IVGCVSW-3381 Break up LayerTests.hpp into more manageable files

Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Change-Id: Icf39434f09fd340ad664cb3b97b8bee6d9da4838

1 files changed, 0 insertions, 194 deletions

diff --git a/src/backends/backendsCommon/test/BatchNormTestImpl.hpp b/src/backends/backendsCommon/test/BatchNormTestImpl.hpp
deleted file mode 100644
index d34a54f992..0000000000
--- a/src/backends/backendsCommon/test/BatchNormTestImpl.hpp
+++ /dev/null
@@ -1,194 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <ResolveType.hpp>
-#include "WorkloadTestUtils.hpp"
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-
-#include <backendsCommon/CpuTensorHandle.hpp>
-#include <backendsCommon/IBackendInternal.hpp>
-#include <backendsCommon/WorkloadFactory.hpp>
-#include <backendsCommon/test/QuantizeHelper.hpp>
-
-#include <test/TensorHelpers.hpp>
-
-#include <DataLayoutIndexed.hpp>
-
-template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
-LayerTestResult<T, 4> BatchNormTestImpl(
-    armnn::IWorkloadFactory& workloadFactory,
-    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
-    const armnn::TensorShape& inputOutputTensorShape,
-    const std::vector<float>& inputValues,
-    const std::vector<float>& expectedOutputValues,
-    float qScale,
-    int32_t qOffset,
-    armnn::DataLayout dataLayout)
-{
-    armnn::TensorInfo inputTensorInfo(inputOutputTensorShape, ArmnnType);
-    armnn::TensorInfo outputTensorInfo(inputOutputTensorShape, ArmnnType);
-
-    armnnUtils::DataLayoutIndexed dataLayoutIndexed(dataLayout);
-
-    armnn::TensorInfo tensorInfo({ inputOutputTensorShape[dataLayoutIndexed.GetChannelsIndex()] },
-                                 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);
-        tensorInfo.SetQuantizationScale(qScale);
-        tensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto inputTensor = MakeTensor<T, 4>(inputTensorInfo,
-                                        QuantizedVector<T>(qScale, qOffset, inputValues));
-
-    // These values are per-channel of the input.
-    auto mean     = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, -2}));
-    auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {4,  9}));
-    auto beta     = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3,  2}));
-    auto gamma    = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {2,  1}));
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-
-    result.outputExpected = MakeTensor<T, 4>(inputTensorInfo,
-                                             QuantizedVector<T>(qScale, qOffset, expectedOutputValues));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ScopedCpuTensorHandle meanTensor(tensorInfo);
-    armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo);
-    armnn::ScopedCpuTensorHandle betaTensor(tensorInfo);
-    armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo);
-
-    armnn::BatchNormalizationQueueDescriptor descriptor;
-    descriptor.m_Mean                    = &meanTensor;
-    descriptor.m_Variance                = &varianceTensor;
-    descriptor.m_Beta                    = &betaTensor;
-    descriptor.m_Gamma                   = &gammaTensor;
-    descriptor.m_Parameters.m_Eps        = 0.0f;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-    armnn::WorkloadInfo info;
-
-    AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]);
-    AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]);
-    AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]);
-    AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]);
-
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateBatchNormalization(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-
-    return result;
-}
-
-
-template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
-LayerTestResult<T,4> BatchNormTestNhwcImpl(
-    armnn::IWorkloadFactory& workloadFactory,
-    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
-    float qScale,
-    int32_t qOffset)
-{
-    const unsigned int width    = 2;
-    const unsigned int height   = 3;
-    const unsigned int channels = 2;
-    const unsigned int num      = 1;
-
-    armnn::TensorInfo inputTensorInfo({num, height, width, channels}, ArmnnType);
-    armnn::TensorInfo outputTensorInfo({num, height, width, channels}, ArmnnType);
-    armnn::TensorInfo tensorInfo({channels}, 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);
-        tensorInfo.SetQuantizationScale(qScale);
-        tensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset,
-        {
-            1.f, 1.f, 4.f, 1.f,
-            4.f, 4.f, 2.f, 1.f,
-            1.f, -2.f, 6.f, 4.f
-        }));
-    // These values are per-channel of the input.
-    auto mean     = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, -2}));
-    auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {4, 9}));
-    auto beta     = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, 2}));
-    auto gamma    = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {2, 1}));
-    LayerTestResult<T,4> ret(outputTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::BatchNormalizationQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle meanTensor(tensorInfo);
-    armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo);
-    armnn::ScopedCpuTensorHandle betaTensor(tensorInfo);
-    armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo);
-
-    AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]);
-    AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]);
-    AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]);
-    AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]);
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Mean             = &meanTensor;
-    data.m_Variance         = &varianceTensor;
-    data.m_Beta             = &betaTensor;
-    data.m_Gamma            = &gammaTensor;
-    data.m_Parameters.m_Eps = 0.0f;
-    data.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC;
-
-    // For each channel:
-    // substract mean, divide by standard deviation (with an epsilon to avoid div by 0),
-    // multiply by gamma and add beta
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset,
-        {
-            1.f, 3.f, 4.f, 3.f,
-            4.f, 4.f, 2.f, 3.f,
-            1.f, 2.f, 6.f, 4.f
-        }));
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateBatchNormalization(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}