text: // Tencent is pleased to support the open source community by making ncnn available.
  //
  // Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
  //
  // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
  // in compliance with the License. You may obtain a copy of the License at
  //
  // https://opensource.org/licenses/BSD-3-Clause
  //
  // Unless required by applicable law or agreed to in writing, software distributed
  // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
  // CONDITIONS OF ANY KIND, either express or implied. See the License for the
  // specific language governing permissions and limitations under the License.
  
  #include "pooling.h"
  
  #include "layer_type.h"
  
  #include <float.h>
  
  namespace ncnn {
  
  Pooling::Pooling()
  {
      one_blob_only = true;
      support_inplace = false;
  }
  
  int Pooling::load_param(const ParamDict& pd)
  {
      pooling_type = pd.get(0, 0);
      kernel_w = pd.get(1, 0);
      kernel_h = pd.get(11, kernel_w);
      stride_w = pd.get(2, 1);
      stride_h = pd.get(12, stride_w);
      pad_left = pd.get(3, 0);
      pad_right = pd.get(14, pad_left);
      pad_top = pd.get(13, pad_left);
      pad_bottom = pd.get(15, pad_top);
      global_pooling = pd.get(4, 0);
      pad_mode = pd.get(5, 0);
      avgpool_count_include_pad = pd.get(6, 0);
      adaptive_pooling = pd.get(7, 0);
      out_w = pd.get(8, 0);
      out_h = pd.get(18, out_w);
  
      return 0;
  }
  
  int Pooling::forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
  {
      // max value in NxN window
      // avg value in NxN window
  
      int w = bottom_blob.w;
      int h = bottom_blob.h;
      int channels = bottom_blob.c;
      size_t elemsize = bottom_blob.elemsize;
  
      //     NCNN_LOGE("Pooling     input %d x %d  pad = %d %d %d %d  ksize=%d %d  stride=%d %d", w, h, pad_left, pad_right, pad_top, pad_bottom, kernel_w, kernel_h, stride_w, stride_h);
      if (global_pooling)
      {
          top_blob.create(channels, elemsize, opt.blob_allocator);
          if (top_blob.empty())
              return -100;
  
          int size = w * h;
  
          if (pooling_type == PoolMethod_MAX)
          {
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int q = 0; q < channels; q++)
              {
                  const float* ptr = bottom_blob.channel(q);
  
                  float max = ptr[0];
                  for (int i = 0; i < size; i++)
                  {
                      max = std::max(max, ptr[i]);
                  }
  
                  top_blob[q] = max;
              }
          }
          else if (pooling_type == PoolMethod_AVE)
          {
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int q = 0; q < channels; q++)
              {
                  const float* ptr = bottom_blob.channel(q);
  
                  float sum = 0.f;
                  for (int i = 0; i < size; i++)
                  {
                      sum += ptr[i];
                  }
  
                  top_blob[q] = sum / size;
              }
          }
  
          return 0;
      }
  
      if (adaptive_pooling)
      {
          top_blob.create(out_w, out_h, channels, elemsize, opt.blob_allocator);
          if (top_blob.empty())
              return -100;
  
          if (pooling_type == PoolMethod_MAX)
          {
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int q = 0; q < channels; q++)
              {
                  const float* inptr = bottom_blob.channel(q);
                  float* outptr = top_blob.channel(q);
  
                  for (int i = 0; i < out_h; i++)
                  {
                      // floor div
                      const int ih0 = h * i / out_h;
                      // ceil div
                      const int ih1 = (h * (i + 1) + out_h - 1) / out_h;
                      for (int j = 0; j < out_w; j++)
                      {
                          // floor div
                          const int iw0 = w * j / out_w;
                          // ceil div
                          const int iw1 = (w * (j + 1) + out_w - 1) / out_w;
  
                          float max = inptr[ih0 * w + iw0];
                          for (int ih = ih0; ih < ih1; ih++)
                          {
                              for (int iw = iw0; iw < iw1; iw++)
                              {
                                  max = std::max(max, inptr[ih * w + iw]);
                              }
                          }
  
