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Merge branch 'develop' into set_stream_infer-shareExData

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This commit is contained in:
Wang Bojun
2023-02-14 14:23:33 +08:00
committed by GitHub
parent 727fdc5863 3aafceed07
commit a0fc0d5e49
14 changed files with 385 additions and 253 deletions
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100
benchmark/cpp/benchmark_ppyolov8.cc
View File

@@ -12,9 +12,9 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "fastdeploy/benchmark/utils.h"
#include "fastdeploy/vision.h"
#include "flags.h"
#include "macros.h"
#include "option.h"
#ifdef WIN32
const char sep = '\\';
@@ -22,104 +22,24 @@ const char sep = '\\';
const char sep = '/';
#endif
bool RunModel(std::string model_dir, std::string image_file, size_t warmup,
size_t repeats, size_t dump_period, std::string cpu_mem_file_name,
std::string gpu_mem_file_name) {
int main(int argc, char* argv[]) {
google::ParseCommandLineFlags(&argc, &argv, true);
auto im = cv::imread(FLAGS_image);
// Initialization
auto option = fastdeploy::RuntimeOption();
if (!CreateRuntimeOption(&option)) {
PrintUsage();
return false;
}
auto model_file = model_dir + sep + "model.pdmodel";
auto params_file = model_dir + sep + "model.pdiparams";
auto config_file = model_dir + sep + "infer_cfg.yml";
if (FLAGS_profile_mode == "runtime") {
option.EnableProfiling(FLAGS_include_h2d_d2h, repeats, warmup);
}
auto model = fastdeploy::vision::detection::PaddleYOLOv8(
auto model_file = FLAGS_model + sep + "model.pdmodel";
auto params_file = FLAGS_model + sep + "model.pdiparams";
auto config_file = FLAGS_model + sep + "infer_cfg.yml";
auto model_ppyolov8 = fastdeploy::vision::detection::PaddleYOLOv8(
model_file, params_file, config_file, option);
if (!model.Initialized()) {
std::cerr << "Failed to initialize." << std::endl;
return false;
}
auto im = cv::imread(image_file);
// For Runtime
if (FLAGS_profile_mode == "runtime") {
fastdeploy::vision::DetectionResult res;
if (!model.Predict(im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return false;
}
double profile_time = model.GetProfileTime() * 1000;
std::cout << "Runtime(ms): " << profile_time << "ms." << std::endl;
BENCHMARK_MODEL(model_ppyolov8, model_ppyolov8.Predict(im, &res))
auto vis_im = fastdeploy::vision::VisDetection(im, res);
cv::imwrite("vis_result.jpg", vis_im);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
} else {
// For End2End
// Step1: warm up for warmup times
std::cout << "Warmup " << warmup << " times..." << std::endl;
for (int i = 0; i < warmup; i++) {
fastdeploy::vision::DetectionResult res;
if (!model.Predict(im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return false;
}
}
std::vector<float> end2end_statis;
// Step2: repeat for repeats times
std::cout << "Counting time..." << std::endl;
fastdeploy::TimeCounter tc;
fastdeploy::vision::DetectionResult res;
for (int i = 0; i < repeats; i++) {
if (FLAGS_collect_memory_info && i % dump_period == 0) {
fastdeploy::benchmark::DumpCurrentCpuMemoryUsage(cpu_mem_file_name);
#if defined(WITH_GPU)
fastdeploy::benchmark::DumpCurrentGpuMemoryUsage(gpu_mem_file_name,
FLAGS_device_id);
#endif
}
tc.Start();
if (!model.Predict(im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return false;
}
tc.End();
end2end_statis.push_back(tc.Duration() * 1000);
}
float end2end = std::accumulate(end2end_statis.end() - repeats,
end2end_statis.end(), 0.f) /
repeats;
std::cout << "End2End(ms): " << end2end << "ms." << std::endl;
auto vis_im = fastdeploy::vision::VisDetection(im, res);
cv::imwrite("vis_result.jpg", vis_im);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}
return true;
}
int main(int argc, char* argv[]) {
google::ParseCommandLineFlags(&argc, &argv, true);
int repeats = FLAGS_repeat;
int warmup = FLAGS_warmup;
int dump_period = FLAGS_dump_period;
std::string cpu_mem_file_name = "result_cpu.txt";
std::string gpu_mem_file_name = "result_gpu.txt";
// Run model
if (RunModel(FLAGS_model, FLAGS_image, warmup, repeats, dump_period,
cpu_mem_file_name, gpu_mem_file_name) != true) {
exit(1);
}
if (FLAGS_collect_memory_info) {
float cpu_mem = fastdeploy::benchmark::GetCpuMemoryUsage(cpu_mem_file_name);
std::cout << "cpu_pss_mb: " << cpu_mem << "MB." << std::endl;
#if defined(WITH_GPU)
float gpu_mem = fastdeploy::benchmark::GetGpuMemoryUsage(gpu_mem_file_name);
std::cout << "gpu_pss_mb: " << gpu_mem << "MB." << std::endl;
#endif
}
return 0;
}

