COMPMID-2111: ConcatenateLayer API should accept an index instead of an enum

Alters the concatenate layer to be layout agnostic and accept an index
as thec concatenation axis instead of an typed layout dependent
enumeration.

Change-Id: I0eaaf919f66a1ba1b09bbfb47c171fc1d4045530
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-on: https://review.mlplatform.org/c/994
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

29 files changed, 111 insertions, 114 deletions

diff --git a/arm_compute/graph/LayerDescriptors.h b/arm_compute/graph/LayerDescriptors.h
index 79099326ec..f52beab523 100644
--- a/arm_compute/graph/LayerDescriptors.h
+++ b/arm_compute/graph/LayerDescriptors.h
@@ -32,7 +32,7 @@ namespace graph
 {
 namespace descriptors
 {
-/** Common node parameters */
+/** Concatenate layer descriptor */
 struct ConcatLayerDescriptor
 {
     /** Default constructor */
diff --git a/arm_compute/graph/Utils.h b/arm_compute/graph/Utils.h
index 4ffccec9be..2fa2f3b627 100644
--- a/arm_compute/graph/Utils.h
+++ b/arm_compute/graph/Utils.h
@@ -110,12 +110,12 @@ void release_default_graph_context(GraphContext &ctx);
 size_t get_dimension_size(const TensorDescriptor &descriptor, const DataLayoutDimension data_layout_dimension);
 /** Get index of a tensor's given dimension depending on its layout
  *
- * @param[in] descriptor            Descriptor
+ * @param[in] data_layout           Data layout of the tensor
  * @param[in] data_layout_dimension Tensor data layout dimension
  *
  * @return Idx of given dimension
  */
-size_t get_dimension_idx(const TensorDescriptor &descriptor, const DataLayoutDimension data_layout_dimension);
+size_t get_dimension_idx(DataLayout data_layout, const DataLayoutDimension data_layout_dimension);
 /** Get the list of driving nodes of a given node
  *
  * @param[in] node Node to find the driving node of
diff --git a/arm_compute/graph/backends/FunctionHelpers.h b/arm_compute/graph/backends/FunctionHelpers.h
index e05f4bc8cf..f6e6286a19 100644
--- a/arm_compute/graph/backends/FunctionHelpers.h
+++ b/arm_compute/graph/backends/FunctionHelpers.h
@@ -28,6 +28,7 @@
 #include "arm_compute/graph/Tensor.h"
 #include "arm_compute/graph/TypePrinter.h"
 #include "arm_compute/graph/Types.h"
+#include "arm_compute/graph/Utils.h"
 #include "arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h"
 #include "arm_compute/graph/backends/Utils.h"
 #include "arm_compute/graph/nodes/Nodes.h"
@@ -321,7 +322,8 @@ std::unique_ptr<arm_compute::IFunction> create_concatenate_layer(ConcatenateLaye
         inputs.push_back(get_backing_tensor<TargetInfo>(node.input(i)));
     }
     typename TargetInfo::TensorType *output      = get_backing_tensor<TargetInfo>(node.output(0));
-    const DataLayoutDimension        concat_axis = node.concatenation_axis();
+    const DataLayout                 data_layout = node.output(0) != nullptr ? node.output(0)->desc().layout : DataLayout::UNKNOWN;
+    const size_t                     concat_axis = get_dimension_idx(data_layout, node.concatenation_axis());
 
     // Create and configure function
     auto func = support::cpp14::make_unique<ConcatenateLayerFunction>();
diff --git a/arm_compute/runtime/CL/functions/CLConcatenateLayer.h b/arm_compute/runtime/CL/functions/CLConcatenateLayer.h
index 5cf09c8ee0..d85a4453d8 100644
--- a/arm_compute/runtime/CL/functions/CLConcatenateLayer.h
+++ b/arm_compute/runtime/CL/functions/CLConcatenateLayer.h
@@ -59,7 +59,7 @@ public:
      * @param[out]    output        Output tensor. Data types supported: Same as @p input.
      * @param[in]     axis          Concatenation axis. Supported underlying concatenation axis are 0, 1 and 2.
      */
-    void configure(const std::vector<ICLTensor *> &inputs_vector, ICLTensor *output, DataLayoutDimension axis);
+    void configure(const std::vector<ICLTensor *> &inputs_vector, ICLTensor *output, size_t axis);
     /** Static function to check if given info will lead to a valid configuration of @ref CLConcatenateLayer
      *
      * @note Input and output tensor dimensions preconditions defer depending on the concatenation axis.
@@ -71,7 +71,7 @@ public:
      *
      * @return a status
      */
-    static Status validate(const std::vector<ITensorInfo *> &inputs_vector, const ITensorInfo *output, DataLayoutDimension axis);
+    static Status validate(const std::vector<ITensorInfo *> &inputs_vector, const ITensorInfo *output, size_t axis);
 
