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Diffstat (limited to 'src/backends/backendsCommon/test/Convolution2dEndToEndTestImpl.hpp')
-rw-r--r-- | src/backends/backendsCommon/test/Convolution2dEndToEndTestImpl.hpp | 134 |
1 files changed, 134 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/Convolution2dEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/Convolution2dEndToEndTestImpl.hpp new file mode 100644 index 0000000000..bc9a94289b --- /dev/null +++ b/src/backends/backendsCommon/test/Convolution2dEndToEndTestImpl.hpp @@ -0,0 +1,134 @@ +// +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include "EndToEndTestImpl.hpp" +#include <armnnUtils/QuantizeHelper.hpp> + +#include <ResolveType.hpp> + +#include <CommonTestUtils.hpp> +#include <armnnTestUtils/DataLayoutUtils.hpp> + +#include <map> +#include <vector> + +namespace +{ + +armnn::INetworkPtr CreateConstConvolution2dNetwork(const armnn::Convolution2dDescriptor& 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, + bool biasEnabled) +{ + using namespace armnn; + + INetworkPtr network(INetwork::Create()); + IConnectableLayer* input = network->AddInputLayer(0, "input"); + IConnectableLayer* weightsLayer = network->AddConstantLayer(weights, "Weights"); + IConnectableLayer* convolution2d = network->AddConvolution2dLayer(descriptor, "convolution2d"); + IConnectableLayer* output = network->AddOutputLayer(0, "output"); + + Connect(input, convolution2d, inputInfo, 0, 0); + Connect(weightsLayer, convolution2d, weightsInfo, 0, 1); + + if(biasEnabled) + { + armnn::IConnectableLayer* biasLayer = network->AddConstantLayer(biases, "Bias"); + Connect(biasLayer, convolution2d, biasInfo, 0, 2); + } + + Connect(convolution2d, output, outputInfo, 0, 0); + + return network; +} + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +void Convolution2dEndToEnd(const std::vector<armnn::BackendId>& backends, + armnn::DataLayout dataLayout, + bool biasEnabled = true) +{ + using namespace armnn; + + const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f; + const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0; + + TensorInfo inputInfo({ 1, 5, 5, 1 }, ArmnnType, qScale, qOffset, true); + TensorInfo outputInfo({ 1, 3, 3, 1 }, ArmnnType, qScale, qOffset); + TensorInfo weightsInfo({ 1, 3, 3, 1 }, ArmnnType, qScale, qOffset, true); + TensorInfo biasesInfo({ 1 }, ArmnnType, qScale * qScale, 0, true); + + std::vector<float> inputData = + { + 1.0f, 5.0f, 2.0f, 3.0f, 5.0f, + 8.0f, 7.0f, 3.0f, 6.0f, 3.0f, + 3.0f, 3.0f, 9.0f, 1.0f, 9.0f, + 4.0f, 1.0f, 8.0f, 1.0f, 3.0f, + 6.0f, 8.0f, 1.0f, 9.0f, 2.0f + }; + + std::vector<float> weightsData = + { + 4.0f, 5.0f, 6.0f, + 0.0f, 0.0f, 0.0f, + 3.0f, 2.0f, 1.0f + }; + + std::vector<float> biasesData = { 1.0f }; + + float bias = biasEnabled ? biasesData[0] : 0.0f; + std::vector<float> expectedOutputData = + { + 65.0f + bias, 76.0f + bias, 91.0f + bias, + 107.0f + bias, 99.0f + bias, 89.0f + bias, + 116.0f + bias, 98.0f + bias, 118.0f + bias, + }; + + Convolution2dDescriptor descriptor; + descriptor.m_PadLeft = 0; + descriptor.m_PadRight = 0; + descriptor.m_PadTop = 0; + descriptor.m_PadBottom = 0; + descriptor.m_StrideX = 1; + descriptor.m_StrideY = 1; + descriptor.m_BiasEnabled = biasEnabled; + descriptor.m_DataLayout = dataLayout; + + if (dataLayout == DataLayout::NCHW) + { + PermuteTensorNhwcToNchw(inputInfo, inputData); + PermuteTensorNhwcToNchw(weightsInfo, weightsData); + PermuteTensorNhwcToNchw(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<T> qBiasesData = armnnUtils::QuantizedVector<T>(biasesData, qScale * qScale, 0); + + ConstTensor weights(weightsInfo, qWeightsData); + ConstTensor biases(biasesInfo, qBiasesData); + + INetworkPtr network = CreateConstConvolution2dNetwork(descriptor, + inputInfo, + weightsInfo, + biasesInfo, + outputInfo, + weights, + biases, + biasEnabled); + + EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), + {{ 0, qInputData }}, + {{ 0, qExpectedOutputData }}, + backends); +} + +} // anonymous namespace |