| // |
| // 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)); |
| } |