147 lines
5.0 KiB
Python
Executable File
147 lines
5.0 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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含时间/空间额外特征的双层 时间->空间->时间 三明治结构模型
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使用 x[...,0] 主通道，x[...,1] time_in_day，x[...,2] day_in_week
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通过独立投影融合三路信息
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无改进
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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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# 构造动态邻接矩阵 (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)
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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(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, 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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# 第一步：时间注意力
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h1 = self.manba1(h)
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# 第二步：空间卷积
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adj = self.graph_constructor()
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h2 = self.gc(h1, adj)
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# 第三步：时间注意力
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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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# 对三路输入分别投影到隐藏维度
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self.main_proj = nn.Linear(self.seq_len, self.hidden_dim)
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self.time_proj = nn.Linear(self.seq_len, self.hidden_dim)
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self.week_proj = nn.Linear(self.seq_len, self.hidden_dim)
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# 双层 时间->空间->时间 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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x_time = x[..., 1] # (B, T, N)
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x_week = x[..., 2] # (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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# 将三路特征分别映射后叠加
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x_main_flat = x_main.permute(0, 2, 1).reshape(B * N, T)
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h_main = self.main_proj(x_main_flat).view(B, N, self.hidden_dim)
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x_time_flat = x_time.permute(0, 2, 1).reshape(B * N, T)
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h_time = self.time_proj(x_time_flat).view(B, N, self.hidden_dim)
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x_week_flat = x_week.permute(0, 2, 1).reshape(B * N, T)
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h_week = self.week_proj(x_week_flat).view(B, N, self.hidden_dim)
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# 初始隐藏表示，融合三路信息
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h0 = h_main + h_time + h_week
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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
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out = self.out_proj(h2)
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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, D_out)
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return out
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# 示例测试
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# args = {'horizon':12,'output_dim':1,'in_len':12,'hidden_dim':64,'num_nodes':307,'embed_dim':16}
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# model = EXP(args)
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# x = torch.randn(16, 12, 307, 3)
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# print(model(x).shape) # (16,12,307,1) |