nn-sdk 1.8.2

nn-sdk是一个基于tensorflow(v1 ,v2),onnx和tensorrt神经网络推理开发包

nn-sdk是一个基于tensorflow(v1 ,v2),onnx和tensorrt神经网络推理开发包

'''
    前言:
        当前支持开发语言c/c++,python,java
        当前支持推理引擎tensorflow(v1,v2) onnxruntime tensorrt,fasttext 注:tensorrt 7,8测试通过(建议8),目前tensorrt只支持linux系统
        当前支持多子图,支持图多输入多输出, 支持pb [tensorflow 1,2] , ckpt [tensorflow] , trt [tensorrt] , fasttext
        当前支持fastertransformer pb [32精度 相对于传统tf,加速1.9x] ,安装 pip install tf2pb  , 进行模型转换
        tf2pb pb模型转换参考: https://pypi.org/project/tf2pb
        模型加密参考test_aes.py,目前支持tensorflow 1 pb模型 , onnx模型 , tensorrt fasttext模型加密
        推荐环境ubuntu16 ubuntu18  ubuntu20 centos7 centos8 windows系列
        python (test_py.py) , c语言 (test.c) , java语言包 (nn_sdk.java)
        使用过程中遇到问题或者有建议可加qq group: 759163831
        更多使用参见: https://github.com/ssbuild

    python 推理demo
    config 字段介绍:
        aes: 加密参考test_aes.py
        engine: 推理引擎 0: tensorflow , 1: onnx , 2: tensorrt 3: fasttext
        log_level: 日志类型 0 fatal , 2 error , 4 info , 8 debug
        model_type: tensorflow 模型类型, 0 pb format , 1 ckpt format
        fastertransformer:  fastertransformer算子选项, 参考 https://pypi.org/project/tf2pb
        ConfigProto: tensorflow 显卡配置
        device_id: GPU id
        engine_version: 推理引擎主版本 tf 0,1  tensorrt 7 或者 8 , fasttext 0需正确配置
        graph: 多子图配置 
            node: 例子: tensorflow 1 input_ids:0 ,  tensorflow 2: input_ids , onnx: input_ids
            data_type: 节点的类型根据模型配置，对于c++/java支持 int int64 long longlong float double str
            shape:  尺寸维度
    更新详情:
    2021-10-7 增加 fasttext 向量和标签推理

'''

# -*- coding: utf-8 -*-
from nn_sdk import *
config = {
    "model_dir": r'/root/model.ckpt',
    "aes":{
        "use":False,
        "key":bytes([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]),
        "iv":bytes([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]),
    },
    "log_level": 4,# fatal 1 , error 2 , info 4 , debug 8
    'engine':0, # 0 tensorflow,  1 onnx , 2  tensorrt , 3 fasttext
    "device_id": 0,
    'tf':{
        #tensorflow2 ConfigProto无效
        "ConfigProto": {
            "log_device_placement": False,
            "allow_soft_placement": True,
            "gpu_options": {
                "allow_growth": True
            },
            "graph_options":{
                "optimizer_options":{
                    "global_jit_level": 1
                }
            },
        },
        "engine_version": 1, # tensorflow版本
        "model_type": 1,# 0 pb , 1 ckpt
        "saved_model":{ # 当model_type为pb模型有效, 普通pb use=False ， 如果是saved_model冻结模型 , 则需启用use并且配置tags
            'use': False, # 是否启用saved_model
            'tags': ['serve'],
            'signature_key': 'serving_default',
        },
        "fastertransformer":{
            "use": False,
            "cuda_version":"11.3", #当前依赖 tf2pb,支持10.2 11.3 ,
        }
    },
    'onnx':{
        "engine_version": 1,# onnxruntime 版本
    },
    'trt':{
        "engine_version": 8,# tensorrt 版本
        "enable_graph": 0,
    },
    'fasttext': {
        "engine_version": 0,# fasttext主版本
        "threshold":0, # 预测k个标签的阈值
        "k":1, # 预测k个标签
        "dump_label": 1, #输出内部标签，用于上层解码
        "predict_label": 1, #获取预测标签 1  , 获取向量  0
    },
    "graph": [
        {
            # 对于Bert模型 shape [max_batch_size,max_seq_lenth],
            # 其中max_batch_size 用于c++ java开辟输入输出缓存,输入不得超过max_batch_size，对于python没有作用，取决于上层用户真实输入
            # python限制max_batch_size 在上层用户输入做
            # 对于fasttext node 对应name可以任意写，但不能少
            "input": [
                {"node":"input_ids:0", "data_type":"int64", "shape":[1, 256]},
                {"node":"input_mask:0", "data_type":"int64", "shape":[1, 256]}
            ],
            "output": [
                {"node":"pred_ids:0", "data_type":"int64", "shape":[1, 256]},
            ],
        }
    ]}

