diff --git a/docs/api_docs/python/README.md b/docs/api_docs/python/README.md index c6bccc576c..789ae76bbc 100644 --- a/docs/api_docs/python/README.md +++ b/docs/api_docs/python/README.md @@ -2,7 +2,7 @@ This directory help to generate Python API documents for FastDeploy. -1. First, to generate the latest api documents, you need to install the latest FastDeploy, refer [build and install](en/build_and_install) to build FastDeploy python wheel package with the latest code. +1. First, to generate the latest api documents, you need to install the latest FastDeploy, refer [build and install](../../cn/build_and_install) to build FastDeploy python wheel package with the latest code. 2. After installed FastDeploy in your python environment, there are some dependencies need to install, execute command `pip install -r requirements.txt` in this directory 3. Execute command `make html` to generate API documents diff --git a/docs/cn/build_and_install/android.md b/docs/cn/build_and_install/android.md index 899ec2985a..fb945ed3a0 100644 --- a/docs/cn/build_and_install/android.md +++ b/docs/cn/build_and_install/android.md @@ -102,4 +102,4 @@ make install 如何使用FastDeploy Android C++ SDK 请参考使用案例文档： - [图像分类Android使用文档](../../../examples/vision/classification/paddleclas/android/README.md) - [目标检测Android使用文档](../../../examples/vision/detection/paddledetection/android/README.md) -- [在 Android 通过 JNI 中使用 FastDeploy C++ SDK](../../../../../docs/cn/faq/use_cpp_sdk_on_android.md) +- [在 Android 通过 JNI 中使用 FastDeploy C++ SDK](../../cn/faq/use_cpp_sdk_on_android.md) diff --git a/docs/cn/faq/use_sdk_on_windows.md b/docs/cn/faq/use_sdk_on_windows.md index be1e1ab0a0..7209d50bb3 100644 --- a/docs/cn/faq/use_sdk_on_windows.md +++ b/docs/cn/faq/use_sdk_on_windows.md @@ -218,7 +218,7 @@ D:\qiuyanjun\fastdeploy_test\infer_ppyoloe\x64\Release\infer_ppyoloe.exe ![image](https://user-images.githubusercontent.com/31974251/192144782-79bccf8f-65d0-4f22-9f41-81751c530319.png) （2）其中infer_ppyoloe.cpp的代码可以直接从examples中的代码拷贝过来： -- [examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc](../../examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc) +- [examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc](../../../examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc) （3）CMakeLists.txt主要包括配置FastDeploy C++ SDK的路径，如果是GPU版本的SDK，还需要配置CUDA_DIRECTORY为CUDA的安装路径，CMakeLists.txt的配置如下： diff --git a/docs/en/faq/use_sdk_on_windows.md b/docs/en/faq/use_sdk_on_windows.md index f75c6d911b..367f6e1df1 100644 --- a/docs/en/faq/use_sdk_on_windows.md +++ b/docs/en/faq/use_sdk_on_windows.md @@ -179,7 +179,7 @@ D:\qiuyanjun\fastdeploy_build\built\fastdeploy-win-x64-gpu-0.2.1\third_libs\inst ![image](https://user-images.githubusercontent.com/31974251/192827842-1f05d435-8a3e-492b-a3b7-d5e88f85f814.png) -Compile successfully, you can see the exe saved in： +Compile successfully, you can see the exe saved in： ```bat D:\qiuyanjun\fastdeploy_test\infer_ppyoloe\x64\Release\infer_ppyoloe.exe @@ -221,7 +221,7 @@ This section is for CMake users and describes how to create CMake projects in Vi ![image](https://user-images.githubusercontent.com/31974251/192144782-79bccf8f-65d0-4f22-9f41-81751c530319.png) （2）The code of infer_ppyoloe.cpp can be copied directly from the code in examples： -- [examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc](../../examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc) +- [examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc](../../../examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc) （3）CMakeLists.txt mainly includes the configuration of the path of FastDeploy C++ SDK, if it is the GPU version of the SDK, you also need to configure CUDA_DIRECTORY as the installation path of CUDA, the configuration of CMakeLists.txt is as follows： @@ -361,7 +361,7 @@ A brief description of the usage is as follows. #### 4.1.2 fastdeploy_init.bat View all dll, lib and include paths in the SDK
