IVGCVSW-5390 'TfLiteDelegate: Implement the Normalisation operators'

* Added L2_NORMALIZATION and LOCAL_RESPONSE_NORMALIZATION opertor support

Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: Ic9e66879cf6469fa8761fb1c9dd5950771f629b0

1 files changed, 181 insertions, 0 deletions

diff --git a/delegate/src/test/NormalizationTestHelper.hpp b/delegate/src/test/NormalizationTestHelper.hpp
new file mode 100644
index 0000000000..26286b1c88
--- /dev/null
+++ b/delegate/src/test/NormalizationTestHelper.hpp
@@ -0,0 +1,181 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "TestUtils.hpp"
+
+#include <armnn_delegate.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/interpreter.h>
+#include <tensorflow/lite/kernels/register.h>
+#include <tensorflow/lite/model.h>
+#include <tensorflow/lite/schema/schema_generated.h>
+#include <tensorflow/lite/version.h>
+
+#include <doctest/doctest.h>
+
+namespace
+{
+
+std::vector<char> CreateNormalizationTfLiteModel(tflite::BuiltinOperator normalizationOperatorCode,
+                                                 tflite::TensorType tensorType,
+                                                 const std::vector<int32_t>& inputTensorShape,
+                                                 const std::vector<int32_t>& outputTensorShape,
+                                                 int32_t radius,
+                                                 float bias,
+                                                 float alpha,
+                                                 float beta,
+                                                 float quantScale = 1.0f,
+                                                 int quantOffset  = 0)
+{
+    using namespace tflite;
+    flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+    auto quantizationParameters =
+        CreateQuantizationParameters(flatBufferBuilder,
+                                     0,
+                                     0,
+                                     flatBufferBuilder.CreateVector<float>({ quantScale }),
+                                     flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
+
+    auto inputTensor = CreateTensor(flatBufferBuilder,
+                                    flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
+                                                                            inputTensorShape.size()),
+                                    tensorType,
+                                    0,
+                                    flatBufferBuilder.CreateString("input"),
+                                    quantizationParameters);
+
+    auto outputTensor = CreateTensor(flatBufferBuilder,
+                                     flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+                                                                             outputTensorShape.size()),
+                                     tensorType,
+                                     1,
+                                     flatBufferBuilder.CreateString("output"),
+                                     quantizationParameters);
+
+    std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, outputTensor };
+
+    std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+    buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
+    buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
+
+    std::vector<int32_t> operatorInputs = {{ 0 }};
+    std::vector<int> subgraphInputs = {{ 0 }};
+
+    tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_L2NormOptions;
+    flatbuffers::Offset<void> operatorBuiltinOptions = CreateL2NormOptions(flatBufferBuilder,
+                                                                           tflite::ActivationFunctionType_NONE).Union();
+
+    if (normalizationOperatorCode == tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION)
+    {
+        operatorBuiltinOptionsType = BuiltinOptions_LocalResponseNormalizationOptions;
+        operatorBuiltinOptions =
+            CreateLocalResponseNormalizationOptions(flatBufferBuilder, radius, bias, alpha, beta).Union();
+    }
+
+    // create operator
+    const std::vector<int32_t> operatorOutputs{{ 1 }};
+    flatbuffers::Offset <Operator> normalizationOperator =
+        CreateOperator(flatBufferBuilder,
+                       0,
+                       flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+                       flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+                       operatorBuiltinOptionsType,
+                       operatorBuiltinOptions);
+
+    const std::vector<int> subgraphOutputs{{ 1 }};
+    flatbuffers::Offset <SubGraph> subgraph =
+        CreateSubGraph(flatBufferBuilder,
+                       flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
+                       flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
+                       flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
+                       flatBufferBuilder.CreateVector(&normalizationOperator, 1));
+
+    flatbuffers::Offset <flatbuffers::String> modelDescription =
+        flatBufferBuilder.CreateString("ArmnnDelegate: Normalization Operator Model");
+    flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
+                                                                         normalizationOperatorCode);
+
+    flatbuffers::Offset <Model> flatbufferModel =
+        CreateModel(flatBufferBuilder,
+                    TFLITE_SCHEMA_VERSION,
+                    flatBufferBuilder.CreateVector(&operatorCode, 1),
+                    flatBufferBuilder.CreateVector(&subgraph, 1),
+                    modelDescription,
+                    flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
+
+    flatBufferBuilder.Finish(flatbufferModel);
+
+    return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
+                             flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
+}
+
+template <typename T>
+void NormalizationTest(tflite::BuiltinOperator normalizationOperatorCode,
+                       tflite::TensorType tensorType,
+                       const std::vector<armnn::BackendId>& backends,
+                       const std::vector<int32_t>& inputShape,
+                       std::vector<int32_t>& outputShape,
+                       std::vector<T>& inputValues,
+                       std::vector<T>& expectedOutputValues,
+                       int32_t radius = 0,
+                       float bias = 0.f,
+                       float alpha = 0.f,
+                       float beta = 0.f,
+                       float quantScale = 1.0f,
+                       int quantOffset  = 0)
+{
+    using namespace tflite;
+    std::vector<char> modelBuffer = CreateNormalizationTfLiteModel(normalizationOperatorCode,
+                                                                   tensorType,
+                                                                   inputShape,
+                                                                   outputShape,
+                                                                   radius,
+                                                                   bias,
+                                                                   alpha,
+                                                                   beta,
+                                                                   quantScale,
+                                                                   quantOffset);
+
+    const Model* tfLiteModel = GetModel(modelBuffer.data());
+    CHECK(tfLiteModel != nullptr);
+
+    std::unique_ptr<Interpreter> armnnDelegateInterpreter;
+    CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+          (&armnnDelegateInterpreter) == kTfLiteOk);
+    CHECK(armnnDelegateInterpreter != nullptr);
+    CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk);
+
+    std::unique_ptr<Interpreter> tfLiteInterpreter;
+    CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+          (&tfLiteInterpreter) == kTfLiteOk);
+    CHECK(tfLiteInterpreter != nullptr);
+    CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk);
+
+    // Create the ArmNN Delegate
+    armnnDelegate::DelegateOptions delegateOptions(backends);
+    std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)>
+        theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
+                         armnnDelegate::TfLiteArmnnDelegateDelete);
+    CHECK(theArmnnDelegate != nullptr);
+    // Modify armnnDelegateInterpreter to use armnnDelegate
+    CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
+
+    // Set input data
+    armnnDelegate::FillInput<T>(tfLiteInterpreter, 0, inputValues);
+    armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, inputValues);
+
+    // Run EnqueueWorkload
+    CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+    CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+    // Compare output data
+    armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, outputShape, expectedOutputValues);
+}
+
+} // anonymous namespace
\ No newline at end of file