mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-22 16:07:51 +08:00
[PaddlePaddle Hackathon4 No.186] Add PaddleDetection Models Deployment Go Examples (#1648)
* [PaddlePaddle Hackathon4 No.186] Add PaddleDetection Models Deployment Go Examples Signed-off-by: wanziyu <ziyuwan@zju.edu.cn> * Fix YOLOv8 Deployment Go Example Signed-off-by: wanziyu <ziyuwan@zju.edu.cn> --------- Signed-off-by: wanziyu <ziyuwan@zju.edu.cn> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
This commit is contained in:
wanziyu
@@ -0,0 +1,57 @@
|
||||
English | [简体中文](README_CN.md)
|
||||
# PaddleDetection Golang Deployment Example
|
||||
|
||||
This directory provides examples that `infer.go` uses CGO to call FastDeploy C API and fast finish the deployment of PaddleDetection models, including PPYOLOE on CPU/GPU.
|
||||
|
||||
Before deployment, two steps require confirmation
|
||||
|
||||
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
|
||||
- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
Taking inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 1.0.4 above (x.x.x>1.0.4) or develop version (x.x.x=0.0.0) is required to support this model.
|
||||
|
||||
### Use Golang and CGO to deploy PPYOLOE model
|
||||
|
||||
Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above.
|
||||
```bash
|
||||
wget https://fastdeploy.bj.bcebos.com/dev/cpp/fastdeploy-linux-x64-0.0.0.tgz
|
||||
tar xvf fastdeploy-linux-x64-0.0.0.tgz
|
||||
```
|
||||
|
||||
Copy FastDeploy C APIs from precompiled library to the current directory.
|
||||
```bash
|
||||
cp -r fastdeploy-linux-x64-0.0.0/include/fastdeploy_capi .
|
||||
```
|
||||
|
||||
Download the PPYOLOE model file and test images.
|
||||
```bash
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
|
||||
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
||||
tar xvf ppyoloe_crn_l_300e_coco.tgz
|
||||
```
|
||||
|
||||
Configure the `cgo CFLAGS: -I` to FastDeploy C API directory path and the `cgo LDFLAGS: -L` to FastDeploy dynamic library path. The FastDeploy dynamic library is located in the `/lib` directory.
|
||||
```bash
|
||||
cgo CFLAGS: -I./fastdeploy_capi
|
||||
cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
|
||||
```
|
||||
|
||||
Use the following command to add Fastdeploy library path to the environment variable.
|
||||
```bash
|
||||
source /Path/to/fastdeploy-linux-x64-0.0.0/fastdeploy_init.sh
|
||||
```
|
||||
|
||||
Compile the Go file `infer.go`.
|
||||
```bash
|
||||
go build infer.go
|
||||
```
|
||||
|
||||
After compiling, use the following command to obtain the predicted results.
|
||||
```bash
|
||||
# CPU inference
|
||||
./infer -model ./ppyoloe_crn_l_300e_coco -image 000000014439.jpg -device 0
|
||||
# GPU inference
|
||||
./infer -model ./ppyoloe_crn_l_300e_coco -image 000000014439.jpg -device 1
|
||||
```
|
||||
|
||||
Then visualized inspection result is saved in the local image `vis_result.jpg`.
|
||||
@@ -0,0 +1,56 @@
|
||||
[English](README.md) | 简体中文
|
||||
# PaddleDetection Golang 部署示例
|
||||
|
||||
本目录下提供`infer.go`, 使用CGO调用FastDeploy C API快速完成PaddleDetection模型PPYOLOE在CPU/GPU上部署的示例
|
||||
|
||||
在部署前，需确认以下两个步骤
|
||||
|
||||
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.4以上(x.x.x>1.0.4)或FastDeploy的Develop版本(x.x.x=0.0.0)
|
||||
### 使用Golang和CGO工具进行PPYOLOE模型推理部署
|
||||
|
||||
在当前目录下，下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
|
||||
```bash
|
||||
wget https://fastdeploy.bj.bcebos.com/dev/cpp/fastdeploy-linux-x64-0.0.0.tgz
|
||||
tar xvf fastdeploy-linux-x64-0.0.0.tgz
|
||||
```
|
||||
|
||||
将FastDeploy C API文件拷贝至当前目录
|
||||
```bash
|
||||
cp -r fastdeploy-linux-x64-0.0.0/include/fastdeploy_capi .
