COMPMID-1740: Fuse batch normalization with Convolution Layer at graph level

Change-Id: I77ca51c2c72783cc26a099a6a9c3210cdbbe822d
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/797
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

6 files changed, 337 insertions, 66 deletions

diff --git a/src/graph/backends/CL/CLFunctionsFactory.cpp b/src/graph/backends/CL/CLFunctionsFactory.cpp
index b9e3ddc0a3..7473ff480f 100644
--- a/src/graph/backends/CL/CLFunctionsFactory.cpp
+++ b/src/graph/backends/CL/CLFunctionsFactory.cpp
@@ -40,7 +40,8 @@ namespace backends
 /** Target specific information structure used to pass information to the layer templates */
 struct CLTargetInfo
 {
-    using TensorType = arm_compute::ICLTensor;
+    using TensorType         = arm_compute::ICLTensor;
+    using TensorConcreteType = CLTensor;
     static Target TargetType;
 };
 
@@ -69,6 +70,14 @@ struct CLEltwiseFunctions
     using Subtraction    = CLArithmeticSubtraction;
     using Multiplication = CLPixelWiseMultiplication;
 };
+
+/** Function and tensor types to be used inside a CL fused convolution/batch normalization layer */
+struct CLFusedLayerTypes
+{
+    using ConvolutionLayer       = CLConvolutionLayer;
+    using FuseBatchNormalization = CLFuseBatchNormalization;
+};
+
 // TODO (isagot01): Remove once we support heterogeneous scheduling at function level
 /** Wrapper for the CPP Function in the OpenCL backend **/
 class CPPWrapperFunction : public IFunction
@@ -192,6 +201,8 @@ std::unique_ptr<IFunction> CLFunctionFactory::create(INode *node, GraphContext &
             return detail::create_flatten_layer<CLFlattenLayer, CLTargetInfo>(*polymorphic_downcast<FlattenLayerNode *>(node));
         case NodeType::FullyConnectedLayer:
             return detail::create_fully_connected_layer<CLFullyConnectedLayer, CLTargetInfo>(*polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
+        case NodeType::FusedConvolutionBatchNormalizationLayer:
+            return detail::create_fused_convolution_batch_normalization_layer<CLFusedLayerTypes, CLTargetInfo>(*polymorphic_downcast<FusedConvolutionBatchNormalizationNode *>(node));
         case NodeType::GenerateProposalsLayer:
             return detail::create_generate_proposals_layer<CLGenerateProposalsLayer, CLTargetInfo>(*polymorphic_downcast<GenerateProposalsLayerNode *>(node), ctx);
         case NodeType::NormalizationLayer:
diff --git a/src/graph/backends/NEON/NEFunctionFactory.cpp b/src/graph/backends/NEON/NEFunctionFactory.cpp
index dc987dd86e..f23845c314 100644
--- a/src/graph/backends/NEON/NEFunctionFactory.cpp
+++ b/src/graph/backends/NEON/NEFunctionFactory.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -46,7 +46,8 @@ namespace backends
 /** Target specific information structure used to pass information to the layer templates */
 struct NETargetInfo
 {
-    using TensorType = arm_compute::ITensor;
+    using TensorType         = arm_compute::ITensor;
+    using TensorConcreteType = arm_compute::Tensor;
     static Target TargetType;
 };
 
@@ -76,6 +77,13 @@ struct NEEltwiseFunctions
     using Multiplication = NEPixelWiseMultiplication;
 };
 
+/** Function and tensor types to be used inside a NEON fused convolution/batch normalization layer */
+struct NEFusedLayerTypes
+{
+    using ConvolutionLayer       = NEConvolutionLayer;
+    using FuseBatchNormalization = NEFuseBatchNormalization;
+};
+
 namespace detail
 {
 // Specialized functions
@@ -210,6 +218,8 @@ std::unique_ptr<IFunction> NEFunctionFactory::create(INode *node, GraphContext &
             return detail::create_flatten_layer<NEFlattenLayer, NETargetInfo>(*polymorphic_downcast<FlattenLayerNode *>(node));
         case NodeType::FullyConnectedLayer:
             return detail::create_fully_connected_layer<NEFullyConnectedLayer, NETargetInfo>(*polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
+        case NodeType::FusedConvolutionBatchNormalizationLayer:
+            return detail::create_fused_convolution_batch_normalization_layer<NEFusedLayerTypes, NETargetInfo>(*polymorphic_downcast<FusedConvolutionBatchNormalizationNode *>(node));
         case NodeType::NormalizationLayer:
             return detail::create_normalization_layer<NENormalizationLayer, NETargetInfo>(*polymorphic_downcast<NormalizationLayerNode *>(node), ctx);
         case NodeType::PermuteLayer:
diff --git a/src/graph/mutators/NodeFusionMutator.cpp b/src/graph/mutators/NodeFusionMutator.cpp
index 9dc02d1ad1..445748caf7 100644
--- a/src/graph/mutators/NodeFusionMutator.cpp
+++ b/src/graph/mutators/NodeFusionMutator.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -23,9 +23,11 @@
  */
 #include "arm_compute/graph/mutators/NodeFusionMutator.h"
 
