diff options
Diffstat (limited to 'src/backends/backendsCommon/test/LayerTests.cpp')
| -rw-r--r-- | src/backends/backendsCommon/test/LayerTests.cpp | 1364 |
1 files changed, 725 insertions, 639 deletions
diff --git a/src/backends/backendsCommon/test/LayerTests.cpp b/src/backends/backendsCommon/test/LayerTests.cpp index 0bf56e2445..3c78c82b6e 100644 --- a/src/backends/backendsCommon/test/LayerTests.cpp +++ b/src/backends/backendsCommon/test/LayerTests.cpp @@ -5,6 +5,7 @@ #include "LayerTests.hpp" #include "WorkloadTestUtils.hpp" #include "TensorUtils.hpp" +#include "TypeUtils.hpp" #include "test/TensorHelpers.hpp" #include "TensorCopyUtils.hpp" @@ -75,12 +76,12 @@ static std::vector<float> ConvInput3x8x16({ static std::vector<float> Bias2({0, 2}); // Helper function that returns either Bias2 or an empty vector depending on whether bias is enabled. -template<typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> boost::multi_array<T, 1> GetBias2(bool biasEnabled, float qScale, int32_t qOffset) { if(biasEnabled) { - armnn::TensorInfo biasDesc({static_cast<unsigned int>(Bias2.size())}, armnn::GetDataType<T>()); + armnn::TensorInfo biasDesc({static_cast<unsigned int>(Bias2.size())}, ArmnnType); boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(qScale, qOffset, Bias2)); return bias; } @@ -90,7 +91,7 @@ boost::multi_array<T, 1> GetBias2(bool biasEnabled, float qScale, int32_t qOffse } } -template<typename T> +template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -100,11 +101,11 @@ LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon( const armnn::DataLayout layout) { // Use common single-batch 3-channel 16x8 image. - armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType<T>()); + armnn::TensorInfo inputDesc({1, 3, 8, 16}, ArmnnType); boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(qScale, qOffset, ConvInput3x8x16)); // Use a 2-element batch with 3-channel 3x5 kernels. - armnn::TensorInfo kernelDesc({2, 3, 5, 3}, armnn::GetDataType<T>()); + armnn::TensorInfo kernelDesc({2, 3, 5, 3}, ArmnnType); boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { 1, 1, 1, @@ -146,7 +147,7 @@ LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon( }))); // Expected output is 2 batch elements of a 1-channel 14x4 image. - armnn::TensorInfo outputDesc({1, 2, 4, 14}, armnn::GetDataType<T>()); + armnn::TensorInfo outputDesc({1, 2, 4, 14}, ArmnnType); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, @@ -162,18 +163,20 @@ LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon( 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }))); - return SimpleConvolution2dTestImpl<T>(workloadFactory, - memoryManager, - input, - kernel, - GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset), - expectedOutput, - qScale, - qOffset, - layout); + return SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>( + workloadFactory, + memoryManager, + input, + kernel, + GetBias2<ArmnnBType>(biasEnabled, qScale, qOffset), + expectedOutput, + qScale, + qOffset, + layout); } -template<typename T> +template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, + typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> SimpleConvolution2d3x3TestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -185,11 +188,11 @@ LayerTestResult<T, 4> SimpleConvolution2d3x3TestCommon( // Use a 3x3 kernel, which exercises ArmCompute's direct convolution path. // Use common single-batch 3-channel 16x8 image. - armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType<T>()); + armnn::TensorInfo inputDesc({1, 3, 8, 16}, ArmnnType); boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(qScale, qOffset, ConvInput3x8x16)); // Use a 2-element batch of 3-channel 3x3 kernels. - armnn::TensorInfo kernelDesc({2, 3, 3, 3}, armnn::GetDataType<T>()); + armnn::TensorInfo kernelDesc({2, 3, 3, 3}, ArmnnType); boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { 1, 1, 1, @@ -219,7 +222,7 @@ LayerTestResult<T, 4> SimpleConvolution2d3x3TestCommon( }))); // Expected output is 1 batch of a 2-channel 14x6 image. - armnn::TensorInfo outputDesc({1, 2, 6, 14}, armnn::GetDataType<T>()); + armnn::TensorInfo outputDesc({1, 2, 6, 14}, ArmnnType); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, @@ -237,18 +240,19 @@ LayerTestResult<T, 4> SimpleConvolution2d3x3TestCommon( 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }))); - return SimpleConvolution2dTestImpl<T>(workloadFactory, - memoryManager, - input, - kernel, - GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset), - expectedOutput, - qScale, - qOffset, - layout); + return SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>( + workloadFactory, + memoryManager, + input, + kernel, + GetBias2<ArmnnBType>(biasEnabled, qScale, qOffset), + expectedOutput, + qScale, + qOffset, + layout); } -template<typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> SimpleConvolution2d3x3NhwcTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -259,7 +263,7 @@ LayerTestResult<T, 4> SimpleConvolution2d3x3NhwcTestCommon( { // Use common single-batch 5x5 image. - armnn::TensorInfo inputDesc({1, 3, 4, 1}, armnn::GetDataType<T>()); + armnn::TensorInfo inputDesc({1, 3, 4, 1}, ArmnnType); boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, { 1, 5, 2, 3, @@ -269,7 +273,7 @@ LayerTestResult<T, 4> SimpleConvolution2d3x3NhwcTestCommon( // Use a 2-element batch of 3-channel 3x3 kernels. - armnn::TensorInfo kernelDesc({1, 3, 3, 1}, armnn::GetDataType<T>()); + armnn::TensorInfo kernelDesc({1, 3, 3, 1}, ArmnnType); boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, { 4, 5, 6, 0, 0, 0, @@ -277,7 +281,7 @@ LayerTestResult<T, 4> SimpleConvolution2d3x3NhwcTestCommon( }); // Expected output is 1 batch of a 5x5 image. - armnn::TensorInfo outputDesc({1, 3, 4, 1}, armnn::GetDataType<T>()); + armnn::TensorInfo outputDesc({1, 3, 4, 1}, ArmnnType); const std::vector<float> outputData = { @@ -288,18 +292,19 @@ LayerTestResult<T, 4> SimpleConvolution2d3x3NhwcTestCommon( boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, outputData); - return SimpleConvolution2dNhwcTestImpl<T>(workloadFactory, - memoryManager, - input, - kernel, - boost::multi_array<T, 1>(), - expectedOutput, - dataLayout, - qScale, - qOffset); + return SimpleConvolution2dNhwcTestImpl<ArmnnType, ArmnnType>( + workloadFactory, + memoryManager, + input, + kernel, + boost::multi_array<T, 1>(), + expectedOutput, + dataLayout, + qScale, + qOffset); } -template<typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> SimpleConvolution2d3x3Stride2x2TestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -309,7 +314,7 @@ LayerTestResult<T, 4> SimpleConvolution2d3x3Stride2x2TestCommon( const armnn::DataLayout& dataLayout) { // Input is a single-batch, 1 channel, 5x5 image. - armnn::TensorInfo inputDesc({1, 5, 5, 1}, armnn::GetDataType<T>()); + armnn::TensorInfo inputDesc({1, 5, 5, 1}, ArmnnType); boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, { 1, 5, 2, 3, 5, @@ -320,7 +325,7 @@ LayerTestResult<T, 4> SimpleConvolution2d3x3Stride2x2TestCommon( }); // Use a 3x3 kernel. - armnn::TensorInfo kernelDesc({1, 3, 3, 1}, armnn::GetDataType<T>()); + armnn::TensorInfo kernelDesc({1, 3, 3, 1}, ArmnnType); boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, { 4, 5, 6, @@ -329,7 +334,7 @@ LayerTestResult<T, 4> SimpleConvolution2d3x3Stride2x2TestCommon( }); // Expected output is a single-batch, 1 channel, 3x3 image. - armnn::TensorInfo outputDesc({1, 3, 3, 1}, armnn::GetDataType<T>()); + armnn::TensorInfo outputDesc({1, 3, 3, 1}, ArmnnType); const std::vector<T> outputData = { @@ -347,21 +352,22 @@ LayerTestResult<T, 4> SimpleConvolution2d3x3Stride2x2TestCommon( uint32_t strideX = 2; uint32_t strideY = 2; - return SimpleConvolution2dNhwcTestImpl<T>(workloadFactory, - memoryManager, - input, - kernel, - boost::multi_array<T, 1>(), - expectedOutput, - dataLayout, - qScale, - qOffset, - padLeft, - padTop, - padRight, - padBottom, - strideX, - strideY); + return SimpleConvolution2dNhwcTestImpl<ArmnnType, ArmnnType>( + workloadFactory, + memoryManager, + input, + kernel, + boost::multi_array<T, 1>(), + expectedOutput, + dataLayout, + qScale, + qOffset, + padLeft, + padTop, + padRight, + padBottom, + strideX, + strideY); } LayerTestResult<float, 4> SimpleConvolution2d3x5Test( @@ -370,7 +376,8 @@ LayerTestResult<float, 4> SimpleConvolution2d3x5Test( bool biasEnabled, const armnn::DataLayout layout) { - return SimpleConvolution2d3x5TestCommon<float>(workloadFactory, memoryManager, 0.f, 0, biasEnabled, layout); + return SimpleConvolution2d3x5TestCommon<armnn::DataType::Float32, armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.f, 0, biasEnabled, layout); } LayerTestResult<uint8_t, 4> SimpleConvolution2d3x5Uint8Test( @@ -379,7 +386,8 @@ LayerTestResult<uint8_t, 4> SimpleConvolution2d3x5Uint8Test( bool biasEnabled, const armnn::DataLayout layout) { - return SimpleConvolution2d3x5TestCommon<uint8_t>(workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout); + return SimpleConvolution2d3x5TestCommon<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>( + workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout); } LayerTestResult<float, 4> SimpleConvolution2d3x3Test( @@ -388,7 +396,8 @@ LayerTestResult<float, 4> SimpleConvolution2d3x3Test( bool biasEnabled, const armnn::DataLayout layout) { - return SimpleConvolution2d3x3TestCommon<float>(workloadFactory, memoryManager, 0.f, 0, biasEnabled, layout); + return SimpleConvolution2d3x3TestCommon<armnn::DataType::Float32, armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.f, 0, biasEnabled, layout); } LayerTestResult<float, 4> SimpleConvolution2d3x3NhwcTest( @@ -396,12 +405,13 @@ LayerTestResult<float, 4> SimpleConvolution2d3x3NhwcTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool biasEnabled) { - return SimpleConvolution2d3x3NhwcTestCommon<float>(workloadFactory, - memoryManager, - 0.f, - 0, - biasEnabled, - armnn::DataLayout::NHWC); + return SimpleConvolution2d3x3NhwcTestCommon<armnn::DataType::Float32>( + workloadFactory, + memoryManager, + 0.f, + 0, + biasEnabled, + armnn::DataLayout::NHWC); } LayerTestResult<float, 4> SimpleConvolution2d3x3Stride2x2Test( @@ -410,12 +420,13 @@ LayerTestResult<float, 4> SimpleConvolution2d3x3Stride2x2Test( bool biasEnabled, const armnn::DataLayout layout) { - return SimpleConvolution2d3x3Stride2x2TestCommon<float>(workloadFactory, - memoryManager, - 0.f, - 0, - biasEnabled, - layout); + return SimpleConvolution2d3x3Stride2x2TestCommon<armnn::DataType::Float32>( + workloadFactory, + memoryManager, + 0.f, + 0, + biasEnabled, + layout); } LayerTestResult<uint8_t, 4> SimpleConvolution2d3x3Uint8Test( @@ -424,10 +435,12 @@ LayerTestResult<uint8_t, 4> SimpleConvolution2d3x3Uint8Test( bool biasEnabled, const armnn::DataLayout layout) { - return SimpleConvolution2d3x3TestCommon<uint8_t>(workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout); + return SimpleConvolution2d3x3TestCommon<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>( + workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout); } -template<typename T> +template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, + typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -436,7 +449,7 @@ LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest int32_t qOffset) { // Use a single-batch 1-channel 3x3 image as input. - armnn::TensorInfo inputDesc({1, 1, 3, 3}, armnn::GetDataType<T>()); + armnn::TensorInfo inputDesc({1, 1, 3, 3}, ArmnnType); boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { 11,21,31, @@ -445,7 +458,7 @@ LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest }))); // Use 1 batch of a 1-channel 2x2 kernel. - armnn::TensorInfo kernelDesc({1, 1, 2, 2}, armnn::GetDataType<T>()); + armnn::TensorInfo kernelDesc({1, 1, 2, 2}, ArmnnType); boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { -11,-21, @@ -461,7 +474,7 @@ LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest //[-11*0 -21*13 -12*0 -22*0 ; -11*13 -21*23 -12*0 -22*0 ; -11*23 -21*33 -12*0 -22*0 ; -11*33 -21*0 -12*0 -22*0 ..] //[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..] //[..