-Go to the root directory of the SDK and run the show command to view all the dll, lib and include paths in the SDK. In the following command, %cd% means the current directory (the root directory of the SDK). +Go to the root directory of the SDK and run the show command to view all the dll, lib and include paths in the SDK. In the following command, %cd% means the current directory (the root directory of the SDK). ```bat D:\path-to-fastdeploy-sdk-dir>fastdeploy_init.bat show %cd% @@ -504,7 +504,7 @@ copy /Y %FASTDEPLOY_HOME%\third_libs\install\yaml-cpp\lib\*.dll Release\ copy /Y %FASTDEPLOY_HOME%\third_libs\install\openvino\bin\*.dll Release\ copy /Y %FASTDEPLOY_HOME%\third_libs\install\openvino\bin\*.xml Release\ copy /Y %FASTDEPLOY_HOME%\third_libs\install\openvino\3rdparty\tbb\bin\*.dll Release\ -``` +``` Note that if you compile the latest SDK or version >0.2.1 by yourself, the opencv and openvino directory structure has changed and the path needs to be modified appropriately. For example： ```bat copy /Y %FASTDEPLOY_HOME%\third_libs\install\opencv\build\x64\vc15\bin\*.dll Release\ diff --git a/docs/en/quantize.md b/docs/en/quantize.md index effce0700b..c4535808e1 100644 --- a/docs/en/quantize.md +++ b/docs/en/quantize.md @@ -27,7 +27,7 @@ FastDeploy基于PaddleSlim, 集成了一键模型量化的工具, 同时, FastDe ### 用户使用FastDeploy一键模型量化工具来量化模型 Fastdeploy基于PaddleSlim, 为用户提供了一键模型量化的工具，请参考如下文档进行模型量化. -- [FastDeploy 一键模型量化](../../tools/quantization/) +- [FastDeploy 一键模型量化](../../tools/auto_compression/) 当用户获得产出的量化模型之后，即可以使用FastDeploy来部署量化模型. diff --git a/examples/text/ernie-3.0/serving/README.md b/examples/text/ernie-3.0/serving/README.md index df969724a2..487a5eddca 100644 --- a/examples/text/ernie-3.0/serving/README.md +++ b/examples/text/ernie-3.0/serving/README.md @@ -168,4 +168,4 @@ entity: 华夏 label: LOC pos: [14, 15] ## 配置修改 -当前分类任务(ernie_seqcls_model/config.pbtxt)默认配置在CPU上运行OpenVINO引擎; 序列标注任务默认配置在GPU上运行Paddle引擎。如果要在CPU/GPU或其他推理引擎上运行, 需要修改配置，详情请参考[配置文档](../../../../../serving/docs/zh_CN/model_configuration.md) +当前分类任务(ernie_seqcls_model/config.pbtxt)默认配置在CPU上运行OpenVINO引擎; 序列标注任务默认配置在GPU上运行Paddle引擎。如果要在CPU/GPU或其他推理引擎上运行, 需要修改配置，详情请参考[配置文档](../../../../serving/docs/zh_CN/model_configuration.md) diff --git a/examples/vision/README.md b/examples/vision/README.md index f439d6e721..347da1be75 100755 --- a/examples/vision/README.md +++ b/examples/vision/README.md @@ -30,4 +30,4 @@ FastDeploy针对飞桨的视觉套件，以及外部热门模型，提供端到 - 加载模型 - 调用`predict`接口 -FastDeploy在各视觉模型部署时，也支持一键切换后端推理引擎，详情参阅[如何切换模型推理引擎](../../docs/runtime/how_to_change_backend.md)。 +FastDeploy在各视觉模型部署时，也支持一键切换后端推理引擎，详情参阅[如何切换模型推理引擎](../../docs/cn/faq/how_to_change_backend.md)。 diff --git a/examples/vision/classification/paddleclas/quantize/cpp/README.md b/examples/vision/classification/paddleclas/quantize/cpp/README.md index e2e625dbd5..76ffa0be9b 100644 --- a/examples/vision/classification/paddleclas/quantize/cpp/README.md +++ b/examples/vision/classification/paddleclas/quantize/cpp/README.md @@ -8,7 +8,7 @@ ### 量化模型准备 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署. -- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.) +- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.) ## 以量化后的ResNet50_Vd模型为例, 进行部署 