COMPMID-1804: Implement Inception-Resnet-V1

Change-Id: I002f99d7b8a816d780b7a941859babc264c92f65
Reviewed-on: https://review.mlplatform.org/366
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 718 insertions, 0 deletions

diff --git a/examples/graph_inception_resnet_v1.cpp b/examples/graph_inception_resnet_v1.cpp
new file mode 100644
index 0000000000..e99f688319
--- /dev/null
+++ b/examples/graph_inception_resnet_v1.cpp
@@ -0,0 +1,718 @@
+/*
+ * 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;
+
+const float batch_norm_epsilon = 0.0010000000474974513f;
+
+/** Example demonstrating how to implement Inception ResNet V1 network using the Compute Library's graph API */
+class InceptionResNetV1Example final : public Example
+{
+public:
+    InceptionResNetV1Example()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), model_input_width(nullptr), model_input_height(nullptr), graph(0, "InceptionResNetV1")
+    {
+        model_input_width  = cmd_parser.add_option<SimpleOption<unsigned int>>("image-width", 512);
+        model_input_height = cmd_parser.add_option<SimpleOption<unsigned int>>("image-height", 512);
+
+        // Add model id option
+        model_input_width->set_help("Input image width.");
+        model_input_height->set_help("Input image height.");
+    }
+    InceptionResNetV1Example(const InceptionResNetV1Example &) = delete;
+    InceptionResNetV1Example &operator=(const InceptionResNetV1Example &) = delete;
+    InceptionResNetV1Example(InceptionResNetV1Example &&)                 = default; // NOLINT
+    InceptionResNetV1Example &operator=(InceptionResNetV1Example &&) = default;      // NOLINT
+    ~InceptionResNetV1Example() override                             = default;
+    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;
+        }
+        // Get input image width and height
+        const unsigned int image_width  = model_input_width->value();
+        const unsigned int image_height = model_input_height->value();
+
+        // Set default layout if needed
+        if(!common_opts.data_layout->is_set() && common_params.target == Target::NEON)
+        {
+            common_params.data_layout = DataLayout::NCHW;
+        }
+
+        // Checks
+        ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+        std::cout << "Image width: " << image_width << std::endl;
+        std::cout << "Image height: " << image_height << std::endl;
+
+        // Create model path
+        std::string data_path  = common_params.data_path;
+        std::string model_path = "/cnn_data/inception_resnet_v1_model/";
+        if(!data_path.empty())
+        {
+            data_path += model_path;
+        }
+
+        // Create a preprocessor object
+        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(0.f, 1.f);
+
+        // Create input descriptor
+        const TensorShape tensor_shape     = permute_shape(TensorShape(image_width, image_height, 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, "Conv2d_1a_3x3_weights.npy", weights_layout),
+                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                  PadStrideInfo(2, 2, 0, 0))
+              .set_name("Conv2d_1a_3x3/convolution")
+              << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
+                                         get_weights_accessor(data_path, "Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
+                                         get_random_accessor(1.f, 1.f),
+                                         get_weights_accessor(data_path, "Conv2d_1a_3x3_BatchNorm_beta.npy"),
+                                         batch_norm_epsilon)
+              .set_name("Conv2d_1a_3x3/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu")
+              // Conv2d_2a_3x3
+              << ConvolutionLayer(3U, 3U, 32U,
+                                  get_weights_accessor(data_path, "Conv2d_2a_3x3_weights.npy", weights_layout),
+                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                  PadStrideInfo(1, 1, 0, 0))
+              .set_name("Conv2d_2a_3x3/convolution")
+              << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_2a_3x3_BatchNorm_moving_mean.npy"),
+                                         get_weights_accessor(data_path, "Conv2d_2a_3x3_BatchNorm_moving_variance.npy"),
+                                         get_random_accessor(1.f, 1.f),
+                                         get_weights_accessor(data_path, "Conv2d_2a_3x3_BatchNorm_beta.npy"),
+                                         batch_norm_epsilon)
+              .set_name("Conv2d_2a_3x3/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu")
+              // Conv2d_2b_3x3
+              << ConvolutionLayer(3U, 3U, 64U,
+                                  get_weights_accessor(data_path, "Conv2d_2b_3x3_weights.npy", weights_layout),
+                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                  PadStrideInfo(1, 1, 1, 1))
+              .set_name("Conv2d_2b_3x3/convolution")
+              << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_2b_3x3_BatchNorm_moving_mean.npy"),
+                                         get_weights_accessor(data_path, "Conv2d_2b_3x3_BatchNorm_moving_variance.npy"),
+                                         get_random_accessor(1.f, 1.f),
+                                         get_weights_accessor(data_path, "Conv2d_2b_3x3_BatchNorm_beta.npy"),
+                                         batch_norm_epsilon)
+              .set_name("Conv2d_2b_3x3/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu")
+              // MaxPool_3a_3x3
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("MaxPool_3a_3x3/MaxPool")
+              // Conv2d_3b_1x1
+              << ConvolutionLayer(1U, 1U, 80U,
+                                  get_weights_accessor(data_path, "Conv2d_3b_1x1_weights.npy", weights_layout),
+                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                  PadStrideInfo(1, 1, 0, 0))
+              .set_name("Conv2d_3b_1x1/convolution")
+              << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_3b_1x1_BatchNorm_moving_mean.npy"),
+                                         get_weights_accessor(data_path, "Conv2d_3b_1x1_BatchNorm_moving_variance.npy"),
+                                         get_random_accessor(1.f, 1.f),
