paddlepaddle / fastdeploy Goto Github PK

此外，OCR中模型的预处理需说明清楚。 如若模型由PaddleOCR导出，或下载，需对哪几个模型做怎样的处理操作

@yunyaoXYY

Please using git, rather than BCE....

使用option.DisablePaddleLogInfo();无法关闭[INFO]日志。
比如加载模型时：

[INFO] fastdeploy/fastdeploy_runtime.cc(265)::Init      Runtime initialized with Backend::ORT in device Device::CPU.
[INFO] fastdeploy/fastdeploy_runtime.cc(278)::Init      Runtime initialized with Backend::PDINFER in device Device::CPU.

镜像是paddlepaddle/fastdeploy:0.3.0-gpu-cuda11.4-trt8.4-21.10

fastdeploy人脸识别python部署，采用多进程进行人脸特征提取及人脸比对，实现通过主进程开启摄像头，获取帧，裁剪人脸roi存到Queue中，识别进程从Queue中获取人脸数据最后报错。
核心代码如下：

报错代码：
[ERROR] csrc/fastdeploy/pybind/main.cc(94)::NumpyDataTypeToOpenCvType NumpyDataTypeToOpenCvType() only support int32/int8/uint8/float32 now.

ps：目前fastdeploy python不熟不支持多线程么，我尝试多线程处理，速度并没有提升。是不是pybind进行耗时处理的方法绑定中没释放gil？

我在windows10 x64用VS2019+cuda10.2+cudnn7.6.5编译总出错

I am trying to run the \examples\runtime\python\infer_onnx_openvino.py on Windows 10

And I get the folowwing error:
[ERROR] csrc/fastdeploy/fastdeploy_runtime.cc(193)::fastdeploy::RuntimeOption::UseOpenVINOBackend The FastDeploy didn't compile with OpenVINO.

How do I compile with OpenVINO backend?

PaddleX导出的模型,怎么办

配置文件 只有
model.yml
pipeline.yml

新手求助
配置：win+VS 2019
按照教程，C++SDK成功运行后想通过C++接口来使用
(SDK参照Windows C++SDK教程实现)
使用教程中的代码(在Debug模式下)：

新建的项目没有配置目录，配置信息如下：
VC++包含目录

VC++库目录

C/C++常规、附加包含目录

配置可以找到fastdeploy、opencv等源文件，运行后报错
错误 LNK1104 无法打开文件“C:\Users\lihao.qin\paddle\FastDeploy\build\fastdeploy-win-x64-gpu-0.2.0\lib.obj”

查看预编译库中没有错误信息中提到的文件，请问该如何解决这个问题？谢谢！

=================================================================================
更换为Release模式，出现新的报错信息。

C++ SDK在linux服务端部署，支持服务化部署吗，服务端用C++服务化部署，客户端用python写，服务端和客户端通过rpc通信，这样能否支持？

I have below error. model downloaded from your exported ones:

with cache or without cache enabled same error.

python3 infer.py --model yolov7-w6-end2end-ort-nms.onnx --image /data/Videos/hiv00131/hiv00131/00000480.png --device gpu --use_trt True
[INFO] fastdeploy/backends/tensorrt/trt_backend.cc(540)::CreateTrtEngineFromOnnx Detect serialized TensorRT Engine file in yolotrt.cache, will load it directly.
[INFO] fastdeploy/backends/tensorrt/trt_backend.cc(106)::LoadTrtCache Build TensorRT Engine from cache file: yolotrt.cache with shape range information as below,
[INFO] fastdeploy/backends/tensorrt/trt_backend.cc(109)::LoadTrtCache Input name: images, shape=[1, 3, 640, 640], min=[1, 3, 640, 640], max=[1, 3, 640, 640]

[INFO] fastdeploy/fastdeploy_runtime.cc(270)::Init Runtime initialized with Backend::TRT in device Device::GPU.
[ERROR] fastdeploy/backends/tensorrt/trt_backend.cc(384)::AllocateOutputsBuffer Cannot find output: num_dets of tensorrt network from the original model.
Aborted (core dumped)

你好，
我这边测试matting抠图cpu方案时，按照readme.txt 操作，明明下载的是gpu的依赖包，为啥安装下时候指定cpu的路径？

视觉模型部署没有 实例分割 吗

硬件：Mac m1
版本：fastdeploy-python 0.2.1
安装方式：pip install numpy opencv-python fastdeploy-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html
运行demo： python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device cpu
报错：