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ..] - armnn::TensorInfo outputDesc({1, 1, 8, 6}, armnn::GetDataType<T>()); + armnn::TensorInfo outputDesc({1, 1, 8, 6}, ArmnnType); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { 0, 0, 0, 0, 0, 0, @@ -474,22 +487,24 @@ LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest 0, 0, 0, 0, 0, 0 }))); - return SimpleConvolution2dTestImpl<T>(workloadFactory, - memoryManager, - input, - kernel, - GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(false, qScale, qOffset), - expectedOutput, - qScale, - qOffset, - layout, - 1, // Padding left. - 2, // Padding top. - 3, // Padding right. - 4); // Padding bottom. + return SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>( + workloadFactory, + memoryManager, + input, + kernel, + GetBias2<ArmnnBType>(false, qScale, qOffset), + expectedOutput, + qScale, + qOffset, + layout, + 1, // Padding left. + 2, // Padding top. + 3, // Padding right. + 4); // Padding bottom. } -template<typename T> +template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, + typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -498,7 +513,7 @@ LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon( int32_t qOffset) { // Use a single-batch 1-channel 5x5 image as input. - armnn::TensorInfo inputDesc({ 1, 1, 5, 5 }, armnn::GetDataType<T>()); + armnn::TensorInfo inputDesc({ 1, 1, 5, 5 }, ArmnnType); boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { 11,21,31,41,51, @@ -509,7 +524,7 @@ LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon( }))); // Use 1 batch of a 1-channel 4x4 kernel. - armnn::TensorInfo kernelDesc({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); + armnn::TensorInfo kernelDesc({ 1, 1, 4, 4 }, ArmnnType); boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { -11,-21,-31,-41, @@ -519,7 +534,7 @@ LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon( }))); // Expected output is 1 batch of a 1-channel 5x5 image. - armnn::TensorInfo outputDesc({ 1, 1, 5, 5 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputDesc({ 1, 1, 5, 5 }, ArmnnType); std::vector<T> myVec(outputDesc.GetNumElements(), 0); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( QuantizedVector<T>(qScale, qOffset, { @@ -530,11 +545,12 @@ LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon( -5032, -7256, -9376, -6142, -3368, }))); - return SimpleConvolution2dTestImpl<T>(workloadFactory, + return SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>( + workloadFactory, memoryManager, input, kernel, - GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(false, qScale, qOffset), + GetBias2<ArmnnBType>(false, qScale, qOffset), expectedOutput, qScale, qOffset, @@ -545,7 +561,8 @@ LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon( 2); // Padding bottom. } -template<typename T> +template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, + typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -555,7 +572,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon( const armnn::DataLayout layout) { // Use a single-batch 2-channel 5x5 image as input. - armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5 }, ArmnnType); auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>( QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { 0, 1, 2, 3, 4, @@ -572,7 +589,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon( }))); // Use a depth multiplier of 1 on a 2-channel 4x4 kernel. - armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, armnn::GetDataType<T>()); + armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, ArmnnType); auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>( QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), { 32, 31, 30, 29, @@ -588,7 +605,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon( // Expected output is 1 batch of a 2-channel 5x5 image. // Calculated using the python tensorflow library with strideX=1, strideY=1. - armnn::TensorInfo outputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 1, 2, 5, 5 }, ArmnnType); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>( QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), { 1062, 1580, 1850, 1530, 1117, @@ -603,11 +620,12 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon( 3100, 4352, 4452, 3517, 2465 }))); - return DepthwiseConvolution2dAsymmetricTestImpl<T>(workloadFactory, + return DepthwiseConvolution2dAsymmetricTestImpl<ArmnnType, ArmnnBType>( + workloadFactory, memoryManager, input, kernel, - GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset), + GetBias2<ArmnnBType>(biasEnabled, qScale, qOffset), expectedOutput, qScale, qOffset, @@ -620,7 +638,8 @@ LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon( 1); // strideY } -template<typename T> +template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, + typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -628,7 +647,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestCommon( int32_t qOffset, bool biasEnabled) { - armnn::TensorInfo inputTensorInfo({ 1, 5, 5, 2}, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo({ 1, 5, 5, 2}, ArmnnType); auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>( QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { 0, 25, @@ -662,7 +681,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestCommon( 24, 49 }))); - armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, armnn::GetDataType<T>()); + armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, ArmnnType); auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>( QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), { 32, 31, 30, 29, @@ -676,7 +695,7 @@ LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestCommon( 4, 3, 2, 1 }))); - armnn::TensorInfo outputTensorInfo({ 1, 5, 5, 2}, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 1, 5, 5, 2}, ArmnnType); boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>( QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), { 1062, 1550, @@ -710,11 +729,12 @@ LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestCommon( 2457, 2465 }))); - return DepthwiseConvolution2dNhwcTestImpl<T>(workloadFactory, + return DepthwiseConvolution2dNhwcTestImpl<ArmnnType, ArmnnBType>( + workloadFactory, memoryManager, input, kernel, - GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset), + GetBias2<ArmnnBType>(biasEnabled, qScale, qOffset), expectedOutput, qScale, qOffset, @@ -732,8 +752,9 @@ Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::DataLayout layout) { - return Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon<float>( - workloadFactory, memoryManager, layout, 0.0f, 0); + return Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon + <armnn::DataType::Float32, armnn::DataType::Float32>( + workloadFactory, memoryManager, layout, 0.0f, 0); } LayerTestResult<float, 4> Convolution2dAsymmetricPaddingTest( @@ -741,7 +762,7 @@ LayerTestResult<float, 4> Convolution2dAsymmetricPaddingTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::DataLayout layout) { - return SimpleConvolution2dAsymmetricPaddingTestCommon<float>( + return SimpleConvolution2dAsymmetricPaddingTestCommon<armnn::DataType::Float32, armnn::DataType::Float32>( workloadFactory, memoryManager, layout, 0.0f, 0); } @@ -751,7 +772,7 @@ LayerTestResult<float, 4> DepthwiseConvolution2dTest( bool biasEnabled, const armnn::DataLayout layout) { - return DepthwiseConvolution2dTestImpl<float, float>( + return DepthwiseConvolution2dTestImpl<armnn::DataType::Float32, armnn::DataType::Float32>( workloadFactory, memoryManager, 0.0f, 0, biasEnabled, layout); } @@ -760,7 +781,8 @@ LayerTestResult<float, 4> DepthwiseConvolution2dDepthNhwcTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool biasEnabled) { - return DepthwiseConvolution2dNhwcTestCommon<float>(workloadFactory, memoryManager, 0.0f, 0, biasEnabled); + return DepthwiseConvolution2dNhwcTestCommon<armnn::DataType::Float32, armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.0f, 0, biasEnabled); } LayerTestResult<float, 4> DepthwiseConvolution2dDepthMul1Test( @@ -769,7 +791,7 @@ LayerTestResult<float, 4> DepthwiseConvolution2dDepthMul1Test( bool biasEnabled, const armnn::DataLayout layout) { - return DepthwiseConvolution2dDepthMul1TestImpl<float, float>( + return DepthwiseConvolution2dDepthMul1TestImpl<armnn::DataType::Float32, armnn::DataType::Float32>( workloadFactory, memoryManager, 0.0f, 0, biasEnabled, layout); } @@ -779,7 +801,7 @@ LayerTestResult<float, 4> DepthwiseConvolution2dAsymmetricTest( bool biasEnabled, const armnn::DataLayout layout) { - return DepthwiseConvolution2dAsymmetricTestCommon<float>( + return DepthwiseConvolution2dAsymmetricTestCommon<armnn::DataType::Float32, armnn::DataType::Float32>( workloadFactory, memoryManager, 0.0f, 0, biasEnabled, layout); } @@ -789,7 +811,7 @@ LayerTestResult<uint8_t, 4> DepthwiseConvolution2dUint8Test( bool biasEnabled, const armnn::DataLayout layout) { - return DepthwiseConvolution2dTestImpl<uint8_t, int32_t>( + return DepthwiseConvolution2dTestImpl<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>( workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout); } @@ -799,7 +821,7 @@ LayerTestResult<uint8_t, 4> DepthwiseConvolution2dDepthMul1Uint8Test( bool biasEnabled, const armnn::DataLayout layout) { - return DepthwiseConvolution2dDepthMul1TestImpl<uint8_t, int32_t>( + return DepthwiseConvolution2dDepthMul1TestImpl<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>( workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout); } @@ -808,7 +830,8 @@ LayerTestResult<float, 4> Convolution1dTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool biasEnabled) { - return Convolution1dTestImpl<float>(workloadFactory, memoryManager, 0.0f, 0, biasEnabled); + return Convolution1dTestImpl<armnn::DataType::Float32, armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.0f, 0, biasEnabled); } LayerTestResult<uint8_t, 4> Convolution1dUint8Test( @@ -816,7 +839,8 @@ LayerTestResult<uint8_t, 4> Convolution1dUint8Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool biasEnabled) { - return Convolution1dTestImpl<uint8_t>(workloadFactory, memoryManager, 0.1f, 128, biasEnabled); + return Convolution1dTestImpl<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>( + workloadFactory, memoryManager, 0.1f, 128, biasEnabled); } LayerTestResult<float,4> CompareConvolution2dTest( @@ -824,30 +848,29 @@ LayerTestResult<float,4> CompareConvolution2dTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, armnn::IWorkloadFactory& refWorkloadFactory) { - return CompareConvolution2dTestImpl<float>(workloadFactory, memoryManager, refWorkloadFactory); + return CompareConvolution2dTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, refWorkloadFactory); } -template<typename T> -LayerTestResult<T,4> CompareDepthwiseConvolution2dTest( +LayerTestResult<float, 4> CompareDepthwiseConvolution2dFloatTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, armnn::IWorkloadFactory& refWorkloadFactory, const armnn::DataLayout layout) { - return CompareDepthwiseConvolution2dTestImpl<T>(workloadFactory, memoryManager, refWorkloadFactory, layout); + return CompareDepthwiseConvolution2dTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, refWorkloadFactory, layout); } -template LayerTestResult<float, 4> CompareDepthwiseConvolution2dTest<float>( - armnn::IWorkloadFactory&, - const armnn::IBackendInternal::IMemoryManagerSharedPtr&, - armnn::IWorkloadFactory&, - const armnn::DataLayout); - -template LayerTestResult<uint8_t, 4> CompareDepthwiseConvolution2dTest<uint8_t>( - armnn::IWorkloadFactory&, - const armnn::IBackendInternal::IMemoryManagerSharedPtr&, - armnn::IWorkloadFactory&, - const armnn::DataLayout); +LayerTestResult<uint8_t, 4> CompareDepthwiseConvolution2dUint8Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + armnn::IWorkloadFactory& refWorkloadFactory, + const armnn::DataLayout layout) +{ + return CompareDepthwiseConvolution2dTestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, refWorkloadFactory, layout); +} LayerTestResult<float,4> SimpleNormalizationAcrossTest( armnn::IWorkloadFactory& workloadFactory, @@ -881,7 +904,7 @@ LayerTestResult<float,2> SimpleSoftmaxTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float beta) { - return SimpleSoftmaxTestImpl<float>(workloadFactory, memoryManager, beta); + return SimpleSoftmaxTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, beta); } LayerTestResult<uint8_t,2> SimpleSoftmaxUint8Test( @@ -889,7 +912,7 @@ LayerTestResult<uint8_t,2> SimpleSoftmaxUint8Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float beta) { - return SimpleSoftmaxTestImpl<uint8_t>(workloadFactory, memoryManager, beta); + return SimpleSoftmaxTestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, beta); } LayerTestResult<float,4> CompareNormalizationTest( @@ -908,7 +931,8 @@ LayerTestResult<float,2> CompareSoftmaxTest( armnn::IWorkloadFactory& refWorkloadFactory, float beta) { - return CompareSoftmaxTestImpl<float>(workloadFactory, memoryManager, refWorkloadFactory, beta); + return CompareSoftmaxTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, refWorkloadFactory, beta); } LayerTestResult<uint8_t,2> CompareSoftmaxUint8Test( @@ -917,46 +941,47 @@ LayerTestResult<uint8_t,2> CompareSoftmaxUint8Test( armnn::IWorkloadFactory& refWorkloadFactory, float beta) { - return CompareSoftmaxTestImpl<uint8_t>(workloadFactory, memoryManager, refWorkloadFactory, beta); + return CompareSoftmaxTestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, refWorkloadFactory, beta); } std::vector<LayerTestResult<float,3>> SplitterTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SplitterTestCommon<float>(workloadFactory, memoryManager); + return SplitterTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager); } std::vector<LayerTestResult<uint8_t,3>> SplitterUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SplitterTestCommon<uint8_t>(workloadFactory, memoryManager, 1.0f, 0); + return SplitterTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 1.0f, 0); } LayerTestResult<float, 3> CopyViaSplitterTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return CopyViaSplitterTestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return CopyViaSplitterTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<uint8_t, 3> CopyViaSplitterUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return CopyViaSplitterTestImpl<uint8_t>(workloadFactory, memoryManager, 1.0f, 0); + return CopyViaSplitterTestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 1.0f, 0); } LayerTestResult<float, 2> LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - armnn::TensorInfo inputDesc({ 2, 2 }, armnn::GetDataType<float>()); + armnn::TensorInfo inputDesc({ 2, 2 }, armnn::DataType::Float32); boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>( { 2., 3., 3., 4. })); - armnn::TensorInfo outputDesc({ 2, 4 }, armnn::GetDataType<float>()); + armnn::TensorInfo outputDesc({ 2, 4 }, armnn::DataType::Float32); boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>( {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f})); @@ -968,12 +993,12 @@ LayerTestResult<float, 2> LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - armnn::TensorInfo inputDesc({ 2, 5 }, armnn::GetDataType<float>()); + armnn::TensorInfo inputDesc({ 2, 5 }, armnn::DataType::Float32); boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>( {0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f, 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f})); - armnn::TensorInfo outputDesc({ 2, 16 }, armnn::GetDataType<float>()); + armnn::TensorInfo outputDesc({ 2, 16 }, armnn::DataType::Float32); boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>( {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f, -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f, @@ -989,12 +1014,12 @@ LayerTestResult<float, 2> LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - armnn::TensorInfo inputDesc({2, 2}, armnn::GetDataType<float>()); + armnn::TensorInfo inputDesc({2, 2}, armnn::DataType::Float32); boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>( {2., 3., 3., 4.