utils.py

import os
import json
import logging
import numpy as np
from datetime import datetime

import tensorflow as tf
import tensorflow.contrib.slim as slim

def prepare_dirs_and_logger(config):
  formatter = logging.Formatter(
      "%(asctime)s:%(levelname)s::%(message)s")
  logger = logging.getLogger('tensorflow')

  for hdlr in logger.handlers:
    logger.removeHandler(hdlr)

  handler = logging.StreamHandler()
  handler.setFormatter(formatter)

  logger.addHandler(handler)
  logger.setLevel(tf.logging.INFO)

  if config.load_path:
    if config.load_path.startswith(config.task):
      config.model_name = config.load_path
    else:
      config.model_name = "{}_{}".format(config.task, config.load_path)
  else:
    config.model_name = "{}_{}".format(config.task, get_time())

  config.model_dir = os.path.join(config.log_dir, config.model_name)

  for path in [config.log_dir, config.data_dir, config.model_dir]:
    if not os.path.exists(path):
      os.makedirs(path)

def get_time():
  return datetime.now().strftime("%Y-%m-%d_%H-%M-%S")

def show_all_variables():
  model_vars = tf.trainable_variables()
  slim.model_analyzer.analyze_vars(model_vars, print_info=True)

def save_config(model_dir, config):
  param_path = os.path.join(model_dir, "params.json")

  tf.logging.info("MODEL dir: %s" % model_dir)
  tf.logging.info("PARAM path: %s" % param_path)

  with open(param_path, 'w') as fp:
    json.dump(config.__dict__, fp,  indent=4, sort_keys=True)