87
benchmark/cpp/benchmark_yolov5.cc Executable file → Normal file
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@@ -12,96 +12,25 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "fastdeploy/benchmark/utils.h"
#include "fastdeploy/vision.h"
#include "flags.h"
#include "macros.h"
#include "option.h"
bool RunModel(std::string model_file, std::string image_file, size_t warmup,
size_t repeats, size_t sampling_interval) {
int main(int argc, char* argv[]) {
google::ParseCommandLineFlags(&argc, &argv, true);
auto im = cv::imread(FLAGS_image);
// Initialization
auto option = fastdeploy::RuntimeOption();
if (!CreateRuntimeOption(&option)) {
PrintUsage();
return false;
}
if (FLAGS_profile_mode == "runtime") {
option.EnableProfiling(FLAGS_include_h2d_d2h, repeats, warmup);
}
auto model = fastdeploy::vision::detection::YOLOv5(model_file, "", option);
if (!model.Initialized()) {
std::cerr << "Failed to initialize." << std::endl;
return false;
}
auto im = cv::imread(image_file);
// For collect memory info
fastdeploy::benchmark::ResourceUsageMonitor resource_moniter(
sampling_interval, FLAGS_device_id);
if (FLAGS_collect_memory_info) {
resource_moniter.Start();
}
// For Runtime
if (FLAGS_profile_mode == "runtime") {
auto model_yolov5 =
fastdeploy::vision::detection::YOLOv5(FLAGS_model, "", option);
fastdeploy::vision::DetectionResult res;
if (!model.Predict(im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return false;
}
double profile_time = model.GetProfileTime() * 1000;
std::cout << "Runtime(ms): " << profile_time << "ms." << std::endl;
BENCHMARK_MODEL(model_yolov5, model_yolov5.Predict(im, &res))
auto vis_im = fastdeploy::vision::VisDetection(im, res);
cv::imwrite("vis_result.jpg", vis_im);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
} else {
// For End2End
// Step1: warm up for warmup times
std::cout << "Warmup " << warmup << " times..." << std::endl;
for (int i = 0; i < warmup; i++) {
fastdeploy::vision::DetectionResult res;
if (!model.Predict(im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return false;
}
}
// Step2: repeat for repeats times
std::cout << "Counting time..." << std::endl;
std::cout << "Repeat " << repeats << " times..." << std::endl;
fastdeploy::vision::DetectionResult res;
fastdeploy::TimeCounter tc;
tc.Start();
for (int i = 0; i < repeats; i++) {
if (!model.Predict(im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return false;
}
}
tc.End();
double end2end = tc.Duration() / repeats * 1000;
std::cout << "End2End(ms): " << end2end << "ms." << std::endl;
auto vis_im = fastdeploy::vision::VisDetection(im, res);
cv::imwrite("vis_result.jpg", vis_im);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}
if (FLAGS_collect_memory_info) {
float cpu_mem = resource_moniter.GetMaxCpuMem();
float gpu_mem = resource_moniter.GetMaxGpuMem();
float gpu_util = resource_moniter.GetMaxGpuUtil();
std::cout << "cpu_pss_mb: " << cpu_mem << "MB." << std::endl;
std::cout << "gpu_pss_mb: " << gpu_mem << "MB." << std::endl;
std::cout << "gpu_util: " << gpu_util << std::endl;
resource_moniter.Stop();
}
return true;
}
int main(int argc, char* argv[]) {
google::ParseCommandLineFlags(&argc, &argv, true);
int repeats = FLAGS_repeat;
int warmup = FLAGS_warmup;
int sampling_interval = FLAGS_sampling_interval;
// Run model
if (!RunModel(FLAGS_model, FLAGS_image, warmup, repeats, sampling_interval)) {
exit(1);
}
return 0;
}

