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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "../DriverTestHelpers.hpp"
#include "../TestTensor.hpp"
#include "../1.1/HalPolicy.hpp"
#include <doctest/doctest.h>
#include <array>
using namespace android::hardware;
using namespace driverTestHelpers;
using namespace armnn_driver;
using HalPolicy = hal_1_1::HalPolicy;
using RequestArgument = V1_0::RequestArgument;
namespace
{
void MeanTestImpl(const TestTensor& input,
const hidl_vec<uint32_t>& axisDimensions,
const int32_t* axisValues,
int32_t keepDims,
const TestTensor& expectedOutput,
bool fp16Enabled,
armnn::Compute computeDevice)
{
auto driver = std::make_unique<ArmnnDriver>(DriverOptions(computeDevice, fp16Enabled));
HalPolicy::Model model = {};
AddInputOperand<HalPolicy>(model, input.GetDimensions());
AddTensorOperand<HalPolicy>(model,
axisDimensions,
const_cast<int32_t*>(axisValues),
HalPolicy::OperandType::TENSOR_INT32);
AddIntOperand<HalPolicy>(model, keepDims);
AddOutputOperand<HalPolicy>(model, expectedOutput.GetDimensions());
model.operations.resize(1);
model.operations[0].type = HalPolicy::OperationType::MEAN;
model.operations[0].inputs = hidl_vec<uint32_t>{ 0, 1, 2 };
model.operations[0].outputs = hidl_vec<uint32_t>{ 3 };
model.relaxComputationFloat32toFloat16 = fp16Enabled;
android::sp<V1_0::IPreparedModel> preparedModel = PrepareModel(model, *driver);
// The request's memory pools will follow the same order as the inputs
V1_0::DataLocation inLoc = {};
inLoc.poolIndex = 0;
inLoc.offset = 0;
inLoc.length = input.GetNumElements() * sizeof(float);
RequestArgument inArg = {};
inArg.location = inLoc;
inArg.dimensions = input.GetDimensions();
// An additional memory pool is needed for the output
V1_0::DataLocation outLoc = {};
outLoc.poolIndex = 1;
outLoc.offset = 0;
outLoc.length = expectedOutput.GetNumElements() * sizeof(float);
RequestArgument outArg = {};
outArg.location = outLoc;
outArg.dimensions = expectedOutput.GetDimensions();
// Make the request based on the arguments
V1_0::Request request = {};
request.inputs = hidl_vec<RequestArgument>{ inArg };
request.outputs = hidl_vec<RequestArgument>{ outArg };
// Set the input data
AddPoolAndSetData(input.GetNumElements(), request, input.GetData());
// Add memory for the output
android::sp<IMemory> outMemory = AddPoolAndGetData<float>(expectedOutput.GetNumElements(), request);
const float* outputData = static_cast<const float*>(static_cast<void*>(outMemory->getPointer()));
if (preparedModel.get() != nullptr)
{
V1_0::ErrorStatus execStatus = Execute(preparedModel, request);
CHECK((int)execStatus == (int)V1_0::ErrorStatus::NONE);
}
const float* expectedOutputData = expectedOutput.GetData();
for (unsigned int i = 0; i < expectedOutput.GetNumElements(); i++)
{
CHECK(outputData[i] == expectedOutputData[i]);
}
}
} // anonymous namespace
TEST_SUITE("MeanTests_CpuRef")
{
TEST_CASE("MeanNoKeepDimsTest_CpuRef")
{
TestTensor input{ armnn::TensorShape{ 4, 3, 2 },
{ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f,
11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f,
20.0f, 21.0f, 22.0f, 23.0f, 24.0f } };
hidl_vec<uint32_t> axisDimensions = { 2 };
int32_t axisValues[] = { 0, 1 };
int32_t keepDims = 0;
TestTensor expectedOutput{ armnn::TensorShape{ 2 }, { 12.0f, 13.0f } };
MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, false, armnn::Compute::CpuRef);
}
TEST_CASE("MeanKeepDimsTest_CpuRef")
{
TestTensor input{ armnn::TensorShape{ 1, 1, 3, 2 }, { 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f } };
hidl_vec<uint32_t> axisDimensions = { 1 };
int32_t axisValues[] = { 2 };
int32_t keepDims = 1;
TestTensor expectedOutput{ armnn::TensorShape{ 1, 1, 1, 2 }, { 2.0f, 2.0f } };
MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, false, armnn::Compute::CpuRef);
}
TEST_CASE("MeanFp16NoKeepDimsTest_CpuRef")
{
TestTensor input{ armnn::TensorShape{ 4, 3, 2 },
{ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f,
11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f,
20.0f, 21.0f, 22.0f, 23.0f, 24.0f } };
hidl_vec<uint32_t> axisDimensions = { 2 };
int32_t axisValues[] = { 0, 1 };
int32_t keepDims = 0;
TestTensor expectedOutput{ armnn::TensorShape{ 2 }, { 12.0f, 13.0f } };
MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, true, armnn::Compute::CpuRef);
