From fd341b85640e208c0e2a0d1c2e445f3d9adeea77 Mon Sep 17 00:00:00 2001 From: jiangjiajun Date: Mon, 18 May 2020 15:11:25 +0800 Subject: [PATCH] fix conflict --- docs/deploy/deploy.md | 68 ------------ docs/deploy/deploy_cpp_linux.md | 136 ------------------------ docs/deploy/deploy_cpp_win_vs2019.md | 148 --------------------------- docs/deploy/images/vs2019_step1.png | Bin 77064 -> 0 bytes docs/deploy/images/vs2019_step2.png | Bin 86315 -> 0 bytes docs/deploy/images/vs2019_step3.png | Bin 390401 -> 0 bytes docs/deploy/images/vs2019_step4.png | Bin 406162 -> 0 bytes docs/deploy/images/vs2019_step5.png | Bin 437763 -> 0 bytes docs/deploy/images/vs2019_step6.png | Bin 85323 -> 0 bytes 9 files changed, 352 deletions(-) delete mode 100644 docs/deploy/deploy.md delete mode 100644 docs/deploy/deploy_cpp_linux.md delete mode 100644 docs/deploy/deploy_cpp_win_vs2019.md delete mode 100644 docs/deploy/images/vs2019_step1.png delete mode 100644 docs/deploy/images/vs2019_step2.png delete mode 100644 docs/deploy/images/vs2019_step3.png delete mode 100644 docs/deploy/images/vs2019_step4.png delete mode 100644 docs/deploy/images/vs2019_step5.png delete mode 100644 docs/deploy/images/vs2019_step6.png diff --git a/docs/deploy/deploy.md b/docs/deploy/deploy.md deleted file mode 100644 index c7d1545..0000000 --- a/docs/deploy/deploy.md +++ /dev/null @@ -1,68 +0,0 @@ -# 模型预测部署 - -本文档指引用户如何采用更高性能地方式来部署使用PaddleX训练的模型。使用本文档模型部署方式,会在模型运算过程中,对模型计算图进行优化,同时减少内存操作,相对比普通的paddlepaddle模型加载和预测方式,预测速度平均可提升1倍,具体各模型性能对比见[预测性能对比](#预测性能对比) - -## 服务端部署 - -### 导出inference模型 - -在服务端部署的模型需要首先将模型导出为inference格式模型,导出的模型将包括`__model__`、`__params__`和`model.yml`三个文名,分别为模型的网络结构,模型权重和模型的配置文件(包括数据预处理参数等等)。在安装完PaddleX后,在命令行终端使用如下命令导出模型到当前目录`inferece_model`下。 -> 可直接下载小度熊分拣模型测试本文档的流程[xiaoduxiong_epoch_12.tar.gz](https://bj.bcebos.com/paddlex/models/xiaoduxiong_epoch_12.tar.gz) - -``` -paddlex --export_inference --model_dir=./xiaoduxiong_epoch_12 --save_dir=./inference_model -``` - -使用TensorRT预测时,需指定模型的图像输入shape:[w,h]。 -**注**: -- 分类模型请保持于训练时输入的shape一致。 -- 指定[w,h]时,w和h中间逗号隔开,不允许存在空格等其他字符 - -``` -paddlex --export_inference --model_dir=./xiaoduxiong_epoch_12 --save_dir=./inference_model --fixed_input_shape=[640,960] -``` - -### Python部署 -PaddleX已经集成了基于Python的高性能预测接口,在安装PaddleX后,可参照如下代码示例,进行预测。相关的接口文档可参考[paddlex.deploy](apis/deploy.md) -> 点击下载测试图片 [xiaoduxiong_test_image.tar.gz](https://bj.bcebos.com/paddlex/datasets/xiaoduxiong_test_image.tar.gz) - -``` -import paddlex as pdx -predictor = pdx.deploy.create_predictor('./inference_model') -result = predictor.predict(image='xiaoduxiong_test_image/JPEGImages/WeChatIMG110.jpeg') -``` - -### C++部署 - -C++部署方案位于目录`deploy/cpp/`下,且独立于PaddleX其他模块。