///////////////////////////////////////////////////////////////////////

// File:        convolve.cpp

// Description: Convolutional layer that stacks the inputs over its rectangle

//              and pulls in random data to fill out-of-input inputs.

//              Output is therefore same size as its input, but deeper.

// Author:      Ray Smith

//

// (C) Copyright 2014, Google Inc.

// 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.

///////////////////////////////////////////////////////////////////////

#ifdef HAVE_CONFIG_H

#  include "config_auto.h"

#endif

#include "convolve.h"

#include "networkscratch.h"

#include "serialis.h"

namespace tesseract {

Convolve::Convolve(const std::string &name, int ni, int half_x, int half_y)

    : Network(NT_CONVOLVE, name, ni, ni * (2 * half_x + 1) * (2 * half_y + 1))

    , half_x_(half_x)

    , half_y_(half_y) {}

// Writes to the given file. Returns false in case of error.

bool Convolve::Serialize(TFile *fp) const {

  return Network::Serialize(fp) && fp->Serialize(&half_x_) && fp->Serialize(&half_y_);

}

// Reads from the given file. Returns false in case of error.

bool Convolve::DeSerialize(TFile *fp) {

  if (!fp->DeSerialize(&half_x_)) {

    return false;

  }

  if (!fp->DeSerialize(&half_y_)) {

    return false;

  }

  no_ = ni_ * (2 * half_x_ + 1) * (2 * half_y_ + 1);

  return true;

}

// Runs forward propagation of activations on the input line.

// See NetworkCpp for a detailed discussion of the arguments.

void Convolve::Forward(bool debug, const NetworkIO &input, const TransposedArray *input_transpose,

                       NetworkScratch *scratch, NetworkIO *output) {

  output->Resize(input, no_);

  int y_scale = 2 * half_y_ + 1;

  StrideMap::Index dest_index(output->stride_map());

  do {

    // Stack x_scale groups of y_scale * ni_ inputs together.

    int t = dest_index.t();

    int out_ix = 0;

    for (int x = -half_x_; x <= half_x_; ++x, out_ix += y_scale * ni_) {

      StrideMap::Index x_index(dest_index);

      if (!x_index.AddOffset(x, FD_WIDTH)) {

        // This x is outside the image.

        output->Randomize(t, out_ix, y_scale * ni_, randomizer_);

      } else {

        int out_iy = out_ix;

        for (int y = -half_y_; y <= half_y_; ++y, out_iy += ni_) {

          StrideMap::Index y_index(x_index);

          if (!y_index.AddOffset(y, FD_HEIGHT)) {

            // This y is outside the image.

            output->Randomize(t, out_iy, ni_, randomizer_);

          } else {

            output->CopyTimeStepGeneral(t, out_iy, ni_, input, y_index.t(), 0);

          }

        }

      }

    }

  } while (dest_index.Increment());

#ifndef GRAPHICS_DISABLED

  if (debug) {

    DisplayForward(*output);

  }

#endif

}

// Runs backward propagation of errors on the deltas line.

// See NetworkCpp for a detailed discussion of the arguments.

bool Convolve::Backward(bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch,

                        NetworkIO *back_deltas) {

  back_deltas->Resize(fwd_deltas, ni_);

  NetworkScratch::IO delta_sum;

  delta_sum.ResizeFloat(fwd_deltas, ni_, scratch);

  delta_sum->Zero();

  int y_scale = 2 * half_y_ + 1;

  StrideMap::Index src_index(fwd_deltas.stride_map());

  do {

    // Stack x_scale groups of y_scale * ni_ inputs together.

    int t = src_index.t();

    int out_ix = 0;

    for (int x = -half_x_; x <= half_x_; ++x, out_ix += y_scale * ni_) {

      StrideMap::Index x_index(src_index);

      if (x_index.AddOffset(x, FD_WIDTH)) {

        int out_iy = out_ix;

        for (int y = -half_y_; y <= half_y_; ++y, out_iy += ni_) {

          StrideMap::Index y_index(x_index);

          if (y_index.AddOffset(y, FD_HEIGHT)) {

            fwd_deltas.AddTimeStepPart(t, out_iy, ni_, delta_sum->f(y_index.t()));

          }

        }

      }

    }

  } while (src_index.Increment());

  back_deltas->CopyAll(*delta_sum);

  return true;

}

} // namespace tesseract.