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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "QuantizeHelper.hpp"
#include "WorkloadTestUtils.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>
#include <test/TensorHelpers.hpp>
#include <algorithm>
template<armnn::DataType ArmnnType, std::size_t n, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, n> SimpleSoftmaxBaseTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float beta,
const armnn::TensorShape& inputShape,
const std::vector<float>& outputData,
const std::vector<float>& inputData,
int axis = 1)
{
using std::exp;
const float qScale = 1.f / 256.f;
const int qOffset = 0;
armnn::TensorInfo inputTensorInfo;
armnn::TensorInfo outputTensorInfo;
inputTensorInfo = armnn::TensorInfo(inputShape, ArmnnType);
inputTensorInfo.SetQuantizationScale(qScale);
inputTensorInfo.SetQuantizationOffset(qOffset);
outputTensorInfo = armnn::TensorInfo(inputShape, ArmnnType);
outputTensorInfo.SetQuantizationScale(qScale);
outputTensorInfo.SetQuantizationOffset(qOffset);
LayerTestResult<T, n> ret(outputTensorInfo);
// Each row is independently softmax'd.
auto input = MakeTensor<T, n>(inputTensorInfo, std::vector<T>(
QuantizedVector<T>(qScale, qOffset, inputData)));
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::SoftmaxQueueDescriptor data;
data.m_Parameters.m_Beta = beta;
data.m_Parameters.m_Axis = axis;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSoftmax(data, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), input.origin());
BOOST_ASSERT(workload);
ExecuteWorkload(*workload, memoryManager);
CopyDataFromITensorHandle(ret.output.origin(), outputHandle.get());
std::vector<T> expectedOutput = std::vector<T>(
QuantizedVector<T>(qScale, qOffset, outputData));
ret.outputExpected = MakeTensor<T, n>(outputTensorInfo, expectedOutput);
return ret;
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 2> SimpleSoftmaxTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float beta)
{
using std::exp;
const armnn::TensorShape inputShape{ 2, 4 };
float x0[4] = { exp((0.f - 1.0f) * beta), exp((1.0f - 1.0f) * beta),
exp((0.0f - 1.0f) * beta), exp((0.0f - 1.0f) * beta) };
float sum0 = x0[0] + x0[1] + x0[2] + x0[3];
float x1[4] = { exp((0.5f - 0.5f) * beta), exp((0.0f - 0.5f) * beta),
exp((0.0f - 0.5f) * beta), exp((0.0f - 0.5f) * beta) };
float sum1 = x1[0] + x1[1] + x1[2] + x1[3];
const std::vector<float> outputData = { x0[0] / sum0, x0[1] / sum0, x0[2] / sum0, x0[3] / sum0,
x1[0] / sum1, x1[1] / sum1, x1[2] / sum1, x1[3] / sum1 };
const std::vector<float> inputData =
{
0.f, 1.f, 0.f, 0.f,
.5f, 0.f, 0.f, 0.f,
};
return SimpleSoftmaxBaseTestImpl<ArmnnType, 2>(workloadFactory, memoryManager, beta,
inputShape, outputData, inputData);
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 2> SimpleSoftmaxTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float beta,
int axis)
{
armnn::TensorShape inputShape;
std::vector<float> inputData;
std::vector<float> outputData;
switch (axis)
{
case -2:
case 0:
{
inputShape = {5, 2};
inputData =
{
17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f
};
outputData =
{
0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f,
0.087144312427294f,
0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f,
7.246299848982885e-08f
};
break;
}
case -1:
case 1:
{
inputShape = {2, 5};
inputData =
{
17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f
};
outputData =
{
0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
7.246299848982885e-08f,
0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
7.246299848982885e-08f
};
break;
}
}
return SimpleSoftmaxBaseTestImpl<ArmnnType, 2>(workloadFactory, memoryManager, beta,
inputShape, outputData, inputData, axis);
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 3> Simple3dSoftmaxTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float beta,
const armnn::TensorShape& inputShape,
const std::vector<float>& outputData,
const std::vector<float>& inputData,
int axis = 1)
{
return SimpleSoftmaxBaseTestImpl<ArmnnType, 3>(workloadFactory, memoryManager, beta,
inputShape, outputData, inputData, axis);
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> Simple4dSoftmaxTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float beta,
const armnn::TensorShape& inputShape,
const std::vector<float>& outputData,
const std::vector<float>& inputData,
int axis = 1)
{
return SimpleSoftmaxBaseTestImpl<ArmnnType, 4>(workloadFactory, memoryManager, beta,
inputShape, outputData, inputData, axis);
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 2> CompareSoftmaxTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
armnn::IWorkloadFactory& refWorkloadFactory,
float beta)
{
const int batchSize = 20;
const int channels = 30;
armnn::TensorInfo inputTensorInfo;
armnn::TensorInfo outputTensorInfo;
unsigned int inputShape[] = { batchSize, channels };
inputTensorInfo = armnn::TensorInfo(2, inputShape, ArmnnType);
outputTensorInfo = armnn::TensorInfo(2, inputShape, ArmnnType);
float qScale = 1.f / 256.f;
int qOffset = 0;
inputTensorInfo.SetQuantizationScale(qScale);
inputTensorInfo.SetQuantizationOffset(qOffset);
outputTensorInfo.SetQuantizationScale(qScale);
outputTensorInfo.SetQuantizationOffset(qOffset);
LayerTestResult<T, 2> ret(outputTensorInfo);
auto input = MakeRandomTensor<T, 2>(inputTensorInfo, 0xF00D, 0.0f, 1.0f);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::SoftmaxQueueDescriptor data;
data.m_Parameters.m_Beta = beta;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
armnn::SoftmaxQueueDescriptor refData = data;
armnn::WorkloadInfo refInfo = info;
SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSoftmax(data, info);
std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateSoftmax(refData, refInfo);
outputHandleRef->Allocate();
inputHandleRef->Allocate();
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0]);
CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0]);
ExecuteWorkload(*workload, memoryManager);
workloadRef->Execute();
CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
CopyDataFromITensorHandle(&ret.outputExpected[0][0], outputHandleRef.get());
return ret;
}
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