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.
  
  #ifdef _MSC_VER
  #define _CRT_SECURE_NO_DEPRECATE
  #endif
  
  #include "caffe.pb.h"
  
  #include <algorithm>
  #include <fstream>
  #include <google/protobuf/io/coded_stream.h>
  #include <google/protobuf/io/zero_copy_stream_impl.h>
  #include <google/protobuf/message.h>
  #include <google/protobuf/text_format.h>
  #include <limits.h>
  #include <limits>
  #include <map>
  #include <math.h>
  #include <set>
  #include <stdio.h>
  
  static bool read_proto_from_text(const char* filepath, google::protobuf::Message* message)
  {
      std::ifstream fs(filepath, std::ifstream::in);
      if (!fs.is_open())
      {
          fprintf(stderr, "open failed %s\n", filepath);
          return false;
      }
  
      google::protobuf::io::IstreamInputStream input(&fs);
      bool success = google::protobuf::TextFormat::Parse(&input, message);
  
      fs.close();
  
      return success;
  }
  
  static bool read_proto_from_binary(const char* filepath, google::protobuf::Message* message)
  {
      std::ifstream fs(filepath, std::ifstream::in | std::ifstream::binary);
      if (!fs.is_open())
      {
          fprintf(stderr, "open failed %s\n", filepath);
          return false;
      }
  
      google::protobuf::io::IstreamInputStream input(&fs);
      google::protobuf::io::CodedInputStream codedstr(&input);
  
  #if GOOGLE_PROTOBUF_VERSION >= 3011000
      codedstr.SetTotalBytesLimit(INT_MAX);
  #else
      codedstr.SetTotalBytesLimit(INT_MAX, INT_MAX / 2);
  #endif
  
      bool success = message->ParseFromCodedStream(&codedstr);
  
      fs.close();
  
      return success;
  }
  
  int main(int argc, char** argv)
  {
      if (!(argc == 3 || argc == 5))
      {
          fprintf(stderr, "Usage: %s [caffeproto] [caffemodel] [ncnnparam] [ncnnbin]\n", argv[0]);
          return -1;
      }
  
      const char* caffeproto = argv[1];
      const char* caffemodel = argv[2];
      const char* ncnn_prototxt = argc == 5 ? argv[3] : "ncnn.param";
      const char* ncnn_modelbin = argc == 5 ? argv[4] : "ncnn.bin";
  
      caffe::NetParameter proto;
      caffe::NetParameter net;
  
      // load
      bool s0 = read_proto_from_text(caffeproto, &proto);
      if (!s0)
      {
          fprintf(stderr, "read_proto_from_text failed\n");
          return -1;
      }
  
      bool s1 = read_proto_from_binary(caffemodel, &net);
      if (!s1)
      {
          fprintf(stderr, "read_proto_from_binary failed\n");
          return -1;
      }
  
      FILE* pp = fopen(ncnn_prototxt, "wb");
      FILE* bp = fopen(ncnn_modelbin, "wb");
  
      // magic
      fprintf(pp, "7767517\n");
  
      // rename mapping for identical bottom top style
      std::map<std::string, std::string> blob_name_decorated;
  
      // bottom blob reference
      std::map<std::string, int> bottom_reference;
  
      // global definition line
      // [layer count] [blob count]
      int layer_count = proto.layer_size();
      std::set<std::string> blob_names;
      for (int i = 0; i < layer_count; i++)
      {
          const caffe::LayerParameter& layer = proto.layer(i);
  
          for (int j = 0; j < layer.bottom_size(); j++)
          {
              std::string blob_name = layer.bottom(j);
              if (blob_name_decorated.find(blob_name) != blob_name_decorated.end())
              {
                  blob_name = blob_name_decorated[blob_name];
              }
  
              blob_names.insert(blob_name);
  
              if (bottom_reference.find(blob_name) == bottom_reference.end())
              {
                  bottom_reference[blob_name] = 1;
              }
              else
              {
                  bottom_reference[blob_name] = bottom_reference[blob_name] + 1;
              }
          }
  
          if (layer.bottom_size() == 1 && layer.top_size() == 1 && layer.bottom(0) == layer.top(0))
          {
              std::string blob_name = layer.top(0) + "_" + layer.name();
              blob_name_decorated[layer.top(0)] = blob_name;
              blob_names.insert(blob_name);
          }
          else
          {
              for (int j = 0; j < layer.top_size(); j++)
              {
                  std::string blob_name = layer.top(j);
                  blob_names.insert(blob_name);
              }
          }
      }
      // remove bottom_reference entry with reference equals to one
      int splitncnn_blob_count = 0;
      std::map<std::string, int>::iterator it = bottom_reference.begin();
      while (it != bottom_reference.end())
      {
          if (it->second == 1)
          {
              bottom_reference.erase(it++);
          }
          else
          {
              splitncnn_blob_count += it->second;
              //             fprintf(stderr, "%s %d\n", it->first.c_str(), it->second);
              ++it;
          }
      }
      fprintf(pp, "%d %d\n", int(layer_count + bottom_reference.size()), int(blob_names.size() + splitncnn_blob_count));
  
      // populate
      blob_name_decorated.clear();
      int internal_split = 0;
      for (int i = 0; i < layer_count; i++)
      {
          const caffe::LayerParameter& layer = proto.layer(i);
  
