1 files changed, 0 insertions, 85 deletions
diff --git a/src/armnn/test/optimizations/RedirectMembersToConstantInputsTests.cpp b/src/armnn/test/optimizations/RedirectMembersToConstantInputsTests.cpp
deleted file mode 100644
index b3f9ed8780..0000000000
--- a/src/armnn/test/optimizations/RedirectMembersToConstantInputsTests.cpp
+++ /dev/null
@@ -1,85 +0,0 @@
-//
-// 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