From 28dcab6c176a3938519809aa9da7321e4ede7623 Mon Sep 17 00:00:00 2001 From: Matteo Martincigh Date: Fri, 19 Oct 2018 16:40:03 +0100 Subject: IVGCVSW-2049 + IVGCVSW-2051 Create the CL Mean Float workload and add the unit tests * Created the ClFloatWorkload class * Added ClMeanValidate validation function * Added helper function to convert the reduction axes from the ArmNN format to ACL's * Added workload tests * Added some unit tests * These changes need the CL pin to be pointing at least to revision 88d871028eeae57f9e4536d0329110eccb5e2890 (COMPMID-1574 Implement ReduceMean in OpenCL) !android-nn-driver:155033 Change-Id: I694fd36be0458c90e158172afde045fcc88c32ae --- include/armnn/Descriptors.hpp | 3 +- src/armnn/test/CreateWorkload.hpp | 33 ++++++++ src/backends/cl/ClLayerSupport.cpp | 11 +-- src/backends/cl/ClWorkloadFactory.cpp | 2 +- src/backends/cl/backend.mk | 1 + src/backends/cl/test/ClCreateWorkloadTests.cpp | 34 ++++++++- src/backends/cl/test/ClLayerTests.cpp | 15 ++++ src/backends/cl/workloads/CMakeLists.txt | 2 + src/backends/cl/workloads/ClMeanWorkload.cpp | 100 +++++++++++++++++++++++++ src/backends/cl/workloads/ClMeanWorkload.hpp | 31 ++++++++ src/backends/cl/workloads/ClWorkloads.hpp | 1 + src/backends/reference/test/RefLayerTests.cpp | 1 + src/backends/test/LayerTests.cpp | 96 +++++++++++++----------- src/backends/test/LayerTests.hpp | 2 + 14 files changed, 281 insertions(+), 51 deletions(-) create mode 100644 src/backends/cl/workloads/ClMeanWorkload.cpp create mode 100644 src/backends/cl/workloads/ClMeanWorkload.hpp diff --git a/include/armnn/Descriptors.hpp b/include/armnn/Descriptors.hpp index 648477e09b..5a7a647ee7 100644 --- a/include/armnn/Descriptors.hpp +++ b/include/armnn/Descriptors.hpp @@ -356,7 +356,8 @@ struct LstmDescriptor struct MeanDescriptor { MeanDescriptor() - : m_KeepDims(false) + : m_Axis() + , m_KeepDims(false) {} MeanDescriptor(const std::vector& axis, bool keepDims) diff --git a/src/armnn/test/CreateWorkload.hpp b/src/armnn/test/CreateWorkload.hpp index 5308a1c1dc..c0d2ab1c7f 100644 --- a/src/armnn/test/CreateWorkload.hpp +++ b/src/armnn/test/CreateWorkload.hpp @@ -1021,4 +1021,37 @@ std::unique_ptr CreateConvertFp32ToFp16Workloa return workload; } +template +std::unique_ptr CreateMeanWorkloadTest(armnn::IWorkloadFactory& factory, armnn::Graph& graph) +{ + // Reduce along the first and second dimensions, and do not keep the reduced dimensions. + MeanDescriptor descriptor({ 1, 2 }, false); + + // Creates the layer we're testing. + Layer* const layer = graph.AddLayer(descriptor, "mean"); + + // Creates extra layers. + Layer* const input = graph.AddLayer(0, "input"); + Layer* const output = graph.AddLayer(0, "output"); + + // Connects up. + armnn::TensorInfo inputTensorInfo({ 1, 3, 7, 4 }, DataType); + armnn::TensorInfo outputTensorInfo({ 1, 4 }, DataType); + Connect(input, layer, inputTensorInfo); + Connect(layer, output, outputTensorInfo); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload(*layer, graph, factory); + + MeanQueueDescriptor queueDescriptor = workload->GetData(); + BOOST_TEST(queueDescriptor.m_Parameters.m_Axis == descriptor.m_Axis); + BOOST_TEST(queueDescriptor.m_Parameters.m_KeepDims == descriptor.m_KeepDims); + BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); + BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + } diff --git a/src/backends/cl/ClLayerSupport.cpp b/src/backends/cl/ClLayerSupport.cpp index 3ca8bb5c46..6c5704d7ab 100644 --- a/src/backends/cl/ClLayerSupport.cpp +++ b/src/backends/cl/ClLayerSupport.cpp @@ -26,6 +26,7 @@ #include "workloads/ClFullyConnectedWorkload.hpp" #include "workloads/ClL2NormalizationFloatWorkload.hpp" #include "workloads/ClLstmFloatWorkload.hpp" +#include "workloads/ClMeanWorkload.hpp" #include "workloads/ClMultiplicationWorkload.hpp" #include "workloads/ClNormalizationFloatWorkload.hpp" #include "workloads/ClPadWorkload.hpp" @@ -372,11 +373,11 @@ bool ClLayerSupport::IsMeanSupported(const TensorInfo& input, const MeanDescriptor& descriptor, Optional reasonIfUnsupported) const { - ignore_unused(input); - ignore_unused(output); - ignore_unused(descriptor); - ignore_unused(reasonIfUnsupported); - return false; + FORWARD_WORKLOAD_VALIDATE_FUNC(ClMeanValidate, + reasonIfUnsupported, + input, + output, + descriptor); } bool ClLayerSupport::IsMergerSupported(const std::vector inputs, diff --git a/src/backends/cl/ClWorkloadFactory.cpp b/src/backends/cl/ClWorkloadFactory.cpp index fd92db34d5..08ee9e922d 100644 --- a/src/backends/cl/ClWorkloadFactory.cpp +++ b/src/backends/cl/ClWorkloadFactory.cpp @@ -303,7 +303,7 @@ std::unique_ptr ClWorkloadFactory::CreateConvertFp32ToFp16( std::unique_ptr ClWorkloadFactory::CreateMean(const MeanQueueDescriptor& descriptor, const WorkloadInfo& info) const { - return MakeWorkload(descriptor, info); + return std::make_unique(descriptor, info); } std::unique_ptr ClWorkloadFactory::CreatePad(const PadQueueDescriptor& descriptor, diff --git a/src/backends/cl/backend.mk b/src/backends/cl/backend.mk index 996db3fbfd..97df8e4903 100644 --- a/src/backends/cl/backend.mk +++ b/src/backends/cl/backend.mk @@ -26,6 +26,7 @@ BACKEND_SOURCES := \ workloads/ClFullyConnectedWorkload.cpp \ workloads/ClL2NormalizationFloatWorkload.cpp \ workloads/ClLstmFloatWorkload.cpp \ + workloads/ClMeanWorkload.cpp \ workloads/ClMultiplicationWorkload.cpp \ workloads/ClNormalizationFloatWorkload.cpp \ workloads/ClPadWorkload.cpp \ diff --git a/src/backends/cl/test/ClCreateWorkloadTests.cpp b/src/backends/cl/test/ClCreateWorkloadTests.cpp index 4f9989405d..2a705de99b 100644 --- a/src/backends/cl/test/ClCreateWorkloadTests.cpp +++ b/src/backends/cl/test/ClCreateWorkloadTests.cpp @@ -14,8 +14,6 @@ #include #include -#include - boost::test_tools::predicate_result CompareIClTensorHandleShape(IClTensorHandle* tensorHandle, std::initializer_list expectedDimensions) { @@ -739,4 +737,36 @@ BOOST_AUTO_TEST_CASE(CreateResizeBilinearFloat16NhwcWorkload) ClResizeBilinearWorkloadTest(DataLayout::NHWC); } +template +static void ClMeanWorkloadTest() +{ + Graph graph; + ClWorkloadFactory factory; + auto workload = CreateMeanWorkloadTest(factory, graph); + + // Checks that inputs/outputs are as we expect them (see definition of CreateMeanWorkloadTest). + MeanQueueDescriptor queueDescriptor = workload->GetData(); + auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); + auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); + + // The first dimension (batch size) in both input and output