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//
// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn_delegate.hpp>
#include <armnnUtils/FloatingPointComparison.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
{
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, flatBufferBuilder.CreateVector({})));
std::array<flatbuffers::Offset<Tensor>, 2> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
tensorShape.size()),
tensorType,
0);
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
tensorShape.size()),
tensorType,
0);
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);
return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
}
void SoftmaxTest(tflite::BuiltinOperator softmaxOperatorCode,
tflite::TensorType tensorType,
std::vector<armnn::BackendId>& backends,
std::vector<int32_t>& shape,
std::vector<float>& inputValues,
std::vector<float>& expectedOutputValues,
float beta = 0)
{
using namespace tflite;
std::vector<char> modelBuffer = CreateSoftmaxTfLiteModel(softmaxOperatorCode,
tensorType,
shape,
beta);
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
auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0];
auto tfLiteInterpreterInputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInputId);
for (unsigned int i = 0; i < inputValues.size(); ++i)
{
tfLiteInterpreterInputData[i] = inputValues[i];
}
auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0];
auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInputId);
for (unsigned int i = 0; i < inputValues.size(); ++i)
{
armnnDelegateInputData[i] = inputValues[i];
}
// Run EnqueWorkload
CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
// Compare output data
auto tfLiteInterpreterOutputId = tfLiteInterpreter->outputs()[0];
auto tfLiteInterpreterOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteInterpreterOutputId);
auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId);
for (size_t i = 0; i < inputValues.size(); ++i)
{
CHECK(armnnUtils::within_percentage_tolerance(expectedOutputValues[i], armnnDelegateOutputData[i], 0.1));
CHECK(armnnUtils::within_percentage_tolerance(tfLiteInterpreterOutputData[i],
armnnDelegateOutputData[i], 0.1));
}
}
/// 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,
std::vector<armnn::BackendId> backends, float beta, std::vector<float> expectedOutput) {
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,
backends,
shape,
input,
expectedOutput,
beta);
}
} // anonymous namespace
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