text: // Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
  //
  // Licensed under the Apache License, Version 2.0 (the "License");
  // you may not use this file except in compliance with the License.
  // You may obtain a copy of the License at
  //
  //     http://www.apache.org/licenses/LICENSE-2.0
  //
  // 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.
  
  #pragma once
  #include <stdio.h>
  #include <cassert>
  #include <string>
  #include <vector>
  #include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
  
  namespace paddle {
  namespace inference {
  namespace tensorrt {
  namespace plugin {
  
  static std::vector<int> CalcOutputSize(const std::vector<int>& input_shape,
                                         const bool& ceil_mode,
                                         const bool& adaptive,
                                         const std::vector<int>& ksize,
                                         const std::vector<int>& strides,
                                         const std::vector<int>& real_paddings) {
    std::vector<int> output_shape = input_shape;
    if (adaptive) {
      output_shape[0] = ksize[0];
      output_shape[1] = ksize[1];
    } else {
      int output_h = 0, output_w = 0;
      if (ceil_mode) {
        output_h = (input_shape[0] - ksize[0] + real_paddings[0] +
                    real_paddings[1] + strides[0] - 1) /
                       strides[0] +
                   1;
        output_w = (input_shape[1] - ksize[1] + real_paddings[2] +
                    real_paddings[3] + strides[1] - 1) /
                       strides[1] +
                   1;
      }
      // TRT will use native layer when ceil_model=false
      /*
      else{
        output_h = (input_shape[0] - ksize[0] + real_paddings[0] +
      real_paddings[1]) / strides[0] + 1;
        output_w = (input_shape[1] - ksize[1] + real_paddings[2] +
      real_paddings[3]) / strides[1] + 1;
      }
      */
      output_shape[0] = output_h;
      output_shape[1] = output_w;
    }
    return output_shape;
  }
  
  class PoolPlugin : public PluginTensorRT {
   public:
    size_t getSerializationSize() const TRT_NOEXCEPT override;
  
    void serialize(void* buffer) const TRT_NOEXCEPT override;
  
    enum class PoolType {
      max = 0,
      avg,
    };
    PoolPlugin() {}
    PoolPlugin(bool ceil_mode, PoolType pool_type, bool adaptive, bool exclusive,
               std::vector<int> ksize, std::vector<int> strides,
               std::vector<int> paddings, std::vector<int> input_shape,
               std::vector<int> real_paddings)
        : ceil_mode_(ceil_mode),
          pool_type_(pool_type),
          adaptive_(adaptive),
          exclusive_(exclusive),
          ksize_(ksize),
          strides_(strides),
          paddings_(paddings),
          real_paddings_(real_paddings),
          input_shape_(input_shape) {
      output_shape_ = input_shape_;
      std::vector<int> output_shape =
          CalcOutputSize({input_shape_[1], input_shape_[2]}, ceil_mode_,
                         adaptive_, ksize_, strides_, real_paddings_);
      output_shape_[1] = output_shape[0];
      output_shape_[2] = output_shape[1];
    }
  
    // It was used for tensorrt deserialization.
    // It should not be called by users.
    PoolPlugin(void const* serialData, size_t serialLength) {
      deserializeBase(serialData, serialLength);
      DeserializeValue(&serialData, &serialLength, &ceil_mode_);
      DeserializeValue(&serialData, &serialLength, &pool_type_);
      DeserializeValue(&serialData, &serialLength, &adaptive_);
      DeserializeValue(&serialData, &serialLength, &exclusive_);
      DeserializeValue(&serialData, &serialLength, &ksize_);
      DeserializeValue(&serialData, &serialLength, &strides_);
      DeserializeValue(&serialData, &serialLength, &paddings_);
      DeserializeValue(&serialData, &serialLength, &real_paddings_);
      DeserializeValue(&serialData, &serialLength, &input_shape_);
      DeserializeValue(&serialData, &serialLength, &output_shape_);
    }
  
