first commit. include both dpmv5 and coco tools.

pascal.m

@@ -0,0 +1,76 @@
+function pascal(cls, n, note, dotrainval, testyear)
+% Train and evaluate a model. 
+%   pascal(cls, n, note, dotrainval, testyear)
+%
+%   The model will be a mixture of n star models, each of which
+%   has 2 latent orientations.
+%
+% Arguments
+%   cls           Object class to train and evaluate
+%   n             Number of aspect ratio clusters to use
+%                 (The final model has 2*n components)
+%   note          Save a note in the model.note field that describes this model
+%   dotrainval    Also evaluate on the trainval dataset
+%                 This is used to collect training data for context rescoring
+%   testyear      Test set year (e.g., '2007', '2011')
+
+startup;
+
+conf = voc_config();
+cachedir = conf.paths.model_dir;
+testset = conf.eval.test_set;
+
+% TODO: should save entire code used for this run
+% Take the code, zip it into an archive named by date
+% print the name of the code archive to the log file
+% add the code name to the training note
+timestamp = datestr(datevec(now()), 'dd.mmm.yyyy:HH.MM.SS');
+
+% Set the note to the training time if none is given
+if nargin < 3
+  note = timestamp;
+end
+
+% Don't evaluate trainval by default
+if nargin < 4
+  dotrainval = false;
+end
+
+if nargin < 5
+  % which year to test on -- a string, e.g., '2007'.
+  testyear = conf.pascal.year;
+end
+
+% Record a log of the training and test procedure
+diary(conf.training.log([cls '-' timestamp]));
+
+% Train a model (and record how long it took)
+th = tic;
+model = pascal_train(cls, n, note);
+toc(th);
+
+% Free the feature vector cache memory
+fv_cache('free');
+
+% Lower threshold to get high recall
+model.thresh = min(conf.eval.max_thresh, model.thresh);
+model.interval = conf.eval.interval;
+
+suffix = testyear;
+
+% Collect detections on the test set
+ds = pascal_test(model, testset, testyear, suffix);
+
+% Evaluate the model without bounding box prediction
+ap1 = pascal_eval(cls, ds, testset, testyear, suffix);
+fprintf('AP = %.4f (without bounding box prediction)\n', ap1)
+
+% Recompute AP after applying bounding box prediction
+[ap1, ap2] = bboxpred_rescore(cls, testset, testyear, suffix);
+fprintf('AP = %.4f (without bounding box prediction)\n', ap1)
+fprintf('AP = %.4f (with bounding box prediction)\n', ap2)
+
+% Compute detections on the trainval dataset (used for context rescoring)
+if dotrainval
+  trainval(cls);
+end