加一个三分类

class2/train_best_pt.py

@@ -0,0 +1,144 @@
+import argparse
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import datasets, transforms
+from torch.utils.data import DataLoader
+from class2.test.mobilenetv3 import MobileNetV3_Large
+import os
+
+def train(args):
+    # 创建输出目录
+    os.makedirs("checkpoints", exist_ok=True)
+
+    # 数据增强 & 预处理
+    transform_train = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.RandomHorizontalFlip(),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                             std=[0.229, 0.224, 0.225])
+    ])
+    transform_val = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                             std=[0.229, 0.224, 0.225])
+    ])
+
+    # 加载数据集
+    train_dataset = datasets.ImageFolder(root=f"{args.data}/train", transform=transform_train)
+    val_dataset = datasets.ImageFolder(root=f"{args.data}/val", transform=transform_val)
+
+    train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=4, pin_memory=True)
+    val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=4, pin_memory=True)
+
+    # 模型
+    model = MobileNetV3_Large(num_classes=2)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = model.to(device)
+
+    # 损失函数 & 优化器
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(model.parameters(), lr=args.lr)
+
+    # 学习率调度器
+    scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
+
+    # ========== 改进的早停 + 保存机制 ==========
+    best_val_acc = 0.0
+    best_epoch = 0
+    patience = args.patience  # 默认 5
+    wait = 0  # 连续未超越最佳的次数
+
+    print(f"开始训练，共 {args.epochs} 轮，早停耐心值: {patience}")
+
+    for epoch in range(args.epochs):
+        # 训练阶段
+        model.train()
+        running_loss = 0.0
+        correct, total = 0, 0
+
+        for images, labels in train_loader:
+            images, labels = images.to(device), labels.to(device)
+            optimizer.zero_grad()
+            outputs = model(images)
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+            running_loss += loss.item()
+            _, predicted = outputs.max(1)
+            total += labels.size(0)
+            correct += predicted.eq(labels).sum().item()
+
+        train_acc = 100. * correct / total
+        avg_loss = running_loss / len(train_loader)
+        scheduler.step()
+
+        # 验证阶段
+        model.eval()
+        correct, total = 0, 0
+        with torch.no_grad():
+            for images, labels in val_loader:
+                images, labels = images.to(device), labels.to(device)
+                outputs = model(images)
+                _, predicted = outputs.max(1)
+                total += labels.size(0)
+                correct += predicted.eq(labels).sum().item()
+
+        val_acc = 100. * correct / total
+
+        print(f"[Epoch {epoch+1:2d}/{args.epochs}] "
+              f"Loss: {avg_loss:.4f} | "
+              f"Train Acc: {train_acc:.2f}% | "
+              f"Val Acc: {val_acc:.2f}% | "
+              f"LR: {scheduler.get_last_lr()[0]:.6f}")
+
+        # ========== 核心逻辑：保存最后一个最高权重 + 改进早停 ==========
+        if val_acc >= best_val_acc:  # ✅ 只要不低于最佳，就视为“保持高水平”
+            if val_acc > best_val_acc:
+                print(f"新的最高验证准确率: {val_acc:.2f}% (原: {best_val_acc:.2f}%)")
+            else:
+                print(f"验证准确率持平历史最佳: {val_acc:.2f}%")
+
+            # 更新最佳
+            best_val_acc = val_acc
+            best_epoch = epoch + 1
+            wait = 0  # ✅ 重置等待计数
+
+            # 保存完整检查点（我们想要“最后一个”最高权重）
+            checkpoint = {
+                'epoch': epoch,
+                'model_state_dict': model.state_dict(),
+                'optimizer_state_dict': optimizer.state_dict(),
+                'scheduler_state_dict': scheduler.state_dict(),
+                'val_acc': val_acc,
+                'best_val_acc': best_val_acc,
+                'args': args
+            }
+            torch.save(checkpoint, "checkpoints/mobilenetv3_last_best.pth")
+            print(f"✅ 已保存：当前最佳权重（epoch {best_epoch}）")
+
+        else:
+            # ✅ 只有严格低于最佳才增加 wait
+            wait += 1
+            print(f"验证准确率下降 ({val_acc:.2f}% < {best_val_acc:.2f}%)，已连续 {wait}/{patience} 次未超越")
+
+            if wait >= patience:
+                print(f"早停触发！连续 {patience} 次验证准确率未达到或超越历史最佳。")
+                break
+
+    # 训练结束
+    print(f"\n✅ 训练完成！")
+    print(f"最终最佳验证准确率: {best_val_acc:.2f}% (第 {best_epoch} 轮)")
+    print(f"最后一个最高权重已保存至: 'checkpoints/mobilenetv3_last_best.pth'")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data", type=str, default="/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/classdata", help="数据集路径")
+    parser.add_argument("--epochs", type=int, default=100, help="训练轮数")
+    parser.add_argument("--batch_size", type=int, default=32, help="批大小")
+    parser.add_argument("--lr", type=float, default=1e-3, help="学习率")
+    parser.add_argument("--patience", type=int, default=5, help="早停耐心值（连续多少次未达到或超越历史最佳才停止）")
+    args = parser.parse_args()
+    train(args)