train.py

@@ -13,7 +13,7 @@ def read_config(config_path):
     # 全局配置
     device = "cuda:0" # 指定设备为cuda:0
     seed = 2023 # 随机种子
-    epochs = 1 # 训练轮数
+    epochs = 100 # 训练轮数
 
     # 拷贝项
     config["basic"]["device"] = device
@@ -91,8 +91,8 @@ if __name__ == "__main__":
     # 调试用
     # model_list = ["iTransformer", "PatchTST", "HI"]
     # model_list = ["ASTRA_v2", "GWN", "REPST", "STAEFormer", "MTGNN"]
-    model_list = ["MTGNN"]
-    # dataset_list = ["AirQuality", "SolarEnergy", "PEMS-BAY", "METR-LA", "BJTaxi-InFlow", "BJTaxi-OutFlow", "NYCBike-InFlow", "NYCBike-OutFlow"]
-    dataset_list = ["AirQuality"]
+    model_list = ["iTransformer"]
+    dataset_list = ["AirQuality", "SolarEnergy", "PEMS-BAY", "METR-LA", "BJTaxi-InFlow", "BJTaxi-OutFlow", "NYCBike-InFlow", "NYCBike-OutFlow"]
+    # dataset_list = ["AirQuality"]
     # dataset_list = ["AirQuality", "SolarEnergy", "METR-LA", "NYCBike-InFlow", "NYCBike-OutFlow"]
-    main(model_list, dataset_list, debug = False)
+    main(model_list, dataset_list, debug = True)

trainer/TSTrainer.py

@@ -3,25 +3,13 @@ from tqdm import tqdm
 from utils.logger import get_logger
 from utils.loss_function import all_metrics
 
-class TSWrapper:
-    def __init__(self, args):
-        self.n = args['data']['num_nodes']
-
-    def forward(self, x):
-        # [b, t, n, c] -> [b*n, t, c]
-        b, t, n, c = x.shape
-        x = x[..., :-2].permute(0, 2, 1, 3).reshape(b * n, t, c-2)
-        return x, b, t, n, c
-
-    def inverse(self, x, b, t, n, c):
-        return x.reshape(b, n, t, c-2).permute(0, 2, 1, 3)
-
 
 class Trainer:
     def __init__(self, model, loss, optimizer,
                  train_loader, val_loader, test_loader,
                  scaler, args, lr_scheduler=None):
 
+        self.config = args
         self.device = args["basic"]["device"]
         self.args = args["train"]
 
@@ -35,7 +23,24 @@ class Trainer:
         self.test_loader = test_loader
         self.scaler = scaler
 
-        self.ts = TSWrapper(args)
+        # ---------- shape magic (replace TSWrapper) ----------
+        self.pack = lambda x: (
+            x[..., :-2]
+            .permute(0, 2, 1, 3)
+            .reshape(-1, x.size(1), x.size(3) - 2),
+            x.shape
+        )
+        self.unpack = lambda y, s: (
+            y.reshape(s[0], s[2], s[1], -1)
+             .permute(0, 2, 1, 3)
+        )
+
+        # ---------- inverse scaler ----------
+        self.inv = lambda x: torch.cat(
+            [s.inverse_transform(x[..., i:i+1]) for i, s in enumerate(self.scaler)],
+            dim=-1
+        )
+
         self._init_paths()
         self._init_logger()
 
@@ -51,7 +56,7 @@ class Trainer:
         self.logger = get_logger(
             self.args["log_dir"],
             name=self.model.__class__.__name__,
-            debug=self.args["debug"],
+            debug=self.args["debug"]
         )
 
     # ---------------- epoch ----------------
@@ -67,21 +72,17 @@ class Trainer:
                 data, target = data.to(self.device), target.to(self.device)
                 label = target[..., :self.args["output_dim"]]
 
-                x, b, t, n, c = self.ts.forward(data)
-                out = self.model(x)
-                out = self.ts.inverse(out, b, t, n, c)
+                x, shp = self.pack(data)
+                out = self.unpack(self.model(x), shp)
 
                 if os.environ.get("TRY") == "True":
-                    if out.shape == label.shape:
-                        print("shape true")
-                        assert False
-                    else: 
-                        print("shape false")
-                        assert False
+                    print(f"out:{out.shape} label:{label.shape}",
+                          "✅" if out.shape == label.shape else "❌")
+                    assert False
 
                 loss = self.loss(out, label)
-                d_out = self.scaler.inverse_transform(out)
-                d_lbl = self.scaler.inverse_transform(label)
+
+                d_out, d_lbl = self.inv(out), self.inv(label)
                 d_loss = self.loss(d_out, d_lbl)
 
                 total_loss += d_loss.item()
@@ -98,9 +99,7 @@ class Trainer:
                         )
                     self.optimizer.step()
 
