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Diffstat (limited to 'src/backends/backendsCommon/test/Conv2dTestImpl.hpp')
-rwxr-xr-xsrc/backends/backendsCommon/test/Conv2dTestImpl.hpp64
1 files changed, 23 insertions, 41 deletions
diff --git a/src/backends/backendsCommon/test/Conv2dTestImpl.hpp b/src/backends/backendsCommon/test/Conv2dTestImpl.hpp
index 37fa0f63d6..2ff66b08d5 100755
--- a/src/backends/backendsCommon/test/Conv2dTestImpl.hpp
+++ b/src/backends/backendsCommon/test/Conv2dTestImpl.hpp
@@ -327,7 +327,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const boost::multi_array<T, 4>& input,
- const boost::multi_array<T, 4>& originalKernel,
+ const boost::multi_array<T, 4>& kernel,
const boost::multi_array<B, 1>& bias,
const boost::multi_array<T, 4>& outputExpected,
float qScale,
@@ -344,10 +344,10 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(
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>(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 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 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]);
@@ -362,8 +362,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(
armnnUtils::GetTensorInfo<T>(inputNum, inputChannels, inputHeight, inputWidth, layout);
armnn::TensorInfo outputTensorInfo =
armnnUtils::GetTensorInfo<T>(outputNum, outputChannels, outputHeight, outputWidth, layout);
- armnn::TensorInfo kernelDesc =
- armnnUtils::GetTensorInfo<T>(kernelChanMul, kernelChannels, kernelHeight, kernelWidth, layout);
+ armnn::TensorInfo kernelDesc({kernelChanMul, kernelChannels, kernelHeight, kernelWidth}, armnn::GetDataType<T>());
armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, armnn::GetDataType<B>());
// Set quantization parameters if the requested type is a quantized type.
@@ -423,13 +422,6 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(
armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
- // Permute the kernel if necessary
- boost::multi_array<T, 4> kernel = boost::multi_array<T, 4>(originalKernel);
- if (layout == armnn::DataLayout::NHWC)
- {
- armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernel.data(), kernel.data());
- }
-
AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
@@ -484,6 +476,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(
unsigned int kernelHeight = 3;
unsigned int kernelWidth = 3;
unsigned int kernelChannels = inputChannels;
+ unsigned int kernelDepthMultiplier = 1;
unsigned int outputHeight = 1;
unsigned int outputWidth = 1;
@@ -494,7 +487,8 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(
armnnUtils::GetTensorInfo<T>(inputNum, inputChannels, inputHeight, inputWidth, layout);
armnn::TensorInfo outputTensorInfo =
armnnUtils::GetTensorInfo<T>(outputNum, outputChannels, outputHeight, outputWidth, layout);
- armnn::TensorInfo kernelDesc = armnnUtils::GetTensorInfo<T>(1, outputChannels, kernelHeight, kernelWidth, layout);
+ armnn::TensorInfo kernelDesc({kernelDepthMultiplier, kernelChannels, kernelHeight, kernelWidth},
+ armnn::GetDataType<T>());
armnn::TensorInfo biasDesc({ outputChannels }, armnn::GetDataType<B>());
// Set quantization parameters if the requested type is a quantized type.
@@ -543,12 +537,6 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(
0.f, 0.f, 0.f,
-1.f, 0.f, -1.f,
}));
- if (layout == 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.
@@ -642,8 +630,8 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
inputBatchSize, inputChannels, inputHeight, inputWidth, layout);
armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo<T>(
outputBatchSize, outputChannels, outputHeight, outputWidth, layout);
- armnn::TensorInfo kernelDesc = armnnUtils::GetTensorInfo<T>(
- depthMultiplier, inputChannels, kernelHeight, kernelWidth, layout);
+ armnn::TensorInfo kernelDesc({depthMultiplier, inputChannels, kernelHeight, kernelWidth},
+ armnn::GetDataType<T>());
armnn::TensorInfo biasDesc({outputChannels}, armnn::GetDataType<B>());
// Set quantization parameters if the requested type is a quantized type.