-#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/GraphBuilder.h"
 #include "arm_compute/graph/Logger.h"
 #include "arm_compute/graph/Utils.h"
+#include "arm_compute/graph/backends/BackendRegistry.h"
+#include "arm_compute/graph/nodes/FusedConvolutionBatchNormalizationNode.h"
 #include "arm_compute/graph/nodes/Nodes.h"
 
 #include "arm_compute/core/utils/misc/Cast.h"
@@ -38,69 +40,156 @@ namespace graph
 {
 namespace detail
 {
+void fuse_convolution_with_batch_normalization(Graph &g, const Edge *output_edge)
+{
+    ARM_COMPUTE_ERROR_ON(output_edge == nullptr);
+
+    auto *conv_node = arm_compute::utils::cast::polymorphic_downcast<ConvolutionLayerNode *>(output_edge->producer());
+    auto *bn_node   = arm_compute::utils::cast::polymorphic_downcast<BatchNormalizationLayerNode *>(output_edge->consumer());
+
+    // Not fusing if number of groups is greater than 1
+    if(conv_node->num_groups() > 1)
+    {
+        return;
+    }
+
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing convolution node with ID : " << output_edge->producer_id()
+                                  << " with BatchNormalization Layer node with ID : " << output_edge->consumer_id() << std::endl);
+
+    // Prevent fusion if fused node has an output accessor
+    if(conv_node->output(0)->accessor() == nullptr)
+    {
+        const Target assigned_target = conv_node->assigned_target();
+
+        // Extract conv inputs
+        const auto   conv_input_id   = conv_node->input_edge(0)->producer_id();
+        const auto   conv_weights_id = conv_node->input_edge(1)->producer_id();
+        const auto   out_quant_info  = conv_node->output(0)->desc().quant_info;
+        const auto   conv_info       = conv_node->convolution_info();
+        const auto   conv_method     = conv_node->convolution_method();
+        const auto   num_groups      = conv_node->num_groups();
+        const auto   act_info        = bn_node->fused_activation();
+        FastMathHint fast_math_hint  = conv_node->fast_math_hint();
+
+        // Extract bn inputs
+        const auto bn_mean_id  = bn_node->input_edge(1)->producer_id();
+        const auto bn_var_id   = bn_node->input_edge(2)->producer_id();
+        const auto bn_beta_id  = bn_node->input_edge(3)->producer_id();
+        const auto bn_gamma_id = bn_node->input_edge(4)->producer_id();
+        const auto epsilon     = bn_node->epsilon();
+
+        // Create the fused node
+        const NodeID fused_id = g.add_node<FusedConvolutionBatchNormalizationNode>(epsilon, conv_info, num_groups, conv_method, fast_math_hint, out_quant_info, act_info);
+
+        if(conv_node->input_edge(2) != nullptr)
+        {
+            auto conv_bias_id = conv_node->input_edge(2)->producer_id();
+            g.add_connection(conv_bias_id, 0, fused_id, 2);
+        }
+
+        // Add connections from the conv/batch_norm inputs to the fused node
+        g.add_connection(conv_input_id, 0, fused_id, 0);
+        g.add_connection(conv_weights_id, 0, fused_id, 1);
+        g.add_connection(bn_mean_id, 0, fused_id, 3);
+        g.add_connection(bn_var_id, 0, fused_id, 4);
+        g.add_connection(bn_beta_id, 0, fused_id, 5);
+        g.add_connection(bn_gamma_id, 0, fused_id, 6);
+
+        auto                     fused_node       = g.node(fused_id);
+        std::vector<NodeIdxPair> bn_driving_nodes = get_driving_nodes(*bn_node);
+
+        // Extract batch normalization node accessor if any
+        auto bn_node_accessor = bn_node->output(0)->extract_accessor();
+        auto bn_node_name     = bn_node->name();
+
+        // Remove batch normalization node
+        g.remove_node(bn_node->id());
+
+        // Get driving nodes of batch normalization node
+        for(auto &driving_node : bn_driving_nodes)
+        {
+            g.add_connection(fused_id, 0, driving_node.node_id, driving_node.index);
+            configure_tensor(fused_node->output(0));
+        }
+        // Update fused node outputs
+        fused_node->output(0)->set_accessor(std::move(bn_node_accessor));
+        fused_node->set_assigned_target(assigned_target);
+        fused_node->set_common_node_parameters(NodeParams{ conv_node->name() + "+" + bn_node_name, assigned_target });
+
+        // Remove convolution node
+        g.remove_node(conv_node->id());
+    }
+    else
+    {
+        ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of convolution with batch normalization due to the presence of an output accessor\n");
+    }
+}
+
 template <typename N>
-void fuse_node_with_activation(Graph                              &g,
-                               const std::set<Activation>         &supported_fused_activations,
-                               std::function<bool(INode &)> const &prec)
+void fuse_node_with_activation(Graph &g, const Edge *output_edge, const std::set<Activation> &supported_fused_activations)
+{
+    ARM_COMPUTE_ERROR_ON(output_edge == nullptr);
+
+    auto *n_node   = arm_compute::utils::cast::polymorphic_downcast<N *>(output_edge->producer());
+    auto *act_node = arm_compute::utils::cast::polymorphic_downcast<ActivationLayerNode *>(output_edge->consumer());
+
+    ARM_COMPUTE_ERROR_ON(act_node->output(0) == nullptr || n_node->output(0) == nullptr);
