pull develop

.gitignore

@@ -4,7 +4,6 @@ __pycache__/
 *$py.class
 
 # C extensions
-*.so
 
 # Distribution / packaging
 .Python

README.md

@@ -64,11 +64,15 @@ $ pip install -r requirements.txt
 * [如何训练U-Net](./turtorial/finetune_unet.md)
 * [如何训练ICNet](./turtorial/finetune_icnet.md)
 * [如何训练PSPNet](./turtorial/finetune_pspnet.md)
+* [如何训练HRNet](./turtorial/finetune_hrnet.md)
 
 ### 预测部署
 
 * [模型导出](./docs/model_export.md)
-* [C++预测库使用](./inference)
+* [使用Python预测](./deploy/python/)
+* [使用C++预测](./deploy/cpp/)
+* [移动端预测部署](./deploy/lite/)
+
 
 ### 高级功能
 

configs/hrnet_w18_pet.yaml

+TRAIN_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling
+EVAL_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling
+AUG:
+    AUG_METHOD: "unpadding" # choice unpadding rangescaling and stepscaling
+    FIX_RESIZE_SIZE: (512, 512) # (width, height), for unpadding
+
+    INF_RESIZE_VALUE: 500  # for rangescaling
+    MAX_RESIZE_VALUE: 600  # for rangescaling
+    MIN_RESIZE_VALUE: 400  # for rangescaling
+
+    MAX_SCALE_FACTOR: 1.25  # for stepscaling
+    MIN_SCALE_FACTOR: 0.75  # for stepscaling
+    SCALE_STEP_SIZE: 0.25  # for stepscaling
+    MIRROR: True
+BATCH_SIZE: 4
+DATASET:
+    DATA_DIR: "./dataset/mini_pet/"
+    IMAGE_TYPE: "rgb"  # choice rgb or rgba
+    NUM_CLASSES: 3
+    TEST_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt"
+    TRAIN_FILE_LIST: "./dataset/mini_pet/file_list/train_list.txt"
+    VAL_FILE_LIST: "./dataset/mini_pet/file_list/val_list.txt"
+    VIS_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt"
+    IGNORE_INDEX: 255
+    SEPARATOR: " "
+FREEZE:
+    MODEL_FILENAME: "__model__"
+    PARAMS_FILENAME: "__params__"
+MODEL:
+    MODEL_NAME: "hrnet"
+    DEFAULT_NORM_TYPE: "bn"
+    HRNET:
+      STAGE2:
+        NUM_CHANNELS: [18, 36]
+      STAGE3:
+        NUM_CHANNELS: [18, 36, 72]
+      STAGE4:
+        NUM_CHANNELS: [18, 36, 72, 144]
+TRAIN:
+    PRETRAINED_MODEL_DIR: "./pretrained_model/hrnet_w18_bn_cityscapes/"
+    MODEL_SAVE_DIR: "./saved_model/hrnet_w18_bn_pet/"
+    SNAPSHOT_EPOCH: 10
+TEST:
+    TEST_MODEL: "./saved_model/hrnet_w18_bn_pet/final"
+SOLVER:
+    NUM_EPOCHS: 100
+    LR: 0.005
+    LR_POLICY: "poly"
+    OPTIMIZER: "sgd"

deploy/README.md

+# PaddleSeg 预测部署
+
+`PaddleSeg`目前支持使用`Python`和`C++`部署在`Windows` 和`Linux` 上, 也可以集成`PaddleServing`服务化部署在 `Linux` 上。
+
+[1. Python预测(支持 Linux 和 Windows)](./python/)
+
+[2. C++预测(支持 Linux 和 Windows)](./cpp/)
+
+[3. 服务化部署(仅支持 Linux)](./serving)
+
+[4. 移动端部署(仅支持Android)](./lite)
+

inference/CMakeLists.txt → deploy/cpp/CMakeLists.txt

@@ -195,8 +195,8 @@ endif(NOT WIN32)
 if(WITH_GPU)
   if(NOT WIN32)
     if (USE_TENSORRT)
-      set(DEPS ${DEPS} ${PADDLE_DIR}/third_party/install/tensorrt/lib/libnvinfer${CMAKE_STATIC_LIBRARY_SUFFIX})
-      set(DEPS ${DEPS} ${PADDLE_DIR}/third_party/install/tensorrt/lib/libnvinfer_plugin${CMAKE_STATIC_LIBRARY_SUFFIX})
+      set(DEPS ${DEPS} ${PADDLE_DIR}/third_party/install/tensorrt/lib/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
+      set(DEPS ${DEPS} ${PADDLE_DIR}/third_party/install/tensorrt/lib/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX})
     endif()
     set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX})
     set(DEPS ${DEPS} ${CUDNN_LIB}/libcudnn${CMAKE_SHARED_LIBRARY_SUFFIX})

inference/README.md → deploy/cpp/README.md

-# PaddleSeg C++预测部署方案
+# PaddleSeg 预测部署方案
 
 
 [1.说明](#1说明)
@@ -11,7 +11,7 @@
 
 ## 1.说明
 
-本目录提供一个跨平台的图像分割模型的C++预测部署方案，用户通过一定的配置，加上少量的代码，即可把模型集成到自己的服务中，完成图像分割的任务。
+本目录提供一个跨平台`PaddlePaddle`图像分割模型的`C++`预测部署方案，用户通过一定的配置，加上少量的代码，即可把模型集成到自己的服务中，完成图像分割的任务。
 
 主要设计的目标包括以下四点：
 - 跨平台，支持在 windows 和 Linux 完成编译、开发和部署
@@ -19,11 +19,13 @@
 - 可扩展性，支持用户针对新模型开发自己特殊的数据预处理、后处理等逻辑
 - 高性能，除了`PaddlePaddle`自身带来的性能优势，我们还针对图像分割的特点对关键步骤进行了性能优化
 
+**注意** 如需要使用`Python`的预测部署方法，请参考：[Python预测部署](../python/)
+
 
 ## 2.主要目录和文件
 
 ```
-inference
+cpp
 ├── demo.cpp # 演示加载模型、读入数据、完成预测任务C++代码
 |
 ├── conf
@@ -88,6 +90,8 @@ deeplabv3p_xception65_humanseg
 DEPLOY:
     # 是否使用GPU预测
     USE_GPU: 1
+    # 是否是PaddleSeg 0.3.0新版本模型
+    USE_PR : 1
     # 模型和参数文件所在目录路径
     MODEL_PATH: "/root/projects/models/deeplabv3p_xception65_humanseg"
     # 模型文件名
@@ -123,11 +127,11 @@ DEPLOY:
 
 `Linux` 系统中执行以下命令：
 ```shell
-./demo --conf=/root/projects/PaddleSeg/inference/conf/humanseg.yaml --input_dir=/root/projects/PaddleSeg/inference/images/humanseg/
+./demo --conf=/root/projects/PaddleSeg/deploy/cpp/conf/humanseg.yaml --input_dir=/root/projects/PaddleSeg/deploy/cpp/images/humanseg/
 ```
 `Windows` 中执行以下命令:
 ```shell
-D:\projects\PaddleSeg\inference\build\Release>demo.exe --conf=D:\\projects\\PaddleSeg\\inference\\conf\\humanseg.yaml --input_dir=D:\\projects\\PaddleSeg\\inference\\images\humanseg\\
+D:\projects\PaddleSeg\deploy\cpp\build\Release>demo.exe --conf=D:\\projects\\PaddleSeg\\deploy\\cpp\\conf\\humanseg.yaml --input_dir=D:\\projects\\PaddleSeg\\deploy\\cpp\\images\humanseg\\
 ```
 
 
@@ -139,7 +143,7 @@ D:\projects\PaddleSeg\inference\build\Release>demo.exe --conf=D:\\projects\\Padd
 | input_dir | 需要预测的图片目录 |
 
 
-配置文件说明请参考上一步，样例程序会扫描input_dir目录下的所有以**jpg或jpeg**为后缀的图片，并生成对应的预测结果（若input_dir目录下没有以**jpg或jpeg**为后缀的图片，程序会报错）。图像分割会对`demo.jpg`的每个像素进行分类，其预测的结果保存在`demo_jpg.png`中。分割预测结果的图不能直接看到效果，必须经过可视化处理。对于二分类的图像分割模型，样例程序自动将预测结果转换成可视化结果，保存在`demo_jpg_scoremap.png`中， 原始尺寸的预测结果在`demo_jpg_recover.png`中，如下图。对于**多分类**的图像分割模型，请参考[可视化脚本使用方法](./docs/vis.md)。
+配置文件说明请参考上一步，样例程序会扫描input_dir目录下的所有以**jpg或jpeg**为后缀的图片，并生成对应的预测结果（若input_dir目录下没有以**jpg或jpeg**为后缀的图片，程序会报错）。图像分割会对`demo.jpg`的每个像素进行分类，其预测的结果保存在`demo_jpg_mask.png`中。分割预测结果的图不能直接看到效果，必须经过可视化处理。对于二分类的图像分割模型。如果需要对预测结果进行**可视化**，请参考[可视化脚本使用方法](./docs/vis.md)。
 
 输入原图  
 ![avatar](images/humanseg/demo2.jpeg)

inference/demo.cpp → deploy/cpp/demo.cpp

@@ -24,7 +24,7 @@ int main(int argc, char** argv) {
     google::ParseCommandLineFlags(&argc, &argv, true);
     if (FLAGS_conf.empty() || FLAGS_input_dir.empty()) {
         std::cout << "Usage: ./predictor --conf=/config/path/to/your/model "
-                  << "--input_dir=/directory/of/your/input/images";
+                  << "--input_dir=/directory/of/your/input/images" << std::endl;
         return -1;
     }
     // 1. create a predictor and init it with conf

inference/docs/linux_build.md → deploy/cpp/docs/linux_build.md

@@ -16,7 +16,7 @@
 1. `mkdir -p /root/projects/ && cd /root/projects`
 2. `git clone https://github.com/PaddlePaddle/PaddleSeg.git`
 
-`C++`预测代码在`/root/projects/PaddleSeg/inference` 目录，该目录不依赖任何`PaddleSeg`下其他目录。
+`C++`预测代码在`/root/projects/PaddleSeg/deploy/cpp` 目录，该目录不依赖任何`PaddleSeg`下其他目录。
 
 
 ### Step2: 下载PaddlePaddle C++ 预测库 fluid_inference
@@ -25,9 +25,9 @@ PaddlePaddle C++ 预测库主要分为CPU版本和GPU版本。其中，针对不
 
 |  版本   | 链接  |
 |  ----  | ----  |
-| CPU版本  | [fluid_inference.tgz](https://bj.bcebos.com/paddlehub/paddle_inference_lib/fluid_inference_linux_cpu_1.6.1.tgz) |
-| CUDA 9.0版本  | [fluid_inference.tgz](https://bj.bcebos.com/paddlehub/paddle_inference_lib/fluid_inference_linux_cuda97_1.6.1.tgz) |
-| CUDA 10.0版本  | [fluid_inference.tgz](https://bj.bcebos.com/paddlehub/paddle_inference_lib/fluid_inference_linux_cuda10_1.6.1.tgz) |
+| CPU版本  | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.6.1-cpu-avx-mkl/fluid_inference.tgz) |
+| CUDA 9.0版本  | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.6.1-gpu-cuda9-cudnn7-avx-mkl/fluid_inference.tgz) |
+| CUDA 10.0版本  | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.6.1-gpu-cuda10-cudnn7-avx-mkl/fluid_inference.tgz) |
 
 
 针对不同的CPU类型、不同的指令集，官方提供更多可用的预测库版本，目前已经推出1.6版本的预测库。其余版本具体请参考以下链接:[C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_usage/deploy/inference/build_and_install_lib_cn.html)
@@ -75,7 +75,7 @@ make install
 在使用**GPU版本**预测库进行编译时，可执行下列操作。**注意**把对应的参数改为你的上述依赖库实际路径：
 
 ```shell
-cd /root/projects/PaddleSeg/inference
+cd /root/projects/PaddleSeg/deploy/cpp
 mkdir build && cd build
 cmake .. -DWITH_GPU=ON  -DPADDLE_DIR=/root/projects/fluid_inference -DCUDA_LIB=/usr/local/cuda/lib64/ -DOPENCV_DIR=/root/projects/opencv3/ -DCUDNN_LIB=/usr/local/cuda/lib64/ -DWITH_STATIC_LIB=OFF
 make
@@ -83,7 +83,7 @@ make
 
 在使用**CPU版本**预测库进行编译时，可执行下列操作。
 ```shell
-cd /root/projects/PaddleSeg/inference
+cd /root/projects/PaddleSeg/cpp
 
 mkdir build && cd build
 cmake .. -DWITH_GPU=OFF  -DPADDLE_DIR=/root/projects/fluid_inference -DOPENCV_DIR=/root/projects/opencv3/ -DWITH_STATIC_LIB=OFF
@@ -98,4 +98,4 @@ make
 ./demo --conf=/path/to/your/conf --input_dir=/path/to/your/input/data/directory
 ```
 
-更详细说明请参考README文档： [预测和可视化部分](../README.md)
\ No newline at end of file
+更详细说明请参考README文档： [预测和可视化部分](../README.md)

inference/docs/vis.md → deploy/cpp/docs/vis.md

@@ -12,7 +12,7 @@ cd inference/tools/
 # 拷贝保存分割预测结果的图片到本目录
 cp XXX/demo_jpg.png .
 # 运行可视化脚本
-python visualize.py demo.jpg demo_jpg.png vis_result.png
+python visualize.py demo.jpg demo_jpg_mask.png vis_result.png
 ```
 
 以下为上述运行可视化脚本例子中每个参数的含义，请根据测试机器中图片的**实际路径**修改对应参数。

inference/docs/windows_vs2015_build.md → deploy/cpp/docs/windows_vs2015_build.md

@@ -15,7 +15,7 @@
 1. 打开`cmd`, 执行 `cd /d D:\projects`
 2. `git clone http://gitlab.baidu.com/Paddle/PaddleSeg.git`
 
-`C++`预测库代码在`D:\projects\PaddleSeg\inference` 目录，该目录不依赖任何`PaddleSeg`下其他目录。
+`C++`预测库代码在`D:\projects\PaddleSeg\deploy\cpp` 目录，该目录不依赖任何`PaddleSeg`下其他目录。
 
 
 ### Step2: 下载PaddlePaddle C++ 预测库 fluid_inference
@@ -24,9 +24,9 @@ PaddlePaddle C++ 预测库主要分为两大版本：CPU版本和GPU版本。其
 
 |  版本   | 链接  |
 |  ----  | ----  |
-| CPU版本  | [fluid_inference_install_dir.zip](https://bj.bcebos.com/paddlehub/paddle_inference_lib/fluid_install_dir_win_cpu_1.6.zip) |
-| CUDA 9.0版本  | [fluid_inference_install_dir.zip](https://bj.bcebos.com/paddlehub/paddle_inference_lib/fluid_inference_install_dir_win_cuda9_1.6.1.zip) |
-| CUDA 10.0版本  | [fluid_inference_install_dir.zip](https://bj.bcebos.com/paddlehub/paddle_inference_lib/fluid_inference_install_dir_win_cuda10_1.6.1.zip) |
+| CPU版本  | [fluid_inference_install_dir.zip](https://paddle-wheel.bj.bcebos.com/1.6.2/win-infer/mkl/cpu/fluid_inference_install_dir.zip) |
+| CUDA 9.0版本  | [fluid_inference_install_dir.zip](https://paddle-wheel.bj.bcebos.com/1.6.2/win-infer/mkl/post97/fluid_inference_install_dir.zip) |
+| CUDA 10.0版本  | [fluid_inference_install_dir.zip](https://paddle-wheel.bj.bcebos.com/1.6.2/win-infer/mkl/post107/fluid_inference_install_dir.zip) |
 
 解压后`D:\projects\fluid_inference`目录包含内容为：
 ```
@@ -70,19 +70,19 @@ call "C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\vcvarsall.bat" amd6
 
 ```bash
 # 切换到预测库所在目录
-cd /d D:\projects\PaddleSeg\inference\
+cd /d D:\projects\PaddleSeg\deply\cpp\
 # 创建构建目录, 重新构建只需要删除该目录即可
 mkdir build
 cd build
 # cmake构建VS项目
-D:\projects\PaddleSeg\inference\build> cmake .. -G "Visual Studio 14 2015 Win64" -DWITH_GPU=ON -DPADDLE_DIR=D:\projects\fluid_inference -DCUDA_LIB=D:\projects\cudalib\v9.0\lib\x64 -DOPENCV_DIR=D:\projects\opencv -T host=x64
+D:\projects\PaddleSeg\deploy\cpp\build> cmake .. -G "Visual Studio 14 2015 Win64" -DWITH_GPU=ON -DPADDLE_DIR=D:\projects\fluid_inference -DCUDA_LIB=D:\projects\cudalib\v9.0\lib\x64 -DOPENCV_DIR=D:\projects\opencv -T host=x64
 ```
 
 在使用**CPU版本**预测库进行编译时，可执行下列操作。
 
 ```bash
 # 切换到预测库所在目录
-cd /d D:\projects\PaddleSeg\inference\
+cd /d D:\projects\PaddleSeg\deploy\cpp\
 # 创建构建目录, 重新构建只需要删除该目录即可
 mkdir build
 cd build
@@ -102,7 +102,7 @@ D:\projects\PaddleSeg\inference\build> msbuild /m /p:Configuration=Release cpp_i
 
 上述`Visual Studio 2015`编译产出的可执行文件在`build\release`目录下，切换到该目录：
 ```
-cd /d D:\projects\PaddleSeg\inference\build\release
+cd /d D:\projects\PaddleSeg\deploy\cpp\build\release
 ```
 
 之后执行命令：

inference/docs/windows_vs2019_build.md → deploy/cpp/docs/windows_vs2019_build.md

@@ -15,7 +15,7 @@ Windows 平台下，我们使用`Visual Studio 2015` 和 `Visual Studio 2019 Com
 
 ### Step1: 下载代码
 
-1. 点击下载源代码：[下载地址](https://github.com/PaddlePaddle/PaddleSeg/archive/release/v0.2.0.zip)
+1. 点击下载源代码：[下载地址](https://github.com/PaddlePaddle/PaddleSeg/archive/release/v0.3.0.zip)
 2. 解压，解压后目录重命名为`PaddleSeg`
 
 以下代码目录路径为`D:\projects\PaddleSeg` 为例。
@@ -27,9 +27,9 @@ PaddlePaddle C++ 预测库主要分为两大版本：CPU版本和GPU版本。其
 
 |  版本   | 链接  |
 |  ----  | ----  |
-| CPU版本  | [fluid_inference_install_dir.zip](https://bj.bcebos.com/paddlehub/paddle_inference_lib/fluid_install_dir_win_cpu_1.6.zip) |
-| CUDA 9.0版本  | [fluid_inference_install_dir.zip](https://bj.bcebos.com/paddlehub/paddle_inference_lib/fluid_inference_install_dir_win_cuda9_1.6.1.zip) |
-| CUDA 10.0版本  | [fluid_inference_install_dir.zip](https://bj.bcebos.com/paddlehub/paddle_inference_lib/fluid_inference_install_dir_win_cuda10_1.6.1.zip) |
+| CPU版本  | [fluid_inference_install_dir.zip](https://paddle-wheel.bj.bcebos.com/1.6.1/win-infer/mkl/cpu/fluid_inference_install_dir.zip) |
+| CUDA 9.0版本  | [fluid_inference_install_dir.zip](https://paddle-wheel.bj.bcebos.com/1.6.1/win-infer/mkl/post97/fluid_inference_install_dir.zip) |
+| CUDA 10.0版本  | [fluid_inference_install_dir.zip](https://paddle-wheel.bj.bcebos.com/1.6.1/win-infer/mkl/post107/fluid_inference_install_dir.zip) |
 
 解压后`D:\projects\fluid_inference`目录包含内容为：
 ```
@@ -74,6 +74,7 @@ fluid_inference
 | *CUDA_LIB  | CUDA的库路径 |
 | OPENCV_DIR  | OpenCV的安装路径 |
 | PADDLE_DIR | Paddle预测库的路径 |
+
 **注意**在使用CPU版本预测库时，需要把CUDA_LIB的勾去掉。
 ![step4](https://paddleseg.bj.bcebos.com/inference/vs2019_step5.png)
 
