COMPMID-797: Switch to new graph.

- Cleaned up build system

Change-Id: If2faa27ee5b31fa8b972836960ab3ef671059c8d
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/126435
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>

1 files changed, 0 insertions, 563 deletions

diff --git a/src/graph2/backends/NEON/NEFunctionFactory.cpp b/src/graph2/backends/NEON/NEFunctionFactory.cpp
deleted file mode 100644
index 933210377d..0000000000
--- a/src/graph2/backends/NEON/NEFunctionFactory.cpp
+++ /dev/null
@@ -1,563 +0,0 @@
-/*
- * 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/graph2/backends/NEON/NEFunctionFactory.h"
-
-#include "arm_compute/core/utils/misc/Cast.h"
-#include "arm_compute/graph2/Graph.h"
-#include "arm_compute/graph2/GraphContext.h"
-#include "arm_compute/graph2/Logger.h"
-#include "arm_compute/graph2/TypePrinter.h"
-#include "arm_compute/graph2/backends/Utils.h"
-#include "arm_compute/graph2/nodes/Nodes.h"
-#include "arm_compute/runtime/NEON/NEFunctions.h"
-#include "support/ToolchainSupport.h"
-
-using namespace arm_compute::utils::cast;
-
-namespace arm_compute
-{
-namespace graph2
-{
-namespace backends
-{
-namespace
-{
-/** Returns backing tensor of a given tensor
- *
- * @param[in] tensor Tensor to extract the backing tensor from
- *
- * @return Backing tensor if present else nullptr
- */
-arm_compute::ITensor *get_backing_tensor(arm_compute::graph2::Tensor *tensor)
-{
-    return ((tensor == nullptr) || (tensor->handle() == nullptr)) ? nullptr : &tensor->handle()->tensor();
-}
-
-/** Create a backend activation layer function
- *
- * @param[in] node Node to create the backend function for
- *
- * @return Backend activation layer function
- */
-std::unique_ptr<IFunction> create_activation_layer(ActivationLayerNode &node)
-{
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON ActivationLayerNode node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
-    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
-    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
-
-    // Extract IO and info
-    ITensor                  *input    = get_backing_tensor(node.input(0));
-    ITensor                  *output   = get_backing_tensor(node.output(0));
-    const ActivationLayerInfo act_info = node.activation_info();
-
-    // Create function
-    auto func = support::cpp14::make_unique<NEActivationLayer>();
-    func->configure(input, output, act_info);
-
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NEActivationLayer"
-                               << " Data Type: " << input->info()->data_type()
-                               << " Shape: " << input->info()->tensor_shape()
-                               << " Activation function: " << act_info.activation()
-                               << " a: " << act_info.a()
-                               << " b: " << act_info.b()
-                               << " InPlace : " << is_in_place_operation(input, output)
-                               << std::endl);
-
-    return std::move(func);
-}
-
-/** Create a backend batch normalization layer function
- *
- * @param[in] node Node to create the backend function for
- *
- * @return Backend batch normalization layer function
- */
-std::unique_ptr<IFunction> create_batch_normalization_layer(BatchNormalizationLayerNode &node)
-{
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON BatchNormalization node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
-
-    // TODO (geopin01) : Var and mean are compulsory, switch function to accept nullptr as beta and/or gamma
-    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 5);
-    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
-
-    // Extract IO and info
-    ITensor                  *input     = get_backing_tensor(node.input(0));
-    ITensor                  *mean      = get_backing_tensor(node.input(1));
-    ITensor                  *var       = get_backing_tensor(node.input(2));
-    ITensor                  *beta      = get_backing_tensor(node.input(3));
-    ITensor                  *gamma     = get_backing_tensor(node.input(4));
-    ITensor                  *output    = get_backing_tensor(node.output(0));
-    const float               epsilon   = node.epsilon();
-    const ActivationLayerInfo fused_act = node.fused_activation();
-
-    // Create and configure function
-    auto func = support::cpp14::make_unique<NEBatchNormalizationLayer>();
-    func->configure(input, output, mean, var, beta, gamma, epsilon, fused_act);
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NEBatchNormalizationLayer"
-                               << " Data Type: " << input->info()->data_type()
-                               << " Shape: " << input->info()->tensor_shape()
-                               << " Epsilon: " << epsilon << " "
-                               << (fused_act.enabled() ? to_string(fused_act.activation()) : "")
-                               << " InPlace : " << is_in_place_operation(input, output)
-                               << std::endl);
