update nlp support

src/jni/PML.java

@@ -9,6 +9,14 @@ public class PML {
      */
     public static native boolean load(String modelDir);
 
+    /**
+     * load seperated model
+     *
+     * @param modelDir model dir
+     * @return isloadsuccess
+     */
+    public static native boolean loadnlp(String modelDir);
+
     /**
      * load combined model
      *

src/jni/paddle_mobile_jni.cpp

@@ -74,6 +74,28 @@ JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_PML_load(JNIEnv *env,
   return static_cast<jboolean>(isLoadOk);
 }
 
+JNIEXPORT jboolean JNICALL
+Java_com_baidu_paddle_PML_loadnlp(JNIEnv *env, jclass thiz, jstring modelPath) {
+  std::lock_guard<std::mutex> lock(shared_mutex);
+  ANDROIDLOGI("load invoked");
+  bool optimize = true;
+  bool isLoadOk = false;
+
+#ifdef ENABLE_EXCEPTION
+  try {
+    isLoadOk = getPaddleMobileInstance()->Load(
+        jstring2cppstring(env, modelPath), optimize, false, 1, true);
+  } catch (paddle_mobile::PaddleMobileException &e) {
+    ANDROIDLOGE("jni got an PaddleMobileException! ", e.what());
+    isLoadOk = false;
+  }
+#else
+  isLoadOk = getPaddleMobileInstance()->Load(jstring2cppstring(env, modelPath),
+                                             optimize, false, 1, true);
+#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);

test/net/test_nlp.cpp

@@ -32,8 +32,7 @@ int main() {
     std::cout << "load cost :" << time_diff(time1, time1) << "ms" << std::endl;
     //    1064 1603 644 699 2878 1219 867 1352 8 1 13 312 479
 
-    std::vector<int64_t> ids{1064, 1603, 644, 699, 2878, 1219, 867,
-                             1352, 8,    1,   13,  312,  479};
+    std::vector<int64_t> ids{1918, 117, 55, 97, 1352, 4272, 1656, 903};
 
     paddle_mobile::framework::LoDTensor words;
     auto size = static_cast<int>(ids.size());
@@ -56,5 +55,32 @@ int main() {
     std::cout << "predict cost :" << time_diff(time3, time4) / 1 << "ms"
               << std::endl;
   }
+
+  auto time2 = time();
+  std::cout << "load cost :" << time_diff(time1, time1) << "ms" << std::endl;
+  //    1064 1603 644 699 2878 1219 867 1352 8 1 13 312 479
+
+  std::vector<int64_t> ids{1791, 656, 1549, 281, 96};
+
+  paddle_mobile::framework::LoDTensor words;
+  auto size = static_cast<int>(ids.size());
+  paddle_mobile::framework::LoD lod{{0, ids.size()}};
+  DDim dims{size, 1};
+  words.Resize(dims);
+  words.set_lod(lod);
+  DLOG << "words lod : " << words.lod();
+  auto *pdata = words.mutable_data<int64_t>();
+  size_t n = words.numel() * sizeof(int64_t);
+  DLOG << "n :" << n;
+  memcpy(pdata, ids.data(), n);
+  DLOG << "words lod 22: " << words.lod();
+  auto time3 = time();
+  for (int i = 0; i < 1; ++i) {
+    auto vec_result = paddle_mobile.PredictLod(words);
+    DLOG << *vec_result;
+  }
+  auto time4 = time();
+  std::cout << "predict cost :" << time_diff(time3, time4) / 1 << "ms"
+            << std::endl;
   return 0;
 }