在本目录执行如下命令即可完成编译,以及量化模型部署. diff --git a/examples/vision/classification/paddleclas/quantize/python/README.md b/examples/vision/classification/paddleclas/quantize/python/README.md index 00fd7bef9c..6b874e77ab 100644 --- a/examples/vision/classification/paddleclas/quantize/python/README.md +++ b/examples/vision/classification/paddleclas/quantize/python/README.md @@ -8,7 +8,7 @@ ### 量化模型准备 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署. -- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.) +- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.) ## 以量化后的ResNet50_Vd模型为例, 进行部署 diff --git a/examples/vision/classification/paddleclas/web/README.md b/examples/vision/classification/paddleclas/web/README.md index 837773cc3f..710dd53ad4 100644 --- a/examples/vision/classification/paddleclas/web/README.md +++ b/examples/vision/classification/paddleclas/web/README.md @@ -6,7 +6,7 @@ ## 前端部署图像分类模型 -图像分类模型web demo使用[**参考文档**](../../../../examples/application/js/web_demo) +图像分类模型web demo使用[**参考文档**](../../../../application/js/web_demo/) ## MobileNet js接口 @@ -34,4 +34,3 @@ console.log(res); - [PaddleClas模型 python部署](../../paddleclas/python/) - [PaddleClas模型 C++部署](../cpp/) - diff --git a/examples/vision/classification/resnet/cpp/README.md b/examples/vision/classification/resnet/cpp/README.md index eb3bff6f48..1180d26c9c 100644 --- a/examples/vision/classification/resnet/cpp/README.md +++ b/examples/vision/classification/resnet/cpp/README.md @@ -4,8 +4,8 @@ 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/environment.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) +- 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 ResNet50 推理为例，在本目录执行如下命令即可完成编译测试 @@ -33,7 +33,7 @@ wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/Ima ``` 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) +- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md) ## ResNet C++接口 @@ -74,4 +74,4 @@ fastdeploy::vision::classification::ResNet( - [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/runtime/how_to_change_backend.md) +- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md) diff --git a/examples/vision/classification/resnet/python/README.md b/examples/vision/classification/resnet/python/README.md index 6315ee06a6..a115bcdf40 100644 --- a/examples/vision/classification/resnet/python/README.md +++ b/examples/vision/classification/resnet/python/README.md @@ -2,8 +2,8 @@ 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/environment.md) -- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/quick_start) +- 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. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md) 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 @@ -69,4 +69,4 @@ fd.vision.classification.ResNet(model_file, params_file, runtime_option=None, mo - [ResNet 模型介绍](..) - [ResNet C++部署](../cpp) - [模型预测结果说明](../../../../../docs/api/vision_results/) -- [如何切换模型推理后端引擎](../../../../../docs/runtime/how_to_change_backend.md) +- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md) diff --git a/examples/vision/detection/paddledetection/quantize/cpp/README.md b/examples/vision/detection/paddledetection/quantize/cpp/README.md index 42bf40acbe..bcc66e7f79 100644 --- a/examples/vision/detection/paddledetection/quantize/cpp/README.md +++ b/examples/vision/detection/paddledetection/quantize/cpp/README.md @@ -9,7 +9,7 @@ ### 量化模型准备 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署. -- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的infer_cfg.yml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.) +- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的infer_cfg.yml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.) ## 