diff --git a/README.md b/README.md index ba6d1e981..8d414580a 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ ## 近期更新 - 🔥 **2022.8.18：发布FastDeploy [release/v0.2.0](https://github.com/PaddlePaddle/FastDeploy/releases/tag/release%2F0.2.0)**
- - **服务端全新升级：一套SDK，覆盖全量模型** + - **服务端全新升级：一套SDK，覆盖全量模型** - 发布基于x86 CPU、NVIDIA GPU的易用、高性能推理引擎SDK，推理速度大幅提升 - 支持ONNXRuntime、Paddle Inference、TensorRT推理引擎 - 支持YOLOv7、YOLOv6、YOLOv5、PP-YOLOE等目标检测最优模型及[Demo示例](examples/vision/detection/) @@ -51,7 +51,7 @@
-### 1.1 快速安装 FastDeploy Python/C++ 库 +### 1.1 快速安装 FastDeploy Python/C++ 库 #### 环境依赖 @@ -63,11 +63,11 @@ - python 3.6\~3.9(Windows 10 3.8\~3.9) #### 安装 CPU Python 版本 -``` +```bash pip install numpy opencv-python fastdeploy-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html ``` #### 安装 GPU Python 版本 -``` +```bash pip install numpy opencv-python fastdeploy-gpu-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html ``` #### 安装 C++ 版本 @@ -93,8 +93,8 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000 import cv2 import fastdeploy.vision as vision -model = vision.detection.PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel", - "ppyoloe_crn_l_300e_coco/model.pdiparams", +model = vision.detection.PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel", + "ppyoloe_crn_l_300e_coco/model.pdiparams", "ppyoloe_crn_l_300e_coco/nfer_cfg.yml") im = cv2.imread("000000014439.jpg") result = model.predict(im.copy()) @@ -114,8 +114,8 @@ cv2.imwrite("vis_image.jpg", vis_im) int main(int argc, char* argv[]) { namespace vision = fastdeploy::vision; - auto model = vision::detection::PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel", - "ppyoloe_crn_l_300e_coco/model.pdiparams", + auto model = vision::detection::PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel", + "ppyoloe_crn_l_300e_coco/model.pdiparams", "ppyoloe_crn_l_300e_coco/infer_cfg.yml"); auto im = cv::imread("000000014439.jpg"); @@ -128,7 +128,7 @@ int main(int argc, char* argv[]) { ``` 更多部署案例请参考[视觉模型部署示例](examples/vision) . - + ## 2. 服务端模型列表 🔥🔥🔥
diff --git a/docs/api/runtime_option.md b/docs/api/runtime_option.md index 30bc5a29a..d30a98b4e 100644 --- a/docs/api/runtime_option.md +++ b/docs/api/runtime_option.md @@ -15,13 +15,13 @@ FastDeploy产品中的Runtime包含多个推理后端，其各关系如下所示 | GPU | 支持 | 支持 | 支持 | 支持 | 在各模型的，均通过`RuntimeOption`来配置推理的后端，以及推理时的参数，例如在python中，加载模型后可通过如下代码打印推理配置 -``` -model = fastdeploy.vision.ultralytics.YOLOv5("yolov5s.onnx") +```python +model = fastdeploy.vision.detection.YOLOv5("yolov5s.onnx") print(model.runtime_option) ``` 可看下如下输出 -``` +```python RuntimeOption( backend : Backend.ORT # 推理后端ONNXRuntime cpu_thread_num : 8 # CPU线程数（仅当使用CPU推理时有效） @@ -70,7 +70,7 @@ RuntimeOption( > * **trt_max_shape**(dict[str : list[int]]): 当模型为动态shape，且实际推理时输入shape也会变化，通过此参数配置输入的最大shape > * **trt_max_batch_size**(int): TensorRT推理时的最大batch数 -``` +```python import fastdeploy as fd option = fd.RuntimeOption() @@ -81,10 +81,11 @@ option.trt_min_shape = {"x": [1, 3, 224, 224]} option.trt_opt_shape = {"x": [4, 3, 224, 224]} option.trt_max_shape = {"x": [8, 3, 224, 224]} -model = fd.vision.ppcls.Model("resnet50/inference.pdmodel", - "resnet50/inference.pdiparams", - "resnet50/inference_cls.yaml", - runtime_option=option) +model = fd.vision.classification.PaddleClasModel( + "resnet50/inference.pdmodel", + "resnet50/inference.pdiparams", + "resnet50/inference_cls.yaml", + runtime_option=option) ``` ## C++ 使用 @@ -112,7 +113,7 @@ model = fd.vision.ppcls.Model("resnet50/inference.pdmodel", > * **trt_max_shape**(map>): 当模型为动态shape，且实际推理时输入shape也会变化，通过此参数配置输入的最大shape > * **trt_max_batch_size**(int): TensorRT推理时的最大batch数 -``` +```c++ #include "fastdeploy/vision.h" int main() { @@ -121,11 +122,11 @@ int main() { option.trt_opt_shape["x"] = {4, 3, 224, 224}; option.trt_max_shape["x"] = {8, 3, 224, 224}; - auto model = fastdeploy::vision::ppcls.Model( + auto model = fastdeploy::vision::classification::PaddleClasModel( "resnet50/inference.pdmodel", "resnet50/inference.pdiparams", "resnet50/inference_cls.yaml", - option); + option); return 0; } ``` diff --git a/docs/api/vision_results/classification_result.md b/docs/api/vision_results/classification_result.md index bf94d0ff1..9dbc56466 100644 --- a/docs/api/vision_results/classification_result.md +++ b/docs/api/vision_results/classification_result.md @@ -6,7 +6,7 @@ ClassifyResult代码定义在`csrcs/fastdeploy/vision/common/result.h`中，用 `fastdeploy::vision::ClassifyResult` -``` +```c++ struct ClassifyResult { std::vector label_ids; std::vector scores; diff --git a/docs/api/vision_results/detection_result.md b/docs/api/vision_results/detection_result.md index a702d4989..346010a30 100644 --- a/docs/api/vision_results/detection_result.md +++ b/docs/api/vision_results/detection_result.md @@ -6,7 +6,7 @@ DetectionResult代码定义在`csrcs/fastdeploy/vision/common/result.h`中，用 `fastdeploy::vision::DetectionResult` -``` +```c++ struct DetectionResult { std::vector> boxes; std::vector scores; diff --git a/docs/api/vision_results/face_detection_result.md b/docs/api/vision_results/face_detection_result.md index 30029a7f0..5bc478fda 100644 --- a/docs/api/vision_results/face_detection_result.md +++ b/docs/api/vision_results/face_detection_result.md @@ -6,7 +6,7 @@ FaceDetectionResult 代码定义在`csrcs/fastdeploy/vision/common/result.h`中 `fastdeploy::vision::FaceDetectionResult` -``` +```c++ struct FaceDetectionResult { std::vector> boxes; std::vector> landmarks; @@ -32,4 +32,3 @@ struct FaceDetectionResult { - **scores**(list of float): 成员变量，表示单张图片检测出来的所有目标置信度 - **landmarks**(list of list(float)): 成员变量，表示单张图片检测出来的所有人脸的关键点 - **landmarks_per_face**(int): 成员变量，表示每个人脸框中的关键点的数量。 - diff --git a/docs/api/vision_results/face_recognition_result.md b/docs/api/vision_results/face_recognition_result.md index 54d6eabb1..8f0cf3448 100644 --- a/docs/api/vision_results/face_recognition_result.md +++ b/docs/api/vision_results/face_recognition_result.md @@ -5,7 +5,7 @@ FaceRecognitionResult 代码定义在`csrcs/fastdeploy/vision/common/result.h` `fastdeploy::vision::FaceRecognitionResult` -``` +```c++ struct FaceRecognitionResult { std::vector embedding; void Clear(); diff --git a/docs/api/vision_results/matting_result.md b/docs/api/vision_results/matting_result.md index 5a61365c2..8a1f8ba4f 100644 --- a/docs/api/vision_results/matting_result.md +++ b/docs/api/vision_results/matting_result.md @@ -6,7 +6,7 @@ MattingResult 代码定义在`csrcs/fastdeploy/vision/common/result.h`中，用 `fastdeploy::vision::MattingResult` -``` +```c++ struct MattingResult { std::vector alpha; std::vector foreground; diff --git a/docs/api/vision_results/segmentation_result.md b/docs/api/vision_results/segmentation_result.md index 7228838e7..d684d1a23 100644 --- a/docs/api/vision_results/segmentation_result.md +++ b/docs/api/vision_results/segmentation_result.md @@ -6,7 +6,7 @@ SegmentationResult代码定义在`csrcs/fastdeploy/vision/common/result.h`中， `fastdeploy::vision::DetectionResult` -``` +```c++ struct DetectionResult { std::vector label_map; std::vector score_map; diff --git a/docs/compile/how_to_build_linux_and_mac.md b/docs/compile/how_to_build_linux_and_mac.md index 1393d89d4..ea6ac86dd 100644 --- a/docs/compile/how_to_build_linux_and_mac.md +++ b/docs/compile/how_to_build_linux_and_mac.md @@ -9,7 +9,7 @@ - TensorRT >= 8.4 (当ENABLE_TRT_BACKEND=ON) ## 编译C++ -``` +```bash git clone https://github.com/PaddlePaddle/FastDeploy.git cd FastDeploy git checkout develop @@ -23,7 +23,7 @@ make install 编译后的预测库即在当前目录下的`fastdeploy-0.0.3` ## 编译Python安装包 -``` +```bash git clone https://github.com/PaddlePaddle/FastDeploy.git cd FastDeploy git checkout develop diff --git a/docs/compile/how_to_build_windows.md b/docs/compile/how_to_build_windows.md index a073e8d41..c0d60a7e9 100644 --- a/docs/compile/how_to_build_windows.md +++ b/docs/compile/how_to_build_windows.md @@ -3,13 +3,13 @@ ## 环境依赖 - cmake >= 3.12 -- g++ >= 8.2 +- Visual Studio 16 2019 - cuda >= 11.2 (当WITH_GPU=ON) - cudnn >= 11.2 (当WITH_GPU=ON) - TensorRT >= 8.4 (当ENABLE_TRT_BACKEND=ON) ## 获取代码 -``` +```bat git clone https://github.com/PaddlePaddle/FastDeploy.git cd FastDeploy git checkout develop @@ -19,10 +19,10 @@ git checkout develop Windows菜单打开`x64 Native Tools Command Prompt for VS 2019`命令工具，其中`CMAKE_INSTALL_PREFIX`用于指定编译后生成的SDK路径 -``` +```bat mkdir build cd build -cmake -G "Visual Studio 16 2019" -A x64 -DCMAKE_INSTALL_PREFIX=D:\Paddle\FastDeploy\build\fastdeploy-win-x64-0.2.0 -DENABLE_ORT_BACKEND=ON -DENABLE_VISION=ON .. +cmake -G "Visual Studio 16 2019" -A x64 -DCMAKE_INSTALL_PREFIX=D:\Paddle\FastDeploy\build\fastdeploy-win-x64-0.2.0 -DENABLE_ORT_BACKEND=ON -DENABLE_VISION=ON .. msbuild fastdeploy.sln /m /p:Configuration=Release /p:Platform=x64 msbuild INSTALL.vcxproj /m /p:Configuration=Release /p:Platform=x64 ``` @@ -31,7 +31,7 @@ msbuild INSTALL.vcxproj /m /p:Configuration=Release /p:Platform=x64 ## 编译Python Wheel包 Python编译时，通过环境变量获取编译选项 -``` +```bat git clone https://github.com/PaddlePaddle/FastDeploy.git cd FastDeploy git checkout develop diff --git a/docs/compile/how_to_use_sdk_on_windows.md b/docs/compile/how_to_use_sdk_on_windows.md new file mode 100644 index 000000000..ba8feb572 --- /dev/null +++ b/docs/compile/how_to_use_sdk_on_windows.md @@ -0,0 +1,63 @@ +# 在 Windows 使用 FastDeploy C++ SDK + +在 Windows 下使用 FastDeploy C++ SDK 与在 Linux 下使用稍有不同。以下以 PPYOLOE 为例进行演示在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/the%20software%20and%20hardware%20requirements.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/quick_start) + +## 环境依赖 + +- cmake >= 3.12 +- Visual Studio 16 2019 +- cuda >= 11.2 (当WITH_GPU=ON) +- cudnn >= 11.2 (当WITH_GPU=ON) +- TensorRT >= 8.4 (当ENABLE_TRT_BACKEND=ON) + +## 下载 FastDeploy Windows 10 C++ SDK +可以从以下链接下载编译好的 FastDeploy Windows 10 C++ SDK，SDK中包含了examples代码。 +```text +https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-win-x64-gpu-0.2.0.zip +``` +## 准备模型文件和测试图片 +可以从以下链接下载模型文件和测试图片，并解压缩 +```text +https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz # (下载后解压缩) +https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg +``` + +## 在 Windows 上编译 PPYOLOE +Windows菜单打开`x64 Native Tools Command Prompt for VS 2019`命令工具，cd到ppyoloe的demo路径 +```bat +cd fastdeploy-win-x64-gpu-0.2.0\examples\vision\detection\paddledetection\cpp +``` +```bat +mkdir build && cd build +cmake .. -G "Visual Studio 16 2019" -A x64 -DFASTDEPLOY_INSTALL_DIR=%cd%\..\..\..\..\..\..\..\fastdeploy-win-x64-gpu-0.2.0 -DCUDA_DIRECTORY="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2" +``` +然后执行 +```bat +msbuild infer_demo.sln /m:4 /p:Configuration=Release /p:Platform=x64 +``` +编译好的exe保存在Release目录下，在运行demo前，需要将模型和测试图片拷贝至该目录。另外，需要在终端指定DLL的搜索路径。请在build目录下执行以下命令。 +```bat +set FASTDEPLOY_PATH=%cd%\..\..\..\..\..\..\..\fastdeploy-win-x64-gpu-0.2.0 +set PATH=%FASTDEPLOY_PATH%\lib;%FASTDEPLOY_PATH%\third_libs\install\onnxruntime\lib;%FASTDEPLOY_PATH%\third_libs\install\opencv-win-x64-3.4.16\build\x64\vc15\bin;%FASTDEPLOY_PATH%\third_libs\install\paddle_inference\paddle\lib;%FASTDEPLOY_PATH%\third_libs\install\paddle_inference\third_party\install\mkldnn\lib;%FASTDEPLOY_PATH%\third_libs\install\paddle_inference\third_party\install\mklml\lib;%FASTDEPLOY_PATH%\third_libs\install\paddle2onnx\lib;%FASTDEPLOY_PATH%\third_libs\install\tensorrt\lib;%FASTDEPLOY_PATH%\third_libs\install\yaml-cpp\lib;%PATH% +``` +注意，需要拷贝onnxruntime.dll到exe所在的目录。 +```bat +copy /Y %FASTDEPLOY_PATH%\third_libs\install\onnxruntime\lib\onnxruntime* Release\ +``` +由于较新的Windows在System32系统目录下自带了onnxruntime.dll，因此就算设置了PATH，系统依然会出现onnxruntime的加载冲突。因此需要先拷贝demo用到的onnxruntime.dll到exe所在的目录。 +```bat +where onnxruntime.dll +C:\Windows\System32\onnxruntime.dll # windows自带的onnxruntime.dll +``` +## 运行 demo +```bat +cd Release +infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 0 # CPU +infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 1 # GPU +infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 2 # GPU + TensorRT +``` diff --git a/docs/develop.md b/docs/develop.md index 1c48e624e..760635959 100644 --- a/docs/develop.md +++ b/docs/develop.md @@ -1,7 +1,7 @@ # 代码提交说明 FastDeploy使用clang-format, cpplint检查和格式化代码，提交代码前，需安装pre-commit -``` +```bash git clone https://github.com/PaddlePaddle/FastDeploy.git cd FastDeploy git checkout develop diff --git a/docs/quick_start/Python_prebuilt_wheels.md b/docs/quick_start/Python_prebuilt_wheels.md index 211615514..f2f6979a9 100644 --- a/docs/quick_start/Python_prebuilt_wheels.md +++ b/docs/quick_start/Python_prebuilt_wheels.md @@ -9,11 +9,11 @@ FastDeploy提供了在Windows/Linux/Mac上的预先编译Python Wheel包，开 - Mac 支持Python3.6~3.9 ## 安装 CPU Python 版本 -``` +```bash pip install fastdeploy-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html ``` ## 安装 GPU Python 版本 -``` +```bash pip install fastdeploy-gpu-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html ``` diff --git a/examples/vision/classification/paddleclas/cpp/README.md b/examples/vision/classification/paddleclas/cpp/README.md index 990d7cd70..1ac870ceb 100644 --- a/examples/vision/classification/paddleclas/cpp/README.md +++ b/examples/vision/classification/paddleclas/cpp/README.md @@ -9,14 +9,14 @@ 以Linux上ResNet50_vd推理为例，在本目录执行如下命令即可完成编译测试 -``` +```bash #下载SDK，编译模型examples代码（SDK中包含了examples代码） wget https://bj.bcebos.com/paddlehub/fastdeploy/libs/0.2.0/fastdeploy-linux-x64-gpu-0.2.0.tgz tar xvf fastdeploy-linux-x64-gpu-0.2.0.tgz cd fastdeploy-linux-x64-gpu-0.2.0/examples/vision/classification/paddleclas/cpp mkdir build cd build -cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.2.0 +cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.2.0 make -j # 下载ResNet50_vd模型文件和测试图片 @@ -33,11 +33,14 @@ wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/Ima ./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 2 ``` +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/compile/how_to_use_sdk_on_windows.md) + ## PaddleClas C++接口 ### PaddleClas类 -``` +```c++ fastdeploy::vision::classification::PaddleClasModel( const string& model_file, const string& params_file, @@ -58,14 +61,14 @@ PaddleClas模型加载和初始化，其中model_file, params_file为训练模 #### Predict函数 -> ``` +> ```c++ > PaddleClasModel::Predict(cv::Mat* im, ClassifyResult* result, int topk = 1) > ``` -> +> > 模型预测接口，输入图像直接输出检测结果。 -> +> > **参数** -> +> > > * **im**: 输入图像，注意需为HWC，BGR格式 > > * **result**: 分类结果，包括label_id，以及相应的置信度, ClassifyResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/) > > * **topk**(int):返回预测概率最高的topk个分类结果，默认为1 diff --git a/examples/vision/classification/paddleclas/python/README.md b/examples/vision/classification/paddleclas/python/README.md index 1a478f89c..78b26e897 100644 --- a/examples/vision/classification/paddleclas/python/README.md +++ b/examples/vision/classification/paddleclas/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer.py`快速完成ResNet50_vd在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd FastDeploy/examples/vision/classification/paddleclas/python @@ -26,7 +26,7 @@ python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg - ``` 运行完成后返回结果如下所示 -``` +```bash ClassifyResult( label_ids: 153, scores: 0.686229, @@ -35,7 +35,7 @@ scores: 0.686229, ## PaddleClasModel Python接口 -``` +```python fd.vision.classification.PaddleClasModel(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE) ``` @@ -51,19 +51,19 @@ PaddleClas模型加载和初始化，其中model_file, params_file为训练模 ### predict函数 -> ``` +> ```python > PaddleClasModel.predict(input_image, topk=1) > ``` -> +> > 模型预测结口，输入图像直接输出检测结果。 -> +> > **参数** -> +> > > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式 > > * **topk**(int):返回预测概率最高的topk个分类结果，默认为1 > **返回** -> +> > > 返回`fastdeploy.vision.ClassifyResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/) diff --git a/examples/vision/detection/nanodet_plus/cpp/README.md b/examples/vision/detection/nanodet_plus/cpp/README.md index 4802063bc..bb0a8dc81 100644 --- a/examples/vision/detection/nanodet_plus/cpp/README.md +++ b/examples/vision/detection/nanodet_plus/cpp/README.md @@ -9,7 +9,7 @@ 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 -``` +```bash mkdir build cd build wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz @@ -34,11 +34,14 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000 +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/compile/how_to_use_sdk_on_windows.md) + ## NanoDetPlus C++接口 ### NanoDetPlus类 -``` +```c++ fastdeploy::vision::detection::NanoDetPlus( const string& model_file, const string& params_file = "", @@ -57,7 +60,7 @@ NanoDetPlus模型加载和初始化，其中model_file为导出的ONNX模型格 #### Predict函数 -> ``` +> ```c++ > NanoDetPlus::Predict(cv::Mat* im, DetectionResult* result, > float conf_threshold = 0.25, > float nms_iou_threshold = 0.5) diff --git a/examples/vision/detection/nanodet_plus/python/README.md b/examples/vision/detection/nanodet_plus/python/README.md index 9f43c523e..007e08850 100644 --- a/examples/vision/detection/nanodet_plus/python/README.md +++ b/examples/vision/detection/nanodet_plus/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer.py`快速完成NanoDetPlus在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vision/detection/nanodet_plus/python/ @@ -30,7 +30,7 @@ python infer.py --model nanodet-plus-m_320.onnx --image 000000014439.jpg --devic ## NanoDetPlus Python接口 -``` +```python fastdeploy.vision.detection.NanoDetPlus(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) ``` @@ -45,7 +45,7 @@ NanoDetPlus模型加载和初始化，其中model_file为导出的ONNX模型格 ### predict函数 -> ``` +> ```python > NanoDetPlus.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` > diff --git a/examples/vision/detection/paddledetection/cpp/README.md b/examples/vision/detection/paddledetection/cpp/README.md index 875e8da32..16deb3fc6 100644 --- a/examples/vision/detection/paddledetection/cpp/README.md +++ b/examples/vision/detection/paddledetection/cpp/README.md @@ -9,7 +9,7 @@ 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 -``` +```bash 以ppyoloe为例进行推理部署 #下载SDK，编译模型examples代码（SDK中包含了examples代码） @@ -34,12 +34,15 @@ tar xvf ppyoloe_crn_l_300e_coco.tgz ./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 2 ``` +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/compile/how_to_use_sdk_on_windows.md) + ## PaddleDetection C++接口 ### 模型类 PaddleDetection目前支持6种模型系列，类名分别为`PPYOLOE`, `PicoDet`, `PaddleYOLOX`, `PPYOLO`, `FasterRCNN`，所有类名的构造函数和预测函数在参数上完全一致，本文档以PPYOLOE为例讲解API -``` +```c++ fastdeploy::vision::detection::PPYOLOE( const string& model_file, const string& params_file, @@ -60,7 +63,7 @@ PaddleDetection PPYOLOE模型加载和初始化，其中model_file为导出的ON #### Predict函数 -> ``` +> ```c++ > PPYOLOE::Predict(cv::Mat* im, DetectionResult* result) > ``` > diff --git a/examples/vision/detection/paddledetection/python/README.md b/examples/vision/detection/paddledetection/python/README.md index cc36ce1ee..ad9587463 100644 --- a/examples/vision/detection/paddledetection/python/README.md +++ b/examples/vision/detection/paddledetection/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer_xxx.py`快速完成PPYOLOE/PicoDet等模型在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vision/detection/paddledetection/python/ @@ -26,13 +26,13 @@ python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439 ``` 运行完成可视化结果如下图所示 -
