diff options
Diffstat (limited to 'src/backends/backendsCommon/test/Convolution2dEndToEndTestImpl.hpp')
| -rw-r--r-- | src/backends/backendsCommon/test/Convolution2dEndToEndTestImpl.hpp | 81 |
1 files changed, 43 insertions, 38 deletions
diff --git a/src/backends/backendsCommon/test/Convolution2dEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/Convolution2dEndToEndTestImpl.hpp index bc9a94289b..f53f97ae88 100644 --- a/src/backends/backendsCommon/test/Convolution2dEndToEndTestImpl.hpp +++ b/src/backends/backendsCommon/test/Convolution2dEndToEndTestImpl.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2022, 2024 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #pragma once @@ -49,46 +49,51 @@ armnn::INetworkPtr CreateConstConvolution2dNetwork(const armnn::Convolution2dDes return network; } -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +template<DataType ArmnnIType, DataType ArmnnWType = ArmnnIType, DataType ArmnnBType = ArmnnIType, + DataType ArmnnOType = ArmnnIType> void Convolution2dEndToEnd(const std::vector<armnn::BackendId>& backends, armnn::DataLayout dataLayout, bool biasEnabled = true) { using namespace armnn; + using IT = ResolveType<ArmnnIType>; + using WT = ResolveType<ArmnnWType>; + using BT = ResolveType<ArmnnBType>; + using OT = ResolveType<ArmnnOType>; - const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f; - const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0; + const float qScale = 1.0f; + const int32_t qOffset = IsQuantizedType<IT>() ? 10 : 0; // offset must be zero for non-quantized types - 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); + TensorInfo inputInfo( { 1, 5, 5, 1 }, ArmnnIType, qScale, qOffset, true); + TensorInfo weightsInfo({ 1, 3, 3, 1 }, ArmnnWType, qScale, qOffset, true); + TensorInfo biasesInfo( { 1 }, ArmnnBType, qScale * qScale, 0, true); + TensorInfo outputInfo( { 1, 3, 3, 1 }, ArmnnOType, qScale, qOffset); 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 - }; + { + 1, 5, 2, 3, 5, + 8, 7, 3, 6, 3, + 3, 3, 9, 1, 9, + 4, 1, 8, 1, 3, + 6, 8, 1, 9, 2 + }; std::vector<float> weightsData = - { - 4.0f, 5.0f, 6.0f, - 0.0f, 0.0f, 0.0f, - 3.0f, 2.0f, 1.0f - }; + { + 4, 5, 6, + 0, 0, 0, + 3, 2, 1 + }; - std::vector<float> biasesData = { 1.0f }; + std::vector<float> biasesData = { 1 }; + float bias = biasEnabled ? biasesData[0] : 0; - 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, - }; + { + 65 + bias, 76 + bias, 91 + bias, + 107 + bias, 99 + bias, 89 + bias, + 116 + bias, 98 + bias, 118 + bias + }; Convolution2dDescriptor descriptor; descriptor.m_PadLeft = 0; @@ -102,16 +107,16 @@ void Convolution2dEndToEnd(const std::vector<armnn::BackendId>& backends, if (dataLayout == DataLayout::NCHW) { - PermuteTensorNhwcToNchw(inputInfo, inputData); + PermuteTensorNhwcToNchw(inputInfo, inputData); PermuteTensorNhwcToNchw(weightsInfo, weightsData); - PermuteTensorNhwcToNchw(outputInfo, expectedOutputData); + 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); + // Convert data + std::vector<IT> qInputData = armnnUtils::QuantizedVector<IT>(inputData, qScale, qOffset); + std::vector<WT> qWeightsData = armnnUtils::QuantizedVector<WT>(weightsData, qScale, qOffset); + std::vector<BT> qBiasesData = armnnUtils::QuantizedVector<BT>(biasesData, qScale * qScale, 0); + std::vector<OT> qExpectedOutputData = armnnUtils::QuantizedVector<OT>(expectedOutputData, qScale, qOffset); ConstTensor weights(weightsInfo, qWeightsData); ConstTensor biases(biasesInfo, qBiasesData); @@ -125,10 +130,10 @@ void Convolution2dEndToEnd(const std::vector<armnn::BackendId>& backends, biases, biasEnabled); - EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), - {{ 0, qInputData }}, - {{ 0, qExpectedOutputData }}, - backends); + EndToEndLayerTestImpl<ArmnnIType, ArmnnOType>(std::move(network), + {{ 0, qInputData }}, + {{ 0, qExpectedOutputData }}, + backends); } } // anonymous namespace |