COMPMID-1176: Adds nodes to the graph.

Nodes added:
-ChannelShuffle
-Resize
-Deconvolution
-Dummy (used for performance analysis and debugging)

Change-Id: Iad19960cbbce6e25532f77bfd34b2292c0ca9781
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/131672
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

9 files changed, 635 insertions, 4 deletions

diff --git a/src/graph/GraphBuilder.cpp b/src/graph/GraphBuilder.cpp
index 4c5d30a33f..a20920a74c 100644
--- a/src/graph/GraphBuilder.cpp
+++ b/src/graph/GraphBuilder.cpp
@@ -28,6 +28,8 @@
 #include "arm_compute/graph/algorithms/BFS.h"
 #include "arm_compute/graph/nodes/Nodes.h"
 
+#include "support/ToolchainSupport.h"
+
 #define CHECK_NODEIDX_PAIR(pair, g) \
     ARM_COMPUTE_ERROR_ON(((pair).node_id >= (g).nodes().size()) || ((g).node((pair).node_id) == nullptr) || ((pair).index >= (g).node((pair).node_id)->num_outputs()));
 
@@ -80,7 +82,7 @@ NodeID create_simple_single_input_output_node(Graph &g, NodeParams &params, Node
     return nid;
 }
 
-NodeID create_grouped_convolution(Graph &g, NodeParams &params, NodeIdxPair input, NodeID weights, NodeID bias,
+NodeID create_grouped_convolution(Graph &g, const NodeParams &params, NodeIdxPair input, NodeID weights, NodeID bias,
                                   PadStrideInfo conv_info, ConvolutionMethod method, FastMathHint fast_math_hint, unsigned int num_groups)
 {
     bool has_bias = (bias != EmptyNodeID);
@@ -102,14 +104,20 @@ NodeID create_grouped_convolution(Graph &g, NodeParams &params, NodeIdxPair inpu
     std::vector<NodeIdxPair> convolution_outputs;
     for(unsigned int i = 0; i < num_groups; ++i)
     {
-        NodeID conv_nid = g.add_node<ConvolutionLayerNode>(conv_info, method, fast_math_hint);
+        NodeParams group_params = params;
+        NodeID     conv_nid     = g.add_node<ConvolutionLayerNode>(conv_info, method, fast_math_hint);
         g.add_connection(input_split, i, conv_nid, 0);
         g.add_connection(weights_split, i, conv_nid, 1);
         if(has_bias)
         {
             g.add_connection(bias_split, i, conv_nid, 2);
         }
-        set_node_params(g, conv_nid, params);
+        // Add group name
+        if(!group_params.name.empty())
+        {
+            group_params.name.append("_g" + arm_compute::support::cpp11::to_string(i));
+        }
+        set_node_params(g, conv_nid, group_params);
         convolution_outputs.push_back({ conv_nid, 0 });
     }
 
@@ -203,6 +211,11 @@ NodeID GraphBuilder::add_batch_normalization_node(Graph &g, NodeParams params, N
     return batch_norm_nid;
 }
 
+NodeID GraphBuilder::add_channel_shuffle_node(Graph &g, NodeParams params, NodeIdxPair input, unsigned int num_groups)
+{
+    return create_simple_single_input_output_node<ChannelShuffleLayerNode>(g, params, input, num_groups);
+}
+
 NodeID GraphBuilder::add_convolution_node(Graph &g, NodeParams params, NodeIdxPair input,
                                           Size2D kernel_spatial_extend, unsigned int depth, PadStrideInfo conv_info,
                                           unsigned int num_groups, ConvolutionMethod method, FastMathHint fast_math_hint,
@@ -262,6 +275,52 @@ NodeID GraphBuilder::add_convolution_node(Graph &g, NodeParams params, NodeIdxPa
     }
 }
 
