refactor: merge multiple project into one and create new project

dl-exp/exp3/modified/train.py

@@ -0,0 +1,79 @@
+from pathlib import Path
+import sys
+import typing
+import torch
+import torchinfo
+import ignite.engine
+import ignite.metrics
+from ignite.engine import Engine, Events
+from ignite.handlers.tqdm_logger import ProgressBar
+from dataset import PoetryDataLoader
+from model import Rnn
+from predict import generate_random_poetry
+import settings
+
+sys.path.append(str(Path(__file__).resolve().parent.parent.parent))
+import gpu_utils
+
+
+class Trainer:
+    """核心训练器"""
+
+    device: torch.device
+    data_loader: PoetryDataLoader
+    model: Rnn
+
+    trainer: Engine
+    pbar: ProgressBar
+
+    def __init__(self):
+        # 创建训练设备，模型和数据加载器。
+        self.device = gpu_utils.get_gpu_device()
+        self.data_loader = PoetryDataLoader(batch_size=settings.N_BATCH_SIZE)
+        self.model = Rnn(self.data_loader.get_vocab_size()).to(self.device)
+        # 展示模型结构。批次为指定批次数量，最大诗歌长度，同时输入一定是int32
+        torchinfo.summary(self.model, 
+                          (settings.N_BATCH_SIZE, settings.POETRY_MAX_LEN),
+                          dtypes=[torch.int32,])
+        
+        # 优化器和损失函数
+        optimizer = torch.optim.Adam(self.model.parameters(), eps=1e-7)
+        criterion = torch.nn.CrossEntropyLoss()
+        # 创建训练器
+        self.trainer = ignite.engine.create_supervised_trainer(
+            self.model, optimizer, criterion, self.device,
+            # 由于PyTorch的交叉熵函数总是要求概率在dim=1，所以要调换一下维度才能传入。
+            model_transform=lambda output: self.__adjust_for_loss(output))
+        # 将训练器关联到进度条
+        self.pbar = ProgressBar(persist=True)
+        self.pbar.attach(self.trainer, output_transform=lambda loss: {"loss": loss})
+        # 每次epoch后，作诗一首看看结果
+        self.trainer.add_event_handler(
+            Events.EPOCH_COMPLETED,
+            lambda: print(generate_random_poetry(self.data_loader.get_tokenizer(), self.model, self.device))
+        )
+
+    def __adjust_for_loss(self, output: torch.Tensor) -> torch.Tensor:
+        return output.permute(0, 2, 1)
+
+    def train_model(self):
+        # 训练模型
+        self.trainer.run(self.data_loader.loader, max_epochs=settings.N_EPOCH)
+
+    def save_model(self):
+        # 确保保存模型的文件夹存在。
+        settings.SAVED_MODEL_PATH.parent.mkdir(parents=True, exist_ok=True)
+        # 仅保存模型参数
+        torch.save(self.model.state_dict(), settings.SAVED_MODEL_PATH)
+        print(f'Model was saved into: {settings.SAVED_MODEL_PATH}')
+
+
+def main():
+    trainer = Trainer()
+    trainer.train_model()
+    trainer.save_model()
+
+
+if __name__ == "__main__":
+    gpu_utils.print_gpu_availability()
+    main()