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Diffstat (limited to 'src/backends/backendsCommon/test/QuantizationEndToEndTestImpl.hpp')
| -rw-r--r-- | src/backends/backendsCommon/test/QuantizationEndToEndTestImpl.hpp | 108 |
1 files changed, 108 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/QuantizationEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/QuantizationEndToEndTestImpl.hpp new file mode 100644 index 0000000000..f5c2eea601 --- /dev/null +++ b/src/backends/backendsCommon/test/QuantizationEndToEndTestImpl.hpp @@ -0,0 +1,108 @@ +// +// Copyright © 2023 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include <CommonTestUtils.hpp> + +#include <armnn/INetwork.hpp> + +#include <ResolveType.hpp> + +#include <doctest/doctest.h> + +namespace +{ + +armnn::INetworkPtr CreateQuantizationNetwork(const armnn::TensorInfo& inputInfo, + const armnn::TensorInfo& outputInfo) +{ + using namespace armnn; + + INetworkPtr network(INetwork::Create()); + + IConnectableLayer *input= network->AddInputLayer(0, "input"); + IConnectableLayer *quantization = network->AddQuantizeLayer("quantization"); + IConnectableLayer *output = network->AddOutputLayer(0, "output"); + + Connect(input, quantization, inputInfo, 0, 0); + Connect(quantization, output, outputInfo, 0, 0); + + return network; +} + +template<armnn::DataType ArmnnIType, armnn::DataType ArmnnOType, + typename Tin = armnn::ResolveType<ArmnnIType>, typename Tout = armnn::ResolveType<ArmnnOType>> +void QuantizeEndToEndLayerTestImpl(const std::vector<armnn::BackendId>& backends, + const armnn::TensorShape& tensorShape, + const std::vector<Tin>& input, + const std::vector<Tout>& expectedOutput, + float scale, + int32_t offset) +{ + using namespace armnn; + + TensorInfo inputInfo(tensorShape, ArmnnIType); + TensorInfo outputInfo(tensorShape, ArmnnOType, scale, offset); + + inputInfo.SetConstant(true); + + // Builds up the structure of the network + INetworkPtr net = CreateQuantizationNetwork(inputInfo, outputInfo); + + CHECK(net); + + const std::map<int, std::vector<Tin>> inputTensorData = { { 0, input } }; + const std::map<int, std::vector<Tout>> expectedOutputData = { { 0, expectedOutput } }; + + EndToEndLayerTestImpl<ArmnnIType, ArmnnOType>(std::move(net), inputTensorData, expectedOutputData, backends); +} + +template<armnn::DataType ArmnnOType, typename Tout = armnn::ResolveType<ArmnnOType>> +void QuantizationEndToEndFloat32(const std::vector<armnn::BackendId>& backends) +{ + using namespace armnn; + + const TensorShape tensorShape({ 1, 1, 1, 5 }); + + std::vector<float> inputData = { 63.5f, 49.5f, 14.0f, 0.0f, 50.0f }; + + float qScale = 0.5f; + int32_t qOffset = 127; + std::vector<Tout> expectedOutputData = armnnUtils::QuantizedVector<Tout>(inputData, qScale, qOffset); + + QuantizeEndToEndLayerTestImpl<DataType::Float32, ArmnnOType>(backends, + tensorShape, + inputData, + expectedOutputData, + qScale, + qOffset); +}; + +template<armnn::DataType ArmnnOType, typename Tout = armnn::ResolveType<ArmnnOType>> +void QuantizationEndToEndFloat16(const std::vector<armnn::BackendId>& backends) +{ + using namespace armnn; + using namespace half_float::literal; + using Half = half_float::half; + + const TensorShape tensorShape({ 1, 1, 1, 5 }); + + std::vector<float> floatInputData = { 63.f, 49.f, 14.f, 0.f, 50.f }; + std::vector<Half> inputData = { 63._h, 49._h, 14._h, 0._h, 50._h }; + + float qScale = 0.25f; + int32_t qOffset = 1; + std::vector<Tout> expectedOutputData = armnnUtils::QuantizedVector<Tout>(floatInputData, qScale, qOffset); + + QuantizeEndToEndLayerTestImpl<DataType::Float16, ArmnnOType>(backends, + tensorShape, + inputData, + expectedOutputData, + qScale, + qOffset); +}; + +}
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