$ python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device cpu
Traceback (most recent call last):
File "/opt/miniconda3/envs/py39/lib/python3.9/site-packages/fastdeploy/c_lib_wrap.py", line 154, in
from .libs.fastdeploy_main import *
ImportError: dlopen(/opt/miniconda3/envs/py39/lib/python3.9/site-packages/fastdeploy/libs/fastdeploy_main.cpython-39-darwin.so, 0x0002): Library not loaded: '@rpath/libfastdeploy.0.2.1.dylib'
Referenced from: '/opt/miniconda3/envs/py39/lib/python3.9/site-packages/fastdeploy/libs/fastdeploy_main.cpython-39-darwin.so'
Reason: tried: '/Users/qiuyanjun/Desktop/dev/fastdeploy_build/FastDeploy/python/.setuptools-cmake-build/third_libs/install/paddle2onnx/lib/libfastdeploy.0.2.1.dylib' (no such file), '/Users/qiuyanjun/Desktop/dev/fastdeploy_build/FastDeploy/python/.setuptools-cmake-build/third_libs/install/onnxruntime/lib/libfastdeploy.0.2.1.dylib' (no such file), '/Users/qiuyanjun/Desktop/dev/fastdeploy_build/FastDeploy/python/.setuptools-cmake-build/third_libs/install/opencv/lib/libfastdeploy.0.2.1.dylib' (no such file), '/Users/qiuyanjun/Desktop/dev/fastdeploy_build/FastDeploy/python/.setuptools-cmake-build/third_libs/install/faster_tokenizer/lib/libfastdeploy.0.2.1.dylib' (no such file), '/Users/qiuyanjun/Desktop/dev/fastdeploy_build/FastDeploy/python/.setuptools-cmake-build/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/paddle2onnx/lib/libfastdeploy.0.2.1.dylib' (no such file), 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'loader_path/$loader_path/third_libs/faster_tokenizer/third_party/include/glog/nlohmann/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/faster_tokenizer/third_party/include/gflags/nlohmann/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/faster_tokenizer/third_party/include/re2/nlohmann/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/faster_tokenizer/third_party/include/nlohmann/nlohmann/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/faster_tokenizer/third_party/lib/nlohmann/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/faster_tokenizer/lib/nlohmann/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/lib/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv/opencv2/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/calib3d/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/superres/calib3d/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/shape/calib3d/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/flann/calib3d/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/core/detail/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/core/hal/detail/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/core/cuda/detail/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/core/cuda/detail/detail/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/core/utils/detail/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/core/opencl/runtime/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/core/opencl/runtime/autogenerated/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/core/opencl/runtime/autogenerated/autogenerated/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/core/detail/autogenerated/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/stitching/detail/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/stitching/detail/detail/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/imgproc/detail/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/imgproc/hal/detail/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/objdetect/detail/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/ml/detail/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/imgcodecs/detail/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/dnn/utils/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/dnn/utils/utils/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/videoio/utils/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/highgui/utils/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/features2d/hal/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/features2d/hal/hal/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/videostab/hal/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/photo/hal/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/include/opencv2/calib3d/hal/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/lib/python2.7/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/lib/pkgconfig/python2.7/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/lib/python2.7/site-packages/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/lib/python2.7/site-packages/cv2/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/lib/python2.7/site-packages/cv2/python-2.7/libfastdeploy.0.2.1.dylib' (no such file), 'loader_path/$loader_path/third_libs/opencv/lib/python2.7/site-packages/cv2/python-2.7/python-2.7/libfastdeploy.0.2.1.dylib' (no such file), '/opt/miniconda3/envs/py39/bin/../lib/libfastdeploy.0.2.1.dylib' (no such file), '/opt/miniconda3/envs/py39/bin/../lib/libfastdeploy.0.2.1.dylib' (no such file), '/usr/local/lib/libfastdeploy.0.2.1.dylib' (no such file), '/usr/lib/libfastdeploy.0.2.1.dylib' (no such file)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/Users/netwu/workspace/PaddlePaddle/FastDeploy/examples/vision/segmentation/paddleseg/python/infer.py", line 1, in
import fastdeploy as fd
File "/opt/miniconda3/envs/py39/lib/python3.9/site-packages/fastdeploy/init.py", line 19, in
from .c_lib_wrap import (Frontend, Backend, FDDataType, TensorInfo, Device,
File "/opt/miniconda3/envs/py39/lib/python3.9/site-packages/fastdeploy/c_lib_wrap.py", line 156, in
raise RuntimeError("FastDeploy initalized failed!")
RuntimeError: FastDeploy initalized failed!

