(self, x):
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# x: (B, N, C) 视 N 维为时间序列长度
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res = x
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x_attn, _ = self.attn(x, x, x)
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x = self.norm1(res + x_attn)
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res2 = x
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x_ffn = self.ffn(x)
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x = self.norm2(res2 + x_ffn)
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return x
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class SandwichBlock(nn.Module):
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"""
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时间-空间-时间 三明治结构
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输入/输出： (B, N, hidden_dim)
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"""
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def __init__(self, num_nodes, embed_dim, hidden_dim):
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super().__init__()
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self.manba1 = MANBA_Block(hidden_dim, hidden_dim * 2)
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self.graph_constructor = DynamicGraphConstructor(num_nodes, embed_dim)
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self.gc = GraphConvBlock(hidden_dim, hidden_dim)
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self.manba2 = MANBA_Block(hidden_dim, hidden_dim * 2)
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def forward(self, h):
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# h: (B, N, hidden_dim)
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h1 = self.manba1(h)
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adj = self.graph_constructor()
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h2 = self.gc(h1, adj)
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h3 = self.manba2(h2)
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return h3
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class EXP(nn.Module):
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def __init__(self, args):
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super().__init__()
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self.horizon = args["horizon"]
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self.output_dim = args["output_dim"]
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self.seq_len = args.get("in_len", 12)
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self.hidden_dim = args.get("hidden_dim", 64)
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self.num_nodes = args["num_nodes"]
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self.embed_dim = args.get("embed_dim", 16)
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# 时序傅里叶块
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self.fourier_block = TemporalFourierBlock(self.seq_len)
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# 输入映射：(B*N, T) -> hidden_dim
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self.input_proj = nn.Linear(self.seq_len, self.hidden_dim)
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# 两层三明治块
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self.sandwich1 = SandwichBlock(self.num_nodes, self.embed_dim, self.hidden_dim)
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self.sandwich2 = SandwichBlock(self.num_nodes, self.embed_dim, self.hidden_dim)
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# 输出映射
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self.out_proj = nn.Linear(self.hidden_dim, self.horizon * self.output_dim)
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def forward(self, x):
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# x: (B, T, N, D_total)
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x_main = x[..., 0] # (B, T, N)
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B, T, N = x_main.shape
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assert T == self.seq_len
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# 时序傅里叶变换 + 残差
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x_freq = self.fourier_block(x_main) # (B, T, N)
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x_main = x_main + x_freq
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# 输入投影 (B, T, N) -> (B*N, T) -> (B, N, hidden_dim)
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x_flat = x_main.permute(0, 2, 1).reshape(B * N, T)
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h0 = self.input_proj(x_flat).view(B, N, self.hidden_dim)
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# 第一层三明治 + 残差
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h1 = self.sandwich1(h0)
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h1 = h1 + h0
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# 第二层三明治
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h2 = self.sandwich2(h1)
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# 输出映射
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out = self.out_proj(h2) # (B, N, H*D_out)
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out = out.view(B, N, self.horizon, self.output_dim)
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out = out.permute(0, 2, 1, 3) # (B, horizon, N, output_dim)
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return out
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