diff options
Diffstat (limited to 'tests/MobileNetDatabase.cpp')
-rw-r--r-- | tests/MobileNetDatabase.cpp | 133 |
1 files changed, 133 insertions, 0 deletions
diff --git a/tests/MobileNetDatabase.cpp b/tests/MobileNetDatabase.cpp new file mode 100644 index 0000000000..66f297c502 --- /dev/null +++ b/tests/MobileNetDatabase.cpp @@ -0,0 +1,133 @@ +// +// 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)); +} |