Merge branch 'zhenghuihuang/jni' into 'incubate/lite'

Add Java JNI APIs

See merge request inference/paddlelite!69

CMakeLists.txt

@@ -147,6 +147,7 @@ endif()
 
 # for lite, both server and mobile framework.
 option(WITH_LITE "Enable lite framework" OFF)
+option(WITH_JAVA "Compile PaddlePaddle with Java JNI lib" OFF)
 option(LITE_WITH_CUDA "Enable CUDA in lite mode" OFF)
 option(LITE_WITH_X86  "Enable X86 in lite mode"  ON)
 option(LITE_WITH_ARM  "Enable ARM in lite mode"  OFF)

paddle/fluid/lite/CMakeLists.txt

@@ -110,7 +110,7 @@ file(WRITE ${__lite_cc_files} "") # clean
 #  LIGHT_DEPS:    LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
 #  HVY_DEPS:      NOT LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
 function(lite_cc_library TARGET)
-    set(options STATIC static SHARED shared)
+    set(options SHARED shared STATIC static MODULE module)
     set(oneValueArgs "")
     set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS ARM_DEPS PROFILE_DEPS LIGHT_DEPS
       HVY_DEPS ARGS)
@@ -126,8 +126,12 @@ function(lite_cc_library TARGET)
             LIGHT_DEPS ${args_LIGHT_DEPS}
             HVY_DEPS ${args_HVY_DEPS}
             )
-    if (${args_SHARED} OR ${args_shared})
+
+    if (args_SHARED OR ARGS_shared)
         cc_library(${TARGET} SRCS ${args_SRCS} DEPS ${deps} ${args_DEPS} SHARED)
+    elseif (args_MODULE OR ARGS_module)
+        add_library(${TARGET} MODULE ${args_SRCS})
+        add_dependencies(${TARGET} ${deps} ${args_DEPS})
     else()
         cc_library(${TARGET} SRCS ${args_SRCS} DEPS ${deps} ${args_DEPS})
     endif()

paddle/fluid/lite/api/CMakeLists.txt

@@ -125,6 +125,10 @@ if (WITH_TESTING)
     add_dependencies(test_paddle_api_lite extern_lite_download_lite_naive_model_tar_gz)
 endif()
 
+if (WITH_JAVA AND LITE_WITH_ARM)
+    add_subdirectory(android/jni)
+endif()
+
 #lite_cc_binary(cxx_api_lite_bin SRCS cxx_api_bin.cc
     #X86_DEPS operator
     #DEPS light_api_lite model_parser_lite target_wrapper_host mir_passes

paddle/fluid/lite/api/android/jni/.gitignore

+/PaddleListTest.class
+/PaddleLite.class
+/bin/

paddle/fluid/lite/api/android/jni/CMakeLists.txt

+if ((NOT WITH_LITE) OR (NOT WITH_JAVA))
+  return()
+endif()
+
+include(UseJava)
+find_package(Java REQUIRED)
+
+# We are only interested in finding jni.h: we do not care about extended JVM
+# functionality or the AWT library.
+set(JAVA_AWT_LIBRARY NotNeeded)
+set(JAVA_JVM_LIBRARY NotNeeded)
+set(JAVA_INCLUDE_PATH2 NotNeeded)
+set(JAVA_AWT_INCLUDE_PATH NotNeeded)
+find_package(JNI REQUIRED)
+
+# Generate PaddlePredictor.jar
+include_directories(${JNI_INCLUDE_DIRS})
+add_jar(PaddlePredictor src/com/baidu/paddle/lite/PaddlePredictor.java)
+get_target_property(_jarFile PaddlePredictor JAR_FILE)
+get_target_property(_classDir PaddlePredictor CLASSDIR)
+set(_stubDir "${CMAKE_CURRENT_BINARY_DIR}")
+
+# Generate paddle_lite_jni.h
+add_custom_target(
+    paddle_lite_jni_header ALL
+    COMMAND ${Java_JAVAH_EXECUTABLE} -verbose
+        -classpath ${_classDir}
+        -o paddle_lite_jni.h
+        -jni
+        com.baidu.paddle.lite.PaddlePredictor
+    DEPENDS PaddlePredictor
+)
+
+# Generate paddle_lite_jni.so
+include_directories(${JNI_INCLUDE_DIRS} ${_classDir} ${_stubDir})
+lite_cc_library(paddle_lite_jni MODULE SRCS paddle_lite_jni.cc 
+    DEPS light_api_lite cxx_api_lite
+        paddle_api_full paddle_api_lite paddle_api_light op_registry_lite
+        ${ops_lite} ${lite_kernel_deps}
+    ARM_DEPS ${arm_kernels})
+if (APPLE)
+    # MacOS only accepts JNI lib ends with .jnilib or .dylib
+    set_target_properties(paddle_lite_jni PROPERTIES SUFFIX ".jnilib")
+elseif (WIN32)
+    # Windows only accepts JNI lib ends with .dll
+    set_target_properties(paddle_lite_jni PROPERTIES SUFFIX ".dll")
+endif (APPLE)
+target_link_libraries(paddle_lite_jni light_api_lite cxx_api_lite
+    paddle_api_full paddle_api_lite paddle_api_light op_registry_lite 
+    ${ops_lite} ${arm_kernels} ${lite_kernel_deps})

