def on_SIGHUP(signum, frame):
cmd = Commands.commands.get("admin")
if cmd and Config.config.get("irclog"):
Logger.irclog("Received SIGHUP, reloading Config")
req = Hooks.Request(Config.config["irclog"][0],
Config.config["irclog"][1], "@SIGHUP", "SIGHUP")
Hooks.run_command(cmd, req, ["reload", "Config"])
def __init__(self, port, flag):
self.reward_range = (-200,200)
self.port=port
self.flag=flag
# Start flightgear sim and establish hooks, against other pilot
time.sleep(flag/5)
if (self.flag % 2 == 0):
exec_string = "~/flightgear/dnc-managed/run_fgfs.sh --telnet=foo,bar,1000,foo,{},bar --aircraft=f15c --prop:/sim/rendering/multithreading-mode=AutomaticSelection --altitude=40000 --vc=400 --enable-fuel-freeze --geometry=300x300 --multiplay=out,10,127.0.0.1,{} --multiplay=in,10,127.0.0.1,{} --callsign=primary".format(self.port, self.port+1, self.port+501)
else:
exec_string = "~/flightgear/dnc-managed/run_fgfs.sh --telnet=foo,bar,1000,foo,{},bar --aircraft=f15c --prop:/sim/rendering/multithreading-mode=AutomaticSelection --altitude=40000 --vc=400 --enable-fuel-freeze --geometry=300x300 --multiplay=out,10,127.0.0.1,{} --multiplay=in,10,127.0.0.1,{} --callsign=primary".format(self.port, self.port+1, self.port-499)
#os.system(exec_string)
proc = subprocess.Popen([exec_string], shell=True,
stdin=None, stdout=None, stderr=None, close_fds=True)
time.sleep(30)
self.hooks = Hooks.Hooks(port)
out = self.hooks.starter()
print(out, file=open("output.txt", "a"))
# Actions of the agent (control schema)
self.action_space = spaces.MultiDiscrete([3,3,3,3])
# Observations of the agent (observation schema)
high = np.array([
1000,1000,10000,1000,1000,1000,10000,10000,10000,10000,10000,10000,1000,1000,10000,1000,1000,1000])
self.observation_space = spaces.Box(-high, high, dtype=np.float16)
def run(self):
import Hooks
#now make sure that the kids can't setuid back to root
groupToRunAs = getattr(Configuration, 'groupToRunAs', None)
userToRunAs = getattr(Configuration, 'userToRunAs', None)
if groupToRunAs is not None or userToRunAs is not None:
# in case we are seteuid something else, which would
# cause setuid or getuid to fail, undo any existing
# seteuid. (The only reason to do this is for the case
# os.getuid()==0, AFAIK).
try:
seteuid = os.seteuid
except AttributeError:
# the OS may not support seteuid, in which
# case everything is hotsy-totsy.
pass
else:
seteuid(os.getuid())
if groupToRunAs is not None:
gid = grp.getgrnam(groupToRunAs)[2]
os.setgid(gid)
if userToRunAs is not None:
uid = pwd.getpwnam(userToRunAs)[2]
os.setuid(uid)
Hooks.ChildStart()
SocketMan.run(self)
def _as(req, arg):
"""
admin"""
_, target, text = req.text.split(" ", 2)
if target[0] == '@':
Global.account_cache[""] = {"@": target[1:]}
target = "@"
if text.find(" ") == -1:
command = text
args = []
else:
command, args = text.split(" ", 1)
args = [a for a in args.split(" ") if len(a) > 0]
if command[0] != '_':
cmd = commands.get(command.lower(), None)
if not cmd.__doc__ or cmd.__doc__.find("admin") == -1 or Irc.is_admin(source):
if cmd:
req = Hooks.FakeRequest(req, target, text)
Hooks.run_command(cmd, req, args)
def _as(req, arg):
"""
admin"""
_, target, text = req.text.split(" ", 2)
if target[0] == '@':
Global.account_cache[""] = {"@": target[1:]}
target = "@"
if text.find(" ") == -1:
command = text
args = []
else:
command, args = text.split(" ", 1)
args = [a for a in args.split(" ") if len(a) > 0]
if command[0] != '_':
cmd = commands.get(command.lower(), None)
if not cmd.__doc__ or cmd.__doc__.find("admin") == -1 or Irc.is_admin(source):
if cmd:
req = Hooks.FakeRequest(req, target, text)
Hooks.run_command(cmd, req, args)
