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, 152 insertions, 142 deletions

diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp
index 11a80a7bd8..b21f2fe5c4 100644
--- a/examples/graph_squeezenet.cpp
+++ b/examples/graph_squeezenet.cpp
@@ -31,33 +31,13 @@
 #include <cstdlib>
 #include <tuple>
 
+using namespace arm_compute::utils;
 using namespace arm_compute::graph;
 using namespace arm_compute::graph_utils;
 using namespace arm_compute::logging;
 
 namespace
 {
-BranchLayer get_expand_fire_node(const std::string &data_path, std::string &&param_path, unsigned int expand1_filt, unsigned int expand3_filt)
-{
-    std::string total_path = "/cnn_data/squeezenet_v1.0_model/" + param_path + "_";
-    SubGraph    i_a;
-    i_a << ConvolutionLayer(
-            1U, 1U, expand1_filt,
-            get_weights_accessor(data_path, total_path + "expand1x1_w.npy"),
-            get_weights_accessor(data_path, total_path + "expand1x1_b.npy"),
-            PadStrideInfo(1, 1, 0, 0))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-
-    SubGraph i_b;
-    i_b << ConvolutionLayer(
-            3U, 3U, expand3_filt,
-            get_weights_accessor(data_path, total_path + "expand3x3_w.npy"),
-            get_weights_accessor(data_path, total_path + "expand3x3_b.npy"),
-            PadStrideInfo(1, 1, 1, 1))
-        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-
-    return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b));
-}
 } // namespace
 
 /** Example demonstrating how to implement Squeezenet's network using the Compute Library's graph API
@@ -65,136 +45,166 @@ BranchLayer get_expand_fire_node(const std::string &data_path, std::string &&par
  * @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_squeezenet(int argc, char **argv)
+class GraphSqueezenetExample : 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(224U, 224U, 3U, 1U), 1, DataType::F32),
+                        get_input_accessor(image, mean_r, mean_g, mean_b))
+              << ConvolutionLayer(
+                  7U, 7U, 96U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_b.npy"),
+                  PadStrideInfo(2, 2, 0, 0))
+              << convolution_hint
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << ConvolutionLayer(
+                  1U, 1U, 16U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire2", 64U, 64U)
+              << ConvolutionLayer(
+                  1U, 1U, 16U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire3", 64U, 64U)
+              << ConvolutionLayer(
+                  1U, 1U, 32U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire4", 128U, 128U)
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << ConvolutionLayer(
+                  1U, 1U, 32U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire5", 128U, 128U)
+              << ConvolutionLayer(
+                  1U, 1U, 48U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire6", 192U, 192U)
+              << ConvolutionLayer(
+                  1U, 1U, 48U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire7", 192U, 192U)
+              << ConvolutionLayer(
+                  1U, 1U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire8", 256U, 256U)
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << ConvolutionLayer(
+                  1U, 1U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire9", 256U, 256U)
+              << ConvolutionLayer(
+                  1U, 1U, 1000U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
+              << FlattenLayer()
+              << 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;
+private:
+    Graph graph{};
 
-    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, 96U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_b.npy"),
-              PadStrideInfo(2, 2, 0, 0))
-          << convolution_hint
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-          << ConvolutionLayer(
-              1U, 1U, 16U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire2", 64U, 64U)
-          << ConvolutionLayer(
-              1U, 1U, 16U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire3", 64U, 64U)
-          << ConvolutionLayer(
-              1U, 1U, 32U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire4", 128U, 128U)
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-          << ConvolutionLayer(
-              1U, 1U, 32U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire5", 128U, 128U)
-          << ConvolutionLayer(
-              1U, 1U, 48U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire6", 192U, 192U)
-          << ConvolutionLayer(
-              1U, 1U, 48U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire7", 192U, 192U)
-          << ConvolutionLayer(
-              1U, 1U, 64U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire8", 256U, 256U)
-          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
-          << ConvolutionLayer(
-              1U, 1U, 64U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << get_expand_fire_node(data_path, "fire9", 256U, 256U)
-          << ConvolutionLayer(
-              1U, 1U, 1000U,
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy"),
-              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
-          << FlattenLayer()
-          << SoftmaxLayer()
-          << Tensor(get_output_accessor(label, 5));
+    BranchLayer get_expand_fire_node(const std::string &data_path, std::string &&param_path, unsigned int expand1_filt, unsigned int expand3_filt)
+    {
+        std::string total_path = "/cnn_data/squeezenet_v1.0_model/" + param_path + "_";
+        SubGraph    i_a;
+        i_a << ConvolutionLayer(
+                1U, 1U, expand1_filt,
+                get_weights_accessor(data_path, total_path + "expand1x1_w.npy"),
+                get_weights_accessor(data_path, total_path + "expand1x1_b.npy"),
+                PadStrideInfo(1, 1, 0, 0))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
-    graph.run();
-}
+        SubGraph i_b;
+        i_b << ConvolutionLayer(
+                3U, 3U, expand3_filt,
+                get_weights_accessor(data_path, total_path + "expand3x3_w.npy"),
+                get_weights_accessor(data_path, total_path + "expand3x3_b.npy"),
+                PadStrideInfo(1, 1, 1, 1))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b));
+    }
+};
 
 /** Main program for Squeezenet v1.0
  *
@@ -203,5 +213,5 @@ void main_graph_squeezenet(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return arm_compute::utils::run_example(argc, argv, main_graph_squeezenet);
+    return arm_compute::utils::run_example<GraphSqueezenetExample>(argc, argv);
 }