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//
// 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
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