                          outptr[j] = max;
                      }
                      outptr += out_w;
                  }
              }
          }
          else if (pooling_type == PoolMethod_AVE)
          {
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int q = 0; q < channels; q++)
              {
                  const float* inptr = bottom_blob.channel(q);
                  float* outptr = top_blob.channel(q);
  
                  for (int i = 0; i < out_h; i++)
                  {
                      // floor div
                      const int ih0 = h * i / out_h;
                      // ceil div
                      const int ih1 = (h * (i + 1) + out_h - 1) / out_h;
                      const int hk = ih1 - ih0;
                      for (int j = 0; j < out_w; j++)
                      {
                          // floor div
                          const int iw0 = w * j / out_w;
                          // ceil div
                          const int iw1 = (w * (j + 1) + out_w - 1) / out_w;
                          const int wk = iw1 - iw0;
  
                          float sum = 0;
                          for (int ih = ih0; ih < ih1; ih++)
                          {
                              for (int iw = iw0; iw < iw1; iw++)
                              {
                                  sum += inptr[ih * w + iw];
                              }
                          }
  
                          outptr[j] = sum / hk / wk;
                      }
  
                      outptr += out_w;
                  }
              }
          }
  
          return 0;
      }
  
      Mat bottom_blob_bordered;
      make_padding(bottom_blob, bottom_blob_bordered, opt);
      if (bottom_blob_bordered.empty())
          return -100;
  
      w = bottom_blob_bordered.w;
      h = bottom_blob_bordered.h;
  
      int outw = (w - kernel_w) / stride_w + 1;
      int outh = (h - kernel_h) / stride_h + 1;
  
      top_blob.create(outw, outh, channels, elemsize, opt.blob_allocator);
      if (top_blob.empty())
          return -100;
  
      const int maxk = kernel_w * kernel_h;
  
      // kernel offsets
      std::vector<int> _space_ofs(maxk);
      int* space_ofs = &_space_ofs[0];
      {
          int p1 = 0;
          int p2 = 0;
          int gap = w - kernel_w;
          for (int i = 0; i < kernel_h; i++)
          {
              for (int j = 0; j < kernel_w; j++)
              {
                  space_ofs[p1] = p2;
                  p1++;
                  p2++;
              }
              p2 += gap;
          }
      }
  
      if (pooling_type == PoolMethod_MAX)
      {
          #pragma omp parallel for num_threads(opt.num_threads)
          for (int q = 0; q < channels; q++)
          {
              const Mat m = bottom_blob_bordered.channel(q);
              float* outptr = top_blob.channel(q);
  
              for (int i = 0; i < outh; i++)
              {
                  for (int j = 0; j < outw; j++)
                  {
                      const float* sptr = m.row(i * stride_h) + j * stride_w;
  
                      float max = sptr[0];
  
                      for (int k = 0; k < maxk; k++)
                      {
                          float val = sptr[space_ofs[k]];
                          max = std::max(max, val);
                      }
  
                      outptr[j] = max;
                  }
  
                  outptr += outw;
              }
          }
      }
      else if (pooling_type == PoolMethod_AVE)
      {
          if (avgpool_count_include_pad == 0)
          {
              int wtailpad = 0;
              int htailpad = 0;
  
              if (pad_mode == 0) // full padding
              {
                  wtailpad = bottom_blob_bordered.w - bottom_blob.w - pad_left - pad_right;
                  htailpad = bottom_blob_bordered.h - bottom_blob.h - pad_top - pad_bottom;
              }
  
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int q = 0; q < channels; q++)
              {
                  const Mat m = bottom_blob_bordered.channel(q);
                  float* outptr = top_blob.channel(q);
  
                  for (int i = 0; i < outh; i++)
                  {
                      int sy0 = i * stride_h;
  
                      for (int j = 0; j < outw; j++)
                      {
                          int sx0 = j * stride_w;
  
                          float sum = 0;
                          int area = 0;
  
                          for (int ki = 0; ki < kernel_h; ki++)
                          {
                              int sy = sy0 + ki;
  
                              if (sy < pad_top)
                                  continue;
  
                              if (sy >= h - pad_bottom - htailpad)
                                  break;
  
