modules/dnn/src/layers/max_unpooling_layer.cpp

   1 // This file is part of OpenCV project.
   2 // It is subject to the license terms in the LICENSE file found in the top-level directory
   3 // of this distribution and at http://opencv.org/license.html.
   4
   5 // Copyright (C) 2016, Intel Corporation, all rights reserved.
   6 // Third party copyrights are property of their respective owners.
   7
   8 /*
   9 Implementation of Batch Normalization layer.
  10 */
  11
  12 #include "../precomp.hpp"
  13 #include "layers_common.hpp"
  14 #include "op_halide.hpp"
  15 #include <opencv2/dnn/shape_utils.hpp>
  16
  17 #include <iostream>
  18
  19 namespace cv
  20 {
  21 namespace dnn
  22 {
  23
  24 class MaxUnpoolLayerImpl : public MaxUnpoolLayer
  25 {
  26 public:
  27     MaxUnpoolLayerImpl(const LayerParams& params)
  28     {
  29         setParamsFrom(params);
  30         poolKernel = Size(params.get<int>("pool_k_w"), params.get<int>("pool_k_h"));
  31         poolPad = Size(params.get<int>("pool_pad_w"), params.get<int>("pool_pad_h"));
  32         poolStride = Size(params.get<int>("pool_stride_w"), params.get<int>("pool_stride_h"));
  33     }
  34
  35     virtual bool supportBackend(int backendId)
  36     {
  37         return backendId == DNN_BACKEND_DEFAULT ||
  38                backendId == DNN_BACKEND_HALIDE && haveHalide() &&
  39                !poolPad.width && !poolPad.height;
  40     }
  41
  42     bool getMemoryShapes(const std::vector<MatShape> &inputs,
  43                          const int requiredOutputs,
  44                          std::vector<MatShape> &outputs,
  45                          std::vector<MatShape> &internals) const
  46     {
  47         CV_Assert(inputs.size() == 2);
  48         CV_Assert(total(inputs[0]) == total(inputs[1]));
  49
  50         MatShape outShape = inputs[0];
  51         outShape[2] = (outShape[2] - 1) * poolStride.height + poolKernel.height - 2 * poolPad.height;
  52         outShape[3] = (outShape[3] - 1) * poolStride.width + poolKernel.width - 2 * poolPad.width;
  53
  54         outputs.clear();
  55         outputs.push_back(outShape);
  56
  57         return false;
  58     }
  59
  60     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
  61     {
  62         CV_TRACE_FUNCTION();
  63         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
  64
  65         Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
  66     }
  67
  68     void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
  69     {
  70         CV_TRACE_FUNCTION();
  71         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
  72
  73         CV_Assert(inputs.size() == 2);
  74         Mat& input = *inputs[0];
  75         Mat& indices = *inputs[1];
  76
  77         CV_Assert(input.total() == indices.total());
  78         CV_Assert(input.size[0] == 1);
  79         CV_Assert(input.isContinuous());
  80
  81         for(int i_n = 0; i_n < outputs.size(); i_n++)
  82         {
  83             Mat& outBlob = outputs[i_n];
  84             outBlob.setTo(0);
  85             CV_Assert(input.size[1] == outBlob.size[1]);
  86             int outPlaneTotal = outBlob.size[2]*outBlob.size[3];
  87
  88             for (int i_c = 0; i_c < input.size[1]; i_c++)
  89             {
  90                 Mat outPlane = getPlane(outBlob, 0, i_c);
  91                 int wh_area = input.size[2]*input.size[3];
  92                 const float* inptr = input.ptr<float>(0, i_c);
  93                 const float* idxptr = indices.ptr<float>(0, i_c);
  94                 float* outptr = outPlane.ptr<float>();
  95
  96                 for(int i_wh = 0; i_wh < wh_area; i_wh++)
  97                 {
  98                     int index = idxptr[i_wh];
  99                     if (!(0 <= index && index < outPlaneTotal))
 100                     {
 101                         std::cerr
 102                             << "i_n=" << i_n << std::endl
 103                             << "i_c=" << i_c << std::endl
 104                             << "i_wh=" << i_wh << std::endl
 105                             << "index=" << index << std::endl
 106                             << "maxval=" << inptr[i_wh] << std::endl
 107                             << "outPlaneTotal=" << outPlaneTotal << std::endl
 108                             << "input.size=" << input.size << std::endl
 109                             << "indices.size=" << indices.size << std::endl
 110                             << "outBlob=" << outBlob.size << std::endl
 111                             ;
 112                         CV_Assert(0 <= index && index < outPlaneTotal);
 113                     }
 114                     outptr[index] = inptr[i_wh];
 115                 }
 116             }
 117         }
 118     }
 119
 120     virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &input)
 121     {
 122 #ifdef HAVE_HALIDE
 123         // Meaningless operation if false because if kernel > stride
 124         // it is not deterministic and if kernel < stride we just
 125         // skip a part of input data (you'd better change your model).
 126         if (poolKernel.width != poolStride.width ||
 127             poolKernel.height != poolStride.height)
 128             CV_Error(cv::Error::StsNotImplemented,
 129                      "Halide backend for maximum unpooling "
 130                      "is not support cases when kernel != stride");
 131
 132         Halide::Var x("x"), y("y"), c("c"), n("n");
 133         Halide::Func top = (name.empty() ? Halide::Func() : Halide::Func(name));
 134         Halide::Buffer<float> inputBuffer = halideBuffer(input[0]);
 135         Halide::Buffer<float> indices = halideBuffer(input[1]);
 136
 137         Halide::Expr pooledX = x / poolKernel.width;
 138         Halide::Expr pooledY = y / poolKernel.height;
 139
 140         const int outW = inputBuffer.width() * poolKernel.width;
 141         top(x, y, c, n) = select(y * outW + x == indices(pooledX, pooledY, c, n),
 142                                  inputBuffer(pooledX, pooledY, c, n), 0.0f);
 143         return Ptr<BackendNode>(new HalideBackendNode(top));
 144 #endif  // HAVE_HALIDE
 145         return Ptr<BackendNode>();
 146     }
 147 };
 148
 149 Ptr<MaxUnpoolLayer> MaxUnpoolLayer::create(const LayerParams& params)
 150 {
 151     return Ptr<MaxUnpoolLayer>(new MaxUnpoolLayerImpl(params));
 152 }
 153
 154 }
 155 }