// // 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 #include #include #include #include #include #include #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" #include // 3-channel 16x8 image used as common input data for a number of Conv2d tests. static std::vector 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 Bias2({0, 2}); // Helper function that returns either Bias2 or an empty vector depending on whether bias is enabled. template boost::multi_array GetBias2(bool biasEnabled, float qScale, int32_t qOffset) { if(biasEnabled) { armnn::TensorInfo biasDesc({static_cast(Bias2.size())}, armnn::GetDataType()); boost::multi_array bias = MakeTensor(biasDesc, QuantizedVector(qScale, qOffset, Bias2)); return bias; } else { return boost::multi_array(); } } template LayerTestResult 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()); boost::multi_array input = MakeTensor(inputDesc, QuantizedVector(qScale, qOffset, ConvInput3x8x16)); // Use a 2-element batch with 3-channel 3x5 kernels. armnn::TensorInfo kernelDesc({2, 3, 5, 3}, armnn::GetDataType()); boost::multi_array kernel = MakeTensor(kernelDesc, std::vector( QuantizedVector(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()); boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( QuantizedVector(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(workloadFactory, input, kernel, GetBias2::Type>(biasEnabled, qScale, qOffset), expectedOutput, qScale, qOffset); } template LayerTestResult 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()); boost::multi_array input = MakeTensor(inputDesc, QuantizedVector(qScale, qOffset, ConvInput3x8x16)); // Use a 2-element batch of 3-channel 3x3 kernels. armnn::TensorInfo kernelDesc({2, 3, 3, 3}, armnn::GetDataType()); boost::multi_array kernel = MakeTensor(kernelDesc, std::vector( QuantizedVector(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()); boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( QuantizedVector(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(workloadFactory, input, kernel, GetBias2::Type>(biasEnabled, qScale, qOffset), expectedOutput, qScale, qOffset); } template LayerTestResult SimpleConvolution2d3x3NhwcTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset, bool biasEnabled, armnn::DataLayout dataLayout) { // Use common single-batch 5x5 image. armnn::TensorInfo inputDesc({1, 3, 4, 1}, armnn::GetDataType()); boost::multi_array input = MakeTensor(inputDesc, { 1, 5, 2, 3, 8, 7, 3, 6, 3, 3, 9, 1 }); // Use a 2-element batch of 3-channel 3x3 kernels. armnn::TensorInfo kernelDesc({1, 3, 3, 1}, armnn::GetDataType()); boost::multi_array kernel = MakeTensor(kernelDesc, { 4, 5, 6, 0, 0, 0, 3, 2, 1 }); // Expected output is 1 batch of a 5x5 image. armnn::TensorInfo outputDesc({1, 3, 4, 1}, armnn::GetDataType()); const std::vector outputData = { 23, 41, 33, 21, 44, 65, 76, 52, 82, 85, 79, 42 }; boost::multi_array expectedOutput = MakeTensor(outputDesc, outputData); return SimpleConvolution2dNhwcTestImpl(workloadFactory, input, kernel, boost::multi_array(), expectedOutput, dataLayout, qScale, qOffset); } LayerTestResult SimpleConvolution2d3x5Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) { return SimpleConvolution2d3x5TestCommon(workloadFactory, 0.f, 0, biasEnabled); } LayerTestResult SimpleConvolution2d3x5Uint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) { return SimpleConvolution2d3x5TestCommon(workloadFactory, 0.5f, 50, biasEnabled); } LayerTestResult SimpleConvolution2d3x3Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) { return SimpleConvolution2d3x3TestCommon(workloadFactory, 0.f, 0, biasEnabled); } LayerTestResult SimpleConvolution2d3x3NhwcTest(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) { return SimpleConvolution2d3x3NhwcTestCommon(workloadFactory, 0.f, 0, biasEnabled, armnn::DataLayout::NHWC); } LayerTestResult SimpleConvolution2d3x3Uint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) { return SimpleConvolution2d3x3TestCommon(workloadFactory, 0.5f, 50, biasEnabled); } template LayerTestResult 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()); boost::multi_array input = MakeTensor(inputDesc, std::vector( QuantizedVector(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()); boost::multi_array kernel = MakeTensor(kernelDesc, std::vector( QuantizedVector(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()); boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( QuantizedVector(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(workloadFactory, input, kernel, GetBias2::Type>(false, qScale, qOffset), expectedOutput, qScale, qOffset, 1, // Padding left. 2, // Padding top. 3, // Padding right. 4); // Padding bottom. } template LayerTestResult 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()); boost::multi_array input = MakeTensor(inputDesc, std::vector( QuantizedVector(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()); boost::multi_array kernel = MakeTensor(kernelDesc, std::vector( QuantizedVector(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()); std::vector myVec(outputDesc.GetNumElements(), 0); boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( QuantizedVector(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(workloadFactory, input, kernel, GetBias2::Type>(false, qScale, qOffset), expectedOutput, qScale, qOffset, 1, // Padding left. 1, // Padding top. 2, // Padding right. 2); // Padding bottom. } template LayerTestResult 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()); auto input = MakeTensor(inputTensorInfo, std::vector( QuantizedVector(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()); auto kernel = MakeTensor(kernelTensorInfo, std::vector( QuantizedVector(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()); boost::multi_array expectedOutput = MakeTensor(outputTensorInfo, std::vector( QuantizedVector(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(workloadFactory, input, kernel, GetBias2::Type>(biasEnabled, qScale, qOffset), expectedOutput, qScale, qOffset, 1, // Padding left. 1, // Padding top. 2, // Padding right. 2, // Padding bottom. 1, // strideX 1); // strideY } template LayerTestResult DepthwiseConvolution2dNhwcTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset, bool biasEnabled) { armnn::TensorInfo inputTensorInfo({ 1, 5, 5, 2}, armnn::GetDataType()); auto input = MakeTensor(inputTensorInfo, std::vector( QuantizedVector(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { 0, 25, 1, 26, 2, 27, 3, 28, 4, 29, 5, 30, 6, 31, 7, 32, 8, 33, 9, 34, 10, 35, 11, 36, 12, 37, 13, 38, 14, 39, 15, 40, 16, 41, 17, 42, 18, 43, 19, 44, 20, 45, 21, 46, 22, 47, 23, 48, 24, 49 }))); armnn::TensorInfo kernelTensorInfo({ 1, 4, 4, 2}, armnn::GetDataType()); auto kernel = MakeTensor(kernelTensorInfo, std::vector( QuantizedVector(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), { 32, 16, 31, 15, 30, 14, 29, 13, 28, 12, 27, 11, 26, 10, 25, 9, 24, 8, 23, 7, 22, 6, 21, 5, 20, 4, 19, 3, 18, 2, 17, 1 }))); armnn::TensorInfo outputTensorInfo({ 1, 5, 5, 2}, armnn::GetDataType()); boost::multi_array expectedOutput = MakeTensor(outputTensorInfo, std::vector( QuantizedVector(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), { 1062, 1550, 1580, 2284, 1850, 2362, 1530, 1955, 1117, 1428, 2140, 2910, 3108, 4206, 3500, 4342, 2842, 3528, 2042, 2536, 3580, 3390, 5068, 4886, 5460, 5022, 4342, 4068, 3062, 2916, 3618, 3566, 5072, 5056, 5390, 5182, 4248, 4133, 2971, 2922, 3074, 3100, 4282, 4352, 4510, 4452, 3533, 3517, 2457, 2465 }))); return DepthwiseConvolution2dNhwcTestImpl(workloadFactory, input, kernel, GetBias2::Type>(biasEnabled, qScale, qOffset), expectedOutput, qScale, qOffset, 1, // Padding left. 1, // Padding top. 2, // Padding right. 2, // Padding bottom. 1, // strideX 1); // strideY } LayerTestResult Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest(armnn::IWorkloadFactory& workloadFactory) { return Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon(workloadFactory, 0.0f, 0); } LayerTestResult Convolution2dAsymmetricPaddingTest(armnn::IWorkloadFactory& workloadFactory) { return SimpleConvolution2dAsymmetricPaddingTestCommon(workloadFactory, 0.0f, 0); } LayerTestResult DepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) { return DepthwiseConvolution2dTestImpl(workloadFactory, 0.0f, 0, biasEnabled); } LayerTestResult DepthwiseConvolution2dDepthNhwcTest(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) { return DepthwiseConvolution2dNhwcTestCommon(workloadFactory, 0.0f, 0, biasEnabled); } LayerTestResult DepthwiseConvolution2dDepthMul1Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) { return DepthwiseConvolution2dDepthMul1TestImpl(workloadFactory, 0.0f, 0, biasEnabled); } LayerTestResult DepthwiseConvolution2dAsymmetricTest(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) { return DepthwiseConvolution2dAsymmetricTestCommon(workloadFactory, 0.0f, 0, biasEnabled); } LayerTestResult DepthwiseConvolution2dUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) { return DepthwiseConvolution2dTestImpl(workloadFactory, 0.5f, 50, biasEnabled); } LayerTestResult DepthwiseConvolution2dDepthMul1Uint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) { return DepthwiseConvolution2dDepthMul1TestImpl(workloadFactory, 0.5f, 50, biasEnabled); } LayerTestResult Convolution1dTest(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) { return Convolution1dTestImpl(workloadFactory, 0.0f, 0, biasEnabled); } LayerTestResult Convolution1dUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) { return Convolution1dTestImpl(workloadFactory, 0.1f, 128, biasEnabled); } LayerTestResult CompareConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, armnn::IWorkloadFactory& refWorkloadFactory) { return CompareConvolution2dTestImpl(workloadFactory, refWorkloadFactory); } template LayerTestResult CompareDepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, armnn::IWorkloadFactory& refWorkloadFactory) { return CompareDepthwiseConvolution2dTestImpl(workloadFactory, refWorkloadFactory); } template LayerTestResult CompareDepthwiseConvolution2dTest( armnn::IWorkloadFactory&, armnn::IWorkloadFactory&); template LayerTestResult CompareDepthwiseConvolution2dTest( armnn::IWorkloadFactory&, armnn::IWorkloadFactory&); LayerTestResult SimpleNormalizationAcrossTest(armnn::IWorkloadFactory& workloadFactory) { auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; auto normChannel = armnn::NormalizationAlgorithmChannel::Across; return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod); } LayerTestResult SimpleNormalizationWithinTest(armnn::IWorkloadFactory& workloadFactory) { auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; auto normChannel = armnn::NormalizationAlgorithmChannel::Within; return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod); } LayerTestResult SimpleNormalizationAcrossNhwcTest(armnn::IWorkloadFactory& workloadFactory) { auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; auto normChannel = armnn::NormalizationAlgorithmChannel::Across; return SimpleNormalizationNhwcClNeonTestImpl(workloadFactory, normChannel, normMethod); } LayerTestResult SimpleSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, float beta) { return SimpleSoftmaxTestImpl(workloadFactory, beta); } LayerTestResult SimpleSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, float beta) { return SimpleSoftmaxTestImpl(workloadFactory, beta); } LayerTestResult