diff options
Diffstat (limited to 'delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp')
| -rw-r--r-- | delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp | 344 |
1 files changed, 182 insertions, 162 deletions
diff --git a/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp b/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp index 9d6ef87e3f..10555aca1a 100644 --- a/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp +++ b/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -19,7 +19,6 @@ #include <doctest/doctest.h> - #include <armnn/utility/IgnoreUnused.hpp> #include <armnn/utility/NumericCast.hpp> #include <armnn/TypesUtils.hpp> @@ -33,7 +32,7 @@ namespace { -template <typename T> +template<typename T> std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType tensorType, int32_t batchSize, int32_t timeSize, @@ -78,7 +77,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType float clippingThresProj, bool isTimeMajor, float quantScale, - int quantOffset = 0) + int quantOffset = 0) { std::vector<int32_t> tensorInfo0{}; @@ -105,39 +104,41 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType std::vector<int> operatorInputs; using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; + flatbuffers::FlatBufferBuilder flatBufferBuilder; std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - std::vector<flatbuffers::Offset<Tensor>> tensors; + std::vector<flatbuffers::Offset<Tensor>> tensors; auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ 1.0f }), - flatBufferBuilder.CreateVector<int64_t>({ 0 })); + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({1.0f}), + flatBufferBuilder.CreateVector<int64_t>({0})); auto weightQuantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({quantScale}), + flatBufferBuilder.CreateVector<int64_t>({quantOffset})); + + buffers.push_back(CreateBuffer(flatBufferBuilder)); + buffers.push_back(CreateBuffer(flatBufferBuilder)); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(inputShape.data(), inputShape.size()), ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("input_0"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); if (hasInputToInputWeights) { buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToInputWeights.data()), - sizeof(T) * inputToInputWeights.size()))); + flatBufferBuilder.CreateVector( + reinterpret_cast<const uint8_t*>(inputToInputWeights.data()), + sizeof(T) * inputToInputWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), tensorInfoInputSize.size()), @@ -145,7 +146,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("inputToInputWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -154,8 +155,9 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToForgetWeights.data()), - sizeof(T) * inputToForgetWeights.size()))); + flatBufferBuilder.CreateVector( + reinterpret_cast<const uint8_t*>(inputToForgetWeights.data()), + sizeof(T) * inputToForgetWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), tensorInfoInputSize.size()), @@ -163,12 +165,13 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("inputToForgetWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToCellWeights.data()), - sizeof(T) * inputToCellWeights.size()))); + flatBufferBuilder.CreateVector( + reinterpret_cast<const uint8_t*>(inputToCellWeights.data()), + sizeof(T) * inputToCellWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), tensorInfoInputSize.size()), @@ -176,12 +179,13 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("inputToCellWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToOutputWeights.data()), - sizeof(T) * inputToOutputWeights.size()))); + flatBufferBuilder.CreateVector( + reinterpret_cast<const uint8_t*>(inputToOutputWeights.data()), + sizeof(T) * inputToOutputWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), tensorInfoInputSize.size()), @@ -189,7 +193,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("inputToOutputWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); if (hasRecurrentToInputWeights) { @@ -204,7 +208,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("recurrentToInputWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -213,7 +217,8 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(recurrentToForgetWeights.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( + recurrentToForgetWeights.data()), sizeof(T) * recurrentToForgetWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoOutputSize.data(), @@ -222,11 +227,12 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("recurrentToForgetWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(recurrentToCellWeights.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( + recurrentToCellWeights.data()), sizeof(T) * recurrentToCellWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoOutputSize.data(), @@ -235,26 +241,28 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("recurrentToCellWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(recurrentToOutputWeights.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( + recurrentToOutputWeights.data()), sizeof(T) * recurrentToOutputWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoOutputSize.data(), tensorInfoOutputSize.size()), tensorType, - buffers.size() - 1 , + buffers.size() - 1, flatBufferBuilder.CreateString("recurrentToOutputWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); if (hasCellToInputWeights) { buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellToInputWeights.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( + cellToInputWeights.data()), sizeof(T) * cellToInputWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -263,7 +271,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("cellToInputWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -274,7 +282,8 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType { buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellToForgetWeights.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( + cellToForgetWeights.data()), sizeof(T) * cellToForgetWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -283,7 +292,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("cellToForgetWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -294,7 +303,8 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType { buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellToOutputWeights.