COMPMID-1151: Templatize FunctionFactories.

Change-Id: Id1c68c3bf442c3fcff265041b260d007db7593cb
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/134027
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 79 insertions, 346 deletions

diff --git a/src/graph/backends/GLES/GCFunctionsFactory.cpp b/src/graph/backends/GLES/GCFunctionsFactory.cpp
index d53daf1109..e6bd5a5f02 100644
--- a/src/graph/backends/GLES/GCFunctionsFactory.cpp
+++ b/src/graph/backends/GLES/GCFunctionsFactory.cpp
@@ -25,16 +25,9 @@
 
 #include "arm_compute/core/utils/misc/Cast.h"
 #include "arm_compute/graph/Graph.h"
-#include "arm_compute/graph/GraphContext.h"
-#include "arm_compute/graph/Logger.h"
-#include "arm_compute/graph/TypePrinter.h"
-#include "arm_compute/graph/Types.h"
-#include "arm_compute/graph/backends/Utils.h"
-#include "arm_compute/graph/nodes/Nodes.h"
+#include "arm_compute/graph/backends/FunctionHelpers.h"
 #include "arm_compute/runtime/GLES_COMPUTE/GCFunctions.h"
 
-#include "support/ToolchainSupport.h"
-
 using namespace arm_compute::utils::cast;
 
 namespace arm_compute
@@ -43,121 +36,48 @@ namespace graph
 {
 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::IGCTensor *get_backing_tensor(arm_compute::graph::Tensor *tensor)
+/** Target specific information structure used to pass information to the layer templates */
+struct GCTargetInfo
 {
-    arm_compute::IGCTensor *backing_tensor = nullptr;
-    if(tensor != nullptr)
-    {
-        ARM_COMPUTE_ERROR_ON(tensor->desc().target != arm_compute::graph::Target::GC);
-        // Get backing tensor handle
-        ITensorHandle *tensor_handle = tensor->handle();
-        // Get backing tensor
-        backing_tensor = (tensor_handle != nullptr) ? polymorphic_cast<IGCTensor *>(&tensor_handle->tensor()) : nullptr;
-    }
+    using TensorType = arm_compute::IGCTensor;
+    static Target TargetType;
+};
 
-    return backing_tensor;
-}
+Target GCTargetInfo::TargetType = Target::GC;
 
-/** 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)
+/** Collection of GC convolution functions */
+struct GCConvolutionLayerFunctions
 {
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE(
-        "Creating GC 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
-    IGCTensor                *input    = get_backing_tensor(node.input(0));
-    IGCTensor                *output   = get_backing_tensor(node.output(0));
-    const ActivationLayerInfo act_info = node.activation_info();
-
-    // Create function
-    auto func = support::cpp14::make_unique<GCActivationLayer>();
-    func->configure(input, output, act_info);
-
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCActivationLayer"
-                               << " 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);
+    using GenericConvolutionLayer = GCConvolutionLayer;
+    using GEMMConvolutionLayer    = GCConvolutionLayer;
+    using DirectConvolutionLayer  = GCDirectConvolutionLayer;
+};
 
-    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)
+/** Collection of GC depthwise convolution functions */
+struct GCDepthwiseConvolutionLayerFunctions
 {
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating GC 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
-    IGCTensor                *input     = get_backing_tensor(node.input(0));
-    IGCTensor                *mean      = get_backing_tensor(node.input(1));
-    IGCTensor                *var       = get_backing_tensor(node.input(2));
-    IGCTensor                *beta      = get_backing_tensor(node.input(3));
-    IGCTensor                *gamma     = get_backing_tensor(node.input(4));
-    IGCTensor                *output    = get_backing_tensor(node.output(0));
-    const float               epsilon   = node.epsilon();
-    const ActivationLayerInfo fused_act = node.fused_activation();
+    using DepthwiseConvolutionLayer3x3 = GCDepthwiseConvolutionLayer3x3;
+};
 
-    // Create and configure function
-    auto func = support::cpp14::make_unique<GCBatchNormalizationLayer>();
-    func->configure(input, output, mean, var, beta, gamma, epsilon, fused_act);
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCBatchNormalizationLayer"
-                               << " 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);
-}
+/** Collection of GC element-wise functions */
+struct GCEltwiseFunctions
+{
+    using Addition       = GCArithmeticAddition;
+    using Multiplication = GCPixelWiseMultiplication;
+};
 
