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, 126 deletions

diff --git a/src/backends/backendsCommon/test/QuantizeTestImpl.hpp b/src/backends/backendsCommon/test/QuantizeTestImpl.hpp
deleted file mode 100644
index fee68f073e..0000000000
--- a/src/backends/backendsCommon/test/QuantizeTestImpl.hpp
+++ /dev/null
@@ -1,126 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include "WorkloadTestUtils.hpp"
-
-#include <test/TensorHelpers.hpp>
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-#include <armnn/TypesUtils.hpp>
-
-#include <backendsCommon/CpuTensorHandle.hpp>
-#include <backendsCommon/IBackendInternal.hpp>
-#include <backendsCommon/WorkloadFactory.hpp>
-
-
-namespace
-{
-
-template<typename T, std::size_t Dim>
-LayerTestResult<T, Dim> QuantizeTestImpl(
-    armnn::IWorkloadFactory& workloadFactory,
-    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
-    const armnn::TensorInfo& inputTensorInfo,
-    const armnn::TensorInfo& outputTensorInfo,
-    const std::vector<float>& inputData,
-    const std::vector<T>& expectedOutputData,
-    armnn::QuantizeQueueDescriptor descriptor)
-{
-    boost::multi_array<float, Dim> input = MakeTensor<float, Dim>(inputTensorInfo, inputData);
-
-    LayerTestResult<T, Dim> ret(outputTensorInfo);
-    ret.outputExpected = MakeTensor<T, Dim>(outputTensorInfo, expectedOutputData);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateQuantize(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), input.data());
-
-    ExecuteWorkload(*workload, memoryManager);
-
-    CopyDataFromITensorHandle(ret.output.data(), outputHandle.get());
-
-    return ret;
-}
-
-template <armnn::DataType ArmnnOutputType, typename T = armnn::ResolveType<ArmnnOutputType>>
-LayerTestResult<T, 4> QuantizeSimpleTest(
-    armnn::IWorkloadFactory& workloadFactory,
-    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
-{
-    armnn::QuantizeQueueDescriptor desc;
-
-    const armnn::TensorInfo inputTensorInfo({1, 2, 2, 3}, armnn::DataType::Float32);
-    const armnn::TensorInfo outputTensorInfo({1, 2, 2, 3}, ArmnnOutputType, 0.5f, 1);
-
-    std::vector<float> inputData = std::vector<float>(
-    {
-        1.0f,   2.0f,  3.0f,
-        4.0f,   5.0f,  6.0f,
-        7.0f,   8.0f,  9.0f,
-        10.0f, 11.0f, 12.0f,
-    });
-
-    std::vector<T> expectedOutputData = std::vector<T>(
-    {
-         3,  5,  7,
-         9, 11, 13,
-        15, 17, 19,
-        21, 23, 25,
-    });
-
-    return QuantizeTestImpl<T, 4>(workloadFactory,
-                                  memoryManager,
-                                  inputTensorInfo,
-                                  outputTensorInfo,
-                                  inputData,
-                                  expectedOutputData,
-                                  desc);
-}
-
-template <armnn::DataType ArmnnOutputType, typename T = armnn::ResolveType<ArmnnOutputType>>
-LayerTestResult<T, 4> QuantizeClampTest(
-    armnn::IWorkloadFactory& workloadFactory,
-    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
-{
-    armnn::QuantizeQueueDescriptor desc;
-
-    const armnn::TensorInfo inputTensorInfo({1, 1, 2, 1}, armnn::DataType::Float32);
-    const armnn::TensorInfo outputTensorInfo({1, 1, 2, 1}, ArmnnOutputType, 0.0001f, 0);
-
-    const T max = std::numeric_limits<T>::max();
-    const T min = std::numeric_limits<T>::lowest();
-
-    std::vector<float> inputData = std::vector<float>(
-    {
-        -100.0f, 100.0f
-    });
-
-    std::vector<T> expectedOutputData = std::vector<T>(
-    {
-        min, max
-    });
-
-    return QuantizeTestImpl<T, 4>(workloadFactory,
-                                  memoryManager,
-                                  inputTensorInfo,
-                                  outputTensorInfo,
-                                  inputData,
-                                  expectedOutputData,
-                                  desc);
-}
-
-} // anonymous namespace