CompareNormalizationTest(armnn::IWorkloadFactory& workloadFactory, armnn::IWorkloadFactory& refWorkloadFactory, armnn::NormalizationAlgorithmChannel normChannel, armnn::NormalizationAlgorithmMethod normMethod) { return CompareNormalizationTestImpl(workloadFactory, refWorkloadFactory, normChannel, normMethod); } LayerTestResult CompareSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, armnn::IWorkloadFactory& refWorkloadFactory, float beta) { return CompareSoftmaxTestImpl(workloadFactory, refWorkloadFactory, beta); } LayerTestResult CompareSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, armnn::IWorkloadFactory& refWorkloadFactory, float beta) { return CompareSoftmaxTestImpl(workloadFactory, refWorkloadFactory, beta); } std::vector> SplitterTest(armnn::IWorkloadFactory& workloadFactory) { return SplitterTestCommon(workloadFactory); } std::vector> SplitterUint8Test(armnn::IWorkloadFactory& workloadFactory) { return SplitterTestCommon(workloadFactory, 1.0f, 0); } LayerTestResult CopyViaSplitterTest(armnn::IWorkloadFactory& workloadFactory) { return CopyViaSplitterTestImpl(workloadFactory, 0.0f, 0); } LayerTestResult CopyViaSplitterUint8Test(armnn::IWorkloadFactory& workloadFactory) { return CopyViaSplitterTestImpl(workloadFactory, 1.0f, 0); } LayerTestResult LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest( armnn::IWorkloadFactory& workloadFactory) { armnn::TensorInfo inputDesc({ 2, 2 }, armnn::GetDataType()); boost::multi_array input = MakeTensor(inputDesc, std::vector( { 2., 3., 3., 4. })); armnn::TensorInfo outputDesc({ 2, 4 }, armnn::GetDataType()); boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f})); return LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput); } LayerTestResult LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest( armnn::IWorkloadFactory& workloadFactory) { armnn::TensorInfo inputDesc({ 2, 5 }, armnn::GetDataType()); boost::multi_array input = MakeTensor(inputDesc, std::vector( {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()); boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( {-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 LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest(armnn::IWorkloadFactory& workloadFactory) { armnn::TensorInfo inputDesc({2, 2}, armnn::GetDataType()); boost::multi_array input = MakeTensor(inputDesc, std::vector( {2., 3., 3., 4.})); armnn::TensorInfo outputDesc({2, 4}, armnn::GetDataType()); boost::multi_array expectedOutput = MakeTensor(outputDesc, std::vector( {{-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f, -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}})); return LstmNoCifgNoPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput); } LayerTestResult 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 ret(outputTensorInfo); ret.outputExpected = MakeTensor(outputTensorInfo, std::vector( { 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(inputTensorInfo1, std::vector( { 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(inputTensorInfo2, std::vector( { 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 wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of input[0]. armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1); std::vector wOrigin2 = {2, 0, 0}; //Extent of the window is defined by size of input[1]. armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2); std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); bool subTensorsSupported = workloadFactory.SupportsSubTensors(); std::unique_ptr inputHandle1 = subTensorsSupported ? workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : workloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr 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 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 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(inputTensorInfo1, std::vector( { 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(inputTensorInfo2, std::vector( { 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 ret(outputTensorInfo); ret.outputExpected = MakeTensor(outputTensorInfo, std::vector( { 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 inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); std::unique_ptr 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 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 LayerTestResult AdditionBroadcastTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) { armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 1}, armnn::GetDataType()); armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 2, 3}, armnn::GetDataType()); armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType()); if (armnn::IsQuantizedType()) { inputTensorInfo1.SetQuantizationScale(qScale); inputTensorInfo1.SetQuantizationOffset(qOffset); inputTensorInfo2.SetQuantizationScale(qScale); inputTensorInfo2.SetQuantizationOffset(qOffset); outputTensorInfo.SetQuantizationScale(qScale); outputTensorInfo.SetQuantizationOffset(qOffset); } auto input1 = MakeTensor(inputTensorInfo1, QuantizedVector(qScale, qOffset, { 0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, })); auto input2 = MakeTensor(inputTensorInfo2, QuantizedVector(qScale, qOffset, { 0.5f, 1.5f, 2.5f, 3.5f, 4.5f, 5.5f, })); LayerTestResult ret(outputTensorInfo); ret.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(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 inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); std::unique_ptr 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 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 LayerTestResult AdditionBroadcast1ElementTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) { armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType()); armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 1, 1}, armnn::GetDataType()); armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType()); if (armnn::IsQuantizedType()) { inputTensorInfo1.SetQuantizationScale(qScale); inputTensorInfo1.SetQuantizationOffset(qOffset); inputTensorInfo2.SetQuantizationScale(qScale); inputTensorInfo2.SetQuantizationOffset(qOffset); outputTensorInfo.SetQuantizationScale(qScale); outputTensorInfo.SetQuantizationOffset(qOffset); } auto input1 = MakeTensor(inputTensorInfo1, QuantizedVector(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(inputTensorInfo2, QuantizedVector(qScale, qOffset, { 0.5f, })); LayerTestResult ret(outputTensorInfo); ret.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(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 inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); std::unique_ptr 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 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 AdditionBroadcastTest(armnn::IWorkloadFactory& workloadFactory) { return AdditionBroadcastTestImpl(workloadFactory, 0.0f, 0); } LayerTestResult AdditionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory) { return AdditionBroadcastTestImpl(workloadFactory, 2.f, 0); } LayerTestResult AdditionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) { return AdditionBroadcast1ElementTestImpl(workloadFactory, 0.0f, 0); } LayerTestResult AdditionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) { return AdditionBroadcast1ElementTestImpl(workloadFactory, 0.1333333f, 128); } LayerTestResult 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(inputTensorInfo1, 1232); auto input2 = MakeRandomTensor(inputTensorInfo2, 456); LayerTestResult ret(outputTensorInfo); std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); std::unique_ptr inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr inputHandle2Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo2); std::unique_ptr 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 workload = workloadFactory.CreateAddition(data, info); std::unique_ptr 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 LayerTestResult DivisionTestHelper(armnn::IWorkloadFactory& workloadFactory, const unsigned int shape0[4], const std::vector& values0, float scale0, int32_t offset0, const unsigned int shape1[4], const std::vector & values1, float scale1, int32_t offset1, const unsigned int outShape[4], const std::vector & outValues, float outScale, int32_t outOffset) { auto dataType = (std::is_same::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(inputTensorInfo0, values0); auto input1 = MakeTensor(inputTensorInfo1, values1); LayerTestResult result(outputTensorInfo); result.outputExpected = MakeTensor(outputTensorInfo, outValues); std::unique_ptr inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr 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 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 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 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 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 output({ INFINITY, INFINITY, -INFINITY, -INFINITY, NAN, NAN, -NAN, -NAN, -INFINITY, -INFINITY, INFINITY, INFINITY, 1, 1, 1, 1 }); return DivisionTestHelper(workloadFactory, shape, input0, 1.0f, 0, shape, input1, 1.0f, 0, shape, output, 1.0f, 0); } LayerTestResult 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 input0({ 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5 }); std::vector input1({ 1, 1, 1, 1, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4 }); std::vector 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(workloadFactory, shape, input0, 1.0f, 0, shape, input1, 1.0f, 0, shape, output, 1.0f, 0); } LayerTestResult DivisionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) { unsigned int shape0[] = { 1, 2, 2, 2 }; std::vector input0({ 2, 4, 6, 8, 10, 12, 14, 16}); unsigned int shape1[] = { 1, 1, 1, 1 }; std::vector input1({ 2 }); std::vector output({ 1, 2, 3, 4, 5, 6, 7, 8}); return DivisionTestHelper(workloadFactory, shape0, input0, 1.0f, 0, shape1, input1, 1.0f, 0, shape0, output, 1.0f, 0); } LayerTestResult DivisionBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory) { unsigned int shape0[] = { 1, 3, 3, 2 }; std::vector 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 input1({ 1, 2 }); std::vector output({ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18}); return DivisionTestHelper(workloadFactory, shape0, input0, 1.0f, 0, shape1, input1, 1.0f, 0, shape0, output, 1.0f, 0); } LayerTestResult 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 input0({2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5 }); std::vector input1({1, 1, 1, 1, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4 }); std::vector output({8, 8, 8, 8, 6, 6, 6, 6, 4, 4, 4, 4, 5, 5, 5, 5}); return DivisionTestHelper(workloadFactory, shape, input0, 1.0f, 0, shape, input1, 1.0f, 0, shape, output, 0.25f, 0); } LayerTestResult DivisionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) { unsigned int shape0[] = { 1, 2, 2, 2 }; std::vector input0({ 2, 4, 6, 8, 10, 12, 14, 16}); unsigned int shape1[] = { 1, 1, 1, 1 }; std::vector input1({ 2 }); std::vector output({ 1, 2, 3, 4, 5, 6, 7, 8}); return DivisionTestHelper(workloadFactory, shape0, input0, 1.0f, 0, shape1, input1, 1.0f, 0, shape0, output, 1.0f, 0); } LayerTestResult DivisionBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory) { unsigned int shape0[] = { 1, 3, 3, 2 }; std::vector 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 input1({ 1, 2 }); std::vector output({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18}); return