73
benchmark/cpp/flags.h
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@@ -15,7 +15,6 @@
#pragma once
#include "gflags/gflags.h"
#include "fastdeploy/utils/perf.h"
DEFINE_string(model, "", "Directory of the inference model.");
DEFINE_string(image, "", "Path of the image file.");
@@ -49,75 +48,3 @@ void PrintUsage() {
std::cout << "Default value of backend: default" << std::endl;
std::cout << "Default value of use_fp16: false" << std::endl;
}
bool CreateRuntimeOption(fastdeploy::RuntimeOption* option) {
if (FLAGS_device == "gpu") {
option->UseGpu(FLAGS_device_id);
if (FLAGS_backend == "ort") {
option->UseOrtBackend();
} else if (FLAGS_backend == "paddle") {
option->UsePaddleInferBackend();
} else if (FLAGS_backend == "trt" || FLAGS_backend == "paddle_trt") {
option->UseTrtBackend();
if (FLAGS_backend == "paddle_trt") {
option->EnablePaddleToTrt();
}
if (FLAGS_use_fp16) {
option->EnableTrtFP16();
}
} else if (FLAGS_backend == "default") {
return true;
} else {
std::cout << "While inference with GPU, only support "
"default/ort/paddle/trt/paddle_trt now, "
<< FLAGS_backend << " is not supported." << std::endl;
return false;
}
} else if (FLAGS_device == "cpu") {
option->SetCpuThreadNum(FLAGS_cpu_thread_nums);
if (FLAGS_backend == "ort") {
option->UseOrtBackend();
} else if (FLAGS_backend == "ov") {
option->UseOpenVINOBackend();
} else if (FLAGS_backend == "paddle") {
option->UsePaddleInferBackend();
} else if (FLAGS_backend == "lite") {
option->UsePaddleLiteBackend();
if (FLAGS_use_fp16) {
option->EnableLiteFP16();
}
} else if (FLAGS_backend == "default") {
return true;
} else {
std::cout << "While inference with CPU, only support "
"default/ort/ov/paddle/lite now, "
<< FLAGS_backend << " is not supported." << std::endl;
return false;
}
} else if (FLAGS_device == "xpu") {
option->UseKunlunXin(FLAGS_device_id);
if (FLAGS_backend == "ort") {
option->UseOrtBackend();
} else if (FLAGS_backend == "paddle") {
option->UsePaddleInferBackend();
} else if (FLAGS_backend == "lite") {
option->UsePaddleLiteBackend();
if (FLAGS_use_fp16) {
option->EnableLiteFP16();
}
} else if (FLAGS_backend == "default") {
return true;
} else {
std::cout << "While inference with XPU, only support "
"default/ort/paddle/lite now, "
<< FLAGS_backend << " is not supported." << std::endl;
return false;
}
} else {
std::cerr << "Only support device CPU/GPU/XPU now, " << FLAGS_device
<< " is not supported." << std::endl;
return false;
}
return true;
}

