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Diffstat (limited to 'src/armnn/backends/test/LayerTests.cpp')
| -rw-r--r-- | src/armnn/backends/test/LayerTests.cpp | 4750 |
1 files changed, 0 insertions, 4750 deletions
diff --git a/src/armnn/backends/test/LayerTests.cpp b/src/armnn/backends/test/LayerTests.cpp deleted file mode 100644 index 4dcc36fdb2..0000000000 --- a/src/armnn/backends/test/LayerTests.cpp +++ /dev/null @@ -1,4750 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include "LayerTests.hpp" - -#include "test/TensorHelpers.hpp" -#include "TensorCopyUtils.hpp" -#include "Permute.hpp" - -#include <boost/test/unit_test.hpp> -#include <boost/assert.hpp> - -#include "armnn/LayerSupport.hpp" - -#include "backends/CpuTensorHandle.hpp" -#include "backends/WorkloadFactory.hpp" - -#ifdef ARMCOMPUTECL_ENABLED -#include "backends/ClTensorHandle.hpp" -#include "backends/ArmComputeTensorUtils.hpp" -#endif - -#include <algorithm> -#include <boost/cast.hpp> - -#include "WorkloadTestUtils.hpp" -#include "Conv2dTestImpl.hpp" -#include "BatchNormTestImpl.hpp" -#include "ActivationTestImpl.hpp" -#include "Pooling2dTestImpl.hpp" -#include "ReshapeTestImpl.hpp" -#include "FullyConnectedTestImpl.hpp" -#include "SplitterTestImpl.hpp" -#include "SoftmaxTestImpl.hpp" -#include "NormTestImpl.hpp" -#include "PermuteTestImpl.hpp" -#include "LstmTestImpl.hpp" -#include "ConvertFp16ToFp32TestImpl.hpp" -#include "ConvertFp32ToFp16TestImpl.hpp" - -// 3-channel 16x8 image used as common input data for a number of Conv2d tests. -static std::vector<float> ConvInput3x8x16({ - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 -}); - -// 2-channel bias used by a number of Conv2d tests. -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> -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>()); - boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(qScale, qOffset, Bias2)); - return bias; - } - else - { - return boost::multi_array<T, 1>(); - } -} - -template<typename T> -LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset, - bool biasEnabled) -{ - // Use common single-batch 3-channel 16x8 image. - armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType<T>()); - 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>()); - boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { - 1, 1, 1, - 1, -1, 1, - 1, 1, 1, - 1, 1, 1, - 1, 1, 1, - - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - - - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - - 1, 1, 1, - 1, 1, 1, - 1, 1, 1, - 1, 1, 1, - 1, 1, 1, - - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - 0, 0, 0 - }))); - - // Expected output is 2 batch elements of a 1-channel 14x4 image. - armnn::TensorInfo outputDesc({1, 2, 4, 14}, armnn::GetDataType<T>()); - 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, - -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, - -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, - -23.5f, -23.5f, -23.5f, - -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, - -23.5f, -23.5f, -23.5f, - - 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 - }))); - - return SimpleConvolution2dTestImpl<T>(workloadFactory, - input, - kernel, - GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset), - expectedOutput, - qScale, - qOffset); -} - -template<typename T> -LayerTestResult<T, 4> SimpleConvolution2d3x3TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset, - bool biasEnabled) -{ - // 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>()); - 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>()); - boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { - 1, 1, 1, - 1, -1, 1, - 1, 1, 1, - - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - - 2, 2, 2, - 2, 2, 2, - 2, 2, 2, - - - 0, 0, 0, - 0, 0, 0, - 0, 0, 0, - - 1, 1, 1, - 1, 1, 1, - 1, 1, 1, - - 0, 0, 0, - 0, 0, 0, - 0, 0, 0 - }))); - - // Expected output is 1 batch of a 2-channel 14x6 image. - armnn::TensorInfo outputDesc({1, 2, 6, 14}, armnn::GetDataType<T>()); - 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, - -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, - -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, - -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, - -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, - -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, - - 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 - }))); - - return SimpleConvolution2dTestImpl<T>(workloadFactory, - input, - kernel, - GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset), - expectedOutput, - qScale, - qOffset); -} - -LayerTestResult<float, 4> SimpleConvolution2d3x5Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return SimpleConvolution2d3x5TestCommon<float>(workloadFactory, 0.f, 0, biasEnabled); -} - -LayerTestResult<uint8_t, 4> SimpleConvolution2d3x5Uint8Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return SimpleConvolution2d3x5TestCommon<uint8_t>(workloadFactory, 0.5f, 50, biasEnabled); -} - -LayerTestResult<float, 4> SimpleConvolution2d3x3Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return SimpleConvolution2d3x3TestCommon<float>(workloadFactory, 0.f, 0, biasEnabled); -} - -LayerTestResult<uint8_t, 4> SimpleConvolution2d3x3Uint8Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return SimpleConvolution2d3x3TestCommon<uint8_t>(workloadFactory, 0.5f, 50, biasEnabled); -} - -template<typename T> -LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon( - armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset) -{ - // Use a single-batch 1-channel 3x3 image as input. - armnn::TensorInfo inputDesc({1, 1, 3, 3}, armnn::GetDataType<T>()); - boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { - 11,21,31, - 12,22,32, - 13,23,33 - }))); - - // Use 1 batch of a 1-channel 2x2 kernel. - armnn::TensorInfo kernelDesc({1, 1, 2, 2}, armnn::GetDataType<T>()); - boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { - -11,-21, - -12,-22, - }))); - -// Expected output is 1 batch of a 1-channel 6x8 image. -// Manually calculated like this: -//[-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 ..] -//[-11*0 -21*0 -12*0 -22*11 ; -11*0 -21*0 -12*11 -22*21 ; -11*0 -21*0 -12*21 -22*31 ; -11*0 -21*0 -12*31 -22*0 ..] -//[-11*0 -21*11 -12*0 -22*12 ; -11*11 -21*21 -12*12 -22*22 ; -11*21 -21*31 -12*22 -22*32 ; -11*31 -21*0 -12*32 -22*0 ..] -//[-11*0 -21*12 -12*0 -22*13 ; -11*12 -21*22 -12*13 -22*23 ; -11*22 -21*32 -12*23 -22*33 ; -11*32 -21*0 -12*33 -22*0 ..] -//[-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>()); - boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { - 0, 0, 0, 0, 0, 0, - -242, -594, -934, -372, 0, 0, - -495, -1190, -1850, -725, 0, 0, - -538, -1256, -1916, -748, 0, 0, - -273, -626, -946, -363, 0, 0, - 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0 - }))); - - return SimpleConvolution2dTestImpl<T>(workloadFactory, - input, - kernel, - GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(false, qScale, qOffset), - expectedOutput, - qScale, - qOffset, - 1, // Padding left. - 2, // Padding top. - 3, // Padding right. - 4); // Padding bottom. -} - -template<typename T> -LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset) -{ - // Use a single-batch 1-channel 5x5 image as input. - armnn::TensorInfo inputDesc({ 1, 1, 5, 5 }, armnn::GetDataType<T>()); - boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { - 11,21,31,41,51, - 12,22,32,42,52, - 13,23,33,43,53, - 14,24,34,44,54, - 15,25,35,45,55, - }))); - - // Use 1 batch of a 1-channel 4x4 kernel. - armnn::TensorInfo kernelDesc({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); - boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { - -11,-21,-31,-41, - -12,-22,-32,-42, - -13,-23,-33,-43, - -14,-24,-34,-44, - }))); - - // Expected output is 1 batch of a 1-channel 5x5 image. - armnn::TensorInfo outputDesc({ 1, 1, 5, 5 }, armnn::GetDataType<T>()); - std::vector<T> myVec(outputDesc.GetNumElements(), 0); - boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { - -7140, -10580, -13940, -9300, -5230, - -9590, -14120, -18520, -12290, -6860, - -9980, -14560, -18960, -12560, -7000, - -7518, -10904, -14144, -9318, -5152, - -5032, -7256, -9376, -6142, -3368, - }))); - - return SimpleConvolution2dTestImpl<T>(workloadFactory, - input, - kernel, - GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(false, qScale, qOffset), - expectedOutput, - qScale, - qOffset, - 1, // Padding left. - 1, // Padding top. - 2, // Padding right. - 2); // Padding bottom. -} - -template<typename T> -LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset, - bool biasEnabled) -{ - // Use a single-batch 2-channel 5x5 image as input. - armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType<T>()); - auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>( - QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { - 0, 1, 2, 3, 4, - 5, 6, 7, 8, 9, - 10, 11, 12, 13, 14, - 15, 16, 17, 18, 19, - 20, 21, 22, 23, 24, - - 25, 26, 27, 28, 29, - 30, 31, 32, 33, 34, - 35, 36, 37, 38, 39, - 40, 41, 42, 43, 44, - 45, 46, 47, 48, 49 - }))); - - // Use a depth multiplier of 1 on a 2-channel 4x4 kernel. - armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, armnn::GetDataType<T>()); - auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>( - QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), { - 32, 31, 30, 29, - 28, 27, 26, 25, - 24, 23, 22, 21, - 20, 19, 18, 17, - - 16, 15, 14, 13, - 12, 11, 10, 9, - 8, 7, 6, 5, - 4, 3, 2, 1 - }))); - - // 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>()); - boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>( - QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), { - 1062, 1580, 1850, 1530, 1117, - 2140, 3108, 3500, 2842, 2042, - 3580, 5068, 5460, 4342, 3062, - 3618, 5072, 5390, 4248, 2971, - 3074, 4282, 4510, 3533, 2457, - 1550, 2284, 2362, 1955, 1428, - 2910, 4206, 4342, 3528, 2536, - 3390, 4886, 5022, 4068, 2916, - 3566, 5056, 5182, 4133, 2922, - 3100, 4352, 4452, 3517, 2465 - }))); - - return DepthwiseConvolution2dAsymmetricTestImpl<T>(workloadFactory, - input, - kernel, - GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset), - expectedOutput, - qScale, - qOffset, - 1, // Padding left. - 1, // Padding top. - 2, // Padding right. - 2, // Padding bottom. - 1, // strideX - 1); // strideY -} - -LayerTestResult<float, 4> -Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest(armnn::IWorkloadFactory& workloadFactory) -{ - return Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon<float>(workloadFactory, 0.0f, 0); -} - -LayerTestResult<float, 4> Convolution2dAsymmetricPaddingTest(armnn::IWorkloadFactory& workloadFactory) -{ - return SimpleConvolution2dAsymmetricPaddingTestCommon<float>(workloadFactory, 0.0f, 0); -} - -LayerTestResult<float, 4> DepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return DepthwiseConvolution2dTestImpl<float, float>(workloadFactory, 0.0f, 0, biasEnabled); -} - -LayerTestResult<float, 4> DepthwiseConvolution2dDepthMul1Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return DepthwiseConvolution2dDepthMul1TestImpl<float, float>(workloadFactory, 0.0f, 0, biasEnabled); -} - -LayerTestResult<float, 4> DepthwiseConvolution2dAsymmetricTest(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return DepthwiseConvolution2dAsymmetricTestCommon<float>(workloadFactory, 0.0f, 0, biasEnabled); -} - -LayerTestResult<uint8_t, 4> DepthwiseConvolution2dUint8Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return DepthwiseConvolution2dTestImpl<uint8_t, int32_t>(workloadFactory, 0.5f, 50, biasEnabled); -} - -LayerTestResult<uint8_t, 4> DepthwiseConvolution2dDepthMul1Uint8Test(armnn::IWorkloadFactory& workloadFactory, - bool biasEnabled) -{ - return DepthwiseConvolution2dDepthMul1TestImpl<uint8_t, int32_t>(workloadFactory, 0.5f, 50, biasEnabled); -} - -LayerTestResult<float, 4> Convolution1dTest(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) -{ - return Convolution1dTestImpl<float>(workloadFactory, 0.0f, 0, biasEnabled); -} - -LayerTestResult<uint8_t, 4> Convolution1dUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) -{ - return Convolution1dTestImpl<uint8_t>(workloadFactory, 0.1f, 128, biasEnabled); -} - -LayerTestResult<float,4> CompareConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory) -{ - return CompareConvolution2dTestImpl<float>(workloadFactory, refWorkloadFactory); -} - -template<typename T> -LayerTestResult<T,4> CompareDepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory) -{ - return CompareDepthwiseConvolution2dTestImpl<T>(workloadFactory, refWorkloadFactory); -} - -template LayerTestResult<float, 4> CompareDepthwiseConvolution2dTest<float>( - armnn::IWorkloadFactory&, armnn::IWorkloadFactory&); -template LayerTestResult<uint8_t, 4> CompareDepthwiseConvolution2dTest<uint8_t>( - armnn::IWorkloadFactory&, armnn::IWorkloadFactory&); - -LayerTestResult<float,4> SimpleNormalizationAcrossTest(armnn::IWorkloadFactory& workloadFactory) -{ - auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; - auto normChannel = armnn::NormalizationAlgorithmChannel::Across; - return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod); -} - -LayerTestResult<float,4> SimpleNormalizationWithinTest(armnn::IWorkloadFactory& workloadFactory) -{ - auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; - auto normChannel = armnn::NormalizationAlgorithmChannel::Within; - return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod); -} - -LayerTestResult<float,2> SimpleSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, float beta) -{ - return