+
## PaddleDetection Python接口 -``` +```python fastdeploy.vision.detection.PPYOLOE(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE) fastdeploy.vision.detection.PicoDet(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE) fastdeploy.vision.detection.PaddleYOLOX(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE) @@ -54,7 +54,7 @@ PaddleDetection模型加载和初始化，其中model_file， params_file为导 ### predict函数 PaddleDetection中各个模型，包括PPYOLOE/PicoDet/PaddleYOLOX/YOLOv3/PPYOLO/FasterRCNN，均提供如下同样的成员函数用于进行图像的检测 -> ``` +> ```python > PPYOLOE.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` > diff --git a/examples/vision/detection/scaledyolov4/README.md b/examples/vision/detection/scaledyolov4/README.md index d54e04d4c..36ec1af0c 100644 --- a/examples/vision/detection/scaledyolov4/README.md +++ b/examples/vision/detection/scaledyolov4/README.md @@ -11,7 +11,7 @@ 访问[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)官方github库，按照指引下载安装，下载`scaledyolov4.pt` 模型，利用 `models/export.py` 得到`onnx`格式文件。如果您导出的`onnx`模型出现问题，可以参考[ScaledYOLOv4#401](https://github.com/WongKinYiu/ScaledYOLOv4/issues/401)的解决办法 - ``` + ```bash #下载ScaledYOLOv4模型文件 Download from the goole drive https://drive.google.com/file/d/1aXZZE999sHMP1gev60XhNChtHPRMH3Fz/view?usp=sharing diff --git a/examples/vision/detection/scaledyolov4/cpp/README.md b/examples/vision/detection/scaledyolov4/cpp/README.md index e5740eb05..04325f822 100644 --- a/examples/vision/detection/scaledyolov4/cpp/README.md +++ b/examples/vision/detection/scaledyolov4/cpp/README.md @@ -9,7 +9,7 @@ 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 -``` +```bash mkdir build cd build wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz @@ -34,11 +34,14 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000 +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/compile/how_to_use_sdk_on_windows.md) + ## ScaledYOLOv4 C++接口 ### ScaledYOLOv4类 -``` +```c++ fastdeploy::vision::detection::ScaledYOLOv4( const string& model_file, const string& params_file = "", @@ -57,7 +60,7 @@ ScaledYOLOv4模型加载和初始化，其中model_file为导出的ONNX模型格 #### Predict函数 -> ``` +> ```c++ > ScaledYOLOv4::Predict(cv::Mat* im, DetectionResult* result, > float conf_threshold = 0.25, > float nms_iou_threshold = 0.5) diff --git a/examples/vision/detection/scaledyolov4/python/README.md b/examples/vision/detection/scaledyolov4/python/README.md index b2055bae3..df563044a 100644 --- a/examples/vision/detection/scaledyolov4/python/README.md +++ b/examples/vision/detection/scaledyolov4/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer.py`快速完成ScaledYOLOv4在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vision/detection/scaledyolov4/python/ @@ -30,7 +30,7 @@ python infer.py --model scaled_yolov4-p5.onnx --image 000000014439.jpg --device ## ScaledYOLOv4 Python接口 -``` +```python fastdeploy.vision.detection.ScaledYOLOv4(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) ``` @@ -45,7 +45,7 @@ ScaledYOLOv4模型加载和初始化，其中model_file为导出的ONNX模型格 ### predict函数 -> ``` +> ```python > ScaledYOLOv4.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` > diff --git a/examples/vision/detection/yolor/README.md b/examples/vision/detection/yolor/README.md index 81e57fde5..ffe29f39f 100644 --- a/examples/vision/detection/yolor/README.md +++ b/examples/vision/detection/yolor/README.md @@ -11,7 +11,7 @@ 访问[YOLOR](https://github.com/WongKinYiu/yolor)官方github库，按照指引下载安装，下载`yolor.pt` 模型，利用 `models/export.py` 得到`onnx`格式文件。如果您导出的`onnx`模型出现精度不达标或者是数据维度的问题，可以参考[yolor#32](https://github.com/WongKinYiu/yolor/issues/32)的解决办法 - ``` + ```bash #下载yolor模型文件 wget https://github.com/WongKinYiu/yolor/releases/download/weights/yolor-d6-paper-570.pt diff --git a/examples/vision/detection/yolor/cpp/README.md b/examples/vision/detection/yolor/cpp/README.md index 7ce7c2d85..2cf9a47fa 100644 --- a/examples/vision/detection/yolor/cpp/README.md +++ b/examples/vision/detection/yolor/cpp/README.md @@ -9,7 +9,7 @@ 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 -``` +```bash mkdir build cd build wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz @@ -34,11 +34,14 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000 +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/compile/how_to_use_sdk_on_windows.md) + ## YOLOR C++接口 ### YOLOR类 -``` +```c++ fastdeploy::vision::detection::YOLOR( const string& model_file, const string& params_file = "", @@ -57,7 +60,7 @@ YOLOR模型加载和初始化，其中model_file为导出的ONNX模型格式。 #### Predict函数 -> ``` +> ```c++ > YOLOR::Predict(cv::Mat* im, DetectionResult* result, > float conf_threshold = 0.25, > float nms_iou_threshold = 0.5) diff --git a/examples/vision/detection/yolor/python/README.md b/examples/vision/detection/yolor/python/README.md index 5b88ed787..7fb76f5f1 100644 --- a/examples/vision/detection/yolor/python/README.md +++ b/examples/vision/detection/yolor/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer.py`快速完成YOLOR在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vision/detection/yolor/python/ @@ -30,7 +30,7 @@ python infer.py --model yolor-p6-paper-541-640-640.onnx --image 000000014439.jpg ## YOLOR Python接口 -``` +```python fastdeploy.vision.detection.YOLOR(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) ``` @@ -45,7 +45,7 @@ YOLOR模型加载和初始化，其中model_file为导出的ONNX模型格式 ### predict函数 -> ``` +> ```python > YOLOR.