text: /*
   * Copyright (c) 2018 ARM Limited.
   *
   * SPDX-License-Identifier: MIT
   *
   * Permission is hereby granted, free of charge, to any person obtaining a copy
   * of this software and associated documentation files (the "Software"), to
   * deal in the Software without restriction, including without limitation the
   * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
   * sell copies of the Software, and to permit persons to whom the Software is
   * furnished to do so, subject to the following conditions:
   *
   * The above copyright notice and this permission notice shall be included in all
   * copies or substantial portions of the Software.
   *
   * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
   * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
   * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
   * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
   * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
   * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
   * SOFTWARE.
   */
  #include "arm_compute/graph.h"
  #include "support/ToolchainSupport.h"
  #include "utils/CommonGraphOptions.h"
  #include "utils/GraphUtils.h"
  #include "utils/Utils.h"
  
  using namespace arm_compute::utils;
  using namespace arm_compute::graph::frontend;
  using namespace arm_compute::graph_utils;
  
  /** Example demonstrating how to implement InceptionV4's network using the Compute Library's graph API */
  class InceptionV4Example final : public Example
  {
  public:
      InceptionV4Example()
          : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "InceptionV4")
      {
      }
      bool do_setup(int argc, char **argv) override
      {
          // Parse arguments
          cmd_parser.parse(argc, argv);
  
          // Consume common parameters
          common_params = consume_common_graph_parameters(common_opts);
  
          // Return when help menu is requested
          if(common_params.help)
          {
              cmd_parser.print_help(argv[0]);
              return false;
          }
  
          // Checks
          ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
          ARM_COMPUTE_EXIT_ON_MSG(common_params.data_type == DataType::F16 && common_params.target == Target::NEON, "F16 NEON not supported for this graph");
  
          // Print parameter values
          std::cout << common_params << std::endl;
  
          // Get trainable parameters data path
          std::string data_path = common_params.data_path;
  
          // Create a preprocessor object
          std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
  
          // Create input descriptor
          const TensorShape tensor_shape     = permute_shape(TensorShape(299U, 299U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
          TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
  
          // Set weights trained layout
          const DataLayout weights_layout = DataLayout::NCHW;
  
          graph << common_params.target
                << common_params.fast_math_hint
                << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false))
                // Conv2d_1a_3x3
                << ConvolutionLayer(3U, 3U, 32U,
                                    get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_weights.npy", weights_layout),
                                    std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
                << BatchNormalizationLayer(get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
                                           get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
                                           get_random_accessor(1.f, 1.f),
                                           get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_BatchNorm_beta.npy"),
                                           0.001f)
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
                // Conv2d_2a_3x3
                << ConvolutionLayer(3U, 3U, 32U,
                                    get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_weights.npy", weights_layout),
                                    std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
                << BatchNormalizationLayer(get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy"),
                                           get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy"),
                                           get_random_accessor(1.f, 1.f),
                                           get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_BatchNorm_beta.npy"),
                                           0.001f)
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
                // Conv2d_2b_3x3
                << ConvolutionLayer(3U, 3U, 64U,
                                    get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_weights.npy", weights_layout),
                                    std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
                << BatchNormalizationLayer(get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_moving_mean.npy"),
                                           get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_moving_variance.npy"),
                                           get_random_accessor(1.f, 1.f),
                                           get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_beta.npy"),
                                           0.001f)
                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          graph << get_mixed_3a(data_path, weights_layout);
          graph << get_mixed_4a(data_path, weights_layout);
          graph << get_mixed_5a(data_path, weights_layout);
          // 4 inception A blocks
          graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5b");
          graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5c");
          graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5d");
          graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5e");
          // reduction A block
          graph << get_reductionA_block(data_path, weights_layout);
          // 7 inception B blocks
          graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6b");
          graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6c");
          graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6d");
          graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6e");
          graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6f");
          graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6g");
          graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6h");
          // reduction B block
          graph << get_reductionB_block(data_path, weights_layout);
          // 3 inception C blocks
          graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7b");
          graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7c");
          graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7d");
          graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
                << FlattenLayer()
                << FullyConnectedLayer(
                    1001U,
                    get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Logits_Logits_weights.npy", weights_layout),
                    get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Logits_Logits_biases.npy"))
                << SoftmaxLayer()
                << OutputLayer(get_output_accessor(common_params, 5));
  