+NodeID GraphBuilder::add_deconvolution_node(Graph &g, NodeParams params, NodeIdxPair input,
+                                            Size2D kernel_spatial_extend, unsigned int depth, PadStrideInfo deconv_info,
+                                            Size2D inner_border, ITensorAccessorUPtr weights_accessor,
+                                            ITensorAccessorUPtr bias_accessor)
+{
+    CHECK_NODEIDX_PAIR(input, g);
+    ARM_COMPUTE_ERROR_ON(depth == 0);
+    ARM_COMPUTE_ERROR_ON((kernel_spatial_extend.width == 0) || (kernel_spatial_extend.height == 0));
+
+    bool has_bias = (bias_accessor != nullptr);
+
+    // Get input tensor descriptor
+    const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]);
+
+    // Create weights node
+    TensorDescriptor w_desc = input_tensor_desc;
+    w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::WIDTH), kernel_spatial_extend.width);
+    w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::HEIGHT), kernel_spatial_extend.height);
+    w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::CHANNEL),
+                     get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL));
+    w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::BATCHES), depth);
+
+    NodeID w_nid = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor));
+
+    // Create bias nodes
+    NodeID b_nid = EmptyNodeID;
+    if(has_bias)
+    {
+        TensorDescriptor b_desc = input_tensor_desc;
+        b_desc.shape            = TensorShape(depth);
+        b_nid                   = add_const_node_with_name(g, params, "Bias", b_desc, std::move(bias_accessor));
+    }
+
+    // Create convolution node and connect
+    NodeID deconv_nid = g.add_node<DeconvolutionLayerNode>(deconv_info, inner_border);
+    g.add_connection(input.node_id, input.index, deconv_nid, 0);
+    g.add_connection(w_nid, 0, deconv_nid, 1);
+    if(has_bias)
+    {
+        g.add_connection(b_nid, 0, deconv_nid, 2);
+    }
+    set_node_params(g, deconv_nid, params);
+
+    return deconv_nid;
+}
+
 NodeID GraphBuilder::add_depth_concatenate_node(Graph &g, NodeParams params, std::vector<NodeIdxPair> inputs)
 {
     ARM_COMPUTE_ERROR_ON(inputs.size() == 0);
@@ -326,6 +385,18 @@ NodeID GraphBuilder::add_depthwise_convolution_node(Graph &g, NodeParams params,
     return conv_nid;
 }
 
+NodeID GraphBuilder::add_dummy_node(Graph &g, NodeParams params, NodeIdxPair input, TensorShape shape)
+{
+    CHECK_NODEIDX_PAIR(input, g);
+
+    NodeID nid = g.add_node<DummyNode>(shape);
+    g.add_connection(input.node_id, input.index, nid, 0);
+
+    set_node_params(g, nid, params);
+
+    return nid;
+}
+
 NodeID GraphBuilder::add_elementwise_node(Graph &g, NodeParams params, NodeIdxPair input0, NodeIdxPair input1, EltwiseOperation operation)
 {
     CHECK_NODEIDX_PAIR(input0, g);
@@ -399,6 +470,12 @@ NodeID GraphBuilder::add_reshape_node(Graph &g, NodeParams params, NodeIdxPair i
     return create_simple_single_input_output_node<ReshapeLayerNode>(g, params, input, shape);
 }
 
+NodeID GraphBuilder::add_resize_node(Graph &g, NodeParams params, NodeIdxPair input, InterpolationPolicy policy,
+                                     float width_scale, float height_scale)
+{
+    return create_simple_single_input_output_node<ResizeLayerNode>(g, params, input, policy, width_scale, height_scale);
+}
+
 NodeID GraphBuilder::add_scale_layer(Graph &g, const NodeParams &params, NodeIdxPair input, ITensorAccessorUPtr mul_accessor, ITensorAccessorUPtr add_accessor)
 {
     CHECK_NODEIDX_PAIR(input, g);
diff --git a/src/graph/backends/CL/CLFunctionsFactory.cpp b/src/graph/backends/CL/CLFunctionsFactory.cpp
index ac04f1063c..90ea81f21a 100644
--- a/src/graph/backends/CL/CLFunctionsFactory.cpp
+++ b/src/graph/backends/CL/CLFunctionsFactory.cpp
@@ -141,6 +141,38 @@ std::unique_ptr<IFunction> create_batch_normalization_layer(BatchNormalizationLa
     return std::move(func);
 }
 
+/** Create a backend channel shuffle layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend channel shuffle layer function
+ */
+std::unique_ptr<IFunction> create_channel_shuffle_layer(ChannelShuffleLayerNode &node)
+{
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+        "Creating CL Channel Shuffle 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
+    ICLTensor         *input      = get_backing_tensor(node.input(0));
+    ICLTensor         *output     = get_backing_tensor(node.output(0));
+    const unsigned int num_groups = node.num_groups();
+
+    // Create function
+    auto func = support::cpp14::make_unique<CLChannelShuffleLayer>();
+    func->configure(input, output, num_groups);
+
+    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLChannelShuffleLayer"
+                               << " Data Type: " << input->info()->data_type()
+                               << " Shape: " << input->info()->tensor_shape()
+                               << " Num groups: " << num_groups
+                               << std::endl);
+
+    return std::move(func);
+}
+
 /** Create a backend convolution layer function
  *
  * @param[in] node Node to create the backend function for
@@ -206,6 +238,46 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node,
     return func;
 }
 
