diff options
author | Aron Virginas-Tar <Aron.Virginas-Tar@arm.com> | 2019-09-12 11:03:09 +0100 |
---|---|---|
committer | Áron Virginás-Tar <aron.virginas-tar@arm.com> | 2019-09-16 09:57:58 +0000 |
commit | 46ff1caca9b4504d04515404b7f61cc97fc42123 (patch) | |
tree | 18382b5424989db21a50bf948ea62aaa98199f1d /src/backends/backendsCommon/test/QuantizedLstmEndToEndTestImpl.cpp | |
parent | 6095ba5f097345510bcae7804bcf4ae123b4f98f (diff) | |
download | armnn-46ff1caca9b4504d04515404b7f61cc97fc42123.tar.gz |
IVGCVSW-3854 Fix QuantizedLstmEndToEndTest on Raspberry Pi
* Do not rely on boost::test_tools::tolerance for comparing integer values,
as this is not supported in all Boost versions
Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Change-Id: I7050a5765d007dc501d9e893b661d8847dd55ad7
Diffstat (limited to 'src/backends/backendsCommon/test/QuantizedLstmEndToEndTestImpl.cpp')
-rw-r--r-- | src/backends/backendsCommon/test/QuantizedLstmEndToEndTestImpl.cpp | 247 |
1 files changed, 247 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/QuantizedLstmEndToEndTestImpl.cpp b/src/backends/backendsCommon/test/QuantizedLstmEndToEndTestImpl.cpp new file mode 100644 index 0000000000..609773ce89 --- /dev/null +++ b/src/backends/backendsCommon/test/QuantizedLstmEndToEndTestImpl.cpp @@ -0,0 +1,247 @@ +// +// Copyright © 2019 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "QuantizedLstmEndToEndTestImpl.hpp" + +#include "CommonTestUtils.hpp" +#include "EndToEndTestImpl.hpp" + +#include <ResolveType.hpp> + +#include <armnn/INetwork.hpp> + +#include <test/TensorHelpers.hpp> + +#include <boost/test/unit_test.hpp> + +#include <type_traits> + +namespace +{ + +using MultiArray = const boost::multi_array<uint8_t, 2>&; + +armnn::INetworkPtr CreateQuantizedLstmNetwork(MultiArray input, + MultiArray expectedOutput) +{ + auto batchSize = boost::numeric_cast<unsigned int>(input.shape()[0]); + auto inputSize = boost::numeric_cast<unsigned int>(input.shape()[1]); + auto outputSize = boost::numeric_cast<unsigned int>(expectedOutput.shape()[1]); + + float inputOutputScale = 0.0078125f; + int32_t inputOutputOffset = 128; + + float weightsScale = 0.00408021f; + int32_t weightsOffset = 100; + + float biasScale = 3.1876640625e-05f; + int32_t biasOffset = 0; + + float cellStateScale = 0.00048828125f; + int32_t cellStateOffset = 0; + + armnn::TensorInfo inputWeightsInfo({outputSize, inputSize}, + armnn::DataType::QuantisedAsymm8, + weightsScale, + weightsOffset); + + armnn::TensorInfo recurrentWeightsInfo({outputSize, outputSize}, + armnn::DataType::QuantisedAsymm8, + weightsScale, + weightsOffset); + + armnn::TensorInfo biasInfo({outputSize}, armnn::DataType::Signed32, biasScale, biasOffset); + + armnn::QuantizedLstmInputParams data; + + const std::vector<uint8_t> inputToInputWeightsVector = {146, 250, 235, 171, 10, 218, 171, 108}; + armnn::ConstTensor inputToInputWeightsTensor(inputWeightsInfo, inputToInputWeightsVector.data()); + + const std::vector<uint8_t> inputToForgetWeightsVector = {24, 50, 132, 179, 158, 110, 3, 169}; + armnn::ConstTensor inputToForgetWeightsTensor(inputWeightsInfo, inputToForgetWeightsVector.data()); + + const std::vector<uint8_t> inputToCellWeightsTensorVector = {133, 34, 29, 49, 206, 109, 54, 183}; + armnn::ConstTensor inputToCellWeightsTensor(inputWeightsInfo, inputToCellWeightsTensorVector.data()); + + const std::vector<uint8_t> inputToOutputWeightsTensorVector = {195, 187, 11, 99, 109, 10, 218, 48}; + armnn::ConstTensor inputToOutputWeightsTensor(inputWeightsInfo, inputToOutputWeightsTensorVector.data()); + + const std::vector<uint8_t> recurrentToInputWeightsTensorVector = + {254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26}; + armnn::ConstTensor recurrentToInputWeightsTensor(recurrentWeightsInfo, recurrentToInputWeightsTensorVector.data()); + + const