})); - armnn::TensorInfo outputDesc({2, 4}, armnn::GetDataType<float>()); + armnn::TensorInfo outputDesc({2, 4}, armnn::DataType::Float32); boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>( {{-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f, -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}})); @@ -1216,16 +1241,16 @@ LayerTestResult<float,4> AdditionTest( return ret; } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> AdditionBroadcastTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 1}, armnn::GetDataType<T>()); - armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 2, 3}, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 1}, ArmnnType); + armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 2, 3}, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, ArmnnType); if (armnn::IsQuantizedType<T>()) { @@ -1294,16 +1319,16 @@ LayerTestResult<T, 4> AdditionBroadcastTestImpl( return ret; } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> AdditionBroadcast1ElementTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType<T>()); - armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 1, 1}, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 3}, ArmnnType); + armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 1, 1}, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, ArmnnType); if (armnn::IsQuantizedType<T>()) { @@ -1371,28 +1396,32 @@ LayerTestResult<float, 4> AdditionBroadcastTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return AdditionBroadcastTestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return AdditionBroadcastTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<uint8_t, 4> AdditionBroadcastUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return AdditionBroadcastTestImpl<uint8_t>(workloadFactory, memoryManager, 2.f, 0); + return AdditionBroadcastTestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, 2.f, 0); } LayerTestResult<float, 4> AdditionBroadcast1ElementTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return AdditionBroadcast1ElementTestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return AdditionBroadcast1ElementTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<uint8_t, 4> AdditionBroadcast1ElementUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return AdditionBroadcast1ElementTestImpl<uint8_t>(workloadFactory, memoryManager, 0.1333333f, 128); + return AdditionBroadcast1ElementTestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, 0.1333333f, 128); } LayerTestResult<float,4> CompareAdditionTest( @@ -1754,24 +1783,24 @@ std::unique_ptr<armnn::IWorkload> CreateWorkload<armnn::GreaterQueueDescriptor>( } namespace { - template <typename Descriptor, typename dataType> - LayerTestResult<dataType, 4> ElementwiseTestHelper + template <typename Descriptor, armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> + LayerTestResult<T, 4> ElementwiseTestHelper (armnn::IWorkloadFactory & workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager, - const unsigned int shape0[4], std::vector<dataType> values0, - const unsigned int shape1[4], std::vector<dataType> values1, - const unsigned int outShape[4], std::vector<dataType> outValues, + const unsigned int shape0[4], std::vector<T> values0, + const unsigned int shape1[4], std::vector<T> values1, + const unsigned int outShape[4], std::vector<T> outValues, float qScale = 0.0f, int qOffset = 0) { const size_t dimensionCount = 4; - armnn::TensorInfo inputTensorInfo0{dimensionCount, shape0, armnn::GetDataType<dataType>()}; - armnn::TensorInfo inputTensorInfo1{dimensionCount, shape1, armnn::GetDataType<dataType>()}; - armnn::TensorInfo outputTensorInfo{dimensionCount, outShape, armnn::GetDataType<dataType>()}; + armnn::TensorInfo inputTensorInfo0{dimensionCount, shape0, ArmnnType}; + armnn::TensorInfo inputTensorInfo1{dimensionCount, shape1, ArmnnType}; + armnn::TensorInfo outputTensorInfo{dimensionCount, outShape, ArmnnType}; - auto input0 = MakeTensor<dataType, 4>(inputTensorInfo0, values0); - auto input1 = MakeTensor<dataType, 4>(inputTensorInfo1, values1); + auto input0 = MakeTensor<T, 4>(inputTensorInfo0, values0); + auto input1 = MakeTensor<T, 4>(inputTensorInfo1, values1); - if (armnn::IsQuantizedType<dataType>()) + if (armnn::IsQuantizedType<T>()) { inputTensorInfo0.SetQuantizationScale(qScale); inputTensorInfo0.SetQuantizationOffset(qOffset); @@ -1783,7 +1812,7 @@ namespace { outputTensorInfo.SetQuantizationOffset(qOffset); } - LayerTestResult<dataType,4> ret(outputTensorInfo); + LayerTestResult<T,4> ret(outputTensorInfo); std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); @@ -1807,7 +1836,7 @@ namespace { CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - ret.outputExpected = MakeTensor<dataType, 4>(outputTensorInfo, outValues); + ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outValues); return ret; } } @@ -1831,15 +1860,15 @@ LayerTestResult<float, 4> EqualSimpleTest(armnn::IWorkloadFactory& workloadFacto std::vector<float> output({ 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1 }); - return ElementwiseTestHelper<armnn::EqualQueueDescriptor, float> - (workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output); + return ElementwiseTestHelper<armnn::EqualQueueDescriptor, armnn::DataType::Float32>( + workloadFactory, + memoryManager, + shape, + input0, + shape, + input1, + shape, + output); } LayerTestResult<float, 4> EqualBroadcast1ElementTest( @@ -1854,15 +1883,15 @@ LayerTestResult<float, 4> EqualBroadcast1ElementTest( std::vector<float> output({ 1, 0, 0, 0, 0, 0, 0, 0}); - return ElementwiseTestHelper<armnn::EqualQueueDescriptor, float> - (workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); + return ElementwiseTestHelper<armnn::EqualQueueDescriptor, armnn::DataType::Float32>( + workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output); } LayerTestResult<float, 4> EqualBroadcast1DVectorTest( @@ -1880,15 +1909,15 @@ LayerTestResult<float, 4> EqualBroadcast1DVectorTest( std::vector<float> output({ 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 }); - return ElementwiseTestHelper<armnn::EqualQueueDescriptor, float> - (workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); + return ElementwiseTestHelper<armnn::EqualQueueDescriptor, armnn::DataType::Float32>( + workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output); } LayerTestResult<uint8_t, 4> EqualUint8Test( @@ -1907,17 +1936,17 @@ LayerTestResult<uint8_t, 4> EqualUint8Test( std::vector<uint8_t> output({ 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0 }); - return ElementwiseTestHelper<armnn::EqualQueueDescriptor, uint8_t > - (workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output, - 1.0f, - 0); + return ElementwiseTestHelper<armnn::EqualQueueDescriptor, armnn::DataType::QuantisedAsymm8>( + workloadFactory, + memoryManager, + shape, + input0, + shape, + input1, + shape, + output, + 1.0f, + 0); } LayerTestResult<uint8_t, 4> EqualBroadcast1ElementUint8Test( @@ -1935,17 +1964,17 @@ LayerTestResult<uint8_t, 4> EqualBroadcast1ElementUint8Test( std::vector<uint8_t> output({ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }); - return ElementwiseTestHelper<armnn::EqualQueueDescriptor, uint8_t > - (workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); + return ElementwiseTestHelper<armnn::EqualQueueDescriptor, armnn::DataType::QuantisedAsymm8>( + workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output, + 1.0f, + 0); } LayerTestResult<uint8_t, 4> EqualBroadcast1DVectorUint8Test( @@ -1963,17 +1992,17 @@ LayerTestResult<uint8_t, 4> EqualBroadcast1DVectorUint8Test( std::vector<uint8_t> output({ 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 }); - return ElementwiseTestHelper<armnn::EqualQueueDescriptor, uint8_t> - (workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); + return ElementwiseTestHelper<armnn::EqualQueueDescriptor, armnn::DataType::QuantisedAsymm8>( + workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output, + 1.0f, + 0); } LayerTestResult<float, 4> GreaterSimpleTest(armnn::IWorkloadFactory& workloadFactory, @@ -1995,15 +2024,15 @@ LayerTestResult<float, 4> GreaterSimpleTest(armnn::IWorkloadFactory& workloadFac std::vector<float> output({ 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0 }); - return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, float> - (workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output); + return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, armnn::DataType::Float32>( + workloadFactory, + memoryManager, + shape, + input0, + shape, + input1, + shape, + output); } LayerTestResult<float, 4> GreaterBroadcast1ElementTest( @@ -2018,15 +2047,15 @@ LayerTestResult<float, 4> GreaterBroadcast1ElementTest( std::vector<float> output({ 0, 1, 1, 1, 1, 1, 1, 1}); - return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, float> - (workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); + return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, armnn::DataType::Float32>( + workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output); } LayerTestResult<float, 4> GreaterBroadcast1DVectorTest( @@ -2044,15 +2073,15 @@ LayerTestResult<float, 4> GreaterBroadcast1DVectorTest( std::vector<float> output({ 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }); - return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, float> - (workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); + return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, armnn::DataType::Float32>( + workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output); } LayerTestResult<uint8_t, 4> GreaterUint8Test( @@ -2071,17 +2100,17 @@ LayerTestResult<uint8_t, 4> GreaterUint8Test( std::vector<uint8_t> output({ 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0 }); - return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, uint8_t > - (workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output, - 1.0f, - 0); + return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, armnn::DataType::QuantisedAsymm8>( + workloadFactory, + memoryManager, + shape, + input0, + shape, + input1, + shape, + output, + 1.0f, + 0); } LayerTestResult<uint8_t, 4> GreaterBroadcast1ElementUint8Test( @@ -2099,17 +2128,17 @@ LayerTestResult<uint8_t, 4> GreaterBroadcast1ElementUint8Test( std::vector<uint8_t> output({ 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }); - return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, uint8_t > - (workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); + return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, armnn::DataType::QuantisedAsymm8>( + workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output, + 1.0f, + 0); } LayerTestResult<uint8_t, 4> GreaterBroadcast1DVectorUint8Test( @@ -2127,17 +2156,17 @@ LayerTestResult<uint8_t, 4> GreaterBroadcast1DVectorUint8Test( std::vector<uint8_t> output({ 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1 }); - return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, uint8_t> - (workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); + return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, armnn::DataType::QuantisedAsymm8>( + workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output, + 1.0f, + 0); } LayerTestResult<float, 