text: // Tencent is pleased to support the open source community by making ncnn available.
  //
  // Copyright (C) 2021 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 "pass_ncnn.h"
  
  namespace pnnx {
  
  namespace ncnn {
  
  class nn_ConvTranspose2d : public GraphRewriterPass
  {
  public:
      const char* match_pattern_graph() const
      {
          return R"PNNXIR(7767517
  3 2
  pnnx.Input              input       0 1 input
  nn.ConvTranspose2d      op_0        1 1 input out in_channels=%in_channels out_channels=%out_channels kernel_size=%kernel_size stride=%stride padding=%padding dilation=%dilation output_padding=%output_padding groups=1 bias=%bias @weight @bias
  pnnx.Output             output      1 0 out
  )PNNXIR";
      }
  
      const char* type_str() const
      {
          return "Deconvolution";
      }
  
      const char* name_str() const
      {
          return "deconv";
      }
  
      void write(Operator* op, const std::map<std::string, Parameter>& captured_params, const std::map<std::string, Attribute>& captured_attrs) const
      {
          op->params["0"] = captured_params.at("out_channels");
          op->params["1"] = captured_params.at("kernel_size").ai[1];
          op->params["11"] = captured_params.at("kernel_size").ai[0];
          op->params["2"] = captured_params.at("dilation").ai[1];
          op->params["12"] = captured_params.at("dilation").ai[0];
          op->params["3"] = captured_params.at("stride").ai[1];
          op->params["13"] = captured_params.at("stride").ai[0];
          op->params["4"] = captured_params.at("padding").ai[1];
          op->params["14"] = captured_params.at("padding").ai[0];
          op->params["18"] = captured_params.at("output_padding").ai[1];
          op->params["19"] = captured_params.at("output_padding").ai[0];
          op->params["5"] = captured_params.at("bias").b ? 1 : 0;
          op->params["6"] = (int)(captured_attrs.at("op_0.weight").data.size() / sizeof(float));
  
          // transpose inch-outch-kh-kw to outch-inch-kh-kw
          const int inch = captured_params.at("in_channels").i;
          const int outch = captured_params.at("out_channels").i;
          const int kh = captured_params.at("kernel_size").ai[0];
          const int kw = captured_params.at("kernel_size").ai[1];
          std::vector<float> new_weight;
          {
              const float* w = (const float*)captured_attrs.at("op_0.weight").data.data();
  
              new_weight.resize(outch * inch * kh * kw);
              float* w2 = (float*)new_weight.data();
              const int maxk = kh * kw;
  
              // reorder weight from inch-outch to outch-inch
              for (int i = 0; i < outch; i++)
              {
                  for (int j = 0; j < inch; j++)
                  {
                      for (int k = 0; k < maxk; k++)
                      {
                          w2[(i * inch + j) * maxk + k] = w[(j * outch + i) * maxk + k];
                      }
                  }
              }
          }
  
          op->attrs["0"] = Attribute();
          op->attrs["0"].data = {0, 0, 0, 0};
          op->attrs["1"] = Attribute({outch, inch, kh, kw}, new_weight);
          if (captured_params.at("bias").b)
              op->attrs["2"] = captured_attrs.at("op_0.bias");
      }
  };
  
  class nn_ConvTranspose2d_1 : public GraphRewriterPass
  {
  public:
      const char* match_pattern_graph() const
      {
          return R"PNNXIR(7767517
  3 2
  pnnx.Input              input       0 1 input
  nn.ConvTranspose2d      op_0        1 1 input out in_channels=%in_channels out_channels=%out_channels kernel_size=%kernel_size stride=%stride padding=%padding dilation=%dilation output_padding=%output_padding groups=%groups bias=%bias @weight @bias
  pnnx.Output             output      1 0 out
  )PNNXIR";
      }
  
      const char* type_str() const
      {
          return "DeconvolutionDepthWise";
      }
  
      const char* name_str() const
      {
          return "deconvdw";
      }
  
      void write(Operator* op, const std::map<std::string, Parameter>& captured_params, const std::map<std::string, Attribute>& captured_attrs) const
      {
          op->params["0"] = captured_params.at("out_channels");
          op->params["1"] = captured_params.at("kernel_size").ai[1];
          op->params["11"] = captured_params.at("kernel_size").ai[0];
          op->params["2"] = captured_params.at("dilation").ai[1];
          op->params["12"] = captured_params.at("dilation").ai[0];
          op->params["3"] = captured_params.at("stride").ai[1];
          op->params["13"] = captured_params.at("stride").ai[0];
          op->params["4"] = captured_params.at("padding").ai[1];
          op->params["14"] = captured_params.at("padding").ai[0];
          op->params["18"] = captured_params.at("output_padding").ai[1];
          op->params["19"] = captured_params.at("output_padding").ai[0];
          op->params["5"] = captured_params.at("bias").b ? 1 : 0;
          op->params["6"] = (int)(captured_attrs.at("op_0.weight").data.size() / sizeof(float));
          op->params["7"] = captured_params.at("groups");
  
          // transpose group-inch/group-outch/group-kh-kw to group-outch/group-inch/group-kh-kw
          const int inch = captured_params.at("in_channels").i;
          const int outch = captured_params.at("out_channels").i;
          const int groups = captured_params.at("groups").i;
          const int kh = captured_params.at("kernel_size").ai[0];
          const int kw = captured_params.at("kernel_size").ai[1];
          std::vector<float> new_weight;
          {
              const float* w = (const float*)captured_attrs.at("op_0.weight").data.data();
  
              new_weight.resize(outch / groups * inch * kh * kw);
              float* w2 = (float*)new_weight.data();
              const int outch_g = outch / groups;
              const int inch_g = inch / groups;
              const int maxk = kh * kw;
  
              for (int g = 0; g < groups; g++)
              {
                  // reorder weight from inch-outch to outch-inch
                  float* wg2 = w2 + g * outch_g * inch_g * maxk;
                  const float* wg = w + g * inch_g * outch_g * maxk;
                  for (int i = 0; i < outch_g; i++)
                  {
                      for (int j = 0; j < inch_g; j++)
                      {
                          for (int k = 0; k < maxk; k++)
                          {
                              wg2[(i * inch_g + j) * maxk + k] = wg[(j * outch_g + i) * maxk + k];
                          }
                      }
                  }
              }
          }
  
          op->attrs["0"] = Attribute();
          op->attrs["0"].data = {0, 0, 0, 0};
          op->attrs["1"] = Attribute({outch / groups, inch, kh, kw}, new_weight);
          if (captured_params.at("bias").b)
              op->attrs["2"] = captured_attrs.at("op_0.bias");
      }
  };
  
  REGISTER_GLOBAL_PNNX_NCNN_GRAPH_REWRITER_PASS(nn_ConvTranspose2d, 20)
  REGISTER_GLOBAL_PNNX_NCNN_GRAPH_REWRITER_PASS(nn_ConvTranspose2d_1, 21)
  
  } // namespace ncnn
  
  } // namespace pnnx
  