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 solve():
print('Running solve')
config = Attention_LapConfig(load=True, args=args)
out = OutputGenerator(config.trainImgPaths)
#img_id = raw_input('Img_id--> ')
img_id = str(262415)
img = Image.open(out.convertIDtoPath(str(img_id)))
img.show()
qn = raw_input('Question--> ')
print(qn)
model = ImageAttentionModel(config)
model.loadTrainedModel(config.restoreModel, config.restoreModelPath)
alpha, pred = model.solve(qn, img_id)
out.displaySingleOutput(alpha, img_id, qn, pred)
''' -a otest -r ./results/Att21Mar1334/att21Mar1334.meta -p ./results/Att21Mar1334/
def solveqn():
print('Running solve')
config = Attention_LapConfig(load=True, args=args)
out = OutputGenerator(config.valImgPaths)
#img_id = raw_input('Img_id--> ')
img_id = str(337826) #214587
img = Image.open(out.convertIDtoPath(str(img_id)))
img.show()
model = QnAttentionModel(config)
model.loadTrainedModel(config.restoreQnImAttModel,
config.restoreQnImAttModelPath)
processor = OnlineProcessor(config.valImgFile, config)
for i in range(5):
qn = raw_input('Question--> ')
print(qn)
qnalpha, alpha, pred = model.solve(qn, img_id, processor)
out.displaySingleOutput(alpha, img_id, qn, pred)
def runVisualise():
print('Running Visuals')
config = Attention_LapConfig(load=True, args=args)
reader = AttModelInputProcessor(config.trainAnnotFile,
config.rawQnTrain,
config.trainImgFile,
config,
is_training=False)
model = ImageAttentionModel(config)
model.loadTrainedModel(config.restoreModel, config.restoreModelPath)
alphas, img_ids, qns, preds = model.runPredict(
reader, config.csvResults, 5, mini=True)
model.destruct()
reader.destruct()
out = OutputGenerator(config.trainImgPaths)
out.displayOutput(alphas, img_ids, qns, preds)
def runVisualiseVal():
print('Running Visuals')
config = Attention_LapConfig(load=True, args=args)
reader = AttModelInputProcessor(config.testAnnotFile,
config.rawQnValTestFile,
config.valImgFile,
config,
is_training=False)
model = ImageAttentionModel(config)
model.loadTrainedModel(config.restoreModel, config.restoreModelPath)
alphas, img_ids, qns, preds, labels = model.runPredict(
reader, config.csvResults, 180, mini=True, chooseBatch=0)
model.destruct()
reader.destruct()
out = OutputGenerator(config.valImgPaths)
out.displayEachSample(alphas, img_ids, qns, preds, labels, saveData=True)
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)