main.py

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

# 定义模型
class FaceRecognitionModel(nn.Module):
    def __init__(self):
        super(FaceRecognitionModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=7)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(32 * 56 * 56, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, num_classes)  # num_classes是类别数

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = x.view(-1, 32 * 56 * 56)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 加载数据集
transform = transforms.Compose([
    transforms.Resize((112, 112)),
    transforms.ToTensor(),
])

train_data = datasets.ImageFolder('path_to_train_data', transform=transform)
val_data = datasets.ImageFolder('path_to_val_data', transform=transform)

train_loader = DataLoader(train_data, batch_size=32, shuffle=True)
val_loader = DataLoader(val_data, batch_size=32, shuffle=False)

# 实例化模型、定义损失函数和优化器
model = FaceRecognitionModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f'Epoch {epoch + 1}, Loss: {running_loss / len(train_loader)}')

    # 验证模型
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for images, labels in val_loader:
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print(f'Validation Accuracy: {100 * correct / total}%')