          // layer definition line, repeated
          // [type] [name] [bottom blob count] [top blob count] [bottom blobs] [top blobs] [layer specific params]
          if (layer.type() == "BN")
          {
              fprintf(pp, "%-16s", "Scale");
          }
          else if (layer.type() == "Convolution")
          {
              const caffe::ConvolutionParameter& convolution_param = layer.convolution_param();
              if (convolution_param.group() != 1)
                  fprintf(pp, "%-16s", "ConvolutionDepthWise");
              else
                  fprintf(pp, "%-16s", "Convolution");
          }
          else if (layer.type() == "ConvolutionDepthwise" || layer.type() == "DepthwiseConvolution")
          {
              fprintf(pp, "%-16s", "ConvolutionDepthWise");
          }
          else if (layer.type() == "Deconvolution")
          {
              const caffe::ConvolutionParameter& convolution_param = layer.convolution_param();
              if (convolution_param.group() != 1)
                  fprintf(pp, "%-16s", "DeconvolutionDepthWise");
              else
                  fprintf(pp, "%-16s", "Deconvolution");
          }
          else if (layer.type() == "MemoryData")
          {
              fprintf(pp, "%-16s", "Input");
          }
          else if (layer.type() == "Python")
          {
              const caffe::PythonParameter& python_param = layer.python_param();
              std::string python_layer_name = python_param.layer();
              if (python_layer_name == "ProposalLayer")
                  fprintf(pp, "%-16s", "Proposal");
              else
                  fprintf(pp, "%-16s", python_layer_name.c_str());
          }
          else if (layer.type() == "ReLU6")
          {
              fprintf(pp, "%-16s", "Clip");
          }
          else if (layer.type() == "Silence")
          {
              fprintf(pp, "%-16s", "Noop");
          }
          else
          {
              fprintf(pp, "%-16s", layer.type().c_str());
          }
          fprintf(pp, " %-16s %d %d", layer.name().c_str(), layer.bottom_size(), layer.top_size());
  
          for (int j = 0; j < layer.bottom_size(); j++)
          {
              std::string blob_name = layer.bottom(j);
              if (blob_name_decorated.find(layer.bottom(j)) != blob_name_decorated.end())
              {
                  blob_name = blob_name_decorated[layer.bottom(j)];
              }
  
              if (bottom_reference.find(blob_name) != bottom_reference.end())
              {
                  int refidx = bottom_reference[blob_name] - 1;
                  bottom_reference[blob_name] = refidx;
  
                  char splitsuffix[256];
                  sprintf(splitsuffix, "_splitncnn_%d", refidx);
                  blob_name = blob_name + splitsuffix;
              }
  
              fprintf(pp, " %s", blob_name.c_str());
          }
  
          // decorated
          if (layer.bottom_size() == 1 && layer.top_size() == 1 && layer.bottom(0) == layer.top(0))
          {
              std::string blob_name = layer.top(0) + "_" + layer.name();
              blob_name_decorated[layer.top(0)] = blob_name;
  
              fprintf(pp, " %s", blob_name.c_str());
          }
          else
          {
              for (int j = 0; j < layer.top_size(); j++)
              {
                  std::string blob_name = layer.top(j);
                  fprintf(pp, " %s", blob_name.c_str());
              }
          }
  
          // find blob binary by layer name
          int netidx;
          for (netidx = 0; netidx < net.layer_size(); netidx++)
          {
              if (net.layer(netidx).name() == layer.name())
              {
                  break;
              }
          }
  
          // layer specific params
          if (layer.type() == "BatchNorm")
          {
              const caffe::LayerParameter& binlayer = net.layer(netidx);
  
              const caffe::BlobProto& mean_blob = binlayer.blobs(0);
              const caffe::BlobProto& var_blob = binlayer.blobs(1);
              fprintf(pp, " 0=%d", (int)mean_blob.data_size());
  
              const caffe::BatchNormParameter& batch_norm_param = layer.batch_norm_param();
              float eps = batch_norm_param.eps();
  
              std::vector<float> ones(mean_blob.data_size(), 1.f);
              fwrite(ones.data(), sizeof(float), ones.size(), bp); // slope
  
              if (binlayer.blobs_size() < 3)
              {
                  fwrite(mean_blob.data().data(), sizeof(float), mean_blob.data_size(), bp);
                  float tmp;
                  for (int j = 0; j < var_blob.data_size(); j++)
                  {
                      tmp = var_blob.data().data()[j] + eps;
                      fwrite(&tmp, sizeof(float), 1, bp);
                  }
              }
              else
              {
                  float scale_factor = binlayer.blobs(2).data().data()[0] == 0 ? 0 : 1 / binlayer.blobs(2).data().data()[0];
                  // premultiply scale_factor to mean and variance
                  float tmp;
                  for (int j = 0; j < mean_blob.data_size(); j++)
                  {
                      tmp = mean_blob.data().data()[j] * scale_factor;
                      fwrite(&tmp, sizeof(float), 1, bp);
                  }
                  for (int j = 0; j < var_blob.data_size(); j++)
                  {
                      tmp = var_blob.data().data()[j] * scale_factor + eps;
                      fwrite(&tmp, sizeof(float), 1, bp);
                  }
              }
  
              std::vector<float> zeros(mean_blob.data_size(), 0.f);
              fwrite(zeros.data(), sizeof(float), zeros.size(), bp); // bias
          }
          else if (layer.type() == "BN")
          {
              const caffe::LayerParameter& binlayer = net.layer(netidx);
  
              const caffe::BlobProto& scale_blob = binlayer.blobs(0);
              const caffe::BlobProto& shift_blob = binlayer.blobs(1);
              fprintf(pp, " 0=%d", (int)scale_blob.data_size());
              fprintf(pp, " 1=1");
  