if Global.account_cache.get("", None):
del Global.account_cache[""]
def run(self):
import Hooks
#now make sure that the kids can't setuid back to root
if hasattr(Configuration, 'groupToRunAs'):
gid = grp.getgrnam(Configuration.groupToRunAs)[2]
os.setgid(gid)
if hasattr(Configuration, 'userToRunAs'):
uid = pwd.getpwnam(Configuration.userToRunAs)[2]
if hasattr(os, 'seteuid'):
os.seteuid(os.getuid())
os.setuid(uid)
Hooks.ChildStart()
SocketMan.run(self)
def DistributedTrain(FLAGS, task_index, TFCluster, TFServer, QueueHook):
is_chief = (task_index == 0)
master = TFServer.target
with tf.device(
tf.train.replica_device_setter(
worker_device="/job:worker/task:%d" % task_index,
cluster=TFCluster)):
#Import data
mnist = input_data.read_data_sets(FLAGS.data_dir + "-" +
str(task_index),
one_hot=True)
#PlaceHolder for Images
x = tf.placeholder(tf.float32, [None, 784])
#PlaceHolder for Labels-OneHot Vector
y_ = tf.placeholder(tf.float32, [None, 10])
#Build the Deep Model
y_conv, keep_prob = Deep.deepnn(x)
#Define loss and optimizer
SummaryDictionary = {}
global_step = tf.train.get_or_create_global_step()
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
labels=y_, logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
SummaryDictionary.update({'cross_entropy': cross_entropy})
with tf.name_scope('adam_optimizer'):
Optimizer = tf.train.AdamOptimizer(1e-4)
SyncOtimizer = tf.train.SyncReplicasOptimizer(
Optimizer,
replicas_to_aggregate=FLAGS.workers,
total_num_replicas=FLAGS.workers,
use_locking=True,
name="sync_replicas")
train_step = SyncOtimizer.minimize(
cross_entropy,
global_step=global_step,
aggregation_method=tf.AggregationMethod.ADD_N)
#Define Metric
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1),
tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
SummaryDictionary.update({'AccuracyMetric': accuracy})
#Added Summaries for TensorBoard Visualization.
ListOfSummaries = []
for key in SummaryDictionary:
ListOfSummaries.append(tf.summary.scalar(key, SummaryDictionary[key]))
#for key, value in (SummaryDictionary).iteritems():
# ListOfSummaries.append(tf.summary.scalar(key,value))
MergedSummaryOperation = tf.summary.merge(ListOfSummaries)
#BatchSize=FLAGS.batch_size
BatchSize = int(FLAGS.batch_size / FLAGS.workers)
SummarySteps = 100
hooks = [QueueHook]
#Hook for Sync replicas
sync_replicas_hook = SyncOtimizer.make_session_run_hook(is_chief,
num_tokens=0)
hooks.append(sync_replicas_hook)
#Hook for StopAtStepHook
hooks.append(tf.train.StopAtStepHook(FLAGS.Iterations))
if is_chief:
#Get training subset for validation
TrainingLenght = mnist.train.images.shape[0]
#idx=np.random.randint(TrainingLenght,size=int(TrainingLenght/10))
idx = np.random.randint(TrainingLenght, size=BatchSize)
TrainingImagesValidation = mnist.train.images[idx]
#print(TrainingImagesValidation.shape)
TrainingLabelsValidation = mnist.train.labels[idx]
#print(TrainingLabelsValidation.shape)
hooks.append(
HOOKS.NewSummarySaverHook(MergedSummaryOperation,
FLAGS.model_dir + "/Training",
SummarySteps,
FLAGS.Iterations,
features=x,
labels=y_,
dropout=keep_prob,
batchx=TrainingImagesValidation,
batchy=TrainingLabelsValidation,
dropout_value=1.0))
hooks.append(
HOOKS.FinalSummaryHook(SummaryDictionary, x, y_, keep_prob,
mnist.test.images, mnist.test.labels, 1.0,
FLAGS.Iterations, "Testing"))
tick = time.time()
Config = None
with tf.train.MonitoredTrainingSession(master=master,
is_chief=is_chief,
hooks=hooks,
checkpoint_dir=FLAGS.model_dir,
save_checkpoint_secs=None,
save_summaries_steps=None,
save_summaries_secs=None,
config=Config) as sess:
step = sess.run(global_step)
while not sess.should_stop(): #and step <= FLAGS.Iterations:
