def main(_):
  # Build the inference graph.
  g = tf.Graph()
  with g.as_default():
    model = inference_wrapper.InferenceWrapper()
    restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
                                               FLAGS.checkpoint_path)
  g.finalize()

  # Create the vocabulary.
  vocab = vocabulary.Vocabulary(FLAGS.vocab_file)

  filenames = []
  for file_pattern in FLAGS.input_files.split(","):
    filenames.extend(tf.gfile.Glob(file_pattern))
  tf.logging.info("Running caption generation on %d files matching %s",
                  len(filenames), FLAGS.input_files)

  with tf.Session(graph=g) as sess:
    # Load the model from checkpoint.
    restore_fn(sess)

    # Prepare the caption generator. Here we are implicitly using the default
    # beam search parameters. See caption_generator.py for a description of the
    # available beam search parameters.
    generator = caption_generator.CaptionGenerator(model, vocab)

    for filename in filenames:
      with tf.gfile.GFile(filename, "r") as f:
        image = f.read()
      captions = generator.beam_search(sess, image)
      print("Captions for image %s:" % os.path.basename(filename))
      for i, caption in enumerate(captions):
        # Ignore begin and end words.
        sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
        sentence = " ".join(sentence)
        print("  %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob))) 

def main(_):
  # Build the inference graph.
  g = tf.Graph()
  with g.as_default():
    model = inference_wrapper.InferenceWrapper()
    restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
                                               FLAGS.checkpoint_path)
  g.finalize()

  # Create the vocabulary.
  vocab = vocabulary.Vocabulary(FLAGS.vocab_file)

  filenames = []
  for file_pattern in FLAGS.input_files.split(","):
    filenames.extend(tf.gfile.Glob(file_pattern))
  tf.logging.info("Running caption generation on %d files matching %s",
                  len(filenames), FLAGS.input_files)

  with tf.Session(graph=g) as sess:
    # Load the model from checkpoint.
    restore_fn(sess)

    # Prepare the caption generator. Here we are implicitly using the default
    # beam search parameters. See caption_generator.py for a description of the
    # available beam search parameters.
    generator = caption_generator.CaptionGenerator(model, vocab)

    for filename in filenames:
      with tf.gfile.GFile(filename, "r") as f:
        image = f.read()
      captions = generator.beam_search(sess, image)
      print("Captions for image %s:" % os.path.basename(filename))
      for i, caption in enumerate(captions):
        # Ignore begin and end words.
        sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
        sentence = " ".join(sentence)
        print("  %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob))) 

def main(_):
  # Build the inference graph.
  g = tf.Graph()
  with g.as_default():
    model = inference_wrapper.InferenceWrapper()
    restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
                                               FLAGS.checkpoint_path)
  g.finalize()

  # Create the vocabulary.
  vocab = vocabulary.Vocabulary(FLAGS.vocab_file)

  filenames = []
  for file_pattern in FLAGS.input_files.split(","):
    filenames.extend(tf.gfile.Glob(file_pattern))
  tf.logging.info("Running caption generation on %d files matching %s",
                  len(filenames), FLAGS.input_files)

  with tf.Session(graph=g) as sess:
    # Load the model from checkpoint.
    restore_fn(sess)

    # Prepare the caption generator. Here we are implicitly using the default
    # beam search parameters. See caption_generator.py for a description of the
    # available beam search parameters.
    generator = caption_generator.CaptionGenerator(model, vocab)

    for filename in filenames:
      with tf.gfile.GFile(filename, "rb") as f:
        image = f.read()
      captions = generator.beam_search(sess, image)
      print("Captions for image %s:" % os.path.basename(filename))
      for i, caption in enumerate(captions):
        # Ignore begin and end words.
        sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
        sentence = " ".join(sentence)
        print("  %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob))) 

def main(_):
  # Build the inference graph.
  g = tf.Graph()
  with g.as_default():
    model = inference_wrapper.InferenceWrapper()
    restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
                                               FLAGS.checkpoint_path)
  g.finalize()

