initial commit

train.py

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+import os
+import torch
+from torch import nn, optim
+from torch.utils.data import DataLoader, random_split
+import numpy as np
+from datetime import datetime
+import argparse
+
+# 导入项目模块（根据你的路径调整）
+from models.rotation_cnn import RotationInvariantCNN  # 模型实现
+from data_units import LayoutDataset, layout_transforms  # 数据集和预处理函数
+
+# 设置随机种子（可选）
+torch.manual_seed(42)
+np.random.seed(42)
+
+def main():
+    """训练流程"""
+    # 解析命令行参数
+    parser = argparse.ArgumentParser(description="Train Rotation-Invariant Layout Matcher")
+    parser.add_argument("--data_dir", type=str, default="./data/train/", help="训练数据目录")
+    parser.add_argument("--val_split", type=float, default=0.2, help="验证集比例")
+    parser.add_argument("--batch_size", type=int, default=16, help="批量大小")
+    parser.add_argument("--epochs", type=int, default=50, help="训练轮次")
+    parser.add_argument("--lr", type=float, default=1e-3, help="学习率")
+    parser.add_argument("--model_save_dir", type=str, default="./models/", help="模型保存路径")
+    args = parser.parse_args()
+
+    # 创建输出目录
+    os.makedirs(args.model_save_dir, exist_ok=True)
+
+    # 数据加载
+    dataset = LayoutDataset(root_dir=args.data_dir, transform=layout_transforms())
+    total_samples = len(dataset)
+    val_size = int(total_samples * args.val_split)
+    train_size = total_samples - val_size
+
+    # 划分训练集和验证集
+    train_dataset, val_dataset = random_split(
+        dataset,
+        [train_size, val_size],
+        generator=torch.Generator().manual_seed(42)
+    )
+    
+    train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=4)
+    val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=4)
+
+    # 初始化模型、损失函数和优化器
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = RotationInvariantCNN().to(device)  # 根据你的模型结构调整参数
+    criterion = nn.CrossEntropyLoss()          # 分类任务示例，根据任务类型选择损失函数
+    optimizer = optim.Adam(model.parameters(), lr=args.lr)
+
+    # 训练循环
+    best_val_loss = float("inf")
+    for epoch in range(1, args.epochs + 1):
+        model.train()
+        train_loss = 0.0
+        for batch_idx, (data, targets) in enumerate(train_loader):
+            data, targets = data.to(device), targets.to(device)
+            
+            # 前向传播
+            outputs = model(data)
+            loss = criterion(outputs, targets)
+
+            # 反向传播和优化
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            train_loss += loss.item()
+            
+            if (batch_idx + 1) % 10 == 0:
+                print(f"Epoch [{epoch}/{args.epochs}] Batch {batch_idx+1}/{len(train_loader)} Loss: {loss.item():.4f}")
+
+        # 验证
+        model.eval()
+        val_loss = 0.0
+        with torch.no_grad():
+            for data, targets in val_loader:
+                data, targets = data.to(device), targets.to(device)
+                outputs = model(data)
+                loss = criterion(outputs, targets)
+                val_loss += loss.item()
+
+        avg_train_loss = train_loss / len(train_loader)
+        avg_val_loss = val_loss / len(val_loader)
+
+        print(f"Epoch {epoch} - Train Loss: {avg_train_loss:.4f}, Val Loss: {avg_val_loss:.4f}")
+
+        # 保存最佳模型
+        if avg_val_loss < best_val_loss:
+            best_val_loss = avg_val_loss
+            torch.save(model.state_dict(), os.path.join(args.model_save_dir, f"best_model_{datetime.now().strftime('%Y%m%d%H%M')}.pth"))
+    
+    print("训练完成！")
+
+if __name__ == "__main__":
+    main()