DivisionTestHelper(workloadFactory, shape0, input0, 1.0f, 0, shape1, input1, 1.0f, 0, shape0, output, 1.0f, 0); } namespace { LayerTestResult MultiplicationTestHelper(armnn::IWorkloadFactory& workloadFactory, const unsigned int shape0[4], const std::vector & values0, const unsigned int shape1[4], const std::vector & values1, const unsigned int outShape[4], const std::vector & 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(inputTensorInfo0, values0); auto input1 = MakeTensor(inputTensorInfo1, values1); LayerTestResult ret(outputTensorInfo); std::unique_ptr inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr 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 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(outputTensorInfo, outValues); return ret; } } // anonymous namespace LayerTestResult 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 input0({ 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4 }); std::vector input1({ 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5 }); std::vector 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 MultiplicationBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) { unsigned int shape0[] = { 1, 2, 2, 2 }; std::vector input0({ 1, 2, 3, 4, 5, 6, 7, 8}); unsigned int shape1[] = { 1, 1, 1, 1 }; std::vector input1({ 2 }); std::vector output({ 2, 4, 6, 8, 10, 12, 14, 16}); return MultiplicationTestHelper(workloadFactory, shape0, input0, shape1, input1, shape0, output); } LayerTestResult MultiplicationBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory) { unsigned int shape0[] = { 1, 3, 3, 2 }; std::vector 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 input1({ 1, 2 }); std::vector 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 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 comparisonResult(outputTensorInfo); auto input0 = MakeRandomTensor(inputTensorInfo0, 803506992); auto input1 = MakeRandomTensor(inputTensorInfo1, 54902257); std::unique_ptr inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); std::unique_ptr inputHandle0Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo0); std::unique_ptr inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr 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 workload = workloadFactory.CreateMultiplication(data, info); std::unique_ptr 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 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(inputTensorInfo, 21312); auto mean = MakeRandomTensor(tensorInfo, 123); auto variance = MakeRandomTensor(tensorInfo, 234, 0.0f); auto beta = MakeRandomTensor(tensorInfo, 123); auto gamma = MakeRandomTensor(tensorInfo, 345); LayerTestResult ret(outputTensorInfo); std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); std::unique_ptr inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr 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 workload = workloadFactory.CreateBatchNormalization(data, info); std::unique_ptr 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 void PermuteTensorData( armnn::IWorkloadFactory& workloadFactory, const armnn::PermutationVector& mappings, armnn::TensorInfo & inputTensorInfo, const T * inputData, std::vector& 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 inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr 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 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 & inputTensorInfos, unsigned int concatDim) { std::vector 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 & 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 newDims(size_t(3), 1u); unsigned int expandedBy = 3 - numDims; for (unsigned int i=0; i & 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 void PermuteInputsForConcat( armnn::IWorkloadFactory& workloadFactory, std::vector & inputTensorInfos, std::vector & inputData, std::vector> & 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 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(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 void PermuteOutputForConcat( armnn::IWorkloadFactory& workloadFactory, const armnn::TensorInfo & tensorInfo, const armnn::PermutationVector & permuteVector, std::unique_ptr && 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 inputData(tensorInfo.GetNumElements()); std::vector outputData; CopyDataFromITensorHandle(&inputData[0], inputDataHandle.get()); PermuteTensorData(workloadFactory, permuteVector, resultTensorInfo, &inputData[0], outputData); ::memcpy(data, &outputData[0], sizeof(T)*outputData.size()); } template void Concatenate(armnn::IWorkloadFactory& workloadFactory, std::initializer_list inputTensorInfosOrig, std::initializer_list 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 inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end()); std::vector inputs = inputsOrig; armnn::TensorInfo outputTensorInfo = outputTensorInfoOrig; armnn::PermutationVector permuteVector{0, 1, 2}; // Holds and automatically releases memory for the reshaped input data. std::vector> 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(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(viewsDescriptor.GetViewOrigin(i), viewsDescriptor.GetViewOrigin(i) + viewsDescriptor.GetNumDimensions())); } std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); std::vector> 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 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 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(workloadFactory, outputTensorInfo, permuteVector, std::move(outputHandle), output); } else { CopyDataFromITensorHandle(output, outputHandle.get()); } } template LayerTestResult Concatenation1dTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) { armnn::TensorInfo inputTensorInfo({ 3 }, armnn::GetDataType()); auto input0 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { 1.0f, 2.0f, 3.0f })); auto input1 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { 4.0f, 5.0f, 6.0f })); auto input2 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { 7.0f, 8.0f, 9.0f })); armnn::TensorInfo outputTensorInfo({ 9 }, armnn::GetDataType()); LayerTestResult result(outputTensorInfo); std::vector output; output.resize(outputTensorInfo.GetNumElements()); Concatenate(workloadFactory, { inputTensorInfo, inputTensorInfo, inputTensorInfo }, { input0.data(), input1.data(), input2.data() }, outputTensorInfo, output.data(), 0); result.output = MakeTensor(outputTensorInfo, output); result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(qScale, qOffset, { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f })); return result; } LayerTestResult Concatenation1dTest(armnn::IWorkloadFactory& workloadFactory) { return Concatenation1dTestImpl(workloadFactory, 0.0f, 0); } template LayerTestResult 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()); auto input0 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { // Batch 0 1.0f, 2.0f, 3.0f, // Batch 1 10.0f, 11.0f, 12.0f, })); auto input1 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { // Batch 0 4.0f, 5.0f, 6.0f, // Batch 1 13.0f, 14.0f, 15.0f, })); auto input2 = MakeTensor(inputTensorInfo, QuantizedVector(qScale, qOffset, { // Batch 0 7.0f, 8.0f, 9.0f, // Batch 1 16.0f, 17.0f, 18.0f, })); LayerTestResult result(outputTensorInfo); std::vector output; output.resize(outputTensorInfo.GetNumElements()); Concatenate(workloadFactory, { inputTensorInfo, inputTensorInfo, inputTensorInfo }, { input0.data(), input1.data(), input2.data() }, outputTensorInfo, output.data(), dimension); result.output = MakeTensor(outputTensorInfo, output); return result; } template LayerTestResult Concatenation2dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) { armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType()); LayerTestResult result = Concatenation2dTestImpl(workloadFactory, outputTensorInfo, 0, qScale, qOffset); result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(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 Concatenation2dDim0Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation2dDim0TestImpl(workloadFactory, 0.0f, 0); } template LayerTestResult Concatenation2dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) { armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType()); LayerTestResult result = Concatenation2dTestImpl(workloadFactory, outputTensorInfo, 1, qScale, qOffset); result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(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 Concatenation2dDim1Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation2dDim1TestImpl(workloadFactory, 0.0f, 0); } template LayerTestResult Concatenation2dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) { armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType()); auto input0 = MakeTensor(input0TensorInfo, QuantizedVector(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()); auto input1 = MakeTensor(input1TensorInfo, QuantizedVector(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()); auto input2 = MakeTensor(input2TensorInfo, QuantizedVector(qScale, qOffset, { // Batch 1 16.0f, 17.0f, 18.0f, })); armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType()); LayerTestResult result(outputTensorInfo); std::vector output; output.resize(outputTensorInfo.GetNumElements()); Concatenate(workloadFactory, { input0TensorInfo, input1TensorInfo, input2TensorInfo }, { input0.data(), input1.data(), input2.data() }, outputTensorInfo, output.data(), 0); result.output = MakeTensor(outputTensorInfo, output); result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(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 Concatenation2dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) { return Concatenation2dDim0DiffInputDimsTestImpl(workloadFactory, 0.0f, 0); } template LayerTestResult Concatenation2dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) { armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType()); auto input0 = MakeTensor(input0TensorInfo, QuantizedVector(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()); auto input1 = MakeTensor(input1TensorInfo, QuantizedVector(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()); auto input2 = MakeTensor(input2TensorInfo, QuantizedVector(qScale, qOffset, { // Batch 0 9.0f, // Batch 1 18.0f })); armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType()); LayerTestResult result(outputTensorInfo); std::vector output; output.resize(outputTensorInfo.GetNumElements()); Concatenate(workloadFactory, { input0TensorInfo, input1TensorInfo, input2TensorInfo }, { input0.data(), input1.data(), input2.data() }, outputTensorInfo, output.data(), 1); result.output = MakeTensor(outputTensorInfo, output); result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(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 Concatenation2dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) { return Concatenation2dDim1DiffInputDimsTestImpl(workloadFactory, 0.0f, 0); } template LayerTestResult Concatenation3dTestImpl(armnn::IWorkloadFactory& workloadFactory, const armnn::TensorInfo& outputTensorInfo, unsigned int dimension, float qScale, int32_t qOffset) { armnn::TensorInfo inputTensorInfo({ 2, 3, 2 }, armnn::GetDataType()); auto input0 = MakeTensor(inputTensorInfo, QuantizedVector(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(inputTensorInfo, QuantizedVector(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(inputTensorInfo, QuantizedVector(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 