+/** Create a backend deconvolution layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend deconvolution layer function
+ */
+std::unique_ptr<IFunction> create_deconvolution_layer(DeconvolutionLayerNode &node, GraphContext &ctx)
+{
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating CL DeconvolutionLayer 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
+    ICLTensor *input   = get_backing_tensor(node.input(0));
+    ICLTensor *weights = get_backing_tensor(node.input(1));
+    ICLTensor *biases  = get_backing_tensor(node.input(2));
+    ICLTensor *output  = get_backing_tensor(node.output(0));
+
+    const PadStrideInfo deconv_info  = node.deconvolution_info();
+    const Size2D        inner_border = node.inner_border();
+
+    // Create and configure function (we assume that functions have been validated before creation)
+    std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, Target::CL);
+    std::unique_ptr<IFunction>      func;
+    std::string                     func_name;
+
+    std::tie(func, func_name) = create_named_memory_managed_function<CLDeconvolutionLayer>(std::string("CLDeconvolutionLayer"), mm,
+                                                                                           input, weights, biases, output,
+                                                                                           deconv_info, inner_border.x(), inner_border.y());
+
+    // 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
@@ -530,6 +602,41 @@ std::unique_ptr<IFunction> create_reshape_layer(ReshapeLayerNode &node)
     return std::move(func);
 }
 
+/** Create a backend resize layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend resize layer function
+ */
+std::unique_ptr<IFunction> create_resize_layer(ResizeLayerNode &node)
+{
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+        "Creating CL Resize 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
+    ICLTensor *input  = get_backing_tensor(node.input(0));
+    ICLTensor *output = get_backing_tensor(node.output(0));
+    ARM_COMPUTE_ERROR_ON(input == nullptr);
+    ARM_COMPUTE_ERROR_ON(output == nullptr);
+    const InterpolationPolicy policy = node.policy();
+
+    // Create and configure function
+    auto func = support::cpp14::make_unique<CLScale>();
+    func->configure(input, output, policy, BorderMode::CONSTANT);
+
+    // Log info
+    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLScale"
+                               << " Data Type: " << input->info()->data_type()
+                               << " Input shape: " << input->info()->tensor_shape()
+                               << " Output shape: " << output->info()->tensor_shape()
+                               << " Interpolation: " << policy
+                               << std::endl);
+
+    return std::move(func);
+}
+
 /** Create a backend softmax layer function
  *
  * @param[in] node Node to create the backend function for
@@ -579,8 +686,12 @@ std::unique_ptr<IFunction> CLFunctionFactory::create(INode *node, GraphContext &
             return create_activation_layer(*polymorphic_downcast<ActivationLayerNode *>(node));
         case NodeType::BatchNormalizationLayer:
             return create_batch_normalization_layer(*polymorphic_downcast<BatchNormalizationLayerNode *>(node));
+        case NodeType::ChannelShuffleLayer:
+            return create_channel_shuffle_layer(*polymorphic_downcast<ChannelShuffleLayerNode *>(node));
         case NodeType::ConvolutionLayer:
             return create_convolution_layer(*polymorphic_downcast<ConvolutionLayerNode *>(node), ctx);
+        case NodeType::DeconvolutionLayer:
+            return create_deconvolution_layer(*polymorphic_downcast<DeconvolutionLayerNode *>(node), ctx);
         case NodeType::DepthConcatenateLayer:
             return create_depth_concatenate_layer(*polymorphic_downcast<DepthConcatenateLayerNode *>(node));
         case NodeType::DepthwiseConvolutionLayer:
@@ -597,6 +708,8 @@ std::unique_ptr<IFunction> CLFunctionFactory::create(INode *node, GraphContext &
             return create_pooling_layer(*polymorphic_downcast<PoolingLayerNode *>(node));
         case NodeType::ReshapeLayer:
             return create_reshape_layer(*polymorphic_downcast<ReshapeLayerNode *>(node));
+        case NodeType::ResizeLayer:
+            return create_resize_layer(*polymorphic_downcast<ResizeLayerNode *>(node));
         case NodeType::SoftmaxLayer:
             return create_softmax_layer(*polymorphic_downcast<SoftmaxLayerNode *>(node), ctx);
         default:
diff --git a/src/graph/backends/CL/CLNodeValidator.cpp b/src/graph/backends/CL/CLNodeValidator.cpp
index c16b2e67df..3e63617478 100644
--- a/src/graph/backends/CL/CLNodeValidator.cpp
+++ b/src/graph/backends/CL/CLNodeValidator.cpp
@@ -47,6 +47,8 @@ Status CLNodeValidator::validate(INode *node)
     NodeType type = node->type();
     switch(type)
     {
+        case NodeType::ChannelShuffleLayer:
+            return detail::validate_channel_shuffle_layer<CLChannelShuffleLayer>(*polymorphic_downcast<ChannelShuffleLayerNode *>(node));
         case NodeType::ConvolutionLayer:
             return detail::validate_convolution_layer<CLConvolutionLayer,
                    CLDirectConvolutionLayer,
diff --git a/src/graph/backends/NEON/NEFunctionFactory.cpp b/src/graph/backends/NEON/NEFunctionFactory.cpp
index 7f97876e57..8376feb265 100644
--- a/src/graph/backends/NEON/NEFunctionFactory.cpp
+++ b/src/graph/backends/NEON/NEFunctionFactory.cpp
@@ -190,6 +190,46 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node,
     return func;
 }
 