@@ -89,7 +90,7 @@ fluid_inference
 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：
 
 ```
-cd /d D:\projects\PaddleSeg\inference\out\build\x64-Release
+cd /d D:\projects\PaddleSeg\deploy\cpp\out\build\x64-Release
 ```
 
 之后执行命令：

inference/predictor/seg_predictor.cpp → deploy/cpp/predictor/seg_predictor.cpp

@@ -36,7 +36,6 @@ namespace PaddleSolution {
         const auto& model_dir = _model_config._model_path;
         const auto& model_filename = _model_config._model_file_name;
         const auto& params_filename = _model_config._param_file_name;
-
         // load paddle model file
         if (_model_config._predictor_mode == "NATIVE") {
             paddle::NativeConfig config;
@@ -52,6 +51,12 @@ namespace PaddleSolution {
             paddle::AnalysisConfig config;
             if (use_gpu) {
                 config.EnableUseGpu(100, 0);
+                if (TRT_MAP.find(_model_config._trt_mode) != TRT_MAP.end()) {
+                    auto precision = TRT_MAP[_model_config._trt_mode];
+                    bool use_cab = (precision == paddle::AnalysisConfig::Precision::kInt8);
+                    config.EnableTensorRtEngine(1 << 30, _model_config._batch_size, 40,
+                        precision, false, use_cab);
+                }
             }
             auto prog_file = utils::path_join(model_dir, model_filename);
             auto param_file = utils::path_join(model_dir, params_filename);
@@ -83,7 +88,6 @@ namespace PaddleSolution {
 
         int blob_out_len = length;
         int seg_out_len = eval_height * eval_width * eval_num_class;
-
         if (blob_out_len != seg_out_len) {
             LOG(ERROR) << " [FATAL] unequal: input vs output [" <<
                 seg_out_len << "|" << blob_out_len << "]" << std::endl;
@@ -97,25 +101,22 @@ namespace PaddleSolution {
         cv::Mat mask_png = cv::Mat(eval_height, eval_width, CV_8UC1);
         mask_png.data = _mask.data();
         std::string nname(fname);
-        auto pos = fname.find(".");
+        auto pos = fname.rfind(".");
         nname[pos] = '_';
-        std::string mask_save_name = nname + ".png";
+        std::string mask_save_name = nname + "_mask.png";
         cv::imwrite(mask_save_name, mask_png);
         cv::Mat scoremap_png = cv::Mat(eval_height, eval_width, CV_8UC1);
         scoremap_png.data = _scoremap.data();
-        std::string scoremap_save_name = nname
-                                       + std::string("_scoremap.png");
+        std::string scoremap_save_name = nname + std::string("_scoremap.png");
         cv::imwrite(scoremap_save_name, scoremap_png);
         std::cout << "save mask of [" << fname << "] done" << std::endl;
 
         if (height && width) {
             int recover_height = *height;
             int recover_width = *width;
-            cv::Mat recover_png = cv::Mat(recover_height,
-                                          recover_width, CV_8UC1);
+            cv::Mat recover_png = cv::Mat(recover_height, recover_width, CV_8UC1);
             cv::resize(scoremap_png, recover_png,
-                       cv::Size(recover_width, recover_height),
-                       0, 0, cv::INTER_CUBIC);
+                cv::Size(recover_width, recover_height), 0, 0, cv::INTER_CUBIC);
             std::string recover_name = nname + std::string("_recover.png");
             cv::imwrite(recover_name, recover_png);
         }
@@ -176,8 +177,13 @@ namespace PaddleSolution {
             }
             paddle::PaddleTensor im_tensor;
             im_tensor.name = "image";
-            im_tensor.shape = std::vector<int>{ batch_size, channels,
-                                                 eval_height, eval_width };
+            if (!_model_config._use_pr) {
+                im_tensor.shape = std::vector<int>{ batch_size, channels,
+                                                eval_height, eval_width };
+            } else {
+                im_tensor.shape = std::vector<int>{ batch_size, eval_height,
+                                                eval_width, channels};
+            }
             im_tensor.data.Reset(input_buffer.data(),
                                  real_buffer_size * sizeof(float));
             im_tensor.dtype = paddle::PaddleDType::FLOAT32;
@@ -202,19 +208,45 @@ namespace PaddleSolution {
                 std::cout << _outputs[0].shape[j] << ",";
             }
             std::cout << ")" << std::endl;
-            const size_t nums = _outputs.front().data.length()
-                              / sizeof(float);
-            if (out_num % batch_size != 0 || out_num != nums) {
-                LOG(ERROR) << "outputs data size mismatch with shape size.";
+
+            size_t nums = _outputs.front().data.length() / sizeof(float);
+            if (_model_config._use_pr) {
+                nums = _outputs.front().data.length() / sizeof(int64_t);
+            }
+            // size mismatch checking
+            bool size_mismatch = out_num % batch_size;
+            size_mismatch |= (!_model_config._use_pr) && (nums != out_num);
+            size_mismatch |= _model_config._use_pr && (nums != eval_height * eval_width);
+            if (size_mismatch) {
+                LOG(ERROR) << "output with a unexpected size";
                 return -1;
             }
 
+            if (_model_config._use_pr) {
+                std::vector<uchar> out_data;
+                out_data.resize(out_num);
+                auto addr = reinterpret_cast<int64_t*>(_outputs[0].data.data());
+                for (int r = 0; r < out_num; ++r) {
+                    out_data[r] = (int)(addr[r]);
+                }
+                for (int r = 0; r < batch_size; ++r) {
+                    cv::Mat mask_png = cv::Mat(eval_height, eval_width, CV_8UC1);
+                    mask_png.data = out_data.data() + eval_height*eval_width*r;
+                    auto name = imgs_batch[r];
+                    auto pos = name.rfind(".");
+                    name[pos] = '_';
+                    std::string mask_save_name = name + "_mask.png";
+                    cv::imwrite(mask_save_name, mask_png);
+                }
+                continue;
+            }
+
             for (int i = 0; i < batch_size; ++i) {
                 float* output_addr = reinterpret_cast<float*>(
                                     _outputs[0].data.data())
-                                   + i * (out_num / batch_size);
+                                   + i * (nums / batch_size);
                 output_mask(imgs_batch[i], output_addr,
-                            out_num / batch_size,
+                            nums / batch_size,
                             &org_height[i],
                             &org_width[i]);
             }
@@ -278,8 +310,14 @@ namespace PaddleSolution {
                 return -1;
             }
             auto im_tensor = _main_predictor->GetInputTensor("image");
-            im_tensor->Reshape({ batch_size, channels,
+            if (!_model_config._use_pr) {
+                im_tensor->Reshape({ batch_size, channels,
                                  eval_height, eval_width });
+            } else {
+                im_tensor->Reshape({ batch_size, eval_height,
+                                eval_width, channels});
+            }
+
             im_tensor->copy_from_cpu(input_buffer.data());
 
             auto t1 = std::chrono::high_resolution_clock::now();
@@ -292,7 +330,6 @@ namespace PaddleSolution {
             auto output_names = _main_predictor->GetOutputNames();
             auto output_t = _main_predictor->GetOutputTensor(
                                               output_names[0]);
-            std::vector<float> out_data;
             std::vector<int> output_shape = output_t->shape();
 
             int out_num = 1;
@@ -303,6 +340,30 @@ namespace PaddleSolution {
             }
             std::cout << ")" << std::endl;
 
+            if (_model_config._use_pr) {
+                    std::vector<int64_t> out_data;
+                    out_data.resize(out_num);
+                    output_t->copy_to_cpu(out_data.data());
+
+                    std::vector<uchar> mask_data;
+                    mask_data.resize(out_num);
+                    auto addr = reinterpret_cast<int64_t*>(out_data.data());
+                    for (int r = 0; r < out_num; ++r) {
+                        mask_data[r] = (int)(addr[r]);
+                    }
+                    for (int r = 0; r < batch_size; ++r) {
+                        cv::Mat mask_png = cv::Mat(eval_height, eval_width, CV_8UC1);
+                        mask_png.data = mask_data.data() + eval_height*eval_width*r;
+                        auto name = imgs_batch[r];
+                        auto pos = name.rfind(".");
+                        name[pos] = '_';
+                        std::string mask_save_name = name + "_mask.png";
+                        cv::imwrite(mask_save_name, mask_png);
+                    }
+                    continue;
+            }
+
+            std::vector<float> out_data;
             out_data.resize(out_num);
             output_t->copy_to_cpu(out_data.data());
             for (int i = 0; i < batch_size; ++i) {

inference/predictor/seg_predictor.h → deploy/cpp/predictor/seg_predictor.h

@@ -55,5 +55,10 @@ class Predictor {
     PaddleSolution::PaddleSegModelConfigPaser _model_config;
     std::shared_ptr<PaddleSolution::ImagePreProcessor> _preprocessor;
     std::unique_ptr<paddle::PaddlePredictor> _main_predictor;
+    std::map<std::string, paddle::AnalysisConfig::Precision> TRT_MAP = {
+        {"FP32", paddle::AnalysisConfig::Precision::kFloat32},
+        {"FP16", paddle::AnalysisConfig::Precision::kHalf},
+        {"INT8", paddle::AnalysisConfig::Precision::kInt8}
+    };
 };
 }  // namespace PaddleSolution

inference/preprocessor/preprocessor_seg.cpp → deploy/cpp/preprocessor/preprocessor_seg.cpp

@@ -40,14 +40,18 @@ namespace PaddleSolution {
             LOG(ERROR) << "Only support rgb(gray) and rgba image.";
             return false;
         }
-
         cv::Size resize_size(_config->_resize[0], _config->_resize[1]);
         int rw = resize_size.width;
         int rh = resize_size.height;
         if (*ori_h != rh || *ori_w != rw) {
             cv::resize(im, im, resize_size, 0, 0, cv::INTER_LINEAR);
         }
-        utils::normalize(im, data, _config->_mean, _config->_std);
+
+        if (!_config->_use_pr) {
+            utils::normalize(im, data, _config->_mean, _config->_std);
+        } else {
+            utils::flatten_mat(im, data);
+        }
         return true;
     }
 

inference/utils/seg_conf_parser.h → deploy/cpp/utils/seg_conf_parser.h

@@ -25,6 +25,7 @@ class PaddleSegModelConfigPaser {
         :_class_num(0),
         _channels(0),
         _use_gpu(0),
+        _use_pr(0),
         _batch_size(1),
         _model_file_name("__model__"),
         _param_file_name("__params__") {
@@ -40,10 +41,12 @@ class PaddleSegModelConfigPaser {
         _class_num = 0;
         _channels = 0;
         _use_gpu = 0;
+        _use_pr = 0;
         _batch_size = 1;
         _model_file_name.clear();
         _model_path.clear();
         _param_file_name.clear();
+        _trt_mode.clear();
     }
 
     std::string process_parenthesis(const std::string& str) {
@@ -70,43 +73,120 @@ class PaddleSegModelConfigPaser {
 
     bool load_config(const std::string& conf_file) {
         reset();
-
-        YAML::Node config = YAML::LoadFile(conf_file);
+        YAML::Node config;
+        try {
+            config = YAML::LoadFile(conf_file);
+        } catch(...) {
+            return false;
+        }
         // 1. get resize
-        auto str = config["DEPLOY"]["EVAL_CROP_SIZE"].as<std::string>();
-        _resize = parse_str_to_vec<int>(process_parenthesis(str));
+        if (config["DEPLOY"]["EVAL_CROP_SIZE"].IsDefined()) {
+            auto str = config["DEPLOY"]["EVAL_CROP_SIZE"].as<std::string>();
+            _resize = parse_str_to_vec<int>(process_parenthesis(str));
+        } else {
+            std::cerr << "Please set EVAL_CROP_SIZE: (xx, xx)" << std::endl;
+            return false;
+        }
 
         // 2. get mean
-        for (const auto& item : config["DEPLOY"]["MEAN"]) {
-            _mean.push_back(item.as<float>());
+        if (config["DEPLOY"]["MEAN"].IsDefined()) {
+            for (const auto& item : config["DEPLOY"]["MEAN"]) {
+                _mean.push_back(item.as<float>());
+            }
+        } else {
+            std::cerr << "Please set MEAN: [xx, xx, xx]" << std::endl;
+            return false;
         }
 
         // 3. get std
-        for (const auto& item : config["DEPLOY"]["STD"]) {
-            _std.push_back(item.as<float>());
+        if(config["DEPLOY"]["STD"].IsDefined()) {
+            for (const auto& item : config["DEPLOY"]["STD"]) {
+                _std.push_back(item.as<float>());
+            }
+        } else {
+            std::cerr << "Please set STD: [xx, xx, xx]" << std::endl;
+            return false;
         }
 
         // 4. get image type
-        _img_type = config["DEPLOY"]["IMAGE_TYPE"].as<std::string>();
+		if (config["DEPLOY"]["IMAGE_TYPE"].IsDefined()) {
+            _img_type = config["DEPLOY"]["IMAGE_TYPE"].as<std::string>();
+        } else {
+            std::cerr << "Please set IMAGE_TYPE: \"rgb\" or \"rgba\"" << std::endl;
+            return false;
+        }
         // 5. get class number
-        _class_num = config["DEPLOY"]["NUM_CLASSES"].as<int>();
+        if (config["DEPLOY"]["NUM_CLASSES"].IsDefined()) {
+            _class_num = config["DEPLOY"]["NUM_CLASSES"].as<int>();
+        } else {
+            std::cerr << "Please set NUM_CLASSES: x" << std::endl;
+            return false;
+        }
         // 7. set model path
-        _model_path = config["DEPLOY"]["MODEL_PATH"].as<std::string>();
+        if (config["DEPLOY"]["MODEL_PATH"].IsDefined()) {
+            _model_path = config["DEPLOY"]["MODEL_PATH"].as<std::string>();
+        } else {
+            std::cerr << "Please set MODEL_PATH: \"/path/to/model_dir\"" << std::endl;
+            return false;
+        }
         // 8. get model file_name
-        _model_file_name = config["DEPLOY"]["MODEL_FILENAME"].as<std::string>();
+        if (config["DEPLOY"]["MODEL_FILENAME"].IsDefined()) {
+            _model_file_name = config["DEPLOY"]["MODEL_FILENAME"].as<std::string>();
+        } else {
+            _model_file_name = "__model__";
+        }
         // 9. get model param file name
-        _param_file_name =
-                        config["DEPLOY"]["PARAMS_FILENAME"].as<std::string>();
+        if (config["DEPLOY"]["PARAMS_FILENAME"].IsDefined()) {
+            _param_file_name
+                = config["DEPLOY"]["PARAMS_FILENAME"].as<std::string>();
+        } else {
+            _param_file_name = "__params__";
+        }
         // 10. get pre_processor
-        _pre_processor = config["DEPLOY"]["PRE_PROCESSOR"].as<std::string>();
+        if (config["DEPLOY"]["PRE_PROCESSOR"].IsDefined()) {
+            _pre_processor = config["DEPLOY"]["PRE_PROCESSOR"].as<std::string>();
+        } else {
+            std::cerr << "Please set PRE_PROCESSOR: \"DetectionPreProcessor\"" << std::endl;
+            return false;
+        }
         // 11. use_gpu
-        _use_gpu = config["DEPLOY"]["USE_GPU"].as<int>();
+        if (config["DEPLOY"]["USE_GPU"].IsDefined()) { 
+            _use_gpu = config["DEPLOY"]["USE_GPU"].as<int>();
+        } else {
+            _use_gpu = 0;
+        }
         // 12. predictor_mode
-        _predictor_mode = config["DEPLOY"]["PREDICTOR_MODE"].as<std::string>();
+        if (config["DEPLOY"]["PREDICTOR_MODE"].IsDefined()) {
+            _predictor_mode = config["DEPLOY"]["PREDICTOR_MODE"].as<std::string>();
+        } else {
+            std::cerr << "Please set PREDICTOR_MODE: \"NATIVE\" or \"ANALYSIS\""  << std::endl;
+            return false;
+        }
         // 13. batch_size
-        _batch_size = config["DEPLOY"]["BATCH_SIZE"].as<int>();
+        if (config["DEPLOY"]["BATCH_SIZE"].IsDefined()) {
+            _batch_size = config["DEPLOY"]["BATCH_SIZE"].as<int>();
+        } else {
+            _batch_size = 1;
+        }
         // 14. channels
-        _channels = config["DEPLOY"]["CHANNELS"].as<int>();
+        if (config["DEPLOY"]["CHANNELS"].IsDefined()) {
+            _channels = config["DEPLOY"]["CHANNELS"].as<int>();
+        } else {
+            std::cerr << "Please set CHANNELS: x"  << std::endl;
+            return false;
+        }
+        // 15. use_pr
+        if (config["DEPLOY"]["USE_PR"].IsDefined()) {
+            _use_pr = config["DEPLOY"]["USE_PR"].as<int>();
+        } else {
+            _use_pr = 0;
+        }
+        // 16. trt_mode
+	if (config["DEPLOY"]["TRT_MODE"].IsDefined()) {
+            _trt_mode = config["DEPLOY"]["TRT_MODE"].as<std::string>();
+        } else {
+            _trt_mode = "";
+        }
         return true;
     }
 
@@ -173,6 +253,10 @@ class PaddleSegModelConfigPaser {
     std::string _predictor_mode;
     // DEPLOY.BATCH_SIZE
     int _batch_size;
+    // DEPLOY.USE_PR: OP Optimized model
+    int _use_pr;
+    // DEPLOY.TRT_MODE: TRT Precesion
+    std::string _trt_mode;
 };
 
