diff options
Diffstat (limited to 'src/backends/test/Conv2dTestImpl.hpp')
| -rwxr-xr-x | src/backends/test/Conv2dTestImpl.hpp | 46 |
1 files changed, 37 insertions, 9 deletions
diff --git a/src/backends/test/Conv2dTestImpl.hpp b/src/backends/test/Conv2dTestImpl.hpp index 41a0d1b095..993794e325 100755 --- a/src/backends/test/Conv2dTestImpl.hpp +++ b/src/backends/test/Conv2dTestImpl.hpp @@ -337,11 +337,12 @@ LayerTestResult<T, 4> SimpleConvolution2dNhwcTestImpl(armnn::IWorkloadFactory& w template<typename T, typename B> LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(armnn::IWorkloadFactory& workloadFactory, const boost::multi_array<T, 4>& input, - const boost::multi_array<T, 4>& kernel, + const boost::multi_array<T, 4>& originalKernel, const boost::multi_array<B, 1>& bias, const boost::multi_array<T, 4>& outputExpected, float qScale, int32_t qOffset, + const armnn::DataLayoutIndexed& layout, uint32_t padLeft = 0, uint32_t padTop = 0, uint32_t padRight = 0, @@ -353,10 +354,10 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(armnn::IWorkloadF unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[1]); unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[2]); unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[3]); - unsigned int kernelChanMul = boost::numeric_cast<unsigned int>(kernel.shape()[0]); - unsigned int kernelChannels = boost::numeric_cast<unsigned int>(kernel.shape()[1]); - unsigned int kernelHeight = boost::numeric_cast<unsigned int>(kernel.shape()[2]); - unsigned int kernelWidth = boost::numeric_cast<unsigned int>(kernel.shape()[3]); + unsigned int kernelChanMul = boost::numeric_cast<unsigned int>(originalKernel.shape()[0]); + unsigned int kernelChannels = boost::numeric_cast<unsigned int>(originalKernel.shape()[1]); + unsigned int kernelHeight = boost::numeric_cast<unsigned int>(originalKernel.shape()[2]); + unsigned int kernelWidth = boost::numeric_cast<unsigned int>(originalKernel.shape()[3]); unsigned int outputNum = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]); unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]); unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]); @@ -367,10 +368,9 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(armnn::IWorkloadF BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels); // Creates the tensors. - armnn::TensorInfo inputTensorInfo({inputNum, inputChannels, inputHeight, inputWidth}, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo({outputNum, outputChannels, outputHeight, outputWidth}, - armnn::GetDataType<T>()); - armnn::TensorInfo kernelDesc({kernelChanMul, kernelChannels, kernelHeight, kernelWidth}, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(inputNum, inputChannels, inputHeight, inputWidth, layout); + armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(outputNum, outputChannels, outputHeight, outputWidth, layout); + armnn::TensorInfo kernelDesc = GetTensorInfo<T>(kernelChanMul, kernelChannels, kernelHeight, kernelWidth, layout); armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, armnn::GetDataType<B>()); // Set quantization parameters if the requested type is a quantized type. @@ -389,6 +389,16 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(armnn::IWorkloadF // Construct the input data. std::vector<T> inputData; inputData.assign(input.data(), input.data() + inputChannels*inputHeight*inputWidth); + + // At this point if we require it permute the input data + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector<T> tmp(inputData.size()); + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); + inputData = tmp; + } + auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData); // Construct the output data, with bias applied, as appropriate. @@ -404,12 +414,29 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(armnn::IWorkloadF } LayerTestResult<T, 4> ret(outputTensorInfo); + + // At this point if we require it permute the expected output + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector<T> tmp(outputData.size()); + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp.data()); + outputData = tmp; + } + ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc); + + // Permute the kernel if necessary + boost::multi_array<T, 4> kernel = boost::multi_array<T, 4>(originalKernel); + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernel.data(), kernel.data()); + } + AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]); armnn::ScopedCpuTensorHandle biasTensor(biasDesc); @@ -428,6 +455,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(armnn::IWorkloadF data.m_Parameters.m_PadTop = padTop; data.m_Parameters.m_PadBottom = padBottom; data.m_Parameters.m_BiasEnabled = biasEnabled; + data.m_Parameters.m_DataLayout = layout.GetDataLayout(); armnn::WorkloadInfo info; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); 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