Revert "COMPMID-915: Create ResNet50 example"

This reverts commit 2e8c7ee2ecebd9783c97bbd602a61989e1247d6b.

Change-Id: Id90691f427a68d01480889f8d5fff190fd72c5a3
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/121176
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

7 files changed, 2 insertions, 566 deletions

diff --git a/arm_compute/graph/Nodes.h b/arm_compute/graph/Nodes.h
index 3009a24fcb..4390c59959 100644
--- a/arm_compute/graph/Nodes.h
+++ b/arm_compute/graph/Nodes.h
@@ -39,7 +39,6 @@
 #include "arm_compute/graph/nodes/PoolingLayer.h"
 #include "arm_compute/graph/nodes/QuantizationLayer.h"
 #include "arm_compute/graph/nodes/ReshapeLayer.h"
-#include "arm_compute/graph/nodes/ResidualLayer.h"
 #include "arm_compute/graph/nodes/SoftmaxLayer.h"
 
 #endif /* __ARM_COMPUTE_GRAPH_NODES_H__ */
diff --git a/arm_compute/graph/Types.h b/arm_compute/graph/Types.h
index a5d6ae8459..3ec0c7a5cf 100644
--- a/arm_compute/graph/Types.h
+++ b/arm_compute/graph/Types.h
@@ -91,7 +91,6 @@ enum class ConvolutionMethodHint
 enum class OperationType
 {
     ActivationLayer,
-    ArithmeticAddition,
     BatchNormalizationLayer,
     ConvolutionLayer,
     DepthConvertLayer,
diff --git a/arm_compute/graph/nodes/ResidualLayer.h b/arm_compute/graph/nodes/ResidualLayer.h
deleted file mode 100644
index 1eecf6f996..0000000000
--- a/arm_compute/graph/nodes/ResidualLayer.h
+++ /dev/null
@@ -1,76 +0,0 @@
-/*
- * 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.
- */
-#ifndef __ARM_COMPUTE_GRAPH_RESIDUAL_LAYER_H__
-#define __ARM_COMPUTE_GRAPH_RESIDUAL_LAYER_H__
-
-#include "arm_compute/graph/GraphContext.h"
-#include "arm_compute/graph/INode.h"
-#include "arm_compute/graph/ITensorObject.h"
-#include "arm_compute/graph/SubGraph.h"
-#include "arm_compute/graph/Types.h"
-
-#include "arm_compute/core/utils/misc/utility.h"
-
-#include <vector>
-
-namespace arm_compute
-{
-namespace graph
-{
-/** Branch Layer node */
-class ResidualLayer final : public INode
-{
-public:
-    /** Default Constructor
-     *
-     * @param[in] sub_graph1 First graph branch
-     * @param[in] sub_graph2 Second graph branch
-     */
-    template <typename... Ts>
-    ResidualLayer(SubGraph &&sub_graph1, SubGraph &&sub_graph2)
-        : _sub_graphs()
-    {
-        _sub_graphs.push_back(arm_compute::support::cpp14::make_unique<SubGraph>(std::move(sub_graph1)));
-        _sub_graphs.push_back(arm_compute::support::cpp14::make_unique<SubGraph>(std::move(sub_graph2)));
-    }
-    /** Default Constructor
-     *
-     * @param[in] sub_graph Sub graph
-     */
-    template <typename... Ts>
-    ResidualLayer(SubGraph &&sub_graph)
-        : _sub_graphs()
-    {
-        _sub_graphs.push_back(arm_compute::support::cpp14::make_unique<SubGraph>(std::move(sub_graph)));
-    }
-
-    // Inherited methods overriden:
-    std::unique_ptr<arm_compute::IFunction> instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output) override;
-
-private:
-    std::vector<std::unique_ptr<SubGraph>> _sub_graphs;
-};
-} // namespace graph
-} // namespace arm_compute
-#endif /* __ARM_COMPUTE_GRAPH_RESIDUAL_LAYER_H__ */
diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp
deleted file mode 100644
index 23432b821b..0000000000
--- a/examples/graph_resnet50.cpp
+++ /dev/null
@@ -1,231 +0,0 @@
-/*
- * 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 "support/ToolchainSupport.h"
-#include "utils/GraphUtils.h"
-#include "utils/Utils.h"
-
-#include <cstdlib>
-
-using namespace arm_compute::utils;
-using namespace arm_compute::graph;
-using namespace arm_compute::graph_utils;
-
-/** Example demonstrating how to implement Microsoft's ResNet50 network using the Compute Library's graph API
- *
- * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
- */
-class GraphResNet50Example : public Example
-{
-public:
-    void do_setup(int argc, char **argv) override
-    {
-        std::string data_path; /* Path to the trainable data */
-        std::string image;     /* Image data */
-        std::string label;     /* Label data */
-
-        constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
-        constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
-        constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
-        constexpr float std    = 1.f;     /* Standard deviation value to divide from the channels */
-
-        // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
-        TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
-
-        // Parse arguments
-        if(argc < 2)
-        {
-            // Print help
-            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 2)
-        {
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n";
-            std::cout << "No data folder provided: using random values\n\n";
-        }
-        else if(argc == 3)
-        {
-            data_path = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n";
-            std::cout << "No image provided: using random values\n\n";
-        }
-        else if(argc == 4)
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
-            std::cout << "No text file with labels provided: skipping output accessor\n\n";
-        }
-        else
-        {
-            data_path = argv[2];
-            image     = argv[3];
-            label     = argv[4];
-        }
-
-        graph << target_hint
-              << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
-                        get_input_accessor(image,
-                                           mean_r, mean_g, mean_b,
-                                           std, std, std, false /* Do not convert to BGR */))
-              << ConvolutionLayer(
-                  7U, 7U, 64U,
-                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_weights.npy"),
-                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                  PadStrideInfo(2, 2, 3, 3))
-              << BatchNormalizationLayer(
-                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_mean.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_variance.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_gamma.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_beta.npy"),
