-o[utput]: Output folder of the rendered results -k: Render top-K answers (default 1) -p[icture]: Picture folder, only required in LaTeX mode (default "img") -r[esults]: Results folder where trained models are stored (default "../results") -f[ile]: Output filename, only required in LaTex mode -dataset: Use DAQUAR/COCO-QA dataset (default "cocoqa") -format: Set output format to HTML/LaTeX (default "html") Input question list format: QID1,Question1,GroundTruthAnswer1 QID2,Question2,GroundTruthAnswer2 ... """ params = ir.parseComparativeParams(sys.argv) urlDict = ir.loadImgUrl(params['dataset'], params['dataFolder']) data = it.loadDataset(params['dataFolder']) maxlen = data['testData'][0].shape[1] print('Parsing input file...') caption, qids, questions, answers = parseInputFile(params['inputFile']) idx = np.array(qids, dtype='int') #inputTestSel = data['testData'][0][idx] #targetTestSel = data['testData'][1][idx] imgids = qids #imgids = inputTestSel[:, 0, 0] inputTest = prep.combine(\ prep.lookupQID(questions, data['questionDict'], maxlen), imgids) targetTest = prep.lookupAnsID(answers, data['ansDict']) questionTypeArray = data['questionTypeArray'][idx]