COMPMID-2985 add data_layout to PoolingLayerInfo

- use data layout from PoolingLayerInfo if it's available
- deprecate constructors without data_layout
- (3RDPARTY_UPDATE) modify examples and test suites to give data layout

Change-Id: Ie9ae8cc4837c339ff69a16a816110be704863c2d
Signed-off-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2603
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

19 files changed, 163 insertions, 125 deletions

diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index 79d02f6ba5..0adc6a8e93 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -69,8 +69,9 @@ public:
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(227U, 227U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(227U, 227U, 3U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -87,7 +88,7 @@ public:
               .set_name("conv1")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu1")
               << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm1")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
               // Layer 2
               << ConvolutionLayer(
                   5U, 5U, 256U,
@@ -97,7 +98,7 @@ public:
               .set_name("conv2")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu2")
               << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm2")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
               // Layer 3
               << ConvolutionLayer(
                   3U, 3U, 384U,
@@ -122,7 +123,7 @@ public:
                   PadStrideInfo(1, 1, 1, 1), 2)
               .set_name("conv5")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu5")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
               // Layer 6
               << FullyConnectedLayer(
                   4096U,
diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp
index b768d282ef..84a10ffce1 100644
--- a/examples/graph_googlenet.cpp
+++ b/examples/graph_googlenet.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -69,8 +69,9 @@ public:
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -85,7 +86,7 @@ public:
                   PadStrideInfo(2, 2, 3, 3))
               .set_name("conv1/7x7_s2")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/relu_7x7")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1/3x3_s2")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1/3x3_s2")
               << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("pool1/norm1")
               << ConvolutionLayer(
                   1U, 1U, 64U,
@@ -102,19 +103,19 @@ public:
               .set_name("conv2/3x3")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2/relu_3x3")
               << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("conv2/norm2")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool2/3x3_s2");
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool2/3x3_s2");
         graph << get_inception_node(data_path, "inception_3a", weights_layout, 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U).set_name("inception_3a/concat");
         graph << get_inception_node(data_path, "inception_3b", weights_layout, 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U).set_name("inception_3b/concat");
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool3/3x3_s2");
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool3/3x3_s2");
         graph << get_inception_node(data_path, "inception_4a", weights_layout, 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U).set_name("inception_4a/concat");
         graph << get_inception_node(data_path, "inception_4b", weights_layout, 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U).set_name("inception_4b/concat");
         graph << get_inception_node(data_path, "inception_4c", weights_layout, 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U).set_name("inception_4c/concat");
         graph << get_inception_node(data_path, "inception_4d", weights_layout, 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U).set_name("inception_4d/concat");
         graph << get_inception_node(data_path, "inception_4e", weights_layout, 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U).set_name("inception_4e/concat");
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool4/3x3_s2");
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool4/3x3_s2");
         graph << get_inception_node(data_path, "inception_5a", weights_layout, 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U).set_name("inception_5a/concat");
         graph << get_inception_node(data_path, "inception_5b", weights_layout, 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U).set_name("inception_5b/concat");
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("pool5/7x7_s1")
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, operation_layout, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("pool5/7x7_s1")
               << FullyConnectedLayer(
                   1000U,
                   get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy", weights_layout),
@@ -195,7 +196,7 @@ private:
             << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_5x5");
 
         SubStream i_d(graph);
-        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL))).set_name(param_path + "/pool")
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL))).set_name(param_path + "/pool")
             << ConvolutionLayer(
                 1U, 1U, d_filt,
                 get_weights_accessor(data_path, total_path + "pool_proj_w.npy", weights_layout),
diff --git a/examples/graph_inception_resnet_v1.cpp b/examples/graph_inception_resnet_v1.cpp
index 89e44edc33..599aa5c8ac 100644
--- a/examples/graph_inception_resnet_v1.cpp
+++ b/examples/graph_inception_resnet_v1.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2019 ARM Limited.
+ * Copyright (c) 2018-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -97,8 +97,9 @@ public:
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(0.f, 1.f);
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(image_width, image_height, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(image_width, image_height, 3U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -146,7 +147,7 @@ public:
               .set_name("Conv2d_2b_3x3/BatchNorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu")
               // MaxPool_3a_3x3
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("MaxPool_3a_3x3/MaxPool")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("MaxPool_3a_3x3/MaxPool")
               // Conv2d_3b_1x1
               << ConvolutionLayer(1U, 1U, 80U,
                                   get_weights_accessor(data_path, "Conv2d_3b_1x1_weights.npy", weights_layout),
@@ -201,7 +202,7 @@ public:
         block8_repeat(data_path, weights_layout, 1, 1.f, false);
 