该方案支持在 Windows 和 Linux 完成编译、二次开发集成和部署运行。具体使用方法和编译: - -- Linux平台:[linux](deploy_cpp_linux.md) -- window平台:[windows](deploy_cpp_win_vs2019.md) - -### 预测性能对比 - -#### 测试环境 - -- CUDA 9.0 -- CUDNN 7.5 -- PaddlePaddle 1.71 -- GPU: Tesla P40 -- AnalysisPredictor 指采用Python的高性能预测方式 -- Executor 指采用paddlepaddle普通的python预测方式 -- Batch Size均为1,耗时单位为ms/image,只计算模型运行时间,不包括数据的预处理和后处理 - -| 模型 | AnalysisPredictor耗时 | Executor耗时 | 输入图像大小 | -| :---- | :--------------------- | :------------ | :------------ | -| resnet50 | 4.84 | 7.57 | 224*224 | -| mobilenet_v2 | 3.27 | 5.76 | 224*224 | -| unet | 22.51 | 34.60 |513*513 | -| deeplab_mobile | 63.44 | 358.31 |1025*2049 | -| yolo_mobilenetv2 | 15.20 | 19.54 | 608*608 | -| faster_rcnn_r50_fpn_1x | 50.05 | 69.58 |800*1088 | -| faster_rcnn_r50_1x | 326.11 | 347.22 | 800*1067 | -| mask_rcnn_r50_fpn_1x | 67.49 | 91.02 | 800*1088 | -| mask_rcnn_r50_1x | 326.11 | 350.94 | 800*1067 | - -## 移动端部署 - -> Lite模型导出正在集成中,即将开源... diff --git a/docs/deploy/deploy_cpp_linux.md b/docs/deploy/deploy_cpp_linux.md deleted file mode 100644 index a2eb2f5..0000000 --- a/docs/deploy/deploy_cpp_linux.md +++ /dev/null @@ -1,136 +0,0 @@ -# Linux平台编译指南 - -## 说明 -本文档在 `Linux`平台使用`GCC 4.8.5` 和 `GCC 4.9.4`测试过,如果需要使用更高G++版本编译使用,则需要重新编译Paddle预测库,请参考: [从源码编译Paddle预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html#id12)。 - -## 前置条件 -* G++ 4.8.2 ~ 4.9.4 -* CUDA 9.0 / CUDA 10.0, CUDNN 7+ (仅在使用GPU版本的预测库时需要) -* CMake 3.0+ - -请确保系统已经安装好上述基本软件,**下面所有示例以工作目录 `/root/projects/`演示**。 - -### Step1: 下载代码 - - `git clone https://github.com/PaddlePaddle/PaddleX.git` - -**说明**:其中`C++`预测代码在`/root/projects/PaddleX/deploy/cpp` 目录,该目录不依赖任何`PaddleX`下其他目录。 - - -### Step2: 下载PaddlePaddle C++ 预测库 fluid_inference - -PaddlePaddle C++ 预测库针对不同的`CPU`,`CUDA`,以及是否支持TensorRT,提供了不同的预编译版本,目前PaddleX依赖于Paddle1.7版本,以下提供了多个不同版本的Paddle预测库: - -| 版本说明 | 预测库(1.7.2版本) | -| ---- | ---- | -| ubuntu14.04_cpu_avx_mkl | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.7.2-cpu-avx-mkl/fluid_inference.tgz) | -| ubuntu14.04_cpu_avx_openblas | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.7.2-cpu-avx-openblas/fluid_inference.tgz) | -| ubuntu14.04_cpu_noavx_openblas | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.7.2-cpu-noavx-openblas/fluid_inference.tgz) | -| ubuntu14.04_cuda9.0_cudnn7_avx_mkl | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.7.2-gpu-cuda9-cudnn7-avx-mkl/fluid_inference.tgz) | -| ubuntu14.04_cuda10.0_cudnn7_avx_mkl | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.7.2-gpu-cuda10-cudnn7-avx-mkl/fluid_inference.tgz ) | -| ubuntu14.04_cuda10.1_cudnn7.6_avx_mkl_trt6 | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.7.2-gpu-cuda10.1-cudnn7.6-avx-mkl-trt6%2Ffluid_inference.tgz) | - -更多和更新的版本,请根据实际情况下载: [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/windows_cpp_inference.html#id1) - -下载并解压后`/root/projects/fluid_inference`目录包含内容为: -``` -fluid_inference -├── paddle # paddle核心库和头文件 -| -├── third_party # 第三方依赖库和头文件 -| -└── version.txt # 版本和编译信息 -``` - -**注意:** 预编译版本除`nv-jetson-cuda10-cudnn7.5-trt5` 以外其它包都是基于`GCC 4.8.5`编译,使用高版本`GCC`可能存在 `ABI`兼容性问题,建议降级或[自行编译预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html#id12)。 - - -### Step4: 编译 - -编译`cmake`的命令在`scripts/build.sh`中,请根据实际情况修改主要参数,其主要内容说明如下: -``` -# 是否使用GPU(即是否使用 CUDA) -WITH_GPU=OFF -# 使用MKL or openblas -WITH_MKL=ON -# 是否集成 TensorRT(仅WITH_GPU=ON 有效) -WITH_TENSORRT=OFF -# TensorRT 的lib路径 -TENSORRT_DIR=/path/to/TensorRT/ -# Paddle 预测库路径 -PADDLE_DIR=/path/to/fluid_inference/ -# Paddle 的预测库是否使用静态库来编译 -# 使用TensorRT时,Paddle的预测库通常为动态库 -WITH_STATIC_LIB=ON -# CUDA 的 lib 路径 -CUDA_LIB=/path/to/cuda/lib/ -# CUDNN 的 lib 路径 -CUDNN_LIB=/path/to/cudnn/lib/ - -# OPENCV 路径, 如果使用自带预编译版本可不修改 -OPENCV_DIR=$(pwd)/deps/opencv3gcc4.8/ -sh $(pwd)/scripts/bootstrap.sh - -# 以下无需改动 -rm -rf build -mkdir -p build -cd build -cmake .. \ - -DWITH_GPU=${WITH_GPU} \ - -DWITH_MKL=${WITH_MKL} \ - -DWITH_TENSORRT=${WITH_TENSORRT} \ - -DTENSORRT_DIR=${TENSORRT_DIR} \ - -DPADDLE_DIR=${PADDLE_DIR} \ - -DWITH_STATIC_LIB=${WITH_STATIC_LIB} \ - -DCUDA_LIB=${CUDA_LIB} \ - -DCUDNN_LIB=${CUDNN_LIB} \ - -DOPENCV_DIR=${OPENCV_DIR} -make - -``` - -修改脚本设置好主要参数后,执行`build`脚本: - ```shell - sh ./scripts/build.sh - ``` - -### Step5: 预测及可视化 -编译成功后,预测demo的可执行程序分别为`build/demo/detector`,`build/demo/classifer`,`build/demo/segmenter`,用户可根据自己的模型类型选择,其主要命令参数说明如下: - -| 参数 | 说明 | -| ---- | ---- | -| model_dir | 导出的预测模型所在路径 | -| image | 要预测的图片文件路径 | -| image_list | 按行存储图片路径的.txt文件 | -| use_gpu | 是否使用 GPU 预测, 支持值为0或1(默认值为0) | -| use_trt | 是否使用 TensorTr 预测, 支持值为0或1(默认值为0) | -| gpu_id | GPU 设备ID, 默认值为0 | -| save_dir | 保存可视化结果的路径, 默认值为"output",classfier无该参数 | - -## 样例 - -可使用[小度熊识别模型](deploy.md#导出inference模型)中导出的`inference_model`和测试图片进行预测。 - -`样例一`: - -不使用`GPU`测试图片 `/path/to/xiaoduxiong.jpeg` - -```shell -./build/demo/detector --model_dir=/path/to/inference_model --image=/path/to/xiaoduxiong.jpeg --save_dir=output -``` -图片文件`可视化预测结果`会保存在`save_dir`参数设置的目录下。 - - -`样例二`: - -使用`GPU`预测多个图片`/path/to/image_list.txt`,image_list.txt内容的格式如下: -``` -/path/to/images/xiaoduxiong1.jpeg -/path/to/images/xiaoduxiong2.jpeg -... -/path/to/images/xiaoduxiongn.jpeg -``` -```shell -./build/demo/detector --model_dir=/path/to/models/inference_model --image_list=/root/projects/images_list.txt --use_gpu=1 --save_dir=output -``` -图片文件`可视化预测结果`会保存在`save_dir`参数设置的目录下。 