70
benchmark/cpp/macros.h Executable file
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@@ -0,0 +1,70 @@
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "fastdeploy/benchmark/utils.h"
#include "fastdeploy/utils/perf.h"
#define BENCHMARK_MODEL(MODEL_NAME, BENCHMARK_FUNC) \
{ \
std::cout << "====" << #MODEL_NAME << "====" << std::endl; \
if (!MODEL_NAME.Initialized()) { \
std::cerr << "Failed to initialize." << std::endl; \
return 0; \
} \
auto __im__ = cv::imread(FLAGS_image); \
fastdeploy::benchmark::ResourceUsageMonitor __resource_moniter__( \
FLAGS_sampling_interval, FLAGS_device_id); \
if (FLAGS_collect_memory_info) { \
__resource_moniter__.Start(); \
} \
if (FLAGS_profile_mode == "runtime") { \
if (!BENCHMARK_FUNC) { \
std::cerr << "Failed to predict." << std::endl; \
return 0; \
} \
double __profile_time__ = MODEL_NAME.GetProfileTime() * 1000; \
std::cout << "Runtime(ms): " << __profile_time__ << "ms." << std::endl; \
} else { \
std::cout << "Warmup " << FLAGS_warmup << " times..." << std::endl; \
for (int __i__ = 0; __i__ < FLAGS_warmup; __i__++) { \
if (!BENCHMARK_FUNC) { \
std::cerr << "Failed to predict." << std::endl; \
return 0; \
} \
} \
std::cout << "Counting time..." << std::endl; \
std::cout << "Repeat " << FLAGS_repeat << " times..." << std::endl; \
fastdeploy::TimeCounter __tc__; \
__tc__.Start(); \
for (int __i__ = 0; __i__ < FLAGS_repeat; __i__++) { \
if (!BENCHMARK_FUNC) { \
std::cerr << "Failed to predict." << std::endl; \
return 0; \
} \
} \
__tc__.End(); \
double __end2end__ = __tc__.Duration() / FLAGS_repeat * 1000; \
std::cout << "End2End(ms): " << __end2end__ << "ms." << std::endl; \
} \
if (FLAGS_collect_memory_info) { \
float __cpu_mem__ = __resource_moniter__.GetMaxCpuMem(); \
float __gpu_mem__ = __resource_moniter__.GetMaxGpuMem(); \
float __gpu_util__ = __resource_moniter__.GetMaxGpuUtil(); \
std::cout << "cpu_pss_mb: " << __cpu_mem__ << "MB." << std::endl; \
std::cout << "gpu_pss_mb: " << __gpu_mem__ << "MB." << std::endl; \
std::cout << "gpu_util: " << __gpu_util__ << std::endl; \
__resource_moniter__.Stop(); \
} \
}

