Introduce 'Inception v3' benchmark (#283)

This commit introduces 'inception v3' benchmark (which is derived from
inception v3 ACL Graph API example).

Signed-off-by: Jonghyun Park <jh1302.park@samsung.com>

diff --git a/benchmark/acl/CMakeLists.txt b/benchmark/acl/CMakeLists.txt
index e9977af..b5c4aad 100644 (file)
--- a/benchmark/acl/CMakeLists.txt
+++ b/benchmark/acl/CMakeLists.txt
@@ -6,6 +6,10 @@ target_link_libraries(arm_compute_benchmark arm_compute_graph)
 add_executable(benchmark_googlenet "benchmark_googlenet.cpp")
 target_link_libraries(benchmark_googlenet arm_compute_benchmark)
 
+# GoogLeNet benchmark
+add_executable(benchmark_inception_v3 "benchmark_inception_v3.cpp")
+target_link_libraries(benchmark_inception_v3 arm_compute_benchmark)
+
 # MobileNet benchmark
 add_executable(benchmark_mobilenet "benchmark_mobilenet.cpp")
 target_link_libraries(benchmark_mobilenet arm_compute_benchmark)

diff --git a/benchmark/acl/benchmark_inception_v3.cpp b/benchmark/acl/benchmark_inception_v3.cpp
new file mode 100644 (file)
index 0000000..3e5df2f
--- /dev/null
+++ b/benchmark/acl/benchmark_inception_v3.cpp
@@ -0,0 +1,743 @@
+/*
+ * Copyright (c) 2017-2018 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/Graph.h"
+#include "arm_compute/graph/Nodes.h"
+#include "arm_compute/graph/SubGraph.h"
+
+#include "Benchmark.h"
+
+#include <cstdlib>
+#include <tuple>
+
+using namespace arm_compute::graph;
+
+inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_input_accessor(void)
+{
+    return get_accessor<InputAccessor>();
+}
+
+inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_random_accessor(float lower, float upper)
+{
+    return get_accessor<InputAccessor>();
+}
+
+inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_weights_accessor(const std::string &path, const std::string &data_file)
+{
+    return get_accessor<InputAccessor>();
+}
+
+inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_output_accessor(void)
+{
+    return get_accessor<OutputAccessor>();
+}
+
+/** Example demonstrating how to implement InceptionV3'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, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ */
+class InceptionV3Example
+{
+public:
+    void do_setup(int argc, char **argv)
+    {
+        std::string data_path; /* Path to the trainable data */
+        std::string image;     /* Image data */
+        std::string label;     /* Label data */
+
+        // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
+        const int  int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
+        TargetHint target_hint     = set_target_hint(int_target_hint);
+
+        // 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(299U, 299U, 3U, 1U), 1, DataType::F32),
+                                       get_input_accessor())
+
+              << ConvolutionLayer(3U, 3U, 32U,
+                                  get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy"),
+                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
+              << BatchNormalizationLayer(get_weights_accessor(data_path,
+                                                              "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
+                                         get_weights_accessor(data_path,
+                                                              "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
+                                         get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
+                                                                                             "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_beta.npy"),
+                                         0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+
+              << ConvolutionLayer(3U, 3U, 32U,
+                                  get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_weights.npy"),
+                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+              << BatchNormalizationLayer(get_weights_accessor(data_path,
+                                                              "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy"),
+                                         get_weights_accessor(data_path,
+                                                              "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy"),
+                                         get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
+                                                                                             "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_beta.npy"),
+                                         0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+
+              << ConvolutionLayer(3U, 3U, 64U,
+                                  get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_weights.npy"),
+                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+              << BatchNormalizationLayer(get_weights_accessor(data_path,
+                                                              "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_mean.npy"),
+                                         get_weights_accessor(data_path,
+                                                              "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_variance.npy"),
+                                         get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
+                                                                                             "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_beta.npy"),
+                                         0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+
+              << ConvolutionLayer(1U, 1U, 80U,
+                                  get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_weights.npy"),
+                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+              << BatchNormalizationLayer(get_weights_accessor(data_path,
+                                                              "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_mean.npy"),
+                                         get_weights_accessor(data_path,
+                                                              "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_variance.npy"),
+                                         get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
+                                                                                             "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_beta.npy"),
+                                         0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+
+              << ConvolutionLayer(3U, 3U, 192U,
+                                  get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_weights.npy"),
+                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+              << BatchNormalizationLayer(get_weights_accessor(data_path,
+                                                              "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_mean.npy"),
+                                         get_weights_accessor(data_path,
+                                                              "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_variance.npy"),
+                                         get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
+                                                                                             "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_beta.npy"),