+
+    // Check if activation is supported for fusion
+    if(supported_fused_activations.count(act_node->activation_info().activation()) == 0)
+    {
+        return;
+    }
+
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing node with ID : " << output_edge->producer_id()
+                                  << " with Activation Layer node with ID : " << output_edge->consumer_id() << std::endl);
+
+    // Prevent fusion if fused node has an output accessor
+    if(n_node->output(0)->accessor() == nullptr)
+    {
+        // Get driving nodes of activation node
+        std::vector<NodeIdxPair> act_driving_nodes = get_driving_nodes(*act_node);
+
+        // Set activation info to fused node
+        n_node->set_fused_activation(act_node->activation_info());
+
+        // Extract activation node accessor if any
+        auto act_node_accessor = act_node->output(0)->extract_accessor();
+
+        // Remove activation node
+        g.remove_node(act_node->id());
+
+        // Update fused node outputs
+        for(auto &driving_node : act_driving_nodes)
+        {
+            g.add_connection(n_node->id(), 0, driving_node.node_id, driving_node.index);
+        }
+
+        // Update accessor to fused node
+        n_node->output(0)->set_accessor(std::move(act_node_accessor));
+    }
+    else
+    {
+        ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of node with activation due to the presence of an output accessor\n");
+    }
+}
+
+template <typename N1, typename N2, typename F, typename... Args>
+void fuse_layer(Graph &g, std::function<bool(INode &)> const &prec, const F fuse_fcn, Args &&... optional_arguments)
 {
     // Not interested in the order of nodes
     for(auto &node : g.nodes())
     {
         // Check if the node is of type N and not a branching node
-        if(node && node->type() == N::node_type && node->output_edges().size() == 1)
+        if(node && node->type() == N1::node_type && node->output_edges().size() == 1)
         {
-            auto output_edge_id = *node->output_edges().begin();
-            auto output_edge    = g.edge(output_edge_id);
+            const auto output_edge_id = *node->output_edges().begin();
+            const auto output_edge    = g.edge(output_edge_id);
+
             // Check if following node is an activation layer node
-            if((output_edge != nullptr) && (output_edge->consumer() != nullptr) && (output_edge->consumer()->type() == NodeType::ActivationLayer))
+            if((output_edge != nullptr) && (output_edge->consumer() != nullptr) && (output_edge->consumer()->type() == N2::node_type) && prec(*output_edge->producer()))
             {
-                auto *n_node   = arm_compute::utils::cast::polymorphic_downcast<N *>(output_edge->producer());
-                auto *act_node = arm_compute::utils::cast::polymorphic_downcast<ActivationLayerNode *>(output_edge->consumer());
-
-                ARM_COMPUTE_ERROR_ON(act_node->output(0) == nullptr || n_node->output(0) == nullptr);
-
-                // Check given precondition
-                if(!prec(*n_node))
-                {
-                    continue;
-                }
-                // Check if activation is supported for fusion
-                if(supported_fused_activations.count(act_node->activation_info().activation()) == 0)
-                {
-                    continue;
-                }
-
-                ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing node with ID : " << output_edge->producer_id()
-                                              << " with Activation Layer node with ID : " << output_edge->consumer_id() << std::endl);
-
-                // Prevent fusion if fused node has an output accessor
-                if(n_node->output(0)->accessor() == nullptr)
-                {
-                    // Get driving nodes of activation node
-                    std::vector<NodeIdxPair> act_driving_nodes = get_driving_nodes(*act_node);
-
-                    // Set activation info to fused node
-                    n_node->set_fused_activation(act_node->activation_info());
-
-                    // Extract activation node accessor if any
-                    auto act_node_accessor = act_node->output(0)->extract_accessor();
-
-                    // Remove activation node
-                    g.remove_node(act_node->id());
-
-                    // Update fused node outputs
-                    for(auto &driving_node : act_driving_nodes)
-                    {
-                        g.add_connection(n_node->id(), 0, driving_node.node_id, driving_node.index);
-                    }
-
-                    // Update accessor to fused node
-                    n_node->output(0)->set_accessor(std::move(act_node_accessor));
-                }
-                else
-                {
-                    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of node with activation due to the presence of an output accessor\n");
-                }
+                fuse_fcn(g, output_edge, optional_arguments...);
             }
         }
     }
@@ -129,9 +218,10 @@ void NodeFusionMutator::mutate(Graph &g)
     };
 
