trans gemm to class && add multi instance support && to unit test

src/operators/math/gemm.h

@@ -35,146 +35,166 @@ namespace paddle_mobile {
 namespace operators {
 namespace math {
 
-/*
+class Gemm {
+ public:
+  /*
 // 将 A 矩阵分块复制到连续内存(ColMajor)
 void PackMatrixA(int m, int k, int m_tail, const float *A, int lda,
-                 float *buffer);
+           float *buffer);
 
 // 将 B 矩阵分块复制到连续内存(ColMajor)
 void PackMatrixB(int k, int n, int n_tail, const float *B, int ldb,
-                 float *buffer);
+           float *buffer);
 */
-
-// 将 A 矩阵分块复制到连续内存(RowMajor)
-void PackMatrixA_4r(int m, int k, int m_tail, const float *A, int lda,
-                    float *buffer);
-void PackMatrixA_6r(int m, int k, int m_tail, const float *A, int lda,
-                    float *buffer);
-void PackMatrixA_8r(int m, int k, int m_tail, const float *A, int lda,
-                    float *buffer);
-void PackMatrixA_omp_6r(int m, int k, int m_tail, const float *A, int lda,
-                        float *buffer);
-void PackMatrixA_omp_8r(int m, int k, int m_tail, const float *A, int lda,
-                        float *buffer);
-
-// 将 B 矩阵分块复制到连续内存(RowMajor)
-void PackMatrixB_8c(int k, int n, int n_tail, const float *B, int ldb,
-                    float *buffer);
-void PackMatrixB_12c(int k, int n, int n_tail, const float *B, int ldb,
-                     float *buffer);
-void PackMatrixB_16c(int k, int n, int n_tail, const float *B, int ldb,
-                     float *buffer);
-void PackMatrixB_omp_8c(int k, int n, int n_tail, const float *B, int ldb,
-                        float *buffer);
-void PackMatrixB_omp_12c(int k, int n, int n_tail, const float *B, int ldb,
-                         float *buffer);
-void PackMatrixB_omp_16c(int k, int n, int n_tail, const float *B, int ldb,
-                         float *buffer);
-
-// 分块矩阵乘法
-void InnerKernel(int mc, int nc, float alpha, const float *a, const float *b,
-                 float beta, float *c, float *C, int ldc, bool relu);
-void InnerKernelWithBias(int mc, int nc, float alpha, const float *a,
+  typedef void (Gemm::*FnPack)(int, int, int, const float *, int, float *);
+  typedef void (Gemm::*FnAddDot)(int, const float *, const float *, float *,
+                                 int);
+  FnPack procPackA;
+  FnPack procPackB;
+  FnAddDot procAddDot;
+
+  // 将 A 矩阵分块复制到连续内存(RowMajor)
+  void PackMatrixA_4r(int m, int k, int m_tail, const float *A, int lda,
+                      float *buffer);
+  void PackMatrixA_6r(int m, int k, int m_tail, const float *A, int lda,
+                      float *buffer);
+  void PackMatrixA_8r(int m, int k, int m_tail, const float *A, int lda,
+                      float *buffer);
+  void PackMatrixA_omp_6r(int m, int k, int m_tail, const float *A, int lda,
+                          float *buffer);
+  void PackMatrixA_omp_8r(int m, int k, int m_tail, const float *A, int lda,
+                          float *buffer);
+
+  // 将 B 矩阵分块复制到连续内存(RowMajor)
+  void PackMatrixB_8c(int k, int n, int n_tail, const float *B, int ldb,
+                      float *buffer);
+  void PackMatrixB_12c(int k, int n, int n_tail, const float *B, int ldb,
+                       float *buffer);
+  void PackMatrixB_16c(int k, int n, int n_tail, const float *B, int ldb,
+                       float *buffer);
+  void PackMatrixB_omp_8c(int k, int n, int n_tail, const float *B, int ldb,
+                          float *buffer);
+  void PackMatrixB_omp_12c(int k, int n, int n_tail, const float *B, int ldb,
+                           float *buffer);
+  void PackMatrixB_omp_16c(int k, int n, int n_tail, const float *B, int ldb,
+                           float *buffer);
+
+  // 分块矩阵乘法
+  void InnerKernel(int mc, int nc, float alpha, const float *a, const float *b,
+                   float beta, float *c, float *C, int ldc, bool relu);
+  void InnerKernelWithBias(int mc, int nc, float alpha, const float *a,
+                           const float *b, float beta, float *c, float *C,
+                           int ldc, bool relu, float *bias);
+
+  void InnerKernelWithBn(int mc, int nc, float alpha, const float *a,
                          const float *b, float beta, float *c, float *C,
-                         int ldc, bool relu, float *bias);
-
-void InnerKernelWithBn(int mc, int nc, float alpha, const float *a,
-                       const float *b, float beta, float *c, float *C, int ldc,
-                       bool relu, float *new_scale, float *new_bias);
-void InnerKernelWithBnAdd(int mc, int nc, float alpha, const float *a,
-                          const float *b, float beta, float *c, float *C,
-                          int ldc, bool relu, float *new_scale, float *new_bias,
+                         int ldc, bool relu, float *new_scale, float *new_bias);
+  void InnerKernelWithBnAdd(int mc, int nc, float alpha, const float *a,
