# 导入所需的库
import tensorflow as tf
from tensorflow.keras.applications import EfficientNetB0
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import SparseCategoricalCrossentropy

# 创建对象检测模型
def create_detection_model(num_classes):
    # 加载预训练的EfficientNetB0模型
    base_model = EfficientNetB0(include_top=False, weights='imagenet')
    
    # 冻结预训练模型的权重
    base_model.trainable = False

    # 添加自定义的分类层
    inputs = tf.keras.Input(shape=(None, None, 3))
    x = base_model(inputs, training=False)
    x = tf.keras.layers.GlobalAveragePooling2D()(x)
    outputs = Dense(num_classes, activation='softmax')(x)
    
    # 创建模型
    model = Model(inputs, outputs)
    
    return model

# 定义损失函数和优化器
loss_fn = SparseCategoricalCrossentropy()
optimizer = Adam(learning_rate=0.001)

# 编译模型
model = create_detection_model(num_classes=10)
model.compile(optimizer=optimizer, loss=loss_fn)

# 加载和预处理数据
# 这里假设您已经准备好了训练数据和标签
train_images = ...
train_labels = ...

# 数据增强
data_augmentation = tf.keras.Sequential([
    tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'),
    tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),
])

# 定义训练过程
@tf.function
def train_step(images, labels):
    # 数据增强
    augmented_images = data_augmentation(images, training=True)
    
    # 前向传播
    with tf.GradientTape() as tape:
        predictions = model(augmented_images, training=True)
        loss = loss_fn(labels, predictions)
    
    # 反向传播
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    
    return loss

# 进行训练
num_epochs = 10
batch_size = 32

for epoch in range(num_epochs):
    print(f"Epoch {epoch+1}/{num_epochs}")
    
    # 分批次训练
    for batch in range(len(train_images) // batch_size):
        start = batch * batch_size
        end = start + batch_size
        images = train_images[start:end]
        labels = train_labels[start:end]
        
        loss = train_step(images, labels)
        
        print(f"Batch {batch+1}/{len(train_images)//batch_size} - Loss: {loss}")