|
||||
```
|
||||
|
||||
下载PPYOLOE模型文件和测试图片
|
||||
```bash
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
|
||||
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
||||
tar xvf ppyoloe_crn_l_300e_coco.tgz
|
||||
```
|
||||
|
||||
配置`infer.go`中的`cgo CFLAGS: -I`参数配置为C API文件路径，`cgo LDFLAGS: -L`参数配置为FastDeploy的动态库路径，动态库位于预编译库的`/lib`目录中
|
||||
```bash
|
||||
cgo CFLAGS: -I./fastdeploy_capi
|
||||
cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
|
||||
```
|
||||
|
||||
将FastDeploy的库路径添加到环境变量
|
||||
```bash
|
||||
source /Path/to/fastdeploy-linux-x64-0.0.0/fastdeploy_init.sh
|
||||
```
|
||||
|
||||
编译Go文件`infer.go`
|
||||
```bash
|
||||
go build infer.go
|
||||
```
|
||||
|
||||
编译完成后，使用如下命令执行可得到预测结果
|
||||
```bash
|
||||
# CPU推理
|
||||
./infer -model ./ppyoloe_crn_l_300e_coco -image 000000014439.jpg -device 0
|
||||
# GPU推理
|
||||
./infer -model ./ppyoloe_crn_l_300e_coco -image 000000014439.jpg -device 1
|
||||
```
|
||||
|
||||
可视化的检测结果图片保存在本地`vis_result.jpg`
|
||||
@@ -0,0 +1,185 @@
|
||||
// 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.
|
||||
|
||||
package main
|
||||
|
||||
// #cgo CFLAGS: -I./fastdeploy_capi
|
||||
// #cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
|
||||
// #include <fastdeploy_capi/vision.h>
|
||||
// #include <stdio.h>
|
||||
// #include <stdbool.h>
|
||||
// #include <stdlib.h>
|
||||
/*
|
||||
#include <stdio.h>
|
||||
#ifdef WIN32
|
||||
const char sep = '\\';
|
||||
#else
|
||||
const char sep = '/';
|
||||
#endif
|
||||
|
||||
char* GetModelFilePath(char* model_dir, char* model_file, int max_size){
|
||||
snprintf(model_file, max_size, "%s%c%s", model_dir, sep, "model.pdmodel");
|
||||
return model_file;
|
||||
}
|
||||
|
||||
char* GetParametersFilePath(char* model_dir, char* params_file, int max_size){
|
||||
snprintf(params_file, max_size, "%s%c%s", model_dir, sep, "model.pdiparams");
|
||||
return params_file;
|
||||
}
|
||||
|
||||
char* GetConfigFilePath(char* model_dir, char* config_file, int max_size){
|
||||
snprintf(config_file, max_size, "%s%c%s", model_dir, sep, "infer_cfg.yml");
|
||||
return config_file;
|
||||
}
|
||||
*/
|
||||
import "C"
|
||||
import (
|
||||
"flag"
|
||||
"fmt"
|
||||
"unsafe"
|
||||
)
|
||||
|
||||
func FDBooleanToGo(b C.FD_C_Bool) bool {
|
||||
var cFalse C.FD_C_Bool
|
||||
if b != cFalse {
|
||||
return true
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
func CpuInfer(modelDir *C.char, imageFile *C.char) {
|
||||
|
||||
var modelFile = (*C.char)(C.malloc(C.size_t(100)))
|
||||
var paramsFile = (*C.char)(C.malloc(C.size_t(100)))
|
||||
var configFile = (*C.char)(C.malloc(C.size_t(100)))
|
||||
var maxSize = 99
|
||||
|
||||
modelFile = C.GetModelFilePath(modelDir, modelFile, C.int(maxSize))
|
||||
paramsFile = C.GetParametersFilePath(modelDir, paramsFile, C.int(maxSize))
|
||||
configFile = C.GetConfigFilePath(modelDir, configFile, C.int(maxSize))
|
||||
|
||||
var option *C.FD_C_RuntimeOptionWrapper = C.FD_C_CreateRuntimeOptionWrapper()
|
||||
C.FD_C_RuntimeOptionWrapperUseCpu(option)
|
||||
|
||||
var model *C.FD_C_PPYOLOEWrapper = C.FD_C_CreatePPYOLOEWrapper(
|
||||
modelFile, paramsFile, configFile, option, C.FD_C_ModelFormat_PADDLE)
|
||||
|
||||
if !FDBooleanToGo(C.FD_C_PPYOLOEWrapperInitialized(model)) {
|
||||
fmt.Printf("Failed to initialize.\n")
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyPPYOLOEWrapper(model)
|
||||
return
|
||||
}
|
||||
|
||||
var image C.FD_C_Mat = C.FD_C_Imread(imageFile)
|
||||
|
||||
var result *C.FD_C_DetectionResult = C.FD_C_CreateDetectionResult()
|
||||
|
||||
if !FDBooleanToGo(C.FD_C_PPYOLOEWrapperPredict(model, image, result)) {
|
||||
fmt.Printf("Failed to predict.\n")