     // Inherited methods overridden:
     void run() override;
@@ -81,5 +81,5 @@ private:
     unsigned int                            _num_inputs;
     unsigned int                            _axis;
 };
-}
+} // namespace arm_compute
 #endif /* __ARM_COMPUTE_CLCONCATENATELAYER_H__ */
diff --git a/arm_compute/runtime/NEON/functions/NEConcatenateLayer.h b/arm_compute/runtime/NEON/functions/NEConcatenateLayer.h
index 7dfbcf9199..f8cda326d2 100644
--- a/arm_compute/runtime/NEON/functions/NEConcatenateLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEConcatenateLayer.h
@@ -59,7 +59,7 @@ public:
      * @param[out]    output        Output tensor. Data types supported: Same as @p input.
      * @param[in]     axis          Concatenation axis. Supported underlying concatenation axis are 0, 1 and 2.
      */
-    void configure(const std::vector<ITensor *> &inputs_vector, ITensor *output, DataLayoutDimension axis);
+    void configure(const std::vector<ITensor *> &inputs_vector, ITensor *output, size_t axis);
     /** Static function to check if given info will lead to a valid configuration of @ref NEConcatenateLayer
      *
      * @note Input and output tensor dimensions preconditions defer depending on the concatenation axis.
@@ -71,7 +71,7 @@ public:
      *
      * @return a status
      */
-    static Status validate(const std::vector<ITensorInfo *> &inputs_vector, const ITensorInfo *output, DataLayoutDimension axis);
+    static Status validate(const std::vector<ITensorInfo *> &inputs_vector, const ITensorInfo *output, size_t axis);
 
     // Inherited methods overridden:
     void run() override;
diff --git a/arm_compute/runtime/NEON/functions/NEDepthConcatenateLayer.h b/arm_compute/runtime/NEON/functions/NEDepthConcatenateLayer.h
index da38151e73..e2f2c4c44c 100644
--- a/arm_compute/runtime/NEON/functions/NEDepthConcatenateLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEDepthConcatenateLayer.h
@@ -89,5 +89,5 @@ private:
     std::unique_ptr<NEFillBorderKernel[]>            _border_handlers_vector;
     unsigned int                                     _num_inputs;
 };
-}
+} // namespace arm_compute
 #endif /* __ARM_COMPUTE_NEDEPTHCONCATENATE_H__ */
diff --git a/src/graph/GraphBuilder.cpp b/src/graph/GraphBuilder.cpp
index b96a242acf..9f8dd69922 100644
--- a/src/graph/GraphBuilder.cpp
+++ b/src/graph/GraphBuilder.cpp
@@ -221,14 +221,15 @@ NodeID GraphBuilder::add_convolution_node(Graph &g, NodeParams params, NodeIdxPa
 
     // Get input tensor descriptor
     const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]);
+    const DataLayout       input_data_layout = input_tensor_desc.layout;
 
     // 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),
+    w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::WIDTH), kernel_spatial_extend.width);
+    w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::HEIGHT), kernel_spatial_extend.height);
+    w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::CHANNEL),
                      get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL) / num_groups);
-    w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::BATCHES), depth);
+    w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::BATCHES), depth);
     if(!weights_quant_info.empty())
     {
         w_desc.quant_info = weights_quant_info;
@@ -275,14 +276,15 @@ NodeID GraphBuilder::add_deconvolution_node(Graph &g, NodeParams params, NodeIdx
 
     // Get input tensor descriptor
     const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]);
+    const DataLayout       input_data_layout = input_tensor_desc.layout;
 
     // 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),
+    w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::WIDTH), kernel_spatial_extend.width);
+    w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::HEIGHT), kernel_spatial_extend.height);
+    w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::CHANNEL),
                      get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL));
-    w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::BATCHES), depth);
+    w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::BATCHES), depth);
 
     NodeID w_nid = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor));
 
@@ -328,12 +330,13 @@ NodeID GraphBuilder::add_depthwise_convolution_node(Graph &g, NodeParams params,
 
     // Get input tensor descriptor
     const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]);
+    const DataLayout       input_data_layout = input_tensor_desc.layout;
 
     // 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),
+    w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::WIDTH), kernel_spatial_extend.width);
+    w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::HEIGHT), kernel_spatial_extend.height);
+    w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::CHANNEL),
                      get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL) * depth_multiplier);
     if(!quant_info.empty())
     {
@@ -595,13 +598,14 @@ NodeID GraphBuilder::add_scale_layer(Graph &g, const NodeParams &params, NodeIdx
 
     // Get input tensor descriptor
     const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]);
+    const DataLayout       input_data_layout = input_tensor_desc.layout;
 