paddle/fluid/lite/api/android/jni/paddle_lite_jni.cc

+/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+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. */
+
+#include "paddle/fluid/lite/api/android/jni/paddle_lite_jni.h"
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "paddle/fluid/lite/kernels/arm/activation_compute.h"
+#include "paddle/fluid/lite/kernels/arm/batch_norm_compute.h"
+#include "paddle/fluid/lite/kernels/arm/concat_compute.h"
+#include "paddle/fluid/lite/kernels/arm/conv_compute.h"
+#include "paddle/fluid/lite/kernels/arm/dropout_compute.h"
+#include "paddle/fluid/lite/kernels/arm/elementwise_compute.h"
+#include "paddle/fluid/lite/kernels/arm/fc_compute.h"
+#include "paddle/fluid/lite/kernels/arm/mul_compute.h"
+#include "paddle/fluid/lite/kernels/arm/pool_compute.h"
+#include "paddle/fluid/lite/kernels/arm/scale_compute.h"
+#include "paddle/fluid/lite/kernels/arm/softmax_compute.h"
+#include "paddle/fluid/lite/kernels/arm/split_compute.h"
+#include "paddle/fluid/lite/kernels/arm/transpose_compute.h"
+
+#include "paddle/fluid/lite/api/light_api.h"
+#include "paddle/fluid/lite/api/paddle_api.h"
+#include "paddle/fluid/lite/api/paddle_lite_factory_helper.h"
+#include "paddle/fluid/lite/api/paddle_use_kernels.h"
+#include "paddle/fluid/lite/api/paddle_use_ops.h"
+#include "paddle/fluid/lite/api/paddle_use_passes.h"
+
+#define ARM_KERNEL_POINTER(kernel_class_name__)                    \
+  std::unique_ptr<paddle::lite::kernels::arm::kernel_class_name__> \
+      p##kernel_class_name__(                                      \
+          new paddle::lite::kernels::arm::kernel_class_name__);
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+using paddle::lite_api::MobileConfig;
+using paddle::lite_api::PaddlePredictor;
+using paddle::lite_api::Tensor;
+
+static std::shared_ptr<PaddlePredictor> predictor;
+
+/**
+ * Not sure why, we have to initial a pointer first for kernels.
+ * Otherwise it throws null pointer error when do KernelRegistor.
+ */
+static void use_arm_kernels() {
+  ARM_KERNEL_POINTER(BatchNormCompute);
+  ARM_KERNEL_POINTER(ConvCompute);
+  ARM_KERNEL_POINTER(ConcatCompute);
+  ARM_KERNEL_POINTER(ElementwiseAddCompute);
+  ARM_KERNEL_POINTER(DropoutCompute);
+  ARM_KERNEL_POINTER(FcCompute);
+  ARM_KERNEL_POINTER(MulCompute);
+  ARM_KERNEL_POINTER(PoolCompute);
+  ARM_KERNEL_POINTER(ReluCompute);
+  ARM_KERNEL_POINTER(ScaleCompute);
+  ARM_KERNEL_POINTER(SoftmaxCompute);
+  ARM_KERNEL_POINTER(SplitCompute);
+  ARM_KERNEL_POINTER(TransposeCompute);
+  ARM_KERNEL_POINTER(Transpose2Compute);
+}
+
+inline std::string jstring_to_cpp_string(JNIEnv *env, jstring jstr) {
+  // In java, a unicode char will be encoded using 2 bytes (utf16).
+  // so jstring will contain characters utf16. std::string in c++ is
+  // essentially a string of bytes, not characters, so if we want to
+  // pass jstring from JNI to c++, we have convert utf16 to bytes.
+  if (!jstr) {
+    return "";
+  }
+  const jclass stringClass = env->GetObjectClass(jstr);
+  const jmethodID getBytes =
+      env->GetMethodID(stringClass, "getBytes", "(Ljava/lang/String;)[B");
+  const jbyteArray stringJbytes = (jbyteArray)env->CallObjectMethod(
+      jstr, getBytes, env->NewStringUTF("UTF-8"));
+
+  size_t length = (size_t)env->GetArrayLength(stringJbytes);
+  jbyte *pBytes = env->GetByteArrayElements(stringJbytes, NULL);
+
+  std::string ret = std::string(reinterpret_cast<char *>(pBytes), length);
+  env->ReleaseByteArrayElements(stringJbytes, pBytes, JNI_ABORT);
+
+  env->DeleteLocalRef(stringJbytes);
+  env->DeleteLocalRef(stringClass);
+  return ret;
+}
+
+inline jfloatArray cpp_array_to_jfloatarray(JNIEnv *env, const float *buf,
+                                            int64_t len) {
+  jfloatArray result = env->NewFloatArray(len);
+  env->SetFloatArrayRegion(result, 0, len, buf);
+  return result;
+}
+
+inline jintArray cpp_array_to_jintarray(JNIEnv *env, const int *buf,
+                                        int64_t len) {
+  jintArray result = env->NewIntArray(len);
+  env->SetIntArrayRegion(result, 0, len, buf);
+  return result;
+}
+
+inline jbyteArray cpp_array_to_jbytearray(JNIEnv *env, const int8_t *buf,
+                                          int64_t len) {
+  jbyteArray result = env->NewByteArray(len);
+  env->SetByteArrayRegion(result, 0, len, buf);
+  return result;
+}
+
+inline std::vector<int64_t> jintarray_to_int64_vector(JNIEnv *env,
+                                                      jintArray dims) {
