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-rw-r--r--delegate/src/test/ReduceTestHelper.hpp228
1 files changed, 0 insertions, 228 deletions
diff --git a/delegate/src/test/ReduceTestHelper.hpp b/delegate/src/test/ReduceTestHelper.hpp
deleted file mode 100644
index f500736080..0000000000
--- a/delegate/src/test/ReduceTestHelper.hpp
+++ /dev/null
@@ -1,228 +0,0 @@
-//
-// Copyright © 2021, 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>
-
-#include <string>
-
-namespace
-{
-
-std::vector<char> CreateReduceTfLiteModel(tflite::BuiltinOperator reduceOperatorCode,
- tflite::TensorType tensorType,
- std::vector<int32_t>& input0TensorShape,
- std::vector<int32_t>& input1TensorShape,
- const std::vector <int32_t>& outputTensorShape,
- std::vector<int32_t>& axisData,
- const bool keepDims,
- float quantScale = 1.0f,
- int quantOffset = 0,
- bool kTfLiteNoQuantizationForQuantized = false)
-{
- using namespace tflite;
- flatbuffers::FlatBufferBuilder flatBufferBuilder;
-
- flatbuffers::Offset<tflite::Buffer> buffers[4] = {
- CreateBuffer(flatBufferBuilder),
- CreateBuffer(flatBufferBuilder),
- CreateBuffer(flatBufferBuilder,
- flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()),
- sizeof(int32_t) * axisData.size())),
- CreateBuffer(flatBufferBuilder)
- };
-
- flatbuffers::Offset<tflite::QuantizationParameters> quantizationParametersAxis
- = CreateQuantizationParameters(flatBufferBuilder);
-
- flatbuffers::Offset<tflite::QuantizationParameters> quantizationParameters;
-
- if (kTfLiteNoQuantizationForQuantized)
- {
- if ((quantScale == 1 || quantScale == 0) && quantOffset == 0)
- {
- // Creates quantization parameter with quantization.type = kTfLiteNoQuantization
- quantizationParameters = CreateQuantizationParameters(flatBufferBuilder);
- }
- else
- {
- // Creates quantization parameter with quantization.type != kTfLiteNoQuantization
- quantizationParameters = CreateQuantizationParameters(
- flatBufferBuilder,
- 0,
- 0,
- flatBufferBuilder.CreateVector<float>({quantScale}),
- flatBufferBuilder.CreateVector<int64_t>({quantOffset}));
- }
- }
- else
- {
- 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"),
- quantizationParameters);
-
- tensors[1] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(),
- input1TensorShape.size()),
- ::tflite::TensorType_INT32,
- 2,
- flatBufferBuilder.CreateString("axis"),
- quantizationParametersAxis);
-
- // Create output tensor
- tensors[2] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
- outputTensorShape.size()),
- tensorType,
- 3,
- flatBufferBuilder.CreateString("output"),
- quantizationParameters);
-
- // Create operator. Reduce operations MIN, MAX, SUM, MEAN, PROD uses ReducerOptions.
- tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ReducerOptions;
- flatbuffers::Offset<void> operatorBuiltinOptions = CreateReducerOptions(flatBufferBuilder, keepDims).Union();
-
- const std::vector<int> operatorInputs{ {0, 1} };
- const std::vector<int> operatorOutputs{ 2 };
- flatbuffers::Offset <Operator> reduceOperator =
- 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(&reduceOperator, 1));
-
- flatbuffers::Offset <flatbuffers::String> modelDescription =
- flatBufferBuilder.CreateString("ArmnnDelegate: Reduce Operator Model");
- flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, reduceOperatorCode);
-
- flatbuffers::Offset <Model> flatbufferModel =
- CreateModel(flatBufferBuilder,
- TFLITE_SCHEMA_VERSION,
- flatBufferBuilder.CreateVector(&operatorCode, 1),
- flatBufferBuilder.CreateVector(&subgraph, 1),
- modelDescription,
- flatBufferBuilder.CreateVector(buffers, 4));
-
- flatBufferBuilder.Finish(flatbufferModel);
-
- return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
- flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
-}
-
-template <typename T>
-void ReduceTest(tflite::BuiltinOperator reduceOperatorCode,
- tflite::TensorType tensorType,
- std::vector<armnn::BackendId>& backends,
- std::vector<int32_t>& input0Shape,
- std::vector<int32_t>& input1Shape,
- std::vector<int32_t>& expectedOutputShape,
- std::vector<T>& input0Values,
- std::vector<int32_t>& input1Values,
- std::vector<T>& expectedOutputValues,
- const bool keepDims,
- float quantScale = 1.0f,
- int quantOffset = 0)
-{
- using namespace tflite;
- std::vector<char> modelBufferArmNN = CreateReduceTfLiteModel(reduceOperatorCode,
- tensorType,
- input0Shape,
- input1Shape,
- expectedOutputShape,
- input1Values,
- keepDims,
- quantScale,
- quantOffset,
- false);
- std::vector<char> modelBufferTFLite = CreateReduceTfLiteModel(reduceOperatorCode,
- tensorType,
- input0Shape,
- input1Shape,
- expectedOutputShape,
- input1Values,
- keepDims,
- quantScale,
- quantOffset,
- true);
-
- const Model* tfLiteModelArmNN = GetModel(modelBufferArmNN.data());
- const Model* tfLiteModelTFLite = GetModel(modelBufferTFLite.data());
-
- // Create TfLite Interpreters
- std::unique_ptr<Interpreter> armnnDelegateInterpreter;
- CHECK(InterpreterBuilder(tfLiteModelArmNN, ::tflite::ops::builtin::BuiltinOpResolver())
- (&armnnDelegateInterpreter) == kTfLiteOk);
- CHECK(armnnDelegateInterpreter != nullptr);
- CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk);
-
- std::unique_ptr<Interpreter> tfLiteInterpreter;
- CHECK(InterpreterBuilder(tfLiteModelTFLite, ::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);
-
- // Run EnqueWorkload
- CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
- CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
-
- // Compare output data
- armnnDelegate::CompareOutputData<T>(tfLiteInterpreter,
- armnnDelegateInterpreter,
- expectedOutputShape,
- 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 18:36:44 +0000