Upload demo.py

demo.py

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+
+import json
+
+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()
+  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)
+    emaSaver.restore(sess, config.weightsFile(epoch))
+
+    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