1 files changed, 0 insertions, 190 deletions
diff --git a/src/backends/backendsCommon/test/FullyConnectedTestImpl.hpp b/src/backends/backendsCommon/test/FullyConnectedTestImpl.hpp
deleted file mode 100644
index 402a3e6d51..0000000000
--- a/src/backends/backendsCommon/test/FullyConnectedTestImpl.hpp
+++ /dev/null
@@ -1,190 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include <ResolveType.hpp>
-#include "WorkloadTestUtils.hpp"
-#include <backendsCommon/IBackendInternal.hpp>
-
-LayerTestResult<float, 2> FullyConnectedFloat32Test(
-    armnn::IWorkloadFactory& workloadFactory,
-    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
-    bool biasEnabled,
-    bool transposeWeights)
-{
-    unsigned int inputWidth = 1;
-    unsigned int inputHeight = 1;
-    unsigned int inputChannels = 5;
-    unsigned int inputNum = 2;
-
-    unsigned int outputChannels = 3;
-    unsigned int outputNum = 2;
-
-    // Define the tensor descriptors.
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-    armnn::TensorInfo weightsDesc;
-    armnn::TensorInfo biasesDesc;
-
-    unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
-    unsigned int outputShape[] = { outputNum, outputChannels };
-    unsigned int weightsShape[] = { inputChannels, outputChannels };
-    if (transposeWeights)
-    {
-        std::swap(weightsShape[0], weightsShape[1]);
-    }
-    unsigned int biasShape[] = { outputChannels };
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::DataType::Float32);
-    weightsDesc = armnn::TensorInfo(2, weightsShape, armnn::DataType::Float32);
-    biasesDesc = armnn::TensorInfo(1, biasShape, armnn::DataType::Float32);
-
-    LayerTestResult<float, 2> result(outputTensorInfo);
-
-    boost::multi_array<float, 4> input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>(
-        {
-            1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
-
-            5.0f, 4.0f, 3.0f, 2.0f, 1.0f
-        })
-    );
-
-    boost::multi_array<float, 2> weights = MakeTensor<float, 2>(weightsDesc, std::vector<float>(
-        {
-            .5f, 2.f, .5f,
-            .5f, 2.f, 1.f,
-            .5f, 2.f, 2.f,
-            .5f, 2.f, 3.f,
-            .5f, 2.f, 4.f
-        }));
-
-    if (transposeWeights)
-    {
-        weights = MakeTensor<float, 2>(weightsDesc, std::vector<float>(
-        {
-            .5f, .5f, .5f, .5f, .5f,
-            2.f, 2.f, 2.f, 2.f, 2.f,
-            .5f, 1.f, 2.f, 3.f, 4.f
-        }));
-    }
-
-
-    std::vector<float> biasValues({0.f, 0.f, 0.f});
-    if (biasEnabled)
-    {
-        biasValues =  std::vector<float>({10.f, 20.f, 30.f});
-    }
-    boost::multi_array<float, 1> bias = MakeTensor<float, 1>(biasesDesc, biasValues);
-
-    result = SimpleFullyConnectedTestImpl<float>(
-        workloadFactory,
-        memoryManager,
-        inputTensorInfo, outputTensorInfo,
-        weightsDesc, biasesDesc,
-        weights, bias, input,
-        biasEnabled, transposeWeights
-    );
-
-    result.outputExpected = MakeTensor<float, 2>(outputTensorInfo, std::vector<float>(
-        {
-            0.5f + 1.0f + 1.5f + 2.0f + 2.5f + biasValues[0],
-            2.0f + 4.0f + 6.0f + 8.0f + 10.f + biasValues[1],
-            0.5f + 2.0f + 6.0f + 12.f + 20.f + biasValues[2],
-
-            2.5f + 2.0f + 1.5f + 1.0f + 0.5f + biasValues[0],
-            10.0f + 8.0f + 6.0f + 4.0f + 2.f + biasValues[1],
-            2.5f + 4.0f + 6.0f + 6.f + 4.f   + biasValues[2]
-        })
-    );
-
-    return result;
-}
-
-//
-// ArmNN variant of the AndroidNN fully_connected_float_large test.
-//
-// Tests the fully connected layer with large values, optionally transposing weights.
-// Note this is templated for consistency, but the nature of this tests makes it unlikely to be useful in Uint8 mode.
-//
-template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
-LayerTestResult<T, 2> FullyConnectedLargeTestCommon(
-    armnn::IWorkloadFactory& workloadFactory,
-    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
-    bool transposeWeights,
-    float qScale = 0.0f,
-    int32_t qOffset = 0)
-{
-    unsigned int inputWidth = 1;
-    unsigned int inputHeight = 1;
-    unsigned int inputChannels = 5;
-    unsigned int inputNum = 1;
-
-    unsigned int outputChannels = 1;
-    unsigned int outputNum = 1;
-
-    // Define the tensor descriptors.
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-    armnn::TensorInfo weightsDesc;
-    armnn::TensorInfo biasesDesc;
-
-    unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
-    unsigned int outputShape[] = { outputNum, outputChannels };
-    unsigned int weightsShape[] = { inputChannels, outputChannels };
-    if (transposeWeights)
-    {
-        std::swap(weightsShape[0], weightsShape[1]);
-    }
-
-    unsigned int biasShape[] = { outputChannels };
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, ArmnnType);
-    outputTensorInfo = armnn::TensorInfo(2, outputShape, ArmnnType);
-    weightsDesc = armnn::TensorInfo(2, weightsShape, ArmnnType);
-    biasesDesc = armnn::TensorInfo(1, biasShape, ArmnnType);
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    LayerTestResult<T, 2> result(outputTensorInfo);
-
-    boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            1.0f, 10.0f, 100.0f, 1000.0f, 10000.0f,
-        })
-    );
-
-    boost::multi_array<T, 2> weights = MakeTensor<T, 2>(weightsDesc,
-        QuantizedVector<T>(qScale, qOffset, {
-            2.0f, 3.0f, 4.0f, 5.0f, 6.0f
-        })
-    );
-
-    std::vector<T> biasValues({900000.f});
-    boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasesDesc, biasValues);
-
-    result = SimpleFullyConnectedTestImpl<T>(
-        workloadFactory,
-        memoryManager,
-        inputTensorInfo, outputTensorInfo,
-        weightsDesc, biasesDesc,
-        weights, bias, input,
-        true, transposeWeights
-    );
-
-    result.outputExpected = MakeTensor<T, 2>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            965432.0f,
-        })
-    );
-
-    return result;
-}