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Diffstat (limited to 'src/backends/backendsCommon/test/Conv2dTestImpl.hpp')
-rwxr-xr-xsrc/backends/backendsCommon/test/Conv2dTestImpl.hpp108
1 files changed, 57 insertions, 51 deletions
diff --git a/src/backends/backendsCommon/test/Conv2dTestImpl.hpp b/src/backends/backendsCommon/test/Conv2dTestImpl.hpp
index 8d292c84bb..24f0825504 100755
--- a/src/backends/backendsCommon/test/Conv2dTestImpl.hpp
+++ b/src/backends/backendsCommon/test/Conv2dTestImpl.hpp
@@ -6,6 +6,7 @@
#include "WorkloadTestUtils.hpp"
#include "TensorUtils.hpp"
+#include "TypeUtils.hpp"
#include <Permute.hpp>
#include <DataLayoutIndexed.hpp>
@@ -70,7 +71,8 @@ void ApplyBias(std::vector<T>& v, float vScale, int32_t vOffset,
}
}
-template<typename T, typename B>
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
+ typename T = armnn::ResolveType<ArmnnType>, typename B = armnn::ResolveType<ArmnnBType>>
LayerTestResult<T, 4> SimpleConvolution2dTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
@@ -115,12 +117,12 @@ LayerTestResult<T, 4> SimpleConvolution2dTestImpl(
// Note these tensors will use two (identical) batches.
armnn::TensorInfo inputTensorInfo =
- armnnUtils::GetTensorInfo<T>(2*inputNum, inputChannels, inputHeight, inputWidth, layout);
+ armnnUtils::GetTensorInfo(2*inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
armnn::TensorInfo outputTensorInfo =
- armnnUtils::GetTensorInfo<T>(2*outputNum, outputChannels, outputHeight, outputWidth, layout);
+ armnnUtils::GetTensorInfo(2*outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
armnn::TensorInfo kernelDesc =
- armnnUtils::GetTensorInfo<T>(kernelDepthMul, kernelChannels, kernelHeight, kernelWidth, layout);
- armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, armnn::GetDataType<B>());
+ armnnUtils::GetTensorInfo(kernelDepthMul, kernelChannels, kernelHeight, kernelWidth, layout, ArmnnType);
+ armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
@@ -230,7 +232,8 @@ LayerTestResult<T, 4> SimpleConvolution2dTestImpl(
return ret;
}
-template<typename T, typename B>
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
+ typename T = armnn::ResolveType<ArmnnType>, typename B = armnn::ResolveType<ArmnnBType>>
LayerTestResult<T, 4> SimpleConvolution2dNhwcTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
@@ -266,11 +269,11 @@ LayerTestResult<T, 4> SimpleConvolution2dNhwcTestImpl(
bool biasEnabled = bias.size() > 0;
// Creates the tensors.
- armnn::TensorInfo inputTensorInfo({inputNum, inputHeight, inputWidth, inputChannels}, armnn::GetDataType<T>());
+ armnn::TensorInfo inputTensorInfo({inputNum, inputHeight, inputWidth, inputChannels}, ArmnnType);
armnn::TensorInfo outputTensorInfo({outputNum, outputHeight, outputWidth, outputChannels},
- armnn::GetDataType<T>());
- armnn::TensorInfo kernelDesc({kernelChanMul, kernelHeight, kernelWidth, kernelChannels}, armnn::GetDataType<T>());
- armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, armnn::GetDataType<B>());
+ ArmnnType);
+ armnn::TensorInfo kernelDesc({kernelChanMul, kernelHeight, kernelWidth, kernelChannels}, ArmnnType);
+ armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
// Construct the input data.
std::vector<T> inputData;
@@ -322,7 +325,8 @@ LayerTestResult<T, 4> SimpleConvolution2dNhwcTestImpl(
return ret;
}
-template<typename T, typename B>
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
+ typename T = armnn::ResolveType<ArmnnType>, typename B = armnn::ResolveType<ArmnnBType>>
LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
@@ -359,11 +363,11 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(
// Creates the tensors.