result(outputTensorInfo); std::vector output; output.resize(outputTensorInfo.GetNumElements()); Concatenate(workloadFactory, { inputTensorInfo, inputTensorInfo, inputTensorInfo }, { input0.data(), input1.data(), input2.data() }, outputTensorInfo, output.data(), dimension); result.output = MakeTensor(outputTensorInfo, output); return result; } template LayerTestResult Concatenation3dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) { armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType()); LayerTestResult result = Concatenation3dTestImpl(workloadFactory, outputTensorInfo, 0, qScale, qOffset); result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(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 Concatenation3dDim0Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation3dDim0TestImpl(workloadFactory, 0.0f, 0); } template LayerTestResult Concatenation3dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) { armnn::TensorInfo outputTensorInfo({ 2, 9, 2 }, armnn::GetDataType()); LayerTestResult result = Concatenation3dTestImpl(workloadFactory, outputTensorInfo, 1, qScale, qOffset); result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(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 Concatenation3dDim1Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation3dDim1TestImpl(workloadFactory, 0.0f, 0); } template LayerTestResult Concatenation3dDim2TestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) { armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType()); LayerTestResult result = Concatenation3dTestImpl(workloadFactory, outputTensorInfo, 2, qScale, qOffset); result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(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 Concatenation3dDim2Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation3dDim2TestImpl(workloadFactory, 0.0f, 0); } template LayerTestResult Concatenation3dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) { armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType()); auto input0 = MakeTensor(input0TensorInfo, QuantizedVector(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()); auto input1 = MakeTensor(input1TensorInfo, QuantizedVector(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()); auto input2 = MakeTensor(input2TensorInfo, QuantizedVector(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()); LayerTestResult result(outputTensorInfo); std::vector output; output.resize(outputTensorInfo.GetNumElements()); Concatenate(workloadFactory, { input0TensorInfo, input1TensorInfo, input2TensorInfo }, { input0.data(), input1.data(), input2.data() }, outputTensorInfo, output.data(), 0); result.output = MakeTensor(outputTensorInfo, output); result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(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 Concatenation3dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) { return Concatenation3dDim0DiffInputDimsTestImpl(workloadFactory, 0.0f, 0); } template LayerTestResult Concatenation3dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) { armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType()); auto input0 = MakeTensor(input0TensorInfo, QuantizedVector(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()); auto input1 = MakeTensor(input1TensorInfo, QuantizedVector(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()); auto input2 = MakeTensor(input2TensorInfo, QuantizedVector(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()); LayerTestResult result(outputTensorInfo); std::vector output; output.resize(outputTensorInfo.GetNumElements()); Concatenate(workloadFactory, { input0TensorInfo, input1TensorInfo, input2TensorInfo }, { input0.data(), input1.data(), input2.data() }, outputTensorInfo, output.data(), 1); result.output = MakeTensor(outputTensorInfo, output); result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(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 Concatenation3dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) { return Concatenation3dDim1DiffInputDimsTestImpl(workloadFactory, 0.0f, 0); } template LayerTestResult Concatenation3dDim2DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) { armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType()); auto input0 = MakeTensor(input0TensorInfo, QuantizedVector(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()); auto input1 = MakeTensor(input1TensorInfo, QuantizedVector(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()); auto input2 = MakeTensor(input2TensorInfo, QuantizedVector(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()); LayerTestResult result(outputTensorInfo); std::vector output; output.resize(outputTensorInfo.GetNumElements()); Concatenate(workloadFactory, { input0TensorInfo, input1TensorInfo, input2TensorInfo }, { input0.data(), input1.data(), input2.data() }, outputTensorInfo, output.data(), 2); result.output = MakeTensor(outputTensorInfo, output); result.outputExpected = MakeTensor(outputTensorInfo, QuantizedVector(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 Concatenation3dDim2DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) { return Concatenation3dDim2DiffInputDimsTestImpl(workloadFactory, 0.0f, 0); } LayerTestResult ResizeBilinearNopTestImpl(armnn::IWorkloadFactory& workloadFactory, const armnn::TensorShape& inputOutputTensorShape, armnn::DataLayout dataLayout) { const armnn::TensorInfo inputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32); const armnn::TensorInfo outputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32); auto input = MakeTensor(inputTensorInfo, std::vector({ 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 result(outputTensorInfo); result.outputExpected = input; std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); armnn::ResizeBilinearQueueDescriptor descriptor; descriptor.m_Parameters.m_DataLayout = dataLayout; armnn::WorkloadInfo info; AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); std::unique_ptr 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 ResizeBilinearNopTest(armnn::IWorkloadFactory& workloadFactory) { // BatchSize = 1, Channels = 1, Height = 4, Width = 4 const armnn::TensorShape inputOutputShape{ 1, 1, 4, 4 }; return ResizeBilinearNopTestImpl(workloadFactory, inputOutputShape, armnn::DataLayout::NCHW); } LayerTestResult ResizeBilinearNopNhwcTest(armnn::IWorkloadFactory& workloadFactory) { // BatchSize = 1, Height = 4, Width = 4, Channels = 1 const armnn::TensorShape inputOutputShape{ 1, 4, 4, 1 }; return ResizeBilinearNopTestImpl(workloadFactory, inputOutputShape, armnn::DataLayout::NHWC); } LayerTestResult SimpleResizeBilinearTestImpl(armnn::IWorkloadFactory& workloadFactory, const armnn::TensorShape& inputTensorShape, const armnn::TensorShape& outputTensorShape, armnn::DataLayout dataLayout) { const armnn::TensorInfo inputTensorInfo(inputTensorShape, armnn::DataType::Float32); const armnn::TensorInfo outputTensorInfo(outputTensorShape, armnn::DataType::Float32); auto input = MakeTensor(inputTensorInfo, std::vector({ 1.0f, 255.0f, 200.0f, 250.0f })); // 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 result(outputTensorInfo); result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ 1.0f })); std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); armnn::ResizeBilinearQueueDescriptor descriptor; descriptor.m_Parameters.m_DataLayout = dataLayout; armnn::WorkloadInfo info; AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); std::unique_ptr 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 SimpleResizeBilinearTest(armnn::IWorkloadFactory& workloadFactory) { // inputShape: BatchSize = 1, Channels = 1, Height = 2, Width = 2 const armnn::TensorShape inputShape{ 1, 1, 2, 2 }; // outputShape: BatchSize = 1, Channels = 1, Height = 1, Width = 1 const armnn::TensorShape outputShape{ 1, 1, 1, 1 }; return SimpleResizeBilinearTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NCHW); } LayerTestResult SimpleResizeBilinearNhwcTest(armnn::IWorkloadFactory& workloadFactory) { // inputShape: BatchSize = 1, Height = 2, Width = 2, Channels = 1 const armnn::TensorShape inputShape{ 1, 2, 2, 1 }; // outputShape: BatchSize = 1, Height = 1, Width = 1, Channels = 1 const armnn::TensorShape outputShape{ 1, 1, 1, 1 }; return SimpleResizeBilinearTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NHWC); } LayerTestResult ResizeBilinearSqMinTestImpl(armnn::IWorkloadFactory& workloadFactory, const armnn::TensorShape& inputTensorShape, const armnn::TensorShape& outputTensorShape, armnn::DataLayout dataLayout) { const armnn::TensorInfo inputTensorInfo(inputTensorShape, armnn::DataType::Float32); const armnn::TensorInfo outputTensorInfo(outputTensorShape, armnn::DataType::Float32); auto input = MakeTensor(inputTensorInfo, std::vector({ 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 result(outputTensorInfo); result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ 1.0f, 3.0f, 3.0f, 5.0f })); std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); armnn::ResizeBilinearQueueDescriptor descriptor; descriptor.m_Parameters.m_DataLayout = dataLayout; armnn::WorkloadInfo info; AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); std::unique_ptr 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 ResizeBilinearSqMinTest(armnn::IWorkloadFactory& workloadFactory) { // inputShape: BatchSize = 1, Channels = 1, Height = 4, Width = 4 const armnn::TensorShape inputShape{ 1, 1, 4, 4 }; // outputShape: BatchSize = 1, Channels = 1, Height = 2, Width = 2 const armnn::TensorShape outputShape{ 1, 1, 2, 2 }; return ResizeBilinearSqMinTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NCHW); } LayerTestResult ResizeBilinearSqMinNhwcTest(armnn::IWorkloadFactory& workloadFactory) { // inputShape: BatchSize = 1, Height = 4, Width = 4, Channels = 1 const armnn::TensorShape inputShape{ 1, 4, 4, 1 }; // outputShape: BatchSize = 1, Height = 2, Width = 2, Channels = 1 const armnn::TensorShape outputShape{ 1, 2, 2, 1 }; return ResizeBilinearSqMinTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NHWC); } LayerTestResult ResizeBilinearMinTestImpl(armnn::IWorkloadFactory& workloadFactory, const armnn::TensorShape& inputTensorShape, const armnn::TensorShape& outputTensorShape, armnn::DataLayout dataLayout) { const armnn::TensorInfo inputTensorInfo(inputTensorShape, armnn::DataType::Float32); const armnn::TensorInfo outputTensorInfo(outputTensorShape, armnn::DataType::Float32); auto input = MakeTensor(inputTensorInfo, std::vector({ 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 result(outputTensorInfo); result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ 1.0f, 2.6666f, 6.0f, 78.5f, 179.3333f, 401.0f })); std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); armnn::ResizeBilinearQueueDescriptor descriptor; descriptor.m_Parameters.m_DataLayout = dataLayout; armnn::WorkloadInfo info; AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); std::unique_ptr 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 ResizeBilinearMinTest(armnn::IWorkloadFactory& workloadFactory) { // inputShape: BatchSize = 1, Channels = 1, Height = 3, Width = 5 const armnn::TensorShape inputShape{ 1, 1, 3, 5 }; // outputShape: BatchSize = 1, Channels = 1, Height = 2, Width = 3 const armnn::TensorShape outputShape{ 1, 1, 2, 3 }; return ResizeBilinearMinTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NCHW); } LayerTestResult ResizeBilinearMinNhwcTest(armnn::IWorkloadFactory& workloadFactory) { // inputShape: BatchSize = 1, Height = 3, Width = 5, Channels = 1 const armnn::TensorShape inputShape{ 1, 3, 5, 1 }; // outputShape: BatchSize = 1, Height = 2, Width = 3, Channels = 1 const armnn::TensorShape outputShape{ 1, 2, 3, 1 }; return ResizeBilinearMinTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NHWC); } LayerTestResult ResizeBilinearMagTestImpl(armnn::IWorkloadFactory& workloadFactory, const armnn::TensorShape& inputTensorShape, const armnn::TensorShape& outputTensorShape, armnn::DataLayout dataLayout) { const armnn::TensorInfo inputTensorInfo(inputTensorShape, armnn::DataType::Float32); const armnn::TensorInfo outputTensorInfo(outputTensorShape, armnn::DataType::Float32); auto input = MakeTensor(inputTensorInfo, std::vector({ 1.0f, 2.0f, 13.0f, 21.0f, 144.0f, 233.0f })); LayerTestResult result(outputTensorInfo); result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ 1.0f, 1.4f, 1.8f, 2.0f, 2.0f, 13.0f, 16.2f, 19.4f, 21.0f, 21.0f, 144.0f, 179.6f, 215.2f, 233.0f, 233.0f })); std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); armnn::ResizeBilinearQueueDescriptor descriptor; descriptor.m_Parameters.m_DataLayout = dataLayout; armnn::WorkloadInfo info; AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); std::unique_ptr 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 ResizeBilinearMagTest(armnn::IWorkloadFactory& workloadFactory) { // inputShape: BatchSize = 1, Channels = 1, Height = 3, Width = 2 const armnn::TensorShape inputShape{ 1, 1, 3, 2 }; // outputShape: BatchSize = 1, Channels = 1, Height = 3, Width = 5 const armnn::TensorShape outputShape{ 1, 1, 3, 5 }; return ResizeBilinearMagTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NCHW); } LayerTestResult ResizeBilinearMagNhwcTest(armnn::IWorkloadFactory& workloadFactory) { // inputShape: BatchSize = 1, Height = 3, Width = 2, Channels = 1 const armnn::TensorShape inputShape{ 1, 3, 2, 1 }; // outputShape: BatchSize = 1, Height = 3, Width = 5, Channels = 1 const armnn::TensorShape outputShape{ 1, 3, 5, 1 }; return ResizeBilinearMagTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NHWC); } LayerTestResult 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(tensorInfo, std::vector({ -10.0f, -5.0f, 0.0f, 5.0f, 10.0f, 10.0f })); LayerTestResult ret(tensorInfo); std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(tensorInfo); std::unique_ptr 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 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(tensorInfo, std::vector({ 0.0f, 63.0f, 128.0f, 191.0f, 255.0f, 255.0f })); return ret; } namespace { LayerTestResult L2NormalizationTestImpl(armnn::IWorkloadFactory& workloadFactory, const armnn::TensorShape& inputOutputTensorShape, const std::vector& inputValues, const std::vector& expectedOutputValues, armnn::DataLayout dataLayout) { const armnn::TensorInfo inputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32); const armnn::TensorInfo outputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32); auto inputTensor = MakeTensor(inputTensorInfo, std::vector(inputValues)); LayerTestResult result(outputTensorInfo); result.outputExpected = MakeTensor(inputTensorInfo, std::vector(expectedOutputValues)); std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); armnn::L2NormalizationQueueDescriptor descriptor; descriptor.m_Parameters.m_DataLayout = dataLayout; armnn::WorkloadInfo info; AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); std::unique_ptr workload = workloadFactory.CreateL2Normalization(descriptor, info); inputHandle->Allocate(); outputHandle->Allocate(); CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0][0]); workloadFactory.Finalize(); workload->Execute(); CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); return result; } float CalcInvL2Norm(std::initializer_list 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); } } // anonymous namespace LayerTestResult L2Normalization1dTest(armnn::IWorkloadFactory& workloadFactory) { // Width: 1 // Height: 1 // Channels: 10 // BatchSize: 1 const armnn::TensorShape inputOutputShape{ 1, 10, 1, 1 }; std::vector inputValues { // Batch 0, Channel 0, Height (1) x Width (1) 1.0f, // Batch 0, Channel 1, Height (1) x Width (1) 2.0f, // Batch 0, Channel 2, Height (1) x Width (1) 3.0f, // Batch 0, Channel 3, Height (1) x Width (1) 4.0f, // Batch 0, Channel 4, Height (1) x Width (1) 5.0f, // Batch 0, Channel 5, Height (1) x Width (1) 6.0f, // Batch 0, Channel 6, Height (1) x Width (1) 7.0f, // Batch 0, Channel 7, Height (1) x Width (1) 8.0f, // Batch 0, Channel 8, Height (1) x Width (1) 9.0f, // Batch 0, Channel 9, Height (1) x Width (1) 10.0f }; const float approxInvL2Norm = 0.050964719f; std::vector expectedOutputValues { // Batch 0, Channel 0, Height (1) x Width (1) 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 }; return L2NormalizationTestImpl(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, armnn::DataLayout::NCHW); } LayerTestResult L2Normalization1dNhwcTest(armnn::IWorkloadFactory& workloadFactory) { // Width: 1 // Height: 1 // Channels: 10 // BatchSize: 1 const armnn::TensorShape inputOutputShape{ 1, 1, 1, 10 }; std::vector inputValues { // Batch 0, Height 0, Width (1) x Channel (10) 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; std::vector expectedOutputValues { // Batch 0, Height 0, Width (1) x Channel (10) 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 }; return L2NormalizationTestImpl(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, armnn::DataLayout::NHWC); } LayerTestResult L2Normalization2dTest(armnn::IWorkloadFactory& workloadFactory) { // Width: 5 // Height: 1 // Channels: 2 // BatchSize: 1 const armnn::TensorShape inputOutputShape{ 1, 2, 1, 5 }; std::vector inputValues { // Batch 0, Channel 0, Height (1) x Width (5) 1.0f, 3.0f, 5.0f, 7.0f, 9.0f, // Batch 0, Channel 1, Height (1) x Width (5) 2.0f, 4.0f, 6.0f, 8.0f, 10.0f }; std::vector expectedOutputValues { // Batch 0, Channel 0, Height (1) x Width (5) 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 }), // Batch 0, Channel 1, Height (1) x Width (5) 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 }) }; return L2NormalizationTestImpl(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, armnn::DataLayout::NCHW); } LayerTestResult L2Normalization2dNhwcTest(armnn::IWorkloadFactory& workloadFactory) { // Width: 5 // Height: 1 // Channels: 2 // BatchSize: 1 const armnn::TensorShape inputOutputShape{ 1, 1, 5, 2 }; std::vector inputValues { // Batch 0, Height 0, Width (5) x Channel (2) 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f }; std::vector expectedOutputValues { // Batch 0, Height 0, Width (5) x Channel (2) 1.0f * CalcInvL2Norm({ 1.0f, 2.0f }), 2.0f * CalcInvL2Norm({ 1.0f, 2.0f }), 3.0f * CalcInvL2Norm({ 3.0f, 4.0f }), 4.0f * CalcInvL2Norm({ 3.0f, 4.0f }), 5.0f * CalcInvL2Norm({ 5.0f, 6.0f }), 6.0f * CalcInvL2Norm({ 5.0f, 6.0f }), 7.0f * CalcInvL2Norm({ 7.0f, 8.0f }), 8.0f * CalcInvL2Norm({ 7.0f, 8.0f }), 9.0f * CalcInvL2Norm({ 9.0f, 10.0f }), 10.0f * CalcInvL2Norm({ 9.0f, 10.0f }) }; return L2NormalizationTestImpl(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, armnn::DataLayout::NHWC); } LayerTestResult L2Normalization3dTest(armnn::IWorkloadFactory& workloadFactory) { // Width: 3 // Height: 4 // Channels: 2 // BatchSize: 1 const armnn::TensorShape inputOutputShape{ 1, 2, 4, 3 }; std::vector inputValues { // Batch 0, Channel 0, Height (4) x Width (3) 119.0f, 21.0f, 150.0f, 149.0f, 32.0f, 179.0f, 15.0f, 227.0f, 141.0f, 147.0f, 199.0f, 220.0f, // Batch 0, Channel 1, Height (4) x Width (3) 110.0f, 140.0f, 73.0f, 211.0f, 212.0f, 89.0f, 24.0f, 138.0f, 188.0f, 162.0f, 12.0f, 161.0f }; std::vector expectedOutputValues { // Batch 0, Channel 0, Height (4) x Width (3) 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 }), // Batch 0, Channel 1, Height (4) x Width (3) 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 }) }; return L2NormalizationTestImpl(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, armnn::DataLayout::NCHW); } LayerTestResult L2Normalization3dNhwcTest(armnn::IWorkloadFactory& workloadFactory) { // Width: 3 // Height: 4 // Channels: 2 // BatchSize: 1 const armnn::TensorShape inputOutputShape{ 1, 4, 3, 2 }; std::vector inputValues { // Batch 0, Height 0, Width (3) x Channel (2) 119.0f, 110.0f, 21.0f, 140.0f, 150.0f, 73.0f, // Batch 0, Height 1, Width (3) x Channel (2) 149.0f, 211.0f, 32.0f, 212.0f, 179.0f, 89.0f, // Batch 0, Height 2, Width (3) x Channel (2) 15.0f, 24.0f, 227.0f, 138.0f, 141.0f, 188.0f, // Batch 0, Height 3, Width (3) x Channel (2) 147.0f, 162.0f, 199.0f, 12.0f, 220.0f, 161.0f }; std::vector expectedOutputValues { // Batch 0, Height 0, Width (3) x Channel (2) 119.0f * CalcInvL2Norm({ 119.0f, 110.0f }), 110.0f * CalcInvL2Norm({ 119.0f, 110.0f }), 21.0f * CalcInvL2Norm({ 21.0f, 140.0f }), 140.0f * CalcInvL2Norm({ 21.0f, 140.0f }), 150.0f * CalcInvL2Norm({ 150.0f, 73.0f }), 73.0f * CalcInvL2Norm({ 150.0f, 73.0f }), // Batch 0, Height 1, Width (3) x Channel (2) 149.0f * CalcInvL2Norm({ 149.0f, 211.0f }), 211.0f * CalcInvL2Norm({ 149.0f, 211.0f }), 32.0f * CalcInvL2Norm({ 32.0f, 212.0f }), 212.0f * CalcInvL2Norm({ 32.0f, 212.0f }), 179.0f * CalcInvL2Norm({ 179.0f, 89.0f }), 89.0f * CalcInvL2Norm({ 179.0f, 89.0f }), // Batch 0, Height 2, Width (3) x Channel (2) 15.0f * CalcInvL2Norm({ 15.0f, 24.0f }), 24.0f * CalcInvL2Norm({ 15.0f, 24.0f }), 227.0f * CalcInvL2Norm({ 227.0f, 138.0f }), 138.0f * CalcInvL2Norm({ 227.0f, 138.0f }), 141.0f * CalcInvL2Norm({ 141.0f, 188.0f }), 188.0f * CalcInvL2Norm({ 141.0f, 188.0f }), // Batch 0, Height 3, Width (3) x Channel (2) 147.0f * CalcInvL2Norm({ 147.0f, 162.0f }), 162.0f * CalcInvL2Norm({ 147.0f, 162.0f }), 199.0f * CalcInvL2Norm({ 199.0f, 12.0f }), 12.0f * CalcInvL2Norm({ 199.0f, 12.0f }), 220.0f * CalcInvL2Norm({ 220.0f, 161.0f }), 161.0f * CalcInvL2Norm({ 220.0f, 161.0f }) }; return L2NormalizationTestImpl(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, armnn::DataLayout::NHWC); } LayerTestResult L2Normalization4dTest(armnn::IWorkloadFactory& workloadFactory) { // Width: 3 // Height: 4 // Channels: 3 // BatchSize: 2 const armnn::TensorShape inputOutputShape{ 2, 3, 4, 3 }; std::vector inputValues { // Batch 0, Channel 0, Height (4) x Width (3) 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, Height (4) x Width (3) 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, Height (4) x Width (3) 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, Height (4) x Width (3) 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, Height (4) x Width (3) 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, Height (4) x Width (3) 97.0f, 145.0f, 215.0f, 115.0f, 116.0f, 238.0f, 226.0f, 16.0f, 132.0f, 92.0f, 125.0f, 88.0f }; std::vector expectedOutputValues { // Batch 0, Channel 0, Height (4) x Width (3) 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, Height (4) x Width (3) 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, Height (4) x Width (3) 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, Height (4) x Width (3) 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, Height (4) x Width (3) 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, Height (4) x Width (3) 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 }) }; return L2NormalizationTestImpl(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, armnn::DataLayout::NCHW); } LayerTestResult L2Normalization4dNhwcTest(armnn::IWorkloadFactory& workloadFactory) { // Width: 3 // Height: 4 // Channels: 3 // BatchSize: 2 const armnn::TensorShape inputOutputShape{ 2, 4, 3, 3 }; std::vector inputValues { // Batch 0, Height 0, Width (3) x Channel (3) 235.0f, 113.0f, 56.0f, 46.0f, 95.0f, 170.0f, 178.0f, 202.0f, 162.0f, // Batch 0, Height 1, Width (3) x Channel (3) 100.0f, 77.0f, 194.0f, 123.0f, 114.0f, 89.0f, 19.0f, 71.0f, 254.0f, // Batch 0, Height 2, Width (3) x Channel (3) 172.0f, 122.0f, 12.0f, 74.0f, 246.0f, 209.0f, 250.0f, 166.0f, 200.0f, // Batch 0, Height 3, Width (3) x Channel (3) 6.0f, 82.0f, 1.0f, 195.0f, 28.0f, 64.0f, 80.0f, 37.0f, 54.0f, // Batch 1, Height 0, Width (3) x Channel (3) 67.0f, 239.0f, 97.0f, 90.0f, 104.0f, 145.0f, 49.0f, 199.0f, 215.0f, // Batch 1, Height 1, Width (3) x Channel (3) 7.0f, 17.0f, 115.0f, 163.0f, 124.0f, 116.0f, 18.0f, 153.0f, 238.0f, // Batch 1, Height 2, Width (3) x Channel (3) 25.0f, 222.0f, 226.0f, 117.0f, 217.0f, 16.0f, 103.0f, 75.0f, 132.0f, // Batch 1, Height 3, Width (3) x Channel (3) 247.0f, 32.0f, 92.0f, 59.0f, 126.0f, 125.0f, 189.0f, 21.0f, 88.0f }; std::vector expectedOutputValues { // Batch 0, Height 0, Width (3) x Channel (3) 235.