         // Logits tail
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a_8x8")
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("Logits/AvgPool_1a_8x8")
               << FlattenLayer().set_name("Logits/Flatten")
               << FullyConnectedLayer(
                   128U,
@@ -592,7 +593,7 @@ private:
 
         // Branch 2
         SubStream i_c(graph);
-        i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3");
+        i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3");
 
         // Concatenate
         graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)).set_name("Mixed_6a/concat");
@@ -695,7 +696,7 @@ private:
 
         // Branch 3
         SubStream i_d(graph);
-        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_7a/Branch_3/MaxPool_1a_3x3");
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_7a/Branch_3/MaxPool_1a_3x3");
 
         // Concatenate
         graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)).set_name("Mixed_7a/concat");
diff --git a/examples/graph_inception_resnet_v2.cpp b/examples/graph_inception_resnet_v2.cpp
index 424884f16f..d2f6e1d576 100644
--- a/examples/graph_inception_resnet_v2.cpp
+++ b/examples/graph_inception_resnet_v2.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2019 ARM Limited.
+ * Copyright (c) 2018-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -79,8 +79,9 @@ public:
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(0.f, 1.f);
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(299U, 299U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(299U, 299U, 3U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -128,7 +129,7 @@ public:
               .set_name("Conv2d_2b_3x3/BatchNorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu")
               // MaxPool_3a_3x3
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("MaxPool_3a_3x3/MaxPool")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("MaxPool_3a_3x3/MaxPool")
               // Conv2d_3b_1x1
               << ConvolutionLayer(1U, 1U, 80U,
                                   get_weights_accessor(data_path, "Conv2d_3b_1x1_weights.npy", weights_layout),
@@ -156,7 +157,7 @@ public:
               .set_name("Conv2d_4a_3x3/BatchNorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu")
               // MaxPool_5a_3x3
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0), true)).set_name("MaxPool_5a_3x3/MaxPool");
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0), true)).set_name("MaxPool_5a_3x3/MaxPool");
 
         block_mixed_5b(data_path, weights_layout);
         block35_repeat(data_path, weights_layout, 10);
@@ -179,7 +180,7 @@ public:
                                          0.0010000000474974513f)
               .set_name("Conv2d_7b_1x1/BatchNorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_7b_1x1/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a_8x8")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("Logits/AvgPool_1a_8x8")
               << FlattenLayer().set_name("Logits/Flatten")
               << FullyConnectedLayer(
                   1001U,
@@ -298,7 +299,7 @@ private:
 
         // Branch 3
         SubStream i_d(graph);
-        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name("Mixed_5b/Branch_3/AvgPool_0a_3x3")
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name("Mixed_5b/Branch_3/AvgPool_0a_3x3")
             << ConvolutionLayer(1U, 1U, 64U,
                                 get_weights_accessor(data_path, "Mixed_5b_Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
                                 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
@@ -374,7 +375,7 @@ private:
 
         // Branch 2
         SubStream i_c(graph);
-        i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3");
+        i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3");
 
         // Concatenate
         graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)).set_name("Mixed_6a/concat");
@@ -477,7 +478,7 @@ private:
 
         // Branch 3
         SubStream i_d(graph);
-        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_7a/Branch_3/MaxPool_1a_3x3");
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_7a/Branch_3/MaxPool_1a_3x3");
 
         // Concatenate
         graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)).set_name("Mixed_7a/concat");
diff --git a/examples/graph_inception_v3.cpp b/examples/graph_inception_v3.cpp
index d49561ae38..03d5dff9be 100644
--- a/examples/graph_inception_v3.cpp
+++ b/examples/graph_inception_v3.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -65,8 +65,9 @@ public:
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(299U, 299U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(299U, 299U, 3U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -115,7 +116,7 @@ public:
               .set_name("Conv2d_2b_3x3/BatchNorm/batchnorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu")
 