+                                         0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+
+              << get_inception_node_A(data_path, "Mixed_5b", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
+                                      32U)
+              << get_inception_node_A(data_path, "Mixed_5c", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
+                                      64U, true)
+              << get_inception_node_A(data_path, "Mixed_5d", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
+                                      64U)
+
+              << get_inception_node_B(data_path, "Mixed_6a", 384U, std::make_tuple(64U, 96U, 96U))
+
+              << get_inception_node_C(data_path, "Mixed_6b", 192U, std::make_tuple(128U, 128U, 192U),
+                                      std::make_tuple(128U, 128U, 128U, 128U, 192U), 192U)
+              << get_inception_node_C(data_path, "Mixed_6c", 192U, std::make_tuple(160U, 160U, 192U),
+                                      std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)
+              << get_inception_node_C(data_path, "Mixed_6d", 192U, std::make_tuple(160U, 160U, 192U),
+                                      std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)
+              << get_inception_node_C(data_path, "Mixed_6e", 192U, std::make_tuple(192U, 192U, 192U),
+                                      std::make_tuple(192U, 192U, 192U, 192U, 192U), 192U)
+
+              << get_inception_node_D(data_path, "Mixed_7a", std::make_tuple(192U, 320U),
+                                      std::make_tuple(192U, 192U, 192U, 192U))
+
+              << get_inception_node_E(data_path, "Mixed_7b", 320U, std::make_tuple(384U, 384U, 384U),
+                                      std::make_tuple(448U, 384U, 384U, 384U), 192U)
+              << get_inception_node_E(data_path, "Mixed_7c", 320U, std::make_tuple(384U, 384U, 384U),
+                                      std::make_tuple(448U, 384U, 384U, 384U), 192U, true)
+
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
+              << ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path,
+                                                                      "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy"),
+                                  get_weights_accessor(data_path,
+                                                       "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_biases.npy"),
+                                  PadStrideInfo(1, 1, 0, 0))
+              << ReshapeLayer(TensorShape(1001U)) << SoftmaxLayer()
+              << Tensor(get_output_accessor());
+
+        // In order to enable the OpenCL tuner, graph_init() has to be called only when all nodes have been instantiated
+        graph.graph_init(int_target_hint == 2);
+    }
+
+    void do_run()
+    {
+        run_benchmark(graph);
+    }
+
+private:
+    Graph graph{};
+
+private:
+    BranchLayer get_inception_node_A(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, unsigned int> c_filters,
+                                     unsigned int d_filt,
+                                     bool         is_name_different = false)
+    {
+        std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
+
+        // This is due to a naming issue in the tf model
+        std::string conv_id0 = "_0a_";
+        std::string conv_id1 = "2d_0b_";
+        if(is_name_different)
+        {
+            conv_id0 = "_0b_";
+            conv_id1 = "_1_0c_";
+        }
+
+        SubGraph i_a;
+        i_a << ConvolutionLayer(
+                1U, 1U, a_filt,
+                get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        SubGraph i_b;
+        i_b << ConvolutionLayer(
+                1U, 1U, std::get<0>(b_filters),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_weights.npy"),
+                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                PadStrideInfo(1, 1, 0, 0))
+            << BatchNormalizationLayer(
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_mean.npy"),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_variance.npy"),
+                get_random_accessor(1.f, 1.f),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_beta.npy"),
+                0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                5U, 5U, std::get<1>(b_filters),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_weights.npy"),
+                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                PadStrideInfo(1, 1, 2, 2))
+            << BatchNormalizationLayer(
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_mean.npy"),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_variance.npy"),
+                get_random_accessor(1.f, 1.f),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_beta.npy"),
+                0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        SubGraph i_c;
+        i_c << ConvolutionLayer(
+                1U, 1U, std::get<0>(c_filters),
+                get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                3U, 3U, std::get<1>(c_filters),
+                get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                3U, 3U, std::get<2>(c_filters),
+                get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        SubGraph i_d;
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true))
+            << ConvolutionLayer(
+                1U, 1U, d_filt,
+                get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"),
+                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, 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));
+    }
+
+    BranchLayer get_inception_node_B(const std::string &data_path, std::string &&param_path,
+                                     unsigned int a_filt,
+                                     std::tuple<unsigned int, unsigned int, unsigned int> b_filters)
+    {
+        std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
+        SubGraph    i_a;
+        i_a << ConvolutionLayer(
+                3U, 3U, a_filt,
+                get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_weights.npy"),
+                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_1x1_BatchNorm_moving_mean.npy"),
+                get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"),
+                get_random_accessor(1.f, 1.f),
+                get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_beta.npy"),
+                0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        SubGraph i_b;
+        i_b << ConvolutionLayer(
+                1U, 1U, std::get<0>(b_filters),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                3U, 3U, std::get<1>(b_filters),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                3U, 3U, std::get<2>(b_filters),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_weights.npy"),
+                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_1x1_BatchNorm_moving_mean.npy"),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"),
+                get_random_accessor(1.f, 1.f),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_beta.npy"),
+                0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        SubGraph i_c;
+        i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f, 0.f));
+
+        return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c));
+    }
+
+    BranchLayer get_inception_node_C(const std::string &data_path, std::string &&param_path,
