189 lines
6.4 KiB
Python
Executable File
189 lines
6.4 KiB
Python
Executable File
import torch
|
||
import torch.nn as nn
|
||
import torch.nn.functional as F
|
||
|
||
"""
|
||
添加空间嵌入
|
||
"""
|
||
|
||
|
||
class DynamicGraphConstructor(nn.Module):
|
||
def __init__(self, node_num, embed_dim):
|
||
super().__init__()
|
||
# 自适应邻接参数
|
||
self.nodevec1 = nn.Parameter(torch.randn(node_num, embed_dim), requires_grad=True)
|
||
self.nodevec2 = nn.Parameter(torch.randn(node_num, embed_dim), requires_grad=True)
|
||
|
||
def forward(self):
|
||
# 构造动态邻接矩阵
|
||
adj = torch.matmul(self.nodevec1, self.nodevec2.T) # (N, N)
|
||
adj = F.relu(adj)
|
||
adj = F.softmax(adj, dim=-1)
|
||
return adj
|
||
|
||
|
||
class GraphConvBlock(nn.Module):
|
||
def __init__(self, input_dim, output_dim):
|
||
super().__init__()
|
||
# 线性变换 + 可选残差投影
|
||
self.theta = nn.Linear(input_dim, output_dim)
|
||
self.residual = (input_dim == output_dim)
|
||
if not self.residual:
|
||
self.res_proj = nn.Linear(input_dim, output_dim)
|
||
|
||
def forward(self, x, adj):
|
||
# x: (B, N, F_in), adj: (N, N)
|
||
res = x
|
||
x = torch.matmul(adj, x) # 邻接乘特征
|
||
x = self.theta(x) # 线性变换
|
||
# 残差连接
|
||
x = x + (res if self.residual else self.res_proj(res))
|
||
return F.relu(x)
|
||
|
||
|
||
class MANBA_Block(nn.Module):
|
||
def __init__(self, input_dim, hidden_dim):
|
||
super().__init__()
|
||
# 空间自注意力 + 前馈网络
|
||
self.attn = nn.MultiheadAttention(embed_dim=input_dim, num_heads=4, batch_first=True)
|
||
self.ffn = nn.Sequential(
|
||
nn.Linear(input_dim, hidden_dim),
|
||
nn.ReLU(),
|
||
nn.Linear(hidden_dim, input_dim)
|
||
)
|
||
self.norm1 = nn.LayerNorm(input_dim)
|
||
self.norm2 = nn.LayerNorm(input_dim)
|
||
|
||
def forward(self, x):
|
||
# x: (B, N, C)
|
||
res = x
|
||
x_attn, _ = self.attn(x, x, x)
|
||
x = self.norm1(res + x_attn)
|
||
res2 = x
|
||
x_ffn = self.ffn(x)
|
||
x = self.norm2(res2 + x_ffn)
|
||
return x
|
||
|
||
|
||
class SandwichBlock(nn.Module):
|
||
def __init__(self, num_nodes, embed_dim, hidden_dim):
|
||
super().__init__()
|
||
self.manba1 = MANBA_Block(hidden_dim, hidden_dim * 2)
|
||
self.graph_constructor = DynamicGraphConstructor(num_nodes, embed_dim)
|
||
self.gc = GraphConvBlock(hidden_dim, hidden_dim)
|
||
self.manba2 = MANBA_Block(hidden_dim, hidden_dim * 2)
|
||
|
||
def forward(self, h):
|
||
# h: (B, N, hidden_dim)
|
||
h1 = self.manba1(h)
|
||
adj = self.graph_constructor()
|
||
h2 = self.gc(h1, adj)
|
||
h3 = self.manba2(h2)
|
||
return h3
|
||
|
||
|
||
class MLP(nn.Module):
|
||
def __init__(self, in_dim, hidden_dims, out_dim, activation=nn.ReLU):
|
||
super().__init__()
|
||
# 多层感知机
|
||
dims = [in_dim] + hidden_dims + [out_dim]
|
||
layers = []
|
||
for i in range(len(dims) - 2):
|
||
layers += [nn.Linear(dims[i], dims[i + 1]), activation()]
|
||
layers += [nn.Linear(dims[-2], dims[-1])]
|
||
self.net = nn.Sequential(*layers)
|
||
|
||
def forward(self, x):
|
||
# 对最后一维做线性映射
|
||
return self.net(x)
|
||
|
||
|
||
