mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
[Doc] Fix dead links (#517)
* first commit for yolov7 * pybind for yolov7 * CPP README.md * CPP README.md * modified yolov7.cc * README.md * python file modify * delete license in fastdeploy/ * repush the conflict part * README.md modified * README.md modified * file path modified * file path modified * file path modified * file path modified * file path modified * README modified * README modified * move some helpers to private * add examples for yolov7 * api.md modified * api.md modified * api.md modified * YOLOv7 * yolov7 release link * yolov7 release link * yolov7 release link * copyright * change some helpers to private * change variables to const and fix documents. * gitignore * Transfer some funtions to private member of class * Transfer some funtions to private member of class * Merge from develop (#9) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> * first commit for yolor * for merge * Develop (#11) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> * Yolor (#16) * Develop (#11) (#12) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> * Develop (#13) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> * documents * documents * documents * documents * documents * documents * documents * documents * documents * documents * documents * documents * Develop (#14) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> Co-authored-by: Jason <928090362@qq.com> * add is_dynamic for YOLO series (#22) * modify ppmatting backend and docs * modify ppmatting docs * fix the PPMatting size problem * fix LimitShort's log * retrigger ci * modify PPMatting docs * modify the way for dealing with LimitShort * add python comments for external models * modify resnet c++ comments * modify C++ comments for external models * modify python comments and add result class comments * fix comments compile error * modify result.h comments * first commit for dead links * first commit for dead links * fix docs deadlinks * fix docs deadlinks * fix examples deadlinks * fix examples deadlinks Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> Co-authored-by: Jason <928090362@qq.com>
This commit is contained in:
ziqi-jin
@@ -2,7 +2,7 @@
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This directory help to generate Python API documents for FastDeploy.
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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.
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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.
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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
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3. Execute command `make html` to generate API documents
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@@ -102,4 +102,4 @@ make install
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如何使用FastDeploy Android C++ SDK 请参考使用案例文档：
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- [图像分类Android使用文档](../../../examples/vision/classification/paddleclas/android/README.md)
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- [目标检测Android使用文档](../../../examples/vision/detection/paddledetection/android/README.md)
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- [在 Android 通过 JNI 中使用 FastDeploy C++ SDK](../../../../../docs/cn/faq/use_cpp_sdk_on_android.md)
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- [在 Android 通过 JNI 中使用 FastDeploy C++ SDK](../../cn/faq/use_cpp_sdk_on_android.md)
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@@ -218,7 +218,7 @@ D:\qiuyanjun\fastdeploy_test\infer_ppyoloe\x64\Release\infer_ppyoloe.exe
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（2）其中infer_ppyoloe.cpp的代码可以直接从examples中的代码拷贝过来：
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- [examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc](../../examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc)
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- [examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc](../../../examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc)
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（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：
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@@ -221,7 +221,7 @@ This section is for CMake users and describes how to create CMake projects in Vi
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（2）The code of infer_ppyoloe.cpp can be copied directly from the code in examples：
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- [examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc](../../examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc)
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- [examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc](../../../examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc)
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（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：
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@@ -27,7 +27,7 @@ FastDeploy基于PaddleSlim, 集成了一键模型量化的工具, 同时, FastDe
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### 用户使用FastDeploy一键模型量化工具来量化模型
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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.
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- [FastDeploy 一键模型量化](../../tools/quantization/)
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- [FastDeploy 一键模型量化](../../tools/auto_compression/)
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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.
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@@ -168,4 +168,4 @@ entity: 华夏 label: LOC pos: [14, 15]
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## 配置修改
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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)
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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)
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@@ -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
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- 加载模型
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- 调用`predict`接口
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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)。
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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)。
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@@ -8,7 +8,7 @@
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### 量化模型准备
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- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
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- 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.)
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- 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.)
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## 以量化后的ResNet50_Vd模型为例, 进行部署
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在本目录执行如下命令即可完成编译,以及量化模型部署.
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@@ -8,7 +8,7 @@
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### 量化模型准备
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- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
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- 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.)
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- 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.)
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## 以量化后的ResNet50_Vd模型为例, 进行部署
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@@ -6,7 +6,7 @@
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## 前端部署图像分类模型
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图像分类模型web demo使用[**参考文档**](../../../../examples/application/js/web_demo)
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图像分类模型web demo使用[**参考文档**](../../../../application/js/web_demo/)
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## MobileNet js接口
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@@ -34,4 +34,3 @@ console.log(res);
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- [PaddleClas模型 python部署](../../paddleclas/python/)
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- [PaddleClas模型 C++部署](../cpp/)
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@@ -4,8 +4,8 @@
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/environment.md)
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- 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)
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- 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)
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- 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)
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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
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@@ -33,7 +33,7 @@ wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/Ima
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```
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以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
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- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/compile/how_to_use_sdk_on_windows.md)
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- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
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## ResNet C++接口
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@@ -74,4 +74,4 @@ fastdeploy::vision::classification::ResNet(
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- [模型介绍](../../)
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- [Python部署](../python)
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- [视觉模型预测结果](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/runtime/how_to_change_backend.md)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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@@ -2,8 +2,8 @@
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/environment.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/quick_start)
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- 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)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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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
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@@ -69,4 +69,4 @@ fd.vision.classification.ResNet(model_file, params_file, runtime_option=None, mo
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- [ResNet 模型介绍](..)
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- [ResNet C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/runtime/how_to_change_backend.md)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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@@ -9,7 +9,7 @@
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### 量化模型准备
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- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
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- 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.
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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.
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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.
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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:
|
||||
@@ -62,7 +62,7 @@ YOLOv7 model loading and initialisation, with model_file being the exported ONNX
|
||||
|
||||
> **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)
|
||||
|
||||
@@ -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.
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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)
|
||||
|
||||
@@ -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)
|
||||
|
||||
@@ -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）
|
||||
|
||||
@@ -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
|
||||
|
||||
|
||||
@@ -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)
|
||||
|
||||
@@ -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**
|
||||
|
||||
|
||||
@@ -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.
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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
|
||||
|
||||
|
||||
@@ -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
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -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）
|
||||
|
||||
Reference in New Issue
Block a user