From d134093a271b60e248942af9757e8236e8f41ac1 Mon Sep 17 00:00:00 2001
From: Nikhil Raj <nikhil.raj@arm.com>
Date: Thu, 18 Oct 2018 14:27:50 +0100
Subject: IVGCVSW-2023 CL and Neon implementation of BatchNorm with NHWC

Change-Id: I962e986607e5d045cd97b9eaeaea2f5ae624db35
---
 src/armnn/test/CreateWorkload.hpp | 26 ++++++++++++++++++--------
 1 file changed, 18 insertions(+), 8 deletions(-)

(limited to 'src/armnn')

diff --git a/src/armnn/test/CreateWorkload.hpp b/src/armnn/test/CreateWorkload.hpp
index db1773a0ce..01c5e9f689 100644
--- a/src/armnn/test/CreateWorkload.hpp
+++ b/src/armnn/test/CreateWorkload.hpp
@@ -133,8 +133,20 @@ std::unique_ptr<WorkloadType> CreateArithmeticWorkloadTest(armnn::IWorkloadFacto
 
 template <typename BatchNormalizationFloat32Workload, armnn::DataType DataType>
 std::unique_ptr<BatchNormalizationFloat32Workload> CreateBatchNormalizationWorkloadTest(
-    armnn::IWorkloadFactory& factory, armnn::Graph& graph, DataLayout dataLayout = DataLayout::NCHW)
+    armnn::IWorkloadFactory& factory, armnn::Graph& graph, DataLayoutIndexed dataLayout = DataLayout::NCHW)
 {
+
+    TensorShape tensorShape;
+    switch (dataLayout.GetDataLayout())
+    {
+        case DataLayout::NHWC:
+            tensorShape = { 2, 4, 4, 3 };
+            break;
+        case DataLayout::NCHW:
+        default:
+            tensorShape = { 2, 3, 4, 4 };
+    }
+
     // Creates the layer we're testing.
     BatchNormalizationDescriptor layerDesc;
     layerDesc.m_Eps = 0.05f;
@@ -156,25 +168,23 @@ std::unique_ptr<BatchNormalizationFloat32Workload> CreateBatchNormalizationWorkl
     Layer* const input = graph.AddLayer<InputLayer>(0, "input");
     Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
 
-    TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{ 2, 3, 1, 1 } : TensorShape{ 2, 1, 1, 3 };
-    TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{ 2, 3, 1, 1 } : TensorShape{ 2, 1, 1, 3 };
-
-    // Connects up.
-    Connect(input, layer, TensorInfo(inputShape, DataType));
-    Connect(layer, output, TensorInfo(outputShape, DataType));
+    //Connects up.
+    armnn::TensorInfo tensorInfo(tensorShape, DataType);
+    Connect(input, layer, tensorInfo);
+    Connect(layer, output, tensorInfo);
     CreateTensorHandles(graph, factory);
 
     // Makes the workload and checks it.
     auto workload = MakeAndCheckWorkload<BatchNormalizationFloat32Workload>(*layer, graph, factory);
     BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData();
     BOOST_TEST(queueDescriptor.m_Parameters.m_Eps == 0.05f);
-    BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));
     BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);
     BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);
     BOOST_TEST((queueDescriptor.m_Mean->GetTensorInfo() == TensorInfo({3}, DataType)));
     BOOST_TEST((queueDescriptor.m_Variance->GetTensorInfo() == TensorInfo({3}, DataType)));
     BOOST_TEST((queueDescriptor.m_Gamma->GetTensorInfo() == TensorInfo({3}, DataType)));
     BOOST_TEST((queueDescriptor.m_Beta->GetTensorInfo() == TensorInfo({3}, DataType)));
+    BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout.GetDataLayout() == dataLayout));
 
     // Returns so we can do extra, backend-specific tests.
     return workload;
-- 
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