class EXP(nn.Module):
|
||
def __init__(self, args):
|
||
super().__init__()
|
||
# 训练 & 输出参数
|
||
self.horizon = args['horizon']
|
||
self.output_dim = args['output_dim']
|
||
self.seq_len = args.get('in_len', 12)
|
||
self.hidden_dim = args.get('hidden_dim', 64)
|
||
self.num_nodes = args['num_nodes']
|
||
self.embed_dim = args.get('embed_dim', 16)
|
||
|
||
# ==== 时间嵌入 ====
|
||
self.time_slots = args.get('time_slots', 24 * 60 // args.get('time_slot', 5))
|
||
self.time_embedding = nn.Embedding(self.time_slots, self.hidden_dim)
|
||
self.day_embedding = nn.Embedding(7, self.hidden_dim)
|
||
self.node_emb = nn.Parameter(torch.empty(self.num_nodes, self.embed_dim))
|
||
|
||
# ==== 空间嵌入 ====
|
||
# 每个节点一个可学习的向量
|
||
self.spatial_embedding = nn.Parameter(
|
||
torch.randn(self.num_nodes, self.hidden_dim),
|
||
requires_grad=True
|
||
)
|
||
|
||
# 输入投影：仅对流量序列做 MLP
|
||
self.input_proj = MLP(
|
||
in_dim=self.seq_len,
|
||
hidden_dims=[self.hidden_dim],
|
||
out_dim=self.hidden_dim
|
||
)
|
||
|
||
# 两个 SandwichBlock
|
||
self.sandwich1 = SandwichBlock(self.num_nodes, self.embed_dim, self.hidden_dim)
|
||
self.sandwich2 = SandwichBlock(self.num_nodes, self.embed_dim, self.hidden_dim)
|
||
|
||
# 输出投影
|
||
self.out_proj = MLP(
|
||
in_dim=self.hidden_dim,
|
||
hidden_dims=[2 * self.hidden_dim],
|
||
out_dim=self.horizon * self.output_dim
|
||
)
|
||
|
||
def forward(self, x):
|
||
"""
|
||
x: (B, T, N, D_total)
|
||
D_total >= 3，其中：
|
||
x[...,0] = 流量 (flow)
|
||
x[...,1] = 当天时间比 (time_in_day，归一化到 [0,1])
|
||
x[...,2] = 星期几 (day_in_week，0–6)
|
||
"""
|
||
# 拆分三条序列
|
||
x_flow = x[..., 0] # (B, T, N)
|
||
x_time = x[..., 1] # (B, T, N)
|
||
x_day = x[..., 2] # (B, T, N)
|
||
|
||
B, T, N = x_flow.shape
|
||
assert T == self.seq_len, f"序列长度应为 {self.seq_len}，但收到 {T}"
|
||
|
||
# 1) MLP 投影流量历史 -> 节点初始特征 h0
|
||
x_flat = x_flow.permute(0, 2, 1).reshape(B * N, T) # (B*N, T)
|
||
h0 = self.input_proj(x_flat).view(B, N, self.hidden_dim) # (B, N, hidden_dim)
|
||
|
||
# 2) 计算离散时间嵌入
|
||
t_idx = (x_time[:, -1, :] * (self.time_slots - 1)).long() # (B, N)
|
||
d_idx = x_day[:, -1, :].long() # (B, N)
|
||
time_emb = self.time_embedding(t_idx) # (B, N, hidden_dim)
|
||
day_emb = self.day_embedding(d_idx) # (B, N, hidden_dim)
|
||
|
||
# 3) 计算空间嵌入并扩展到 batch 大小
|
||
# node_emb = []
|
||
# node_emb.append(self.node_emb.unsqueeze(0).expand(
|
||
# B, -1, -1).transpose(1, 2).unsqueeze(-1))
|
||
# spatial_emb = torch.stack(node_emb)
|
||
spatial_emb = self.spatial_embedding.unsqueeze(0).expand(B, N, self.hidden_dim) # -> (B, N, hidden_dim)
|
||
|
||
# 4) 将三种嵌入相加到 h0
|
||
h0 = h0 + time_emb + day_emb + spatial_emb
|
||
|
||
# 5) 两层 Sandwich + 残差连接
|
||
h1 = self.sandwich1(h0)
|
||
h1 = h1 + h0
|
||
h2 = self.sandwich2(h1)
|
||
|
||
# 6) 输出投影 -> (B, horizon, N, output_dim)
|
||
out = self.out_proj(h2) # (B, N, horizon*out_dim)
|
||
out = out.view(B, N, self.horizon, self.output_dim)
|
||
out = out.permute(0, 2, 1, 3) # (B, horizon, N, output_dim)
|
||
return out
|