优化神经网络，缩减模型大小

gui/train.py

@@ -0,0 +1,92 @@
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import warnings
+from tqdm import tqdm
+from torchvision import datasets, transforms
+from torch.utils.data import DataLoader, random_split
+from model import CNNClassifier
+
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+# 数据预处理
+transform = transforms.Compose([
+    transforms.Resize((1024, 800)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# 加载数据集
+data_dir = './datasets'
+full_dataset = datasets.ImageFolder(data_dir, transform=transform)
+
+# 划分训练集和测试集
+train_size = int(0.8 * len(full_dataset))
+test_size = len(full_dataset) - train_size
+train_dataset, test_dataset = random_split(full_dataset, [train_size, test_size])
+
+# 创建数据加载器
+train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
+test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False)
+
+# 初始化模型
+num_classes = len(full_dataset.classes)
+model = CNNClassifier(num_classes)
+
+# 初始化损失函数和优化器
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+if __name__ == '__main__':
+    # 训练模型
+    num_epochs = 16
+    model.to(device)
+    for epoch in range(num_epochs):
+        model.train()
+        running_loss = 0.0
+        train_loader_tqdm = tqdm(train_loader, desc=f'Epoch {epoch + 1}/{num_epochs}', unit='batch')
+        for i, (images, labels) in enumerate(train_loader_tqdm):
+            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()
+            train_loader_tqdm.set_postfix(loss=running_loss / (i + 1))
+
+        print(f'Epoch {epoch + 1}/{num_epochs}, Loss: {running_loss / len(train_loader)}')
+
+    # 保存模型
+    model_path = 'rdp_model.pth'
+    torch.save(model.state_dict(), model_path)
+
+    # 测试模型
+    load_saved_model = True
+    if load_saved_model and os.path.exists(model_path):
+        model = CNNClassifier(num_classes)
+        model.load_state_dict(torch.load(model_path, weights_only=True))
+        model.to(device)
+
+    model.eval()
+    correct = 0
+    total = 0
+
+    test_loader_tqdm = tqdm(test_loader, desc='Testing', unit='batch')
+    with torch.no_grad():
+        for images, labels in test_loader_tqdm:
+            images, labels = images.to(device), labels.to(device)
+            outputs = model(images)
+            _, predicted = torch.max(outputs.data, 1)
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()
+            test_loader_tqdm.set_postfix(accuracy=100 * correct / total)
+
+    print(f'Accuracy on test set: {100 * correct / total}%')