-        y_pred = torch.cat(y_pred)
-        y_true = torch.cat(y_true)
-
+        y_pred, y_true = torch.cat(y_pred), torch.cat(y_true)
         mae, rmse, mape = all_metrics(
             y_pred, y_true,
             self.args["mae_thresh"],
@@ -110,23 +109,28 @@ class Trainer:
         self.logger.info(
             f"Epoch #{epoch:02d} {mode:<5} "
             f"MAE:{mae:5.2f} RMSE:{rmse:5.2f} "
-            f"MAPE:{mape:7.4f} Time:{time.time()-start:.2f}s"
+            f"MAPE:{mape:7.4f} "
+            f"Time:{time.time() - start:.2f}s"
         )
+
         return total_loss / len(loader)
 
     # ---------------- train ----------------
     def train(self):
-        best, best_test = float("inf"), float("inf")
-        best_w, best_test_w = None, None
+        best = best_test = float("inf")
+        best_w = best_test_w = None
         patience = 0
 
         self.logger.info("Training started")
 
         for epoch in range(1, self.args["epochs"] + 1):
             losses = {
-                "train": self._run_epoch(epoch, self.train_loader, "train"),
-                "val":   self._run_epoch(epoch, self.val_loader, "val"),
-                "test":  self._run_epoch(epoch, self.test_loader, "test"),
+                k: self._run_epoch(epoch, l, k)
+                for k, l in [
+                    ("train", self.train_loader),
+                    ("val", self.val_loader),
+                    ("test", self.test_loader)
+                ]
             }
 
             if losses["train"] > 1e6:
@@ -171,15 +175,14 @@ class Trainer:
                 data, target = data.to(self.device), target.to(self.device)
                 label = target[..., :self.args["output_dim"]]
 
-                x, b, t, n, c = self.ts.forward(data)
-                out = self.model(x)
-                out = self.ts.inverse(out, b, t, n, c)
+                x, shp = self.pack(data)
+                out = self.unpack(self.model(x), shp)
 
                 y_pred.append(out.cpu())
                 y_true.append(label.cpu())
 
-        d_pred = self.scaler.inverse_transform(torch.cat(y_pred))
-        d_true = self.scaler.inverse_transform(torch.cat(y_true))
+        d_pred = self.inv(torch.cat(y_pred))
+        d_true = self.inv(torch.cat(y_true))
 
         for t in range(d_true.shape[1]):
             mae, rmse, mape = all_metrics(
@@ -188,11 +191,15 @@ class Trainer:
                 self.args["mape_thresh"]
             )
             self.logger.info(
-                f"Horizon {t+1:02d} MAE:{mae:.4f} RMSE:{rmse:.4f} MAPE:{mape:.4f}"
+                f"Horizon {t+1:02d} "
+                f"MAE:{mae:.4f} RMSE:{rmse:.4f} MAPE:{mape:.4f}"
             )
-        
-        avg_mae, avg_rmse, avg_mape = all_metrics(d_pred, d_true, self.args["mae_thresh"], self.args["mape_thresh"])
-        self.logger.info(
-            f"AVG MAE:{avg_mae:.4f} AVG RMSE:{avg_rmse:.4f} AVG MAPE:{avg_mape:.4f}"
-        )
 
+        mae, rmse, mape = all_metrics(
+            d_pred, d_true,
+            self.args["mae_thresh"],
+            self.args["mape_thresh"]
+        )
+        self.logger.info(
+            f"AVG MAE:{mae:.4f} AVG RMSE:{rmse:.4f} AVG MAPE:{mape:.4f}"
+        )

trainer/Trainer.py

@@ -3,59 +3,29 @@ from tqdm import tqdm
 from utils.logger import get_logger
 from utils.loss_function import all_metrics
 