is singular thus it has been reduced by ACL. + BOOST_TEST(CompareIClTensorHandleShape(inputHandle, { 3, 7, 4 })); + BOOST_TEST(CompareIClTensorHandleShape(outputHandle, { 4 })); +} + +BOOST_AUTO_TEST_CASE(CreateMeanFloat32Workload) +{ + ClMeanWorkloadTest(); +} + +BOOST_AUTO_TEST_CASE(CreateMeanFloat16Workload) +{ + ClMeanWorkloadTest(); +} + +BOOST_AUTO_TEST_CASE(CreateMeanUint8Workload) +{ + ClMeanWorkloadTest(); +} + BOOST_AUTO_TEST_SUITE_END() diff --git a/src/backends/cl/test/ClLayerTests.cpp b/src/backends/cl/test/ClLayerTests.cpp index d5e941977a..937c58c689 100755 --- a/src/backends/cl/test/ClLayerTests.cpp +++ b/src/backends/cl/test/ClLayerTests.cpp @@ -274,6 +274,21 @@ ARMNN_AUTO_TEST_CASE(SimpleConvertFp32ToFp16, SimpleConvertFp32ToFp16Test) ARMNN_AUTO_TEST_CASE(AdditionAfterMaxPool, AdditionAfterMaxPoolTest) +// Mean +ARMNN_AUTO_TEST_CASE(MeanUint8Simple, MeanUint8SimpleTest) +ARMNN_AUTO_TEST_CASE(MeanUint8SimpleAxis, MeanUint8SimpleAxisTest) +ARMNN_AUTO_TEST_CASE(MeanUint8KeepDims, MeanUint8KeepDimsTest) +ARMNN_AUTO_TEST_CASE(MeanUint8MultipleDims, MeanUint8MultipleDimsTest) +ARMNN_AUTO_TEST_CASE(MeanVtsUint8, MeanVtsUint8Test) + +ARMNN_AUTO_TEST_CASE(MeanFloatSimple, MeanFloatSimpleTest) +ARMNN_AUTO_TEST_CASE(MeanFloatSimpleAxis, MeanFloatSimpleAxisTest) +ARMNN_AUTO_TEST_CASE(MeanFloatKeepDims, MeanFloatKeepDimsTest) +ARMNN_AUTO_TEST_CASE(MeanFloatMultipleDims, MeanFloatMultipleDimsTest) +ARMNN_AUTO_TEST_CASE(MeanVtsFloat1, MeanVtsFloat1Test) +ARMNN_AUTO_TEST_CASE(MeanVtsFloat2, MeanVtsFloat2Test) +ARMNN_AUTO_TEST_CASE(MeanVtsFloat3, MeanVtsFloat3Test) + // ============================================================================ // COMPARE tests diff --git a/src/backends/cl/workloads/CMakeLists.txt b/src/backends/cl/workloads/CMakeLists.txt index 59a45facea..86c3804244 100644 --- a/src/backends/cl/workloads/CMakeLists.txt +++ b/src/backends/cl/workloads/CMakeLists.txt @@ -30,6 +30,8 @@ list(APPEND armnnClBackendWorkloads_sources ClL2NormalizationFloatWorkload.hpp ClLstmFloatWorkload.cpp ClLstmFloatWorkload.hpp + ClMeanWorkload.cpp + ClMeanWorkload.hpp ClMergerWorkload.hpp ClMultiplicationWorkload.cpp ClMultiplicationWorkload.hpp diff --git a/src/backends/cl/workloads/ClMeanWorkload.cpp b/src/backends/cl/workloads/ClMeanWorkload.cpp new file mode 100644 index 0000000000..7e9649b1b6 --- /dev/null +++ b/src/backends/cl/workloads/ClMeanWorkload.cpp @@ -0,0 +1,100 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "ClMeanWorkload.hpp" + +#include +#include + +#include "ClWorkloadUtils.hpp" + +namespace +{ + +void ConvertArmnnAxesToAclCoordinates(size_t inputDimensions, + unsigned int originalInputRank, + const std::vector& armnnAxes, + arm_compute::Coordinates& outAclCoords) +{ + if (armnnAxes.empty()) + { + // If no reduction axes were provided, then the input must be reduced along all dimensions. + // Since arm_compute::CLReduceMean does not accept an empty vector as the reduction dimensions, we then + // manually create a vector including all the input dimensions (in reversed order) as: + // + // { inputDimensions - 1, inputDimensions - 2, ..., 1, 0 } + // + outAclCoords.set_num_dimensions(inputDimensions); + std::generate(outAclCoords.begin(), outAclCoords.end(), [d = inputDimensions - 1] () mutable { return d--; }); + } + else + { + // Create a vector of reduction dimensions (in reversed order) with the given reduction axes. + // + // Adjust the given reduction axes according to the original rank of the input tensor (before ACL applied any + // dimension correction). + // For example, if the input tensor originally had 4 dimensions, and one of the reduction axes was 2, then the + // new value for that reduction axis should be 1. + // + // Example: + // ArmNN input shape = { 1, 1, 3, 2 } -> ACL input shape = { 2, 3 } + // ArmNN reduction axis = { 2 } -> ACL reduction axis = { 1 } + // ArmNN reduction axis = { 3 } -> ACL reduction axis = { 0 } + // + // The transformation: ACL reduction axis index = original rank - ArmNN reduction axis index - 1 + // + outAclCoords.set_num_dimensions(armnnAxes.size()); + std::transform(armnnAxes.begin(), armnnAxes.end(), + outAclCoords.begin(), + [originalInputRank](unsigned int i){ return originalInputRank - i - 1; }); + } +} + +} // anonymous namespace + +namespace armnn +{ +using namespace armcomputetensorutils; + +arm_compute::Status ClMeanValidate(const TensorInfo& input, + const TensorInfo& output, + const MeanDescriptor& desc) +{ + const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input); + const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output); + + arm_compute::Coordinates coords; + ConvertArmnnAxesToAclCoordinates(aclInputInfo.num_dimensions(), + input.GetNumDimensions(), + desc.m_Axis, + coords); + + return arm_compute::CLReduceMean::validate(&aclInputInfo, coords, desc.m_KeepDims, &aclOutputInfo); +} + +ClMeanWorkload::ClMeanWorkload(const MeanQueueDescriptor& descriptor, const WorkloadInfo& info) + : BaseWorkload(descriptor, info) +{ + m_Data.ValidateInputsOutputs("ClMeanWorkload", 1, 1); + + arm_compute::ICLTensor& input = static_cast(m_Data.m_Inputs[0])->GetTensor(); + arm_compute::ICLTensor& output = static_cast(m_Data.m_Outputs[0])->GetTensor(); + + arm_compute::Coordinates coords; + ConvertArmnnAxesToAclCoordinates(input.info()->num_dimensions(), + info.m_InputTensorInfos[0].GetNumDimensions(), + m_Data.m_Parameters.m_Axis, + coords); + + m_Layer.configure(&input, coords, m_Data.m_Parameters.m_KeepDims, &output); +} + +void ClMeanWorkload::Execute() const +{ + ARMNN_SCOPED_PROFILING_EVENT_CL("ClMeanWorkload_Execute"); + m_Layer.run(); +} + +} //namespace armnn diff --git a/src/backends/cl/workloads/ClMeanWorkload.hpp b/src/backends/cl/workloads/ClMeanWorkload.hpp new file mode 100644 index 0000000000..c9f0356e04 --- /dev/null +++ b/src/backends/cl/workloads/ClMeanWorkload.hpp @@ -0,0 +1,31 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include + +#include + +namespace armnn +{ + +arm_compute::Status ClMeanValidate(const TensorInfo& input, + const TensorInfo& output, + const MeanDescriptor& desc); + +class ClMeanWorkload : public BaseWorkload +{ +public: + ClMeanWorkload(const MeanQueueDescriptor& descriptor, const WorkloadInfo& info); + + void Execute() const override; + +private: + // Not using CLMeanStdDev, as 4D input tensor support for Mean has been added to a new function called CLReduceMean. + mutable arm_compute::CLReduceMean m_Layer; +}; + +} //namespace armnn diff --git