remove in-time accuracy display

exp2/modified/train.py

@@ -6,7 +6,6 @@ import torchinfo
 import ignite.engine
 import ignite.metrics
 from ignite.engine import Engine, Events
-from ignite.metrics import Accuracy, Loss
 from ignite.handlers.tqdm_logger import ProgressBar
 from dataset import MnistDataLoaders
 from model import Cnn
@@ -104,7 +103,6 @@ class Trainer:
     model: Cnn
 
     trainer: Engine
-    trainer_accuracy: Accuracy
     evaluator: Engine
     pbar: ProgressBar
 
@@ -121,42 +119,15 @@ class Trainer:
         criterion = torch.nn.CrossEntropyLoss()
         # 创建训练器
         self.trainer = ignite.engine.create_supervised_trainer(
-            self.model, optimizer, criterion, self.device,
+            self.model, optimizer, criterion, self.device)
-            # 输出转换为这种形式，因为后面的测量需要用其中一些参数。
-            # 默认的输出只输出loss。
-            output_transform=lambda x, y, y_pred, loss: {
-                'loss': loss.item(),
-                'y': y,
-                'y_pred': y_pred
-            }
-        )
-        # 设置训练器测量数据
-        self.trainer_accuracy = Accuracy(
-            device=self.device,
-            # 转换为Accuracy需要的形式。
-            output_transform=lambda o: (o['y_pred'], o['y'])
-        )
-        self.trainer_accuracy.attach(self.trainer, 'accuracy')
-        # YYC MARK: 这里要手动reset一下，不然第一次运行没有accuracy
-        self.trainer_accuracy.reset()
-        # 每次epoch前重置accuracy
-        self.trainer.add_event_handler(
-            Events.EPOCH_STARTED,
-            lambda: self.trainer_accuracy.reset()
-        )
         # 将训练器关联到进度条
         self.pbar = ProgressBar(persist=True)
-        self.pbar.attach(self.trainer, 
+        self.pbar.attach(self.trainer, output_transform=lambda o: {"loss": o})
-                         metric_names=['accuracy'],
-                         output_transform=lambda o: {"loss": o['loss']})
-        # 训练完毕后保存模型
-        self.trainer.add_event_handler(
-            Events.COMPLETED,
-            lambda: self.save_model()
-        )
 
         # 创建测试的评估器的评估量
         evaluator_metrics = {
+            # 这个Accuracy要的是logits，而不是possibilities，
+            # 所以依然是不需要softmax处理后的结果。
             "accuracy": ignite.metrics.Accuracy(device=self.device),
             "loss": ignite.metrics.Loss(criterion, device=self.device)
         }
@@ -164,13 +135,9 @@ class Trainer:
         self.evaluator = ignite.engine.create_supervised_evaluator(
             self.model, metrics=evaluator_metrics, device=self.device)
         
-    def run(self):
+    def train_model(self):
         # 训练模型
         self.trainer.run(self.data_source.train_loader, max_epochs=settings.N_EPOCH)
-        # 测试模型并输出结果
-        self.evaluator.run(self.data_source.test_loader)
-        metrics = self.evaluator.state.metrics
-        print(f"Test Done. Accuracy: {metrics['accuracy']:.4f} Loss: {metrics['loss']:.4f}")
 
     def save_model(self):
         # 确保保存模型的文件夹存在。
@@ -179,9 +146,18 @@ class Trainer:
         torch.save(self.model.state_dict(), settings.SAVED_MODEL_PATH)
         print(f'Model was saved into: {settings.SAVED_MODEL_PATH}')
 
+    def test_model(self):
+        # 测试模型并输出结果
+        self.evaluator.run(self.data_source.test_loader)
+        metrics = self.evaluator.state.metrics
+        print(f"Accuracy: {metrics['accuracy']:.4f} Loss: {metrics['loss']:.4f}")
+
+
 def main():
     trainer = Trainer()
-    trainer.run()
+    trainer.train_model()
+    trainer.save_model()
+    trainer.test_model()
 
 
 if __name__ == "__main__":