更新EXP模型，添加周期性数据处理和时间特征，优化数据加载器和训练器，支持新的EXP32模型结构

config/EXP/PEMSD4.yaml

@@ -12,12 +12,14 @@ data:
   add_day_in_week: True
   steps_per_day: 288
   days_per_week: 7
+  cycle: 288
 
 model:
   batch_size: 64
   input_dim: 1
   output_dim: 1
   in_len: 12
+  cycle_len: 288
 
 
 train:
@@ -36,6 +38,7 @@ train:
   max_grad_norm: 5
   real_value: True
 
+
 test:
   mae_thresh: null
   mape_thresh: 0.0

dataloader/EXPdataloader.py

@@ -1,94 +1,95 @@
-from lib.normalization import normalize_dataset
-
 import numpy as np
 import gc
 import os
 import torch
 import h5py
+from lib.normalization import normalize_dataset
 
 
 def get_dataloader(args, normalizer='std', single=True):
-    data = load_st_dataset(args['type'], args['sample'])  # 加载数据
+    # args should now include 'cycle'
-    L, N, F = data.shape  # 数据形状
+    data = load_st_dataset(args['type'], args['sample'])  # [T, N, F]
+    L, N, F = data.shape
 
-    # Step 1: data -> x,y
+    # compute cycle index
+    cycle_arr = np.arange(L) % args['cycle']  # length-L array
+
+    # Step 1: sliding windows for X and Y
     x = add_window_x(data, args['lag'], args['horizon'], single)
     y = add_window_y(data, args['lag'], args['horizon'], single)
+    # window count = M = L - lag - horizon + 1
+    M = x.shape[0]
 
-    del data
+    # Step 2: time features
-    gc.collect()
+    time_in_day = np.tile(
-
+        np.array([i % args['steps_per_day'] / args['steps_per_day'] for i in range(L)]),
-    # Step 2: time_in_day, day_in_week -> day, week
+        (N, 1)
-    time_in_day = [i % args['steps_per_day'] / args['steps_per_day'] for i in range(L)]
+    ).T.reshape(L, N, 1)
-    time_in_day = np.tile(np.array(time_in_day), [1, N, 1]).transpose((2, 1, 0))
+    day_in_week = np.tile(
-    day_in_week = [(i // args['steps_per_day']) % args['days_per_week'] for i in range(L)]
+        np.array([(i // args['steps_per_day']) % args['days_per_week'] for i in range(L)]),
-    day_in_week = np.tile(np.array(day_in_week), [1, N, 1]).transpose((2, 1, 0))
+        (N, 1)
+    ).T.reshape(L, N, 1)
 
     x_day = add_window_x(time_in_day, args['lag'], args['horizon'], single)
     x_week = add_window_x(day_in_week, args['lag'], args['horizon'], single)
-
-    # Step 3 day, week, x, y --> x, y
     x = np.concatenate([x, x_day, x_week], axis=-1)
+    # del x_day, x_week
+    # gc.collect()
 
-    del x_day, x_week
+    # Step 3: extract cycle index per window: take value at end of sequence
-    gc.collect()
+    cycle_win = np.array([cycle_arr[i + args['lag']] for i in range(M)])  # shape [M]
 
-    # Step 4 x,y --> x_train, x_val, x_test, y_train, y_val, y_test
+    # Step 4: split into train/val/test
     if args['test_ratio'] > 1:
         x_train, x_val, x_test = split_data_by_days(x, args['val_ratio'], args['test_ratio'])
+        y_train, y_val, y_test = split_data_by_days(y, args['val_ratio'], args['test_ratio'])
+        c_train, c_val, c_test = split_data_by_days(cycle_win, args['val_ratio'], args['test_ratio'])
     else:
         x_train, x_val, x_test = split_data_by_ratio(x, args['val_ratio'], args['test_ratio'])
+        y_train, y_val, y_test = split_data_by_ratio(y, args['val_ratio'], args['test_ratio'])
+        c_train, c_val, c_test = split_data_by_ratio(cycle_win, args['val_ratio'], args['test_ratio'])
+    # del x, y, cycle_win
+    # gc.collect()
 