-
-    return std::move(func);
-}
-
-/** Create a backend convolution layer function
- *
- * @param[in] node Node to create the backend function for
- *
- * @return Backend convolution layer function
- */
-std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node, GraphContext &ctx)
-{
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON ConvolutionLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
-    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 3);
-    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
-
-    // Extract IO and info
-    ITensor                *input          = get_backing_tensor(node.input(0));
-    ITensor                *weights        = get_backing_tensor(node.input(1));
-    ITensor                *biases         = get_backing_tensor(node.input(2));
-    ITensor                *output         = get_backing_tensor(node.output(0));
-    const PadStrideInfo     conv_info      = node.convolution_info();
-    const ConvolutionMethod conv_algorithm = node.convolution_method();
-
-    // Create and configure function (we assume that functions have been validated before creation)
-    std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, Target::NEON);
-    std::unique_ptr<IFunction>      func;
-    std::string                     func_name;
-    if(conv_algorithm == ConvolutionMethod::DIRECT)
-    {
-        std::tie(func, func_name) = create_named_memory_managed_function<NEDirectConvolutionLayer>(std::string("NEDirectConvolutionLayer"), mm,
-                                                                                                   input, weights, biases, output, conv_info);
-    }
-    else if(conv_algorithm == ConvolutionMethod::GEMM)
-    {
-        std::tie(func, func_name) = create_named_memory_managed_function<NEGEMMConvolutionLayer>(std::string("NEGEMMConvolutionLayer"), mm,
-                                                                                                 input, weights, biases, output, conv_info);
-    }
-    else if(conv_algorithm == ConvolutionMethod::WINOGRAD)
-    {
-        std::tie(func, func_name) = create_named_memory_managed_function<NEWinogradLayer>(std::string("NEWinogradLayer"), mm,
-                                                                                          input, weights, biases, output, conv_info);
-    }
-    else
-    {
-        std::tie(func, func_name) = create_named_memory_managed_function<NEConvolutionLayer>(std::string("NEConvolutionLayer"), mm,
-                                                                                             input, weights, biases, output, conv_info);
-    }
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
-                               << " Data Type: " << input->info()->data_type()
-                               << " Input shape: " << input->info()->tensor_shape()
-                               << " Weights shape: " << weights->info()->tensor_shape()
-                               << " Output shape: " << output->info()->tensor_shape()
-                               << std::endl);
-    return func;
-}
-
-/** Create a backend layer depth concatenate function
- *
- * @param[in] node Node to create the backend function for
- *
- * @return Backend depth concatenate layer function
- */
-std::unique_ptr<arm_compute::IFunction> create_depth_concatenate_layer(DepthConcatenateLayerNode &node)
-{
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON DepthConcatenate node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
-    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
-
-    // Return nullptr if depth concatenate is switched off
-    if(!node.is_enabled())
-    {
-        return nullptr;
-    }
-
-    // Extract IO and info
-    std::vector<arm_compute::ITensor *> inputs;
-    for(unsigned int i = 0; i < node.num_inputs(); ++i)
-    {
-        inputs.push_back(get_backing_tensor(node.input(i)));
-    }
-    ITensor *output = get_backing_tensor(node.output(0));
-
-    // Create and configure function
-    auto func = support::cpp14::make_unique<NEDepthConcatenateLayer>();
-    func->configure(inputs, output);
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NEDepthConcatenateLayer"
-                               << " Data Type: " << output->info()->data_type()
-                               << " Shape: " << output->info()->tensor_shape()
-                               << " Num Inputs: " << inputs.size()
-                               << std::endl);
-
-    return std::move(func);
-}
-
-/** Create a backend layer depth-wise convolution function
- *
- * @param[in] node Node to create the backend function for
- *
- * @return Backend depth-wise convolution layer function
- */
-std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvolutionLayerNode &node)
-{
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON DepthwiseConvolutionLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
-    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 3);
