def testSummariesAreFlushedToDisk(self):
checkpoint_dir = os.path.join(self.get_temp_dir(),
'summaries_are_flushed')
logdir = os.path.join(self.get_temp_dir(),
'summaries_are_flushed_eval')
if gfile.Exists(logdir):
gfile.DeleteRecursively(logdir)
# Train a Model to completion:
self._train_model(checkpoint_dir, num_steps=300)
# Create the model (which can be restored).
inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
logistic_classifier(inputs)
names_to_values = {'bread': 3.4, 'cheese': 4.5, 'tomato': 2.0}
for k in names_to_values:
v = names_to_values[k]
summary_lib.scalar(k, v)
evaluation.evaluate_repeatedly(checkpoint_dir=checkpoint_dir,
hooks=[
evaluation.SummaryAtEndHook(logdir),
],
max_number_of_evaluations=1)
self._verify_summaries(logdir, names_to_values)
def testSummaryAtEndHookWithoutSummaries(self):
logdir = os.path.join(self.get_temp_dir(),
'summary_at_end_hook_without_summaires')
if gfile.Exists(logdir):
gfile.DeleteRecursively(logdir)
with ops.Graph().as_default():
# Purposefully don't add any summaries. The hook will just dump the
# GraphDef event.
hook = evaluation.SummaryAtEndHook(log_dir=logdir)
hook.begin()
with self.cached_session() as session:
hook.after_create_session(session, None)
hook.end(session)
self._verify_events(logdir, {})
def evaluation_loop(master,
checkpoint_dir,
logdir,
num_evals=1,
initial_op=None,
initial_op_feed_dict=None,
init_fn=None,
eval_op=None,
eval_op_feed_dict=None,
final_op=None,
final_op_feed_dict=None,
summary_op=_USE_DEFAULT,
summary_op_feed_dict=None,
variables_to_restore=None,
eval_interval_secs=60,
max_number_of_evaluations=None,
session_config=None,
timeout=None,
timeout_fn=None,
hooks=None):
"""Runs TF-Slim's Evaluation Loop.
Args:
master: The BNS address of the TensorFlow master.
checkpoint_dir: The directory where checkpoints are stored.
logdir: The directory where the TensorFlow summaries are written to.
num_evals: The number of times to run `eval_op`.
initial_op: An operation run at the beginning of evaluation.
initial_op_feed_dict: A feed dictionary to use when executing `initial_op`.
init_fn: An optional callable to be executed after `init_op` is called. The
callable must accept one argument, the session being initialized.
eval_op: A operation run `num_evals` times.
eval_op_feed_dict: The feed dictionary to use when executing the `eval_op`.
final_op: An operation to execute after all of the `eval_op` executions. The
value of `final_op` is returned.
final_op_feed_dict: A feed dictionary to use when executing `final_op`.
summary_op: The summary_op to evaluate after running TF-Slims metric ops. By
default the summary_op is set to tf.compat.v1.summary.merge_all().
summary_op_feed_dict: An optional feed dictionary to use when running the
`summary_op`.
variables_to_restore: A list of TensorFlow variables to restore during
evaluation. If the argument is left as `None` then
slim.variables.GetVariablesToRestore() is used.
eval_interval_secs: The minimum number of seconds between evaluations.
max_number_of_evaluations: the max number of iterations of the evaluation.
If the value is left as 'None', the evaluation continues indefinitely.
session_config: An instance of `tf.compat.v1.ConfigProto` that will be used
to configure the `Session`. If left as `None`, the default will be used.
timeout: The maximum amount of time to wait between checkpoints. If left as
`None`, then the process will wait indefinitely.
timeout_fn: Optional function to call after a timeout. If the function
returns True, then it means that no new checkpoints will be generated and
the iterator will exit. The function is called with no arguments.
hooks: A list of additional `SessionRunHook` objects to pass during repeated
evaluations.
Returns:
The value of `final_op` or `None` if `final_op` is `None`.
"""
if summary_op == _USE_DEFAULT:
summary_op = summary.merge_all()
all_hooks = [
evaluation.StopAfterNEvalsHook(num_evals),
]
if summary_op is not None:
all_hooks.append(
evaluation.SummaryAtEndHook(log_dir=logdir,
summary_op=summary_op,
feed_dict=summary_op_feed_dict))
if hooks is not None:
# Add custom hooks if provided.
all_hooks.extend(hooks)
saver = None
if variables_to_restore is not None:
saver = tf_saver.Saver(variables_to_restore)
return evaluation.evaluate_repeatedly(
checkpoint_dir,
master=master,
scaffold=monitored_session.Scaffold(
init_op=initial_op,
init_feed_dict=initial_op_feed_dict,
init_fn=init_fn,
saver=saver),
eval_ops=eval_op,
feed_dict=eval_op_feed_dict,
final_ops=final_op,
final_ops_feed_dict=final_op_feed_dict,
eval_interval_secs=eval_interval_secs,
hooks=all_hooks,
config=session_config,
max_number_of_evaluations=max_number_of_evaluations,
timeout=timeout,
timeout_fn=timeout_fn)
def evaluate_once(master,
checkpoint_path,
logdir,
num_evals=1,
initial_op=None,
initial_op_feed_dict=None,
eval_op=None,
eval_op_feed_dict=None,
final_op=None,
final_op_feed_dict=None,
summary_op=_USE_DEFAULT,
summary_op_feed_dict=None,
variables_to_restore=None,
session_config=None,
hooks=None):
"""Evaluates the model at the given checkpoint path.