+                            const float *b, float beta, float *c, float *C,
+                            int ldc, bool relu, float *new_scale,
+                            float *new_bias, float *bias);
+  void InnerKernelWithPRelu(int mc, int nc, const float *a, const float *b,
+                            float *c, float *C, int ldc, float *p,
+                            std::string mode, float *bias, float *bias1);
+  /*
+  // 向量矩阵乘法 (M = 1)
+  void VectorKernel(int m, int n, int k, float alpha, const float *A, int lda,
+                    const float *B, int ldb, float beta, float *C, int ldc,
+                    bool relu);
+
+  void VectorKernelWithBn(int m, int n, int k, float alpha, const float *A,
+                          int lda, const float *B, int ldb, float beta, float
+  *C, int ldc, bool relu, float *new_scale, float *new_bias);
+  */
+
+  // 计算一个更小的 C 矩阵分块
+  void AddDot4x4(int k, const float *a, const float *b, float *c, int ldc);
+  void AddDot4x8(int k, const float *a, const float *b, float *c, int ldc);
+  void AddDot6x8(int k, const float *a, const float *b, float *c, int ldc);
+  void AddDot8x12(int k, const float *a, const float *b, float *c, int ldc);
+  void AddDot6x16(int k, const float *a, const float *b, float *c, int ldc);
+
+  // 分块矩阵乘法结果回写
+  // C = A * B
+  void WriteBasic(int mc, int nc, float *c, float *C, int ldc);
+  // C = alpha * A * B + beta * C
+  void WriteWithAlphaBeta(int mc, int nc, float *c, float *C, int ldc);
+  // C = A * B + C
+  void WriteWithAdd(int mc, int nc, float *c, float *C, int ldc);
+  // C = A * B + bias
+  void WriteWithAddV1(int mc, int nc, float *c, float *C, int ldc, float *bias);
+  // C = A * B + C, relu(C)
+  void WriteWithAddRelu(int mc, int nc, float *c, float *C, int ldc);
+  // C = A * B + C,prelu(C)
+  void WriteWithAddPRelu(int mc, int nc, float *c, float *C, int ldc, float *p,
+                         std::string mode, float *bias, float *bias1);
+  // C = A * B + bias ,relu(C)
+  void WriteWithAddReluV1(int mc, int nc, float *c, float *C, int ldc,
                           float *bias);
-void InnerKernelWithPRelu(int mc, int nc, const float *a, const float *b,
-                          float *c, float *C, int ldc, float *p,
-                          std::string mode, float *bias, float *bias1);
-/*
-// 向量矩阵乘法 (M = 1)
-void VectorKernel(int m, int n, int k, float alpha, const float *A, int lda,
-                  const float *B, int ldb, float beta, float *C, int ldc,
-                  bool relu);
-
-void VectorKernelWithBn(int m, int n, int k, float alpha, const float *A,
-                        int lda, const float *B, int ldb, float beta, float *C,
-                        int ldc, bool relu, float *new_scale, float *new_bias);
-*/
+  // C = A * B, batchnorm(C)
+  void WriteWithBn(int mc, int nc, float *c, float *C, int ldc,
+                   float *new_scale, float *new_bias);
+  // C = A * B, batchnorm(C), relu(C)
+  void WriteWithBnRelu(int mc, int nc, float *c, float *C, int ldc,
+                       float *new_scale, float *new_bias);
+  void WriteWithBnAddRelu(int mc, int nc, float *c, float *C, int ldc,
+                          float *new_scale, float *new_bias, float *bias1);
+  /*
+  // 向量矩阵乘法结果回写
+  // C = A * B
+  void VecWriteBasic(int n, float *c, float *C, int ldc);
+  // C = alpha * A * B + beta * C
+  void VecWriteWithAlphaBeta(int n, float *c, float *C, int ldc);
+  // C = A * B + C
+  void VecWriteWithAdd(int n, float *c, float *C, int ldc);
+  // C = A * B + C, relu(C)
+  void VecWriteWithAddRelu(int n, float *c, float *C, int ldc);
+  // C = A * B, batchnorm(C)
+  void VecWriteWithBn(int n, float *c, float *C, int ldc, float *new_scale,
+                      float *new_bias);
+  // C = A * B, batchnorm(C), relu(C)
+  void VecWriteWithBnRelu(int n, float *c, float *C, int ldc, float *new_scale,
+                          float *new_bias);
+  */
+
+  // 32位 float 矩阵乘法
+  void Sgemm(int m, int n, int k, float alpha, const float *A, int lda,
+             const float *B, int ldb, float beta, float *C, int ldc, bool relu,
+             float *bias);
+
+  // 32位 float 矩阵乘法, 并对结果进行 batchnrom
+  void SgemmWithBn(int m, int n, int k, float alpha, const float *A, int lda,
+                   const float *B, int ldb, float beta, float *C, int ldc,
+                   bool relu, float *new_scale, float *new_bias, float *bias);
+  void SgemmWithPRelu(int m, int n, int k, const float *A, int lda,
+                      const float *B, int ldb, float *C, int ldc, float *p,
+                      std::string mode, float *bias, float *bias1);
+
+  // 32位 float 矩阵乘法（openmp 多线程版本）
+  void Sgemm_omp(int m, int n, int k, float alpha, const float *A, int lda,
+                 const float *B, int ldb, float beta, float *C, int ldc,
+                 bool relu, float *bias);
 
