update

acl/src/acl_mat.cpp

@@ -174,7 +174,7 @@ aclMat &aclMat::operator*=(const aclMat &m) {
   vector<aclMat> output_Mat;
   vector<aclDataBuffer *> inputBuffers_;
   vector<aclDataBuffer *> outputBuffers_;
-  aclMat newMat{this->rows, m.cols, this->type(), this->acl_context};
+  aclMat newMat {this->rows, m.cols, this->type(), this->acl_context};
 
   input_Mat.emplace_back(*this);
   input_Mat.emplace_back(m);

acl/src/gemm.cpp

@@ -53,7 +53,7 @@ void Convolution(const aclMat& src, const aclMat& kernel, aclMat& dest,
                  const vector<int64_t>& padsList, int stream_id) {
   vector<aclDataBuffer*> inputBuffers_;
   vector<aclDataBuffer*> outputBuffers_;
-  vector<int64_t> dilationsList{1, 1, 1, 1};
+  vector<int64_t> dilationsList {1, 1, 1, 1};
   string opType = "Conv2D";
   int dest_rows =
       (src.rows + padsList[0] + padsList[1] - (1 * (kernel.rows - 1) + 1)) /
@@ -65,9 +65,9 @@ void Convolution(const aclMat& src, const aclMat& kernel, aclMat& dest,
       1;
   aclMat acl_dest {dest_rows, dest_cols, src.type(), src.acl_context};
 
-  vector<int64_t> shape{1, 1, src.rows, src.cols};
-  vector<int64_t> shape1{1, 1, kernel.rows, kernel.cols};
-  vector<int64_t> shape2{1, 1, acl_dest.rows, acl_dest.cols};
+  vector<int64_t> shape {1, 1, src.rows, src.cols};
+  vector<int64_t> shape1 {1, 1, kernel.rows, kernel.cols};
+  vector<int64_t> shape2 {1, 1, acl_dest.rows, acl_dest.cols};
 
   aclDataType dataType = type_transition(src.depth());
   aclFormat format = ACL_FORMAT_NCHW;

acl/src/matrices.cpp

@@ -105,15 +105,15 @@ void transpose(const aclMat &src, aclMat &dest, int stream_id) {
   OperatorDesc opDesc("Transpose");
   aclDataType dataType = type_transition(src.depth());
 
-  vector<int64_t> inputShape1{1, src.rows, src.cols, src.channels()};
+  vector<int64_t> inputShape1 {1, src.rows, src.cols, src.channels()};
   opDesc.AddInputTensorDesc(dataType, inputShape1.size(), inputShape1.data(),
                             ACL_FORMAT_ND);
 
-  vector<int64_t> inputShape2{4};
+  vector<int64_t> inputShape2 {4};
   opDesc.AddInputTensorDesc(ACL_INT32, inputShape2.size(), inputShape2.data(),
                             ACL_FORMAT_ND);
 
-  vector<int64_t> outputShape{-1, -1, -1, -1};
+  vector<int64_t> outputShape {-1, -1, -1, -1};
   opDesc.AddOutputTensorDesc(dataType, outputShape.size(), outputShape.data(),
                              ACL_FORMAT_ND);
 
@@ -198,12 +198,12 @@ void split(const aclMat &src, vector<aclMat> &mv, int stream_id) {
   aclDataType dataType = type_transition(src.depth());
 
   int cols = src.step / src.elemSize();
-  vector<int64_t> inputShape1{1, src.rows, cols, src.channels()};
+  vector<int64_t> inputShape1 {1, src.rows, cols, src.channels()};
   opDesc.AddInputTensorDesc(dataType, inputShape1.size(), inputShape1.data(),
                             ACL_FORMAT_ND);
 
   for (int i = 0; i < num_split; ++i) {
-    vector<int64_t> outputShape{1, src.rows, cols, 1};
+    vector<int64_t> outputShape {1, src.rows, cols, 1};
     opDesc.AddOutputTensorDesc(dataType, outputShape.size(), outputShape.data(),
                                ACL_FORMAT_ND);
   }
@@ -239,15 +239,15 @@ static void flip_(const aclMat &src, aclMat &dest, int axis, int stream_id) {
   OperatorDesc opDesc("ReverseV2");
   aclDataType dataType = type_transition(src.depth());
 
