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