-
 class Trainer:
-    def __init__(self, model, loss, optimizer,
-                 train_loader, val_loader, test_loader,
-                 scaler, args, lr_scheduler=None):
-
-        self.config = args
-        self.device = args["basic"]["device"]
-        self.args = args["train"]
-
-        self.model = model.to(self.device)
-        self.loss = loss
-        self.optimizer = optimizer
-        self.lr_scheduler = lr_scheduler
-
-        self.train_loader = train_loader
-        self.val_loader = val_loader or test_loader
-        self.test_loader = test_loader
+    def __init__(self, model, loss, optimizer, train_loader, val_loader, test_loader, scaler, args, lr_scheduler=None):
+        self.device, self.args = args["basic"]["device"], args["train"]
+        self.model, self.loss, self.optimizer, self.lr_scheduler = model.to(self.device), loss, optimizer, lr_scheduler
+        self.train_loader, self.val_loader, self.test_loader = train_loader, val_loader or test_loader, test_loader
         self.scaler = scaler
-
-        # ===== 新增：统一反归一化接口（单 scaler / 多 scaler 通吃）=====
-        self.inv = (
-            (lambda x: self.scaler.inverse_transform(x))
-            if not isinstance(self.scaler, (list, tuple))
-            else (lambda x: torch.cat(
-                [s.inverse_transform(x[..., i:i+1])
-                 for i, s in enumerate(self.scaler)],
-                dim=-1))
-        )
-
+        self.inv = lambda x: torch.cat([s.inverse_transform(x[..., i:i+1]) for i, s in enumerate(self.scaler)], dim=-1)  # 对每个维度调用反归一化器后cat
         self._init_paths()
         self._init_logger()
 
     # ---------------- init ----------------
     def _init_paths(self):
         d = self.args["log_dir"]
-        self.best_path = os.path.join(d, "best_model.pth")
-        self.best_test_path = os.path.join(d, "best_test_model.pth")
+        self.best_path, self.best_test_path = os.path.join(d, "best_model.pth"), os.path.join(d, "best_test_model.pth")
 
     def _init_logger(self):
-        if not self.args["debug"]:
-            os.makedirs(self.args["log_dir"], exist_ok=True)
-        self.logger = get_logger(
-            self.args["log_dir"],
-            name=self.model.__class__.__name__,
-            debug=self.args["debug"],
-        )
+        if not self.args["debug"]: os.makedirs(self.args["log_dir"], exist_ok=True)
+        self.logger = get_logger(self.args["log_dir"], name=self.model.__class__.__name__, debug=self.args["debug"])
 
     # ---------------- epoch ----------------
     def _run_epoch(self, epoch, loader, mode):
         is_train = mode == "train"
         self.model.train() if is_train else self.model.eval()
-
         total_loss, start = 0.0, time.time()
         y_pred, y_true = [], []
 
@@ -63,20 +33,12 @@ class Trainer:
             for data, target in tqdm(loader, desc=f"{mode} {epoch}", total=len(loader)):
                 data, target = data.to(self.device), target.to(self.device)
                 label = target[..., :self.args["output_dim"]]
-
                 out = self.model(data)
-
-                if os.environ.get("TRY") == "True":
-                    print(f"out: {out.shape}, label: {label.shape}")
-                    assert False
-
+                if os.environ.get("TRY") == "True": print(f"out: {out.shape}, label: {label.shape} \
+                    {'✅' if out.shape == label.shape else '❌'}"); assert False
                 loss = self.loss(out, label)
-
-                # ===== 修改点：反归一化 =====
-                d_out = self.inv(out)
-                d_lbl = self.inv(label)
+                d_out, d_lbl = self.inv(out), self.inv(label)  # 反归一化
                 d_loss = self.loss(d_out, d_lbl)
-
                 total_loss += d_loss.item()
                 y_pred.append(d_out.detach().cpu())
                 y_true.append(d_lbl.detach().cpu())
@@ -84,27 +46,12 @@ class Trainer:
                 if is_train and self.optimizer:
                     self.optimizer.zero_grad()
                     loss.backward()
-                    if self.args["grad_norm"]:
-                        torch.nn.utils.clip_grad_norm_(
-                            self.model.parameters(),
-                            self.args["max_grad_norm"]
-                        )
+                    if self.args["grad_norm"]: torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args["max_grad_norm"])
                     self.optimizer.step()
 