          // Finalize graph
          GraphConfig config;
          config.num_threads = common_params.threads;
          config.use_tuner   = common_params.enable_tuner;
          config.tuner_file  = common_params.tuner_file;
  
          graph.finalize(common_params.target, config);
  
          return true;
      }
  
      void do_run() override
      {
          graph.run();
      }
  
  private:
      CommandLineParser  cmd_parser;
      CommonGraphOptions common_opts;
      CommonGraphParams  common_params;
      Stream             graph;
  
  private:
      ConcatLayer get_mixed_3a(const std::string &data_path, DataLayout weights_layout)
      {
          std::string total_path = "/cnn_data/inceptionv4_model/Mixed_3a_";
  
          SubStream i_a(graph);
          i_a << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true));
  
          SubStream i_b(graph);
          i_b << ConvolutionLayer(3U, 3U, 96U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          return ConcatLayer(std::move(i_a), std::move(i_b));
      }
  
      ConcatLayer get_mixed_4a(const std::string &data_path, DataLayout weights_layout)
      {
          std::string total_path = "/cnn_data/inceptionv4_model/Mixed_4a_";
  
          SubStream i_a(graph);
          i_a << ConvolutionLayer(1U, 1U, 64U,
                                  get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(3U, 3U, 96U,
                                  get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_b(graph);
          i_b << ConvolutionLayer(1U, 1U, 64U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(7U, 1U, 64U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 3, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(1U, 7U, 64U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 3))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(3U, 3U, 96U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          return ConcatLayer(std::move(i_a), std::move(i_b));
      }
  
      ConcatLayer get_mixed_5a(const std::string &data_path, DataLayout weights_layout)
      {
          std::string total_path = "/cnn_data/inceptionv4_model/Mixed_5a_";
  
          SubStream i_a(graph);
          i_a << ConvolutionLayer(3U, 3U, 192U,
                                  get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_b(graph);
          i_b << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true));
  
          return ConcatLayer(std::move(i_a), std::move(i_b));
      }
  
      ConcatLayer get_inceptionA_block(const std::string &data_path, DataLayout weights_layout, std::string &&param_path)
      {
          std::string total_path = "/cnn_data/inceptionv4_model/" + param_path + "_";
  
          SubStream i_a(graph);
          i_a << ConvolutionLayer(1U, 1U, 96U,
                                  get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_b(graph);
          i_b << ConvolutionLayer(1U, 1U, 64U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(3U, 3U, 96U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_c(graph);
          i_c << ConvolutionLayer(1U, 1U, 64U,
                                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(3U, 3U, 96U,
                                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(3U, 3U, 96U,
                                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_d(graph);
          i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true))
              << ConvolutionLayer(1U, 1U, 96U,
                                  get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
      }
  
      ConcatLayer get_reductionA_block(const std::string &data_path, DataLayout weights_layout)
      {
          std::string total_path = "/cnn_data/inceptionv4_model/Mixed_6a_";
  
          SubStream i_a(graph);
          i_a << ConvolutionLayer(3U, 3U, 384U,
                                  get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_b(graph);
          i_b << ConvolutionLayer(1U, 1U, 192U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(3U, 3U, 224U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(3U, 3U, 256U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_c(graph);
          i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true));
  
          return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c));
      }
  
      ConcatLayer get_inceptionB_block(const std::string &data_path, DataLayout weights_layout, std::string &&param_path)
      {
          std::string total_path = "/cnn_data/inceptionv4_model/" + param_path + "_";
  
          SubStream i_a(graph);
          i_a << ConvolutionLayer(1U, 1U, 384U,
                                  get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_b(graph);
          i_b << ConvolutionLayer(1U, 1U, 192U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(7U, 1U, 224U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 3, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(1U, 7U, 256U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 3))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_c(graph);
          i_c << ConvolutionLayer(1U, 1U, 192U,
                                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(1U, 7U, 192U,
                                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 3))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(7U, 1U, 224U,
                                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 3, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(1U, 7U, 224U,
                                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 3))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(7U, 1U, 256U,
                                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 3, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_d(graph);
          i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true))
              << ConvolutionLayer(1U, 1U, 128U,
                                  get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
      }
  
      ConcatLayer get_reductionB_block(const std::string &data_path, DataLayout weights_layout)
      {
          std::string total_path = "/cnn_data/inceptionv4_model/Mixed_7a_";
  