              fwrite(scale_blob.data().data(), sizeof(float), scale_blob.data_size(), bp);
              fwrite(shift_blob.data().data(), sizeof(float), shift_blob.data_size(), bp);
          }
          else if (layer.type() == "Concat")
          {
              const caffe::ConcatParameter& concat_param = layer.concat_param();
              int axis = concat_param.axis() - 1;
              fprintf(pp, " 0=%d", axis);
          }
          else if (layer.type() == "Convolution" || layer.type() == "ConvolutionDepthwise" || layer.type() == "DepthwiseConvolution")
          {
              const caffe::LayerParameter& binlayer = net.layer(netidx);
  
              const caffe::BlobProto& weight_blob = binlayer.blobs(0);
              const caffe::ConvolutionParameter& convolution_param = layer.convolution_param();
              fprintf(pp, " 0=%d", convolution_param.num_output());
              if (convolution_param.has_kernel_w() && convolution_param.has_kernel_h())
              {
                  fprintf(pp, " 1=%d", convolution_param.kernel_w());
                  fprintf(pp, " 11=%d", convolution_param.kernel_h());
              }
              else
              {
                  fprintf(pp, " 1=%d", convolution_param.kernel_size(0));
              }
              fprintf(pp, " 2=%d", convolution_param.dilation_size() != 0 ? convolution_param.dilation(0) : 1);
              if (convolution_param.has_stride_w() && convolution_param.has_stride_h())
              {
                  fprintf(pp, " 3=%d", convolution_param.stride_w());
                  fprintf(pp, " 13=%d", convolution_param.stride_h());
              }
              else
              {
                  fprintf(pp, " 3=%d", convolution_param.stride_size() != 0 ? convolution_param.stride(0) : 1);
              }
              if (convolution_param.has_pad_w() && convolution_param.has_pad_h())
              {
                  fprintf(pp, " 4=%d", convolution_param.pad_w());
                  fprintf(pp, " 14=%d", convolution_param.pad_h());
              }
              else
              {
                  fprintf(pp, " 4=%d", convolution_param.pad_size() != 0 ? convolution_param.pad(0) : 0);
              }
              fprintf(pp, " 5=%d", convolution_param.bias_term());
              fprintf(pp, " 6=%d", weight_blob.data_size());
  
              int num_group = 1;
              if (layer.type() == "ConvolutionDepthwise" || layer.type() == "DepthwiseConvolution")
              {
                  num_group = convolution_param.num_output();
              }
              else
              {
                  num_group = convolution_param.group();
              }
  
              if (num_group != 1)
              {
                  fprintf(pp, " 7=%d", num_group);
              }
  
              for (int j = 0; j < binlayer.blobs_size(); j++)
              {
                  int quantize_tag = 0;
                  const caffe::BlobProto& blob = binlayer.blobs(j);
  
                  // we will not quantize the bias values
                  if (j == 0)
                  {
                      // write quantize tag first
                      fwrite(&quantize_tag, sizeof(int), 1, bp);
  
                      // write original data
                      fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
                  }
                  else
                  {
                      // write original data
                      fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
                  }
              }
          }
          else if (layer.type() == "Crop")
          {
              const caffe::CropParameter& crop_param = layer.crop_param();
              int num_offset = crop_param.offset_size();
              if (num_offset == 1)
              {
                  int offset = crop_param.offset(0);
                  int axis = crop_param.axis() - 1;
                  if (axis == 0)
                  {
                      fprintf(pp, " 0=%d", offset);
                      fprintf(pp, " 1=%d", offset);
                      fprintf(pp, " 2=%d", offset);
                  }
                  else if (axis == 1)
                  {
                      fprintf(pp, " 0=%d", offset);
                      fprintf(pp, " 1=%d", offset);
                  }
                  else if (axis == 2)
                  {
                      fprintf(pp, " 0=%d", offset);
                  }
              }
              else if (num_offset == 2)
              {
                  int woffset = crop_param.offset(1);
                  int hoffset = crop_param.offset(0);
                  fprintf(pp, " 0=%d", woffset);
                  fprintf(pp, " 1=%d", hoffset);
              }
              else if (num_offset == 3)
              {
                  int woffset = crop_param.offset(2);
                  int hoffset = crop_param.offset(1);
                  int coffset = crop_param.offset(0);
                  fprintf(pp, " 0=%d", woffset);
                  fprintf(pp, " 1=%d", hoffset);
                  fprintf(pp, " 2=%d", coffset);
              }
          }
          else if (layer.type() == "Deconvolution")
          {
              const caffe::LayerParameter& binlayer = net.layer(netidx);
  
              const caffe::BlobProto& weight_blob = binlayer.blobs(0);
              const caffe::ConvolutionParameter& convolution_param = layer.convolution_param();
              fprintf(pp, " 0=%d", convolution_param.num_output());
              if (convolution_param.has_kernel_w() && convolution_param.has_kernel_h())
              {
                  fprintf(pp, " 1=%d", convolution_param.kernel_w());
                  fprintf(pp, " 11=%d", convolution_param.kernel_h());
              }
              else
              {
                  fprintf(pp, " 1=%d", convolution_param.kernel_size(0));
              }
              fprintf(pp, " 2=%d", convolution_param.dilation_size() != 0 ? convolution_param.dilation(0) : 1);
              if (convolution_param.has_stride_w() && convolution_param.has_stride_h())
              {
                  fprintf(pp, " 3=%d", convolution_param.stride_w());
                  fprintf(pp, " 13=%d", convolution_param.stride_h());
              }
              else
              {
                  fprintf(pp, " 3=%d", convolution_param.stride_size() != 0 ? convolution_param.stride(0) : 1);
              }
              if (convolution_param.has_pad_w() && convolution_param.has_pad_h())
              {
                  fprintf(pp, " 4=%d", convolution_param.pad_w());
                  fprintf(pp, " 14=%d", convolution_param.pad_h());
              }
              else
              {
                  fprintf(pp, " 4=%d", convolution_param.pad_size() != 0 ? convolution_param.pad(0) : 0);
              }
              fprintf(pp, " 5=%d", convolution_param.bias_term());
              fprintf(pp, " 6=%d", weight_blob.data_size());
  