-    del x
+    # Step 5: normalization on X only
-    gc.collect()
-
-    # Normalization
     scaler = normalize_dataset(x_train[..., :args['input_dim']], normalizer, args['column_wise'])
     x_train[..., :args['input_dim']] = scaler.transform(x_train[..., :args['input_dim']])
     x_val[..., :args['input_dim']] = scaler.transform(x_val[..., :args['input_dim']])
     x_test[..., :args['input_dim']] = scaler.transform(x_test[..., :args['input_dim']])
 
-
+    # add time features to Y
     y_day = add_window_y(time_in_day, args['lag'], args['horizon'], single)
     y_week = add_window_y(day_in_week, args['lag'], args['horizon'], single)
-
-    del time_in_day, day_in_week
-    gc.collect()
-
     y = np.concatenate([y, y_day, y_week], axis=-1)
+    # del y_day, y_week, time_in_day, day_in_week
+    # gc.collect()
 
-    del y_day, y_week
+    # split Y time-augmented
-    gc.collect()
-
-    # Split Y
     if args['test_ratio'] > 1:
         y_train, y_val, y_test = split_data_by_days(y, args['val_ratio'], args['test_ratio'])
     else:
         y_train, y_val, y_test = split_data_by_ratio(y, args['val_ratio'], args['test_ratio'])
+    # del y
 
-    del y
+    # Step 6: create dataloaders including cycle index
-    gc.collect()
+    train_loader = data_loader_with_cycle(x_train, y_train, c_train, args['batch_size'], shuffle=True, drop_last=True)
+    val_loader = data_loader_with_cycle(x_val, y_val, c_val, args['batch_size'], shuffle=False, drop_last=True)
+    test_loader = data_loader_with_cycle(x_test, y_test, c_test, args['batch_size'], shuffle=False, drop_last=False)
 
-    # Step 5: x_train y_train x_val y_val x_test y_test --> train val test
+    return train_loader, val_loader, test_loader, scaler
-    # train_dataloader = data_loader(x_train[..., :args['input_dim']], y_train[..., :args['input_dim']], args['batch_size'], shuffle=True, drop_last=True)
-    train_dataloader = data_loader(x_train, y_train, args['batch_size'], shuffle=True, drop_last=True)
 
-    del x_train, y_train
-    gc.collect()
 
-    # val_dataloader = data_loader(x_val[..., :args['input_dim']], y_val[..., :args['input_dim']], args['batch_size'], shuffle=False, drop_last=True)
+def data_loader_with_cycle(X, Y, C, batch_size, shuffle=True, drop_last=True):
-    val_dataloader = data_loader(x_val, y_val, args['batch_size'], shuffle=False, drop_last=True)
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    X_t = torch.tensor(X, dtype=torch.float32, device=device)
+    Y_t = torch.tensor(Y, dtype=torch.float32, device=device)
+    C_t = torch.tensor(C, dtype=torch.long, device=device).unsqueeze(-1)  # [B,1]
+    dataset = torch.utils.data.TensorDataset(X_t, Y_t, C_t)
+    loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last)
+    return loader
 
-    del x_val, y_val
+# Rest of the helper functions (load_st_dataset, split_data..., add_window_x/y) unchanged
-    gc.collect()
 
-    # test_dataloader = data_loader(x_test[..., :args['input_dim']], y_test[..., :args['input_dim']], args['batch_size'], shuffle=False, drop_last=False)
-    test_dataloader = data_loader(x_test, y_test, args['batch_size'], shuffle=False, drop_last=False)
-
-    del x_test, y_test
-    gc.collect()
-
-    return train_dataloader, val_dataloader, test_dataloader, scaler
 
 def load_st_dataset(dataset, sample):
     # output B, N, D

dataloader/loader_selector.py

@@ -1,11 +1,13 @@
 from dataloader.cde_loader.cdeDataloader import get_dataloader as cde_loader
 from dataloader.PeMSDdataloader import get_dataloader as normal_loader
 from dataloader.DCRNNdataloader import get_dataloader as DCRNN_loader
+from dataloader.EXPdataloader import get_dataloader as EXP_loader
 
 def get_dataloader(config, normalizer, single):
     match config['model']['type']:
         case 'STGNCDE': return cde_loader(config['data'], normalizer, single)
         case 'DCRNN': return DCRNN_loader(config['data'], normalizer, single)
+        case 'EXP': return EXP_loader(config['data'], normalizer, single)
         case _: return normal_loader(config['data'], normalizer, single)
 