-    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
-
-    // Extract IO and info
-    ITensor                         *input         = get_backing_tensor(node.input(0));
-    ITensor                         *weights       = get_backing_tensor(node.input(1));
-    ITensor                         *biases        = get_backing_tensor(node.input(2));
-    ITensor                         *output        = get_backing_tensor(node.output(0));
-    const PadStrideInfo              conv_info     = node.convolution_info();
-    const DepthwiseConvolutionMethod dwc_algorithm = node.depthwise_convolution_method();
-
-    // Create and configure function (we assume that functions have been validated before creation)
-    std::unique_ptr<IFunction> func;
-    std::string                func_name;
-    if(dwc_algorithm == DepthwiseConvolutionMethod::OPTIMIZED_3x3)
-    {
-        std::tie(func, func_name) = create_named_function<NEDepthwiseConvolutionLayer3x3>(std::string("NEDepthwiseConvolutionLayer3x3"),
-                                                                                          input, weights, biases, output, conv_info);
-    }
-    else
-    {
-        std::tie(func, func_name) = create_named_function<NEDepthwiseConvolutionLayer>(std::string("NEDepthwiseConvolutionLayer"),
-                                                                                       input, weights, biases, output, conv_info);
-    }
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
-                               << " Data Type: " << input->info()->data_type()
-                               << " Input shape: " << input->info()->tensor_shape()
-                               << " Weights shape: " << weights->info()->tensor_shape()
-                               << " Output shape: " << output->info()->tensor_shape()
-                               << std::endl);
-    return func;
-}
-
-/** Create a backend element-wise operation layer function
- *
- * @param[in] node Node to create the backend function for
- *
- * @return Backend element-wise operation layer function
- */
-std::unique_ptr<IFunction> create_eltwise_layer(EltwiseLayerNode &node)
-{
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON EltwiseLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
-    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 2);
-    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
-
-    // Extract IO and info
-    ITensor               *input1     = get_backing_tensor(node.input(0));
-    ITensor               *input2     = get_backing_tensor(node.input(1));
-    ITensor               *output     = get_backing_tensor(node.output(0));
-    const EltwiseOperation eltwise_op = node.eltwise_operation();
-    ARM_COMPUTE_ERROR_ON(input1 == nullptr);
-    ARM_COMPUTE_ERROR_ON(input2 == nullptr);
-    ARM_COMPUTE_ERROR_ON(output == nullptr);
-
-    std::unique_ptr<IFunction> func = nullptr;
-    std::string                func_name;
-    if(eltwise_op == EltwiseOperation::ADD)
-    {
-        std::tie(func, func_name) = create_named_function<NEArithmeticAddition>(std::string("NEArithmeticAddition"),
-                                                                                input1, input2, output, ConvertPolicy::SATURATE);
-    }
-    else if(eltwise_op == EltwiseOperation::SUB)
-    {
-        std::tie(func, func_name) = create_named_function<NEArithmeticSubtraction>(std::string("NEArithmeticSubtraction"),
-                                                                                   input1, input2, output, ConvertPolicy::SATURATE);
-    }
-    else if(eltwise_op == EltwiseOperation::MUL)
-    {
-        std::tie(func, func_name) = create_named_function<NEPixelWiseMultiplication>(std::string("NEPixelWiseMultiplication"),
-                                                                                     input1, input2, output, 1.f,
-                                                                                     ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
-    }
-    else
-    {
-        ARM_COMPUTE_ERROR("Unsupported element-wise operation!");
-    }
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
-                               << " Data Type: " << input1->info()->data_type()
-                               << " Shape : " << input1->info()->tensor_shape()
-                               << std::endl);
-
-    return func;
-}
-
-/** Create a backend flatten layer function
- *
- * @param[in] node Node to create the backend function for
- *
- * @return Backend flatten layer function
- */
-std::unique_ptr<IFunction> create_flatten_layer(FlattenLayerNode &node)
-{
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON FlattenLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
-    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
-    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
-
-    // Extract IO and info
-    ITensor *input  = get_backing_tensor(node.input(0));
-    ITensor *output = get_backing_tensor(node.output(0));