              int group = convolution_param.group();
              if (group != 1)
              {
                  fprintf(pp, " 7=%d", group);
              }
  
              int quantized_weight = 0;
              fwrite(&quantized_weight, sizeof(int), 1, bp);
  
              int maxk = 0;
              if (convolution_param.has_kernel_w() && convolution_param.has_kernel_h())
              {
                  maxk = convolution_param.kernel_w() * convolution_param.kernel_h();
              }
              else
              {
                  maxk = convolution_param.kernel_size(0) * convolution_param.kernel_size(0);
              }
              for (int g = 0; g < group; g++)
              {
                  // reorder weight from inch-outch to outch-inch
                  int num_output = convolution_param.num_output() / group;
                  int num_input = weight_blob.data_size() / maxk / num_output / group;
                  const float* weight_data_ptr = weight_blob.data().data() + g * maxk * num_output * num_input;
                  for (int k = 0; k < num_output; k++)
                  {
                      for (int j = 0; j < num_input; j++)
                      {
                          fwrite(weight_data_ptr + (j * num_output + k) * maxk, sizeof(float), maxk, bp);
                      }
                  }
              }
  
              for (int j = 1; j < binlayer.blobs_size(); j++)
              {
                  const caffe::BlobProto& blob = binlayer.blobs(j);
                  fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
              }
          }
          else if (layer.type() == "DetectionOutput")
          {
              const caffe::DetectionOutputParameter& detection_output_param = layer.detection_output_param();
              const caffe::NonMaximumSuppressionParameter& nms_param = detection_output_param.nms_param();
              fprintf(pp, " 0=%d", detection_output_param.num_classes());
              fprintf(pp, " 1=%e", nms_param.nms_threshold());
              fprintf(pp, " 2=%d", nms_param.top_k());
              fprintf(pp, " 3=%d", detection_output_param.keep_top_k());
              fprintf(pp, " 4=%e", detection_output_param.confidence_threshold());
          }
          else if (layer.type() == "Dropout")
          {
              const caffe::DropoutParameter& dropout_param = layer.dropout_param();
              if (dropout_param.has_scale_train() && !dropout_param.scale_train())
              {
                  float scale = 1.f - dropout_param.dropout_ratio();
                  fprintf(pp, " 0=%e", scale);
              }
          }
          else if (layer.type() == "Eltwise")
          {
              const caffe::EltwiseParameter& eltwise_param = layer.eltwise_param();
              int coeff_size = eltwise_param.coeff_size();
              fprintf(pp, " 0=%d", (int)eltwise_param.operation());
              fprintf(pp, " -23301=%d", coeff_size);
              for (int j = 0; j < coeff_size; j++)
              {
                  fprintf(pp, ",%e", eltwise_param.coeff(j));
              }
          }
          else if (layer.type() == "ELU")
          {
              const caffe::ELUParameter& elu_param = layer.elu_param();
              fprintf(pp, " 0=%e", elu_param.alpha());
          }
          else if (layer.type() == "Embed")
          {
              const caffe::LayerParameter& binlayer = net.layer(netidx);
  
              const caffe::BlobProto& weight_blob = binlayer.blobs(0);
              const caffe::EmbedParameter& embed_param = layer.embed_param();
              fprintf(pp, " 0=%d", embed_param.num_output());
              fprintf(pp, " 1=%d", embed_param.input_dim());
              fprintf(pp, " 2=%d", embed_param.bias_term());
              fprintf(pp, " 3=%d", weight_blob.data_size());
  
              for (int j = 0; j < binlayer.blobs_size(); j++)
              {
                  int quantize_tag = 0;
                  const caffe::BlobProto& blob = binlayer.blobs(j);
  
                  // write quantize tag first
                  if (j == 0)
                      fwrite(&quantize_tag, sizeof(int), 1, bp);
  
                  // write original data
                  fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
              }
          }
          else if (layer.type() == "InnerProduct")
          {
              const caffe::LayerParameter& binlayer = net.layer(netidx);
  
              const caffe::BlobProto& weight_blob = binlayer.blobs(0);
              const caffe::InnerProductParameter& inner_product_param = layer.inner_product_param();
              fprintf(pp, " 0=%d", inner_product_param.num_output());
              fprintf(pp, " 1=%d", inner_product_param.bias_term());
              fprintf(pp, " 2=%d", weight_blob.data_size());
  
              for (int j = 0; j < binlayer.blobs_size(); j++)
              {
                  int quantize_tag = 0;
                  const caffe::BlobProto& blob = binlayer.blobs(j);
  
                  // we will not quantize the bias values
                  if (j == 0)
                  {
                      // write quantize tag first
                      fwrite(&quantize_tag, sizeof(int), 1, bp);
  