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyPPYOLOEWrapper(model)
|
||||
C.FD_C_DestroyMat(image)
|
||||
C.free(unsafe.Pointer(result))
|
||||
return
|
||||
}
|
||||
|
||||
var visImage C.FD_C_Mat = C.FD_C_VisDetection(image, result, 0.5, 1, 0.5)
|
||||
|
||||
C.FD_C_Imwrite(C.CString("vis_result.jpg"), visImage)
|
||||
fmt.Printf("Visualized result saved in ./vis_result.jpg\n")
|
||||
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyPPYOLOEWrapper(model)
|
||||
C.FD_C_DestroyDetectionResult(result)
|
||||
C.FD_C_DestroyMat(image)
|
||||
C.FD_C_DestroyMat(visImage)
|
||||
}
|
||||
|
||||
func GpuInfer(modelDir *C.char, imageFile *C.char) {
|
||||
|
||||
var modelFile = (*C.char)(C.malloc(C.size_t(100)))
|
||||
var paramsFile = (*C.char)(C.malloc(C.size_t(100)))
|
||||
var configFile = (*C.char)(C.malloc(C.size_t(100)))
|
||||
var maxSize = 99
|
||||
|
||||
modelFile = C.GetModelFilePath(modelDir, modelFile, C.int(maxSize))
|
||||
paramsFile = C.GetParametersFilePath(modelDir, paramsFile, C.int(maxSize))
|
||||
configFile = C.GetConfigFilePath(modelDir, configFile, C.int(maxSize))
|
||||
|
||||
var option *C.FD_C_RuntimeOptionWrapper = C.FD_C_CreateRuntimeOptionWrapper()
|
||||
C.FD_C_RuntimeOptionWrapperUseGpu(option, 0)
|
||||
|
||||
var model *C.FD_C_PPYOLOEWrapper = C.FD_C_CreatePPYOLOEWrapper(
|
||||
modelFile, paramsFile, configFile, option, C.FD_C_ModelFormat_PADDLE)
|
||||
|
||||
if !FDBooleanToGo(C.FD_C_PPYOLOEWrapperInitialized(model)) {
|
||||
fmt.Printf("Failed to initialize.\n")
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyPPYOLOEWrapper(model)
|
||||
return
|
||||
}
|
||||
|
||||
var image C.FD_C_Mat = C.FD_C_Imread(imageFile)
|
||||
|
||||
var result *C.FD_C_DetectionResult = C.FD_C_CreateDetectionResult()
|
||||
|
||||
if !FDBooleanToGo(C.FD_C_PPYOLOEWrapperPredict(model, image, result)) {
|
||||
fmt.Printf("Failed to predict.\n")
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyPPYOLOEWrapper(model)
|
||||
C.FD_C_DestroyMat(image)
|
||||
C.free(unsafe.Pointer(result))
|
||||
return
|
||||
}
|
||||
|
||||
var visImage C.FD_C_Mat = C.FD_C_VisDetection(image, result, 0.5, 1, 0.5)
|
||||
|
||||
C.FD_C_Imwrite(C.CString("vis_result.jpg"), visImage)
|
||||
fmt.Printf("Visualized result saved in ./vis_result.jpg\n")
|
||||
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyPPYOLOEWrapper(model)
|
||||
C.FD_C_DestroyDetectionResult(result)
|
||||
C.FD_C_DestroyMat(image)
|
||||
C.FD_C_DestroyMat(visImage)
|
||||
}
|
||||
|
||||
var (
|
||||
modelDir string
|
||||
imageFile string
|
||||
deviceType int
|
||||
)
|
||||
|
||||
func init() {
|
||||
flag.StringVar(&modelDir, "model", "", "paddle detection model to use")
|
||||
flag.StringVar(&imageFile, "image", "", "image to predict")
|
||||
flag.IntVar(&deviceType, "device", 0, "The data type of run_option is int, 0: run with cpu; 1: run with gpu")
|
||||
}
|
||||
|
||||
func main() {
|
||||
flag.Parse()
|
||||
|
||||
if modelDir != "" && imageFile != "" {
|
||||
if deviceType == 0 {
|
||||
CpuInfer(C.CString(modelDir), C.CString(imageFile))
|
||||
} else if deviceType == 1 {
|
||||
GpuInfer(C.CString(modelDir), C.CString(imageFile))
|
||||
}
|
||||
} else {
|
||||
fmt.Printf("Usage: ./infer -model path/to/model_dir -image path/to/image -device run_option \n")
|
||||
fmt.Printf("e.g ./infer -model ./ppyoloe_crn_l_300e_coco -image 000000014439.jpg -device 0 \n")
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,56 @@
|
||||
English | [简体中文](README_CN.md)
|
||||
# YOLOv5 Golang Deployment Example
|
||||
|
||||
This directory provides examples that `infer.go` uses CGO to call FastDeploy C API and finish the deployment of YOLOv5 model on CPU/GPU.