 }  // namespace PaddleSolution

inference/utils/utils.h → deploy/cpp/utils/utils.h

@@ -23,7 +23,8 @@
 #include <opencv2/highgui/highgui.hpp>
 
 #ifdef _WIN32
-#include <filesystem>
+#define GLOG_NO_ABBREVIATED_SEVERITIES
+#include <windows.h>
 #else
 #include <dirent.h>
 #include <sys/types.h>
@@ -67,15 +68,21 @@ namespace utils {
     // scan a directory and get all files with input extensions
     inline std::vector<std::string> get_directory_images(
                     const std::string& path, const std::string& exts) {
+        std::string pattern(path);
+        pattern.append("\\*");
         std::vector<std::string> imgs;
-        for (const auto& item :
-                    std::experimental::filesystem::directory_iterator(path)) {
-            auto suffix = item.path().extension().string();
-            if (exts.find(suffix) != std::string::npos && suffix.size() > 0) {
-                auto fullname = path_join(path,
-                                          item.path().filename().string());
-                imgs.push_back(item.path().string());
-            }
+        WIN32_FIND_DATA data;
+        HANDLE hFind;
+        if ((hFind = FindFirstFile(pattern.c_str(), &data)) != INVALID_HANDLE_VALUE) {
+            do {
+                auto fname = std::string(data.cFileName);
+                auto pos = fname.rfind(".");
+                auto ext = fname.substr(pos + 1);
+                if (ext.size() > 1 && exts.find(ext) != std::string::npos) {
+                    imgs.push_back(path + "\\" + data.cFileName);
+                }
+            } while (FindNextFile(hFind, &data) != 0);
+            FindClose(hFind);
         }
         return imgs;
     }
@@ -103,6 +110,25 @@ namespace utils {
         }
     }
 
+    // flatten a cv::mat
+    inline void flatten_mat(cv::Mat& im, float* data) {
+        int rh = im.rows;
+        int rw = im.cols;
+        int rc = im.channels();
+        #pragma omp parallel for
+        for (int h = 0; h < rh; ++h) {
+            const uchar* ptr = im.ptr<uchar>(h);
+            int im_index = 0;
+            int top_index = h * rw * rc;
+            for (int w = 0; w < rw; ++w) {
+                for (int c = 0; c < rc; ++c) {
+                    float pixel = static_cast<float>(ptr[im_index++]);
+                    data[top_index++] = pixel;
+                }
+            }
+        }
+    }
+
     // argmax
     inline void argmax(float* out, std::vector<int>& shape,
                        std::vector<uchar>& mask, std::vector<uchar>& scoremap) {

deploy/lite/README.md

+# 人像分割在移动端的部署
+
+## 1.介绍
+以人像分割在安卓端的部署为例，介绍如何使用[Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite)对分割模型进行移动端的部署。文档第二节介绍如何使用人像分割安卓端的demo，后面几章节介绍如何将PaddleSeg的Model部署到安卓设备。
+
+## 2.安卓Demo使用
+
+### 2.1 要求
+* Android Studio 3.4；
+* Android手机或开发板；
+
+### 2.2 安装
+* git clone https://github.com/PaddlePaddle/PaddleSeg.git  ；
+* 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"/PaddleSeg/lite/humanseg-android-demo/"目录，然后点击右下角的"Open"按钮即可导入工程
+* 通过USB连接Android手机或开发板；
+* 载入工程后，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；
+* 手机上会出现Demo的主界面，选择"Image Segmentation"图标，进入的人像分割示例程序；
+* 在人像分割Demo中，默认会载入一张人像图像，并会在图像下方给出CPU的预测结果；
+* 在人像分割Demo中，你还可以通过上方的"Gallery"和"Take Photo"按钮从相册或相机中加载测试图像；
+
+### 2.3 其他
+此安卓demo基于[Paddle-Lite-Demo](https://github.com/PaddlePaddle/Paddle-Lite-Demo)开发，更多的细节请参考该repo。<br>
+*注意：demo中拍照时照片会自动压缩，想测试拍照原图效果，可使用手机相机拍照后从相册中打开进行预测。*
+
+### 2.4 效果展示
+<img src="example/human_1.png"  width="20%" ><img src="example/human_2.png"  width="20%" ><img src="example/human_3.png"  width="20%" >
+## 3.模型导出
+此demo的人像分割模型为[下载链接](https://paddleseg.bj.bcebos.com/models/humanseg_mobilenetv2_1_0_bn_freeze_model_pr_po.zip)，是基于Deeplab_v3+mobileNet_v2的humanseg模型，关于humanseg的介绍移步[特色垂类分割模型](./contrib)，更多的分割模型导出可参考：[模型导出](https://github.com/PaddlePaddle/PaddleSeg/blob/release/v0.2.0/docs/model_export.md)
+
+## 4.模型转换
+
+### 4.1模型转换工具
+准备好PaddleSeg导出来的模型和参数文件后，需要使用Paddle-Lite提供的model_optimize_tool对模型进行优化，并转换成Paddle-Lite支持的文件格式，这里有两种方式来实现：
+
+* 手动编译model_optimize_tool
+详细的模型转换方法参考paddlelite提供的官方文档：[模型转化方法](https://paddlepaddle.github.io/Paddle-Lite/v2.0.0/model_optimize_tool/)，从PaddleSeg里面导出来的模型使用model_optimize_tool即可导出model.nb和param.nb文件。
+
+* 使用预编译版本的model_optimize_tool，最新的预编译文件参考[release](https://github.com/PaddlePaddle/Paddle-Lite/releases/)，此demo使用的版本为[model_optimize_tool](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.0.0/model_optimize_tool) ；
+
+	*注意：如果运行失败，请在[Paddle-Lite源码编译](https://paddlepaddle.github.io/Paddle-Lite/v2.0.0/source_compile/)的开发环境中使用model_optimize_tool*
+
+### 4.2 更新模型
+将优化好的model.nb和param.nb文件，替换app/src/main/assets/image_segmentation/
+models/deeplab_mobilenet_for_cpu下面的文件即可。
+
+## 5. 更新预测库
+Paddle-Lite的编译目前支持Docker，Linux和Mac OS开发环境，建议使用Docker开发环境，以免存在各种依赖问题，同时也提供了预编译版本的预测库。准备Paddle-Lite在安卓端的预测库，主要包括三个文件：
+
+* PaddlePredictor.jar；
+* arm64-v8a/libpaddle_lite_jni.so；
+*  armeabi-v7a/libpaddle_lite_jni.so；
+
+下面分别介绍两种方法：
+
+* 使用预编译版本的预测库，最新的预编译文件参考：[release](https://github.com/PaddlePaddle/Paddle-Lite/releases/)，此demo使用的版本：
+
+	* arm64-v8a: [inference_lite_lib.android.armv8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.0.0/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.full_publish.tar.gz) ；
+	
+	* armeabi-v7a: [inference_lite_lib.android.armv7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.0.0/inference_lite_lib.android.armv7.gcc.c++_shared.with_extra.full_publish.tar.gz) ；
+
+	解压上面两个文件，PaddlePredictor.jar位于任一文件夹：inference_lite_lib.android.xxx/java/jar/PaddlePredictor.jar；
+	    
+	解压上述inference_lite_lib.android.armv8文件，arm64-v8a/libpaddle_lite_jni.so位于：inference_lite_lib.android.armv8/java/so/libpaddle_lite_jni.so；
+	
+	解压上述inference_lite_lib.android.armv7文件，armeabi-v7a/libpaddle_lite_jni.so位于：inference_lite_lib.android.armv7/java/so/libpaddle_lite_jni.so；
+
+* 手动编译Paddle-Lite预测库
+开发环境的准备和编译方法参考：[Paddle-Lite源码编译](https://paddlepaddle.github.io/Paddle-Lite/v2.0.0/source_compile/)。
+
+准备好上述文件，即可参考[java_api](https://paddlepaddle.github.io/Paddle-Lite/v2.0.0/java_api_doc/)在安卓端进行推理。具体使用预测库的方法可参考[Paddle-Lite-Demo](https://github.com/PaddlePaddle/Paddle-Lite-Demo)中更新预测库部分的文档。

deploy/lite/humanseg-android-demo/.gitignore

+*.iml
+.gradle
+/local.properties
+/.idea/caches
+/.idea/libraries
+/.idea/modules.xml
+/.idea/workspace.xml
+/.idea/navEditor.xml
+/.idea/assetWizardSettings.xml
+.DS_Store
+/build
+/captures
+.externalNativeBuild

deploy/lite/humanseg-android-demo/app/.gitignore

+/build

deploy/lite/humanseg-android-demo/app/build.gradle

+apply plugin: 'com.android.application'
+
+android {
+    compileSdkVersion 28
+    defaultConfig {
+        applicationId "com.baidu.paddle.lite.demo"
+        minSdkVersion 15
+        targetSdkVersion 28
+        versionCode 1
+        versionName "1.0"
+        testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"
+    }
+    buildTypes {
+        release {
+            minifyEnabled false
+            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
+        }
+    }
+}
+
+dependencies {
+    implementation fileTree(include: ['*.jar'], dir: 'libs')
+    implementation 'com.android.support:appcompat-v7:28.0.0'
+    implementation 'com.android.support.constraint:constraint-layout:1.1.3'
+    implementation 'com.android.support:design:28.0.0'
+    testImplementation 'junit:junit:4.12'
+    androidTestImplementation 'com.android.support.test:runner:1.0.2'
+    androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'
+    implementation files('libs/PaddlePredictor.jar')
+}

deploy/lite/humanseg-android-demo/app/gradle/wrapper/gradle-wrapper.properties

+#Mon Nov 25 17:01:58 CST 2019
+distributionBase=GRADLE_USER_HOME
+distributionPath=wrapper/dists
+zipStoreBase=GRADLE_USER_HOME
+zipStorePath=wrapper/dists
+distributionUrl=https\://services.gradle.org/distributions/gradle-5.4.1-all.zip

deploy/lite/humanseg-android-demo/app/gradlew

+#!/usr/bin/env sh
+
+##############################################################################
+##
+##  Gradle start up script for UN*X
+##
+##############################################################################
+
+# Attempt to set APP_HOME
+# Resolve links: $0 may be a link
+PRG="$0"
+# Need this for relative symlinks.
+while [ -h "$PRG" ] ; do
+    ls=`ls -ld "$PRG"`
+    link=`expr "$ls" : '.*-> \(.*\)$'`
+    if expr "$link" : '/.*' > /dev/null; then
+        PRG="$link"
+    else
+        PRG=`dirname "$PRG"`"/$link"
+    fi
+done
+SAVED="`pwd`"
+cd "`dirname \"$PRG\"`/" >/dev/null
+APP_HOME="`pwd -P`"
+cd "$SAVED" >/dev/null
+
+APP_NAME="Gradle"
+APP_BASE_NAME=`basename "$0"`
+
+# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+DEFAULT_JVM_OPTS=""
+
+# Use the maximum available, or set MAX_FD != -1 to use that value.
+MAX_FD="maximum"
+
+warn () {
+    echo "$*"
+}
+
+die () {
+    echo
+    echo "$*"
+    echo
+    exit 1
+}
+
+# OS specific support (must be 'true' or 'false').
+cygwin=false
+msys=false
+darwin=false
+nonstop=false
+case "`uname`" in
+  CYGWIN* )
+    cygwin=true
+    ;;
+  Darwin* )
+    darwin=true
+    ;;
+  MINGW* )
+    msys=true
+    ;;
+  NONSTOP* )
+    nonstop=true
+    ;;
+esac
+
+CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
+
+# Determine the Java command to use to start the JVM.
+if [ -n "$JAVA_HOME" ] ; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
+        # IBM's JDK on AIX uses strange locations for the executables
+        JAVACMD="$JAVA_HOME/jre/sh/java"
+    else
+        JAVACMD="$JAVA_HOME/bin/java"
+    fi
+    if [ ! -x "$JAVACMD" ] ; then
+        die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+    fi
+else
+    JAVACMD="java"
+    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+fi
+
+# Increase the maximum file descriptors if we can.
+if [ "$cygwin" = "false" -a "$darwin" = "false" -a "$nonstop" = "false" ] ; then
+    MAX_FD_LIMIT=`ulimit -H -n`
+    if [ $? -eq 0 ] ; then
+        if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
+            MAX_FD="$MAX_FD_LIMIT"
+        fi
+        ulimit -n $MAX_FD
+        if [ $? -ne 0 ] ; then
+            warn "Could not set maximum file descriptor limit: $MAX_FD"
+        fi
+    else
+        warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
+    fi
+fi
+
+# For Darwin, add options to specify how the application appears in the dock
+if $darwin; then
+    GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
+fi
+
+# For Cygwin, switch paths to Windows format before running java
+if $cygwin ; then
+    APP_HOME=`cygpath --path --mixed "$APP_HOME"`
+    CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
+    JAVACMD=`cygpath --unix "$JAVACMD"`
+
+    # We build the pattern for arguments to be converted via cygpath
+    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
+    SEP=""
+    for dir in $ROOTDIRSRAW ; do
+        ROOTDIRS="$ROOTDIRS$SEP$dir"
+        SEP="|"
+    done
+    OURCYGPATTERN="(^($ROOTDIRS))"
+    # Add a user-defined pattern to the cygpath arguments
+    if [ "$GRADLE_CYGPATTERN" != "" ] ; then
+        OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
+    fi
+    # Now convert the arguments - kludge to limit ourselves to /bin/sh
+    i=0
+    for arg in "$@" ; do
+        CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
+        CHECK2=`echo "$arg"|egrep -c "^-"`                                 ### Determine if an option
+
+        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition
+            eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
+        else
+            eval `echo args$i`="\"$arg\""
+        fi
+        i=$((i+1))
+    done
+    case $i in
+        (0) set -- ;;
+        (1) set -- "$args0" ;;
+        (2) set -- "$args0" "$args1" ;;
+        (3) set -- "$args0" "$args1" "$args2" ;;
+        (4) set -- "$args0" "$args1" "$args2" "$args3" ;;
+        (5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
+        (6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
+        (7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
+        (8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
+        (9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
+    esac
+fi
+
+# Escape application args
+save () {
+    for i do printf %s\\n "$i" | sed "s/'/'\\\\''/g;1s/^/'/;\$s/\$/' \\\\/" ; done
+    echo " "
+}
+APP_ARGS=$(save "$@")
+
+# Collect all arguments for the java command, following the shell quoting and substitution rules
+eval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS "\"-Dorg.gradle.appname=$APP_BASE_NAME\"" -classpath "\"$CLASSPATH\"" org.gradle.wrapper.GradleWrapperMain "$APP_ARGS"
+
+# by default we should be in the correct project dir, but when run from Finder on Mac, the cwd is wrong
+if [ "$(uname)" = "Darwin" ] && [ "$HOME" = "$PWD" ]; then
+  cd "$(dirname "$0")"
+fi
+
+exec "$JAVACMD" "$@"

deploy/lite/humanseg-android-demo/app/gradlew.bat

+@if "%DEBUG%" == "" @echo off
+@rem ##########################################################################
+@rem
+@rem  Gradle startup script for Windows
+@rem
+@rem ##########################################################################
+
+@rem Set local scope for the variables with windows NT shell
+if "%OS%"=="Windows_NT" setlocal
+
+set DIRNAME=%~dp0
+if "%DIRNAME%" == "" set DIRNAME=.
+set APP_BASE_NAME=%~n0
+set APP_HOME=%DIRNAME%
+
+@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+set DEFAULT_JVM_OPTS=
+
+@rem Find java.exe
+if defined JAVA_HOME goto findJavaFromJavaHome
+
+set JAVA_EXE=java.exe
+%JAVA_EXE% -version >NUL 2>&1
+if "%ERRORLEVEL%" == "0" goto init
+
+echo.
+echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:findJavaFromJavaHome
+set JAVA_HOME=%JAVA_HOME:"=%
+set JAVA_EXE=%JAVA_HOME%/bin/java.exe
+
+if exist "%JAVA_EXE%" goto init
+
+echo.
+echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:init
+@rem Get command-line arguments, handling Windows variants
+
+if not "%OS%" == "Windows_NT" goto win9xME_args
+
+:win9xME_args
+@rem Slurp the command line arguments.
+set CMD_LINE_ARGS=
+set _SKIP=2
+
+:win9xME_args_slurp
+if "x%~1" == "x" goto execute
+
+set CMD_LINE_ARGS=%*
+
+:execute
+@rem Setup the command line
+
+set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
+
+@rem Execute Gradle
+"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%
+
+:end
+@rem End local scope for the variables with windows NT shell
+if "%ERRORLEVEL%"=="0" goto mainEnd
+
+:fail
+rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
+rem the _cmd.exe /c_ return code!
+if  not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
+exit /b 1
+
+:mainEnd
+if "%OS%"=="Windows_NT" endlocal
+
+:omega

deploy/lite/humanseg-android-demo/app/proguard-rules.pro

+# Add project specific ProGuard rules here.
+# You can control the set of applied configuration files using the
+# proguardFiles setting in build.gradle.
+#
+# For more details, see
+#   http://developer.android.com/guide/developing/tools/proguard.html
+
+# If your project uses WebView with JS, uncomment the following
+# and specify the fully qualified class name to the JavaScript interface
+# class:
+#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
+#   public *;
+#}
+
+# Uncomment this to preserve the line number information for
+# debugging stack traces.
+#-keepattributes SourceFile,LineNumberTable
+
+# If you keep the line number information, uncomment this to
+# hide the original source file name.
+#-renamesourcefileattribute SourceFile

deploy/lite/humanseg-android-demo/app/src/androidTest/java/com/baidu/paddle/lite/demo/ExampleInstrumentedTest.java

+package com.baidu.paddle.lite.demo;
+
+import android.content.Context;
+import android.support.test.InstrumentationRegistry;
+import android.support.test.runner.AndroidJUnit4;
+
+import org.junit.Test;
+import org.junit.runner.RunWith;
+
+import static org.junit.Assert.*;
+
+/**
+ * Instrumented test, which will execute on an Android device.
+ *
+ * @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
+ */
+@RunWith(AndroidJUnit4.class)
+public class ExampleInstrumentedTest {
+    @Test
+    public void useAppContext() {
+        // Context of the app under test.
+        Context appContext = InstrumentationRegistry.getTargetContext();
+
+        assertEquals("com.baidu.paddle.lite.demo", appContext.getPackageName());
+    }
+}

deploy/lite/humanseg-android-demo/app/src/main/AndroidManifest.xml

+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+          package="com.baidu.paddle.lite.demo">
+
+    <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/>
+    <uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE"/>
+    <uses-permission android:name="android.permission.CAMERA"/>
+
+    <application
+            android:allowBackup="true"
+            android:largeHeap="true"
+            android:icon="@mipmap/ic_launcher"
+            android:label="@string/app_name"
+            android:roundIcon="@mipmap/ic_launcher_round"
+            android:supportsRtl="true"
+            android:theme="@style/AppTheme">
+        <activity android:name=".MainActivity">
+            <intent-filter>
+                <action android:name="android.intent.action.MAIN"/>
+
+                <category android:name="android.intent.category.LAUNCHER"/>
+            </intent-filter>
+        </activity>
+        <activity
+                android:name=".segmentation.ImgSegActivity"
+                android:label="Image Segmentation"/>
+        <activity
+                android:name=".segmentation.ImgSegSettingsActivity"
+                android:label="Settings">
+        </activity>
+    </application>
+
+</manifest>
\ No newline at end of file

deploy/lite/humanseg-android-demo/app/src/main/assets/image_segmentation/labels/label_list

+background
+human

deploy/lite/humanseg-android-demo/app/src/main/java/com/baidu/paddle/lite/demo/AppCompatPreferenceActivity.java