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` > diff --git a/examples/vision/detection/yolov5/cpp/README.md b/examples/vision/detection/yolov5/cpp/README.md index c430e0ebe..7a6e55335 100644 --- a/examples/vision/detection/yolov5/cpp/README.md +++ b/examples/vision/detection/yolov5/cpp/README.md @@ -9,7 +9,7 @@ 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 -``` +```bash mkdir build cd build wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz @@ -34,11 +34,14 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000 +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/compile/how_to_use_sdk_on_windows.md) + ## YOLOv5 C++接口 ### YOLOv5类 -``` +```c++ fastdeploy::vision::detection::YOLOv5( const string& model_file, const string& params_file = "", @@ -57,7 +60,7 @@ YOLOv5模型加载和初始化，其中model_file为导出的ONNX模型格式。 #### Predict函数 -> ``` +> ```c++ > YOLOv5::Predict(cv::Mat* im, DetectionResult* result, > float conf_threshold = 0.25, > float nms_iou_threshold = 0.5) diff --git a/examples/vision/detection/yolov5/python/README.md b/examples/vision/detection/yolov5/python/README.md index 48680e39a..9a8a44a11 100644 --- a/examples/vision/detection/yolov5/python/README.md +++ b/examples/vision/detection/yolov5/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer.py`快速完成YOLOv5在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vision/detection/yolov5/python/ @@ -30,7 +30,7 @@ python infer.py --model yolov5s.onnx --image 000000014439.jpg --device gpu --use ## YOLOv5 Python接口 -``` +```python fastdeploy.vision.detection.YOLOv5(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) ``` @@ -45,7 +45,7 @@ YOLOv5模型加载和初始化，其中model_file为导出的ONNX模型格式 ### predict函数 -> ``` +> ```python > YOLOv5.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` > diff --git a/examples/vision/detection/yolov5lite/README.md b/examples/vision/detection/yolov5lite/README.md index 9c6d0ece8..e8f72099b 100644 --- a/examples/vision/detection/yolov5lite/README.md +++ b/examples/vision/detection/yolov5lite/README.md @@ -12,7 +12,7 @@ - 自动获取 访问[YOLOv5Lite](https://github.com/ppogg/YOLOv5-Lite) 官方github库，按照指引下载安装，下载`yolov5-lite-xx.onnx` 模型(Tips：官方提供的ONNX文件目前是没有decode模块的) - ``` + ```bash #下载yolov5-lite模型文件(.onnx) Download from https://drive.google.com/file/d/1bJByk9eoS6pv8Z3N4bcLRCV3i7uk24aU/view 官方Repo也支持百度云下载 @@ -27,7 +27,7 @@ 首先需要参考[YOLOv5-Lite#189](https://github.com/ppogg/YOLOv5-Lite/pull/189)的解决办法，修改代码。 - ``` + ```bash #下载yolov5-lite模型文件(.pt) Download from https://drive.google.com/file/d/1oftzqOREGqDCerf7DtD5BZp9YWELlkMe/view 官方Repo也支持百度云下载 @@ -39,7 +39,7 @@ ``` - 导出无decode模块的ONNX文件(不需要修改代码) - ``` + ```bash #下载yolov5-lite模型文件 Download from https://drive.google.com/file/d/1oftzqOREGqDCerf7DtD5BZp9YWELlkMe/view 官方Repo也支持百度云下载 diff --git a/examples/vision/detection/yolov5lite/cpp/README.md b/examples/vision/detection/yolov5lite/cpp/README.md index d22550aeb..f548cb1bd 100644 --- a/examples/vision/detection/yolov5lite/cpp/README.md +++ b/examples/vision/detection/yolov5lite/cpp/README.md @@ -9,7 +9,7 @@ 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 -``` +```bash mkdir build cd build wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz @@ -34,11 +34,14 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000 +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/compile/how_to_use_sdk_on_windows.md) + ## YOLOv5Lite C++接口 ### YOLOv5Lite类 -``` +```c++ fastdeploy::vision::detection::YOLOv5Lite( const string& model_file, const string& params_file = "", @@ -57,7 +60,7 @@ YOLOv5Lite模型加载和初始化，其中model_file为导出的ONNX模型格 #### Predict函数 -> ``` +> ```c++ > YOLOv5Lite::Predict(cv::Mat* im, DetectionResult* result, > float conf_threshold = 0.25, > float nms_iou_threshold = 0.5) diff --git a/examples/vision/detection/yolov5lite/python/README.md b/examples/vision/detection/yolov5lite/python/README.md index 096917924..c1df09e97 100644 --- a/examples/vision/detection/yolov5lite/python/README.md +++ b/examples/vision/detection/yolov5lite/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer.py`快速完成YOLOv5Lite在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vision/detection/yolov5lite/python/ @@ -30,7 +30,7 @@ python infer.py --model v5Lite-g-sim-640.onnx --image 000000014439.jpg --device ## YOLOv5Lite Python接口 -``` +```python fastdeploy.vision.detection.YOLOv5Lite(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) ``` @@ -45,7 +45,7 @@ YOLOv5Lite模型加载和初始化，其中model_file为导出的ONNX模型格 ### predict函数 -> ``` +> ```python > YOLOv5Lite.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` > diff --git a/examples/vision/detection/yolov6/cpp/README.md b/examples/vision/detection/yolov6/cpp/README.md index 753805fe8..ceeb286b8 100644 --- a/examples/vision/detection/yolov6/cpp/README.md +++ b/examples/vision/detection/yolov6/cpp/README.md @@ -9,7 +9,7 @@ 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 -``` +```bash mkdir build cd build wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz @@ -34,11 +34,14 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000 +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/compile/how_to_use_sdk_on_windows.md) + ## YOLOv6 C++接口 ### YOLOv6类 -``` +```c++ fastdeploy::vision::detection::YOLOv6( const string& model_file, const string& params_file = "", @@ -57,7 +60,7 @@ YOLOv6模型加载和初始化，其中model_file为导出的ONNX模型格式。 #### Predict函数 -> ``` +> ```c++ > YOLOv6::Predict(cv::Mat* im, DetectionResult* result, > float conf_threshold = 0.25, > float nms_iou_threshold = 0.5) diff --git a/examples/vision/detection/yolov6/python/README.md b/examples/vision/detection/yolov6/python/README.md index 691815072..8d769fff2 100644 --- a/examples/vision/detection/yolov6/python/README.md +++ b/examples/vision/detection/yolov6/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer.py`快速完成YOLOv6在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vision/detection/yolov6/python/ @@ -31,7 +31,7 @@ python infer.py --model yolov6s.onnx --image 000000014439.jpg --device gpu --use ## YOLOv6 Python接口 -``` +```python fastdeploy.vision.detection.YOLOv6(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) ``` @@ -46,7 +46,7 @@ YOLOv6模型加载和初始化，其中model_file为导出的ONNX模型格式 ### predict函数 -> ``` +> ```python > YOLOv6.