  # Create the vocabulary.
  vocab = vocabulary.Vocabulary(FLAGS.vocab_file)

  filenames = []
  for file_pattern in FLAGS.input_files.split(","):
    filenames.extend(tf.gfile.Glob(file_pattern))
  tf.logging.info("Running caption generation on %d files matching %s",
                  len(filenames), FLAGS.input_files)

  with tf.Session(graph=g) as sess:
    # Load the model from checkpoint.
    restore_fn(sess)

    # Prepare the caption generator. Here we are implicitly using the default
    # beam search parameters. See caption_generator.py for a description of the
    # available beam search parameters.
    generator = caption_generator.CaptionGenerator(model, vocab)

    for filename in filenames:
      with tf.gfile.GFile(filename, "r") as f:
        image = f.read()
      captions = generator.beam_search(sess, image)
      print("Captions for image %s:" % os.path.basename(filename))
      for i, caption in enumerate(captions):
        # Ignore begin and end words.
        sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
        sentence = " ".join(sentence)
        print("  %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob))) 

def main(_):
  # Build the inference graph.
  g = tf.Graph()
  with g.as_default():
    model = inference_wrapper.InferenceWrapper()
    restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
                                               FLAGS.checkpoint_path)
  g.finalize()

  # Create the vocabulary.
  vocab = vocabulary.Vocabulary(FLAGS.vocab_file)

  filenames = []
  for file_pattern in FLAGS.input_files.split(","):
    filenames.extend(tf.gfile.Glob(file_pattern))
  tf.logging.info("Running caption generation on %d files matching %s",
                  len(filenames), FLAGS.input_files)

  with tf.Session(graph=g) as sess:
    # Load the model from checkpoint.
    restore_fn(sess)

    # Prepare the caption generator. Here we are implicitly using the default
    # beam search parameters. See caption_generator.py for a description of the
    # available beam search parameters.
    generator = caption_generator.CaptionGenerator(model, vocab)

    for filename in filenames:
      with tf.gfile.GFile(filename, "r") as f:
        image = f.read()
      captions = generator.beam_search(sess, image)
      print("Captions for image %s:" % os.path.basename(filename))
      for i, caption in enumerate(captions):
        # Ignore begin and end words.
        sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
        sentence = " ".join(sentence)
        print("  %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob))) 

def main(_):
  # Build the inference graph.
  g = tf.Graph()
  with g.as_default():
    model = inference_wrapper.InferenceWrapper()
    restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
                                               FLAGS.checkpoint_path)
  g.finalize()

  # Create the vocabulary.
  vocab = vocabulary.Vocabulary(FLAGS.vocab_file)

  filenames = []
  for file_pattern in FLAGS.input_files.split(","):
    filenames.extend(tf.gfile.Glob(file_pattern))
  tf.logging.info("Running caption generation on %d files matching %s",
                  len(filenames), FLAGS.input_files)

  with tf.Session(graph=g) as sess:
    # Load the model from checkpoint.
    restore_fn(sess)

    # Prepare the caption generator. Here we are implicitly using the default
    # beam search parameters. See caption_generator.py for a description of the
    # available beam search parameters.
    generator = caption_generator.CaptionGenerator(model, vocab)

    for filename in filenames:
      with tf.gfile.GFile(filename, "r") as f:
        image = f.read()
      captions = generator.beam_search(sess, image)
      print("Captions for image %s:" % os.path.basename(filename))
      for i, caption in enumerate(captions):
        # Ignore begin and end words.
        sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
        sentence = " ".join(sentence)
        print("  %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob))) 

def main(_):
  # Build the inference graph.
  g = tf.Graph()
  with g.as_default():
    model = inference_wrapper.InferenceWrapper()
    restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
                                               FLAGS.checkpoint_path)
  g.finalize()

