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Diffstat (limited to 'src/backends/backendsCommon')
-rw-r--r-- | src/backends/backendsCommon/test/TransposeConvolution2dEndToEndTestImpl.hpp | 153 |
1 files changed, 153 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/TransposeConvolution2dEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/TransposeConvolution2dEndToEndTestImpl.hpp new file mode 100644 index 0000000000..9d6312ea53 --- /dev/null +++ b/src/backends/backendsCommon/test/TransposeConvolution2dEndToEndTestImpl.hpp @@ -0,0 +1,153 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include "QuantizeHelper.hpp" + +#include <armnn/ArmNN.hpp> + +#include <Permute.hpp> +#include <ResolveType.hpp> + +#include <backendsCommon/test/CommonTestUtils.hpp> + +#include <boost/test/unit_test.hpp> + +#include <map> +#include <vector> + +namespace +{ + +INetworkPtr CreateTransposeConvolution2dNetwork(const armnn::TransposeConvolution2dDescriptor& descriptor, + const armnn::TensorInfo& inputInfo, + const armnn::TensorInfo& outputInfo, + const armnn::ConstTensor& weights, + const armnn::Optional<armnn::ConstTensor>& biases) +{ + using namespace armnn; + + INetworkPtr network(INetwork::Create()); + IConnectableLayer* input = network->AddInputLayer(0, "input"); + IConnectableLayer* transposeConvolution2d = + network->AddTransposeConvolution2dLayer(descriptor, weights, biases, "transposeConvolution2d"); + IConnectableLayer* output = network->AddOutputLayer(0, "output"); + + Connect(input, transposeConvolution2d, inputInfo, 0, 0); + Connect(transposeConvolution2d, output, outputInfo, 0, 0); + + return network; +} + +} // anonymous namespace + +template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType> +void TransposeConvolution2dEndToEnd(const std::vector<armnn::BackendId>& backends, + armnn::DataLayout dataLayout) +{ + using namespace armnn; + using T = ResolveType<ArmnnType>; + + constexpr unsigned int batches = 1u; + constexpr unsigned int channels = 1u; + + constexpr unsigned int wInput = 3u; + constexpr unsigned int hInput = wInput; + + constexpr unsigned int wOutput = 5u; + constexpr unsigned int hOutput = wOutput; + + constexpr unsigned int wWeights = 3u; + constexpr unsigned int hWeights = wWeights; + + TensorShape inputShape = MakeTensorShape(batches, channels, hInput, wInput, dataLayout); + TensorShape outputShape = MakeTensorShape(batches, channels, hOutput, wOutput, dataLayout); + TensorShape weightsShape = MakeTensorShape(batches, channels, hWeights, wWeights, dataLayout); + + const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f; + const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0; + + TensorInfo inputInfo(inputShape, ArmnnType, qScale, qOffset); + TensorInfo outputInfo(outputShape, ArmnnType, qScale, qOffset); + TensorInfo weightsInfo(weightsShape, ArmnnType, qScale, qOffset); + TensorInfo biasesInfo({ channels }, ArmnnBType, qScale * qScale, 0); + + std::vector<float> inputData = + { + 1.f, 1.f, 1.f, + 1.f, 1.f, 1.f, + 1.f, 1.f, 1.f + }; + + std::vector<float> weightsData = + { + 1.f, 2.f, 3.f, + 4.f, 5.f, 6.f, + 7.f, 8.f, 9.f + }; + + std::vector<float> biasesData = { 1.f }; + + std::vector<float> expectedOutputData = + { + 6.f, 11.f, 6.f, 11.f, 6.f, + 11.f, 21.f, 11.f, 21.f, 11.f, + 6.f, 11.f, 6.f, 11.f, 6.f, + 11.f, 21.f, 11.f, 21.f, 11.f, + 6.f, 11.f, 6.f, 11.f, 6.f + }; + + TransposeConvolution2dDescriptor descriptor; + descriptor.m_PadLeft = 1; + descriptor.m_PadRight = 1; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 1; + descriptor.m_StrideX = 2; + descriptor.m_StrideY = 2; + descriptor.m_BiasEnabled = true; + descriptor.m_DataLayout = dataLayout; + + // swizzle data if needed + if (dataLayout == armnn::DataLayout::NHWC) + { + constexpr size_t dataTypeSize = sizeof(float); + const armnn::PermutationVector nchwToNhwc = { 0, 3, 1, 2 }; + + std::vector<float> tmp(inputData.size()); + armnnUtils::Permute(inputInfo.GetShape(), nchwToNhwc, inputData.data(), tmp.data(), dataTypeSize); + inputData = tmp; + + tmp.resize(weightsData.size()); + armnnUtils::Permute(weightsInfo.GetShape(), nchwToNhwc, weightsData.data(), tmp.data(), dataTypeSize); + weightsData = tmp; + + tmp.resize(expectedOutputData.size()); + armnnUtils::Permute(outputInfo.GetShape(), nchwToNhwc, expectedOutputData.data(), tmp.data(), dataTypeSize); + expectedOutputData = tmp; + } + + // quantize data + std::vector<T> qInputData = QuantizedVector<T>(qScale, qOffset, inputData); + std::vector<T> qWeightsData = QuantizedVector<T>(qScale, qOffset, weightsData); + std::vector<T> qExpectedOutputData = QuantizedVector<T>(qScale, qOffset, expectedOutputData); + + using BT = ResolveType<ArmnnBType>; + std::vector<BT> qBiasesData = QuantizedVector<BT>(qScale * qScale, 0, biasesData); + + ConstTensor weights(weightsInfo, qWeightsData); + ConstTensor biases(biasesInfo, qBiasesData); + + INetworkPtr network = CreateTransposeConvolution2dNetwork(descriptor, + inputInfo, + outputInfo, + weights, + Optional<ConstTensor>(biases)); + + + EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), + { { 0, qInputData } }, + { { 0, qExpectedOutputData } }, + backends); +}
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