     // Fusion mutations
-    detail::fuse_node_with_activation<BatchNormalizationLayerNode>(g, supported_fused_activations, empty_prec);
-    detail::fuse_node_with_activation<ConvolutionLayerNode>(g, supported_fused_activations, empty_prec);
-    detail::fuse_node_with_activation<DepthwiseConvolutionLayerNode>(g, supported_fused_activations, qs8_prec);
+    detail::fuse_layer<BatchNormalizationLayerNode, ActivationLayerNode>(g, empty_prec, detail::fuse_node_with_activation<BatchNormalizationLayerNode>, supported_fused_activations);
+    detail::fuse_layer<ConvolutionLayerNode, ActivationLayerNode>(g, empty_prec, detail::fuse_node_with_activation<ConvolutionLayerNode>, supported_fused_activations);
+    detail::fuse_layer<DepthwiseConvolutionLayerNode, ActivationLayerNode>(g, qs8_prec, detail::fuse_node_with_activation<DepthwiseConvolutionLayerNode>, supported_fused_activations);
+    detail::fuse_layer<ConvolutionLayerNode, BatchNormalizationLayerNode>(g, empty_prec, detail::fuse_convolution_with_batch_normalization);
 }
 } // namespace graph
 } // namespace arm_compute
diff --git a/src/graph/nodes/ActivationLayerNode.cpp b/src/graph/nodes/ActivationLayerNode.cpp
index 414684cf30..85cb10bbdb 100644
--- a/src/graph/nodes/ActivationLayerNode.cpp
+++ b/src/graph/nodes/ActivationLayerNode.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -67,7 +67,7 @@ TensorDescriptor ActivationLayerNode::configure_output(size_t idx) const
 
 NodeType ActivationLayerNode::type() const
 {
-    return NodeType::ActivationLayer;
+    return ActivationLayerNode::node_type;
 }
 