-                  0.001f)
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)));
-
-        add_residual_block(data_path, "block1", 64, 3, 2);
-        add_residual_block(data_path, "block2", 128, 4, 2);
-        add_residual_block(data_path, "block3", 256, 6, 2);
-        add_residual_block(data_path, "block4", 512, 3, 1);
-
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
-              << ConvolutionLayer(
-                  1U, 1U, 1000U,
-                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_weights.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_biases.npy"),
-                  PadStrideInfo(1, 1, 0, 0))
-              << FlattenLayer()
-              << SoftmaxLayer()
-              << Tensor(get_output_accessor(label, 5));
-    }
-    void do_run() override
-    {
-        // Run graph
-        graph.run();
-    }
-
-private:
-    Graph graph{};
-
-    void add_residual_block(const std::string &data_path, const std::string &name, unsigned int base_depth, unsigned int num_units, unsigned int stride)
-    {
-        for(unsigned int i = 0; i < num_units; ++i)
-        {
-            std::stringstream unit;
-            unit << "/cnn_data/resnet50_model/" << name << "_unit_" << (i + 1) << "_bottleneck_v1_";
-            std::string unit_name = unit.str();
-
-            unsigned int middle_stride = 1;
-
-            if(i == num_units - 1)
-            {
-                middle_stride = stride;
-            }
-
-            SubGraph right;
-            right << ConvolutionLayer(
-                      1U, 1U, base_depth,
-                      get_weights_accessor(data_path, unit_name + "conv1_weights.npy"),
-                      std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                      PadStrideInfo(1, 1, 0, 0))
-                  << BatchNormalizationLayer(
-                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_mean.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_variance.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_gamma.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_beta.npy"),
-                      0.001f)
-                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-
-                  << ConvolutionLayer(
-                      3U, 3U, base_depth,
-                      get_weights_accessor(data_path, unit_name + "conv2_weights.npy"),
-                      std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                      PadStrideInfo(middle_stride, middle_stride, 1, 1))
-                  << BatchNormalizationLayer(
-                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_mean.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_variance.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_gamma.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_beta.npy"),
-                      0.001f)
-                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-
-                  << ConvolutionLayer(
-                      1U, 1U, base_depth * 4,
-                      get_weights_accessor(data_path, unit_name + "conv3_weights.npy"),
-                      std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                      PadStrideInfo(1, 1, 0, 0))
-                  << BatchNormalizationLayer(
-                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_mean.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_variance.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_gamma.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_beta.npy"),
-                      0.001f);
-
-            if(i == 0)
-            {
-                SubGraph left;
-                left << ConvolutionLayer(
-                         1U, 1U, base_depth * 4,
-                         get_weights_accessor(data_path, unit_name + "shortcut_weights.npy"),
-                         std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                         PadStrideInfo(1, 1, 0, 0))
-                     << BatchNormalizationLayer(
-                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_mean.npy"),
-                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_variance.npy"),
-                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_gamma.npy"),
-                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_beta.npy"),
-                         0.001f);
-
-                graph << ResidualLayer(std::move(left), std::move(right));
-            }
-            else if(middle_stride > 1)
-            {
-                SubGraph left;
-                left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0)))
-                     // TODO (alegil01) : Remove once we understand why a single node graph does not run in CL
-                     << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f, 0.f));
-
-                graph << ResidualLayer(std::move(left), std::move(right));
-            }
-            else
-            {
-                graph << ResidualLayer(std::move(right));
-            }
-
-            graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-        }
-    }
-};
-
-/** Main program for ResNet50
- *
- * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
- */
-int main(int argc, char **argv)
-{
-    return arm_compute::utils::run_example<GraphResNet50Example>(argc, argv);
-}
diff --git a/src/graph/nodes/ResidualLayer.cpp b/src/graph/nodes/ResidualLayer.cpp
deleted file mode 100644
index 87404f9e1f..0000000000
--- a/src/graph/nodes/ResidualLayer.cpp
+++ /dev/null
@@ -1,199 +0,0 @@
-/*
- * 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/nodes/ResidualLayer.h"
-
-#include "arm_compute/graph/Error.h"
-#include "arm_compute/graph/Graph.h"
-#include "arm_compute/graph/NodeContext.h"
-#include "arm_compute/graph/OperationRegistry.h"
-#include "arm_compute/graph/SubGraph.h"
-#include "arm_compute/graph/Tensor.h"
-#include "arm_compute/runtime/IFunction.h"
-#include "support/ToolchainSupport.h"
-#include "utils/Utils.h"
-
-#include <memory>
-#include <tuple>
-#include <vector>
-
-using namespace arm_compute::graph;
-
-/** Residual function */
-class ResidualFunction final : public arm_compute::IFunction