armnn::TensorInfo inputTensorInfo =
- armnnUtils::GetTensorInfo<T>(inputNum, inputChannels, inputHeight, inputWidth, layout);
+ armnnUtils::GetTensorInfo(inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
armnn::TensorInfo outputTensorInfo =
- armnnUtils::GetTensorInfo<T>(outputNum, outputChannels, outputHeight, outputWidth, layout);
- armnn::TensorInfo kernelDesc({kernelChanMul, kernelChannels, kernelHeight, kernelWidth}, armnn::GetDataType<T>());
- armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, armnn::GetDataType<B>());
+ armnnUtils::GetTensorInfo(outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
+ armnn::TensorInfo kernelDesc({kernelChanMul, kernelChannels, kernelHeight, kernelWidth}, ArmnnType);
+ armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
// Set quantization parameters if the requested type is a quantized type.
if (armnn::IsQuantizedType<T>())
@@ -459,7 +463,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(
return ret;
}
-template<typename T, typename B>
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
@@ -468,6 +472,8 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(
bool biasEnabled,
const armnn::DataLayout layout)
{
+ using B = armnn::ResolveType<ArmnnBType>;
+
unsigned int inputHeight = 3;
unsigned int inputWidth = 3;
unsigned int inputChannels = 2;
@@ -484,12 +490,12 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(
unsigned int outputNum = inputNum;
armnn::TensorInfo inputTensorInfo =
- armnnUtils::GetTensorInfo<T>(inputNum, inputChannels, inputHeight, inputWidth, layout);
+ armnnUtils::GetTensorInfo(inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
armnn::TensorInfo outputTensorInfo =
- armnnUtils::GetTensorInfo<T>(outputNum, outputChannels, outputHeight, outputWidth, layout);
+ armnnUtils::GetTensorInfo(outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
armnn::TensorInfo kernelDesc({kernelDepthMultiplier, kernelChannels, kernelHeight, kernelWidth},
- armnn::GetDataType<T>());
- armnn::TensorInfo biasDesc({ outputChannels }, armnn::GetDataType<B>());
+ ArmnnType);
+ armnn::TensorInfo biasDesc({ outputChannels }, ArmnnBType);
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
@@ -602,7 +608,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(
return ret;
}
-template<typename T, typename B>
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
@@ -611,6 +617,8 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
bool biasEnabled,
const armnn::DataLayout layout)
{
+ using B = armnn::ResolveType<ArmnnBType>;
+
unsigned int depthMultiplier = 2;
unsigned int inputHeight = 8;
@@ -626,13 +634,13 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
unsigned int outputChannels = inputChannels * depthMultiplier;
unsigned int outputBatchSize = inputBatchSize;
- armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo<T>(
- inputBatchSize, inputChannels, inputHeight, inputWidth, layout);
- armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo<T>(
- outputBatchSize, outputChannels, outputHeight, outputWidth, layout);
+ armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo(
+ inputBatchSize, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
+ armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo(
+ outputBatchSize, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
armnn::TensorInfo kernelDesc({depthMultiplier, inputChannels, kernelHeight, kernelWidth},
- armnn::GetDataType<T>());
- armnn::TensorInfo biasDesc({outputChannels}, armnn::GetDataType<B>());
+ ArmnnType);
+ armnn::TensorInfo biasDesc({outputChannels}, ArmnnBType);
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
@@ -803,7 +811,8 @@ LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
return ret;
}
-template<typename T, typename B>
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
+ typename T = armnn::ResolveType<ArmnnType>, typename B = armnn::ResolveType<ArmnnBType>>
LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
@@ -836,11 +845,11 @@ LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestImpl(
unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]);
// Creates the tensors.
- armnn::TensorInfo inputTensorInfo({inputNum, inputHeight, inputWidth, inputChannels}, armnn::GetDataType<T>());
+ armnn::TensorInfo inputTensorInfo({inputNum, inputHeight, inputWidth, inputChannels}, ArmnnType);
armnn::TensorInfo outputTensorInfo({outputNum, outputHeight, outputWidth, outputChannels},
- 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>());
+ ArmnnType);
+ armnn::TensorInfo kernelDesc({kernelChanMul, kernelChannels, kernelHeight, kernelWidth}, ArmnnType);
+ armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
// Set quantization parameters if the requested type is a quantized type.
if (armnn::IsQuantizedType<T>())
@@ -904,7 +913,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestImpl(
return ret;
}
-template<typename T>
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T,4> Convolution1dTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
@@ -912,8 +921,7 @@ LayerTestResult<T,4> Convolution1dTestImpl(
int32_t qOffset,
bool biasEnabled)
{
- using B = typename FullyConnectedBiasTypeForInputType<T>::Type;
-
+ using B = armnn::ResolveType<ArmnnBType>;
// Until we have a specialist 1D convolution layer, we can fake one using
// 2D convolution with the final dimension set to 1.