|
||||
|
||||
Before deployment, two steps require confirmation
|
||||
|
||||
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
|
||||
- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
Taking inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 1.0.4 above (x.x.x>1.0.4) or develop version (x.x.x=0.0.0) is required to support this model.
|
||||
|
||||
### Use Golang and CGO to deploy YOLOv5 model
|
||||
|
||||
Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above.
|
||||
```bash
|
||||
wget https://fastdeploy.bj.bcebos.com/dev/cpp/fastdeploy-linux-x64-0.0.0.tgz
|
||||
tar xvf fastdeploy-linux-x64-0.0.0.tgz
|
||||
```
|
||||
|
||||
Copy FastDeploy C APIs from precompiled library to the current directory.
|
||||
```bash
|
||||
cp -r fastdeploy-linux-x64-0.0.0/include/fastdeploy_capi .
|
||||
```
|
||||
|
||||
Download the YOLOv5 ONNX model file and test images
|
||||
```bash
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
|
||||
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
||||
```
|
||||
|
||||
Configure the `cgo CFLAGS: -I` to FastDeploy C API directory path and the `cgo LDFLAGS: -L` to FastDeploy dynamic library path. The FastDeploy dynamic library is located in the `/lib` directory.
|
||||
```bash
|
||||
cgo CFLAGS: -I./fastdeploy_capi
|
||||
cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
|
||||
```
|
||||
|
||||
Use the following command to add Fastdeploy library path to the environment variable.
|
||||
```bash
|
||||
source /Path/to/fastdeploy-linux-x64-0.0.0/fastdeploy_init.sh
|
||||
```
|
||||
|
||||
Compile the Go file `infer.go`.
|
||||
```bash
|
||||
go build infer.go
|
||||
```
|
||||
|
||||
After compiling, use the following command to obtain the predicted results.
|
||||
```bash
|
||||
# CPU inference
|
||||
./infer -model yolov5s.onnx -image 000000014439.jpg -device 0
|
||||
# GPU inference
|
||||
./infer -model yolov5s.onnx -image 000000014439.jpg -device 1
|
||||
```
|
||||
|
||||
Then visualized inspection result is saved in the local image `vis_result.jpg`.
|
||||
@@ -0,0 +1,55 @@
|
||||
[English](README.md) | 简体中文
|
||||
# YOLOv5 Golang 部署示例
|
||||
|
||||
本目录下提供`infer.go`, 使用CGO调用FastDeploy C API快速完成YOLOv5模型在CPU/GPU上部署的示例
|
||||
|
||||
在部署前，需确认以下两个步骤
|
||||
|
||||
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.4以上(x.x.x>1.0.4)或FastDeploy的Develop版本(x.x.x=0.0.0)
|
||||
### 使用Golang和CGO工具进行YOLOv5模型推理部署
|
||||
|
||||
在当前目录下，下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
|
||||
```bash
|
||||
wget https://fastdeploy.bj.bcebos.com/dev/cpp/fastdeploy-linux-x64-0.0.0.tgz
|
||||
tar xvf fastdeploy-linux-x64-0.0.0.tgz
|
||||
```
|
||||
|
||||
将FastDeploy C API文件拷贝至当前目录
|
||||
```bash
|
||||
cp -r fastdeploy-linux-x64-0.0.0/include/fastdeploy_capi .
