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, 149 insertions, 143 deletions

diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp
index 746d558389..1e9601b492 100644
--- a/examples/graph_googlenet.cpp
+++ b/examples/graph_googlenet.cpp
@@ -31,167 +31,173 @@
 #include <cstdlib>
 #include <tuple>
 
+using namespace arm_compute::utils;
 using namespace arm_compute::graph;
 using namespace arm_compute::graph_utils;
 
-namespace
-{
-BranchLayer get_inception_node(const std::string &data_path, std::string &&param_path,
-                               unsigned int a_filt,
-                               std::tuple<unsigned int, unsigned int> b_filters,
-                               std::tuple<unsigned int, unsigned int> c_filters,
-                               unsigned int d_filt)
-{
-    std::string total_path = "/cnn_data/googlenet_model/" + param_path + "/" + param_path + "_";
-    SubGraph    i_a;
-    i_a << ConvolutionLayer(
-            1U, 1U, a_filt,
-            get_weights_accessor(data_path, total_path + "1x1_w.npy"),
-            get_weights_accessor(data_path, total_path + "1x1_b.npy"),
-            PadStrideInfo(1, 1, 0, 0))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-
-    SubGraph i_b;
-    i_b << ConvolutionLayer(
-            1U, 1U, std::get<0>(b_filters),
-            get_weights_accessor(data_path, total_path + "3x3_reduce_w.npy"),
-            get_weights_accessor(data_path, total_path + "3x3_reduce_b.npy"),
-            PadStrideInfo(1, 1, 0, 0))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-        << ConvolutionLayer(
-            3U, 3U, std::get<1>(b_filters),
-            get_weights_accessor(data_path, total_path + "3x3_w.npy"),
-            get_weights_accessor(data_path, total_path + "3x3_b.npy"),
-            PadStrideInfo(1, 1, 1, 1))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-
-    SubGraph i_c;
-    i_c << ConvolutionLayer(
-            1U, 1U, std::get<0>(c_filters),
-            get_weights_accessor(data_path, total_path + "5x5_reduce_w.npy"),
-            get_weights_accessor(data_path, total_path + "5x5_reduce_b.npy"),
-            PadStrideInfo(1, 1, 0, 0))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-        << ConvolutionLayer(
-            5U, 5U, std::get<1>(c_filters),
-            get_weights_accessor(data_path, total_path + "5x5_w.npy"),
-            get_weights_accessor(data_path, total_path + "5x5_b.npy"),
-            PadStrideInfo(1, 1, 2, 2))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-
-    SubGraph i_d;
-    i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)))
-        << ConvolutionLayer(
-            1U, 1U, d_filt,
-            get_weights_accessor(data_path, total_path + "pool_proj_w.npy"),
-            get_weights_accessor(data_path, total_path + "pool_proj_b.npy"),
-            PadStrideInfo(1, 1, 0, 0))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-
-    return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
-}
-} // namespace
-
 /** Example demonstrating how to implement Googlenet's network using the Compute Library's graph API
  *
  * @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_googlenet(int argc, char **argv)
+class GraphGooglenetExample : 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";
+        // 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(224U, 224U, 3U, 1U), 1, DataType::F32),
+                        get_input_accessor(image, mean_r, mean_g, mean_b))
+              << ConvolutionLayer(
+                  7U, 7U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"),
+                  PadStrideInfo(2, 2, 3, 3))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
+              << convolution_hint
+              << ConvolutionLayer(
+                  1U, 1U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << ConvolutionLayer(
+                  3U, 3U, 192U,
+                  get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1))
+              << 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, DimensionRoundingType::CEIL)))
+              << get_inception_node(data_path, "inception_3a", 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U)
+              << get_inception_node(data_path, "inception_3b", 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U)
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << get_inception_node(data_path, "inception_4a", 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U)
+              << get_inception_node(data_path, "inception_4b", 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U)
+              << get_inception_node(data_path, "inception_4c", 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U)
+              << get_inception_node(data_path, "inception_4d", 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U)
+              << get_inception_node(data_path, "inception_4e", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U)
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << get_inception_node(data_path, "inception_5a", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U)
+              << get_inception_node(data_path, "inception_5b", 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U)
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
+              << FullyConnectedLayer(
+                  1000U,
+                  get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_b.npy"))
+              << SoftmaxLayer()
+              << Tensor(get_output_accessor(label, 5));
     }
