def predAnalysis(args):
print('Running Val Test')
predFile = 'Pred_QnAtt47.9.csv'
config = Attention_GPUConfig(load=True, args=args)
valTestReader = AttModelInputProcessor(config.testAnnotFile,
config.rawQnValTestFile,
config.valImgFile,
config,
is_training=False)
model = QnAttentionModel(config)
model.loadTrainedModel(config.restoreModel, config.restoreModelPath)
model.runPredict(valTestReader, predFile)
model.destruct()
valTestReader.destruct()
def visQnImgAtt():
print('Running qn Visuals')
config = Attention_LapConfig(load=True, args=args)
reader = AttModelInputProcessor(config.testAnnotFile,
config.rawQnValTestFile,
config.valImgFile,
config,
is_training=False)
#reader = AttModelInputProcessor(config.trainAnnotFile,
# config.rawQnTrain,
# config.trainImgFile,
# config,
# is_training=False)
model = QnAttentionModel(config)
saveData = True
model.loadTrainedModel(config.restoreQuAttSigmoidModel,
config.restoreQuAttSigmoidModelPath)
#model.loadTrainedModel(config.restoreQnImAttModel,
# config.restoreQnImAttModelPath)
qnAlphas, alphas, img_ids, qns, preds, topk, labs = model.runPredict(
reader, config.csvResults, 200, mini=True, chooseBatch=30)
model.destruct()
out = OutputGenerator(config.valImgPaths)
#out = OutputGenerator(config.trainImgPaths)
#out.displayQnImgAttention(qnAlphas, alphas, img_ids, qns, preds, topk, labs,saveData)
out.displayQnImgAttSaveSplit(qnAlphas, alphas, img_ids, qns, preds, topk, labs,saveData)
def visQnImgAtt():
print('Running qn Visuals')
config = Attention_LapConfig(load=True, args=args)
reader = AttModelInputProcessor(config.testAnnotFile,
config.rawQnValTestFile,
config.valImgFile,
config,
is_training=False)
model = QnAttentionModel(config)
model.loadTrainedModel(config.restoreQnImAttModel,
config.restoreQnImAttModelPath)
qnAlphas, alphas, img_ids, qns, preds, topk = model.runPredict(
reader, config.csvResults, 5, mini=True)
model.destruct()
out = OutputGenerator(config.valImgPaths)
out.displayQnImgAttention(qnAlphas, alphas, img_ids, qns, preds, topk)
def validateInternalTestSet(args, model=None, restoreModelPath=None):
from vqaTools.vqaInternal import VQA
from vqaTools.vqaEval import VQAEval
#config = Attention_LapConfig(load=True, args)
config = Attention_GPUConfig(load=True, args=args)
print('Running Validation Test on Model')
valTestReader = AttModelInputProcessor(config.testAnnotFile,
config.rawQnValTestFile,
config.valImgFile,
config,
is_training=False)
if restoreModelPath is None:
restoreModel = config.restoreModel
restoreModelPath = config.restoreModelPath
if model is None:
if args.att == 'qn':
print('Attention over question and image model')
model = QnAttentionModel(config)
elif args.att == 'im':
print('Attention over image model')
model = ImageAttentionModel(config)
model.loadTrainedModel(restoreModel, restoreModelPath)
predFile = '{}PredsAtt{}.csv'.format(restoreModelPath, args.att)
results, strictAcc = model.runPredict(valTestReader, predFile)
model.destruct()
valTestReader.destruct()
print('predictions made')
vqa = VQA(config.testAnnotFileUnresolved, config.originalValQns)
vqaRes = vqa.loadRes(results, config.originalValQns)
vqaEval = VQAEval(vqa, vqaRes, n=2)
vqaEval.evaluate()
print('Writing to file..')
writeToFile(vqaEval, restoreModelPath, vqa, vqaRes, args, strictAcc)
print('Internal test complete')