                      // write original data
                      fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
                  }
                  else
                  {
                      // write original data
                      fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
                  }
              }
          }
          else if (layer.type() == "Input")
          {
              const caffe::InputParameter& input_param = layer.input_param();
              const caffe::BlobShape& bs = input_param.shape(0);
              if (bs.dim_size() == 4)
              {
                  fprintf(pp, " 0=%zd", size_t(bs.dim(3)));
                  fprintf(pp, " 1=%zd", size_t(bs.dim(2)));
                  fprintf(pp, " 2=%zd", size_t(bs.dim(1)));
              }
              else if (bs.dim_size() == 3)
              {
                  fprintf(pp, " 0=%zd", size_t(bs.dim(2)));
                  fprintf(pp, " 1=%zd", size_t(bs.dim(1)));
              }
              else if (bs.dim_size() == 2)
              {
                  fprintf(pp, " 0=%zd", size_t(bs.dim(1)));
              }
          }
          else if (layer.type() == "Interp")
          {
              const caffe::InterpParameter& interp_param = layer.interp_param();
              fprintf(pp, " 0=%d", 2);
              fprintf(pp, " 1=%e", (float)interp_param.zoom_factor());
              fprintf(pp, " 2=%e", (float)interp_param.zoom_factor());
              fprintf(pp, " 3=%d", interp_param.height());
              fprintf(pp, " 4=%d", interp_param.width());
          }
          else if (layer.type() == "LRN")
          {
              const caffe::LRNParameter& lrn_param = layer.lrn_param();
              fprintf(pp, " 0=%d", lrn_param.norm_region());
              fprintf(pp, " 1=%d", lrn_param.local_size());
              fprintf(pp, " 2=%e", lrn_param.alpha());
              fprintf(pp, " 3=%e", lrn_param.beta());
          }
          else if (layer.type() == "LSTM")
          {
              const caffe::LayerParameter& binlayer = net.layer(netidx);
  
              const caffe::BlobProto& weight_blob = binlayer.blobs(0);
              const caffe::RecurrentParameter& recurrent_param = layer.recurrent_param();
              fprintf(pp, " 0=%d", recurrent_param.num_output());
              fprintf(pp, " 1=%d", weight_blob.data_size());
  
              for (int j = 0; j < binlayer.blobs_size(); j++)
              {
                  int quantize_tag = 0;
                  const caffe::BlobProto& blob = binlayer.blobs(j);
  
                  // write quantize tag first
                  fwrite(&quantize_tag, sizeof(int), 1, bp);
  
                  // write original data
                  fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
              }
          }
          else if (layer.type() == "MemoryData")
          {
              const caffe::MemoryDataParameter& memory_data_param = layer.memory_data_param();
              fprintf(pp, " 0=%d", memory_data_param.width());
              fprintf(pp, " 1=%d", memory_data_param.height());
              fprintf(pp, " 2=%d", memory_data_param.channels());
          }
          else if (layer.type() == "MVN")
          {
              const caffe::MVNParameter& mvn_param = layer.mvn_param();
              fprintf(pp, " 0=%d", mvn_param.normalize_variance());
              fprintf(pp, " 1=%d", mvn_param.across_channels());
              fprintf(pp, " 2=%e", mvn_param.eps());
          }
          else if (layer.type() == "Normalize")
          {
              const caffe::LayerParameter& binlayer = net.layer(netidx);
              const caffe::BlobProto& scale_blob = binlayer.blobs(0);
              const caffe::NormalizeParameter& norm_param = layer.norm_param();
              fprintf(pp, " 0=%d", norm_param.across_spatial());
              fprintf(pp, " 1=%d", norm_param.channel_shared());
              fprintf(pp, " 2=%e", norm_param.eps());
              fprintf(pp, " 3=%d", scale_blob.data_size());
  