以量化后的PP-YOLOE-l模型为例, 进行部署 在本目录执行如下命令即可完成编译,以及量化模型部署. diff --git a/examples/vision/detection/paddledetection/quantize/python/README.md b/examples/vision/detection/paddledetection/quantize/python/README.md index cecb5a1401..3efcd232b9 100644 --- a/examples/vision/detection/paddledetection/quantize/python/README.md +++ b/examples/vision/detection/paddledetection/quantize/python/README.md @@ -8,7 +8,7 @@ ### 量化模型准备 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署. -- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的infer_cfg.yml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.) +- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的infer_cfg.yml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.) ## 以量化后的PP-YOLOE-l模型为例, 进行部署 diff --git a/examples/vision/detection/yolov5/quantize/cpp/README.md b/examples/vision/detection/yolov5/quantize/cpp/README.md index 7d76bad514..5afaecce4b 100644 --- a/examples/vision/detection/yolov5/quantize/cpp/README.md +++ b/examples/vision/detection/yolov5/quantize/cpp/README.md @@ -9,7 +9,7 @@ ### 量化模型准备 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署. -- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署. +- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署. ## 以量化后的YOLOv5s模型为例, 进行部署 在本目录执行如下命令即可完成编译,以及量化模型部署. diff --git a/examples/vision/detection/yolov5/quantize/python/README.md b/examples/vision/detection/yolov5/quantize/python/README.md index 9aa03a8cc0..28086d8b5b 100644 --- a/examples/vision/detection/yolov5/quantize/python/README.md +++ b/examples/vision/detection/yolov5/quantize/python/README.md @@ -8,7 +8,7 @@ ### 量化模型准备 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署. -- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署. +- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署. ## 以量化后的YOLOv5s模型为例, 进行部署 diff --git a/examples/vision/detection/yolov6/quantize/cpp/README.md b/examples/vision/detection/yolov6/quantize/cpp/README.md index bf2208fab7..53a05cab7c 100644 --- a/examples/vision/detection/yolov6/quantize/cpp/README.md +++ b/examples/vision/detection/yolov6/quantize/cpp/README.md @@ -9,7 +9,7 @@ ### 量化模型准备 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署. -- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署. +- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署. ## 以量化后的YOLOv6s模型为例, 进行部署 在本目录执行如下命令即可完成编译,以及量化模型部署. diff --git a/examples/vision/detection/yolov6/quantize/python/README.md b/examples/vision/detection/yolov6/quantize/python/README.md index 5f70a02c84..889fe2f113 100644 --- a/examples/vision/detection/yolov6/quantize/python/README.md +++ b/examples/vision/detection/yolov6/quantize/python/README.md @@ -8,7 +8,7 @@ ### 量化模型准备 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署. -- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署. +- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署. ## 以量化后的YOLOv6s模型为例, 进行部署 ```bash diff --git a/examples/vision/detection/yolov7/python/README_EN.md b/examples/vision/detection/yolov7/python/README_EN.md index 64ce3b6ed4..57b341dd75 100644 --- a/examples/vision/detection/yolov7/python/README_EN.md +++ b/examples/vision/detection/yolov7/python/README_EN.md @@ -4,8 +4,8 @@ English | [简体中文](README.md) Two steps before deployment: -- 1. The hardware and software environment meets the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/docs_en/environment.md) -- 2. Install FastDeploy Python whl package. Please refer to [FastDeploy Python