-// 计算一个更小的 C 矩阵分块
-void AddDot4x4(int k, const float *a, const float *b, float *c, int ldc);
-void AddDot4x8(int k, const float *a, const float *b, float *c, int ldc);
-void AddDot6x8(int k, const float *a, const float *b, float *c, int ldc);
-void AddDot8x12(int k, const float *a, const float *b, float *c, int ldc);
-void AddDot6x16(int k, const float *a, const float *b, float *c, int ldc);
-
-// 分块矩阵乘法结果回写
-// C = A * B
-void WriteBasic(int mc, int nc, float *c, float *C, int ldc);
-// C = alpha * A * B + beta * C
-void WriteWithAlphaBeta(int mc, int nc, float *c, float *C, int ldc);
-// C = A * B + C
-void WriteWithAdd(int mc, int nc, float *c, float *C, int ldc);
-// C = A * B + bias
-void WriteWithAddV1(int mc, int nc, float *c, float *C, int ldc, float *bias);
-// C = A * B + C, relu(C)
-void WriteWithAddRelu(int mc, int nc, float *c, float *C, int ldc);
-// C = A * B + C,prelu(C)
-void WriteWithAddPRelu(int mc, int nc, float *c, float *C, int ldc, float *p,
-                       std::string mode, float *bias, float *bias1);
-// C = A * B + bias ,relu(C)
-void WriteWithAddReluV1(int mc, int nc, float *c, float *C, int ldc,
-                        float *bias);
-// C = A * B, batchnorm(C)
-void WriteWithBn(int mc, int nc, float *c, float *C, int ldc, float *new_scale,
-                 float *new_bias);
-// C = A * B, batchnorm(C), relu(C)
-void WriteWithBnRelu(int mc, int nc, float *c, float *C, int ldc,
-                     float *new_scale, float *new_bias);
-void WriteWithBnAddRelu(int mc, int nc, float *c, float *C, int ldc,
-                        float *new_scale, float *new_bias, float *bias1);
-/*
-// 向量矩阵乘法结果回写
-// C = A * B
-void VecWriteBasic(int n, float *c, float *C, int ldc);
-// C = alpha * A * B + beta * C
-void VecWriteWithAlphaBeta(int n, float *c, float *C, int ldc);
-// C = A * B + C
-void VecWriteWithAdd(int n, float *c, float *C, int ldc);
-// C = A * B + C, relu(C)
-void VecWriteWithAddRelu(int n, float *c, float *C, int ldc);
-// C = A * B, batchnorm(C)
-void VecWriteWithBn(int n, float *c, float *C, int ldc, float *new_scale,
-                    float *new_bias);
-// C = A * B, batchnorm(C), relu(C)
-void VecWriteWithBnRelu(int n, float *c, float *C, int ldc, float *new_scale,
-                        float *new_bias);
-*/
+  // 32位 float 矩阵乘法, 并对结果进行 batchnrom（openmp 多线程版本）
+  void SgemmWithBn_omp(int m, int n, int k, float alpha, const float *A,
+                       int lda, const float *B, int ldb, float beta, float *C,
+                       int ldc, bool relu, float *new_scale, float *new_bias,
+                       float *bias);
+
+  void SgemmWithPRelu_omp(int m, int n, int k, const float *A, int lda,
+                          const float *B, int ldb, float *C, int ldc, float *p,
+                          std::string mode, float *bias, float *bias1);
 