std::vector<uint8_t> recurrentToForgetWeightsTensorVector = + {137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253}; + armnn::ConstTensor recurrentToForgetWeightsTensor(recurrentWeightsInfo, + recurrentToForgetWeightsTensorVector.data()); + + const std::vector<uint8_t> recurrentToCellWeightsTensorVector = + {172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216}; + armnn::ConstTensor recurrentToCellWeightsTensor(recurrentWeightsInfo, recurrentToCellWeightsTensorVector.data()); + + const std::vector<uint8_t> recurrentToOutputWeightsTensorVector = + {106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98}; + armnn::ConstTensor recurrentToOutputWeightsTensor(recurrentWeightsInfo, + recurrentToOutputWeightsTensorVector.data()); + + const std::vector<int32_t> inputGateBiasTensorVector = {-7876, 13488, -726, 32839}; + armnn::ConstTensor inputGateBiasTensor(biasInfo, inputGateBiasTensorVector.data()); + + const std::vector<int32_t> forgetGateBiasTensorVector = {9206, -46884, -11693, -38724}; + armnn::ConstTensor forgetGateBiasTensor(biasInfo, forgetGateBiasTensorVector.data()); + + const std::vector<int32_t> cellBiasTensorVector = {39481, 48624, 48976, -21419}; + armnn::ConstTensor cellBiasTensor(biasInfo, cellBiasTensorVector.data()); + + const std::vector<int32_t> outputGateBiasTensorVector = {-58999, -17050, -41852, -40538}; + armnn::ConstTensor outputGateBiasTensor(biasInfo, outputGateBiasTensorVector.data()); + + data.m_InputToInputWeights = &inputToInputWeightsTensor; + data.m_InputToForgetWeights = &inputToForgetWeightsTensor; + data.m_InputToCellWeights = &inputToCellWeightsTensor; + data.m_InputToOutputWeights = &inputToOutputWeightsTensor; + data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; + data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; + data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; + data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; + data.m_InputGateBias = &inputGateBiasTensor; + data.m_ForgetGateBias = &forgetGateBiasTensor; + data.m_CellBias = &cellBiasTensor; + data.m_OutputGateBias = &outputGateBiasTensor; + + armnn::INetworkPtr net(armnn::INetwork::Create()); + + armnn::IConnectableLayer* const inputLayer = net->AddInputLayer(0); + armnn::IConnectableLayer* const cellStateIn = net->AddInputLayer(1); + armnn::IConnectableLayer* const outputStateIn = net->AddInputLayer(2); + armnn::IConnectableLayer* const quantizedLstmLayer = net->AddQuantizedLstmLayer(data, "quantizedLstm"); + armnn::IConnectableLayer* const cellStateOut = net->AddOutputLayer(0); + armnn::IConnectableLayer* const outputStateOut = net->AddOutputLayer(1); + + armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, + armnn::DataType::QuantisedAsymm8, + inputOutputScale, + inputOutputOffset); + + armnn::TensorInfo cellStateInTensorInfo({batchSize , outputSize}, + armnn::DataType::QuantisedSymm16, + cellStateScale, + cellStateOffset); + + armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, + armnn::DataType::QuantisedAsymm8, + inputOutputScale, + inputOutputOffset); + + armnn::TensorInfo cellStateOutTensorInfo({batchSize, outputSize}, + armnn::DataType::QuantisedSymm16, + cellStateScale, + cellStateOffset); + + armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, + armnn::DataType::QuantisedAsymm8, + inputOutputScale, + inputOutputOffset); + + // connect up + // inputs + Connect(inputLayer, quantizedLstmLayer, inputTensorInfo, 0, 0); + Connect(cellStateIn, quantizedLstmLayer, cellStateInTensorInfo, 0, 1); + Connect(outputStateIn, quantizedLstmLayer, outputStateInTensorInfo, 0, 2); + + // outputs + Connect(quantizedLstmLayer, cellStateOut, cellStateOutTensorInfo, 0, 0); + Connect(quantizedLstmLayer, outputStateOut, outputTensorInfo, 1, 