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, 117 insertions, 109 deletions

diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index 6423fe48d3..8705c8ed1e 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -31,6 +31,7 @@
 #include <iostream>
 #include <memory>
 
+using namespace arm_compute::utils;
 using namespace arm_compute::graph;
 using namespace arm_compute::graph_utils;
 
@@ -39,122 +40,129 @@ using namespace arm_compute::graph_utils;
  * @param[in] argc Number of arguments
  * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
  */
-void main_graph_alexnet(int argc, char **argv)
+class GraphAlexnetExample : public Example
 {
-    std::string data_path; /* Path to the trainable data */
-    std::string image;     /* Image data */
-    std::string label;     /* Label data */
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        std::string data_path; /* Path to the trainable data */
+        std::string image;     /* Image data */
+        std::string label;     /* Label data */
 
-    constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
-    constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
-    constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
+        constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
+        constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
+        constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
 
-    // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
-    TargetHint            target_hint      = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
-    ConvolutionMethodHint convolution_hint = target_hint == TargetHint::NEON ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT;
+        // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
+        TargetHint            target_hint      = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
+        ConvolutionMethodHint convolution_hint = target_hint == TargetHint::NEON ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT;
 
-    // Parse arguments
-    if(argc < 2)
-    {
-        // Print help
-        std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
-        std::cout << "No data folder provided: using random values\n\n";
-    }
-    else if(argc == 2)
-    {
-        std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n";
-        std::cout << "No data folder provided: using random values\n\n";
-    }
-    else if(argc == 3)
-    {
-        data_path = argv[2];
-        std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n";
-        std::cout << "No image provided: using random values\n\n";
-    }
-    else if(argc == 4)
-    {
-        data_path = argv[2];
-        image     = argv[3];
-        std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
-        std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        // Parse arguments
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
+            std::cout << "No data folder provided: using random values\n\n";
+        }
+        else if(argc == 2)
+        {
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n";
+            std::cout << "No data folder provided: using random values\n\n";
+        }
+        else if(argc == 3)
+        {
+            data_path = argv[2];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n";
+            std::cout << "No image provided: using random values\n\n";
+        }
+        else if(argc == 4)
+        {
+            data_path = argv[2];
+            image     = argv[3];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
+            std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        }
+        else
+        {
+            data_path = argv[2];
+            image     = argv[3];
+            label     = argv[4];
+        }
+
+        graph << target_hint
+              << Tensor(TensorInfo(TensorShape(227U, 227U, 3U, 1U), 1, DataType::F32),
+                        get_input_accessor(image, mean_r, mean_g, mean_b))
+              // Layer 1
+              << ConvolutionLayer(
+                  11U, 11U, 96U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"),
+                  PadStrideInfo(4, 4, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 2
+              << convolution_hint
+              << ConvolutionLayer(
+                  5U, 5U, 256U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"),
+                  PadStrideInfo(1, 1, 2, 2), 2)
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 3
+              << ConvolutionLayer(
+                  3U, 3U, 384U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 4
+              << ConvolutionLayer(
+                  3U, 3U, 384U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1), 2)
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 5
+              << ConvolutionLayer(
+                  3U, 3U, 256U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1), 2)
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
+              // Layer 6
+              << FullyConnectedLayer(
+                  4096U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy"))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 7
+              << FullyConnectedLayer(
+                  4096U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy"))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              // Layer 8
+              << FullyConnectedLayer(
+                  1000U,
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
+              // Softmax
+              << SoftmaxLayer()
+              << Tensor(get_output_accessor(label, 5));
     }
-    else
+    void do_run() override
     {
-        data_path = argv[2];
-        image     = argv[3];
-        label     = argv[4];
+        // Run graph
+        graph.run();
     }
 
-    Graph graph;
-
-    graph << target_hint
-          << Tensor(TensorInfo(TensorShape(227U, 227U, 3U, 1U), 1, DataType::F32),
-                    get_input_accessor(image, mean_r, mean_g, mean_b))
-          // Layer 1
-          << ConvolutionLayer(
-              11U, 11U, 96U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"),
-              PadStrideInfo(4, 4, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 2
-          << convolution_hint
-          << ConvolutionLayer(
-              5U, 5U, 256U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"),
-              PadStrideInfo(1, 1, 2, 2), 2)
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 3
-          << ConvolutionLayer(
-              3U, 3U, 384U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 4
-          << ConvolutionLayer(
-              3U, 3U, 384U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"),
-              PadStrideInfo(1, 1, 1, 1), 2)
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 5
-          << ConvolutionLayer(
-              3U, 3U, 256U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"),
-              PadStrideInfo(1, 1, 1, 1), 2)
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
-          // Layer 6
-          << FullyConnectedLayer(
-              4096U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy"))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 7
-          << FullyConnectedLayer(
-              4096U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy"))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          // Layer 8
-          << FullyConnectedLayer(
-              1000U,
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
-          // Softmax
-          << SoftmaxLayer()
-          << Tensor(get_output_accessor(label, 5));
-
-    // Run graph
-    graph.run();
-}
+private:
+    Graph graph{};
+};
 
 /** Main program for AlexNet
  *
@@ -163,5 +171,5 @@ void main_graph_alexnet(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return arm_compute::utils::run_example(argc, argv, main_graph_alexnet);
+    return arm_compute::utils::run_example<GraphAlexnetExample>(argc, argv);
 }