SimpleSoftmaxTestImpl<float>(workloadFactory, beta); -} - -LayerTestResult<uint8_t,2> SimpleSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, float beta) -{ - return SimpleSoftmaxTestImpl<uint8_t>(workloadFactory, beta); -} - -LayerTestResult<float,4> CompareNormalizationTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::NormalizationAlgorithmChannel normChannel, - armnn::NormalizationAlgorithmMethod normMethod) -{ - return CompareNormalizationTestImpl(workloadFactory, refWorkloadFactory, normChannel, normMethod); -} - -LayerTestResult<float,2> CompareSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - float beta) -{ - return CompareSoftmaxTestImpl<float>(workloadFactory, refWorkloadFactory, beta); -} - -LayerTestResult<uint8_t,2> CompareSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - float beta) -{ - return CompareSoftmaxTestImpl<uint8_t>(workloadFactory, refWorkloadFactory, beta); -} - -std::vector<LayerTestResult<float,3>> SplitterTest(armnn::IWorkloadFactory& workloadFactory) -{ - return SplitterTestCommon<float>(workloadFactory); -} - -std::vector<LayerTestResult<uint8_t,3>> SplitterUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return SplitterTestCommon<uint8_t>(workloadFactory, 1.0f, 0); -} - -LayerTestResult<float, 3> CopyViaSplitterTest(armnn::IWorkloadFactory& workloadFactory) -{ - return CopyViaSplitterTestImpl<float>(workloadFactory, 0.0f, 0); -} - -LayerTestResult<uint8_t, 3> CopyViaSplitterUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return CopyViaSplitterTestImpl<uint8_t>(workloadFactory, 1.0f, 0); -} - -LayerTestResult<float, 2> LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest( - armnn::IWorkloadFactory& workloadFactory) -{ - armnn::TensorInfo inputDesc({ 2, 2 }, armnn::GetDataType<float>()); - 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>()); - 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})); - return LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput); -} - -LayerTestResult<float, 2> LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest( - armnn::IWorkloadFactory& workloadFactory) -{ - armnn::TensorInfo inputDesc({ 2, 5 }, armnn::GetDataType<float>()); - 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>()); - 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, - -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f, - 0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f, - -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f, - 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, - 0.02168f})); - return LstmLayerFloat32NoCifgWithPeepholeWithProjectionTestImpl(workloadFactory, input, expectedOutput); -} - -LayerTestResult<float, 2> LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest(armnn::IWorkloadFactory& workloadFactory) -{ - armnn::TensorInfo inputDesc({2, 2}, armnn::GetDataType<float>()); - 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>()); - 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}})); - - return LstmNoCifgNoPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput); -} - -LayerTestResult<float,3> MergerTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int outputWidth = 3; - unsigned int outputHeight = 6; - unsigned int outputChannels = 3; - - unsigned int inputWidth1 = 3; - unsigned int inputHeight1 = 6; - unsigned int inputChannels1 = 2; - - unsigned int inputWidth2 = 3; - unsigned int inputHeight2 = 6; - unsigned int inputChannels2 = 1; - - // Define the tensor descriptors. - armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::Float32); - armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::Float32); - armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::Float32); - - LayerTestResult<float,3> ret(outputTensorInfo); - - ret.outputExpected = MakeTensor<float, 3>(outputTensorInfo, std::vector<float>( - { - 1.0f, 2.0f, 3.0f, - 4.0f, 5.0f, 6.0f, - 7.0f, 8.0f, 9.0f, - 10.0f, 11.0f, 12.0f, - 13.0f, 14.0f, 15.0f, - 16.0f, 17.0f, 18.0f, - - 19.0f, 20.0f, 21.0f, - 22.0f, 23.0f, 24.0f, - 25.0f, 26.0f, 27.0f, - 28.0f, 29.0f, 30.0f, - 31.0f, 32.0f, 33.0f, - 34.0f, 35.0f, 36.0f, - - 37.0f, 38.0f, 39.0f, - 40.0f, 41.0f, 42.0f, - 43.0f, 44.0f, 45.0f, - 46.0f, 47.0f, 48.0f, - 49.0f, 50.0f, 51.0f, - 52.0f, 53.0f, 54.0f, - }) - ); - - auto input1 = MakeTensor<float, 3>(inputTensorInfo1, std::vector<float>( - { - 1.0f, 2.0f, 3.0f, - 4.0f, 5.0f, 6.0f, - 7.0f, 8.0f, 9.0f, - 10.0f, 11.0f, 12.0f, - 13.0f, 14.0f, 15.0f, - 16.0f, 17.0f, 18.0f, - - 19.0f, 20.0f, 21.0f, - 22.0f, 23.0f, 24.0f, - 25.0f, 26.0f, 27.0f, - 28.0f, 29.0f, 30.0f, - 31.0f, 32.0f, 33.0f, - 34.0f, 35.0f, 36.0f, - }) - ); - - auto input2 = MakeTensor<float, 3>(inputTensorInfo2, std::vector<float>( - { - 37.0f, 38.0f, 39.0f, - 40.0f, 41.0f, 42.0f, - 43.0f, 44.0f, 45.0f, - 46.0f, 47.0f, 48.0f, - 49.0f, 50.0f, 51.0f, - 52.0f, 53.0f, 54.0f, - }) - ); - - std::vector<unsigned int> wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of input[0]. - armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1); - - std::vector<unsigned int> wOrigin2 = {2, 0, 0}; //Extent of the window is defined by size of input[1]. - armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2); - - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - bool subTensorsSupported = workloadFactory.SupportsSubTensors(); - - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = - subTensorsSupported ? - workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : - workloadFactory.CreateTensorHandle(inputTensorInfo1); - - std::unique_ptr<armnn::ITensorHandle> inputHandle2 = - subTensorsSupported ? - workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : - workloadFactory.CreateTensorHandle(inputTensorInfo2); - - armnn::MergerQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - data.m_ViewOrigins.push_back(window1); - data.m_ViewOrigins.push_back(window2); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMerger(data, info); - - inputHandle1->Allocate(); - inputHandle2->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); - - return ret; -} - -LayerTestResult<float,4> AdditionTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int batchSize = 2; - unsigned int channels = 2; - unsigned int height = 2; - unsigned int width = 3; - - armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; - armnn::TensorInfo outputTensorInfo; - - unsigned int shape[] = {batchSize, channels, height, width}; - - inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - - - auto input1 = MakeTensor<float, 4>(inputTensorInfo1, std::vector<float>( - { - 0.0f, 2.0f, 1.0f, - 0.2f, 1.0f, 2.0f, - - 1.0f, 2.0f, 1.0f, - 0.2f, 1.0f, 2.0f, - - 0.0f, 2.0f, 1.0f, - 4.2f, 1.0f, 2.0f, - - 0.0f, 0.0f, 1.0f, - 0.2f, 1.0f, 2.0f, - })); - - auto input2 = MakeTensor<float, 4>(inputTensorInfo2, std::vector<float>( - { - 1.0f, 2.0f, 1.0f, - 0.0f, 1.0f, 2.0f, - - 1.0f, 2.0f, -2.0f, - 0.2f, 1.0f, 2.0f, - - 0.0f, 2.0f, 1.0f, - 4.2f, 0.0f, -3.0f, - - 0.0f, 0.0f, 1.0f, - 0.7f, 1.0f, 5.0f, - })); - - LayerTestResult<float,4> ret(outputTensorInfo); - ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>( - { - 1.0f, 4.0f, 2.0f, - 0.2f, 2.0f, 4.0f, - - 2.0f, 4.0f, -1.0f, - 0.4f, 2.0f, 4.0f, - - 0.0f, 4.0f, 2.0f, - 8.4f, 1.0f, -1.0f, - - 0.0f, 0.0f, 2.0f, - 0.9f, 2.0f, 7.0f, - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::AdditionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info); - - inputHandle1->Allocate(); - inputHandle2->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} - -template <typename T> -LayerTestResult<T, 4> AdditionBroadcastTestImpl(armnn::IWorkloadFactory& workloadFactory, - 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>()); - - if (armnn::IsQuantizedType<T>()) - { - inputTensorInfo1.SetQuantizationScale(qScale); - inputTensorInfo1.SetQuantizationOffset(qOffset); - inputTensorInfo2.SetQuantizationScale(qScale); - inputTensorInfo2.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, - { - 0.0f, - 1.0f, - - 2.0f, - 3.0f, - - 4.0f, - 5.0f, - })); - - auto input2 = MakeTensor<T, 4>(inputTensorInfo2, QuantizedVector<T>(qScale, qOffset, - { - 0.5f, 1.5f, 2.5f, - 3.5f, 4.5f, 5.5f, - })); - - LayerTestResult<T,4> ret(outputTensorInfo); - ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, - { - 0.5f, 1.5f, 2.5f, - 4.5f, 5.5f, 6.5f, - - 2.5f, 3.5f, 4.5f, - 6.5f, 7.5f, 8.5f, - - 4.5f, 5.5f, 6.5f, - 8.5f, 9.5f, 10.5f, - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::AdditionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info); - - inputHandle1->Allocate(); - inputHandle2->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} - -template <typename T> -LayerTestResult<T, 4> AdditionBroadcast1ElementTestImpl(armnn::IWorkloadFactory& workloadFactory, - 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>()); - - if (armnn::IsQuantizedType<T>()) - { - inputTensorInfo1.SetQuantizationScale(qScale); - inputTensorInfo1.SetQuantizationOffset(qOffset); - inputTensorInfo2.SetQuantizationScale(qScale); - inputTensorInfo2.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, - { - 0.0f, 1.0f, 2.0f, - 3.0f, 4.0f, 5.0f, - 6.0f, 7.0f, 8.0f, - 9.0f, 10.0f, 11.0f, - 12.0f, 13.0f, 14.0f, - 15.0f, 16.0f, 17.0f, - })); - - auto input2 = MakeTensor<T, 4>(inputTensorInfo2, QuantizedVector<T>(qScale, qOffset, - { - 0.5f, - })); - - LayerTestResult<T,4> ret(outputTensorInfo); - ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, - { - 0.5f, 1.5f, 2.5f, - 3.5f, 4.5f, 5.5f, - 6.5f, 7.5f, 8.5f, - 9.5f, 10.5f, 11.5f, - 12.5f, 13.5f, 14.5f, - 15.5f, 16.5f, 17.5f, - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::AdditionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info); - - inputHandle1->Allocate(); - inputHandle2->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - return ret; -} - -LayerTestResult<float, 4> AdditionBroadcastTest(armnn::IWorkloadFactory& workloadFactory) -{ - return AdditionBroadcastTestImpl<float>(workloadFactory, 0.0f, 0); -} - -LayerTestResult<uint8_t, 4> AdditionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return AdditionBroadcastTestImpl<uint8_t>(workloadFactory, 2.f, 0); -} - -LayerTestResult<float, 4> AdditionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) -{ - return AdditionBroadcast1ElementTestImpl<float>(workloadFactory, 0.0f, 0); -} - -LayerTestResult<uint8_t, 4> AdditionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return AdditionBroadcast1ElementTestImpl<uint8_t>(workloadFactory, 0.1333333f, 128); -} - -LayerTestResult<float,4> CompareAdditionTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory) -{ - unsigned int batchSize = 4; - unsigned int channels = 1; - unsigned int height = 2; - unsigned int width = 3; - - armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; - armnn::TensorInfo outputTensorInfo; - - unsigned int shape[] = {batchSize, channels, height, width}; - - inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - - auto input1 = MakeRandomTensor<float, 4>(inputTensorInfo1, 1232); - auto input2 = MakeRandomTensor<float, 4>(inputTensorInfo2, 456); - - LayerTestResult<float,4> ret(outputTensorInfo); - - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - std::unique_ptr<armnn::ITensorHandle> inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> inputHandle2Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo2); - std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::AdditionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - armnn::AdditionQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo1, inputHandle1Ref.get()); - SetWorkloadInput(refData, refInfo, 1, inputTensorInfo2, inputHandle2Ref.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info); - std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateAddition(refData, refInfo); - - inputHandle1->Allocate(); - inputHandle2->Allocate(); - outputHandle->Allocate(); - inputHandle1Ref->Allocate(); - inputHandle2Ref->Allocate(); - outputHandleRef->Allocate(); - - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle2Ref.get(), &input2[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - refWorkloadFactory.Finalize(); - workloadRef->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); - - return ret; -} - -namespace { -template <typename T> -LayerTestResult<T, 4> DivisionTestHelper(armnn::IWorkloadFactory& workloadFactory, - const unsigned int shape0[4], - const std::vector<T>& values0, - float scale0, - int32_t offset0, - const unsigned int shape1[4], - const std::vector<T> & values1, - float scale1, - int32_t offset1, - const unsigned int outShape[4], - const std::vector<T> & outValues, - float outScale, - int32_t outOffset) -{ - auto dataType = (std::is_same<T, uint8_t>::value ? - armnn::DataType::QuantisedAsymm8 : - armnn::DataType::Float32); - - armnn::TensorInfo inputTensorInfo0(4, shape0, dataType); - armnn::TensorInfo inputTensorInfo1(4, shape1, dataType); - armnn::TensorInfo outputTensorInfo(4, outShape, dataType); - - inputTensorInfo0.SetQuantizationScale(scale0); - inputTensorInfo0.SetQuantizationOffset(offset0); - - inputTensorInfo1.SetQuantizationScale(scale1); - inputTensorInfo1.SetQuantizationOffset(offset1); - - outputTensorInfo.SetQuantizationScale(outScale); - outputTensorInfo.SetQuantizationOffset(outOffset); - - auto input0 = MakeTensor<T, 4>(inputTensorInfo0, values0); - auto input1 = MakeTensor<T, 4>(inputTensorInfo1, values1); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outValues); - - std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::DivisionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDivision(data, info); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - return result; -} -} // anonymous namespace - -LayerTestResult<float,4> DivisionByZeroTest(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - unsigned