-// 32位 float 矩阵乘法
-void Sgemm(int m, int n, int k, float alpha, const float *A, int lda,
-           const float *B, int ldb, float beta, float *C, int ldc, bool relu,
-           float *bias);
+ private:
+  int MC = 0;
+  int KC = 0;
+  int NC = 0;
 
-// 32位 float 矩阵乘法, 并对结果进行 batchnrom
-void SgemmWithBn(int m, int n, int k, float alpha, const float *A, int lda,
-                 const float *B, int ldb, float beta, float *C, int ldc,
-                 bool relu, float *new_scale, float *new_bias, float *bias);
-void SgemmWithPRelu(int m, int n, int k, const float *A, int lda,
-                    const float *B, int ldb, float *C, int ldc, float *p,
-                    std::string mode, float *bias, float *bias1);
-
-// 32位 float 矩阵乘法（openmp 多线程版本）
-void Sgemm_omp(int m, int n, int k, float alpha, const float *A, int lda,
-               const float *B, int ldb, float beta, float *C, int ldc,
-               bool relu, float *bias);
-
-// 32位 float 矩阵乘法, 并对结果进行 batchnrom（openmp 多线程版本）
-void SgemmWithBn_omp(int m, int n, int k, float alpha, const float *A, int lda,
-                     const float *B, int ldb, float beta, float *C, int ldc,
-                     bool relu, float *new_scale, float *new_bias, float *bias);
-
-void SgemmWithPRelu_omp(int m, int n, int k, const float *A, int lda,
-                        const float *B, int ldb, float *C, int ldc, float *p,
-                        std::string mode, float *bias, float *bias1);
+  float *packedA;
+  float *packedB;
+  float *packedC;
+  float *zero;
+};
 
