| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "Resize.hpp" |
| |
| #include "TensorBufferArrayView.hpp" |
| |
| #include <boost/numeric/conversion/cast.hpp> |
| |
| #include <cmath> |
| #include <algorithm> |
| |
| using namespace armnnUtils; |
| |
| namespace armnn |
| { |
| |
| namespace |
| { |
| |
| inline float Lerp(float a, float b, float w) |
| { |
| return w * b + (1.f - w) * a; |
| } |
| |
| inline double EuclideanDistance(float Xa, float Ya, const unsigned int Xb, const unsigned int Yb) |
| { |
| return std::sqrt(pow(Xa - boost::numeric_cast<float>(Xb), 2) + pow(Ya - boost::numeric_cast<float>(Yb), 2)); |
| } |
| |
| }// anonymous namespace |
| |
| void Resize(Decoder<float>& in, |
| const TensorInfo& inputInfo, |
| Encoder<float>& out, |
| const TensorInfo& outputInfo, |
| DataLayoutIndexed dataLayout, |
| armnn::ResizeMethod resizeMethod) |
| { |
| // 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 batchSize = inputInfo.GetShape()[0]; |
| const unsigned int channelCount = inputInfo.GetShape()[dataLayout.GetChannelsIndex()]; |
| |
| const unsigned int inputHeight = inputInfo.GetShape()[dataLayout.GetHeightIndex()]; |
| const unsigned int inputWidth = inputInfo.GetShape()[dataLayout.GetWidthIndex()]; |
| const unsigned int outputHeight = outputInfo.GetShape()[dataLayout.GetHeightIndex()]; |
| const unsigned int outputWidth = outputInfo.GetShape()[dataLayout.GetWidthIndex()]; |
| |
| // 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); |
| |
| TensorShape inputShape = inputInfo.GetShape(); |
| TensorShape outputShape = outputInfo.GetShape(); |
| |
| for (unsigned int n = 0; n < batchSize; ++n) |
| { |
| for (unsigned int c = 0; c < channelCount; ++c) |
| { |
| 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); |
| |
| float interpolatedValue; |
| switch (resizeMethod) |
| { |
| case armnn::ResizeMethod::Bilinear: |
| { |
| in[dataLayout.GetIndex(inputShape, n, c, y0, x0)]; |
| float input1 = in.Get(); |
| in[dataLayout.GetIndex(inputShape, n, c, y0, x1)]; |
| float input2 = in.Get(); |
| in[dataLayout.GetIndex(inputShape, n, c, y1, x0)]; |
| float input3 = in.Get(); |
| in[dataLayout.GetIndex(inputShape, n, c, y1, x1)]; |
| float input4 = in.Get(); |
| |
| const float ly0 = Lerp(input1, input2, xw); // lerp along row y0. |
| const float ly1 = Lerp(input3, input4, xw); // lerp along row y1. |
| interpolatedValue = Lerp(ly0, ly1, yw); |
| break; |
| } |
| case armnn::ResizeMethod::NearestNeighbor: |
| { |
| // calculate euclidean distance to the 4 neighbours |
| auto distance00 = EuclideanDistance(fix, fiy, x0, y0); |
| auto distance01 = EuclideanDistance(fix, fiy, x0, y1); |
| auto distance10 = EuclideanDistance(fix, fiy, x1, y0); |
| auto distance11 = EuclideanDistance(fix, fiy, x1, y1); |
| |
| auto minimum = std::min( { distance00, distance01, distance10, distance11 } ); |
| |
| unsigned int xNearest = 0; |
| unsigned int yNearest = 0; |
| |
| if (minimum == distance00) |
| { |
| xNearest = x0; |
| yNearest = y0; |
| } |
| else if (minimum == distance01) |
| { |
| xNearest = x0; |
| yNearest = y1; |
| } |
| else if (minimum == distance10) |
| { |
| xNearest = x1; |
| yNearest = y0; |
| } |
| else if (minimum == distance11) |
| { |
| xNearest = x1; |
| yNearest = y1; |
| } |
| else |
| { |
| throw armnn::InvalidArgumentException("Resize Nearest Neighbor failure"); |
| } |
| |
| in[dataLayout.GetIndex(inputShape, n, c, yNearest, xNearest)]; |
| interpolatedValue = in.Get(); |
| break; |
| } |
| default: |
| throw armnn::InvalidArgumentException("Unknown resize method: " + |
| std::to_string(static_cast<int>(resizeMethod))); |
| } |
| out[dataLayout.GetIndex(outputShape, n, c, y, x)]; |
| out.Set(interpolatedValue); |
| } |
| } |
| } |
| } |
| } |
| |
| } //namespace armnn |