From cec6b655d9f6ddb73c721ca47a7d67eeaad5c043 Mon Sep 17 00:00:00 2001 From: Nikhil Raj Date: Fri, 12 Oct 2018 13:51:57 +0100 Subject: IVGCVSW-1922 Unit test for DepthwiseConvolution with NHWC Change-Id: I3e6e5b9a62f30d03c05bd7178adea8f4c8275da8 --- src/backends/neon/test/NeonCreateWorkloadTests.cpp | 39 ++++++++++++++++++++++ src/backends/neon/test/NeonLayerTests.cpp | 1 + .../NeonDepthwiseConvolutionFloatWorkload.cpp | 8 +++-- .../NeonDepthwiseConvolutionUint8Workload.cpp | 8 +++-- 4 files changed, 52 insertions(+), 4 deletions(-) (limited to 'src/backends/neon') diff --git a/src/backends/neon/test/NeonCreateWorkloadTests.cpp b/src/backends/neon/test/NeonCreateWorkloadTests.cpp index 244002f132..ac0451f11b 100644 --- a/src/backends/neon/test/NeonCreateWorkloadTests.cpp +++ b/src/backends/neon/test/NeonCreateWorkloadTests.cpp @@ -219,6 +219,45 @@ BOOST_AUTO_TEST_CASE(CreateConvolution2dFloatNhwcWorkload) NeonCreateConvolution2dWorkloadTest(DataLayout::NHWC); } +template +static void NeonCreateDepthWiseConvolutionWorkloadTest(DataLayout dataLayout) +{ + Graph graph; + NeonWorkloadFactory factory; + + auto workload = CreateDepthwiseConvolution2dWorkloadTest(factory, graph, dataLayout); + + // Checks that inputs/outputs are as we expect them (see definition of CreateNormalizationWorkloadTest). + DepthwiseConvolution2dQueueDescriptor queueDescriptor = workload->GetData(); + auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); + auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); + + std::initializer_list inputShape = (dataLayout == DataLayout::NCHW) + ? std::initializer_list({ 2, 2, 5, 5 }) + : std::initializer_list({ 2, 5, 5, 2 }); + std::initializer_list outputShape = (dataLayout == DataLayout::NCHW) + ? std::initializer_list({ 2, 2, 5, 5 }) + : std::initializer_list({ 2, 5, 5, 2 }); + + BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, DataType))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, DataType))); +} + +BOOST_AUTO_TEST_CASE(CreateDepthWiseConvolution2dFloat32NhwcWorkload) +{ + NeonCreateDepthWiseConvolutionWorkloadTest(DataLayout::NHWC); +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +BOOST_AUTO_TEST_CASE(CreateDepthWiseConvolution2dFloat16NhwcWorkload) +{ + NeonCreateDepthWiseConvolutionWorkloadTest(DataLayout::NHWC); +} +#endif + template static void NeonCreateFullyConnectedWorkloadTest() { diff --git a/src/backends/neon/test/NeonLayerTests.cpp b/src/backends/neon/test/NeonLayerTests.cpp index 2d4ee996a4..36138b3c3f 100644 --- a/src/backends/neon/test/NeonLayerTests.cpp +++ b/src/backends/neon/test/NeonLayerTests.cpp @@ -84,6 +84,7 @@ BOOST_AUTO_TEST_CASE(Conv2dUtils) // Depthwise Convolution ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dDepthMul1, DepthwiseConvolution2dDepthMul1Test, true) +ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dDepthNhwc, DepthwiseConvolution2dDepthNhwcTest, false) ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dDepthMul1, DepthwiseConvolution2dDepthMul1Test, false) ARMNN_AUTO_TEST_CASE(DepthwiseConvolution2dDepthMul1Uint8, DepthwiseConvolution2dDepthMul1Uint8Test, true) ARMNN_AUTO_TEST_CASE(UnbiasedDepthwiseConvolution2dDepthMul1Uint8, DepthwiseConvolution2dDepthMul1Uint8Test, false) diff --git a/src/backends/neon/workloads/NeonDepthwiseConvolutionFloatWorkload.cpp b/src/backends/neon/workloads/NeonDepthwiseConvolutionFloatWorkload.cpp index 742a768b94..4b266f3288 100644 --- a/src/backends/neon/workloads/NeonDepthwiseConvolutionFloatWorkload.cpp +++ b/src/backends/neon/workloads/NeonDepthwiseConvolutionFloatWorkload.cpp @@ -20,12 +20,12 @@ NeonDepthwiseConvolutionFloatWorkload::NeonDepthwiseConvolutionFloatWorkload( const TensorInfo& weightInfo = m_Data.m_Weight->GetTensorInfo(); m_KernelTensor = std::make_unique(); - BuildArmComputeTensor(*m_KernelTensor, weightInfo, descriptor.m_DataLayout); + BuildArmComputeTensor(*m_KernelTensor, weightInfo, m_Data.m_Parameters.m_DataLayout); if (m_Data.m_Parameters.m_BiasEnabled) { m_BiasTensor = std::make_unique(); - BuildArmComputeTensor(*m_BiasTensor, m_Data.m_Bias->GetTensorInfo(), descriptor.m_DataLayout); + BuildArmComputeTensor(*m_BiasTensor, m_Data.m_Bias->GetTensorInfo(), m_Data.m_Parameters.m_DataLayout); } arm_compute::PadStrideInfo padStrideInfo(m_Data.m_Parameters.m_StrideX, @@ -41,6 +41,10 @@ NeonDepthwiseConvolutionFloatWorkload::NeonDepthwiseConvolutionFloatWorkload( arm_compute::ITensor& input = static_cast(m_Data.m_Inputs[0])->GetTensor(); arm_compute::ITensor& output = static_cast(m_Data.m_Outputs[0])->GetTensor(); + arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout); + input.info()->set_data_layout(aclDataLayout); + output.info()->set_data_layout(aclDataLayout); + bool use3x3Optimisation = weightInfo.GetShape()[3] == 3 && weightInfo.GetShape()[2] == 3; if (use3x3Optimisation) { diff --git a/src/backends/neon/workloads/NeonDepthwiseConvolutionUint8Workload.cpp b/src/backends/neon/workloads/NeonDepthwiseConvolutionUint8Workload.cpp index 722b778eba..6c6c2dfb6c 100644 --- a/src/backends/neon/workloads/NeonDepthwiseConvolutionUint8Workload.cpp +++ b/src/backends/neon/workloads/NeonDepthwiseConvolutionUint8Workload.cpp @@ -20,12 +20,12 @@ NeonDepthwiseConvolutionUint8Workload::NeonDepthwiseConvolutionUint8Workload( const TensorInfo& weightInfo = m_Data.m_Weight->GetTensorInfo(); m_KernelTensor = std::make_unique(); - BuildArmComputeTensor(*m_KernelTensor, weightInfo, descriptor.m_DataLayout); + BuildArmComputeTensor(*m_KernelTensor, weightInfo, m_Data.m_Parameters.m_DataLayout); if (m_Data.m_Parameters.m_BiasEnabled) { m_BiasTensor = std::make_unique(); - BuildArmComputeTensor(*m_BiasTensor, m_Data.m_Bias->GetTensorInfo(), descriptor.m_DataLayout); + BuildArmComputeTensor(*m_BiasTensor, m_Data.m_Bias->GetTensorInfo(), m_Data.m_Parameters.m_DataLayout); } arm_compute::PadStrideInfo padStrideInfo(m_Data.m_Parameters.m_StrideX, @@ -41,6 +41,10 @@ NeonDepthwiseConvolutionUint8Workload::NeonDepthwiseConvolutionUint8Workload( arm_compute::ITensor& input = static_cast(m_Data.m_Inputs[0])->GetTensor(); arm_compute::ITensor& output = static_cast(m_Data.m_Outputs[0])->GetTensor(); + arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout); + input.info()->set_data_layout(aclDataLayout); + output.info()->set_data_layout(aclDataLayout); + bool use3x3Optimisation = weightInfo.GetShape()[3] == 3 && weightInfo.GetShape()[2] == 3; if (use3x3Optimisation) { -- cgit v1.2.1