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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "InferenceTestImage.hpp"
#include "ImagePreprocessor.hpp"
#include "Permute.hpp"
#include <armnn/TypesUtils.hpp>
#include <boost/numeric/conversion/cast.hpp>
#include <boost/assert.hpp>
#include <boost/format.hpp>
#include <iostream>
#include <fcntl.h>
#include <array>
template <typename TDataType>
unsigned int ImagePreprocessor<TDataType>::GetLabelAndResizedImageAsFloat(unsigned int testCaseId,
std::vector<float> & result)
{
testCaseId = testCaseId % boost::numeric_cast<unsigned int>(m_ImageSet.size());
const ImageSet& imageSet = m_ImageSet[testCaseId];
const std::string fullPath = m_BinaryDirectory + imageSet.first;
InferenceTestImage image(fullPath.c_str());
// this ResizeBilinear result is closer to the tensorflow one than STB.
// there is still some difference though, but the inference results are
// similar to tensorflow for MobileNet
result = image.Resize(m_Width, m_Height, CHECK_LOCATION(),
InferenceTestImage::ResizingMethods::BilinearAndNormalized,
m_Mean, m_Stddev);
if (m_DataFormat == DataFormat::NCHW)
{
const armnn::PermutationVector NHWCToArmNN = { 0, 2, 3, 1 };
armnn::TensorShape dstShape({1, 3, m_Height, m_Width});
std::vector<float> tempImage(result.size());
armnnUtils::Permute<float>(dstShape, NHWCToArmNN, result.data(), tempImage.data());
result.swap(tempImage);
}
return imageSet.second;
}
template <>
std::unique_ptr<ImagePreprocessor<float>::TTestCaseData>
ImagePreprocessor<float>::GetTestCaseData(unsigned int testCaseId)
{
std::vector<float> resized;
auto label = GetLabelAndResizedImageAsFloat(testCaseId, resized);
return std::make_unique<TTestCaseData>(label, std::move(resized));
}
template <>
std::unique_ptr<ImagePreprocessor<uint8_t>::TTestCaseData>
ImagePreprocessor<uint8_t>::GetTestCaseData(unsigned int testCaseId)
{
std::vector<float> resized;
auto label = GetLabelAndResizedImageAsFloat(testCaseId, resized);
size_t resizedSize = resized.size();
std::vector<uint8_t> quantized(resized.size());
for (size_t i=0; i<resizedSize; ++i)
{
quantized[i] = armnn::Quantize<uint8_t>(resized[i],
m_Scale,
m_Offset);
}
return std::make_unique<TTestCaseData>(label, std::move(quantized));
}
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