a/src/backends/cl/workloads/ClWorkloads.hpp b/src/backends/cl/workloads/ClWorkloads.hpp index 63de744be5..eeca40364c 100644 --- a/src/backends/cl/workloads/ClWorkloads.hpp +++ b/src/backends/cl/workloads/ClWorkloads.hpp @@ -16,6 +16,7 @@ #include "ClL2NormalizationFloatWorkload.hpp" #include "ClLstmFloatWorkload.hpp" #include "ClMergerWorkload.hpp" +#include "ClMeanWorkload.hpp" #include "ClMultiplicationWorkload.hpp" #include "ClNormalizationFloatWorkload.hpp" #include "ClPermuteWorkload.hpp" diff --git a/src/backends/reference/test/RefLayerTests.cpp b/src/backends/reference/test/RefLayerTests.cpp index 30f5b10b0e..38ce94d257 100644 --- a/src/backends/reference/test/RefLayerTests.cpp +++ b/src/backends/reference/test/RefLayerTests.cpp @@ -292,6 +292,7 @@ ARMNN_AUTO_TEST_CASE(MeanFloatKeepDims, MeanFloatKeepDimsTest) ARMNN_AUTO_TEST_CASE(MeanFloatMultipleDims, MeanFloatMultipleDimsTest) ARMNN_AUTO_TEST_CASE(MeanVtsFloat1, MeanVtsFloat1Test) ARMNN_AUTO_TEST_CASE(MeanVtsFloat2, MeanVtsFloat2Test) +ARMNN_AUTO_TEST_CASE(MeanVtsFloat3, MeanVtsFloat3Test) ARMNN_AUTO_TEST_CASE(AdditionAfterMaxPool, AdditionAfterMaxPoolTest) diff --git a/src/backends/test/LayerTests.cpp b/src/backends/test/LayerTests.cpp index 5c7a887552..e5a4258043 100755 --- a/src/backends/test/LayerTests.cpp +++ b/src/backends/test/LayerTests.cpp @@ -5807,20 +5807,19 @@ LayerTestResult PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& w namespace { + template LayerTestResult MeanTestHelper(armnn::IWorkloadFactory& workloadFactory, - const unsigned int* inputShape, - const std::vector& inputData, - const std::vector& axis, - bool keepDims, - const unsigned int* outputShape, - const std::vector& outputData, - float scale = 1.0f, - int32_t offset = 0) + const unsigned int* inputShape, + const std::vector& inputData, + const std::vector& axis, + bool keepDims, + const unsigned int* outputShape, + const std::vector& outputData, + float scale = 1.0f, + int32_t offset = 0) { - auto dataType = (std::is_same::value ? - armnn::DataType::QuantisedAsymm8 : - armnn::DataType::Float32); + auto dataType = (std::is_same::value ? armnn::DataType::QuantisedAsymm8 : armnn::DataType::Float32); armnn::TensorInfo inputTensorInfo(InputDim, inputShape, dataType); armnn::TensorInfo outputTensorInfo(OutputDim, outputShape, dataType); @@ -5860,6 +5859,7 @@ LayerTestResult MeanTestHelper(armnn::IWorkloadFactory& workloadFa return result; } + } // anonymous namespace LayerTestResult MeanUint8SimpleTest(armnn::IWorkloadFactory& workloadFactory) @@ -5881,7 +5881,7 @@ LayerTestResult MeanUint8SimpleAxisTest(armnn::IWorkloadFactory& wor std::vector input({ 1, 1, 2, 2, 3, 3 }); std::vector output({ 2, 2 }); - return MeanTestHelper(workloadFactory, inputShape, input, {2}, false, outputShape, output); + return MeanTestHelper(workloadFactory, inputShape, input, { 2 }, false, outputShape, output); } LayerTestResult MeanUint8KeepDimsTest(armnn::IWorkloadFactory& workloadFactory) @@ -5892,7 +5892,7 @@ LayerTestResult MeanUint8KeepDimsTest(armnn::IWorkloadFactory& workl std::vector input({ 1, 1, 2, 2, 3, 3 }); std::vector output({ 2, 2 }); - return MeanTestHelper(workloadFactory, inputShape, input, {2}, true, outputShape, output); + return