graph TD
A[云端训练] --> B[模型压缩]
B --> C[边缘部署]
C --> D[实时推理]
D --> E[结果反馈]subgraph 边缘设备
C1[TensorRT引擎] --> C2[硬件加速]
C3[内存优化] --> C4[功耗控制]
endsubgraph 优化链路
B1[知识蒸馏] --> B2[量化]
B2 --> B3[剪枝]
B3 --> B4[神经架构搜索]
end

import tensorflow as tf
from tensorflow_model_optimization.quantization.keras import quantize_annotate_layer# 训练后量化（PTQ）
def post_train_quantize(model):converter = tf.lite.TFLiteConverter.from_keras_model(model)converter.optimizations = [tf.lite.Optimize.DEFAULT]return converter.convert()# 量化感知训练（QAT）
def quant_aware_train(model):annotated_model = tf.keras.models.clone_model(model,clone_function=lambda layer: quantize_annotate_layer(layer) )return tfmot.quantization.keras.quantize_apply(annotated_model)

import tensorflow_model_optimization as tfmotprune_params = {'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(initial_sparsity=0.30,final_sparsity=0.80,begin_step=1000,end_step=3000),'block_size': (1, 1),'block_pooling_type': 'AVG'
}def adaptive_pruning(model):for i, layer in enumerate(model.layers):if isinstance(layer, tf.keras.layers.Conv2D):# 深度可分离卷积层采用更高稀疏度if 'depthwise' in layer.name:prune_params['final_sparsity'] = 0.90else:prune_params['final_sparsity'] = 0.75model.layers[i] = tfmot.sparsity.keras.prune_low_magnitude(layer, **prune_params)return model

import tensorrt as trtdef build_trt_engine(onnx_path, precision_mode='FP16'):logger = trt.Logger(trt.Logger.WARNING)builder = trt.Builder(logger)network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))parser = trt.OnnxParser(network, logger)# 解析ONNX模型with open(onnx_path, 'rb') as model:parser.parse(model.read())# 配置优化参数config = builder.create_builder_config()config.set_flag(trt.BuilderFlag.FP16) if precision_mode == 'FP16' else Noneconfig.max_workspace_size = 1 << 30  # 1GB# 层融合优化config.set_tactic_sources(trt.TacticSource.CUBLAS_LT)profile = builder.create_optimization_profile()profile.set_shape("input", (1,224,224,3), (8,224,224,3), (16,224,224,3))config.add_optimization_profile(profile)return builder.build_engine(network, config)

// 卷积加速示例
void conv2d_3x3_neon(float* output, const float* input, const float* kernel, int width, int height, int channels) {for (int y = 0; y < height-2; ++y) {for (int x = 0; x < width-2; x+=4) { // 4通道并行float32x4_t sum = vdupq_n_f32(0.0f);for (int ky = 0; ky < 3; ++ky) {for (int kx = 0; kx < 3; ++kx) {// 加载输入向量float32x4_t in_vec = vld1q_f32(input + (y+ky)*width*channels + (x+kx)*channels);// 加载核向量float32x4_t k_vec = vdupq_n_f32(kernel[ky*3+kx]);// 乘累加sum = vmlaq_f32(sum, in_vec, k_vec);}}// 存储结果vst1q_f32(output + y*width + x, sum);}}
}

def adaptive_model_selector(input_data):complexity = calculate_complexity(input_data)if complexity < 10:  # 简单场景return "MobileNetV3-Small"elif complexity < 50:return "EfficientNet-Lite"else:  # 复杂场景return "ResNet-Edge"

import onnxruntime as ort
from onnxruntime.transformers.optimizer import optimize_modeldef runtime_graph_optimize(model_path, input_shape):# 动态形状支持opt_model = optimize_model(model_path,model_type='bert',num_heads=12,hidden_size=768,optimization_options={'enable_gelu': True,'enable_layer_norm': True,'use_multi_head_attention': True})# 根据输入形状优化opt_model.optimize_for_inputs(inputs=[np.zeros(input_shape, dtype=np.float32)],device='cuda')return opt_model

graph TB
A[模型输入] --> B[异常检测]
B --> C[安全推理]
C --> D[输出过滤]subgraph 异常检测
B1[数据范围校验] --> B2[对抗样本检测]
endsubgraph 安全推理
C1[内存隔离] --> C2[执行超时控制]
endsubgraph 输出过滤
D1[置信度阈值] --> D2[敏感内容过滤]
end

# 安全推理封装
def secure_infer(model, input_data):# 输入校验if np.max(input_data) > 1.0 or np.min(input_data) < 0.0:raise ValueError("Input data out of [0,1] range")# 内存隔离执行with ProcessPoolExecutor(max_workers=1) as executor:future = executor.submit(model.predict, input_data)try:result = future.result(timeout=100)  # 100ms超时except TimeoutError:terminate_process()# 输出过滤if result.confidence < 0.6:return "低置信度结果"return sanitize_output(result)