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Diffstat (limited to 'src/backends/test/Conv2dTestImpl.hpp')
-rwxr-xr-xsrc/backends/test/Conv2dTestImpl.hpp143
1 files changed, 86 insertions, 57 deletions
diff --git a/src/backends/test/Conv2dTestImpl.hpp b/src/backends/test/Conv2dTestImpl.hpp
index 993794e325..7a3f452515 100755
--- a/src/backends/test/Conv2dTestImpl.hpp
+++ b/src/backends/test/Conv2dTestImpl.hpp
@@ -123,7 +123,6 @@ LayerTestResult<T, 4> SimpleConvolution2dTestImpl(armnn::IWorkloadFactory& workl
// Note these tensors will use two (identical) batches.
- // NOTE: if layout is unknown we will get an exception at this point.
armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(2*inputNum, inputChannels, inputHeight, inputWidth, layout);
armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(
2*outputNum, outputChannels, outputHeight, outputWidth, layout);
@@ -600,7 +599,8 @@ template<typename T, typename B>
LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
float qScale,
int32_t qOffset,
- bool biasEnabled)
+ bool biasEnabled,
+ const armnn::DataLayoutIndexed& layout)
{
unsigned int depthMultiplier = 2;
@@ -617,11 +617,12 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& wo
unsigned int outputChannels = inputChannels * depthMultiplier;
unsigned int outputBatchSize = inputBatchSize;
- armnn::TensorInfo inputTensorInfo({inputBatchSize, inputChannels, inputHeight, inputWidth},
- armnn::GetDataType<T>());
- armnn::TensorInfo outputTensorInfo({outputBatchSize, outputChannels, outputHeight, outputWidth},
- armnn::GetDataType<T>());
- armnn::TensorInfo kernelDesc({depthMultiplier, inputChannels, kernelHeight, kernelWidth}, armnn::GetDataType<T>());
+ armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(
+ inputBatchSize, inputChannels, inputHeight, inputWidth, layout);
+ armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(
+ outputBatchSize, outputChannels, outputHeight, outputWidth, layout);
+ armnn::TensorInfo kernelDesc = GetTensorInfo<T>(
+ depthMultiplier, inputChannels, kernelHeight, kernelWidth, layout);
armnn::TensorInfo biasDesc({outputChannels}, armnn::GetDataType<B>());
// Set quantization parameters if the requested type is a quantized type.
@@ -637,59 +638,74 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& wo
biasDesc.SetQuantizationOffset(0);
}
- auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(
- QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), {
- 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
- 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
- 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
- 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
- 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
- 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
- 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
- 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
- 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
- })));
+ // NOTE: originalInputData is in NCHW format
+ std::vector<T> originalInputData = std::vector<T>(
+ QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), {
+ 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
+ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
+ 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
+ 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
+ 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
+ 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
+ 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
+ 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+ }));
+ std::vector<T> inputData = originalInputData;
+ // 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)
+ {
+ armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, originalInputData.data(), inputData.data());
+ }
+ auto input = MakeTensor<T, 4>(inputTensorInfo, inputData);
std::vector<B> biasV(QuantizedVector<B>(biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
{0, 2, 1, -1}));
auto bias = MakeTensor<B, 1>(biasDesc, biasV);
- auto kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
- QuantizedVector<T>(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), {
- 1, 1, 1,
- 1, -1, 1,
- 1, 1, 1,
- 1, 1, 1,
- 1, 1, 1,
-
- 2, 2, 2,
- 2, 2, 2,
- 2, 2, 2,
- 2, 2, 2,
- 2, 2, 2,
-
- 0, 0, 0,
- 0, -1, 0,
- 0, 0, 0,
- 0, 0, 0,
- 0, 0, 0,
-
- 0, 0, 0,
- 0, 0, 0,
- 0, 1, 0,
- 0, 0, 0,
- 0, 0, 0
- })));
+ std::vector<T> originalKernelData = std::vector<T>(
+ QuantizedVector<T>(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), {
+ 1, 1, 1,
+ 1, -1, 1,
+ 1, 1, 1,
+ 1, 1, 1,
+ 1, 1, 1,
+
+ 2, 2, 2,
+ 2, 2, 2,
+ 2, 2, 2,
+ 2, 2, 2,
+ 2, 2, 2,
+
+ 0, 0, 0,
+ 0, -1, 0,
+ 0, 0, 0,
+ 0, 0, 0,
+ 0, 0, 0,
+
+ 0, 0, 0,
+ 0, 0, 0,
+ 0, 1, 0,
+ 0, 0, 0,
+ 0, 0, 0
+ }));
+ std::vector<T> kernelData = originalKernelData;
+ if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
+ {
+ armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernelData.data(), kernelData.data());
+ }
+ auto kernel = MakeTensor<T, 4>(kernelDesc, kernelData);
// Manually calculated.
- std::vector<T> outputImage = std::vector<T>(
+ std::vector<T> originalOutputImage = std::vector<T>(
QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), {
3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f,
6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f,
@@ -723,12 +739,23 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& wo
// Optionally apply bias to output image.
if(biasEnabled)
{
- ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
- biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
- outputWidth, outputHeight);
+ ApplyBias(originalOutputImage,
+ outputTensorInfo.GetQuantizationScale(),
+ outputTensorInfo.GetQuantizationOffset(),
+ biasV,
+ biasDesc.GetQuantizationScale(),
+ biasDesc.GetQuantizationOffset(),
+ outputWidth,
+ outputHeight);
}
LayerTestResult<T, 4> ret(outputTensorInfo);
+ std::vector<T> outputImage = originalOutputImage;
+ if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
+ {
+ armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, originalOutputImage.data(), outputImage.data());
+ }
+
ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputImage);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
@@ -754,6 +781,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& wo
data.m_Parameters.m_PadTop = 1;
data.m_Parameters.m_PadBottom = 1;
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();
@@ -1132,7 +1160,8 @@ LayerTestResult<T, 4> CompareDepthwiseConvolution2dTestImpl(armnn::IWorkloadFact
auto input = MakeRandomTensor<T, 4>(inputTensorInfo, 124908, 0.0f, 255.0f);
auto kernel = MakeRandomTensor<T, 4>(kernelDesc, 891234, 0.0f, 255.0f);
- auto bias = MakeRandomTensor<typename FullyConnectedBiasTypeForInputType<T>::Type, 1>(biasDesc, 1028, 0.0f, 255.0f);
+ auto bias = MakeRandomTensor<typename FullyConnectedBiasTypeForInputType<T>::Type, 1>(
+ biasDesc, 1028, 0.0f, 255.0f);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-16 04:23:06 +0000