  # Create the vocabulary.
  vocab = vocabulary.Vocabulary(FLAGS.vocab_file)

  filenames = []
  for file_pattern in FLAGS.input_files.split(","):
    filenames.extend(tf.gfile.Glob(file_pattern))
  tf.logging.info("Running caption generation on %d files matching %s",
                  len(filenames), FLAGS.input_files)

  with tf.Session(graph=g) as sess:
    # Load the model from checkpoint.
    restore_fn(sess)

    # Prepare the caption generator. Here we are implicitly using the default
    # beam search parameters. See caption_generator.py for a description of the
    # available beam search parameters.
    generator = caption_generator.CaptionGenerator(model, vocab)

    for filename in filenames:
      with tf.gfile.GFile(filename, "r") as f:
        image = f.read()
      captions = generator.beam_search(sess, image)
      print("Captions for image %s:" % os.path.basename(filename))
      for i, caption in enumerate(captions):
        # Ignore begin and end words.
        sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
        sentence = " ".join(sentence)
        print("  %d) %s (xyzj=%f)" % (i, sentence, math.exp(caption.logprob))) 

def main(_):
  # Build the inference graph.
  g = tf.Graph()
  with g.as_default():
    model = inference_wrapper.InferenceWrapper()
    restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
                                               FLAGS.checkpoint_path)
  g.finalize()

  # Create the vocabulary.
  vocab = vocabulary.Vocabulary(FLAGS.vocab_file)

  filenames = []
  for file_pattern in FLAGS.input_files.split(","):
    filenames.extend(tf.gfile.Glob(file_pattern))
  tf.logging.info("Running caption generation on %d files matching %s",
                  len(filenames), FLAGS.input_files)

  with tf.Session(graph=g) as sess:
    # Load the model from checkpoint.
    restore_fn(sess)

    # Prepare the caption generator. Here we are implicitly using the default
    # beam search parameters. See caption_generator.py for a description of the
    # available beam search parameters.
    generator = caption_generator.CaptionGenerator(model, vocab)

    for filename in filenames:
      with tf.gfile.GFile(filename, "r") as f:
        image = f.read()
      captions = generator.beam_search(sess, image)
      print("Captions for image %s:" % os.path.basename(filename))
      for i, caption in enumerate(captions):
        # Ignore begin and end words.
        sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
        sentence = " ".join(sentence)
        print("  %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob))) 

def main(_):
  # Build the inference graph.
  g = tf.Graph()
  with g.as_default():
    model = inference_wrapper.InferenceWrapper()
    restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
                                               FLAGS.checkpoint_path)
  g.finalize()

  # Create the vocabulary.
  vocab = vocabulary.Vocabulary(FLAGS.vocab_file)

  filenames = []
  for file_pattern in FLAGS.input_files.split(","):
    filenames.extend(tf.gfile.Glob(file_pattern))
  tf.logging.info("Running caption generation on %d files matching %s",
                  len(filenames), FLAGS.input_files)

  with tf.Session(graph=g) as sess:
    # Load the model from checkpoint.
    restore_fn(sess)

    # Prepare the caption generator. Here we are implicitly using the default
    # beam search parameters. See caption_generator.py for a description of the
    # available beam search parameters.
    generator = caption_generator.CaptionGenerator(model, vocab)

    for filename in filenames:
      with tf.gfile.GFile(filename, "r") as f:
        image = f.read()
      captions = generator.beam_search(sess, image)
      print("Captions for image %s:" % os.path.basename(filename))
      for i, caption in enumerate(captions):
        # Ignore begin and end words.
        sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
        sentence = " ".join(sentence)
        print("  %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob))) 

def main(_):
  # Build the inference graph.
  g = tf.Graph()
  with g.as_default():
    model = inference_wrapper.InferenceWrapper()
    restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
                                               FLAGS.checkpoint_path)
  g.finalize()

