IVGCVSW-7555 Restructure Delegate
* New folders created:
* common is for common code where TfLite API is not used
* classic is for existing delegate implementations
* opaque is for new opaque delegate implementation,
* tests is for shared between existing Delegate and Opaque Delegate which have test utils to work which delegate to use.
* Existing delegate is built to libarmnnDelegate.so and opaque delegate is built as libarmnnOpaqueDelegate.so
* Opaque structure is introduced but no API is added yet.
* CmakeList.txt and delegate/CMakeList.txt have been modified and 2 new CmakeList.txt added
* Rename BUILD_ARMNN_TFLITE_DELEGATE as BUILD_CLASSIC_DELEGATE
* Rename BUILD_ARMNN_TFLITE_OPAQUE_DELEGATE as BUILD_OPAQUE_DELEGATE
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: Ib682b9ad0ac8d8acdc4ec6d9099bb0008a9fe8ed
diff --git a/delegate/test/Pooling2dTestHelper.hpp b/delegate/test/Pooling2dTestHelper.hpp
new file mode 100644
index 0000000..6de85b6
--- /dev/null
+++ b/delegate/test/Pooling2dTestHelper.hpp
@@ -0,0 +1,196 @@
+//
+// Copyright © 2020, 2023 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 <schema_generated.h>
+#include <tensorflow/lite/version.h>
+
+#include <doctest/doctest.h>
+
+namespace
+{
+
+std::vector<char> CreatePooling2dTfLiteModel(
+ tflite::BuiltinOperator poolingOperatorCode,
+ tflite::TensorType tensorType,
+ const std::vector <int32_t>& inputTensorShape,
+ const std::vector <int32_t>& outputTensorShape,
+ tflite::Padding padding = tflite::Padding_SAME,
+ int32_t strideWidth = 0,
+ int32_t strideHeight = 0,
+ int32_t filterWidth = 0,
+ int32_t filterHeight = 0,
+ tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE,
+ float quantScale = 1.0f,
+ int quantOffset = 0)
+{
+ using namespace tflite;
+ flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+ flatbuffers::Offset<tflite::Buffer> buffers[3] = {CreateBuffer(flatBufferBuilder),
+ CreateBuffer(flatBufferBuilder),
+ CreateBuffer(flatBufferBuilder)};
+
+ auto quantizationParameters =
+ CreateQuantizationParameters(flatBufferBuilder,
+ 0,
+ 0,
+ flatBufferBuilder.CreateVector<float>({ quantScale }),
+ flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
+
+ flatbuffers::Offset<Tensor> tensors[2] {
+ CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(inputTensorShape),
+ tensorType,
+ 1,
+ flatBufferBuilder.CreateString("input"),
+ quantizationParameters),
+
+ CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputTensorShape),
+ tensorType,
+ 2,
+ flatBufferBuilder.CreateString("output"),
+ quantizationParameters)
+ };
+
+ // create operator
+ tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_Pool2DOptions;
+ flatbuffers::Offset<void> operatorBuiltinOptions = CreatePool2DOptions(flatBufferBuilder,
+ padding,
+ strideWidth,
+ strideHeight,
+ filterWidth,
+ filterHeight,
+ fusedActivation).Union();
+
+ const std::vector<int32_t> operatorInputs{0};
+ const std::vector<int32_t> operatorOutputs{1};
+ flatbuffers::Offset <Operator> poolingOperator =
+ CreateOperator(flatBufferBuilder,
+ 0,
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs),
+ operatorBuiltinOptionsType,
+ operatorBuiltinOptions);
+
+ const int subgraphInputs[1] = {0};
+ const int subgraphOutputs[1] = {1};
+ flatbuffers::Offset <SubGraph> subgraph =
+ CreateSubGraph(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(tensors, 2),
+ flatBufferBuilder.CreateVector<int32_t>(subgraphInputs, 1),
+ flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs, 1),
+ flatBufferBuilder.CreateVector(&poolingOperator, 1));
+
+ flatbuffers::Offset <flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: Pooling2d Operator Model");
+ flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, poolingOperatorCode);
+
+ flatbuffers::Offset <Model> flatbufferModel =
+ CreateModel(flatBufferBuilder,
+ TFLITE_SCHEMA_VERSION,
+ flatBufferBuilder.CreateVector(&operatorCode, 1),
+ flatBufferBuilder.CreateVector(&subgraph, 1),
+ modelDescription,
+ flatBufferBuilder.CreateVector(buffers, 3));
+
+ flatBufferBuilder.Finish(flatbufferModel);
+
+ return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
+ flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
+}
+
+template <typename T>
+void Pooling2dTest(tflite::BuiltinOperator poolingOperatorCode,
+ tflite::TensorType tensorType,
+ std::vector<armnn::BackendId>& backends,
+ std::vector<int32_t>& inputShape,
+ std::vector<int32_t>& outputShape,
+ std::vector<T>& inputValues,
+ std::vector<T>& expectedOutputValues,
+ tflite::Padding padding = tflite::Padding_SAME,
+ int32_t strideWidth = 0,
+ int32_t strideHeight = 0,
+ int32_t filterWidth = 0,
+ int32_t filterHeight = 0,
+ tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE,
+ float quantScale = 1.0f,
+ int quantOffset = 0)
+{
+ using namespace tflite;
+ std::vector<char> modelBuffer = CreatePooling2dTfLiteModel(poolingOperatorCode,
+ tensorType,
+ inputShape,
+ outputShape,
+ padding,
+ strideWidth,
+ strideHeight,
+ filterWidth,
+ filterHeight,
+ fusedActivation,
+ quantScale,
+ quantOffset);
+
+ const Model* tfLiteModel = GetModel(modelBuffer.data());
+ CHECK(tfLiteModel != nullptr);
+ // Create TfLite Interpreters
+ 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
+ auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0];
+ auto tfLiteDelegateInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInputId);
+ for (unsigned int i = 0; i < inputValues.size(); ++i)
+ {
+ tfLiteDelegateInputData[i] = inputValues[i];
+ }
+
+ auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0];
+ auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInputId);
+ for (unsigned int i = 0; i < inputValues.size(); ++i)
+ {
+ armnnDelegateInputData[i] = inputValues[i];
+ }
+
+ // Run EnqueueWorkload
+ CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+ armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, outputShape, expectedOutputValues);
+}
+
+} // anonymous namespace
+
+
+
+