+/*
+ * Copyright (C) 2014 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.baidu.paddle.lite.demo;
+
+import android.content.res.Configuration;
+import android.os.Bundle;
+import android.preference.PreferenceActivity;
+import android.support.annotation.LayoutRes;
+import android.support.annotation.Nullable;
+import android.support.v7.app.ActionBar;
+import android.support.v7.app.AppCompatDelegate;
+import android.support.v7.widget.Toolbar;
+import android.view.MenuInflater;
+import android.view.View;
+import android.view.ViewGroup;
+
+/**
+ * A {@link android.preference.PreferenceActivity} which implements and proxies the necessary calls
+ * to be used with AppCompat.
+ * <p>
+ * This technique can be used with an {@link android.app.Activity} class, not just
+ * {@link android.preference.PreferenceActivity}.
+ */
+public abstract class AppCompatPreferenceActivity extends PreferenceActivity {
+    private AppCompatDelegate mDelegate;
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        getDelegate().installViewFactory();
+        getDelegate().onCreate(savedInstanceState);
+        super.onCreate(savedInstanceState);
+    }
+
+    @Override
+    protected void onPostCreate(Bundle savedInstanceState) {
+        super.onPostCreate(savedInstanceState);
+        getDelegate().onPostCreate(savedInstanceState);
+    }
+
+    public ActionBar getSupportActionBar() {
+        return getDelegate().getSupportActionBar();
+    }
+
+    public void setSupportActionBar(@Nullable Toolbar toolbar) {
+        getDelegate().setSupportActionBar(toolbar);
+    }
+
+    @Override
+    public MenuInflater getMenuInflater() {
+        return getDelegate().getMenuInflater();
+    }
+
+    @Override
+    public void setContentView(@LayoutRes int layoutResID) {
+        getDelegate().setContentView(layoutResID);
+    }
+
+    @Override
+    public void setContentView(View view) {
+        getDelegate().setContentView(view);
+    }
+
+    @Override
+    public void setContentView(View view, ViewGroup.LayoutParams params) {
+        getDelegate().setContentView(view, params);
+    }
+
+    @Override
+    public void addContentView(View view, ViewGroup.LayoutParams params) {
+        getDelegate().addContentView(view, params);
+    }
+
+    @Override
+    protected void onPostResume() {
+        super.onPostResume();
+        getDelegate().onPostResume();
+    }
+
+    @Override
+    protected void onTitleChanged(CharSequence title, int color) {
+        super.onTitleChanged(title, color);
+        getDelegate().setTitle(title);
+    }
+
+    @Override
+    public void onConfigurationChanged(Configuration newConfig) {
+        super.onConfigurationChanged(newConfig);
+        getDelegate().onConfigurationChanged(newConfig);
+    }
+
+    @Override
+    protected void onStop() {
+        super.onStop();
+        getDelegate().onStop();
+    }
+
+    @Override
+    protected void onDestroy() {
+        super.onDestroy();
+        getDelegate().onDestroy();
+    }
+
+    public void invalidateOptionsMenu() {
+        getDelegate().invalidateOptionsMenu();
+    }
+
+    private AppCompatDelegate getDelegate() {
+        if (mDelegate == null) {
+            mDelegate = AppCompatDelegate.create(this, null);
+        }
+        return mDelegate;
+    }
+}

deploy/lite/humanseg-android-demo/app/src/main/java/com/baidu/paddle/lite/demo/CommonActivity.java

+package com.baidu.paddle.lite.demo;
+
+import android.Manifest;
+import android.app.ProgressDialog;
+import android.content.ContentResolver;
+import android.content.Intent;
+import android.content.pm.PackageManager;
+import android.database.Cursor;
+import android.graphics.Bitmap;
+import android.graphics.BitmapFactory;
+import android.net.Uri;
+import android.os.Bundle;
+import android.os.Environment;
+import android.os.Handler;
+import android.os.HandlerThread;
+import android.os.Message;
+import android.provider.MediaStore;
+import android.support.annotation.NonNull;
+import android.support.v4.app.ActivityCompat;
+import android.support.v4.content.ContextCompat;
+import android.support.v4.content.FileProvider;
+import android.support.v7.app.ActionBar;
+import android.support.v7.app.AppCompatActivity;
+import android.util.Log;
+import android.view.Menu;
+import android.view.MenuInflater;
+import android.view.MenuItem;
+import android.widget.Toast;
+
+import java.io.File;
+import java.io.IOException;
+import java.text.SimpleDateFormat;
+import java.util.Date;
+
+public class CommonActivity extends AppCompatActivity {
+    private static final String TAG = CommonActivity.class.getSimpleName();
+    public static final int OPEN_GALLERY_REQUEST_CODE = 0;
+    public static final int TAKE_PHOTO_REQUEST_CODE = 1;
+
+    public static final int REQUEST_LOAD_MODEL = 0;
+    public static final int REQUEST_RUN_MODEL = 1;
+    public static final int RESPONSE_LOAD_MODEL_SUCCESSED = 0;
+    public static final int RESPONSE_LOAD_MODEL_FAILED = 1;
+    public static final int RESPONSE_RUN_MODEL_SUCCESSED = 2;
+    public static final int RESPONSE_RUN_MODEL_FAILED = 3;
+
+    protected ProgressDialog pbLoadModel = null;
+    protected ProgressDialog pbRunModel = null;
+
+    protected Handler receiver = null; // receive messages from worker thread
+    protected Handler sender = null; // send command to worker thread
+    protected HandlerThread worker = null; // worker thread to load&run model
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        ActionBar supportActionBar = getSupportActionBar();
+        if (supportActionBar != null) {
+            supportActionBar.setDisplayHomeAsUpEnabled(true);
+        }
+
+        receiver = new Handler() {
+            @Override
+            public void handleMessage(Message msg) {
+                switch (msg.what) {
+                    case RESPONSE_LOAD_MODEL_SUCCESSED:
+                        pbLoadModel.dismiss();
+                        onLoadModelSuccessed();
+                        break;
+                    case RESPONSE_LOAD_MODEL_FAILED:
+                        pbLoadModel.dismiss();
+                        Toast.makeText(CommonActivity.this, "Load model failed!", Toast.LENGTH_SHORT).show();
+                        onLoadModelFailed();
+                        break;
+                    case RESPONSE_RUN_MODEL_SUCCESSED:
+                        pbRunModel.dismiss();
+                        onRunModelSuccessed();
+                        break;
+                    case RESPONSE_RUN_MODEL_FAILED:
+                        pbRunModel.dismiss();
+                        Toast.makeText(CommonActivity.this, "Run model failed!", Toast.LENGTH_SHORT).show();
+                        onRunModelFailed();
+                        break;
+                    default:
+                        break;
+                }
+            }
+        };
+
+        worker = new HandlerThread("Predictor Worker");
+        worker.start();
+        sender = new Handler(worker.getLooper()) {
+            public void handleMessage(Message msg) {
+                switch (msg.what) {
+                    case REQUEST_LOAD_MODEL:
+                        // load model and reload test image
+                        if (onLoadModel()) {
+                            receiver.sendEmptyMessage(RESPONSE_LOAD_MODEL_SUCCESSED);
+                        } else {
+                            receiver.sendEmptyMessage(RESPONSE_LOAD_MODEL_FAILED);
+                        }
+                        break;
+                    case REQUEST_RUN_MODEL:
+                        // run model if model is loaded
+                        if (onRunModel()) {
+                            receiver.sendEmptyMessage(RESPONSE_RUN_MODEL_SUCCESSED);
+                        } else {
+                            receiver.sendEmptyMessage(RESPONSE_RUN_MODEL_FAILED);
+                        }
+                        break;
+                    default:
+                        break;
+                }
+            }
+        };
+    }
+
+    public void loadModel() {
+        pbLoadModel = ProgressDialog.show(this, "", "Loading model...", false, false);
+        sender.sendEmptyMessage(REQUEST_LOAD_MODEL);
+    }
+
+    public void runModel() {
+        pbRunModel = ProgressDialog.show(this, "", "Running model...", false, false);
+        sender.sendEmptyMessage(REQUEST_RUN_MODEL);
+    }
+
+    public boolean onLoadModel() {
+        return true;
+    }
+
+    public boolean onRunModel() {
+        return true;
+    }
+
+    public void onLoadModelSuccessed() {
+    }
+
+    public void onLoadModelFailed() {
+    }
+
+    public void onRunModelSuccessed() {
+    }
+
+    public void onRunModelFailed() {
+    }
+
+    public void onImageChanged(Bitmap image) {
+    }
+
+    public void onImageChanged(String path) {
+
+    }
+    public void onSettingsClicked() {
+    }
+
+    @Override
+    public boolean onCreateOptionsMenu(Menu menu) {
+        MenuInflater inflater = getMenuInflater();
+        inflater.inflate(R.menu.menu_action_options, menu);
+        return true;
+    }
+
+    @Override
+    public boolean onOptionsItemSelected(MenuItem item) {
+        switch (item.getItemId()) {
+            case android.R.id.home:
+                finish();
+                break;
+            case R.id.open_gallery:
+                if (requestAllPermissions()) {
+                    openGallery();
+                }
+                break;
+            case R.id.take_photo:
+                if (requestAllPermissions()) {
+                    takePhoto();
+                }
+                break;
+            case R.id.settings:
+                if (requestAllPermissions()) {
+                    // make sure we have SDCard r&w permissions to load model from SDCard
+                    onSettingsClicked();
+                }
+                break;
+        }
+        return super.onOptionsItemSelected(item);
+    }
+
+    @Override
+    public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions,
+                                           @NonNull int[] grantResults) {
+        super.onRequestPermissionsResult(requestCode, permissions, grantResults);
+        if (grantResults[0] != PackageManager.PERMISSION_GRANTED || grantResults[1] != PackageManager.PERMISSION_GRANTED) {
+            Toast.makeText(this, "Permission Denied", Toast.LENGTH_SHORT).show();
+        }
+    }
+
+    private boolean requestAllPermissions() {
+        if (ContextCompat.checkSelfPermission(this, Manifest.permission.WRITE_EXTERNAL_STORAGE)
+                != PackageManager.PERMISSION_GRANTED || ContextCompat.checkSelfPermission(this,
+                Manifest.permission.CAMERA)
+                != PackageManager.PERMISSION_GRANTED) {
+            ActivityCompat.requestPermissions(this, new String[]{Manifest.permission.WRITE_EXTERNAL_STORAGE,
+                            Manifest.permission.CAMERA},
+                    0);
+            return false;
+        }
+        return true;
+    }
+
+    private void openGallery() {
+        Intent intent = new Intent(Intent.ACTION_PICK, null);
+        intent.setDataAndType(MediaStore.Images.Media.EXTERNAL_CONTENT_URI, "image/*");
+        startActivityForResult(intent, OPEN_GALLERY_REQUEST_CODE);
+    }
+
+    private void takePhoto() {
+        Intent takePhotoIntent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);
+        if (takePhotoIntent.resolveActivity(getPackageManager()) != null) {
+            startActivityForResult(takePhotoIntent, TAKE_PHOTO_REQUEST_CODE);
+        }
+    }
+
+    @Override
+    protected void onActivityResult(int requestCode, int resultCode, Intent data) {
+        super.onActivityResult(requestCode, resultCode, data);
+        if (resultCode == RESULT_OK && data != null) {
+            switch (requestCode) {
+                case OPEN_GALLERY_REQUEST_CODE:
+                    try {
+                        ContentResolver resolver = getContentResolver();
+                        Uri uri = data.getData();
+                        Bitmap image = MediaStore.Images.Media.getBitmap(resolver, uri);
+                        String[] proj = {MediaStore.Images.Media.DATA};
+                        Cursor cursor = managedQuery(uri, proj, null, null, null);
+                        cursor.moveToFirst();
+                        onImageChanged(image);
+                    } catch (IOException e) {
+                        Log.e(TAG, e.toString());
+                    }
+                    break;
+
+                case TAKE_PHOTO_REQUEST_CODE:
+                    Bitmap image = (Bitmap) data.getParcelableExtra("data");
+                    onImageChanged(image);
+
+                    break;
+                default:
+                    break;
+            }
+        }
+    }
+
+    @Override
+    protected void onResume() {
+        super.onResume();
+    }
+
+    @Override
+    protected void onDestroy() {
+        worker.quit();
+        super.onDestroy();
+    }
+}

deploy/lite/humanseg-android-demo/app/src/main/java/com/baidu/paddle/lite/demo/MainActivity.java

+package com.baidu.paddle.lite.demo;
+
+import android.content.Intent;
+import android.content.SharedPreferences;
+import android.os.Bundle;
+import android.preference.PreferenceManager;
+import android.support.v7.app.AppCompatActivity;
+import android.util.Log;
+import android.view.View;
+
+import com.baidu.paddle.lite.demo.segmentation.ImgSegActivity;
+
+public class MainActivity extends AppCompatActivity implements View.OnClickListener {
+    private static final String TAG = MainActivity.class.getSimpleName();
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        setContentView(R.layout.activity_main);
+
+        // clear all setting items to avoid app crashing due to the incorrect settings
+        SharedPreferences sharedPreferences = PreferenceManager.getDefaultSharedPreferences(this);
+        SharedPreferences.Editor editor = sharedPreferences.edit();
+        editor.clear();
+        editor.commit();
+    }
+
+    @Override
+    public void onClick(View v) {
+        switch (v.getId()) {
+            case R.id.v_img_seg: {
+                Intent intent = new Intent(MainActivity.this, ImgSegActivity.class);
+                startActivity(intent);
+            } break;
+        }
+    }
+
+    @Override
+    protected void onDestroy() {
+        super.onDestroy();
+        System.exit(0);
+    }
+}

deploy/lite/humanseg-android-demo/app/src/main/java/com/baidu/paddle/lite/demo/Predictor.java

+package com.baidu.paddle.lite.demo;
+
+import android.content.Context;
+import android.util.Log;
+import com.baidu.paddle.lite.*;
+
+import java.util.ArrayList;
+import java.util.Date;
+
+public class Predictor {
+    private static final String TAG = Predictor.class.getSimpleName();
+
+    public boolean isLoaded = false;
+    public int warmupIterNum = 0;
+    public int inferIterNum = 1;
+    protected Context appCtx = null;
+    public int cpuThreadNum = 1;
+    public String cpuPowerMode = "LITE_POWER_HIGH";
+    public String modelPath = "";
+    public String modelName = "";
+    protected PaddlePredictor paddlePredictor = null;
+    protected float inferenceTime = 0;
+
+    public Predictor() {
+    }
+
+    public boolean init(Context appCtx, String modelPath, int cpuThreadNum, String cpuPowerMode) {
+        this.appCtx = appCtx;
+        isLoaded = loadModel(modelPath, cpuThreadNum, cpuPowerMode);
+        return isLoaded;
+    }
+
+    protected boolean loadModel(String modelPath, int cpuThreadNum, String cpuPowerMode) {
+        // release model if exists
+        releaseModel();
+
+        // load model
+        if (modelPath.isEmpty()) {
+            return false;
+        }
+        String realPath = modelPath;
+        if (!modelPath.substring(0, 1).equals("/")) {
+            // read model files from custom file_paths if the first character of mode file_paths is '/'
+            // otherwise copy model to cache from assets
+            realPath = appCtx.getCacheDir() + "/" + modelPath;
+            Utils.copyDirectoryFromAssets(appCtx, modelPath, realPath);
+        }
+        if (realPath.isEmpty()) {
+            return false;
+        }
+        MobileConfig config = new MobileConfig();
+        config.setModelDir(realPath);
+        config.setThreads(cpuThreadNum);
+        if (cpuPowerMode.equalsIgnoreCase("LITE_POWER_HIGH")) {
+            config.setPowerMode(PowerMode.LITE_POWER_HIGH);
+        } else if (cpuPowerMode.equalsIgnoreCase("LITE_POWER_LOW")) {
+            config.setPowerMode(PowerMode.LITE_POWER_LOW);
+        } else if (cpuPowerMode.equalsIgnoreCase("LITE_POWER_FULL")) {
+            config.setPowerMode(PowerMode.LITE_POWER_FULL);
+        } else if (cpuPowerMode.equalsIgnoreCase("LITE_POWER_NO_BIND")) {
+            config.setPowerMode(PowerMode.LITE_POWER_NO_BIND);
+        } else if (cpuPowerMode.equalsIgnoreCase("LITE_POWER_RAND_HIGH")) {
+            config.setPowerMode(PowerMode.LITE_POWER_RAND_HIGH);
+        } else if (cpuPowerMode.equalsIgnoreCase("LITE_POWER_RAND_LOW")) {
+            config.setPowerMode(PowerMode.LITE_POWER_RAND_LOW);
+        } else {
+            Log.e(TAG, "unknown cpu power mode!");
+            return false;
+        }
+        paddlePredictor = PaddlePredictor.createPaddlePredictor(config);
+
+        this.cpuThreadNum = cpuThreadNum;
+        this.cpuPowerMode = cpuPowerMode;
+        this.modelPath = realPath;
+        this.modelName = realPath.substring(realPath.lastIndexOf("/") + 1);
+        return true;
+    }
+
+    public void releaseModel() {
+        paddlePredictor = null;
+        isLoaded = false;
+        cpuThreadNum = 1;
+        cpuPowerMode = "LITE_POWER_HIGH";
+        modelPath = "";
+        modelName = "";
+    }
+
+    public Tensor getInput(int idx) {
+        if (!isLoaded()) {
+            return null;
+        }
+        return paddlePredictor.getInput(idx);
+    }
+
+    public Tensor getOutput(int idx) {
+        if (!isLoaded()) {
+            return null;
+        }
+        return paddlePredictor.getOutput(idx);
+    }
+
+    public boolean runModel() {
+        if (!isLoaded()) {
+            return false;
+        }
+        // warm up
+        for (int i = 0; i < warmupIterNum; i++){
+            paddlePredictor.run();
+        }
+        // inference
+        Date start = new Date();
+        for (int i = 0; i < inferIterNum; i++) {
+            paddlePredictor.run();
+        }
+        Date end = new Date();
+        inferenceTime = (end.getTime() - start.getTime()) / (float) inferIterNum;
+        return true;
+    }
+
+    public boolean isLoaded() {
+        return paddlePredictor != null && isLoaded;
+    }
+
+    public String modelPath() {
+        return modelPath;
+    }
+
+    public String modelName() {
+        return modelName;
+    }
+
+    public int cpuThreadNum() {
+        return cpuThreadNum;
+    }
+
+    public String cpuPowerMode() {
+        return cpuPowerMode;
+    }
+
+    public float inferenceTime() {
+        return inferenceTime;
+    }
+}

deploy/lite/humanseg-android-demo/app/src/main/java/com/baidu/paddle/lite/demo/Utils.java