batch = mnist.train.next_batch(BatchSize)
_, step = sess.run([train_step, global_step],
feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 0.5
})
tack = time.time()
print("Training Time: " + str(tack - tick))
def on_SIGHUP(signum, frame):
cmd = Commands.commands.get("admin")
if cmd and Config.config.get("irclog"):
Logger.irclog("Received SIGHUP, reloading Config")
req = Hooks.Request(Config.config["irclog"][0], Config.config["irclog"][1], "@SIGHUP", "SIGHUP")
Hooks.run_command(cmd, req, ["reload", "Config"])
def run(self):
import Hooks
Hooks.ChildStart()
SocketMan.run(self)
def train(_):
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
#PlaceHolder for Images
x = tf.placeholder(tf.float32, [None, 784])
#PlaceHolder for Labels-OneHot Vector
y_ = tf.placeholder(tf.float32, [None, 10])
#Build the Deep Model
y_conv, keep_prob = Deep.deepnn(x)
#Define loss and optimizer
global_step = tf.train.get_or_create_global_step()
SummaryDictionary = {}
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
SummaryDictionary.update({'cross_entropy': cross_entropy})
#Define Optimizer and global step
with tf.name_scope('adam_optimizer'):
Optimizer = tf.train.AdamOptimizer(1e-4)
train_step = Optimizer.minimize(cross_entropy, global_step=global_step)
#Define Metric
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
SummaryDictionary.update({'AccuracyMetric': accuracy})
#Added Summaries for TensorBoard Visualization.
ListOfSummaries = []
for key in SummaryDictionary:
ListOfSummaries.append(tf.summary.scalar(key, SummaryDictionary[key]))
#for key, value in (SummaryDictionary).iteritems():
# ListOfSummaries.append(tf.summary.scalar(key,value))
MergedSummaryOperation = tf.summary.merge(ListOfSummaries)
BatchSize = FLAGS.batch_size
SummarySteps = 100
hooks = []
#Get a sub set of the training DataSet for validation purpouses during Training
#Get training subset for validation
TrainingLenght = mnist.train.images.shape[0]
#idx=np.random.randint(TrainingLenght,size=int(TrainingLenght/10))
idx = np.random.randint(TrainingLenght, size=BatchSize)
TrainingImagesValidation = mnist.train.images[idx]
#print(TrainingImagesValidation.shape)
TrainingLabelsValidation = mnist.train.labels[idx]
#print(TrainingLabelsValidation.shape)
#Create Hooks for saving summaries or Logging
hooks.append(
HOOKS.NewSummarySaverHook(MergedSummaryOperation,
FLAGS.model_dir,
SummarySteps,
FLAGS.Iterations,
features=x,
labels=y_,
dropout=keep_prob,
batchx=TrainingImagesValidation,
batchy=TrainingLabelsValidation,
dropout_value=1.0))
"""
hooks.append(HOOKS.FinalSummaryHook(SummaryDictionary,x, y_, keep_prob, mnist.train.images, mnist.train.labels, 1.0, FLAGS.Iterations,"Training"))
hooks.append(HOOKS.NewSummarySaverHook(MergedSummaryOperation, FLAGS.model_dir+"/Testing", SummarySteps, FLAGS.Iterations,
features=x,labels=y_,dropout=keep_prob,batchx=mnist.test.images, batchy=mnist.test.labels, dropout_value=1.0)
)
"""
hooks.append(
HOOKS.FinalSummaryHook(SummaryDictionary, x, y_, keep_prob,
mnist.test.images, mnist.test.labels, 1.0,
FLAGS.Iterations, "Testing"))
tick = time.time()
hooks.append(tf.train.StopAtStepHook(last_step=FLAGS.Iterations))
with tf.train.MonitoredTrainingSession(master="",
is_chief=True,
hooks=hooks,
checkpoint_dir=FLAGS.model_dir,
save_checkpoint_secs=None,
save_summaries_steps=None) as sess:
step = sess.run(global_step)
while not sess.should_stop(): #and step < FLAGS.Iterations:
batch = mnist.train.next_batch(BatchSize)
_, step = sess.run([train_step, global_step],
feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 0.5
})
tack = time.time()
print("Training Time: " + str(tack - tick))
