def close(self):
try:
if not self._coord.should_stop():
self._coord.request_stop()
self._coord.join(self._coordinated_threads_to_join)
except Exception: # pylint: disable=broad-except
# Don't raise exception at close
pass
finally:
WrappedSession.close(self)
def close(self):
try:
if not self._coord.should_stop():
self._coord.request_stop()
self._coord.join(self._coordinated_threads_to_join)
except Exception: # pylint: disable=broad-except
# Don't raise exception at close
pass
finally:
WrappedSession.close(self)
def __init__(self, sess, coord, coordinated_threads_to_join):
"""Create a new `CoordinatedSession`.
Args:
sess: A `tf.Session` object. The wrapped session.
coord: A `tf.train.Coordinator` object.
coordinated_threads_to_join: A list of threads.
"""
WrappedSession.__init__(self, sess)
self._coord = coord
self._coordinated_threads_to_join = coordinated_threads_to_join
def __init__(self, sess, coord, coordinated_threads_to_join):
"""Create a new `CoordinatedSession`.
Args:
sess: A `tf.Session` object. The wrapped session.
coord: A `tf.train.Coordinator` object.
coordinated_threads_to_join: A list of threads.
"""
WrappedSession.__init__(self, sess)
self._coord = coord
self._coordinated_threads_to_join = coordinated_threads_to_join
def __init__(self, sess_factory):
"""Create a new `RecoverableSession`.
The value returned by calling `sess_factory()` will be the
session wrapped by this recoverable session.
Args:
sess_factory: A callable with no arguments that returns a
`tf.Session` when called.
"""
self._factory = sess_factory
WrappedSession.__init__(self, sess_factory())
def __init__(self, sess_factory):
"""Create a new `RecoverableSession`.
The value returned by calling `sess_factory()` will be the
session wrapped by this recoverable session.
Args:
sess_factory: A callable with no arguments that returns a
`tf.Session` when called.
"""
self._factory = sess_factory
WrappedSession.__init__(self, sess_factory())
def __init__(self, sess, monitors, global_step_tensor):
"""Initializes a MonitoredSession object.
Args:
sess: A `tf.Session` or a `WrappedSession` object.
monitors: An iterable of `tf.contrib.learn.BaseMonitor' objects.
global_step_tensor: A 'Tensor' which holds a scalar int value.
"""
WrappedSession.__init__(self, sess)
self._monitors = monitors
self._should_stop = False
self._global_step_tensor = global_step_tensor
self._last_step = None
def __init__(self, sess, monitors, global_step_tensor):
"""Initializes a MonitoredSession object.
Args:
sess: A `tf.Session` or a `WrappedSession` object.
monitors: An iterable of `tf.contrib.learn.BaseMonitor' objects.
global_step_tensor: A 'Tensor' which holds a scalar int value.
"""
WrappedSession.__init__(self, sess)
self._monitors = monitors
self._should_stop = False
self._global_step_tensor = global_step_tensor
self._last_step = None
def run(self, fetches, feed_dict=None, options=None, run_metadata=None):
"""See base class."""
if self.should_stop():
raise RuntimeError('Run called even after should_stop requested.')
if self._last_step is None:
self._last_step = WrappedSession.run(self, self._global_step_tensor)
monitors_step = self._last_step + 1
monitor_fetches = []
for monitor in self._monitors:
monitor_requests = monitor.step_begin(monitors_step)
if monitor_requests:
# TODO(ispir): remove following restriction after b/30136815 fixed
if not isinstance(monitor_requests, list):
raise ValueError('Monitor.step_begin should return a list.')
monitor_fetches.extend(monitor_requests)
actual_fetches = {
'caller': fetches,
self._global_step_tensor: self._global_step_tensor,
'monitors': [_as_graph_element(f, self.graph) for f in monitor_fetches]
}
# Do session run.
outputs = WrappedSession.run(self,
fetches=actual_fetches,
feed_dict=feed_dict,
options=options,
run_metadata=run_metadata)
self._last_step = outputs[self._global_step_tensor]
# Call monitors step_end and stop if one of them tells to stop.
if monitor_fetches:
monitor_outputs = dict(zip(monitor_fetches, outputs['monitors']))
else:
monitor_outputs = {}
for monitor in self._monitors:
induce_stop = monitor.step_end(monitors_step, monitor_outputs)
self._should_stop = self._should_stop or induce_stop
# Call the post_step methods.
for monitor in self._monitors:
monitor.post_step(monitors_step, self._sess)
return outputs['caller']
def run(self, fetches, feed_dict=None, options=None, run_metadata=None):
"""See base class."""
if self.should_stop():
raise RuntimeError('run called even after should_stop requested.')
if self._last_step is None:
self._last_step = WrappedSession.run(self,
self._global_step_tensor)
logging.info('Initialized step to: %d', self._last_step)
monitors_step = self._last_step + 1
monitor_fetches = []
for monitor in self._monitors:
monitor_fetches.extend(monitor.step_begin(monitors_step))
actual_fetches = {
'caller': fetches,
self._global_step_tensor: self._global_step_tensor,
'monitors':
[_as_graph_element(f, self.graph) for f in monitor_fetches]
}
# Do session run.
outputs = WrappedSession.run(self,
fetches=actual_fetches,
feed_dict=feed_dict,
options=options,
run_metadata=run_metadata)
self._last_step = outputs[self._global_step_tensor]
# Call monitors step_end and stop if one of them tells to stop.
if monitor_fetches:
monitor_outputs = dict(zip(monitor_fetches, outputs['monitors']))
else:
monitor_outputs = {}
for monitor in self._monitors:
induce_stop = monitor.step_end(monitors_step, monitor_outputs)
self._should_stop = self._should_stop or induce_stop
return outputs['caller']
def run(self, fetches, feed_dict=None, options=None, run_metadata=None):
"""See base class."""
if self.should_stop():
raise RuntimeError('run called even after should_stop requested.')
if self._last_step is None:
self._last_step = WrappedSession.run(self, self._global_step_tensor)
logging.info('Initialized step to: %d', self._last_step)
monitors_step = self._last_step + 1
monitor_fetches = []
for monitor in self._monitors:
monitor_fetches.extend(monitor.step_begin(monitors_step))
actual_fetches = {
'caller': fetches,
self._global_step_tensor: self._global_step_tensor,
'monitors': [_as_graph_element(f, self.graph) for f in monitor_fetches]
}
# Do session run.
outputs = WrappedSession.run(self,
fetches=actual_fetches,
feed_dict=feed_dict,
options=options,
run_metadata=run_metadata)
self._last_step = outputs[self._global_step_tensor]
# Call monitors step_end and stop if one of them tells to stop.
if monitor_fetches:
monitor_outputs = dict(zip(monitor_fetches, outputs['monitors']))
else:
monitor_outputs = {}
for monitor in self._monitors:
induce_stop = monitor.step_end(monitors_step, monitor_outputs)
self._should_stop = self._should_stop or induce_stop
return outputs['caller']
def run(self, fetches, **kwargs):
self.args_called = dict(kwargs)
# Call run only with fetches since we directly pass other arguments.
return WrappedSession.run(self, fetches)
def __init__(self, sess):
WrappedSession.__init__(self, sess)
self.args_called = {}
def run(self, fetches, **kwargs):
self.args_called = dict(kwargs)
# Call run only with fetches since we directly pass other arguments.
return WrappedSession.run(self, fetches)
def __init__(self, sess):
WrappedSession.__init__(self, sess)
self.args_called = {}