-    else if(argc == 4)
+    void do_run() override
     {
-        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";
+        // Run graph
+        graph.run();
     }
-    else
+
+private:
+    Graph graph{};
+
+    BranchLayer get_inception_node(const std::string &data_path, std::string &&param_path,
+                                   unsigned int a_filt,
+                                   std::tuple<unsigned int, unsigned int> b_filters,
+                                   std::tuple<unsigned int, unsigned int> c_filters,
+                                   unsigned int d_filt)
     {
-        data_path = argv[2];
-        image     = argv[3];
-        label     = argv[4];
-    }
+        std::string total_path = "/cnn_data/googlenet_model/" + param_path + "/" + param_path + "_";
+        SubGraph    i_a;
+        i_a << ConvolutionLayer(
+                1U, 1U, a_filt,
+                get_weights_accessor(data_path, total_path + "1x1_w.npy"),
+                get_weights_accessor(data_path, total_path + "1x1_b.npy"),
+                PadStrideInfo(1, 1, 0, 0))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
-    Graph graph;
+        SubGraph i_b;
+        i_b << ConvolutionLayer(
+                1U, 1U, std::get<0>(b_filters),
+                get_weights_accessor(data_path, total_path + "3x3_reduce_w.npy"),
+                get_weights_accessor(data_path, total_path + "3x3_reduce_b.npy"),
+                PadStrideInfo(1, 1, 0, 0))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                3U, 3U, std::get<1>(b_filters),
+                get_weights_accessor(data_path, total_path + "3x3_w.npy"),
+                get_weights_accessor(data_path, total_path + "3x3_b.npy"),
+                PadStrideInfo(1, 1, 1, 1))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
-    graph << target_hint
-          << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
-                    get_input_accessor(image, mean_r, mean_g, mean_b))
-          << ConvolutionLayer(
-              7U, 7U, 64U,
-              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"),
-              PadStrideInfo(2, 2, 3, 3))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-          << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
-          << convolution_hint
-          << ConvolutionLayer(
-              1U, 1U, 64U,
-              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << ConvolutionLayer(
-              3U, 3U, 192U,
-              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"),
-              PadStrideInfo(1, 1, 1, 1))
-          << 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, DimensionRoundingType::CEIL)))
-          << get_inception_node(data_path, "inception_3a", 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U)
-          << get_inception_node(data_path, "inception_3b", 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U)
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-          << get_inception_node(data_path, "inception_4a", 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U)
-          << get_inception_node(data_path, "inception_4b", 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U)
-          << get_inception_node(data_path, "inception_4c", 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U)
-          << get_inception_node(data_path, "inception_4d", 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U)
-          << get_inception_node(data_path, "inception_4e", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U)
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-          << get_inception_node(data_path, "inception_5a", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U)
-          << get_inception_node(data_path, "inception_5b", 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U)
-          << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
-          << FullyConnectedLayer(
-              1000U,
-              get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_b.npy"))
-          << SoftmaxLayer()
-          << Tensor(get_output_accessor(label, 5));
+        SubGraph i_c;
+        i_c << ConvolutionLayer(
+                1U, 1U, std::get<0>(c_filters),
+                get_weights_accessor(data_path, total_path + "5x5_reduce_w.npy"),
+                get_weights_accessor(data_path, total_path + "5x5_reduce_b.npy"),
+                PadStrideInfo(1, 1, 0, 0))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                5U, 5U, std::get<1>(c_filters),
+                get_weights_accessor(data_path, total_path + "5x5_w.npy"),
+                get_weights_accessor(data_path, total_path + "5x5_b.npy"),
+                PadStrideInfo(1, 1, 2, 2))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
-    graph.run();
-}
+        SubGraph i_d;
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)))
+            << ConvolutionLayer(
+                1U, 1U, d_filt,
+                get_weights_accessor(data_path, total_path + "pool_proj_w.npy"),
+                get_weights_accessor(data_path, total_path + "pool_proj_b.npy"),
+                PadStrideInfo(1, 1, 0, 0))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
+    }
+};
 
 /** Main program for Googlenet
  *
@@ -200,5 +206,5 @@ void main_graph_googlenet(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return arm_compute::utils::run_example(argc, argv, main_graph_googlenet);
+    return arm_compute::utils::run_example<GraphGooglenetExample>(argc, argv);
 }