| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "../DriverTestHelpers.hpp" |
| #include "../TestTensor.hpp" |
| |
| #include "../1.1/HalPolicy.hpp" |
| |
| #include <boost/test/data/test_case.hpp> |
| |
| #include <array> |
| |
| BOOST_AUTO_TEST_SUITE(MeanTests) |
| |
| using namespace android::hardware; |
| using namespace driverTestHelpers; |
| using namespace armnn_driver; |
| |
| using HalPolicy = hal_1_1::HalPolicy; |
| |
| namespace |
| { |
| |
| #ifndef ARMCOMPUTECL_ENABLED |
| static const std::array<armnn::Compute, 1> COMPUTE_DEVICES = {{ armnn::Compute::CpuRef }}; |
| #else |
| static const std::array<armnn::Compute, 2> COMPUTE_DEVICES = {{ armnn::Compute::CpuRef, armnn::Compute::GpuAcc }}; |
| #endif |
| |
| 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 |
| 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 |
| 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); |
| BOOST_TEST(execStatus == V1_0::ErrorStatus::NONE); |
| } |
| |
| const float* expectedOutputData = expectedOutput.GetData(); |
| for (unsigned int i = 0; i < expectedOutput.GetNumElements(); i++) |
| { |
| BOOST_TEST(outputData[i] == expectedOutputData[i]); |
| } |
| } |
| |
| } // anonymous namespace |
| |
| BOOST_DATA_TEST_CASE(MeanNoKeepDimsTest, COMPUTE_DEVICES) |
| { |
| 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, sample); |
| } |
| |
| BOOST_DATA_TEST_CASE(MeanKeepDimsTest, COMPUTE_DEVICES) |
| { |
| 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, sample); |
| } |
| |
| BOOST_DATA_TEST_CASE(MeanFp16NoKeepDimsTest, COMPUTE_DEVICES) |
| { |
| 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, sample); |
| } |
| |
| BOOST_DATA_TEST_CASE(MeanFp16KeepDimsTest, COMPUTE_DEVICES) |
| { |
| 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, sample); |
| } |
| |
| BOOST_AUTO_TEST_SUITE_END() |