def test_exclude_by_regex(self):
tf.Variable(tf.zeros((3, 2)), trainable=True)
with tf.variable_scope('foo'):
tf.Variable(tf.zeros((5, 2)), trainable=True)
with tf.variable_scope('bar'):
tf.Variable(tf.zeros((1, 2)), trainable=True)
self.assertEqual(0, count_weights(exclude=r'.*'))
self.assertEqual(6, count_weights(exclude=r'(^|/)foo/.*'))
self.assertEqual(16, count_weights(exclude=r'.*/bar/.*'))
def test_exclude_by_regex(self):
tf.Variable(tf.zeros((3, 2)), trainable=True)
with tf.variable_scope('foo'):
tf.Variable(tf.zeros((5, 2)), trainable=True)
with tf.variable_scope('bar'):
tf.Variable(tf.zeros((1, 2)), trainable=True)
self.assertEqual(0, count_weights(exclude=r'.*'))
self.assertEqual(6, count_weights(exclude=r'(^|/)foo/.*'))
self.assertEqual(16, count_weights(exclude=r'.*/bar/.*'))
def test_non_default_graph(self):
graph = tf.Graph()
with graph.as_default():
tf.Variable(tf.zeros((5, 3)), trainable=True)
tf.Variable(tf.zeros((8, 2)), trainable=False)
self.assertNotEqual(graph, tf.get_default_graph)
self.assertEqual(15, count_weights(graph=graph))
def test_restrict_invalid_scope(self):
tf.Variable(tf.zeros((3, 2)), trainable=True)
with tf.variable_scope('foo'):
tf.Variable(tf.zeros((5, 2)), trainable=True)
with tf.variable_scope('bar'):
tf.Variable(tf.zeros((1, 2)), trainable=True)
self.assertEqual(0, count_weights('bar'))
def test_trainable_and_non_trainable(self): tf.Variable(tf.zeros((5, 3)), trainable=True) tf.Variable(tf.zeros((8, 2)), trainable=False) tf.Variable(tf.zeros((1, 1)), trainable=True) tf.Variable(tf.zeros((5,)), trainable=True) tf.Variable(tf.zeros((3, 1)), trainable=False) self.assertEqual(15 + 1 + 5, count_weights())
def define_simulation_graph(batch_env, algo_cls, config):
"""Define the algortihm and environment interaction.
Args:
batch_env: In-graph environments object.
algo_cls: Constructor of a batch algorithm.
config: Configuration object for the algorithm.
Returns:
Object providing graph elements via attributes.
"""
# pylint: disable=unused-variable
step = tf.Variable(0, False, dtype=tf.int32, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
should_log = tf.placeholder(tf.bool, name='should_log')
do_report = tf.placeholder(tf.bool, name='do_report')
force_reset = tf.placeholder(tf.bool, name='force_reset')
# Extended
is_optimizing_offense = tf.placeholder(tf.bool,
name='is_optimizing_offense')
algo = algo_cls(batch_env,
step,
is_training,
should_log,
config,
is_optimizing_offense=is_optimizing_offense)
done, score, summary, gail_summary = tools.simulate(
batch_env, algo, should_log, force_reset)
message = 'Graph contains {} trainable variables.'
tf.logging.info(message.format(tools.count_weights()))
# pylint: enable=unused-variable
return tools.AttrDict(locals())
def test_restrict_invalid_scope(self):
tf.Variable(tf.zeros((3, 2)), trainable=True)
with tf.variable_scope('foo'):
tf.Variable(tf.zeros((5, 2)), trainable=True)
with tf.variable_scope('bar'):
tf.Variable(tf.zeros((1, 2)), trainable=True)
self.assertEqual(0, count_weights('bar'))
def test_non_default_graph(self):
graph = tf.Graph()
with graph.as_default():
tf.Variable(tf.zeros((5, 3)), trainable=True)
tf.Variable(tf.zeros((8, 2)), trainable=False)
self.assertNotEqual(graph, tf.get_default_graph)
self.assertEqual(15, count_weights(graph=graph))
def define_simulation_graph(batch_env, algo_cls, config):
"""Define the algortihm and environment interaction.