Installation](../../../../../docs/docs_en/quick_start) +- 1. The hardware and software environment meets the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md) +- 2. Install FastDeploy Python whl package. Please refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md) This doc provides a quick `infer.py` demo of YOLOv7 deployment on CPU/GPU, and accelerated GPU deployment by TensorRT. Run the following command: @@ -21,7 +21,7 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000 # CPU Inference python infer.py --model yolov7.onnx --image 000000014439.jpg --device cpu -# GPU +# GPU python infer.py --model yolov7.onnx --image 000000014439.jpg --device gpu # GPU上使用TensorRT推理 python infer.py --model yolov7.onnx --image 000000014439.jpg --device gpu --use_trt True @@ -51,18 +51,18 @@ YOLOv7 model loading and initialisation, with model_file being the exported ONNX > ```python > YOLOv7.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5) > ``` -> +> > Model prediction interface with direct output of detection results from the image input. -> +> > **Parameters** -> +> > > * **image_data**(np.ndarray): Input image. Images need to be in HWC or BGR format > > * **conf_threshold**(float): Filter threshold for detection box confidence > > * **nms_iou_threshold**(float): iou thresholds during NMS processing > **Return** -> -> > Return to`fastdeploy.vision.DetectionResult`Struct. For more details, please refer to [Vision Model Results](../../../../../docs/docs_en/api/vision_results/) +> +> > Return to`fastdeploy.vision.DetectionResult`Struct. For more details, please refer to [Vision Model Results](../../../../../docs/api/vision_results/) ### Class Member Variables @@ -80,5 +80,5 @@ Users can modify the following pre-processing parameters for their needs. This w - [YOLOv7 Model Introduction](..) - [YOLOv7 C++ Deployment](../cpp) -- [Vision Model Results](../../../../../docs/docs_en/api/vision_results/) -- [how to change inference backend](../../../../../docs/docs_en/runtime/how_to_change_inference_backend.md) +- [Vision Model Results](../../../../../docs/api/vision_results/) +- [how to change inference backend](../../../../../docs/en/faq/how_to_change_backend.md) diff --git a/examples/vision/detection/yolov7/quantize/cpp/README.md b/examples/vision/detection/yolov7/quantize/cpp/README.md index 53110591e0..dc78745284 100644 --- a/examples/vision/detection/yolov7/quantize/cpp/README.md +++ b/examples/vision/detection/yolov7/quantize/cpp/README.md @@ -9,7 +9,7 @@ ### 量化模型准备 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署. -- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署. +- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署. ## 以量化后的YOLOv7模型为例, 进行部署 在本目录执行如下命令即可完成编译,以及量化模型部署. diff --git a/examples/vision/detection/yolov7/quantize/python/README.md b/examples/vision/detection/yolov7/quantize/python/README.md index ac1c44889b..e82dc46159 100644 --- a/examples/vision/detection/yolov7/quantize/python/README.md +++ b/examples/vision/detection/yolov7/quantize/python/README.md @@ -8,7 +8,7 @@ ### 量化模型准备 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署. -- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署. +- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署. ## 以量化后的YOLOv7模型为例, 进行部署 ```bash diff --git a/examples/vision/keypointdetection/det_keypoint_unite/python/README.md b/examples/vision/keypointdetection/det_keypoint_unite/python/README.md index e401b655a8..1b2fc0f181 100644 --- a/examples/vision/keypointdetection/det_keypoint_unite/python/README.md +++ b/examples/vision/keypointdetection/det_keypoint_unite/python/README.md @@ -71,4 +71,4 @@ PPTinyPosePipeline模型加载和初始化，其中det_model是使用`fd.vision. - [Pipeline 模型介绍](..) - [Pipeline C++部署](../cpp) - [模型预测结果说明](../../../../../docs/api/vision_results/) -- [如何切换模型推理后端引擎](../../../../../docs/runtime/how_to_change_backend.md) +- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md) diff --git a/examples/vision/keypointdetection/tiny_pose/python/README.md b/examples/vision/keypointdetection/tiny_pose/python/README.md index 2d95e7a2fd..f8835e00ec 100644 --- a/examples/vision/keypointdetection/tiny_pose/python/README.md +++ b/examples/vision/keypointdetection/tiny_pose/python/README.md @@ -76,4 +76,4 @@ PP-TinyPose模型加载和初始化，其中model_file, params_file以及config_ - [PP-TinyPose 模型介绍](..) - [PP-TinyPose C++部署](../cpp) - [模型预测结果说明](../../../../../docs/api/vision_results/) -- [如何切换模型推理后端引擎](../../../../../docs/runtime/how_to_change_backend.md) +- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md) diff --git a/examples/vision/matting/ppmatting/cpp/README.md b/examples/vision/matting/ppmatting/cpp/README.md index a2eaeee1a2..04809ca699 100644 --- a/examples/vision/matting/ppmatting/cpp/README.md +++ b/examples/vision/matting/ppmatting/cpp/README.md @@ -7,7 +7,7 @@ - 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 PP-Matting 推理为例，在本目录执行如下命令即可完成编译测试（如若只需在CPU上部署，可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md/CPP_prebuilt_libraries.md)下载CPU推理库） +以Linux上 PP-Matting 推理为例，在本目录执行如下命令即可完成编译测试（如若只需在CPU上部署，可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)下载CPU推理库） ```bash #下载SDK，编译模型examples代码（SDK中包含了examples代码） diff --git a/examples/vision/matting/rvm/cpp/README.md b/examples/vision/matting/rvm/cpp/README.md index 1e4ade6eb9..ff80ecaf7d 100755 --- a/examples/vision/matting/rvm/cpp/README.md +++ b/examples/vision/matting/rvm/cpp/README.md @@ -5,7 +5,7 @@ - 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 RobustVideoMatting 推理为例，在本目录执行如下命令即可完成编译测试（如若只需在CPU上部署，可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md/CPP_prebuilt_libraries.md)下载CPU推理库） +以Linux上 RobustVideoMatting 推理为例，在本目录执行如下命令即可完成编译测试（如若只需在CPU上部署，可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)下载CPU推理库） 本目录下提供`infer.cc`快速完成RobustVideoMatting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 diff --git a/examples/vision/ocr/PP-OCRv3/mini_program/README.md b/examples/vision/ocr/PP-OCRv3/mini_program/README.md index 6bbe854b42..447a02e725 100644 --- a/examples/vision/ocr/PP-OCRv3/mini_program/README.md +++ b/examples/vision/ocr/PP-OCRv3/mini_program/README.md @@ -16,7 +16,7 @@ import * as ocr from "@paddle-js-models/ocr"; await ocr.init(detConfig, recConfig); const res = await ocr.recognize(img, option, postConfig); ``` -ocr模型加载和初始化，其中模型为Paddle.js模型格式，js模型转换方式参考[文档](../../../../application/web_demo/README.md) +ocr模型加载和初始化，其中模型为Paddle.js模型格式，js模型转换方式参考[文档](../../../../application/js/web_demo/README.md) **init函数参数** @@ -37,5 +37,4 @@ ocr模型加载和初始化，其中模型为Paddle.js模型格式，js模型转 - [PP-OCRv3 C++部署](../cpp) - [模型预测结果说明](../../../../../docs/api/vision_results/) - [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md) -- [PP-OCRv3模型web demo文档](../../../../application/web_demo/README.md) - +- [PP-OCRv3模型web demo文档](../../../../application/js/web_demo/README.md) diff --git a/examples/vision/ocr/PP-OCRv3/web/README.md b/examples/vision/ocr/PP-OCRv3/web/README.md index 7217783104..3afd247612 100644 --- a/examples/vision/ocr/PP-OCRv3/web/README.md +++ b/examples/vision/ocr/PP-OCRv3/web/README.md @@ -16,7 +16,7 @@ import * as ocr from "@paddle-js-models/ocr"; await ocr.init(detConfig, recConfig); const