4> MaximumSimpleTest(armnn::IWorkloadFactory& workloadFactory, @@ -2159,15 +2188,15 @@ LayerTestResult<float, 4> MaximumSimpleTest(armnn::IWorkloadFactory& workloadFac std::vector<float> output({ 2, 2, 2, 2, 5, 5, 5, 5, 4, 4, 4, 4, 5, 5, 5, 5 }); - return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, float> - (workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output); + return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, armnn::DataType::Float32>( + workloadFactory, + memoryManager, + shape, + input0, + shape, + input1, + shape, + output); } LayerTestResult<float, 4> MaximumBroadcast1ElementTest( @@ -2182,15 +2211,15 @@ LayerTestResult<float, 4> MaximumBroadcast1ElementTest( std::vector<float> output({ 2, 2, 3, 4, 5, 6, 7, 8}); - return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, float> - (workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); + return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, armnn::DataType::Float32>( + workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output); } LayerTestResult<float, 4> MaximumBroadcast1DVectorTest( @@ -2208,15 +2237,15 @@ LayerTestResult<float, 4> MaximumBroadcast1DVectorTest( std::vector<float> output({ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 }); - return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, float> - (workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); + return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, armnn::DataType::Float32>( + workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output); } LayerTestResult<uint8_t, 4> MaximumUint8Test( @@ -2235,17 +2264,17 @@ LayerTestResult<uint8_t, 4> MaximumUint8Test( std::vector<uint8_t> output({ 2, 2, 2, 2, 6, 6, 6, 6, 4, 4, 4, 4, 5, 5, 5, 5 }); - return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, uint8_t > - (workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output, - 1.0f, - 0); + return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, armnn::DataType::QuantisedAsymm8>( + workloadFactory, + memoryManager, + shape, + input0, + shape, + input1, + shape, + output, + 1.0f, + 0); } LayerTestResult<uint8_t, 4> MaximumBroadcast1ElementUint8Test( @@ -2263,17 +2292,17 @@ LayerTestResult<uint8_t, 4> MaximumBroadcast1ElementUint8Test( std::vector<uint8_t> output({ 2, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 }); - return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, uint8_t > - (workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); + return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, armnn::DataType::QuantisedAsymm8>( + workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output, + 1.0f, + 0); } LayerTestResult<uint8_t, 4> MaximumBroadcast1DVectorUint8Test( @@ -2291,17 +2320,17 @@ LayerTestResult<uint8_t, 4> MaximumBroadcast1DVectorUint8Test( std::vector<uint8_t> output({ 1, 10, 3, 4, 10, 6, 7, 10, 9, 10, 11, 12 }); - return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, uint8_t> - (workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); + return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, armnn::DataType::QuantisedAsymm8>( + workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output, + 1.0f, + 0); } LayerTestResult<float, 4> MinimumBroadcast1ElementTest1( @@ -2316,14 +2345,15 @@ LayerTestResult<float, 4> MinimumBroadcast1ElementTest1( std::vector<float> output({ 1, 2, 2, 2, 2, 2, 2, 2}); - return ElementwiseTestHelper<armnn::MinimumQueueDescriptor, float>(workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); + return ElementwiseTestHelper<armnn::MinimumQueueDescriptor, armnn::DataType::Float32>( + workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output); } @@ -2339,14 +2369,15 @@ LayerTestResult<float, 4> MinimumBroadcast1ElementTest2( std::vector<float> output({ 1, 5, 3, 2, 5, 5, 1, 5}); - return ElementwiseTestHelper<armnn::MinimumQueueDescriptor, float>(workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); + return ElementwiseTestHelper<armnn::MinimumQueueDescriptor, armnn::DataType::Float32>( + workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output); } LayerTestResult<uint8_t, 4> MinimumBroadcast1DVectorUint8Test( @@ -2364,16 +2395,17 @@ LayerTestResult<uint8_t, 4> MinimumBroadcast1DVectorUint8Test( std::vector<uint8_t> output({ 1, 2, 3, 1, 2, 1, 1, 1, 2, 1, 2, 3 }); - return ElementwiseTestHelper<armnn::MinimumQueueDescriptor, uint8_t>(workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); + return ElementwiseTestHelper<armnn::MinimumQueueDescriptor, armnn::DataType::QuantisedAsymm8>( + workloadFactory, + memoryManager, + shape0, + input0, + shape1, + input1, + shape0, + output, + 1.0f, + 0); } namespace { @@ -3044,20 +3076,20 @@ void Concatenate( } } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 1> Concatenation1dTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo inputTensorInfo({ 3 }, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo({ 3 }, ArmnnType); auto input0 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 1.0f, 2.0f, 3.0f })); auto input1 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 4.0f, 5.0f, 6.0f })); auto input2 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 7.0f, 8.0f, 9.0f })); - armnn::TensorInfo outputTensorInfo({ 9 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 9 }, ArmnnType); LayerTestResult<T, 1> result(outputTensorInfo); @@ -3083,10 +3115,10 @@ LayerTestResult<float, 1> Concatenation1dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation1dTestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return Concatenation1dTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 2> Concatenation2dTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -3095,7 +3127,7 @@ LayerTestResult<T, 2> Concatenation2dTestImpl( const float qScale, const int32_t qOffset) { - armnn::TensorInfo inputTensorInfo({ 2, 3 }, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo({ 2, 3 }, ArmnnType); auto input0 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0 @@ -3137,17 +3169,18 @@ LayerTestResult<T, 2> Concatenation2dTestImpl( return result; } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 2> Concatenation2dDim0TestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 6, 3 }, ArmnnType); + + LayerTestResult<T, 2> result = Concatenation2dTestImpl<ArmnnType>( + workloadFactory, memoryManager, outputTensorInfo, 0, qScale, qOffset); - LayerTestResult<T, 2> result = - Concatenation2dTestImpl<T>(workloadFactory, memoryManager, outputTensorInfo, 0, qScale, qOffset); result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0 1.0f, 2.0f, 3.0f, @@ -3175,20 +3208,21 @@ LayerTestResult<float, 2> Concatenation2dDim0Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation2dDim0TestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return Concatenation2dDim0TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 2> Concatenation2dDim1TestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 2, 9 }, ArmnnType); + + LayerTestResult<T, 2> result = Concatenation2dTestImpl<ArmnnType>( + workloadFactory, memoryManager, outputTensorInfo, 1, qScale, qOffset); - LayerTestResult<T, 2> result = - Concatenation2dTestImpl<T>(workloadFactory, memoryManager, outputTensorInfo, 1, qScale, qOffset); result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, @@ -3204,17 +3238,17 @@ LayerTestResult<float, 2> Concatenation2dDim1Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation2dDim1TestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return Concatenation2dDim1TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 2> Concatenation2dDim0DiffInputDimsTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType<T>()); + armnn::TensorInfo input0TensorInfo({ 2, 3 }, ArmnnType); auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0 1.0f, 2.0f, 3.0f, @@ -3223,7 +3257,7 @@ LayerTestResult<T, 2> Concatenation2dDim0DiffInputDimsTestImpl( 10.0f, 11.0f, 12.0f, })); - armnn::TensorInfo input1TensorInfo({ 3, 3 }, armnn::GetDataType<T>()); + armnn::TensorInfo input1TensorInfo({ 3, 3 }, ArmnnType); auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0 4.0f, 5.0f, 6.0f, @@ -3235,13 +3269,13 @@ LayerTestResult<T, 2> Concatenation2dDim0DiffInputDimsTestImpl( 7.0f, 8.0f, 9.0f, })); - armnn::TensorInfo input2TensorInfo({ 1, 3 }, armnn::GetDataType<T>()); + armnn::TensorInfo input2TensorInfo({ 1, 3 }, ArmnnType); auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 1 16.0f, 17.0f, 18.0f, })); - armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 6, 3 }, ArmnnType); LayerTestResult<T, 2> result(outputTensorInfo); std::vector<T> output; @@ -3282,17 +3316,18 @@ LayerTestResult<float, 2> Concatenation2dDim0DiffInputDimsTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation2dDim0DiffInputDimsTestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return Concatenation2dDim0DiffInputDimsTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 2> Concatenation2dDim1DiffInputDimsTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType<T>()); + armnn::TensorInfo input0TensorInfo({ 2, 3 }, ArmnnType); auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0 1.0f, 2.0f, 3.0f, @@ -3301,7 +3336,7 @@ LayerTestResult<T, 2> Concatenation2dDim1DiffInputDimsTestImpl( 10.0f, 11.0f, 12.0f, })); - armnn::TensorInfo input1TensorInfo({ 2, 5 }, armnn::GetDataType<T>()); + armnn::TensorInfo input1TensorInfo({ 2, 5 }, ArmnnType); auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, @@ -3310,7 +3345,7 @@ LayerTestResult<T, 2> Concatenation2dDim1DiffInputDimsTestImpl( 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, })); - armnn::TensorInfo input2TensorInfo({ 2, 1 }, armnn::GetDataType<T>()); + armnn::TensorInfo input2TensorInfo({ 2, 1 }, ArmnnType); auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0 9.0f, @@ -3319,7 +3354,7 @@ LayerTestResult<T, 2> Concatenation2dDim1DiffInputDimsTestImpl( 18.0f })); - armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 2, 9 }, ArmnnType); LayerTestResult<T, 2> result(outputTensorInfo); std::vector<T> output; @@ -3348,10 +3383,11 @@ LayerTestResult<float, 2> Concatenation2dDim1DiffInputDimsTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation2dDim1DiffInputDimsTestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return Concatenation2dDim1DiffInputDimsTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 3> Concatenation3dTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -3361,7 +3397,7 @@ LayerTestResult<T, 3> Concatenation3dTestImpl( float qScale, int32_t qOffset) { - armnn::TensorInfo inputTensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo({ 2, 3, 2 }, ArmnnType); auto input0 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0, Channel 0 @@ -3439,17 +3475,18 @@ LayerTestResult<T, 3> Concatenation3dTestImpl( return result; } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 3> Concatenation3dDim0TestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, ArmnnType); + + LayerTestResult<T, 3> result = Concatenation3dTestImpl<ArmnnType>( + workloadFactory, memoryManager, outputTensorInfo, 0, true, qScale, qOffset); - LayerTestResult<T, 3> result = - Concatenation3dTestImpl<T>(workloadFactory, memoryManager, outputTensorInfo, 0, true, qScale, qOffset); result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0, Channel 0 1.0f, 2.0f, @@ -3513,20 +3550,20 @@ LayerTestResult<float, 3> Concatenation3dDim0Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation3dDim0TestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return Concatenation3dDim0TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 3> Concatenation3dDim1TestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 2, 9, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 2, 9, 2 }, ArmnnType); - LayerTestResult<T, 3> result = - Concatenation3dTestImpl<T>(workloadFactory, memoryManager, outputTensorInfo, 1, true, qScale, qOffset); + LayerTestResult<T, 3> result = Concatenation3dTestImpl<ArmnnType>( + workloadFactory, memoryManager, outputTensorInfo, 1, true, qScale, qOffset); result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0, Channel 0 @@ -3591,10 +3628,10 @@ LayerTestResult<float, 3> Concatenation3dDim1Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation3dDim1TestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return Concatenation3dDim1TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 3> Concatenation3dDim2TestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -3602,10 +3639,10 @@ LayerTestResult<T, 3> Concatenation3dDim2TestImpl( float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, ArmnnType); - LayerTestResult<T, 3> result = - Concatenation3dTestImpl<T>(workloadFactory, memoryManager, outputTensorInfo, 2, useSubtensor, qScale, qOffset); + LayerTestResult<T, 3> result = Concatenation3dTestImpl<ArmnnType>( + workloadFactory, memoryManager, outputTensorInfo, 2, useSubtensor, qScale, qOffset); result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0, Channel 0 @@ -3635,17 +3672,18 @@ LayerTestResult<float, 3> Concatenation3dDim2Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concatenation3dDim2TestImpl<float>(workloadFactory, memoryManager, useSubtensor, 0.0f, 0); + return