seq_length = 256
input_ids = [[1] * seq_length]
input_mask = [[1] * seq_length]
sdk_inf = csdk_object(config)
if sdk_inf.valid():
    net_stage = 0
    ret, out = sdk_inf.process(net_stage, input_ids,input_mask)
    print(ret)
    print(out)
    sdk_inf.close()

nn-sdk.java java包demo,配置参考python

package nn_sdk;
//包名必须是nn_sdk
public class nn_sdk {
    //输入输出内存节点，名字跟图配置一样，根据图对象修改此属性。 
	public float [] input_ids = null;//推理图的输入,
	//public float [] input_mask = null;//推理图的输入,
	public float[] pred_ids =   null;//推理的结果保存

	public nn_sdk() {
		//初始化配置图的输入输出内存
		input_ids = new float[1 * 20];
		pred_ids =  new float[1 * 20];
		for(int i =0;i<20;i++) {
			input_ids[i] = 1;
			pred_ids[i] = 0;
		}
	}
	
	//推理函数
	public native static int  sdk_init_cc();
	public native static int  sdk_uninit_cc();
	public native static long sdk_new_cc(String json);
	public native static int  sdk_delete_cc(long handle);
	public native static int sdk_process_cc(long handle, int net_state, nn_sdk data);
	
	static {
		//动态库的绝对路径windows是 engine_csdk.pyd , linux是 engine_csdk.so
		System.load("E:\\algo_text\\nn_csdk\\build_py36\\Release\\engine_csdk.pyd");
	}

	public static void main(String[] args){  
		System.out.println("java main...........");
		
	   nn_sdk instance = new nn_sdk();
	   sdk_init_cc();
	   //配置参考 python
	   String json =  "{" + "\"model_dir\": \"E:/algo_text/nn_csdk/nn_csdk/py_test_ckpt/model.ckpt\"," + "\n" +
	   "\"log_level\": 4," + "\n" +
	   "\"engine\": 0," + "\n" +
	   "\"device_id\": 0," + "\n" +
	   "\"tf\":{ " + "\n" +
           "\"ConfigProto\": {" + "\n" +
                     "\"log_device_placement\":false," + "\n" +
                      "\"allow_soft_placement\":true," + "\n" +
                     "\"gpu_options\":{\"allow_growth\": true}" + "\n" +
           "}," + "\n" +
			"\"engine_version\": 1," + "\n" +
			"\"model_type\":1, " + "\n" +
	    "}" + "\n" +
	   "\"graph\": [" + "\n" +
				    "{" + "\n" +
                        "\"input\": [{\"node\":\"input_ids:0\", \"data_type\":\"float\", \"shape\":[1, 20]}]," + "\n" +
                        "\"output\" : [{\"node\":\"pred_ids:0\", \"data_type\":\"float\", \"shape\":[1, 20]}]" + "\n" +
	                "}" + "\n" +
		        "]" + "\n" +
	    "}";
				

	  System.out.println(json);
	   
	  long handle = sdk_new_cc(json);
	  System.out.println(handle);
	   
	  int code = sdk_process_cc(handle,0,instance);
	  System.out.printf("sdk_process_cc %d \n" ,code);
	  if(code == 0) {
		  for(int i = 0;i<20 ; i++) {
			  System.out.printf("%f ",instance.pred_ids[i]);
		  }
		  System.out.println();
	  }
	  sdk_delete_cc(handle);
	   sdk_uninit_cc();
	   System.out.println("end");
	}
}

nn-sdk c包

#ifndef __CC_SDK_H__
#define __CC_SDK_H__
#include <stdio.h>
#ifdef _WIN32
#ifdef _CC_SDK_EXPORT
#define CC_SDK_EXPORT _declspec( dllexport )
#else
#define CC_SDK_EXPORT _declspec(dllimport)
#endif
#else
#define CC_SDK_EXPORT
#endif

#ifdef __cplusplus
extern "C" {
#endif
	typedef long long SDK_HANDLE_CC;

	CC_SDK_EXPORT int sdk_init_cc();

	CC_SDK_EXPORT int sdk_uninit_cc();

	CC_SDK_EXPORT SDK_HANDLE_CC sdk_new_cc(const char* json);

	CC_SDK_EXPORT int sdk_delete_cc(SDK_HANDLE_CC handle);

	CC_SDK_EXPORT int sdk_process_cc(SDK_HANDLE_CC handle, void** final_result, int net_stage, void** input_buffer_list);

