mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 11:56:44 +08:00
aa6931bee9
* add onnx_ort_runtime demo * rm in requirements * support batch eval * fixed MattingResults bug * move assignment for DetectionResult * integrated x2paddle * add model convert readme * update readme * re-lint * add processor api * Add MattingResult Free * change valid_cpu_backends order * add ppocr benchmark * mv bs from 64 to 32 * fixed quantize.md * fixed quantize bugs * Add Monitor for benchmark * update mem monitor * Set trt_max_batch_size default 1 * fixed ocr benchmark bug * support yolov5 in serving * Fixed yolov5 serving * Fixed postprocess * update yolov5 to 7.0 * add poros runtime demos * update readme * Support poros abi=1 * rm useless note * deal with comments * support pp_trt for ppseg * fixed symlink problem * Add is_mini_pad and stride for yolov5 * Add yolo series for paddle format * fixed bugs * fixed bug * support yolov5seg * fixed bug * refactor yolov5seg * fixed bug * mv Mask int32 to uint8 * add yolov5seg example * rm log info * fixed code style * add yolov5seg example in python * fixed dtype bug * update note * deal with comments * get sorted index * add yolov5seg test case * Add GPL-3.0 License * add round func * deal with comments * deal with commens Co-authored-by: Jason <jiangjiajun@baidu.com>
75 lines
3.0 KiB
Markdown
75 lines
3.0 KiB
Markdown
# YOLOv5Seg C++部署示例
|
||
|
||
本目录下提供`infer.cc`快速完成YOLOv5Seg在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
|
||
|
||
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CPU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1.0.3以上(x.x.x>=1.0.3)
|
||
|
||
```bash
|
||
mkdir build
|
||
cd build
|
||
# 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
|
||
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
|
||
tar xvf fastdeploy-linux-x64-x.x.x.tgz
|
||
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
|
||
make -j
|
||
|
||
# 1. 下载官方转换好的 YOLOv5Seg ONNX 模型文件和测试图片
|
||
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s-seg.onnx
|
||
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
||
|
||
# CPU推理
|
||
./infer_demo yolov5s-seg.onnx 000000014439.jpg 0
|
||
# GPU推理
|
||
./infer_demo yolov5s-seg.onnx 000000014439.jpg 1
|
||
# GPU上TensorRT推理
|
||
./infer_demo yolov5s-seg.onnx 000000014439.jpg 2
|
||
```
|
||
运行完成可视化结果如下图所示
|
||
|
||
<img width="640" src="https://user-images.githubusercontent.com/19977378/209955620-657bdd1d-574c-40a2-b05d-42b9e5a15ae8.png">
|
||
|
||
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
|
||
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
|
||
|
||
## YOLOv5Seg C++接口
|
||
|
||
### YOLOv5Seg类
|
||
|
||
```c++
|
||
fastdeploy::vision::detection::YOLOv5Seg(
|
||
const string& model_file,
|
||
const string& params_file = "",
|
||
const RuntimeOption& runtime_option = RuntimeOption(),
|
||
const ModelFormat& model_format = ModelFormat::ONNX)
|
||
```
|
||
|
||
YOLOv5Seg模型加载和初始化，其中model_file为导出的ONNX模型格式。
|
||
|
||
**参数**
|
||
|
||
> * **model_file**(str): 模型文件路径
|
||
> * **params_file**(str): 参数文件路径，当模型格式为ONNX时，此参数传入空字符串即可
|
||
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
|
||
> * **model_format**(ModelFormat): 模型格式，默认为ONNX格式
|
||
|
||
#### Predict函数
|
||
|
||
```c++
|
||
YOLOv5Seg::Predict(const cv::Mat& img, DetectionResult* result)
|
||
```
|
||
|
||
**参数**
|
||
|
||
> > * **im**: 输入图像，注意需为HWC，BGR格式
|
||
> > * **result**: 检测结果，包括检测框，各个框的置信度, DetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||
|
||
- [模型介绍](../../)
|
||
- [Python部署](../python)
|
||
- [视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|