From 58f3919fb34a1aae42857c53360f1d569f5d31f9 Mon Sep 17 00:00:00 2001 From: Sadik Armagan Date: Mon, 17 Sep 2018 14:14:39 +0100 Subject: IVGCVSW-1651 Add Support for Relu on TF Lite parser * Added Relu and Relu6 Support for the TfLite Parser. Change-Id: I3cc5e4922910e556f25b633eae6d2d361cea61b5 --- CMakeLists.txt | 1 + src/armnnTfLiteParser/TfLiteParser.cpp | 80 ++++++++++++++++++++++++--- src/armnnTfLiteParser/TfLiteParser.hpp | 8 +-- src/armnnTfLiteParser/test/Activations.cpp | 87 ++++++++++++++++++++++++++++++ 4 files changed, 167 insertions(+), 9 deletions(-) create mode 100644 src/armnnTfLiteParser/test/Activations.cpp diff --git a/CMakeLists.txt b/CMakeLists.txt index 0fc3f1ccd7..e4ed9b4515 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -727,6 +727,7 @@ if(BUILD_UNIT_TESTS) src/armnnTfLiteParser/test/GetTensorIds.cpp src/armnnTfLiteParser/test/GetSubgraphInputsOutputs.cpp src/armnnTfLiteParser/test/GetInputsOutputs.cpp + src/armnnTfLiteParser/test/Activations.cpp ) endif() diff --git a/src/armnnTfLiteParser/TfLiteParser.cpp b/src/armnnTfLiteParser/TfLiteParser.cpp index 2c70b481e4..dd1f5773af 100644 --- a/src/armnnTfLiteParser/TfLiteParser.cpp +++ b/src/armnnTfLiteParser/TfLiteParser.cpp @@ -455,6 +455,8 @@ TfLiteParser::TfLiteParser() m_ParserFunctions[tflite::BuiltinOperator_DEPTHWISE_CONV_2D] = &TfLiteParser::ParseDepthwiseConv2D; m_ParserFunctions[tflite::BuiltinOperator_SOFTMAX] = &TfLiteParser::ParseSoftmax; m_ParserFunctions[tflite::BuiltinOperator_SQUEEZE] = &TfLiteParser::ParseSqueeze; + m_ParserFunctions[tflite::BuiltinOperator_RELU] = &TfLiteParser::ParseRelu; + m_ParserFunctions[tflite::BuiltinOperator_RELU6] = &TfLiteParser::ParseRelu6; } void TfLiteParser::ResetParser() @@ -692,7 +694,7 @@ void TfLiteParser::ParseAveragePool2D(size_t subgraphIndex, size_t operatorIndex // we need to add the activation layer and fortunately we don't need to care about the data layout // beause the activation function is element-wise, so it is OK to have the activation after the trailing // swizzle layer - layer = AddActivationLayer(permuteLayers.second, 0, options->fused_activation_function); + layer = AddFusedActivationLayer(permuteLayers.second, 0, options->fused_activation_function); // register the output connection slots for the layer, connections are made after all layers have been created auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); @@ -770,7 +772,7 @@ void TfLiteParser::ParseConv2D(size_t subgraphIndex, size_t operatorIndex) // we need to add the activation layer and fortunately we don't need to care about the data layout // beause the activation function is element-wise, so it is OK to have the activation after the trailing // swizzle layer - layer = AddActivationLayer(permuteLayers.second, 0, options->fused_activation_function); + layer = AddFusedActivationLayer(permuteLayers.second, 0, options->fused_activation_function); // register the output connection slots for the layer, connections are made after all layers have been created auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); @@ -845,7 +847,7 @@ void TfLiteParser::ParseDepthwiseConv2D(size_t subgraphIndex, size_t operatorInd // we need to add the activation layer and fortunately we don't need to care about the data layout // beause the activation function is element-wise, so it is OK to have the activation after the trailing // swizzle layer - layer = AddActivationLayer(permuteLayers.second, 0, options->fused_activation_function); + layer = AddFusedActivationLayer(permuteLayers.second, 0, options->fused_activation_function); // register the output connection slots for the layer, connections are made after all layers have been created auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); @@ -965,9 +967,75 @@ void TfLiteParser::ParseSqueeze(size_t subgraphIndex, size_t operatorIndex) RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); } -armnn::IConnectableLayer* TfLiteParser::AddActivationLayer(armnn::IConnectableLayer* prevLayer, - unsigned int outputSlot, - tflite::ActivationFunctionType activationType) +void TfLiteParser::ParseRelu(size_t subgraphIndex, size_t operatorIndex) +{ + CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); + + const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; + boost::ignore_unused(operatorPtr); + + auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); + CHECK_VALID_SIZE(inputs.size(), 1); + + auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); + CHECK_VALID_SIZE(outputs.size(), 1); + + auto layerName = str(boost::format("Activation:RELU:%1%:%2%") % subgraphIndex % operatorIndex); + ActivationDescriptor activationDesc; + activationDesc.m_Function = ActivationFunction::ReLu; + IConnectableLayer* const layer = + m_Network->AddActivationLayer(activationDesc, layerName.c_str()); + + TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); + layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); + + // register the input connection slots for the layer, connections are made after all layers have been created + // only the tensors for the inputs are relevant, exclude the const tensors + auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); + RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); + + // register the output connection slots for the layer, connections