Adding DISFA trained models.

exe/releases/package_windows_executables.m

@@ -1,5 +1,5 @@
 clear;
-version = '0.4.1';
+version = '1.0.0';
 
 out_x86 = sprintf('OpenFace_%s_win_x86', version);
 out_x64 = sprintf('OpenFace_%s_win_x64', version);

matlab_version/AU_training/experiments/DISFA/Script_HOG_SVM_train_dyn.m

@@ -53,4 +53,14 @@ for a=1:numel(aus)
 
     save(name, 'model', 'accuracies', 'F1s', 'corrs', 'rms', 'ccc', 'prediction', 'valid_labels');        
 
+    name = sprintf('classifiers/AU_%d_dyn.dat', au);
+
+    pos_lbl = model.Label(1);
+    neg_lbl = model.Label(2);
+    
+    w = model.w(1:end-1)';
+    b = model.w(end);
+
+    svs = bsxfun(@times, PC, 1./scaling') * w;
+    write_lin_svm(name, means, svs, b, pos_lbl, neg_lbl);
 end

matlab_version/AU_training/experiments/DISFA/Script_HOG_SVM_train_stat.m

@@ -53,4 +53,15 @@ for a=1:numel(aus)
 
     save(name, 'model', 'accuracies', 'F1s', 'corrs', 'rms', 'ccc', 'prediction', 'valid_labels'); 
     
+    name = sprintf('classifiers/AU_%d_stat.dat', au);
+
+    pos_lbl = model.Label(1);
+    neg_lbl = model.Label(2);
+    
+    w = model.w(1:end-1)';
+    b = model.w(end);
+
+    svs = bsxfun(@times, PC, 1./scaling') * w;
+    write_lin_svm(name, means, svs, b, pos_lbl, neg_lbl);
+
 end

matlab_version/AU_training/experiments/DISFA/Script_HOG_SVR_train_dyn.m

@@ -0,0 +1,68 @@
+% Change to your downloaded location
+clear
+addpath('C:\liblinear\matlab')
+addpath('../training_code')
+addpath('../utilities')
+
+%% load shared definitions and AU data
+shared_defs;
+
+% Set up the hyperparameters to be validated
+hyperparams.c = 10.^(-7:1:3);
+hyperparams.p = 10.^(-2);
+
+hyperparams.validate_params = {'c', 'p'};
+
+% Set the training function
+svr_train = @svr_train_linear_shift;
+    
+% Set the test function (the first output will be used for validation)
+svr_test = @svr_test_linear_shift;
+
+%%
+for a=1:numel(aus)
+    
+    au = aus(a);
+    
+    rest_aus = setdiff(all_aus, au);        
+
+    % make sure validation data's labels are balanced
+    [users_train, users_valid] = get_balanced_fold(DISFA_dir, users, au, 1/4, 1);
+
+    % need to split the rest
+    [train_samples, train_labels, valid_samples, valid_labels, ~, PC, means, scaling, valid_ids, valid_success] = Prepare_HOG_AU_data_generic_dynamic(users_train, users_valid, au, rest_aus, DISFA_dir, hog_data_dir);
+    
+    train_samples = sparse(train_samples);
+    valid_samples = sparse(valid_samples);
+
+    %% Validate here
+    hyperparams.success = valid_success;
+    hyperparams.valid_samples = valid_samples;
+    hyperparams.valid_labels = valid_labels;
+    hyperparams.vid_ids = valid_ids;   
+    
+    [ best_params, ~ ] = validate_grid_search_no_par(svr_train, svr_test, false, train_samples, train_labels, valid_samples, valid_labels, hyperparams);
+
+    model = svr_train(train_labels, train_samples, best_params);        
+    model.success = valid_success;
+    model.vid_ids = valid_ids;
+    
+    [~, prediction] = svr_test(valid_labels, valid_samples, model);
+
+    name = sprintf('regressors/AU_%d_dyn_intensity.mat', au);
+
+    [ accuracies, F1s, corrs, ccc, rms, classes ] = evaluate_regression_results( prediction, valid_labels );    
+
+    save(name, 'model', 'accuracies', 'F1s', 'corrs', 'rms', 'ccc', 'prediction', 'valid_labels');        
+
+    % Write out the model
+    name = sprintf('regressors/AU_%d_dynamic_intensity.dat', au);
+   
+    w = model.w(1:end-1)';
+    b = model.w(end);
+
+    svs = bsxfun(@times, PC, 1./scaling') * w;
+    
+    write_lin_dyn_svr(name, means, svs, b, model.cutoff);    
+
+end

matlab_version/AU_training/experiments/DISFA/Script_HOG_SVR_train_stat.m

@@ -0,0 +1,68 @@
+% Change to your downloaded location
+clear
+addpath('C:\liblinear\matlab')
+addpath('../training_code')
+addpath('../utilities')
+
+%% load shared definitions and AU data
+shared_defs;
+
+% Set up the hyperparameters to be validated
+hyperparams.c = 10.^(-7:1:3);
+hyperparams.p = 10.^(-2);
+
+hyperparams.validate_params = {'c', 'p'};
+
+% Set the training function
+svr_train = @svr_train_linear;
+    
+% Set the test function (the first output will be used for validation)
+svr_test = @svr_test_linear;
+
+%%
+for a=1:numel(aus)
+    
+    au = aus(a);
+    
+    rest_aus = setdiff(all_aus, au);        
+
+    % make sure validation data's labels are balanced
+    [users_train, users_valid] = get_balanced_fold(DISFA_dir, users, au, 1/4, 1);
+
+    % need to split the rest
+    [train_samples, train_labels, valid_samples, valid_labels, ~, PC, means, scaling, valid_ids, valid_success] = Prepare_HOG_AU_data_generic(users_train, users_valid, au, rest_aus, DISFA_dir, hog_data_dir);
+    
+    train_samples = sparse(train_samples);
+    valid_samples = sparse(valid_samples);
+
+    %% Validate here
+    hyperparams.success = valid_success;
+    hyperparams.valid_samples = valid_samples;
+    hyperparams.valid_labels = valid_labels;
+    hyperparams.vid_ids = valid_ids;   
+    
+    [ best_params, ~ ] = validate_grid_search_no_par(svr_train, svr_test, false, train_samples, train_labels, valid_samples, valid_labels, hyperparams);
+
+    model = svr_train(train_labels, train_samples, best_params);        
+    model.success = valid_success;
+    model.vid_ids = valid_ids;
+    
+    [~, prediction] = svr_test(valid_labels, valid_samples, model);
+
+    name = sprintf('regressors/AU_%d_static_intensity.mat', au);
+
+    [ accuracies, F1s, corrs, ccc, rms, classes ] = evaluate_regression_results( prediction, valid_labels );    
+
+    save(name, 'model', 'accuracies', 'F1s', 'corrs', 'rms', 'ccc', 'prediction', 'valid_labels');        
+
+    % Write out the model
+    name = sprintf('regressors/AU_%d_static_intensity.dat', au);
+   
+    w = model.w(1:end-1)';
+    b = model.w(end);
+
+    svs = bsxfun(@times, PC, 1./scaling') * w;
+    
+    write_lin_svr(name, means, svs, b);    
+
+end