-{
-public:
-    /** Default Constructor */
-    ResidualFunction(GraphContext &ctx, ITensorObject *output)
-        : _ctx(ctx), _input(nullptr), _output(output), _func(nullptr), _graphs(), _graph_outputs()
-    {
-    }
-
-    /** Prevent instances from being copy constructed */
-    ResidualFunction(const ResidualFunction &) = delete;
-    /** Prevent instances from being copy assigned */
-    const ResidualFunction &operator=(const ResidualFunction &) = delete;
-    /** Prevent instances from being move constructed */
-    ResidualFunction(ResidualFunction &&) = delete;
-    /** Prevent instances from being move assigned */
-    ResidualFunction &operator=(ResidualFunction &&) = delete;
-    /** Default destructor */
-    ~ResidualFunction() override = default;
-
-    /** Set the input (when using only one sub graph)
-     *
-     * @param[in] input Input to set
-     */
-    void set_input(std::unique_ptr<ITensorObject> input)
-    {
-        _input = std::move(input);
-    }
-
-    /** Registers graph to be executed by the residual function
-     *
-     * @param[in] graph  Graph to register
-     * @param[in] output Output to register
-     */
-    void register_graph(std::unique_ptr<Graph> graph, std::unique_ptr<ITensorObject> output)
-    {
-        _graphs.push_back(std::move(graph));
-        _graph_outputs.push_back(std::move(output));
-    }
-
-    /** Configure the function */
-    void configure()
-    {
-        ARM_COMPUTE_ERROR_ON(_graphs.size() < 1 || _graphs.size() > 2);
-        TargetHint target_hint = _ctx.hints().target_hint();
-
-        // Create node context
-        NodeContext node_ctx(OperationType::ArithmeticAddition);
-        node_ctx.set_target(target_hint);
-
-        if(_graphs.size() == 1)
-        {
-            arm_compute::ITensor *in = _input->tensor();
-            node_ctx.add_input(in);
-        }
-
-        for(auto &o : _graph_outputs)
-        {
-            arm_compute::ITensor *in = o->tensor();
-            node_ctx.add_input(in);
-        }
-
-        arm_compute::ITensor *out = _output->tensor();
-        auto_init_if_empty(*out->info(), *_graph_outputs[0]->tensor()->info());
-        node_ctx.add_output(out);
-
-        _func = OperationRegistry::get().find_operation(OperationType::ArithmeticAddition, target_hint)->configure(node_ctx);
-
-        for(auto &o : _graph_outputs)
-        {
-            o->allocate();
-        }
-    }
-
-    // Inherited methods overriden:
-    void run() override
-    {
-        ARM_COMPUTE_ERROR_ON(_graphs.size() < 1 || _graphs.size() > 2);
-
-        for(auto &g : _graphs)
-        {
-            ARM_COMPUTE_ERROR_ON(g.get() == nullptr);
-            g->run();
-        }
-
-        _func->run();
-    }
-
-private:
-    GraphContext                                _ctx;
-    std::unique_ptr<ITensorObject>              _input;
-    ITensorObject                              *_output;
-    std::unique_ptr<arm_compute::IFunction>     _func;
-    std::vector<std::unique_ptr<Graph>>         _graphs;
-    std::vector<std::unique_ptr<ITensorObject>> _graph_outputs;
-};
-
-std::unique_ptr<arm_compute::IFunction> ResidualLayer::instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output)
-{
-    ARM_COMPUTE_ERROR_ON_UNALLOCATED_TENSOR_OBJECT(input, output);
-    ARM_COMPUTE_ERROR_ON(dynamic_cast<Tensor *>(input) == nullptr);
-    ARM_COMPUTE_ERROR_ON(dynamic_cast<Tensor *>(output) == nullptr);
-
-    // Create residual function
-    auto func = arm_compute::support::cpp14::make_unique<ResidualFunction>(ctx, output);
-
-    if(_sub_graphs.size() == 1)
-    {
-        std::unique_ptr<ITensorObject> original_in;
-        original_in = arm_compute::support::cpp14::make_unique<SubTensor>(*dynamic_cast<Tensor *>(input),
-                                                                          input->tensor()->info()->tensor_shape(),
-                                                                          Coordinates());
-        func->set_input(std::move(original_in));
-    }
-
-    // Constuct all sub-graphs given the input/output
-    for(auto &sg : _sub_graphs)
-    {
-        ARM_COMPUTE_ERROR_ON(sg.get() == nullptr);
-
-        // IO buffers
-        std::unique_ptr<ITensorObject> in;
-        std::unique_ptr<ITensorObject> out;
-        std::unique_ptr<ITensorObject> func_in;
-
-        // Create input sub-tensor
-        if(!sg->has_input())
-        {
-            in = arm_compute::support::cpp14::make_unique<SubTensor>(*dynamic_cast<Tensor *>(input),
-                                                                     input->tensor()->info()->tensor_shape(),
-                                                                     Coordinates());
-        }
-
-        // Create output sub-tensor
-        if(!sg->has_output())
-        {
-            ITensorInfo *info = input->tensor()->info();
-            func_in           = arm_compute::support::cpp14::make_unique<Tensor>(TensorInfo(info->num_channels(), info->data_type(), info->fixed_point_position()));
-            func_in->set_target(ctx.hints().target_hint());
-            out = arm_compute::support::cpp14::make_unique<SubTensor>(func_in->tensor(),
-                                                                      TensorShape(),
-                                                                      Coordinates(0, 0, 0),
-                                                                      func_in->target(),
-                                                                      true);
-        }
-
-        // Construct sub_graph
-        auto g = sg->construct(ctx, std::move(in), std::move(out));
-
-        // Register graph to function
-        func->register_graph(std::move(g), std::move(func_in));
-    }
-
-    func->configure();
-
-    return std::move(func);
-}
diff --git a/src/graph/operations/CLSimpleOperations.cpp b/src/graph/operations/CLSimpleOperations.cpp
index fe56122009..94e3fe15f7 100644
--- a/src/graph/operations/CLSimpleOperations.cpp
+++ b/src/graph/operations/CLSimpleOperations.cpp
@@ -66,34 +66,6 @@ REGISTER_SIMPLE_OPERATION(CLActivationLayerOperation, OPENCL, OperationType::Act
     return std::move(activation);
 }
 
