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Diffstat (limited to 'src/backends/backendsCommon/test/Conv2dTestImpl.hpp')
-rwxr-xr-xsrc/backends/backendsCommon/test/Conv2dTestImpl.hpp41
1 files changed, 21 insertions, 20 deletions
diff --git a/src/backends/backendsCommon/test/Conv2dTestImpl.hpp b/src/backends/backendsCommon/test/Conv2dTestImpl.hpp
index d99b7f7fa3..6685a8edd2 100755
--- a/src/backends/backendsCommon/test/Conv2dTestImpl.hpp
+++ b/src/backends/backendsCommon/test/Conv2dTestImpl.hpp
@@ -14,6 +14,7 @@
#include <test/TensorHelpers.hpp>
#include "QuantizeHelper.hpp"
+#include <backendsCommon/DataLayoutIndexed.hpp>
#include <backendsCommon/CpuTensorHandle.hpp>
#include <backendsCommon/IBackendInternal.hpp>
#include <backendsCommon/WorkloadFactory.hpp>
@@ -75,7 +76,7 @@ LayerTestResult<T, 4> SimpleConvolution2dTestImpl(
const boost::multi_array<T, 4>& originalOutputExpected,
float qScale,
int32_t qOffset,
- const armnn::DataLayoutIndexed& layout = armnn::DataLayout::NCHW,
+ const armnn::DataLayout layout = armnn::DataLayout::NCHW,
uint32_t padLeft = 0,
uint32_t padTop = 0,
uint32_t padRight = 0,
@@ -137,7 +138,7 @@ LayerTestResult<T, 4> SimpleConvolution2dTestImpl(
// 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)
+ if (layout == armnn::DataLayout::NHWC)
{
std::vector<T> tmp(inputData.size());
armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
@@ -166,7 +167,7 @@ LayerTestResult<T, 4> SimpleConvolution2dTestImpl(
outputData.insert(outputData.end(), outputImage.begin(), outputImage.end());
// at this point if we require it permute the expected output
- if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
+ if (layout == armnn::DataLayout::NHWC)
{
std::vector<T> tmp(outputData.size());
armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp.data());
@@ -187,7 +188,7 @@ LayerTestResult<T, 4> SimpleConvolution2dTestImpl(
armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
// Permute the kernel if necessary
boost::multi_array<T, 4> kernel = boost::multi_array<T, 4>(originalKernel);
- if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
+ if (layout == armnn::DataLayout::NHWC)
{
armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernel.data(), kernel.data());
}
@@ -210,7 +211,7 @@ LayerTestResult<T, 4> SimpleConvolution2dTestImpl(
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();
+ data.m_Parameters.m_DataLayout = layout;
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution2d(data, info);
inputHandle->Allocate();
@@ -327,7 +328,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(
const boost::multi_array<T, 4>& outputExpected,
float qScale,
int32_t qOffset,
- const armnn::DataLayoutIndexed& layout,
+ const armnn::DataLayout layout,
uint32_t padLeft = 0,
uint32_t padTop = 0,
uint32_t padRight = 0,
@@ -377,7 +378,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(
// 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)
+ if (layout == armnn::DataLayout::NHWC)
{
std::vector<T> tmp(inputData.size());
armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
@@ -401,7 +402,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(
LayerTestResult<T, 4> ret(outputTensorInfo);
// At this point if we require it permute the expected output
- if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
+ if (layout == armnn::DataLayout::NHWC)
{
std::vector<T> tmp(outputData.size());
armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp.data());
@@ -417,7 +418,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(
// Permute the kernel if necessary
boost::multi_array<T, 4> kernel = boost::multi_array<T, 4>(originalKernel);
- if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
+ if (layout == armnn::DataLayout::NHWC)
{
armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernel.data(), kernel.data());
}
@@ -440,7 +441,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(
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();
+ data.m_Parameters.m_DataLayout = layout;
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
@@ -466,7 +467,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(
float qScale,
int32_t qOffset,
bool biasEnabled,
- const armnn::DataLayoutIndexed& layout)
+ const armnn::DataLayout layout)
{
unsigned int inputHeight = 3;
unsigned int inputWidth = 3;
@@ -511,7 +512,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(
}));
// 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)
+ if (layout == armnn::DataLayout::NHWC)
{
std::vector<T> tmp(inputData.size());
armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
@@ -533,7 +534,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(
0.f, 0.f, 0.f,
-1.f, 0.f, -1.f,
}));
- if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
+ if (layout == armnn::DataLayout::NHWC)
{
std::vector<T> tmp(kernelData.size());
armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, kernelData.data(), tmp.data());
@@ -557,7 +558,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(
}
LayerTestResult<T, 4> ret(outputTensorInfo);
- if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
+ if (layout == armnn::DataLayout::NHWC)
{
std::vector<T> tmp(outputImage.size());
armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputImage.data(), tmp.data());
@@ -589,7 +590,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(
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();
+ data.m_Parameters.m_DataLayout = layout;
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
inputHandle->Allocate();
@@ -611,7 +612,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
float qScale,
int32_t qOffset,
bool biasEnabled,
- const armnn::DataLayoutIndexed& layout)
+ const armnn::DataLayout layout)
{
unsigned int depthMultiplier = 2;
@@ -672,7 +673,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
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)
+ if (layout == armnn::DataLayout::NHWC)
{
armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, originalInputData.data(), inputData.data());
}
@@ -709,7 +710,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
0, 0, 0
}));
std::vector<T> kernelData = originalKernelData;
- if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
+ if (layout == armnn::DataLayout::NHWC)
{
armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernelData.data(), kernelData.data());
}
@@ -762,7 +763,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
LayerTestResult<T, 4> ret(outputTensorInfo);
std::vector<T> outputImage = originalOutputImage;
- if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
+ if (layout == armnn::DataLayout::NHWC)
{
armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, originalOutputImage.data(), outputImage.data());
}
@@ -792,7 +793,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
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();
+ data.m_Parameters.m_DataLayout = layout;
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
inputHandle->Allocate();
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-18 01:26:03 +0000