92
benchmark/cpp/option.h Executable file
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@@ -0,0 +1,92 @@
// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "fastdeploy/vision.h"
static bool CreateRuntimeOption(fastdeploy::RuntimeOption* option) {
if (FLAGS_profile_mode == "runtime") {
option->EnableProfiling(FLAGS_include_h2d_d2h, FLAGS_repeat, FLAGS_warmup);
}
if (FLAGS_device == "gpu") {
option->UseGpu(FLAGS_device_id);
if (FLAGS_backend == "ort") {
option->UseOrtBackend();
} else if (FLAGS_backend == "paddle") {
option->UsePaddleInferBackend();
} else if (FLAGS_backend == "trt" || FLAGS_backend == "paddle_trt") {
option->UseTrtBackend();
if (FLAGS_backend == "paddle_trt") {
option->EnablePaddleToTrt();
}
if (FLAGS_use_fp16) {
option->EnableTrtFP16();
}
} else if (FLAGS_backend == "default") {
return true;
} else {
std::cout << "While inference with GPU, only support "
"default/ort/paddle/trt/paddle_trt now, "
<< FLAGS_backend << " is not supported." << std::endl;
return false;
}
} else if (FLAGS_device == "cpu") {
option->SetCpuThreadNum(FLAGS_cpu_thread_nums);
if (FLAGS_backend == "ort") {
option->UseOrtBackend();
} else if (FLAGS_backend == "ov") {
option->UseOpenVINOBackend();
} else if (FLAGS_backend == "paddle") {
option->UsePaddleInferBackend();
} else if (FLAGS_backend == "lite") {
option->UsePaddleLiteBackend();
if (FLAGS_use_fp16) {
option->EnableLiteFP16();
}
} else if (FLAGS_backend == "default") {
return true;
} else {
std::cout << "While inference with CPU, only support "
"default/ort/ov/paddle/lite now, "
<< FLAGS_backend << " is not supported." << std::endl;
return false;
}
} else if (FLAGS_device == "xpu") {
option->UseKunlunXin(FLAGS_device_id);
if (FLAGS_backend == "ort") {
option->UseOrtBackend();
} else if (FLAGS_backend == "paddle") {
option->UsePaddleInferBackend();
} else if (FLAGS_backend == "lite") {
option->UsePaddleLiteBackend();
if (FLAGS_use_fp16) {
option->EnableLiteFP16();
}
} else if (FLAGS_backend == "default") {
return true;
} else {
std::cout << "While inference with XPU, only support "
"default/ort/paddle/lite now, "
<< FLAGS_backend << " is not supported." << std::endl;
return false;
}
} else {
std::cerr << "Only support device CPU/GPU/XPU now, " << FLAGS_device
<< " is not supported." << std::endl;
return false;
}
return true;
}

1
fastdeploy/runtime/option_pybind.cc
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@@ -35,6 +35,7 @@ void BindOption(pybind11::module& m) {
.def(pybind11::init())
.def("set_model_path", &RuntimeOption::SetModelPath)
.def("set_model_buffer", &RuntimeOption::SetModelBuffer)
.def("set_encryption_key", &RuntimeOption::SetEncryptionKey)
.def("use_gpu", &RuntimeOption::UseGpu)
.def("use_cpu", &RuntimeOption::UseCpu)
.def("use_rknpu2", &RuntimeOption::UseRKNPU2)

28
fastdeploy/runtime/runtime.cc
View File

@@ -104,7 +104,33 @@ bool AutoSelectBackend(RuntimeOption& option) {
bool Runtime::Init(const RuntimeOption& _option) {
option = _option;
// decrypt encrypted model
if ("" != option.encryption_key_) {
#ifdef ENABLE_ENCRYPTION
if (option.model_from_memory_) {
option.model_file = Decrypt(option.model_file, option.encryption_key_);
if (!(option.params_file.empty())) {
option.params_file =
Decrypt(option.params_file, option.encryption_key_);
}
} else {
std::string model_buffer = "";
FDASSERT(ReadBinaryFromFile(option.model_file, &model_buffer),
"Fail to read binary from model file");
option.model_file = Decrypt(model_buffer, option.encryption_key_);
if (!(option.params_file.empty())) {
std::string params_buffer = "";
FDASSERT(ReadBinaryFromFile(option.params_file, &params_buffer),
"Fail to read binary from parameter file");
option.params_file = Decrypt(params_buffer, option.encryption_key_);
}
option.model_from_memory_ = true;
}
#else
FDERROR << "The FastDeploy didn't compile with encryption function."
<< std::endl;
#endif
}
// Choose default backend by model format and device if backend is not
// specified
if (option.backend == Backend::UNKNOWN) {