 

model/EXP/EXP32.py

@@ -0,0 +1,168 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# ------------------------- CycleNet Component -------------------------
+class RecurrentCycle(nn.Module):
+    """Efficient cyclic data removal/addition."""
+    def __init__(self, cycle_len, channel_size):
+        super().__init__()
+        self.cycle_len = cycle_len
+        self.channel_size = channel_size
+        # 初始化周期缓存：shape (cycle_len, channel_size)
+        self.data = nn.Parameter(torch.zeros(cycle_len, channel_size))
+
+    def forward(self, index, length):
+        # index: (B,), length: seq_len 或 pred_len
+        B = index.size(0)
+        # 生成 [0,1,...,length-1] 的偏移，shape (1, length)
+        arange = torch.arange(length, device=index.device).unsqueeze(0)
+        # 对每条样本的起始 index 加 arange 并对 cycle_len 取模
+        idx = (index.unsqueeze(1) + arange) % self.cycle_len  # (B, length)
+        # 返回对应的周期值 (B, length, channel_size)
+        return self.data[idx]
+
+# ------------------------- Core Blocks -------------------------
+class DynamicGraphConstructor(nn.Module):
+    def __init__(self, node_num, embed_dim):
+        super().__init__()
+        self.nodevec1 = nn.Parameter(torch.randn(node_num, embed_dim))
+        self.nodevec2 = nn.Parameter(torch.randn(node_num, embed_dim))
+
+    def forward(self):
+        adj = F.relu(torch.matmul(self.nodevec1, self.nodevec2.T))
+        return F.softmax(adj, dim=-1)
+
+class GraphConvBlock(nn.Module):
+    def __init__(self, input_dim, output_dim):
+        super().__init__()
+        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):
+        res = x
+        x = torch.matmul(adj, x)
+        x = self.theta(x)
+        if not self.residual:
+            res = self.res_proj(res)
+        return F.relu(x + res)
+
+class MANBA_Block(nn.Module):
+    def __init__(self, input_dim, hidden_dim):
+        super().__init__()
+        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):
+        res = x
+        x_attn, _ = self.attn(x, x, x)
+        x = self.norm1(res + x_attn)
+        res2 = x
+        x_ffn = self.ffn(x)
+        return self.norm2(res2 + x_ffn)
+
+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):
+        h1 = self.manba1(h)
+        adj = self.graph_constructor()
+        h2 = self.gc(h1, adj)
+        return self.manba2(h2)
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, hidden_dims, out_dim, activation=nn.ReLU):
+        super().__init__()
+        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.append(nn.Linear(dims[-2], dims[-1]))
+        self.net = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.net(x)
+
+# ------------------------- EXP with CycleNet -------------------------
+class EXP(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+        self.horizon     = args['horizon']          # 预测步长
+        self.output_dim  = args['output_dim']       # 输出维度 (一般=1)
+        self.seq_len     = args.get('in_len', 12)   # 输入序列长度
+        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', 288)
+        self.time_embedding = nn.Embedding(self.time_slots, self.hidden_dim)
+        self.day_embedding  = nn.Embedding(7, self.hidden_dim)
+
+        # CycleNet
+        self.cycleQueue = RecurrentCycle(cycle_len=args['cycle_len'], channel_size=self.num_nodes)
+
+        # 输入投影 (序列长度 -> 隐藏维度)
+        self.input_proj = MLP(self.seq_len, [self.hidden_dim], self.hidden_dim)
+
+        # 两层 Sandwich
+        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(self.hidden_dim, [2*self.hidden_dim], self.horizon * self.output_dim)
+
+    def forward(self, x, cycle_index):
+        # x: (B, T, N, D>=3)
+        # 1) 拆流量和时间特征，保证丢掉通道维
+        x_flow = x[..., 0]    # -> (B, T, N) or (B, T, N, 1) 如果之前切片错用了0:1
+        x_time = x[..., 1]
+        x_day  = x[..., 2]
+
+        B, T, N = x_flow.shape
+        # DEBUG 打印(可删除)
+        # print("DEBUG x_flow.dim(), shape:", x_flow.dim(), x_flow.shape)
+
+        # 2) 去周期化
+        cyc = self.cycleQueue(cycle_index, T).squeeze(1)  # (B, T, N)
+        x_flow = x_flow - cyc
+
+        # 3) 序列投影
+        h0 = x_flow.permute(0, 2, 1).reshape(B * N, T)  # -> (B*N, T)
+        h0 = self.input_proj(h0).view(B, N, self.hidden_dim)
+
+        # 4) 加时间嵌入
+        t_idx = (x_time[:, -1] * (self.time_slots - 1)).long()  # (B, N)
+        d_idx = x_day[:, -1].long()                             # (B, N)
+        h0 = h0 + self.time_embedding(t_idx) + self.day_embedding(d_idx)
+
+        # 5) Sandwich Blocks
+        h1 = self.sandwich1(h0) + h0
+        h2 = self.sandwich2(h1)
+
+        # 6) 输出投影并 reshape
+        out = self.out_proj(h2)                             # (B, N, H*O)
+        out = out.view(B, N, self.horizon, self.output_dim) # (B, N, H, O)
+        out = out.permute(0, 2, 1, 3)                       # (B, H, N, O)
+
+        # 加回周期
+        idx_out = (cycle_index + self.seq_len) % self.cycleQueue.cycle_len
+        cyc_out = self.cycleQueue(idx_out, self.horizon)  # (B, 1, H, N)
+        # squeeze 掉第1维并 unsqueeze 最后一维
+        cyc_out = cyc_out.squeeze(1).unsqueeze(-1)  # (B, H, N, 1)
+        # 加回周期分量
+        return out + cyc_out