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_3a_3x3/MaxPool")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_3a_3x3/MaxPool")
 
               << ConvolutionLayer(1U, 1U, 80U,
                                   get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_weights.npy", weights_layout),
@@ -145,7 +146,7 @@ public:
               .set_name("Conv2d_4a_3x3/BatchNorm/batchnorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu")
 
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_5a_3x3/MaxPool");
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_5a_3x3/MaxPool");
 
         graph << get_inception_node_A(data_path, "Mixed_5b", weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
                                       32U)
@@ -183,7 +184,7 @@ public:
                                       std::make_tuple(448U, 384U, 384U, 384U), 192U, true)
               .set_name("Mixed_7c/concat");
 
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("Logits/AvgPool_1a_8x8/AvgPool")
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, operation_layout, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("Logits/AvgPool_1a_8x8/AvgPool")
               << ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path,
                                                                       "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy", weights_layout),
                                   get_weights_accessor(data_path,
@@ -328,7 +329,9 @@ private:
             << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_3x3/Relu");
 
         SubStream i_d(graph);
-        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL),
+                                             true))
+            .set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
             << ConvolutionLayer(
                 1U, 1U, d_filt,
                 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
@@ -413,7 +416,7 @@ private:
             << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_1x1/Relu");
 
         SubStream i_c(graph);
-        i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool");
+        i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool");
 
         return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c));
     }
@@ -558,7 +561,9 @@ private:
             << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_1x7/Relu");
 
         SubStream i_d(graph);
-        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL),
+                                             true))
+            .set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
             << ConvolutionLayer(
                 1U, 1U, d_filt,
                 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
@@ -671,7 +676,7 @@ private:
             << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_3x3/Relu");
 
         SubStream i_c(graph);
-        i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool");
+        i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool");
 
         return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c));
     }
@@ -824,7 +829,9 @@ private:
         i_c << ConcatLayer(std::move(i_c1), std::move(i_c2)).set_name(param_path + "/Branch_2/concat");
 
         SubStream i_d(graph);
-        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL),
+                                             true))
+            .set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
             << ConvolutionLayer(
                 1U, 1U, d_filt,
                 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
diff --git a/examples/graph_inception_v4.cpp b/examples/graph_inception_v4.cpp
index a322b2268d..7893930993 100644
--- a/examples/graph_inception_v4.cpp
+++ b/examples/graph_inception_v4.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2019 ARM Limited.
+ * Copyright (c) 2018-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -65,8 +65,9 @@ public:
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(299U, 299U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(299U, 299U, 3U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -135,7 +136,7 @@ public:
         graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7b").set_name("Mixed_7b/concat");
         graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7c").set_name("Mixed_7c/concat");
         graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7d").set_name("Mixed_7d/concat");
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a/AvgPool")
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("Logits/AvgPool_1a/AvgPool")
               << FlattenLayer().set_name("Logits/Flatten")
               << FullyConnectedLayer(
                   1001U,
@@ -188,7 +189,9 @@ private:
         std::string total_path = "/cnn_data/inceptionv4_model/Mixed_3a_";
 
         SubStream i_a(graph);
-        i_a << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_3a/Branch_0/MaxPool_0a_3x3/MaxPool");
+        i_a << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL),
+                                             true))
+            .set_name("Mixed_3a/Branch_0/MaxPool_0a_3x3/MaxPool");
 
         SubStream i_b(graph);
         i_b << ConvolutionLayer(3U, 3U, 96U,
@@ -301,7 +304,9 @@ private:
             << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5a/Branch_0/Conv2d_1a_3x3/Relu");
 
         SubStream i_b(graph);
-        i_b << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_5a/Branch_1/MaxPool_1a_3x3/MaxPool");
+        i_b << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL),
+                                             true))
+            .set_name("Mixed_5a/Branch_1/MaxPool_1a_3x3/MaxPool");
 
         return ConcatLayer(std::move(i_a), std::move(i_b));
     }
@@ -383,7 +388,9 @@ private:
             << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_3x3/Relu");
 
         SubStream i_d(graph);
-        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL),
+                                             true))
+            .set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
             << ConvolutionLayer(1U, 1U, 96U,
                                 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
                                 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
@@ -452,7 +459,9 @@ private:
             << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/Relu");
 