// I don't anticipate this being particularly slow, given that convolution is implemented
@@ -928,10 +936,10 @@ LayerTestResult<T,4> Convolution1dTestImpl(
unsigned int stride = 1;
unsigned int outputSize = 7; // (inputSize + 2 * padSize - kernelSize + 1) / stride.
- armnn::TensorInfo inputInfo({batchSize, inputChannels, inputSize, 1}, armnn::GetDataType<T>());
- armnn::TensorInfo outputInfo({batchSize, outputChannels, outputSize, 1}, armnn::GetDataType<T>());
- armnn::TensorInfo kernelInfo({outputChannels, inputChannels, kernelSize, 1}, armnn::GetDataType<T>());
- armnn::TensorInfo biasInfo({outputChannels}, armnn::GetDataType<B>());
+ armnn::TensorInfo inputInfo({batchSize, inputChannels, inputSize, 1}, ArmnnType);
+ armnn::TensorInfo outputInfo({batchSize, outputChannels, outputSize, 1}, ArmnnType);
+ armnn::TensorInfo kernelInfo({outputChannels, inputChannels, kernelSize, 1}, ArmnnType);
+ armnn::TensorInfo biasInfo({outputChannels}, ArmnnBType);
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
@@ -1023,9 +1031,7 @@ LayerTestResult<T,4> Convolution1dTestImpl(
return ret;
}
-
-
-template<typename T>
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T,4> CompareConvolution2dTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
@@ -1059,10 +1065,10 @@ LayerTestResult<T,4> CompareConvolution2dTestImpl(
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>());
- kernelDesc = armnn::TensorInfo(4, kernelShape, armnn::GetDataType<T>());
- biasDesc = armnn::TensorInfo(1, biasShape, armnn::GetDataType<T>());
+ inputTensorInfo = armnn::TensorInfo(4, inputShape, ArmnnType);
+ outputTensorInfo = armnn::TensorInfo(4, outputShape, ArmnnType);
+ kernelDesc = armnn::TensorInfo(4, kernelShape, ArmnnType);
+ biasDesc = armnn::TensorInfo(1, biasShape, ArmnnType);
LayerTestResult<T,4> ret(outputTensorInfo);
@@ -1123,7 +1129,7 @@ LayerTestResult<T,4> CompareConvolution2dTestImpl(
return ret;
}
-template<typename T>
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> CompareDepthwiseConvolution2dTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
@@ -1178,11 +1184,11 @@ LayerTestResult<T, 4> CompareDepthwiseConvolution2dTestImpl(
float outputQScale = armnn::IsQuantizedType<T>() ? 2.0f : 0;
int32_t qOffset = 0;
- inputTensorInfo = armnn::TensorInfo(4, inputShape.data(), armnn::GetDataType<T>(), inputsQScale, qOffset);
- outputTensorInfo = armnn::TensorInfo(4, outputShape.data(), armnn::GetDataType<T>(), outputQScale, qOffset);
- kernelDesc = armnn::TensorInfo(4, kernelShape.data(), armnn::GetDataType<T>(), inputsQScale, qOffset);
+ inputTensorInfo = armnn::TensorInfo(4, inputShape.data(), ArmnnType, inputsQScale, qOffset);
+ outputTensorInfo = armnn::TensorInfo(4, outputShape.data(), ArmnnType, outputQScale, qOffset);
+ kernelDesc = armnn::TensorInfo(4, kernelShape.data(), ArmnnType, inputsQScale, qOffset);
biasDesc = armnn::TensorInfo(
- 1, biasShape.data(), armnn::GetBiasDataType(armnn::GetDataType<T>()), inputsQScale, qOffset);
+ 1, biasShape.data(), armnn::GetBiasDataType(ArmnnType), inputsQScale, qOffset);
LayerTestResult<T, 4> ret(outputTensorInfo);
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-02 14:55:46 +0000