IVGCVSW-5401 Implement the FILL operator
* Added FILL operator to TfLite ArmNN Delegate
* Added unit tests
Signed-off-by: David Monahan <david.monahan@arm.com>
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: I335ef469ff773fa4305eb87f6e93ae9c03fc6997
diff --git a/delegate/src/test/FillTestHelper.hpp b/delegate/src/test/FillTestHelper.hpp
new file mode 100644
index 0000000..e6890a2
--- /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