#ifdef __cplusplus
}
#endif


#endif

#include <stdio.h>
#include "nn_sdk.h"

int main(){
    if (0 != sdk_init_cc()) {
		return -1;
	}
    printf("配置参考 python.........\n");
	const char* json_data = "{\n\
    \"model_dir\": \"/root/model.ckpt\",\n\
    \"log_level\":8, \n\
     \"device_id\":0, \n\
    \"tf\":{ \n\
         \"ConfigProto\": {\n\
            \"log_device_placement\":0,\n\
            \"allow_soft_placement\":1,\n\
            \"gpu_options\":{\"allow_growth\": 1}\n\
        },\n\
        \"engine_version\": 1,\n\
        \"model_type\":1 ,\n\
    },\n\
    \"graph\": [\n\
        {\n\
            \"input\": [{\"node\":\"input_ids:0\", \"data_type\":\"float\", \"shape\":[1, 10]}],\n\
            \"output\" : [{\"node\":\"pred_ids:0\", \"data_type\":\"float\", \"shape\":[1, 10]}]\n\
        }\n\
    ]\n\
}";
	printf("%s\n", json_data);
	auto handle = sdk_new_cc(json_data);
	const int INPUT_NUM = 1;
	const int OUTPUT_NUM = 1;
	const int M = 1;
	const int N = 10;
	int *input[INPUT_NUM] = { 0 };
	float* result[OUTPUT_NUM] = { 0 };
	int element_input_size = sizeof(int);
	int element_output_size = sizeof(float);
	for (int i = 0; i < OUTPUT_NUM; ++i) {
		result[i] = (float*)malloc(M * N * element_output_size);
		memset(result[i], 0, M * N * element_output_size);
	}
	for(int i =0;i<INPUT_NUM;++i){
		input[i] = (int*)malloc(M * N * element_input_size);
		memset(input[i], 0, M * N * element_input_size);
		for (int j = 0; j < N; ++j) {
			input[i][j] = i;
		}
	}

	int code = sdk_process_cc(handle, (void**)result, 0, (void**)input);
	if (code == 0) {
		printf("result\n");
		for (int i = 0; i < N; ++i) {
			printf("%f ", result[0][i]);
		}
		printf("\n");
	}
	for (int i = 0; i < INPUT_NUM; ++i) {
		free(input[i]);
	}
	for (int i = 0; i < OUTPUT_NUM; ++i) {
		free(result[i]);
	}
	sdk_delete_cc(handle);
	sdk_uninit_cc();
	return 0;
}

aes加密示例

# -*- coding: UTF-8 -*-

import sys
#sys.path.append(r'E:\algo_text\nn_csdk\cmake_py36\Release')
from nn_sdk.engine_csdk import sdk_aes_encode_decode

def test_string():
    data1 = {
        "mode":0,# 0 加密 ， 1 解密
        "key": bytes([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]),
        "iv": bytes([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]),
        "data": bytes([1,2,3,5,255])
    }

    code,encrypt = sdk_aes_encode_decode(data1)
    print(code,encrypt)

    data2 = {
        "mode":1,
        "key": bytes([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]),
        "iv": bytes([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]),
        "data": encrypt
    }

    code,plain = sdk_aes_encode_decode(data2)
    print(code,plain)

def test_encode_file(in_filename,out_filename):

    with open(in_filename,mode='rb') as f:
        data = f.read()
    if len(data) == 0 :
        return -1
    data1 = {
        "mode": 0,  # 0 加密 ， 1 解密
        "key": bytes([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]),
        "iv": bytes([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]),
        "data": bytes(data)
    }

    code, encrypt = sdk_aes_encode_decode(data1)
    if code != 0:
        return code
    with open(out_filename, mode='wb') as f:
        f.write(encrypt)
    return code
def test_decode_file(in_filename,out_filename):
    with open(in_filename, mode='rb') as f:
        data = f.read()
    if len(data) == 0:
        return -1
    data1 = {
        "mode": 1,  # 0 加密 ， 1 解密
        "key": bytes([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]),
        "iv": bytes([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]),
        "data": bytes(data)
    }

    code, plain = sdk_aes_encode_decode(data1)
    if code != 0:
        return code
    with open(out_filename, mode='wb') as f:
        f.write(plain)
    return code

test_encode_file(r'C:\Users\acer\Desktop\img\a.txt',r'C:\Users\acer\Desktop\img\a.txt.encode')
test_decode_file(r'C:\Users\acer\Desktop\img\a.txt.encode',r'C:\Users\acer\Desktop\img\a.txt.decode')