-/** 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)
+namespace detail
 {
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating GC 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);
+template <>
+std::unique_ptr<IFunction> create_convolution_layer<GCConvolutionLayerFunctions, GCTargetInfo>(ConvolutionLayerNode &node, GraphContext &ctx)
+{
+    validate_node<GCTargetInfo>(node, 3 /* expected inputs */, 1 /* expected outputs */);
 
     // Extract IO and info
-    IGCTensor *input   = get_backing_tensor(node.input(0));
-    IGCTensor *weights = get_backing_tensor(node.input(1));
-    IGCTensor *biases  = get_backing_tensor(node.input(2));
-    IGCTensor *output  = get_backing_tensor(node.output(0));
+    GCTargetInfo::TensorType *input   = get_backing_tensor<GCTargetInfo>(node.input(0));
+    GCTargetInfo::TensorType *weights = get_backing_tensor<GCTargetInfo>(node.input(1));
+    GCTargetInfo::TensorType *biases  = get_backing_tensor<GCTargetInfo>(node.input(2));
+    GCTargetInfo::TensorType *output  = get_backing_tensor<GCTargetInfo>(node.output(0));
 
     if(is_data_type_quantized_asymmetric(input->info()->data_type()))
     {
@@ -168,19 +88,21 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node,
     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::GC);
+    std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, GCTargetInfo::TargetType);
     std::unique_ptr<IFunction>      func;
     std::string                     func_name;
 
     if(conv_algorithm == ConvolutionMethod::DIRECT)
     {
-        std::tie(func, func_name) = create_named_function<GCDirectConvolutionLayer>(
-                                        std::string("GCDirectConvolutionLayer"), input, weights, biases, output, conv_info);
+        std::tie(func, func_name) = create_named_function<GCConvolutionLayerFunctions::DirectConvolutionLayer>(
+                                        std::string("DirectConvolutionLayer"),
+                                        input, weights, biases, output, conv_info);
     }
     else
     {
-        std::tie(func, func_name) = create_named_memory_managed_function<GCConvolutionLayer>(std::string("GCConvolutionLayer"), mm,
-                                                                                             input, weights, biases, output, conv_info);
+        std::tie(func, func_name) = create_named_memory_managed_function<GCConvolutionLayerFunctions::GenericConvolutionLayer>(
+                                        std::string("ConvolutionLayer"), mm,
+                                        input, weights, biases, output, conv_info);
     }
 
     // Log info
@@ -195,64 +117,16 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node,
     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 GC 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::IGCTensor *> inputs;
-    for(unsigned int i = 0; i < node.num_inputs(); ++i)
-    {
-        inputs.push_back(get_backing_tensor(node.input(i)));
-    }
-    IGCTensor *output = get_backing_tensor(node.output(0));
-
-    // Create and configure function
-    auto func = support::cpp14::make_unique<GCDepthConcatenateLayer>();
-    func->configure(inputs, output);
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCDepthConcatenateLayer"
-                               << " 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)
+template <>
+std::unique_ptr<IFunction> create_depthwise_convolution_layer<GCDepthwiseConvolutionLayerFunctions, GCTargetInfo>(DepthwiseConvolutionLayerNode &node)
 {
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE(
-        "Creating GC 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);
+    validate_node<GCTargetInfo>(node, 3 /* expected inputs */, 1 /* expected outputs */);
 
     // Extract IO and info
-    IGCTensor *input   = get_backing_tensor(node.input(0));
-    IGCTensor *weights = get_backing_tensor(node.input(1));
-    IGCTensor *biases  = get_backing_tensor(node.input(2));
-    IGCTensor *output  = get_backing_tensor(node.output(0));
+    GCTargetInfo::TensorType *input   = get_backing_tensor<GCTargetInfo>(node.input(0));
+    GCTargetInfo::TensorType *weights = get_backing_tensor<GCTargetInfo>(node.input(1));
+    GCTargetInfo::TensorType *biases  = get_backing_tensor<GCTargetInfo>(node.input(2));
+    GCTargetInfo::TensorType *output  = get_backing_tensor<GCTargetInfo>(node.output(0));
 
     if(is_data_type_quantized_asymmetric(input->info()->data_type()))
     {
@@ -267,8 +141,9 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti
     std::string                func_name;
     if(dwc_algorithm == DepthwiseConvolutionMethod::OPTIMIZED_3x3)
     {
-        std::tie(func, func_name) = create_named_function<GCDepthwiseConvolutionLayer3x3>(
-                                        std::string("GCDepthwiseConvolutionLayer3x3"), input, weights, biases, output, conv_info);
+        std::tie(func, func_name) = create_named_function<GCDepthwiseConvolutionLayerFunctions::DepthwiseConvolutionLayer3x3>(
+                                        std::string("DepthwiseConvolutionLayer3x3"),
+                                        input, weights, biases, output, conv_info);
     }
     else
     {
@@ -277,6 +152,7 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti
 