+/** Create a backend deconvolution layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend deconvolution layer function
+ */
+std::unique_ptr<IFunction> create_deconvolution_layer(DeconvolutionLayerNode &node, GraphContext &ctx)
+{
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON DeconvolutionLayer 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 deconv_info  = node.deconvolution_info();
+    const Size2D        inner_border = node.inner_border();
+
+    // Create and configure function (we assume that functions have been validated before creation)
+    std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, Target::CL);
+    std::unique_ptr<IFunction>      func;
+    std::string                     func_name;
+
+    std::tie(func, func_name) = create_named_memory_managed_function<NEDeconvolutionLayer>(std::string("NEDeconvolutionLayer"), mm,
+                                                                                           input, weights, biases, output,
+                                                                                           deconv_info, inner_border.x(), inner_border.y());
+
+    // 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
@@ -503,6 +543,41 @@ std::unique_ptr<IFunction> create_reshape_layer(ReshapeLayerNode &node)
     return std::move(func);
 }
 
+/** Create a backend resize layer function
+ *
+ * @param[in] node Node to create the backend function for
+ *
+ * @return Backend resize layer function
+ */
+std::unique_ptr<IFunction> create_resize_layer(ResizeLayerNode &node)
+{
+    ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+        "Creating NEON Resize 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);
+    const InterpolationPolicy policy = node.policy();
+
+    // Create and configure function
+    auto func = support::cpp14::make_unique<NEScale>();
+    func->configure(input, output, policy, BorderMode::CONSTANT);
+
+    // Log info
+    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NEScale"
+                               << " Data Type: " << input->info()->data_type()
+                               << " Input shape: " << input->info()->tensor_shape()
+                               << " Output shape: " << output->info()->tensor_shape()
+                               << " Interpolation: " << policy
+                               << std::endl);
+
+    return std::move(func);
+}
+
 /** Create a backend softmax layer function
  *
  * @param[in] node Node to create the backend function for
@@ -553,6 +628,8 @@ std::unique_ptr<IFunction> NEFunctionFactory::create(INode *node, GraphContext &
             return create_batch_normalization_layer(*polymorphic_downcast<BatchNormalizationLayerNode *>(node));
         case NodeType::ConvolutionLayer:
             return create_convolution_layer(*polymorphic_downcast<ConvolutionLayerNode *>(node), ctx);
+        case NodeType::DeconvolutionLayer:
+            return create_deconvolution_layer(*polymorphic_downcast<DeconvolutionLayerNode *>(node), ctx);
         case NodeType::DepthConcatenateLayer:
             return create_depth_concatenate_layer(*polymorphic_downcast<DepthConcatenateLayerNode *>(node));
         case NodeType::DepthwiseConvolutionLayer:
@@ -569,6 +646,8 @@ std::unique_ptr<IFunction> NEFunctionFactory::create(INode *node, GraphContext &
             return create_pooling_layer(*polymorphic_downcast<PoolingLayerNode *>(node));
         case NodeType::ReshapeLayer:
             return create_reshape_layer(*polymorphic_downcast<ReshapeLayerNode *>(node));
+        case NodeType::ResizeLayer:
+            return create_resize_layer(*polymorphic_downcast<ResizeLayerNode *>(node));
         case NodeType::SoftmaxLayer:
             return create_softmax_layer(*polymorphic_downcast<SoftmaxLayerNode *>(node), ctx);
         default:
diff --git a/src/graph/backends/NEON/NENodeValidator.cpp b/src/graph/backends/NEON/NENodeValidator.cpp
