1 files changed, 64 insertions, 0 deletions
diff --git a/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp b/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp
index 531a0dd92a..34e5f6d3b6 100644
--- a/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp
+++ b/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp
@@ -58,4 +58,68 @@ TEST_CASE("ConvertConstantsFloatToHalfTest")
     CHECK(data[3] == Half(4.0f));
 }
 
+
+TEST_CASE("ConvertConstantsFloatToHalfTest_constant")
+{
+    armnn::Graph graph;
+
+    // Create the simple test network with Weights and Biases as inputs to a FullyConnected layer.
+    auto input   = graph.AddLayer<armnn::InputLayer>(0, "Input");
+    auto weights = graph.AddLayer<armnn::ConstantLayer>("Weights");
+    auto biases  = graph.AddLayer<armnn::ConstantLayer>("Biases");
+
+    armnn::FullyConnectedDescriptor desc;
+    desc.m_BiasEnabled = true;
+    desc.m_ConstantWeights = true;
+    auto fcLayer = graph.AddLayer<armnn::FullyConnectedLayer>(desc, "FullyConnected");
+    auto output  = graph.AddLayer<armnn::OutputLayer>(1, "Output");
+
+    float expectedWeightsData[] = { 1.0f, 2.0f, 3.0f, 4.0f };
+    float expectedBiasesData[]  = { 2.0f, 2.0f };
+
+    const armnn::TensorInfo inputInfo  ({ 1, 2, 2, 3 }, armnn::DataType::Float16);
+    const armnn::TensorInfo outputInfo ({ 1, 2, 2, 3 }, armnn::DataType::Float16);
+    const armnn::TensorInfo weightsInfo({ 4 }, armnn::DataType::Float32, 0.0f, 0, true);
+    const armnn::TensorInfo biasesInfo ({ 2 }, armnn::DataType::Float32, 0.0f, 0, true);
+
+    // Set the m_LayerOutput for the optimizer to point to.
+    armnn::ConstTensor weightsTensor(weightsInfo, &expectedWeightsData);
+    armnn::ConstTensor biasesTensor(biasesInfo, &expectedBiasesData);
+    weights->m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(weightsTensor);
+    biases->m_LayerOutput  = std::make_unique<armnn::ScopedTensorHandle>(biasesTensor);
+
+    input->GetOutputSlot().SetTensorInfo(inputInfo);
+    weights->GetOutputSlot().SetTensorInfo(weightsInfo);
+    biases->GetOutputSlot().SetTensorInfo(biasesInfo);
+    fcLayer->GetOutputSlot().SetTensorInfo(outputInfo);
+
+    // Connect up the layers
+    input->GetOutputSlot(0).Connect(fcLayer->GetInputSlot(0));
+    weights->GetOutputSlot(0).Connect(fcLayer->GetInputSlot(1));
+    biases->GetOutputSlot(0).Connect(fcLayer->GetInputSlot(2));
+    fcLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+    // Check tensor data type before conversion
+    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(weights->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float16);
+
+    // Check whether weights data matches expected fp16 data
+    const Half* data = weights->m_LayerOutput->GetConstTensor<Half>();
+    CHECK(data[0] == Half(1.0f));
+    CHECK(data[1] == Half(2.0f));
+    CHECK(data[2] == Half(3.0f));
+    CHECK(data[3] == Half(4.0f));
+
+    // Check whether bias data matches expected fp16 data
+    const Half* biasData = biases->m_LayerOutput->GetConstTensor<Half>();
+    CHECK(biasData[0] == Half(2.0f));
+    CHECK(biasData[1] == Half(2.0f));
+}
+
+
 }
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