hi, 这里记录了一些 在微信群里的问题, 主要涉及到 FastDeploy 和 paddleserving 两个库相关的问题, 可能放在这里更合适一点, 可以让更多的人看到, 希望可以解答一下, 感谢🙏

• 问一下，fastdeploy 跟 paddle serving 区别是什么;

• paddle serving 的开发似乎不太活跃了, 请问长期来看还会继续支持吗? 还是完全用 fastdeploy 替代?

• 它(指FastDeploy)比 padddle serving 优势在哪里，方便，安全，速度 还是互补的关系

不会用啊 还是和lite.ai.toolkit一样要先自己编译吗?
话说好不容易学会lite.ai.toolkit的编译 难受

请问一下，FD后期是否有对paddledetection 危险行为识别等pipeline部署的支持，还有paddlecls中的物品识别？目前有需求做异常行为识别，人/车辆属性分析等等。谢谢

• 环境：

windwos10 VS2019 3060 6G笔记本显卡

• 问题：
  调用ppyoloe，yolov7和maskrcnn,ocr，均是在example下正常，但是封装dll后均报错
  yolov7和ppyoloe是模型无法生成trt文件，maskrcnn是，cpu gpu和trt都崩溃，cpu和gpu模式初始化成功，predict崩溃;
  以ppyoloe为例：
  封装dll代码：

• .h文件：

__declspec(dllexport) int __stdcall fastdeploy_model_trt_convert(const char* _modeldir, int _trtmodeltype = 1, int _gpu_id = 0);

• .cpp文件

#include "fastdeploy/vision.h"

#ifdef WIN32
const char sep = '\\';
#else
const char sep = '/';
#endif

#ifdef WIN32
#define ACCESS(fileName,accessMode) _access(fileName,accessMode)
#else
#define ACCESS(fileName,accessMode) access(fileName,accessMode)
#endif

__declspec(dllexport) int __stdcall fastdeploy_model_trt_convert(const char* _modeldir, int _trtmodeltype /*= 1*/, int _gpu_id /*= 0*/)
{
	int gpuid = 0;
	if (_gpu_id < 0)
	{
		gpuid = 0;
	}
	else {
		gpuid = _gpu_id;
	}

	auto option = fastdeploy::RuntimeOption();
	option.UseGpu(gpuid);
	option.UseTrtBackend();

	auto model_file = (std::string)_modeldir + sep + "model.pdmodel";
	auto params_file = (std::string)_modeldir + sep + "model.pdiparams";
	auto config_file = (std::string)_modeldir + sep + "infer_cfg.yml";

	std::string  trtpath_file = "";
	if (_trtmodeltype == 1)
	{
		trtpath_file = (std::string)_modeldir + sep + "deploy_fp16.trt";
		option.EnableTrtFP16();
	}
	else if (_trtmodeltype == 0) {
		trtpath_file = (std::string)_modeldir + sep + "deploy_fp32.trt";
	}
	else {
		trtpath_file = (std::string)_modeldir + sep + "deploy_fp8.trt";
	}

	option.SetTrtCacheFile(trtpath_file);
	auto model = fastdeploy::vision::detection::PPYOLOE(model_file, params_file, config_file, option);

	if (!model.Initialized()) {
		return -2;
	}

	if (ACCESS(trtpath_file.c_str(),0) == -1)
	{
		return -3;
	}

	return 0;
}

控制台调用程序：

int main()
{
    std::string modeldir = ".\\ppyoloe";
    fastdeploy_model_trt_convert(modeldir.c_str());
    std::cout << "Hello World!\n";
}

