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///////////////////////////////////////////////////////////////////////
// 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.
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