              fwrite(scale_blob.data().data(), sizeof(float), scale_blob.data_size(), bp);
          }
          else if (layer.type() == "Permute")
          {
              const caffe::PermuteParameter& permute_param = layer.permute_param();
              int order_size = permute_param.order_size();
              int order_type = 0;
              if (order_size == 0)
                  order_type = 0;
              if (order_size == 1)
              {
                  int order0 = permute_param.order(0);
                  if (order0 == 0)
                      order_type = 0;
                  // permute with N not supported
              }
              if (order_size == 2)
              {
                  int order0 = permute_param.order(0);
                  int order1 = permute_param.order(1);
                  if (order0 == 0)
                  {
                      if (order1 == 1) // 0 1 2 3
                          order_type = 0;
                      else if (order1 == 2) // 0 2 1 3
                          order_type = 2;
                      else if (order1 == 3) // 0 3 1 2
                          order_type = 4;
                  }
                  // permute with N not supported
              }
              if (order_size == 3 || order_size == 4)
              {
                  int order0 = permute_param.order(0);
                  int order1 = permute_param.order(1);
                  int order2 = permute_param.order(2);
                  if (order0 == 0)
                  {
                      if (order1 == 1)
                      {
                          if (order2 == 2) // 0 1 2 3
                              order_type = 0;
                          if (order2 == 3) // 0 1 3 2
                              order_type = 1;
                      }
                      else if (order1 == 2)
                      {
                          if (order2 == 1) // 0 2 1 3
                              order_type = 2;
                          if (order2 == 3) // 0 2 3 1
                              order_type = 3;
                      }
                      else if (order1 == 3)
                      {
                          if (order2 == 1) // 0 3 1 2
                              order_type = 4;
                          if (order2 == 2) // 0 3 2 1
                              order_type = 5;
                      }
                  }
                  // permute with N not supported
              }
              fprintf(pp, " 0=%d", order_type);
          }
          else if (layer.type() == "Pooling")
          {
              const caffe::PoolingParameter& pooling_param = layer.pooling_param();
              fprintf(pp, " 0=%d", pooling_param.pool());
              if (pooling_param.has_kernel_w() && pooling_param.has_kernel_h())
              {
                  fprintf(pp, " 1=%d", pooling_param.kernel_w());
                  fprintf(pp, " 11=%d", pooling_param.kernel_h());
              }
              else
              {
                  fprintf(pp, " 1=%d", pooling_param.kernel_size());
              }
              if (pooling_param.has_stride_w() && pooling_param.has_stride_h())
              {
                  fprintf(pp, " 2=%d", pooling_param.stride_w());
                  fprintf(pp, " 12=%d", pooling_param.stride_h());
              }
              else
              {
                  fprintf(pp, " 2=%d", pooling_param.stride());
              }
              if (pooling_param.has_pad_w() && pooling_param.has_pad_h())
              {
                  fprintf(pp, " 3=%d", pooling_param.pad_w());
                  fprintf(pp, " 13=%d", pooling_param.pad_h());
              }
              else
              {
                  fprintf(pp, " 3=%d", pooling_param.pad());
              }
              fprintf(pp, " 4=%d", pooling_param.has_global_pooling() ? pooling_param.global_pooling() : 0);
          }
          else if (layer.type() == "Power")
          {
              const caffe::PowerParameter& power_param = layer.power_param();
              fprintf(pp, " 0=%e", power_param.power());
              fprintf(pp, " 1=%e", power_param.scale());
              fprintf(pp, " 2=%e", power_param.shift());
          }
          else if (layer.type() == "PReLU")
          {
              const caffe::LayerParameter& binlayer = net.layer(netidx);
              const caffe::BlobProto& slope_blob = binlayer.blobs(0);
              fprintf(pp, " 0=%d", slope_blob.data_size());
              fwrite(slope_blob.data().data(), sizeof(float), slope_blob.data_size(), bp);
          }
          else if (layer.type() == "PriorBox")
          {
              const caffe::PriorBoxParameter& prior_box_param = layer.prior_box_param();
  
              int num_aspect_ratio = prior_box_param.aspect_ratio_size();
              for (int j = 0; j < prior_box_param.aspect_ratio_size(); j++)
              {
                  float ar = prior_box_param.aspect_ratio(j);
                  if (fabs(ar - 1.) < 1e-6)
                  {
                      num_aspect_ratio--;
                  }
              }
  
              float variances[4] = {0.1f, 0.1f, 0.1f, 0.1f};
              if (prior_box_param.variance_size() == 4)
              {
                  variances[0] = prior_box_param.variance(0);
                  variances[1] = prior_box_param.variance(1);
                  variances[2] = prior_box_param.variance(2);
                  variances[3] = prior_box_param.variance(3);
              }
              else if (prior_box_param.variance_size() == 1)
              {
                  variances[0] = prior_box_param.variance(0);
                  variances[1] = prior_box_param.variance(0);
                  variances[2] = prior_box_param.variance(0);
                  variances[3] = prior_box_param.variance(0);
              }
  
              int flip = prior_box_param.has_flip() ? prior_box_param.flip() : 1;
              int clip = prior_box_param.has_clip() ? prior_box_param.clip() : 0;
              int image_width = -233;
              int image_height = -233;
              if (prior_box_param.has_img_size())
              {
                  image_width = prior_box_param.img_size();
                  image_height = prior_box_param.img_size();
              }
              else if (prior_box_param.has_img_w() && prior_box_param.has_img_h())
              {
                  image_width = prior_box_param.img_w();
                  image_height = prior_box_param.img_h();
              }
  
              float step_width = -233;
              float step_height = -233;
              if (prior_box_param.has_step())
              {
                  step_width = prior_box_param.step();
                  step_height = prior_box_param.step();
              }
              else if (prior_box_param.has_step_w() && prior_box_param.has_step_h())
              {
                  step_width = prior_box_param.step_w();
                  step_height = prior_box_param.step_h();
              }
  
              fprintf(pp, " -23300=%d", prior_box_param.min_size_size());
              for (int j = 0; j < prior_box_param.min_size_size(); j++)
              {
                  fprintf(pp, ",%e", prior_box_param.min_size(j));
              }
              fprintf(pp, " -23301=%d", prior_box_param.max_size_size());
              for (int j = 0; j < prior_box_param.max_size_size(); j++)
              {
                  fprintf(pp, ",%e", prior_box_param.max_size(j));
              }
              fprintf(pp, " -23302=%d", num_aspect_ratio);
              for (int j = 0; j < prior_box_param.aspect_ratio_size(); j++)
              {
                  float ar = prior_box_param.aspect_ratio(j);
                  if (fabs(ar - 1.) < 1e-6)
                  {
                      continue;
                  }
                  fprintf(pp, ",%e", ar);
              }
              fprintf(pp, " 3=%e", variances[0]);
              fprintf(pp, " 4=%e", variances[1]);
              fprintf(pp, " 5=%e", variances[2]);
              fprintf(pp, " 6=%e", variances[3]);
              fprintf(pp, " 7=%d", flip);
              fprintf(pp, " 8=%d", clip);
              fprintf(pp, " 9=%d", image_width);
              fprintf(pp, " 10=%d", image_height);
              fprintf(pp, " 11=%e", step_width);
              fprintf(pp, " 12=%e", step_height);
              fprintf(pp, " 13=%e", prior_box_param.offset());
          }
          else if (layer.type() == "PSROIPooling")
          {
              const caffe::PSROIPoolingParameter& psroi_pooling_param = layer.psroi_pooling_param();
              fprintf(pp, " 0=%d", psroi_pooling_param.group_size());
              fprintf(pp, " 1=%d", psroi_pooling_param.group_size());
              fprintf(pp, " 2=%e", psroi_pooling_param.spatial_scale());
              fprintf(pp, " 3=%d", psroi_pooling_param.output_dim());
          }
          else if (layer.type() == "Python")
          {
              const caffe::PythonParameter& python_param = layer.python_param();
              std::string python_layer_name = python_param.layer();
              if (python_layer_name == "ProposalLayer")
              {
                  int feat_stride = 16;
                  sscanf(python_param.param_str().c_str(), "'feat_stride': %d", &feat_stride);
  