Concatenation3dDim2TestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, useSubtensor, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 3> Concatenation3dDim0DiffInputDimsTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType); auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0, Channel 0 1.0f, 2.0f, @@ -3666,7 +3704,7 @@ LayerTestResult<T, 3> Concatenation3dDim0DiffInputDimsTestImpl( 23.0f, 24.0f })); - armnn::TensorInfo input1TensorInfo({ 1, 3, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo input1TensorInfo({ 1, 3, 2 }, ArmnnType); auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0, Channel 0 7.0f, 8.0f, @@ -3678,7 +3716,7 @@ LayerTestResult<T, 3> Concatenation3dDim0DiffInputDimsTestImpl( 11.0f, 12.0f, })); - armnn::TensorInfo input2TensorInfo({ 3, 3, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo input2TensorInfo({ 3, 3, 2 }, ArmnnType); auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0, Channel 0 25.0f, 26.0f, @@ -3708,7 +3746,7 @@ LayerTestResult<T, 3> Concatenation3dDim0DiffInputDimsTestImpl( 35.0f, 36.0f })); - armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, ArmnnType); LayerTestResult<T, 3> result(outputTensorInfo); std::vector<T> output; @@ -3785,17 +3823,18 @@ LayerTestResult<float, 3> Concatenation3dDim0DiffInputDimsTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation3dDim0DiffInputDimsTestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return Concatenation3dDim0DiffInputDimsTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 3> Concatenation3dDim1DiffInputDimsTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType); auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0, Channel 0 1.0f, 2.0f, @@ -3816,7 +3855,7 @@ LayerTestResult<T, 3> Concatenation3dDim1DiffInputDimsTestImpl( 23.0f, 24.0f })); - armnn::TensorInfo input1TensorInfo({ 2, 4, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo input1TensorInfo({ 2, 4, 2 }, ArmnnType); auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0, Channel 0 7.0f, 8.0f, @@ -3843,7 +3882,7 @@ LayerTestResult<T, 3> Concatenation3dDim1DiffInputDimsTestImpl( 15.0f, 16.0f, })); - armnn::TensorInfo input2TensorInfo({ 2, 1, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo input2TensorInfo({ 2, 1, 2 }, ArmnnType); auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0, Channel 0 17.0f, 18.0f, @@ -3852,7 +3891,7 @@ LayerTestResult<T, 3> Concatenation3dDim1DiffInputDimsTestImpl( 31.0f, 32.0f, })); - armnn::TensorInfo outputTensorInfo({ 2, 8, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 2, 8, 2 }, ArmnnType); LayerTestResult<T, 3> result(outputTensorInfo); std::vector<T> output; @@ -3923,10 +3962,11 @@ LayerTestResult<float, 3> Concatenation3dDim1DiffInputDimsTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation3dDim1DiffInputDimsTestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return Concatenation3dDim1DiffInputDimsTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 3> Concatenation3dDim2DiffInputDimsTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -3934,7 +3974,7 @@ LayerTestResult<T, 3> Concatenation3dDim2DiffInputDimsTestImpl( float qScale, int32_t qOffset) { - armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, ArmnnType); auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0, Channel 0 1.0f, 2.0f, @@ -3955,7 +3995,7 @@ LayerTestResult<T, 3> Concatenation3dDim2DiffInputDimsTestImpl( 23.0f, 24.0f })); - armnn::TensorInfo input1TensorInfo({ 2, 3, 1 }, armnn::GetDataType<T>()); + armnn::TensorInfo input1TensorInfo({ 2, 3, 1 }, ArmnnType); auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0, Channel 0 7.0f, @@ -3976,7 +4016,7 @@ LayerTestResult<T, 3> Concatenation3dDim2DiffInputDimsTestImpl( 29.0f })); - armnn::TensorInfo input2TensorInfo({ 2, 3, 3 }, armnn::GetDataType<T>()); + armnn::TensorInfo input2TensorInfo({ 2, 3, 3 }, ArmnnType); auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { // Batch 0, Channel 0 13.0f, 14.0f, 50.0f, @@ -3997,7 +4037,7 @@ LayerTestResult<T, 3> Concatenation3dDim2DiffInputDimsTestImpl( 35.0f, 36.0f, 55.0f, })); - armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, ArmnnType); LayerTestResult<T, 3> result(outputTensorInfo); std::vector<T> output; @@ -4039,10 +4079,11 @@ LayerTestResult<float, 3> Concatenation3dDim2DiffInputDimsTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concatenation3dDim2DiffInputDimsTestImpl<float>(workloadFactory, memoryManager, useSubtensor, 0.0f, 0); + return Concatenation3dDim2DiffInputDimsTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, useSubtensor, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> Concatenation4dTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -4052,7 +4093,7 @@ LayerTestResult<T, 4> Concatenation4dTestImpl( float qScale, int32_t qOffset) { - armnn::TensorInfo inputTensorInfo({ 1, 3, 2, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo({ 1, 3, 2, 2 }, ArmnnType); auto input0 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 1.0f, 2.0f, @@ -4099,17 +4140,18 @@ LayerTestResult<T, 4> Concatenation4dTestImpl( return result; } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> Concatenation4dDim0TestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 3, 3, 2, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType); + + LayerTestResult<T, 4> result = Concatenation4dTestImpl<ArmnnType>( + workloadFactory, memoryManager, outputTensorInfo, 0, true, qScale, qOffset); - LayerTestResult<T, 4> result = Concatenation4dTestImpl<T>(workloadFactory, memoryManager, outputTensorInfo, 0, - true, qScale, qOffset); result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 1.0f, 2.0f, 3.0f, 4.0f, @@ -4139,20 +4181,21 @@ LayerTestResult<float, 4> Concatenation4dDim0Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation4dDim0TestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return Concatenation4dDim0TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> Concatenation4dDim1TestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 1, 9, 2, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 1, 9, 2, 2 }, ArmnnType); + + LayerTestResult<T, 4> result = Concatenation4dTestImpl<ArmnnType>( + workloadFactory, memoryManager, outputTensorInfo, 1, true, qScale, qOffset); - LayerTestResult<T, 4> result = Concatenation4dTestImpl<T>(workloadFactory, memoryManager, outputTensorInfo, 1, - true, qScale, qOffset); result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 1.0f, 2.0f, 3.0f, 4.0f, @@ -4183,20 +4226,21 @@ LayerTestResult<float, 4> Concatenation4dDim1Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation4dDim1TestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return Concatenation4dDim1TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> Concatenation4dDim2TestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, float qScale, int32_t qOffset) { - armnn::TensorInfo outputTensorInfo({ 1, 3, 6, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 1, 3, 6, 2 }, ArmnnType); + + LayerTestResult<T, 4> result = Concatenation4dTestImpl<ArmnnType>( + workloadFactory, memoryManager, outputTensorInfo, 2, true, qScale, qOffset); - LayerTestResult<T, 4> result = Concatenation4dTestImpl<T>(workloadFactory, memoryManager, outputTensorInfo, 2, - true, qScale, qOffset); result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 1.0f, 2.0f, 3.0f, 4.0f, @@ -4227,10 +4271,10 @@ LayerTestResult<float, 4> Concatenation4dDim2Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation4dDim2TestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return Concatenation4dDim2TestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> Concatenation4dDim3TestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -4238,10 +4282,11 @@ LayerTestResult<T, 4> Concatenation4dDim3TestImpl( int32_t qOffset, bool useSubtensor) { - armnn::TensorInfo outputTensorInfo({ 1, 3, 2, 6 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 1, 3, 2, 6 }, ArmnnType); + + LayerTestResult<T, 4> result = Concatenation4dTestImpl<ArmnnType>( + workloadFactory, memoryManager, outputTensorInfo, 3, useSubtensor, qScale, qOffset); - LayerTestResult<T, 4> result = Concatenation4dTestImpl<T>(workloadFactory, memoryManager, outputTensorInfo, 3, - useSubtensor, qScale, qOffset); result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 1.0f, 2.0f, 11.0f, 12.0f, @@ -4273,10 +4318,11 @@ LayerTestResult<float, 4> Concatenation4dDim3Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concatenation4dDim3TestImpl<float>(workloadFactory, memoryManager, 0.0f, 0, useSubtensor); + return Concatenation4dDim3TestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.0f, 0, useSubtensor); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> Concatenation4dDiffShapeDim0TestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -4284,7 +4330,7 @@ LayerTestResult<T, 4> Concatenation4dDiffShapeDim0TestImpl( int32_t qOffset) { unsigned int dimension = 0; - armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType); auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(qScale, qOffset, { 1.0f, 2.0f, @@ -4295,7 +4341,7 @@ LayerTestResult<T, 4> Concatenation4dDiffShapeDim0TestImpl( 11.0f, 12.0f })); - armnn::TensorInfo inputTensorInfo1({ 2, 3, 2, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo1({ 2, 3, 2, 2 }, ArmnnType); auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, { 11.0f, 12.0f, @@ -4314,7 +4360,7 @@ LayerTestResult<T, 4> Concatenation4dDiffShapeDim0TestImpl( })); - armnn::TensorInfo outputTensorInfo({ 3, 3, 2, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType); LayerTestResult<T, 4> result(outputTensorInfo); @@ -4360,10 +4406,11 @@ LayerTestResult<float, 4> Concatenation4dDiffShapeDim0Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation4dDiffShapeDim0TestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return Concatenation4dDiffShapeDim0TestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> Concatenation4dDiffShapeDim1TestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -4371,7 +4418,7 @@ LayerTestResult<T, 4> Concatenation4dDiffShapeDim1TestImpl( int32_t qOffset) { unsigned int dimension = 1; - armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType); auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(qScale, qOffset, { 1.0f, 2.0f, @@ -4382,7 +4429,7 @@ LayerTestResult<T, 4> Concatenation4dDiffShapeDim1TestImpl( 11.0f, 12.0f })); - armnn::TensorInfo inputTensorInfo1({ 1, 2, 2, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo1({ 1, 2, 2, 2 }, ArmnnType); auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, { 11.0f, 12.0f, @@ -4392,7 +4439,7 @@ LayerTestResult<T, 4> Concatenation4dDiffShapeDim1TestImpl( })); - armnn::TensorInfo outputTensorInfo({ 1, 5, 2, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 1, 5, 2, 2 }, ArmnnType); LayerTestResult<T, 4> result(outputTensorInfo); @@ -4428,10 +4475,11 @@ LayerTestResult<float, 4> Concatenation4dDiffShapeDim1Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation4dDiffShapeDim1TestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return Concatenation4dDiffShapeDim1TestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> Concatenation4dDiffShapeDim2TestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -4439,7 +4487,7 @@ LayerTestResult<T, 4> Concatenation4dDiffShapeDim2TestImpl( int32_t qOffset) { unsigned int dimension = 2; - armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType); auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(qScale, qOffset, { 1.0f, 2.0f, @@ -4450,7 +4498,7 @@ LayerTestResult<T, 4> Concatenation4dDiffShapeDim2TestImpl( 11.0f, 12.0f })); - armnn::TensorInfo inputTensorInfo1({ 1, 3, 3, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo1({ 1, 3, 3, 2 }, ArmnnType); auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, { 11.0f, 12.0f, @@ -4464,7 +4512,7 @@ LayerTestResult<T, 4> Concatenation4dDiffShapeDim2TestImpl( 27.0f, 28.0f })); - armnn::TensorInfo outputTensorInfo({ 1, 3, 5, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 1, 3, 5, 2 }, ArmnnType); LayerTestResult<T, 4> result(outputTensorInfo); @@ -4507,10 +4555,11 @@ LayerTestResult<float, 4> Concatenation4dDiffShapeDim2Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation4dDiffShapeDim2TestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return Concatenation4dDiffShapeDim2TestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.0f, 0); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> Concatenation4dDiffShapeDim3TestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -4519,7 +4568,7 @@ LayerTestResult<T, 4> Concatenation4dDiffShapeDim3TestImpl( bool useSubtensor) { unsigned int dimension = 3; - armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType); auto input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(qScale, qOffset, { 1.0f, 2.0f, @@ -4530,7 +4579,7 @@ LayerTestResult<T, 4> Concatenation4dDiffShapeDim3TestImpl( 11.0f, 12.0f })); - armnn::TensorInfo inputTensorInfo1({ 1, 3, 2, 3 }, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo1({ 1, 3, 2, 3 }, ArmnnType); auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, { 11.0f, 12.0f, 13.0f, @@ -4543,7 +4592,7 @@ LayerTestResult<T, 