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Merge branch 'zhenghuihuang/jni-feature' into 'incubate/lite'
Add java demo See merge request inference/paddlelite!86
2 parents dcd0cf5 + 9b479f6 commit dbcc745

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‎paddle/fluid/lite/CMakeLists.txt

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@@ -98,7 +98,7 @@ add_custom_target(lite_compile_deps COMMAND echo 1)
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set(offline_lib_registry_file "${CMAKE_BINARY_DIR}/lite_libs.txt")
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file(WRITE ${offline_lib_registry_file} "") # clean
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set(__lite_cc_files "";"")
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set(__lite_cc_files "" ; "")
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set(__lite_cc_files "${CMAKE_BINARY_DIR}/lite_cc_files.txt")
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file(WRITE ${__lite_cc_files} "") # clean
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# cc_library with branch support.
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#cc_library(inference_mobile_lib DEPS light_api_lite)
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# copy cpp mobile_light demo/lib
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add_custom_target(publish_inference_mobile_lib ${TARGET}
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COMMAND mkdir -p "${INFER_LITE_PUBLISH_ROOT}/cxx/lib"
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COMMAND mkdir -p "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
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COMMAND cp "${CMAKE_BINARY_DIR}/libpaddle_api_light_bundled.a" "${INFER_LITE_PUBLISH_ROOT}/cxx/lib"
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COMMAND cp -r "${CMAKE_SOURCE_DIR}/paddle/fluid/lite/demo/cxx/mobile_light" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
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)
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# copy java mobile_light demo/lib
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add_custom_target(publish_java_inference_mobile_lib ${TARGET}
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COMMAND mkdir -p "${INFER_LITE_PUBLISH_ROOT}/java/so"
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COMMAND mkdir -p "${INFER_LITE_PUBLISH_ROOT}/demo/java"
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COMMAND cp "${CMAKE_BINARY_DIR}/paddle/fluid/lite/api/android/jni/libpaddle_lite_jni.so" "${INFER_LITE_PUBLISH_ROOT}/java/so"
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COMMAND cp -r "${CMAKE_SOURCE_DIR}/paddle/fluid/lite/api/android/jni/src" "${INFER_LITE_PUBLISH_ROOT}/java"
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COMMAND cp -r "${CMAKE_SOURCE_DIR}/paddle/fluid/lite/demo/java/android" "${INFER_LITE_PUBLISH_ROOT}/demo/java"
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COMMAND cp -r "${CMAKE_BINARY_DIR}/third_party/install/inception_v4_simple"
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"${INFER_LITE_PUBLISH_ROOT}/demo/java/android/PaddlePredictor/app/src/main/assets"
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COMMAND cp -r "${CMAKE_BINARY_DIR}/third_party/install/lite_naive_model"
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"${INFER_LITE_PUBLISH_ROOT}/demo/java/android/PaddlePredictor/app/src/main/assets"
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COMMAND cp -r "${CMAKE_BINARY_DIR}/third_party/install/mobilenet_v1"
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"${INFER_LITE_PUBLISH_ROOT}/demo/java/android/PaddlePredictor/app/src/main/assets"
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COMMAND cp -r "${CMAKE_BINARY_DIR}/third_party/install/mobilenet_v2_relu"
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"${INFER_LITE_PUBLISH_ROOT}/demo/java/android/PaddlePredictor/app/src/main/assets"
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COMMAND cp -r "${CMAKE_BINARY_DIR}/third_party/install/resnet50"
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"${INFER_LITE_PUBLISH_ROOT}/demo/java/android/PaddlePredictor/app/src/main/assets"
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COMMAND cp "${CMAKE_BINARY_DIR}/paddle/fluid/lite/api/android/jni/libpaddle_lite_jni.so"
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"${INFER_LITE_PUBLISH_ROOT}/demo/java/android/PaddlePredictor/app/src/main/jniLibs/arm7"
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COMMAND cp "${CMAKE_BINARY_DIR}/paddle/fluid/lite/api/android/jni/libpaddle_lite_jni.so"
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"${INFER_LITE_PUBLISH_ROOT}/demo/java/android/PaddlePredictor/app/src/main/jniLibs/arm8"
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COMMAND cp "${CMAKE_BINARY_DIR}/paddle/fluid/lite/api/android/jni/libpaddle_lite_jni.so"
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"${INFER_LITE_PUBLISH_ROOT}/demo/java/android/PaddlePredictor/app/src/main/jniLibs/arm64-v8a"
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COMMAND cp "${CMAKE_BINARY_DIR}/paddle/fluid/lite/api/android/jni/libpaddle_lite_jni.so"
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"${INFER_LITE_PUBLISH_ROOT}/demo/java/android/PaddlePredictor/app/src/main/jniLibs/armeabi-v7a"
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COMMAND cp "${CMAKE_BINARY_DIR}/paddle/fluid/lite/api/android/jni/libpaddle_lite_jni.so"
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"${INFER_LITE_PUBLISH_ROOT}/demo/java/android/PaddlePredictor/app/src/main/jniLibs/x86"
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)
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add_dependencies(publish_java_inference_mobile_lib paddle_lite_jni)
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add_dependencies(publish_inference_lite publish_java_inference_mobile_lib)