     // Create mul node
     TensorDescriptor mul_desc = input_tensor_desc;
-    const size_t     C        = input_tensor_desc.shape[get_dimension_idx(mul_desc, DataLayoutDimension::CHANNEL)];
-    mul_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::WIDTH), 1);
-    mul_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::HEIGHT), 1);
-    mul_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::CHANNEL), C);
+    const size_t     C        = input_tensor_desc.shape[get_dimension_idx(input_data_layout, DataLayoutDimension::CHANNEL)];
+    mul_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::WIDTH), 1);
+    mul_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::HEIGHT), 1);
+    mul_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::CHANNEL), C);
     NodeID      mul_const_nid   = add_const_node_with_name(g, params, "Mul", mul_desc, std::move(mul_accessor));
     NodeIdxPair mul_const_nidxp = { mul_const_nid, 0 };
 
diff --git a/src/graph/Utils.cpp b/src/graph/Utils.cpp
index 71a6fc582b..4c34dd85a5 100644
--- a/src/graph/Utils.cpp
+++ b/src/graph/Utils.cpp
@@ -119,12 +119,12 @@ void setup_requested_backend_context(GraphContext &ctx, Target target)
 size_t get_dimension_size(const TensorDescriptor &descriptor, const DataLayoutDimension data_layout_dimension)
 {
     ARM_COMPUTE_ERROR_ON_MSG(descriptor.layout == DataLayout::UNKNOWN, "Cannot retrieve the dimension index for an unknown layout!");
-    return descriptor.shape[get_dimension_idx(descriptor, data_layout_dimension)];
+    return descriptor.shape[get_dimension_idx(descriptor.layout, data_layout_dimension)];
 }
 
-size_t get_dimension_idx(const TensorDescriptor &descriptor, const DataLayoutDimension data_layout_dimension)
+size_t get_dimension_idx(DataLayout data_layout, const DataLayoutDimension data_layout_dimension)
 {
-    ARM_COMPUTE_ERROR_ON_MSG(descriptor.layout == DataLayout::UNKNOWN, "Cannot retrieve the dimension index for an unknown layout!");
+    ARM_COMPUTE_ERROR_ON_MSG(data_layout == DataLayout::UNKNOWN, "Cannot retrieve the dimension index for an unknown layout!");
 
     /* Return the index based on the data layout
      * [N C H W]
@@ -134,13 +134,13 @@ size_t get_dimension_idx(const TensorDescriptor &descriptor, const DataLayoutDim
     switch(data_layout_dimension)
     {
         case DataLayoutDimension::CHANNEL:
-            return (descriptor.layout == DataLayout::NCHW) ? 2 : 0;
+            return (data_layout == DataLayout::NCHW) ? 2 : 0;
             break;
         case DataLayoutDimension::HEIGHT:
-            return (descriptor.layout == DataLayout::NCHW) ? 1 : 2;
+            return (data_layout == DataLayout::NCHW) ? 1 : 2;
             break;
         case DataLayoutDimension::WIDTH:
-            return (descriptor.layout == DataLayout::NCHW) ? 0 : 1;
+            return (data_layout == DataLayout::NCHW) ? 0 : 1;
             break;
         case DataLayoutDimension::BATCHES:
             return 3;
diff --git a/src/graph/mutators/DepthConcatSubTensorMutator.cpp b/src/graph/mutators/DepthConcatSubTensorMutator.cpp
index 0e0a26b886..7994541b78 100644
--- a/src/graph/mutators/DepthConcatSubTensorMutator.cpp
+++ b/src/graph/mutators/DepthConcatSubTensorMutator.cpp
@@ -62,9 +62,9 @@ void DepthConcatSubTensorMutator::mutate(Graph &g)
             // Get output tensor
             auto output_tensor = node->output(0);
 
-            // Check concatenation axis (Sub-tensor optimization is support for concatenation axis >=2)
+            // Check concatenation axis (Sub-tensor optimization is supported for concatenation axis >=2)
             auto *concat_node = arm_compute::utils::cast::polymorphic_downcast<ConcatenateLayerNode *>(node);
-            if(output_tensor == nullptr || get_dimension_idx(output_tensor->desc(), concat_node->concatenation_axis()) < 2)
+            if(output_tensor == nullptr || get_dimension_idx(output_tensor->desc().layout, concat_node->concatenation_axis()) < 2)
             {
                 continue;
             }
diff --git a/src/graph/mutators/GroupedConvolutionMutator.cpp b/src/graph/mutators/GroupedConvolutionMutator.cpp
index d69d2cd7d0..3d53f49218 100644
--- a/src/graph/mutators/GroupedConvolutionMutator.cpp
+++ b/src/graph/mutators/GroupedConvolutionMutator.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -47,12 +47,12 @@ NodeID create_grouped_convolution(Graph &g, const NodeParams &params, NodeIdxPai
 
     // Split input
     const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]);
-    const unsigned int     input_idx         = get_dimension_idx(input_tensor_desc, DataLayoutDimension::CHANNEL);
+    const unsigned int     input_idx         = get_dimension_idx(input_tensor_desc.layout, DataLayoutDimension::CHANNEL);
     NodeID                 input_split       = GraphBuilder::add_split_node(g, params, input, num_groups, input_idx);
 
