增量预训练（Incremental Pre-training）是一种在已有预训练模型的基础上，使用新的数据继续训练模型的过程。

对于人脸识别模型，以下是详细的增量预训练步骤：

  1. 准备工作

import torch
from insightface.model_zoo import get_model
from insightface.utils import face_align
import numpy as np

# 加载预训练模型
model = get_model('buffalo_l_rec_500k.onnx')

  1. 数据准备

def prepare_training_data(image_paths, labels):
    """
    准备训练数据
    Args:
        image_paths: 图片路径列表
        labels: 对应的标签列表
    Returns:
        处理后的图片数据和标签
    """
    images = []
    for img_path in image_paths:
        # 读取图片
        img = cv2.imread(img_path)
        # 人脸对齐
        aligned_img = face_align.align(img, landmark=None)
        # 归一化
        aligned_img = (aligned_img - 127.5) / 127.5
        images.append(aligned_img)
    
    return np.array(images), np.array(labels)

  1. 增量训练代码

def incremental_train(model, train_images, train_labels, epochs=10, batch_size=32):
    """
    增量训练函数
    Args:
        model: 预训练模型
        train_images: 训练图片
        train_labels: 训练标签
        epochs: 训练轮数
        batch_size: 批次大小
    """
    # 设置优化器
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
    
    # 设置损失函数 (ArcFace Loss)
    criterion = torch.nn.CrossEntropyLoss()
    
    # 训练模式
    model.train()
    
    for epoch in range(epochs):
        total_loss = 0
        for i in range(0, len(train_images), batch_size):
            batch_images = train_images[i:i+batch_size]
            batch_labels = train_labels[i:i+batch_size]
            
            # 前向传播
            embeddings = model(batch_images)
            
            # 计算损失
            loss = criterion(embeddings, batch_labels)
            
            # 反向传播
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
            total_loss += loss.item()
        
        print(f'Epoch {epoch+1}/{epochs}, Loss: {total_loss/len(train_images):.4f}')
    
    return model

  1. 模型保存

def save_model(model, save_path):
    """
    保存训练后的模型
    Args:
        model: 训练后的模型
        save_path: 保存路径
    """
    torch.save(model.state_dict(), save_path)

  1. 完整训练流程

def main():
    # 1. 准备数据
    train_images, train_labels = prepare_training_data(image_paths, labels)
    
    # 2. 加载预训练模型
    model = get_model('buffalo_l_rec_500k.onnx')
    
    # 3. 增量训练
    trained_model = incremental_train(
        model, 
        train_images, 
        train_labels,
        epochs=20,
        batch_size=64
    )
    
    # 4. 保存模型
    save_model(trained_model, 'new_model.pth')

  1. 训练技巧和注意事项：

# 数据增强
def augment_image(image):
    # 随机翻转
    if np.random.rand() > 0.5:
        image = cv2.flip(image, 1)
    
    # 随机亮度调整
    brightness = np.random.uniform(0.8, 1.2)
    image = image * brightness
    
    # 随机噪声
    noise = np.random.normal(0, 0.01, image.shape)
    image = image + noise
    
    return np.clip(image, -1, 1)

# 学习率调度
def adjust_learning_rate(optimizer, epoch):
    """调整学习率"""
    lr = 0.001 * (0.1 ** (epoch // 10))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr

  1. 评估模型

def evaluate_model(model, test_images, test_labels):
    """
    评估模型性能
    """
    model.eval()
    correct = 0
    total = 0
    
    with torch.no_grad():
        for images, labels in zip(test_images, test_labels):
            embeddings = model(images)
            # 计算相似度
            similarities = cosine_similarity(embeddings)
            predictions = np.argmax(similarities, axis=1)
            correct += (predictions == labels).sum()
            total += len(labels)
    
    accuracy = correct / total
    print(f'Test Accuracy: {accuracy:.4f}')
    return accuracy

  1. 使用建议：

  • 数据质量要高，确保人脸清晰、对齐准确

  • 新数据要与原始训练数据分布相似

  • 使用较小的学习率进行增量训练

  • 定期保存检查点

  • 监控训练过程中的损失和准确率

  • 使用验证集防止过拟合

  1. 常见问题处理：

# 处理类别不平衡
def handle_class_imbalance(labels):
    from collections import Counter
    label_counts = Counter(labels)
    max_count = max(label_counts.values())
    
    # 计算类别权重
    class_weights = {label: max_count/count for label, count in label_counts.items()}
    
    # 在损失函数中使用权重
    weights = torch.tensor([class_weights[label] for label in labels])
    criterion = torch.nn.CrossEntropyLoss(weight=weights)
    
    return criterion

  1. 部署更新后的模型：

def update_model_in_production(new_model_path):
    """
    更新生产环境中的模型
    """
    # 1. 备份当前模型
    backup_current_model()
    
    # 2. 加载新模型
    new_model = load_model(new_model_path)
    
    # 3. 验证新模型
    if validate_model(new_model):
        # 4. 部署新模型
        deploy_model(new_model)
    else:
        # 5. 如果验证失败，回滚
        rollback_model()