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import json
import os
import werkzeug
import tensorflow as tf
from config import config, parseArgs, configPDF
from extract_feature import get_img_feat, build_model
from main import setSession, loadWeights, setSavers
from model import MACnet
from preprocess import Preprocesser
import warnings
def predict(image, question):
parseArgs()
config.parallel = True
config.evalTrain = True
config.retainVal = True
config.useEMA = True
config.lrReduce = True
config.adam = True
config.clip = True
config.memoryVariationalDropout = True
config.relu='ELU'
config.encBi = True
config.wrdEmbRandom = True
config.wrdEmbUniform = True
config.outQuestion = True
config.initCtrl='Q'
config.controlContextual = True
config.controlInputUnshared = True
config.readProjInputs = True
config.readMemConcatKB = True
config.readMemConcatProj = True
config.readMemProj = True
config.readCtrl = True
config.writeMemProj = True
config.restore = True
config.expName = 'PDF_exp_extra'
config.netLength = 16
configPDF()
with open(config.configFile(), "a+") as outFile:
json.dump(vars(config), outFile)
if config.gpus != "":
config.gpusNum = len(config.gpus.split(","))
os.environ["CUDA_VISIBLE_DEVICES"] = config.gpus
tf.reset_default_graph()
tf.Graph().as_default()
tf.logging.set_verbosity(tf.logging.ERROR)
cnn_model = build_model()
imageData = get_img_feat(cnn_model, image)
preprocessor = Preprocesser()
qData, embeddings, answerDict = preprocessor.preprocessData(question)
model = MACnet(embeddings, answerDict)
init = tf.global_variables_initializer()
savers = setSavers(model)
saver, emaSaver = savers["saver"], savers["emaSaver"]
sessionConfig = setSession()
data = {'data': qData, 'image': imageData}
with tf.Session(config=sessionConfig) as sess:
sess.graph.finalize()
# epoch = loadWeights(sess, saver, init)
print('###############', config.weightsFile(25))
os.system('ls -l ./weights/PDF_exp_extra')
emaSaver.restore(sess, config.weightsFile(25))
evalRes = model.runBatch(sess, data['data'], data['image'], False)
answer = None
if evalRes in ['top', 'bottom']:
answer = 'The caption at the %s side of the object.' % evalRes
elif evalRes in ['True', 'False']:
answer = 'There is at least one title object in this image.'
else:
answer = 'This image contain %s specific object(s).' % evalRes
return answer |