int shape[] = { batchSize, channelCount, height, width }; - - std::vector<float> input0({ - 1.f, 1.f, 1.f, 1.f, 0.f, 0.f, 0.f, 0.f, - -1.f, -1.f, -1.f, -1.f, 5.f, 5.f, 5.f, 5.f }); - - std::vector<float> input1({ - 0.f, 0.f, -0.f, -0.f, 0.f, 0.f, -0.f, -0.f, - 0.f, 0.f, -0.f, -0.f, 5.f, 5.f, 5.f, 5.f }); - - std::vector<float> output({ - INFINITY, INFINITY, -INFINITY, -INFINITY, NAN, NAN, -NAN, -NAN, - -INFINITY, -INFINITY, INFINITY, INFINITY, 1, 1, 1, 1 }); - - return DivisionTestHelper<float>(workloadFactory, - shape, input0, 1.0f, 0, - shape, input1, 1.0f, 0, - shape, output, 1.0f, 0); -} - -LayerTestResult<float,4> DivisionTest(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - unsigned int shape[] = { batchSize, channelCount, height, width }; - - std::vector<float> input0({ - 2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - std::vector<float> input1({ - 1, 1, 1, 1, 2, 2, 2, 2, - 4, 4, 4, 4, 4, 4, 4, 4 }); - - std::vector<float> output({ - 2, 2, 2, 2, 1.5, 1.5, 1.5, 1.5, - 1, 1, 1, 1, 1.25, 1.25, 1.25, 1.25 }); - - - return DivisionTestHelper<float>(workloadFactory, - shape, input0, 1.0f, 0, - shape, input1, 1.0f, 0, - shape, output, 1.0f, 0); -} - -LayerTestResult<float, 4> DivisionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int shape0[] = { 1, 2, 2, 2 }; - std::vector<float> input0({ 2, 4, 6, 8, 10, 12, 14, 16}); - - unsigned int shape1[] = { 1, 1, 1, 1 }; - std::vector<float> input1({ 2 }); - - std::vector<float> output({ 1, 2, 3, 4, 5, 6, 7, 8}); - - - return DivisionTestHelper<float>(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<float, 4> DivisionBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int shape0[] = { 1, 3, 3, 2 }; - std::vector<float> input0({ - 1, 4, 3, 8, 5, 12, - 7, 16, 9, 20, 11, 24, - 13, 28, 15, 32, 17, 36}); - - unsigned int shape1[] = { 1, 1, 1, 2 }; - std::vector<float> input1({ 1, 2 }); - - std::vector<float> output({ - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12, - 13, 14, 15, 16, 17, 18}); - - return DivisionTestHelper<float>(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - - -LayerTestResult<uint8_t,4> DivisionUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - unsigned int shape[] = { batchSize, channelCount, height, width }; - - std::vector<uint8_t> input0({2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - std::vector<uint8_t> input1({1, 1, 1, 1, 2, 2, 2, 2, - 4, 4, 4, 4, 4, 4, 4, 4 }); - - std::vector<uint8_t> output({8, 8, 8, 8, 6, 6, 6, 6, - 4, 4, 4, 4, 5, 5, 5, 5}); - - - return DivisionTestHelper<uint8_t>(workloadFactory, - shape, input0, 1.0f, 0, - shape, input1, 1.0f, 0, - shape, output, 0.25f, 0); -} - -LayerTestResult<uint8_t, 4> DivisionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int shape0[] = { 1, 2, 2, 2 }; - std::vector<uint8_t> input0({ 2, 4, 6, 8, 10, 12, 14, 16}); - - unsigned int shape1[] = { 1, 1, 1, 1 }; - std::vector<uint8_t> input1({ 2 }); - - std::vector<uint8_t> output({ 1, 2, 3, 4, 5, 6, 7, 8}); - - return DivisionTestHelper<uint8_t>(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<uint8_t, 4> DivisionBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int shape0[] = { 1, 3, 3, 2 }; - std::vector<uint8_t> input0({1, 4, 3, 8, 5, 12, - 7, 16, 9, 20, 11, 24, - 13, 28, 15, 32, 17, 36}); - - unsigned int shape1[] = { 1, 1, 1, 2 }; - std::vector<uint8_t> input1({ 1, 2 }); - - std::vector<uint8_t> output({1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12, - 13, 14, 15, 16, 17, 18}); - - return DivisionTestHelper<uint8_t>(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -namespace { -LayerTestResult<float,4> MultiplicationTestHelper(armnn::IWorkloadFactory& workloadFactory, - const unsigned int shape0[4], - const std::vector<float> & values0, - const unsigned int shape1[4], - const std::vector<float> & values1, - const unsigned int outShape[4], - const std::vector<float> & outValues) -{ - const size_t dimensionCount = 4; - armnn::TensorInfo inputTensorInfo0{dimensionCount, shape0, armnn::DataType::Float32}; - armnn::TensorInfo inputTensorInfo1{dimensionCount, shape1, armnn::DataType::Float32}; - armnn::TensorInfo outputTensorInfo{dimensionCount, outShape, armnn::DataType::Float32}; - - auto input0 = MakeTensor<float, 4>(inputTensorInfo0, values0); - auto input1 = MakeTensor<float, 4>(inputTensorInfo1, values1); - - LayerTestResult<float,4> ret(outputTensorInfo); - - std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::MultiplicationQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outValues); - return ret; -} -} // anonymous namespace - - -LayerTestResult<float,4> MultiplicationTest(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - unsigned int shape[] = { batchSize, channelCount, height, width }; - - std::vector<float> input0({ - 1, 1, 1, 1, 2, 2, 2, 2, - 3, 3, 3, 3, 4, 4, 4, 4 }); - - std::vector<float> input1({ - 2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - std::vector<float> output({ - 2, 2, 2, 2, 6, 6, 6, 6, - 12, 12, 12, 12, 20, 20, 20, 20 }); - - return MultiplicationTestHelper(workloadFactory, - shape, - input0, - shape, - input1, - shape, - output); -} - -LayerTestResult<float, 4> MultiplicationBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int shape0[] = { 1, 2, 2, 2 }; - std::vector<float> input0({ 1, 2, 3, 4, 5, 6, 7, 8}); - - unsigned int shape1[] = { 1, 1, 1, 1 }; - std::vector<float> input1({ 2 }); - - std::vector<float> output({ 2, 4, 6, 8, 10, 12, 14, 16}); - - return MultiplicationTestHelper(workloadFactory, - shape0, - input0, - shape1, - input1, - shape0, - output); -} - -LayerTestResult<float, 4> MultiplicationBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int shape0[] = { 1, 3, 3, 2 }; - std::vector<float> input0({ - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12, - 13, 14, 15, 16, 17, 18}); - - unsigned int shape1[] = { 1, 1, 1, 2 }; - std::vector<float> input1({ 1, 2 }); - - std::vector<float> output({ - 1, 4, 3, 8, 5, 12, - 7, 16, 9, 20, 11, 24, - 13, 28, 15, 32, 17, 36}); - - return MultiplicationTestHelper(workloadFactory, - shape0, - input0, - shape1, - input1, - shape0, - output); -} - -LayerTestResult<float,4> CompareMultiplicationTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory) -{ - const unsigned int width = 16; - const unsigned int height = 32; - const unsigned int channelCount = 2; - const unsigned int batchSize = 5; - - armnn::TensorInfo inputTensorInfo0; - armnn::TensorInfo inputTensorInfo1; - armnn::TensorInfo outputTensorInfo; - - constexpr unsigned int shape[] = { batchSize, channelCount, height, width }; - - inputTensorInfo0 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - - LayerTestResult<float,4> comparisonResult(outputTensorInfo); - - auto input0 = MakeRandomTensor<float, 4>(inputTensorInfo0, 803506992); - auto input1 = MakeRandomTensor<float, 4>(inputTensorInfo1, 54902257); - - std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - std::unique_ptr<armnn::ITensorHandle> inputHandle0Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr<armnn::ITensorHandle> inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::MultiplicationQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - armnn::MultiplicationQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo0, inputHandle0Ref.get()); - SetWorkloadInput(refData, refInfo, 1, inputTensorInfo1, inputHandle1Ref.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info); - std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateMultiplication(refData, refInfo); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - inputHandle0Ref->Allocate(); - inputHandle1Ref->Allocate(); - outputHandleRef->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle0Ref.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - refWorkloadFactory.Finalize(); - workloadRef->Execute(); - - CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get()); - CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get()); - - return comparisonResult; -} - -LayerTestResult<float,4> CompareBatchNormTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory) -{ - const unsigned int width = 2; - const unsigned int height = 3; - const unsigned int channels = 5; - const unsigned int batchSize = 3; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - armnn::TensorInfo tensorInfo; - - constexpr unsigned int shape[] = {batchSize, channels, height, width}; - constexpr unsigned int tensorShape[] = {channels}; - - inputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - tensorInfo = armnn::TensorInfo(1, tensorShape, armnn::DataType::Float32); - - auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 21312); - - auto mean = MakeRandomTensor<float, 1>(tensorInfo, 123); - auto variance = MakeRandomTensor<float, 1>(tensorInfo, 234, 0.0f); - auto beta = MakeRandomTensor<float, 1>(tensorInfo, 123); - auto gamma = MakeRandomTensor<float, 1>(tensorInfo, 345); - - LayerTestResult<float,4> ret(outputTensorInfo); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::BatchNormalizationQueueDescriptor data; - armnn::WorkloadInfo info; - armnn::ScopedCpuTensorHandle meanTensor(tensorInfo); - armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo); - armnn::ScopedCpuTensorHandle betaTensor(tensorInfo); - armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo); - - AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]); - AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]); - AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]); - AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]); - - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - data.m_Mean = &meanTensor; - data.m_Variance = &varianceTensor; - data.m_Beta = &betaTensor; - data.m_Gamma = &gammaTensor; - data.m_Parameters.m_Eps = 0.01f; - - armnn::BatchNormalizationQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateBatchNormalization(data, info); - std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateBatchNormalization(refData, refInfo); - - inputHandle->Allocate(); - outputHandle->Allocate(); - inputHandleRef->Allocate(); - outputHandleRef->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - refWorkloadFactory.Finalize(); - workloadRef->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); - - return ret; -} - -template<typename T> -void PermuteTensorData( - armnn::IWorkloadFactory& workloadFactory, - const armnn::PermutationVector& mappings, - armnn::TensorInfo & inputTensorInfo, - const T * inputData, - std::vector<T>& outputData) -{ - BOOST_ASSERT_MSG(inputData != nullptr, "inputData must not be null"); - if (inputData == nullptr) - { - // Nullptr is an error in the test. By returning without doing the concatenation - // I expect the caller to fail the test. It still makes sense to report this as - // an assert for Debug builds. - return; - } - - armnn::TensorInfo outputTensorInfo = armnnUtils::Permuted(inputTensorInfo, mappings); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::PermuteQueueDescriptor queueDescriptor; - queueDescriptor.m_Parameters = armnn::PermuteDescriptor{mappings}; - armnn::WorkloadInfo workloadInfo; - AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePermute(queueDescriptor, workloadInfo); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), inputData); - - workload->Execute(); - - outputData.resize(outputTensorInfo.GetNumElements()); - CopyDataFromITensorHandle(&outputData[0], outputHandle.get()); - inputTensorInfo = outputTensorInfo; -} - -armnn::OriginsDescriptor CreateMergerDescriptorForConcatenation( - const std::vector<armnn::TensorInfo> & inputTensorInfos, - unsigned int concatDim) -{ - std::vector<armnn::TensorShape> shapes; - shapes.reserve(inputTensorInfos.size()); - for (const armnn::TensorInfo& it: inputTensorInfos) - { - shapes.push_back(it.GetShape()); - } - - return armnn::CreateMergerDescriptorForConcatenation(shapes.begin(), - shapes.end(), - concatDim); -} - -// -// Concatenation is only supported for N and C dimensions for NCHW. In case of -// <4 dimensions we need to make sure that the concat dimensions are at least -// the 3rd slowest iterating one. -// - -bool NeedPermuteForConcat( - const std::vector<armnn::TensorInfo> & inputTensorInfos, - unsigned int concatDim) -{ - // See note above. Additionally we expect the input shapes to have the - // same number of dimensions. - unsigned int nDimensions = 0; - - // Determine the number of dimensions as well as sanity check them - // agains test implementation issues. - for (auto && tensorInfo : inputTensorInfos) - { - if (!nDimensions) - { - nDimensions = tensorInfo.GetShape().GetNumDimensions(); - } - else - { - BOOST_ASSERT_MSG(nDimensions == tensorInfo.GetShape().GetNumDimensions(), - "Input shapes must have the same number of dimensions"); - } - } - - return (nDimensions-concatDim) < 3; -} - -armnn::TensorShape ExpandTensorShapeTo3dForPermute(const armnn::TensorShape & inputShape) -{ - unsigned int numDims = inputShape.GetNumDimensions(); - if (numDims >= 3) - { - // Nothing to do if the inputShape has at least 3 dimensions. - return inputShape; - } - - std::vector<unsigned int> newDims(size_t(3), 1u); - unsigned int expandedBy = 3 - numDims; - for (unsigned int i=0; i<numDims; ++i) - { - newDims[expandedBy+i] = inputShape[i]; - } - return armnn::TensorShape(3u, &newDims[0]); -} - -void Generate3dPermuteVectorForConcat( - unsigned int numDimensions, - unsigned int & concatDim, - std::pair<armnn::PermutationVector, armnn::PermutationVector> & permutations) -{ - BOOST_ASSERT_MSG(numDimensions <= 3, - "Only dimensions 1,2 and 3 are supported by this helper"); - - unsigned int expandedBy = 3 - numDimensions; - unsigned int