     // Split weights
     const TensorDescriptor weights_tensor_desc = get_tensor_descriptor(g, g.node(weights)->outputs()[0]);
-    const unsigned int     batch_idx           = get_dimension_idx(weights_tensor_desc, DataLayoutDimension::BATCHES);
+    const unsigned int     batch_idx           = get_dimension_idx(weights_tensor_desc.layout, DataLayoutDimension::BATCHES);
     NodeID                 weights_split       = GraphBuilder::add_split_node(g, params, { weights, 0 }, num_groups, batch_idx);
 
     // Split bias
diff --git a/src/graph/nodes/ConcatenateLayerNode.cpp b/src/graph/nodes/ConcatenateLayerNode.cpp
index bbdc4dc029..48da8b6e9e 100644
--- a/src/graph/nodes/ConcatenateLayerNode.cpp
+++ b/src/graph/nodes/ConcatenateLayerNode.cpp
@@ -67,7 +67,7 @@ TensorDescriptor ConcatenateLayerNode::compute_output_descriptor(const std::vect
     ARM_COMPUTE_ERROR_ON(input_descriptors.size() == 0);
 
     TensorDescriptor output_descriptor = input_descriptors[0];
-    const int        axis_idx          = get_dimension_idx(output_descriptor, axis);
+    const int        axis_idx          = get_dimension_idx(output_descriptor.layout, axis);
 
     // Extract shapes
     std::vector<const TensorShape *> shapes;
diff --git a/src/graph/nodes/ConvolutionLayerNode.cpp b/src/graph/nodes/ConvolutionLayerNode.cpp
index 15c7ff68f8..1c8dcaecfc 100644
--- a/src/graph/nodes/ConvolutionLayerNode.cpp
+++ b/src/graph/nodes/ConvolutionLayerNode.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -97,10 +97,11 @@ TensorDescriptor ConvolutionLayerNode::compute_output_descriptor(const TensorDes
 
     std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
 
+    const DataLayout data_layout       = input_descriptor.layout;
     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]);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::WIDTH), output_width);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::HEIGHT), output_height);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL), weights_descriptor.shape[3]);
 
     return output_descriptor;
 }
diff --git a/src/graph/nodes/DeconvolutionLayerNode.cpp b/src/graph/nodes/DeconvolutionLayerNode.cpp
index e7ccffd04f..b1a6db7ccc 100644
--- a/src/graph/nodes/DeconvolutionLayerNode.cpp
+++ b/src/graph/nodes/DeconvolutionLayerNode.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -66,10 +66,11 @@ TensorDescriptor DeconvolutionLayerNode::compute_output_descriptor(const TensorD
                                                                             info.pad().first, info.pad().second,
                                                                             info.stride().first, info.stride().second);
 
+    const DataLayout data_layout       = input_descriptor.layout;
     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]);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::WIDTH), output_width);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::HEIGHT), output_height);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL), weights_descriptor.shape[3]);
 
     return output_descriptor;
 }
diff --git a/src/graph/nodes/DepthwiseConvolutionLayerNode.cpp b/src/graph/nodes/DepthwiseConvolutionLayerNode.cpp
index 935902d3fd..cdd9e7b601 100644
--- a/src/graph/nodes/DepthwiseConvolutionLayerNode.cpp
+++ b/src/graph/nodes/DepthwiseConvolutionLayerNode.cpp
@@ -86,10 +86,11 @@ TensorDescriptor DepthwiseConvolutionLayerNode::compute_output_descriptor(const
 
     std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
 
+    const DataLayout data_layout       = input_descriptor.layout;
     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), input_channels * depth_multiplier);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::WIDTH), output_width);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::HEIGHT), output_height);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL), input_channels * depth_multiplier);
 
     return output_descriptor;
 }
diff --git a/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp b/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp
index 27a348fa69..c304a6c605 100644
--- a/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp
+++ b/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp
@@ -102,10 +102,11 @@ TensorDescriptor FusedConvolutionBatchNormalizationNode::compute_output_descript
 
     std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
 
+    const DataLayout data_layout       = input_descriptor.layout;
     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]);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::WIDTH), output_width);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::HEIGHT), output_height);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL), weights_descriptor.shape[3]);
 
     return output_descriptor;
 }
diff --git a/src/graph/nodes/PoolingLayerNode.cpp b/src/graph/nodes/PoolingLayerNode.cpp
index 26c145ae31..48b93c9158 100644
--- a/src/graph/nodes/PoolingLayerNode.cpp
+++ b/src/graph/nodes/PoolingLayerNode.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -57,9 +57,10 @@ TensorDescriptor PoolingLayerNode::compute_output_descriptor(const TensorDescrip
 
     std::tie(pooled_width, pooled_height) = scaled_dimensions(input_width, input_height, pool_size_x, pool_size_y, info.pad_stride_info());
 
