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, 125 insertions, 119 deletions

diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp
index c468093e56..193e5c336e 100644
--- a/examples/graph_mobilenet.cpp
+++ b/examples/graph_mobilenet.cpp
@@ -29,142 +29,148 @@
 
 #include <cstdlib>
 
+using namespace arm_compute::utils;
 using namespace arm_compute::graph;
 using namespace arm_compute::graph_utils;
 
-namespace
-{
-BranchLayer get_dwsc_node(const std::string &data_path, std::string &&param_path,
-                          unsigned int  conv_filt,
-                          PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info)
-{
-    std::string total_path = "/cnn_data/mobilenet_v1_model/" + param_path + "_";
-    SubGraph    sg;
-    sg << DepthwiseConvolutionLayer(
-           3U, 3U,
-           get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"),
-           std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-           dwc_pad_stride_info,
-           true)
-       << BatchNormalizationLayer(
-           get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"),
-           get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"),
-           get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"),
-           get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"),
-           0.001f)
-       << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
-       << ConvolutionLayer(
-           1U, 1U, conv_filt,
-           get_weights_accessor(data_path, total_path + "pointwise_weights.npy"),
-           std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-           conv_pad_stride_info)
-       << BatchNormalizationLayer(
-           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"),
-           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"),
-           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"),
-           get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"),
-           0.001f)
-       << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
-
-    return BranchLayer(std::move(sg));
-}
-} // namespace
-
 /** Example demonstrating how to implement MobileNet'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_mobilenet(int argc, char **argv)
+class GraphMobilenetExample : 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))
+              << convolution_hint
+              << ConvolutionLayer(
+                  3U, 3U, 32U,
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_weights.npy"),
+                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                  PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))
+              << BatchNormalizationLayer(
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_moving_mean.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_moving_variance.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_beta.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_gamma.npy"),
+                  0.001f)
+
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+              << get_dwsc_node(data_path, "Conv2d_1", 64, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_2", 128, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_3", 128, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_4", 256, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_5", 256, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_6", 512, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_7", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_8", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_9", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_10", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_11", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_12", 1024, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << get_dwsc_node(data_path, "Conv2d_13", 1024, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
+              << ConvolutionLayer(
+                  1U, 1U, 1001U,
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Logits_Conv2d_1c_1x1_weights.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Logits_Conv2d_1c_1x1_biases.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ReshapeLayer(TensorShape(1001U))
+              << 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))
-          << convolution_hint
-          << ConvolutionLayer(
-              3U, 3U, 32U,
-              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_weights.npy"),
-              std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-              PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))
-          << BatchNormalizationLayer(
-              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_moving_mean.npy"),
-              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_moving_variance.npy"),
-              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_beta.npy"),
-              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_gamma.npy"),
-              0.001f)
-
-          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
-          << get_dwsc_node(data_path, "Conv2d_1", 64, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_2", 128, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_3", 128, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_4", 256, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_5", 256, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_6", 512, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_7", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_8", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_9", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_10", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_11", 512, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_12", 1024, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << get_dwsc_node(data_path, "Conv2d_13", 1024, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0))
-          << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
-          << ConvolutionLayer(
-              1U, 1U, 1001U,
-              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Logits_Conv2d_1c_1x1_weights.npy"),
-              get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Logits_Conv2d_1c_1x1_biases.npy"),
-              PadStrideInfo(1, 1, 0, 0))
-          << ReshapeLayer(TensorShape(1001U))
-          << SoftmaxLayer()
-          << Tensor(get_output_accessor(label, 5));
+    BranchLayer get_dwsc_node(const std::string &data_path, std::string &&param_path,
+                              unsigned int  conv_filt,
+                              PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info)
+    {
+        std::string total_path = "/cnn_data/mobilenet_v1_model/" + param_path + "_";
+        SubGraph    sg;
+        sg << DepthwiseConvolutionLayer(
+               3U, 3U,
+               get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"),
+               std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+               dwc_pad_stride_info,
+               true)
+           << BatchNormalizationLayer(
+               get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"),
+               get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"),
+               get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"),
+               get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"),
+               0.001f)
+           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+           << ConvolutionLayer(
+               1U, 1U, conv_filt,
+               get_weights_accessor(data_path, total_path + "pointwise_weights.npy"),
+               std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+               conv_pad_stride_info)
+           << BatchNormalizationLayer(
+               get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"),
+               get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"),
+               get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"),
+               get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"),
+               0.001f)
+           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
 
-    graph.run();
-}
+        return BranchLayer(std::move(sg));
+    }
+};
 
 /** Main program for MobileNetV1
  *
@@ -173,5 +179,5 @@ void main_graph_mobilenet(int argc, char **argv)
  */
 int main(int argc, char **argv)
 {
-    return arm_compute::utils::run_example(argc, argv, main_graph_mobilenet);
+    return arm_compute::utils::run_example<GraphMobilenetExample>(argc, argv);
 }