Args:
master: The BNS address of the TensorFlow master.
checkpoint_path: The path to a checkpoint to use for evaluation.
logdir: The directory where the TensorFlow summaries are written to.
num_evals: The number of times to run `eval_op`.
initial_op: An operation run at the beginning of evaluation.
initial_op_feed_dict: A feed dictionary to use when executing `initial_op`.
eval_op: A operation run `num_evals` times.
eval_op_feed_dict: The feed dictionary to use when executing the `eval_op`.
final_op: An operation to execute after all of the `eval_op` executions. The
value of `final_op` is returned.
final_op_feed_dict: A feed dictionary to use when executing `final_op`.
summary_op: The summary_op to evaluate after running TF-Slims metric ops. By
default the summary_op is set to tf.compat.v1.summary.merge_all().
summary_op_feed_dict: An optional feed dictionary to use when running the
`summary_op`.
variables_to_restore: A list of TensorFlow variables to restore during
evaluation. If the argument is left as `None` then
slim.variables.GetVariablesToRestore() is used.
session_config: An instance of `tf.compat.v1.ConfigProto` that will be used
to configure the `Session`. If left as `None`, the default will be used.
hooks: A list of additional `SessionRunHook` objects to pass during the
evaluation.
Returns:
The value of `final_op` or `None` if `final_op` is `None`.
"""
if summary_op == _USE_DEFAULT:
summary_op = summary.merge_all()
all_hooks = [
evaluation.StopAfterNEvalsHook(num_evals),
]
if summary_op is not None:
all_hooks.append(
evaluation.SummaryAtEndHook(log_dir=logdir,
summary_op=summary_op,
feed_dict=summary_op_feed_dict))
if hooks is not None:
all_hooks.extend(hooks)
saver = None
if variables_to_restore is not None:
saver = tf_saver.Saver(variables_to_restore)
return evaluation.evaluate_once(checkpoint_path,
master=master,
scaffold=monitored_session.Scaffold(
init_op=initial_op,
init_feed_dict=initial_op_feed_dict,
saver=saver),
eval_ops=eval_op,
feed_dict=eval_op_feed_dict,
final_ops=final_op,
final_ops_feed_dict=final_op_feed_dict,
hooks=all_hooks,
config=session_config)
def evaluation_loop(master,
checkpoint_dir,
logdir,
num_evals=1,
initial_op=None,
initial_op_feed_dict=None,
eval_op=None,
eval_op_feed_dict=None,
final_op=None,
final_op_feed_dict=None,
summary_op=_USE_DEFAULT,
summary_op_feed_dict=None,
variables_to_restore=None,
eval_interval_secs=60,
max_number_of_evaluations=None,
session_config=None,
timeout=None):
"""Runs TF-Slim's Evaluation Loop.
Args:
master: The BNS address of the TensorFlow master.
checkpoint_dir: The directory where checkpoints are stored.
logdir: The directory where the TensorFlow summaries are written to.
num_evals: The number of times to run `eval_op`.
initial_op: An operation run at the beginning of evaluation.
initial_op_feed_dict: A feed dictionary to use when executing `initial_op`.
eval_op: A operation run `num_evals` times.
eval_op_feed_dict: The feed dictionary to use when executing the `eval_op`.
final_op: An operation to execute after all of the `eval_op` executions. The
value of `final_op` is returned.
final_op_feed_dict: A feed dictionary to use when executing `final_op`.
summary_op: The summary_op to evaluate after running TF-Slims metric ops. By
default the summary_op is set to tf.summary.merge_all().
summary_op_feed_dict: An optional feed dictionary to use when running the
`summary_op`.
variables_to_restore: A list of TensorFlow variables to restore during
evaluation. If the argument is left as `None` then
slim.variables.GetVariablesToRestore() is used.
eval_interval_secs: The minimum number of seconds between evaluations.
max_number_of_evaluations: the max number of iterations of the evaluation.
If the value is left as 'None', the evaluation continues indefinitely.
session_config: An instance of `tf.ConfigProto` that will be used to
configure the `Session`. If left as `None`, the default will be used.
timeout: The maximum amount of time to wait between checkpoints. If left as
`None`, then the process will wait indefinitely.
Returns:
The value of `final_op` or `None` if `final_op` is `None`.
"""
if summary_op == _USE_DEFAULT:
summary_op = summary.merge_all()
hooks = [
evaluation.StopAfterNEvalsHook(num_evals),
]
if summary_op is not None:
hooks.append(
evaluation.SummaryAtEndHook(logdir, summary_op,
summary_op_feed_dict))
return evaluation.evaluate_repeatedly(
checkpoint_dir,
master=master,
scaffold=monitored_session.Scaffold(
init_op=initial_op, init_feed_dict=initial_op_feed_dict),
eval_ops=eval_op,
feed_dict=eval_op_feed_dict,
final_ops=final_op,
final_ops_feed_dict=final_op_feed_dict,
variables_to_restore=variables_to_restore,
eval_interval_secs=eval_interval_secs,
hooks=hooks,
config=session_config,
max_number_of_evaluations=max_number_of_evaluations,
timeout=timeout)