Simulate a single step in all environments. See more details in `tools.simulate`.
Args:
batch_env: In-graph environments object.
algo_cls: Constructor of a batch algorithm.
config: Configuration object for the algorithm.
Returns:
Object providing graph elements via attributes.
"""
# pylint: disable=unused-variable
step = tf.Variable(0, False, dtype=tf.int32, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
should_log = tf.placeholder(tf.bool, name='should_log')
do_report = tf.placeholder(tf.bool, name='do_report')
force_reset = tf.placeholder(tf.bool, name='force_reset')
algo = algo_cls(batch_env, step, is_training, should_log, config)
should_step = tf.placeholder(tf.bool, name='should_step')
use_external_action = tf.placeholder(tf.bool, name='use_external_action')
external_action = tf.placeholder(batch_env.action_info[0],
shape=batch_env.action_info[1],
name='external_action')
done, score, summary = tools.simulate(batch_env, algo, should_step,
use_external_action, external_action,
should_log, force_reset)
message = 'Graph contains {} trainable variables.'
tf.logging.info(message.format(tools.count_weights()))
# pylint: enable=unused-variable
return tools.AttrDict(locals())
def test_trainable_and_non_trainable(self):
tf.Variable(tf.zeros((5, 3)), trainable=True)
tf.Variable(tf.zeros((8, 2)), trainable=False)
tf.Variable(tf.zeros((1, 1)), trainable=True)
tf.Variable(tf.zeros((5, )), trainable=True)
tf.Variable(tf.zeros((3, 1)), trainable=False)
self.assertEqual(15 + 1 + 5, count_weights())
def testing(config, off_data, off_label, def_data, def_label, outdir):
"""
Args
----
config : Object providing configurations via attributes.
Yields
------
score : Evaluation scores.
"""
# split into train and eval
off_train_data, off_eval_data = np.split(off_data,
[off_data.shape[0] * 9 // 10])
off_train_label, off_eval_label = np.split(off_label,
[off_data.shape[0] * 9 // 10])
def_train_data, def_eval_data = np.split(def_data,
[def_data.shape[0] * 9 // 10])
def_train_label, def_eval_label = np.split(def_label,
[def_data.shape[0] * 9 // 10])
print(off_train_data.shape)
print(off_eval_data.shape)
print(off_train_label.shape)
print(off_eval_label.shape)
print(def_train_data.shape)
print(def_eval_data.shape)
print(def_train_label.shape)
print(def_eval_label.shape)
# graph
tf.reset_default_graph()
if FLAGS.config == 'offense':
model = pretrain_model.PretrainOffense(config)
elif FLAGS.config == 'defense':
model = pretrain_model.PretrainDefense(config)
else:
raise ValueError('{} is not an available config'.format(FLAGS.config))
message = 'Graph contains {} trainable variables.'
tf.logging.info(message.format(tools.count_weights()))
saver = utility.define_saver()
sess_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=config.log_device_placement)
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
if FLAGS.debug:
sess = tf_debug.LocalCLIDebugWrapperSession(sess,
ui_type=FLAGS.ui_type)
utility.initialize_variables(sess, saver, config.logdir, resume=True)
vis_result(sess, model, off_train_data, off_train_label,
def_train_data, def_train_label,
os.path.join(outdir, 'train'), 3)
vis_result(sess, model, off_eval_data, off_eval_label, def_eval_data,
def_eval_label, os.path.join(outdir, 'eval'), 3)
def define_simulation_graph(batch_env, algo_cls, config):
"""Define the algorithm and environment interaction.
Args:
batch_env: In-graph environments object.
algo_cls: Constructor of a batch algorithm.
config: Configuration object for the algorithm.
Returns:
Object providing graph elements via attributes.
