def loadOfficialTest(args, restoreModel=None, restoreModelPath=None):
#config = Attention_LapConfig(load=True, args)
config = Attention_GPUConfig(load=True, args=args)
testReader = TestProcessor(qnFile=config.testOfficialDevQns,
imgFile=config.testOfficialImgFeatures,
config=config)
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)
if restoreModel is None:
model.loadTrainedModel(config.restoreModel, config.restoreModelPath)
else:
model.loadTrainedModel(restoreModel, restoreModelPath)
if restoreModelPath is None:
testOfficialResultFile = config.testOfficialResultFile
else:
testOfficialResultFile = '{}AttSubmission.json'.format(restoreModelPath)
model.runTest(testReader, testOfficialResultFile)
testReader.destruct()
print('Official test complete')
return model
def runValTest(args):
#Val set's split -- test
print('Running Val Test')
config = Attention_LapConfig(load=True, args=args)
valTestReader = TestProcessor(qnFile=config.valTestQns,
imgFile=config.valImgFile,
config=config)
#valTestReader = TrainProcessors(config.testAnnotFile,
# config.rawQnValTestFile,
# config.valImgFile,
# config,
# is_training=False)
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(config.restoreQnImAttModel,
config.restoreQnImAttModelPath)
model.runTest(valTestReader, 'testResFile.json')
model.destruct()
valTestReader.destruct()