 }  // namespace math
 }  // namespace operators

src/operators/math/gru_compute.cpp

@@ -28,19 +28,22 @@ struct GRUUnitFunctor<CPU, T> {
   static void compute(GRUMetaValue<T> value, int frame_size, int batch_size,
                       const ActivationType active_node,
                       const ActivationType active_gate) {
+    Gemm gemm;
     if (value.prev_out_value) {
-      Sgemm(batch_size, frame_size * 2, frame_size, 1, value.prev_out_value,
-            frame_size, value.gate_weight, frame_size * 2, 1, value.gate_value,
-            frame_size * 3, false, nullptr);
+      gemm.Sgemm(batch_size, frame_size * 2, frame_size, 1,
+                 value.prev_out_value, frame_size, value.gate_weight,
+                 frame_size * 2, 1, value.gate_value, frame_size * 3, false,
+                 nullptr);
     }
 
     forward_reset_output(forward::gru_resetOutput<T>(), value, frame_size,
                          batch_size, active_gate);
 
     if (value.prev_out_value) {
-      Sgemm(batch_size, frame_size, frame_size, 1, value.reset_output_value,
-            frame_size, value.state_weight, frame_size, 1,
-            value.gate_value + frame_size * 2, frame_size * 3, false, nullptr);
+      gemm.Sgemm(batch_size, frame_size, frame_size, 1,
+                 value.reset_output_value, frame_size, value.state_weight,
+                 frame_size, 1, value.gate_value + frame_size * 2,
+                 frame_size * 3, false, nullptr);
     }
 
     forward_final_output(forward::gru_finalOutput<T>(), value, frame_size,

src/operators/math/math_function.cpp

@@ -36,6 +36,7 @@ void matmul<float>(const framework::Tensor &matrix_a, bool trans_a,
   int M = dim_out[0];
   int N = dim_out[1];
   int K = (!trans_a) ? dim_a[1] : dim_a[0];
+  Gemm gemm;
 
   if (trans_a) {
     int numel = matrix_a.numel();
@@ -50,20 +51,24 @@ void matmul<float>(const framework::Tensor &matrix_a, bool trans_a,
         a[index++] = tmp[i * n + j];
       }
     }
+
 #ifdef _OPENMP
-    Sgemm_omp(M, N, K, alpha, a, K, matrix_b.data<float>(), N, beta,
-              matrix_out->data<float>(), N, relu, bias);
+
+    gemm.Sgemm_omp(M, N, K, alpha, a, K, matrix_b.data<float>(), N, beta,
+                   matrix_out->data<float>(), N, relu, bias);
 #else
-    Sgemm(M, N, K, alpha, a, K, matrix_b.data<float>(), N, beta,
-          matrix_out->data<float>(), N, relu, bias);
+    gemm.Sgemm(M, N, K, alpha, a, K, matrix_b.data<float>(), N, beta,
+               matrix_out->data<float>(), N, relu, bias);
 #endif
   } else {
 #ifdef _OPENMP
-    Sgemm_omp(M, N, K, alpha, matrix_a.data<float>(), K, matrix_b.data<float>(),
-              N, beta, matrix_out->data<float>(), N, relu, bias);
+    gemm.Sgemm_omp(M, N, K, alpha, matrix_a.data<float>(), K,
+                   matrix_b.data<float>(), N, beta, matrix_out->data<float>(),
+                   N, relu, bias);
 #else
-    Sgemm(M, N, K, alpha, matrix_a.data<float>(), K, matrix_b.data<float>(), N,
-          beta, matrix_out->data<float>(), N, relu, bias);
+    gemm.Sgemm(M, N, K, alpha, matrix_a.data<float>(), K,
+               matrix_b.data<float>(), N, beta, matrix_out->data<float>(), N,
+               relu, bias);
 #endif
   }
 }
@@ -74,6 +79,7 @@ void matmulWithBn<float>(const framework::Tensor &matrix_a, bool trans_a,
                          float alpha, framework::Tensor *matrix_out, float beta,
                          bool relu, framework::Tensor *new_scale,
                          framework::Tensor *new_bias, int group, float *bias) {
+  Gemm gemm;
   auto dim_a = matrix_a.dims();
   auto dim_b = matrix_b.dims();
   auto dim_out = matrix_out->dims();
@@ -86,21 +92,22 @@ void matmulWithBn<float>(const framework::Tensor &matrix_a, bool trans_a,
   int K = (!trans_a) ? dim_a[1] : dim_a[0];
 
