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save all variables to make sure we save the vec_normalize normalization

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Peter Zhokhov
2018-08-01 10:12:19 -07:00
parent 151e48009e
commit 3528f7b992
7 changed files with 20 additions and 20 deletions
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4
baselines/a2c/a2c.py
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@@ -74,8 +74,8 @@ class Model(object):
self.step = step_model.step
self.value = step_model.value
self.initial_state = step_model.initial_state
self.save = functools.partial(tf_util.save_variables, sess=sess, variables=params)
self.load = functools.partial(tf_util.load_variables, sess=sess, variables=params)
self.save = functools.partial(tf_util.save_variables, sess=sess)
self.load = functools.partial(tf_util.load_variables, sess=sess)
tf.global_variables_initializer().run(session=sess)

4
baselines/acktr/acktr_disc.py
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@@ -83,8 +83,8 @@ class Model(object):
self.train = train
self.save = functools.partial(save_variables, variables=params, sess=sess)
self.load = functools.partial(load_variables, variables=params, sess=sess)
self.save = functools.partial(save_variables, sess=sess)
self.load = functools.partial(load_variables, sess=sess)
self.train_model = train_model
self.step_model = step_model
self.step = step_model.step

1
baselines/bench/benchmarks.py
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@@ -86,6 +86,7 @@ register_benchmark({
'description': 'Some small 2D MuJoCo tasks, run for 1M timesteps',
'tasks': [{'env_id': _envid, 'trials': 6, 'num_timesteps': int(1e6)} for _envid in _mujocosmall]
})
register_benchmark({
'name': 'MujocoWalkers',
'description': 'MuJoCo forward walkers, run for 8M, humanoid 100M',

8
baselines/common/running_mean_std.py
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@@ -46,10 +46,10 @@ class TfRunningMeanStd(object):
_batch_count = tf.placeholder(shape=(), dtype=tf.float64)
with tf.variable_scope(scope, reuse=False):
_mean = tf.get_variable('mean', initializer=np.zeros(shape))
_var = tf.get_variable('std', initializer=np.ones(shape))
_count = tf.get_variable('count', initializer=np.ones(shape=())*epsilon)
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
_mean = tf.get_variable('mean', initializer=np.zeros(shape, 'float64'), dtype=tf.float64)
_var = tf.get_variable('std', initializer=np.ones(shape, 'float64'), dtype=tf.float64)
_count = tf.get_variable('count', initializer=np.ones((), 'float64')*epsilon, dtype=tf.float64)
delta = _batch_mean - _mean
tot_count = _count + _batch_count

14
baselines/common/tf_util.py
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@@ -325,20 +325,20 @@ def save_state(fname, sess=None):
# The methods above and below are clearly doing the same thing, and in a rather similar way
# TODO: ensure there is no subtle differences and remove one
def save_variables(save_path, variables, sess=None):
if sess is None:
sess = get_session()
def save_variables(save_path, variables=None, sess=None):
sess = sess or get_session()
variables = variables or tf.trainable_variables()
ps = sess.run(variables)
save_dict = {v.name: value for v, value in zip(variables, ps)}
os.makedirs(os.path.dirname(save_path), exist_ok=True)
joblib.dump(save_dict, save_path)
def load_variables(load_path, variables, sess=None):
if sess is None:
sess = get_session()
def load_variables(load_path, variables=None, sess=None):
sess = sess or get_session()
variables = variables or tf.trainable_variables()
loaded_params = joblib.load(load_path)
loaded_params = joblib.load(os.path.expanduser(load_path))
restores = []
for v in variables:
restores.append(v.assign(loaded_params[v.name]))

5
baselines/ppo2/ppo2.py
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@@ -81,9 +81,8 @@ class Model(object):
self.value = act_model.value
self.initial_state = act_model.initial_state
# If you want to load weights, also save/load observation scaling inside VecNormalize ?
self.save = functools.partial(save_variables, sess=sess, variables=params)
self.load = functools.partial(load_variables, sess=sess, variables=params)
self.save = functools.partial(save_variables, sess=sess)
self.load = functools.partial(load_variables, sess=sess)
if MPI.COMM_WORLD.Get_rank() == 0:
initialize()