+                                         get_weights_accessor(data_path, "Conv2d_3b_1x1_BatchNorm_beta.npy"),
+                                         batch_norm_epsilon)
+              .set_name("Conv2d_3b_1x1/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_3b_1x1/Relu")
+              // Conv2d_4a_3x3
+              << ConvolutionLayer(3U, 3U, 192U,
+                                  get_weights_accessor(data_path, "Conv2d_4a_3x3_weights.npy", weights_layout),
+                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                  PadStrideInfo(1, 1, 0, 0))
+              .set_name("Conv2d_4a_3x3/convolution")
+              << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_4a_3x3_BatchNorm_moving_mean.npy"),
+                                         get_weights_accessor(data_path, "Conv2d_4a_3x3_BatchNorm_moving_variance.npy"),
+                                         get_random_accessor(1.f, 1.f),
+                                         get_weights_accessor(data_path, "Conv2d_4a_3x3_BatchNorm_beta.npy"),
+                                         batch_norm_epsilon)
+              .set_name("Conv2d_4a_3x3/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu")
+              // Conv2d_4b_3x3
+              << ConvolutionLayer(3U, 3U, 256U,
+                                  get_weights_accessor(data_path, "Conv2d_4b_3x3_weights.npy", weights_layout),
+                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                  PadStrideInfo(2, 2, 0, 0))
+              .set_name("Conv2d_4a_3x3/convolution")
+              << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_4b_3x3_BatchNorm_moving_mean.npy"),
+                                         get_weights_accessor(data_path, "Conv2d_4b_3x3_BatchNorm_moving_variance.npy"),
+                                         get_random_accessor(1.f, 1.f),
+                                         get_weights_accessor(data_path, "Conv2d_4b_3x3_BatchNorm_beta.npy"),
+                                         batch_norm_epsilon)
+              .set_name("Conv2d_4b_3x3/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4b_3x3/Relu");
+
+        // 5 x Inception-resnet-A
+        block35_repeat(data_path, weights_layout, 5);
+        // Reduction-A
+        reduction_a(data_path, weights_layout);
+        // 10 x Inception-Resnet-B
+        block17_repeat(data_path, weights_layout, 10);
+        // Reduction-B
+        reduction_b(data_path, weights_layout);
+        // 5 x Inception-resnet-C
+        block8_repeat(data_path, weights_layout, 5, 0.2f, true);
+
+        block8_repeat(data_path, weights_layout, 1, 1.f, false);
+
+        // Logits tail
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a_8x8")
+              << FlattenLayer().set_name("Logits/Flatten")
+              << FullyConnectedLayer(
+                  128U,
+                  get_weights_accessor(data_path, "Logits_Logits_weights.npy", weights_layout),
+                  get_weights_accessor(data_path, "Logits_Logits_biases.npy"))
+              .set_name("Logits/Logits")
+              << OutputLayer(arm_compute::support::cpp14::make_unique<DummyAccessor>(0));
+
+        // 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;
+    SimpleOption<unsigned int> *model_input_width{ nullptr };
+    SimpleOption<unsigned int> *model_input_height{ nullptr };
+    Stream                      graph;
+
+private:
+    void block35_repeat(const std::string &data_path, DataLayout weights_layout, unsigned int num_blocks)
+    {
+        for(unsigned int i = 0; i < num_blocks; ++i)
+        {
+            std::stringstream unit_path_ss;
+            unit_path_ss << "Repeat_block35_" << (i + 1) << "_";
+            std::stringstream unit_name_ss;
+            unit_name_ss << "Repeat/block35_" << (i + 1) << "/";
+
+            std::string unit_path = unit_path_ss.str();
+            std::string unit_name = unit_name_ss.str();
+
+            // Create left and write substreams
+            SubStream i_l(graph);
+            SubStream i_r(graph);
+
+            // Branch 0
+            SubStream i_la(i_l);
+            i_la << ConvolutionLayer(1U, 1U, 32U,
+                                     get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_weights.npy", weights_layout),
+                                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                     PadStrideInfo(1, 1, 0, 0))
+                 .set_name(unit_name + "Branch_0/Conv2d_1x1/convolution")
+                 << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_mean.npy"),
+                                            get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_variance.npy"),
+                                            get_random_accessor(1.f, 1.f),
+                                            get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_beta.npy"),
+                                            batch_norm_epsilon)
+                 .set_name(unit_name + "Branch_0/Conv2d_1x1/BatchNorm")
+                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_0/Conv2d_1x1/Relu");
+
+            // Branch 1
+            SubStream i_lb(i_l);
+            i_lb << ConvolutionLayer(1U, 1U, 32U,
+                                     get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
+                                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                     PadStrideInfo(1, 1, 0, 0))
+                 .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/convolution")
+                 << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+                                            get_random_accessor(1.f, 1.f),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+                                            batch_norm_epsilon)
+                 .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/BatchNorm")
+                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0a_1x1/Relu")
+                 << ConvolutionLayer(3U, 3U, 32U,
+                                     get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout),
+                                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                     PadStrideInfo(1, 1, 1, 1))
+                 .set_name(unit_name + "Branch_1/Conv2d_0b_3x3/convolution")