         SubStream i_c(graph);
-        i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3/MaxPool");
+        i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL),
+                                             true))
+            .set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3/MaxPool");
 
         return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c));
     }
@@ -567,7 +576,9 @@ private:
             << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_1x7/Relu");
 
         SubStream i_d(graph);
-        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL),
+                                             true))
+            .set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
             << ConvolutionLayer(1U, 1U, 128U,
                                 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
                                 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
@@ -658,7 +669,9 @@ private:
             << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/Relu");
 
         SubStream i_c(graph);
-        i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_7a/Branch_2/MaxPool_1a_3x3/MaxPool");
+        i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL),
+                                             true))
+            .set_name("Mixed_7a/Branch_2/MaxPool_1a_3x3/MaxPool");
 
         return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c));
     }
@@ -811,7 +824,9 @@ private:
         i_c << ConcatLayer(std::move(i_c1), std::move(i_c2)).set_name(param_path + "/Branch_2/concat");
 
         SubStream i_d(graph);
-        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
+        i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL),
+                                             true))
+            .set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
             << ConvolutionLayer(1U, 1U, 256U,
                                 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
                                 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp
index 9936ea52ae..7b475c2c03 100644
--- a/examples/graph_lenet.cpp
+++ b/examples/graph_lenet.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -66,8 +66,9 @@ public:
         unsigned int batches   = 4; /** Number of batches */
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(28U, 28U, 1U, batches), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(28U, 28U, 1U, batches), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -82,14 +83,14 @@ public:
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
               .set_name("conv1")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
               << ConvolutionLayer(
                   5U, 5U, 50U,
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy", weights_layout),
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
               .set_name("conv2")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
               << FullyConnectedLayer(
                   500U,
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy", weights_layout),
diff --git a/examples/graph_mnist.cpp b/examples/graph_mnist.cpp
index eb66138df4..56d5c96282 100644
--- a/examples/graph_mnist.cpp
+++ b/examples/graph_mnist.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019 ARM Limited.
+ * Copyright (c) 2019-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -70,8 +70,9 @@ public:
         }
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(28U, 28U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(28U, 28U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         const QuantizationInfo in_quant_info = QuantizationInfo(0.003921568859368563f, 0);
 
@@ -106,7 +107,7 @@ public:
                   PadStrideInfo(1U, 1U, 1U, 1U), 1, conv_quant_info.at(1).first, conv_quant_info.at(1).second)
               .set_name("conv1")
 
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("maxpool1")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("maxpool1")
 
               << ConvolutionLayer(
                   3U, 3U, 32U,
@@ -115,7 +116,7 @@ public:
                   PadStrideInfo(1U, 1U, 1U, 1U), 1, conv_quant_info.at(2).first, conv_quant_info.at(2).second)
               .set_name("conv2")
 
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("maxpool2")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("maxpool2")
 
               << FullyConnectedLayer(
                   10U,
diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp
index 5a39dc0ffe..23653c52c8 100644
--- a/examples/graph_mobilenet.cpp
+++ b/examples/graph_mobilenet.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -166,7 +166,7 @@ private:
         graph << get_dwsc_node_float(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
         graph << get_dwsc_node_float(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
         graph << get_dwsc_node_float(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a")
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool_1a")
               << ConvolutionLayer(
                   1U, 1U, 1001U,
                   get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW),
@@ -269,7 +269,7 @@ private:
                                       point_weights_quant_info.at(11));
         graph << get_dwsc_node_qasymm(data_path, "Conv2d_13", 1024U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(12),
                                       point_weights_quant_info.at(12))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool_1a")
               << ConvolutionLayer(
                   1U, 1U, 1001U,
                   get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"),
diff --git a/examples/graph_mobilenet_v2.cpp b/examples/graph_mobilenet_v2.cpp
index 337d68524c..1f5b0ec1ac 100644
--- a/examples/graph_mobilenet_v2.cpp
+++ b/examples/graph_mobilenet_v2.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2019 ARM Limited.
+ * Copyright (c) 2018-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -188,7 +188,7 @@ private:
               .set_name("Conv_1/BatchNorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
               .set_name("Conv_1/Relu6")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool")
               << ConvolutionLayer(1U, 1U, 1001U,
                                   get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW),
                                   get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
@@ -398,7 +398,7 @@ private:
                                   PadStrideInfo(1, 1, 0, 0), 1, conv_weights_quant_info.at(1))
               .set_name("Conv_1")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv_1/Relu6")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool")
               << ConvolutionLayer(1U, 1U, 1001U,
                                   get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"),
                                   get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp
index 84355131d0..7af058e042 100644
--- a/examples/graph_resnet50.cpp
+++ b/examples/graph_resnet50.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -67,8 +67,9 @@ public:
                                                                                                                    false /* Do not convert to BGR */);
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -90,14 +91,14 @@ public:
                   0.0000100099996416f)
               .set_name("conv1/BatchNorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool1/MaxPool");
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool1/MaxPool");
 