diff --git a/docs/deploy/deploy_cpp_win_vs2019.md b/docs/deploy/deploy_cpp_win_vs2019.md deleted file mode 100644 index 2572236..0000000 --- a/docs/deploy/deploy_cpp_win_vs2019.md +++ /dev/null @@ -1,148 +0,0 @@ -# Visual Studio 2019 Community CMake 编译指南 - -## 说明 -Windows 平台下,我们使用`Visual Studio 2019 Community` 进行了测试。微软从`Visual Studio 2017`开始即支持直接管理`CMake`跨平台编译项目,但是直到`2019`才提供了稳定和完全的支持,所以如果你想使用CMake管理项目编译构建,我们推荐你使用`Visual Studio 2019`环境下构建。 - -## 前置条件 -* Visual Studio 2019 -* CUDA 9.0 / CUDA 10.0, CUDNN 7+ (仅在使用GPU版本的预测库时需要) -* CMake 3.0+ - -请确保系统已经安装好上述基本软件,我们使用的是`VS2019`的社区版。 - -**下面所有示例以工作目录为 `D:\projects`演示**。 - -### Step1: 下载代码 - -下载源代码 -```shell -d: -mkdir projects -cd projects -git clone https://github.com/PaddlePaddle/PaddleX.git -``` - -**说明**:其中`C++`预测代码在`PaddleX/deploy/cpp` 目录,该目录不依赖任何`PaddleX`下其他目录。 - - -### Step2: 下载PaddlePaddle C++ 预测库 fluid_inference - -PaddlePaddle C++ 预测库针对不同的`CPU`,`CUDA`,以及是否支持TensorRT,提供了不同的预编译版本,目前PaddleX依赖于Paddle1.7版本,以下提供了多个不同版本的Paddle预测库: - -| 版本说明 | 预测库(1.7.2版本) | 编译器 | 构建工具| cuDNN | CUDA -| ---- | ---- | ---- | ---- | ---- | ---- | -| cpu_avx_mkl | [fluid_inference.zip](https://paddle-wheel.bj.bcebos.com/1.7.2/win-infer/mkl/cpu/fluid_inference_install_dir.zip) | MSVC 2015 update 3 | CMake v3.16.0 | -| cpu_avx_openblas | [fluid_inference.zip](https://paddle-wheel.bj.bcebos.com/1.7.2/win-infer/open/cpu/fluid_inference_install_dir.zip) | MSVC 2015 update 3 | CMake v3.16.0 | -| cuda9.0_cudnn7_avx_mkl | [fluid_inference.zip](https://paddle-wheel.bj.bcebos.com/1.7.2/win-infer/mkl/post97/fluid_inference_install_dir.zip) | MSVC 2015 update 3 | CMake v3.16.0 | 7.4.1 | 9.0 | -| cuda9.0_cudnn7_avx_openblas | [fluid_inference.zip](https://paddle-wheel.bj.bcebos.com/1.7.2/win-infer/open/post97/fluid_inference_install_dir.zip) | MSVC 2015 update 3 | CMake v3.16.0 | 7.4.1 | 9.0 | -| cuda10.0_cudnn7_avx_mkl | [fluid_inference.zip](https://paddle-wheel.bj.bcebos.com/1.7.2/win-infer/mkl/post107/fluid_inference_install_dir.zip) | MSVC 2015 update 3 | CMake v3.16.0 | 7.5.0 | 9.0 | - - -更多和更新的版本,请根据实际情况下载: [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html#id1) - -解压后`D:\projects\fluid_inference*\`目录下主要包含的内容为: -``` -├── \paddle\ # paddle核心库和头文件 -| -├── \third_party\ # 第三方依赖库和头文件 -| -└── \version.txt # 版本和编译信息 -``` - -### Step3: 安装配置OpenCV - -1. 在OpenCV官网下载适用于Windows平台的3.4.6版本, [下载地址](https://sourceforge.net/projects/opencvlibrary/files/3.4.6/opencv-3.4.6-vc14_vc15.exe/download) -2. 运行下载的可执行文件,将OpenCV解压至指定目录,如`D:\projects\opencv` -3. 配置环境变量,如下流程所示 - - 我的电脑->属性->高级系统设置->环境变量 - - 在系统变量中找到Path(如没有,自行创建),并双击编辑 - - 新建,将opencv路径填入并保存,如`D:\projects\opencv\build\x64\vc14\bin` - -### Step4: 使用Visual Studio 2019直接编译CMake - -1. 