-  vector<int64_t> inputShape1{1, src.rows, src.cols, src.channels()};
+  vector<int64_t> inputShape1 {1, src.rows, src.cols, src.channels()};
   opDesc.AddInputTensorDesc(dataType, inputShape1.size(), inputShape1.data(),
                             ACL_FORMAT_ND);
 
-  vector<int64_t> inputShape2{1};
+  vector<int64_t> inputShape2 {1};
   opDesc.AddInputTensorDesc(ACL_INT32, inputShape2.size(), inputShape2.data(),
                             ACL_FORMAT_ND);
 
-  vector<int64_t> outputShape{1, dest.rows, dest.cols, dest.channels()};
+  vector<int64_t> outputShape {1, dest.rows, dest.cols, dest.channels()};
   opDesc.AddOutputTensorDesc(dataType, outputShape.size(), outputShape.data(),
                              ACL_FORMAT_ND);
 

acl/src/operator_desc.cpp

@@ -67,11 +67,11 @@ OperatorDesc CreateOpDesc(const string opType, const vector<aclMat>& input_Mat,
   for (i = 0; i < input_Mat.size(); ++i) {
     if (config == TWO_DIMS) {
       int cols = input_Mat[i].step / input_Mat[i].elemSize();
-      vector<int64_t> shape{input_Mat[i].rows, cols};
+      vector<int64_t> shape {input_Mat[i].rows, cols};
       opDesc.AddInputTensorDesc(dataType, shape.size(), shape.data(), format);
     } else if (config == FOUR_DIMS) {
       int cols = input_Mat[i].step / input_Mat[i].elemSize();
-      vector<int64_t> shape{1, input_Mat[i].rows, cols,
+      vector<int64_t> shape {1, input_Mat[i].rows, cols,
                             input_Mat[i].channels()};
       opDesc.AddInputTensorDesc(dataType, shape.size(), shape.data(), format);
     }
@@ -80,11 +80,11 @@ OperatorDesc CreateOpDesc(const string opType, const vector<aclMat>& input_Mat,
   for (i = 0; i < output_Mat.size(); ++i) {
     if (config == TWO_DIMS) {
       int cols = output_Mat[i].step / output_Mat[i].elemSize();
-      vector<int64_t> shape{output_Mat[i].rows, cols};
+      vector<int64_t> shape {output_Mat[i].rows, cols};
       opDesc.AddOutputTensorDesc(dataType, shape.size(), shape.data(), format);
     } else if (config == FOUR_DIMS) {
       int cols = output_Mat[i].step / output_Mat[i].elemSize();
-      vector<int64_t> shape{1, output_Mat[i].rows, cols,
+      vector<int64_t> shape {1, output_Mat[i].rows, cols,
                             output_Mat[i].channels()};
       opDesc.AddOutputTensorDesc(dataType, shape.size(), shape.data(), format);
     }

acl/test/test_acl.cpp

@@ -18,7 +18,7 @@ void PERF_TEST::Test_operator_add_perf(aclCxt *acl_context) {
   constexpr int rand_data_range = 1;
   constexpr int min_format_flag = 128;
 
-  vector<int> type{CV_8UC1, CV_32FC1, CV_32SC1, CV_64FC1};
+  vector<int> type {CV_8UC1, CV_32FC1, CV_32SC1, CV_64FC1};
   for (size_t i = 0; i < type.size(); ++i) {
     test.PrintLog("Perf test : Function: operator+=()", type[i]);
     for (val = start_val; val <= valmax; val *= base) {
@@ -72,7 +72,7 @@ void PERF_TEST::Test_operator_sub_perf(aclCxt *acl_context) {
   constexpr int rand_data_range2 = 32;
   constexpr int min_format_flag = 128;
 
-  vector<int> type{CV_8UC1, CV_32FC1, CV_32SC1, CV_64FC1};
+  vector<int> type {CV_8UC1, CV_32FC1, CV_32SC1, CV_64FC1};
   for (size_t i = 0; i < type.size(); ++i) {
     test.PrintLog("Perf test : Function: operator-=()", type[i]);
     for (val = start_val; val <= valmax; val *= base) {
@@ -124,7 +124,7 @@ void PERF_TEST::Test_operator_div_perf(aclCxt *acl_context) {
   constexpr int s_val4 = 4, s_val8 = 8;
   constexpr int min_format_flag = 128;
 