                  int base_size = 16;
                  //                 float ratio;
                  //                 float scale;
                  int pre_nms_topN = 6000;
                  int after_nms_topN = 300;
                  float nms_thresh = 0.7f;
                  int min_size = 16;
                  fprintf(pp, " 0=%d", feat_stride);
                  fprintf(pp, " 1=%d", base_size);
                  fprintf(pp, " 2=%d", pre_nms_topN);
                  fprintf(pp, " 3=%d", after_nms_topN);
                  fprintf(pp, " 4=%e", nms_thresh);
                  fprintf(pp, " 5=%d", min_size);
              }
          }
          else if (layer.type() == "ReLU")
          {
              const caffe::ReLUParameter& relu_param = layer.relu_param();
              if (relu_param.has_negative_slope())
              {
                  fprintf(pp, " 0=%e", relu_param.negative_slope());
              }
          }
          else if (layer.type() == "ReLU6")
          {
              float min = 0.f;
              float max = 6.f;
              fprintf(pp, " 0=%e", min);
              fprintf(pp, " 1=%e", max);
          }
          else if (layer.type() == "Reorg")
          {
              const caffe::ReorgParameter& reorg_param = layer.reorg_param();
              fprintf(pp, " 0=%d", reorg_param.stride());
          }
          else if (layer.type() == "Reshape")
          {
              const caffe::ReshapeParameter& reshape_param = layer.reshape_param();
              const caffe::BlobShape& bs = reshape_param.shape();
              if (bs.dim_size() == 1)
              {
                  fprintf(pp, " 0=%zd 1=-233 2=-233", size_t(bs.dim(0)));
              }
              else if (bs.dim_size() == 2)
              {
                  fprintf(pp, " 0=%zd 1=-233 2=-233", size_t(bs.dim(1)));
              }
              else if (bs.dim_size() == 3)
              {
                  fprintf(pp, " 0=%zd 1=%zd 2=-233", size_t(bs.dim(2)), size_t(bs.dim(1)));
              }
              else // bs.dim_size() == 4
              {
                  fprintf(pp, " 0=%zd 1=%zd 2=%zd", size_t(bs.dim(3)), size_t(bs.dim(2)), size_t(bs.dim(1)));
              }
              fprintf(pp, " 3=0"); // permute
          }
          else if (layer.type() == "ROIAlign")
          {
              const caffe::ROIAlignParameter& roi_align_param = layer.roi_align_param();
              fprintf(pp, " 0=%d", roi_align_param.pooled_w());
              fprintf(pp, " 1=%d", roi_align_param.pooled_h());
              fprintf(pp, " 2=%e", roi_align_param.spatial_scale());
              fprintf(pp, " 3=%d", 0);
              fprintf(pp, " 4=%d", false);
              fprintf(pp, " 5=%d", 0);
          }
          else if (layer.type() == "ROIPooling")
          {
              const caffe::ROIPoolingParameter& roi_pooling_param = layer.roi_pooling_param();
              fprintf(pp, " 0=%d", roi_pooling_param.pooled_w());
              fprintf(pp, " 1=%d", roi_pooling_param.pooled_h());
              fprintf(pp, " 2=%e", roi_pooling_param.spatial_scale());
          }
          else if (layer.type() == "Scale")
          {
              const caffe::LayerParameter& binlayer = net.layer(netidx);
  
              const caffe::ScaleParameter& scale_param = layer.scale_param();
              bool scale_weight = scale_param.bias_term() ? (binlayer.blobs_size() == 2) : (binlayer.blobs_size() == 1);
              if (scale_weight)
              {
                  const caffe::BlobProto& weight_blob = binlayer.blobs(0);
                  fprintf(pp, " 0=%d", int(weight_blob.data_size()));
              }
              else
              {
                  fprintf(pp, " 0=-233");
              }
  
              fprintf(pp, " 1=%d", scale_param.bias_term());
  
