| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "InferenceTestImage.hpp" |
| #include "ImagePreprocessor.hpp" |
| |
| #include <armnn/TypesUtils.hpp> |
| |
| #include <armnnUtils/Permute.hpp> |
| #include <armnn/utility/NumericCast.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 % armnn::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, m_Scale); |
| |
| // duplicate data across the batch |
| for (unsigned int i = 1; i < m_BatchSize; i++) |
| { |
| result.insert(result.end(), result.begin(), result.begin() + armnn::numeric_cast<int>(GetNumImageElements())); |
| } |
| |
| if (m_DataFormat == DataFormat::NCHW) |
| { |
| const armnn::PermutationVector NHWCToArmNN = { 0, 2, 3, 1 }; |
| armnn::TensorShape dstShape({m_BatchSize, 3, m_Height, m_Width}); |
| std::vector<float> tempImage(result.size()); |
| armnnUtils::Permute(dstShape, NHWCToArmNN, result.data(), tempImage.data(), sizeof(float)); |
| 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] = static_cast<uint8_t>(resized[i]); |
| } |
| |
| return std::make_unique<TTestCaseData>(label, std::move(quantized)); |
| } |