res = await ocr.recognize(img, option, postConfig); ``` -ocr模型加载和初始化，其中模型为Paddle.js模型格式，js模型转换方式参考[文档](../../../../application/web_demo/README.md) +ocr模型加载和初始化，其中模型为Paddle.js模型格式，js模型转换方式参考[文档](../../../../application/js/web_demo/README.md) **init函数参数** diff --git a/examples/vision/segmentation/paddleseg/quantize/cpp/README.md b/examples/vision/segmentation/paddleseg/quantize/cpp/README.md index fa334fba41..9c1ec4b6aa 100644 --- a/examples/vision/segmentation/paddleseg/quantize/cpp/README.md +++ b/examples/vision/segmentation/paddleseg/quantize/cpp/README.md @@ -8,7 +8,7 @@ ### 量化模型准备 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署. -- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的deploy.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.) +- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的deploy.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.) ## 以量化后的PP_LiteSeg_T_STDC1_cityscapes模型为例, 进行部署 在本目录执行如下命令即可完成编译,以及量化模型部署. diff --git a/examples/vision/segmentation/paddleseg/quantize/python/README.md b/examples/vision/segmentation/paddleseg/quantize/python/README.md index 9fd3b900bc..2e06ae145c 100644 --- a/examples/vision/segmentation/paddleseg/quantize/python/README.md +++ b/examples/vision/segmentation/paddleseg/quantize/python/README.md @@ -8,7 +8,7 @@ ### 量化模型准备 - 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署. -- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的deploy.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.) +- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的deploy.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.) ## 以量化后的PP_LiteSeg_T_STDC1_cityscapes模型为例, 进行部署 diff --git a/examples/vision/segmentation/paddleseg/rknpu2/python/README.md b/examples/vision/segmentation/paddleseg/rknpu2/python/README.md index 74aeed2a07..64460c68e4 100644 --- a/examples/vision/segmentation/paddleseg/rknpu2/python/README.md +++ b/examples/vision/segmentation/paddleseg/rknpu2/python/README.md @@ -4,7 +4,7 @@ - 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/rknpu2.md) -【注意】如你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../matting) +【注意】如你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../../matting/) 本目录下提供`infer.py`快速完成PPHumanseg在RKNPU上部署的示例。执行如下脚本即可完成 diff --git a/examples/vision/segmentation/paddleseg/web/README.md b/examples/vision/segmentation/paddleseg/web/README.md index 2402b18a73..6c214347c0 100644 --- a/examples/vision/segmentation/paddleseg/web/README.md +++ b/examples/vision/segmentation/paddleseg/web/README.md @@ -7,7 +7,7 @@ ## 前端部署PP-Humanseg v1模型 -PP-Humanseg v1模型web demo部署及使用参考[文档](../../../../application/web_demo/README.md) +PP-Humanseg v1模型web demo部署及使用参考[文档](../../../../application/js/web_demo/README.md) ## PP-Humanseg v1 js接口 @@ -41,7 +41,3 @@ humanSeg.blurBackground(res) **drawHumanSeg()函数参数** > * **seg_values**(number[]): 输入参数，一般是getGrayValue函数计算的结果作为输入 - - - - diff --git a/examples/vision/tracking/pptracking/cpp/README.md b/examples/vision/tracking/pptracking/cpp/README.md index 9adef9dc79..dd46425868 100644 --- a/examples/vision/tracking/pptracking/cpp/README.md +++ b/examples/vision/tracking/pptracking/cpp/README.md @@ -7,7 +7,7 @@ - 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 PP-Tracking 推理为例，在本目录执行如下命令即可完成编译测试（如若只需在CPU上部署，可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md/CPP_prebuilt_libraries.md)下载CPU推理库） +以Linux上 PP-Tracking 推理为例，在本目录执行如下命令即可完成编译测试（如若只需在CPU上部署，可在[Fastdeploy C++预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)下载CPU推理库） ```bash #下载SDK，编译模型examples代码（SDK中包含了examples代码）