-  vector<int> type{CV_8UC1, CV_32FC1, CV_32SC1, CV_64FC1};
+  vector<int> type {CV_8UC1, CV_32FC1, CV_32SC1, CV_64FC1};
   for (size_t i = 0; i < type.size(); ++i) {
     test.PrintLog("Perf test : Function: operator/=()", type[i]);
     for (val = start_val; val <= valmax; val *= base) {
@@ -172,7 +172,7 @@ void PERF_TEST::Test_operator_mul_perf(aclCxt *acl_context) {
   constexpr int rand_data_range = 1;
   constexpr int min_format_flag = 128;
 
-  vector<int> type{CV_32FC1};
+  vector<int> type {CV_32FC1};
   for (size_t i = 0; i < type.size(); ++i) {
     for (val = start_val; val <= valmax; val *= base) {
       n = cycle_index;

acl/test/test_common.hpp

@@ -3,10 +3,10 @@
 
 #include "test_precomp.hpp"
 
-typedef enum TestDatatype { INT = 1, FLOAT } TestDatatype;
+using TestDatatype = enum TestDatatype { INT = 1, FLOAT };
 
 class CV_EXPORTS Common_Test {
- public:
+public:
   Common_Test();
   ~Common_Test();
   CV_EXPORTS bool Test_Diff(

acl/test/test_correctness.cpp

@@ -43,7 +43,7 @@
 // arising in any way out of the use of this software, even if advised of the
 // possibility of such damage.
 //
-// M*/
+// M */
 
 #include "test_correctness.hpp"
 

acl/test/test_correctness.hpp

@@ -27,7 +27,7 @@ class CV_EXPORTS AclMat_Test {
   /* test aclMat (const Mat &m, cv::acl::aclCxt* acl_context, aclrtMemMallocPolicy policy
    * = ACL_MEM_MALLOC_HUGE_FIRST) */
   CV_EXPORTS void Test_constructor_MAT(cv::acl::aclCxt *acl_context);
-  /* test upload download*/
+  /* test upload download */
   CV_EXPORTS void Test_DATA_TRANSFER(cv::acl::aclCxt *acl_context);
   /* test upload_2d download_2d */
   CV_EXPORTS void Test_DATA_TRANSFERASYNC(cv::acl::aclCxt *acl_context);

acl/test/test_gemm.cpp

@@ -13,7 +13,7 @@ void PERF_TEST::Test_MatMul(aclCxt *acl_context) {
   int cycle_index = 10;  // 100;
   double begin, end, time, acltime;
   Common_Test test;
-  vector<int> type{CV_32FC1};
+  vector<int> type {CV_32FC1};
   constexpr int base = 2;
   constexpr int start_val = 8;
   constexpr int rand_data_range = 32;
@@ -68,7 +68,7 @@ void PERF_TEST::Test_Convolution(aclCxt *acl_context) {
   int cycle_index = 10;
   double begin, end, time, acltime;
   Common_Test test;
-  vector<int> type{CV_32FC1};
+  vector<int> type {CV_32FC1};
   constexpr int base = 2;
   constexpr int start_val = 8;
   constexpr int min_format_flag = 128;
@@ -78,9 +78,9 @@ void PERF_TEST::Test_Convolution(aclCxt *acl_context) {
 
   for (size_t i = 0; i < type.size(); ++i) {
     for (val = start_val; val <= valmax; val *= base) {
-      Mat mat_src(val, val, type[i], Scalar{s_val1, s_val2});
-      Mat mat_kernel(kernel_val, kernel_val, type[i], Scalar(s_val1, s_val4));
-      Mat mat_dest(val, val, type[i], Scalar{s_val6});
+      Mat mat_src(val, val, type[i], Scalar {s_val1, s_val2});
+      Mat mat_kernel(kernel_val, kernel_val, type[i], Scalar (s_val1, s_val4));
+      Mat mat_dest(val, val, type[i], Scalar {s_val6});
 
       aclMat aclmat_src(val, val, type[i], mat_src.data, acl_context);
       aclMat aclmat_kernel(kernel_val, kernel_val, type[i], mat_kernel.data, acl_context);
@@ -92,8 +92,8 @@ void PERF_TEST::Test_Convolution(aclCxt *acl_context) {
       end = static_cast<double>(getTickCount());
       time = (end - begin) / getTickFrequency() / cycle_index;
 
