IVGCVSW-2026 + IVGCVSW-2027 Add FullyConnected Support to TfLiteParser
Change-Id: Id48f97ee33e2fd650a1ee3365ef66bdfc514a586
diff --git a/CMakeLists.txt b/CMakeLists.txt
index fab3d9d..5cdc07d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -414,6 +414,7 @@
src/armnnTfLiteParser/test/Concatenation.cpp
src/armnnTfLiteParser/test/Conv2D.cpp
src/armnnTfLiteParser/test/DepthwiseConvolution2D.cpp
+ src/armnnTfLiteParser/test/FullyConnected.cpp
src/armnnTfLiteParser/test/MaxPool2D.cpp
src/armnnTfLiteParser/test/Reshape.cpp
src/armnnTfLiteParser/test/Softmax.cpp
diff --git a/src/armnnTfLiteParser/TfLiteParser.cpp b/src/armnnTfLiteParser/TfLiteParser.cpp
index 216c090..8b1d3e6 100644
--- a/src/armnnTfLiteParser/TfLiteParser.cpp
+++ b/src/armnnTfLiteParser/TfLiteParser.cpp
@@ -456,6 +456,7 @@
m_ParserFunctions[tflite::BuiltinOperator_CONCATENATION] = &TfLiteParser::ParseConcatenation;
m_ParserFunctions[tflite::BuiltinOperator_CONV_2D] = &TfLiteParser::ParseConv2D;
m_ParserFunctions[tflite::BuiltinOperator_DEPTHWISE_CONV_2D] = &TfLiteParser::ParseDepthwiseConv2D;
+ m_ParserFunctions[tflite::BuiltinOperator_FULLY_CONNECTED] = &TfLiteParser::ParseFullyConnected;
m_ParserFunctions[tflite::BuiltinOperator_MAX_POOL_2D] = &TfLiteParser::ParseMaxPool2D;
m_ParserFunctions[tflite::BuiltinOperator_RELU] = &TfLiteParser::ParseRelu;
m_ParserFunctions[tflite::BuiltinOperator_RELU6] = &TfLiteParser::ParseRelu6;
@@ -1219,6 +1220,75 @@
RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});
}
+void TfLiteParser::ParseFullyConnected(size_t subgraphIndex, size_t operatorIndex)
+{
+ CHECK_MODEL(m_Model, subgraphIndex, operatorIndex);
+
+ const auto & operatorRfr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];
+ const auto options = operatorRfr->builtin_options.AsFullyConnectedOptions();
+
+ CHECK_SUPPORTED_FUSED_ACTIVATION(options, subgraphIndex, operatorIndex);
+
+ FullyConnectedDescriptor desc;
+ desc.m_BiasEnabled = false;
+
+ auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex);
+ auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex);
+ CHECK_VALID_SIZE(outputs.size(), 1);
+
+ armnn::TensorInfo filterTensorInfo = ToTensorInfo(inputs[1]);
+
+ // Fully Connected Layer accepts two dimensional weights input
+ int32_t weightsDimension = static_cast<int32_t>(filterTensorInfo.GetNumDimensions());
+ if (weightsDimension != 2)
+ {
+ throw ParseException(
+ boost::str(
+ boost::format(
+ "Dimension %1% for Fully Connected weights is not supported by Armnn. "
+ "Node %2%")
+ % weightsDimension
+ % CHECK_LOCATION().AsString()));
+ }
+
+ auto filterTensorAndData = CreateConstTensor(inputs[1], filterTensorInfo, false);
+ armnn::IConnectableLayer* layer;
+ auto layerName = boost::str(boost::format("FullyConnected:%1%:%2%") % subgraphIndex % operatorIndex);
+
+ if (inputs.size() == 3)
+ {
+ desc.m_BiasEnabled = true;
+ TensorInfo biasTensorInfo = ToTensorInfo(inputs[2]);
+ auto biasTensorAndData = CreateConstTensor(inputs[2], biasTensorInfo, false);
+ layer = m_Network->AddFullyConnectedLayer(desc,
+ filterTensorAndData.first,
+ biasTensorAndData.first,
+ layerName.c_str());
+ }
+ else
+ {
+ layer = m_Network->AddFullyConnectedLayer(desc,
+ filterTensorAndData.first,
+ layerName.c_str());
+ }
+ BOOST_ASSERT(layer != nullptr);
+
+ armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+
+ // register the input connection slot for the layer
+ // 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]});
+
+ // we need to add the activation layer and fortunately we don't need to care about the data layout
+ armnn::IConnectableLayer* fusedActivationLayer = AddFusedActivationLayer(layer, 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, fusedActivationLayer, {outputTensorIndexes[0]});
+}
+
armnn::IConnectableLayer* TfLiteParser::AddFusedActivationLayer(armnn::IConnectableLayer* prevLayer,
unsigned int outputSlot,
tflite::ActivationFunctionType activationType)
diff --git a/src/armnnTfLiteParser/TfLiteParser.hpp b/src/armnnTfLiteParser/TfLiteParser.hpp
index 35f0b64..76e539a 100644