  # Create the vocabulary.
  vocab = vocabulary.Vocabulary(FLAGS.vocab_file)

  filenames = []
  for file_pattern in FLAGS.input_files.split(","):
    filenames.extend(tf.gfile.Glob(file_pattern))
  tf.logging.info("Running caption generation on %d files matching %s",
                  len(filenames), FLAGS.input_files)

  with tf.Session(graph=g) as sess:
    # Load the model from checkpoint.
    restore_fn(sess)

    # Prepare the caption generator. Here we are implicitly using the default
    # beam search parameters. See caption_generator.py for a description of the
    # available beam search parameters.
    generator = caption_generator.CaptionGenerator(model, vocab)

    for filename in filenames:
      with tf.gfile.GFile(filename, "rb") as f:
        image = f.read()
      captions = generator.beam_search(sess, image)
      print("Captions for image %s:" % os.path.basename(filename))
      for i, caption in enumerate(captions):
        # Ignore begin and end words.
        sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
        sentence = " ".join(sentence)
        print("  %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob))) 

def main(_):
  # Build the inference graph.
  g = tf.Graph()
  with g.as_default():
    model = inference_wrapper.InferenceWrapper()
    restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
                                               FLAGS.checkpoint_path)
  g.finalize()

  # Create the vocabulary.
  vocab = vocabulary.Vocabulary(FLAGS.vocab_file)

  filenames = []
  for file_pattern in FLAGS.input_files.split(","):
    filenames.extend(tf.gfile.Glob(file_pattern))
  tf.logging.info("Running caption generation on %d files matching %s",
                  len(filenames), FLAGS.input_files)

  with tf.Session(graph=g) as sess:
    # Load the model from checkpoint.
    restore_fn(sess)

    # Prepare the caption generator. Here we are implicitly using the default
    # beam search parameters. See caption_generator.py for a description of the
    # available beam search parameters.
    generator = caption_generator.CaptionGenerator(model, vocab)

    for filename in filenames:
      with tf.gfile.GFile(filename, "rb") as f:
        image = f.read()
      captions = generator.beam_search(sess, image)
      print("Captions for image %s:" % os.path.basename(filename))
      for i, caption in enumerate(captions):
        # Ignore begin and end words.
        sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
        sentence = " ".join(sentence)
        print("  %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob))) 

def main(_):
  # Build the inference graph.
  g = tf.Graph()
  with g.as_default():
    model = inference_wrapper.InferenceWrapper()
    restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
                                               FLAGS.checkpoint_path)
  g.finalize()

  # Create the vocabulary.
  vocab = vocabulary.Vocabulary(FLAGS.vocab_file)

  filenames = []
  for file_pattern in FLAGS.input_files.split(","):
    filenames.extend(tf.gfile.Glob(file_pattern))
  tf.logging.info("Running caption generation on %d files matching %s",
                  len(filenames), FLAGS.input_files)

  with tf.Session(graph=g) as sess:
    # Load the model from checkpoint.
    restore_fn(sess)

    # Prepare the caption generator. Here we are implicitly using the default
    # beam search parameters. See caption_generator.py for a description of the
    # available beam search parameters.
    generator = caption_generator.CaptionGenerator(model, vocab)

    for filename in filenames:
      with tf.gfile.GFile(filename, "rb") as f:
        image = f.read()
      captions = generator.beam_search(sess, image)
      print("Captions for image %s:" % os.path.basename(filename))
      for i, caption in enumerate(captions):
        # Ignore begin and end words.
        sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
        sentence = " ".join(sentence)
        print("  %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob))) 

def main(_):
  # Build the inference graph.
  g = tf.Graph()
  with g.as_default():
    model = inference_wrapper.InferenceWrapper()
    restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
                                               FLAGS.checkpoint_path)
    # preprocessing compute graph
    image_placeholder = tf.placeholder(dtype=tf.string, shape=[])
    preprocessor = model.model.process_image(image_placeholder)

  g.finalize()