+package com.baidu.paddle.lite.demo;
+
+import android.content.Context;
+import android.os.Environment;
+
+import java.io.*;
+
+public class Utils {
+    private static final String TAG = Utils.class.getSimpleName();
+
+    public static void copyFileFromAssets(Context appCtx, String srcPath, String dstPath) {
+        if (srcPath.isEmpty() || dstPath.isEmpty()) {
+            return;
+        }
+        InputStream is = null;
+        OutputStream os = null;
+        try {
+            is = new BufferedInputStream(appCtx.getAssets().open(srcPath));
+            os = new BufferedOutputStream(new FileOutputStream(new File(dstPath)));
+            byte[] buffer = new byte[1024];
+            int length = 0;
+            while ((length = is.read(buffer)) != -1) {
+                os.write(buffer, 0, length);
+            }
+        } catch (FileNotFoundException e) {
+            e.printStackTrace();
+        } catch (IOException e) {
+            e.printStackTrace();
+        } finally {
+            try {
+                os.close();
+                is.close();
+            } catch (IOException e) {
+                e.printStackTrace();
+            }
+        }
+    }
+
+    public static void copyDirectoryFromAssets(Context appCtx, String srcDir, String dstDir) {
+        if (srcDir.isEmpty() || dstDir.isEmpty()) {
+            return;
+        }
+        try {
+            if (!new File(dstDir).exists()) {
+                new File(dstDir).mkdirs();
+            }
+            for (String fileName : appCtx.getAssets().list(srcDir)) {
+                String srcSubPath = srcDir + File.separator + fileName;
+                String dstSubPath = dstDir + File.separator + fileName;
+                if (new File(srcSubPath).isDirectory()) {
+                    copyDirectoryFromAssets(appCtx, srcSubPath, dstSubPath);
+                } else {
+                    copyFileFromAssets(appCtx, srcSubPath, dstSubPath);
+                }
+            }
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+
+    public static float[] parseFloatsFromString(String string, String delimiter) {
+        String[] pieces = string.trim().toLowerCase().split(delimiter);
+        float[] floats = new float[pieces.length];
+        for (int i = 0; i < pieces.length; i++) {
+            floats[i] = Float.parseFloat(pieces[i].trim());
+        }
+        return floats;
+    }
+
+    public static long[] parseLongsFromString(String string, String delimiter) {
+        String[] pieces = string.trim().toLowerCase().split(delimiter);
+        long[] longs = new long[pieces.length];
+        for (int i = 0; i < pieces.length; i++) {
+            longs[i] = Long.parseLong(pieces[i].trim());
+        }
+        return longs;
+    }
+
+    public static String getSDCardDirectory() {
+        return Environment.getExternalStorageDirectory().getAbsolutePath();
+    }
+
+    public static boolean isSupportedNPU() {
+        String hardware = android.os.Build.HARDWARE;
+        return hardware.equalsIgnoreCase("kirin810") || hardware.equalsIgnoreCase("kirin990");
+    }
+}

deploy/lite/humanseg-android-demo/app/src/main/java/com/baidu/paddle/lite/demo/segmentation/ImgSegActivity.java

+package com.baidu.paddle.lite.demo.segmentation;
+
+import android.content.Intent;
+import android.content.SharedPreferences;
+import android.graphics.Bitmap;
+import android.graphics.BitmapFactory;
+import android.os.Bundle;
+import android.preference.PreferenceManager;
+import android.text.method.ScrollingMovementMethod;
+import android.util.Log;
+import android.view.Menu;
+import android.widget.ImageView;
+import android.widget.TextView;
+import android.widget.Toast;
+
+import com.baidu.paddle.lite.demo.CommonActivity;
+import com.baidu.paddle.lite.demo.R;
+import com.baidu.paddle.lite.demo.Utils;
+import com.baidu.paddle.lite.demo.segmentation.config.Config;
+import com.baidu.paddle.lite.demo.segmentation.preprocess.Preprocess;
+import com.baidu.paddle.lite.demo.segmentation.visual.Visualize;
+
+import java.io.File;
+import java.io.IOException;
+import java.io.InputStream;
+
+public class ImgSegActivity extends CommonActivity {
+    private static final String TAG = ImgSegActivity.class.getSimpleName();
+
+    protected TextView tvInputSetting;
+    protected ImageView ivInputImage;
+    protected TextView tvOutputResult;
+    protected TextView tvInferenceTime;
+
+    // model config
+    Config config = new Config();
+
+    protected ImgSegPredictor predictor = new ImgSegPredictor();
+
+    Preprocess preprocess = new Preprocess();
+
+    Visualize visualize = new Visualize();
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+
+        super.onCreate(savedInstanceState);
+        setContentView(R.layout.activity_img_seg);
+        tvInputSetting = findViewById(R.id.tv_input_setting);
+        ivInputImage = findViewById(R.id.iv_input_image);
+        tvInferenceTime = findViewById(R.id.tv_inference_time);
+        tvOutputResult = findViewById(R.id.tv_output_result);
+        tvInputSetting.setMovementMethod(ScrollingMovementMethod.getInstance());
+        tvOutputResult.setMovementMethod(ScrollingMovementMethod.getInstance());
+    }
+
+    @Override
+    public boolean onLoadModel() {
+        return super.onLoadModel() && predictor.init(ImgSegActivity.this, config);
+    }
+
+    @Override
+    public boolean onRunModel() {
+        return super.onRunModel() && predictor.isLoaded() && predictor.runModel(preprocess,visualize);
+    }
+
+    @Override
+    public void onLoadModelSuccessed() {
+        super.onLoadModelSuccessed();
+        // load test image from file_paths and run model
+        try {
+            if (config.imagePath.isEmpty()) {
+                return;
+            }
+            Bitmap image = null;
+            // read test image file from custom file_paths if the first character of mode file_paths is '/', otherwise read test
+            // image file from assets
+            if (!config.imagePath.substring(0, 1).equals("/")) {
+                InputStream imageStream = getAssets().open(config.imagePath);
+                image = BitmapFactory.decodeStream(imageStream);
+            } else {
+                if (!new File(config.imagePath).exists()) {
+                    return;
+                }
+                image = BitmapFactory.decodeFile(config.imagePath);
+            }
+            if (image != null && predictor.isLoaded()) {
+                predictor.setInputImage(image);
+                runModel();
+            }
+        } catch (IOException e) {
+            Toast.makeText(ImgSegActivity.this, "Load image failed!", Toast.LENGTH_SHORT).show();
+            e.printStackTrace();
+        }
+    }
+
+    @Override
+    public void onLoadModelFailed() {
+        super.onLoadModelFailed();
+    }
+
+    @Override
+    public void onRunModelSuccessed() {
+        super.onRunModelSuccessed();
+        // obtain results and update UI
+        tvInferenceTime.setText("Inference time: " + predictor.inferenceTime() + " ms");
+        Bitmap outputImage = predictor.outputImage();
+        if (outputImage != null) {
+            ivInputImage.setImageBitmap(outputImage);
+        }
+        tvOutputResult.setText(predictor.outputResult());
+        tvOutputResult.scrollTo(0, 0);
+    }
+
+    @Override
+    public void onRunModelFailed() {
+        super.onRunModelFailed();
+    }
+
+    @Override
+    public void onImageChanged(Bitmap image) {
+        super.onImageChanged(image);
+        // rerun model if users pick test image from gallery or camera
+        if (image != null && predictor.isLoaded()) {
+//            predictor.setConfig(config);
+            predictor.setInputImage(image);
+            runModel();
+        }
+    }
+
+    @Override
+    public void onImageChanged(String path) {
+        super.onImageChanged(path);
+        Bitmap image = BitmapFactory.decodeFile(path);
+        predictor.setInputImage(image);
+            runModel();
+    }
+    public void onSettingsClicked() {
+        super.onSettingsClicked();
+        startActivity(new Intent(ImgSegActivity.this, ImgSegSettingsActivity.class));
+    }
+
+    @Override
+    public boolean onPrepareOptionsMenu(Menu menu) {
+        boolean isLoaded = predictor.isLoaded();
+        menu.findItem(R.id.open_gallery).setEnabled(isLoaded);
+        menu.findItem(R.id.take_photo).setEnabled(isLoaded);
+        return super.onPrepareOptionsMenu(menu);
+    }
+
+    @Override
+    protected void onResume() {
+        Log.i(TAG,"begin onResume");
+        super.onResume();
+
+        SharedPreferences sharedPreferences = PreferenceManager.getDefaultSharedPreferences(this);
+        boolean settingsChanged = false;
+        String model_path = sharedPreferences.getString(getString(R.string.ISG_MODEL_PATH_KEY),
+                getString(R.string.ISG_MODEL_PATH_DEFAULT));
+        String label_path = sharedPreferences.getString(getString(R.string.ISG_LABEL_PATH_KEY),
+                getString(R.string.ISG_LABEL_PATH_DEFAULT));
+        String image_path = sharedPreferences.getString(getString(R.string.ISG_IMAGE_PATH_KEY),
+                getString(R.string.ISG_IMAGE_PATH_DEFAULT));
+        settingsChanged |= !model_path.equalsIgnoreCase(config.modelPath);
+        settingsChanged |= !label_path.equalsIgnoreCase(config.labelPath);
+        settingsChanged |= !image_path.equalsIgnoreCase(config.imagePath);
+        int cpu_thread_num = Integer.parseInt(sharedPreferences.getString(getString(R.string.ISG_CPU_THREAD_NUM_KEY),
+                getString(R.string.ISG_CPU_THREAD_NUM_DEFAULT)));
+        settingsChanged |= cpu_thread_num != config.cpuThreadNum;
+        String cpu_power_mode =
+                sharedPreferences.getString(getString(R.string.ISG_CPU_POWER_MODE_KEY),
+                        getString(R.string.ISG_CPU_POWER_MODE_DEFAULT));
+        settingsChanged |= !cpu_power_mode.equalsIgnoreCase(config.cpuPowerMode);
+        String input_color_format =
+                sharedPreferences.getString(getString(R.string.ISG_INPUT_COLOR_FORMAT_KEY),
+                        getString(R.string.ISG_INPUT_COLOR_FORMAT_DEFAULT));
+        settingsChanged |= !input_color_format.equalsIgnoreCase(config.inputColorFormat);
+        long[] input_shape =
+                Utils.parseLongsFromString(sharedPreferences.getString(getString(R.string.ISG_INPUT_SHAPE_KEY),
+                        getString(R.string.ISG_INPUT_SHAPE_DEFAULT)), ",");
+
+        settingsChanged |= input_shape.length != config.inputShape.length;
+
+        if (!settingsChanged) {
+            for (int i = 0; i < input_shape.length; i++) {
+                settingsChanged |= input_shape[i] != config.inputShape[i];
+            }
+        }
+
+        if (settingsChanged) {
+            config.init(model_path,label_path,image_path,cpu_thread_num,cpu_power_mode,
+                    input_color_format,input_shape);
+            preprocess.init(config);
+            // update UI
+            tvInputSetting.setText("Model: " + config.modelPath.substring(config.modelPath.lastIndexOf("/") + 1) + "\n" + "CPU" +
+                    " Thread Num: " + Integer.toString(config.cpuThreadNum) + "\n" + "CPU Power Mode: " + config.cpuPowerMode);
+            tvInputSetting.scrollTo(0, 0);
+            // reload model if configure has been changed
+            loadModel();
+        }
+    }
+
+    @Override
+    protected void onDestroy() {
+        if (predictor != null) {
+            predictor.releaseModel();
+        }
+        super.onDestroy();
+    }
+}

deploy/lite/humanseg-android-demo/app/src/main/java/com/baidu/paddle/lite/demo/segmentation/ImgSegPredictor.java

+package com.baidu.paddle.lite.demo.segmentation;
+
+import android.content.Context;
+import android.graphics.Bitmap;
+import android.util.Log;
+
+import com.baidu.paddle.lite.Tensor;
+import com.baidu.paddle.lite.demo.Predictor;
+import com.baidu.paddle.lite.demo.segmentation.config.Config;
+import com.baidu.paddle.lite.demo.segmentation.preprocess.Preprocess;
+import com.baidu.paddle.lite.demo.segmentation.visual.Visualize;
+
+import java.io.InputStream;
+import java.util.Date;
+import java.util.Vector;
+
+import static android.graphics.Color.blue;
+import static android.graphics.Color.green;
+import static android.graphics.Color.red;
+
+public class ImgSegPredictor extends Predictor {
+    private static final String TAG = ImgSegPredictor.class.getSimpleName();
+    protected Vector<String> wordLabels = new Vector<String>();
+
+    Config config;
+
+    protected Bitmap inputImage = null;
+    protected Bitmap scaledImage = null;
+    protected Bitmap outputImage = null;
+    protected String outputResult = "";
+    protected float preprocessTime = 0;
+    protected float postprocessTime = 0;
+
+    public ImgSegPredictor() {
+        super();
+    }
+
+    public boolean init(Context appCtx, Config config) {
+
+        if (config.inputShape.length != 4) {
+            Log.i(TAG, "size of input shape should be: 4");
+            return false;
+        }
+        if (config.inputShape[0] != 1) {
+            Log.i(TAG, "only one batch is supported in the image classification demo, you can use any batch size in " +
+                    "your Apps!");
+            return false;
+        }
+        if (config.inputShape[1] != 1 && config.inputShape[1] != 3) {
+            Log.i(TAG, "only one/three channels are supported in the image classification demo, you can use any " +
+                    "channel size in your Apps!");
+            return false;
+        }
+        if (!config.inputColorFormat.equalsIgnoreCase("RGB") && !config.inputColorFormat.equalsIgnoreCase("BGR")) {
+            Log.i(TAG, "only RGB and BGR color format is supported.");
+            return false;
+        }
+        super.init(appCtx, config.modelPath, config.cpuThreadNum, config.cpuPowerMode);
+        if (!super.isLoaded()) {
+            return false;
+        }
+        this.config = config;
+
+        return isLoaded;
+    }
+
+    protected boolean loadLabel(String labelPath) {
+        wordLabels.clear();
+        // load word labels from file
+        try {
+            InputStream assetsInputStream = appCtx.getAssets().open(labelPath);
+            int available = assetsInputStream.available();
+            byte[] lines = new byte[available];
+            assetsInputStream.read(lines);
+            assetsInputStream.close();
+            String words = new String(lines);
+            String[] contents = words.split("\n");
+            for (String content : contents) {
+                wordLabels.add(content);
+            }
+            Log.i(TAG, "word label size: " + wordLabels.size());
+        } catch (Exception e) {
+            Log.e(TAG, e.getMessage());
+            return false;
+        }
+        return true;
+    }
+
+    public Tensor getInput(int idx) {
+        return super.getInput(idx);
+    }
+
+    public Tensor getOutput(int idx) {
+        return super.getOutput(idx);
+    }
+
+    public boolean runModel(Bitmap image) {
+        setInputImage(image);
+        return runModel();
+    }
+
+    public boolean runModel(Preprocess preprocess, Visualize visualize) {
+        if (inputImage == null) {
+            return false;
+        }
+
+        // set input shape
+        Tensor inputTensor = getInput(0);
+
+        inputTensor.resize(config.inputShape);
+
+        // pre-process image
+        Date start = new Date();
+
+        preprocess.init(config);
+
+        preprocess.to_array(scaledImage);
+
+        // feed input tensor with pre-processed data
+
+        inputTensor.setData(preprocess.inputData);
+
+        Date end = new Date();
+        preprocessTime = (float) (end.getTime() - start.getTime());
+
+        // inference
+        super.runModel();
+
+        Tensor outputTensor = getOutput(0);
+
+        // post-process
+        this.outputImage = visualize.draw(inputImage,outputTensor);
+
+        postprocessTime = (float) (end.getTime() - start.getTime());
+
+        start = new Date();
+        outputResult = new String();
+        end = new Date();
+
+        return true;
+    }
+
+    public void setConfig(Config config){
+        this.config = config;
+    }
+
+    public Bitmap inputImage() {
+        return inputImage;
+    }
+
+    public Bitmap outputImage() {
+        return outputImage;
+    }
+
+    public String outputResult() {
+        return outputResult;
+    }
+
+    public float preprocessTime() {
+        return preprocessTime;
+    }
+
+    public float postprocessTime() {
+        return postprocessTime;
+    }
+
+    public void setInputImage(Bitmap image) {
+        if (image == null) {
+            return;
+        }
+        // scale image to the size of input tensor
+        Bitmap rgbaImage = image.copy(Bitmap.Config.ARGB_8888, true);
+
+        Bitmap scaleImage = Bitmap.createScaledBitmap(rgbaImage, (int) this.config.inputShape[3], (int) this.config.inputShape[2], true);
+        this.inputImage = rgbaImage;
+        this.scaledImage = scaleImage;
+    }
+
+}

deploy/lite/humanseg-android-demo/app/src/main/java/com/baidu/paddle/lite/demo/segmentation/ImgSegSettingsActivity.java