+    const DataLayout data_layout       = input_descriptor.layout;
     TensorDescriptor output_descriptor = input_descriptor;
-    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::WIDTH), pooled_width);
-    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::HEIGHT), pooled_height);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::WIDTH), pooled_width);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::HEIGHT), pooled_height);
 
     return output_descriptor;
 }
diff --git a/src/graph/nodes/ReorgLayerNode.cpp b/src/graph/nodes/ReorgLayerNode.cpp
index 6b83f6b90c..21ad451c3e 100644
--- a/src/graph/nodes/ReorgLayerNode.cpp
+++ b/src/graph/nodes/ReorgLayerNode.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -53,10 +53,11 @@ TensorDescriptor ReorgLayerNode::compute_output_descriptor(const TensorDescripto
     ARM_COMPUTE_ERROR_ON_MSG((input_width % stride != 0), "The width of the input tensor must be a multiple of stride");
     ARM_COMPUTE_ERROR_ON_MSG((input_height % stride != 0), "The height of the input tensor must be a multiple of stride");
 
+    const DataLayout data_layout       = input_descriptor.layout;
     TensorDescriptor output_descriptor = input_descriptor;
-    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::WIDTH), input_width / stride);
-    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::HEIGHT), input_height / stride);
-    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::CHANNEL), input_channel * stride * stride);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::WIDTH), input_width / stride);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::HEIGHT), input_height / stride);
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL), input_channel * stride * stride);
 
     return output_descriptor;
 }
diff --git a/src/graph/nodes/ResizeLayerNode.cpp b/src/graph/nodes/ResizeLayerNode.cpp
index a6aa7bfe5c..a399229013 100644
--- a/src/graph/nodes/ResizeLayerNode.cpp
+++ b/src/graph/nodes/ResizeLayerNode.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -68,9 +68,10 @@ TensorDescriptor ResizeLayerNode::configure_output(size_t idx) const
     const Tensor *src = input(0);
     ARM_COMPUTE_ERROR_ON(src == nullptr);
 
+    const DataLayout data_layout = src->desc().layout;
     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);
+    size_t           width_idx   = get_dimension_idx(data_layout, DataLayoutDimension::WIDTH);
+    size_t           height_idx  = get_dimension_idx(data_layout, 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));
 
diff --git a/src/graph/nodes/UpsampleLayerNode.cpp b/src/graph/nodes/UpsampleLayerNode.cpp
index bdd39e8ebd..88af122a59 100644
--- a/src/graph/nodes/UpsampleLayerNode.cpp
+++ b/src/graph/nodes/UpsampleLayerNode.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -54,9 +54,10 @@ TensorDescriptor UpsampleLayerNode::compute_output_descriptor(const TensorDescri
     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 DataLayout data_layout       = input_descriptor.layout;
     TensorDescriptor output_descriptor = input_descriptor;
-    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::WIDTH), input_width * info.x());
-    output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::HEIGHT), input_height * info.y());
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::WIDTH), input_width * info.x());
+    output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::HEIGHT), input_height * info.y());
 
     return output_descriptor;
 }
diff --git a/src/runtime/CL/functions/CLConcatenateLayer.cpp b/src/runtime/CL/functions/CLConcatenateLayer.cpp
index 7edea3efac..b9b3c5bb80 100644
--- a/src/runtime/CL/functions/CLConcatenateLayer.cpp
+++ b/src/runtime/CL/functions/CLConcatenateLayer.cpp
@@ -44,10 +44,10 @@ CLConcatenateLayer::CLConcatenateLayer()
 {
 }
 
-void CLConcatenateLayer::configure(const std::vector<ICLTensor *> &inputs_vector, ICLTensor *output, DataLayoutDimension axis)
+void CLConcatenateLayer::configure(const std::vector<ICLTensor *> &inputs_vector, ICLTensor *output, size_t axis)
 {
     ARM_COMPUTE_ERROR_ON(output == nullptr);
-    _axis       = get_data_layout_dimension_index(output->info()->data_layout(), axis);
+    _axis       = axis;
     _num_inputs = inputs_vector.size();
 
     std::vector<ITensorInfo *> inputs_vector_info(inputs_vector.size());
@@ -135,30 +135,29 @@ void CLConcatenateLayer::configure(const std::vector<ICLTensor *> &inputs_vector
     }
 }
 
-Status CLConcatenateLayer::validate(const std::vector<ITensorInfo *> &inputs_vector, const ITensorInfo *output, DataLayoutDimension axis)
+Status CLConcatenateLayer::validate(const std::vector<ITensorInfo *> &inputs_vector, const ITensorInfo *output, size_t axis)
 {
     ARM_COMPUTE_RETURN_ERROR_ON(output == nullptr);
     const unsigned int num_inputs = inputs_vector.size();
 
