-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]