+package com.baidu.paddle.lite.demo.segmentation;
+
+import android.content.SharedPreferences;
+import android.os.Bundle;
+import android.preference.CheckBoxPreference;
+import android.preference.EditTextPreference;
+import android.preference.ListPreference;
+import android.support.v7.app.ActionBar;
+
+import com.baidu.paddle.lite.demo.AppCompatPreferenceActivity;
+import com.baidu.paddle.lite.demo.R;
+import com.baidu.paddle.lite.demo.Utils;
+
+import java.util.ArrayList;
+import java.util.List;
+
+public class ImgSegSettingsActivity extends AppCompatPreferenceActivity implements SharedPreferences.OnSharedPreferenceChangeListener {
+    ListPreference lpChoosePreInstalledModel = null;
+    CheckBoxPreference cbEnableCustomSettings = null;
+    EditTextPreference etModelPath = null;
+    EditTextPreference etLabelPath = null;
+    EditTextPreference etImagePath = null;
+    ListPreference lpCPUThreadNum = null;
+    ListPreference lpCPUPowerMode = null;
+    ListPreference lpInputColorFormat = null;
+    EditTextPreference etInputShape = null;
+    EditTextPreference etInputMean = null;
+    EditTextPreference etInputStd = null;
+
+    List<String> preInstalledModelPaths = null;
+    List<String> preInstalledLabelPaths = null;
+    List<String> preInstalledImagePaths = null;
+    List<String> preInstalledInputShapes = null;
+    List<String> preInstalledCPUThreadNums = null;
+    List<String> preInstalledCPUPowerModes = null;
+    List<String> preInstalledInputColorFormats = null;
+    List<String> preInstalledInputMeans = null;
+    List<String> preInstalledInputStds = null;
+
+    @Override
+    public void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        addPreferencesFromResource(R.xml.settings_img_seg);
+        ActionBar supportActionBar = getSupportActionBar();
+        if (supportActionBar != null) {
+            supportActionBar.setDisplayHomeAsUpEnabled(true);
+        }
+
+        // initialized pre-installed models
+        preInstalledModelPaths = new ArrayList<String>();
+        preInstalledLabelPaths = new ArrayList<String>();
+        preInstalledImagePaths = new ArrayList<String>();
+        preInstalledInputShapes = new ArrayList<String>();
+        preInstalledCPUThreadNums = new ArrayList<String>();
+        preInstalledCPUPowerModes = new ArrayList<String>();
+        preInstalledInputColorFormats = new ArrayList<String>();
+        preInstalledInputMeans = new ArrayList<String>();
+        preInstalledInputStds = new ArrayList<String>();
+        // add deeplab_mobilenet_for_cpu
+        preInstalledModelPaths.add(getString(R.string.ISG_MODEL_PATH_DEFAULT));
+        preInstalledLabelPaths.add(getString(R.string.ISG_LABEL_PATH_DEFAULT));
+        preInstalledImagePaths.add(getString(R.string.ISG_IMAGE_PATH_DEFAULT));
+        preInstalledCPUThreadNums.add(getString(R.string.ISG_CPU_THREAD_NUM_DEFAULT));
+        preInstalledCPUPowerModes.add(getString(R.string.ISG_CPU_POWER_MODE_DEFAULT));
+        preInstalledInputColorFormats.add(getString(R.string.ISG_INPUT_COLOR_FORMAT_DEFAULT));
+        preInstalledInputShapes.add(getString(R.string.ISG_INPUT_SHAPE_DEFAULT));
+
+        // initialize UI components
+        lpChoosePreInstalledModel =
+                (ListPreference) findPreference(getString(R.string.ISG_CHOOSE_PRE_INSTALLED_MODEL_KEY));
+        String[] preInstalledModelNames = new String[preInstalledModelPaths.size()];
+        for (int i = 0; i < preInstalledModelPaths.size(); i++) {
+            preInstalledModelNames[i] =
+                    preInstalledModelPaths.get(i).substring(preInstalledModelPaths.get(i).lastIndexOf("/") + 1);
+        }
+        lpChoosePreInstalledModel.setEntries(preInstalledModelNames);
+        lpChoosePreInstalledModel.setEntryValues(preInstalledModelPaths.toArray(new String[preInstalledModelPaths.size()]));
+        cbEnableCustomSettings =
+                (CheckBoxPreference) findPreference(getString(R.string.ISG_ENABLE_CUSTOM_SETTINGS_KEY));
+        etModelPath = (EditTextPreference) findPreference(getString(R.string.ISG_MODEL_PATH_KEY));
+        etModelPath.setTitle("Model Path (SDCard: " + Utils.getSDCardDirectory() + ")");
+        etLabelPath = (EditTextPreference) findPreference(getString(R.string.ISG_LABEL_PATH_KEY));
+        etImagePath = (EditTextPreference) findPreference(getString(R.string.ISG_IMAGE_PATH_KEY));
+        lpCPUThreadNum =
+                (ListPreference) findPreference(getString(R.string.ISG_CPU_THREAD_NUM_KEY));
+        lpCPUPowerMode =
+                (ListPreference) findPreference(getString(R.string.ISG_CPU_POWER_MODE_KEY));
+        lpInputColorFormat =
+                (ListPreference) findPreference(getString(R.string.ISG_INPUT_COLOR_FORMAT_KEY));
+        etInputShape = (EditTextPreference) findPreference(getString(R.string.ISG_INPUT_SHAPE_KEY));
+    }
+
+    private void reloadPreferenceAndUpdateUI() {
+        SharedPreferences sharedPreferences = getPreferenceScreen().getSharedPreferences();
+        boolean enableCustomSettings =
+                sharedPreferences.getBoolean(getString(R.string.ISG_ENABLE_CUSTOM_SETTINGS_KEY), false);
+        String modelPath = sharedPreferences.getString(getString(R.string.ISG_CHOOSE_PRE_INSTALLED_MODEL_KEY),
+                getString(R.string.ISG_MODEL_PATH_DEFAULT));
+        int modelIdx = lpChoosePreInstalledModel.findIndexOfValue(modelPath);
+        if (modelIdx >= 0 && modelIdx < preInstalledModelPaths.size()) {
+            if (!enableCustomSettings) {
+                SharedPreferences.Editor editor = sharedPreferences.edit();
+                editor.putString(getString(R.string.ISG_MODEL_PATH_KEY), preInstalledModelPaths.get(modelIdx));
+                editor.putString(getString(R.string.ISG_LABEL_PATH_KEY), preInstalledLabelPaths.get(modelIdx));
+                editor.putString(getString(R.string.ISG_IMAGE_PATH_KEY), preInstalledImagePaths.get(modelIdx));
+                editor.putString(getString(R.string.ISG_CPU_THREAD_NUM_KEY), preInstalledCPUThreadNums.get(modelIdx));
+                editor.putString(getString(R.string.ISG_CPU_POWER_MODE_KEY), preInstalledCPUPowerModes.get(modelIdx));
+                editor.putString(getString(R.string.ISG_INPUT_COLOR_FORMAT_KEY),
+                        preInstalledInputColorFormats.get(modelIdx));
+                editor.putString(getString(R.string.ISG_INPUT_SHAPE_KEY), preInstalledInputShapes.get(modelIdx));
+                editor.commit();
+            }
+            lpChoosePreInstalledModel.setSummary(modelPath);
+        }
+        cbEnableCustomSettings.setChecked(enableCustomSettings);
+        etModelPath.setEnabled(enableCustomSettings);
+        etLabelPath.setEnabled(enableCustomSettings);
+        etImagePath.setEnabled(enableCustomSettings);
+        lpCPUThreadNum.setEnabled(enableCustomSettings);
+        lpCPUPowerMode.setEnabled(enableCustomSettings);
+        lpInputColorFormat.setEnabled(enableCustomSettings);
+        etInputShape.setEnabled(enableCustomSettings);
+        etInputMean.setEnabled(enableCustomSettings);
+        etInputStd.setEnabled(enableCustomSettings);
+        modelPath = sharedPreferences.getString(getString(R.string.ISG_MODEL_PATH_KEY),
+                getString(R.string.ISG_MODEL_PATH_DEFAULT));
+        String labelPath = sharedPreferences.getString(getString(R.string.ISG_LABEL_PATH_KEY),
+                getString(R.string.ISG_LABEL_PATH_DEFAULT));
+        String imagePath = sharedPreferences.getString(getString(R.string.ISG_IMAGE_PATH_KEY),
+                getString(R.string.ISG_IMAGE_PATH_DEFAULT));
+        String cpuThreadNum = sharedPreferences.getString(getString(R.string.ISG_CPU_THREAD_NUM_KEY),
+                getString(R.string.ISG_CPU_THREAD_NUM_DEFAULT));
+        String cpuPowerMode = sharedPreferences.getString(getString(R.string.ISG_CPU_POWER_MODE_KEY),
+                getString(R.string.ISG_CPU_POWER_MODE_DEFAULT));
+        String inputColorFormat = sharedPreferences.getString(getString(R.string.ISG_INPUT_COLOR_FORMAT_KEY),
+                getString(R.string.ISG_INPUT_COLOR_FORMAT_DEFAULT));
+        String inputShape = sharedPreferences.getString(getString(R.string.ISG_INPUT_SHAPE_KEY),
+                getString(R.string.ISG_INPUT_SHAPE_DEFAULT));
+        etModelPath.setSummary(modelPath);
+        etModelPath.setText(modelPath);
+        etLabelPath.setSummary(labelPath);
+        etLabelPath.setText(labelPath);
+        etImagePath.setSummary(imagePath);
+        etImagePath.setText(imagePath);
+        lpCPUThreadNum.setValue(cpuThreadNum);
+        lpCPUThreadNum.setSummary(cpuThreadNum);
+        lpCPUPowerMode.setValue(cpuPowerMode);
+        lpCPUPowerMode.setSummary(cpuPowerMode);
+        lpInputColorFormat.setValue(inputColorFormat);
+        lpInputColorFormat.setSummary(inputColorFormat);
+        etInputShape.setSummary(inputShape);
+        etInputShape.setText(inputShape);
+    }
+
+    @Override
+    protected void onResume() {
+        super.onResume();
+        getPreferenceScreen().getSharedPreferences().registerOnSharedPreferenceChangeListener(this);
+        reloadPreferenceAndUpdateUI();
+    }
+
+    @Override
+    protected void onPause() {
+        super.onPause();
+        getPreferenceScreen().getSharedPreferences().unregisterOnSharedPreferenceChangeListener(this);
+    }
+
+    @Override
+    public void onSharedPreferenceChanged(SharedPreferences sharedPreferences, String key) {
+        if (key.equals(getString(R.string.ISG_CHOOSE_PRE_INSTALLED_MODEL_KEY))) {
+            SharedPreferences.Editor editor = sharedPreferences.edit();
+            editor.putBoolean(getString(R.string.ISG_ENABLE_CUSTOM_SETTINGS_KEY), false);
+            editor.commit();
+        }
+        reloadPreferenceAndUpdateUI();
+    }
+}

deploy/lite/humanseg-android-demo/app/src/main/java/com/baidu/paddle/lite/demo/segmentation/config/Config.java

+package com.baidu.paddle.lite.demo.segmentation.config;
+
+import android.graphics.Bitmap;
+
+public class Config {
+
+    public String modelPath = "";
+    public String labelPath = "";
+    public String imagePath = "";
+    public int cpuThreadNum = 1;
+    public String cpuPowerMode = "";
+
+    public String inputColorFormat = "";
+    public long[] inputShape = new long[]{};
+
+
+    public void init(String modelPath, String labelPath, String imagePath, int cpuThreadNum,
+                     String cpuPowerMode, String inputColorFormat,long[] inputShape){
+
+        this.modelPath = modelPath;
+        this.labelPath = labelPath;
+        this.imagePath = imagePath;
+        this.cpuThreadNum = cpuThreadNum;
+        this.cpuPowerMode = cpuPowerMode;
+
+        this.inputColorFormat = inputColorFormat;
+        this.inputShape = inputShape;
+    }
+
+    public void setInputShape(Bitmap inputImage){
+        this.inputShape[0] = 1;
+        this.inputShape[1] = 3;
+
+        this.inputShape[2] = inputImage.getHeight();
+        this.inputShape[3] = inputImage.getWidth();
+
+    }
+
+}

deploy/lite/humanseg-android-demo/app/src/main/java/com/baidu/paddle/lite/demo/segmentation/preprocess/Preprocess.java

+package com.baidu.paddle.lite.demo.segmentation.preprocess;
+
+import android.graphics.Bitmap;
+import android.util.Log;
+
+import com.baidu.paddle.lite.demo.segmentation.config.Config;
+
+import static android.graphics.Color.blue;
+import static android.graphics.Color.green;
+import static android.graphics.Color.red;
+
+public class Preprocess {
+
+    private static final String TAG = Preprocess.class.getSimpleName();
+
+    Config config;
+    int channels;
+    int width;
+    int height;
+
+    public  float[] inputData;
+
+    public void init(Config config){
+        this.config = config;
+        this.channels = (int) config.inputShape[1];
+        this.height = (int) config.inputShape[2];
+        this.width = (int) config.inputShape[3];
+        this.inputData = new float[channels * width * height];
+    }
+
+    public boolean to_array(Bitmap inputImage){
+
+        if (channels == 3) {
+            int[] channelIdx = null;
+            if (config.inputColorFormat.equalsIgnoreCase("RGB")) {
+                channelIdx = new int[]{0, 1, 2};
+            } else if (config.inputColorFormat.equalsIgnoreCase("BGR")) {
+                channelIdx = new int[]{2, 1, 0};
+            } else {
+                Log.i(TAG, "unknown color format " + config.inputColorFormat + ", only RGB and BGR color format is " +
+                        "supported!");
+                return false;
+            }
+            int[] channelStride = new int[]{width * height, width * height * 2};
+
+            for (int y = 0; y < height; y++) {
+                for (int x = 0; x < width; x++) {
+                    int color = inputImage.getPixel(x, y);
+                    float[] rgb = new float[]{(float) red(color) , (float) green(color) ,
+                            (float) blue(color)};
+                    inputData[y * width + x] = rgb[channelIdx[0]] ;
+                    inputData[y * width + x + channelStride[0]] = rgb[channelIdx[1]] ;
+                    inputData[y * width + x + channelStride[1]] = rgb[channelIdx[2]];
+                }
+            }
+        } else if (channels == 1) {
+            for (int y = 0; y < height; y++) {
+                for (int x = 0; x < width; x++) {
+                    int color = inputImage.getPixel(x, y);
+                    float gray = (float) (red(color) + green(color) + blue(color));
+                    inputData[y * width + x] = gray;
+                }
+            }
+        } else {
+            Log.i(TAG, "unsupported channel size " + Integer.toString(channels) + ",  only channel 1 and 3 is " +
+                    "supported!");
+            return false;
+        }
+        return true;
+
+    }
+
+}

deploy/lite/humanseg-android-demo/app/src/main/java/com/baidu/paddle/lite/demo/segmentation/visual/Visualize.java

+package com.baidu.paddle.lite.demo.segmentation.visual;
+
+import android.graphics.Bitmap;
+import android.graphics.Canvas;
+import android.graphics.Matrix;
+import android.graphics.Paint;
+import android.util.Log;
+
+import com.baidu.paddle.lite.Tensor;
+
+public class Visualize {
+    private static final String TAG = Visualize.class.getSimpleName();
+
+    public Bitmap draw(Bitmap inputImage, Tensor outputTensor){
+
+        final int[] colors_map = {0xFF000000, 0xFFFFFF00};
+
+        float[] output = outputTensor.getFloatData();
+        long outputShape[] = outputTensor.shape();
+        long outputSize = 1;
+
+        for (long s : outputShape) {
+            outputSize *= s;
+        }
+
+        int[] objectColor = new int[(int)outputSize];
+
+        for(int i=0;i<output.length;i++){
+            objectColor[i] = colors_map[(int)output[i]];
+        }
+
+        Bitmap.Config config = inputImage.getConfig();
+        Bitmap outputImage = null;
+        if(outputShape.length==3){
+            outputImage = Bitmap.createBitmap(objectColor, (int)outputShape[2], (int)outputShape[1], config);
+            outputImage = Bitmap.createScaledBitmap(outputImage, inputImage.getWidth(), inputImage.getHeight(),true);
+        }
+
+        else if (outputShape.length==4){
+            outputImage = Bitmap.createBitmap(objectColor, (int)outputShape[3], (int)outputShape[2], config);
+        }
+        Bitmap bmOverlay = Bitmap.createBitmap(inputImage.getWidth(), inputImage.getHeight() , inputImage.getConfig());
+        Canvas canvas = new Canvas(bmOverlay);
+        canvas.drawBitmap(inputImage, new Matrix(), null);
+
+        Paint paint = new Paint();
+        paint.setAlpha(0x80);
+        canvas.drawBitmap(outputImage, 0, 0, paint);
+
+        return bmOverlay;
+
+    }
+}

deploy/lite/humanseg-android-demo/app/src/main/res/drawable-v24/ic_launcher_foreground.xml

+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:aapt="http://schemas.android.com/aapt"
+    android:width="108dp"
+    android:height="108dp"
+    android:viewportWidth="108"
+    android:viewportHeight="108">
+    <path
+        android:fillType="evenOdd"
+        android:pathData="M32,64C32,64 38.39,52.99 44.13,50.95C51.37,48.37 70.14,49.57 70.14,49.57L108.26,87.69L108,109.01L75.97,107.97L32,64Z"
+        android:strokeWidth="1"
+        android:strokeColor="#00000000">
+        <aapt:attr name="android:fillColor">
+            <gradient
+                android:endX="78.5885"
+                android:endY="90.9159"
+                android:startX="48.7653"
+                android:startY="61.0927"
+                android:type="linear">
+                <item
+                    android:color="#44000000"
+                    android:offset="0.0" />
+                <item
+                    android:color="#00000000"
+                    android:offset="1.0" />
+            </gradient>
+        </aapt:attr>
+    </path>
+    <path
+        android:fillColor="#FFFFFF"
+        android:fillType="nonZero"
+        android:pathData="M66.94,46.02L66.94,46.02C72.44,50.07 76,56.61 76,64L32,64C32,56.61 35.56,50.11 40.98,46.06L36.18,41.19C35.45,40.45 35.45,39.3 36.18,38.56C36.91,37.81 38.05,37.81 38.78,38.56L44.25,44.05C47.18,42.57 50.48,41.71 54,41.71C57.48,41.71 60.78,42.57 63.68,44.05L69.11,38.56C69.84,37.81 70.98,37.81 71.71,38.56C72.44,39.3 72.44,40.45 71.71,41.19L66.94,46.02ZM62.94,56.92C64.08,56.92 65,56.01 65,54.88C65,53.76 64.08,52.85 62.94,52.85C61.8,52.85 60.88,53.76 60.88,54.88C60.88,56.01 61.8,56.92 62.94,56.92ZM45.06,56.92C46.2,56.92 47.13,56.01 47.13,54.88C47.13,53.76 46.2,52.85 45.06,52.85C43.92,52.85 43,53.76 43,54.88C43,56.01 43.92,56.92 45.06,56.92Z"
+        android:strokeWidth="1"
+        android:strokeColor="#00000000" />
+</vector>

deploy/lite/humanseg-android-demo/app/src/main/res/drawable/ic_launcher_background.xml

+<?xml version="1.0" encoding="utf-8"?>
+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    android:width="108dp"
+    android:height="108dp"
+    android:viewportWidth="108"
+    android:viewportHeight="108">
+    <path
+        android:fillColor="#008577"
+        android:pathData="M0,0h108v108h-108z" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M9,0L9,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,0L19,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M29,0L29,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M39,0L39,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M49,0L49,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M59,0L59,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M69,0L69,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M79,0L79,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M89,0L89,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M99,0L99,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,9L108,9"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,19L108,19"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,29L108,29"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,39L108,39"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,49L108,49"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,59L108,59"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,69L108,69"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,79L108,79"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,89L108,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,99L108,99"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,29L89,29"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,39L89,39"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,49L89,49"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,59L89,59"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,69L89,69"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,79L89,79"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M29,19L29,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M39,19L39,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M49,19L49,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M59,19L59,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M69,19L69,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M79,19L79,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+</vector>

deploy/lite/humanseg-android-demo/app/src/main/res/layout/activity_img_seg.xml

+<?xml version="1.0" encoding="utf-8"?>
+<android.support.constraint.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
+                                             xmlns:app="http://schemas.android.com/apk/res-auto"
+                                             xmlns:tools="http://schemas.android.com/tools"
+                                             android:layout_width="match_parent"
+                                             android:layout_height="match_parent"
+                                             tools:context=".segmentation.ImgSegActivity">
+
+    <RelativeLayout
+            android:layout_width="match_parent"
+            android:layout_height="match_parent">
+
+        <LinearLayout
+                android:id="@+id/v_input_info"
+                android:layout_width="fill_parent"
+                android:layout_height="wrap_content"
+                android:layout_alignParentTop="true"
+                android:orientation="vertical">
+            <TextView
+                    android:id="@+id/tv_input_setting"
+                    android:layout_width="wrap_content"
+                    android:layout_height="wrap_content"
+                    android:scrollbars="vertical"
+                    android:layout_marginLeft="12dp"
+                    android:layout_marginRight="12dp"
+                    android:layout_marginTop="10dp"
+                    android:layout_marginBottom="5dp"
+                    android:lineSpacingExtra="4dp"
+                    android:singleLine="false"
+                    android:maxLines="6"
+                    android:text=""/>
+
+        </LinearLayout>
+
+        <RelativeLayout
+                android:layout_width="match_parent"
+                android:layout_height="match_parent"
+                android:layout_above="@+id/v_output_info"
+                android:layout_below="@+id/v_input_info">
+
+            <ImageView
+                    android:id="@+id/iv_input_image"
+                    android:layout_width="400dp"
+                    android:layout_height="400dp"
+                    android:layout_centerHorizontal="true"
+                    android:layout_centerVertical="true"
+                    android:layout_marginLeft="12dp"
+                    android:layout_marginRight="12dp"
+                    android:layout_marginTop="5dp"
+                    android:layout_marginBottom="5dp"
+                    android:adjustViewBounds="true"
+                    android:scaleType="fitCenter"/>
+        </RelativeLayout>
+
+
+        <RelativeLayout
+                android:id="@+id/v_output_info"
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:layout_alignParentBottom="true"
+                android:layout_centerHorizontal="true">
+
+            <TextView
+                    android:id="@+id/tv_output_result"
+                    android:layout_width="wrap_content"
+                    android:layout_height="wrap_content"
+                    android:layout_alignParentTop="true"
+                    android:layout_centerHorizontal="true"
+                    android:layout_centerVertical="true"
+                    android:scrollbars="vertical"
+                    android:layout_marginLeft="12dp"
+                    android:layout_marginRight="12dp"
+                    android:layout_marginTop="5dp"
+                    android:layout_marginBottom="5dp"
+                    android:textAlignment="center"
+                    android:lineSpacingExtra="5dp"
+                    android:singleLine="false"
+                    android:maxLines="5"
+                    android:text=""/>
+
+            <TextView
+                    android:id="@+id/tv_inference_time"
+                    android:layout_width="wrap_content"
+                    android:layout_height="wrap_content"
+                    android:layout_below="@+id/tv_output_result"
+                    android:layout_centerHorizontal="true"
+                    android:layout_centerVertical="true"
+                    android:textAlignment="center"
+                    android:layout_marginLeft="12dp"
+                    android:layout_marginRight="12dp"
+                    android:layout_marginTop="5dp"
+                    android:layout_marginBottom="10dp"
+                    android:text=""/>
+
+        </RelativeLayout>
+
+    </RelativeLayout>
+
+</android.support.constraint.ConstraintLayout>
\ No newline at end of file