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
     ARM_COMPUTE_RETURN_ERROR_ON(num_inputs < 2);
-    const unsigned int _axis = get_data_layout_dimension_index(inputs_vector[0]->data_layout(), axis);
 
     // Output auto inizialitation if not yet initialized
     TensorInfo  tmp_output_info = *output->clone();
     TensorShape output_shape{};
-    if(_axis == Window::DimZ)
+    if(axis == Window::DimZ)
     {
         output_shape = arm_compute::misc::shape_calculator::calculate_depth_concatenate_shape(inputs_vector);
     }
     else
     {
-        output_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(inputs_vector, _axis);
+        output_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(inputs_vector, axis);
     }
     auto_init_if_empty(tmp_output_info, output_shape, 1, inputs_vector[0]->data_type());
 
     unsigned int offset = 0;
-    switch(_axis)
+    switch(axis)
     {
         case Window::DimX:
         {
@@ -180,7 +179,7 @@ Status CLConcatenateLayer::validate(const std::vector<ITensorInfo *> &inputs_vec
                     {
                         ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
                         ARM_COMPUTE_RETURN_ON_ERROR(CLWidthConcatenateLayerKernel::validate(input, offset, &tmp_output_info));
-                        offset += input->dimension(_axis);
+                        offset += input->dimension(axis);
                     }
                     break;
             }
@@ -191,7 +190,7 @@ Status CLConcatenateLayer::validate(const std::vector<ITensorInfo *> &inputs_vec
             for(const auto &input : inputs_vector)
             {
                 ARM_COMPUTE_RETURN_ON_ERROR(CLHeightConcatenateLayerKernel::validate(input, offset, &tmp_output_info));
-                offset += input->dimension(_axis);
+                offset += input->dimension(axis);
             }
             break;
         }
@@ -200,7 +199,7 @@ Status CLConcatenateLayer::validate(const std::vector<ITensorInfo *> &inputs_vec
             for(const auto &input : inputs_vector)
             {
                 ARM_COMPUTE_RETURN_ON_ERROR(CLDepthConcatenateLayerKernel::validate(input, offset, &tmp_output_info));
-                offset += input->dimension(_axis);
+                offset += input->dimension(axis);
             }
             break;
         }
diff --git a/src/runtime/CL/functions/CLPadLayer.cpp b/src/runtime/CL/functions/CLPadLayer.cpp
index f88cb388be..dba7f23f3b 100644
--- a/src/runtime/CL/functions/CLPadLayer.cpp
+++ b/src/runtime/CL/functions/CLPadLayer.cpp
@@ -141,7 +141,7 @@ void CLPadLayer::configure_reflect_symmetric_mode(ICLTensor *input, ICLTensor *o
             }
             // Concatenate the padding before and after with the input.
             ICLTensor *out = (static_cast<int32_t>(i) == last_padding_dimension) ? output : &_concat_results[i];
-            _concat_functions[i].configure(concat_vector, out, get_index_data_layout_dimension(prev->info()->data_layout(), i));
+            _concat_functions[i].configure(concat_vector, out, i);
             prev = out;
         }
     }
diff --git a/src/runtime/NEON/functions/NEConcatenateLayer.cpp b/src/runtime/NEON/functions/NEConcatenateLayer.cpp
index fa7b91c3ca..e02c0c2c7a 100644
--- a/src/runtime/NEON/functions/NEConcatenateLayer.cpp
+++ b/src/runtime/NEON/functions/NEConcatenateLayer.cpp
@@ -44,10 +44,10 @@ NEConcatenateLayer::NEConcatenateLayer()
 {
 }
 
-void NEConcatenateLayer::configure(const std::vector<ITensor *> &inputs_vector, ITensor *output, DataLayoutDimension axis)
+void NEConcatenateLayer::configure(const std::vector<ITensor *> &inputs_vector, ITensor *output, size_t axis)
 {
     ARM_COMPUTE_ERROR_ON(output == nullptr);
-    _axis       = get_data_layout_dimension_index(output->info()->data_layout(), axis);
+    _axis       = axis;
     _num_inputs = inputs_vector.size();
 
     std::vector<ITensorInfo *> inputs_vector_info;
@@ -104,22 +104,21 @@ void NEConcatenateLayer::configure(const std::vector<ITensor *> &inputs_vector,
     }
 }
 
-Status NEConcatenateLayer::validate(const std::vector<ITensorInfo *> &inputs_vector, const ITensorInfo *output, DataLayoutDimension axis)
+Status NEConcatenateLayer::validate(const std::vector<ITensorInfo *> &inputs_vector, const ITensorInfo *output, size_t axis)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
     ARM_COMPUTE_RETURN_ERROR_ON(inputs_vector.size() < 2);
-    const unsigned int _axis = get_data_layout_dimension_index(inputs_vector[0]->data_layout(), axis);
 
