DeepLabV3+模型训练伪代码

DeepLabV3+是一种用于图像语义分割的深度学习模型，它结合了特征金字塔网络（FPN）、空间金字塔池化（SPP）以及改进的ASPP模块。以下是使用Python和TensorFlow库进行简化的DeepLabV3+模型训练的伪代码示例：

import tensorflow as tf
from tensorflow.keras import Model
from deeplabv3_plus import build_deeplabv3_plus

# 定义超参数
learning_rate = 0.0001
batch_size = 8
epochs = 50
input_shape = (512, 512, 3)  # 输入图片尺寸

# 加载预训练模型作为基础网络
base_model = build_deeplabv3_plus(input_shape)
base_model.trainable = False  # 预训练层不参与训练

# 构建新的分类头
output_layer = create_classifier_layers(base_model.output, num_classes=NUM_CLASSES)

# 创建完整的模型
model = Model(inputs=base_model.input, outputs=output_layer)

# 编译模型
optimizer = tf.keras.optimizers.Adam(learning_rate)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer=optimizer, loss=loss_fn, metrics=['accuracy'])

# 数据加载和预处理
train_dataset, val_dataset = load_data(batch_size=batch_size)
data_augmentation = get_data_augmentation_pipeline()

# 训练模型
for epoch in range(epochs):
    model.fit(
        train_dataset,
        epochs=1,
        validation_data=val_dataset,
        callbacks=[EarlyStopping(patience=5), ModelCheckpoint('deeplabv3+.h5', save_best_only=True)],
        steps_per_epoch=len(train_dataset),
        validation_steps=len(val_dataset),
        data_augmentation=data_augmentation
    )

# 使用最佳权重加载模型
best_weights_path = 'deeplabv3+.h5'
model.load_weights(best_weights_path)