index e438e79c76..1c17f92fa1 100644
--- a/src/graph/backends/NEON/NENodeValidator.cpp
+++ b/src/graph/backends/NEON/NENodeValidator.cpp
@@ -47,6 +47,8 @@ Status NENodeValidator::validate(INode *node)
     NodeType type = node->type();
     switch(type)
     {
+        case NodeType::ChannelShuffleLayer:
+            return ARM_COMPUTE_CREATE_ERROR(arm_compute::ErrorCode::RUNTIME_ERROR, "Channel Shuffle is unsupported for NEON");
         case NodeType::ConvolutionLayer:
             return detail::validate_convolution_layer<NEConvolutionLayer,
                    NEDirectConvolutionLayer,
@@ -55,7 +57,6 @@ Status NENodeValidator::validate(INode *node)
         case NodeType::DepthwiseConvolutionLayer:
             return detail::validate_depthwise_convolution_layer<NEDepthwiseConvolutionLayer,
                    NEDepthwiseConvolutionLayer3x3>(*polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
-
         default:
             return Status{};
     }
diff --git a/src/graph/nodes/ChannelShuffleLayerNode.cpp b/src/graph/nodes/ChannelShuffleLayerNode.cpp
new file mode 100644
index 0000000000..08fcce1192
--- /dev/null
+++ b/src/graph/nodes/ChannelShuffleLayerNode.cpp
@@ -0,0 +1,78 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/graph/nodes/ChannelShuffleLayerNode.h"
+
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/INodeVisitor.h"
+
+namespace arm_compute
+{
+namespace graph
+{
+ChannelShuffleLayerNode::ChannelShuffleLayerNode(unsigned int num_groups)
+    : _num_groups(num_groups)
+{
+    _input_edges.resize(1, EmptyEdgeID);
+    _outputs.resize(1, NullTensorID);
+}
+
+unsigned int ChannelShuffleLayerNode::num_groups() const
+{
+    return _num_groups;
+}
+
+bool ChannelShuffleLayerNode::forward_descriptors()
+{
+    if((input_id(0) != 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 ChannelShuffleLayerNode::configure_output(size_t idx) const
+{
+    ARM_COMPUTE_UNUSED(idx);
+    ARM_COMPUTE_ERROR_ON(idx >= _outputs.size());
+
+    const Tensor *src = input(0);
+    ARM_COMPUTE_ERROR_ON(src == nullptr);
+
+    return src->desc();
+}
+
+NodeType ChannelShuffleLayerNode::type() const
+{
+    return NodeType::ChannelShuffleLayer;
+}
+
+void ChannelShuffleLayerNode::accept(INodeVisitor &v)
+{
+    v.visit(*this);
+}
+} // namespace graph
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/graph/nodes/DeconvolutionLayerNode.cpp b/src/graph/nodes/DeconvolutionLayerNode.cpp
new file mode 100644
index 0000000000..9329ae3c23
--- /dev/null
+++ b/src/graph/nodes/DeconvolutionLayerNode.cpp
@@ -0,0 +1,113 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/graph/nodes/DeconvolutionLayerNode.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
+{
+DeconvolutionLayerNode::DeconvolutionLayerNode(PadStrideInfo info, Size2D inner_border)
+    : _info(std::move(info)), _inner_border(inner_border)
+{
+    _input_edges.resize(3, EmptyEdgeID);
+    _outputs.resize(1, NullTensorID);
+}
+
+PadStrideInfo DeconvolutionLayerNode::deconvolution_info() const
+{
+    return _info;
+}
+
+Size2D DeconvolutionLayerNode::inner_border() const
+{
+    return _inner_border;
+}
+
+TensorDescriptor DeconvolutionLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
+                                                                   const TensorDescriptor &weights_descriptor,
+                                                                   const PadStrideInfo    &info,
+                                                                   const Size2D           &inner_border)
+{
+    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) = deconvolution_output_dimensions(input_width, input_height,
+                                                                            kernel_width, kernel_height,
+                                                                            info.pad().first, info.pad().second,