‎paddle/fluid/lite/demo/java/README.md

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# Java Android Demo
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要编译和跑起 ./android 文件夹下的 Android demo 程序 PaddlePredictor，你需要准备：
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1. 一台能运行安卓程序的安卓手机
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2. 一台带有AndroidStudio的开发机
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## 如果你使用我们的 cmake 生成 demo 程序，
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可以直接把 `${INFER_LITE_PUBLISH_ROOT}/demo/java/android/PaddlePredictor` 载入你的AndroidStudio，
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运行，查看本文末尾的程序运行显示结果。
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## 手动编译（给测试人员需要更新 demo 模型或 .so 库可阅读）
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###编译：
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首先在PaddleLite的开发Docker镜像中，拉取最新PaddleLite代码，编译对应你手机架构的预测库，
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下面我们以arm8 架构举例。进入paddlelite 目录，运行以下cmake 和make 命令：
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```
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mkdir -p build.lite.android.arm8.gcc
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cd build.lite.android.arm8.gcc
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cmake .. \
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-DWITH_GPU=OFF \
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-DWITH_MKL=OFF \
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-DWITH_LITE=ON \
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-DLITE_WITH_JAVA=ON \
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-DLITE_WITH_CUDA=OFF \
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-DLITE_WITH_X86=OFF \
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-DLITE_WITH_ARM=ON \
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-DLITE_WITH_LIGHT_WEIGHT_FRAMEWORK=ON \
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-DWITH_TESTING=ON \
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-DARM_TARGET_OS=android -DARM_TARGET_ARCH_ABI=armv8 -DARM_TARGET_LANG=gcc
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make -j 4
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```
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Make完成后查看要存在`build.lite.android.arm8.gcc/paddle/fluid/lite/api/android/jni/libpaddle_lite_jni.so`
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这个文件。该文件为PaddleLite c++ 动态链接库。接下来Android Java 代码会load 这个库来跑c++ 代码
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### 把.so动态库拷贝进安卓demo程序：
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把本文件夹下 demo/PaddlePredictor 载入到AndroidStudio。把上一步提到的`libpaddle_lite_jni.so`
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拷贝进 `PaddlePredictor/app/src/main/jinLibs/所有架构文件夹下` 比如文件夹arm8里要包含该 .so文件：
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### 把demo使用到的模型文件拷贝进安卓程序：
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在 `build.lite.android.arm8.gcc/third_party/install` 文件夹下，把以下我们的模型文件夹拷贝进
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`PaddlePredictor/app/src/main/assets` 这个文件夹
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需要拷贝的模型文件：
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inception_v4_simple
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lite_naive_model
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mobilenet_v1
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mobilenet_v2_relu
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resnet50
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## 运行 Android 程序结果
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以上准备工作完成，就可以开始Build ，安装，和跑安卓demo程序。当你运行PaddlePredictor 程序时，大概会等10秒，
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然后看到类似以下字样：
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lite_naive_model output: 50.213173, -28.872887
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expected: 50.2132, -28.8729
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inception_v4_simple test:true
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time: 2078 ms
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resnet50 test:true
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time: 2078 ms
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mobilenet_v1 test:true
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time: 2078 ms
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mobilenet_v2 test:true
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time: 2078 ms
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该 demo 程序跑我们的 5 个模型，第一个模型结果将真正的头两个数字输出，并在第二行附上期望的正确值。你应该要
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看到他们的误差小于0.001。后面四个模型如果你看到 test:true 字样，说明模型输出通过了我们在 demo 程序里对其输出
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的测试。time 代表该测试花费的时间。

‎paddle/fluid/lite/demo/java/android/.gitignore

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

‎paddle/fluid/lite/demo/java/android/PaddlePredictor/app/.gitignore

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/build

‎paddle/fluid/lite/demo/java/android/PaddlePredictor/app/proguard-rules.pro

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

‎paddle/fluid/lite/demo/java/android/PaddlePredictor/app/src/androidTest/java/com/baidu/paddle/lite/ExampleInstrumentedTest.java

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

‎paddle/fluid/lite/demo/java/android/PaddlePredictor/app/src/main/AndroidManifest.xml

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<?xml version="1.0" encoding="utf-8"?>
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<manifest xmlns:android="http://schemas.android.com/apk/res/android"
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package="com.baidu.paddle.lite">
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<application
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android:allowBackup="true"
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android:icon="@mipmap/ic_launcher"
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android:label="@string/app_name"
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android:roundIcon="@mipmap/ic_launcher_round"
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android:supportsRtl="true"
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android:theme="@style/AppTheme">
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<activity android:name=".MainActivity">
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<intent-filter>
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<action android:name="android.intent.action.MAIN" />
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<category android:name="android.intent.category.LAUNCHER" />
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</intent-filter>
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</activity>
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</application>
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</manifest>

‎paddle/fluid/lite/demo/java/android/PaddlePredictor/app/src/main/assets/README.txt

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After build PaddleLite in your build folder, copy following models in this directory:
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inception_v4_simple
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lite_naive_model
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mobilenet_v1
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mobilenet_v2_relu
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resnet50

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