}
TEST_CASE("MeanFp16KeepDimsTest_CpuRef")
{
TestTensor input{ armnn::TensorShape{ 1, 1, 3, 2 }, { 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f } };
hidl_vec<uint32_t> axisDimensions = { 1 };
int32_t axisValues[] = { 2 };
int32_t keepDims = 1;
TestTensor expectedOutput{ armnn::TensorShape{ 1, 1, 1, 2 }, { 2.0f, 2.0f } };
MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, true, armnn::Compute::CpuRef);
}
}
#ifdef ARMCOMPUTECL_ENABLED
TEST_SUITE("MeanTests_CpuAcc")
{
TEST_CASE("MeanNoKeepDimsTest_CpuAcc")
{
TestTensor input{ armnn::TensorShape{ 4, 3, 2 },
{ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f,
11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f,
20.0f, 21.0f, 22.0f, 23.0f, 24.0f } };
hidl_vec<uint32_t> axisDimensions = { 2 };
int32_t axisValues[] = { 0, 1 };
int32_t keepDims = 0;
TestTensor expectedOutput{ armnn::TensorShape{ 2 }, { 12.0f, 13.0f } };
MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, false, armnn::Compute::CpuAcc);
}
TEST_CASE("MeanKeepDimsTest_CpuAcc")
{
TestTensor input{ armnn::TensorShape{ 1, 1, 3, 2 }, { 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f } };
hidl_vec<uint32_t> axisDimensions = { 1 };
int32_t axisValues[] = { 2 };
int32_t keepDims = 1;
TestTensor expectedOutput{ armnn::TensorShape{ 1, 1, 1, 2 }, { 2.0f, 2.0f } };
MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, false, armnn::Compute::CpuAcc);
}
TEST_CASE("MeanFp16NoKeepDimsTest_CpuAcc")
{
TestTensor input{ armnn::TensorShape{ 4, 3, 2 },
{ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f,
11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f,
20.0f, 21.0f, 22.0f, 23.0f, 24.0f } };
hidl_vec<uint32_t> axisDimensions = { 2 };
int32_t axisValues[] = { 0, 1 };
int32_t keepDims = 0;
TestTensor expectedOutput{ armnn::TensorShape{ 2 }, { 12.0f, 13.0f } };
MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, true, armnn::Compute::CpuAcc);
}
TEST_CASE("MeanFp16KeepDimsTest_CpuAcc")
{
TestTensor input{ armnn::TensorShape{ 1, 1, 3, 2 }, { 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f } };
hidl_vec<uint32_t> axisDimensions = { 1 };
int32_t axisValues[] = { 2 };
int32_t keepDims = 1;
TestTensor expectedOutput{ armnn::TensorShape{ 1, 1, 1, 2 }, { 2.0f, 2.0f } };
MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, true, armnn::Compute::CpuAcc);
}
}
TEST_SUITE("MeanTests_GpuAcc")
{
TEST_CASE("MeanNoKeepDimsTest_GpuAcc")
{
TestTensor input{ armnn::TensorShape{ 4, 3, 2 },
{ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f,
11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f,
20.0f, 21.0f, 22.0f, 23.0f, 24.0f } };
hidl_vec<uint32_t> axisDimensions = { 2 };
int32_t axisValues[] = { 0, 1 };
int32_t keepDims = 0;
TestTensor expectedOutput{ armnn::TensorShape{ 2 }, { 12.0f, 13.0f } };
MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, false, armnn::Compute::GpuAcc);
}
TEST_CASE("MeanKeepDimsTest_GpuAcc")
{
TestTensor input{ armnn::TensorShape{ 1, 1, 3, 2 }, { 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f } };
hidl_vec<uint32_t> axisDimensions = { 1 };
int32_t axisValues[] = { 2 };
int32_t keepDims = 1;
TestTensor expectedOutput{ armnn::TensorShape{ 1, 1, 1, 2 }, { 2.0f, 2.0f } };
MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, false, armnn::Compute::GpuAcc);
}
TEST_CASE("MeanFp16NoKeepDimsTest_GpuAcc")
{
TestTensor input{ armnn::TensorShape{ 4, 3, 2 },
{ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f,
11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f,
20.0f, 21.0f, 22.0f, 23.0f, 24.0f } };
hidl_vec<uint32_t> axisDimensions = { 2 };
int32_t axisValues[] = { 0, 1 };
int32_t keepDims = 0;
TestTensor expectedOutput{ armnn::TensorShape{ 2 }, { 12.0f, 13.0f } };
MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, true, armnn::Compute::GpuAcc);
}
TEST_CASE("MeanFp16KeepDimsTest_GpuAcc")
{
TestTensor input{ armnn::TensorShape{ 1, 1, 3, 2 }, { 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f } };
hidl_vec<uint32_t> axisDimensions = { 1 };
int32_t axisValues[] = { 2 };
int32_t keepDims = 1;
TestTensor expectedOutput{ armnn::TensorShape{ 1, 1, 1, 2 }, { 2.0f, 2.0f } };
MeanTestImpl(input, axisDimensions, axisValues, keepDims, expectedOutput, true, armnn::Compute::GpuAcc);
}
}
#endif
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