4> Concatenation4dDiffShapeDim3TestImpl( 26.0f, 27.0f, 28.0f })); - armnn::TensorInfo outputTensorInfo({ 1, 3, 2, 5 }, armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ 1, 3, 2, 5 }, ArmnnType); LayerTestResult<T, 4> result(outputTensorInfo); @@ -4576,7 +4625,8 @@ LayerTestResult<float, 4> Concatenation4dDiffShapeDim3Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concatenation4dDiffShapeDim3TestImpl<float>(workloadFactory, memoryManager, 0.0f, 0, useSubtensor); + return Concatenation4dDiffShapeDim3TestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.0f, 0, useSubtensor); } LayerTestResult<float, 4> ResizeBilinearNopTest( @@ -4584,8 +4634,11 @@ LayerTestResult<float, 4> ResizeBilinearNopTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::DataLayout dataLayout) { - const armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo<float>(1, 2, 4, 4, dataLayout); - const armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo<float>(1, 2, 4, 4, dataLayout); + const armnn::TensorInfo inputTensorInfo = + armnnUtils::GetTensorInfo(1, 2, 4, 4, dataLayout, armnn::DataType::Float32); + + const armnn::TensorInfo outputTensorInfo = + armnnUtils::GetTensorInfo(1, 2, 4, 4, dataLayout, armnn::DataType::Float32); std::vector<float> inputData({ 1.0f, 2.0f, 3.0f, 4.0f, @@ -4638,8 +4691,11 @@ LayerTestResult<float, 4> SimpleResizeBilinearTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::DataLayout dataLayout) { - const armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo<float>(1, 2, 2, 2, dataLayout); - const armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo<float>(1, 2, 1, 1, dataLayout); + const armnn::TensorInfo inputTensorInfo = + armnnUtils::GetTensorInfo(1, 2, 2, 2, dataLayout, armnn::DataType::Float32); + + const armnn::TensorInfo outputTensorInfo = + armnnUtils::GetTensorInfo(1, 2, 1, 1, dataLayout, armnn::DataType::Float32); std::vector<float> inputData({ 1.0f, 255.0f, @@ -4704,8 +4760,11 @@ LayerTestResult<float, 4> ResizeBilinearSqMinTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::DataLayout dataLayout) { - const armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo<float>(1, 2, 4, 4, dataLayout); - const armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo<float>(1, 2, 2, 2, dataLayout); + const armnn::TensorInfo inputTensorInfo = + armnnUtils::GetTensorInfo(1, 2, 4, 4, dataLayout, armnn::DataType::Float32); + + const armnn::TensorInfo outputTensorInfo = + armnnUtils::GetTensorInfo(1, 2, 2, 2, dataLayout, armnn::DataType::Float32); std::vector<float> inputData({ 1.0f, 2.0f, 3.0f, 4.0f, @@ -4770,8 +4829,11 @@ LayerTestResult<float, 4> ResizeBilinearMinTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::DataLayout dataLayout) { - const armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo<float>(1, 2, 3, 5, dataLayout); - const armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo<float>(1, 2, 2, 3, dataLayout); + const armnn::TensorInfo inputTensorInfo = + armnnUtils::GetTensorInfo(1, 2, 3, 5, dataLayout, armnn::DataType::Float32); + + const armnn::TensorInfo outputTensorInfo = + armnnUtils::GetTensorInfo(1, 2, 2, 3, dataLayout, armnn::DataType::Float32); std::vector<float> inputData({ 1.0f, 2.0f, 3.0f, 5.0f, 8.0f, @@ -4834,8 +4896,11 @@ LayerTestResult<float, 4> ResizeBilinearMagTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::DataLayout dataLayout) { - const armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo<float>(1, 2, 3, 2, dataLayout); - const armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo<float>(1, 2, 3, 5, dataLayout); + const armnn::TensorInfo inputTensorInfo = + armnnUtils::GetTensorInfo(1, 2, 3, 2, dataLayout, armnn::DataType::Float32); + + const armnn::TensorInfo outputTensorInfo = + armnnUtils::GetTensorInfo(1, 2, 3, 5, dataLayout, armnn::DataType::Float32); std::vector<float> inputData({ 1.0f, 2.0f, @@ -5021,7 +5086,7 @@ float CalcInvL2Norm(std::initializer_list<float> elements) } // anonymous namespace -template<typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 2> Pad2dTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -5031,8 +5096,8 @@ LayerTestResult<T, 2> Pad2dTestCommon( const armnn::TensorShape inputShape{ 3, 3 }; const armnn::TensorShape outputShape{ 7, 7 }; - const armnn::TensorInfo inputTensorInfo(inputShape, armnn::GetDataType<T>()); - const armnn::TensorInfo outputTensorInfo(outputShape, armnn::GetDataType<T>()); + const armnn::TensorInfo inputTensorInfo(inputShape, ArmnnType); + const armnn::TensorInfo outputTensorInfo(outputShape, ArmnnType); std::vector<T> inputValues( QuantizedVector<T>(qScale, qOffset, @@ -5089,7 +5154,7 @@ LayerTestResult<T, 2> Pad2dTestCommon( return result; } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 3> Pad3dTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -5099,8 +5164,8 @@ LayerTestResult<T, 3> Pad3dTestCommon( const armnn::TensorShape inputShape{ 2, 2, 2 }; const armnn::TensorShape outputShape{ 3, 5, 6 }; - const armnn::TensorInfo inputTensorInfo(inputShape, armnn::GetDataType<T>()); - const armnn::TensorInfo outputTensorInfo(outputShape, armnn::GetDataType<T>()); + const armnn::TensorInfo inputTensorInfo(inputShape, ArmnnType); + const armnn::TensorInfo outputTensorInfo(outputShape, ArmnnType); std::vector<T> inputValues( QuantizedVector<T>(qScale,qOffset, @@ -5173,7 +5238,7 @@ LayerTestResult<T, 3> Pad3dTestCommon( return result; } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> Pad4dTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -5183,8 +5248,8 @@ LayerTestResult<T, 4> Pad4dTestCommon( const armnn::TensorShape inputShape{ 2, 2, 3, 2 }; const armnn::TensorShape outputShape{ 4, 5, 7, 4 }; - const armnn::TensorInfo inputTensorInfo(inputShape, armnn::GetDataType<T>()); - const armnn::TensorInfo outputTensorInfo(outputShape, armnn::GetDataType<T>()); + const armnn::TensorInfo inputTensorInfo(inputShape, ArmnnType); + const armnn::TensorInfo outputTensorInfo(outputShape, ArmnnType); std::vector<T> inputValues( QuantizedVector<T>(qScale,qOffset, @@ -5414,42 +5479,42 @@ LayerTestResult<uint8_t, 2> PadUint82dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Pad2dTestCommon<uint8_t>(workloadFactory, memoryManager, 1.0f, 0); + return Pad2dTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 1.0f, 0); } LayerTestResult<uint8_t, 3> PadUint83dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Pad3dTestCommon<uint8_t>(workloadFactory, memoryManager, 1.0f, 0); + return Pad3dTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 1.0f, 0); } LayerTestResult<uint8_t, 4> PadUint84dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Pad4dTestCommon<uint8_t>(workloadFactory, memoryManager, 1.0f, 0); + return Pad4dTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 1.0f, 0); } LayerTestResult<float, 2> PadFloat322dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Pad2dTestCommon<float>(workloadFactory, memoryManager, 0.0f, 0); + return Pad2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 3> PadFloat323dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Pad3dTestCommon<float>(workloadFactory, memoryManager, 0.0f, 0); + return Pad3dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 4> PadFloat324dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Pad4dTestCommon<float>(workloadFactory, memoryManager, 0.0f, 0); + return Pad4dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<float, 4> L2Normalization1dTest( @@ -5777,7 +5842,7 @@ LayerTestResult<float, 4> L2Normalization4dTest( inputValues, expectedOutputValues, layout); } -template <typename T> +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> ConstantTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -5794,11 +5859,9 @@ LayerTestResult<T, 4> ConstantTestImpl( constexpr unsigned int outputChannels = inputChannels; constexpr unsigned int outputBatchSize = inputBatchSize; - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, ArmnnType); - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::GetDataType<T>()); + armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, ArmnnType); // Set quantization parameters if the requested type is a quantized type. if(armnn::IsQuantizedType<T>()) @@ -5876,14 +5939,14 @@ LayerTestResult<float, 4> ConstantTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return ConstantTestImpl<float>(workloadFactory, memoryManager, 0.0f, 0); + return ConstantTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0); } LayerTestResult<uint8_t, 4> ConstantTestUint8( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return ConstantTestImpl<uint8_t>(workloadFactory, memoryManager, 1.0f, 0); + return ConstantTestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 1.0f, 0); } LayerTestResult<uint8_t, 3> MergerUint8Test( @@ -6934,9 +6997,10 @@ LayerTestResult<float, 4> BatchNormTest( 2.f, 4.f }; - return BatchNormTestImpl<float>(workloadFactory, memoryManager, - inputOutputShape, inputValues, expectedOutputValues, - 0.f, 0, armnn::DataLayout::NCHW); + return BatchNormTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, + inputOutputShape, inputValues, expectedOutputValues, + 0.f, 0, armnn::DataLayout::NCHW); } LayerTestResult<float, 4> BatchNormNhwcTest( @@ -6978,9 +7042,10 @@ LayerTestResult<float, 4> BatchNormNhwcTest( 6.f, 4.f }; - return BatchNormTestImpl<float>(workloadFactory, memoryManager, - inputOutputShape, inputValues, expectedOutputValues, - 0.f, 0, armnn::DataLayout::NHWC); + return BatchNormTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, + inputOutputShape, inputValues, expectedOutputValues, + 0.f, 0, armnn::DataLayout::NHWC); } LayerTestResult<uint8_t, 4> BatchNormUint8Test( @@ -7018,9 +7083,10 @@ LayerTestResult<uint8_t, 4> BatchNormUint8Test( 2.f, 4.f }; - return BatchNormTestImpl<uint8_t>(workloadFactory, memoryManager, - inputOutputShape, inputValues, expectedOutputValues, - 1.f/20.f, 50, armnn::DataLayout::NCHW); + return BatchNormTestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, + inputOutputShape, inputValues, expectedOutputValues, + 1.f/20.f, 50, armnn::DataLayout::NCHW); } LayerTestResult<uint8_t, 4> BatchNormUint8NhwcTest( @@ -7062,65 +7128,68 @@ LayerTestResult<uint8_t, 4> BatchNormUint8NhwcTest( 6.f, 4.f }; - return BatchNormTestImpl<uint8_t>(workloadFactory, memoryManager, - inputOutputShape, inputValues, expectedOutputValues, - 1.f/20.f, 50, armnn::DataLayout::NHWC); + return BatchNormTestImpl<armnn::DataType::QuantisedAsymm8> + (workloadFactory, memoryManager, + inputOutputShape, inputValues, expectedOutputValues, + 1.f/20.f, 50, armnn::DataLayout::NHWC); } LayerTestResult<uint8_t, 4> ConstantUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return ConstantTestImpl<uint8_t>(workloadFactory, memoryManager, 2e-6f, 1); + return ConstantTestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 2e-6f, 1); } LayerTestResult<uint8_t, 1> Concatenation1dUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation1dTestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1); + return Concatenation1dTestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 2> Concatenation2dDim0Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation2dDim0TestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1); + return Concatenation2dDim0TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 2> Concatenation2dDim1Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation2dDim1TestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1); + return Concatenation2dDim1TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 2> Concatenation2dDim0DiffInputDimsUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation2dDim0DiffInputDimsTestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1); + return Concatenation2dDim0DiffInputDimsTestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 2> Concatenation2dDim1DiffInputDimsUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation2dDim1DiffInputDimsTestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1); + return Concatenation2dDim1DiffInputDimsTestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 3> Concatenation3dDim0Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation3dDim0TestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1); + return Concatenation3dDim0TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 3> Concatenation3dDim1Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation3dDim1TestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1); + return Concatenation3dDim1TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 3> Concatenation3dDim2Uint8Test( @@ -7128,21 +7197,23 @@ LayerTestResult<uint8_t, 3> Concatenation3dDim2Uint8Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concatenation3dDim2TestImpl<uint8_t>(workloadFactory, memoryManager, useSubtensor, 0.5f, -1); + return Concatenation3dDim2TestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, useSubtensor, 0.5f, -1); } LayerTestResult<uint8_t, 3> Concatenation3dDim0DiffInputDimsUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation3dDim0TestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1); + return Concatenation3dDim0TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 