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author | jimfly01 <jim.flynn@arm.com> | 2018-10-26 16:50:13 +0100 |
---|---|---|
committer | Matteo Martincigh <matteo.martincigh@arm.com> | 2018-11-01 12:14:22 +0000 |
commit | b9c8963c3d393baf27edf37ab732fa76ee53af50 (patch) | |
tree | 545713a0e0896cabfc107c30a6310a26bf3b67e6 /src/backends/test/Conv2dTestImpl.hpp | |
parent | 7af00da9068c71ae6f66eaa488048feea62e7aa9 (diff) | |
download | armnn-b9c8963c3d393baf27edf37ab732fa76ee53af50.tar.gz |
IVGCVSW-2074: Updated the DepthwiseConvolution2dDepthMul1 for NHWC
Change-Id: Ibaa0b909680a6f2a155e4ca6fa24e1144d6e6e73
Diffstat (limited to 'src/backends/test/Conv2dTestImpl.hpp')
-rwxr-xr-x | src/backends/test/Conv2dTestImpl.hpp | 71 |
1 files changed, 47 insertions, 24 deletions
diff --git a/src/backends/test/Conv2dTestImpl.hpp b/src/backends/test/Conv2dTestImpl.hpp index 9bb36fb344..3791fb0a8e 100755 --- a/src/backends/test/Conv2dTestImpl.hpp +++ b/src/backends/test/Conv2dTestImpl.hpp @@ -457,7 +457,8 @@ template<typename T, typename B> LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset, - bool biasEnabled) + bool biasEnabled, + const armnn::DataLayoutIndexed& layout) { unsigned int inputHeight = 3; unsigned int inputWidth = 3; @@ -473,10 +474,9 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(armnn::IWorkloadFa unsigned int outputChannels = kernelChannels; unsigned int outputNum = inputNum; - armnn::TensorInfo inputTensorInfo({ inputNum, inputChannels, inputHeight, inputWidth }, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo({ outputNum, outputChannels, outputHeight, outputWidth }, - armnn::GetDataType<T>()); - armnn::TensorInfo kernelDesc({ 1, outputChannels, 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>(1, outputChannels, kernelHeight, kernelWidth, layout); armnn::TensorInfo biasDesc({ outputChannels }, armnn::GetDataType<B>()); // Set quantization parameters if the requested type is a quantized type. @@ -491,32 +491,47 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(armnn::IWorkloadFa biasDesc.SetQuantizationScale(qScale*qScale); biasDesc.SetQuantizationOffset(0); } + std::vector<T> inputData = std::vector<T>( + QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { + 1.f, 2.f, 1.f, + 2.f, 1.f, 2.f, + 1.f, 2.f, 1.f, - auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>( - QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { - 1.f, 2.f, 1.f, - 2.f, 1.f, 2.f, - 1.f, 2.f, 1.f, - - 1.f, 2.f, 1.f, - 2.f, 1.f, 2.f, - 1.f, 2.f, 1.f, - }))); + 1.f, 2.f, 1.f, + 2.f, 1.f, 2.f, + 1.f, 2.f, 1.f, + })); + // 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 input = MakeTensor<T, 4>(inputTensorInfo, inputData); std::vector<B> biasV(QuantizedVector<B>(biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(), {0, 2})); auto bias = MakeTensor<B, 1>(biasDesc, biasV); - auto kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( - QuantizedVector<T>(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), { - 1.f, 0.f, 1.f, - 0.f, 0.f, 0.f, - -1.f, 0.f, -1.f, + std::vector<T> kernelData = std::vector<T>( + QuantizedVector<T>(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), { + 1.f, 0.f, 1.f, + 0.f, 0.f, 0.f, + -1.f, 0.f, -1.f, - 1.f, 0.f, 1.f, - 0.f, 0.f, 0.f, - -1.f, 0.f, -1.f, - }))); + 1.f, 0.f, 1.f, + 0.f, 0.f, 0.f, + -1.f, 0.f, -1.f, + })); + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector<T> tmp(kernelData.size()); + armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, kernelData.data(), tmp.data()); + kernelData = tmp; + } + auto kernel = MakeTensor<T, 4>(kernelDesc, kernelData); // Manually calculated. std::vector<T> outputImage( @@ -534,6 +549,13 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(armnn::IWorkloadFa } LayerTestResult<T, 4> ret(outputTensorInfo); + if (layout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector<T> tmp(outputImage.size()); + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputImage.data(), tmp.data()); + outputImage = tmp; + } + ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputImage); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); @@ -559,6 +581,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(armnn::IWorkloadFa data.m_Parameters.m_PadTop = 0; data.m_Parameters.m_PadBottom = 0; data.m_Parameters.m_BiasEnabled = biasEnabled; + data.m_Parameters.m_DataLayout = layout.GetDataLayout(); std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info); inputHandle->Allocate(); |