-      vector<int64_t> strides{1, 1, 1, 1};
-      vector<int64_t> pads{1, 1, 1, 1};
+      vector<int64_t> strides {1, 1, 1, 1};
+      vector<int64_t> pads {1, 1, 1, 1};
       n = (cycle_index - 1);
       Convolution(aclmat_src, aclmat_kernel, aclmat_dest, strides, pads, 0);
       wait_stream(acl_context, 0);

acl/test/test_mathfuncs.cpp

@@ -18,14 +18,14 @@ void PERF_TEST::Test_Abs(aclCxt *acl_context) {
   constexpr int start_val = 8;
   constexpr int min_format_flag = 128;
 
-  vector<int> type{CV_32FC1, CV_32SC1};
+  vector<int> type {CV_32FC1, CV_32SC1};
   for (size_t i = 0; i < type.size(); ++i) {
     test.PrintLog("Perf test : Function: Abs()", type[i]);
     for (val = start_val; val <= valmax; val *= base) {
       n = cycle_index;
-      Mat mat_src(val, val, type[i], Scalar{s_val_2});
-      Mat mat_dest(val, val, type[i], Scalar{s_val_4});
-      Mat mat_dest1(val, val, type[i], Scalar{s_val_6});
+      Mat mat_src(val, val, type[i], Scalar {s_val_2});
+      Mat mat_dest(val, val, type[i], Scalar {s_val_4});
+      Mat mat_dest1(val, val, type[i], Scalar {s_val_6});
 
       aclMat aclmat_src(val, val, type[i], mat_src.data, acl_context);
       aclMat aclmat_dest(val, val, type[i], mat_dest.data, acl_context);
@@ -68,7 +68,7 @@ void PERF_TEST::Test_Pow(aclCxt *acl_context) {
   constexpr int rand_data_range = 32;
   constexpr int min_format_flag = 128;
 
-  vector<int> type{CV_8UC1, CV_32FC1, CV_32SC1};
+  vector<int> type {CV_8UC1, CV_32FC1, CV_32SC1};
   for (size_t i = 0; i < type.size(); ++i) {
     test.PrintLog("Perf test : Function: Pow()", type[i]);
     for (val = start_val; val <= valmax; val *= base) {
@@ -119,7 +119,7 @@ void PERF_TEST::Test_Sqrt(aclCxt *acl_context) {
   constexpr int min_format_flag = 128;
   constexpr int rand_data_range = 32;
 
-  vector<int> type{CV_32FC1, CV_64FC1};
+  vector<int> type {CV_32FC1, CV_64FC1};
   for (size_t i = 0; i < type.size(); ++i) {
     for (val = start_val; val <= valmax; val *= base) {
       n = cycle_index;
@@ -169,7 +169,7 @@ void PERF_TEST::Test_Add(aclCxt *acl_context) {
   constexpr int min_format_flag = 128;
   constexpr int rand_data_range = 32;
 
-  vector<int> type{CV_8UC1, CV_32FC1, CV_32SC1, CV_64FC1};
+  vector<int> type {CV_8UC1, CV_32FC1, CV_32SC1, CV_64FC1};
   for (size_t i = 0; i < type.size(); ++i) {
     for (val = start_val; val <= valmax; val *= base) {
       n = cycle_index;
@@ -225,7 +225,7 @@ void PERF_TEST::Test_Divide(aclCxt *acl_context) {
   constexpr int rand_data_range1 = 32;
   constexpr int rand_data_range2 = 4;
 
-  vector<int> type{CV_8UC1, CV_32FC1, CV_32SC1};
+  vector<int> type {CV_8UC1, CV_32FC1, CV_32SC1};
   for (size_t i = 0; i < type.size(); ++i) {
     for (val = start_val; val <= valmax; val *= base) {
       n = cycle_index;
@@ -279,7 +279,7 @@ void PERF_TEST::Test_Exp(aclCxt *acl_context) {
   constexpr int rand_data_range1 = 32;
   constexpr int rand_data_range2 = 2;
 