3> Concatenation3dDim1DiffInputDimsUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation3dDim1DiffInputDimsTestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1); + return Concatenation3dDim1DiffInputDimsTestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 3> Concatenation3dDim2DiffInputDimsUint8Test( @@ -7150,56 +7221,61 @@ LayerTestResult<uint8_t, 3> Concatenation3dDim2DiffInputDimsUint8Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concatenation3dDim2DiffInputDimsTestImpl<uint8_t>(workloadFactory, memoryManager, useSubtensor, 0.5f, -1); + return Concatenation3dDim2DiffInputDimsTestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, useSubtensor, 0.5f, -1); } LayerTestResult<uint8_t, 4> Concatenation4dDim0Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation4dDim0TestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1); + return Concatenation4dDim0TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 4> Concatenation4dDim1Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation4dDim1TestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1); + return Concatenation4dDim1TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 4> Concatenation4dDim2Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation4dDim2TestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1); + return Concatenation4dDim2TestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 4> Concatenation4dDim3Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concatenation4dDim3TestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1, useSubtensor); + return Concatenation4dDim3TestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, 0.5f, -1, useSubtensor); } LayerTestResult<uint8_t, 4> Concatenation4dDiffShapeDim0Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation4dDiffShapeDim0TestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1); + return Concatenation4dDiffShapeDim0TestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 4> Concatenation4dDiffShapeDim1Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation4dDiffShapeDim1TestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1); + return Concatenation4dDiffShapeDim1TestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 4> Concatenation4dDiffShapeDim2Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Concatenation4dDiffShapeDim2TestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1); + return Concatenation4dDiffShapeDim2TestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, 0.5f, -1); } LayerTestResult<uint8_t, 4> Concatenation4dDiffShapeDim3Uint8Test( @@ -7207,7 +7283,8 @@ LayerTestResult<uint8_t, 4> Concatenation4dDiffShapeDim3Uint8Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool useSubtensor) { - return Concatenation4dDiffShapeDim3TestImpl<uint8_t>(workloadFactory, memoryManager, 0.5f, -1, useSubtensor); + return Concatenation4dDiffShapeDim3TestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, 0.5f, -1, useSubtensor); } LayerTestResult<float, 4> SimpleMaxPooling2dSize2x2Stride2x2Test( @@ -7215,7 +7292,8 @@ LayerTestResult<float, 4> SimpleMaxPooling2dSize2x2Stride2x2Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool forceNoPadding) { - return SimpleMaxPooling2dSize2x2Stride2x2TestCommon<float>(workloadFactory, memoryManager, forceNoPadding); + return SimpleMaxPooling2dSize2x2Stride2x2TestCommon<armnn::DataType::Float32>( + workloadFactory, memoryManager, forceNoPadding); } LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize2x2Stride2x2Uint8Test( @@ -7223,7 +7301,7 @@ LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize2x2Stride2x2Uint8Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool forceNoPadding) { - return SimpleMaxPooling2dSize2x2Stride2x2TestCommon<uint8_t>( + return SimpleMaxPooling2dSize2x2Stride2x2TestCommon<armnn::DataType::QuantisedAsymm8>( workloadFactory, memoryManager, forceNoPadding, 3.0f, -5); } @@ -7232,7 +7310,8 @@ LayerTestResult<float, 4> SimpleMaxPooling2dSize3x3Stride2x4Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool forceNoPadding) { - return SimpleMaxPooling2dSize3x3Stride2x4TestCommon<float>(workloadFactory, memoryManager, forceNoPadding); + return SimpleMaxPooling2dSize3x3Stride2x4TestCommon<armnn::DataType::Float32>( + workloadFactory, memoryManager, forceNoPadding); } LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize3x3Stride2x4Uint8Test( @@ -7240,7 +7319,7 @@ LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize3x3Stride2x4Uint8Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool forceNoPadding) { - return SimpleMaxPooling2dSize3x3Stride2x4TestCommon<uint8_t>( + return SimpleMaxPooling2dSize3x3Stride2x4TestCommon<armnn::DataType::QuantisedAsymm8>( workloadFactory, memoryManager, forceNoPadding, 0.1f, 128); } @@ -7249,7 +7328,7 @@ LayerTestResult<float, 4> SimpleMaxPooling2dTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::DataLayout dataLayout) { - return SimpleMaxPooling2dTestCommon<float>(workloadFactory, memoryManager, dataLayout); + return SimpleMaxPooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, dataLayout); } LayerTestResult<uint8_t, 4> SimpleMaxPooling2dUint8Test( @@ -7257,7 +7336,7 @@ LayerTestResult<uint8_t, 4> SimpleMaxPooling2dUint8Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::DataLayout dataLayout) { - return SimpleMaxPooling2dTestCommon<uint8_t>(workloadFactory, memoryManager, dataLayout); + return SimpleMaxPooling2dTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, dataLayout); } LayerTestResult<float, 4> SimpleAveragePooling2dTest( @@ -7265,7 +7344,7 @@ LayerTestResult<float, 4> SimpleAveragePooling2dTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::DataLayout dataLayout) { - return SimpleAveragePooling2dTestCommon<float>(workloadFactory, memoryManager, dataLayout); + return SimpleAveragePooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, dataLayout); } LayerTestResult<uint8_t, 4> SimpleAveragePooling2dUint8Test( @@ -7273,7 +7352,7 @@ LayerTestResult<uint8_t, 4> SimpleAveragePooling2dUint8Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::DataLayout dataLayout) { - return SimpleAveragePooling2dTestCommon<uint8_t>( + return SimpleAveragePooling2dTestCommon<armnn::DataType::QuantisedAsymm8>( workloadFactory, memoryManager, dataLayout, 0.5, -1); } @@ -7282,7 +7361,7 @@ LayerTestResult<float, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool forceNoPadding) { - return IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon<float>( + return IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon<armnn::DataType::Float32>( workloadFactory, memoryManager, forceNoPadding); } @@ -7290,14 +7369,15 @@ LayerTestResult<float, 4> LargeTensorsAveragePooling2dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return LargeTensorsAveragePooling2dTestCommon<float>(workloadFactory, memoryManager); + return LargeTensorsAveragePooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> LargeTensorsAveragePooling2dUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return LargeTensorsAveragePooling2dTestCommon<uint8_t>(workloadFactory, memoryManager, 0.5, -1); + return LargeTensorsAveragePooling2dTestCommon<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, 0.5, -1); } LayerTestResult<float, 4> SimpleL2Pooling2dTest( @@ -7305,7 +7385,7 @@ LayerTestResult<float, 4> SimpleL2Pooling2dTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::DataLayout dataLayout) { - return SimpleL2Pooling2dTestCommon<float>(workloadFactory, memoryManager, dataLayout); + return SimpleL2Pooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, dataLayout); } LayerTestResult<uint8_t, 4> SimpleL2Pooling2dUint8Test( @@ -7313,91 +7393,91 @@ LayerTestResult<uint8_t, 4> SimpleL2Pooling2dUint8Test( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::DataLayout dataLayout) { - return SimpleL2Pooling2dTestCommon<uint8_t>(workloadFactory, memoryManager, dataLayout); + return SimpleL2Pooling2dTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, dataLayout); } LayerTestResult<float, 4> L2Pooling2dSize3Stride1Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return L2Pooling2dSize3Stride1TestCommon<float>(workloadFactory, memoryManager); + return L2Pooling2dSize3Stride1TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride1Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return L2Pooling2dSize3Stride1TestCommon<uint8_t>(workloadFactory, memoryManager); + return L2Pooling2dSize3Stride1TestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<float, 4> L2Pooling2dSize3Stride3Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return L2Pooling2dSize3Stride3TestCommon<float>(workloadFactory, memoryManager); + return L2Pooling2dSize3Stride3TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride3Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return L2Pooling2dSize3Stride3TestCommon<uint8_t>(workloadFactory, memoryManager); + return L2Pooling2dSize3Stride3TestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<float, 4> L2Pooling2dSize3Stride4Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return L2Pooling2dSize3Stride4TestCommon<float>(workloadFactory, memoryManager); + return L2Pooling2dSize3Stride4TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride4Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return L2Pooling2dSize3Stride4TestCommon<uint8_t>(workloadFactory, memoryManager); + return L2Pooling2dSize3Stride4TestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<float, 4> L2Pooling2dSize7Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return L2Pooling2dSize7TestCommon<float>(workloadFactory, memoryManager); + return L2Pooling2dSize7TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> L2Pooling2dSize7Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return L2Pooling2dSize7TestCommon<uint8_t>(workloadFactory, memoryManager); + return L2Pooling2dSize7TestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<float, 4> L2Pooling2dSize9Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return L2Pooling2dSize9TestCommon<float>(workloadFactory, memoryManager); + return L2Pooling2dSize9TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> L2Pooling2dSize9Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return L2Pooling2dSize9TestCommon<uint8_t>(workloadFactory, memoryManager); + return L2Pooling2dSize9TestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<float, 4> AsymmetricNonSquarePooling2dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return AsymmetricNonSquarePooling2dTestCommon<float>(workloadFactory, memoryManager); + return AsymmetricNonSquarePooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> AsymmetricNonSquarePooling2dUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return AsymmetricNonSquarePooling2dTestCommon<uint8_t>(workloadFactory, memoryManager); + return AsymmetricNonSquarePooling2dTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<float, 4> ComparePooling2dTest( @@ -7406,7 +7486,7 @@ LayerTestResult<float, 4> ComparePooling2dTest( armnn::IWorkloadFactory& refWorkloadFactory, armnn::PoolingAlgorithm poolingType) { - return ComparePooling2dTestCommon<float>( + return ComparePooling2dTestCommon<armnn::DataType::Float32>( workloadFactory, memoryManager, refWorkloadFactory, poolingType); } @@ -7416,7 +7496,7 @@ LayerTestResult<uint8_t, 4> ComparePooling2dUint8Test( armnn::IWorkloadFactory& refWorkloadFactory, armnn::PoolingAlgorithm poolingType) { - return ComparePooling2dTestCommon<uint8_t>( + return ComparePooling2dTestCommon<armnn::DataType::QuantisedAsymm8>( workloadFactory, memoryManager, refWorkloadFactory, poolingType, 0.1f, 128); } @@ -7425,105 +7505,111 @@ LayerTestResult<float, 2> FullyConnectedLargeTest( const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool transposeWeights) { - return FullyConnectedLargeTestCommon<float>(workloadFactory, memoryManager, transposeWeights); + return FullyConnectedLargeTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, transposeWeights); } LayerTestResult<float, 4> IgnorePaddingSimpleMaxPooling2dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return IgnorePaddingSimpleMaxPooling2dTestCommon<float>(workloadFactory, memoryManager); + return IgnorePaddingSimpleMaxPooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> IgnorePaddingSimpleMaxPooling2dUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return IgnorePaddingSimpleMaxPooling2dTestCommon<uint8_t>(workloadFactory, memoryManager, 1.0f, -5); + return IgnorePaddingSimpleMaxPooling2dTestCommon<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, 1.0f, -5); } LayerTestResult<float, 4> IgnorePaddingMaxPooling2dSize3Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return IgnorePaddingMaxPooling2dSize3TestCommon<float>(workloadFactory, memoryManager); + return IgnorePaddingMaxPooling2dSize3TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> IgnorePaddingMaxPooling2dSize3Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return IgnorePaddingMaxPooling2dSize3TestCommon<uint8_t>(workloadFactory, memoryManager, 1.0f, -5); + return IgnorePaddingMaxPooling2dSize3TestCommon<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, 1.0f, -5); } LayerTestResult<float, 