-
-    // Create and configure function
-    auto func = support::cpp14::make_unique<NEFlattenLayer>();
-    func->configure(input, output);
-    ARM_COMPUTE_ERROR_ON(input == nullptr);
-    ARM_COMPUTE_ERROR_ON(output == nullptr);
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NEFlattenLayer"
-                               << " Data Type: " << input->info()->data_type()
-                               << " Input shape: " << input->info()->tensor_shape()
-                               << " Output shape: " << output->info()->tensor_shape()
-                               << std::endl);
-
-    return std::move(func);
-}
-
-/** Create a backend fully connected layer function
- *
- * @param[in] node Node to create the backend function for
- *
- * @return Backend fully connected layer function
- */
-std::unique_ptr<IFunction> create_fully_connected_layer(FullyConnectedLayerNode &node, GraphContext &ctx)
-{
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON FullyConnectedLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
-    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 3);
-    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
-
-    // Extract IO and info
-    ITensor *input   = get_backing_tensor(node.input(0));
-    ITensor *weights = get_backing_tensor(node.input(1));
-    ITensor *biases  = get_backing_tensor(node.input(2));
-    ITensor *output  = get_backing_tensor(node.output(0));
-
-    // Create and configure function
-    auto func = support::cpp14::make_unique<NEFullyConnectedLayer>(get_memory_manager(ctx, Target::NEON));
-    func->configure(input, weights, biases, output);
-    ARM_COMPUTE_ERROR_ON(input == nullptr);
-    ARM_COMPUTE_ERROR_ON(weights == nullptr);
-    ARM_COMPUTE_ERROR_ON(output == nullptr);
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NEFullyConnectedLayer"
-                               << " Data Type: " << input->info()->data_type()
-                               << " Input shape: " << input->info()->tensor_shape()
-                               << " Weights shape: " << weights->info()->tensor_shape()
-                               << " Output shape: " << output->info()->tensor_shape()
-                               << std::endl);
-
-    return std::move(func);
-}
-
-/** Create a backend normalization layer function
- *
- * @param[in] node Node to create the backend function for
- *
- * @return Backend normalization layer function
- */
-std::unique_ptr<IFunction> create_normalization_layer(NormalizationLayerNode &node, GraphContext &ctx)
-{
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON NormalizationLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
-    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
-    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
-
-    // Extract IO and info
-    ITensor                     *input     = get_backing_tensor(node.input(0));
-    ITensor                     *output    = get_backing_tensor(node.output(0));
-    const NormalizationLayerInfo norm_info = node.normalization_info();
-    ARM_COMPUTE_ERROR_ON(input == nullptr);
-    ARM_COMPUTE_ERROR_ON(output == nullptr);
-
-    // Create and configure function
-    auto func = support::cpp14::make_unique<NENormalizationLayer>(get_memory_manager(ctx, Target::NEON));
-    func->configure(input, output, norm_info);
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NENormalizationLayer"
-                               << " Data Type: " << input->info()->data_type()
-                               << " Input shape: " << input->info()->tensor_shape()
-                               << " Output shape: " << output->info()->tensor_shape()
-                               << " Normalization info: " << norm_info.type()
-                               << std::endl);
-
-    return std::move(func);
-}
-
-/** Create a backend pooling layer function
- *
- * @param[in] node Node to create the backend function for
- *
- * @return Backend pooling layer function
- */
-std::unique_ptr<IFunction> create_pooling_layer(PoolingLayerNode &node)
-{
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON PoolingLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
-    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
-    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
-
-    // Extract IO and info
-    ITensor               *input     = get_backing_tensor(node.input(0));
-    ITensor               *output    = get_backing_tensor(node.output(0));
-    const PoolingLayerInfo pool_info = node.pooling_info();
-    ARM_COMPUTE_ERROR_ON(input == nullptr);
-    ARM_COMPUTE_ERROR_ON(output == nullptr);
-
-    // Create and configure function
-    auto func = support::cpp14::make_unique<NEPoolingLayer>();
-    func->configure(input, output, pool_info);
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NEPoolingLayer"
-                               << " Data Type: " << input->info()->data_type()
-                               << " Input shape: " << input->info()->tensor_shape()