 #ifdef _OPENMP
-  SgemmWithBn_omp(M, N, K, alpha, matrix_a.data<float>(), K,
-                  matrix_b.data<float>(), N, beta, matrix_out->data<float>(), N,
-                  relu, new_scale->data<float>() + group,
-                  new_bias->data<float>() + group, bias);
+  gemm.SgemmWithBn_omp(
+      M, N, K, alpha, matrix_a.data<float>(), K, matrix_b.data<float>(), N,
+      beta, matrix_out->data<float>(), N, relu,
+      new_scale->data<float>() + group, new_bias->data<float>() + group, bias);
 #else
-  SgemmWithBn(M, N, K, alpha, matrix_a.data<float>(), K, matrix_b.data<float>(),
-              N, beta, matrix_out->data<float>(), N, relu,
-              new_scale->data<float>() + group, new_bias->data<float>() + group,
-              bias);
+  gemm.SgemmWithBn(M, N, K, alpha, matrix_a.data<float>(), K,
+                   matrix_b.data<float>(), N, beta, matrix_out->data<float>(),
+                   N, relu, new_scale->data<float>() + group,
+                   new_bias->data<float>() + group, bias);
 #endif
 }
 void matmulWithPRelu(const framework::Tensor &matrix_a, bool trans_a,
                      const framework::Tensor &matrix_b, bool trans_b,
                      framework::Tensor *matrix_out, float *p, std::string mode,
                      float *bias, float *bias1) {
+  Gemm gemm;
   auto dim_a = matrix_a.dims();
   auto dim_b = matrix_b.dims();
   auto dim_out = matrix_out->dims();
@@ -113,11 +120,13 @@ void matmulWithPRelu(const framework::Tensor &matrix_a, bool trans_a,
   int K = (!trans_a) ? dim_a[1] : dim_a[0];
 
 #ifdef _OPENMP
-  SgemmWithPRelu_omp(M, N, K, matrix_a.data<float>(), K, matrix_b.data<float>(),
-                     N, matrix_out->data<float>(), N, p, mode, bias, bias1);
+  gemm.SgemmWithPRelu_omp(M, N, K, matrix_a.data<float>(), K,
+                          matrix_b.data<float>(), N, matrix_out->data<float>(),
+                          N, p, mode, bias, bias1);
 #else
-  SgemmWithPRelu(M, N, K, matrix_a.data<float>(), K, matrix_b.data<float>(), N,
-                 matrix_out->data<float>(), N, p, mode, bias, bias1);
+  gemm.SgemmWithPRelu(M, N, K, matrix_a.data<float>(), K,
+                      matrix_b.data<float>(), N, matrix_out->data<float>(), N,
+                      p, mode, bias, bias1);
 
 #endif
 }

test/CMakeLists.txt

@@ -35,8 +35,8 @@ if (CON GREATER -1)
     ADD_EXECUTABLE(test-yolo net/test_yolo.cpp test_helper.h test_include.h executor_for_test.h)
     target_link_libraries(test-yolo paddle-mobile)
     # gen test
-    ADD_EXECUTABLE(test_yolo_combined net/test_yolo_combined.cpp test_helper.h test_include.h executor_for_test.h)
-    target_link_libraries(test_yolo_combined paddle-mobile)
+    ADD_EXECUTABLE(test-yolo-combined net/test_yolo_combined.cpp test_helper.h test_include.h executor_for_test.h)
+    target_link_libraries(test-yolo-combined paddle-mobile)
     set(FOUND_MATCH ON)
 
 endif ()
@@ -323,5 +323,10 @@ if (NOT FOUND_MATCH)
     target_link_libraries(test-fssd paddle-mobile)
 
 
+    # gen test
+    ADD_EXECUTABLE(test-multi-process net/test_multi_inference_predict.cpp test_helper.h test_include.h)
+    target_link_libraries(test-multi-process paddle-mobile)
+
+
     #add_library(test-lib-size SHARED common/test_lib_size.h common/test_lib_size.cpp)
 endif ()

test/common/test_gemm_accuracy.cpp

@@ -83,8 +83,9 @@ int do_sgemm(int m, int n, int k, bool relu, int t1, int t2, int pr) {
     }
   }
 