4> IgnorePaddingSimpleAveragePooling2dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return IgnorePaddingSimpleAveragePooling2dTestCommon<float>(workloadFactory, memoryManager); + return IgnorePaddingSimpleAveragePooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> IgnorePaddingSimpleAveragePooling2dUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return IgnorePaddingSimpleAveragePooling2dTestCommon<uint8_t>(workloadFactory, memoryManager); + return IgnorePaddingSimpleAveragePooling2dTestCommon<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager); } LayerTestResult<float, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<float>(workloadFactory, memoryManager); + return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<armnn::DataType::Float32>( + workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<uint8_t>(workloadFactory, memoryManager); + return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager); } LayerTestResult<float, 4> IgnorePaddingAveragePooling2dSize3Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return IgnorePaddingAveragePooling2dSize3TestCommon<float>(workloadFactory, memoryManager); + return IgnorePaddingAveragePooling2dSize3TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> IgnorePaddingAveragePooling2dSize3Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return IgnorePaddingAveragePooling2dSize3TestCommon<uint8_t>(workloadFactory, memoryManager); + return IgnorePaddingAveragePooling2dSize3TestCommon<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager); } LayerTestResult<float, 4> IgnorePaddingSimpleL2Pooling2dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return IgnorePaddingSimpleL2Pooling2dTestCommon<float>(workloadFactory, memoryManager); + return IgnorePaddingSimpleL2Pooling2dTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> IgnorePaddingSimpleL2Pooling2dUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return IgnorePaddingSimpleL2Pooling2dTestCommon<uint8_t>(workloadFactory, memoryManager); + return IgnorePaddingSimpleL2Pooling2dTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<float, 4> IgnorePaddingL2Pooling2dSize3Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return IgnorePaddingL2Pooling2dSize3TestCommon<float>(workloadFactory, memoryManager); + return IgnorePaddingL2Pooling2dSize3TestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> IgnorePaddingL2Pooling2dSize3Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return IgnorePaddingL2Pooling2dSize3TestCommon<uint8_t>(workloadFactory, memoryManager); + return IgnorePaddingL2Pooling2dSize3TestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<float, 4> SimplePermuteFloat32Test( @@ -7800,8 +7886,8 @@ LayerTestResult<float, 4> AdditionAfterMaxPoolTest( // 4, 5, 6 // 7, 8, 9 - armnn::TensorInfo poolingInputTensorInfo({ 1, 1, 3, 3}, armnn::GetDataType<float>()); - armnn::TensorInfo poolingOutputTensorInfo({ 1, 1, 2, 2}, armnn::GetDataType<float>()); + armnn::TensorInfo poolingInputTensorInfo({ 1, 1, 3, 3}, armnn::DataType::Float32); + armnn::TensorInfo poolingOutputTensorInfo({ 1, 1, 2, 2}, armnn::DataType::Float32); boost::multi_array<float, 4> poolingInput = MakeTensor<float,4>(poolingInputTensorInfo, {1, 2, 3, @@ -7846,8 +7932,8 @@ LayerTestResult<float, 4> AdditionAfterMaxPoolTest( // 12, 16 // 24, 28 - armnn::TensorInfo addInputTensorInfo({ 1,1,2,2}, armnn::GetDataType<float>()); - armnn::TensorInfo addOutputTensorInfo({ 1,1,2,2}, armnn::GetDataType<float>()); + armnn::TensorInfo addInputTensorInfo({ 1,1,2,2}, armnn::DataType::Float32); + armnn::TensorInfo addOutputTensorInfo({ 1,1,2,2}, armnn::DataType::Float32); boost::multi_array<float, 4> addInput = MakeTensor<float,4>(addInputTensorInfo, {12, 16, @@ -7898,112 +7984,112 @@ LayerTestResult<float, 4> SpaceToBatchNdSimpleFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdSimpleTest<float>(workloadFactory, memoryManager); + return SpaceToBatchNdSimpleTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 4> SpaceToBatchNdMultiChannelsFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdMultiChannelsTest<float>(workloadFactory, memoryManager); + return SpaceToBatchNdMultiChannelsTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 4> SpaceToBatchNdMultiBlockFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdMultiBlockTest<float>(workloadFactory, memoryManager); + return SpaceToBatchNdMultiBlockTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 4> SpaceToBatchNdPaddingFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdPaddingTest<float>(workloadFactory, memoryManager); + return SpaceToBatchNdPaddingTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> SpaceToBatchNdSimpleUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdSimpleTest<uint8_t>(workloadFactory, memoryManager); + return SpaceToBatchNdSimpleTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> SpaceToBatchNdMultiChannelsUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdMultiChannelsTest<uint8_t>(workloadFactory, memoryManager); + return SpaceToBatchNdMultiChannelsTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> SpaceToBatchNdMultiBlockUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdMultiBlockTest<uint8_t>(workloadFactory, memoryManager); + return SpaceToBatchNdMultiBlockTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> SpaceToBatchNdPaddingUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdPaddingTest<uint8_t>(workloadFactory, memoryManager); + return SpaceToBatchNdPaddingTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<float, 4> SpaceToBatchNdSimpleNHWCFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdSimpleNHWCTest<float>(workloadFactory, memoryManager); + return SpaceToBatchNdSimpleNHWCTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 4> SpaceToBatchNdMultiChannelsNHWCFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdMultiChannelsNHWCTest<float>(workloadFactory, memoryManager); + return SpaceToBatchNdMultiChannelsNHWCTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 4> SpaceToBatchNdMultiBlockNHWCFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdMultiBlockNHWCTest<float>(workloadFactory, memoryManager); + return SpaceToBatchNdMultiBlockNHWCTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 4> SpaceToBatchNdPaddingNHWCFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdPaddingNHWCTest<float>(workloadFactory, memoryManager); + return SpaceToBatchNdPaddingNHWCTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> SpaceToBatchNdSimpleNHWCUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdSimpleNHWCTest<uint8_t>(workloadFactory, memoryManager); + return SpaceToBatchNdSimpleNHWCTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> SpaceToBatchNdMultiChannelsNHWCUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdMultiChannelsNHWCTest<uint8_t>(workloadFactory, memoryManager); + return SpaceToBatchNdMultiChannelsNHWCTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> SpaceToBatchNdMultiBlockNHWCUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdMultiBlockNHWCTest<uint8_t>(workloadFactory, memoryManager); + return SpaceToBatchNdMultiBlockNHWCTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> SpaceToBatchNdPaddingNHWCUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return SpaceToBatchNdPaddingNHWCTest<uint8_t>(workloadFactory, memoryManager); + return SpaceToBatchNdPaddingNHWCTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } namespace { @@ -8263,126 +8349,126 @@ LayerTestResult<float, 4> StridedSlice4DFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSlice4DTest<float>(workloadFactory, memoryManager); + return StridedSlice4DTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 4> StridedSlice4DReverseFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSlice4DReverseTest<float>(workloadFactory, memoryManager); + return StridedSlice4DReverseTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 4> StridedSliceSimpleStrideFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSliceSimpleStrideTest<float>(workloadFactory, memoryManager); + return StridedSliceSimpleStrideTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 4> StridedSliceSimpleRangeMaskFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSliceSimpleRangeMaskTest<float>(workloadFactory, memoryManager); + return StridedSliceSimpleRangeMaskTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 2> StridedSliceShrinkAxisMaskFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSliceShrinkAxisMaskTest<float>(workloadFactory, memoryManager); + return StridedSliceShrinkAxisMaskTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 3> StridedSlice3DFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSlice3DTest<float>(workloadFactory, memoryManager); + return StridedSlice3DTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 3> StridedSlice3DReverseFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSlice3DReverseTest<float>(workloadFactory, memoryManager); + return StridedSlice3DReverseTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 2> StridedSlice2DFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSlice2DTest<float>(workloadFactory, memoryManager); + return StridedSlice2DTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 2> StridedSlice2DReverseFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSlice2DReverseTest<float>(workloadFactory, memoryManager); + return StridedSlice2DReverseTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> StridedSlice4DUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSlice4DTest<uint8_t>(workloadFactory, memoryManager); + return StridedSlice4DTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> StridedSlice4DReverseUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSlice4DReverseTest<uint8_t>(workloadFactory, memoryManager); + return StridedSlice4DReverseTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> StridedSliceSimpleStrideUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSliceSimpleStrideTest<uint8_t>(workloadFactory, memoryManager); + return StridedSliceSimpleStrideTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> StridedSliceSimpleRangeMaskUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSliceSimpleRangeMaskTest<uint8_t>(workloadFactory, memoryManager); + return StridedSliceSimpleRangeMaskTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 2> StridedSliceShrinkAxisMaskUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSliceShrinkAxisMaskTest<uint8_t>(workloadFactory, memoryManager); + return StridedSliceShrinkAxisMaskTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 3> StridedSlice3DUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSlice3DTest<uint8_t>(workloadFactory, memoryManager); + return StridedSlice3DTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 3> StridedSlice3DReverseUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSlice3DReverseTest<uint8_t>(workloadFactory, memoryManager); + return StridedSlice3DReverseTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 2> StridedSlice2DUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSlice2DTest<uint8_t>(workloadFactory, memoryManager); + return StridedSlice2DTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 2> StridedSlice2DReverseUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return StridedSlice2DReverseTest<uint8_t>(workloadFactory, memoryManager); + return StridedSlice2DReverseTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> BatchToSpaceNdNhwcUintTest2( armnn::IWorkloadFactory& workloadFactory, @@ -8517,56 +8603,56 @@ LayerTestResult<float, 4> Debug4DFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Debug4DTest<float>(workloadFactory, memoryManager); + return Debug4DTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 3> Debug3DFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Debug3DTest<float>(workloadFactory, memoryManager); + return Debug3DTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 2> Debug2DFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Debug2DTest<float>(workloadFactory, memoryManager); + return Debug2DTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<float, 1> Debug1DFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Debug1DTest<float>(workloadFactory, memoryManager); + return Debug1DTest<armnn::DataType::Float32>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> Debug4DUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Debug4DTest<uint8_t>(workloadFactory, memoryManager); + return Debug4DTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 3> Debug3DUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Debug3DTest<uint8_t>(workloadFactory, memoryManager); + return Debug3DTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 2> Debug2DUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Debug2DTest<uint8_t>(workloadFactory, memoryManager); + return Debug2DTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 1> Debug1DUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) { - return Debug1DTest<uint8_t>(workloadFactory, memoryManager); + return Debug1DTest<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager); } LayerTestResult<uint8_t, 4> PreCompiledConvolution2dTest( |