     // Output auto inizialitation if not yet initialized
     TensorInfo  tmp_output_info = *output->clone();
     TensorShape output_shape{};
-    if(_axis == Window::DimZ)
+    if(axis == Window::DimZ)
     {
         output_shape = arm_compute::misc::shape_calculator::calculate_depth_concatenate_shape(inputs_vector);
     }
     else
     {
-        output_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(inputs_vector, _axis);
+        output_shape = arm_compute::misc::shape_calculator::calculate_concatenate_shape(inputs_vector, axis);
     }
     auto_init_if_empty(tmp_output_info, output_shape, 1, inputs_vector[0]->data_type());
 
@@ -127,7 +126,7 @@ Status NEConcatenateLayer::validate(const std::vector<ITensorInfo *> &inputs_vec
     for(const auto &input : inputs_vector)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
-        switch(_axis)
+        switch(axis)
         {
             case Window::DimX:
             {
@@ -147,7 +146,7 @@ Status NEConcatenateLayer::validate(const std::vector<ITensorInfo *> &inputs_vec
             default:
                 ARM_COMPUTE_ERROR("Axis not supported");
         }
-        offset += input->dimension(_axis);
+        offset += input->dimension(axis);
     }
 
     return Status{};
diff --git a/src/runtime/NEON/functions/NEPadLayer.cpp b/src/runtime/NEON/functions/NEPadLayer.cpp
index 62a7d4559b..6af2ee8868 100644
--- a/src/runtime/NEON/functions/NEPadLayer.cpp
+++ b/src/runtime/NEON/functions/NEPadLayer.cpp
@@ -182,7 +182,7 @@ void NEPadLayer::configure_reflect_symmetric_mode(ITensor *input, ITensor *outpu
             }
             // Concatenate the padding before and after with the input.
             ITensor *out = (i == _num_dimensions - 1) ? output : &_concat_results[i];
-            _concat_functions[i].configure(concat_vector, out, get_index_data_layout_dimension(input->info()->data_layout(), i));
+            _concat_functions[i].configure(concat_vector, out, i);
             if(i != _num_dimensions - 1)
             {
                 _concat_results[i].allocator()->allocate();
diff --git a/tests/validation/CL/DepthConcatenateLayer.cpp b/tests/validation/CL/DepthConcatenateLayer.cpp
index f4a693ca7d..5da8a34351 100644
--- a/tests/validation/CL/DepthConcatenateLayer.cpp
+++ b/tests/validation/CL/DepthConcatenateLayer.cpp
@@ -73,7 +73,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
         inputs_vector_info_raw.emplace_back(&input);
     }
 
-    bool is_valid = bool(CLConcatenateLayer::validate(inputs_vector_info_raw, &output_info.clone()->set_is_resizable(false), DataLayoutDimension::CHANNEL));
+    bool is_valid = bool(CLConcatenateLayer::validate(inputs_vector_info_raw, &output_info.clone()->set_is_resizable(false), 2));
     ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
 }
 // clang-format on
@@ -95,7 +95,7 @@ TEST_CASE(Configuration, framework::DatasetMode::ALL)
     // Create and configure function
     CLConcatenateLayer concat_layer;
 
-    concat_layer.configure({ &src1, &src2, &src3 }, &dst, DataLayoutDimension::CHANNEL);
+    concat_layer.configure({ &src1, &src2, &src3 }, &dst, 2);
 }
 template <typename T>
 using CLDepthConcatenateLayerFixture = ConcatenateLayerValidationFixture<CLTensor, ICLTensor, CLAccessor, CLConcatenateLayer, T>;
diff --git a/tests/validation/CL/WidthConcatenateLayer.cpp b/tests/validation/CL/WidthConcatenateLayer.cpp
index 0ca6d72bff..2c1eb7fada 100644
--- a/tests/validation/CL/WidthConcatenateLayer.cpp
+++ b/tests/validation/CL/WidthConcatenateLayer.cpp
@@ -77,7 +77,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
         inputs_vector_info_raw.emplace_back(&input);
     }
 
-    bool is_valid = bool(CLConcatenateLayer::validate(inputs_vector_info_raw,&output_info.clone()->set_is_resizable(true),DataLayoutDimension::WIDTH ));
+    bool is_valid = bool(CLConcatenateLayer::validate(inputs_vector_info_raw,&output_info.clone()->set_is_resizable(true), 0));
     ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
 }
 // clang-format on
@@ -99,7 +99,7 @@ TEST_CASE(Configuration, framework::DatasetMode::ALL)
     // Create and configure function
     CLConcatenateLayer concat_layer;
 