3
fastdeploy/runtime/runtime.h
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@@ -23,6 +23,9 @@
#include "fastdeploy/core/fd_tensor.h"
#include "fastdeploy/runtime/runtime_option.h"
#include "fastdeploy/utils/perf.h"
#ifdef ENABLE_ENCRYPTION
#include "fastdeploy/encryption/include/decrypt.h"
#endif
/** \brief All C++ FastDeploy APIs are defined inside this namespace
*

9
fastdeploy/runtime/runtime_option.cc
View File

@@ -36,6 +36,15 @@ void RuntimeOption::SetModelBuffer(const std::string& model_buffer,
model_from_memory_ = true;
}
void RuntimeOption::SetEncryptionKey(const std::string& encryption_key) {
#ifdef ENABLE_ENCRYPTION
encryption_key_ = encryption_key;
#else
FDERROR << "The FastDeploy didn't compile with encryption function."
<< std::endl;
#endif
}
void RuntimeOption::UseGpu(int gpu_id) {
#ifdef WITH_GPU
device = Device::GPU;

8
fastdeploy/runtime/runtime_option.h
View File

@@ -59,6 +59,12 @@ struct FASTDEPLOY_DECL RuntimeOption {
const std::string& params_buffer = "",
const ModelFormat& format = ModelFormat::PADDLE);
/** \brief When loading encrypted model, encryption_key is required to decrypte model
*
* \param[in] encryption_key The key for decrypting model
*/
void SetEncryptionKey(const std::string& encryption_key);
/// Use cpu to inference, the runtime will inference on CPU by default
void UseCpu();
/// Use Nvidia GPU to inference
@@ -179,6 +185,8 @@ struct FASTDEPLOY_DECL RuntimeOption {
/// format of input model
ModelFormat model_format = ModelFormat::PADDLE;
std::string encryption_key_ = "";
// for cpu inference
// default will let the backend choose their own default value
int cpu_thread_num = -1;

6
python/fastdeploy/runtime.py
View File

@@ -195,6 +195,12 @@ class RuntimeOption:
return self._option.set_model_buffer(model_buffer, params_buffer,
model_format)
def set_encryption_key(self, encryption_key):
"""When loading encrypted model, encryption_key is required to decrypte model
:param encryption_key: (str)The key for decrypting model
"""
return self._option.set_encryption_key(encryption_key)
def use_gpu(self, device_id=0):
"""Inference with Nvidia GPU

46
tutorials/encrypt_model/README.md Normal file
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@@ -0,0 +1,46 @@
English | [中文](README_CN.md)
# FastDeploy generates an encrypted model
This directory provides `encrypt.py` to quickly complete the encryption of the model and parameter files of ResNet50_vd
## encryption
```bash
# Download deployment example code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/tutorials/encrypt_model
# Download the ResNet50_vd model file
wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
tar -xvf ResNet50_vd_infer.tgz
python encrypt.py --model_file ResNet50_vd_infer/inference.pdmodel --params_file ResNet50_vd_infer/inference.pdiparams --encrypted_model_dir ResNet50_vd_infer_encrypt
```
>> **Note** After the encryption is completed, the ResNet50_vd_infer_encrypt folder will be generated, including `__model__.encrypted`, `__params__.encrypted`, `encryption_key.txt` three files, where `encryption_key.txt` contains the encrypted key. At the same time, you need to copy the `inference_cls.yaml` configuration file in the original folder to the ResNet50_vd_infer_encrypt folder for subsequent deployment
### Python encryption interface
Use the encrypted interface through the following interface settings
```python
import fastdeploy as fd
import os
# when key is not given, key will be automatically generated.
# otherwise, the file will be encrypted by specific key
encrypted_model, key = fd.encryption.encrypt(model_file.read())
encrypted_params, key= fd.encryption.encrypt(params_file.read(), key)
```
### FastDeploy deployment encryption model (decryption)
Through the setting of the following interface, FastDeploy can deploy the encryption model
```python
import fastdeploy as fd
option = fd.RuntimeOption()
option.set_encryption_key(key)
```
```C++
fastdeploy::RuntimeOption option;
option.SetEncryptionKey(key)
```
>> **Note** For more details about RuntimeOption, please refer to [RuntimeOption Python Documentation](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/runtime_option.html), [ RuntimeOption C++ Documentation](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/structfastdeploy_1_1RuntimeOption.html)