+                 << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
+                                            get_random_accessor(1.f, 1.f),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"),
+                                            batch_norm_epsilon)
+                 .set_name(unit_name + "Branch_1/Conv2d_0b_3x3/BatchNorm")
+                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0b_3x3/Relu");
+
+            // Branch 2
+            SubStream i_lc(i_l);
+            i_lc << ConvolutionLayer(1U, 1U, 32U,
+                                     get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout),
+                                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                     PadStrideInfo(1, 1, 0, 0))
+                 .set_name(unit_name + "Branch_2/Conv2d_0a_1x1/convolution")
+                 << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+                                            get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+                                            get_random_accessor(1.f, 1.f),
+                                            get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+                                            batch_norm_epsilon)
+                 .set_name(unit_name + "Branch_2/Conv2d_0a_1x1/BatchNorm")
+                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_2/Conv2d_0a_1x1/Relu")
+                 << ConvolutionLayer(3U, 3U, 32U,
+                                     get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout),
+                                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                     PadStrideInfo(1, 1, 1, 1))
+                 .set_name(unit_name + "Branch_2/Conv2d_0b_3x3/convolution")
+                 << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
+                                            get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
+                                            get_random_accessor(1.f, 1.f),
+                                            get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
+                                            batch_norm_epsilon)
+                 .set_name(unit_name + "Branch_2/Conv2d_0b_3x3/BatchNorm")
+                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_2/Conv2d_0b_3x3/Relu")
+                 << ConvolutionLayer(3U, 3U, 32U,
+                                     get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_3x3_weights.npy", weights_layout),
+                                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                     PadStrideInfo(1, 1, 1, 1))
+                 .set_name(unit_name + "Branch_2/Conv2d_0c_3x3/convolution")
+                 << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"),
+                                            get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"),
+                                            get_random_accessor(1.f, 1.f),
+                                            get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"),
+                                            batch_norm_epsilon)
+                 .set_name(unit_name + "Branch_2/Conv2d_0c_3x3/BatchNorm")
+                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_2/Conv2d_0c_3x3/Relu");
+
+            // Concatenate
+            i_l << ConcatLayer(std::move(i_la), std::move(i_lb), std::move(i_lc)).set_name(unit_name + "concat")
+                << ConvolutionLayer(1U, 1U, 256U,
+                                    get_weights_accessor(data_path, unit_path + "Conv2d_1x1_weights.npy", weights_layout),
+                                    get_weights_accessor(data_path, unit_path + "Conv2d_1x1_biases.npy", weights_layout),
+                                    PadStrideInfo(1, 1, 0, 0))
+                .set_name(unit_name + "Conv2d_1x1/convolution")
+                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 0.17f, 0.f)).set_name(unit_name + "mul");
+
+            graph << EltwiseLayer(std::move(i_l), std::move(i_r), EltwiseOperation::Add).set_name(unit_name + "add")
+                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu");
+        }
+    }
+
+    void block17_repeat(const std::string &data_path, DataLayout weights_layout, unsigned int num_blocks)
+    {
+        for(unsigned int i = 0; i < num_blocks; ++i)
+        {
+            std::stringstream unit_path_ss;
+            unit_path_ss << "Repeat_1_block17_" << (i + 1) << "_";
+            std::stringstream unit_name_ss;
+            unit_name_ss << "Repeat_1/block17_" << (i + 1) << "/";
+
+            std::string unit_path = unit_path_ss.str();
+            std::string unit_name = unit_name_ss.str();
+
+            // Create left and write substreams
+            SubStream i_l(graph);
+            SubStream i_r(graph);
+
+            // Branch 0
+            SubStream i_la(i_l);
+            i_la << ConvolutionLayer(1U, 1U, 128U,
+                                     get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_weights.npy", weights_layout),
+                                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                     PadStrideInfo(1, 1, 0, 0))
+                 .set_name(unit_name + "Branch_0/Conv2d_1x1/convolution")
+                 << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_mean.npy"),
+                                            get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_variance.npy"),
+                                            get_random_accessor(1.f, 1.f),
+                                            get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_beta.npy"),
+                                            batch_norm_epsilon)
+                 .set_name(unit_name + "Branch_0/Conv2d_1x1/BatchNorm")
+                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_0/Conv2d_1x1/Relu");
+
+            // Branch 1
+            SubStream i_lb(i_l);
+            i_lb << ConvolutionLayer(1U, 1U, 128U,
+                                     get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
+                                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                     PadStrideInfo(1, 1, 0, 0))