+                                                                            inner_border.x(), inner_border.y(),
+                                                                            info.stride().first, info.stride().second);
+
+    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 DeconvolutionLayerNode::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 DeconvolutionLayerNode::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, _inner_border);
+    return output_info;
+}
+
+NodeType DeconvolutionLayerNode::type() const
+{
+    return NodeType::DeconvolutionLayer;
+}
+
+void DeconvolutionLayerNode::accept(INodeVisitor &v)
+{
+    v.visit(*this);
+}
+} // namespace graph
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/graph/nodes/DummyNode.cpp b/src/graph/nodes/DummyNode.cpp
new file mode 100644
index 0000000000..e6411810de
--- /dev/null
+++ b/src/graph/nodes/DummyNode.cpp
@@ -0,0 +1,78 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/graph/nodes/DummyNode.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/INodeVisitor.h"
+#include "arm_compute/graph/Tensor.h"
+
+namespace arm_compute
+{
+namespace graph
+{
+DummyNode::DummyNode(TensorShape shape)
+    : _shape(shape)
+{
+    _input_edges.resize(1, EmptyEdgeID);
+    _outputs.resize(1, NullTensorID);
+}
+
+bool DummyNode::forward_descriptors()
+{
+    if((input_id(0) != 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 DummyNode::configure_output(size_t idx) const
+{
+    ARM_COMPUTE_UNUSED(idx);
+    ARM_COMPUTE_ERROR_ON(idx >= _outputs.size());
+
+    const Tensor *src = input(0);
+    ARM_COMPUTE_ERROR_ON(src == nullptr);
+
+    TensorDescriptor output_desc = src->desc();
+    output_desc.shape            = _shape;
+
+    return output_desc;
+}
+
+NodeType DummyNode::type() const
+{
+    return NodeType::Dummy;
+}
+
+void DummyNode::accept(INodeVisitor &v)
+{
+    v.visit(*this);
+}
+} // namespace graph
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/graph/nodes/ResizeLayerNode.cpp b/src/graph/nodes/ResizeLayerNode.cpp
new file mode 100644
index 0000000000..a6aa7bfe5c
--- /dev/null
+++ b/src/graph/nodes/ResizeLayerNode.cpp
@@ -0,0 +1,90 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/graph/nodes/ResizeLayerNode.h"
+
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/INodeVisitor.h"
+#include "arm_compute/graph/Utils.h"
+
+namespace arm_compute
+{
+namespace graph
+{
+ResizeLayerNode::ResizeLayerNode(InterpolationPolicy policy, float scale_width, float scale_height)
+    : _policy(policy), _scale_width(scale_width), _scale_height(scale_height)
+{
+    _input_edges.resize(1, EmptyEdgeID);
+    _outputs.resize(1, NullTensorID);
+}
+
+InterpolationPolicy ResizeLayerNode::policy() const
+{
+    return _policy;
+}
+
+std::pair<float, float> ResizeLayerNode::scaling_factor() const
+{
+    return std::make_pair(_scale_width, _scale_height);
+}
+
+bool ResizeLayerNode::forward_descriptors()
+{
+    if((input_id(0) != 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 ResizeLayerNode::configure_output(size_t idx) const
+{
+    ARM_COMPUTE_UNUSED(idx);
+    ARM_COMPUTE_ERROR_ON(idx >= _outputs.size());
+
+    const Tensor *src = input(0);
+    ARM_COMPUTE_ERROR_ON(src == nullptr);
+
+    TensorDescriptor output_desc = src->desc();
+    size_t           width_idx   = get_dimension_idx(output_desc, DataLayoutDimension::WIDTH);
+    size_t           height_idx  = get_dimension_idx(output_desc, DataLayoutDimension::HEIGHT);
+    output_desc.shape.set(width_idx, static_cast<int>(output_desc.shape[width_idx] * _scale_width));
+    output_desc.shape.set(height_idx, static_cast<int>(output_desc.shape[height_idx] * _scale_height));
+
+    return output_desc;
+}
+
+NodeType ResizeLayerNode::type() const
+{
+    return NodeType::ResizeLayer;
+}
+
+void ResizeLayerNode::accept(INodeVisitor &v)
+{
+    v.visit(*this);
+}
+} // namespace graph
+} // namespace arm_compute
\ No newline at end of file