+  int dim_size = env->GetArrayLength(dims);
+  jint *dim_nums = env->GetIntArrayElements(dims, nullptr);
+  std::vector<int64_t> dim_vec(dim_nums, dim_nums + dim_size);
+  env->ReleaseIntArrayElements(dims, dim_nums, 0);
+  return dim_vec;
+}
+
+inline static int64_t product(const std::vector<int64_t> &vec) {
+  if (vec.empty()) {
+    return 0;
+  }
+  int64_t result = 1;
+  for (int64_t d : vec) {
+    result *= d;
+  }
+  return result;
+}
+
+JNIEXPORT jboolean JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_loadMobileModel(JNIEnv *env,
+                                                           jclass thiz,
+                                                           jstring model_path) {
+  if (predictor != nullptr) {
+    return JNI_FALSE;
+  }
+  use_arm_kernels();
+  MobileConfig config;
+  std::string model_dir = jstring_to_cpp_string(env, model_path);
+  config.set_model_dir(model_dir);
+  predictor = paddle::lite_api::CreatePaddlePredictor(config);
+  return JNI_TRUE;
+}
+
+JNIEXPORT jboolean JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_clear(JNIEnv *env, jclass thiz) {
+  if (predictor == nullptr) {
+    return JNI_FALSE;
+  }
+  predictor.reset();
+  return JNI_TRUE;
+}
+
+JNIEXPORT jboolean JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_setInput__I_3I_3F(
+    JNIEnv *env, jclass thiz, jint offset, jintArray dims, jfloatArray buf) {
+  std::vector<int64_t> ddim = jintarray_to_int64_vector(env, dims);
+
+  int len = env->GetArrayLength(buf);
+  if ((int64_t)len != product(ddim)) {
+    return JNI_FALSE;
+  }
+
+  float *buffer = env->GetFloatArrayElements(buf, nullptr);
+  std::unique_ptr<Tensor> tensor =
+      predictor->GetInput(static_cast<int>(offset));
+  tensor->Resize(ddim);
+  float *input = tensor->mutable_data<float>();
+  for (int i = 0; i < len; ++i) {
+    input[i] = buffer[i];
+  }
+  return JNI_TRUE;
+}
+
+JNIEXPORT jboolean JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_setInput__I_3I_3B(
+    JNIEnv *env, jclass thiz, jint offset, jintArray dims, jbyteArray buf) {
+  std::vector<int64_t> ddim = jintarray_to_int64_vector(env, dims);
+
+  int len = env->GetArrayLength(buf);
+  if ((int64_t)len != product(ddim)) {
+    return JNI_FALSE;
+  }
+
+  jbyte *buffer = env->GetByteArrayElements(buf, nullptr);
+  std::unique_ptr<Tensor> tensor =
+      predictor->GetInput(static_cast<int>(offset));
+  tensor->Resize(ddim);
+  int8_t *input = tensor->mutable_data<int8_t>();
+  for (int i = 0; i < len; ++i) {
+    input[i] = (int8_t)buffer[i];
+  }
+
+  return JNI_TRUE;
+}
+
+JNIEXPORT jboolean JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_run(JNIEnv *, jclass) {
+  predictor->Run();
+  return JNI_TRUE;
+}
+
+JNIEXPORT jfloatArray JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_getFloatOutput(JNIEnv *env,
+                                                          jclass thiz,
+                                                          jint offset) {
+  std::unique_ptr<const Tensor> tensor =
+      predictor->GetOutput(static_cast<int>(offset));
+  int64_t len = product(tensor->shape());
+  return cpp_array_to_jfloatarray(env, tensor->data<float>(), len);
+}
+
+JNIEXPORT jbyteArray JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_getByteOutput(JNIEnv *env,
+                                                         jclass thiz,
+                                                         jint offset) {
+  std::unique_ptr<const Tensor> tensor =
+      predictor->GetOutput(static_cast<int>(offset));
+  int64_t len = product(tensor->shape());
+  return cpp_array_to_jbytearray(env, tensor->data<int8_t>(), len);
+}
+
+JNIEXPORT jfloatArray JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_fetchFloat(JNIEnv *env, jclass thiz,
+                                                      jstring name) {
+  std::string cpp_name = jstring_to_cpp_string(env, name);
+  std::unique_ptr<const Tensor> tensor = predictor->GetTensor(cpp_name);
+  int64_t len = product(tensor->shape());
+  return cpp_array_to_jfloatarray(env, tensor->data<float>(), len);
+}
+
+JNIEXPORT jbyteArray JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_fetchByte(JNIEnv *env, jclass thiz,
+                                                     jstring name) {
+  std::string cpp_name = jstring_to_cpp_string(env, name);
+  std::unique_ptr<const Tensor> tensor = predictor->GetTensor(cpp_name);
+  int64_t len = product(tensor->shape());
+  return cpp_array_to_jbytearray(env, tensor->data<int8_t>(), len);
+}
+
+#ifdef __cplusplus
+}
+#endif