         add_residual_block(data_path, "block1", weights_layout, 64, 3, 2);
         add_residual_block(data_path, "block2", weights_layout, 128, 4, 2);
         add_residual_block(data_path, "block3", weights_layout, 256, 6, 2);
         add_residual_block(data_path, "block4", weights_layout, 512, 3, 1);
 
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool5")
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool5")
               << ConvolutionLayer(
                   1U, 1U, 1000U,
                   get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_weights.npy", weights_layout),
@@ -220,7 +221,7 @@ private:
             else if(middle_stride > 1)
             {
                 SubStream left(graph);
-                left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)).set_name(unit_name + "shortcut/MaxPool");
+                left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, common_params.data_layout, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)).set_name(unit_name + "shortcut/MaxPool");
 
                 graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(unit_name + "add");
             }
diff --git a/examples/graph_resnet_v2_50.cpp b/examples/graph_resnet_v2_50.cpp
index e2325151bc..7d6b9aa3fd 100644
--- a/examples/graph_resnet_v2_50.cpp
+++ b/examples/graph_resnet_v2_50.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2019 ARM Limited.
+ * Copyright (c) 2018-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -70,8 +70,9 @@ public:
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -85,7 +86,7 @@ public:
                   get_weights_accessor(data_path, "conv1_biases.npy", weights_layout),
                   PadStrideInfo(2, 2, 3, 3))
               .set_name("conv1/convolution")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool1/MaxPool");
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool1/MaxPool");
 
         add_residual_block(data_path, "block1", weights_layout, 64, 3, 2);
         add_residual_block(data_path, "block2", weights_layout, 128, 4, 2);
@@ -100,7 +101,7 @@ public:
                   0.000009999999747378752f)
               .set_name("postnorm/BatchNorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("postnorm/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool5")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool5")
               << ConvolutionLayer(
                   1U, 1U, 1001U,
                   get_weights_accessor(data_path, "logits_weights.npy", weights_layout),
@@ -174,7 +175,7 @@ private:
             {
                 if(middle_stride != 1)
                 {
-                    shortcut << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)).set_name(unit_name + "shortcut/MaxPool");
+                    shortcut << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, common_params.data_layout, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)).set_name(unit_name + "shortcut/MaxPool");
                 }
             }
             else
diff --git a/examples/graph_resnext50.cpp b/examples/graph_resnext50.cpp
index 4e505a05e5..2c50594b0c 100644
--- a/examples/graph_resnext50.cpp
+++ b/examples/graph_resnext50.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2019 ARM Limited.
+ * Copyright (c) 2018-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -65,8 +65,9 @@ public:
         std::string data_path = common_params.data_path;
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -84,14 +85,14 @@ public:
                   PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR))
               .set_name("conv0/Convolution")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0");
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0");
 
         add_residual_block(data_path, weights_layout, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1);
         add_residual_block(data_path, weights_layout, 512, 2, 4, 2);
         add_residual_block(data_path, weights_layout, 1024, 3, 6, 2);
         add_residual_block(data_path, weights_layout, 2048, 4, 3, 2);
 