model/model_selector.py

@@ -15,7 +15,7 @@ from model.STGODE.STGODE import ODEGCN
 from model.PDG2SEQ.PDG2Seqb import PDG2Seq
 from model.STID.STID import STID
 from model.STAEFormer.STAEFormer import STAEformer
-from model.EXP.EXP31 import EXP as EXP
+from model.EXP.EXP32 import EXP as EXP
 
 def model_selector(model):
     match model['type']:

trainer/E32Trainer.py

@@ -39,6 +39,7 @@ class Trainer:
         is_train = (mode == 'train')
         self.model.train() if is_train else self.model.eval()
         total_loss = 0.0
+        epoch_time = time.time()
 
         with torch.set_grad_enabled(is_train), \
              tqdm(total=len(dataloader), desc=f'{mode.capitalize()} Epoch {epoch}') as pbar:
@@ -83,7 +84,8 @@ class Trainer:
                 pbar.set_postfix(loss=loss.item())
 
         avg_loss = total_loss / len(dataloader)
-        self.logger.info(f'{mode.capitalize()} Epoch {epoch}: avg Loss: {avg_loss:.6f}')
+        self.logger.info(
+            f'{mode.capitalize()} Epoch {epoch}: average Loss: {avg_loss:.6f}, time: {time.time() - epoch_time:.2f} s')
         return avg_loss
 
     def train_epoch(self, epoch):
@@ -154,9 +156,9 @@ class Trainer:
         y_pred, y_true = [], []
 
         with torch.no_grad():
-            for data, target in data_loader:
+            for data, target, cycle_index in data_loader:
                 label = target[..., :args['output_dim']]
-                output = model(data)
+                output = model(data, cycle_index)
                 y_pred.append(output)
                 y_true.append(label)
 

trainer/trainer_selector.py

@@ -2,7 +2,7 @@ from trainer.Trainer import Trainer
 from trainer.cdeTrainer.cdetrainer import Trainer as cdeTrainer
 from trainer.DCRNN_Trainer import Trainer as DCRNN_Trainer
 from trainer.PDG2SEQ_Trainer import Trainer as PDG2SEQ_Trainer
-from trainer.EXP_trainer import Trainer as EXP_Trainer
+from trainer.E32Trainer import Trainer as EXP_Trainer
 
 
 def select_trainer(model, loss, optimizer, train_loader, val_loader, test_loader, scaler, args,