utilities.py

#Utilities
import numpy as np
import pickle
import json
import random
import matplotlib
if not True:
	matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
import logging
from datetime import datetime
import tensorflow as tf
import tensorflow.contrib.slim as slim
from numpyencoder import NumpyEncoder

import sys


class Logger(object):
	# writes the outputs to a file
	def __init__(self,config):
		self.terminal = sys.stdout
		self.log = open(os.path.join(config.model_dir,"logfile.log"), "a",0)

	def write(self, message):
		self.terminal.write(message)
		self.log.write(message)  

	def flush(self):
		#this flush method is needed for python 3 compatibility.
		#this handles the flush command by doing nothing.
		#you might want to specify some extra behavior here.
		pass    


def prepare_dirs_and_logger(config):
	# set format of the logger
	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)
	
	# create load paths, if they don't exist
	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:
		if config.iftl:
			tl = 1
		else:
			tl = 0
		config.model_name = "{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}_{}".format(config.task, get_time(),
			config.gameConfig, config.tlBaseBrain, config.NoHiLayer, 
			config.demandUp, config.cp1, config.cp2, config.cp3, config.cp4, config.ch1, config.ch2, config.ch3, config.ch4,
			config.distCoeff, config.NoAgent, config.maxEpisodesTrain, config.lr0, config.multPerdInpt, config.dnnUpCnt, tl, 
			config.actionUp, config.demandDistribution, config.action_step, config.data_id)
		  
	config.model_dir = os.path.join(config.log_dir, config.model_name)

	for path in [config.pre_model_dir, config.log_dir, config.model_dir, 
				os.path.join(config.model_dir,'saved_figures'),
				os.path.join(config.model_dir,'model1'),
				os.path.join(config.model_dir,'model2'),
				os.path.join(config.model_dir,'model3'),
				os.path.join(config.model_dir,'model4'),
				os.path.join(config.pre_model_dir,'brain3'),
				os.path.join(config.pre_model_dir,'brain4'),
				os.path.join(config.pre_model_dir,'brain5'),
				os.path.join(config.pre_model_dir,'brain6'),				
				os.path.join(config.pre_model_dir,'brain7'),				
				os.path.join(config.pre_model_dir,'brain8'),				
				os.path.join(config.pre_model_dir,'brain9'),				
				os.path.join(config.pre_model_dir,'brain10'),				
				os.path.join(config.log_dir,'reports')]:
				
		if not os.path.exists(path):
			os.makedirs(path)
			

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


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

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


# input-output functions
def save(obj,name):
	pickle.dump(obj, open(name, "wb"))

def load(name):
	return pickle.load(open(name, "rb"))

def save_json(obj,name):
	with open(name, 'w') as outfile:
		json.dump(obj, outfile, indent=4, sort_keys=True, cls=NumpyEncoder)