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Diffstat (limited to 'tests/MobileNetDatabase.cpp')
| -rw-r--r-- | tests/MobileNetDatabase.cpp | 133 |
1 files changed, 0 insertions, 133 deletions
diff --git a/tests/MobileNetDatabase.cpp b/tests/MobileNetDatabase.cpp deleted file mode 100644 index 66f297c502..0000000000 --- a/tests/MobileNetDatabase.cpp +++ /dev/null @@ -1,133 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// See LICENSE file in the project root for full license information. -// -#include "InferenceTestImage.hpp" -#include "MobileNetDatabase.hpp" - -#include <boost/numeric/conversion/cast.hpp> -#include <boost/assert.hpp> -#include <boost/format.hpp> - -#include <iostream> -#include <fcntl.h> -#include <array> - -namespace -{ - -inline float Lerp(float a, float b, float w) -{ - return w * b + (1.f - w) * a; -} - -inline void PutData(std::vector<float> & data, - const unsigned int width, - const unsigned int x, - const unsigned int y, - const unsigned int c, - float value) -{ - data[(3*((y*width)+x)) + c] = value; -} - -std::vector<float> -ResizeBilinearAndNormalize(const InferenceTestImage & image, - const unsigned int outputWidth, - const unsigned int outputHeight) -{ - std::vector<float> out; - out.resize(outputWidth * outputHeight * 3); - - // We follow the definition of TensorFlow and AndroidNN: The top-left corner of a texel in the output - // image is projected into the input image to figure out the interpolants and weights. Note that this - // will yield different results than if projecting the centre of output texels. - - const unsigned int inputWidth = image.GetWidth(); - const unsigned int inputHeight = image.GetHeight(); - - // How much to scale pixel coordinates in the output image to get the corresponding pixel coordinates - // in the input image - const float scaleY = boost::numeric_cast<float>(inputHeight) / boost::numeric_cast<float>(outputHeight); - const float scaleX = boost::numeric_cast<float>(inputWidth) / boost::numeric_cast<float>(outputWidth); - - uint8_t rgb_x0y0[3]; - uint8_t rgb_x1y0[3]; - uint8_t rgb_x0y1[3]; - uint8_t rgb_x1y1[3]; - - for (unsigned int y = 0; y < outputHeight; ++y) - { - // Corresponding real-valued height coordinate in input image - const float iy = boost::numeric_cast<float>(y) * scaleY; - - // Discrete height coordinate of top-left texel (in the 2x2 texel area used for interpolation) - const float fiy = floorf(iy); - const unsigned int y0 = boost::numeric_cast<unsigned int>(fiy); - - // Interpolation weight (range [0,1]) - const float yw = iy - fiy; - - for (unsigned int x = 0; x < outputWidth; ++x) - { - // Real-valued and discrete width coordinates in input image - const float ix = boost::numeric_cast<float>(x) * scaleX; - const float fix = floorf(ix); - const unsigned int x0 = boost::numeric_cast<unsigned int>(fix); - - // Interpolation weight (range [0,1]) - const float xw = ix - fix; - - // Discrete width/height coordinates of texels below and to the right of (x0, y0) - const unsigned int x1 = std::min(x0 + 1, inputWidth - 1u); - const unsigned int y1 = std::min(y0 + 1, inputHeight - 1u); - - std::tie(rgb_x0y0[0], rgb_x0y0[1], rgb_x0y0[2]) = image.GetPixelAs3Channels(x0, y0); - std::tie(rgb_x1y0[0], rgb_x1y0[1], rgb_x1y0[2]) = image.GetPixelAs3Channels(x1, y0); - std::tie(rgb_x0y1[0], rgb_x0y1[1], rgb_x0y1[2]) = image.GetPixelAs3Channels(x0, y1); - std::tie(rgb_x1y1[0], rgb_x1y1[1], rgb_x1y1[2]) = image.GetPixelAs3Channels(x1, y1); - - for (unsigned c=0; c<3; ++c) - { - const float ly0 = Lerp(float(rgb_x0y0[c]), float(rgb_x1y0[c]), xw); - const float ly1 = Lerp(float(rgb_x0y1[c]), float(rgb_x1y1[c]), xw); - const float l = Lerp(ly0, ly1, yw); - PutData(out, outputWidth, x, y, c, l/255.0f); - } - } - } - - return out; -} - -} // end of anonymous namespace - - -MobileNetDatabase::MobileNetDatabase(const std::string& binaryFileDirectory, - unsigned int width, - unsigned int height, - const std::vector<ImageSet>& imageSet) -: m_BinaryDirectory(binaryFileDirectory) -, m_Height(height) -, m_Width(width) -, m_ImageSet(imageSet) -{ -} - -std::unique_ptr<MobileNetDatabase::TTestCaseData> -MobileNetDatabase::GetTestCaseData(unsigned int testCaseId) -{ - 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 - std::vector<float> resized(ResizeBilinearAndNormalize(image, m_Width, m_Height)); - - const unsigned int label = imageSet.second; - return std::make_unique<TTestCaseData>(label, std::move(resized)); -} |