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_level1.h"
  
  #include "../pass_level3/fuse_expression.h"
  
  #include "../utils.h"
  
  namespace pnnx {
  
  class Conv2d : public FuseModulePass
  {
  public:
      const char* match_type_str() const
      {
          return "__torch__.torch.nn.modules.conv.Conv2d";
      }
  
      const char* type_str() const
      {
          return "nn.Conv2d";
      }
  
      void write(Operator* op, const std::shared_ptr<torch::jit::Graph>& graph, const torch::jit::Module& mod) const
      {
          //         {
          //             pnnx::Graph pnnx_graph;
          //
          //             pnnx_graph.load(mod, graph);
          //
          //             pnnx::fuse_expression(pnnx_graph);
          //
          //             pnnx_graph.save("tmp.param", "tmp.bin");
          //         }
  
          const torch::jit::Node* convolution = find_node_by_kind(graph, "aten::_convolution");
          const torch::jit::Node* convolution_mode = find_node_by_kind(graph, "aten::_convolution_mode");
          const torch::jit::Node* reflection_pad2d = find_node_by_kind(graph, "aten::reflection_pad2d");
          const torch::jit::Node* replication_pad2d = find_node_by_kind(graph, "aten::replication_pad2d");
  
          if (convolution_mode)
          {
              convolution = convolution_mode;
          }
  
          const auto& weight = mod.attr("weight").toTensor();
  
          op->params["groups"] = convolution->namedInput("groups");
          op->params["in_channels"] = weight.size(1) * op->params["groups"].i;
          op->params["out_channels"] = weight.size(0);
          op->params["kernel_size"] = Parameter{weight.size(2), weight.size(3)};
          op->params["stride"] = convolution->namedInput("stride");
          if (reflection_pad2d)
          {
              op->params["padding_mode"] = "reflect";
              op->params["padding"] = reflection_pad2d->namedInput("padding");
              std::vector<int>& padding = op->params["padding"].ai;
              if (padding.size() == 4)
              {
                  // Conv2d only accepts tuple of two integers
                  if (padding[0] == padding[1] && padding[1] == padding[2] && padding[2] == padding[3])
                  {
                      padding.resize(2);
                  }
                  else if (padding[0] == padding[2] && padding[1] == padding[3] && padding[0] != padding[1])
                  {
                      padding.resize(0);
                      op->params["padding"].s = "same";
                  }
              }
          }
          else if (replication_pad2d)
          {
              op->params["padding_mode"] = "replicate";
              op->params["padding"] = replication_pad2d->namedInput("padding");
              std::vector<int>& padding = op->params["padding"].ai;
              if (padding.size() == 4)
              {
                  // Conv2d only accepts tuple of two integers
                  if (padding[0] == padding[1] && padding[1] == padding[2] && padding[2] == padding[3])
                  {
                      padding.resize(2);
                  }
                  else if (padding[0] == padding[2] && padding[1] == padding[3] && padding[0] != padding[1])
                  {
                      padding.resize(0);
                      op->params["padding"].s = "same";
                  }
              }
          }
          else
          {
              op->params["padding"] = convolution->namedInput("padding");
          }
          op->params["dilation"] = convolution->namedInput("dilation");
          op->params["bias"] = mod.hasattr("bias");
  
          op->attrs["weight"] = weight;
          if (mod.hasattr("bias"))
          {
              op->attrs["bias"] = mod.attr("bias").toTensor();
          }
      }
  };
  
  REGISTER_GLOBAL_PNNX_FUSE_MODULE_PASS(Conv2d)
  
  } // namespace pnnx
  