COMPMID-782 Port examples to the new format

Change-Id: Ib178a97c080ff650094d02ee49e2a0aa22376dd0
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/115717
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

1 files changed, 141 insertions, 131 deletions

diff --git a/examples/cl_sgemm.cpp b/examples/cl_sgemm.cpp
index 939870fabf..f2c63985f6 100644
--- a/examples/cl_sgemm.cpp
+++ b/examples/cl_sgemm.cpp
@@ -36,174 +36,184 @@
 using namespace arm_compute;
 using namespace utils;
 
-void main_cl_sgemm(int argc, char **argv)
+class CLSGEMMExample : public Example
 {
-    NPYLoader npy0, npy1, npy2;
-    CLTensor  src0, src1, src2, dst;
-    float     alpha = 1.0f, beta = 0.0f;
-
-    CLTuner tuner;
-    CLScheduler::get().default_init(&tuner);
-
-    std::ifstream stream;
-    if(argc > 1)
+public:
+    void do_setup(int argc, char **argv) override
     {
-        stream.open(argv[1], std::fstream::in);
-    }
+        NPYLoader npy0, npy1, npy2;
+        alpha = 1.0f;
+        beta  = 0.0f;
 
-    if(argc < 3 || (argc < 4 && stream.bad()))
-    {
-        // Print help
-        std::cout << "Usage: 1) ./build/cl_sgemm input_matrix_1.npy input_matrix_2.npy [input_matrix_3.npy] [alpha = 1] [beta = 0]\n";
-        std::cout << "       2) ./build/cl_sgemm M N K [alpha = 1.0f] [beta = 0.0f]\n\n";
-        std::cout << "Too few or no input_matrices provided. Using M=7, N=3, K=5, alpha=1.0f and beta=0.0f\n\n";
-
-        src0.allocator()->init(TensorInfo(TensorShape(5U, 7U), 1, DataType::F32));
-        src1.allocator()->init(TensorInfo(TensorShape(3U, 5U), 1, DataType::F32));
-        src2.allocator()->init(TensorInfo(TensorShape(3U, 7U), 1, DataType::F32));
-    }
-    else
-    {
-        if(stream.good()) /* case file1.npy file2.npy [file3.npy] [alpha = 1.0f] [beta = 0.0f] */
+        CLScheduler::get().default_init(&tuner);
+
+        std::ifstream stream;
+        if(argc > 1)
         {
-            npy0.open(argv[1]);
-            npy0.init_tensor(src0, DataType::F32);
-            npy1.open(argv[2]);
-            npy1.init_tensor(src1, DataType::F32);
+            stream.open(argv[1], std::fstream::in);
+        }
 
-            if(argc > 3)
+        if(argc < 3 || (argc < 4 && stream.bad()))
+        {
+            // Print help
+            std::cout << "Usage: 1) ./build/cl_sgemm input_matrix_1.npy input_matrix_2.npy [input_matrix_3.npy] [alpha = 1] [beta = 0]\n";
+            std::cout << "       2) ./build/cl_sgemm M N K [alpha = 1.0f] [beta = 0.0f]\n\n";
+            std::cout << "Too few or no input_matrices provided. Using M=7, N=3, K=5, alpha=1.0f and beta=0.0f\n\n";
+
+            src0.allocator()->init(TensorInfo(TensorShape(5U, 7U), 1, DataType::F32));
+            src1.allocator()->init(TensorInfo(TensorShape(3U, 5U), 1, DataType::F32));
+            src2.allocator()->init(TensorInfo(TensorShape(3U, 7U), 1, DataType::F32));
+        }
+        else
+        {
+            if(stream.good()) /* case file1.npy file2.npy [file3.npy] [alpha = 1.0f] [beta = 0.0f] */
             {
-                stream.close();
-                stream.clear();
-                stream.open(argv[3], std::fstream::in);
-                if(stream.good()) /* case with third file */
-                {
-                    npy2.open(argv[3]);
-                    npy2.init_tensor(src2, DataType::F32);
+                npy0.open(argv[1]);
+                npy0.init_tensor(src0, DataType::F32);
+                npy1.open(argv[2]);
+                npy1.init_tensor(src1, DataType::F32);
 
