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-rw-r--r--delegate/src/test/ElementwiseBinaryTestHelper.hpp243
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diff --git a/delegate/src/test/ElementwiseBinaryTestHelper.hpp b/delegate/src/test/ElementwiseBinaryTestHelper.hpp
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index 09a715e7f1..0000000000
--- a/delegate/src/test/ElementwiseBinaryTestHelper.hpp
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@@ -1,243 +0,0 @@
-//
-// 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 <tensorflow/lite/schema/schema_generated.h>
-#include <tensorflow/lite/version.h>
-
-#include <doctest/doctest.h>
-
-namespace
-{
-
-template <typename T>
-std::vector<char> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator binaryOperatorCode,
- tflite::ActivationFunctionType activationType,
- tflite::TensorType tensorType,
- const std::vector <int32_t>& input0TensorShape,
- const std::vector <int32_t>& input1TensorShape,
- const std::vector <int32_t>& outputTensorShape,
- std::vector<T>& input1Values,
- bool constantInput = false,
- float quantScale = 1.0f,
- int quantOffset = 0)
-{
- using namespace tflite;
- flatbuffers::FlatBufferBuilder flatBufferBuilder;
-
- std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
- buffers.push_back(CreateBuffer(flatBufferBuilder));
- buffers.push_back(CreateBuffer(flatBufferBuilder));
- if (constantInput)
- {
- buffers.push_back(
- CreateBuffer(flatBufferBuilder,
- flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(input1Values.data()),
- sizeof(T) * input1Values.size())));
- }
- else
- {
- buffers.push_back(CreateBuffer(flatBufferBuilder));
- }
- buffers.push_back(CreateBuffer(flatBufferBuilder));
-
- auto quantizationParameters =
- CreateQuantizationParameters(flatBufferBuilder,
- 0,
- 0,
- flatBufferBuilder.CreateVector<float>({ quantScale }),
- flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
-
-
- std::array<flatbuffers::Offset<Tensor>, 3> tensors;
- tensors[0] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(),
- input0TensorShape.size()),
- tensorType,
- 1,
- flatBufferBuilder.CreateString("input_0"),
- quantizationParameters);
- tensors[1] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(),
- input1TensorShape.size()),
- tensorType,
- 2,
- flatBufferBuilder.CreateString("input_1"),
- quantizationParameters);
- tensors[2] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
- outputTensorShape.size()),
- tensorType,
- 3,
- flatBufferBuilder.CreateString("output"),
- quantizationParameters);
-
- // create operator
- tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE;
- flatbuffers::Offset<void> operatorBuiltinOptions = 0;
- switch (binaryOperatorCode)
- {
- case BuiltinOperator_ADD:
- {
- operatorBuiltinOptionsType = BuiltinOptions_AddOptions;
- operatorBuiltinOptions = CreateAddOptions(flatBufferBuilder, activationType).Union();
- break;
- }
- case BuiltinOperator_DIV:
- {
- operatorBuiltinOptionsType = BuiltinOptions_DivOptions;
- operatorBuiltinOptions = CreateDivOptions(flatBufferBuilder, activationType).Union();
- break;
- }
- case BuiltinOperator_MAXIMUM:
- {
- operatorBuiltinOptionsType = BuiltinOptions_MaximumMinimumOptions;
- operatorBuiltinOptions = CreateMaximumMinimumOptions(flatBufferBuilder).Union();
- break;
- }
- case BuiltinOperator_MINIMUM:
- {
- operatorBuiltinOptionsType = BuiltinOptions_MaximumMinimumOptions;
- operatorBuiltinOptions = CreateMaximumMinimumOptions(flatBufferBuilder).Union();
- break;
- }
- case BuiltinOperator_MUL:
- {
- operatorBuiltinOptionsType = BuiltinOptions_MulOptions;
- operatorBuiltinOptions = CreateMulOptions(flatBufferBuilder, activationType).Union();
- break;
- }
- case BuiltinOperator_SUB:
- {
- operatorBuiltinOptionsType = BuiltinOptions_SubOptions;
- operatorBuiltinOptions = CreateSubOptions(flatBufferBuilder, activationType).Union();
- break;
- }
- case BuiltinOperator_FLOOR_DIV:
- {
- operatorBuiltinOptionsType = tflite::BuiltinOptions_FloorDivOptions;
- operatorBuiltinOptions = CreateSubOptions(flatBufferBuilder, activationType).Union();
- break;
- }
- default:
- break;
- }
- const std::vector<int32_t> operatorInputs{0, 1};
- const std::vector<int32_t> operatorOutputs{2};
- flatbuffers::Offset <Operator> elementwiseBinaryOperator =
- 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(&elementwiseBinaryOperator, 1));
-
- flatbuffers::Offset <flatbuffers::String> modelDescription =
- flatBufferBuilder.CreateString("ArmnnDelegate: Elementwise Binary Operator Model");
- flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, binaryOperatorCode);
-
- 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 ElementwiseBinaryTest(tflite::BuiltinOperator binaryOperatorCode,
- tflite::ActivationFunctionType activationType,
- tflite::TensorType tensorType,
- std::vector<armnn::BackendId>& backends,
- std::vector<int32_t>& input0Shape,
- std::vector<int32_t>& input1Shape,
- std::vector<int32_t>& outputShape,
- std::vector<T>& input0Values,
- std::vector<T>& input1Values,
- std::vector<T>& expectedOutputValues,
- float quantScale = 1.0f,
- int quantOffset = 0,
- bool constantInput = false)
-{
- using namespace tflite;
- std::vector<char> modelBuffer = CreateElementwiseBinaryTfLiteModel<T>(binaryOperatorCode,
- activationType,
- tensorType,
- input0Shape,
- input1Shape,
- outputShape,
- input1Values,
- constantInput,
- quantScale,
- quantOffset);
-
- const Model* tfLiteModel = GetModel(modelBuffer.data());
- // 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
- armnnDelegate::FillInput<T>(tfLiteInterpreter, 0, input0Values);
- armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, input0Values);
- if (!constantInput)
- {
- armnnDelegate::FillInput<T>(tfLiteInterpreter, 1, input1Values);
- armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 1, input1Values);
- }
- // Run EnqueWorkload
- CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
- CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
-
- // Compare output data
- armnnDelegate::CompareOutputData<T>(tfLiteInterpreter,
- armnnDelegateInterpreter,
- outputShape,
- expectedOutputValues);
- armnnDelegateInterpreter.reset(nullptr);
-}
-
-} // anonymous namespace \ No newline at end of file
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-25 20:09:06 +0000