Release 18.03
diff --git a/tests/MobileNetDatabase.cpp b/tests/MobileNetDatabase.cpp
new file mode 100644
index 0000000..66f297c
--- /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));
+}