-                    if(argc > 4)
+                if(argc > 3)
+                {
+                    stream.close();
+                    stream.clear();
+                    stream.open(argv[3], std::fstream::in);
+                    if(stream.good()) /* case with third file */
                     {
-                        // Convert string to float
-                        alpha = strtof(argv[4], nullptr);
+                        npy2.open(argv[3]);
+                        npy2.init_tensor(src2, DataType::F32);
 
-                        if(argc > 5)
+                        if(argc > 4)
                         {
                             // Convert string to float
-                            beta = strtof(argv[5], nullptr);
+                            alpha = strtof(argv[4], nullptr);
+
+                            if(argc > 5)
+                            {
+                                // Convert string to float
+                                beta = strtof(argv[5], nullptr);
+                            }
                         }
                     }
-                }
-                else /* case without third file */
-                {
-                    alpha = strtof(argv[3], nullptr);
-
-                    if(argc > 4)
+                    else /* case without third file */
                     {
-                        beta = strtof(argv[4], nullptr);
+                        alpha = strtof(argv[3], nullptr);
+
+                        if(argc > 4)
+                        {
+                            beta = strtof(argv[4], nullptr);
+                        }
                     }
                 }
             }
-        }
-        else /* case M N K [alpha = 1.0f] [beta = 0.0f] */
-        {
-            size_t M = strtol(argv[1], nullptr, 10);
-            size_t N = strtol(argv[2], nullptr, 10);
-            size_t K = strtol(argv[3], nullptr, 10);
-
-            src0.allocator()->init(TensorInfo(TensorShape(K, M), 1, DataType::F32));
-            src1.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::F32));
-            src2.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::F32));
-
-            if(argc > 4)
+            else /* case M N K [alpha = 1.0f] [beta = 0.0f] */
             {
-                alpha = strtof(argv[4], nullptr);
+                size_t M = strtol(argv[1], nullptr, 10);
+                size_t N = strtol(argv[2], nullptr, 10);
+                size_t K = strtol(argv[3], nullptr, 10);
+
+                src0.allocator()->init(TensorInfo(TensorShape(K, M), 1, DataType::F32));
+                src1.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::F32));
+                src2.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::F32));
 
-                if(argc > 5)
+                if(argc > 4)
                 {
-                    beta = strtof(argv[5], nullptr);
+                    alpha = strtof(argv[4], nullptr);
+
+                    if(argc > 5)
+                    {
+                        beta = strtof(argv[5], nullptr);
+                    }
                 }
             }
         }
-    }
 
-    init_sgemm_output(dst, src0, src1, DataType::F32);
+        init_sgemm_output(dst, src0, src1, DataType::F32);
 
-    // Configure function
-    CLGEMM sgemm;
-    sgemm.configure(&src0, &src1, (src2.info()->total_size() > 0) ? &src2 : nullptr, &dst, alpha, beta);
+        // Configure function
+        sgemm.configure(&src0, &src1, (src2.info()->total_size() > 0) ? &src2 : nullptr, &dst, alpha, beta);
 
-    // Allocate all the images
-    src0.allocator()->allocate();
-    src1.allocator()->allocate();
-    dst.allocator()->allocate();
+        // Allocate all the images
+        src0.allocator()->allocate();
+        src1.allocator()->allocate();
+        dst.allocator()->allocate();
 
-    // Fill the input images with either the data provided or random data
-    if(npy0.is_open())
-    {
-        npy0.fill_tensor(src0);
-        npy1.fill_tensor(src1);
+        // Fill the input images with either the data provided or random data
+        if(npy0.is_open())
+        {
+            npy0.fill_tensor(src0);
+            npy1.fill_tensor(src1);
+
+            output_filename = "sgemm_out.npy";
+            is_fortran      = npy0.is_fortran();
 