     // Log info
     ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
+                               << " Target " << GCTargetInfo::TargetType
                                << " Data Type: " << input->info()->data_type()
                                << " Input QuantInfo: " << input->info()->quantization_info()
                                << " Weights QuantInfo: " << weights->info()->quantization_info()
@@ -287,13 +163,8 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti
     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)
+template <>
+std::unique_ptr<IFunction> create_eltwise_layer<GCEltwiseFunctions, GCTargetInfo>(EltwiseLayerNode &node)
 {
     ARM_COMPUTE_LOG_GRAPH_VERBOSE(
         "Creating GC EltwiseLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
@@ -301,11 +172,11 @@ std::unique_ptr<IFunction> create_eltwise_layer(EltwiseLayerNode &node)
     ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
 
     // Extract IO and info
-    IGCTensor             *input1         = get_backing_tensor(node.input(0));
-    IGCTensor             *input2         = get_backing_tensor(node.input(1));
-    IGCTensor             *output         = get_backing_tensor(node.output(0));
-    const EltwiseOperation eltwise_op     = node.eltwise_operation();
-    const ConvertPolicy    convert_policy = node.convert_policy();
+    GCTargetInfo::TensorType *input1         = get_backing_tensor<GCTargetInfo>(node.input(0));
+    GCTargetInfo::TensorType *input2         = get_backing_tensor<GCTargetInfo>(node.input(1));
+    GCTargetInfo::TensorType *output         = get_backing_tensor<GCTargetInfo>(node.output(0));
+    const EltwiseOperation    eltwise_op     = node.eltwise_operation();
+    const ConvertPolicy       convert_policy = node.convert_policy();
     ARM_COMPUTE_ERROR_ON(input1 == nullptr);
     ARM_COMPUTE_ERROR_ON(input2 == nullptr);
     ARM_COMPUTE_ERROR_ON(output == nullptr);
@@ -314,9 +185,9 @@ std::unique_ptr<IFunction> create_eltwise_layer(EltwiseLayerNode &node)
     std::string                func_name;
     if(eltwise_op == EltwiseOperation::ADD)
     {
-        std::tie(func, func_name) = create_named_function<GCArithmeticAddition>(std::string("GCArithmeticAddition"),
-                                                                                input1, input2, output,
-                                                                                convert_policy);
+        std::tie(func, func_name) = create_named_function<GCEltwiseFunctions::Addition>(
+                                        std::string("GCArithmeticAddition"),
+                                        input1, input2, output, convert_policy);
     }
     else if(eltwise_op == EltwiseOperation::SUB)
     {
@@ -324,8 +195,9 @@ std::unique_ptr<IFunction> create_eltwise_layer(EltwiseLayerNode &node)
     }
     else if(eltwise_op == EltwiseOperation::MUL)
     {
-        std::tie(func, func_name) = create_named_function<GCPixelWiseMultiplication>(
-                                        std::string("GCPixelWiseMultiplication"), input1, input2, output, 1.f);
+        std::tie(func, func_name) = create_named_function<GCEltwiseFunctions::Multiplication>(
+                                        std::string("PixelWiseMultiplication"),
+                                        input1, input2, output, 1.f);
     }
     else
     {
@@ -333,157 +205,16 @@ std::unique_ptr<IFunction> create_eltwise_layer(EltwiseLayerNode &node)
     }
 
     // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
+    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.type()
+                               << " Target " << GCTargetInfo::TargetType
+                               << " Operation " << func_name
                                << " Data Type: " << input1->info()->data_type()
                                << " Shape : " << input1->info()->tensor_shape()
                                << std::endl);
 