--- a/src/armnnTfLiteParser/TfLiteParser.hpp
+++ b/src/armnnTfLiteParser/TfLiteParser.hpp
@@ -94,6 +94,7 @@
void ParseConcatenation(size_t subgraphIndex, size_t operatorIndex);
void ParseConv2D(size_t subgraphIndex, size_t operatorIndex);
void ParseDepthwiseConv2D(size_t subgraphIndex, size_t operatorIndex);
+ void ParseFullyConnected(size_t subgraphIndex, size_t operatorIndex);
void ParseMaxPool2D(size_t subgraphIndex, size_t operatorIndex);
void ParseRelu(size_t subgraphIndex, size_t operatorIndex);
void ParseRelu6(size_t subgraphIndex, size_t operatorIndex);
diff --git a/src/armnnTfLiteParser/test/FullyConnected.cpp b/src/armnnTfLiteParser/test/FullyConnected.cpp
new file mode 100644
index 0000000..2853fe9
--- /dev/null
+++ b/src/armnnTfLiteParser/test/FullyConnected.cpp
@@ -0,0 +1,154 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <boost/test/unit_test.hpp>
+#include "ParserFlatbuffersFixture.hpp"
+#include "../TfLiteParser.hpp"
+
+#include <string>
+#include <iostream>
+
+BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)
+
+struct FullyConnectedFixture : public ParserFlatbuffersFixture
+{
+ explicit FullyConnectedFixture(const std::string& inputShape,
+ const std::string& outputShape,
+ const std::string& filterShape,
+ const std::string& filterData,
+ const std::string biasShape = "",
+ const std::string biasData = "")
+ {
+ std::string inputTensors = "[ 0, 2 ]";
+ std::string biasTensor = "";
+ std::string biasBuffer = "";
+ if (biasShape.size() > 0 && biasData.size() > 0)
+ {
+ inputTensors = "[ 0, 2, 3 ]";
+ biasTensor = R"(
+ {
+ "shape": )" + biasShape + R"( ,
+ "type": "INT32",
+ "buffer": 3,
+ "name": "biasTensor",
+ "quantization": {
+ "min": [ 0.0 ],
+ "max": [ 255.0 ],
+ "scale": [ 1.0 ],
+ "zero_point": [ 0 ],
+ }
+ } )";
+ biasBuffer = R"(
+ { "data": )" + biasData + R"(, }, )";
+ }
+ m_JsonString = R"(
+ {
+ "version": 3,
+ "operator_codes": [ { "builtin_code": "FULLY_CONNECTED" } ],
+ "subgraphs": [ {
+ "tensors": [
+ {
+ "shape": )" + inputShape + R"(,
+ "type": "UINT8",
+ "buffer": 0,
+ "name": "inputTensor",
+ "quantization": {
+ "min": [ 0.0 ],
+ "max": [ 255.0 ],
+ "scale": [ 1.0 ],
+ "zero_point": [ 0 ],
+ }
+ },
+ {
+ "shape": )" + outputShape + R"(,
+ "type": "UINT8",
+ "buffer": 1,
+ "name": "outputTensor",
+ "quantization": {
+ "min": [ 0.0 ],
+ "max": [ 511.0 ],
+ "scale": [ 2.0 ],
+ "zero_point": [ 0 ],
+ }
+ },
+ {
+ "shape": )" + filterShape + R"(,
+ "type": "UINT8",
+ "buffer": 2,
+ "name": "filterTensor",
+ "quantization": {
+ "min": [ 0.0 ],
+ "max": [ 255.0 ],
+ "scale": [ 1.0 ],
+ "zero_point": [ 0 ],
+ }
+ }, )" + biasTensor + R"(
+ ],
+ "inputs": [ 0 ],
+ "outputs": [ 1 ],
+ "operators": [
+ {
+ "opcode_index": 0,
+ "inputs": )" + inputTensors + R"(,
+ "outputs": [ 1 ],
+ "builtin_options_type": "FullyConnectedOptions",
+ "builtin_options": {
+ "fused_activation_function": "NONE"
+ },
+ "custom_options_format": "FLEXBUFFERS"
+ }
+ ],
+ } ],
+ "buffers" : [
+ { },
+ { },
+ { "data": )" + filterData + R"(, }, )"
+ + biasBuffer + R"(
+ ]
+ }
+ )";
+ SetupSingleInputSingleOutput("inputTensor", "outputTensor");
+ }
+};
+
+struct FullyConnectedWithNoBiasFixture : FullyConnectedFixture
+{
+ FullyConnectedWithNoBiasFixture()
+ : FullyConnectedFixture("[ 1, 4, 1, 1 ]", // inputShape
+ "[ 1, 1 ]", // outputShape
+ "[ 4, 1 ]", // filterShape
+ "[ 2, 3, 4, 5 ]") // filterData
+ {}
+};
+
+BOOST_FIXTURE_TEST_CASE(FullyConnectedWithNoBias, FullyConnectedWithNoBiasFixture)
+{
+ RunTest<2, uint8_t>(
+ 0,
+ { 10, 20, 30, 40 },
+ { 400/2 });
+}
+
+struct FullyConnectedWithBiasFixture : FullyConnectedFixture
+{
+ FullyConnectedWithBiasFixture()
+ : FullyConnectedFixture("[ 1, 4, 1, 1 ]", // inputShape
+ "[ 1, 1 ]", // outputShape
+ "[ 4, 1 ]", // filterShape
+ "[ 2, 3, 4, 5 ]", // filterData
+ "[ 1 ]", // biasShape
+ "[ 10, 0, 0, 0 ]" ) // biasData
+ {}
+};
+
+BOOST_FIXTURE_TEST_CASE(ParseFullyConnectedWithBias, FullyConnectedWithBiasFixture)
+{
+ RunTest<2, uint8_t>(
+ 0,
+ { 10, 20, 30, 40 },
+ { (400+10)/2 });
+}
+
+BOOST_AUTO_TEST_SUITE_END()