|
||||
```
|
||||
|
||||
下载官方转换好的 YOLOv5 ONNX 模型文件和测试图片
|
||||
```bash
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
|
||||
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
||||
```
|
||||
|
||||
配置`infer.go`中的`cgo CFLAGS: -I`参数配置为C API文件路径，`cgo LDFLAGS: -L`参数配置为FastDeploy的动态库路径，动态库位于预编译库的`/lib`目录中
|
||||
```bash
|
||||
cgo CFLAGS: -I./fastdeploy_capi
|
||||
cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
|
||||
```
|
||||
|
||||
将FastDeploy的库路径添加到环境变量
|
||||
```bash
|
||||
source /Path/to/fastdeploy-linux-x64-0.0.0/fastdeploy_init.sh
|
||||
```
|
||||
|
||||
编译Go文件`infer.go`
|
||||
```bash
|
||||
go build infer.go
|
||||
```
|
||||
|
||||
编译完成后，使用如下命令执行可得到预测结果
|
||||
```bash
|
||||
# CPU推理
|
||||
./infer -model yolov5s.onnx -image 000000014439.jpg -device 0
|
||||
# GPU推理
|
||||
./infer -model yolov5s.onnx -image 000000014439.jpg -device 1
|
||||
```
|
||||
|
||||
可视化的检测结果图片保存在本地`vis_result.jpg`
|
||||
@@ -0,0 +1,144 @@
|
||||
// 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.
|
||||
|
||||
package main
|
||||
|
||||
// #cgo CFLAGS: -I./fastdeploy_capi
|
||||
// #cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
|
||||
// #include <fastdeploy_capi/vision.h>
|
||||
// #include <stdio.h>
|
||||
// #include <stdbool.h>
|
||||
// #include <stdlib.h>
|
||||
import "C"
|
||||
import (
|
||||
"flag"
|
||||
"fmt"
|
||||
"unsafe"
|
||||
)
|
||||
|
||||
func FDBooleanToGo(b C.FD_C_Bool) bool {
|
||||
var cFalse C.FD_C_Bool
|
||||
if b != cFalse {
|
||||
return true
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
func CpuInfer(modelFile *C.char, imageFile *C.char) {
|
||||
|
||||
var option *C.FD_C_RuntimeOptionWrapper = C.FD_C_CreateRuntimeOptionWrapper()
|
||||
C.FD_C_RuntimeOptionWrapperUseCpu(option)
|
||||
|
||||
var model *C.FD_C_YOLOv5Wrapper = C.FD_C_CreateYOLOv5Wrapper(
|
||||
modelFile, C.CString(""), option, C.FD_C_ModelFormat_ONNX)
|
||||
|
||||
if !FDBooleanToGo(C.FD_C_YOLOv5WrapperInitialized(model)) {
|
||||
fmt.Printf("Failed to initialize.\n")
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyYOLOv5Wrapper(model)
|
||||
return
|
||||
}
|
||||
|
||||
var image C.FD_C_Mat = C.FD_C_Imread(imageFile)
|
||||
|
||||
var result *C.FD_C_DetectionResult = C.FD_C_CreateDetectionResult()
|
||||
|
||||
if !FDBooleanToGo(C.FD_C_YOLOv5WrapperPredict(model, image, result)) {
|
||||
fmt.Printf("Failed to predict.\n")
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyYOLOv5Wrapper(model)
|
||||
C.FD_C_DestroyMat(image)
|
||||
C.free(unsafe.Pointer(result))
|
||||
return
|
||||
}
|
||||
|
||||
var visImage C.FD_C_Mat = C.FD_C_VisDetection(image, result, 0.5, 1, 0.5)
|
||||
|
||||
C.FD_C_Imwrite(C.CString("vis_result.jpg"), visImage)
|
||||
fmt.Printf("Visualized result saved in ./vis_result.jpg\n")
|
||||
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyYOLOv5Wrapper(model)
|
||||
C.FD_C_DestroyDetectionResult(result)
|
||||
C.FD_C_DestroyMat(image)
|
||||
C.FD_C_DestroyMat(visImage)
|
||||
}
|
||||
|
||||
func GpuInfer(modelFile *C.char, imageFile *C.char) {
|
||||
|
||||
var option *C.FD_C_RuntimeOptionWrapper = C.FD_C_CreateRuntimeOptionWrapper()
|
||||
C.FD_C_RuntimeOptionWrapperUseGpu(option, 0)
|
||||
|
||||
var model *C.FD_C_YOLOv5Wrapper = C.FD_C_CreateYOLOv5Wrapper(
|
||||
modelFile, C.CString(""), option, C.FD_C_ModelFormat_ONNX)
|
||||
|
||||
if !FDBooleanToGo(C.FD_C_YOLOv5WrapperInitialized(model)) {
|
||||
fmt.Printf("Failed to initialize.\n")
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyYOLOv5Wrapper(model)
|
||||
return
|
||||
}
|
||||
|
||||
var image C.FD_C_Mat = C.FD_C_Imread(imageFile)
|
||||
|
||||
var result *C.FD_C_DetectionResult = C.FD_C_CreateDetectionResult()
