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Diffstat (limited to 'src/backends/backendsCommon/test/Convolution3dEndToEndTestImpl.hpp')
-rw-r--r-- | src/backends/backendsCommon/test/Convolution3dEndToEndTestImpl.hpp | 167 |
1 files changed, 167 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/Convolution3dEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/Convolution3dEndToEndTestImpl.hpp new file mode 100644 index 0000000000..33bf9a180b --- /dev/null +++ b/src/backends/backendsCommon/test/Convolution3dEndToEndTestImpl.hpp @@ -0,0 +1,167 @@ +// +// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include "EndToEndTestImpl.hpp" +#include "QuantizeHelper.hpp" + +#include <ResolveType.hpp> + +#include <backendsCommon/test/CommonTestUtils.hpp> +#include <backendsCommon/test/DataLayoutUtils.hpp> + +#include <map> +#include <vector> + +namespace +{ + +armnn::INetworkPtr CreateConvolution3dNetwork(const armnn::Convolution3dDescriptor& descriptor, + const armnn::TensorInfo& inputInfo, + const armnn::TensorInfo& weightsInfo, + const armnn::TensorInfo& biasInfo, + const armnn::TensorInfo& outputInfo, + const armnn::ConstTensor& weights, + const armnn::ConstTensor& biases) +{ + using namespace armnn; + + INetworkPtr network(INetwork::Create()); + IConnectableLayer* input = network->AddInputLayer(0, "input"); + armnn::IConnectableLayer* weightsLayer = network->AddConstantLayer(weights, "Weights"); + armnn::IConnectableLayer* biasLayer = network->AddConstantLayer(biases, "Bias"); + IConnectableLayer* convolution3d = network->AddConvolution3dLayer(descriptor, "convolution3d"); + IConnectableLayer* output = network->AddOutputLayer(0, "output"); + + Connect(input, convolution3d, inputInfo, 0, 0); + Connect(weightsLayer, convolution3d, weightsInfo, 0, 1); + Connect(biasLayer, convolution3d, biasInfo, 0, 2); + Connect(convolution3d, output, outputInfo, 0, 0); + + return network; +} + +} // anonymous namespace + +template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType> +void Convolution3dEndToEnd(const std::vector<armnn::BackendId>& backends, + armnn::DataLayout dataLayout) +{ + using namespace armnn; + using T = ResolveType<ArmnnType>; + using BT = ResolveType<ArmnnBType>; + + const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f; + const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0; + + TensorInfo inputInfo({ 1, 5, 5, 5, 1 }, ArmnnType, qScale, qOffset); + TensorInfo outputInfo({ 1, 2, 2, 2, 1 }, ArmnnType, qScale, qOffset); + TensorInfo weightsInfo({ 3, 3, 3, 1, 1 }, ArmnnType, qScale, qOffset, true); + TensorInfo biasesInfo({ 1 }, ArmnnBType, qScale * qScale, 0, true); + + std::vector<float> inputData = + { + 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, 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, + 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, + 60.0f, 61.0f, 62.0f, 63.0f, 64.0f, + 65.0f, 66.0f, 67.0f, 68.0f, 69.0f, + + 70.0f, 71.0f, 72.0f, 73.0f, 74.0f, + 75.0f, 76.0f, 77.0f, 78.0f, 79.0f, + 80.0f, 81.0f, 82.0f, 83.0f, 84.0f, + 85.0f, 86.0f, 87.0f, 88.0f, 89.0f, + 90.0f, 91.0f, 92.0f, 93.0f, 94.0f, + 95.0f, 96.0f, 97.0f, 98.0f, 99.0f, + + 100.0f, 101.0f, 102.0f, 103.0f, 104.0f, + 105.0f, 106.0f, 107.0f, 108.0f, 109.0f, + 110.0f, 111.0f, 112.0f, 113.0f, 114.0f, + 115.0f, 116.0f, 117.0f, 118.0f, 119.0f, + 120.0f, 121.0f, 122.0f, 123.0f, 124.0f + }; + + std::vector<float> weightsData = + { + 1.0f, 1.0f, 1.0f, + 1.0f, 1.0f, 1.0f, + 1.0f, 1.0f, 1.0f, + + 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, + 0.0f, 0.0f, 0.0f, + + 1.0f, 1.0f, 1.0f, + 1.0f, 1.0f, 1.0f, + 1.0f, 1.0f, 1.0f, + }; + + std::vector<float> biasesData = { 1.f }; + + std::vector<float> expectedOutputData = + { + 559.0f, 595.0f, + + 739.0f, 775.0f, + + 1459.0f, 1495.0f, + + 1639.0f, 1675.0f, + }; + + Convolution3dDescriptor descriptor; + descriptor.m_PadLeft = 0; + descriptor.m_PadRight = 0; + descriptor.m_PadTop = 0; + descriptor.m_PadBottom = 0; + descriptor.m_PadFront = 0; + descriptor.m_PadBack = 0; + descriptor.m_StrideX = 2; + descriptor.m_StrideY = 2; + descriptor.m_StrideZ = 2; + descriptor.m_BiasEnabled = true; + descriptor.m_DataLayout = dataLayout; + + // Permute input and output if NCDHW. + if (dataLayout == DataLayout::NCDHW) + { + PermuteTensorNdhwcToNcdhw(inputInfo, inputData); + PermuteTensorNdhwcToNcdhw(outputInfo, expectedOutputData); + } + + // Quantize data + std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset); + std::vector<T> qWeightsData = armnnUtils::QuantizedVector<T>(weightsData, qScale, qOffset); + std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset); + + std::vector<BT> qBiasesData = armnnUtils::QuantizedVector<BT>(biasesData, qScale * qScale, 0); + + ConstTensor weights(weightsInfo, qWeightsData); + ConstTensor biases(biasesInfo, qBiasesData); + + INetworkPtr network = CreateConvolution3dNetwork(descriptor, + inputInfo, + weightsInfo, + biasesInfo, + outputInfo, + weights, + biases); + + EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), + { { 0, qInputData } }, + { { 0, qExpectedOutputData } }, + backends); +} |