+                                     unsigned int a_filt,
+                                     std::tuple<unsigned int, unsigned int, unsigned int> b_filters,
+                                     std::tuple<unsigned int, unsigned int, unsigned int, unsigned int, unsigned int> c_filters,
+                                     unsigned int d_filt)
+    {
+        std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
+        SubGraph    i_a;
+        i_a << ConvolutionLayer(
+                1U, 1U, a_filt,
+                get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        SubGraph i_b;
+        i_b << ConvolutionLayer(
+                1U, 1U, std::get<0>(b_filters),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                7U, 1U, std::get<1>(b_filters),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                1U, 7U, std::get<2>(b_filters),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        SubGraph i_c;
+        i_c << ConvolutionLayer(
+                1U, 1U, std::get<0>(c_filters),
+                get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                1U, 7U, std::get<1>(c_filters),
+                get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                7U, 1U, std::get<2>(c_filters),
+                get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                1U, 7U, std::get<3>(c_filters),
+                get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                7U, 1U, std::get<4>(c_filters),
+                get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        SubGraph i_d;
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true))
+            << ConvolutionLayer(
+                1U, 1U, d_filt,
+                get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"),
+                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, 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));
+    }
+
+    BranchLayer get_inception_node_D(const std::string &data_path, std::string &&param_path,
+                                     std::tuple<unsigned int, unsigned int>      a_filters,
+                                     std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> b_filters)
+    {
+        std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
+        SubGraph    i_a;
+        i_a << ConvolutionLayer(
+                1U, 1U, std::get<0>(a_filters),
+                get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                3U, 3U, std::get<1>(a_filters),
+                get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        SubGraph i_b;
+        i_b << ConvolutionLayer(
+                1U, 1U, std::get<0>(b_filters),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                7U, 1U, std::get<1>(b_filters),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                1U, 7U, std::get<2>(b_filters),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                3U, 3U, std::get<3>(b_filters),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        SubGraph i_c;
+        i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f, 0.f));
+
+        return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c));
+    }
+
+    BranchLayer get_inception_node_E(const std::string &data_path, std::string &&param_path,
+                                     unsigned int a_filt,
+                                     std::tuple<unsigned int, unsigned int, unsigned int> b_filters,
+                                     std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> c_filters,
+                                     unsigned int d_filt,
+                                     bool         is_name_different = false)
+    {
+        // This is due to a naming issue in the tf model
+        std::string conv_id = "_0b_";
+        if(is_name_different)
+        {
+            conv_id = "_0c_";
+        }
+
+        std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
+        SubGraph    i_a;
+        i_a << ConvolutionLayer(
+                1U, 1U, a_filt,
+                get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        SubGraph i_b1;
+        i_b1 << ConvolutionLayer(
+                 3U, 1U, std::get<1>(b_filters),
+                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_weights.npy"),
+                 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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        SubGraph i_b2;
+        i_b2 << ConvolutionLayer(
+                 1U, 3U, std::get<2>(b_filters),
+                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_weights.npy"),
+                 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                 PadStrideInfo(1, 1, 0, 1))
+             << BatchNormalizationLayer(
+                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_mean.npy"),
+                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_variance.npy"),
+                 get_random_accessor(1.f, 1.f),
+                 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_beta.npy"),
+                 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        SubGraph i_b;
+        i_b << ConvolutionLayer(
+                1U, 1U, std::get<0>(b_filters),
+                get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_b1), std::move(i_b2));
+
+        SubGraph i_c1;
+        i_c1 << ConvolutionLayer(
+                 3U, 1U, std::get<2>(c_filters),
+                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_weights.npy"),
+                 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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        SubGraph i_c2;
+        i_c2 << ConvolutionLayer(
+                 1U, 3U, std::get<3>(c_filters),
+                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_weights.npy"),
+                 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                 PadStrideInfo(1, 1, 0, 1))
+             << BatchNormalizationLayer(
+                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_mean.npy"),
+                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_variance.npy"),
+                 get_random_accessor(1.f, 1.f),
+                 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_beta.npy"),
+                 0.001f, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+        SubGraph i_c;
+        i_c << ConvolutionLayer(
+                1U, 1U, std::get<0>(c_filters),
+                get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << ConvolutionLayer(
+                3U, 3U, std::get<1>(c_filters),
+                get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"),
+                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, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+            << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_c1), std::move(i_c2));
+
+        SubGraph i_d;
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true))
+            << ConvolutionLayer(
+                1U, 1U, d_filt,
+                get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"),
+                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, 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 Inception V3
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ */
+int main(int argc, char **argv)
+{
+    InceptionV3Example example;
+
+    example.do_setup(argc, argv);
+    example.do_run();
+
+    return 0;
+}