expandedConcatAxis = concatDim + expandedBy; - - if (expandedConcatAxis == 2) - { - concatDim = 0; - armnn::PermutationVector forwardPermutation({1, 2, 0}); - armnn::PermutationVector reversePermutation({2, 0, 1}); - permutations = std::make_pair(forwardPermutation, reversePermutation); - } - else if (expandedConcatAxis == 1) - { - concatDim = 0; - armnn::PermutationVector forwardPermutation({2, 0, 1}); - armnn::PermutationVector reversePermutation({1, 2, 0}); - permutations = std::make_pair(forwardPermutation, reversePermutation); - } - else - { - BOOST_ASSERT(expandedConcatAxis == 0); - concatDim = 0; - } -} - -// -// Permute the input tensors so we can do a supported concatenation. -// Also treat lower than 3d tensors as 3d by adding dummy 1 dimensions -// at the front. Finally this function tells what the output shape -// of the permuted concatenated tensor is going to be. -// -template <typename T> -void PermuteInputsForConcat( - armnn::IWorkloadFactory& workloadFactory, - std::vector<armnn::TensorInfo> & inputTensorInfos, - std::vector<T *> & inputData, - std::vector<std::vector<T>> & inputDataStorage, - armnn::PermutationVector & permuteVector, - unsigned int & concatDim, - armnn::TensorInfo & outputTensorInfo) -{ - BOOST_ASSERT_MSG(inputTensorInfos.size() > 1, - "Expecting more than one tensor to be concatenated here"); - - unsigned int numDims = 0; - unsigned int nthInput = 0; - const armnn::PermutationVector identity({0, 1, 2}); - - std::pair<armnn::PermutationVector, armnn::PermutationVector> permutations = - std::make_pair(identity, identity); - - inputDataStorage.resize(inputData.size()); - - for (auto && tensorInfo : inputTensorInfos) - { - if (numDims == 0) - { - numDims = tensorInfo.GetShape().GetNumDimensions(); - Generate3dPermuteVectorForConcat(numDims, concatDim, permutations); - // Store the reverese permutation. - permuteVector = permutations.second; - BOOST_ASSERT_MSG(!permuteVector.IsEqual(identity), - "Test logic error, we don't need permutation, so we shouldn't arrive here"); - } - else - { - BOOST_ASSERT_MSG(numDims == tensorInfo.GetShape().GetNumDimensions(), - "All inputs must have the same number of dimensions"); - } - - armnn::TensorInfo newTensorInfo = tensorInfo; - newTensorInfo.SetShape(ExpandTensorShapeTo3dForPermute(tensorInfo.GetShape())); - - PermuteTensorData<T>(workloadFactory, - permutations.first, - newTensorInfo, - inputData[nthInput], - inputDataStorage[nthInput]); - - inputData[nthInput] = inputDataStorage[nthInput].data(); - inputTensorInfos[nthInput] = newTensorInfo; - - ++nthInput; - } - - outputTensorInfo.SetShape( - armnnUtils::Permuted( - ExpandTensorShapeTo3dForPermute(outputTensorInfo.GetShape()), - permutations.first)); -} - - -// -// This is the pair of PermuteInputsForConcat(...) which permutes back -// the output of the concatenation so we can check it against an expected -// output. -// -template <typename T> -void PermuteOutputForConcat( - armnn::IWorkloadFactory& workloadFactory, - const armnn::TensorInfo & tensorInfo, - const armnn::PermutationVector & permuteVector, - std::unique_ptr<armnn::ITensorHandle> && inputDataHandle, - T * data) -{ - BOOST_ASSERT_MSG(data != nullptr, "data must not be null"); - if (data == nullptr) - { - // Nullptr is an error in the test. By returning without doing the permutation - // I expect the caller to fail the test. It still makes sense to report this as - // an assert for Debug builds. - return; - } - - armnn::TensorInfo resultTensorInfo = tensorInfo; - std::vector<T> inputData(tensorInfo.GetNumElements()); - std::vector<T> outputData; - - CopyDataFromITensorHandle(&inputData[0], inputDataHandle.get()); - - PermuteTensorData<T>(workloadFactory, - permuteVector, - resultTensorInfo, - &inputData[0], - outputData); - - ::memcpy(data, &outputData[0], sizeof(T)*outputData.size()); -} - -template <typename T> -void Concatenate(armnn::IWorkloadFactory& workloadFactory, - std::initializer_list<const armnn::TensorInfo> inputTensorInfosOrig, - std::initializer_list<T *> inputsOrig, - const armnn::TensorInfo& outputTensorInfoOrig, - T * output, - unsigned int concatDim) -{ - BOOST_ASSERT_MSG(output != nullptr, "output must not be null"); - if (output == nullptr) - { - // Nullptr is an error in the test. By returning without doing the permutation - // I expect the caller to fail the test. It still makes sense to report this as - // an assert for Debug builds. - return; - } - - armnn::MergerQueueDescriptor queueDescriptor; - - // Saves a copy of the parameters which we might need to change. - std::vector<armnn::TensorInfo> inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end()); - std::vector<T *> inputs = inputsOrig; - armnn::TensorInfo outputTensorInfo = outputTensorInfoOrig; - - armnn::PermutationVector permuteVector{0, 1, 2}; - - // Holds and automatically releases memory for the reshaped input data. - std::vector<std::vector<T>> tmpInputDataStorage; - - const size_t inputCount = inputTensorInfos.size(); - - bool needPermuteForConcat = NeedPermuteForConcat(inputTensorInfos, concatDim); - - if (needPermuteForConcat) - { - // - // We need to permute the inputs, because concatenation along - // the requested axis is not supported. - // - PermuteInputsForConcat<T>(workloadFactory, - inputTensorInfos, - inputs, - tmpInputDataStorage, - permuteVector, - concatDim, - outputTensorInfo); - } - - armnn::OriginsDescriptor viewsDescriptor = CreateMergerDescriptorForConcatenation(inputTensorInfos, concatDim); - - queueDescriptor.m_ViewOrigins.reserve(viewsDescriptor.GetNumViews()); - for (unsigned int i = 0; i < viewsDescriptor.GetNumViews(); ++i) - { - queueDescriptor.m_ViewOrigins.emplace_back(std::vector<unsigned int>(viewsDescriptor.GetViewOrigin(i), - viewsDescriptor.GetViewOrigin(i) + viewsDescriptor.GetNumDimensions())); - } - - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - std::vector<std::unique_ptr<armnn::ITensorHandle>> inputHandles; - inputHandles.reserve(inputCount); - - const bool subTensorsSupported = workloadFactory.SupportsSubTensors(); - for (unsigned int i = 0; i < inputCount; ++i) - { - const armnn::TensorInfo& inputTensorInfo = inputTensorInfos[i]; - - std::unique_ptr<armnn::ITensorHandle> inputHandle = subTensorsSupported ? - workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo.GetShape(), - queueDescriptor.m_ViewOrigins[i].m_Origin.data()) - : workloadFactory.CreateTensorHandle(inputTensorInfo); - - inputHandles.emplace_back(std::move(inputHandle)); - } - - armnn::WorkloadInfo workloadInfo; - - for (unsigned int i = 0; i < inputCount; ++i) - { - AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfos[i], inputHandles[i].get()); - } - - AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMerger(queueDescriptor, workloadInfo); - - for (auto& inputHandle : inputHandles) - { - inputHandle->Allocate(); - } - - outputHandle->Allocate(); - - unsigned int nextInputId = 0; - for (auto& inputHandle : inputHandles) - { - CopyDataToITensorHandle(inputHandle.get(), inputs[nextInputId]); - ++nextInputId; - } - - workloadFactory.Finalize(); - workload->Execute(); - - if (needPermuteForConcat) - { - PermuteOutputForConcat<T>(workloadFactory, - outputTensorInfo, - permuteVector, - std::move(outputHandle), - output); - } - else - { - CopyDataFromITensorHandle(output, outputHandle.get()); - } -} - -template <typename T> -LayerTestResult<T, 1> Concatenation1dTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) -{ - armnn::TensorInfo inputTensorInfo({ 3 }, armnn::GetDataType<T>()); - - 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>()); - - LayerTestResult<T, 1> result(outputTensorInfo); - - std::vector<T> output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate<T>(workloadFactory, - { inputTensorInfo, inputTensorInfo, inputTensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - 0); - - result.output = MakeTensor<T, 1>(outputTensorInfo, output); - result.outputExpected = MakeTensor<T, 1>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f - })); - - return result; -} - -LayerTestResult<float, 1> Concatenation1dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation1dTestImpl<float>(workloadFactory, 0.0f, 0); -} - -template <typename T> -LayerTestResult<T, 2> Concatenation2dTestImpl(armnn::IWorkloadFactory& workloadFactory, - const armnn::TensorInfo& outputTensorInfo, - unsigned int dimension, - const float qScale, - const int32_t qOffset) -{ - armnn::TensorInfo inputTensorInfo({ 2, 3 }, armnn::GetDataType<T>()); - - auto input0 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, - - // Batch 1 - 10.0f, 11.0f, 12.0f, - })); - - auto input1 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 4.0f, 5.0f, 6.0f, - - // Batch 1 - 13.0f, 14.0f, 15.0f, - })); - - auto input2 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 7.0f, 8.0f, 9.0f, - - // Batch 1 - 16.0f, 17.0f, 18.0f, - })); - - LayerTestResult<T, 2> result(outputTensorInfo); - - std::vector<T> output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate<T>(workloadFactory, - { inputTensorInfo, inputTensorInfo, inputTensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - dimension); - - result.output = MakeTensor<T, 2>(outputTensorInfo, output); - return result; -} - -template <typename T> -LayerTestResult<T, 2> Concatenation2dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, int32_t qOffset) -{ - armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType<T>()); - - LayerTestResult<T, 2> result = Concatenation2dTestImpl<T>(workloadFactory, outputTensorInfo, 0, qScale, qOffset); - result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, - - // Batch 1 - 10.0f, 11.0f, 12.0f, - - // Batch 2 - 4.0f, 5.0f, 6.0f, - - // Batch 3 - 13.0f, 14.0f, 15.0f, - - // Batch 4 - 7.0f, 8.0f, 9.0f, - - // Batch 5 - 16.0f, 17.0f, 18.0f, - })); - - return result; -} - -LayerTestResult<float, 2> Concatenation2dDim0Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim0TestImpl<float>(workloadFactory, 0.0f, 0); -} - -template <typename T> -LayerTestResult<T, 2> Concatenation2dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, int32_t qOffset) -{ - armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType<T>()); - - LayerTestResult<T, 2> result = Concatenation2dTestImpl<T>(workloadFactory, 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, - - // Batch 1 - 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f - })); - - return result; -} - -LayerTestResult<float, 2> Concatenation2dDim1Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim1TestImpl<float>(workloadFactory, 0.0f, 0); -} - -template <typename T> -LayerTestResult<T, 2> Concatenation2dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, - int32_t qOffset) -{ - armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType<T>()); - auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, - - // Batch 1 - 10.0f, 11.0f, 12.0f, - })); - - armnn::TensorInfo input1TensorInfo({ 3, 3 }, armnn::GetDataType<T>()); - auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 4.0f, 5.0f, 6.0f, - - // Batch 1 - 13.0f, 14.0f, 15.0f, - - // Batch 0 - 7.0f, 8.0f, 9.0f, - })); - - armnn::TensorInfo input2TensorInfo({ 1, 3 }, armnn::GetDataType<T>()); - 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>()); - LayerTestResult<T, 2> result(outputTensorInfo); - - std::vector<T> output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate<T>(workloadFactory, - { input0TensorInfo, input1TensorInfo, input2TensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - 0); - - result.output = MakeTensor<T, 2>(outputTensorInfo, output); - result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, - - // Batch 1 - 10.0f, 11.0f, 12.0f, - - // Batch 2 - 4.0f, 5.0f, 6.0f, - - // Batch 3 - 13.0f, 14.0f, 15.0f, - - // Batch 4 - 7.0f, 8.0f, 9.0f, - - // Batch 5 - 16.0f, 17.0f, 18.0f, - })); - - return result; -} - -LayerTestResult<float, 2> Concatenation2dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim0DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0); -} - -template <typename T> -LayerTestResult<T, 2> Concatenation2dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, - int32_t qOffset) -{ - armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType<T>()); - auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 1.0f, 2.0f, 3.0f, - - // Batch 1 - 10.0f, 11.0f, 12.0f, - })); - - armnn::TensorInfo input1TensorInfo({ 2, 5 }, armnn::GetDataType<T>()); - auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, - - // Batch 1 - 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, - })); - - armnn::TensorInfo input2TensorInfo({ 2, 1 }, armnn::GetDataType<T>()); - auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0 - 9.0f, - - // Batch 1 - 18.0f - })); - - armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType<T>()); - LayerTestResult<T, 2> result(outputTensorInfo); - - std::vector<T> output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate<T>(workloadFactory, - { input0TensorInfo, input1TensorInfo, input2TensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - 1); - - result.output = MakeTensor<T, 2>(outputTensorInfo, output); - 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, - - // Batch 1 - 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, - })); - - return result; -} - -LayerTestResult<float, 2> Concatenation2dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim1DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0); -} - -template <typename T> -LayerTestResult<T, 3> Concatenation3dTestImpl(armnn::IWorkloadFactory& workloadFactory, - const armnn::TensorInfo& outputTensorInfo, - unsigned int dimension, - float qScale, - int32_t