-/* Arithmetic addition */
-REGISTER_SIMPLE_OPERATION(CLArithmeticAdditionOperation, OPENCL, OperationType::ArithmeticAddition)
-{
-    ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 2);
-    ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
-    ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)) == nullptr);
-    ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.input(1)) == nullptr);
-    ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr);
-
-    // Extract IO and info
-    auto *in1 = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0));
-    auto *in2 = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(1));
-    auto *out = dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0));
-
-    auto addition = arm_compute::support::cpp14::make_unique<arm_compute::CLArithmeticAddition>();
-    addition->configure(in1, in2, out, ConvertPolicy::SATURATE);
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLArithmeticAddition"
-                               << " Data Type: " << in1->info()->data_type()
-                               << " Input 1 shape: " << in1->info()->tensor_shape()
-                               << " Input 2 shape: " << in2->info()->tensor_shape()
-                               << " Output shape: " << out->info()->tensor_shape()
-                               << std::endl);
-
-    return std::move(addition);
-}
-
 /* Batch Normalization Layer */
 REGISTER_SIMPLE_OPERATION(CLBatchNormalizationLayerOperation, OPENCL, OperationType::BatchNormalizationLayer)
 {
@@ -492,4 +464,4 @@ REGISTER_SIMPLE_OPERATION(CLSoftmaxLayerOperation, OPENCL, OperationType::Softma
                                << std::endl);
 