paddle/fluid/lite/api/android/jni/paddle_lite_jni.h

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// 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.
+
+/* DO NOT EDIT THIS FILE - it is machine generated */
+#include <jni.h>
+/* Header for class com_baidu_paddle_lite_PaddlePredictor */
+
+#ifndef PADDLE_FLUID_LITE_API_ANDROID_JNI_PADDLE_LITE_JNI_H_
+#define PADDLE_FLUID_LITE_API_ANDROID_JNI_PADDLE_LITE_JNI_H_
+#ifdef __cplusplus
+extern "C" {
+#endif
+/*
+ * Class:     com_baidu_paddle_lite_PaddlePredictor
+ * Method:    loadMobileModel
+ * Signature: (Ljava/lang/String;)Z
+ */
+JNIEXPORT jboolean JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_loadMobileModel(JNIEnv *, jclass,
+                                                           jstring);
+
+/*
+ * Class:     com_baidu_paddle_lite_PaddlePredictor
+ * Method:    clear
+ * Signature: ()Z
+ */
+JNIEXPORT jboolean JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_clear(JNIEnv *, jclass);
+
+/*
+ * Class:     com_baidu_paddle_lite_PaddlePredictor
+ * Method:    setInput
+ * Signature: (I[I[F)Z
+ */
+JNIEXPORT jboolean JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_setInput__I_3I_3F(JNIEnv *, jclass,
+                                                             jint, jintArray,
+                                                             jfloatArray);
+
+/*
+ * Class:     com_baidu_paddle_lite_PaddlePredictor
+ * Method:    setInput
+ * Signature: (I[I[B)Z
+ */
+JNIEXPORT jboolean JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_setInput__I_3I_3B(JNIEnv *, jclass,
+                                                             jint, jintArray,
+                                                             jbyteArray);
+
+/*
+ * Class:     com_baidu_paddle_lite_PaddlePredictor
+ * Method:    run
+ * Signature: ()Z
+ */
+JNIEXPORT jboolean JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_run(JNIEnv *, jclass);
+
+/*
+ * Class:     com_baidu_paddle_lite_PaddlePredictor
+ * Method:    getFloatOutput
+ * Signature: (I)[F
+ */
+JNIEXPORT jfloatArray JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_getFloatOutput(JNIEnv *, jclass,
+                                                          jint);
+
+/*
+ * Class:     com_baidu_paddle_lite_PaddlePredictor
+ * Method:    getByteOutput
+ * Signature: (I)[B
+ */
+JNIEXPORT jbyteArray JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_getByteOutput(JNIEnv *, jclass,
+                                                         jint);
+
+/*
+ * Class:     com_baidu_paddle_lite_PaddlePredictor
+ * Method:    fetchFloat
+ * Signature: (Ljava/lang/String;)[F
+ */
+JNIEXPORT jfloatArray JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_fetchFloat(JNIEnv *, jclass,
+                                                      jstring);
+
+/*
+ * Class:     com_baidu_paddle_lite_PaddlePredictor
+ * Method:    fetchByte
+ * Signature: (Ljava/lang/String;)[B
+ */
+JNIEXPORT jbyteArray JNICALL
+Java_com_baidu_paddle_lite_PaddlePredictor_fetchByte(JNIEnv *, jclass, jstring);
+
+#ifdef __cplusplus
+}
+#endif
+#endif  // PADDLE_FLUID_LITE_API_ANDROID_JNI_PADDLE_LITE_JNI_H_