打开Visual Studio 2019 Community,点击`继续但无需代码` - -![step2](images/vs2019_step1.png) - -2. 点击: `文件`->`打开`->`CMake` - -![step2.1](images/vs2019_step2.png) - -选择项目代码所在路径,并打开`CMakeList.txt`: - -![step2.2](images/vs2019_step3.png) - -3. 点击:`项目`->`PADDLEX_INFERENCE的CMake设置` - -![step3](images/vs2019_step4.png) - -4. 点击`浏览`,分别设置编译选项指定`CUDA`、`OpenCV`、`Paddle预测库`的路径 - -依赖库路径的含义说明如下(带*表示仅在使用**GPU版本**预测库时指定, 其中CUDA库版本尽量对齐,**使用9.0、10.0版本,不使用9.2、10.1等版本CUDA库**): - -| 参数名 | 含义 | -| ---- | ---- | -| *CUDA_LIB | CUDA的库路径, 注:请将CUDNN的cudnn.lib文件拷贝到CUDA_LIB路径下 | -| OPENCV_DIR | OpenCV的安装路径, | -| PADDLE_DIR | Paddle c++预测库的路径 | - -**注意:** 1. 使用`CPU`版预测库,请把`WITH_GPU`的`值`去掉勾 2. 如果使用的是`openblas`版本,请把`WITH_MKL`的`值`去掉勾 - -![step4](images/vs2019_step5.png) - -**设置完成后**, 点击上图中`保存并生成CMake缓存以加载变量`。 - -5. 点击`生成`->`全部生成` - -![step6](images/vs2019_step6.png) - - -### Step5: 预测及可视化 - -上述`Visual Studio 2019`编译产出的可执行文件在`out\build\x64-Release`目录下,打开`cmd`,并切换到该目录: - -``` -d: -cd D:\projects\PaddleX\deploy\cpp\out\build\x64-Release -``` - -编译成功后,预测demo的入口程序为`demo\detector`,`demo\classifer`,`demo\segmenter`,用户可根据自己的模型类型选择,其主要命令参数说明如下: - -| 参数 | 说明 | -| ---- | ---- | -| model_dir | 导出的预测模型所在路径 | -| image | 要预测的图片文件路径 | -| image_list | 按行存储图片路径的.txt文件 | -| use_gpu | 是否使用 GPU 预测, 支持值为0或1(默认值为0) | -| gpu_id | GPU 设备ID, 默认值为0 | -| save_dir | 保存可视化结果的路径, 默认值为"output",classfier无该参数 | - - -## 样例 - -可使用[小度熊识别模型](deploy.md#导出inference模型)中导出的`inference_model`和测试图片进行预测。 - -`样例一`: - -不使用`GPU`测试图片 `\\path\\to\\xiaoduxiong.jpeg` - -```shell -.\detector --model_dir=\\path\\to\\inference_model --image=D:\\images\\xiaoduxiong.jpeg --save_dir=output - -``` -图片文件`可视化预测结果`会保存在`save_dir`参数设置的目录下。 - - -`样例二`: - -使用`GPU`预测多个图片`\\path\\to\\image_list.txt`,image_list.txt内容的格式如下: -``` -\\path\\to\\images\\xiaoduxiong1.jpeg -\\path\\to\\images\\xiaoduxiong2.jpeg -... -\\path\\to\\images\\xiaoduxiongn.jpeg -``` -```shell -.\detector --model_dir=\\path\\to\\inference_model --image_list=\\path\\to\\images_list.txt --use_gpu=1 --save_dir=output -``` -图片文件`可视化预测结果`会保存在`save_dir`参数设置的目录下。 diff --git a/docs/deploy/images/vs2019_step1.png b/docs/deploy/images/vs2019_step1.png deleted file mode 100644 index 58fdf2fd624f3c2d26f7c78814c4a67cc323e495..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 77064 zcmeFYb9ZIm)-4=66;y27c2X7Fwr#s&n-x2$WXHB`+qSJ+^*hh|oVw?Hf%lKw+H9-s zwq{#D^ReKvXOkvP2{_a9=cB zbXO=55lDDW00B%icx*KRnpHp^(E~6NsxuDFlH4*RjlRj_>bk9LNBZmNaPwi7W2fuw zJ1EdTwI+!~04k6ex=Cp7JI`^o{k78@K^L&G0I<;3(1DoHSXC9lkCnSO*H=4ifv>mH z5n)=NAMd`b!gR7dz(6p9oN?6K1^HemxUw8X_7Ff~oF|KuBZ$&h(1}U?W_}d9*;{** 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