@@ -692,7 +680,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
{0, 2, 1, -1}));
auto bias = MakeTensor<B, 1>(biasDesc, biasV);
- std::vector<T> originalKernelData = std::vector<T>(
+ std::vector<T> kernelData = std::vector<T>(
QuantizedVector<T>(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), {
1, 1, 1,
1, -1, 1,
@@ -717,12 +705,8 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
0, 1, 0,
0, 0, 0,
0, 0, 0
+
}));
- std::vector<T> kernelData = originalKernelData;
- if (layout == armnn::DataLayout::NHWC)
- {
- armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernelData.data(), kernelData.data());
- }
auto kernel = MakeTensor<T, 4>(kernelDesc, kernelData);
// Manually calculated.
@@ -840,9 +824,9 @@ LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestImpl(
unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[2]);
unsigned int kernelChanMul = boost::numeric_cast<unsigned int>(kernel.shape()[0]);
- unsigned int kernelChannels = boost::numeric_cast<unsigned int>(kernel.shape()[3]);
- unsigned int kernelHeight = boost::numeric_cast<unsigned int>(kernel.shape()[1]);
- unsigned int kernelWidth = boost::numeric_cast<unsigned int>(kernel.shape()[2]);
+ 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 outputNum = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]);
unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]);
@@ -853,7 +837,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestImpl(
armnn::TensorInfo inputTensorInfo({inputNum, inputHeight, inputWidth, inputChannels}, armnn::GetDataType<T>());
armnn::TensorInfo outputTensorInfo({outputNum, outputHeight, outputWidth, outputChannels},
armnn::GetDataType<T>());
- armnn::TensorInfo kernelDesc({kernelChanMul, kernelHeight, kernelWidth, kernelChannels}, armnn::GetDataType<T>());
+ armnn::TensorInfo kernelDesc({kernelChanMul, kernelChannels, kernelHeight, kernelWidth}, armnn::GetDataType<T>());
armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, armnn::GetDataType<B>());
// Set quantization parameters if the requested type is a quantized type.
@@ -1068,10 +1052,10 @@ LayerTestResult<T,4> CompareConvolution2dTestImpl(
armnn::TensorInfo kernelDesc;
armnn::TensorInfo biasDesc;
- unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth};
- unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};
- unsigned int kernelShape[] = {outputChannels, inputChannels, kernelHeight, kernelWidth};
- unsigned int biasShape[] = {outputChannels};
+ unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth};
+ unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};
+ unsigned int kernelShape[] = {outputChannels, inputChannels, kernelHeight, kernelWidth};
+ unsigned int biasShape[] = {outputChannels};
inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>());
outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>());
@@ -1171,19 +1155,17 @@ LayerTestResult<T, 4> CompareDepthwiseConvolution2dTestImpl(
std::vector<unsigned int> inputShape;
std::vector<unsigned int> outputShape;
- std::vector<unsigned int> kernelShape;
- std::vector<unsigned int> biasShape= { outputChannels };
+ std::vector<unsigned int> kernelShape{ channelMultiplier, inputChannels, kernelHeight, kernelWidth };
+ std::vector<unsigned int> biasShape{ outputChannels };
switch (layout.GetDataLayout())
{
case armnn::DataLayout::NCHW:
inputShape = { inputNum, inputChannels, inputHeight, inputWidth };
outputShape = { outputNum, outputChannels, outputHeight, outputWidth };
- kernelShape = { channelMultiplier, inputChannels, kernelHeight, kernelWidth };
break;
case armnn::DataLayout ::NHWC:
inputShape = { inputNum, inputHeight, inputWidth, inputChannels };
outputShape = { outputNum, outputHeight, outputWidth, outputChannels };
- kernelShape = { channelMultiplier, kernelHeight, kernelWidth, inputChannels };
break;
default:
throw armnn::InvalidArgumentException("unknown data layout ["
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-30 10:42:32 +0000