|
||||
|
||||
if !FDBooleanToGo(C.FD_C_YOLOv5WrapperPredict(model, image, result)) {
|
||||
fmt.Printf("Failed to predict.\n")
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyYOLOv5Wrapper(model)
|
||||
C.FD_C_DestroyMat(image)
|
||||
C.free(unsafe.Pointer(result))
|
||||
return
|
||||
}
|
||||
|
||||
var visImage C.FD_C_Mat = C.FD_C_VisDetection(image, result, 0.5, 1, 0.5)
|
||||
|
||||
C.FD_C_Imwrite(C.CString("vis_result.jpg"), visImage)
|
||||
fmt.Printf("Visualized result saved in ./vis_result.jpg\n")
|
||||
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyYOLOv5Wrapper(model)
|
||||
C.FD_C_DestroyDetectionResult(result)
|
||||
C.FD_C_DestroyMat(image)
|
||||
C.FD_C_DestroyMat(visImage)
|
||||
}
|
||||
|
||||
var (
|
||||
modelFile string
|
||||
imageFile string
|
||||
deviceType int
|
||||
)
|
||||
|
||||
func init() {
|
||||
flag.StringVar(&modelFile, "model", "", "paddle detection model to use")
|
||||
flag.StringVar(&imageFile, "image", "", "image to predict")
|
||||
flag.IntVar(&deviceType, "device", 0, "The data type of run_option is int, 0: run with cpu; 1: run with gpu")
|
||||
}
|
||||
|
||||
func main() {
|
||||
flag.Parse()
|
||||
|
||||
if modelFile != "" && imageFile != "" {
|
||||
if deviceType == 0 {
|
||||
CpuInfer(C.CString(modelFile), C.CString(imageFile))
|
||||
} else if deviceType == 1 {
|
||||
GpuInfer(C.CString(modelFile), C.CString(imageFile))
|
||||
}
|
||||
} else {
|
||||
fmt.Printf("Usage: ./infer -model path/to/model_dir -image path/to/image -device run_option \n")
|
||||
fmt.Printf("e.g ./infer -model yolov5s.onnx -image 000000014439.jpg -device 0 \n")
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,56 @@
|
||||
English | [简体中文](README_CN.md)
|
||||
# YOLOv8 Golang Deployment Example
|
||||
|
||||
This directory provides examples that `infer.go` uses CGO to call FastDeploy C API and finish the deployment of YOLOv8 model on CPU/GPU.
|
||||
|
||||
Before deployment, two steps require confirmation
|
||||
|
||||
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
|
||||
- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
Taking inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 1.0.4 above (x.x.x>1.0.4) or develop version (x.x.x=0.0.0) is required to support this model.
|
||||
|
||||
### Use Golang and CGO to deploy YOLOv8 model
|
||||
|
||||
Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above.
|
||||
```bash
|
||||
wget https://fastdeploy.bj.bcebos.com/dev/cpp/fastdeploy-linux-x64-0.0.0.tgz
|
||||
tar xvf fastdeploy-linux-x64-0.0.0.tgz
|
||||
```
|
||||
|
||||
Copy FastDeploy C APIs from precompiled library to the current directory.
|
||||
```bash
|
||||
cp -r fastdeploy-linux-x64-0.0.0/include/fastdeploy_capi .
|
||||
```
|
||||
|
||||
Download the YOLOv8 ONNX model file and test images
|
||||
```bash
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov8s.onnx
|
||||
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
||||
```
|
||||
|
||||
Configure the `cgo CFLAGS: -I` to FastDeploy C API directory path and the `cgo LDFLAGS: -L` to FastDeploy dynamic library path. The FastDeploy dynamic library is located in the `/lib` directory.
|
||||
```bash
|
||||
cgo CFLAGS: -I./fastdeploy_capi
|
||||
cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
|
||||
```
|
||||
|
||||
Use the following command to add Fastdeploy library path to the environment variable.
|
||||
```bash
|
||||
source /Path/to/fastdeploy-linux-x64-0.0.0/fastdeploy_init.sh
|
||||
```
|
||||
|
||||
Compile the Go file `infer.go`.
|
||||
```bash
|
||||
go build infer.go
|
||||
```
|
||||
|
||||
After compiling, use the following command to obtain the predicted results.