+                 .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/convolution")
+                 << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+                                            get_random_accessor(1.f, 1.f),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+                                            batch_norm_epsilon)
+                 .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/BatchNorm")
+                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0a_1x1/Relu")
+                 << ConvolutionLayer(7U, 1U, 128U,
+                                     get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout),
+                                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                     PadStrideInfo(1, 1, 3, 0))
+                 .set_name(unit_name + "Branch_1/Conv2d_0b_1x7/convolution")
+                 << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
+                                            get_random_accessor(1.f, 1.f),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
+                                            batch_norm_epsilon)
+                 .set_name(unit_name + "Branch_1/Conv2d_0b_1x7/BatchNorm")
+                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0b_1x7/Relu")
+                 << ConvolutionLayer(1U, 7U, 128U,
+                                     get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout),
+                                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                     PadStrideInfo(1, 1, 0, 3))
+                 .set_name(unit_name + "Branch_1/Conv2d_0c_7x1/convolution")
+                 << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
+                                            get_random_accessor(1.f, 1.f),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
+                                            batch_norm_epsilon)
+                 .set_name(unit_name + "Branch_1/Conv2d_0c_7x1/BatchNorm")
+                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0c_7x1/Relu");
+
+            // Concatenate
+            i_l << ConcatLayer(std::move(i_la), std::move(i_lb)).set_name(unit_name + "concat")
+                << ConvolutionLayer(1U, 1U, 896U,
+                                    get_weights_accessor(data_path, unit_path + "Conv2d_1x1_weights.npy", weights_layout),
+                                    get_weights_accessor(data_path, unit_path + "Conv2d_1x1_biases.npy", weights_layout),
+                                    PadStrideInfo(1, 1, 0, 0))
+                .set_name(unit_name + "Conv2d_1x1/convolution")
+                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 0.10f, 0.f)).set_name(unit_name + "mul");
+
+            graph << EltwiseLayer(std::move(i_l), std::move(i_r), EltwiseOperation::Add).set_name(unit_name + "add")
+                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu");
+        }
+    }
+
+    void block8_repeat(const std::string &data_path, DataLayout weights_layout, unsigned int num_blocks, float scale, bool has_activation)
+    {
+        for(unsigned int i = 0; i < num_blocks; ++i)
+        {
+            std::stringstream unit_path_ss;
+            std::stringstream unit_name_ss;
+            if(num_blocks != 1)
+            {
+                unit_path_ss << "Repeat_2_block8_" << (i + 1) << "_";
+                unit_name_ss << "Repeat_2/block8_" << (i + 1) << "/";
+            }
+            else
+            {
+                unit_path_ss << "Block8_";
+                unit_name_ss << "Block8/";
+            }
+
+            std::string unit_path = unit_path_ss.str();
+            std::string unit_name = unit_name_ss.str();
+
+            // Create left and write substreams
+            SubStream i_l(graph);
+            SubStream i_r(graph);
+
+            // Branch 0
+            SubStream i_la(i_l);
+            i_la << ConvolutionLayer(1U, 1U, 192U,
+                                     get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_weights.npy", weights_layout),
+                                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                     PadStrideInfo(1, 1, 0, 0))
+                 .set_name(unit_name + "Branch_0/Conv2d_1x1/convolution")
+                 << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_mean.npy"),
+                                            get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_variance.npy"),
+                                            get_random_accessor(1.f, 1.f),
+                                            get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_beta.npy"),
+                                            batch_norm_epsilon)
+                 .set_name(unit_name + "Branch_0/Conv2d_1x1/BatchNorm")
+                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_0/Conv2d_1x1/Relu");
+
+            // Branch 1
+            SubStream i_lb(i_l);
+            i_lb << ConvolutionLayer(1U, 1U, 192U,
+                                     get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
+                                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                     PadStrideInfo(1, 1, 0, 0))
+                 .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/convolution")
+                 << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+                                            get_random_accessor(1.f, 1.f),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+                                            batch_norm_epsilon)
+                 .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/BatchNorm")
+                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0a_1x1/Relu")
+                 << ConvolutionLayer(3U, 1U, 192U,
+                                     get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x3_weights.npy", weights_layout),
+                                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                     PadStrideInfo(1, 1, 1, 0))
+                 .set_name(unit_name + "Branch_1/Conv2d_0b_1x3/convolution")
+                 << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy"),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy"),
+                                            get_random_accessor(1.f, 1.f),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"),