0); + + return net; +} + +// Checks if two values of an arithmetic type are close enough to each other +// with regard to a given tolerance value. +template<typename T> +typename std::enable_if<std::is_arithmetic<T>::value, bool>::type +IsCloseEnough(T value1, T value2, T tolerance) +{ + if (tolerance < 0) + { + throw armnn::InvalidArgumentException("Tolerance cannot be < 0"); + } + + T diff = value1 >= value2 ? static_cast<T>(value1 - value2) : static_cast<T>(value2 - value1); + return diff <= tolerance; +} + +} // anonymous namespace + +void QuantizedLstmEndToEnd(const std::vector<armnn::BackendId>& backends) +{ + std::vector<uint8_t> inputVector = {166, 179, 50, 150}; + armnn::TensorInfo inputDesc({2, 2}, armnn::DataType::QuantisedAsymm8); + boost::multi_array<uint8_t, 2> input = MakeTensor<uint8_t, 2>(inputDesc, inputVector); + + std::vector<int16_t> cellStateInVector = {876, 1034, 955, -909, 761, 1029, 796, -1036}; + armnn::TensorInfo cellStateInDesc({2, 4}, armnn::DataType::QuantisedSymm16); + boost::multi_array<int16_t, 2> cellStateIn = MakeTensor<int16_t, 2>(cellStateInDesc, cellStateInVector); + + std::vector<uint8_t> outputStateInVector = {136, 150, 140, 115, 135, 152, 138, 112}; + armnn::TensorInfo outputStateInDesc({2, 4}, armnn::DataType::QuantisedAsymm8); + boost::multi_array<uint8_t, 2> outputStateIn = MakeTensor<uint8_t, 2>(outputStateInDesc, outputStateInVector); + + std::vector<int16_t> cellStateOutVector = {1485, 1177, 1373, -1023, 1019, 1355, 1097, -1235}; + armnn::TensorInfo cellStateOutVectorDesc({2, 4}, armnn::DataType::QuantisedSymm16); + boost::multi_array<int16_t, 2> cellStateOut = MakeTensor<int16_t, 2>(cellStateOutVectorDesc, cellStateOutVector); + + std::vector<uint8_t> outputStateOutVector = {140, 151, 146, 112, 136, 156, 142, 112}; + armnn::TensorInfo outputDesc({2, 4}, armnn::DataType::QuantisedAsymm8); + boost::multi_array<uint8_t, 2> outputStateOut = MakeTensor<uint8_t, 2>(outputDesc, outputStateOutVector); + + // Builds up the structure of the network + armnn::INetworkPtr net = CreateQuantizedLstmNetwork(input, outputStateOut); + + BOOST_TEST_CHECKPOINT("create a network"); + + IRuntime::CreationOptions options; + IRuntimePtr runtime(IRuntime::Create(options)); + + // optimize the network + IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); + + // Loads it into the runtime. + NetworkId netId; + runtime->LoadNetwork(netId, std::move(optNet)); + + InputTensors inputTensors; + inputTensors.reserve(3); + + // input + inputTensors.push_back({0, ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputVector.data())}); + inputTensors.push_back({1, ConstTensor(runtime->GetInputTensorInfo(netId, 1), cellStateInVector.data())}); + inputTensors.push_back({2, ConstTensor(runtime->GetInputTensorInfo(netId, 2), outputStateInVector.data())}); + + OutputTensors outputTensors; + outputTensors.reserve(2); + + //output + std::vector<int16_t > cellStateOutResult(cellStateOutVector.size()); + std::vector<uint8_t > outputStateOutResult(outputStateOutVector.size()); + outputTensors.push_back({0, Tensor(runtime->GetOutputTensorInfo(netId, 0), cellStateOutResult.data())}); + outputTensors.push_back({1, Tensor(runtime->GetOutputTensorInfo(netId, 1), outputStateOutResult.data())}); + + // Does the inference. + runtime->EnqueueWorkload(netId, inputTensors, outputTensors); + + // Checks the results + constexpr int16_t toleranceInt16 = 2; + for (unsigned int i = 0u; i < cellStateOutResult.size(); ++i) + { + BOOST_CHECK(IsCloseEnough(cellStateOutVector[i], cellStateOutResult[i], toleranceInt16)); + } + + constexpr uint8_t toleranceUint8 = 1; + for (unsigned int i = 0u; i < outputStateOutResult.size(); ++i) + { + BOOST_TEST(IsCloseEnough(outputStateOutVector[i], outputStateOutResult[i], toleranceUint8)); + } +} |