-    concat_layer.configure({ &src1, &src2, &src3 }, &dst, DataLayoutDimension::WIDTH);
+    concat_layer.configure({ &src1, &src2, &src3 }, &dst, 0);
 }
 
 template <typename T>
diff --git a/tests/validation/NEON/DepthConcatenateLayer.cpp b/tests/validation/NEON/DepthConcatenateLayer.cpp
index 1b355ae17d..0ddb220d34 100644
--- a/tests/validation/NEON/DepthConcatenateLayer.cpp
+++ b/tests/validation/NEON/DepthConcatenateLayer.cpp
@@ -55,13 +55,13 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
                                                   TensorInfo(TensorShape(23U, 27U, 4U), 1, DataType::F32),
                                                   TensorInfo(TensorShape(16U, 27U, 6U), 1, DataType::F32)
         })),
-                                                              framework::dataset::make("OutputInfo", {  TensorInfo(TensorShape(23U, 27U, 9U), 1, DataType::F16),
-                                                                                                        TensorInfo(TensorShape(25U, 12U, 9U), 1, DataType::F32),
-                                                                                                        TensorInfo(TensorShape(23U, 27U, 8U), 1, DataType::F32),
-                                                                                                        TensorInfo(TensorShape(16U, 27U, 12U), 1, DataType::F32)
-                                                              })),
-                                                          framework::dataset::make("Expected", { false, false, false, true })),
-               input_info1, input_info2, output_info,expected)
+        framework::dataset::make("OutputInfo", {  TensorInfo(TensorShape(23U, 27U, 9U), 1, DataType::F16),
+                                                  TensorInfo(TensorShape(25U, 12U, 9U), 1, DataType::F32),
+                                                  TensorInfo(TensorShape(23U, 27U, 8U), 1, DataType::F32),
+                                                  TensorInfo(TensorShape(16U, 27U, 12U), 1, DataType::F32)
+        })),
+        framework::dataset::make("Expected", { false, false, false, true })),
+        input_info1, input_info2, output_info,expected)
 {
     std::vector<TensorInfo> inputs_vector_info;
     inputs_vector_info.emplace_back(std::move(input_info1));
@@ -73,7 +73,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
         inputs_vector_info_raw.emplace_back(&input);
     }
 
-    bool is_valid = bool(NEConcatenateLayer::validate(inputs_vector_info_raw, &output_info.clone()->set_is_resizable(false), DataLayoutDimension::CHANNEL));
+    bool is_valid = bool(NEConcatenateLayer::validate(inputs_vector_info_raw, &output_info.clone()->set_is_resizable(false), 2));
     ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
 }
 // clang-format on
diff --git a/tests/validation/NEON/HeightConcatenateLayer.cpp b/tests/validation/NEON/HeightConcatenateLayer.cpp
index 0d08824645..9c23fb9bd3 100644
--- a/tests/validation/NEON/HeightConcatenateLayer.cpp
+++ b/tests/validation/NEON/HeightConcatenateLayer.cpp
@@ -77,7 +77,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
         inputs_vector_info_raw.emplace_back(&input);
     }
 
-    bool is_valid = bool(NEConcatenateLayer::validate(inputs_vector_info_raw, &output_info.clone()->set_is_resizable(true), DataLayoutDimension::HEIGHT));
+    bool is_valid = bool(NEConcatenateLayer::validate(inputs_vector_info_raw, &output_info.clone()->set_is_resizable(true), 1));
     ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
 }
 // clang-format on
diff --git a/tests/validation/NEON/WidthConcatenateLayer.cpp b/tests/validation/NEON/WidthConcatenateLayer.cpp
index 20df3f4d7d..ed840ef325 100644
--- a/tests/validation/NEON/WidthConcatenateLayer.cpp
+++ b/tests/validation/NEON/WidthConcatenateLayer.cpp
@@ -75,8 +75,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
         inputs_vector_info_raw.emplace_back(&input);
     }
 
-    bool is_valid = bool(NEConcatenateLayer::validate(inputs_vector_info_raw,
-                                                           &output_info.clone()->set_is_resizable(true),DataLayoutDimension::WIDTH));
+    bool is_valid = bool(NEConcatenateLayer::validate(inputs_vector_info_raw, &output_info.clone()->set_is_resizable(true), 0));
     ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
 }
 // clang-format on
diff --git a/tests/validation/fixtures/ConcatenateLayerFixture.h b/tests/validation/fixtures/ConcatenateLayerFixture.h
index db09957c09..39d4f9f95d 100644
--- a/tests/validation/fixtures/ConcatenateLayerFixture.h
+++ b/tests/validation/fixtures/ConcatenateLayerFixture.h
@@ -112,21 +112,7 @@ protected:
 
         // Create and configure function
         FunctionType concat;
-        switch(axis)
-        {
-            case 0:
-                concat.configure(src_ptrs, &dst, DataLayoutDimension::WIDTH);
-                break;
-            case 1:
-                concat.configure(src_ptrs, &dst, DataLayoutDimension::HEIGHT);
-                break;
-            case 2:
-                concat.configure(src_ptrs, &dst, DataLayoutDimension::CHANNEL);
-                break;
-            default:
-                ARM_COMPUTE_ERROR("Not supported");
-                break;
-        }
+        concat.configure(src_ptrs, &dst, axis);
 
         for(auto &src : srcs)
         {