  # Create the vocabulary.
  vocab = vocabulary.Vocabulary(FLAGS.vocab_file)

  filenames = []
  for file_pattern in FLAGS.input_files.split(","):
    filenames.extend(tf.gfile.Glob(file_pattern))
  tf.logging.info("Running caption generation on %d files matching %s",
                  len(filenames), FLAGS.input_files)


  with tf.Session(graph=g) as sess:
    # Load the model from checkpoint.
    restore_fn(sess)


    # Prepare the caption generator. Here we are implicitly using the default
    # beam search parameters. See caption_generator.py for a description of the
    # available beam search parameters.
    generator = caption_generator.CaptionGenerator(model, vocab)

    for filename in filenames:
      _, file_extension = os.path.splitext(filename)
      if file_extension == ".npy":
        # load numpy array
        image = np.squeeze(np.load(filename))
      else:
        with tf.gfile.GFile(filename, "rb") as f:
          image = f.read()
          image = sess.run(preprocessor, {image_placeholder: image})
          print('raw image shape is', image.shape)
    
      captions = generator.beam_search(sess, image)
      print("Captions for image %s:" % os.path.basename(filename))
      for i, caption in enumerate(captions):
        # Ignore begin and end words.
        sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
        sentence = " ".join(sentence)
        print("  %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob)))
        print(caption.sentence)
        # print(generator.new_caption_prob(sess, caption.sentence, image))
        print(model.new_caption_prob(sess, caption.sentence, image))
      
      new_sentence = "kite"
      new_sentence = new_sentence.split()
      print("My new sentence:", new_sentence)
      new_caption = [vocab.start_id]+[vocab.word_to_id(w) for w in new_sentence] + [vocab.end_id]
      print("My new id:", new_caption)
      print(model.new_caption_prob(sess, new_caption, image)) 

def main(_):
  # Build the inference graph.
  g = tf.Graph()
  with g.as_default():
    model = inference_wrapper.InferenceWrapper()
    restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
                                               FLAGS.checkpoint_path)
    # preprocessing compute graph
    image_placeholder = tf.placeholder(dtype=tf.string, shape=[])
    preprocessor = model.model.process_image(image_placeholder)

  g.finalize()

  # Create the vocabulary.
  vocab = vocabulary.Vocabulary(FLAGS.vocab_file)

  filenames = []
  for file_pattern in FLAGS.input_files.split(","):
    filenames.extend(tf.gfile.Glob(file_pattern))
  tf.logging.info("Running caption generation on %d files matching %s",
                  len(filenames), FLAGS.input_files)


  with tf.Session(graph=g) as sess:
    # Load the model from checkpoint.
    restore_fn(sess)


    # Prepare the caption generator. Here we are implicitly using the default
    # beam search parameters. See caption_generator.py for a description of the
    # available beam search parameters.
    generator = caption_generator.CaptionGenerator(model, vocab)

    for filename in filenames:
      _, file_extension = os.path.splitext(filename)
      if file_extension == ".npy":
        # load numpy array
        image = np.squeeze(np.load(filename))
      else:
        with tf.gfile.GFile(filename, "rb") as f:
          image = f.read()
          image = sess.run(preprocessor, {image_placeholder: image})
          print('raw image shape is', image.shape)
    
      captions = generator.beam_search(sess, image)
      print("Captions for image %s:" % os.path.basename(filename))
      for i, caption in enumerate(captions):
        # Ignore begin and end words.
        sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
        sentence = " ".join(sentence)
        print("  %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob)))
        print(caption.sentence)
        # print(generator.new_caption_prob(sess, caption.sentence, image))
        print(model.new_caption_prob(sess, caption.sentence, image))
      
      new_sentence = "kite"
      new_sentence = new_sentence.split()
      print("My new sentence:", new_sentence)
      new_caption = [vocab.start_id]+[vocab.word_to_id(w) for w in new_sentence] + [vocab.end_id]
      print("My new id:", new_caption)
      print(model.new_caption_prob(sess, new_caption, image))