-                               << " Output shape: " << output->info()->tensor_shape()
-                               << " Pooling info: " << pool_info.pool_type()
-                               << std::endl);
-
-    return std::move(func);
-}
-
-/** Create a backend reshape layer function
- *
- * @param[in] node Node to create the backend function for
- *
- * @return Backend reshape layer function
- */
-std::unique_ptr<IFunction> create_reshape_layer(ReshapeLayerNode &node)
-{
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON ReshapeLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
-    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
-    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
-
-    // Extract IO and info
-    ITensor *input  = get_backing_tensor(node.input(0));
-    ITensor *output = get_backing_tensor(node.output(0));
-    ARM_COMPUTE_ERROR_ON(input == nullptr);
-    ARM_COMPUTE_ERROR_ON(output == nullptr);
-
-    // Create and configure function
-    auto func = support::cpp14::make_unique<NEReshapeLayer>();
-    func->configure(input, output);
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NEReshapeLayer"
-                               << " Data Type: " << input->info()->data_type()
-                               << " Input shape: " << input->info()->tensor_shape()
-                               << " Output shape: " << output->info()->tensor_shape()
-                               << std::endl);
-
-    return std::move(func);
-}
-
-/** Create a backend softmax layer function
- *
- * @param[in] node Node to create the backend function for
- *
- * @return Backend softmax layer function
- */
-std::unique_ptr<IFunction> create_softmax_layer(SoftmaxLayerNode &node, GraphContext &ctx)
-{
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON SoftmaxLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
-    ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1);
-    ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
-
-    // Extract IO and info
-    ITensor    *input  = get_backing_tensor(node.input(0));
-    ITensor    *output = get_backing_tensor(node.output(0));
-    const float beta   = node.beta();
-    ARM_COMPUTE_ERROR_ON(input == nullptr);
-    ARM_COMPUTE_ERROR_ON(output == nullptr);
-
-    // Create and configure function
-    auto func = support::cpp14::make_unique<NESoftmaxLayer>(get_memory_manager(ctx, Target::NEON));
-    func->configure(input, output, beta);
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NESoftmaxLayer"
-                               << " Data Type: " << input->info()->data_type()
-                               << " Input shape: " << input->info()->tensor_shape()
-                               << " Output shape: " << output->info()->tensor_shape()
-                               << std::endl);
-
-    return std::move(func);
-}
-} // namespace
-
-std::unique_ptr<IFunction> NEFunctionFactory::create(INode *node, GraphContext &ctx)
-{
-    if(node == nullptr)
-    {
-        return nullptr;
-    }
-
-    NodeType type = node->type();
-    switch(type)
-    {
-        case NodeType::ActivationLayer:
-            return create_activation_layer(*polymorphic_downcast<ActivationLayerNode *>(node));
-        case NodeType::BatchNormalizationLayer:
-            return create_batch_normalization_layer(*polymorphic_downcast<BatchNormalizationLayerNode *>(node));
-        case NodeType::ConvolutionLayer:
-            return create_convolution_layer(*polymorphic_downcast<ConvolutionLayerNode *>(node), ctx);
-        case NodeType::DepthConcatenateLayer:
-            return create_depth_concatenate_layer(*polymorphic_downcast<DepthConcatenateLayerNode *>(node));
-        case NodeType::DepthwiseConvolutionLayer:
-            return create_depthwise_convolution_layer(*polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
-        case NodeType::EltwiseLayer:
-            return create_eltwise_layer(*polymorphic_downcast<EltwiseLayerNode *>(node));
-        case NodeType::FlattenLayer:
-            return create_flatten_layer(*polymorphic_downcast<FlattenLayerNode *>(node));
-        case NodeType::FullyConnectedLayer:
-            return create_fully_connected_layer(*polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
-        case NodeType::NormalizationLayer:
-            return create_normalization_layer(*polymorphic_downcast<NormalizationLayerNode *>(node), ctx);
-        case NodeType::PoolingLayer:
-            return create_pooling_layer(*polymorphic_downcast<PoolingLayerNode *>(node));
-        case NodeType::ReshapeLayer:
-            return create_reshape_layer(*polymorphic_downcast<ReshapeLayerNode *>(node));
-        case NodeType::SoftmaxLayer:
-            return create_softmax_layer(*polymorphic_downcast<SoftmaxLayerNode *>(node), ctx);
-        default:
-            return nullptr;
-    }
-}
-} // namespace backends
-} // namespace graph2
-} // namespace arm_compute
\ No newline at end of file