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), 113.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), 56.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), 46.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), 95.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), 170.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), 178.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), 202.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), 162.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), // Batch 0, Height 1, Width (3) x Channel (3) 100.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), 77.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), 194.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), 123.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), 114.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), 89.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), 19.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), 71.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), 254.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), // Batch 0, Height 2, Width (3) x Channel (3) 172.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), 122.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), 12.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), 74.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), 246.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), 209.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), 250.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), 166.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), 200.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), // Batch 0, Height 3, Width (3) x Channel (3) 6.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), 82.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), 1.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), 195.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), 28.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), 64.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), 80.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), 37.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), 54.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), // Batch 1, Height 0, Width (3) x Channel (3) 67.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), 239.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), 97.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), 90.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), 104.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), 145.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), 49.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), 199.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), 215.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), // Batch 1, Height 1, Width (3) x Channel (3) 7.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), 17.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), 115.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), 163.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), 124.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), 116.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), 18.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), 153.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), 238.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), // Batch 1, Height 2, Width (3) x Channel (3) 25.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), 222.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), 226.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), 117.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), 217.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), 16.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), 103.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), 75.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), 132.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), // Batch 1, Height 3, Width (3) x Channel (3) 247.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), 32.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), 92.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), 59.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), 126.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), 125.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), 189.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), 21.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), 88.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }) }; return L2NormalizationTestImpl(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, armnn::DataLayout::NHWC); } template LayerTestResult 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()); armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, armnn::GetDataType()); // Set quantization parameters if the requested type is a quantized type. if(armnn::IsQuantizedType()) { inputTensorInfo.SetQuantizationScale(qScale); inputTensorInfo.SetQuantizationOffset(qOffset); outputTensorInfo.SetQuantizationScale(qScale); outputTensorInfo.SetQuantizationOffset(qOffset); } auto input = MakeTensor(inputTensorInfo, std::vector( QuantizedVector(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 result(outputTensorInfo); result.outputExpected = input; std::unique_ptr 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 workload = workloadFactory.CreateConstant(descriptor, info); outputHandle->Allocate(); workloadFactory.Finalize(); workload->Execute(); CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); return result; } LayerTestResult ConstantTest(armnn::IWorkloadFactory& workloadFactory) { return ConstantTestImpl(workloadFactory, 0.0f, 0); } LayerTestResult ConstantTestUint8(armnn::IWorkloadFactory& workloadFactory) { return ConstantTestImpl(workloadFactory, 1.0f, 0); } LayerTestResult 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 ret(outputTensorInfo); ret.outputExpected = MakeTensor(outputTensorInfo, std::vector( { 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(inputTensorInfo1, std::vector( { 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(inputTensorInfo2, std::vector( { 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, }) ); std::vector wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1); std::vector wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1]. armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2); std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); bool subTensorsSupported = workloadFactory.SupportsSubTensors(); std::unique_ptr inputHandle1 = subTensorsSupported ? workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : workloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr 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 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 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(inputTensorInfo1, std::vector( { 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(inputTensorInfo1, std::vector( { 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 result(outputTensorInfo); result.outputExpected = MakeTensor(outputTensorInfo, std::vector( { 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 inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); std::unique_ptr 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 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 MultiplicationUint8TestHelper(armnn::IWorkloadFactory& workloadFactory, const unsigned int shape0[4], const std::vector & values0, float scale0, int32_t offset0, const unsigned int shape1[4], const std::vector & values1, float scale1, int32_t offset1, const unsigned int outShape[4], const std::vector & 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(inputTensorInfo0, values0); auto input1 = MakeTensor(inputTensorInfo1, values1); LayerTestResult result(outputTensorInfo); result.outputExpected = MakeTensor(outputTensorInfo, outValues); std::unique_ptr inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr 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 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 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 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 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 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 MultiplicationBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) { const unsigned int shape0[] = { 1, 2, 2, 3 }; const unsigned int shape1[] = { 1, 1, 1, 1 }; std::vector input0({ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 }); std::vector input1({2}); std::vector 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 MultiplicationBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory) { const unsigned int shape0[] = { 1, 2, 2, 3 }; const unsigned int shape1[] = { 1, 1, 1, 3 }; std::vector input0({ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 }); std::vector input1({1, 2, 3}); std::vector 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 LayerTestResult SubtractionTestHelper(armnn::IWorkloadFactory& workloadFactory, const unsigned int shape0[4], const std::vector& values0, float scale0, int32_t offset0, const unsigned int shape1[4], const std::vector & values1, float scale1, int32_t offset1, const unsigned int outShape[4], const std::vector & outValues, float outScale, int32_t outOffset) { auto dataType = (std::is_same::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(inputTensorInfo0, values0); auto input1 = MakeTensor(inputTensorInfo1, values1); LayerTestResult result(outputTensorInfo); result.outputExpected = MakeTensor(outputTensorInfo, outValues); std::unique_ptr inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); std::unique_ptr inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); std::unique_ptr 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 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 SubtractionUint8Test(armnn::IWorkloadFactory& workloadFactory) { const unsigned int shape0[] = { 1, 1, 2, 2 }; const unsigned int shape1[] = { 1, 1, 2, 2 }; std::vector