124 lines
3.8 KiB
Python
124 lines
3.8 KiB
Python
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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含残差版本 + 时间-空间-时间（三明治结构）
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"""
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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(torch.randn(node_num, embed_dim), requires_grad=True)
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self.nodevec2 = nn.Parameter(torch.randn(node_num, embed_dim), requires_grad=True)
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def forward(self):
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# (N, D) @ (D, N) -> (N, N)
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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) # (B, N, C)
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x = self.theta(x)
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# 残差连接
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if self.residual:
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x = x + res
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else:
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x = x + 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(embed_dim=input_dim, num_heads=4, batch_first=True)
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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, T, C) 或 (B, N, C) 当 N 视为 时间维度
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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 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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# 动态图构建
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self.graph = DynamicGraphConstructor(self.num_nodes, embed_dim=16)
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# 输入映射层
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self.input_proj = nn.Linear(self.seq_len, self.hidden_dim)
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# 图卷积
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self.gc = GraphConvBlock(self.hidden_dim, self.hidden_dim)
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# MANBA block（时间建模）
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self.manba = MANBA_Block(self.hidden_dim, self.hidden_dim * 2)
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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 = x[..., 0] # 只用主通道 (B, T, N)
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B, T, N = x.shape
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assert T == self.seq_len
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# 输入投影 (B, T, N) -> (B, N, T) -> (B*N, T) -> (B*N, hidden_dim)
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x_flat = x.permute(0, 2, 1).reshape(B * N, T)
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h = self.input_proj(x_flat) # (B*N, hidden_dim)
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h = h.view(B, N, self.hidden_dim) # (B, N, hidden_dim)
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# === 时间建模（首次） ===
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# 将 N 视作 时间维度进行注意力
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h_time1 = self.manba(h) # (B, N, hidden_dim)
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# 动态图构建
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adj = self.graph() # (N, N)
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# === 空间建模 ===
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h_space = self.gc(h_time1, adj) # (B, N, hidden_dim)
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# === 时间建模（再一次） ===
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h_time2 = self.manba(h_space) # (B, N, hidden_dim)
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# 输出映射
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out = self.out_proj(h_time2) # (B, N, horizon * output_dim)
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out = out.view(B, N, self.horizon, self.output_dim).permute(0, 2, 1, 3)
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return out # (B, horizon, N, output_dim)
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