+                                            batch_norm_epsilon)
+                 .set_name(unit_name + "Branch_1/Conv2d_0b_1x3/BatchNorm")
+                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0b_1x3/Relu")
+                 << ConvolutionLayer(1U, 3U, 192U,
+                                     get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_weights.npy", weights_layout),
+                                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                     PadStrideInfo(1, 1, 0, 1))
+                 .set_name(unit_name + "Branch_1/Conv2d_0c_3x1/convolution")
+                 << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_mean.npy"),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_variance.npy"),
+                                            get_random_accessor(1.f, 1.f),
+                                            get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_beta.npy"),
+                                            batch_norm_epsilon)
+                 .set_name(unit_name + "Branch_1/Conv2d_0c_3x1/BatchNorm")
+                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0c_3x1/Relu");
+
+            // Concatenate
+            i_l << ConcatLayer(std::move(i_la), std::move(i_lb)).set_name(unit_name + "concat")
+                << ConvolutionLayer(1U, 1U, 1792U,
+                                    get_weights_accessor(data_path, unit_path + "Conv2d_1x1_weights.npy", weights_layout),
+                                    get_weights_accessor(data_path, unit_path + "Conv2d_1x1_biases.npy", weights_layout),
+                                    PadStrideInfo(1, 1, 0, 0))
+                .set_name(unit_name + "Conv2d_1x1/convolution");
+
+            // Scale result
+            if(scale != 1.f)
+            {
+                i_l << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, scale, 0.f)).set_name(unit_name + "mul");
+            }
+
+            // Residual add
+            graph << EltwiseLayer(std::move(i_l), std::move(i_r), EltwiseOperation::Add).set_name(unit_name + "add");
+
+            // Apply activation if needed
+            if(has_activation)
+            {
+                graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu");
+            }
+        }
+    }
+
+    void reduction_a(const std::string &data_path, DataLayout weights_layout)
+    {
+        // Branch 0
+        SubStream i_a(graph);
+        i_a << ConvolutionLayer(3U, 3U, 384U,
+                                get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
+                                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                PadStrideInfo(2, 2, 0, 0))
+            .set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/convolution")
+            << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
+                                       get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
+                                       get_random_accessor(1.f, 1.f),
+                                       get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
+                                       batch_norm_epsilon)
+            .set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/Relu");
+
+        // Branch 1
+        SubStream i_b(graph);
+        i_b << ConvolutionLayer(1U, 1U, 192U,
+                                get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
+                                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                PadStrideInfo(1, 1, 0, 0))
+            .set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/convolution")
+            << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+                                       get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+                                       get_random_accessor(1.f, 1.f),
+                                       get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+                                       batch_norm_epsilon)
+            .set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/Relu")
+            << ConvolutionLayer(3U, 3U, 192U,
+                                get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout),
+                                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                PadStrideInfo(1, 1, 1, 1))
+            .set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/convolution")
+            << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
+                                       get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
+                                       get_random_accessor(1.f, 1.f),
+                                       get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"),
+                                       batch_norm_epsilon)
+            .set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/Relu")
+            << ConvolutionLayer(3U, 3U, 256U,
+                                get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout),
+                                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                PadStrideInfo(2, 2, 0, 0))
+            .set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/convolution")
+            << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
+                                       get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
+                                       get_random_accessor(1.f, 1.f),
+                                       get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
+                                       batch_norm_epsilon)
+            .set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/Relu");
+
+        // Branch 2
+        SubStream i_c(graph);
+        i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3");
+
+        // Concatenate
+        graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)).set_name("Mixed_6a/concat");
+    }
+
+    void reduction_b(const std::string &data_path, DataLayout weights_layout)
+    {
+        // Branch 0
+        SubStream i_a(graph);
+        i_a << ConvolutionLayer(1U, 1U, 256U,
+                                get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
