#码力全开·技术π对#如何在TensorFlow Lite中优化模型以降低Android端内存占用？

# 选择混合量化（部分层保持FP32）
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
converter.optimizations = [tf.lite.Optimize.DEFAULT]  # 默认量化配置
tflite_quant_model = converter.convert()

# 使用TensorFlow Model Optimization Toolkit
import tensorflow_model_optimization as tfmot
pruned_model = tfmot.sparsity.keras.prune_low_magnitude(original_model)
pruned_model.compile(...)  # 重新编译后再转换

# 检查设备支持的加速器类型
val compatList = CompatibilityList()
val options = Interpreter.Options().apply {
    if (compatList.isDelegateSupportedOnThisDevice) {
        addDelegate(NnApiDelegate())
    }
}

# 使用模型分析工具
bazel run tensorflow/lite/tools:visualize model.tflite model.html

# Android端完整优化示例
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.uint8  # 输入输出统一类型
converter.inference_output_type = tf.uint8
tflite_model = converter.convert()

# 使用Android Profiler
adb shell dumpsys meminfo <package_name> | grep "Native Heap"

// 启用详细日志
interpreter.setNumThreads(4)
interpreter.run(inputs, outputs)

val options = Interpreter.Options().apply {
    addDelegate(GpuDelegate())
}