48
tutorials/encrypt_model/README_CN.md Normal file
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@@ -0,0 +1,48 @@
[English](README.md) | 中文
# 使用FastDeploy生成加密模型
本目录下提供`encrypt.py`快速完成ResNet50_vd的模型和参数文件加密
FastDeploy支持对称加密的方案，通过调用OpenSSL中的对称加密算法（AES）对模型进行加密并产生密钥
## 加密
```bash
#下载加密示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/tutorials/encrypt_model
# 下载ResNet50_vd模型文件
wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
tar -xvf ResNet50_vd_infer.tgz
python encrypt.py --model_file ResNet50_vd_infer/inference.pdmodel --params_file ResNet50_vd_infer/inference.pdiparams --encrypted_model_dir ResNet50_vd_infer_encrypt
```
>> **注意** 加密完成后会生成ResNet50_vd_infer_encrypt文件夹，包含`__model__.encrypted`,`__params__.encrypted`,`encryption_key.txt`三个文件，其中`encryption_key.txt`包含加密后的秘钥，同时需要将原文件夹中的、`inference_cls.yaml`配置文件 拷贝至ResNet50_vd_infer_encrypt文件夹，以便后续部署使用
### Python加密接口
通过如下接口的设定，使用加密接口（解密）
```python
import fastdeploy as fd
import os
# when key is not given, key will be automatically generated.
# otherwise, the file will be encrypted by specific key
encrypted_model, key = fd.encryption.encrypt(model_file.read())
encrypted_params, key= fd.encryption.encrypt(params_file.read(), key)
```
### FastDeploy 部署加密模型
通过如下接口的设定，完成加密模型的推理
```python
import fastdeploy as fd
option = fd.RuntimeOption()
option.set_encryption_key(key)
```
```C++
fastdeploy::RuntimeOption option;
option.SetEncryptionKey(key)
```
>> **注意** RuntimeOption的更多详细信息，请参考[RuntimeOption Python文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/runtime_option.html)，[RuntimeOption C++文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/structfastdeploy_1_1RuntimeOption.html)

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import fastdeploy as fd
import os
def parse_arguments():
import argparse
import ast
parser = argparse.ArgumentParser()
parser.add_argument(
"--encrypted_model_dir",
required=False,
help="Path of model directory.")
parser.add_argument(
"--model_file", required=True, help="Path of model file directory.")
parser.add_argument(
"--params_file",
required=True,
help="Path of parameters file directory.")
return parser.parse_args()
if __name__ == "__main__":
args = parse_arguments()
model_buffer = open(args.model_file, 'rb')
params_buffer = open(args.params_file, 'rb')
encrypted_model, key = fd.encryption.encrypt(model_buffer.read())
# use the same key to encrypt parameter file
encrypted_params, key = fd.encryption.encrypt(params_buffer.read(), key)
encrypted_model_dir = "encrypt_model_dir"
if args.encrypted_model_dir:
encrypted_model_dir = args.encrypted_model_dir
model_buffer.close()
params_buffer.close()
os.mkdir(encrypted_model_dir)
with open(os.path.join(encrypted_model_dir, "__model__.encrypted"),
"w") as f:
f.write(encrypted_model)
with open(os.path.join(encrypted_model_dir, "__params__.encrypted"),
"w") as f:
f.write(encrypted_params)
with open(os.path.join(encrypted_model_dir, "encryption_key.txt"),
"w") as f:
f.write(key)
print("encryption key: ", key)
print("encryption success")