接口设置了SetTrtCacheFile(trtpath_file);，并且模型初始化成功了fastdeploy::vision::detection::PPYOLOE和model.Initialized()均返回正常，但是没有生成trt文件：

if (ACCESS(trtpath_file.c_str(),0) == -1)
	{
		return -3;
	}

返回了-3

模型 （CV、NLP、Speech、Cross-modal）

• PP-Tracking(Doing)
• Robust Video Matting(Doing)

平台& 硬件 （云、边、端）

• Android ARM CPU
• • Picodet(Doing)
• • PaddleClas(Doing)
• RKNN2
• • YOLOv5(Doing)

如果你希望FastDeploy支持新的AI模型/新硬件，或与FastDeploy共建生态，欢迎提供如下信息我们：

1. 硬件型号：例如: 晶晨A311D
2. 操作系统：例如：Linux
3. 网络名称：例如：PP-PicoDet
4. 模型链接：例如：https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet 
5. 详细描述：例如：希望将PP-PicoDet-M模型用到自己车辆检测场景中实现服务化部署，但是对算法前后处理不理解，部署困难。
6. 联系方式：[email protected]

反馈一个bug,文档中写的是./easyedge_serving ./RES可以启动http服务

但实际需要在RES后面添加一个参数，任意一个数字


这里模型采用的是PP-YOLO的物体检测

你好：
我们这边在2022 win10平台测试matting时候，发现在模型加载的时候crash了，dll依赖都是我们本地编译的的，没有使用预编译的库，在linux平台测试正常，麻烦后续关注下？谢谢

有支持C#部署的打算吗

请问3D Object Detection计划什么时候可以支持

我在自己编译FastDeploy时，发现很多third_party都是直接下载使用的，感觉这好像是贵司的传统？下载的预编译库还带了个OpenCV3和onnxruntime的动态链接库，然而我自己的环境已经安装了特定版本的OpenCV4，onnxruntime，yaml-cpp等，我却无法链接这些已经存在的库，很容易出现多版本、静动态库混用的问题。所以能否增加使用系统已安装库的编译选项？

I was hoping to do a light deployment of text detection on a Linux server using FastDeploy in Python.

However, PP-OCR and later versions aren't listed in the supported model list for server side deployment.

I have tried to import it in any case along the lines of the tutorial for PPYOLOE but not sure how to pass the required det_model and cls_model parameters. I presume there isn't a way of doing this and this is just a stub?

Wondering if PP-OCR may be made available for server side deployment at some stage?

让用户自己选择模型的文件夹

那么我应该怎么判断模型种类

然后判断 逻辑处理 对应的

vision.classification.PaddleClasModel(model_file, params_file, config_file,)
vision.detection.YOLOv3(model_file, params_file, config_file,)
vision.detection.PPYOLO(model_file, params_file, config_file)
vision.detection.FasterRCNN(model_file, params_file, config_file)
vision.detection.MaskRCNN(model_file, params_file, config_file)

是否增加一个判断模型种类的函数

Hi,

Is it possible to support ultralytics/yolov5#8956 models?

Thanks

如果你希望FastDeploy支持新的AI模型/新硬件，或与FastDeploy共建生态，欢迎随时联系我们 （If you want FastDeploy to support new AI models/new hardware, or build an ecosystem with FastDeploy, please feel free to contact us）

1. 硬件型号 (hardware)：例如: 晶晨A311D (Example:Amlogic A311D)

2. 系统 (OS)：例如：Linux (Example:Linux)

3. 网络名称 (model name)：例如：PP-PicoDet (Example:PP-PicoDet)

4. 模型链接（model link）： 例如：https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet (Example:https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet)

5. 详细描述（Detailed Description）：例如：希望将PP-PicoDet-M模型用到自己车辆检测场景中实现服务化部署，但是对算法前后处理不理解，部署困难。（Example: I hope to use the PP-PicoDet-M model in my own vehicle detection scene to achieve service deployment, but I do not understand the pre- and post-processing of the algorithm, and the deployment is difficult.）

6. 联系方式（contact info）：[email protected]

[WARNING] csrc/fastdeploy/fastdeploy_runtime.cc(141)::fastdeploy::RuntimeOption::UseGpu The FastDeploy didn't compile with GPU, will force to use CPU.

Hello my friends -- we are a NYC based team interested to explore using the PaddlePaddle framework. Do you know any english-speaking members of the PaddlePaddle community that we may connect with?