|
||||
```bash
|
||||
# CPU inference
|
||||
./infer -model yolov8s.onnx -image 000000014439.jpg -device 0
|
||||
# GPU inference
|
||||
./infer -model yolov8s.onnx -image 000000014439.jpg -device 1
|
||||
```
|
||||
|
||||
Then visualized inspection result is saved in the local image `vis_result.jpg`.
|
||||
@@ -0,0 +1,55 @@
|
||||
[English](README.md) | 简体中文
|
||||
# YOLOv8 Golang 部署示例
|
||||
|
||||
本目录下提供`infer.go`, 使用CGO调用FastDeploy C API快速完成YOLOv8模型在CPU/GPU上部署的示例
|
||||
|
||||
在部署前，需确认以下两个步骤
|
||||
|
||||
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.4以上(x.x.x>1.0.4)或FastDeploy的Develop版本(x.x.x=0.0.0)
|
||||
### 使用Golang和CGO工具进行YOLOv8模型推理部署
|
||||
|
||||
在当前目录下，下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
|
||||
```bash
|
||||
wget https://fastdeploy.bj.bcebos.com/dev/cpp/fastdeploy-linux-x64-0.0.0.tgz
|
||||
tar xvf fastdeploy-linux-x64-0.0.0.tgz
|
||||
```
|
||||
|
||||
将FastDeploy C API文件拷贝至当前目录
|
||||
```bash
|
||||
cp -r fastdeploy-linux-x64-0.0.0/include/fastdeploy_capi .
|
||||
```
|
||||
|
||||
下载官方转换好的 YOLOv8 ONNX 模型文件和测试图片
|
||||
```bash
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov8s.onnx
|
||||
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
||||
```
|
||||
|
||||
配置`infer.go`中的`cgo CFLAGS: -I`参数配置为C API文件路径，`cgo LDFLAGS: -L`参数配置为FastDeploy的动态库路径，动态库位于预编译库的`/lib`目录中
|
||||
```bash
|
||||
cgo CFLAGS: -I./fastdeploy_capi
|
||||
cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
|
||||
```
|
||||
|
||||
将FastDeploy的库路径添加到环境变量
|
||||
```bash
|
||||
source /Path/to/fastdeploy-linux-x64-0.0.0/fastdeploy_init.sh
|
||||
```
|
||||
|
||||
编译Go文件`infer.go`
|
||||
```bash
|
||||
go build infer.go
|
||||
```
|
||||
|
||||
编译完成后，使用如下命令执行可得到预测结果
|
||||
```bash
|
||||
# CPU推理
|
||||
./infer -model yolov8s.onnx -image 000000014439.jpg -device 0
|
||||
# GPU推理
|
||||
./infer -model yolov8s.onnx -image 000000014439.jpg -device 1
|
||||
```
|
||||
|
||||
可视化的检测结果图片保存在本地`vis_result.jpg`
|
||||
@@ -0,0 +1,144 @@
|
||||
// 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.
|
||||
|
||||
package main
|
||||
|
||||
// #cgo CFLAGS: -I./fastdeploy_capi
|
||||
// #cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
|
||||
// #include <fastdeploy_capi/vision.h>
|
||||
// #include <stdio.h>
|
||||
// #include <stdbool.h>
|
||||
// #include <stdlib.h>
|
||||
import "C"
|
||||
import (
|
||||
"flag"
|
||||
"fmt"
|
||||
"unsafe"
|
||||
)
|
||||
|
||||
func FDBooleanToGo(b C.FD_C_Bool) bool {
|
||||
var cFalse C.FD_C_Bool
|
||||
if b != cFalse {
|
||||
return true
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
func CpuInfer(modelFile *C.char, imageFile *C.char) {
|
||||
|
||||
var option *C.FD_C_RuntimeOptionWrapper = C.FD_C_CreateRuntimeOptionWrapper()
|
||||
C.FD_C_RuntimeOptionWrapperUseCpu(option)
|
||||
|
||||
var model *C.FD_C_YOLOv8Wrapper = C.FD_C_CreateYOLOv8Wrapper(
|
||||
modelFile, C.CString(""), option, C.FD_C_ModelFormat_ONNX)
|
||||
|
||||
if !FDBooleanToGo(C.FD_C_YOLOv8WrapperInitialized(model)) {
|
||||
fmt.Printf("Failed to initialize.\n")
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyYOLOv8Wrapper(model)
|
||||
return
|
||||
}
|
||||
|
||||
var image C.FD_C_Mat = C.FD_C_Imread(imageFile)
|
||||
|
