Created by: buptwds

模型使用的是线性回归的例子http://paddlepaddle.org/docs/develop/documentation/zh/getstarted/concepts/use_concepts_cn.html 预测样例main.c,https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/examples/model_inference/dense/main.c 把预测样例中的main.c改成infer.c，并生成libpaddle_infer.so，infer.c代码如下：

#include <stdlib.h>
#include <paddle/capi.h>
#include "common.h"

#define MERGE_PATH "models/merge_model"

float infer(float data[], int length) {
  // Initalize the PaddlePaddle runtime environment.
  char* argv[] = {"--use_gpu=False"};
  CHECK(paddle_init(1, (char**)argv));

  long size;
  void* buf = read_config(MERGE_PATH, &size);

  paddle_gradient_machine machine;
  CHECK(paddle_gradient_machine_create_for_inference_with_parameters(&machine, buf, size));

  paddle_arguments in_args = paddle_arguments_create_none();

  CHECK(paddle_arguments_resize(in_args, 1));

  paddle_matrix mat =
      paddle_matrix_create(/* height = batch size */ 1,
                           /* width = dimensionality of the data layer */ 2,
                           /* whether to use GPU */ false);

  paddle_real* array;
  CHECK(paddle_matrix_get_row(mat, 0, &array));

  for(int i = 0; i < length; i++) {
      array[i] = data[i];
  }

  // Assign the matrix to the argument.
  CHECK(paddle_arguments_set_value(in_args, 0, mat));

  // Create the output argument.
  paddle_arguments out_args = paddle_arguments_create_none();

  // Invoke the forward computation.
  CHECK(paddle_gradient_machine_forward(machine,
                                        in_args,
                                        out_args,
                                        /* is train taks or not */ false));

  // Create the matrix to hold the forward result of the neural network.
  paddle_matrix prob = paddle_matrix_create_none();
  // Access the matrix of the output argument, the predicted result is stored in
  CHECK(paddle_arguments_get_value(out_args, 0, prob));

  //uint64_t height;
  //uint64_t width;
  paddle_real* predict = (paddle_real*)malloc(sizeof(paddle_real) * 1);
  //CHECK(paddle_matrix_get_shape(prob, &height, &width));
  CHECK(paddle_matrix_get_row(prob, 0, &predict));

  float result = *predict;
  //printf("Prob: \n");
  //for (int i = 0; i < height * width; ++i) {
  //  printf("predict in infer is %.4f ", *predict);
  //}
  //printf("predict in infer is %.4f\n", *predict);

  //cleaning up.
  CHECK(paddle_matrix_destroy(prob));
  CHECK(paddle_arguments_destroy(out_args));
  CHECK(paddle_matrix_destroy(mat));
  CHECK(paddle_arguments_destroy(in_args));
  CHECK(paddle_gradient_machine_destroy(machine));

  return result;
}

生成so的过程如下： gcc -fPIC -c infer.c -o infer.o gcc -shared infer.o -o lib/libpaddle_infer.so -Llib -lpaddle_capi_shared

然后使用java的jna链接libpaddle_infer.so，并调用infer.c中的infer函数，java代码：

package capi;
import com.sun.jna.Library;
import com.sun.jna.Native;

public class Main {

    public interface Inference extends Library {

        Inference INSTANTCE = (Inference) Native.loadLibrary("paddle_infer", Inference.class);
        float infer(float sample[], int length);
    }

    public static void main(String[] args) {
        float[] data = {1, 2};
        float predict = Inference.INSTANTCE.infer(data, 2);
        System.out.println("predict: " + predict);
    }
}

不稳定复现以下错误，运行多次才会出现一次（但可以看到，即使出错也能正常打印出predict的值）：

I0205 08:43:00.892827  7502 Util.cpp:166] commandline:  --use_gpu=False
predict: 1.4857053
*** Aborted at 1517820180 (unix time) try "date -d @1517820180" if you are using GNU date ***
PC: @                0x0 (unknown)
*** SIGSEGV (@0x7f6d5299b580) received by PID 7501 (TID 0x7f6d0f45d700) from PID 1385805184; stack trace: ***
    @     0x7f6d5256e390 (unknown)
    @     0x7f6d516a8964 check_pending_signals()
    @     0x7f6d5169f823 signal_thread_entry()
    @     0x7f6d517faaa3 JavaThread::thread_main_inner()
    @     0x7f6d517fabec JavaThread::run()
    @     0x7f6d516a9a28 java_start()
    @     0x7f6d525646ba start_thread
    @     0x7f6d51e803dd clone
    @                0x0 (unknown)
Segmentation fault