input0({ 10, 12, 14, 16 }); std::vector input1({ 1, 2, 1, 2 }); std::vector output({ 3, 3, 5, 5 }); return SubtractionTestHelper(workloadFactory, shape0, input0, 0.5f, 2, shape1, input1, 1.0f, 0, shape0, output, 1.0f, 0); } LayerTestResult SubtractionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) { const unsigned int shape0[] = { 1, 1, 2, 2 }; const unsigned int shape1[] = { 1, 1, 1, 1 }; std::vector input0({ 10, 12, 14, 16 }); std::vector input1({ 2 }); std::vector output({ 5, 6, 7, 8 }); return SubtractionTestHelper(workloadFactory, shape0, input0, 0.5f, 2, shape1, input1, 1.0f, 0, shape0, output, 1.0f, 3); } LayerTestResult SubtractionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory) { const unsigned int shape0[] = { 1, 1, 2, 2 }; const unsigned int shape1[] = { 1, 1, 2, 1 }; std::vector input0({ 10, 12, 14, 16 }); std::vector input1({ 2, 1 }); std::vector output({ 8, 11, 12, 15 }); return SubtractionTestHelper(workloadFactory, shape0, input0, 1.0f, 0, shape1, input1, 1.0f, 0, shape0, output, 1.0f, 0); } LayerTestResult SubtractionTest(armnn::IWorkloadFactory& workloadFactory) { const unsigned int shape0[] = { 1, 1, 2, 2 }; const unsigned int shape1[] = { 1, 1, 2, 2 }; std::vector input0({ 1, 2, 3, 4 }); std::vector input1({ 1, -1, 0, 2 }); std::vector output({ 0, 3, 3, 2 }); return SubtractionTestHelper(workloadFactory, shape0, input0, 1.0f, 0, shape1, input1, 1.0f, 0, shape0, output, 1.0f, 0); } LayerTestResult SubtractionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) { const unsigned int shape0[] = { 1, 1, 2, 2 }; const unsigned int shape1[] = { 1, 1, 1, 1 }; std::vector input0({ 1, 2, 3, 4 }); std::vector input1({ 10 }); std::vector output({ -9, -8, -7, -6 }); return SubtractionTestHelper(workloadFactory, shape0, input0, 1.0f, 0, shape1, input1, 1.0f, 0, shape0, output, 1.0f, 0); } LayerTestResult SubtractionBroadcastTest(armnn::IWorkloadFactory& workloadFactory) { const unsigned int shape0[] = { 1, 1, 2, 2 }; const unsigned int shape1[] = { 1, 1, 1, 2 }; std::vector input0({ 1, 2, 3, 4 }); std::vector input1({ 10, -5 }); std::vector output({ -9, 7, -7, 9 }); return SubtractionTestHelper(workloadFactory, shape0, input0, 1.0f, 0, shape1, input1, 1.0f, 0, shape0, output, 1.0f, 0); } LayerTestResult 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(inputTensorInfo, std::vector({ 1, 2, 3, 4, 2, 3, 4, 5, 3, 4, 5, 6, 4, 5, 6, 7 })); LayerTestResult result(outputTensorInfo); result.outputExpected = input; std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr 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 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 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(inputTensorInfo, std::vector({ 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 result(outputTensorInfo); result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ 1 })); std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr 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 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 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(inputTensorInfo, std::vector({ 1, 2, 3, 4, 2, 3, 4, 5, 3, 4, 5, 6, 4, 5, 6, 7 })); LayerTestResult result(outputTensorInfo); result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ 1, 3, 3, 5 })); std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr 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 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 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(inputTensorInfo, std::vector({ 1, 2, 3, // 3.0, 4.5, 6.0 5, 8, 13 // 9.0, 13.5, 21.0 })); LayerTestResult result(outputTensorInfo); result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ 1, 3 // 3.0, 5.25 })); std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr 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 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 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(inputTensorInfo, std::vector({ 24, 228, // 0.183005, 2.379065, 105, 128, // 1.05497, 1.302565 230, 71 // 2.400595, 0.68896 })); LayerTestResult result(outputTensorInfo); result.outputExpected = MakeTensor(outputTensorInfo, std::vector({ 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 inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr 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 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 BatchNormTest(armnn::IWorkloadFactory& workloadFactory) { auto ret = BatchNormTestImpl(workloadFactory, 0.f, 0); return ret; } LayerTestResult BatchNormUint8Test(armnn::IWorkloadFactory& workloadFactory) { auto ret = BatchNormTestImpl(workloadFactory, 1.f/20.f, 50); return ret; } LayerTestResult ConstantUint8Test(armnn::IWorkloadFactory& workloadFactory) { return ConstantTestImpl(workloadFactory, 2e-6f, 1); } LayerTestResult Concatenation1dUint8Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation1dTestImpl(workloadFactory, 0.5f, -1); } LayerTestResult Concatenation2dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation2dDim0TestImpl(workloadFactory, 0.5f, -1); } LayerTestResult Concatenation2dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation2dDim1TestImpl(workloadFactory, 0.5f, -1); } LayerTestResult Concatenation2dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation2dDim0DiffInputDimsTestImpl(workloadFactory, 0.5f, -1); } LayerTestResult Concatenation2dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation2dDim1DiffInputDimsTestImpl(workloadFactory, 0.5f, -1); } LayerTestResult Concatenation3dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation3dDim0TestImpl(workloadFactory, 0.5f, -1); } LayerTestResult Concatenation3dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation3dDim1TestImpl(workloadFactory, 0.5f, -1); } LayerTestResult Concatenation3dDim2Uint8Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation3dDim2TestImpl(workloadFactory, 0.5f, -1); } LayerTestResult Concatenation3dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation3dDim0TestImpl(workloadFactory, 0.5f, -1); } LayerTestResult Concatenation3dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation3dDim1DiffInputDimsTestImpl(workloadFactory, 0.5f, -1); } LayerTestResult Concatenation3dDim2DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) { return Concatenation3dDim2DiffInputDimsTestImpl(workloadFactory, 0.5f, -1); } LayerTestResult SimpleMaxPooling2dSize2x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, bool forceNoPadding) { return SimpleMaxPooling2dSize2x2Stride2x2TestCommon(workloadFactory, forceNoPadding); } LayerTestResult SimpleMaxPooling2dSize2x2Stride2x2Uint8Test(armnn::IWorkloadFactory& workloadFactory, bool forceNoPadding) { return SimpleMaxPooling2dSize2x2Stride2x2TestCommon(workloadFactory, forceNoPadding, 3.0f, -5); } LayerTestResult SimpleMaxPooling2dSize3x3Stride2x4Test(armnn::IWorkloadFactory& workloadFactory, bool forceNoPadding) { return SimpleMaxPooling2dSize3x3Stride2x4TestCommon(workloadFactory, forceNoPadding); } LayerTestResult SimpleMaxPooling2dSize3x3Stride2x4Uint8Test(armnn::IWorkloadFactory& workloadFactory, bool forceNoPadding) { return SimpleMaxPooling2dSize3x3Stride2x4TestCommon(workloadFactory, forceNoPadding, 0.1f, 128); } LayerTestResult SimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory) { return SimpleAveragePooling2dTestCommon(workloadFactory); } LayerTestResult SimpleAveragePooling2dNhwcTest(armnn::IWorkloadFactory& workloadFactory) { return SimpleAveragePooling2dNhwcTestCommon(workloadFactory); } LayerTestResult SimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) { return SimpleAveragePooling2dTestCommon(workloadFactory, 0.5, -1); } LayerTestResult IgnorePaddingAveragePooling2dSize3x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, bool forceNoPadding) { return IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon(workloadFactory, forceNoPadding); } LayerTestResult LargeTensorsAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory) { return LargeTensorsAveragePooling2dTestCommon(workloadFactory); } LayerTestResult LargeTensorsAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) { return LargeTensorsAveragePooling2dTestCommon(workloadFactory, 0.5, -1); } LayerTestResult SimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory) { return SimpleL2Pooling2dTestCommon(workloadFactory); } LayerTestResult SimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) { return SimpleL2Pooling2dTestCommon(workloadFactory); } LayerTestResult L2Pooling2dSize3Stride1Test(armnn::IWorkloadFactory& workloadFactory) { return L2Pooling2dSize3Stride1TestCommon(workloadFactory); } LayerTestResult L2Pooling2dSize3Stride1Uint8Test(armnn::IWorkloadFactory& workloadFactory) { return L2Pooling2dSize3Stride1TestCommon(workloadFactory); } LayerTestResult L2Pooling2dSize3Stride3Test(armnn::IWorkloadFactory& workloadFactory) { return L2Pooling2dSize3Stride3TestCommon(workloadFactory); } LayerTestResult L2Pooling2dSize3Stride3Uint8Test(armnn::IWorkloadFactory& workloadFactory) { return L2Pooling2dSize3Stride3TestCommon(workloadFactory); } LayerTestResult L2Pooling2dSize3Stride4Test(armnn::IWorkloadFactory& workloadFactory) { return L2Pooling2dSize3Stride4TestCommon(workloadFactory); } LayerTestResult L2Pooling2dSize3Stride4Uint8Test(armnn::IWorkloadFactory& workloadFactory) { return L2Pooling2dSize3Stride4TestCommon(workloadFactory); } LayerTestResult L2Pooling2dSize7Test(armnn::IWorkloadFactory& workloadFactory) { return L2Pooling2dSize7TestCommon(workloadFactory); } LayerTestResult L2Pooling2dSize7Uint8Test(armnn::IWorkloadFactory& workloadFactory) { return L2Pooling2dSize7TestCommon(workloadFactory); } LayerTestResult L2Pooling2dSize9Test(armnn::IWorkloadFactory& workloadFactory) { return L2Pooling2dSize9TestCommon(workloadFactory); } LayerTestResult L2Pooling2dSize9Uint8Test(armnn::IWorkloadFactory& workloadFactory) { return L2Pooling2dSize9TestCommon(workloadFactory); } LayerTestResult AsymmetricNonSquarePooling2dTest(armnn::IWorkloadFactory& workloadFactory) { return AsymmetricNonSquarePooling2dTestCommon(workloadFactory); } LayerTestResult AsymmetricNonSquarePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) { return AsymmetricNonSquarePooling2dTestCommon(workloadFactory); } LayerTestResult ComparePooling2dTest(armnn::IWorkloadFactory& workloadFactory, armnn::IWorkloadFactory& refWorkloadFactory, armnn::PoolingAlgorithm poolingType) { return ComparePooling2dTestCommon(workloadFactory, refWorkloadFactory, poolingType); } LayerTestResult ComparePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory, armnn::IWorkloadFactory& refWorkloadFactory, armnn::PoolingAlgorithm poolingType) { return ComparePooling2dTestCommon(workloadFactory, refWorkloadFactory, poolingType, 0.1f, 128); } LayerTestResult FullyConnectedLargeTest(armnn::IWorkloadFactory& workloadFactory, bool transposeWeights) { return FullyConnectedLargeTestCommon(workloadFactory, transposeWeights); } LayerTestResult IgnorePaddingSimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory) { return IgnorePaddingSimpleMaxPooling2dTestCommon(workloadFactory); } LayerTestResult IgnorePaddingSimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) { return IgnorePaddingSimpleMaxPooling2dTestCommon(workloadFactory, 1.0f, -5); } LayerTestResult IgnorePaddingMaxPooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory) { return IgnorePaddingMaxPooling2dSize3TestCommon(workloadFactory); } LayerTestResult IgnorePaddingMaxPooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory) { return IgnorePaddingMaxPooling2dSize3TestCommon(workloadFactory, 1.0f, -5); } LayerTestResult IgnorePaddingSimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory) { return IgnorePaddingSimpleAveragePooling2dTestCommon(workloadFactory); } LayerTestResult IgnorePaddingSimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) { return IgnorePaddingSimpleAveragePooling2dTestCommon(workloadFactory); } LayerTestResult IgnorePaddingSimpleAveragePooling2dNoPaddingTest(armnn::IWorkloadFactory& workloadFactory) { return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon(workloadFactory); } LayerTestResult IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test( armnn::IWorkloadFactory& workloadFactory) { return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon(workloadFactory); } LayerTestResult IgnorePaddingAveragePooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory) { return IgnorePaddingAveragePooling2dSize3TestCommon(workloadFactory); } LayerTestResult IgnorePaddingAveragePooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory) { return IgnorePaddingAveragePooling2dSize3TestCommon(workloadFactory); } LayerTestResult IgnorePaddingSimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory) { return IgnorePaddingSimpleL2Pooling2dTestCommon(workloadFactory); } LayerTestResult IgnorePaddingSimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) { return IgnorePaddingSimpleL2Pooling2dTestCommon(workloadFactory); } LayerTestResult IgnorePaddingL2Pooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory) { return IgnorePaddingL2Pooling2dSize3TestCommon(workloadFactory); } LayerTestResult IgnorePaddingL2Pooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory) { return IgnorePaddingL2Pooling2dSize3TestCommon(workloadFactory); } LayerTestResult SimplePermuteFloat32Test(armnn::IWorkloadFactory& workloadFactory) { return SimplePermuteFloat32TestCommon(workloadFactory); }; LayerTestResult SimplePermuteUint8Test(armnn::IWorkloadFactory& workloadFactory) { return SimplePermuteUint8TestCommon(workloadFactory); }; LayerTestResult PermuteFloat32ValueSet1Test(armnn::IWorkloadFactory& workloadFactory) { return PermuteFloat32ValueSet1TestCommon(workloadFactory); }; LayerTestResult PermuteFloat32ValueSet2Test(armnn::IWorkloadFactory& workloadFactory) { return PermuteFloat32ValueSet2TestCommon(workloadFactory); }; LayerTestResult PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& workloadFactory) { return PermuteFloat32ValueSet3TestCommon(workloadFactory); }; namespace { template LayerTestResult MeanTestHelper(armnn::IWorkloadFactory& workloadFactory, const unsigned int* inputShape, const std::vector& inputData, const std::vector& axis, bool keepDims, const unsigned int* outputShape, const std::vector& outputData, float scale = 1.0f, int32_t offset = 0) { auto dataType = (std::is_same::value ? armnn::DataType::QuantisedAsymm8 : armnn::DataType::Float32); armnn::TensorInfo inputTensorInfo(InputDim, inputShape, dataType); armnn::TensorInfo outputTensorInfo(OutputDim, outputShape, dataType); inputTensorInfo.SetQuantizationScale(scale); inputTensorInfo.SetQuantizationOffset(offset); outputTensorInfo.SetQuantizationScale(scale); outputTensorInfo.SetQuantizationOffset(offset); auto input = MakeTensor(inputTensorInfo, inputData); LayerTestResult result(outputTensorInfo); result.outputExpected = MakeTensor(outputTensorInfo, outputData); std::unique_ptr inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); armnn::MeanQueueDescriptor data; data.m_Parameters.m_Axis = axis; data.m_Parameters.m_KeepDims = keepDims; armnn::WorkloadInfo info; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); std::unique_ptr workload = workloadFactory.CreateMean(data, info); inputHandle->Allocate(); outputHandle->Allocate(); CopyDataToITensorHandle(inputHandle.get(), input.origin()); workloadFactory.Finalize(); workload->Execute(); CopyDataFromITensorHandle(result.output.origin(), outputHandle.get()); return result; } } // anonymous namespace LayerTestResult MeanUint8SimpleTest(armnn::IWorkloadFactory& workloadFactory) { const unsigned int inputShape[] = { 3, 2 }; const unsigned int outputShape[] = { 1 }; std::vector input({ 1, 1, 2, 2, 3, 3 }); std::vector output({ 2 }); return MeanTestHelper(workloadFactory, inputShape, input, {}, false, outputShape, output); } LayerTestResult MeanUint8SimpleAxisTest(armnn::IWorkloadFactory& workloadFactory) { const unsigned int inputShape[] = { 1, 1, 3, 2 }; const unsigned int outputShape[] = { 1, 1, 2 }; std::vector input({ 1, 1, 2, 2, 3, 3 }); std::vector output({ 2, 2 }); return MeanTestHelper(workloadFactory, inputShape, input, {2}, false, outputShape, output); } LayerTestResult MeanUint8KeepDimsTest(armnn::IWorkloadFactory& workloadFactory) { const unsigned int inputShape[] = { 1, 1, 3, 2 }; const unsigned int outputShape[] = { 1, 1, 1, 2 }; std::vector input({ 1, 1, 2, 2, 3, 3 }); std::vector output({ 2, 2 }); return MeanTestHelper(workloadFactory, inputShape, input, {2}, true, outputShape, output); } LayerTestResult MeanUint8MultipleDimsTest(armnn::IWorkloadFactory& workloadFactory) { const unsigned int inputShape[] = { 2, 3, 1, 2 }; const unsigned int outputShape[] = { 1, 3, 1, 1 }; std::vector input({ 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6}); std::vector output({ 1, 3, 5 }); return MeanTestHelper(workloadFactory, inputShape, input, {0, 3}, true, outputShape, output); } LayerTestResult MeanVtsUint8Test(armnn::IWorkloadFactory& workloadFactory) { const unsigned int inputShape[] = {4, 3, 2}; const unsigned int outputShape[] = { 2 }; std::vector input({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}); std::vector output({12, 13}); return MeanTestHelper(workloadFactory, inputShape, input, {0, 1}, false, outputShape, output, 0.8f, 5); } LayerTestResult MeanFloatSimpleTest(armnn::IWorkloadFactory& workloadFactory) { const unsigned int inputShape[] = { 3, 2 }; const unsigned int outputShape[] = { 1 }; std::vector input({ 1., 1., 2., 2., 3., 3. }); std::vector output({ 2. }); return MeanTestHelper(workloadFactory, inputShape, input, {}, false, outputShape, output); } LayerTestResult MeanFloatSimpleAxisTest(armnn::IWorkloadFactory& workloadFactory) { const unsigned int inputShape[] = { 2, 3, 1, 2 }; const unsigned int outputShape[] = { 3, 1, 2 }; std::vector input({ 1., 2., 3., 4., 5., 6., 1., 2., 3., 4., 5., 6.}); std::vector output({ 1., 2., 3., 4., 5., 6. }); return MeanTestHelper(workloadFactory, inputShape, input, {0}, false, outputShape, output); } LayerTestResult MeanFloatKeepDimsTest(armnn::IWorkloadFactory& workloadFactory) { const unsigned int inputShape[] = { 1, 1, 3, 2 }; const unsigned int outputShape[] = { 1, 1, 1, 2 }; std::vector input({ 1., 1., 2., 2., 3., 3. }); std::vector output({ 2., 2. }); return MeanTestHelper(workloadFactory, inputShape, input, {2}, true, outputShape, output); } LayerTestResult MeanFloatMultipleDimsTest(armnn::IWorkloadFactory& workloadFactory) { const unsigned int inputShape[] = { 2, 3, 1, 2 }; const unsigned int outputShape[] = { 1, 3, 1, 1 }; std::vector input({ 1., 2., 3., 4., 5., 6., 1., 2., 3., 4., 5., 6.}); std::vector output({ 1.5, 3.5, 5.5 }); return MeanTestHelper(workloadFactory, inputShape, input, {0, 3}, true, outputShape, output); } LayerTestResult MeanVtsFloat1Test(armnn::IWorkloadFactory& workloadFactory) { const unsigned int inputShape[] = {4, 3, 2}; const unsigned int outputShape[] = { 2 }; std::vector input({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}); std::vector output({12.0f, 13.0f}); return MeanTestHelper(workloadFactory, inputShape, input, {0, 1}, false, outputShape, output); } LayerTestResult MeanVtsFloat2Test(armnn::IWorkloadFactory& workloadFactory) { const unsigned int inputShape[] = {4, 3, 2}; const unsigned int outputShape[] = {1, 3, 1 }; std::vector input({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}); std::vector output({10.5f, 12.5f, 14.5f}); return MeanTestHelper(workloadFactory, inputShape, input, {0, 2}, true, outputShape, output); } LayerTestResult AdditionAfterMaxPoolTest(armnn::IWorkloadFactory& workloadFactory) { // Create Initial Tensor // 1, 2, 3 // 4, 5, 6 // 7, 8, 9 armnn::TensorInfo poolingInputTensorInfo({ 1, 1, 3, 3}, armnn::GetDataType()); armnn::TensorInfo poolingOutputTensorInfo({ 1, 1, 2, 2}, armnn::GetDataType()); boost::multi_array poolingInput = MakeTensor(poolingInputTensorInfo, {1, 2, 3, 4, 5, 6, 7, 8, 9 }); std::unique_ptr poolingInputHandle = workloadFactory.CreateTensorHandle(poolingInputTensorInfo); std::unique_ptr poolingOutputHandle = workloadFactory.CreateTensorHandle(poolingOutputTensorInfo); // Apply MaxPool poolSize = 1x1, stride=2x2 // Result = // 1, 3 // 7, 9 armnn::Pooling2dDescriptor descriptor; descriptor.m_PoolHeight = 1; descriptor.m_PoolWidth = 1; descriptor.m_StrideX = 2; descriptor.m_StrideY = 2; descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; armnn::Pooling2dQueueDescriptor queueDescriptor; queueDescriptor.m_Parameters = descriptor; armnn::WorkloadInfo workloadInfo; AddInputToWorkload(queueDescriptor, workloadInfo, poolingInputTensorInfo, poolingInputHandle.get()); AddOutputToWorkload(queueDescriptor, workloadInfo, poolingOutputTensorInfo, poolingOutputHandle.get()); // Create the MaxPool std::unique_ptr workload = workloadFactory.CreatePooling2d(queueDescriptor, workloadInfo); //LayerTestResult result(poolingOutputTensorInfo); auto shape( GetTensorShapeAsArray<4>(poolingOutputTensorInfo)); boost::multi_array resultMaxPool; resultMaxPool.resize(shape); // Create addition with another tensor the same size // This would be the result to apply a Conv2d with kernel ones(2) and stride 1x1 // with the initial tensor. // 12, 16 // 24, 28 armnn::TensorInfo addInputTensorInfo({ 1,1,2,2}, armnn::GetDataType()); armnn::TensorInfo addOutputTensorInfo({ 1,1,2,2}, armnn::GetDataType()); boost::multi_array addInput = MakeTensor(addInputTensorInfo, {12, 16, 24, 28, }); // Expected output tensor after MaxPool and Addition. LayerTestResult addRet(addOutputTensorInfo); addRet.outputExpected = MakeTensor(addOutputTensorInfo, std::vector( { 13, 19, 31, 37 })); std::unique_ptr addInputHandle = workloadFactory.CreateTensorHandle(addInputTensorInfo); std::unique_ptr addOutputHandle = workloadFactory.CreateTensorHandle(addOutputTensorInfo); armnn::AdditionQueueDescriptor data; armnn::WorkloadInfo info; // Add the output of the MaxPool and the new tensor AddInputToWorkload(data, info, poolingOutputTensorInfo, poolingOutputHandle.get()); AddInputToWorkload(data, info, addInputTensorInfo, addInputHandle.get()); AddOutputToWorkload(data, info, addOutputTensorInfo, addOutputHandle.get()); std::unique_ptr addWorkload = workloadFactory.CreateAddition(data, info); poolingInputHandle->Allocate(); poolingOutputHandle->Allocate(); addInputHandle->Allocate(); addOutputHandle->Allocate(); CopyDataToITensorHandle(poolingInputHandle.get(), &poolingInput[0][0][0][0]); CopyDataFromITensorHandle(&resultMaxPool[0][0][0][0], poolingOutputHandle.get()); CopyDataToITensorHandle(poolingOutputHandle.get(), &resultMaxPool[0][0][0][0]); CopyDataToITensorHandle(addInputHandle.get(), &addInput[0][0][0][0]); workload->Execute(); addWorkload->Execute(); CopyDataFromITensorHandle(&addRet.output[0][0][0][0], addOutputHandle.get()); workloadFactory.Finalize(); return addRet; }