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool1")
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool1")
               << FlattenLayer().set_name("predictions/Reshape")
               << OutputLayer(get_npy_output_accessor(common_params.labels, TensorShape(2048U), DataType::F32));
 
diff --git a/examples/graph_shufflenet.cpp b/examples/graph_shufflenet.cpp
index 0a67f5873a..0b977982b5 100644
--- a/examples/graph_shufflenet.cpp
+++ b/examples/graph_shufflenet.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2019 ARM Limited.
+ * Copyright (c) 2018-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -81,8 +81,9 @@ public:
         }
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -107,7 +108,7 @@ public:
                   1e-5f)
               .set_name("Conv1/BatchNorm")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv1/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 1, 1))).set_name("pool1/MaxPool");
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 1, 1))).set_name("pool1/MaxPool");
 
         // Stage 2
         add_residual_block(data_path, DataLayout::NCHW, 0U /* unit */, 112U /* depth */, 2U /* stride */);
@@ -131,7 +132,7 @@ public:
         add_residual_block(data_path, DataLayout::NCHW, 14U /* unit */, 544U /* depth */, 1U /* stride */);
         add_residual_block(data_path, DataLayout::NCHW, 15U /* unit */, 544U /* depth */, 1U /* stride */);
 
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("predictions/AvgPool")
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("predictions/AvgPool")
               << FlattenLayer().set_name("predictions/Reshape")
               << FullyConnectedLayer(
                   1000U,
@@ -181,7 +182,7 @@ private:
 
         if(stride == 2)
         {
-            right_ss << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(2, 2, 1, 1))).set_name(unit_name + "/pool_1/AveragePool");
+            right_ss << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(2, 2, 1, 1))).set_name(unit_name + "/pool_1/AveragePool");
             dwc_info = PadStrideInfo(2, 2, 1, 1);
         }
 
diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp
index 234def150d..35fceb4e98 100644
--- a/examples/graph_squeezenet.cpp
+++ b/examples/graph_squeezenet.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -66,8 +66,9 @@ public:
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -82,7 +83,7 @@ public:
                   PadStrideInfo(2, 2, 0, 0))
               .set_name("conv1")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv1")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1")
               << ConvolutionLayer(
                   1U, 1U, 16U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy", weights_layout),
@@ -107,7 +108,7 @@ public:
               .set_name("fire4/squeeze1x1")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire4/relu_squeeze1x1");
         graph << get_expand_fire_node(data_path, "fire4", weights_layout, 128U, 128U).set_name("fire4/concat");
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool4")
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool4")
               << ConvolutionLayer(
                   1U, 1U, 32U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy", weights_layout),
@@ -140,7 +141,7 @@ public:
               .set_name("fire8/squeeze1x1")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire8/relu_squeeze1x1");
         graph << get_expand_fire_node(data_path, "fire8", weights_layout, 256U, 256U).set_name("fire8/concat");
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool8")
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool8")
               << ConvolutionLayer(
                   1U, 1U, 64U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy", weights_layout),
@@ -156,7 +157,7 @@ public:
                   PadStrideInfo(1, 1, 0, 0))
               .set_name("conv10")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv10")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool10")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool10")
               << FlattenLayer().set_name("flatten")
               << SoftmaxLayer().set_name("prob")
               << OutputLayer(get_output_accessor(common_params, 5));
diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp
index b43c8ffdad..f648b6337d 100644
--- a/examples/graph_squeezenet_v1_1.cpp
+++ b/examples/graph_squeezenet_v1_1.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2019 ARM Limited.
+ * Copyright (c) 2018-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -66,8 +66,9 @@ public:
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(227U, 227U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(227U, 227U, 3U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -82,7 +83,7 @@ public:
                   PadStrideInfo(2, 2, 0, 0))
               .set_name("conv1")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv1")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1")
               << ConvolutionLayer(
                   1U, 1U, 16U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire2_squeeze1x1_w.npy", weights_layout),
@@ -99,7 +100,7 @@ public:
               .set_name("fire3/squeeze1x1")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire3/relu_squeeze1x1");
         graph << get_expand_fire_node(data_path, "fire3", weights_layout, 64U, 64U).set_name("fire3/concat");
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool3")
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool3")
               << ConvolutionLayer(
                   1U, 1U, 32U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire4_squeeze1x1_w.npy", weights_layout),
@@ -116,7 +117,7 @@ public:
               .set_name("fire5/squeeze1x1")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire5/relu_squeeze1x1");
         graph << get_expand_fire_node(data_path, "fire5", weights_layout, 128U, 128U).set_name("fire5/concat");
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool5")
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool5")
               << ConvolutionLayer(
                   1U, 1U, 48U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire6_squeeze1x1_w.npy", weights_layout),
@@ -156,7 +157,7 @@ public:
                   PadStrideInfo(1, 1, 0, 0))
               .set_name("conv10")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv10")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool10")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool10")
               << FlattenLayer().set_name("flatten")
               << SoftmaxLayer().set_name("prob")
               << OutputLayer(get_output_accessor(common_params, 5));
diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp
index 2c7f614f64..f6996dadd5 100644
--- a/examples/graph_vgg16.cpp
+++ b/examples/graph_vgg16.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -76,8 +76,9 @@ public:
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -102,7 +103,7 @@ public:
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv1_2")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_2/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
               // Layer 3
               << ConvolutionLayer(
                   3U, 3U, 128U,
@@ -119,7 +120,7 @@ public:
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv2_2")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_2/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
               // Layer 5
               << ConvolutionLayer(
                   3U, 3U, 256U,
@@ -144,7 +145,7 @@ public:
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv3_3")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_3/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool3")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool3")
               // Layer 8
               << ConvolutionLayer(
                   3U, 3U, 512U,
@@ -169,7 +170,7 @@ public:
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv4_3")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_3/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool4")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool4")
               // Layer 11
               << ConvolutionLayer(
                   3U, 3U, 512U,
@@ -194,7 +195,7 @@ public:
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv5_3")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_3/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
               // Layer 14
               << FullyConnectedLayer(
                   4096U,
diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp
index e3733d859c..f9f5c213d5 100644
--- a/examples/graph_vgg19.cpp
+++ b/examples/graph_vgg19.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -75,8 +75,9 @@ public:
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -99,7 +100,7 @@ public:
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv1_2")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_2/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
               // Layer 2
               << ConvolutionLayer(
                   3U, 3U, 128U,
@@ -115,7 +116,7 @@ public:
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv2_2")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_2/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
               // Layer 3
               << ConvolutionLayer(
                   3U, 3U, 256U,
@@ -145,7 +146,7 @@ public:
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv3_4")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_4/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool3")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool3")
               // Layer 4
               << ConvolutionLayer(
                   3U, 3U, 512U,
@@ -175,7 +176,7 @@ public:
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv4_4")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_4/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool4")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool4")
               // Layer 5
               << ConvolutionLayer(
                   3U, 3U, 512U,
@@ -205,7 +206,7 @@ public:
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv5_4")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_4/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
               // Layer 6
               << FullyConnectedLayer(
                   4096U,
diff --git a/examples/neon_cnn.cpp b/examples/neon_cnn.cpp
index 6f26af7af4..ee6f46d28b 100644
--- a/examples/neon_cnn.cpp
+++ b/examples/neon_cnn.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2016-2018 ARM Limited.
+ * Copyright (c) 2016-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -131,6 +131,8 @@ public:
         const TensorShape out_shape_softmax(out_shape_fc0.x());
         out_softmax.allocator()->init(TensorInfo(out_shape_softmax, 1, DataType::F32));
 
+        constexpr auto data_layout = DataLayout::NCHW;
+
         /* -----------------------End: [Initialize tensors] */
 
         /* [Configure functions] */
@@ -142,7 +144,7 @@ public:
         act0.configure(&out_conv0, &out_act0, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
         // in:32x32x8, out:16x16x8 (2x2 pooling), Pool type function: Max
-        pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */)));
+        pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2, data_layout, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */)));
 
         // in:16x16x8: 3x3 convolution, 16 output features maps (OFM)
         conv1->configure(&out_pool0, &weights1, &biases1, &out_conv1, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 1 /* pad_x */, 1 /* pad_y */));
@@ -151,7 +153,7 @@ public:
         act1.configure(&out_conv1, &out_act1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
         // in:16x16x16, out:8x8x16 (2x2 pooling), Pool type function: Average
-        pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */)));
+        pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2, data_layout, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */)));
 
         // in:8x8x16, out:128
         fc0->configure(&out_pool1, &weights2, &biases2, &out_fc0);