     return std::move(smx);
-}
+}
\ No newline at end of file
diff --git a/src/graph/operations/NESimpleOperations.cpp b/src/graph/operations/NESimpleOperations.cpp
index 4154b9a59c..265bed6b7a 100644
--- a/src/graph/operations/NESimpleOperations.cpp
+++ b/src/graph/operations/NESimpleOperations.cpp
@@ -66,34 +66,6 @@ REGISTER_SIMPLE_OPERATION(NEActivationLayerOperation, NEON, OperationType::Activ
     return std::move(activation);
 }
 
-/* Arithmetic addition */
-REGISTER_SIMPLE_OPERATION(NEArithmeticAdditionOperation, NEON, OperationType::ArithmeticAddition)
-{
-    ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 2);
-    ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1);
-    ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr);
-    ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(1)) == nullptr);
-    ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr);
-
-    // Extract IO and info
-    auto *in1 = dynamic_cast<arm_compute::ITensor *>(ctx.input(0));
-    auto *in2 = dynamic_cast<arm_compute::ITensor *>(ctx.input(1));
-    auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0));
-
-    auto addition = arm_compute::support::cpp14::make_unique<arm_compute::NEArithmeticAddition>();
-    addition->configure(in1, in2, out, ConvertPolicy::SATURATE);
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEArithmeticAddition"
-                               << " Data Type: " << in1->info()->data_type()
-                               << " Input 1 shape: " << in1->info()->tensor_shape()
-                               << " Input 2 shape: " << in2->info()->tensor_shape()
-                               << " Output shape: " << out->info()->tensor_shape()
-                               << std::endl);
-
-    return std::move(addition);
-}
-
 /* Batch Normalization Layer */
 REGISTER_SIMPLE_OPERATION(NEBatchNormalizationLayerOperation, NEON, OperationType::BatchNormalizationLayer)
 {
@@ -492,4 +464,4 @@ REGISTER_SIMPLE_OPERATION(NESoftmaxLayerOperation, NEON, OperationType::SoftmaxL
                                << std::endl);
 
     return std::move(smx);
-}
+}
\ No newline at end of file