                              for (int kj = 0; kj < kernel_w; kj++)
                              {
                                  int sx = sx0 + kj;
  
                                  if (sx < pad_left)
                                      continue;
  
                                  if (sx >= w - pad_right - wtailpad)
                                      break;
  
                                  float val = m.row(sy)[sx];
                                  sum += val;
                                  area += 1;
                              }
                          }
  
                          outptr[j] = sum / area;
                      }
  
                      outptr += outw;
                  }
              }
          }
          else // if (avgpool_count_include_pad == 1)
          {
              #pragma omp parallel for num_threads(opt.num_threads)
              for (int q = 0; q < channels; q++)
              {
                  const Mat m = bottom_blob_bordered.channel(q);
                  float* outptr = top_blob.channel(q);
  
                  for (int i = 0; i < outh; i++)
                  {
                      for (int j = 0; j < outw; j++)
                      {
                          const float* sptr = m.row(i * stride_h) + j * stride_w;
  
                          float sum = 0;
  
                          for (int k = 0; k < maxk; k++)
                          {
                              float val = sptr[space_ofs[k]];
                              sum += val;
                          }
  
                          outptr[j] = sum / maxk;
                      }
  
                      outptr += outw;
                  }
              }
          }
      }
  
      return 0;
  }
  
  void Pooling::make_padding(const Mat& bottom_blob, Mat& bottom_blob_bordered, const Option& opt) const
  {
      int w = bottom_blob.w;
      int h = bottom_blob.h;
  
      bottom_blob_bordered = bottom_blob;
  
      float pad_value = 0.f;
      if (pooling_type == PoolMethod_MAX)
      {
          pad_value = bottom_blob.elemsize == 1 ? -128.f : -FLT_MAX;
      }
      else if (pooling_type == PoolMethod_AVE)
      {
          pad_value = 0.f;
      }
  
      int wtailpad = 0;
      int htailpad = 0;
  
      if (pad_mode == 0) // full padding
      {
          int wtail = (w + pad_left + pad_right - kernel_w) % stride_w;
          int htail = (h + pad_top + pad_bottom - kernel_h) % stride_h;
  
          if (wtail != 0)
              wtailpad = stride_w - wtail;
          if (htail != 0)
              htailpad = stride_h - htail;
  
          Option opt_b = opt;
          opt_b.blob_allocator = opt.workspace_allocator;
          copy_make_border(bottom_blob, bottom_blob_bordered, pad_top, pad_bottom + htailpad, pad_left, pad_right + wtailpad, BORDER_CONSTANT, pad_value, opt_b);
      }
      else if (pad_mode == 1) // valid padding
      {
          Option opt_b = opt;
          opt_b.blob_allocator = opt.workspace_allocator;
          copy_make_border(bottom_blob, bottom_blob_bordered, pad_top, pad_bottom, pad_left, pad_right, BORDER_CONSTANT, pad_value, opt_b);
      }
      else if (pad_mode == 2) // tensorflow padding=SAME or onnx padding=SAME_UPPER
      {
          int wpad = kernel_w + (w - 1) / stride_w * stride_w - w;
          int hpad = kernel_h + (h - 1) / stride_h * stride_h - h;
          if (wpad > 0 || hpad > 0)
          {
              Option opt_b = opt;
              opt_b.blob_allocator = opt.workspace_allocator;
              copy_make_border(bottom_blob, bottom_blob_bordered, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, BORDER_CONSTANT, pad_value, opt_b);
          }
      }
      else if (pad_mode == 3) // onnx padding=SAME_LOWER
      {
          int wpad = kernel_w + (w - 1) / stride_w * stride_w - w;
          int hpad = kernel_h + (h - 1) / stride_h * stride_h - h;
          if (wpad > 0 || hpad > 0)
          {
              Option opt_b = opt;
              opt_b.blob_allocator = opt.workspace_allocator;
              copy_make_border(bottom_blob, bottom_blob_bordered, hpad - hpad / 2, hpad / 2, wpad - wpad / 2, wpad / 2, BORDER_CONSTANT, pad_value, opt_b);
          }
      }
  }
  
  } // namespace ncnn
  