-        if(npy2.is_open())
+            if(npy2.is_open())
+            {
+                src2.allocator()->allocate();
+                npy2.fill_tensor(src2);
+            }
+        }
+        else
         {
             src2.allocator()->allocate();
-            npy2.fill_tensor(src2);
+
+            fill_random_tensor(src0, -1.f, 1.f);
+            fill_random_tensor(src1, -1.f, 1.f);
+            fill_random_tensor(src2, -1.f, 1.f);
         }
-    }
-    else
-    {
-        src2.allocator()->allocate();
 
-        fill_random_tensor(src0, -1.f, 1.f);
-        fill_random_tensor(src1, -1.f, 1.f);
-        fill_random_tensor(src2, -1.f, 1.f);
+        // Dummy run for CLTuner
+        sgemm.run();
     }
-
-    // Dummy run for CLTuner
-    sgemm.run();
-
-    auto start = std::chrono::high_resolution_clock::now();
-
-    // Execute the function
-    sgemm.run();
-
-    // Make sure all the OpenCL jobs are done executing:
-    CLScheduler::get().sync();
-
-    auto stop = std::chrono::high_resolution_clock::now();
-
-    if(!npy0.is_open()) /* If the inputs were not files, print the results */
+    void do_run() override
     {
-        std::cout << "\nMatrix 1:" << std::endl;
-        src0.map(true);
-        src0.print(std::cout, IOFormatInfo());
-        src0.unmap();
-
-        std::cout << "Matrix 2:" << std::endl;
-        src1.map(true);
-        src1.print(std::cout, IOFormatInfo());
-        src1.unmap();
-
-        std::cout << "Matrix 3:" << std::endl;
-        src2.map(true);
-        src2.print(std::cout, IOFormatInfo());
-        src2.unmap();
-
-        std::cout << "Alpha:" << alpha << "\n\n";
-        std::cout << "Beta:" << beta << "\n\n";
-
-        std::cout << "Output Matrix:" << std::endl;
-        dst.map(true);
-        dst.print(std::cout, IOFormatInfo());
-        dst.unmap();
+        // Execute the function
+        sgemm.run();
+
+        // Make sure all the OpenCL jobs are done executing:
+        CLScheduler::get().sync();
     }
-    else /* Save to .npy file */
+    void do_teardown() override
     {
-        save_to_npy(dst, "sgemm_out.npy", npy0.is_fortran());
+        if(output_filename.empty()) /* If the inputs were not files, print the results */
+        {
+            std::cout << "\nMatrix 1:" << std::endl;
+            src0.map(true);
+            src0.print(std::cout, IOFormatInfo());
+            src0.unmap();
+
+            std::cout << "Matrix 2:" << std::endl;
+            src1.map(true);
+            src1.print(std::cout, IOFormatInfo());
+            src1.unmap();
+
+            std::cout << "Matrix 3:" << std::endl;
+            src2.map(true);
+            src2.print(std::cout, IOFormatInfo());
+            src2.unmap();
+
+            std::cout << "Alpha:" << alpha << "\n\n";
+            std::cout << "Beta:" << beta << "\n\n";
+
+            std::cout << "Output Matrix:" << std::endl;
+            dst.map(true);
+            dst.print(std::cout, IOFormatInfo());
+            dst.unmap();
+        }
+        else /* Save to .npy file */
+        {
+            save_to_npy(dst, output_filename, is_fortran);
+        }
     }
 
-    auto delta = std::chrono::duration_cast<std::chrono::microseconds>(stop - start);
-    std::cout << "Time elapsed: " << delta.count() << "us." << std::endl;
-}
+private:
+    CLTensor    src0{}, src1{}, src2{}, dst{};
+    CLGEMM      sgemm{};
+    CLTuner     tuner{};
+    float       alpha{}, beta{};
+    bool        is_fortran{};
+    std::string output_filename{};
+};
 
 /** Main program for sgemm test
  *
@@ -212,5 +222,5 @@ void main_cl_sgemm(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return utils::run_example(argc, argv, main_cl_sgemm);
+    return utils::run_example<CLSGEMMExample>(argc, argv);
 }