-  vector<int> type{CV_32FC1, CV_64FC1};
+  vector<int> type {CV_32FC1, CV_64FC1};
   for (size_t i = 0; i < type.size(); ++i) {
     for (val = start_val; val <= valmax; val *= base) {
       n = cycle_index;
@@ -329,7 +329,7 @@ void PERF_TEST::Test_Log(aclCxt *acl_context) {
   constexpr int min_format_flag = 128;
   constexpr int rand_data_range = 32;
 
-  vector<int> type{CV_32FC1, CV_64FC1};
+  vector<int> type {CV_32FC1, CV_64FC1};
   for (size_t i = 0; i < type.size(); ++i) {
     for (val = start_val; val <= valmax; val *= base) {
       n = cycle_index;
@@ -379,7 +379,7 @@ void PERF_TEST::Test_Max(aclCxt *acl_context) {
   constexpr int min_format_flag = 128;
   constexpr int rand_data_range = 32;
 
-  vector<int> type{CV_32FC2, CV_32SC2, CV_64FC2};
+  vector<int> type {CV_32FC2, CV_32SC2, CV_64FC2};
   for (size_t i = 0; i < type.size(); ++i) {
     for (val = start_val; val <= valmax; val *= base) {
       n = cycle_index;
@@ -434,7 +434,7 @@ void PERF_TEST::Test_Min(aclCxt *acl_context) {
   constexpr int min_format_flag = 128;
   constexpr int rand_data_range = 32;
 
-  vector<int> type{CV_32FC3, CV_32SC3, CV_64FC3};
+  vector<int> type {CV_32FC3, CV_32SC3, CV_64FC3};
   for (size_t i = 0; i < type.size(); ++i) {
     for (val = start_val; val <= valmax; val *= base) {
       n = cycle_index;

acl/test/test_matrices.cpp

@@ -19,9 +19,9 @@ void PERF_TEST::Test_Merge(aclCxt *acl_context) {
   constexpr int s_val1 = 1, s_val2 = 2, s_val3 = 3;
   constexpr int min_format_flag = 128;
 
-  vector<int> srcType{CV_8UC1, CV_32FC1, CV_32SC1};
+  vector<int> srcType {CV_8UC1, CV_32FC1, CV_32SC1};
 
-  vector<int> destType{CV_8UC3, CV_32FC3, CV_32SC3};
+  vector<int> destType {CV_8UC3, CV_32FC3, CV_32SC3};
   for (size_t i = 0; i < srcType.size(); ++i) {
     test.PrintLog("Perf test : Function: merge()", srcType[i]);
     for (val = start_val; val <= valmax; val *= base) {
@@ -88,7 +88,7 @@ void PERF_TEST::Test_Transpose(aclCxt *acl_context) {
   constexpr int rand_data_range = 32;
   constexpr int min_format_flag = 128;
 
-  vector<int> type{CV_32FC1, CV_32SC1};
+  vector<int> type {CV_32FC1, CV_32SC1};
   for (size_t i = 0; i < type.size(); ++i) {
     test.PrintLog("Perf test : Function: transpose()", type[i]);
     for (val = start_val; val <= valmax; val *= base) {
@@ -141,8 +141,8 @@ void PERF_TEST::Test_Split(aclCxt *acl_context) {
   constexpr int min_format_flag = 128;
   constexpr int index0 = 0, index1 = 1, index2 = 2;
 
-  vector<int> srcType{CV_8UC3, CV_32FC3, CV_32SC3};
-  vector<int> destType{CV_8UC1, CV_32FC1, CV_32SC1};
+  vector<int> srcType {CV_8UC3, CV_32FC3, CV_32SC3};
+  vector<int> destType {CV_8UC1, CV_32FC1, CV_32SC1};
   for (size_t i = 0; i < srcType.size(); ++i) {
     test.PrintLog("Perf test : Function: split()", srcType[i]);
     for (val = start_val; val <= valmax; val *= base) {
@@ -212,7 +212,7 @@ void PERF_TEST::Test_Flip(aclCxt *acl_context) {
   constexpr int rand_data_range = 32;
   constexpr int min_format_flag = 128;
 
