--- /dev/null
+//
+// 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));
+ }
+}
#pragma once
-#include "CommonTestUtils.hpp"
-#include "EndToEndTestImpl.hpp"
+#include <armnn/BackendId.hpp>
-#include <armnn/INetwork.hpp>
-#include <ResolveType.hpp>
-#include <test/TensorHelpers.hpp>
+#include <vector>
-#include <boost/test/unit_test.hpp>
-
-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;
-}
-
-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.
- for (unsigned int i = 0; i < cellStateOutResult.size(); ++i)
- {
- BOOST_TEST(cellStateOutVector[i] == cellStateOutResult[i], boost::test_tools::tolerance(1.0f));
- }
-
- for (unsigned int i = 0; i < outputStateOutResult.size(); ++i)
- {
- BOOST_TEST(outputStateOutVector[i] == outputStateOutResult[i], boost::test_tools::tolerance(1.0f));
- }
-}
-
-} // anonymous namespace
+void QuantizedLstmEndToEnd(const std::vector<armnn::BackendId>& backends);
projects / platform / upstream / armnn.git / commitdiff