IVGCVSW-1651 Add Support for Relu on TF Lite parser
* Added Relu and Relu6 Support for the TfLite Parser.
Change-Id: I3cc5e4922910e556f25b633eae6d2d361cea61b5
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 0fc3f1c..e4ed9b4 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -727,6 +727,7 @@
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 2c70b48..dd1f577 100644
--- a/src/armnnTfLiteParser/TfLiteParser.cpp
+++ b/src/armnnTfLiteParser/TfLiteParser.cpp
@@ -455,6 +455,8 @@
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 @@
// 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 @@
// 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 @@
// 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 @@
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 964be85..b9f81e4 100644
--- a/src/armnnTfLiteParser/TfLiteParser.hpp
+++ b/src/armnnTfLiteParser/TfLiteParser.hpp
@@ -93,6 +93,8 @@
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 @@
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 0000000..a30d464
--- /dev/null
+++ b/src/armnnTfLiteParser/test/Activations.cpp
@@ -0,0 +1,87 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <boost/test/unit_test.hpp>
+#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()