are made after all layers have been created + auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); + RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); +} + +void TfLiteParser::ParseRelu6(size_t subgraphIndex, size_t operatorIndex) +{ + CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); + + const auto & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; + boost::ignore_unused(operatorPtr); + + auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); + CHECK_VALID_SIZE(inputs.size(), 1); + + auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); + CHECK_VALID_SIZE(outputs.size(), 1); + + auto layerName = str(boost::format("Activation:RELU6:%1%:%2%") % subgraphIndex % operatorIndex); + ActivationDescriptor activationDesc; + activationDesc.m_Function = ActivationFunction::BoundedReLu; + activationDesc.m_A = 6.0f; + activationDesc.m_B = 0.0f; + IConnectableLayer* const layer = + m_Network->AddActivationLayer(activationDesc, layerName.c_str()); + + TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]); + layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); + + // register the input connection slots for the layer, connections are made after all layers have been created + // only the tensors for the inputs are relevant, exclude the const tensors + auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex)); + RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]}); + + // register the output connection slots for the layer, connections are made after all layers have been created + auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); + RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); +} + +armnn::IConnectableLayer* TfLiteParser::AddFusedActivationLayer(armnn::IConnectableLayer* prevLayer, + unsigned int outputSlot, + tflite::ActivationFunctionType activationType) { ActivationDescriptor activationDesc; std::string layerName = prevLayer->GetName(); diff --git a/src/armnnTfLiteParser/TfLiteParser.hpp b/src/armnnTfLiteParser/TfLiteParser.hpp index 964be857ab..b9f81e4118 100644 --- a/src/armnnTfLiteParser/TfLiteParser.hpp +++ b/src/armnnTfLiteParser/TfLiteParser.hpp @@ -93,6 +93,8 @@ private: void ParseDepthwiseConv2D(size_t subgraphIndex, size_t operatorIndex); void ParseSoftmax(size_t subgraphIndex, size_t operatorIndex); void ParseSqueeze(size_t subgraphIndex, size_t operatorIndex); + void ParseRelu(size_t subgraphIndex, size_t operatorIndex); + void ParseRelu6(size_t subgraphIndex, size_t operatorIndex); void RegisterProducerOfTensor(size_t subgraphIndex, size_t tensorIndex, armnn::IOutputSlot* slot); void RegisterConsumerOfTensor(size_t subgraphIndex, size_t tensorIndex, armnn::IInputSlot* slot); @@ -111,9 +113,9 @@ private: void ResetParser(); /// Attach an activation layer to the one passed as a parameter - armnn::IConnectableLayer* AddActivationLayer(armnn::IConnectableLayer* layer, - unsigned int outputSlot, - tflite::ActivationFunctionType activationType); + armnn::IConnectableLayer* AddFusedActivationLayer(armnn::IConnectableLayer* layer, + unsigned int outputSlot, + tflite::ActivationFunctionType activationType); // SupportedDataStorage's purpose is to hold data till we pass over to the network. // We don't care about the content, and we want a single datatype to simplify the code. diff --git a/src/armnnTfLiteParser/test/Activations.cpp b/src/armnnTfLiteParser/test/Activations.cpp new file mode 100644 index 0000000000..a30d46408c --- /dev/null +++ b/src/armnnTfLiteParser/test/Activations.cpp @@ -0,0 +1,87 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include +#include "ParserFlatbuffersFixture.hpp" +#include "../TfLiteParser.hpp" + +BOOST_AUTO_TEST_SUITE(TensorflowLiteParser) + +struct ActivationFixture : ParserFlatbuffersFixture +{ + + explicit ActivationFixture(std::string activationFunction, std::string dataType) + { + m_JsonString = R"( + { + "version": 3, + "operator_codes": [ { "builtin_code": )" + activationFunction + R"( } ], + "subgraphs": [ { + "tensors": [ + { + "shape": [ 1, 7 ], + "type": )" + dataType + R"(, + "buffer": 0, + "name": "inputTensor", + "quantization": { + "min": [ 0.0 ], + "max": [ 255.0 ], + "scale": [ 1.0 ], + "zero_point": [ 0 ], + } + }, + { + "shape": [ 1, 7 ], + "type": )" + dataType + R"(, + "buffer": 1, + "name": "outputTensor", + "quantization": { + "min": [ 0.0 ], + "max": [ 255.0 ], + "scale": [ 1.0 ], + "zero_point": [ 0 ], + } + } + ], + "inputs": [ 0 ], + "outputs": [ 1 ], + "operators": [ + { + "opcode_index": 0, + "inputs": [ 0 ], + "outputs": [ 1 ], + "custom_options_format": "FLEXBUFFERS" + } + ], + } ], + "buffers" : [ {}, {} ] + } + )"; + SetupSingleInputSingleOutput("inputTensor", "outputTensor"); + } + +}; + +struct ReLuFixture : ActivationFixture +{ + ReLuFixture() : ActivationFixture("RELU", "FLOAT32") {} +}; +BOOST_FIXTURE_TEST_CASE(ParseReLu, ReLuFixture) +{ + RunTest<2, float>(0, { -1.0f, -0.5f, 1.25f, -3.0f, 0.0f, 0.5f, -0.75f }, + { 0.0f, 0.0f, 1.25f, 0.0f, 0.0f, 0.5f, 0.0f }); +} + +struct ReLu6Fixture : ActivationFixture +{ + ReLu6Fixture() : ActivationFixture("RELU6", "FLOAT32") {} +}; +BOOST_FIXTURE_TEST_CASE(ParseReLu6, ReLu6Fixture) +{ + RunTest<2, float>(0, { -1.0f, -0.5f, 7.25f, -3.0f, 0.0f, 0.5f, -0.75f }, + { 0.0f, 0.0f, 6.0f, 0.0f, 0.0f, 0.5f, 0.0f }); +} + +BOOST_AUTO_TEST_SUITE_END() -- cgit v1.2.1