From 61d1242c3be46f3ff499df917b92d48c7da1d1e2 Mon Sep 17 00:00:00 2001 From: FlyingQianMM <245467267@qq.com> Date: Mon, 13 Jul 2020 19:37:58 +0800 Subject: [PATCH] add arm deploy for human segmentation --- docs/examples/human_segmentation.md | 104 +++++++++++++++++++- docs/examples/images/beauty.png | Bin 0 -> 592092 bytes examples/human_segmentation/README.md | 104 +++++++++++++++++++- examples/human_segmentation/data/beauty.png | Bin 0 -> 592092 bytes 4 files changed, 204 insertions(+), 4 deletions(-) create mode 100644 docs/examples/images/beauty.png create mode 100644 examples/human_segmentation/data/beauty.png diff --git a/docs/examples/human_segmentation.md b/docs/examples/human_segmentation.md index 7bed8f1..014b22b 100644 --- a/docs/examples/human_segmentation.md +++ b/docs/examples/human_segmentation.md @@ -27,6 +27,21 @@ |humanseg_mobile_quant | 1.6M | 24.93ms | 执行以下脚本下载全部的预训练模型: + +* 下载PaddleX源码: + +```bash +git clone https://github.com/PaddlePaddle/PaddleX +``` + +* 下载预训练模型的代码位于`PaddleX/examples/human_segmentation`,进入该目录: + +```bash +cd PaddleX/examples/human_segmentation +``` + +* 执行下载 + ```bash python pretrain_weights/download_pretrain_weights.py ``` @@ -35,6 +50,8 @@ python pretrain_weights/download_pretrain_weights.py [supervise.ly](https://supervise.ly/)发布了人像分割数据集**Supervisely Persons**, 本案例从中随机抽取一小部分数据并转化成PaddleX可直接加载的数据格式,运行以下代码可下载该数据、以及手机前置摄像头拍摄的人像测试视频`video_test.mp4`. +* 下载测试数据的代码位于`PaddleX/xamples/human_segmentation`,进入该目录并执行下载: + ```bash python data/download_data.py ``` @@ -49,9 +66,21 @@ python data/download_data.py 安装的相关问题参考[PaddleX安装](../../docs/install.md) +* 下载PaddleX源码: + +```bash +git clone https://github.com/PaddlePaddle/PaddleX +``` + +* 视频流人像分割和背景替换的执行文件均位于`PaddleX/examples/human_segmentation`,进入该目录: + +```bash +cd PaddleX/examples/human_segmentation +``` + ### 光流跟踪辅助的视频流人像分割 -本案例将DIS(Dense Inverse Search-basedmethod)光流跟踪算法的预测结果与PaddleX的分割结果进行融合,以此改善视频流人像分割的效果。运行以下代码进行体验: +本案例将DIS(Dense Inverse Search-basedmethod)光流跟踪算法的预测结果与PaddleX的分割结果进行融合,以此改善视频流人像分割的效果。运行以下代码进行体验,以下代码位于`PaddleX/xamples/human_segmentation`: * 通过电脑摄像头进行实时分割处理 @@ -70,7 +99,7 @@ python video_infer.py --model_dir pretrain_weights/humanseg_mobile_inference --v ### 人像背景替换 -本案例还实现了人像背景替换功能,根据所选背景对人像的背景画面进行替换,背景可以是一张图片,也可以是一段视频。 +本案例还实现了人像背景替换功能,根据所选背景对人像的背景画面进行替换,背景可以是一张图片,也可以是一段视频。人像背景替换的代码位于`PaddleX/xamples/human_segmentation`,进入该目录并执行: * 通过电脑摄像头进行实时背景替换处理, 通过'--background_video_path'传入背景视频 ```bash @@ -105,6 +134,18 @@ python bg_replace.py --model_dir pretrain_weights/humanseg_mobile_inference --im 安装的相关问题参考[PaddleX安装](../../docs/install.md) +* 下载PaddleX源码: + +```bash +git clone https://github.com/PaddlePaddle/PaddleX +``` + +* 人像分割训练、评估、预测、模型导出、离线量化的执行文件均位于`PaddleX/examples/human_segmentation`,进入该目录: + +```bash +cd PaddleX/examples/human_segmentation +``` + ### 模型训练 使用下述命令进行基于预训练模型的模型训练,请确保选用的模型结构`model_type`与模型参数`pretrain_weights`匹配。如果不需要本案例提供的测试数据,可更换数据、选择合适的模型并调整训练参数。 @@ -203,3 +244,62 @@ python quant_offline.py --model_dir output/best_model \ * `--image_shape`: 网络输入图像大小(w, h) ## Paddle-Lite移动端部署 + +本案例将人像分割模型在移动端进行部署,部署流程展示如下,通用的移动端部署流程参见[PaddleLite移动端部署](../../docs/deploy/paddlelite/android.md)。 + +### 1. 将PaddleX模型导出为inference模型 + +本案例使用humanseg_mobile_quant预训练模型,该模型已经是inference模型,不需要再执行模型导出步骤。如果不使用预训练模型,则执行上一章节`模型训练`中的`模型导出`将自己训练的模型导出为inference格式。 + +### 2. 将inference模型优化为PaddleLite模型 + +下载并解压 [模型优化工具opt](https://bj.bcebos.com/paddlex/deploy/lite/model_optimize_tool_11cbd50e.tar.gz),进入模型优化工具opt所在路径后,执行以下命令: + +``` bash +./opt --model_file= \ + --param_file= \ + --valid_targets=arm \ + --optimize_out_type=naive_buffer \ + --optimize_out=model_output_name +``` + +| 参数 | 说明 | +| ---- | ---- | +| --model_file | 导出inference模型中包含的网络结构文件:`__model__`所在的路径| +| --param_file | 导出inference模型中包含的参数文件:`__params__`所在的路径| +| --valid_targets |指定模型可执行的backend,这里请指定为`arm`| +| --optimize_out_type | 输出模型类型,目前支持两种类型:protobuf和naive_buffer,其中naive_buffer是一种更轻量级的序列化/反序列化,这里请指定为`naive_buffer`| +| --optimize_out | 输出模型的名称 | + +更详细的使用方法和参数含义请参考: [使用opt转化模型](https://paddle-lite.readthedocs.io/zh/latest/user_guides/opt/opt_bin.html) + +### 3. 移动端预测 + +PaddleX提供了基于PaddleX Android SDK的安卓demo,可供用户体验图像分类、目标检测、实例分割和语义分割,该demo位于`PaddleX/deploy/lite/android/demo`,用户将模型、配置文件和测试图片拷贝至该demo下进行预测。 + +#### 3.1 前置依赖 + +* Android Studio 3.4 +* Android手机或开发板 + +#### 3.2 拷贝模型、配置文件和测试图片 + +* 将Lite模型(.nb文件)拷贝到`PaddleX/deploy/lite/android/demo/app/src/main/assets/model/`目录下, 根据.nb文件的名字,修改文件`PaddleX/deploy/lite/android/demo/app/src/main/res/values/strings.xml`中的`MODEL_PATH_DEFAULT`; + +* 将配置文件(.yml文件)拷贝到`PaddleX/deploy/lite/android/demo/app/src/main/assets/config/`目录下,根据.yml文件的名字,修改文件`PaddleX/deploy/lite/android/demo/app/src/main/res/values/strings.xml`中的`YAML_PATH_DEFAULT`; + +* 将测试图片拷贝到`PaddleX/deploy/lite/android/demo/app/src/main/assets/images/`目录下,根据图片文件的名字,修改文件`PaddleX/deploy/lite/android/demo/app/src/main/res/values/strings.xml`中的`IMAGE_PATH_DEFAULT`。 + +#### 3.3 导入工程并运行 + +* 打开Android Studio,在"Welcome to Android Studio"窗口点击"Open an existing Android Studio project",在弹出的路径选择窗口中进入`PaddleX/deploy/lite/android/demo`目录,然后点击右下角的"Open"按钮,导入工程; + +* 通过USB连接Android手机或开发板; + +* 工程编译完成后,点击菜单栏的Run->Run 'App'按钮,在弹出的"Select Deployment Target"窗口选择已经连接的Android设备,然后点击"OK"按钮; + +* 运行成功后,Android设备将加载一个名为PaddleX Demo的App,默认会加载一个测试图片,同时还支持拍照和从图库选择照片进行预测。 + +测试图片及其分割结果如下所示: + +![](./images/beauty.png) diff --git a/docs/examples/images/beauty.png b/docs/examples/images/beauty.png new file mode 100644 index 0000000000000000000000000000000000000000..21343fa343a33620981041179e0fc8c6a717c598 GIT binary patch literal 592092 zcmeEuWl&t-(}0FOSBB>@cqp1|{KU7&dei<#{s(|6kYh55H;RXqLtX~}g2m}q$P zs9*7=UgomrR!to%k5{)IiR`(h9UdNL9cNXwweBhK8C)C|-fBRWm#$VkveUnzMFjk} z{QswcPF4b_X0_cL4g`AI|03bkfqW0%B3&OF1{A4ByBZzNMFuKhaw%eAwGTzBhNYN| zd{qM(SbGQhNN?9Cm{g&-4-@TFv4%(~Dmj)JPp;=2Z!}z}i#9aQ)p4HnWMZY^g1d(TqwjftJPoJY^jxICEFKko(8dw$`+@))fhm%Hj!1 z``bEGF^V)BzTyO2B+)xx7 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+ +本案例将人像分割模型在移动端进行部署,部署流程展示如下,通用的移动端部署流程参见[PaddleLite移动端部署](../../docs/deploy/paddlelite/android.md)。 + +### 1. 将PaddleX模型导出为inference模型 + +本案例使用humanseg_mobile_quant预训练模型,该模型已经是inference模型,不需要再执行模型导出步骤。如果不使用预训练模型,则执行上一章节`模型训练`中的`模型导出`将自己训练的模型导出为inference格式。 + +### 2. 将inference模型优化为PaddleLite模型 + +下载并解压 [模型优化工具opt](https://bj.bcebos.com/paddlex/deploy/lite/model_optimize_tool_11cbd50e.tar.gz),进入模型优化工具opt所在路径后,执行以下命令: + +``` bash +./opt --model_file= \ + --param_file= \ + --valid_targets=arm \ + --optimize_out_type=naive_buffer \ + --optimize_out=model_output_name +``` + +| 参数 | 说明 | +| ---- | ---- | +| --model_file | 导出inference模型中包含的网络结构文件:`__model__`所在的路径| +| --param_file | 导出inference模型中包含的参数文件:`__params__`所在的路径| +| --valid_targets |指定模型可执行的backend,这里请指定为`arm`| +| --optimize_out_type | 输出模型类型,目前支持两种类型:protobuf和naive_buffer,其中naive_buffer是一种更轻量级的序列化/反序列化,这里请指定为`naive_buffer`| +| --optimize_out | 输出模型的名称 | + +更详细的使用方法和参数含义请参考: [使用opt转化模型](https://paddle-lite.readthedocs.io/zh/latest/user_guides/opt/opt_bin.html) + +### 3. 移动端预测 + +PaddleX提供了基于PaddleX Android SDK的安卓demo,可供用户体验图像分类、目标检测、实例分割和语义分割,该demo位于`PaddleX/deploy/lite/android/demo`,用户将模型、配置文件和测试图片拷贝至该demo下进行预测。 + +#### 3.1 前置依赖 + +* Android Studio 3.4 +* Android手机或开发板 + +#### 3.2 拷贝模型、配置文件和测试图片 + +* 将Lite模型(.nb文件)拷贝到`PaddleX/deploy/lite/android/demo/app/src/main/assets/model/`目录下, 根据.nb文件的名字,修改文件`PaddleX/deploy/lite/android/demo/app/src/main/res/values/strings.xml`中的`MODEL_PATH_DEFAULT`; + +* 将配置文件(.yml文件)拷贝到`PaddleX/deploy/lite/android/demo/app/src/main/assets/config/`目录下,根据.yml文件的名字,修改文件`PaddleX/deploy/lite/android/demo/app/src/main/res/values/strings.xml`中的`YAML_PATH_DEFAULT`; + +* 将测试图片拷贝到`PaddleX/deploy/lite/android/demo/app/src/main/assets/images/`目录下,根据图片文件的名字,修改文件`PaddleX/deploy/lite/android/demo/app/src/main/res/values/strings.xml`中的`IMAGE_PATH_DEFAULT`。 + +#### 3.3 导入工程并运行 + +* 打开Android Studio,在"Welcome to Android Studio"窗口点击"Open an existing Android Studio project",在弹出的路径选择窗口中进入`PaddleX/deploy/lite/android/demo`目录,然后点击右下角的"Open"按钮,导入工程; + +* 通过USB连接Android手机或开发板; + +* 工程编译完成后,点击菜单栏的Run->Run 'App'按钮,在弹出的"Select Deployment Target"窗口选择已经连接的Android设备,然后点击"OK"按钮; + +* 运行成功后,Android设备将加载一个名为PaddleX Demo的App,默认会加载一个测试图片,同时还支持拍照和从图库选择照片进行预测。 + +测试图片及其分割结果如下所示: + +![](./data/beauty.png) diff --git a/examples/human_segmentation/data/beauty.png b/examples/human_segmentation/data/beauty.png new file mode 100644 index 0000000000000000000000000000000000000000..21343fa343a33620981041179e0fc8c6a717c598 GIT binary patch literal 592092 zcmeEuWl&t-(}0FOSBB>@cqp1|{KU7&dei<#{s(|6kYh55H;RXqLtX~}g2m}q$P zs9*7=UgomrR!to%k5{)IiR`(h9UdNL9cNXwweBhK8C)C|-fBRWm#$VkveUnzMFjk} z{QswcPF4b_X0_cL4g`AI|03bkfqW0%B3&OF1{A4ByBZzNMFuKhaw%eAwGTzBhNYN| zd{qM(SbGQhNN?9Cm{g&-4-@TFv4%(~Dmj)JPp;=2Z!}z}i#9aQ)p4HnWMZY^g1d(TqwjftJPoJY^jxICEFKko(8dw$`+@))fhm%Hj!1 z``bEGF^V)BzTyO2B+)xx7 z{&O>55y(or!%58S(wLpzLvh^6X;_biR~%0k2I35l+8mDQY9?YTz=LyEf(WSB=4}(r 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