-        y_pred = torch.cat(y_pred)
-        y_true = torch.cat(y_true)
-
-        mae, rmse, mape = all_metrics(
-            y_pred, y_true,
-            self.args["mae_thresh"],
-            self.args["mape_thresh"]
-        )
-
-        self.logger.info(
-            f"Epoch #{epoch:02d} {mode:<5} "
-            f"MAE:{mae:5.2f} RMSE:{rmse:5.2f} "
-            f"MAPE:{mape:7.4f} Time:{time.time()-start:.2f}s"
-        )
+        y_pred, y_true = torch.cat(y_pred), torch.cat(y_true)
+        mae, rmse, mape = all_metrics(y_pred, y_true, self.args["mae_thresh"], self.args["mape_thresh"])
+        self.logger.info(f"Epoch #{epoch:02d} {mode:<5} MAE:{mae:5.2f} RMSE:{rmse:5.2f} MAPE:{mape:7.4f} Time:{time.time()-start:.2f}s")
         return total_loss / len(loader)
 
     # ---------------- train ----------------
@@ -112,37 +59,24 @@ class Trainer:
         best, best_test = float("inf"), float("inf")
         best_w, best_test_w = None, None
         patience = 0
-
         self.logger.info("Training started")
 
         for epoch in range(1, self.args["epochs"] + 1):
             losses = {
                 "train": self._run_epoch(epoch, self.train_loader, "train"),
-                "val":   self._run_epoch(epoch, self.val_loader, "val"),
-                "test":  self._run_epoch(epoch, self.test_loader, "test"),
+                "val": self._run_epoch(epoch, self.val_loader, "val"),
+                "test": self._run_epoch(epoch, self.test_loader, "test"),
             }
 
-            if losses["train"] > 1e6:
-                self.logger.warning("Gradient explosion detected")
-                break
-
-            if losses["val"] < best:
-                best, patience = losses["val"], 0
-                best_w = copy.deepcopy(self.model.state_dict())
-            else:
-                patience += 1
-
-            if self.args["early_stop"] and patience == self.args["early_stop_patience"]:
-                break
-
-            if losses["test"] < best_test:
-                best_test = losses["test"]
-                best_test_w = copy.deepcopy(self.model.state_dict())
+            if losses["train"] > 1e6: self.logger.warning("Gradient explosion detected"); break
+            if losses["val"] < best: best, patience, best_w = losses["val"], 0, copy.deepcopy(self.model.state_dict())
+            else: patience += 1
+            if self.args["early_stop"] and patience == self.args["early_stop_patience"]: break
+            if losses["test"] < best_test: best_test, best_test_w = losses["test"], copy.deepcopy(self.model.state_dict())
 
         if not self.args["debug"]:
             torch.save(best_w, self.best_path)
             torch.save(best_test_w, self.best_test_path)
-
         self._final_test(best_w, best_test_w)
 
     # ---------------- final test ----------------
@@ -164,25 +98,10 @@ class Trainer:
                 y_pred.append(self.model(data).cpu())
                 y_true.append(label.cpu())
 
-        # ===== 修改点：反归一化 =====
-        d_pred = self.inv(torch.cat(y_pred))
-        d_true = self.inv(torch.cat(y_true))
-
+        d_pred, d_true = self.inv(torch.cat(y_pred)), self.inv(torch.cat(y_true))  # 反归一化
         for t in range(d_true.shape[1]):
-            mae, rmse, mape = all_metrics(
-                d_pred[:, t], d_true[:, t],
-                self.args["mae_thresh"],
-                self.args["mape_thresh"]
-            )
-            self.logger.info(
-                f"Horizon {t+1:02d} MAE:{mae:.4f} RMSE:{rmse:.4f} MAPE:{mape:.4f}"
-            )
+            mae, rmse, mape = all_metrics(d_pred[:, t], d_true[:, t], self.args["mae_thresh"], self.args["mape_thresh"])
+            self.logger.info(f"Horizon {t+1:02d} MAE:{mae:.4f} RMSE:{rmse:.4f} MAPE:{mape:.4f}")
 
-        avg_mae, avg_rmse, avg_mape = all_metrics(
-            d_pred, d_true,
-            self.args["mae_thresh"],
-            self.args["mape_thresh"]
-        )
-        self.logger.info(
-            f"AVG MAE:{avg_mae:.4f} AVG RMSE:{avg_rmse:.4f} AVG MAPE:{avg_mape:.4f}"
-        )
+        avg_mae, avg_rmse, avg_mape = all_metrics(d_pred, d_true, self.args["mae_thresh"], self.args["mape_thresh"])
+        self.logger.info(f"AVG MAE:{avg_mae:.4f} AVG RMSE:{avg_rmse:.4f} AVG MAPE:{avg_mape:.4f}")