IVGCVSW-7546 SPACE_TO_DEPTH support added in tflite parser

Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: I4f5016841c31b183440f31c1e177bc41d2b8dbb7

4 files changed, 128 insertions, 1 deletions

diff --git a/docs/05_01_parsers.dox b/docs/05_01_parsers.dox
index 39fe536fe9..b0115626d6 100644
--- a/docs/05_01_parsers.dox
+++ b/docs/05_01_parsers.dox
@@ -1,4 +1,4 @@
-/// Copyright (c) 2022 Arm Ltd and Contributors. All rights reserved.
+/// Copyright (c) 2022-2023 Arm Ltd and Contributors. All rights reserved.
 ///
 /// SPDX-License-Identifier: MIT
 ///
@@ -176,6 +176,7 @@ The Arm NN SDK TensorFlow Lite parser currently supports the following operators
 - SLICE
 - SOFTMAX
 - SPACE_TO_BATCH
+- SPACE_TO_DEPTH
 - SPLIT
 - SPLIT_V
 - SQUEEZE
diff --git a/src/armnnTfLiteParser/TfLiteParser.cpp b/src/armnnTfLiteParser/TfLiteParser.cpp
index 279f804a03..b7e2762ade 100644
--- a/src/armnnTfLiteParser/TfLiteParser.cpp
+++ b/src/armnnTfLiteParser/TfLiteParser.cpp
@@ -790,6 +790,7 @@ TfLiteParserImpl::TfLiteParserImpl(const Optional<ITfLiteParser::TfLiteParserOpt
     m_ParserFunctions[tflite::BuiltinOperator_SLICE]                   = &TfLiteParserImpl::ParseSlice;
     m_ParserFunctions[tflite::BuiltinOperator_SOFTMAX]                 = &TfLiteParserImpl::ParseSoftmax;
     m_ParserFunctions[tflite::BuiltinOperator_SPACE_TO_BATCH_ND]       = &TfLiteParserImpl::ParseSpaceToBatchND;
+    m_ParserFunctions[tflite::BuiltinOperator_SPACE_TO_DEPTH]          = &TfLiteParserImpl::ParseSpaceToDepth;
     m_ParserFunctions[tflite::BuiltinOperator_SPLIT]                   = &TfLiteParserImpl::ParseSplit;
     m_ParserFunctions[tflite::BuiltinOperator_SPLIT_V]                 = &TfLiteParserImpl::ParseSplitV;
     m_ParserFunctions[tflite::BuiltinOperator_SQUEEZE]                 = &TfLiteParserImpl::ParseSqueeze;
@@ -2138,6 +2139,42 @@ void TfLiteParserImpl::ParseSpaceToBatchND(size_t subgraphIndex, size_t operator
     RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});
 }
 