deploy/lite/humanseg-android-demo/app/src/main/res/layout/activity_main.xml

+<?xml version="1.0" encoding="utf-8"?>
+<android.support.constraint.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
+                                             xmlns:app="http://schemas.android.com/apk/res-auto"
+                                             xmlns:tools="http://schemas.android.com/tools"
+                                             android:layout_width="fill_parent"
+                                             android:layout_height="fill_parent"
+                                             tools:context=".MainActivity">
+
+    <ScrollView
+            android:layout_width="fill_parent"
+            android:layout_height="fill_parent"
+            android:fadingEdge="vertical"
+            android:scrollbars="vertical">
+        <LinearLayout
+                android:layout_width="fill_parent"
+                android:layout_height="fill_parent"
+                android:orientation="vertical">
+
+            <LinearLayout
+                    android:layout_width="fill_parent"
+                    android:layout_height="300dp"
+                    android:orientation="horizontal">
+                <RelativeLayout
+                        android:id="@+id/v_img_seg"
+                        android:layout_width="wrap_content"
+                        android:layout_height="fill_parent"
+                        android:layout_weight="1"
+                        android:clickable="true"
+                        android:onClick="onClick">
+
+                    <ImageView
+                            android:id="@+id/iv_img_seg_image"
+                            android:layout_width="200dp"
+                            android:layout_height="200dp"
+                            android:layout_centerHorizontal="true"
+                            android:layout_centerVertical="true"
+                            android:layout_margin="12dp"
+                            android:adjustViewBounds="true"
+                            android:src="@drawable/image_segementation"
+                            android:scaleType="fitCenter"/>
+
+                    <TextView
+                            android:id="@+id/iv_img_seg_title"
+                            android:layout_below="@id/iv_img_seg_image"
+                            android:layout_width="wrap_content"
+                            android:layout_height="wrap_content"
+                            android:layout_centerHorizontal="true"
+                            android:layout_margin="8dp"
+                            android:text="Image Segmentation"
+                            android:textStyle="bold"
+                            android:textAllCaps="false"
+                            android:singleLine="false"/>
+                </RelativeLayout>
+
+            </LinearLayout>
+        </LinearLayout>
+    </ScrollView>
+</android.support.constraint.ConstraintLayout>
\ No newline at end of file

deploy/lite/humanseg-android-demo/app/src/main/res/menu/menu_action_options.xml

+<menu xmlns:android="http://schemas.android.com/apk/res/android"
+      xmlns:app="http://schemas.android.com/apk/res-auto">
+    <group android:id="@+id/pick_image">
+        <item
+                android:id="@+id/open_gallery"
+                android:title="Open Gallery"
+                app:showAsAction="withText"/>
+
+        <item
+                android:id="@+id/take_photo"
+                android:title="Take Photo"
+                app:showAsAction="withText"/>
+    </group>
+
+    <group>
+        <item
+                android:id="@+id/settings"
+                android:title="Settings..."
+                app:showAsAction="withText"/>
+    </group>
+</menu>

deploy/lite/humanseg-android-demo/app/src/main/res/mipmap-anydpi-v26/ic_launcher.xml

+<?xml version="1.0" encoding="utf-8"?>
+<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
+    <background android:drawable="@drawable/ic_launcher_background" />
+    <foreground android:drawable="@drawable/ic_launcher_foreground" />
+</adaptive-icon>
\ No newline at end of file

deploy/lite/humanseg-android-demo/app/src/main/res/mipmap-anydpi-v26/ic_launcher_round.xml

+<?xml version="1.0" encoding="utf-8"?>
+<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
+    <background android:drawable="@drawable/ic_launcher_background" />
+    <foreground android:drawable="@drawable/ic_launcher_foreground" />
+</adaptive-icon>
\ No newline at end of file

deploy/lite/humanseg-android-demo/app/src/main/res/values/arrays.xml

+<?xml version="1.0" encoding="utf-8"?>
+<resources>
+    <string-array name="cpu_thread_num_entries">
+        <item>1 threads</item>
+        <item>2 threads</item>
+        <item>4 threads</item>
+        <item>8 threads</item>
+    </string-array>
+    <string-array name="cpu_thread_num_values">
+        <item>1</item>
+        <item>2</item>
+        <item>4</item>
+        <item>8</item>
+    </string-array>
+    <string-array name="cpu_power_mode_entries">
+        <item>HIGH(only big cores)</item>
+        <item>LOW(only LITTLE cores)</item>
+        <item>FULL(all cores)</item>
+        <item>NO_BIND(depends on system)</item>
+        <item>RAND_HIGH</item>
+        <item>RAND_LOW</item>
+    </string-array>
+    <string-array name="cpu_power_mode_values">
+        <item>LITE_POWER_HIGH</item>
+        <item>LITE_POWER_LOW</item>
+        <item>LITE_POWER_FULL</item>
+        <item>LITE_POWER_NO_BIND</item>
+        <item>LITE_POWER_RAND_HIGH</item>
+        <item>LITE_POWER_RAND_LOW</item>
+    </string-array>
+    <string-array name="input_color_format_entries">
+        <item>BGR color format</item>
+        <item>RGB color format</item>
+    </string-array>
+    <string-array name="input_color_format_values">
+        <item>BGR</item>
+        <item>RGB</item>
+    </string-array>
+</resources>
\ No newline at end of file

deploy/lite/humanseg-android-demo/app/src/main/res/values/colors.xml

+<?xml version="1.0" encoding="utf-8"?>
+<resources>
+    <color name="colorPrimary">#008577</color>
+    <color name="colorPrimaryDark">#00574B</color>
+    <color name="colorAccent">#D81B60</color>
+</resources>

deploy/lite/humanseg-android-demo/app/src/main/res/values/strings.xml

+<resources>
+<string name="app_name">Segmentation-demo</string>
+<!-- image segmentation settings -->
+<string name="ISG_CHOOSE_PRE_INSTALLED_MODEL_KEY">ISG_CHOOSE_PRE_INSTALLED_MODEL_KEY</string>
+<string name="ISG_ENABLE_CUSTOM_SETTINGS_KEY">ISG_ENABLE_CUSTOM_SETTINGS_KEY</string>
+<string name="ISG_MODEL_PATH_KEY">ISG_MODEL_PATH_KEY</string>
+<string name="ISG_LABEL_PATH_KEY">ISG_LABEL_PATH_KEY</string>
+<string name="ISG_IMAGE_PATH_KEY">ISG_IMAGE_PATH_KEY</string>
+<string name="ISG_CPU_THREAD_NUM_KEY">ISG_CPU_THREAD_NUM_KEY</string>
+<string name="ISG_CPU_POWER_MODE_KEY">ISG_CPU_POWER_MODE_KEY</string>
+<string name="ISG_INPUT_COLOR_FORMAT_KEY">ISG_INPUT_COLOR_FORMAT_KEY</string>
+<string name="ISG_INPUT_SHAPE_KEY">ISG_INPUT_SHAPE_KEY</string>
+<string name="ISG_MODEL_PATH_DEFAULT">image_segmentation/models/deeplab_mobilenet_for_cpu</string>
+<string name="ISG_LABEL_PATH_DEFAULT">image_segmentation/labels/label_list</string>
+<string name="ISG_IMAGE_PATH_DEFAULT">image_segmentation/images/human.jpg</string>
+<string name="ISG_CPU_THREAD_NUM_DEFAULT">1</string>
+<string name="ISG_CPU_POWER_MODE_DEFAULT">LITE_POWER_HIGH</string>
+<string name="ISG_INPUT_COLOR_FORMAT_DEFAULT">RGB</string>
+<string name="ISG_INPUT_SHAPE_DEFAULT">1,3,513,513</string>
+</resources>

deploy/lite/humanseg-android-demo/app/src/main/res/values/styles.xml

+<resources>
+
+    <!-- Base application theme. -->
+    <style name="AppTheme" parent="Theme.AppCompat.Light.DarkActionBar">
+        <!-- Customize your theme here. -->
+        <item name="colorPrimary">@color/colorPrimary</item>
+        <item name="colorPrimaryDark">@color/colorPrimaryDark</item>
+        <item name="colorAccent">@color/colorAccent</item>
+        <item name="actionOverflowMenuStyle">@style/OverflowMenuStyle</item>
+    </style>
+
+    <style name="OverflowMenuStyle" parent="Widget.AppCompat.Light.PopupMenu.Overflow">
+        <item name="overlapAnchor">false</item>
+    </style>
+
+    <style name="AppTheme.NoActionBar">
+        <item name="windowActionBar">false</item>
+        <item name="windowNoTitle">true</item>
+    </style>
+
+    <style name="AppTheme.AppBarOverlay" parent="ThemeOverlay.AppCompat.Dark.ActionBar"/>
+
+    <style name="AppTheme.PopupOverlay" parent="ThemeOverlay.AppCompat.Light"/>
+
+</resources>

deploy/lite/humanseg-android-demo/app/src/main/res/xml/settings_img_seg.xml

+<?xml version="1.0" encoding="utf-8"?>
+<PreferenceScreen xmlns:android="http://schemas.android.com/apk/res/android" >
+    <PreferenceCategory android:title="Model Settings">
+        <ListPreference
+                android:defaultValue="@string/ISG_MODEL_PATH_DEFAULT"
+                android:key="@string/ISG_CHOOSE_PRE_INSTALLED_MODEL_KEY"
+                android:negativeButtonText="@null"
+                android:positiveButtonText="@null"
+                android:title="Choose pre-installed models" />
+        <CheckBoxPreference
+                android:defaultValue="false"
+                android:key="@string/ISG_ENABLE_CUSTOM_SETTINGS_KEY"
+                android:summaryOn="Enable"
+                android:summaryOff="Disable"
+                android:title="Enable custom settings"/>
+        <EditTextPreference
+                android:key="@string/ISG_MODEL_PATH_KEY"
+                android:defaultValue="@string/ISG_MODEL_PATH_DEFAULT"
+                android:title="Model Path" />
+        <EditTextPreference
+                android:key="@string/ISG_LABEL_PATH_KEY"
+                android:defaultValue="@string/ISG_LABEL_PATH_DEFAULT"
+                android:title="Label Path" />
+        <EditTextPreference
+                android:key="@string/ISG_IMAGE_PATH_KEY"
+                android:defaultValue="@string/ISG_IMAGE_PATH_DEFAULT"
+                android:title="Image Path" />
+    </PreferenceCategory>
+    <PreferenceCategory android:title="CPU Settings">
+        <ListPreference
+                android:defaultValue="@string/ISG_CPU_THREAD_NUM_DEFAULT"
+                android:key="@string/ISG_CPU_THREAD_NUM_KEY"
+                android:negativeButtonText="@null"
+                android:positiveButtonText="@null"
+                android:title="CPU Thread Num"
+                android:entries="@array/cpu_thread_num_entries"
+                android:entryValues="@array/cpu_thread_num_values"/>
+        <ListPreference
+                android:defaultValue="@string/ISG_CPU_POWER_MODE_DEFAULT"
+                android:key="@string/ISG_CPU_POWER_MODE_KEY"
+                android:negativeButtonText="@null"
+                android:positiveButtonText="@null"
+                android:title="CPU Power Mode"
+                android:entries="@array/cpu_power_mode_entries"
+                android:entryValues="@array/cpu_power_mode_values"/>
+    </PreferenceCategory>
+    <PreferenceCategory android:title="Input Settings">
+        <ListPreference
+                android:defaultValue="@string/ISG_INPUT_COLOR_FORMAT_DEFAULT"
+                android:key="@string/ISG_INPUT_COLOR_FORMAT_KEY"
+                android:negativeButtonText="@null"
+                android:positiveButtonText="@null"
+                android:title="Input Color Format: BGR or RGB"
+                android:entries="@array/input_color_format_entries"
+                android:entryValues="@array/input_color_format_values"/>
+        <EditTextPreference
+                android:key="@string/ISG_INPUT_SHAPE_KEY"
+                android:defaultValue="@string/ISG_INPUT_SHAPE_DEFAULT"
+                android:title="Input Shape: (1,1,h,w) or (1,3,h,w)" />
+    </PreferenceCategory>
+</PreferenceScreen>
+

deploy/lite/humanseg-android-demo/app/src/test/java/com/baidu/paddle/lite/demo/ExampleUnitTest.java

+package com.baidu.paddle.lite.demo;
+
+import org.junit.Test;
+
+import static org.junit.Assert.*;
+
+/**
+ * Example local unit test, which will execute on the development machine (host).
+ *
+ * @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
+ */
+public class ExampleUnitTest {
+    @Test
+    public void addition_isCorrect() {
+        assertEquals(4, 2 + 2);
+    }
+}
\ No newline at end of file

deploy/lite/humanseg-android-demo/build.gradle

+// Top-level build file where you can add configuration options common to all sub-projects/modules.
+
+buildscript {
+    repositories {
+        google()
+        jcenter()
+        
+    }
+    dependencies {
+        classpath 'com.android.tools.build:gradle:3.4.0'
+        
+        // NOTE: Do not place your application dependencies here; they belong
+        // in the individual module build.gradle files
+    }
+}
+
+allprojects {
+    repositories {
+        google()
+        jcenter()
+        
+    }
+}
+
+task clean(type: Delete) {
+    delete rootProject.buildDir
+}

deploy/lite/humanseg-android-demo/gradle.properties

+# Project-wide Gradle settings.
+# IDE (e.g. Android Studio) users:
+# Gradle settings configured through the IDE *will override*
+# any settings specified in this file.
+# For more details on how to configure your build environment visit
+# http://www.gradle.org/docs/current/userguide/build_environment.html
+# Specifies the JVM arguments used for the daemon process.
+# The setting is particularly useful for tweaking memory settings.
+org.gradle.jvmargs=-Xmx1536m
+# When configured, Gradle will run in incubating parallel mode.
+# This option should only be used with decoupled projects. More details, visit
+# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
+# org.gradle.parallel=true
+
+

deploy/lite/humanseg-android-demo/gradle/wrapper/gradle-wrapper.properties

+#Thu Aug 22 15:05:37 CST 2019
+distributionBase=GRADLE_USER_HOME
+distributionPath=wrapper/dists
+zipStoreBase=GRADLE_USER_HOME
+zipStorePath=wrapper/dists
+distributionUrl=https\://services.gradle.org/distributions/gradle-5.1.1-all.zip

deploy/lite/humanseg-android-demo/gradlew

+#!/usr/bin/env sh
+
+##############################################################################
+##
+##  Gradle start up script for UN*X
+##
+##############################################################################
+
+# Attempt to set APP_HOME
+# Resolve links: $0 may be a link
+PRG="$0"
+# Need this for relative symlinks.
+while [ -h "$PRG" ] ; do
+    ls=`ls -ld "$PRG"`
+    link=`expr "$ls" : '.*-> \(.*\)$'`
+    if expr "$link" : '/.*' > /dev/null; then
+        PRG="$link"
+    else
+        PRG=`dirname "$PRG"`"/$link"
+    fi
+done
+SAVED="`pwd`"
+cd "`dirname \"$PRG\"`/" >/dev/null
+APP_HOME="`pwd -P`"
+cd "$SAVED" >/dev/null
+
+APP_NAME="Gradle"
+APP_BASE_NAME=`basename "$0"`
+
+# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+DEFAULT_JVM_OPTS=""
+
+# Use the maximum available, or set MAX_FD != -1 to use that value.
+MAX_FD="maximum"
+
+warn () {
+    echo "$*"
+}
+
+die () {
+    echo
+    echo "$*"
+    echo
+    exit 1
+}
+
+# OS specific support (must be 'true' or 'false').
+cygwin=false
+msys=false
+darwin=false
+nonstop=false
+case "`uname`" in
+  CYGWIN* )
+    cygwin=true
+    ;;
+  Darwin* )
+    darwin=true
+    ;;
+  MINGW* )
+    msys=true
+    ;;
+  NONSTOP* )
+    nonstop=true
+    ;;
+esac
+
+CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
+
+# Determine the Java command to use to start the JVM.
+if [ -n "$JAVA_HOME" ] ; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
+        # IBM's JDK on AIX uses strange locations for the executables
+        JAVACMD="$JAVA_HOME/jre/sh/java"
+    else
+        JAVACMD="$JAVA_HOME/bin/java"
+    fi
+    if [ ! -x "$JAVACMD" ] ; then
+        die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+    fi
+else
+    JAVACMD="java"
+    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+fi
+
+# Increase the maximum file descriptors if we can.
+if [ "$cygwin" = "false" -a "$darwin" = "false" -a "$nonstop" = "false" ] ; then
+    MAX_FD_LIMIT=`ulimit -H -n`
+    if [ $? -eq 0 ] ; then
+        if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
+            MAX_FD="$MAX_FD_LIMIT"
+        fi
+        ulimit -n $MAX_FD
+        if [ $? -ne 0 ] ; then
+            warn "Could not set maximum file descriptor limit: $MAX_FD"
+        fi
+    else
+        warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
+    fi
+fi
+
+# For Darwin, add options to specify how the application appears in the dock
+if $darwin; then
+    GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
+fi
+
+# For Cygwin, switch paths to Windows format before running java
+if $cygwin ; then
+    APP_HOME=`cygpath --path --mixed "$APP_HOME"`
+    CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
+    JAVACMD=`cygpath --unix "$JAVACMD"`
+
+    # We build the pattern for arguments to be converted via cygpath
+    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
+    SEP=""
+    for dir in $ROOTDIRSRAW ; do
+        ROOTDIRS="$ROOTDIRS$SEP$dir"
+        SEP="|"
+    done
+    OURCYGPATTERN="(^($ROOTDIRS))"
+    # Add a user-defined pattern to the cygpath arguments
+    if [ "$GRADLE_CYGPATTERN" != "" ] ; then
+        OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
+    fi
+    # Now convert the arguments - kludge to limit ourselves to /bin/sh
+    i=0
+    for arg in "$@" ; do
+        CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
+        CHECK2=`echo "$arg"|egrep -c "^-"`                                 ### Determine if an option
+
+        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition
+            eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
+        else
+            eval `echo args$i`="\"$arg\""
+        fi
+        i=$((i+1))
+    done
+    case $i in
+        (0) set -- ;;
+        (1) set -- "$args0" ;;
+        (2) set -- "$args0" "$args1" ;;
+        (3) set -- "$args0" "$args1" "$args2" ;;
+        (4) set -- "$args0" "$args1" "$args2" "$args3" ;;
+        (5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
+        (6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
+        (7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
+        (8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
+        (9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
+    esac
+fi
+
+# Escape application args
+save () {
+    for i do printf %s\\n "$i" | sed "s/'/'\\\\''/g;1s/^/'/;\$s/\$/' \\\\/" ; done
+    echo " "
+}
+APP_ARGS=$(save "$@")
+
+# Collect all arguments for the java command, following the shell quoting and substitution rules
+eval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS "\"-Dorg.gradle.appname=$APP_BASE_NAME\"" -classpath "\"$CLASSPATH\"" org.gradle.wrapper.GradleWrapperMain "$APP_ARGS"
+
+# by default we should be in the correct project dir, but when run from Finder on Mac, the cwd is wrong
+if [ "$(uname)" = "Darwin" ] && [ "$HOME" = "$PWD" ]; then
+  cd "$(dirname "$0")"
+fi
+
+exec "$JAVACMD" "$@"

