1 files changed, 85 insertions, 0 deletions
diff --git a/src/armnn/test/optimizations/RedirectMembersToConstantInputsTests.cpp b/src/armnn/test/optimizations/RedirectMembersToConstantInputsTests.cpp
new file mode 100644
index 0000000000..46b06a55c7
--- /dev/null
+++ b/src/armnn/test/optimizations/RedirectMembersToConstantInputsTests.cpp
@@ -0,0 +1,85 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "../TestUtils.hpp"
+
+#include <Optimizer.hpp>
+
+#include <doctest/doctest.h>
+
+TEST_SUITE("Optimizer")
+{
+using namespace armnn::optimizations;
+
+TEST_CASE("RedirectMembersToConstantInputsFullyConnectedTest")
+{
+    armnn::Graph graph;
+
+    const armnn::TensorInfo inputInfo  ({ 1, 2, 2, 3 }, armnn::DataType::Float32);
+    const armnn::TensorInfo outputInfo ({ 1, 2, 2, 3 }, armnn::DataType::Float32);
+    const armnn::TensorInfo weightsInfo({ 4 }, armnn::DataType::Float32, 0.0f, 0, true);
+    const armnn::TensorInfo biasesInfo ({ 2 }, armnn::DataType::Float32, 0.0f, 0, true);
+
+    // Check if isConstant is enabled for weights and biases tensor info.
+    CHECK(weightsInfo.IsConstant());
+    CHECK(biasesInfo.IsConstant());
+
+    armnn::FullyConnectedDescriptor desc;
+    desc.m_BiasEnabled = true;
+    desc.m_ConstantWeights = false;
+
+    // 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");
+    auto fcLayer = graph.AddLayer<armnn::FullyConnectedLayer>(desc, "FullyConnected");
+    auto output  = graph.AddLayer<armnn::OutputLayer>(1, "Output");
+
+    float expectedWeightsData[] = { 1.0f, 1.0f, 1.0f, 1.0f };
+    float expectedBiasesData[]  = { 2.0f, 2.0f };
+
+    // 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));
+
+    // Member variables should be null before optimization.
+    CHECK(fcLayer->m_Weight == nullptr);
+    CHECK(fcLayer->m_Bias == nullptr);
+
+    // Run the optimizer
+    armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(RedirectMembersToConstantInputs()));
+
+    // Check if member variables are not null and shape is set correctly.
+    CHECK(fcLayer->m_Weight != nullptr);
+    CHECK(fcLayer->m_Bias != nullptr);
+    CHECK(fcLayer->m_Weight->GetTensorInfo().GetShape() == weightsInfo.GetShape());
+    CHECK(fcLayer->m_Bias->GetTensorInfo().GetShape() == biasesInfo.GetShape());
+
+    // Check whether data matches expected float data
+    const float* weightsData = fcLayer->m_Weight->GetConstTensor<float>();
+    CHECK(weightsData[0] == expectedWeightsData[0]);
+    CHECK(weightsData[1] == expectedWeightsData[1]);
+    CHECK(weightsData[2] == expectedWeightsData[2]);
+    CHECK(weightsData[3] == expectedWeightsData[3]);
+
+    const float* biasesData = fcLayer->m_Bias->GetConstTensor<float>();
+    CHECK(biasesData[0] == expectedBiasesData[0]);
+    CHECK(biasesData[1] == expectedBiasesData[1]);
+}
+
+}
+\ No newline at end of file