MeanTestHelper(workloadFactory, inputShape, input, { 2 }, true, outputShape, output); } LayerTestResult MeanUint8MultipleDimsTest(armnn::IWorkloadFactory& workloadFactory) @@ -5900,22 +5900,23 @@ LayerTestResult MeanUint8MultipleDimsTest(armnn::IWorkloadFactory& w const unsigned int inputShape[] = { 2, 3, 1, 2 }; const unsigned int outputShape[] = { 1, 3, 1, 1 }; - std::vector input({ 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6}); + std::vector input({ 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6 }); std::vector output({ 1, 3, 5 }); - return MeanTestHelper(workloadFactory, inputShape, input, {0, 3}, true, outputShape, output); + return MeanTestHelper(workloadFactory, inputShape, input, { 0, 3 }, true, outputShape, output); } LayerTestResult MeanVtsUint8Test(armnn::IWorkloadFactory& workloadFactory) { - const unsigned int inputShape[] = {4, 3, 2}; + const unsigned int inputShape[] = { 4, 3, 2 }; const unsigned int outputShape[] = { 2 }; - std::vector input({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}); - std::vector output({12, 13}); + std::vector input({ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, + 24 }); + std::vector output({ 12, 13 }); - return MeanTestHelper(workloadFactory, inputShape, input, {0, 1}, false, outputShape, - output, 0.8f, 5); + return MeanTestHelper(workloadFactory, inputShape, input, { 0, 1 }, false, outputShape, + output, 0.8f, 5); } LayerTestResult MeanFloatSimpleTest(armnn::IWorkloadFactory& workloadFactory) @@ -5923,8 +5924,8 @@ LayerTestResult MeanFloatSimpleTest(armnn::IWorkloadFactory& workloadF const unsigned int inputShape[] = { 3, 2 }; const unsigned int outputShape[] = { 1 }; - std::vector input({ 1., 1., 2., 2., 3., 3. }); - std::vector output({ 2. }); + std::vector input({ 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f }); + std::vector output({ 2.0f }); return MeanTestHelper(workloadFactory, inputShape, input, {}, false, outputShape, output); } @@ -5934,10 +5935,10 @@ LayerTestResult MeanFloatSimpleAxisTest(armnn::IWorkloadFactory& workl const unsigned int inputShape[] = { 2, 3, 1, 2 }; const unsigned int outputShape[] = { 3, 1, 2 }; - std::vector input({ 1., 2., 3., 4., 5., 6., 1., 2., 3., 4., 5., 6.}); - std::vector output({ 1., 2., 3., 4., 5., 6. }); + std::vector input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f }); + std::vector output({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f }); - return MeanTestHelper(workloadFactory, inputShape, input, {0}, false, outputShape, output); + return MeanTestHelper(workloadFactory, inputShape, input, { 0 }, false, outputShape, output); } LayerTestResult MeanFloatKeepDimsTest(armnn::IWorkloadFactory& workloadFactory) @@ -5945,10 +5946,10 @@ LayerTestResult MeanFloatKeepDimsTest(armnn::IWorkloadFactory& workloa const unsigned int inputShape[] = { 1, 1, 3, 2 }; const unsigned int outputShape[] = { 1, 1, 1, 2 }; - std::vector input({ 1., 1., 2., 2., 3., 3. }); - std::vector output({ 2., 2. }); + std::vector input({ 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f }); + std::vector output({ 2.0f, 2.0f }); - return MeanTestHelper(workloadFactory, inputShape, input, {2}, true, outputShape, output); + return MeanTestHelper(workloadFactory, inputShape, input, { 2 }, true, outputShape, output); } LayerTestResult MeanFloatMultipleDimsTest(armnn::IWorkloadFactory& workloadFactory) @@ -5956,34 +5957,45 @@ LayerTestResult MeanFloatMultipleDimsTest(armnn::IWorkloadFactory& wor const unsigned int inputShape[] = { 2, 3, 1, 2 }; const unsigned int outputShape[] = { 1, 3, 1, 1 }; - std::vector input({ 1., 2., 3., 4., 5., 6., 1., 2., 3., 4., 5., 6.