              for (int j = 0; j < binlayer.blobs_size(); j++)
              {
                  const caffe::BlobProto& blob = binlayer.blobs(j);
                  fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
              }
          }
          else if (layer.type() == "ShuffleChannel")
          {
              const caffe::ShuffleChannelParameter& shuffle_channel_param = layer.shuffle_channel_param();
              fprintf(pp, " 0=%d", shuffle_channel_param.group());
          }
          else if (layer.type() == "Slice")
          {
              const caffe::SliceParameter& slice_param = layer.slice_param();
              if (slice_param.slice_point_size() == 0)
              {
                  int num_slice = layer.top_size();
                  fprintf(pp, " -23300=%d", num_slice);
                  for (int j = 0; j < num_slice; j++)
                  {
                      fprintf(pp, ",-233");
                  }
              }
              else
              {
                  int num_slice = slice_param.slice_point_size() + 1;
                  fprintf(pp, " -23300=%d", num_slice);
                  int prev_offset = 0;
                  for (int j = 0; j < slice_param.slice_point_size(); j++)
                  {
                      int offset = slice_param.slice_point(j);
                      fprintf(pp, ",%d", offset - prev_offset);
                      prev_offset = offset;
                  }
                  fprintf(pp, ",-233");
              }
              int axis = 0;
              if (slice_param.has_axis())
              {
                  axis = slice_param.axis() - 1;
              }
              else if (slice_param.has_slice_dim())
              {
                  axis = slice_param.slice_dim() - 1;
              }
              fprintf(pp, " 1=%d", axis);
          }
          else if (layer.type() == "Softmax")
          {
              const caffe::SoftmaxParameter& softmax_param = layer.softmax_param();
              int dim = softmax_param.axis() - 1;
              fprintf(pp, " 0=%d", dim);
              fprintf(pp, " 1=1");
          }
          else if (layer.type() == "Threshold")
          {
              const caffe::ThresholdParameter& threshold_param = layer.threshold_param();
              fprintf(pp, " 0=%e", threshold_param.threshold());
          }
          else if (layer.type() == "YoloDetectionOutput")
          {
              const caffe::YoloDetectionOutputParameter& yolo_detection_output_param = layer.yolo_detection_output_param();
  
              fprintf(pp, " 0=%d", yolo_detection_output_param.num_classes());
              fprintf(pp, " 1=%d", yolo_detection_output_param.num_box());
              fprintf(pp, " 2=%e", yolo_detection_output_param.confidence_threshold());
              fprintf(pp, " 3=%e", yolo_detection_output_param.nms_threshold());
  
              int num_bias = yolo_detection_output_param.biases_size();
              fprintf(pp, " -23304=%d", num_bias);
              for (int j = 0; j < num_bias; j++)
              {
                  fprintf(pp, ",%e", yolo_detection_output_param.biases(j));
              }
          }
          else if (layer.type() == "Yolov3DetectionOutput")
          {
              const caffe::Yolov3DetectionOutputParameter& yolov3_detection_output_param = layer.yolov3_detection_output_param();
  
              fprintf(pp, " 0=%d", yolov3_detection_output_param.num_classes());
              fprintf(pp, " 1=%d", yolov3_detection_output_param.num_box());
              fprintf(pp, " 2=%e", yolov3_detection_output_param.confidence_threshold());
              fprintf(pp, " 3=%e", yolov3_detection_output_param.nms_threshold());
  
              int num_bias = yolov3_detection_output_param.biases_size();
              fprintf(pp, " -23304=%d", num_bias);
              for (int j = 0; j < num_bias; j++)
              {
                  fprintf(pp, ",%e", yolov3_detection_output_param.biases(j));
              }
              int num_mask = yolov3_detection_output_param.mask_size();
              fprintf(pp, " -23305=%d", num_mask);
              for (int j = 0; j < num_mask; j++)
              {
                  fprintf(pp, ",%e", (float)yolov3_detection_output_param.mask(j));
              }
              int num_anchors = yolov3_detection_output_param.anchors_scale_size();
              fprintf(pp, " -23306=%d", num_anchors);
              for (int j = 0; j < num_anchors; j++)
              {
                  fprintf(pp, ",%e", (float)yolov3_detection_output_param.anchors_scale(j));
              }
              fprintf(pp, " 7=%d", yolov3_detection_output_param.mask_group_num());
          }
          fprintf(pp, "\n");
  
          // add split layer if top reference larger than one
          if (layer.bottom_size() == 1 && layer.top_size() == 1 && layer.bottom(0) == layer.top(0))
          {
              std::string blob_name = blob_name_decorated[layer.top(0)];
              if (bottom_reference.find(blob_name) != bottom_reference.end())
              {
                  int refcount = bottom_reference[blob_name];
                  if (refcount > 1)
                  {
                      char splitname[256];
                      sprintf(splitname, "splitncnn_%d", internal_split);
                      fprintf(pp, "%-16s %-16s %d %d", "Split", splitname, 1, refcount);
                      fprintf(pp, " %s", blob_name.c_str());
  
                      for (int j = 0; j < refcount; j++)
                      {
                          fprintf(pp, " %s_splitncnn_%d", blob_name.c_str(), j);
                      }
                      fprintf(pp, "\n");
  
                      internal_split++;
                  }
              }
          }
          else
          {
              for (int j = 0; j < layer.top_size(); j++)
              {
                  std::string blob_name = layer.top(j);
                  if (bottom_reference.find(blob_name) != bottom_reference.end())
                  {
                      int refcount = bottom_reference[blob_name];
                      if (refcount > 1)
                      {
                          char splitname[256];
                          sprintf(splitname, "splitncnn_%d", internal_split);
                          fprintf(pp, "%-16s %-16s %d %d", "Split", splitname, 1, refcount);
                          fprintf(pp, " %s", blob_name.c_str());
  
                          for (int j = 0; j < refcount; j++)
                          {
                              fprintf(pp, " %s_splitncnn_%d", blob_name.c_str(), j);
                          }
                          fprintf(pp, "\n");
  
                          internal_split++;
                      }
                  }
              }
          }
      }
  
      fclose(pp);
      fclose(bp);
  
      return 0;
  }
  