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` > diff --git a/examples/vision/detection/yolov7/README.md b/examples/vision/detection/yolov7/README.md index e3701f558..14ff1ae46 100644 --- a/examples/vision/detection/yolov7/README.md +++ b/examples/vision/detection/yolov7/README.md @@ -10,7 +10,7 @@ ## 导出ONNX模型 -``` +```bash # 下载yolov7模型文件 wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt diff --git a/examples/vision/detection/yolov7/cpp/README.md b/examples/vision/detection/yolov7/cpp/README.md index 6c2500b67..a4f4232e7 100644 --- a/examples/vision/detection/yolov7/cpp/README.md +++ b/examples/vision/detection/yolov7/cpp/README.md @@ -9,7 +9,7 @@ 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 -``` +```bash mkdir build cd build wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz @@ -34,11 +34,14 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000 +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/compile/how_to_use_sdk_on_windows.md) + ## YOLOv7 C++接口 ### YOLOv7类 -``` +```c++ fastdeploy::vision::detection::YOLOv7( const string& model_file, const string& params_file = "", @@ -57,7 +60,7 @@ YOLOv7模型加载和初始化，其中model_file为导出的ONNX模型格式。 #### Predict函数 -> ``` +> ```c++ > YOLOv7::Predict(cv::Mat* im, DetectionResult* result, > float conf_threshold = 0.25, > float nms_iou_threshold = 0.5) diff --git a/examples/vision/detection/yolov7/python/README.md b/examples/vision/detection/yolov7/python/README.md index 19874bc75..29dbae78c 100644 --- a/examples/vision/detection/yolov7/python/README.md +++ b/examples/vision/detection/yolov7/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer.py`快速完成YOLOv7在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vision/detection/yolov7/python/ @@ -30,7 +30,7 @@ python infer.py --model yolov7.onnx --image 000000014439.jpg --device gpu --use_ ## YOLOv7 Python接口 -``` +```python fastdeploy.vision.detection.YOLOv7(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) ``` @@ -45,7 +45,7 @@ YOLOv7模型加载和初始化，其中model_file为导出的ONNX模型格式 ### predict函数 -> ``` +> ```python > YOLOv7.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` > diff --git a/examples/vision/detection/yolox/cpp/README.md b/examples/vision/detection/yolox/cpp/README.md index aebdca1f1..0f8a0e623 100644 --- a/examples/vision/detection/yolox/cpp/README.md +++ b/examples/vision/detection/yolox/cpp/README.md @@ -9,7 +9,7 @@ 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 -``` +```bash mkdir build cd build wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz @@ -34,11 +34,14 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000 +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/compile/how_to_use_sdk_on_windows.md) + ## YOLOX C++接口 ### YOLOX类 -``` +```c++ fastdeploy::vision::detection::YOLOX( const string& model_file, const string& params_file = "", @@ -57,7 +60,7 @@ YOLOX模型加载和初始化，其中model_file为导出的ONNX模型格式。 #### Predict函数 -> ``` +> ```c++ > YOLOX::Predict(cv::Mat* im, DetectionResult* result, > float conf_threshold = 0.25, > float nms_iou_threshold = 0.5) diff --git a/examples/vision/detection/yolox/python/README.md b/examples/vision/detection/yolox/python/README.md index 021feae86..f3471dee7 100644 --- a/examples/vision/detection/yolox/python/README.md +++ b/examples/vision/detection/yolox/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer.py`快速完成YOLOX在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vision/detection/yolox/python/ @@ -30,7 +30,7 @@ python infer.py --model yolox_s.onnx --image 000000014439.jpg --device gpu --use ## YOLOX Python接口 -``` +```python fastdeploy.vision.detection.YOLOX(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) ``` @@ -45,7 +45,7 @@ YOLOX模型加载和初始化，其中model_file为导出的ONNX模型格式 ### predict函数 -> ``` +> ```python > YOLOX.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` > diff --git a/examples/vision/facedet/retinaface/cpp/README.md b/examples/vision/facedet/retinaface/cpp/README.md index c2e0429fb..b501ae4dd 100644 --- a/examples/vision/facedet/retinaface/cpp/README.md +++ b/examples/vision/facedet/retinaface/cpp/README.md @@ -9,7 +9,7 @@ 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 -``` +```bash mkdir build cd build wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz @@ -34,11 +34,13 @@ wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/li +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/compile/how_to_use_sdk_on_windows.md) ## RetinaFace C++接口 ### RetinaFace类 -``` +```c++ fastdeploy::vision::facedet::RetinaFace( const string& model_file, const string& params_file = "", @@ -57,7 +59,7 @@ RetinaFace模型加载和初始化，其中model_file为导出的ONNX模型格 #### Predict函数 -> ``` +> ```c++ > RetinaFace::Predict(cv::Mat* im, FaceDetectionResult* result, > float conf_threshold = 0.25, > float nms_iou_threshold = 0.5) diff --git a/examples/vision/facedet/retinaface/python/README.md b/examples/vision/facedet/retinaface/python/README.md index e03c48ccd..211fd6efc 100644 --- a/examples/vision/facedet/retinaface/python/README.md +++ b/examples/vision/facedet/retinaface/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer.py`快速完成RetinaFace在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vision//retinaface/python/ @@ -30,7 +30,7 @@ python infer.py --model Pytorch_RetinaFace_mobile0.25-640-640.onnx --image test_ ## RetinaFace Python接口 -``` +```python fastdeploy.vision.facedet.RetinaFace(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) ``` @@ -45,7 +45,7 @@ RetinaFace模型加载和初始化，其中model_file为导出的ONNX模型格 ### predict函数 -> ``` +> ```python > RetinaFace.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` > diff --git a/examples/vision/facedet/scrfd/README.md b/examples/vision/facedet/scrfd/README.md index 93ff8b998..a2aaffce8 100644 --- a/examples/vision/facedet/scrfd/README.md +++ b/examples/vision/facedet/scrfd/README.md @@ -8,7 +8,7 @@ ## 导出ONNX模型 - ``` + ```bash #下载scrfd模型文件 e.g. download from https://onedrive.live.com/?authkey=%21ABbFJx2JMhNjhNA&id=4A83B6B633B029CC%215542&cid=4A83B6B633B029CC diff --git a/examples/vision/facedet/scrfd/cpp/README.md b/examples/vision/facedet/scrfd/cpp/README.md index 88fb29426..3d129470b 100644 --- a/examples/vision/facedet/scrfd/cpp/README.md +++ b/examples/vision/facedet/scrfd/cpp/README.md @@ -9,7 +9,7 @@ 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 -``` +```bash mkdir build cd build wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz @@ -34,11 +34,14 @@ wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/li +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/compile/how_to_use_sdk_on_windows.md) + ## SCRFD C++接口 ### SCRFD类 -``` +```c++ fastdeploy::vision::facedet::SCRFD( const string& model_file, const string& params_file = "", @@ -57,7 +60,7 @@ SCRFD模型加载和初始化，其中model_file为导出的ONNX模型格式。 #### Predict函数 -> ``` +> ```c++ > SCRFD::Predict(cv::Mat* im, FaceDetectionResult* result, > float conf_threshold = 0.25, > float nms_iou_threshold = 0.5) diff --git a/examples/vision/facedet/scrfd/python/README.md b/examples/vision/facedet/scrfd/python/README.md index 0a5f9ded3..7e7fea420 100644 --- a/examples/vision/facedet/scrfd/python/README.md +++ b/examples/vision/facedet/scrfd/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer.py`快速完成SCRFD在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vision/facedet/scrfd/python/ @@ -30,7 +30,7 @@ python infer.py --model scrfd_500m_bnkps_shape640x640.onnx --image test_lite_fac ## SCRFD Python接口 -``` +```python fastdeploy.vision.facedet.SCRFD(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) ``` @@ -45,7 +45,7 @@ SCRFD模型加载和初始化，其中model_file为导出的ONNX模型格式 ### predict函数 -> ``` +> ```python > SCRFD.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` > diff --git a/examples/vision/facedet/ultraface/cpp/README.md b/examples/vision/facedet/ultraface/cpp/README.md index 79cc92334..3189c3f0b 100644 --- a/examples/vision/facedet/ultraface/cpp/README.md +++ b/examples/vision/facedet/ultraface/cpp/README.md @@ -9,7 +9,7 @@ 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 -``` +```bash mkdir build cd build wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz @@ -34,11 +34,14 @@ wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/li +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/compile/how_to_use_sdk_on_windows.md) + ## UltraFace C++接口 ### UltraFace类 -``` +```c++ fastdeploy::vision::facedet::UltraFace( const string& model_file, const string& params_file = "", @@ -57,7 +60,7 @@ UltraFace模型加载和初始化，其中model_file为导出的ONNX模型格式 #### Predict函数 -> ``` +> ```c++ > UltraFace::Predict(cv::Mat* im, FaceDetectionResult* result, > float conf_threshold = 0.25, > float nms_iou_threshold = 0.5) diff --git a/examples/vision/facedet/ultraface/python/README.md b/examples/vision/facedet/ultraface/python/README.md index 60c63020f..efa37290b 100644 --- a/examples/vision/facedet/ultraface/python/README.md +++ b/examples/vision/facedet/ultraface/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer.py`快速完成UltraFace在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vision/facedet/ultraface/python/ @@ -30,7 +30,7 @@ python infer.py --model version-RFB-320.onnx --image test_lite_face_detector_3.j ## UltraFace Python接口 -``` +```python fastdeploy.vision.facedet.UltraFace(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) ``` @@ -45,7 +45,7 @@ UltraFace模型加载和初始化，其中model_file为导出的ONNX模型格式 ### predict函数 -> ``` +> ```python > UltraFace.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` > diff --git a/examples/vision/facedet/yolov5face/cpp/README.md b/examples/vision/facedet/yolov5face/cpp/README.md index c2afde648..8c0242f98 100644 --- a/examples/vision/facedet/yolov5face/cpp/README.md +++ b/examples/vision/facedet/yolov5face/cpp/README.md @@ -9,7 +9,7 @@ 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 -``` +```bash mkdir build cd build wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz @@ -34,11 +34,14 @@ wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/li +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/compile/how_to_use_sdk_on_windows.md) + ## YOLOv5Face C++接口 ### YOLOv5Face类 -``` +```c++ fastdeploy::vision::facedet::YOLOv5Face( const string& model_file, const string& params_file = "", @@ -57,7 +60,7 @@ YOLOv5Face模型加载和初始化，其中model_file为导出的ONNX模型格 #### Predict函数 -> ``` +> ```c++ > YOLOv5Face::Predict(cv::Mat* im, FaceDetectionResult* result, > float conf_threshold = 0.25, > float nms_iou_threshold = 0.5) diff --git a/examples/vision/facedet/yolov5face/python/README.md b/examples/vision/facedet/yolov5face/python/README.md index a029cb839..ef0f571eb 100644 --- a/examples/vision/facedet/yolov5face/python/README.md +++ b/examples/vision/facedet/yolov5face/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer.py`快速完成YOLOv5Face在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vision/facedet/yolov5face/python/ @@ -30,7 +30,7 @@ python infer.py --model yolov5s-face.onnx --image test_lite_face_detector_3.jpg ## YOLOv5Face Python接口 -``` +```python fastdeploy.vision.facedet.YOLOv5Face(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) ``` @@ -45,7 +45,7 @@ YOLOv5Face模型加载和初始化，其中model_file为导出的ONNX模型格 ### predict函数 -> ``` +> ```python > YOLOv5Face.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` > diff --git a/examples/vision/faceid/insightface/README.md b/examples/vision/faceid/insightface/README.md index 2318d24ab..d2b5b4b31 100644 --- a/examples/vision/faceid/insightface/README.md +++ b/examples/vision/faceid/insightface/README.md @@ -18,7 +18,7 @@ 访问[ArcFace](https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch)官方github库，按照指引下载安装，下载pt模型文件，利用 `torch2onnx.py` 得到`onnx`格式文件。 * 下载ArcFace模型文件 - ``` + ```bash Link: https://pan.baidu.com/share/init?surl=CL-l4zWqsI1oDuEEYVhj-g code: e8pw ``` diff --git a/examples/vision/faceid/insightface/cpp/README.md b/examples/vision/faceid/insightface/cpp/README.md index cc06f7bda..547c527a3 100644 --- a/examples/vision/faceid/insightface/cpp/README.md +++ b/examples/vision/faceid/insightface/cpp/README.md @@ -9,7 +9,7 @@ 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 -``` +```bash mkdir build cd build wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz @@ -40,11 +40,14 @@ wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_2.JPG
+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/compile/how_to_use_sdk_on_windows.md) + ## InsightFace C++接口 ### ArcFace类 -``` +```c++ fastdeploy::vision::faceid::ArcFace( const string& model_file, const string& params_file = "", @@ -56,7 +59,7 @@ ArcFace模型加载和初始化，其中model_file为导出的ONNX模型格式 ### CosFace类 -``` +```c++ fastdeploy::vision::faceid::CosFace( const string& model_file, const string& params_file = "", @@ -68,7 +71,7 @@ CosFace模型加载和初始化，其中model_file为导出的ONNX模型格式 ### PartialFC类 -``` +```c++ fastdeploy::vision::faceid::PartialFC( const string& model_file, const string& params_file = "", @@ -80,7 +83,7 @@ PartialFC模型加载和初始化，其中model_file为导出的ONNX模型格式 ### VPL类 -``` +```c++ fastdeploy::vision::faceid::VPL( const string& model_file, const string& params_file = "", @@ -98,7 +101,7 @@ VPL模型加载和初始化，其中model_file为导出的ONNX模型格式。 #### Predict函数 -> ``` +> ```c++ > ArcFace::Predict(cv::Mat* im, FaceRecognitionResult* result, > float conf_threshold = 0.25, > float nms_iou_threshold = 0.5) diff --git a/examples/vision/faceid/insightface/python/README.md b/examples/vision/faceid/insightface/python/README.md index 9bf352d57..7f61df114 100644 --- a/examples/vision/faceid/insightface/python/README.md +++ b/examples/vision/faceid/insightface/python/README.md @@ -8,7 +8,7 @@ 以ArcFace为例子, 提供`infer_arcface.py`快速完成ArcFace在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vision/faceid/insightface/python/ @@ -35,7 +35,7 @@ python infer_arcface.py --model ms1mv3_arcface_r100.onnx --face test_lite_focal_ -``` +```bash Prediction Done! --- [Face 0]:FaceRecognitionResult: [Dim(512), Min(-2.309220), Max(2.372197), Mean(0.016987)] --- [Face 1]:FaceRecognitionResult: [Dim(512), Min(-2.288258), Max(1.995104), Mean(-0.003400)] @@ -46,7 +46,7 @@ Detect Done! Cosine 01: 0.814385, Cosine 02:-0.059388 ## InsightFace Python接口 -``` +```python fastdeploy.vision.faceid.ArcFace(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) fastdeploy.vision.faceid.CosFace(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) fastdeploy.vision.faceid.PartialFC(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) @@ -64,7 +64,7 @@ ArcFace模型加载和初始化，其中model_file为导出的ONNX模型格式 ### predict函数 -> ``` +> ```python > ArcFace.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` > diff --git a/examples/vision/matting/modnet/cpp/README.md b/examples/vision/matting/modnet/cpp/README.md index 97a9fb623..18c4fc94e 100644 --- a/examples/vision/matting/modnet/cpp/README.md +++ b/examples/vision/matting/modnet/cpp/README.md @@ -9,7 +9,7 @@ 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 -``` +```bash mkdir build cd build wget https://https://bj.bcebos.com/paddlehub/fastdeploy/cpp/fastdeploy-linux-x64-gpu-0.2.0.tgz @@ -36,11 +36,14 @@ wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/li +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/compile/how_to_use_sdk_on_windows.md) + ## MODNet C++接口 ### MODNet类 -``` +```c++ fastdeploy::vision::matting::MODNet( const string& model_file, const string& params_file = "", @@ -59,7 +62,7 @@ MODNet模型加载和初始化，其中model_file为导出的ONNX模型格式。 #### Predict函数 -> ``` +> ```c++ > MODNet::Predict(cv::Mat* im, MattingResult* result, > float conf_threshold = 0.25, > float nms_iou_threshold = 0.5) diff --git a/examples/vision/matting/modnet/python/README.md b/examples/vision/matting/modnet/python/README.md index 2cd02ddf6..0d441c400 100644 --- a/examples/vision/matting/modnet/python/README.md +++ b/examples/vision/matting/modnet/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer.py`快速完成MODNet在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vision/matting/modnet/python/ @@ -31,7 +31,7 @@ python infer.py --model modnet_photographic_portrait_matting.onnx --image test_l ## MODNet Python接口 -``` +```python fastdeploy.vision.matting.MODNet(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX) ``` @@ -46,7 +46,7 @@ MODNet模型加载和初始化，其中model_file为导出的ONNX模型格式 ### predict函数 -> ``` +> ```python > MODNet.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` > diff --git a/examples/vision/segmentation/paddleseg/cpp/README.md b/examples/vision/segmentation/paddleseg/cpp/README.md index 04bc54761..0ecf54dae 100644 --- a/examples/vision/segmentation/paddleseg/cpp/README.md +++ b/examples/vision/segmentation/paddleseg/cpp/README.md @@ -9,13 +9,13 @@ 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 -``` +```bash mkdir build cd build wget https://bj.bcebos.com/paddlehub/fastdeploy/libs/0.2.0/fastdeploy-linux-x64-gpu-0.2.0.tgz tar xvf fastdeploy-linux-x64-gpu-0.2.0.tgz cd fastdeploy-linux-x64-gpu-0.2.0/examples/vision/segmentation/paddleseg/cpp/build -cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.2.0 +cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.2.0 make -j # 下载Unet模型文件和测试图片 @@ -33,15 +33,18 @@ wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png ``` 运行完成可视化结果如下图所示 -
+
+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/compile/how_to_use_sdk_on_windows.md) + ## PaddleSeg C++接口 ### PaddleSeg类 -``` +```c++ fastdeploy::vision::segmentation::PaddleSegModel( const string& model_file, const string& params_file = "", @@ -62,7 +65,7 @@ PaddleSegModel模型加载和初始化，其中model_file为导出的Paddle模 #### Predict函数 -> ``` +> ```c++ > PaddleSegModel::Predict(cv::Mat* im, DetectionResult* result) > ``` > diff --git a/examples/vision/segmentation/paddleseg/python/README.md b/examples/vision/segmentation/paddleseg/python/README.md index a93d99ae3..226f967ec 100644 --- a/examples/vision/segmentation/paddleseg/python/README.md +++ b/examples/vision/segmentation/paddleseg/python/README.md @@ -7,7 +7,7 @@ 本目录下提供`infer.py`快速完成Unet在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 -``` +```bash #下载部署示例代码 git clone https://github.com/PaddlePaddle/FastDeploy.git cd FastDeploy/examples/vision/segmentation/paddleseg/python @@ -26,13 +26,13 @@ python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_ ``` 运行完成可视化结果如下图所示 -
+
## PaddleSegModel Python接口 -``` +```python fd.vision.segmentation.PaddleSegModel(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE) ``` @@ -48,7 +48,7 @@ PaddleSeg模型加载和初始化，其中model_file, params_file以及config_fi ### predict函数 -> ``` +> ```python > PaddleSegModel.predict(input_image) > ``` > diff --git a/requirements.txt b/requirements.txt index 62c894c20..b4fc12689 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,4 @@ +wheel requests tqdm numpy