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 "convolution.h"
  
  #include "layer_type.h"
  
  namespace ncnn {
  
  DEFINE_LAYER_CREATOR(Convolution)
  
  Convolution::Convolution()
  {
      one_blob_only = true;
      support_inplace = false;
  }
  
  int Convolution::load_param(const ParamDict& pd)
  {
      num_output = pd.get(0, 0);
      kernel_w = pd.get(1, 0);
      kernel_h = pd.get(11, kernel_w);
      dilation_w = pd.get(2, 1);
      dilation_h = pd.get(12, dilation_w);
      stride_w = pd.get(3, 1);
      stride_h = pd.get(13, stride_w);
      pad_w = pd.get(4, 0);
      pad_h = pd.get(14, pad_w);
      bias_term = pd.get(5, 0);
      weight_data_size = pd.get(6, 0);
  
      return 0;
  }
  
  int Convolution::load_model(const ModelBin& mb)
  {
      weight_data = mb.load(weight_data_size, 0);
      if (weight_data.empty())
          return -100;
  
      if (bias_term)
      {
          bias_data = mb.load(num_output, 1);
          if (bias_data.empty())
              return -100;
      }
  
      return 0;
  }
  
  int Convolution::forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
  {
      // convolv with NxN kernel
      // value = value + bias
  
      // flattened blob, implement as InnerProduct
      if (bottom_blob.dims == 1 && kernel_w == 1 && kernel_h == 1)
      {
          int num_input = weight_data_size / num_output;
          if (bottom_blob.w == num_input)
          {
              // call InnerProduct
              ncnn::Layer* op = ncnn::create_layer(ncnn::LayerType::InnerProduct);
  
              // set param
              ncnn::ParamDict pd;
              pd.set(0, num_output);
              pd.set(1, bias_term);
              pd.set(2, weight_data_size);
  
              op->load_param(pd);
  
              // set weights
              ncnn::Mat weights[2];
              weights[0] = weight_data;
              weights[1] = bias_data;
  
              op->load_model(ModelBinFromMatArray(weights));
  
              // forward
              op->forward(bottom_blob, top_blob, opt);
  
              delete op;
  
              return 0;
          }
      }
  
      int w = bottom_blob.w;
      int h = bottom_blob.h;
      int channels = bottom_blob.c;
      size_t elemsize = bottom_blob.elemsize;
  
  //     fprintf(stderr, "Convolution input %d x %d  pad = %d %d  ksize=%d %d  stride=%d %d\n", w, h, pad_w, pad_h, kernel_w, kernel_h, stride_w, stride_h);
  
      const int kernel_extent_w = dilation_w * (kernel_w - 1) + 1;
      const int kernel_extent_h = dilation_h * (kernel_h - 1) + 1;
  
      Mat bottom_blob_bordered = bottom_blob;
      if (pad_w > 0 || pad_h > 0)
      {
          copy_make_border(bottom_blob, bottom_blob_bordered, pad_h, pad_h, pad_w, pad_w, BORDER_CONSTANT, 0.f, opt.workspace_allocator, opt.num_threads);
          if (bottom_blob_bordered.empty())
              return -100;
  
          w = bottom_blob_bordered.w;
          h = bottom_blob_bordered.h;
      }
      else if (pad_w == -233 && pad_h == -233)
      {
          int wpad = kernel_extent_w + (w - 1) / stride_w * stride_w - w;
          int hpad = kernel_extent_h + (h - 1) / stride_h * stride_h - h;
          if (wpad > 0 || hpad > 0)
          {
              copy_make_border(bottom_blob, bottom_blob_bordered, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, BORDER_CONSTANT, 0.f, opt.workspace_allocator, opt.num_threads);
              if (bottom_blob_bordered.empty())
                  return -100;
          }
  
          w = bottom_blob_bordered.w;
          h = bottom_blob_bordered.h;
      }
  
      int outw = (w - kernel_extent_w) / stride_w + 1;
      int outh = (h - kernel_extent_h) / stride_h + 1;
  
      top_blob.create(outw, outh, num_output, 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 * dilation_h - kernel_w * dilation_w;
          for (int i = 0; i < kernel_h; i++)
          {
              for (int j = 0; j < kernel_w; j++)
              {
                  space_ofs[p1] = p2;
                  p1++;
                  p2 += dilation_w;
              }
              p2 += gap;
          }
      }
  
      // num_output
      #pragma omp parallel for num_threads(opt.num_threads)
      for (int p=0; p<num_output; p++)
      {
          float* outptr = top_blob.channel(p);
  
          for (int i = 0; i < outh; i++)
          {
              for (int j = 0; j < outw; j++)
              {
                  float sum = 0.f;
  
                  if (bias_term)
                      sum = bias_data[p];
  
                  const float* kptr = (const float*)weight_data + maxk * channels * p;
  
                  // channels
                  for (int q=0; q<channels; q++)
                  {
                      const Mat m = bottom_blob_bordered.channel(q);
                      const float* sptr = m.row(i*stride_h) + j*stride_w;
  
                      for (int k = 0; k < maxk; k++) // 29.23
                      {
                          float val = sptr[ space_ofs[k] ]; // 20.72
                          float w = kptr[k];
                          sum += val * w; // 41.45
                      }
  
                      kptr += maxk;
                  }
  
                  outptr[j] = sum;
              }
  
              outptr += outw;
          }
      }
  
      return 0;
  }
  
  } // namespace ncnn
  