+                                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                PadStrideInfo(1, 1, 0, 0))
+            .set_name("Mixed_7a/Branch_0/Conv2d_0a_1x1/convolution")
+            << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+                                       get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+                                       get_random_accessor(1.f, 1.f),
+                                       get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+                                       batch_norm_epsilon)
+            .set_name("Mixed_7a/Branch_0/Conv2d_0a_1x1/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_0/Conv2d_0a_1x1/Relu")
+            << ConvolutionLayer(3U, 3U, 384U,
+                                get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
+                                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                PadStrideInfo(2, 2, 0, 0))
+            .set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/convolution")
+            << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
+                                       get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
+                                       get_random_accessor(1.f, 1.f),
+                                       get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
+                                       batch_norm_epsilon)
+            .set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/Relu");
+
+        // Branch 1
+        SubStream i_b(graph);
+        i_b << ConvolutionLayer(1U, 1U, 256U,
+                                get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
+                                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                PadStrideInfo(1, 1, 0, 0))
+            .set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/convolution")
+            << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+                                       get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+                                       get_random_accessor(1.f, 1.f),
+                                       get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+                                       batch_norm_epsilon)
+            .set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/Relu")
+            << ConvolutionLayer(3U, 3U, 256U,
+                                get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout),
+                                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                PadStrideInfo(2, 2, 0, 0))
+            .set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/convolution")
+            << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
+                                       get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
+                                       get_random_accessor(1.f, 1.f),
+                                       get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
+                                       batch_norm_epsilon)
+            .set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/Relu");
+
+        // Branch 2
+        SubStream i_c(graph);
+        i_c << ConvolutionLayer(1U, 1U, 256U,
+                                get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout),
+                                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                PadStrideInfo(1, 1, 0, 0))
+            .set_name("Mixed_7a/Branch_2/Conv2d_0a_1x1/convolution")
+            << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+                                       get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+                                       get_random_accessor(1.f, 1.f),
+                                       get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+                                       batch_norm_epsilon)
+            .set_name("Mixed_7a/Branch_2/Conv2d_0a_1x1/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_2/Conv2d_0a_1x1/Relu")
+            << ConvolutionLayer(3U, 3U, 256U,
+                                get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout),
+                                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                PadStrideInfo(1, 1, 1, 1))
+            .set_name("Mixed_7a/Branch_2/Conv2d_0b_3x3/convolution")
+            << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
+                                       get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
+                                       get_random_accessor(1.f, 1.f),
+                                       get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
+                                       batch_norm_epsilon)
+            .set_name("Mixed_7a/Branch_2/Conv2d_0b_3x3/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_2/Conv2d_0b_3x3/Relu")
+            << ConvolutionLayer(3U, 3U, 256U,
+                                get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_weights.npy", weights_layout),
+                                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                PadStrideInfo(2, 2, 0, 0))
+            .set_name("Mixed_7a/Branch_2/Conv2d_1a_3x3/convolution")
+            << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
+                                       get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
+                                       get_random_accessor(1.f, 1.f),
+                                       get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm_beta.npy"),
+                                       batch_norm_epsilon)
+            .set_name("Mixed_7a/Branch_2/Conv2d_1a_3x3/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_2/Conv2d_1a_3x3/Relu");
+
+        // Branch 3
+        SubStream i_d(graph);
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_7a/Branch_3/MaxPool_1a_3x3");
+
+        // Concatenate
+        graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)).set_name("Mixed_7a/concat");
+    }
+};
+
+/** Main program for Inception ResNet V1
+ *
+ * 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<InceptionResNetV1Example>(argc, argv);
+}