Add Square as Mul in the TfLite Parser

Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: I5760da9be2ed63fdfbdd5d3d7b771c310592798d

5 files changed, 117 insertions, 0 deletions

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6dae8e485f..291154a491 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -717,6 +717,7 @@ if(BUILD_UNIT_TESTS)
              src/armnnTfLiteParser/test/Split.cpp
              src/armnnTfLiteParser/test/SplitV.cpp
              src/armnnTfLiteParser/test/Squeeze.cpp
+             src/armnnTfLiteParser/test/Square.cpp
              src/armnnTfLiteParser/test/SquaredDifference.cpp
              src/armnnTfLiteParser/test/StridedSlice.cpp
              src/armnnTfLiteParser/test/Sub.cpp
diff --git a/docs/05_01_parsers.dox b/docs/05_01_parsers.dox
index cfce7ec580..0be5f30b0d 100644
--- a/docs/05_01_parsers.dox
+++ b/docs/05_01_parsers.dox
@@ -185,6 +185,7 @@ The Arm NN SDK TensorFlow Lite parser currently supports the following operators
 - SPLIT_V
 - SQUEEZE
 - SQRT
+- SQUARE
 - SQUARE_DIFFERENCE
 - STRIDED_SLICE
 - SUB
diff --git a/src/armnnTfLiteParser/TfLiteParser.cpp b/src/armnnTfLiteParser/TfLiteParser.cpp
index f44a747815..078786cb93 100644
--- a/src/armnnTfLiteParser/TfLiteParser.cpp
+++ b/src/armnnTfLiteParser/TfLiteParser.cpp
@@ -820,6 +820,7 @@ TfLiteParserImpl::TfLiteParserImpl(const Optional<ITfLiteParser::TfLiteParserOpt
     m_ParserFunctions[tflite::BuiltinOperator_SPLIT]                   = &TfLiteParserImpl::ParseSplit;
     m_ParserFunctions[tflite::BuiltinOperator_SPLIT_V]                 = &TfLiteParserImpl::ParseSplitV;
     m_ParserFunctions[tflite::BuiltinOperator_SQUEEZE]                 = &TfLiteParserImpl::ParseSqueeze;
+    m_ParserFunctions[tflite::BuiltinOperator_SQUARE]                  = &TfLiteParserImpl::ParseSquare;
     m_ParserFunctions[tflite::BuiltinOperator_SQUARED_DIFFERENCE]      = &TfLiteParserImpl::ParseSquaredDifference;
     m_ParserFunctions[tflite::BuiltinOperator_STRIDED_SLICE]           = &TfLiteParserImpl::ParseStridedSlice;
     m_ParserFunctions[tflite::BuiltinOperator_SUB]                     = &TfLiteParserImpl::ParseSub;
@@ -4703,6 +4704,33 @@ void TfLiteParserImpl::ParseSqrt(size_t subgraphIndex, size_t operatorIndex)
     ParseElementwiseUnary(subgraphIndex, operatorIndex, armnn::UnaryOperation::Sqrt);
 }
 
+void TfLiteParserImpl::ParseSquare(size_t subgraphIndex, size_t operatorIndex)
+{
+    CHECK_MODEL(m_Model, subgraphIndex, operatorIndex);
+
+    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);
+
+    armnn::TensorInfo inputTensorInfo  = InputTensorInfo(subgraphIndex, operatorIndex, 0);
+
+    auto layerName = fmt::format("Square:{}:{}", subgraphIndex, operatorIndex);
+    IConnectableLayer* layer = m_Network->AddElementwiseBinaryLayer(BinaryOperation::Mul, layerName.c_str());
+    ARMNN_ASSERT(layer != nullptr);
+
+    TensorInfo outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0, 0});
+    CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, "Input 0", "Output 0");
+    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+
+    auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
+    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[0]});
+
+    auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex));
+    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});
+}
+
 void TfLiteParserImpl::ParseSquaredDifference(size_t subgraphIndex, size_t operatorIndex)
 {
     CHECK_MODEL(m_Model, subgraphIndex, operatorIndex);
diff --git a/src/armnnTfLiteParser/TfLiteParser.hpp b/src/armnnTfLiteParser/TfLiteParser.hpp
index dad2d67f3c..eb452895a2 100644
--- a/src/armnnTfLiteParser/TfLiteParser.hpp
+++ b/src/armnnTfLiteParser/TfLiteParser.hpp
@@ -188,6 +188,7 @@ private:
     void ParseSplit(size_t subgraphIndex, size_t operatorIndex);
     void ParseSplitV(size_t subgraphIndex, size_t operatorIndex);
     void ParseSqueeze(size_t subgraphIndex, size_t operatorIndex);
+    void ParseSquare(size_t subgraphIndex, size_t operatorIndex);
     void ParseSquaredDifference(size_t subgraphIndex, size_t operatorIndex);
     void ParseStridedSlice(size_t subgraphIndex, size_t operatorIndex);
     void ParseSub(size_t subgraphIndex, size_t operatorIndex);
diff --git a/src/armnnTfLiteParser/test/Square.cpp b/src/armnnTfLiteParser/test/Square.cpp
new file mode 100644
index 0000000000..331448aee8
--- /dev/null
+++ b/src/armnnTfLiteParser/test/Square.cpp
@@ -0,0 +1,86 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ParserFlatbuffersFixture.hpp"
+
+TEST_SUITE("TensorflowLiteParser_Square")
+{
+struct SquareFixture : public ParserFlatbuffersFixture
+{
+    explicit SquareFixture(const std::string & inputShape,
+                           const std::string & outputShape)
+    {
+        m_JsonString = R"(
+            {
+                "version": 3,
+                "operator_codes": [ { "builtin_code": "SQUARE" } ],
+                "subgraphs": [ {
+                    "tensors": [
+                        {
+                            "shape": )" + inputShape + R"(,
+                            "type": "FLOAT32",
+                            "buffer": 0,
+                            "name": "inputTensor",
+                            "quantization": {
+                                "min": [ 0.0 ],
+                                "max": [ 255.0 ],
+                                "scale": [ 1.0 ],
+                                "zero_point": [ 0 ],
+                            }
+                        },
+                        {
+                            "shape": )" + outputShape + R"( ,
+                            "type": "FLOAT32",
+                            "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 ],
+                            "builtin_options_type": "SquareOptions",
+                            "custom_options_format": "FLEXBUFFERS"
+                        }
+                    ],
+                } ],
+                "buffers" : [
+                    { },
+                    { }
+                ]
+            }
+        )";
+        Setup();
+    }
+};
+
+struct SimpleSquareFixture : public SquareFixture
+{
+    SimpleSquareFixture() : SquareFixture("[ 1, 2, 2, 3 ]", "[ 1, 2, 2, 3 ]") {}
+};
+
+TEST_CASE_FIXTURE(SimpleSquareFixture, "ParseSquare")
+{
+    using armnn::DataType;
+    RunTest<4, DataType::Float32>(0, {{ "inputTensor", { 0.0f,  1.0f,  2.0f,
+                                                         3.0f,  4.0f,  5.0f,
+                                                         6.0f,  7.0f,  8.0f,
+                                                         9.0f, 10.0f, 11.0f }}} ,
+                                     {{ "outputTensor", { 0.0f,    1.0f,   4.0f,
+                                                          9.0f,   16.0f,  25.0f,
+                                                          36.0f,  49.0f,  64.0f,
+                                                          81.0f, 100.0f, 121.0f }}} );
+}
+
+}