187 lines
6.6 KiB
Python
187 lines
6.6 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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使用多层感知机替换输入输出的 proj 层，
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并在 EXP 模型中添加显式的空间嵌入（Spatial Embedding）。
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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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# 自适应邻接参数
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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
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adj = torch.matmul(self.nodevec1, self.nodevec2.T) # (N, N)
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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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# 线性变换 + 可选残差投影
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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, F_in), adj: (N, N)
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res = x
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x = torch.matmul(adj, x) # 邻接乘特征
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x = self.theta(x) # 线性变换
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# 残差连接
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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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# 空间自注意力 + 前馈网络
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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)
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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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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 MLP(nn.Module):
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def __init__(self, in_dim, hidden_dims, out_dim, activation=nn.ReLU):
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super().__init__()
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# 多层感知机
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dims = [in_dim] + hidden_dims + [out_dim]
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layers = []
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for i in range(len(dims) - 2):
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layers += [nn.Linear(dims[i], dims[i+1]), activation()]
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layers += [nn.Linear(dims[-2], dims[-1])]
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self.net = nn.Sequential(*layers)
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def forward(self, x):
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# 对最后一维做线性映射
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return self.net(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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# 训练 & 输出参数
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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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# ==== 时间嵌入 ====
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self.time_slots = args.get('time_slots', 24 * 60 // args.get('time_slot', 5))
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self.time_embedding = nn.Embedding(self.time_slots, self.hidden_dim)
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self.day_embedding = nn.Embedding(7, self.hidden_dim)
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# ==== 空间嵌入 ====
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# 每个节点一个可学习的向量
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self.spatial_embedding = nn.Parameter(
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torch.randn(self.num_nodes, self.hidden_dim),
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requires_grad=True
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)
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# 输入投影：仅对流量序列做 MLP
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self.input_proj = MLP(
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in_dim = self.seq_len,
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hidden_dims = [self.hidden_dim],
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out_dim = self.hidden_dim
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)
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# 两个 SandwichBlock
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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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# 输出投影
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self.out_proj = MLP(
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in_dim = self.hidden_dim,
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hidden_dims = [2 * self.hidden_dim],
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out_dim = self.horizon * self.output_dim
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)
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def forward(self, x):
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"""
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x: (B, T, N, D_total)
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D_total >= 3，其中：
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x[...,0] = 流量 (flow)
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x[...,1] = 当天时间比 (time_in_day，归一化到 [0,1])
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x[...,2] = 星期几 (day_in_week，0–6)
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"""
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# 拆分三条序列
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x_flow = x[..., 0] # (B, T, N)
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x_time = x[..., 1] # (B, T, N)
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x_day = x[..., 2] # (B, T, N)
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B, T, N = x_flow.shape
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assert T == self.seq_len, f"序列长度应为 {self.seq_len}，但收到 {T}"
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# 1) MLP 投影流量历史 -> 节点初始特征 h0
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x_flat = x_flow.permute(0, 2, 1).reshape(B * N, T) # (B*N, T)
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h0 = self.input_proj(x_flat).view(B, N, self.hidden_dim) # (B, N, hidden_dim)
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# 2) 计算离散时间嵌入
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t_idx = (x_time[:, -1, :] * (self.time_slots - 1)).long() # (B, N)
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d_idx = x_day[:, -1, :].long() # (B, N)
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time_emb = self.time_embedding(t_idx) # (B, N, hidden_dim)
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day_emb = self.day_embedding(d_idx) # (B, N, hidden_dim)
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# 3) 计算空间嵌入并扩展到 batch 大小
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node_idx = torch.arange(N, device=x.device) # (N,)
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spatial_emb = self.spatial_embedding[node_idx] # (N, hidden_dim)
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spatial_emb = spatial_emb.unsqueeze(0).expand(B, -1, -1) # (B, N, hidden_dim)
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# 4) 将三种嵌入相加到 h0
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h0 = h0 + time_emb + day_emb + spatial_emb
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# 5) 两层 Sandwich + 残差连接
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h1 = self.sandwich1(h0)
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h1 = h1 + h0
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h2 = self.sandwich2(h1)
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# 6) 输出投影 -> (B, horizon, N, output_dim)
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out = self.out_proj(h2) # (B, N, horizon*out_dim)
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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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