qOffset) -{ - armnn::TensorInfo inputTensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>()); - - auto input0 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f - })); - - auto input1 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 7.0f, 8.0f, - - // Batch 0, Channel 1 - 9.0f, 10.0f, - - // Batch 0, Channel 2 - 11.0f, 12.0f, - - // Batch 1, Channel 0 - 25.0f, 26.0f, - - // Batch 1, Channel 1 - 27.0f, 28.0f, - - // Batch 1, Channel 2 - 29.0f, 30.0f - })); - - auto input2 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 13.0f, 14.0f, - - // Batch 0, Channel 1 - 15.0f, 16.0f, - - // Batch 0, Channel 2 - 17.0f, 18.0f, - - // Batch 1, Channel 0 - 31.0f, 32.0f, - - // Batch 1, Channel 1 - 33.0f, 34.0f, - - // Batch 1, Channel 2 - 35.0f, 36.0f - })); - - LayerTestResult<T, 3> result(outputTensorInfo); - - std::vector<T> output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate<T>(workloadFactory, - { inputTensorInfo, inputTensorInfo, inputTensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - dimension); - - result.output = MakeTensor<T, 3>(outputTensorInfo, output); - return result; -} - -template <typename T> -LayerTestResult<T, 3> Concatenation3dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, - int32_t qOffset) -{ - armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType<T>()); - - LayerTestResult<T, 3> result = Concatenation3dTestImpl<T>(workloadFactory, outputTensorInfo, 0, - qScale, qOffset); - result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f, - - // Batch 2, Channel 0 - 7.0f, 8.0f, - - // Batch 2, Channel 1 - 9.0f, 10.0f, - - // Batch 2, Channel 2 - 11.0f, 12.0f, - - // Batch 3, Channel 0 - 25.0f, 26.0f, - - // Batch 3, Channel 1 - 27.0f, 28.0f, - - // Batch 3, Channel 2 - 29.0f, 30.0f, - - // Batch 4, Channel 0 - 13.0f, 14.0f, - - // Batch 4, Channel 1 - 15.0f, 16.0f, - - // Batch 4, Channel 2 - 17.0f, 18.0f, - - // Batch 5, Channel 0 - 31.0f, 32.0f, - - // Batch 5, Channel 1 - 33.0f, 34.0f, - - // Batch 5, Channel 2 - 35.0f, 36.0f - })); - return result; -} - -LayerTestResult<float, 3> Concatenation3dDim0Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim0TestImpl<float>(workloadFactory, 0.0f, 0); -} - -template <typename T> -LayerTestResult<T, 3> Concatenation3dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, int32_t qOffset) -{ - armnn::TensorInfo outputTensorInfo({ 2, 9, 2 }, armnn::GetDataType<T>()); - - LayerTestResult<T, 3> result = Concatenation3dTestImpl<T>(workloadFactory, outputTensorInfo, 1, qScale, qOffset); - result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 0, Channel 3 - 7.0f, 8.0f, - - // Batch 0, Channel 4 - 9.0f, 10.0f, - - // Batch 0, Channel 5 - 11.0f, 12.0f, - - // Batch 0, Channel 6 - 13.0f, 14.0f, - - // Batch 0, Channel 7 - 15.0f, 16.0f, - - // Batch 0, Channel 8 - 17.0f, 18.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f, - - // Batch 1, Channel 3 - 25.0f, 26.0f, - - // Batch 1, Channel 4 - 27.0f, 28.0f, - - // Batch 1, Channel 5 - 29.0f, 30.0f, - - // Batch 1, Channel 6 - 31.0f, 32.0f, - - // Batch 1, Channel 7 - 33.0f, 34.0f, - - // Batch 1, Channel 8 - 35.0f, 36.0f - })); - - return result; -} - -LayerTestResult<float, 3> Concatenation3dDim1Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim1TestImpl<float>(workloadFactory, 0.0f, 0); -} - -template <typename T> -LayerTestResult<T, 3> Concatenation3dDim2TestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, int32_t qOffset) -{ - armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType<T>()); - - LayerTestResult<T, 3> result = Concatenation3dTestImpl<T>(workloadFactory, outputTensorInfo, 2, qScale, qOffset); - result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, 7.0f, 8.0f, 13.0f, 14.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, 9.0f, 10.0f, 15.0f, 16.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, 11.0f, 12.0f, 17.0f, 18.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, 25.0f, 26.0f, 31.0f, 32.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, 27.0f, 28.0f, 33.0f, 34.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f, 29.0f, 30.0f, 35.0f, 36.0f, - })); - - return result; -} - -LayerTestResult<float, 3> Concatenation3dDim2Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim2TestImpl<float>(workloadFactory, 0.0f, 0); -} - -template <typename T> -LayerTestResult<T, 3> Concatenation3dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, - int32_t qOffset) -{ - armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>()); - auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f - })); - - armnn::TensorInfo input1TensorInfo({ 1, 3, 2 }, armnn::GetDataType<T>()); - auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 7.0f, 8.0f, - - // Batch 0, Channel 1 - 9.0f, 10.0f, - - // Batch 0, Channel 2 - 11.0f, 12.0f, - })); - - armnn::TensorInfo input2TensorInfo({ 3, 3, 2 }, armnn::GetDataType<T>()); - auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 25.0f, 26.0f, - - // Batch 0, Channel 1 - 27.0f, 28.0f, - - // Batch 0, Channel 2 - 29.0f, 30.0f, - - // Batch 1, Channel 0 - 13.0f, 14.0f, - - // Batch 1, Channel 1 - 15.0f, 16.0f, - - // Batch 1, Channel 2 - 17.0f, 18.0f, - - // Batch 2, Channel 0 - 31.0f, 32.0f, - - // Batch 2, Channel 1 - 33.0f, 34.0f, - - // Batch 2, Channel 2 - 35.0f, 36.0f - })); - - armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType<T>()); - LayerTestResult<T, 3> result(outputTensorInfo); - - std::vector<T> output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate<T>(workloadFactory, - { input0TensorInfo, input1TensorInfo, input2TensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - 0); - - result.output = MakeTensor<T, 3>(outputTensorInfo, output); - result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f, - - // Batch 2, Channel 0 - 7.0f, 8.0f, - - // Batch 2, Channel 1 - 9.0f, 10.0f, - - // Batch 2, Channel 2 - 11.0f, 12.0f, - - // Batch 3, Channel 0 - 25.0f, 26.0f, - - // Batch 3, Channel 1 - 27.0f, 28.0f, - - // Batch 3, Channel 2 - 29.0f, 30.0f, - - // Batch 4, Channel 0 - 13.0f, 14.0f, - - // Batch 4, Channel 1 - 15.0f, 16.0f, - - // Batch 4, Channel 2 - 17.0f, 18.0f, - - // Batch 5, Channel 0 - 31.0f, 32.0f, - - // Batch 5, Channel 1 - 33.0f, 34.0f, - - // Batch 5, Channel 2 - 35.0f, 36.0f - })); - - return result; -} - -LayerTestResult<float, 3> Concatenation3dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim0DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0); -} - -template <typename T> -LayerTestResult<T, 3> Concatenation3dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, - int32_t qOffset) -{ - armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>()); - auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f - })); - - armnn::TensorInfo input1TensorInfo({ 2, 4, 2 }, armnn::GetDataType<T>()); - auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 7.0f, 8.0f, - - // Batch 0, Channel 1 - 9.0f, 10.0f, - - // Batch 0, Channel 2 - 11.0f, 12.0f, - - // Batch 0, Channel 3 - 25.0f, 26.0f, - - // Batch 1, Channel 0 - 27.0f, 28.0f, - - // Batch 1, Channel 1 - 29.0f, 30.0f, - - // Batch 1, Channel 2 - 13.0f, 14.0f, - - // Batch 1, Channel 3 - 15.0f, 16.0f, - })); - - armnn::TensorInfo input2TensorInfo({ 2, 1, 2 }, armnn::GetDataType<T>()); - auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 17.0f, 18.0f, - - // Batch 1, Channel 0 - 31.0f, 32.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 2, 8, 2 }, armnn::GetDataType<T>()); - LayerTestResult<T, 3> result(outputTensorInfo); - - std::vector<T> output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate<T>(workloadFactory, - { input0TensorInfo, input1TensorInfo, input2TensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - 1); - - result.output = MakeTensor<T, 3>(outputTensorInfo, output); - result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 0, Channel 3 - 7.0f, 8.0f, - - // Batch 0, Channel 4 - 9.0f, 10.0f, - - // Batch 0, Channel 5 - 11.0f, 12.0f, - - // Batch 0, Channel 6 - 25.0f, 26.0f, - - // Batch 0, Channel 7 - 17.0f, 18.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f, - - // Batch 1, Channel 3 - 27.0f, 28.0f, - - // Batch 1, Channel 4 - 29.0f, 30.0f, - - // Batch 1, Channel 5 - 13.0f, 14.0f, - - // Batch 1, Channel 6 - 15.0f, 16.0f, - - // Batch 1, Channel 7 - 31.0f, 32.0f, - })); - - return result; -} - -LayerTestResult<float, 3> Concatenation3dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim1DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0); -} - -template <typename T> -LayerTestResult<T, 3> Concatenation3dDim2DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, - int32_t qOffset) -{ - armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>()); - auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f - })); - - armnn::TensorInfo input1TensorInfo({ 2, 3, 1 }, armnn::GetDataType<T>()); - auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 7.0f, - - // Batch 0, Channel 1 - 9.0f, - - // Batch 0, Channel 2 - 11.0f, - - // Batch 1, Channel 0 - 25.0f, - - // Batch 1, Channel 1 - 27.0f, - - // Batch 1, Channel 2 - 29.0f - })); - - armnn::TensorInfo input2TensorInfo({ 2, 3, 3 }, armnn::GetDataType<T>()); - auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 13.0f, 14.0f, 50.0f, - - // Batch 0, Channel 1 - 15.0f, 16.0f, 51.0f, - - // Batch 0, Channel 2 - 17.0f, 18.0f, 52.0f, - - // Batch 1, Channel 0 - 31.0f, 32.0f, 53.0f, - - // Batch 1, Channel 1 - 33.0f, 34.0f, 54.0f, - - // Batch 1, Channel 2 - 35.0f, 36.0f, 55.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType<T>()); - LayerTestResult<T, 3> result(outputTensorInfo); - - std::vector<T> output; - output.resize(outputTensorInfo.GetNumElements()); - Concatenate<T>(workloadFactory, - { input0TensorInfo, input1TensorInfo, input2TensorInfo }, - { input0.data(), input1.data(), input2.data() }, - outputTensorInfo, - output.data(), - 2); - - result.output = MakeTensor<T, 3>(outputTensorInfo, output); - result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 1.0f, 2.0f, 7.0f, 13.0f, 14.0f, 50.0f, - - // Batch 0, Channel 1 - 3.0f, 4.0f, 9.0f, 15.0f, 16.0f, 51.0f, - - // Batch 0, Channel 2 - 5.0f, 6.0f, 11.0f, 17.0f, 18.0f, 52.0f, - - // Batch 1, Channel 0 - 19.0f, 20.0f, 25.0f, 31.0f, 32.0f, 53.0f, - - // Batch 1, Channel 1 - 21.0f, 22.0f, 27.0f, 33.0f, 34.0f, 54.0f, - - // Batch 1, Channel 2 - 23.0f, 24.0f, 29.0f, 35.0f, 36.0f, 55.0f, - })); - - return result; -} - -LayerTestResult<float, 3> Concatenation3dDim2DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim2DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0); -} - -LayerTestResult<float, 4> ResizeBilinearNopTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 4; - constexpr unsigned int inputHeight = 4; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth; - constexpr unsigned int outputHeight = inputHeight; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ - 1.0f, 2.0f, 3.0f, 4.0f, - 2.0f, 3.0f, 4.0f, 5.0f, - 3.0f, 4.0f, 5.0f, 6.0f, - 4.0f, 5.0f, 6.0f, 7.0f - })); - - LayerTestResult<float, 4> result(outputTensorInfo); - result.outputExpected = input; - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult<float, 4> SimpleResizeBilinearTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 2; - constexpr unsigned int inputHeight = 2; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth / 2; - constexpr unsigned int outputHeight = inputHeight / 2; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ - 1.0f, 255.0f, - 200.0f, 250.f, - })); - - // The 'resize bilinear' operation projects the top-left corner of output texels into the input image, - // then figures out the interpolants and weights. Note this is different to projecting the centre of the - // output texel - and thus we'll expect the output 1x1 matrix to contain, as its single element, the value - // that was at position (0,0) of the input matrix (rather than an average, which we would expect if projecting - // the centre). - LayerTestResult<float, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>({ - 1.0f - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult<float, 4> ResizeBilinearSqMinTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 4; - constexpr unsigned int inputHeight = 4; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth / 2; - constexpr unsigned int outputHeight = inputHeight / 2; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ - 1.0f, 2.0f, 3.0f, 4.0f, - 2.0f, 3.0f, 4.0f, 5.0f, - 3.0f, 4.0f, 5.0f, 6.0f, - 4.0f, 5.0f, 6.0f, 7.0f - })); - - LayerTestResult<float, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>({ - 1.f, 3.f, - 3.f, 5.f - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult<float, 4> ResizeBilinearMinTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 5; - constexpr unsigned int inputHeight = 3; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = 3; - constexpr unsigned int outputHeight = 2; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ - 1.0f, 2.0f, 3.0f, 5.0f, 8.0f, - 13.0f, 21.0f, 34.0f, 55.0f, 89.0f, - 144.0f, 233.0f, 377.0f, 610.0f, 987.0f - })); - - LayerTestResult<float, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>({ - 1.0f, 2.6666f, 6.0f, - 78.5f, 179.3333f, 401.f - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult<float, 4> ResizeBilinearMagTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 2; - constexpr unsigned int inputHeight = 3; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = 5; - constexpr unsigned int outputHeight = 3; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ - 1.0f, 2.0f, - 13.0f, 21.0f, - 144.0f, 233.0f - })); - - LayerTestResult<float, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>({ - 1.0f, 