     return 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 GC 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
-    IGCTensor *input   = get_backing_tensor(node.input(0));
-    IGCTensor *weights = get_backing_tensor(node.input(1));
-    IGCTensor *biases  = get_backing_tensor(node.input(2));
-    IGCTensor *output  = get_backing_tensor(node.output(0));
-
-    // Create and configure function
-    auto func = support::cpp14::make_unique<GCFullyConnectedLayer>(get_memory_manager(ctx, Target::GC));
-    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 GCFullyConnectedLayer"
-                               << " Data Type: " << input->info()->data_type()
-                               << " Input shape: " << input->info()->tensor_shape()
-                               << " Weights shape: " << weights->info()->tensor_shape()
-                               << " Biases Shape: " << biases->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)
-{
-    ARM_COMPUTE_LOG_GRAPH_VERBOSE(
-        "Creating GC 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
-    IGCTensor                   *input     = get_backing_tensor(node.input(0));
-    IGCTensor                   *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<GCNormalizationLayer>();
-    func->configure(input, output, norm_info);
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCNormalizationLayer"
-                               << " 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 GC 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
-    IGCTensor             *input     = get_backing_tensor(node.input(0));
-    IGCTensor             *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<GCPoolingLayer>();
-    func->configure(input, output, pool_info);
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCPoolingLayer"
-                               << " 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 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 GC 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
-    IGCTensor *input  = get_backing_tensor(node.input(0));
-    IGCTensor *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<GCSoftmaxLayer>(get_memory_manager(ctx, Target::CL));
-    func->configure(input, output, beta);
-
-    // Log info
-    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCSoftmaxLayer"
-                               << " 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
+} //namespace detail
 
 std::unique_ptr<IFunction> GCFunctionFactory::create(INode *node, GraphContext &ctx)
 {
@@ -496,25 +227,27 @@ std::unique_ptr<IFunction> GCFunctionFactory::create(INode *node, GraphContext &
     switch(type)
     {
         case NodeType::ActivationLayer:
-            return create_activation_layer(*polymorphic_downcast<ActivationLayerNode *>(node));
+            return detail::create_activation_layer<GCActivationLayer, GCTargetInfo>(*polymorphic_downcast<ActivationLayerNode *>(node));
         case NodeType::BatchNormalizationLayer:
-            return create_batch_normalization_layer(*polymorphic_downcast<BatchNormalizationLayerNode *>(node));
+            return detail::create_batch_normalization_layer<GCBatchNormalizationLayer, GCTargetInfo>(*polymorphic_downcast<BatchNormalizationLayerNode *>(node));
         case NodeType::ConvolutionLayer:
-            return create_convolution_layer(*polymorphic_downcast<ConvolutionLayerNode *>(node), ctx);
+            return detail::create_convolution_layer<GCConvolutionLayerFunctions, GCTargetInfo>(*polymorphic_downcast<ConvolutionLayerNode *>(node), ctx);
         case NodeType::DepthConcatenateLayer:
-            return create_depth_concatenate_layer(*polymorphic_downcast<DepthConcatenateLayerNode *>(node));
+            return detail::create_depth_concatenate_layer<GCDepthConcatenateLayer, GCTargetInfo>(*polymorphic_downcast<DepthConcatenateLayerNode *>(node));
         case NodeType::DepthwiseConvolutionLayer:
-            return create_depthwise_convolution_layer(*polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
+            return detail::create_depthwise_convolution_layer<GCDepthwiseConvolutionLayerFunctions, GCTargetInfo>(*polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
         case NodeType::EltwiseLayer:
-            return create_eltwise_layer(*polymorphic_downcast<EltwiseLayerNode *>(node));
+            return detail::create_eltwise_layer<GCEltwiseFunctions, GCTargetInfo>(*polymorphic_downcast<EltwiseLayerNode *>(node));
         case NodeType::FullyConnectedLayer:
-            return create_fully_connected_layer(*polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
+            return detail::create_fully_connected_layer<GCFullyConnectedLayer, GCTargetInfo>(*polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
         case NodeType::NormalizationLayer:
-            return create_normalization_layer(*polymorphic_downcast<NormalizationLayerNode *>(node));
+            return detail::create_normalization_layer<GCNormalizationLayer, GCTargetInfo>(*polymorphic_downcast<NormalizationLayerNode *>(node), ctx);
         case NodeType::PoolingLayer:
-            return create_pooling_layer(*polymorphic_downcast<PoolingLayerNode *>(node));
+            return detail::create_pooling_layer<GCPoolingLayer, GCTargetInfo>(*polymorphic_downcast<PoolingLayerNode *>(node));
+        case NodeType::ResizeLayer:
+            return detail::create_resize_layer<GCScale, GCTargetInfo>(*polymorphic_downcast<ResizeLayerNode *>(node));
         case NodeType::SoftmaxLayer:
-            return create_softmax_layer(*polymorphic_downcast<SoftmaxLayerNode *>(node), ctx);
+            return detail::create_softmax_layer<GCSoftmaxLayer, GCTargetInfo>(*polymorphic_downcast<SoftmaxLayerNode *>(node), ctx);
         default:
             return nullptr;
     }