-  paddle_mobile::operators::math::SgemmWithBn(
-      m, n, k, 0.9, a, lda, b, ldb, 0.3, c, ldc, relu, scale, bias, nullptr);
+  paddle_mobile::operators::math::Gemm gemm;
+  gemm.SgemmWithBn(m, n, k, 0.9, a, lda, b, ldb, 0.3, c, ldc, relu, scale, bias,
+                   nullptr);
   int eq = 0;
   int neq = 0;
   for (int i = 0; i < m * n; ++i) {

test/net/test_multi_inference_predict.cpp

+/* Copyright (c) 2018 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 <iostream>
+#include <thread>  // NOLINT
+#include "../test_helper.h"
+#include "../test_include.h"
+
+void fun_yolo();
+int fun_mobilenet();
+int main() {
+  paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile2;
+
+  //  fun_yolo();
+  //  fun_mobilenet();
+
+  std::thread t1(fun_yolo);
+  std::thread t2(fun_mobilenet);
+
+  t1.join();
+  t2.join();
+
+  return 0;
+}
+
+void fun_yolo() {
+  paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
+  paddle_mobile.SetThreadNum(4);
+  //  ../../../test/models/googlenet
+  //  ../../../test/models/mobilenet
+  auto time1 = time();
+  if (paddle_mobile.Load(g_yolo, true)) {
+    auto time2 = time();
+    std::cout << "load cost :" << time_diff(time1, time1) << "ms" << std::endl;
+
+    vector<int64_t> dims{1, 3, 227, 227};
+    Tensor input_tensor;
+    SetupTensor<float>(&input_tensor, {1, 3, 227, 227}, static_cast<float>(0),
+                       static_cast<float>(1));
+
+    vector<float> input(input_tensor.data<float>(),
+                        input_tensor.data<float>() + input_tensor.numel());
+
+    auto time3 = time();
+    for (int i = 0; i < 10; ++i) {
+      paddle_mobile.Predict(input, dims);
+    }
+    auto time4 = time();
+    std::cout << "thread 1:   predict cost :" << time_diff(time3, time4) / 10
+              << "ms" << std::endl;
+  }
+}
+
+int fun_mobilenet() {
+  paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
+  paddle_mobile.SetThreadNum(4);
+  auto time1 = time();
+  //  auto isok = paddle_mobile.Load(std::string(g_mobilenet_detect) + "/model",
+  //                     std::string(g_mobilenet_detect) + "/params", true);
+
+  auto isok = paddle_mobile.Load(g_mobilenet, true);
+  if (isok) {
+    auto time2 = time();
+    std::cout << "load cost :" << time_diff(time1, time1) << "ms" << std::endl;
+
+    vector<float> input;
+    vector<int64_t> dims{1, 3, 224, 224};
+    GetInput<float>(g_test_image_1x3x224x224_banana, &input, dims);
+
+    auto vec_result = paddle_mobile.Predict(input, dims);
+    auto biggest = max_element(begin(vec_result), end(vec_result));
+    std::cout << " Max element is " << *biggest << " at position "
+              << distance(begin(vec_result), biggest) << std::endl;
+
+    // 预热十次
+    for (int i = 0; i < 10; ++i) {
+      auto vec_result = paddle_mobile.Predict(input, dims);
+    }
+    auto time3 = time();
+    for (int i = 0; i < 10; ++i) {
+      auto vec_result = paddle_mobile.Predict(input, dims);
+    }
+    DLOG << vec_result;
+    auto time4 = time();
+    std::cout << "thread 2:  predict cost :" << time_diff(time3, time4) / 10
+              << "ms" << std::endl;
+  }
+
+  std::cout << "如果结果Nan请查看: test/images/g_test_image_1x3x224x224_banana "
+               "是否存在?"
+            << std::endl;
+  return 0;
+}