deploy/lite/humanseg-android-demo/gradlew.bat

+@if "%DEBUG%" == "" @echo off
+@rem ##########################################################################
+@rem
+@rem  Gradle startup script for Windows
+@rem
+@rem ##########################################################################
+
+@rem Set local scope for the variables with windows NT shell
+if "%OS%"=="Windows_NT" setlocal
+
+set DIRNAME=%~dp0
+if "%DIRNAME%" == "" set DIRNAME=.
+set APP_BASE_NAME=%~n0
+set APP_HOME=%DIRNAME%
+
+@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+set DEFAULT_JVM_OPTS=
+
+@rem Find java.exe
+if defined JAVA_HOME goto findJavaFromJavaHome
+
+set JAVA_EXE=java.exe
+%JAVA_EXE% -version >NUL 2>&1
+if "%ERRORLEVEL%" == "0" goto init
+
+echo.
+echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:findJavaFromJavaHome
+set JAVA_HOME=%JAVA_HOME:"=%
+set JAVA_EXE=%JAVA_HOME%/bin/java.exe
+
+if exist "%JAVA_EXE%" goto init
+
+echo.
+echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:init
+@rem Get command-line arguments, handling Windows variants
+
+if not "%OS%" == "Windows_NT" goto win9xME_args
+
+:win9xME_args
+@rem Slurp the command line arguments.
+set CMD_LINE_ARGS=
+set _SKIP=2
+
+:win9xME_args_slurp
+if "x%~1" == "x" goto execute
+
+set CMD_LINE_ARGS=%*
+
+:execute
+@rem Setup the command line
+
+set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
+
+@rem Execute Gradle
+"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%
+
+:end
+@rem End local scope for the variables with windows NT shell
+if "%ERRORLEVEL%"=="0" goto mainEnd
+
+:fail
+rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
+rem the _cmd.exe /c_ return code!
+if  not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
+exit /b 1
+
+:mainEnd
+if "%OS%"=="Windows_NT" endlocal
+
+:omega

deploy/lite/humanseg-android-demo/settings.gradle

+include ':app'

deploy/python/README.md

+# PaddleSeg Python 预测部署方案
+
+## 1. 说明
+本方案旨在提供一个`PaddlePaddle`跨平台图像分割模型的`Python`预测部署方案作为参考，用户通过一定的配置，加上少量的代码，即可把模型集成到自己的服务中，完成图像分割的任务。
+
+如果**硬件支持**(如`Tesla V100 GPU`等)，本程序支持使用`Nvidia TensorRT`进行`FP32`和`FP16`两种精度进行推理性能加速。
+
+## 2. 依赖前置条件
+* Python2.7/Python3
+
+## 3. 目录结构和文件说明
+```
+├── infer.py #  核心代码，完成分割模型的预测以及结果可视化
+├── requirements.txt # 依赖的Python包
+└── README.md # 说明文档
+```
+
+## 4. 环境安装和准备
+### 4.1 安装 PaddlePaddle
+
+如何选择合适版本的`PaddlePaddle`版本进行安装，可参考: [PaddlePaddle安装教程](https://www.paddlepaddle.org.cn/install/doc/)
+
+**注意**: 如硬件支持且需要使用`TensorRT`支持`FP16`半精度优化等， 则**本步骤**需要自行安装`TensorRT`并编译`PaddlePaddle`, 点击查看[编译安装参考文档](docs/compile_paddle_with_tensorrt.md)
+
+### 4.2:安装Python依赖包
+在**当前**目录下, 使用`pip`安装`Python`依赖包
+```bash
+pip install -r requirements.txt
+```
+### 4.3 安装`OpenCV` 相关依赖库
+预测代码中需要使用`OpenCV`，所以还需要`OpenCV`安装相关的动态链接库。
+
+`Ubuntu`下安装相关链接库：
+```bash
+apt-get install -y libglib2.0-0 libsm6 libxext6 libxrender-dev
+```
+
+CentOS 下安装相关链接库：
+```bash
+yum install -y libXext libSM libXrender
+```
+
+## 5. 开始预测
+### 5.1 准备模型
+请使用[模型导出工具](../../docs/model_export.md) 导出您的模型, 或点击下载我们的[人像分割样例模型](https://bj.bcebos.com/paddleseg/inference/human_freeze_model.zip)用于测试。
+
+模型导出的目录通常包括三个文件: 
+```
+├── model #  模型文件
+├── params # 参数文件
+└── deploy.yaml # 配置文件，用于C++或Python预测
+```
+配置文件的主要字段及其含义如下:
+```yaml
+DEPLOY:
+    # 是否使用GPU预测
+    USE_GPU: 1
+    # 模型和参数文件所在目录路径
+    MODEL_PATH: "/root/projects/models/deeplabv3p_xception65_humanseg"
+    # 模型文件名
+    MODEL_FILENAME: "__model__"
+    # 参数文件名
+    PARAMS_FILENAME: "__params__"
+    # 预测图片的的标准输入尺寸，输入尺寸不一致会做resize
+    EVAL_CROP_SIZE: (513, 513)
+    # 均值
+    MEAN: [0.5, 0.5, 0.5]
+    # 方差
+    STD: [0.5, 0.5, 0.5]
+    # 分类类型数
+    NUM_CLASSES: 2
+    # 图片通道数
+    CHANNELS : 3
+    # 预测模式，支持 NATIVE 和 ANALYSIS
+    PREDICTOR_MODE: "ANALYSIS"
+    # 每次预测的 batch_size
+    BATCH_SIZE : 3
+```
+### 5.2 执行预测程序
+在终端输入以下命令进行预测:
+```bash
+python infer.py --conf=/path/to/deploy.yaml --input_dir/path/to/images_directory --use_pr=False
+```
+参数说明如下:
+
+| 参数 | 是否必须|含义 |
+|-------|-------|----------|
+| conf | Yes|模型配置的Yaml文件路径 |
+| input_dir |Yes| 需要预测的图片目录 |
+| use_pr |No|是否使用优化模型，默认为False|
+
+* 优化模型：使用`PaddleSeg 0.3.0`版导出的为优化模型, 此前版本导出的模型即为未优化版本。优化模型把图像的预处理以及后处理部分融入到模型网络中使用`GPU` 完成，相比原来`CPU` 中的处理提升了计算性能。
+
+**注意**: 如果硬件支持且安装的是从源码编译集成`TensorRT`的`PaddlePaddle`, 则可以使用参数`--trt_mode=fp16` 表示开启`FP16` 精度优化, 使用`trt_mode=fp32` 表示使用`FP32` 精度。
+
+运行后程序会扫描`input_dir` 目录下所有指定格式图片，并生成`预测mask`和`可视化的结果`。
+
+对于图片`a.jpeg`, `预测mask` 存在`a_jpeg.png` 中，而可视化结果则在`a_jpeg_result.png` 中。
+
+输入样例:
+![avatar](../cpp/images/humanseg/demo2.jpeg)
+
+输出结果:  
+![avatar](../cpp/images/humanseg/demo2.jpeg_result.png)

deploy/python/docs/PaddleSeg_Infer_Benchmark.md

+# PaddleSeg 分割模型预测性能测试
+
+## 测试软件环境
+- CUDA 9.0
+- CUDNN 7.6
+- TensorRT-5.1.5
+- PaddlePaddle v1.6.1
+- Ubuntu 16.04
+- GPU: Tesla V100
+- CPU：Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
+
+## 测试方法
+- 输入采用 1000张RGB图片，batch_size 统一为 1。
+- 重复跑多轮，去掉第一轮预热时间，计后续几轮的平均时间：包括数据拷贝到GPU，预测引擎计算时间，预测结果拷贝回CPU 时间。
+- 采用Fluid C++预测引擎
+- 测试时开启了 FLAGS_cudnn_exhaustive_search=True，使用exhaustive方式搜索卷积计算算法。
+- 对于每个模型，同事测试了`OP`优化模型和原生模型的推理速度, 并分别就是否开启`FP16`和`FP32`的进行了测试
+
+
+
+## 推理速度测试数据
+
+**说明**： `OP优化模型`指的是`PaddleSeg 0.3.0`版以后导出的新版模型，把图像的预处理和后处理部分放入 GPU 中进行加速，提高性能。每个模型包含了三种`eval_crop_size`：`192x192`/`512x512`/`768x768`。
+
+<table width="1440">
+<tbody>
+<tr>
+<td rowspan="2" width="432">
+<p>模型</p>
+</td>
+<td colspan="3" width="535">
+<p>原始模型&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;(单位 ms/image)</p>
+</td>
+<td colspan="3" width="588">
+<p>OP 优化模型&nbsp;&nbsp;&nbsp;&nbsp;(单位 ms/image)</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>Fluid</p>
+</td>
+<td>
+<p>Fluid-TRT FP32</p>
+</td>
+<td>
+<p>Fluid-TRT FP16</p>
+</td>
+<td>
+<p>Fluid</p>
+</td>
+<td>
+<p>Fluid-TRT FP32</p>
+</td>
+<td>
+<p>Fluid-TRT FP16</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>deeplabv3p_mobilenetv2-1-0_bn_192x192</p>
+</td>
+<td>
+<p>4.717</p>
+</td>
+<td>
+<p>3.085</p>
+</td>
+<td>
+<p>2.607</p>
+</td>
+<td>
+<p>3.705</p>
+</td>
+<td>
+<p>2.09</p>
+</td>
+<td>
+<p>1.775</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>deeplabv3p_mobilenetv2-1-0_bn_512x512</p>
+</td>
+<td>
+<p>15.848</p>
+</td>
+<td>
+<p>14.243</p>
+</td>
+<td>
+<p>13.699</p>
+</td>
+<td>
+<p>8.284</p>
+</td>
+<td>
+<p>6.972</p>
+</td>
+<td>
+<p>6.013</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>deeplabv3p_mobilenetv2-1-0_bn_768x768</p>
+</td>
+<td>
+<p>63.148</p>
+</td>
+<td>
+<p>61.133</p>
+</td>
+<td>
+<p>59.262</p>
+</td>
+<td>
+<p>16.242</p>
+</td>
+<td>
+<p>13.624</p>
+</td>
+<td>
+<p>12.018</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>deeplabv3p_xception65_bn_192x192</p>
+</td>
+<td>
+<p>9.703</p>
+</td>
+<td>
+<p>9.393</p>
+</td>
+<td>
+<p>6.46</p>
+</td>
+<td>
+<p>8.555</p>
+</td>
+<td>
+<p>8.202</p>
+</td>
+<td>
+<p>5.15</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>deeplabv3p_xception65_bn_512x512</p>
+</td>
+<td>
+<p>30.944</p>
+</td>
+<td>
+<p>30.031</p>
+</td>
+<td>
+<p>20.716</p>
+</td>
+<td>
+<p>23.571</p>
+</td>
+<td>
+<p>22.601</p>
+</td>
+<td>
+<p>13.327</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>deeplabv3p_xception65_bn_768x768</p>
+</td>
+<td>
+<p>92.109</p>
+</td>
+<td>
+<p>89.338</p>
+</td>
+<td>
+<p>43.342</p>
+</td>
+<td>
+<p>44.341</p>
+</td>
+<td>
+<p>41.945</p>
+</td>
+<td>
+<p>25.486</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>icnet_bn_192x192</p>
+</td>
+<td>
+<p>5.706</p>
+</td>
+<td>
+<p>5.057</p>
+</td>
+<td>
+<p>4.515</p>
+</td>
+<td>
+<p>4.694</p>
+</td>
+<td>
+<p>4.066</p>
+</td>
+<td>
+<p>3.369</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>icnet_bn_512x512</p>
+</td>
+<td>
+<p>18.326</p>
+</td>
+<td>
+<p>16.971</p>
+</td>
+<td>
+<p>16.663</p>
+</td>
+<td>
+<p>10.576</p>
+</td>
+<td>
+<p>9.779</p>
+</td>
+<td>
+<p>9.389</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>icnet_bn_768x768</p>
+</td>
+<td>
+<p>67.542</p>
+</td>
+<td>
+<p>65.436</p>
+</td>
+<td>
+<p>64.197</p>
+</td>
+<td>
+<p>18.464</p>
+</td>
+<td>
+<p>17.881</p>
+</td>
+<td>
+<p>16.958</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>pspnet101_bn_192x192</p>
+</td>
+<td>
+<p>20.978</p>
+</td>
+<td>
+<p>18.089</p>
+</td>
+<td>
+<p>11.946</p>
+</td>
+<td>
+<p>20.102</p>
+</td>
+<td>
+<p>17.128</p>
+</td>
+<td>
+<p>11.011</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>pspnet101_bn_512x512</p>
+</td>
+<td>
+<p>72.085</p>
+</td>
+<td>
+<p>71.114</p>
+</td>
+<td>
+<p>43.009</p>
+</td>
+<td>
+<p>64.584</p>
+</td>
+<td>
+<p>63.715</p>
+</td>
+<td>
+<p>35.806</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>pspnet101_bn_768x768</p>
+</td>
+<td>
+<p>160.552</p>
+</td>
+<td>
+<p>157.791</p>
+</td>
+<td>
+<p>110.544</p>
+</td>
+<td>
+<p>111.996</p>
+</td>
+<td>
+<p>111.22</p>
+</td>
+<td>
+<p>69.646</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>pspnet50_bn_192x192</p>
+</td>
+<td>
+<p>13.854</p>
+</td>
+<td>
+<p>12.491</p>
+</td>
+<td>
+<p>9.357</p>
+</td>
+<td>
+<p>12.889</p>
+</td>
+<td>
+<p>11.479</p>
+</td>
+<td>
+<p>8.516</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>pspnet50_bn_512x512</p>
+</td>
+<td>
+<p>55.868</p>
+</td>
+<td>
+<p>55.205</p>
+</td>
+<td>
+<p>39.659</p>
+</td>
+<td>
+<p>48.647</p>
+</td>
+<td>
+<p>48.076</p>
+</td>
+<td>
+<p>32.403</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>pspnet50_bn_768x768</p>
+</td>
+<td>
+<p>135.268</p>
+</td>
+<td>
+<p>131.268</p>
+</td>
+<td>
+<p>109.732</p>
+</td>
+<td>
+<p>85.167</p>
+</td>
+<td>
+<p>84.615</p>
+</td>
+<td>
+<p>65.483</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>unet_bn_coco_192x192</p>
+</td>
+<td>
+<p>7.557</p>
+</td>
+<td>
+<p>7.979</p>
+</td>
+<td>
+<p>8.049</p>
+</td>
+<td>
+<p>4.933</p>
+</td>
+<td>
+<p>4.952</p>
+</td>
+<td>
+<p>4.959</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>unet_bn_coco_512x512</p>
+</td>
+<td>
+<p>37.131</p>
+</td>
+<td>
+<p>36.668</p>
+</td>
+<td>
+<p>36.706</p>
+</td>
+<td>
+<p>26.857</p>
+</td>
+<td>
+<p>26.917</p>
+</td>
+<td>
+<p>26.928</p>
+</td>
+</tr>
+<tr>
+<td>
+<p>unet_bn_coco_768x768</p>
+</td>
+<td>
+<p>110.578</p>
+</td>
+<td>
+<p>110.031</p>
+</td>
+<td>
+<p>109.979</p>
+</td>
+<td>
+<p>59.118</p>
+</td>
+<td>
+<p>59.173</p>
+</td>
+<td>
+<p>59.124</p>
+</td>
+</tr>
+</tbody>
+</table>
+<p>&nbsp;</p>
+
+## 数据分析
+### 1. 新版OP优化模型的加速效果
+下图是`PaddleSeg 0.3.0`进行OP优化的模型和原模型的性能数据对比(以512x512 为例)：
+![OP加速对比](https://paddleseg.bj.bcebos.com/inference/benchmark/op_opt_512x512.png)
+
+`分析`：
+- 优化模型的加速效果在各模型上都很明显，最高优化效果可达100%
+- 模型的 `eval_crop_size`越大，加速效果越明显
+
+
+### 2. 使用 TensorRT 开启 FP16 和 FP32 优化效果分析
+
+在原始模型上的加速效果：
+
+![优化模型](https://paddleseg.bj.bcebos.com/inference/benchmark/trt_opt_origin_512x512.png)
+
+在优化模型上的加速效果：
+
+![原始模型](https://paddleseg.bj.bcebos.com/inference/benchmark/trt_opt_new_512x512.png)
+
+
+`分析`：
+- unet和icnet模型，使用Fluid-TensorRT的加速效果不明显，甚至没有加速。
+- deeplabv3p_mobilenetv2模型，Fluid-TensorRT在原生模型的加速效果不明显，仅3%-5%的加速效果。在优化模型的加速效果可以达到20%。
+- `deeplabv3_xception`、`pspnet50` 和 `pspnet101`模型，`fp16`加速效果很明显，在`768x768` 的size下加速效果最高可达110%。
+
+ 
+
+### 3. 不同的EVAL_CROP_SIZE对图片想能的影响
+
+在 `deeplabv3p_xception`上的数据对比图：
+![xception](https://paddleseg.bj.bcebos.com/inference/benchmark/xception.png)
+
+在`deeplabv3p_mobilenet`上的数据对比图：
+![xception](https://paddleseg.bj.bcebos.com/inference/benchmark/mobilenet.png)
+在`unet`上的测试数据对比图：
+![xception](https://paddleseg.bj.bcebos.com/inference/benchmark/unet.png)
+在`icnet`上的测试数据对比图：
+![xception](https://paddleseg.bj.bcebos.com/inference/benchmark/unet.png)
+在`pspnet101`上的测试数据对比图：
+![xception](https://paddleseg.bj.bcebos.com/inference/benchmark/pspnet101.png)
+在`pspnet50`上的测试数据对比图：
+![xception](https://paddleseg.bj.bcebos.com/inference/benchmark/pspnet50.png)
+
+`分析`：
+- 对于同一模型，`eval_crop_size`越大，推理速度越慢
+- 同一模型，不管是 TensorRT 优化还是 OP 优化，`eval_crop_size`越大效果越明显
+