+void TfLiteParserImpl::ParseSpaceToDepth(size_t subgraphIndex, size_t operatorIndex)
+{
+    CHECK_MODEL(m_Model, subgraphIndex, operatorIndex);
+
+    TfLiteParserImpl::TensorRawPtrVector inputs = GetInputs(m_Model, subgraphIndex, operatorIndex);
+    CHECK_VALID_SIZE(inputs.size(), 1);
+    TfLiteParserImpl::TensorRawPtrVector outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex);
+    CHECK_VALID_SIZE(outputs.size(), 1);
+
+    armnn::SpaceToDepthDescriptor descriptor;
+
+    const auto& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];
+    const auto* options = operatorPtr->builtin_options.AsSpaceToDepthOptions();
+    auto blockSize = options->block_size;
+    if (blockSize < 2)
+    {
+        throw ParseException(
+                fmt::format("Operation has invalid block size: {} Block size should be >= 2 {}",
+                            blockSize,
+                            CHECK_LOCATION().AsString()));
+    }
+    descriptor.m_BlockSize = armnn::numeric_cast<uint32_t>(blockSize);
+
+    auto layerName = fmt::format("SpaceToDepth:{}:{}", subgraphIndex, operatorIndex);
+    IConnectableLayer* layer = m_Network->AddSpaceToDepthLayer(descriptor, layerName.c_str());
+    ARMNN_ASSERT(layer != nullptr);
+    TensorInfo outputTensorInfo = OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, 0, {0});
+    layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+
+    auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
+    RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});
+
+    auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex));
+    RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});
+}
+
 armnn::TensorInfo TfLiteParserImpl::OutputShapeOfSqueeze(std::vector<uint32_t> squeezeDims,
                                                          const armnn::TensorInfo& inputTensorInfo)
 {
diff --git a/src/armnnTfLiteParser/TfLiteParser.hpp b/src/armnnTfLiteParser/TfLiteParser.hpp
index 327b8a8651..ec03c23a54 100644
--- a/src/armnnTfLiteParser/TfLiteParser.hpp
+++ b/src/armnnTfLiteParser/TfLiteParser.hpp
@@ -181,6 +181,7 @@ private:
     void ParseSoftmax(size_t subgraphIndex, size_t operatorIndex);
     void ParseSqrt(size_t subgraphIndex, size_t operatorIndex);
     void ParseSpaceToBatchND(size_t subgraphIndex, size_t operatorIndex);
+    void ParseSpaceToDepth(size_t subgraphIndex, size_t operatorIndex);
     void ParseSplit(size_t subgraphIndex, size_t operatorIndex);
     void ParseSplitV(size_t subgraphIndex, size_t operatorIndex);
     void ParseSqueeze(size_t subgraphIndex, size_t operatorIndex);
diff --git a/src/armnnTfLiteParser/test/SpaceToDepth.cpp b/src/armnnTfLiteParser/test/SpaceToDepth.cpp
new file mode 100644
index 0000000000..5f9e32d202
--- /dev/null
+++ b/src/armnnTfLiteParser/test/SpaceToDepth.cpp
@@ -0,0 +1,88 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ParserFlatbuffersFixture.hpp"
+
+
+TEST_SUITE("TensorflowLiteParser_SpaceToDepth")
+{
+struct SpaceToDepthFixture : public ParserFlatbuffersFixture
+{
+    explicit SpaceToDepthFixture(const std::string& inputShape,
+                                 const std::string& outputShape,
+                                 const std::string& dataType = "FLOAT32",
+                                 const std::string& scale = "1.0",
+                                 const std::string& offset = "0")
+    {
+        m_JsonString = R"(
+            {
+                "version": 3,
+                "operator_codes": [ { "builtin_code": "DEPTH_TO_SPACE" } ],
+                "subgraphs": [ {
+                    "tensors": [
+                        {
+                            "shape": )" + inputShape + R"(,
+                            "type": )" + dataType + R"(,
+                            "buffer": 0,
+                            "name": "inputTensor",
+                            "quantization": {
+                                "min": [ 0.0 ],
+                                "max": [ 255.0 ],
+                                "scale": [ )" + scale + R"( ],
+                                "zero_point": [ )" + offset + R"( ],
+                            }
+                        },
+                        {
+                             "shape": )" + outputShape + R"(,
+                             "type": )" + dataType + R"(,
+                             "buffer": 1,
+                             "name": "outputTensor",
+                             "quantization": {
+                                "min": [ 0.0 ],
+                                "max": [ 255.0 ],
+                                "scale": [ )" + scale + R"( ],
+                                "zero_point": [ )" + offset + R"( ],
+                            }
+                        }
+                    ],
+                    "inputs": [ 0 ],
+                    "outputs": [ 1 ],
+                    "operators": [
+                        {
+                            "opcode_index": 0,
+                            "inputs": [ 0 ],
+                            "outputs": [ 1 ],
+                            "builtin_options_type": "SpaceToDepthOptions",
+                            "builtin_options": {
+                              "block_size": 2
+                            },
+                            "custom_options_format": "FLEXBUFFERS"
+                        }
+                    ],
+                } ],
+                "buffers" : [
+                    { },
+                    { },
+                ]
+            }
+        )";
+      SetupSingleInputSingleOutput("inputTensor", "outputTensor");
+    }
+};
+
+struct SimpleSpaceToDepthFixture : public SpaceToDepthFixture
+{
+    SimpleSpaceToDepthFixture() : SpaceToDepthFixture("[ 1, 2, 2, 1 ]", "[ 1, 1, 1, 4 ]") {}
+};
+
+TEST_CASE_FIXTURE(SimpleSpaceToDepthFixture, "ParseSpaceToDepth")
+{
+    RunTest<4, armnn::DataType::Float32>
+        (0,
+         {{ "inputTensor",  { 1.f,  2.f,  3.f,  4.f }}},
+         {{ "outputTensor", { 1.f,  2.f,  3.f,  4.f }}} );
+}
+
+}