paddle/fluid/lite/api/android/jni/src/com/baidu/paddle/lite/.gitignore

+/PaddleLite.class
+/PaddleLiteTest.class

paddle/fluid/lite/api/android/jni/src/com/baidu/paddle/lite/PaddlePredictor.java

+/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+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;
+
+/** Java Native Interface (JNI) class for Paddle Lite APIs */
+public class PaddlePredictor {
+
+    /** name of C++ JNI lib */
+    private final static String JNI_LIB_NAME = "paddle_lite_jni";
+
+    /* load the C++ JNI lib */
+    static {
+        System.loadLibrary(JNI_LIB_NAME);
+    }
+
+    /**
+     * Loads mobile lite model, which is the model after optimizing passes.
+     *
+     * @param modelPath model file path
+     * @return true if load successfully
+     */
+    public static native boolean loadMobileModel(String modelPath);
+
+    /**
+     * Clears the current loaded model.
+     * 
+     * @return true if a loaded model has been cleared.
+     */
+    public static native boolean clear();
+
+    /**
+     * Set input data on offset-th column of feed data
+     *
+     * @param offset the offset-th column of feed data will be set
+     * @param buf    the input data
+     * @param dims   dimension format of the input image
+     * @return true if set successfully
+     */
+    public static native boolean setInput(int offset, int[] dims, float[] buf);
+
+    /**
+     * Set input data on offset-th column of feed data
+     *
+     * @param offset the offset-th column of feed data will be set
+     * @param buf    the input data
+     * @param dims   dimension format of the input image
+     * @return true if set successfully
+     */
+    public static native boolean setInput(int offset, int[] dims, byte[] buf);
+
+    /**
+     * Run the predict model
+     * 
+     * @return true if run successfully
+     */
+    public static native boolean run();
+
+    /**
+     * Get offset-th column of output data as float
+     *
+     * @param offset the offset-th column of output data will be returned
+     * @return model predict output
+     */
+    public static native float[] getFloatOutput(int offset);
+
+    /**
+     * Get offset-th column of output data as byte (int8 in C++ side)
+     *
+     * @param offset the offset-th column of output data will be returned
+     * @return model predict output
+     */
+    public static native byte[] getByteOutput(int offset);
+
+    /**
+     * Fetches a Tensor's value as Float data
+     * 
+     * @param name Tensor's name
+     * @return values of the Tensor
+     */
+    public static native float[] fetchFloat(String name);
+
+    /**
+     * Fetches a Tensor's value as byte data (int8 at C++ side)
+     * 
+     * @param name Tensor's name
+     * @return values of the Tensor
+     */
+    public static native byte[] fetchByte(String name);
+
+    /**
+     * Main function for test
+     */
+    public static void main(String[] args) {
+        System.out.println("Load native library successfully");
+    }
+}

paddle/fluid/lite/api/android/jni/test/com/baidu/paddle/lite/PaddlePredictorTest.java

+/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+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;
+
+import org.junit.jupiter.api.Test;
+
+import static org.junit.Assert.assertEquals;
+
+class PaddlePredictorTest {
+
+    @Test
+    public void run_defaultModel() {
+        PaddlePredictor.loadMobileModel("");
+
+        float[] inputBuffer = new float[10000];
+        for (int i = 0; i < 10000; ++i) {
+            inputBuffer[i] = i;
+        }
+        int[] dims = { 100, 100 };
+
+        PaddlePredictor.setInput(0, dims, inputBuffer);
+        PaddlePredictor.run();
+        float[] output = PaddlePredictor.getFloatOutput(0);
+
+        assertEquals(output[0], 50.2132f, 1e-3f);
+        assertEquals(output[1], -28.8729f, 1e-3f);
+
+        PaddlePredictor.clear();
+    }
+
+}