Merge remote-tracking branch 'origin/develop' into develop

src/jni/PML.java

+package com.baidu.paddle;
+
+public class PML {
+    /**
+     * load seperated model
+     *
+     * @param modelDir model dir
+     * @return isloadsuccess
+     */
+    public static native boolean load(String modelDir);
+
+    /**
+     * load combined model
+     *
+     * @param modelPath model file path
+     * @param paramPath param file path
+     * @return isloadsuccess
+     */
+    public static native boolean loadCombined(String modelPath, String paramPath);
+
+    /**
+     * load model and qualified params
+     *
+     * @param modelDir qualified model dir
+     * @return isloadsuccess
+     */
+    public static native boolean loadQualified(String modelDir);
+
+    /**
+     * load model and qualified combined params
+     *
+     * @param modelPath model file path
+     * @param paramPath qualified param path
+     * @return isloadsuccess
+     */
+    public static native boolean loadCombinedQualified(String modelPath, String paramPath);
+
+    /**
+     * predict image
+     *
+     * @param buf   of pretreated image (as your model like)
+     * @param ddims format of your input
+     * @return result
+     */
+    public static native float[] predictImage(float[] buf, int[] ddims);
+
+
+    public static native float[] predictYuv(byte[] buf, int imgWidth, int imgHeight, int[] ddims, float[] meanValues);
+
+    /**
+     * clear model data
+     */
+    public static native void clear();
+
+    /**
+     * setThread num when u enable openmp
+     *
+     * @param threadCount threadCount
+     */
+    public static native void setThread(int threadCount);
+
+
+}

src/jni/paddle_mobile_jni.cpp

@@ -20,6 +20,12 @@ limitations under the License. */
 #include "framework/tensor.h"
 #include "io/paddle_mobile.h"
 
+#ifdef ENABLE_EXCEPTION
+
+#include "common/enforce.h"
+
+#endif
+
 #ifdef __cplusplus
 extern "C" {
 #endif
@@ -33,17 +39,10 @@ using std::string;
 
 extern const char *ANDROID_LOG_TAG =
     "paddle_mobile LOG built on " __DATE__ " " __TIME__;
-static PaddleMobile<CPU> *shared_paddle_mobile_instance = nullptr;
+paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
+static std::mutex shared_mutex;
 
-// toDo mutex lock
-// static std::mutex shared_mutex;
-
-PaddleMobile<CPU> *getPaddleMobileInstance() {
-  if (nullptr == shared_paddle_mobile_instance) {
-    shared_paddle_mobile_instance = new PaddleMobile<CPU>();
-  }
-  return shared_paddle_mobile_instance;
-}
+PaddleMobile<CPU> *getPaddleMobileInstance() { return &paddle_mobile; }
 
 string jstring2cppstring(JNIEnv *env, jstring jstr) {
   const char *cstr = env->GetStringUTFChars(jstr, 0);
@@ -55,43 +54,111 @@ string jstring2cppstring(JNIEnv *env, jstring jstr) {
 JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_PML_load(JNIEnv *env,
                                                           jclass thiz,
                                                           jstring modelPath) {
+  std::lock_guard<std::mutex> lock(shared_mutex);
   ANDROIDLOGI("load invoked");
   bool optimize = true;
-  return getPaddleMobileInstance()->Load(jstring2cppstring(env, modelPath),
+  bool isLoadOk = false;
+
+#ifdef ENABLE_EXCEPTION
+  try {
+    isLoadOk = getPaddleMobileInstance()->Load(
+        jstring2cppstring(env, modelPath), optimize);
+  } catch (paddle_mobile::PaddleMobileException &e) {
+    ANDROIDLOGE("jni got an PaddleMobileException! ", e.what());
+    isLoadOk = false;
+  }
+#else
+  isLoadOk = getPaddleMobileInstance()->Load(jstring2cppstring(env, modelPath),
                                              optimize);
+#endif
+  return static_cast<jboolean>(isLoadOk);
 }
 
 JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_PML_loadQualified(
     JNIEnv *env, jclass thiz, jstring modelPath) {
+  std::lock_guard<std::mutex> lock(shared_mutex);
+
   ANDROIDLOGI("loadQualified invoked");
   bool optimize = true;
   bool qualified = true;
-  return getPaddleMobileInstance()->Load(jstring2cppstring(env, modelPath),
+  bool isLoadOk = false;
+
+#ifdef ENABLE_EXCEPTION
+  try {
+    isLoadOk = getPaddleMobileInstance()->Load(
+        jstring2cppstring(env, modelPath), optimize, qualified);
+  } catch (paddle_mobile::PaddleMobileException &e) {
+    ANDROIDLOGE("jni got an PaddleMobileException! ", e.what());
+    isLoadOk = false;
+  }
+#else
+  isLoadOk = getPaddleMobileInstance()->Load(jstring2cppstring(env, modelPath),
                                              optimize, qualified);
+#endif
+
+  return static_cast<jboolean>(isLoadOk);
 }
 
 JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_PML_loadCombined(
     JNIEnv *env, jclass thiz, jstring modelPath, jstring paramPath) {
+  std::lock_guard<std::mutex> lock(shared_mutex);
   ANDROIDLOGI("loadCombined invoked");
   bool optimize = true;
-  return getPaddleMobileInstance()->Load(jstring2cppstring(env, modelPath),
+  bool isLoadOk = false;
+
+#ifdef ENABLE_EXCEPTION
+  try {
+    isLoadOk = getPaddleMobileInstance()->Load(
+        jstring2cppstring(env, modelPath), jstring2cppstring(env, paramPath),
+        optimize);
+  } catch (paddle_mobile::PaddleMobileException &e) {
+    ANDROIDLOGE("jni got an PaddleMobileException! ", e.what());
+    isLoadOk = false;
+  }
+#else
+  isLoadOk = getPaddleMobileInstance()->Load(jstring2cppstring(env, modelPath),
                                              jstring2cppstring(env, paramPath),
                                              optimize);
+#endif
+  return static_cast<jboolean>(isLoadOk);
 }
 
 JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_PML_loadCombinedQualified(
     JNIEnv *env, jclass thiz, jstring modelPath, jstring paramPath) {
+  std::lock_guard<std::mutex> lock(shared_mutex);
   ANDROIDLOGI("loadCombinedQualified invoked");
   bool optimize = true;
   bool qualified = true;
-  return getPaddleMobileInstance()->Load(jstring2cppstring(env, modelPath),
+  bool isLoadOk = false;
+
+#ifdef ENABLE_EXCEPTION
+  try {
+    isLoadOk = getPaddleMobileInstance()->Load(
+        jstring2cppstring(env, modelPath), jstring2cppstring(env, paramPath),
+        optimize, qualified);
+  } catch (paddle_mobile::PaddleMobileException &e) {
+    ANDROIDLOGE("jni got an PaddleMobileException! ", e.what());
+    isLoadOk = false;
+  }
+#else
+  isLoadOk = getPaddleMobileInstance()->Load(jstring2cppstring(env, modelPath),
                                              jstring2cppstring(env, paramPath),
                                              optimize, qualified);
+#endif
+  return static_cast<jboolean>(isLoadOk);
 }
 
 JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predictImage(
     JNIEnv *env, jclass thiz, jfloatArray buf, jintArray ddims) {
+  std::lock_guard<std::mutex> lock(shared_mutex);
+
   ANDROIDLOGI("predictImage invoked");
+  jfloatArray result = NULL;
+
+#ifdef ENABLE_EXCEPTION
+  ANDROIDLOGE("ENABLE_EXCEPTION!");
+
+  try {
     jsize ddim_size = env->GetArrayLength(ddims);
     if (ddim_size != 4) {
       ANDROIDLOGE("ddims size not equal to 4");
@@ -100,7 +167,6 @@ JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predictImage(
     framework::DDim ddim = framework::make_ddim(
         {ddim_ptr[0], ddim_ptr[1], ddim_ptr[2], ddim_ptr[3]});
     int length = framework::product(ddim);
-  jfloatArray result = NULL;
     int count = 0;
     float *dataPointer = nullptr;
     if (nullptr != buf) {
@@ -112,12 +178,52 @@ JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predictImage(
     for (int i = 0; i < length; i++) {
       input_ptr[i] = dataPointer[i];
     }
-  auto output = shared_paddle_mobile_instance->Predict(input);
+    auto output = getPaddleMobileInstance()->Predict(input);
     count = output->numel();
     result = env->NewFloatArray(count);
     env->SetFloatArrayRegion(result, 0, count, output->data<float>());
     env->ReleaseIntArrayElements(ddims, ddim_ptr, 0);
+    env->DeleteLocalRef(ddims);
+    env->ReleaseFloatArrayElements(buf, dataPointer, 0);
+    env->DeleteLocalRef(buf);
+
+  } catch (paddle_mobile::PaddleMobileException &e) {
+    ANDROIDLOGE("jni got an PaddleMobileException! ", e.what());
+  }
+
+#else
+  jsize ddim_size = env->GetArrayLength(ddims);
+  if (ddim_size != 4) {
+    ANDROIDLOGE("ddims size not equal to 4");
+  }
+  jint *ddim_ptr = env->GetIntArrayElements(ddims, NULL);
+  framework::DDim ddim = framework::make_ddim(
+      {ddim_ptr[0], ddim_ptr[1], ddim_ptr[2], ddim_ptr[3]});
+  int length = framework::product(ddim);
+  int count = 0;
+  float *dataPointer = nullptr;
+  if (nullptr != buf) {
+    dataPointer = env->GetFloatArrayElements(buf, NULL);
+  }
+  framework::Tensor input;
+  input.Resize(ddim);
+  auto input_ptr = input.mutable_data<float>();
+  for (int i = 0; i < length; i++) {
+    input_ptr[i] = dataPointer[i];
+  }
+  auto output = getPaddleMobileInstance()->Predict(input);
+  count = output->numel();
+  result = env->NewFloatArray(count);
+  env->SetFloatArrayRegion(result, 0, count, output->data<float>());
+  env->ReleaseIntArrayElements(ddims, ddim_ptr, 0);
+  env->DeleteLocalRef(ddims);
+  env->ReleaseFloatArrayElements(buf, dataPointer, 0);
+  env->DeleteLocalRef(buf);
+  env->DeleteLocalRef(dataPointer);
+#endif
+
   ANDROIDLOGI("predictImage finished");
+
   return result;
 }
 
@@ -170,7 +276,48 @@ void convert_nv21_to_matrix(uint8_t *nv21, float *matrix, int width, int height,
 JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predictYuv(
     JNIEnv *env, jclass thiz, jbyteArray yuv_, jint imgwidth, jint imgHeight,
     jintArray ddims, jfloatArray meanValues) {
+  std::lock_guard<std::mutex> lock(shared_mutex);
+
   ANDROIDLOGI("predictYuv invoked");
+  jfloatArray result = NULL;
+
+#ifdef ENABLE_EXCEPTION
+  try {
+    jsize ddim_size = env->GetArrayLength(ddims);
+    if (ddim_size != 4) {
+      ANDROIDLOGE("ddims size not equal to 4");
+    }
+    jint *ddim_ptr = env->GetIntArrayElements(ddims, NULL);
+    framework::DDim ddim = framework::make_ddim(
+        {ddim_ptr[0], ddim_ptr[1], ddim_ptr[2], ddim_ptr[3]});
+    int length = framework::product(ddim);
+    float matrix[length];
+    jbyte *yuv = env->GetByteArrayElements(yuv_, NULL);
+    float *meansPointer = nullptr;
+    if (nullptr != meanValues) {
+      meansPointer = env->GetFloatArrayElements(meanValues, NULL);
+    }
+    convert_nv21_to_matrix((uint8_t *)yuv, matrix, imgwidth, imgHeight, ddim[3],
+                           ddim[2], meansPointer);
+    int count = 0;
+    framework::Tensor input;
+    input.Resize(ddim);
+    auto input_ptr = input.mutable_data<float>();
+    for (int i = 0; i < length; i++) {
+      input_ptr[i] = matrix[i];
+    }
+    auto output = getPaddleMobileInstance()->Predict(input);
+    count = output->numel();
+    result = env->NewFloatArray(count);
+    env->SetFloatArrayRegion(result, 0, count, output->data<float>());
+    env->ReleaseByteArrayElements(yuv_, yuv, 0);
+    env->ReleaseIntArrayElements(ddims, ddim_ptr, 0);
+    env->ReleaseFloatArrayElements(meanValues, meansPointer, 0);
+    ANDROIDLOGI("predictYuv finished");
+  } catch (paddle_mobile::PaddleMobileException &e) {
+    ANDROIDLOGE("jni got an PaddleMobileException! ", e.what());
+  }
+#else
   jsize ddim_size = env->GetArrayLength(ddims);
   if (ddim_size != 4) {
     ANDROIDLOGE("ddims size not equal to 4");
@@ -187,7 +334,6 @@ JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predictYuv(
   }
   convert_nv21_to_matrix((uint8_t *)yuv, matrix, imgwidth, imgHeight, ddim[3],
                          ddim[2], meansPointer);
-  jfloatArray result = NULL;
   int count = 0;
   framework::Tensor input;
   input.Resize(ddim);
@@ -195,7 +341,7 @@ JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predictYuv(
   for (int i = 0; i < length; i++) {
     input_ptr[i] = matrix[i];
   }
-  auto output = shared_paddle_mobile_instance->Predict(input);
+  auto output = getPaddleMobileInstance()->Predict(input);
   count = output->numel();
   result = env->NewFloatArray(count);
   env->SetFloatArrayRegion(result, 0, count, output->data<float>());
@@ -203,19 +349,44 @@ JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predictYuv(
   env->ReleaseIntArrayElements(ddims, ddim_ptr, 0);
   env->ReleaseFloatArrayElements(meanValues, meansPointer, 0);
   ANDROIDLOGI("predictYuv finished");
+#endif
+
   return result;
 }
 