-  vector<int> type{CV_8UC1, CV_32FC1, CV_32SC1, CV_64FC1};
+  vector<int> type {CV_8UC1, CV_32FC1, CV_32SC1, CV_64FC1};
   for (size_t i = 0; i < type.size(); ++i) {
     test.PrintLog("Perf test : Function: flip()", type[i]);
     for (val = start_val; val <= valmax; val *= base) {

acl/test/test_perf.hpp

@@ -4,7 +4,7 @@
 #include "test_precomp.hpp"
 
 class PERF_TEST {
- public:
+public:
   CV_EXPORTS void Test_operator_add_perf(cv::acl::aclCxt *acl_context);
   CV_EXPORTS void Test_operator_sub_perf(cv::acl::aclCxt *acl_context);
   CV_EXPORTS void Test_operator_div_perf(cv::acl::aclCxt *acl_context);

samples/example.cpp

@@ -1,126 +1,122 @@
+#include <iostream>
+
+#include "acl/acl.h"
+#include "opencv2/acl/acl.hpp"
 #include "opencv2/core.hpp"
+#include "opencv2/highgui.hpp"
 #include "opencv2/imgcodecs.hpp"
 #include "opencv2/imgproc.hpp"
-#include "opencv2/highgui.hpp"
-#include "opencv2/acl/acl.hpp"
-#include "acl/acl.h"
-#include <iostream>
 
 using namespace cv;
 using namespace cv::acl;
 using namespace std;
 
 /**
- * @brief A simple example of the split and merge functions,Using a different Stream
- * 
+ * @brief A simple example of the split and merge functions, Using a different
+ * Stream
  */
 int test_func1(aclCxt *acl_context_0) {
-    Mat src = imread("../cat1.jpg");
-    if (src.empty()) {
-        cerr << "could not image !" << endl;
-        return -1;
-    }
+  Mat src = imread("../cat1.jpg");
+  if (src.empty()) {
+    cerr << "could not image !" << endl;
+    return -1;
+  }
 
-    // 上传数据到aclMat对象中
-    aclMat acl_src(src, acl_context_0); 
-    aclMat acl_dest1;
-    aclMat acl_dest2;
-    aclMat acl_dest3;
+  // 上传数据到aclMat对象中
+  aclMat acl_src (src, acl_context_0);
+  aclMat acl_dest1;
+  aclMat acl_dest2;
+  aclMat acl_dest3;
 
-    vector<aclMat> mv;
-    mv.emplace_back(acl_dest1);
-    mv.emplace_back(acl_dest2);
-    mv.emplace_back(acl_dest3);
+  vector<aclMat> mv;
+  mv.emplace_back(acl_dest1);
+  mv.emplace_back(acl_dest2);
+  mv.emplace_back(acl_dest3);
 
-    imshow("src", src);
-    split(acl_src, mv, 0);
-    wait_stream(acl_context_0, 0);
+  imshow("src", src);
+  split(acl_src, mv, 0);
+  wait_stream(acl_context_0, 0);
 
-    //下载aclMat数据到Mat类中 
-    Mat dest1 = mv.data()[0].operator cv::Mat();
-    Mat dest2 = mv.data()[1].operator cv::Mat();
-    Mat dest3 = mv.data()[2].operator cv::Mat();
+  // 下载aclMat数据到Mat类中
+  Mat dest1 = mv.data()[0].operator cv::Mat();
+  Mat dest2 = mv.data()[1].operator cv::Mat();
+  Mat dest3 = mv.data()[2].operator cv::Mat();
 
-    imshow("dest1", dest1);
-    imshow("dest2", dest2);
-    imshow("dest3", dest3);
+  imshow("dest1", dest1);
+  imshow("dest2", dest2);
+  imshow("dest3", dest3);
 
-    aclMat acl_imgdest; 
+  aclMat acl_imgdest;
 
-    merge(mv, acl_imgdest, 1);
-    wait_stream(acl_context_0, 1);
-    Mat imgdest = acl_imgdest.operator cv::Mat(); 
-    imshow("imgdest", imgdest);
+  merge(mv, acl_imgdest, 1);
+  wait_stream(acl_context_0, 1);
+  Mat imgdest = acl_imgdest.operator cv::Mat();
+  imshow("imgdest", imgdest);
 }
 
