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250 lines
11 KiB
250 lines
11 KiB
//
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// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved.
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// SPDX-License-Identifier: MIT
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//
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#pragma once
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#include "TestUtils.hpp"
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#include <armnn_delegate.hpp>
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#include <DelegateTestInterpreter.hpp>
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#include <flatbuffers/flatbuffers.h>
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#include <tensorflow/lite/kernels/register.h>
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#include <tensorflow/lite/version.h>
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#include <schema_generated.h>
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#include <doctest/doctest.h>
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namespace
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{
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std::vector<char> CreateNormalizationTfLiteModel(tflite::BuiltinOperator normalizationOperatorCode,
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tflite::TensorType tensorType,
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const std::vector<int32_t>& inputTensorShape,
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const std::vector<int32_t>& outputTensorShape,
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int32_t radius,
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float bias,
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float alpha,
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float beta,
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float quantScale = 1.0f,
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int quantOffset = 0)
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{
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using namespace tflite;
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flatbuffers::FlatBufferBuilder flatBufferBuilder;
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auto quantizationParameters =
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CreateQuantizationParameters(flatBufferBuilder,
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0,
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0,
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flatBufferBuilder.CreateVector<float>({ quantScale }),
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flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
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auto inputTensor = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
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inputTensorShape.size()),
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tensorType,
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1,
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flatBufferBuilder.CreateString("input"),
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quantizationParameters);
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auto outputTensor = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
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outputTensorShape.size()),
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tensorType,
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2,
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flatBufferBuilder.CreateString("output"),
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quantizationParameters);
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std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, outputTensor };
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std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
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buffers.push_back(CreateBuffer(flatBufferBuilder));
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buffers.push_back(CreateBuffer(flatBufferBuilder));
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buffers.push_back(CreateBuffer(flatBufferBuilder));
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std::vector<int32_t> operatorInputs = { 0 };
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std::vector<int> subgraphInputs = { 0 };
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tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_L2NormOptions;
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flatbuffers::Offset<void> operatorBuiltinOptions = CreateL2NormOptions(flatBufferBuilder,
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tflite::ActivationFunctionType_NONE).Union();
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if (normalizationOperatorCode == tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION)
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{
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operatorBuiltinOptionsType = BuiltinOptions_LocalResponseNormalizationOptions;
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operatorBuiltinOptions =
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CreateLocalResponseNormalizationOptions(flatBufferBuilder, radius, bias, alpha, beta).Union();
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}
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// create operator
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const std::vector<int32_t> operatorOutputs{ 1 };
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flatbuffers::Offset <Operator> normalizationOperator =
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CreateOperator(flatBufferBuilder,
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0,
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flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
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flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
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operatorBuiltinOptionsType,
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operatorBuiltinOptions);
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const std::vector<int> subgraphOutputs{ 1 };
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flatbuffers::Offset <SubGraph> subgraph =
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CreateSubGraph(flatBufferBuilder,
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flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
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flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
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flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
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flatBufferBuilder.CreateVector(&normalizationOperator, 1));
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flatbuffers::Offset <flatbuffers::String> modelDescription =
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flatBufferBuilder.CreateString("ArmnnDelegate: Normalization Operator Model");
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flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
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normalizationOperatorCode);
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flatbuffers::Offset <Model> flatbufferModel =
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CreateModel(flatBufferBuilder,
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TFLITE_SCHEMA_VERSION,
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flatBufferBuilder.CreateVector(&operatorCode, 1),
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flatBufferBuilder.CreateVector(&subgraph, 1),
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modelDescription,
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flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
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flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
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return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
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flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
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}
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template <typename T>
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void NormalizationTest(tflite::BuiltinOperator normalizationOperatorCode,
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tflite::TensorType tensorType,
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const std::vector<armnn::BackendId>& backends,
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const std::vector<int32_t>& inputShape,
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std::vector<int32_t>& outputShape,
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std::vector<T>& inputValues,
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std::vector<T>& expectedOutputValues,
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int32_t radius = 0,
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float bias = 0.f,
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float alpha = 0.f,
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float beta = 0.f,
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float quantScale = 1.0f,
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int quantOffset = 0)
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{
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using namespace delegateTestInterpreter;
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std::vector<char> modelBuffer = CreateNormalizationTfLiteModel(normalizationOperatorCode,
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tensorType,
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inputShape,
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outputShape,
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radius,
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bias,
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alpha,
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beta,
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quantScale,
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quantOffset);
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// Setup interpreter with just TFLite Runtime.
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auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
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CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
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CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
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CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
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std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
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std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
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// Setup interpreter with Arm NN Delegate applied.
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auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
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CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
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CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
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CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
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std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
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std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
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armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
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armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape);
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tfLiteInterpreter.Cleanup();
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armnnInterpreter.Cleanup();
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}
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void L2NormalizationTest(std::vector<armnn::BackendId>& backends)
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{
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// Set input data
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std::vector<int32_t> inputShape { 1, 1, 1, 10 };
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std::vector<int32_t> outputShape { 1, 1, 1, 10 };
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std::vector<float> inputValues
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{
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1.0f,
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2.0f,
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3.0f,
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4.0f,
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5.0f,
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6.0f,
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7.0f,
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8.0f,
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9.0f,
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10.0f
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};
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const float approxInvL2Norm = 0.050964719f;
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std::vector<float> expectedOutputValues
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{
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1.0f * approxInvL2Norm,
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2.0f * approxInvL2Norm,
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3.0f * approxInvL2Norm,
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4.0f * approxInvL2Norm,
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5.0f * approxInvL2Norm,
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6.0f * approxInvL2Norm,
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7.0f * approxInvL2Norm,
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8.0f * approxInvL2Norm,
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9.0f * approxInvL2Norm,
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10.0f * approxInvL2Norm
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};
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NormalizationTest<float>(tflite::BuiltinOperator_L2_NORMALIZATION,
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::tflite::TensorType_FLOAT32,
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backends,
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inputShape,
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outputShape,
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inputValues,
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expectedOutputValues);
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}
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void LocalResponseNormalizationTest(std::vector<armnn::BackendId>& backends,
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int32_t radius,
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float bias,
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float alpha,
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float beta)
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{
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// Set input data
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std::vector<int32_t> inputShape { 2, 2, 2, 1 };
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std::vector<int32_t> outputShape { 2, 2, 2, 1 };
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std::vector<float> inputValues
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{
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1.0f, 2.0f,
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3.0f, 4.0f,
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5.0f, 6.0f,
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7.0f, 8.0f
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};
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std::vector<float> expectedOutputValues
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{
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0.5f, 0.400000006f, 0.300000012f, 0.235294119f,
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0.192307696f, 0.16216217f, 0.140000001f, 0.123076923f
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};
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NormalizationTest<float>(tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION,
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::tflite::TensorType_FLOAT32,
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backends,
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inputShape,
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outputShape,
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inputValues,
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expectedOutputValues,
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radius,
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bias,
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alpha,
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beta);
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}
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} // anonymous namespace
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