1 files changed, 160 insertions, 0 deletions
diff --git a/delegate/src/test/FillTestHelper.hpp b/delegate/src/test/FillTestHelper.hpp
new file mode 100644
index 0000000000..e6890a2b2d
--- /dev/null
+++ b/delegate/src/test/FillTestHelper.hpp
@@ -0,0 +1,160 @@
+//
+// 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
+{
+
+template <typename T>
+std::vector<char> CreateFillTfLiteModel(tflite::BuiltinOperator fillOperatorCode,
+                                        tflite::TensorType tensorType,
+                                        const std::vector<int32_t>& inputShape,
+                                        const std::vector <int32_t>& tensorShape,
+                                        const std::vector<T> fillValue)
+{
+    using namespace tflite;
+    flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+    std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+    buffers.push_back(
+        CreateBuffer(flatBufferBuilder,
+                     flatBufferBuilder.CreateVector({})));
+    buffers.push_back(
+        CreateBuffer(flatBufferBuilder,
+                     flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(tensorShape.data()),
+                                                    sizeof(int32_t) * tensorShape.size())));
+    buffers.push_back(
+        CreateBuffer(flatBufferBuilder,
+                     flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(fillValue.data()),
+                                                    sizeof(T) * fillValue.size())));
+
+    std::array<flatbuffers::Offset<Tensor>, 3> tensors;
+    tensors[0] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(inputShape.data(),
+                                                                      inputShape.size()),
+                              tflite::TensorType_INT32,
+                              1,
+                              flatBufferBuilder.CreateString("dims"));
+
+    std::vector<int32_t> fillShape = {};
+    tensors[1] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(fillShape.data(),
+                                                                      fillShape.size()),
+                              tensorType,
+                              2,
+                              flatBufferBuilder.CreateString("value"));
+
+    tensors[2] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
+                                                                      tensorShape.size()),
+                              tensorType,
+                              0,
+                              flatBufferBuilder.CreateString("output"));
+
+    tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_FillOptions;
+    flatbuffers::Offset<void> operatorBuiltinOptions = CreateFillOptions(flatBufferBuilder).Union();
+
+    // create operator
+    const std::vector<int> operatorInputs{ {0, 1} };
+    const std::vector<int> operatorOutputs{ 2 };
+    flatbuffers::Offset <Operator> fillOperator =
+        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> subgraphInputs{ {0, 1} };
+    const std::vector<int> subgraphOutputs{ 2 };
+    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(&fillOperator, 1));
+
+    flatbuffers::Offset <flatbuffers::String> modelDescription =
+        flatBufferBuilder.CreateString("ArmnnDelegate: Fill Operator Model");
+    flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
+                                                                         fillOperatorCode);
+
+    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 FillTest(tflite::BuiltinOperator fillOperatorCode,
+              tflite::TensorType tensorType,
+              const std::vector<armnn::BackendId>& backends,
+              std::vector<int32_t >& inputShape,
+              std::vector<int32_t >& tensorShape,
+              std::vector<T>& expectedOutputValues,
+              T fillValue)
+{
+    using namespace tflite;
+    std::vector<char> modelBuffer = CreateFillTfLiteModel<T>(fillOperatorCode,
+                                                             tensorType,
+                                                             inputShape,
+                                                             tensorShape,
+                                                             {fillValue});
+
+    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);
+
+    // Run EnqueueWorkload
+    CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+    CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+    armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, armnnDelegateInterpreter, tensorShape, expectedOutputValues);
+}
+
+} // anonymous namespace