 void ActivationLayerNode::accept(INodeVisitor &v)
@@ -75,4 +75,4 @@ void ActivationLayerNode::accept(INodeVisitor &v)
     v.visit(*this);
 }
 } // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp b/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp
new file mode 100644
index 0000000000..27a348fa69
--- /dev/null
+++ b/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp
@@ -0,0 +1,152 @@
+/*
+ * Copyright (c) 2019 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/FusedConvolutionBatchNormalizationNode.h"
+
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/INodeVisitor.h"
+#include "arm_compute/graph/Utils.h"
+
+namespace arm_compute
+{
+namespace graph
+{
+FusedConvolutionBatchNormalizationNode::FusedConvolutionBatchNormalizationNode(float epsilon, PadStrideInfo info,
+                                                                               unsigned int      num_groups,
+                                                                               ConvolutionMethod method,
+                                                                               FastMathHint      fast_math_hint,
+                                                                               QuantizationInfo out_quant_info, ActivationLayerInfo fused_activation)
+    : _epsilon(epsilon), _info(std::move(info)), _num_groups(num_groups), _method(method), _fast_math_hint(fast_math_hint), _out_quant_info(out_quant_info), _fused_activation(fused_activation)
+{
+    _input_edges.resize(7, EmptyEdgeID);
+    _outputs.resize(1, NullTensorID);
+}
+
+void FusedConvolutionBatchNormalizationNode::set_convolution_method(ConvolutionMethod method)
+{
+    _method = method;
+}
+
+float FusedConvolutionBatchNormalizationNode::epsilon() const
+{
+    return _epsilon;
+}
+
+ConvolutionMethod FusedConvolutionBatchNormalizationNode::convolution_method() const
+{
+    return _method;
+}
+
+void FusedConvolutionBatchNormalizationNode::set_fast_math_hint(FastMathHint hint)
+{
+    _fast_math_hint = hint;
+}
+
+FastMathHint FusedConvolutionBatchNormalizationNode::fast_math_hint() const
+{
+    return _fast_math_hint;
+}
+
+PadStrideInfo FusedConvolutionBatchNormalizationNode::convolution_info() const
+{
+    return _info;
+}
+
+unsigned int FusedConvolutionBatchNormalizationNode::num_groups() const
+{
+    return _num_groups;
+}
+
+ActivationLayerInfo FusedConvolutionBatchNormalizationNode::fused_activation() const
+{
+    return _fused_activation;
+}
+
+void FusedConvolutionBatchNormalizationNode::set_fused_activation(ActivationLayerInfo fused_activation)
+{
+    _fused_activation = fused_activation;
+}
+
+TensorDescriptor FusedConvolutionBatchNormalizationNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
+                                                                                   const TensorDescriptor &weights_descriptor,
+                                                                                   const PadStrideInfo    &info)
+{
+    unsigned int output_width  = 0;
+    unsigned int output_height = 0;
+
+    const unsigned int input_width   = get_dimension_size(input_descriptor, DataLayoutDimension::WIDTH);
+    const unsigned int input_height  = get_dimension_size(input_descriptor, DataLayoutDimension::HEIGHT);
+    const unsigned int kernel_width  = get_dimension_size(weights_descriptor, DataLayoutDimension::WIDTH);
+    const unsigned int kernel_height = get_dimension_size(weights_descriptor, DataLayoutDimension::HEIGHT);
+
+    std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
+
+    TensorDescriptor output_descriptor = input_descriptor;
+    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::WIDTH), output_width);
+    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::HEIGHT), output_height);
+    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::CHANNEL), weights_descriptor.shape[3]);
+
+    return output_descriptor;
+}
+
+bool FusedConvolutionBatchNormalizationNode::forward_descriptors()
+{
+    if((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
+    {
+        Tensor *dst = output(0);
+        ARM_COMPUTE_ERROR_ON(dst == nullptr);
+        dst->desc() = configure_output(0);
+        return true;
+    }
+    return false;
+}
+
+TensorDescriptor FusedConvolutionBatchNormalizationNode::configure_output(size_t idx) const
+{
+    ARM_COMPUTE_UNUSED(idx);
+    const Tensor *src     = input(0);
+    const Tensor *weights = input(1);
+
+    ARM_COMPUTE_ERROR_ON(src == nullptr || weights == nullptr);
+
+    TensorDescriptor output_info = compute_output_descriptor(src->desc(), weights->desc(), _info);
+    if(!_out_quant_info.empty())
+    {
+        output_info.quant_info = _out_quant_info;
+    }
+
+    return output_info;
+}
+
+NodeType FusedConvolutionBatchNormalizationNode::type() const
+{
+    return FusedConvolutionBatchNormalizationNode::node_type;
+}
+
+void FusedConvolutionBatchNormalizationNode::accept(INodeVisitor &v)
+{
+    v.visit(*this);
+}
+} // namespace graph
+} // namespace arm_compute
diff --git a/src/graph/printers/DotGraphPrinter.cpp b/src/graph/printers/DotGraphPrinter.cpp
index ef156ea252..c939de1b64 100644
--- a/src/graph/printers/DotGraphPrinter.cpp
+++ b/src/graph/printers/DotGraphPrinter.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -77,6 +77,14 @@ void DotGraphVisitor::visit(EltwiseLayerNode &n)
     _info = ss.str();
 }
 
+void DotGraphVisitor::visit(FusedConvolutionBatchNormalizationNode &n)
+{
+    ARM_COMPUTE_UNUSED(n);
+    std::stringstream ss;
+    ss << "FusedConvolutionBatchNormalizationNode";
+    _info = ss.str();
+}
+
 void DotGraphVisitor::visit(NormalizationLayerNode &n)
 {
     std::stringstream ss;