Python logging.info方法代码示例

# 需要导入模块: from tensorflow import logging [as 别名]
# 或者: from tensorflow.logging import info [as 别名]
def export_model(self, global_step_val, saver, save_path, session):

    # If the model has already been exported at this step, return.
    if global_step_val == self.last_model_export_step:
      return

    last_checkpoint = saver.save(session, save_path, global_step_val)

    model_dir = "{0}/export/step_{1}".format(self.train_dir, global_step_val)
    logging.info("%s: Exporting the model at step %s to %s.",
                 task_as_string(self.task), global_step_val, model_dir)

    self.model_exporter.export_model(
        model_dir=model_dir, 
        global_step_val=global_step_val,
        last_checkpoint=last_checkpoint) 

# 需要导入模块: from tensorflow import logging [as 别名]
# 或者: from tensorflow.logging import info [as 别名]
def start_server_if_distributed(self):
    """Starts a server if the execution is distributed."""

    if self.cluster:
      logging.info("%s: Starting trainer within cluster %s.",
                   task_as_string(self.task), self.cluster.as_dict())
      server = start_server(self.cluster, self.task)
      target = server.target
      device_fn = tf.train.replica_device_setter(
          ps_device="/job:ps",
          worker_device="/job:%s/task:%d" % (self.task.type, self.task.index),
          cluster=self.cluster)
    else:
      target = ""
      device_fn = ""
    return (target, device_fn) 

# 需要导入模块: from tensorflow import logging [as 别名]
# 或者: from tensorflow.logging import info [as 别名]
def get_meta_filename(self, start_new_model, train_dir):
    if start_new_model:
      logging.info("%s: Flag 'start_new_model' is set. Building a new model.",
                   task_as_string(self.task))
      return None
    
    latest_checkpoint = tf.train.latest_checkpoint(train_dir)
    if not latest_checkpoint: 
      logging.info("%s: No checkpoint file found. Building a new model.",
                   task_as_string(self.task))
      return None
    
    meta_filename = latest_checkpoint + ".meta"
    if not gfile.Exists(meta_filename):
      logging.info("%s: No meta graph file found. Building a new model.",
                     task_as_string(self.task))
      return None
    else:
      return meta_filename 

# 需要导入模块: from tensorflow import logging [as 别名]
# 或者: from tensorflow.logging import info [as 别名]
def get_input_data_tensors(reader,
                           data_pattern,
                           batch_size=256):
  logging.info("Using batch size of " + str(batch_size) + " for evaluation.")
  with tf.name_scope("eval_input"):
    files = gfile.Glob(data_pattern)
    if not files:
      raise IOError("Unable to find the evaluation files.")
    logging.info("number of evaluation files: " + str(len(files)))
    files.sort()
    filename_queue = tf.train.string_input_producer(
        files, shuffle=False, num_epochs=1)
    eval_data = reader.prepare_reader(filename_queue)
    return tf.train.batch(
        eval_data,
        batch_size=batch_size,
        capacity=4 * batch_size,
        allow_smaller_final_batch=True,
        enqueue_many=True) 

# 需要导入模块: from tensorflow import logging [as 别名]
# 或者: from tensorflow.logging import info [as 别名]
def get_input_evaluation_tensors(reader,
                                 data_pattern,
                                 batch_size=256):
  logging.info("Using batch size of " + str(batch_size) + " for evaluation.")
  with tf.name_scope("eval_input"):
    files = gfile.Glob(data_pattern)
    if not files:
      print data_pattern, files
      raise IOError("Unable to find the evaluation files.")
    logging.info("number of evaluation files: " + str(len(files)))
    files.sort()
    filename_queue = tf.train.string_input_producer(
        files, shuffle=False, num_epochs=1)
    eval_data = reader.prepare_reader(filename_queue)
    return tf.train.batch(
        eval_data,
        batch_size=batch_size,
        capacity=3 * batch_size,
        allow_smaller_final_batch=True,
        enqueue_many=True) 

# 需要导入模块: from tensorflow import logging [as 别名]
# 或者: from tensorflow.logging import info [as 别名]
def get_input_data_tensors(reader,
                           data_pattern,
                           batch_size=256):
  logging.info("Using batch size of " + str(batch_size) + " for evaluation.")
  with tf.name_scope("eval_input"):
    files = gfile.Glob(data_pattern)
    if not files:
      raise IOError("Unable to find the evaluation files.")
    logging.info("number of evaluation files: " + str(len(files)))
    files.sort()
    filename_queue = tf.train.string_input_producer(
        files, shuffle=False, num_epochs=1)
    eval_data = reader.prepare_reader(filename_queue)
    return tf.train.batch(
        eval_data,
        batch_size=batch_size,
        capacity=batch_size,
        allow_smaller_final_batch=True,
        enqueue_many=True) 