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( + cellToOutputWeights.data()), sizeof(T) * cellToOutputWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -303,7 +313,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("cellToOutputWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -322,7 +332,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("inputGateBias"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -331,7 +341,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(forgetGateBias.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(forgetGateBias.data()), sizeof(float) * forgetGateBias.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -339,11 +349,11 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("forgetGateBias"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(cellBias.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellBias.data()), sizeof(float) * cellBias.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -351,11 +361,11 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("cellBias"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(outputGateBias.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(outputGateBias.data()), sizeof(float) * outputGateBias.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -363,14 +373,15 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("outputGateBias"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); if (hasProjectionWeights) { buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(projectionWeights.data()), - sizeof(T) * projectionWeights.size()))); + flatBufferBuilder.CreateVector( + reinterpret_cast<const uint8_t*>(projectionWeights.data()), + sizeof(T) * projectionWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(projectionWeightDimensions.data(), projectionWeightDimensions.size()), @@ -378,7 +389,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.size() - 1, flatBufferBuilder.CreateString("projectionWeights"), weightQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -389,22 +400,23 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType { buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(projectionBias.data()), - sizeof(float) * projectionBias.size()))); + flatBufferBuilder.CreateVector( + reinterpret_cast<const uint8_t*>(projectionBias.data()), + sizeof(float) * projectionBias.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(projectionBiasDimensions.data(), projectionBiasDimensions.size()), ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("projectionBias"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { operatorInputs.push_back(kTfLiteOptionalTensor); } - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputStateInDimensions.data(), outputStateInDimensions.size()), @@ -413,9 +425,9 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType flatBufferBuilder.CreateString("outputStateInInfo"), quantizationParameters, true)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(cellStateInDimensions.data(), cellStateInDimensions.size()), @@ -424,22 +436,22 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType flatBufferBuilder.CreateString("cellStateInInfo"), quantizationParameters, true)); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); if (hasInputLayerNormWeights) { buffers.push_back( CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t *>(inputLayerNormWeights.data()), - sizeof(float) * inputLayerNormWeights.size()))); + reinterpret_cast<const uint8_t*>(inputLayerNormWeights.data()), + sizeof(float) * inputLayerNormWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), tensorInfoNumUnits.size()), ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("inputLayerNormWeights"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -451,15 +463,15 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.push_back( CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t *>(forgetLayerNormWeights.data()), - sizeof(float) * forgetLayerNormWeights.size()))); + reinterpret_cast<const uint8_t*>(forgetLayerNormWeights.data()), + sizeof(float) * forgetLayerNormWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), tensorInfoNumUnits.size()), ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("forgetLayerNormWeights"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -470,7 +482,8 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType { buffers.push_back( CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(cellLayerNormWeights.data()), + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( + cellLayerNormWeights.data()), sizeof(float) * cellLayerNormWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -478,7 +491,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("cellLayerNormWeights"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { @@ -490,7 +503,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType buffers.push_back( CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t *>(outputLayerNormWeights.data()), + reinterpret_cast<const uint8_t*>(outputLayerNormWeights.data()), sizeof(float) * outputLayerNormWeights.size()))); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), @@ -498,58 +511,63 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType ::tflite::TensorType_FLOAT32, buffers.size() - 1, flatBufferBuilder.CreateString("outputLayerNormWeights"))); - operatorInputs.push_back(buffers.size() - 1); + operatorInputs.push_back(tensors.size() - 1); } else { operatorInputs.push_back(kTfLiteOptionalTensor); } - int outputBufferId = buffers.size(); - buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder)); tensors.push_back(CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputShape.data(), outputShape.size()), ::tflite::TensorType_FLOAT32, - outputBufferId, + buffers.size() - 1, flatBufferBuilder.CreateString("output"))); std::vector<int> operatorOutputs; - operatorOutputs.push_back(buffers.size() - 1); + operatorOutputs.push_back(tensors.size() - 1); // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_UnidirectionalSequenceLSTMOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = - CreateUnidirectionalSequenceLSTMOptions(flatBufferBuilder, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor).Union(); + tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_UnidirectionalSequenceLSTMOptions; + flatbuffers::Offset<void> operatorBuiltinOptions = + CreateUnidirectionalSequenceLSTMOptions(flatBufferBuilder, + activationFunction, + clippingThresCell, + clippingThresProj, + isTimeMajor).Union(); flatbuffers::Offset<Operator> lstmOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - operatorBuiltinOptionsType, operatorBuiltinOptions); - - flatbuffers::Offset <SubGraph> subgraph = - CreateSubGraph(flatBufferBuilder, - flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - flatBufferBuilder.CreateVector(&lstmOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: UnidirectionalSequenceLSTM Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = - CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM); - - flatbuffers::Offset <Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(&operatorCode, 1), - flatBufferBuilder.CreateVector(&subgraph, 1), - modelDescription, - flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), + operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), + operatorOutputs.size()), + operatorBuiltinOptionsType, operatorBuiltinOptions); + + flatbuffers::Offset<SubGraph> subgraph = + CreateSubGraph(flatBufferBuilder, + flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), + operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), + operatorOutputs.size()), + flatBufferBuilder.CreateVector(&lstmOperator, 1)); + + flatbuffers::Offset<flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString( + "ArmnnDelegate: UnidirectionalSequenceLSTM Operator Model"); + flatbuffers::Offset<OperatorCode> operatorCode = + CreateOperatorCode(flatBufferBuilder, + tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM); + + flatbuffers::Offset<Model> flatbufferModel = + CreateModel(flatBufferBuilder, + TFLITE_SCHEMA_VERSION, + flatBufferBuilder.CreateVector(&operatorCode, 1), + flatBufferBuilder.CreateVector(&subgraph, 1), + modelDescription, + flatBufferBuilder.CreateVector(buffers)); flatBufferBuilder.Finish(flatbufferModel); @@ -557,7 +575,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); } -template <typename T> +template<typename T> void UnidirectionalSequenceLstmTestImpl(std::vector<armnn::BackendId>& backends, tflite::TensorType tensorType, int32_t batchSize, @@ -609,69 +627,69 @@ void UnidirectionalSequenceLstmTestImpl(std::vector<armnn::BackendId>& backends, using namespace tflite; std::vector<char> modelBuffer = CreateUnidirectionalSequenceLstmTfLiteModel(tensorType, - batchSize, - timeSize, - inputSize, - outputSize, - numUnits, - hasInputToInputWeights, - inputToInputWeights, - inputToForgetWeights, - inputToCellWeights, - inputToOutputWeights, - hasRecurrentToInputWeights, - recurrentToInputWeights, - recurrentToForgetWeights, - recurrentToCellWeights, - recurrentToOutputWeights, - hasCellToInputWeights, - cellToInputWeights, - hasCellToForgetWeights, - cellToForgetWeights, - hasCellToOutputWeights, - cellToOutputWeights, - hasInputGateBias, - inputGateBias, - forgetGateBias, - cellBias, - outputGateBias, - hasProjectionWeights, - projectionWeights, - hasProjectionBias, - projectionBias, - hasInputLayerNormWeights, - inputLayerNormWeights, - hasForgetLayerNormWeights, - forgetLayerNormWeights, - hasCellLayerNormWeights, - cellLayerNormWeights, - hasOutputLayerNormWeights, - outputLayerNormWeights, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor, - quantScale); + batchSize, + timeSize, + inputSize, + outputSize, + numUnits, + hasInputToInputWeights, + inputToInputWeights, + inputToForgetWeights, + inputToCellWeights, + inputToOutputWeights, + hasRecurrentToInputWeights, + recurrentToInputWeights, + recurrentToForgetWeights, + recurrentToCellWeights, + recurrentToOutputWeights, + hasCellToInputWeights, + cellToInputWeights, + hasCellToForgetWeights, + cellToForgetWeights, + hasCellToOutputWeights, + cellToOutputWeights, + hasInputGateBias, + inputGateBias, + forgetGateBias, + cellBias, + outputGateBias, + hasProjectionWeights, + projectionWeights, + hasProjectionBias, + projectionBias, + hasInputLayerNormWeights, + inputLayerNormWeights, + hasForgetLayerNormWeights, + forgetLayerNormWeights, + hasCellLayerNormWeights, + cellLayerNormWeights, + hasOutputLayerNormWeights, + outputLayerNormWeights, + activationFunction, + clippingThresCell, + clippingThresProj, + isTimeMajor, + quantScale); const Model* tfLiteModel = GetModel(modelBuffer.data()); // Create TfLite Interpreters std::unique_ptr<Interpreter> armnnDelegateInterpreter; CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&armnnDelegateInterpreter) == kTfLiteOk); + (&armnnDelegateInterpreter) == kTfLiteOk); CHECK(armnnDelegateInterpreter != nullptr); CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); std::unique_ptr<Interpreter> tfLiteInterpreter; CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&tfLiteInterpreter) == kTfLiteOk); + (&tfLiteInterpreter) == kTfLiteOk); CHECK(tfLiteInterpreter != nullptr); CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); // Create the ArmNN Delegate armnnDelegate::DelegateOptions delegateOptions(backends); std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - armnnDelegate::TfLiteArmnnDelegateDelete); + theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), + armnnDelegate::TfLiteArmnnDelegateDelete); CHECK(theArmnnDelegate != nullptr); // Modify armnnDelegateInterpreter to use armnnDelegate CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); @@ -684,7 +702,7 @@ void UnidirectionalSequenceLstmTestImpl(std::vector<armnn::BackendId>& backends, tfLiteDelageInputData[i] = inputValues[i]; } - auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; + auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInputId); for (unsigned int i = 0; i < inputValues.size(); ++i) { @@ -696,10 +714,10 @@ void UnidirectionalSequenceLstmTestImpl(std::vector<armnn::BackendId>& backends, CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); // Compare output data - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; + auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; auto tfLiteDelagateOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); + auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; + auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); if (tensorType == ::tflite::TensorType_INT8) { @@ -713,8 +731,10 @@ void UnidirectionalSequenceLstmTestImpl(std::vector<armnn::BackendId>& backends, } else { - armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData, expectedOutputValues.size()); - armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteDelagateOutputData, expectedOutputValues.size()); + armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData, + expectedOutputValues.size()); + armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteDelagateOutputData, + expectedOutputValues.size()); armnnDelegate::CompareData(tfLiteDelagateOutputData, armnnDelegateOutputData, expectedOutputValues.size()); } } |