Python configuration.ModelConfig方法代码示例

# 需要导入模块: from im2txt import configuration [as 别名]
# 或者: from im2txt.configuration import ModelConfig [as 别名]
def setUp(self):
    super(ShowAndTellModelTest, self).setUp()
    self._model_config = configuration.ModelConfig() 

# 需要导入模块: from im2txt import configuration [as 别名]
# 或者: from im2txt.configuration import ModelConfig [as 别名]
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))) 

# 需要导入模块: from im2txt import configuration [as 别名]
# 或者: from im2txt.configuration import ModelConfig [as 别名]
def run():
  """Runs evaluation in a loop, and logs summaries to TensorBoard."""
  # Create the evaluation directory if it doesn't exist.
  eval_dir = FLAGS.eval_dir
  if not tf.gfile.IsDirectory(eval_dir):
    tf.logging.info("Creating eval directory: %s", eval_dir)
    tf.gfile.MakeDirs(eval_dir)

  g = tf.Graph()
  with g.as_default():
    # Build the model for evaluation.
    model_config = configuration.ModelConfig()
    model_config.input_file_pattern = FLAGS.input_file_pattern
    model = show_and_tell_model.ShowAndTellModel(model_config, mode="eval")
    model.build()

    # Create the Saver to restore model Variables.
    saver = tf.train.Saver()

    # Create the summary operation and the summary writer.
    summary_op = tf.summary.merge_all()
    summary_writer = tf.summary.FileWriter(eval_dir)

    g.finalize()

    # Run a new evaluation run every eval_interval_secs.
    while True:
      start = time.time()
      tf.logging.info("Starting evaluation at " + time.strftime(
          "%Y-%m-%d-%H:%M:%S", time.localtime()))
      run_once(model, saver, summary_writer, summary_op)
      time_to_next_eval = start + FLAGS.eval_interval_secs - time.time()
      if time_to_next_eval > 0:
        time.sleep(time_to_next_eval) 

# 需要导入模块: from im2txt import configuration [as 别名]
# 或者: from im2txt.configuration import ModelConfig [as 别名]
def __init__(self, model_path, vocab_path):
        self.model_path = model_path
        self.vocab_path = vocab_path
        self.g = tf.Graph()
        with self.g.as_default():
            self.model = inference_wrapper.InferenceWrapper()
            self.restore_fn = self.model.build_graph_from_config(
                    configuration.ModelConfig(), model_path)
        self.g.finalize()
        self.vocab = vocabulary.Vocabulary(vocab_path)
        self.generator = caption_generator.CaptionGenerator(self.model,
                                                            self.vocab)
        self.sess = tf.Session(graph=self.g)
        self.restore_fn(self.sess)
        return 

# 需要导入模块: from im2txt import configuration [as 别名]
# 或者: from im2txt.configuration import ModelConfig [as 别名]
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))) 

# 需要导入模块: from im2txt import configuration [as 别名]
# 或者: from im2txt.configuration import ModelConfig [as 别名]
def predict(self, sess, image_raw_feed, input_feed, mask_feed):
    tf.logging.info("Building model.")
    start_vars = set(x.name for x in tf.global_variables())
    self.build_model(configuration.ModelConfig(), image_raw_feed, input_feed, mask_feed)
    end_vars = tf.global_variables()
    restore_vars = [x for x in end_vars if x.name not in start_vars]
    saver = tf.train.Saver(var_list = restore_vars)
    restore_fn = self._create_restore_fn(FLAGS.checkpoint_path, saver)
    restore_fn(sess)
    sum_log_probs = sess.graph.get_tensor_by_name("batch_loss:0")
    logits = self.model.logits
    softmax = sess.graph.get_tensor_by_name("softmax:0")
    # return sum_log_probs, logits, softmax
    return sum_log_probs, softmax, logits 

# 需要导入模块: from im2txt import configuration [as 别名]
# 或者: from im2txt.configuration import ModelConfig [as 别名]
def run():
  """Runs evaluation in a loop, and logs summaries to TensorBoard."""
  # Create the evaluation directory if it doesn't exist.
  eval_dir = FLAGS.eval_dir
  if not tf.gfile.IsDirectory(eval_dir):
    tf.logging.info("Creating eval directory: %s", eval_dir)
    tf.gfile.MakeDirs(eval_dir)

  g = tf.Graph()
  with g.as_default():
    # Build the model for evaluation.
    model_config = configuration.ModelConfig()
    model_config.input_file_pattern = FLAGS.input_file_pattern
    model = show_and_tell_model.ShowAndTellModel(model_config, mode="eval")
    model.build()

    # Create the Saver to restore model Variables.
    saver = tf.train.Saver()

    # Create the summary operation and the summary writer.
    summary_op = tf.merge_all_summaries()
    summary_writer = tf.train.SummaryWriter(eval_dir)

    g.finalize()

    # Run a new evaluation run every eval_interval_secs.
    while True:
      start = time.time()
      tf.logging.info("Starting evaluation at " + time.strftime(
          "%Y-%m-%d-%H:%M:%S", time.localtime()))
      run_once(model, saver, summary_writer, summary_op)
      time_to_next_eval = start + FLAGS.eval_interval_secs - time.time()
      if time_to_next_eval > 0:
        time.sleep(time_to_next_eval) 

# 需要导入模块: from im2txt import configuration [as 别名]
# 或者: from im2txt.configuration import ModelConfig [as 别名]
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)))