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//
// Copyright © 2020, 2023-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "TestUtils.hpp"
#include <armnn_delegate.hpp>
#include <DelegateTestInterpreter.hpp>
#include <tensorflow/lite/version.h>
namespace
{
std::vector<char> CreateSoftmaxTfLiteModel(tflite::BuiltinOperator softmaxOperatorCode,
tflite::TensorType tensorType,
const std::vector <int32_t>& tensorShape,
float beta)
{
using namespace tflite;
flatbuffers::FlatBufferBuilder flatBufferBuilder;
std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
buffers.push_back(CreateBuffer(flatBufferBuilder));
buffers.push_back(CreateBuffer(flatBufferBuilder));
buffers.push_back(CreateBuffer(flatBufferBuilder));
std::array<flatbuffers::Offset<Tensor>, 2> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
tensorShape.size()),
tensorType,
1);
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
tensorShape.size()),
tensorType,
2);
const std::vector<int32_t> operatorInputs({0});
const std::vector<int32_t> operatorOutputs({1});
flatbuffers::Offset<Operator> softmaxOperator;
flatbuffers::Offset<flatbuffers::String> modelDescription;
flatbuffers::Offset<OperatorCode> operatorCode;
switch (softmaxOperatorCode)
{
case tflite::BuiltinOperator_SOFTMAX:
softmaxOperator =
CreateOperator(flatBufferBuilder,
0,
flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
BuiltinOptions_SoftmaxOptions,
CreateSoftmaxOptions(flatBufferBuilder, beta).Union());
modelDescription = flatBufferBuilder.CreateString("ArmnnDelegate: Softmax Operator Model");
operatorCode = CreateOperatorCode(flatBufferBuilder,
tflite::BuiltinOperator_SOFTMAX);
break;
case tflite::BuiltinOperator_LOG_SOFTMAX:
softmaxOperator =
CreateOperator(flatBufferBuilder,
0,
flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
BuiltinOptions_LogSoftmaxOptions,
CreateLogSoftmaxOptions(flatBufferBuilder).Union());
flatBufferBuilder.CreateString("ArmnnDelegate: Log-Softmax Operator Model");
operatorCode = CreateOperatorCode(flatBufferBuilder,
tflite::BuiltinOperator_LOG_SOFTMAX);
break;
default:
break;
}
const std::vector<int32_t> subgraphInputs({0});
const std::vector<int32_t> subgraphOutputs({1});
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(&softmaxOperator, 1));
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, armnnDelegate::FILE_IDENTIFIER);
return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
}
void SoftmaxTest(tflite::BuiltinOperator softmaxOperatorCode,
tflite::TensorType tensorType,
std::vector<int32_t>& shape,
std::vector<float>& inputValues,
std::vector<float>& expectedOutputValues,
const std::vector<armnn::BackendId>& backends = {},
float beta = 0)
{
using namespace delegateTestInterpreter;
std::vector<char> modelBuffer = CreateSoftmaxTfLiteModel(softmaxOperatorCode,
tensorType,
shape,
beta);
// Setup interpreter with just TFLite Runtime.
auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(tfLiteInterpreter.FillInputTensor<float>(inputValues, 0) == kTfLiteOk);
CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
std::vector<float> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<float>(0);
std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
// Setup interpreter with Arm NN Delegate applied.
auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends));
CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(armnnInterpreter.FillInputTensor<float>(inputValues, 0) == kTfLiteOk);
CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
std::vector<float> armnnOutputValues = armnnInterpreter.GetOutputResult<float>(0);
std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
armnnDelegate::CompareOutputData<float>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, shape);
tfLiteInterpreter.Cleanup();
armnnInterpreter.Cleanup();
}
/// Convenience function to run softmax and log-softmax test cases
/// \param operatorCode tflite::BuiltinOperator_SOFTMAX or tflite::BuiltinOperator_LOG_SOFTMAX
/// \param backends armnn backends to target
/// \param beta multiplicative parameter to the softmax function
/// \param expectedOutput to be checked against transformed input
void SoftmaxTestCase(tflite::BuiltinOperator operatorCode, float beta,
std::vector<float> expectedOutput, const std::vector<armnn::BackendId> backends = {})
{
std::vector<float> input = {
1.0, 2.5, 3.0, 4.5, 5.0,
-1.0, -2.5, -3.0, -4.5, -5.0};
std::vector<int32_t> shape = {2, 5};
SoftmaxTest(operatorCode,
tflite::TensorType_FLOAT32,
shape,
input,
expectedOutput,
backends,
beta);
}
} // anonymous namespace
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