 JNIEXPORT void JNICALL Java_com_baidu_paddle_PML_setThread(JNIEnv *env,
                                                            jclass thiz,
                                                            jint threadCount) {
+  std::lock_guard<std::mutex> lock(shared_mutex);
+
   ANDROIDLOGI("setThreadCount %d", threadCount);
+#ifdef ENABLE_EXCEPTION
+  try {
     getPaddleMobileInstance()->SetThreadNum((int)threadCount);
+  } catch (paddle_mobile::PaddleMobileException &e) {
+    ANDROIDLOGE("jni got an PaddleMobileException! ", e.what());
+  }
+#else
+  getPaddleMobileInstance()->SetThreadNum((int)threadCount);
+
+#endif
 }
 
 JNIEXPORT void JNICALL Java_com_baidu_paddle_PML_clear(JNIEnv *env,
                                                        jclass thiz) {
+  std::lock_guard<std::mutex> lock(shared_mutex);
+
+#ifdef ENABLE_EXCEPTION
+  try {
+    getPaddleMobileInstance()->Clear();
+
+  } catch (paddle_mobile::PaddleMobileException &e) {
+    ANDROIDLOGE("jni got an PaddleMobileException! ", e.what());
+  }
+#else
   getPaddleMobileInstance()->Clear();
+
+#endif
 }
 
 }  // namespace jni

src/jni/paddle_mobile_jni.h

@@ -73,7 +73,7 @@ JNIEXPORT void JNICALL Java_com_baidu_paddle_PML_setThread(JNIEnv *env,
 /**
  * clear data of the net when destroy for android
  */
-JNIEXPORT void JNICALL Java_com_baidu_paddle_PMLL_clear(JNIEnv *env,
+JNIEXPORT void JNICALL Java_com_baidu_paddle_PML_clear(JNIEnv *env,
                                                        jclass thiz);
 }  // namespace jni
 }  // namespace paddle_mobile

src/operators/feed_op.h

@@ -49,6 +49,7 @@ class FeedOp : public framework::OperatorBase<DeviceType> {
     auto input_ptr = input->data<float>();
     Tensor *output = param_.Out();
     auto output_ptr = output->mutable_data<half>();
+    auto out_address = output->fpga_args().scale_pointer();
     fpga::BypassArgs args;
     args.convert_type = fpga::DATA_FP32_TO_FP16;
     args.layout_type = fpga::LAYOUT_CHW_TO_HWC;
@@ -59,6 +60,7 @@ class FeedOp : public framework::OperatorBase<DeviceType> {
     args.image.pad_height = 0;
     args.image.pad_width = 0;
     args.output.address = output_ptr;
+    args.output.scale_address = out_address;
     fpga::PerformBypass(args);
   }