init_filt_oper.m

Standard model object recognition matlab code

function [f,s,all_oper] = init_filt_oper (level)

% FUNCTION [f,s,all_oper] = init_filt_oper (level)
%
% This function pre-computes all the necessary filters and gets the
% definition of operations to be used in each layer/levels.  The
% levels is a variable length vector that determines the
% layer/level of model computation.  For example,
%  If level = [1], filters up to S1.
%  If level = [1 1], filters for S1 and C1.
%  etc., etc., etc.
%
% Filters for the bypass (denoted with "b", usually) are also
% computed here.
%
% Hence, level indicates [S1 C1 S2b C2b S2 C2 S3 C3 VTU].
%
% For simplicitiy, the program will only work for computing:
%  Only upto C1     : [1 1],
%  Bypass route only: [1 1 1 1] or [0 0 1 1],
%  Main route only  : [1 1 0 0 1 1...] or [0 0 0 0 1 1...],
%  Both routes      : [1 1 1 1 1 1...] or [0 0 1 1 1 1...].
% Program may produce errors or errorneous results for other cases.
%
% Note that for scale-mixing filter, we assume some dummy stimulus
% size, but the correct size is used in comp_mix_scale.m
%
% The resulting filter, along with some major parameters, will be
% saved in a file named Filt_128x128.mat (128x128 is stim_size).
% Be careful, because it may overwrite the existing filter file.

% Define all operations in each layer.
global all_quanta;
all_quanta = [0 0 0 0 0 0 0 0 0];

% It's all normdp and sftmax!
global all_oper;
all_oper = ['normsp'; 'fulmax'; ...  % S1, C1: V1, simple/complex
      'normdp'; 'sftmax'; ... % S2b, C2b: bypass route
      'normdp'; 'sftmax'; ... % S2, C2: V4
      'normdp'; 'sftmax'; ... % S3, C3: PIT
      'normdp']; % VTU: AIT

global learn_file; % which features to load?
% Example learn_file's.
%learn_file = cell(9,1);
%learn_file{3} = './Data/f_S2b_natural_img.mat';
%learn_file{5} = './Data/f_S2_natural_img.mat';
%learn_file{7} = './Data/f_S3_natural_img.mat';
%learn_file{9} = './Data/f_S4_natural_img.mat';

% Do some quick error checks.
stat = aux_error_check(level);

% In case there are some different number of features than defined
% in init_filter_def, keep a record with num_feat.
num_feat = zeros(length(level),1);

f = [];
s = [];

% This global variable is the name of filter file that can be used
% between different sessions.
global filt_file_use;

% Check if a saved filter exists.
if length(filt_file_use)>0
  filt_file      = filt_file_use;
  filt_file_save = filt_file_use;
else
  filt_file      = 'Filt.mat'; % Default name.
  filt_file_save = 'Filt.mat';
end
IsMatch = check_filt_file (level, filt_file);

if IsMatch
  load(filt_file);
  disp(['Re-using filter ' filt_file '.']);

else
  disp(['Saving filter ' filt_file '.']);
  for i = 1:length(level)
    disp(['   Setting up filters for Layer ' num2str(i)]);
    f_tmp = 0;
    s_tmp = 0;
    m_tmp = 0;

    if level(i) > 0
    switch i
      case(1)
	f_tmp = filt_get_S1;
      case(2)
	f_tmp = filt_get_C1;

      case({3, 5, 7, 9}) % S2b, S2 and S3 (S4)
	[a,b,c,d]   = init_filter_def(i);
	num_feat(i) = max(b(4,:));
	f_tmp = filt_get_S_layer(learn_file{i},a,b(4,1),c);

      case({4, 6, 8}) % C2b, C2 and C3
	[a,b,c,d]   = init_filter_def(i);
	num_feat(i) = num_feat(i-1);
	f_tmp = filt_get_C_layer(b(1:3,:),c);
	m_tmp = d;
	s_tmp = filt_scalemixing (m_tmp,num_feat(i));
	
      otherwise
	error('Layer undefined.');
    end
    end

    f = setfield(f, ['f' num2str(i)], f_tmp);
    s = setfield(s, ['f' num2str(i)], s_tmp);
    s = setfield(s, ['m' num2str(i)], m_tmp);
  end
  disp('   Saving.');

  % Now loop thru the filters, and set un-used filters (level(i)==0)
  % to null, because in main_model.m, the loop will look at if the
  % current filter is defined.
  for i = 1:length(level)
    if level(i)==0
      f = setfield(f, ['f' num2str(i)], []);
      s = setfield(s, ['f' num2str(i)], []);
      s = setfield(s, ['m' num2str(i)], []);
    end
  end

  % This will overwrite the file with the same name.  Be careful.
  % The global variables are saved under different names, to avoid
  % warning during check_filt_file routine.
  o_save = all_oper;
  q_save = all_quanta;
  l_save = learn_file;
  save(filt_file_save,'f','s','level','o_save','l_save','q_save');
  disp(['Done setting up ' filt_file '.']);
end


function IsMatch = check_filt_file (lvl, filt_file);

% FUNCTION IsMatch = check_filt_file (lvl, filt_file);
%
% Check if a saved filter exists.

global all_quanta all_oper learn_file;

if exist(filt_file, 'file')
  % Note we are not comparing learn_file.
  load (filt_file);

  is_level = strcmp(num2str(lvl),num2str(level));
  is_quant = strcmp(num2str(all_quanta),num2str(q_save));
  is_oper  = strcmp(all_oper,o_save);
  %is_learn = strcmp(this_learn,l_save);

  IsMatch  = is_level & is_quant & is_oper;
else
  IsMatch  = 0;
end