          SubStream i_a(graph);
          i_a << ConvolutionLayer(1U, 1U, 192U,
                                  get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(3U, 3U, 192U,
                                  get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_b(graph);
          i_b << ConvolutionLayer(1U, 1U, 256U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(7U, 1U, 256U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 3, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(1U, 7U, 320U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 3))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(3U, 3U, 320U,
                                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_c(graph);
          i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true));
  
          return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c));
      }
  
      ConcatLayer get_inceptionC_block(const std::string &data_path, DataLayout weights_layout, std::string &&param_path)
      {
          std::string total_path = "/cnn_data/inceptionv4_model/" + param_path + "_";
  
          SubStream i_a(graph);
          i_a << ConvolutionLayer(1U, 1U, 256U,
                                  get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_b(graph);
          i_b << ConvolutionLayer(
                  1U, 1U, 384U,
                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                  PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(
                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
                  get_random_accessor(1.f, 1.f),
                  get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                  0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_b1(i_b);
          i_b1 << ConvolutionLayer(
                   3U, 1U, 256U,
                   get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_weights.npy", weights_layout),
                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                   PadStrideInfo(1, 1, 1, 0))
               << BatchNormalizationLayer(
                   get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy"),
                   get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy"),
                   get_random_accessor(1.f, 1.f),
                   get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"),
                   0.001f)
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_b2(i_b);
          i_b2 << ConvolutionLayer(
                   1U, 3U, 256U,
                   get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_weights.npy", weights_layout),
                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                   PadStrideInfo(1, 1, 0, 1))
               << BatchNormalizationLayer(
                   get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_mean.npy"),
                   get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_variance.npy"),
                   get_random_accessor(1.f, 1.f),
                   get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_beta.npy"),
                   0.001f)
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          // Merge b1 and b2
          i_b << ConcatLayer(std::move(i_b1), std::move(i_b2));
  
          SubStream i_c(graph);
          i_c << ConvolutionLayer(
                  1U, 1U, 384U,
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout),
                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                  PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
                  get_random_accessor(1.f, 1.f),
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
                  0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(
                  1U, 3U, 448U,
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_weights.npy", weights_layout),
                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                  PadStrideInfo(1, 1, 0, 1))
              << BatchNormalizationLayer(
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_BatchNorm_moving_mean.npy"),
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_BatchNorm_moving_variance.npy"),
                  get_random_accessor(1.f, 1.f),
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_BatchNorm_beta.npy"),
                  0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
              << ConvolutionLayer(
                  3U, 1U, 512U,
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_weights.npy", weights_layout),
                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                  PadStrideInfo(1, 1, 1, 0))
              << BatchNormalizationLayer(
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_mean.npy"),
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_variance.npy"),
                  get_random_accessor(1.f, 1.f),
                  get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_beta.npy"),
                  0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_c1(i_c);
          i_c1 << ConvolutionLayer(
                   3U, 1U, 256U,
                   get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_weights.npy", weights_layout),
                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                   PadStrideInfo(1, 1, 1, 0))
               << BatchNormalizationLayer(
                   get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_moving_mean.npy"),
                   get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_moving_variance.npy"),
                   get_random_accessor(1.f, 1.f),
                   get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_beta.npy"),
                   0.001f)
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          SubStream i_c2(i_c);
          i_c2 << ConvolutionLayer(
                   1U, 3U, 256U,
                   get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_weights.npy", weights_layout),
                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                   PadStrideInfo(1, 1, 0, 1))
               << BatchNormalizationLayer(
                   get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_moving_mean.npy"),
                   get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_moving_variance.npy"),
                   get_random_accessor(1.f, 1.f),
                   get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_beta.npy"),
                   0.001f)
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          // Merge i_c1 and i_c2
          i_c << ConcatLayer(std::move(i_c1), std::move(i_c2));
  
          SubStream i_d(graph);
          i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true))
              << ConvolutionLayer(1U, 1U, 256U,
                                  get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
              << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
                                         get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
                                         get_random_accessor(1.f, 1.f),
                                         get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
                                         0.001f)
              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
  
          return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
      }
  };
  
  /** Main program for Inception V4
   *
   * Model is based on:
   *      https://arxiv.org/abs/1602.07261
   *      "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning"
   *      Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
   *
   * @note To list all the possible arguments execute the binary appended with the --help option
   *
   * @param[in] argc Number of arguments
   * @param[in] argv Arguments
   */
  int main(int argc, char **argv)
  {
      return arm_compute::utils::run_example<InceptionV4Example>(argc, argv);
  }
  