1.4f, 1.8f, 2.f, 2.f, - 13.f, 16.2f, 19.4f, 21.f, 21.f, - 144.f, 179.6f, 215.2f, 233.f, 233.f - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult<float, 2> FakeQuantizationTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int width = 2; - constexpr unsigned int height = 3; - - const armnn::TensorInfo tensorInfo({height, width }, - armnn::DataType::Float32); - auto input = MakeTensor<float, 2>(tensorInfo, std::vector<float>({ - -10.0f, -5.0f, - 0.0f, 5.0f, - 10.0f, 10.0f - })); - - LayerTestResult<float, 2> ret(tensorInfo); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(tensorInfo); - - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(tensorInfo); - - armnn::FakeQuantizationQueueDescriptor data; - armnn::WorkloadInfo info; - - AddInputToWorkload(data, info, tensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, tensorInfo, outputHandle.get()); - float min = -10.f; - float max = 10.f; - - data.m_Parameters.m_Min = min; - data.m_Parameters.m_Max = max; - - armnn::PassthroughCpuTensorHandle refHandle(tensorInfo, &ret.outputExpected[0][0]); - armnn::FakeQuantizationQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadOutput(refData, refInfo, 0, tensorInfo, &refHandle); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateFakeQuantization(data, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); - - ret.outputExpected = MakeTensor<float, 2>(tensorInfo, std::vector<float>({ - 0.0f, 63.0f, - 128.0f, 191.0f, - 255.0f, 255.0f - })); - return ret; -} - -LayerTestResult<float, 4> L2Normalization1dTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 1; - constexpr unsigned int inputHeight = 1; - constexpr unsigned int inputChannels = 10; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth; - constexpr unsigned int outputHeight = inputHeight; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ - 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f - })); - - const float approxInvL2Norm = 0.050964719f; - LayerTestResult<float, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ - 1.0f * approxInvL2Norm, - 2.0f * approxInvL2Norm, - 3.0f * approxInvL2Norm, - 4.0f * approxInvL2Norm, - 5.0f * approxInvL2Norm, - 6.0f * approxInvL2Norm, - 7.0f * approxInvL2Norm, - 8.0f * approxInvL2Norm, - 9.0f * approxInvL2Norm, - 10.0f * approxInvL2Norm - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::L2NormalizationQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -namespace -{ - -float CalcInvL2Norm(std::initializer_list<float> elements) -{ - const float reduction = std::accumulate(elements.begin(), elements.end(), 0.0f, - [](float acc, float element) { return acc + element * element; }); - return 1.0f / sqrtf(reduction); -} - -} - -LayerTestResult<float, 4> L2Normalization2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 5; - constexpr unsigned int inputHeight = 1; - constexpr unsigned int inputChannels = 2; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth; - constexpr unsigned int outputHeight = inputHeight; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ - 1.0f, 3.0f, 5.0f, 7.0f, 9.0f, - 2.0f, 4.0f, 6.0f, 8.0f, 10.0f - })); - - LayerTestResult<float, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ - 1.0f * CalcInvL2Norm({ 1.0f, 2.0f }), - 3.0f * CalcInvL2Norm({ 3.0f, 4.0f }), - 5.0f * CalcInvL2Norm({ 5.0f, 6.0f }), - 7.0f * CalcInvL2Norm({ 7.0f, 8.0f }), - 9.0f * CalcInvL2Norm({ 9.0f, 10.0f }), - - 2.0f * CalcInvL2Norm({ 1.0f, 2.0f }), - 4.0f * CalcInvL2Norm({ 3.0f, 4.0f }), - 6.0f * CalcInvL2Norm({ 5.0f, 6.0f }), - 8.0f * CalcInvL2Norm({ 7.0f, 8.0f }), - 10.0f * CalcInvL2Norm({ 9.0f, 10.0f }) - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::L2NormalizationQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult<float, 4> L2Normalization3dTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 3; - constexpr unsigned int inputHeight = 4; - constexpr unsigned int inputChannels = 2; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth; - constexpr unsigned int outputHeight = inputHeight; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ - // Channel 0 - 119.0f, 21.0f, 150.0f, - 149.0f, 32.0f, 179.0f, - 15.0f, 227.0f, 141.0f, - 147.0f, 199.0f, 220.0f, - - // Channel 1 - 110.0f, 140.0f, 73.0f, - 211.0f, 212.0f, 89.0f, - 24.0f, 138.0f, 188.0f, - 162.0f, 12.0f, 161.0f, - })); - - LayerTestResult<float, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ - 119.0f * CalcInvL2Norm({ 119.0f, 110.0f }), - 21.0f * CalcInvL2Norm({ 21.0f, 140.0f }), - 150.0f * CalcInvL2Norm({ 150.0f, 73.0f }), - 149.0f * CalcInvL2Norm({ 149.0f, 211.0f }), - 32.0f * CalcInvL2Norm({ 32.0f, 212.0f }), - 179.0f * CalcInvL2Norm({ 179.0f, 89.0f }), - 15.0f * CalcInvL2Norm({ 15.0f, 24.0f }), - 227.0f * CalcInvL2Norm({ 227.0f, 138.0f }), - 141.0f * CalcInvL2Norm({ 141.0f, 188.0f }), - 147.0f * CalcInvL2Norm({ 147.0f, 162.0f }), - 199.0f * CalcInvL2Norm({ 199.0f, 12.0f }), - 220.0f * CalcInvL2Norm({ 220.0f, 161.0f }), - - 110.0f * CalcInvL2Norm({ 119.0f, 110.0f }), - 140.0f * CalcInvL2Norm({ 21.0f, 140.0f }), - 73.0f * CalcInvL2Norm({ 150.0f, 73.0f }), - 211.0f * CalcInvL2Norm({ 149.0f, 211.0f }), - 212.0f * CalcInvL2Norm({ 32.0f, 212.0f }), - 89.0f * CalcInvL2Norm({ 179.0f, 89.0f }), - 24.0f * CalcInvL2Norm({ 15.0f, 24.0f }), - 138.0f * CalcInvL2Norm({ 227.0f, 138.0f }), - 188.0f * CalcInvL2Norm({ 141.0f, 188.0f }), - 162.0f * CalcInvL2Norm({ 147.0f, 162.0f }), - 12.0f * CalcInvL2Norm({ 199.0f, 12.0f }), - 161.0f * CalcInvL2Norm({ 220.0f, 161.0f }), - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::L2NormalizationQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult<float, 4> L2Normalization4dTest(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 3; - constexpr unsigned int inputHeight = 4; - constexpr unsigned int inputChannels = 3; - constexpr unsigned int inputBatchSize = 2; - - constexpr unsigned int outputWidth = inputWidth; - constexpr unsigned int outputHeight = inputHeight; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - const armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::Float32); - - auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ - // Batch 0, Channel 0 - 235.0f, 46.0f, 178.0f, - 100.0f, 123.0f, 19.0f, - 172.0f, 74.0f, 250.0f, - 6.0f, 195.0f, 80.0f, - - // Batch 0, Channel 1 - 113.0f, 95.0f, 202.0f, - 77.0f, 114.0f, 71.0f, - 122.0f, 246.0f, 166.0f, - 82.0f, 28.0f, 37.0f, - - // Batch 0, Channel 2 - 56.0f, 170.0f, 162.0f, - 194.0f, 89.0f, 254.0f, - 12.0f, 209.0f, 200.0f, - 1.0f, 64.0f, 54.0f, - - // Batch 1, Channel 0 - 67.0f, 90.0f, 49.0f, - 7.0f, 163.0f, 18.0f, - 25.0f, 117.0f, 103.0f, - 247.0f, 59.0f, 189.0f, - - // Batch 1, Channel 1 - 239.0f, 104.0f, 199.0f, - 17.0f, 124.0f, 153.0f, - 222.0f, 217.0f, 75.0f, - 32.0f, 126.0f, 21.0f, - - // Batch 1, Channel 2 - 97.0f, 145.0f, 215.0f, - 115.0f, 116.0f, 238.0f, - 226.0f, 16.0f, 132.0f, - 92.0f, 125.0f, 88.0f, - })); - - LayerTestResult<float, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ - - // Batch 0, Channel 0 - 235.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), - 46.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), - 178.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), - 100.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), - 123.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), - 19.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), - 172.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), - 74.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), - 250.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), - 6.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), - 195.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), - 80.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), - - // Batch 0, Channel 1 - 113.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), - 95.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), - 202.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), - 77.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), - 114.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), - 71.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), - 122.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), - 246.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), - 166.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), - 82.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), - 28.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), - 37.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), - - // Batch 0, Channel 2 - 56.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), - 170.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), - 162.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), - 194.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), - 89.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), - 254.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), - 12.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), - 209.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), - 200.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), - 1.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), - 64.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), - 54.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), - - // Batch 1, Channel 0 - 67.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), - 90.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), - 49.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), - 7.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), - 163.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), - 18.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), - 25.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), - 117.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), - 103.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), - 247.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), - 59.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), - 189.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), - - // Batch 1, Channel 1 - 239.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), - 104.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), - 199.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), - 17.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), - 124.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), - 153.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), - 222.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), - 217.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), - 75.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), - 32.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), - 126.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), - 21.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), - - // Batch 1, Channel 2 - 97.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), - 145.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), - 215.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), - 115.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), - 116.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), - 238.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), - 226.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), - 16.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), - 132.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), - 92.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), - 125.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), - 88.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::L2NormalizationQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -template <typename T> -LayerTestResult<T, 4> ConstantTestImpl(armnn::IWorkloadFactory& workloadFactory, - float qScale, - int32_t qOffset) -{ - constexpr unsigned int inputWidth = 3; - constexpr unsigned int inputHeight = 4; - constexpr unsigned int inputChannels = 3; - constexpr unsigned int inputBatchSize = 2; - - constexpr unsigned int outputWidth = inputWidth; - constexpr unsigned int outputHeight = inputHeight; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::GetDataType<T>()); - - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::GetDataType<T>()); - - // Set quantization parameters if the requested type is a quantized type. - if(armnn::IsQuantizedType<T>()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>( - QuantizedVector<T>(qScale, qOffset, { - // Batch 0, Channel 0 - 235.0f, 46.0f, 178.0f, - 100.0f, 123.0f, 19.0f, - 172.0f, 74.0f, 250.0f, - 6.0f, 195.0f, 80.0f, - - // Batch 0, Channel 1 - 113.0f, 95.0f, 202.0f, - 77.0f, 114.0f, 71.0f, - 122.0f, 246.0f, 166.0f, - 82.0f, 28.0f, 37.0f, - - // Batch 0, Channel 2 - 56.0f, 170.0f, 162.0f, - 194.0f, 89.0f, 254.0f, - 12.0f, 209.0f, 200.0f, - 1.0f, 64.0f, 54.0f, - - // Batch 1, Channel 0 - 67.0f, 90.0f, 49.0f, - 7.0f, 163.0f, 18.0f, - 25.0f, 117.0f, 103.0f, - 247.0f, 59.0f, 189.0f, - - // Batch 1, Channel 1 - 239.0f, 104.0f, 199.0f, - 17.0f, 124.0f, 153.0f, - 222.0f, 217.0f, 75.0f, - 32.0f, 126.0f, 21.0f, - - // Batch 1, Channel 2 - 97.0f, 145.0f, 215.0f, - 115.0f, 116.0f, 238.0f, - 226.0f, 16.0f, 132.0f, - 92.0f, 125.0f, 88.0f, - }))); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = input; - - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ScopedCpuTensorHandle constantTensor(inputTensorInfo); - AllocateAndCopyDataToITensorHandle(&constantTensor, &input[0][0][0][0]); - - armnn::ConstantQueueDescriptor descriptor; - descriptor.m_LayerOutput = &constantTensor; - - armnn::WorkloadInfo info; - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConstant(descriptor, info); - - outputHandle->Allocate(); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult<float, 