}); - std::vector output({ 1.5, 3.5, 5.5 }); + std::vector input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f }); + std::vector output({ 1.5f, 3.5f, 5.5f }); - return MeanTestHelper(workloadFactory, inputShape, input, {0, 3}, true, outputShape, output); + return MeanTestHelper(workloadFactory, inputShape, input, { 0, 3 }, true, outputShape, output); } LayerTestResult MeanVtsFloat1Test(armnn::IWorkloadFactory& workloadFactory) { - const unsigned int inputShape[] = {4, 3, 2}; + const unsigned int inputShape[] = { 4, 3, 2 }; const unsigned int outputShape[] = { 2 }; - std::vector input({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}); - std::vector output({12.0f, 13.0f}); + std::vector input({ 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 }); + std::vector output({ 12.0f, 13.0f }); - return MeanTestHelper(workloadFactory, inputShape, input, {0, 1}, false, outputShape, output); + return MeanTestHelper(workloadFactory, inputShape, input, { 0, 1 }, false, outputShape, output); } LayerTestResult MeanVtsFloat2Test(armnn::IWorkloadFactory& workloadFactory) { - const unsigned int inputShape[] = {4, 3, 2}; - const unsigned int outputShape[] = {1, 3, 1 }; + const unsigned int inputShape[] = { 4, 3, 2 }; + const unsigned int outputShape[] = { 1, 3, 1 }; + + std::vector input({ 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 }); + std::vector output({ 10.5f, 12.5f, 14.5f }); + + return MeanTestHelper(workloadFactory, inputShape, input, { 0, 2 }, true, outputShape, output); +} + +LayerTestResult MeanVtsFloat3Test(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int inputShape[] = { 1, 2, 2, 1 }; + const unsigned int outputShape[] = { 1, 2, 1 }; - std::vector input({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}); - std::vector output({10.5f, 12.5f, 14.5f}); + std::vector input({ 1.0f, 2.0f, 3.0f, 4.0f }); + std::vector output({ 1.5f, 3.5f }); - return MeanTestHelper(workloadFactory, inputShape, input, {0, 2}, true, outputShape, output); + return MeanTestHelper(workloadFactory, inputShape, input, { 2 }, false, outputShape, output); } LayerTestResult AdditionAfterMaxPoolTest(armnn::IWorkloadFactory& workloadFactory) diff --git a/src/backends/test/LayerTests.hpp b/src/backends/test/LayerTests.hpp index 9aae5da6e0..ebd38419c4 100644 --- a/src/backends/test/LayerTests.hpp +++ b/src/backends/test/LayerTests.hpp @@ -388,4 +388,6 @@ LayerTestResult MeanFloatKeepDimsTest(armnn::IWorkloadFactory& workloa LayerTestResult MeanFloatMultipleDimsTest(armnn::IWorkloadFactory& workloadFactory); LayerTestResult MeanVtsFloat1Test(armnn::IWorkloadFactory& workloadFactory); LayerTestResult MeanVtsFloat2Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult MeanVtsFloat3Test(armnn::IWorkloadFactory& workloadFactory); + LayerTestResult AdditionAfterMaxPoolTest(armnn::IWorkloadFactory& workloadFactory); -- cgit v1.2.1