# 需要导入模块: from tensorflow import logging [as 别名]
# 或者: from tensorflow.logging import info [as 别名]
def get_input_data_tensors(reader,
                           data_pattern,
                           batch_size=256):
  logging.info("Using batch size of " + str(batch_size) + " for evaluation.")
  with tf.name_scope("eval_input"):
    files = gfile.Glob(data_pattern)
    if not files:
      raise IOError("Unable to find the evaluation files.")
    logging.info("number of evaluation files: " + str(len(files)))
    files.sort()
    filename_queue = tf.train.string_input_producer(
        files, shuffle=False, num_epochs=1)
    eval_data = reader.prepare_reader(filename_queue)
    return tf.train.batch(
        eval_data,
        batch_size=batch_size,
        capacity=3 * batch_size,
        allow_smaller_final_batch=True,
        enqueue_many=True) 

# 需要导入模块: from tensorflow import logging [as 别名]
# 或者: from tensorflow.logging import info [as 别名]
def get_input_evaluation_tensors(reader,
                                 data_pattern,
                                 batch_size=256):
  logging.info("Using batch size of " + str(batch_size) + " for evaluation.")
  with tf.name_scope("eval_input"):
    files = gfile.Glob(data_pattern)
    if not files:
      print data_pattern, files
      raise IOError("Unable to find the evaluation files.")
    logging.info("number of evaluation files: " + str(len(files)))
    files.sort()
    filename_queue = tf.train.string_input_producer(
        files, shuffle=False, num_epochs=1)
    eval_data = reader.prepare_reader(filename_queue)
    return tf.train.batch(
        eval_data,
        batch_size=batch_size,
        capacity=3 * FLAGS.batch_size,
        allow_smaller_final_batch=True,
        enqueue_many=True) 

# 需要导入模块: from tensorflow import logging [as 别名]
# 或者: from tensorflow.logging import info [as 别名]
def get_input_data_tensors(reader,
                           data_pattern,
                           batch_size=256,
                           num_epochs=None):
  logging.info("Using batch size of " + str(batch_size) + " for training.")
  with tf.name_scope("train_input"):
    files = gfile.Glob(data_pattern)
    if not files:
      raise IOError("Unable to find training files. data_pattern='" +
                    data_pattern + "'.")
    logging.info("Number of training files: %s.", str(len(files)))
    files.sort()
    filename_queue = tf.train.string_input_producer(
        files, num_epochs=num_epochs, shuffle=False)
    training_data = reader.prepare_reader(filename_queue)

    return tf.train.batch(
        training_data,
        batch_size=batch_size,
        capacity=FLAGS.batch_size * 4,
        allow_smaller_final_batch=True,
        enqueue_many=True) 

# 需要导入模块: from tensorflow import logging [as 别名]
# 或者: from tensorflow.logging import info [as 别名]
def start_server_if_distributed(self):
        """Starts a server if the execution is distributed."""

        if self.cluster:
            logging.info("%s: Starting trainer within cluster %s.",
                         task_as_string(self.task), self.cluster.as_dict())
            server = start_server(self.cluster, self.task)
            target = server.target
            device_fn = tf.train.replica_device_setter(
                ps_device="/job:ps",
                worker_device="/job:%s/task:%d" % (self.task.type, self.task.index),
                cluster=self.cluster)
        else:
            target = ""
            device_fn = ""
        return (target, device_fn) 

# 需要导入模块: from tensorflow import logging [as 别名]
# 或者: from tensorflow.logging import info [as 别名]
def get_meta_filename(self, start_new_model, train_dir):
        if start_new_model:
            logging.info("%s: Flag 'start_new_model' is set. Building a new model.",
                         task_as_string(self.task))
            return None

        latest_checkpoint = tf.train.latest_checkpoint(train_dir)
        if not latest_checkpoint:
            logging.info("%s: No checkpoint file found. Building a new model.",
                         task_as_string(self.task))
            return None

        meta_filename = latest_checkpoint + ".meta"
        if not gfile.Exists(meta_filename):
            logging.info("%s: No meta graph file found. Building a new model.",
                         task_as_string(self.task))
            return None
        else:
            return meta_filename 