"""
# pylint: disable=unused-variable
step = tf.Variable(0, False, dtype=tf.int32, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
should_log = tf.placeholder(tf.bool, name='should_log')
do_report = tf.placeholder(tf.bool, name='do_report')
force_reset = tf.placeholder(tf.bool, name='force_reset')
algo = algo_cls(batch_env, step, is_training, should_log, config)
done, score, summary = tools.simulate(
batch_env, algo, should_log, force_reset)
message = 'Graph contains {} trainable variables.'
tf.logging.info(message.format(tools.count_weights()))
# pylint: enable=unused-variable
return tools.AttrDict(locals())
def test_include_scopes(self):
tf.Variable(tf.zeros((3, 2)), trainable=True)
with tf.variable_scope('foo'):
tf.Variable(tf.zeros((5, 2)), trainable=True)
self.assertEqual(6 + 10, count_weights())
def test_ignore_non_trainable(self):
tf.Variable(tf.zeros((5, 3)), trainable=False)
tf.Variable(tf.zeros((1, 1)), trainable=False)
tf.Variable(tf.zeros((5, )), trainable=False)
self.assertEqual(0, count_weights())
def test_count_trainable(self):
tf.Variable(tf.zeros((5, 3)), trainable=True)
tf.Variable(tf.zeros((1, 1)), trainable=True)
tf.Variable(tf.zeros((5, )), trainable=True)
self.assertEqual(15 + 1 + 5, count_weights())
def test_ignore_non_trainable(self): tf.Variable(tf.zeros((5, 3)), trainable=False) tf.Variable(tf.zeros((1, 1)), trainable=False) tf.Variable(tf.zeros((5,)), trainable=False) self.assertEqual(0, count_weights())
def test_include_scopes(self):
tf.Variable(tf.zeros((3, 2)), trainable=True)
with tf.variable_scope('foo'):
tf.Variable(tf.zeros((5, 2)), trainable=True)
self.assertEqual(6 + 10, count_weights())
def train(config, data, label, outdir):
""" Training and evaluation entry point yielding scores.
Args
----
config : Object providing configurations via attributes.
Yields
------
score : Evaluation scores.
"""
# normalization
env = BBallPretrainEnv()
min_ = env.observation_space.low
max_ = env.observation_space.high
data = 2 * (data - min_) / (max_ - min_) - 1
# split into train and eval
train_data, eval_data = np.split(data, [data.shape[0] * 9 // 10])
train_label, eval_label = np.split(label, [data.shape[0] * 9 // 10])
print(train_data.shape)
print(train_label.shape)
print(eval_data.shape)
print(eval_label.shape)
# graph
tf.reset_default_graph()
if FLAGS.config == 'offense':
model = pretrain_model.PretrainOffense(config)
elif FLAGS.config == 'defense':
model = pretrain_model.PretrainDefense(config)
else:
raise ValueError('{} is not an available config'.format(FLAGS.config))
# model = config.model(config)
message = 'Graph contains {} trainable variables.'
tf.logging.info(message.format(tools.count_weights()))
saver = utility.define_saver()
sess_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=config.log_device_placement)
sess_config.gpu_options.allow_growth = True
# summary writter
train_writter = tf.summary.FileWriter(os.path.join(config.logdir, 'train'),
tf.get_default_graph())
# summary writter
eval_writter = tf.summary.FileWriter(os.path.join(config.logdir, 'eval'),
tf.get_default_graph())
with tf.Session(config=sess_config) as sess:
if FLAGS.debug:
sess = tf_debug.LocalCLIDebugWrapperSession(sess,
ui_type=FLAGS.ui_type)
utility.initialize_variables(sess,
saver,
config.logdir,
resume=FLAGS.resume)
for epoch_idx in range(config.num_epochs):
tf.logging.info('Number of epochs: {}'.format(epoch_idx))
training(sess, model, train_data, train_label, config,
train_writter)
evaluating(sess, model, eval_data, eval_label, config,
eval_writter)
if (epoch_idx + 1) % config.checkpoint_every == 0:
tf.gfile.MakeDirs(config.logdir)
filename = os.path.join(config.logdir, 'model.ckpt')
saver.save(sess, filename, (epoch_idx + 1) * config.batch_size)
def test_count_trainable(self): tf.Variable(tf.zeros((5, 3)), trainable=True) tf.Variable(tf.zeros((1, 1)), trainable=True) tf.Variable(tf.zeros((5,)), trainable=True) self.assertEqual(15 + 1 + 5, count_weights())