Thank you
Zev

If you want FastDeploy to support new AI models/new hardware, or build an ecosystem with FastDeploy, please feel free to contact us：

1. Hardware: Example:Amlogic A311D
2. OS: Example:Linux
3. Model name: Example:PP-PicoDet
4. Model link: Example:https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet
5. Detailed Description: Example: I hope to use the PP-PicoDet-M model in my own vehicle detection scene to achieve service deployment, but I do not understand the pre- and post-processing of the algorithm, and the deployment is difficult.
6. Contact info:[email protected]

怎么cmake？可以像lite.ai.tookit一样出一套流程吗

ocr的PPOCRSystemv3编译完成后打开出现上述错误,其他demo比如classification可以正常Release生成infer_demo.exe,是否完成ocr的支持?

你好，
我这边在搭建环境过程中，看见好多third_party都是需要直接下载使用的，不知道是否可行在不同平台自己拿源码编译，有没有变异文档类似资料？

按照教程替换模型与demo图片，如上述报错，如何修改输入呢？
模型是ppyoloe
输入是2k图

我新增了rknpu的代码，在编译环节已经加入一起编译了

但是当编译完成后，我的测试文件引用fastdeploy库时却出现未定义的情况

fastdeploy::RKNPU2Backend::GetSDKAndDeviceVersion()是在rknpu2_backend.cc中定义的，并且在rknpu2_backend.h中已经声明了

1、能否检测人脸的姿态角
2、能否做一个python和c++的检测和识别串起来的案例，比如输入一张图片，图片里面有多个人脸，能分别输出每个人脸的的坐标和每个人脸的向量，做一个整体的案例最大化的压缩预测的时间

模型如下

cmake报错如下

尝试跑C++ 推理示例 报错
gcc demo.cpp
-I./FastDeploy/build/fastdeploy-0.0.3/include/
-I./FastDeploy/build/fastdeploy-0.0.3/third_libs/install/opencv/include/
-I./FastDeploy/build/third_libs/install/opencv/include/
-lfastdeploy
-lopencv_core
-lstdc++
-ldl
-o demo.so \

/usr/bin/ld: /tmp/ccxAZsgp.o: in function main': demo.cpp:(.text+0x1b1): undefined reference to cv::imread(cv::String const&, int)'
/usr/bin/ld: demo.cpp:(.text+0x2a0): undefined reference to `cv::imwrite(cv::String const&, cv::_InputArray const&, std::vector<int, std::allocator > const&)'
collect2: error: ld returned 1 exit status
make: *** [Makefile:2: demo.so] Error 1

Python如何解决模型加密问题

训练出来的模型 不想给别人用

python可以加密成pyd文件 或者用nuitka打包编译

但是模型怎么办

不知道合适方便编译一个 python 3.10 for windows 64bit的版本的whl安装包,谢谢!

使用PP_LiteSeg训练模型：
训练命令：
python export.py --config configs/quick_start/pp_liteseg_optic_disc_512x512_1k.yml --model_path output/iter_10000/model.pdparams --save_dir output --input_shape 1 3 512 512

训练完后，使用python测试效果：

效果还可以。

模型导出：
python export.py --config configs/quick_start/pp_liteseg_optic_disc_512x512_1k.yml --model_path output/iter_10000/model.pdparams --save_dir output --input_shape 1 3 512 512
其中，yaml参数文件为：
Deploy:
input_shape:

• 1
• 3
• 512
• 512
  model: model.pdmodel
  output_dtype: int32
  output_op: argmax
  params: model.pdiparams
  transforms:
• type: Normalize

然后使用FD推理，cpu，效果：

但是使用https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/vision/segmentation/paddleseg下的[PP-LiteSeg-T(STDC1)-cityscapes](https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_T_STDC1_cityscapes_without_argmax_infer.tgz)提供的模型推理，正常，其中yaml文件为：
Deploy:
input_shape:

• -1
• 3
• -1
• -1
  model: model.pdmodel
  output_dtype: float32
  output_op: none
  params: model.pdiparams
  transforms:
• type: Normalize

能否提供C#的DEMO和教程

请问有在Jetson上部署OCR的Demo嘛

大概看了一下，功能各方面好像和paddlex有很多重复，而且貌似paddle系列的很多东西都有类似重复的东西