1 files changed, 14 insertions, 7 deletions
diff --git a/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp b/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp
index 34e5f6d3b6..118907e703 100644
--- a/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp
+++ b/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp
@@ -1,12 +1,12 @@
 //
-// Copyright © 2017 Arm Ltd. All rights reserved.
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
 #include <TestUtils.hpp>
 
-#include <Optimizer.hpp>
 #include <Half.hpp>
+#include <Optimizer.hpp>
 
 #include <doctest/doctest.h>
 
@@ -25,33 +25,38 @@ TEST_CASE("ConvertConstantsFloatToHalfTest")
     // Create const tensor from fp32 data
     unsigned int dims[] = { 4, 1, 1, 1 };
     std::vector<float> floatWeights{ 1.0f, 2.0f, 3.0f, 4.0f };
-    armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float32, 0.0f, 0, true), floatWeights);
+    armnn::TensorInfo weightsInfo = armnn::TensorInfo(4, dims, armnn::DataType::Float32, 0.0f, 0, true);
+    armnn::ConstTensor weights(weightsInfo, floatWeights);
 
     // Create simple test network
     auto input = graph.AddLayer<armnn::InputLayer>(0, "input");
     input->GetOutputSlot().SetTensorInfo(info);
 
     auto fc      = graph.AddLayer<armnn::FullyConnectedLayer>(armnn::FullyConnectedDescriptor(), "fc");
-    fc->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(weights);
     fc->GetOutputSlot().SetTensorInfo(info);
 
+    auto weightsLayer = graph.AddLayer<armnn::ConstantLayer>("weights");
+    weightsLayer->m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(weights);
+    weightsLayer->GetOutputSlot().SetTensorInfo(weightsInfo);
+
     auto output = graph.AddLayer<armnn::OutputLayer>(1, "output");
 
     // Connect up the layers
     input->GetOutputSlot().Connect(fc->GetInputSlot(0));
+    weightsLayer->GetOutputSlot().Connect(fc->GetInputSlot(1));
     fc->GetOutputSlot().Connect(output->GetInputSlot(0));
 
     // Check tensor data type before conversion
-    CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32);
+    CHECK(weightsLayer->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float32);
 
     // Run the optimizer
     armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsFloatToHalf()));
 
     // Check tensor data type after conversion
-    CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float16);
+    CHECK(weightsLayer->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float16);
 
     // Check whether data matches expected fp16 data
-    const Half* data = fc->m_Weight->GetConstTensor<Half>();
+    const Half* data = weightsLayer->m_LayerOutput->GetConstTensor<Half>();
     CHECK(data[0] == Half(1.0f));
     CHECK(data[1] == Half(2.0f));
     CHECK(data[2] == Half(3.0f));
@@ -100,12 +105,14 @@ TEST_CASE("ConvertConstantsFloatToHalfTest_constant")
     fcLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
 
     // Check tensor data type before conversion
+    CHECK(5 == graph.GetNumLayers());
     CHECK(weights->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float32);
 
     // Run the optimizer
     armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsFloatToHalf()));
 
     // Check tensor data type after conversion
+    CHECK(5 == graph.GetNumLayers());
     CHECK(weights->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float16);
 
     // Check whether weights data matches expected fp16 data