-
 /**
- * @brief A demo of use a stream to synchronize multiple functions 
- * 
+ * @brief A demo of use a stream to synchronize multiple functions
  */
 int test_func2(aclCxt *acl_context_0) {
-    Mat src = imread("../cat1.jpg");
-    if (src.empty()) {
-        cerr << "src could not image !" << endl;
-        return -1;
-    }
+  Mat src = imread("../cat1.jpg");
+  if (src.empty()) {
+    cerr << "src could not image !" << endl;
+    return -1;
+  }
 
-    Mat src1 = imread("../cat1.jpg");
-    if (src1.empty()) {
-        cerr << "src1 could not image !" << endl;
-        return -1;
-    }
+  Mat src1 = imread("../cat1.jpg");
+  if (src1.empty()) {
+    cerr << "src1 could not image !" << endl;
+    return -1;
+  }
 
+  // 待split操作数据上传数据到aclMat对象中
+  aclMat acl_src(src, acl_context_0);
+  aclMat acl_dest1;
+  aclMat acl_dest2;
+  aclMat acl_dest3;
+  vector<aclMat> mv;
+  mv.emplace_back(acl_dest1);
+  mv.emplace_back(acl_dest2);
+  mv.emplace_back(acl_dest3);
 
-    // 待split操作数据上传数据到aclMat对象中
-    aclMat acl_src(src, acl_context_0); 
-    aclMat acl_dest1;
-    aclMat acl_dest2;
-    aclMat acl_dest3;
-    vector<aclMat> mv;
-    mv.emplace_back(acl_dest1);
-    mv.emplace_back(acl_dest2);
-    mv.emplace_back(acl_dest3);
+  // 待flip数据上传到aclMat对象中
+  aclMat acl_flip_src(src1, acl_context_0);
+  aclMat acl_flip_dest(src1.rows, src1.cols, src1.type(), acl_context_0);
 
-    // 待flip数据上传到aclMat对象中
-    aclMat acl_flip_src(src1, acl_context_0);
-    aclMat acl_flip_dest(src1.rows, src1.cols, src1.type(), acl_context_0);
+  imshow("src", src);
+  imshow("src1", src1);
 
-    imshow("src", src);
-    imshow("src1", src1);
+  // 将函数挂载到1号stream上
+  split(acl_src, mv, 1);
+  flip(acl_flip_src, acl_flip_dest, 0, 1);
 
-    // 将函数挂载到1号stream上
-    split(acl_src, mv, 1);
-    flip(acl_flip_src, acl_flip_dest, 0, 1);
+  // 等待1号stream中的任务执行完毕,stream内部按序执行任务
+  wait_stream(acl_context_0, 1);
 
-    // 等待1号stream中的任务执行完毕,stream内部按序执行任务
-    wait_stream(acl_context_0, 1);
+  // 下载split操作完成的数据
+  Mat dest1 = mv.data()[0].operator cv::Mat();
+  Mat dest2 = mv.data()[1].operator cv::Mat();
+  Mat dest3 = mv.data()[2].operator cv::Mat();
 
-    // 下载split操作完成的数据
-    Mat dest1 = mv.data()[0].operator cv::Mat();
-    Mat dest2 = mv.data()[1].operator cv::Mat();
-    Mat dest3 = mv.data()[2].operator cv::Mat();
+  Mat flip_dest = acl_flip_dest.operator cv::Mat();
 
-    Mat flip_dest = acl_flip_dest.operator cv::Mat();
+  imshow("split_dest1", dest1);
+  imshow("split_dest2", dest2);
+  imshow("split_dest3", dest3);
 
-    imshow("split_dest1", dest1);
-    imshow("split_dest2", dest2);
-    imshow("split_dest3", dest3);
-
-    imshow("flip_dest", flip_dest);
+  imshow("flip_dest", flip_dest);
 }
 
-int main()
-{
-    // 初始化
-    aclCxt *acl_context_0 = set_device("../acl.json", 1, 2);
+int main() {
+  // 初始化
+  aclCxt *acl_context_0 = set_device("../acl.json", 1, 2);
 
-    test_func2(acl_context_0);
+  test_func2(acl_context_0);
 
-    // 去初始化
-    release_device(acl_context_0);    
-	waitKey(0);
+  // 去初始化
+  release_device (acl_context_0);
+  waitKey(0);
 
-    return 0;
+  return 0;
 }
-