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Upload demo.py
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demo.py
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import json
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import werkzeug
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import tensorflow as tf
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from config import config, parseArgs, configPDF
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from extract_feature import get_img_feat, build_model
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from main import setSession, loadWeights, setSavers
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from model import MACnet
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from preprocess import Preprocesser
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import warnings
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def predict(image, question):
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parseArgs()
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configPDF()
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with open(config.configFile(), "a+") as outFile:
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json.dump(vars(config), outFile)
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if config.gpus != "":
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config.gpusNum = len(config.gpus.split(","))
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os.environ["CUDA_VISIBLE_DEVICES"] = config.gpus
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tf.reset_default_graph()
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tf.Graph().as_default()
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tf.logging.set_verbosity(tf.logging.ERROR)
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cnn_model = build_model()
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imageData = get_img_feat(cnn_model, image)
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preprocessor = Preprocesser()
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qData, embeddings, answerDict = preprocessor.preprocessData(question)
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model = MACnet(embeddings, answerDict)
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init = tf.global_variables_initializer()
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savers = setSavers(model)
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saver, emaSaver = savers["saver"], savers["emaSaver"]
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sessionConfig = setSession()
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data = {'data': qData, 'image': imageData}
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with tf.Session(config=sessionConfig) as sess:
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sess.graph.finalize()
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epoch = loadWeights(sess, saver, init)
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emaSaver.restore(sess, config.weightsFile(epoch))
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evalRes = model.runBatch(sess, data['data'], data['image'], False)
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answer = None
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if evalRes in ['top', 'bottom']:
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answer = 'The caption at the %s side of the object.' % evalRes
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elif evalRes in ['True', 'False']:
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answer = 'There is at least one title object in this image.'
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else:
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answer = 'This image contain %s specific object(s).' % evalRes
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return answer
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