    PoolPlugin* clone() const TRT_NOEXCEPT override;
  
    const char* getPluginType() const TRT_NOEXCEPT override {
      return "pool_plugin";
    }
    int getNbOutputs() const TRT_NOEXCEPT override { return 1; }
    nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims* inputs,
                                       int nbInputDims) TRT_NOEXCEPT override;
    int initialize() TRT_NOEXCEPT override { return 0; }
  #if IS_TRT_VERSION_LT(8000)
    int enqueue(int batchSize, const void* const* inputs, void** outputs,
  #else
    int enqueue(int batchSize, const void* const* inputs, void* const* outputs,
  #endif
                void* workspace, cudaStream_t stream) TRT_NOEXCEPT override;
  
   private:
    bool ceil_mode_;
    PoolType pool_type_;
    bool adaptive_;
    bool exclusive_;
    std::vector<int> ksize_;
    std::vector<int> strides_;
    std::vector<int> paddings_;
    std::vector<int> real_paddings_;
    std::vector<int> input_shape_;
    std::vector<int> output_shape_;
  };
  
  class PoolPluginCreator : public TensorRTPluginCreator {
   public:
    const char* getPluginName() const TRT_NOEXCEPT override {
      return "pool_plugin";
    }
  
    const char* getPluginVersion() const TRT_NOEXCEPT override { return "1"; }
  
    nvinfer1::IPluginV2* deserializePlugin(
        const char* name, const void* serial_data,
        size_t serial_length) TRT_NOEXCEPT override {
      return new PoolPlugin(serial_data, serial_length);
    }
  };
  REGISTER_TRT_PLUGIN_V2(PoolPluginCreator);
  
  #if IS_TRT_VERSION_GE(6000)
  class PoolPluginDynamic : public DynamicPluginTensorRT {
   public:
    PoolPluginDynamic() {}
    PoolPluginDynamic(const bool& ceil_mode, const std::string& pool_type,
                      const bool& adaptive, bool exclusive,
                      const std::vector<int>& ksize,
                      const std::vector<int>& strides,
                      const std::vector<int>& paddings, const bool& is_global)
        : ceil_mode_(ceil_mode),
          pool_type_(pool_type),
          adaptive_(adaptive),
          exclusive_(exclusive),
          ksize_(ksize),
          strides_(strides),
          paddings_(paddings),
          is_global_(is_global) {}
  
    PoolPluginDynamic(void const* serialData, size_t serialLength);
    ~PoolPluginDynamic() {}
    nvinfer1::IPluginV2DynamicExt* clone() const TRT_NOEXCEPT override;
  
    const char* getPluginType() const TRT_NOEXCEPT override {
      return "pool_plugin_dynamic";
    }
    int getNbOutputs() const TRT_NOEXCEPT override { return 1; }
    int initialize() TRT_NOEXCEPT override { return 0; }
  
    size_t getSerializationSize() const TRT_NOEXCEPT override;
    void serialize(void* buffer) const TRT_NOEXCEPT override;
  
    nvinfer1::DimsExprs getOutputDimensions(
        int output_index, const nvinfer1::DimsExprs* inputs, int nb_inputs,
        nvinfer1::IExprBuilder& expr_builder) TRT_NOEXCEPT override;
  
    bool supportsFormatCombination(int pos,
                                   const nvinfer1::PluginTensorDesc* inOut,
                                   int nbInputs,
                                   int nbOutputs) TRT_NOEXCEPT override;
  
    void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in,
                         int nbInputs,
                         const nvinfer1::DynamicPluginTensorDesc* out,
                         int nbOutputs) TRT_NOEXCEPT override {}
  
    size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs,
                            int nbInputs,
                            const nvinfer1::PluginTensorDesc* outputs,
                            int nbOutputs) const TRT_NOEXCEPT override {
      return 0;
    }
  
    int enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
                const nvinfer1::PluginTensorDesc* outputDesc,
                const void* const* inputs, void* const* outputs, void* workspace,
                cudaStream_t stream) TRT_NOEXCEPT override;
    nvinfer1::DataType getOutputDataType(
        int index, const nvinfer1::DataType* inputTypes,
        int nbInputs) const TRT_NOEXCEPT override;
  
    void destroy() TRT_NOEXCEPT override { delete this; }
  
   private:
    bool ceil_mode_;
    std::string pool_type_;
    bool adaptive_;
    bool exclusive_;
    std::vector<int> ksize_;
    std::vector<int> strides_;
    std::vector<int> paddings_;
    bool is_global_;
  };
  
  class PoolPluginDynamicCreator : public TensorRTPluginCreator {
   public:
    const char* getPluginName() const TRT_NOEXCEPT override {
      return "pool_plugin_dynamic";
    }
  
    const char* getPluginVersion() const TRT_NOEXCEPT override { return "1"; }
  
    nvinfer1::IPluginV2* deserializePlugin(
        const char* name, const void* serial_data,
        size_t serial_length) TRT_NOEXCEPT override {
      return new PoolPluginDynamic(serial_data, serial_length);
    }
  };
  REGISTER_TRT_PLUGIN_V2(PoolPluginDynamicCreator);
  #endif
  
  }  // namespace plugin
  }  // namespace tensorrt
  }  // namespace inference
  }  // namespace paddle
  