优化训练器更新提示，删除Informer冗余代码

model/Informer/model.py

@@ -1,4 +1,3 @@
-
 import torch
 import torch.nn as nn
 
@@ -9,12 +8,6 @@ from model.Informer.head import TemporalProjectionHead
 
 
 class InformerEncoder(nn.Module):
-    """
-    Informer Encoder-only
-    - Only uses x
-    - No normalization
-    - Multi-channel friendly
-    """
 
     def __init__(self, configs):
         super().__init__()
@@ -25,41 +18,20 @@ class InformerEncoder(nn.Module):
         Attn = ProbAttention if configs["attn"] == "prob" else FullAttention
 
         # Embedding
-        self.embedding = DataEmbedding(
-            configs["enc_in"],
-            configs["d_model"],
-            configs["dropout"],
-        )
+        self.embedding = DataEmbedding(configs["enc_in"], configs["d_model"], configs["dropout"])
 
-        # Encoder (Informer)
+        # Encoder(Attn-Conv-Norm)
         self.encoder = Encoder(
-            [
-                EncoderLayer(
-                    AttentionLayer(
-                        Attn(
-                            False,
-                            configs["factor"],
-                            attention_dropout=configs["dropout"],
-                            output_attention=False,
-                        ),
-                        configs["d_model"],
-                        configs["n_heads"],
-                        mix=False,
-                    ),
-                    configs["d_model"],
-                    configs["d_ff"],
-                    dropout=configs["dropout"],
-                    activation=configs["activation"],
-                )
-                for _ in range(configs["e_layers"])
-            ],
-            [
-                ConvLayer(configs["d_model"])
-                for _ in range(configs["e_layers"] - 1)
-            ]
-            if configs.get("distil", False)
-            else None,
-            norm_layer=nn.LayerNorm(configs["d_model"]),
+            [EncoderLayer(
+                # Attn
+                AttentionLayer(Attn(False, configs["factor"], configs["dropout"], False),
+                             configs["d_model"], configs["n_heads"], False),
+                configs["d_model"], configs["d_ff"], configs["dropout"], configs["activation"])
+                for _ in range(configs["e_layers"])],
+             # Conv
+            [ConvLayer(configs["d_model"]) for _ in range(configs["e_layers"] - 1)]
+            # Norm
+            if configs.get("distil") else None, norm_layer=nn.LayerNorm(configs["d_model"])
         )
 
         # Forecast Head
@@ -70,9 +42,6 @@ class InformerEncoder(nn.Module):
         )
 
     def forward(self, x_enc):
-        """
-        x_enc: [B, L, C]
-        """
         x = self.embedding(x_enc)
         x, _ = self.encoder(x)
         out = self.head(x)

train.py

@@ -12,9 +12,9 @@ def read_config(config_path):
         config = yaml.safe_load(file)
 
     # 全局配置
-    device = "cpu"  # 指定设备为cuda:0
+    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
@@ -102,11 +102,11 @@ def main(model_list, data, debug=False):
 if __name__ == "__main__":
     # 调试用
     # model_list = ["iTransformer", "PatchTST", "HI"]
-    model_list = ["iTransformer", "Informer"]
+    model_list = ["Informer"]
     # model_list = ["PatchTST"]
     # dataset_list = ["AirQuality"]
-    dataset_list = ["SolarEnergy"]
+    # dataset_list = ["SolarEnergy"]
     # dataset_list = ["BJTaxi-InFlow", "BJTaxi-OutFlow"]
-    # dataset_list = ["AirQuality", "PEMS-BAY", "SolarEnergy", "NYCBike-InFlow", "NYCBike-OutFlow", "METR-LA"]
+    dataset_list = ["SolarEnergy", "NYCBike-InFlow", "NYCBike-OutFlow", "METR-LA"]
     # dataset_list = ["BJTaxi-OutFlow"]
-    main(model_list, dataset_list, debug=True)
+    main(model_list, dataset_list, debug=False)

trainer/TSTrainer.py

@@ -76,10 +76,14 @@ class Trainer:
             }
 
             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())
+            if losses["val"] < best:
+                best, patience, best_w = losses["val"], 0, copy.deepcopy(self.model.state_dict())
+                self.logger.info(f"Best model updated at Epoch {epoch:02d}#")
             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 losses["test"] < best_test:
+                best_test, best_test_w = losses["test"], copy.deepcopy(self.model.state_dict())
+                self.logger.info(f"Best test model saved at Epoch {epoch:02d}#")
 
         if not self.args["debug"]:
             torch.save(best_w, self.best_path)

trainer/Trainer.py

@@ -71,10 +71,14 @@ class Trainer:
             }
 
             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())
+            if losses["val"] < best:
+                best, patience, best_w = losses["val"], 0, copy.deepcopy(self.model.state_dict())
+                self.logger.info(f"Best model updated at Epoch {epoch:02d}#")
             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 losses["test"] < best_test:
+                best_test, best_test_w = losses["test"], copy.deepcopy(self.model.state_dict())
+                self.logger.info(f"Best test model saved at Epoch {epoch:02d}#")
 
         if not self.args["debug"]:
             torch.save(best_w, self.best_path)