// nnet/nnet-average-pooling-component.h
  
  // Copyright 2014  Brno University of Technology (author: Karel Vesely)
  
  // See ../../COPYING for clarification regarding multiple authors
  //
  // 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
  //
  // THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  // KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
  // WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
  // MERCHANTABLITY OR NON-INFRINGEMENT.
  // See the Apache 2 License for the specific language governing permissions and
  // limitations under the License.
  
  
  #ifndef KALDI_NNET_NNET_AVERAGE_POOLING_COMPONENT_H_
  #define KALDI_NNET_NNET_AVERAGE_POOLING_COMPONENT_H_
  
  #include <string>
  #include <vector>
  
  #include "nnet/nnet-component.h"
  #include "nnet/nnet-utils.h"
  #include "cudamatrix/cu-math.h"
  
  namespace kaldi {
  namespace nnet1 {
  
  /**
   * AveragePoolingComponent :
   * The input/output matrices are split to submatrices with width 'pool_stride_'.
   * The pooling is done over 3rd axis, of the set of 2d matrices.
   * Our pooling supports overlaps, overlaps occur when (pool_step_ < pool_size_).
   */
  class AveragePoolingComponent : public Component {
   public:
    AveragePoolingComponent(int32 dim_in, int32 dim_out):
      Component(dim_in, dim_out),
      pool_size_(0),
      pool_step_(0),
      pool_stride_(0)
    { }
  
    ~AveragePoolingComponent()
    { }
  
    Component* Copy() const { return new AveragePoolingComponent(*this); }
    ComponentType GetType() const { return kAveragePoolingComponent; }
  
    void InitData(std::istream &is) {
      // parse config
      std::string token;
      while (is >> std::ws, !is.eof()) {
        ReadToken(is, false, &token);
        /**/ if (token == "<PoolSize>") ReadBasicType(is, false, &pool_size_);
        else if (token == "<PoolStep>") ReadBasicType(is, false, &pool_step_);
        else if (token == "<PoolStride>") ReadBasicType(is, false, &pool_stride_);
        else KALDI_ERR << "Unknown token " << token << ", a typo in config?"
                       << " (PoolSize|PoolStep|PoolStride)";
      }
      // check
      KALDI_ASSERT(pool_size_ != 0 && pool_step_ != 0 && pool_stride_ != 0);
    }
  
    void ReadData(std::istream &is, bool binary) {
      // pooling hyperparameters
      ExpectToken(is, binary, "<PoolSize>");
      ReadBasicType(is, binary, &pool_size_);
      ExpectToken(is, binary, "<PoolStep>");
      ReadBasicType(is, binary, &pool_step_);
      ExpectToken(is, binary, "<PoolStride>");
      ReadBasicType(is, binary, &pool_stride_);
  
      //
      // Sanity checks:
      //
      // number of patches:
      KALDI_ASSERT(input_dim_ % pool_stride_ == 0);
      int32 num_patches = input_dim_ / pool_stride_;
      // number of pools:
      KALDI_ASSERT((num_patches - pool_size_) % pool_step_ == 0);
      int32 num_pools = 1 + (num_patches - pool_size_) / pool_step_;
      // check output dim:
      KALDI_ASSERT(output_dim_ == num_pools * pool_stride_);
      //
    }
  
    void WriteData(std::ostream &os, bool binary) const {
      // pooling hyperparameters
      WriteToken(os, binary, "<PoolSize>");
      WriteBasicType(os, binary, pool_size_);
      WriteToken(os, binary, "<PoolStep>");
      WriteBasicType(os, binary, pool_step_);
      WriteToken(os, binary, "<PoolStride>");
      WriteBasicType(os, binary, pool_stride_);
    }
  
    void PropagateFnc(const CuMatrixBase<BaseFloat> &in,
                      CuMatrixBase<BaseFloat> *out) {
      // useful dims
      int32 num_patches = input_dim_ / pool_stride_;
      int32 num_pools = 1 + (num_patches - pool_size_) / pool_step_;
  
      // do the average-pooling (pools indexed by q)
      for (int32 q = 0; q < num_pools; q++) {
        // get output buffer of the pool
        CuSubMatrix<BaseFloat> pool(out->ColRange(q*pool_stride_, pool_stride_));
        pool.SetZero();  // reset,
        for (int32 r = 0; r < pool_size_; r++) {  // sum
          int32 p = r + q * pool_step_;  // p = input patch
          pool.AddMat(1.0, in.ColRange(p*pool_stride_, pool_stride_));
        }
        pool.Scale(1.0 / pool_size_);  // divide by #summands
      }
    }
  
    void BackpropagateFnc(const CuMatrixBase<BaseFloat> &in,
                          const CuMatrixBase<BaseFloat> &out,
                          const CuMatrixBase<BaseFloat> &out_diff,
                          CuMatrixBase<BaseFloat> *in_diff) {
      // useful dims
      int32 num_patches = input_dim_ / pool_stride_;
      int32 num_pools = 1 + (num_patches - pool_size_) / pool_step_;
  
      //
      // here we note how many diff matrices are summed for each input patch,
      std::vector<int32> patch_summands(num_patches, 0);
      // this metainfo will be used to divide diff of patches
      // used in more than one pool.
      //
  
      in_diff->SetZero();  // reset
  
      for (int32 q = 0; q < num_pools; q++) {  // sum
        for (int32 r = 0; r < pool_size_; r++) {
          int32 p = r + q * pool_step_;
          CuSubMatrix<BaseFloat> tgt(in_diff->ColRange(p*pool_stride_, pool_stride_));
          CuSubMatrix<BaseFloat> src(out_diff.ColRange(q*pool_stride_, pool_stride_));
          tgt.AddMat(1.0, src);
          patch_summands[p] += 1;
        }
      }
  
      // divide diff by average-pooling-dim (derivative of averaging)
      in_diff->Scale(1.0 / pool_size_);
  
      // divide diff by #summands (compensate for patches used in more pools)
      for (int32 p = 0; p < num_patches; p++) {
        CuSubMatrix<BaseFloat> tgt(in_diff->ColRange(p*pool_stride_, pool_stride_));
        KALDI_ASSERT(patch_summands[p] > 0);  // patch at least in one pool
        tgt.Scale(1.0/patch_summands[p]);
      }
    }
  
   private:
    int32 pool_size_,    // input patches used for pooling
          pool_step_,    // shift used for pooling (allow overlapping pools)
          pool_stride_;  // stride used to cut input to a vector of matrices
  };
  
  }  // namespace nnet1
  }  // namespace kaldi
  
  #endif  // KALDI_NNET_NNET_AVERAGE_POOLING_COMPONENT_H_