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// Copyright (C) 2003--2004 Ronan Collobert (collober@idiap.ch)
//
// This file is part of Torch 3.1.
//
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions
// are met:
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
// 3. The name of the author may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
// IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
// OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
// IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
// INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
// NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
// THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#ifndef SPATIAL_CONVOLUTION_INC
#define SPATIAL_CONVOLUTION_INC
#include "GradientMachine.h"
namespace Torch {
/** Class for doing convolution over images.
Suppose you put #n_input_planes# images in each input frame.
The images are in one big vector: each input frame has a size of
#n_input_planes*input_height*input_width#. (image after image).
Thus, #n_inputs = n_input_planes*input_height*input_width#.
Then, for each output planes, it computes the convolution
of \emph{all} input image planes with a kernel of size #k_w*k_w*n_input_planes#.
The output image size is computed in the constructor and
put in #output_height# and #output_width#.
#n_outputs = n_output_planes*output_height*output_width#.
Note that, depending of the size of your kernel, several (last) columns
or rows of the input image could be lost.
Note also that \emph{no} non-linearity is applied in this layer.
@author Ronan Collobert (collober@idiap.ch)
*/
class SpatialConvolution : public GradientMachine
{
public:
/// Kernel size (height and width).
int k_w;
/// 'x' translation \emph{in the input image} after each application of the kernel.
int d_x;
/// 'y' translation \emph{in the input image} after each application of the kernel.
int d_y;
/// Number of input images.
int n_input_planes;
/// Number of output images.
int n_output_planes;
/// Height of each input image.
int input_height;
/// Width of each input image.
int input_width;
/// Height of each output image.
int output_height;
/// Width of each output image.
int output_width;
/** #weights[i]# means kernel-weights for output plane #i#.
#weights[i]# contains #n_input_planes# times #k_w*k_w# weights.
*/
real **weights;
/// Derivatives associated to #weights#.
real **der_weights;
/// #biases[i]# is the bias for output plane #i#.
real *biases;
/// Derivatives associated to #biases#.
real *der_biases;
/// Create a convolution layer...
SpatialConvolution(int n_input_planes_, int n_output_planes_, int width_, int height_, int k_w_=5, int d_x_=1, int d_y_=1);
//-----
void reset_();
virtual void reset();
virtual void frameForward(int t, real *f_inputs, real *f_outputs);
virtual void frameBackward(int t, real *f_inputs, real *beta_, real *f_outputs, real *alpha_);
virtual ~SpatialConvolution();
};
}
#endif
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