||||
var result *C.FD_C_DetectionResult = C.FD_C_CreateDetectionResult()
|
||||
|
||||
if !FDBooleanToGo(C.FD_C_YOLOv8WrapperPredict(model, image, result)) {
|
||||
fmt.Printf("Failed to predict.\n")
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyYOLOv8Wrapper(model)
|
||||
C.FD_C_DestroyMat(image)
|
||||
C.free(unsafe.Pointer(result))
|
||||
return
|
||||
}
|
||||
|
||||
var visImage C.FD_C_Mat = C.FD_C_VisDetection(image, result, 0.5, 1, 0.5)
|
||||
|
||||
C.FD_C_Imwrite(C.CString("vis_result.jpg"), visImage)
|
||||
fmt.Printf("Visualized result saved in ./vis_result.jpg\n")
|
||||
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyYOLOv8Wrapper(model)
|
||||
C.FD_C_DestroyDetectionResult(result)
|
||||
C.FD_C_DestroyMat(image)
|
||||
C.FD_C_DestroyMat(visImage)
|
||||
}
|
||||
|
||||
func GpuInfer(modelFile *C.char, imageFile *C.char) {
|
||||
|
||||
var option *C.FD_C_RuntimeOptionWrapper = C.FD_C_CreateRuntimeOptionWrapper()
|
||||
C.FD_C_RuntimeOptionWrapperUseGpu(option, 0)
|
||||
|
||||
var model *C.FD_C_YOLOv8Wrapper = C.FD_C_CreateYOLOv8Wrapper(
|
||||
modelFile, C.CString(""), option, C.FD_C_ModelFormat_ONNX)
|
||||
|
||||
if !FDBooleanToGo(C.FD_C_YOLOv8WrapperInitialized(model)) {
|
||||
fmt.Printf("Failed to initialize.\n")
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyYOLOv8Wrapper(model)
|
||||
return
|
||||
}
|
||||
|
||||
var image C.FD_C_Mat = C.FD_C_Imread(imageFile)
|
||||
|
||||
var result *C.FD_C_DetectionResult = C.FD_C_CreateDetectionResult()
|
||||
|
||||
if !FDBooleanToGo(C.FD_C_YOLOv8WrapperPredict(model, image, result)) {
|
||||
fmt.Printf("Failed to predict.\n")
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyYOLOv8Wrapper(model)
|
||||
C.FD_C_DestroyMat(image)
|
||||
C.free(unsafe.Pointer(result))
|
||||
return
|
||||
}
|
||||
|
||||
var visImage C.FD_C_Mat = C.FD_C_VisDetection(image, result, 0.5, 1, 0.5)
|
||||
|
||||
C.FD_C_Imwrite(C.CString("vis_result.jpg"), visImage)
|
||||
fmt.Printf("Visualized result saved in ./vis_result.jpg\n")
|
||||
|
||||
C.FD_C_DestroyRuntimeOptionWrapper(option)
|
||||
C.FD_C_DestroyYOLOv8Wrapper(model)
|
||||
C.FD_C_DestroyDetectionResult(result)
|
||||
C.FD_C_DestroyMat(image)
|
||||
C.FD_C_DestroyMat(visImage)
|
||||
}
|
||||
|
||||
var (
|
||||
modelFile string
|
||||
imageFile string
|
||||
deviceType int
|
||||
)
|
||||
|
||||
func init() {
|
||||
flag.StringVar(&modelFile, "model", "", "paddle detection model to use ")
|
||||
flag.StringVar(&imageFile, "image", "", "image to predict")
|
||||
flag.IntVar(&deviceType, "device", 0, "The data type of run_option is int, 0: run with cpu; 1: run with gpu")
|
||||
}
|
||||
|
||||
func main() {
|
||||
flag.Parse()
|
||||
|
||||
if modelFile != "" && imageFile != "" {
|
||||
if deviceType == 0 {
|
||||
CpuInfer(C.CString(modelFile), C.CString(imageFile))
|
||||
} else if deviceType == 1 {
|
||||
GpuInfer(C.CString(modelFile), C.CString(imageFile))
|
||||
}
|
||||
} else {
|
||||
fmt.Printf("Usage: ./infer -model path/to/model_dir -image path/to/image -device run_option \n")
|
||||
fmt.Printf("e.g ./infer -model yolov8s.onnx -image 000000014439.jpg -device 0 \n")
|
||||
}
|
||||
|
||||
}
|
||||
Reference in New Issue
Block a user