// Copyright (C) 2003--2004 Ronan Collobert (collober@idiap.ch)
//                
// This file is part of Torch 3.1.
//
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#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
