Python show_and_tell_model.ShowAndTellModel方法代码示例

# 需要导入模块: from im2txt import show_and_tell_model [as 别名]
# 或者: from im2txt.show_and_tell_model import ShowAndTellModel [as 别名]
def build_inputs(self):
    if self.mode == "inference":
      # Inference mode doesn't read from disk, so defer to parent.
      return super(ShowAndTellModel, self).build_inputs()
    else:
      # Replace disk I/O with random Tensors.
      self.images = tf.random_uniform(
          shape=[self.config.batch_size, self.config.image_height,
                 self.config.image_width, 3],
          minval=-1,
          maxval=1)
      self.input_seqs = tf.random_uniform(
          [self.config.batch_size, 15],
          minval=0,
          maxval=self.config.vocab_size,
          dtype=tf.int64)
      self.target_seqs = tf.random_uniform(
          [self.config.batch_size, 15],
          minval=0,
          maxval=self.config.vocab_size,
          dtype=tf.int64)
      self.input_mask = tf.ones_like(self.input_seqs) 

# 需要导入模块: from im2txt import show_and_tell_model [as 别名]
# 或者: from im2txt.show_and_tell_model import ShowAndTellModel [as 别名]
def testBuildForTraining(self):
    model = ShowAndTellModel(self._model_config, mode="train")
    model.build()

    self._checkModelParameters()

    expected_shapes = {
        # [batch_size, image_height, image_width, 3]
        model.images: (32, 299, 299, 3),
        # [batch_size, sequence_length]
        model.input_seqs: (32, 15),
        # [batch_size, sequence_length]
        model.target_seqs: (32, 15),
        # [batch_size, sequence_length]
        model.input_mask: (32, 15),
        # [batch_size, embedding_size]
        model.image_embeddings: (32, 512),
        # [batch_size, sequence_length, embedding_size]
        model.seq_embeddings: (32, 15, 512),
        # Scalar
        model.total_loss: (),
        # [batch_size * sequence_length]
        model.target_cross_entropy_losses: (480,),
        # [batch_size * sequence_length]
        model.target_cross_entropy_loss_weights: (480,),
    }
    self._checkOutputs(expected_shapes) 

# 需要导入模块: from im2txt import show_and_tell_model [as 别名]
# 或者: from im2txt.show_and_tell_model import ShowAndTellModel [as 别名]
def testBuildForEval(self):
    model = ShowAndTellModel(self._model_config, mode="eval")
    model.build()

    self._checkModelParameters()

    expected_shapes = {
        # [batch_size, image_height, image_width, 3]
        model.images: (32, 299, 299, 3),
        # [batch_size, sequence_length]
        model.input_seqs: (32, 15),
        # [batch_size, sequence_length]
        model.target_seqs: (32, 15),
        # [batch_size, sequence_length]
        model.input_mask: (32, 15),
        # [batch_size, embedding_size]
        model.image_embeddings: (32, 512),
        # [batch_size, sequence_length, embedding_size]
        model.seq_embeddings: (32, 15, 512),
        # Scalar
        model.total_loss: (),
        # [batch_size * sequence_length]
        model.target_cross_entropy_losses: (480,),
        # [batch_size * sequence_length]
        model.target_cross_entropy_loss_weights: (480,),
    }
    self._checkOutputs(expected_shapes) 

# 需要导入模块: from im2txt import show_and_tell_model [as 别名]
# 或者: from im2txt.show_and_tell_model import ShowAndTellModel [as 别名]
def testBuildForInference(self):
    model = ShowAndTellModel(self._model_config, mode="inference")
    model.build()

    self._checkModelParameters()

    # Test feeding an image to get the initial LSTM state.
    images_feed = np.random.rand(1, 299, 299, 3)
    feed_dict = {model.images: images_feed}
    expected_shapes = {
        # [batch_size, embedding_size]
        model.image_embeddings: (1, 512),
        # [batch_size, 2 * num_lstm_units]
        "lstm/initial_state:0": (1, 1024),
    }
    self._checkOutputs(expected_shapes, feed_dict)

    # Test feeding a batch of inputs and LSTM states to get softmax output and
    # LSTM states.
    input_feed = np.random.randint(0, 10, size=3)
    state_feed = np.random.rand(3, 1024)
    feed_dict = {"input_feed:0": input_feed, "lstm/state_feed:0": state_feed}
    expected_shapes = {
        # [batch_size, 2 * num_lstm_units]
        "lstm/state:0": (3, 1024),
        # [batch_size, vocab_size]
        "softmax:0": (3, 12000),
    }
    self._checkOutputs(expected_shapes, feed_dict) 

# 需要导入模块: from im2txt import show_and_tell_model [as 别名]
# 或者: from im2txt.show_and_tell_model import ShowAndTellModel [as 别名]
def build_model(self, model_config):
    model = show_and_tell_model.ShowAndTellModel(model_config, mode="inference")
    model.build()
    return model 

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