# 需要导入模块: from tensorflow import logging [as 别名]
# 或者: from tensorflow.logging import info [as 别名]
def main(unused_argv):
  logging.set_verbosity(tf.logging.INFO)

  if not FLAGS.json_prediction_files_pattern:
    raise ValueError(
        "The flag --json_prediction_files_pattern must be specified.")

  if not FLAGS.csv_output_file:
    raise ValueError("The flag --csv_output_file must be specified.")

  logging.info("Looking for prediction files with pattern: %s", 
               FLAGS.json_prediction_files_pattern)

  file_paths = gfile.Glob(FLAGS.json_prediction_files_pattern)  
  logging.info("Found files: %s", file_paths)

  logging.info("Writing submission file to: %s", FLAGS.csv_output_file)
  with gfile.Open(FLAGS.csv_output_file, "w+") as output_file:
    output_file.write(get_csv_header())

    for file_path in file_paths:
      logging.info("processing file: %s", file_path)

      with gfile.Open(file_path) as input_file:

        for line in input_file: 
          json_data = json.loads(line)
          output_file.write(to_csv_row(json_data))

    output_file.flush()
  logging.info("done") 

# 需要导入模块: from tensorflow import logging [as 别名]
# 或者: from tensorflow.logging import info [as 别名]
def get_input_data_tensors(reader, data_pattern, batch_size, num_readers=1):
  """Creates the section of the graph which reads the input data.

  Args:
    reader: A class which parses the input data.
    data_pattern: A 'glob' style path to the data files.
    batch_size: How many examples to process at a time.
    num_readers: How many I/O threads to use.

  Returns:
    A tuple containing the features tensor, labels tensor, and optionally a
    tensor containing the number of frames per video. The exact dimensions
    depend on the reader being used.

  Raises:
    IOError: If no files matching the given pattern were found.
  """
  with tf.name_scope("input"):
    files = gfile.Glob(data_pattern)
    if not files:
      raise IOError("Unable to find input files. data_pattern='" +
                    data_pattern + "'")
    logging.info("number of input files: " + str(len(files)))
    filename_queue = tf.train.string_input_producer(
        files, num_epochs=1, shuffle=False)
    examples_and_labels = [reader.prepare_reader(filename_queue)
                           for _ in range(num_readers)]

    video_id_batch, video_batch, unused_labels, num_frames_batch = (
        tf.train.batch_join(examples_and_labels,
                            batch_size=batch_size,
                            allow_smaller_final_batch = True,
                            enqueue_many=True))
    return video_id_batch, video_batch, num_frames_batch 

# 需要导入模块: from tensorflow import logging [as 别名]
# 或者: from tensorflow.logging import info [as 别名]
def get_input_evaluation_tensors(reader,
                                 data_pattern,
                                 batch_size=1024,
                                 num_readers=1):
  """Creates the section of the graph which reads the evaluation data.

  Args:
    reader: A class which parses the training data.
    data_pattern: A 'glob' style path to the data files.
    batch_size: How many examples to process at a time.
    num_readers: How many I/O threads to use.

  Returns:
    A tuple containing the features tensor, labels tensor, and optionally a
    tensor containing the number of frames per video. The exact dimensions
    depend on the reader being used.

  Raises:
    IOError: If no files matching the given pattern were found.
  """
  logging.info("Using batch size of " + str(batch_size) + " for evaluation.")
  with tf.name_scope("eval_input"):
    files = gfile.Glob(data_pattern)
    if not files:
      raise IOError("Unable to find the evaluation files.")
    logging.info("number of evaluation files: " + str(len(files)))
    filename_queue = tf.train.string_input_producer(
        files, shuffle=False, num_epochs=1)
    eval_data = [
        reader.prepare_reader(filename_queue) for _ in range(num_readers)
    ]
    return tf.train.batch_join(
        eval_data,
        batch_size=batch_size,
        capacity=3 * batch_size,
        allow_smaller_final_batch=True,
        enqueue_many=True) 

# 需要导入模块: from tensorflow import logging [as 别名]
# 或者: from tensorflow.logging import info [as 别名]
def remove_training_directory(self, train_dir):
    """Removes the training directory."""
    try:
      logging.info(
          "%s: Removing existing train directory.",
          task_as_string(self.task))
      gfile.DeleteRecursively(train_dir)
    except:
      logging.error(
          "%s: Failed to delete directory " + train_dir +
          " when starting a new model. Please delete it manually and" +
          " try again.", task_as_string(self.task))