4> ConstantTest(armnn::IWorkloadFactory& workloadFactory) -{ - return ConstantTestImpl<float>(workloadFactory, 0.0f, 0); -} - -LayerTestResult<uint8_t, 4> ConstantTestUint8(armnn::IWorkloadFactory& workloadFactory) -{ - return ConstantTestImpl<uint8_t>(workloadFactory, 1.0f, 0); -} - -LayerTestResult<uint8_t, 3> MergerUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int outputWidth = 3; - unsigned int outputHeight = 6; - unsigned int outputChannels = 3; - - unsigned int inputWidth1 = 3; - unsigned int inputHeight1 = 6; - unsigned int inputChannels1 = 2; - - unsigned int inputWidth2 = 3; - unsigned int inputHeight2 = 6; - unsigned int inputChannels2 = 1; - - // Defines the tensor descriptors. - armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::QuantisedAsymm8); - armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::QuantisedAsymm8); - armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::QuantisedAsymm8); - - // Arbitrary scale and offsets. They don't really matter as the merger operator doesn't dequantize/quantize them. - const float scale = 0.13497836f; - const int32_t offset = -7; - - outputTensorInfo.SetQuantizationScale(scale); - outputTensorInfo.SetQuantizationOffset(offset); - inputTensorInfo1.SetQuantizationScale(scale); - inputTensorInfo1.SetQuantizationOffset(offset); - inputTensorInfo2.SetQuantizationScale(scale); - inputTensorInfo2.SetQuantizationOffset(offset); - - LayerTestResult<uint8_t, 3> ret(outputTensorInfo); - - ret.outputExpected = MakeTensor<uint8_t, 3>(outputTensorInfo, std::vector<uint8_t>( - { - 1, 2, 3, - 4, 5, 6, - 7, 8, 9, - 10, 11, 12, - 13, 14, 15, - 16, 17, 18, - - 19, 20, 21, - 22, 23, 24, - 25, 26, 27, - 28, 29, 30, - 31, 32, 33, - 34, 35, 36, - - 37, 38, 39, - 40, 41, 42, - 43, 44, 45, - 46, 47, 48, - 49, 50, 51, - 52, 53, 54, - }) - ); - - auto input1 = MakeTensor<uint8_t, 3>(inputTensorInfo1, std::vector<uint8_t>( - { - 1, 2, 3, - 4, 5, 6, - 7, 8, 9, - 10, 11, 12, - 13, 14, 15, - 16, 17, 18, - - 19, 20, 21, - 22, 23, 24, - 25, 26, 27, - 28, 29, 30, - 31, 32, 33, - 34, 35, 36, - }) - ); - - auto input2 = MakeTensor<uint8_t, 3>(inputTensorInfo2, std::vector<uint8_t>( - { - 37, 38, 39, - 40, 41, 42, - 43, 44, 45, - 46, 47, 48, - 49, 50, 51, - 52, 53, 54, - }) - ); - - std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. - armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1); - - std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1]. - armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2); - - - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - bool subTensorsSupported = workloadFactory.SupportsSubTensors(); - - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = - subTensorsSupported ? - workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : - workloadFactory.CreateTensorHandle(inputTensorInfo1); - - std::unique_ptr<armnn::ITensorHandle> inputHandle2 = - subTensorsSupported ? - workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : - workloadFactory.CreateTensorHandle(inputTensorInfo2); - - - armnn::MergerQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - data.m_ViewOrigins.push_back(window1); - data.m_ViewOrigins.push_back(window2); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMerger(data, info); - - inputHandle1->Allocate(); - inputHandle2->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); - - return ret; -} - -LayerTestResult<uint8_t, 4> AdditionUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int batchSize = 1; - unsigned int channels = 2; - unsigned int height = 2; - unsigned int width = 3; - - const float scale = 7.0f; - const int32_t offset = 3; - - armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; - armnn::TensorInfo outputTensorInfo; - - const unsigned int shape[] = { batchSize, channels, height, width }; - inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8); - inputTensorInfo1.SetQuantizationScale(scale); - inputTensorInfo1.SetQuantizationOffset(offset); - - inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8); - inputTensorInfo2.SetQuantizationScale(scale); - inputTensorInfo2.SetQuantizationOffset(offset); - - outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(scale); - outputTensorInfo.SetQuantizationOffset(offset); - - // See dequantized values to the right. - auto input1 = MakeTensor<uint8_t, 4>(inputTensorInfo1, std::vector<uint8_t>( - { - 63, 35, 77, 70, 56, 112, // 420, 224, 518, 469, 371, 763 - 203, 28, 252, 168, 245, 91 // 1400, 175, 1743, 1155, 1694, 616 - })); - - // See dequantized values to the right. - auto input2 = MakeTensor<uint8_t, 4>(inputTensorInfo1, std::vector<uint8_t>( - { - 21, 7, 175, 231, 175, 210, // 126, 28, 1204, 1596, 1204, 1449 - 126, 161, 63, 21, 105, 126 // 861, 1106, 420, 126, 714, 861 - })); - - // See dequantized values to the right. - LayerTestResult<uint8_t, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>( - { - 81, 39, 249, 255, 228, 255, // 546, 252, 1722, 2065(clamped), 1575, 2212(clamped) - 255, 186, 255, 186, 255, 214, // 2261(clamped), 1281, 2163(clamped), 1281, 2408(clamped), 1477 - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::AdditionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info); - - inputHandle1->Allocate(); - inputHandle2->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - return result; -} - -namespace -{ -LayerTestResult<uint8_t, 4> MultiplicationUint8TestHelper(armnn::IWorkloadFactory& workloadFactory, - const unsigned int shape0[4], - const std::vector<uint8_t> & values0, - float scale0, - int32_t offset0, - const unsigned int shape1[4], - const std::vector<uint8_t> & values1, - float scale1, - int32_t offset1, - const unsigned int outShape[4], - const std::vector<uint8_t> & outValues, - float outScale, - int32_t outOffset) -{ - armnn::TensorInfo inputTensorInfo0(4, shape0, armnn::DataType::QuantisedAsymm8); - armnn::TensorInfo inputTensorInfo1(4, shape1, armnn::DataType::QuantisedAsymm8); - armnn::TensorInfo outputTensorInfo(4, outShape, armnn::DataType::QuantisedAsymm8); - - inputTensorInfo0.SetQuantizationScale(scale0); - inputTensorInfo0.SetQuantizationOffset(offset0); - - inputTensorInfo1.SetQuantizationScale(scale1); - inputTensorInfo1.SetQuantizationOffset(offset1); - - outputTensorInfo.SetQuantizationScale(outScale); - outputTensorInfo.SetQuantizationOffset(outOffset); - - auto input0 = MakeTensor<uint8_t, 4>(inputTensorInfo0, values0); - auto input1 = MakeTensor<uint8_t, 4>(inputTensorInfo1, values1); - - LayerTestResult<uint8_t, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, outValues); - - std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::MultiplicationQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - return result; -} -} // anonymous namespace - -LayerTestResult<uint8_t, 4> MultiplicationUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - unsigned int batchSize = 1; - unsigned int channels = 2; - unsigned int height = 2; - unsigned int width = 3; - const unsigned int shape[] = { batchSize, channels, height, width }; - - // See dequantized values to the right. - std::vector<uint8_t> input0({ - 62, 37, 3, 172, 13, 111, // 244, 144, 8, 684, 48, 440, - 188, 20, 73, 31, 23, 31 // 748, 76, 288, 120, 88, 120 - }); - - // See dequantized values to the right. - std::vector<uint8_t> input1({ - 126, 240, 252, 183, 121, 247, // 384, 726, 762, 555, 369, 747, - 48, 115, 151, 79, 78, 97 // 150, 351, 459, 243, 240, 297 - }); - - // See dequantized values to the right. - std::vector<uint8_t> output( - { - 64, 72, 0, 255, 8, 236, // 93696, 104544, 6096(clamped), 379620(clamped), 17712, 328680, - 77, 15, 92, 16, 10, 21, // 112200, 26676, 132192, 29160, 21120, 35640 - }); - - return MultiplicationUint8TestHelper(workloadFactory, - shape, - input0, - 4.0f, - 1, - shape, - input1, - 3.0f, - -2, - shape, - output, - 1366.255f, // Scale/offset chosen to have output values out of range. - -5); -} - -LayerTestResult<uint8_t, 4> MultiplicationBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector<uint8_t> input0({ - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 - }); - - std::vector<uint8_t> input1({2}); - - std::vector<uint8_t> output({ - 2, 4, 6, 8, 10, 12, - 14, 16, 18, 20, 22, 24 - }); - - return MultiplicationUint8TestHelper(workloadFactory, - shape0, - input0, - 1.0f, - 0, - shape1, - input1, - 1.0f, - 0, - shape0, - output, - 1.0f, - 0); -} - -LayerTestResult<uint8_t, 4> MultiplicationBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 3 }; - - std::vector<uint8_t> input0({ - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 - }); - - std::vector<uint8_t> input1({1, 2, 3}); - - std::vector<uint8_t> output({ - 1, 4, 9, 4, 10, 18, - 7, 16, 27, 10, 22, 36 - }); - - return MultiplicationUint8TestHelper(workloadFactory, - shape0, - input0, - 1.0f, - 0, - shape1, - input1, - 1.0f, - 0, - shape0, - output, - 1.0f, - 0); -} - -namespace -{ -template <typename T> -LayerTestResult<T, 4> SubtractionTestHelper(armnn::IWorkloadFactory& workloadFactory, - const unsigned int shape0[4], - const std::vector<T>& values0, - float scale0, - int32_t offset0, - const unsigned int shape1[4], - const std::vector<T> & values1, - float scale1, - int32_t offset1, - const unsigned int outShape[4], - const std::vector<T> & outValues, - float outScale, - int32_t outOffset) -{ - auto dataType = (std::is_same<T, uint8_t>::value ? - armnn::DataType::QuantisedAsymm8 : - armnn::DataType::Float32); - - armnn::TensorInfo inputTensorInfo0(4, shape0, dataType); - armnn::TensorInfo inputTensorInfo1(4, shape1, dataType); - armnn::TensorInfo outputTensorInfo(4, outShape, dataType); - - inputTensorInfo0.SetQuantizationScale(scale0); - inputTensorInfo0.SetQuantizationOffset(offset0); - - inputTensorInfo1.SetQuantizationScale(scale1); - inputTensorInfo1.SetQuantizationOffset(offset1); - - outputTensorInfo.SetQuantizationScale(outScale); - outputTensorInfo.SetQuantizationOffset(outOffset); - - auto input0 = MakeTensor<T, 4>(inputTensorInfo0, values0); - auto input1 = MakeTensor<T, 4>(inputTensorInfo1, values1); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outValues); - - std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::SubtractionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSubtraction(data, info); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - return result; -} -} // anonymous namespace - -LayerTestResult<uint8_t, 4> SubtractionUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 2, 2 }; - - std::vector<uint8_t> input0({ 10, 12, 14, 16 }); - std::vector<uint8_t> input1({ 1, 2, 1, 2 }); - std::vector<uint8_t> output({ 3, 3, 5, 5 }); - - return SubtractionTestHelper(workloadFactory, - shape0, input0, 0.5f, 2, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<uint8_t, 4> SubtractionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector<uint8_t> input0({ 10, 12, 14, 16 }); - std::vector<uint8_t> input1({ 2 }); - std::vector<uint8_t> output({ 5, 6, 7, 8 }); - - return SubtractionTestHelper(workloadFactory, - shape0, input0, 0.5f, 2, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 3); -} - -LayerTestResult<uint8_t, 4> SubtractionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 2, 1 }; - - std::vector<uint8_t> input0({ 10, 12, 14, 16 }); - std::vector<uint8_t> input1({ 2, 1 }); - std::vector<uint8_t> output({ 8, 11, 12, 15 }); - - return SubtractionTestHelper(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<float, 4> SubtractionTest(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 2, 2 }; - - std::vector<float> input0({ 1, 2, 3, 4 }); - std::vector<float> input1({ 1, -1, 0, 2 }); - std::vector<float> output({ 0, 3, 3, 2 }); - - return SubtractionTestHelper(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<float, 4> SubtractionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector<float> input0({ 1, 2, 3, 4 }); - std::vector<float> input1({ 10 }); - std::vector<float> output({ -9, -8, -7, -6 }); - - return SubtractionTestHelper(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<float, 4> SubtractionBroadcastTest(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 1, 2 }; - - std::vector<float> input0({ 1, 2, 3, 4 }); - std::vector<float> input1({ 10, -5 }); - std::vector<float> output({ -9, 7, -7, 9 }); - - return SubtractionTestHelper(workloadFactory, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<uint8_t, 4> ResizeBilinearNopUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 4; - constexpr unsigned int inputHeight = 4; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth; - constexpr unsigned int outputHeight = inputHeight; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::QuantisedAsymm8); - inputTensorInfo.SetQuantizationScale(1.5f); - inputTensorInfo.SetQuantizationOffset(-3); - - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(1.5f); - outputTensorInfo.SetQuantizationOffset(-3); - - auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({ - 1, 2, 3, 4, - 2, 3, 4, 5, - 3, 4, 5, 6, - 4, 5, 6, 7 - })); - - LayerTestResult<uint8_t, 4> result(outputTensorInfo); - result.outputExpected = input; - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult<uint8_t, 4> SimpleResizeBilinearUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 2; - constexpr unsigned int inputHeight = 2; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth / 2; - constexpr unsigned int outputHeight = inputHeight / 2; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::QuantisedAsymm8); - inputTensorInfo.SetQuantizationScale(0.1567f); - inputTensorInfo.SetQuantizationOffset(1); - - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(0.1567f); - outputTensorInfo.SetQuantizationOffset(1); - - auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({ - 1, 255, - 200, 250 - })); - - // The 'resize bilinear' operation projects the top-left corner of output texels into the input image, - // then figures out the interpolants and weights. Note this is different to projecting the centre of the - // output texel - and thus we'll expect the output 1x1 matrix to contain, as its single element, the value - // that was at position (0,0) of the input matrix (rather than an average, which we would expect if projecting - // the centre). - LayerTestResult<uint8_t, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({ - 1 - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult<uint8_t, 4> ResizeBilinearSqMinUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 4; - constexpr unsigned int inputHeight = 4; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = inputWidth / 2; - constexpr unsigned int outputHeight = inputHeight / 2; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::QuantisedAsymm8); - inputTensorInfo.SetQuantizationScale(3.141592f); - inputTensorInfo.SetQuantizationOffset(3); - - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(3.141592f); - outputTensorInfo.SetQuantizationOffset(3); - - auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({ - 1, 2, 3, 4, - 2, 3, 4, 5, - 3, 4, 5, 6, - 4, 5, 6, 7 - })); - - LayerTestResult<uint8_t, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({ - 1, 3, - 3, 5 - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult<uint8_t, 4> ResizeBilinearMinUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 3; - constexpr unsigned int inputHeight = 2; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = 2; - constexpr unsigned int outputHeight = 1; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::QuantisedAsymm8); - inputTensorInfo.SetQuantizationScale(1.5f); - inputTensorInfo.SetQuantizationOffset(-1); - - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(1.5f); - outputTensorInfo.SetQuantizationOffset(-1); - - auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({ - 1, 2, 3, // 3.0, 4.5, 6.0 - 5, 8, 13 // 9.0, 13.5, 21.0 - })); - - LayerTestResult<uint8_t, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({ - 1, 3 // 3.0, 5.25 - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult<uint8_t, 4> ResizeBilinearMagUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - constexpr unsigned int inputWidth = 2; - constexpr unsigned int inputHeight = 3; - constexpr unsigned int inputChannels = 1; - constexpr unsigned int inputBatchSize = 1; - - constexpr unsigned int outputWidth = 5; - constexpr unsigned int outputHeight = 3; - constexpr unsigned int outputChannels = inputChannels; - constexpr unsigned int outputBatchSize = inputBatchSize; - - armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, - armnn::DataType::QuantisedAsymm8); - inputTensorInfo.SetQuantizationScale(0.010765f); - inputTensorInfo.SetQuantizationOffset(7); - - armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, - armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(0.010132f); - outputTensorInfo.SetQuantizationOffset(-18); - - auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({ - 24, 228, // 0.183005, 2.379065, - 105, 128, // 1.05497, 1.302565 - 230, 71 // 2.400595, 0.68896 - })); - - LayerTestResult<uint8_t, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({ - 0, 87, 173, 217, 217, // 0.18300501, 1.06142902, 1.93985295, 2.37906504, 2.37906504 - 86, 96, 106, 111, 111, // 1.05497003, 1.15400803, 1.25304604, 1.30256498, 1.30256498 - 219, 151, 84, 50, 50 // 2.40059495, 1.71594095, 1.03128707, 0.68896002, 0.68896002 - })); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeBilinearQueueDescriptor descriptor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workloadFactory.Finalize(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -LayerTestResult<float, 4> BatchNormTest(armnn::IWorkloadFactory& workloadFactory) -{ - auto ret = BatchNormTestImpl<float>(workloadFactory, 0.f, 0); - return ret; -} - -LayerTestResult<uint8_t, 4> BatchNormUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - auto ret = BatchNormTestImpl<uint8_t>(workloadFactory, 1.f/20.f, 50); - return ret; -} - -LayerTestResult<uint8_t, 4> ConstantUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return ConstantTestImpl<uint8_t>(workloadFactory, 2e-6f, 1); -} - -LayerTestResult<uint8_t, 1> Concatenation1dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation1dTestImpl<uint8_t>(workloadFactory, 0.5f, -1); -} - -LayerTestResult<uint8_t, 2> Concatenation2dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim0TestImpl<uint8_t>(workloadFactory, 0.5f, -1); -} - -LayerTestResult<uint8_t, 2> Concatenation2dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim1TestImpl<uint8_t>(workloadFactory, 0.5f, -1); -} - -LayerTestResult<uint8_t, 2> Concatenation2dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim0DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1); -} - -LayerTestResult<uint8_t, 2> Concatenation2dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation2dDim1DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1); -} - -LayerTestResult<uint8_t, 3> Concatenation3dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim0TestImpl<uint8_t>(workloadFactory, 0.5f, -1); -} - -LayerTestResult<uint8_t, 3> Concatenation3dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim1TestImpl<uint8_t>(workloadFactory, 0.5f, -1); -} - -LayerTestResult<uint8_t, 3> Concatenation3dDim2Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim2TestImpl<uint8_t>(workloadFactory, 0.5f, -1); -} - -LayerTestResult<uint8_t, 3> Concatenation3dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim0TestImpl<uint8_t>(workloadFactory, 0.5f, -1); -} - -LayerTestResult<uint8_t, 3> Concatenation3dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim1DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1); -} - -LayerTestResult<uint8_t, 3> Concatenation3dDim2DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return Concatenation3dDim2DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1); -} - -LayerTestResult<float, 4> SimpleMaxPooling2dSize2x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding) -{ - return SimpleMaxPooling2dSize2x2Stride2x2TestCommon<float>(workloadFactory, forceNoPadding); -} - -LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize2x2Stride2x2Uint8Test(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding) -{ - return SimpleMaxPooling2dSize2x2Stride2x2TestCommon<uint8_t>(workloadFactory, forceNoPadding, 3.0f, -5); -} - -LayerTestResult<float, 4> SimpleMaxPooling2dSize3x3Stride2x4Test(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding) -{ - return SimpleMaxPooling2dSize3x3Stride2x4TestCommon<float>(workloadFactory, forceNoPadding); -} - -LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize3x3Stride2x4Uint8Test(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding) -{ - return SimpleMaxPooling2dSize3x3Stride2x4TestCommon<uint8_t>(workloadFactory, forceNoPadding, 0.1f, 128); -} - -LayerTestResult<float, 4> SimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return SimpleAveragePooling2dTestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> SimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return SimpleAveragePooling2dTestCommon<uint8_t>(workloadFactory, 0.5, -1); -} - -LayerTestResult<float, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding) -{ - return IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon<float>(workloadFactory, forceNoPadding); -} - -LayerTestResult<float, 4> LargeTensorsAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return LargeTensorsAveragePooling2dTestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> LargeTensorsAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return LargeTensorsAveragePooling2dTestCommon<uint8_t>(workloadFactory, 0.5, -1); -} - -LayerTestResult<float, 4> SimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return SimpleL2Pooling2dTestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> SimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return SimpleL2Pooling2dTestCommon<uint8_t>(workloadFactory); -} - -LayerTestResult<float, 4> L2Pooling2dSize3Stride1Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize3Stride1TestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride1Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize3Stride1TestCommon<uint8_t>(workloadFactory); -} - -LayerTestResult<float, 4> L2Pooling2dSize3Stride3Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize3Stride3TestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride3Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize3Stride3TestCommon<uint8_t>(workloadFactory); -} - -LayerTestResult<float, 4> L2Pooling2dSize3Stride4Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize3Stride4TestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride4Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize3Stride4TestCommon<uint8_t>(workloadFactory); -} - -LayerTestResult<float, 4> L2Pooling2dSize7Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize7TestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> L2Pooling2dSize7Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize7TestCommon<uint8_t>(workloadFactory); -} - -LayerTestResult<float, 4> L2Pooling2dSize9Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize9TestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> L2Pooling2dSize9Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return L2Pooling2dSize9TestCommon<uint8_t>(workloadFactory); -} - -LayerTestResult<float, 4> AsymmetricNonSquarePooling2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return AsymmetricNonSquarePooling2dTestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> AsymmetricNonSquarePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return AsymmetricNonSquarePooling2dTestCommon<uint8_t>(workloadFactory); -} - -LayerTestResult<float, 4> ComparePooling2dTest(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::PoolingAlgorithm poolingType) -{ - return ComparePooling2dTestCommon<float>(workloadFactory, refWorkloadFactory, poolingType); -} - -LayerTestResult<uint8_t, 4> ComparePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::PoolingAlgorithm poolingType) -{ - return ComparePooling2dTestCommon<uint8_t>(workloadFactory, refWorkloadFactory, poolingType, 0.1f, 128); -} - -LayerTestResult<float, 2> FullyConnectedLargeTest(armnn::IWorkloadFactory& workloadFactory, - bool transposeWeights) -{ - return FullyConnectedLargeTestCommon<float>(workloadFactory, transposeWeights); -} - -LayerTestResult<float, 4> IgnorePaddingSimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleMaxPooling2dTestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> IgnorePaddingSimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleMaxPooling2dTestCommon<uint8_t>(workloadFactory, 1.0f, -5); -} - -LayerTestResult<float, 4> IgnorePaddingMaxPooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingMaxPooling2dSize3TestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> IgnorePaddingMaxPooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingMaxPooling2dSize3TestCommon<uint8_t>(workloadFactory, 1.0f, -5); -} - -LayerTestResult<float, 4> IgnorePaddingSimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleAveragePooling2dTestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> IgnorePaddingSimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleAveragePooling2dTestCommon<uint8_t>(workloadFactory); -} - -LayerTestResult<float, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingTest(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test( - armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<uint8_t>(workloadFactory); -} - -LayerTestResult<float, 4> IgnorePaddingAveragePooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingAveragePooling2dSize3TestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> IgnorePaddingAveragePooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingAveragePooling2dSize3TestCommon<uint8_t>(workloadFactory); -} - -LayerTestResult<float, 4> IgnorePaddingSimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleL2Pooling2dTestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> IgnorePaddingSimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingSimpleL2Pooling2dTestCommon<uint8_t>(workloadFactory); -} - -LayerTestResult<float, 4> IgnorePaddingL2Pooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingL2Pooling2dSize3TestCommon<float>(workloadFactory); -} - -LayerTestResult<uint8_t, 4> IgnorePaddingL2Pooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return IgnorePaddingL2Pooling2dSize3TestCommon<uint8_t>(workloadFactory); -} - -LayerTestResult<float, 4> SimplePermuteFloat32Test(armnn::IWorkloadFactory& workloadFactory) -{ - return SimplePermuteFloat32TestCommon(workloadFactory); -}; - -LayerTestResult<uint8_t, 4> SimplePermuteUint8Test(armnn::IWorkloadFactory& workloadFactory) -{ - return SimplePermuteUint8TestCommon(workloadFactory); -}; - -LayerTestResult<float, 4> PermuteFloat32ValueSet1Test(armnn::IWorkloadFactory& workloadFactory) -{ - return PermuteFloat32ValueSet1TestCommon(workloadFactory); -}; - -LayerTestResult<float, 4> PermuteFloat32ValueSet2Test(armnn::IWorkloadFactory& workloadFactory) -{ - return PermuteFloat32ValueSet2TestCommon(workloadFactory); -}; - -LayerTestResult<float, 4> PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& workloadFactory) -{ - return PermuteFloat32ValueSet3TestCommon(workloadFactory); -};
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