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-rw-r--r--delegate/src/test/FullyConnectedTestHelper.hpp255
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diff --git a/delegate/src/test/FullyConnectedTestHelper.hpp b/delegate/src/test/FullyConnectedTestHelper.hpp
deleted file mode 100644
index a3f009a863..0000000000
--- a/delegate/src/test/FullyConnectedTestHelper.hpp
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@@ -1,255 +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> CreateFullyConnectedTfLiteModel(tflite::TensorType tensorType,
- tflite::ActivationFunctionType activationType,
- const std::vector <int32_t>& inputTensorShape,
- const std::vector <int32_t>& weightsTensorShape,
- const std::vector <int32_t>& biasTensorShape,
- std::vector <int32_t>& outputTensorShape,
- std::vector <T>& weightsData,
- bool constantWeights = true,
- float quantScale = 1.0f,
- int quantOffset = 0,
- float outputQuantScale = 2.0f,
- int outputQuantOffset = 0)
-{
- using namespace tflite;
- flatbuffers::FlatBufferBuilder flatBufferBuilder;
- std::array<flatbuffers::Offset<tflite::Buffer>, 5> buffers;
- buffers[0] = CreateBuffer(flatBufferBuilder);
- buffers[1] = CreateBuffer(flatBufferBuilder);
-
- auto biasTensorType = ::tflite::TensorType_FLOAT32;
- if (tensorType == ::tflite::TensorType_INT8)
- {
- biasTensorType = ::tflite::TensorType_INT32;
- }
- if (constantWeights)
- {
- buffers[2] = CreateBuffer(flatBufferBuilder,
- flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(weightsData.data()),
- sizeof(T) * weightsData.size()));
-
- if (tensorType == ::tflite::TensorType_INT8)
- {
- std::vector<int32_t> biasData = { 10 };
- buffers[3] = CreateBuffer(flatBufferBuilder,
- flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()),
- sizeof(int32_t) * biasData.size()));
-
- }
- else
- {
- std::vector<float> biasData = { 10 };
- buffers[3] = CreateBuffer(flatBufferBuilder,
- flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()),
- sizeof(float) * biasData.size()));
- }
- }
- else
- {
- buffers[2] = CreateBuffer(flatBufferBuilder);
- buffers[3] = CreateBuffer(flatBufferBuilder);
- }
- buffers[4] = CreateBuffer(flatBufferBuilder);
-
- auto quantizationParameters =
- CreateQuantizationParameters(flatBufferBuilder,
- 0,
- 0,
- flatBufferBuilder.CreateVector<float>({ quantScale }),
- flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
-
- auto outputQuantizationParameters =
- CreateQuantizationParameters(flatBufferBuilder,
- 0,
- 0,
- flatBufferBuilder.CreateVector<float>({ outputQuantScale }),
- flatBufferBuilder.CreateVector<int64_t>({ outputQuantOffset }));
-
- std::array<flatbuffers::Offset<Tensor>, 4> tensors;
- tensors[0] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
- inputTensorShape.size()),
- tensorType,
- 1,
- flatBufferBuilder.CreateString("input_0"),
- quantizationParameters);
- tensors[1] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(weightsTensorShape.data(),
- weightsTensorShape.size()),
- tensorType,
- 2,
- flatBufferBuilder.CreateString("weights"),
- quantizationParameters);
- tensors[2] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(biasTensorShape.data(),
- biasTensorShape.size()),
- biasTensorType,
- 3,
- flatBufferBuilder.CreateString("bias"),
- quantizationParameters);
-
- tensors[3] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
- outputTensorShape.size()),
- tensorType,
- 4,
- flatBufferBuilder.CreateString("output"),
- outputQuantizationParameters);
-
-
- // create operator
- tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_FullyConnectedOptions;
- flatbuffers::Offset<void> operatorBuiltinOptions =
- CreateFullyConnectedOptions(flatBufferBuilder,
- activationType,
- FullyConnectedOptionsWeightsFormat_DEFAULT, false).Union();
-
- const std::vector<int> operatorInputs{0, 1, 2};
- const std::vector<int> operatorOutputs{3};
- flatbuffers::Offset <Operator> fullyConnectedOperator =
- 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, 2};
- const std::vector<int> subgraphOutputs{3};
- 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(&fullyConnectedOperator, 1));
-
- flatbuffers::Offset <flatbuffers::String> modelDescription =
- flatBufferBuilder.CreateString("ArmnnDelegate: FullyConnected Operator Model");
- flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
- tflite::BuiltinOperator_FULLY_CONNECTED);
-
- 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 FullyConnectedTest(std::vector<armnn::BackendId>& backends,
- tflite::TensorType tensorType,
- tflite::ActivationFunctionType activationType,
- const std::vector <int32_t>& inputTensorShape,
- const std::vector <int32_t>& weightsTensorShape,
- const std::vector <int32_t>& biasTensorShape,
- std::vector <int32_t>& outputTensorShape,
- std::vector <T>& inputValues,
- std::vector <T>& expectedOutputValues,
- std::vector <T>& weightsData,
- bool constantWeights = true,
- float quantScale = 1.0f,
- int quantOffset = 0)
-{
- using namespace tflite;
-
- std::vector<char> modelBuffer = CreateFullyConnectedTfLiteModel(tensorType,
- activationType,
- inputTensorShape,
- weightsTensorShape,
- biasTensorShape,
- outputTensorShape,
- weightsData,
- constantWeights,
- 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, inputValues);
- armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, inputValues);
-
- if (!constantWeights)
- {
- armnnDelegate::FillInput<T>(tfLiteInterpreter, 1, weightsData);
- armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 1, weightsData);
-
- if (tensorType == ::tflite::TensorType_INT8)
- {
- std::vector <int32_t> biasData = {10};
- armnnDelegate::FillInput<int32_t>(tfLiteInterpreter, 2, biasData);
- armnnDelegate::FillInput<int32_t>(armnnDelegateInterpreter, 2, biasData);
- }
- else
- {
- std::vector<float> biasData = {10};
- armnnDelegate::FillInput<float>(tfLiteInterpreter, 2, biasData);
- armnnDelegate::FillInput<float>(armnnDelegateInterpreter, 2, biasData);
- }
- }
-
- // Run EnqueWorkload
- CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
- CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
-
- // Compare output data
- armnnDelegate::CompareOutputData<T>(tfLiteInterpreter,
- armnnDelegateInterpreter,
- outputTensorShape,
- 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 20:20:54 +0000