def testEvalOpAndFinalOp(self):
checkpoint_dir = os.path.join(self.get_temp_dir(),
'eval_ops_and_final_ops')
# Train a model for a single step to get a checkpoint.
self._train_model(checkpoint_dir, num_steps=1)
checkpoint_path = evaluation.wait_for_new_checkpoint(checkpoint_dir)
# Create the model so we have something to restore.
inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
logistic_classifier(inputs)
num_evals = 5
final_increment = 9.0
my_var = variables.local_variable(0.0, name='MyVar')
eval_ops = state_ops.assign_add(my_var, 1.0)
final_ops = array_ops.identity(my_var) + final_increment
final_ops_values = evaluation.evaluate_once(
checkpoint_path=checkpoint_path,
eval_ops=eval_ops,
final_ops={'value': final_ops},
hooks=[
evaluation.StopAfterNEvalsHook(num_evals),
])
self.assertEqual(final_ops_values['value'],
num_evals + final_increment)
def testEvaluatePerfectModel(self):
checkpoint_dir = os.path.join(self.get_temp_dir(),
'evaluate_perfect_model_once')
# Train a Model to completion:
self._train_model(checkpoint_dir, num_steps=300)
# Run
inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
labels = constant_op.constant(self._labels, dtype=dtypes.float32)
logits = logistic_classifier(inputs)
predictions = math_ops.round(logits)
accuracy, update_op = metric_ops.streaming_accuracy(
predictions, labels)
checkpoint_path = evaluation.wait_for_new_checkpoint(checkpoint_dir)
final_ops_values = evaluation.evaluate_once(
checkpoint_path=checkpoint_path,
eval_ops=update_op,
final_ops={'accuracy': accuracy},
hooks=[
evaluation.StopAfterNEvalsHook(1),
])
self.assertTrue(final_ops_values['accuracy'] > .99)
def testEvalOpAndFinalOp(self):
checkpoint_dir = os.path.join(self.get_temp_dir(), 'eval_ops_and_final_ops')
# Train a model for a single step to get a checkpoint.
self._train_model(checkpoint_dir, num_steps=1)
checkpoint_path = evaluation.wait_for_new_checkpoint(checkpoint_dir)
# Create the model so we have something to restore.
inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
logistic_classifier(inputs)
num_evals = 5
final_increment = 9.0
my_var = variables.local_variable(0.0, name='MyVar')
eval_ops = state_ops.assign_add(my_var, 1.0)
final_ops = array_ops.identity(my_var) + final_increment
final_ops_values = evaluation.evaluate_once(
checkpoint_path=checkpoint_path,
eval_ops=eval_ops,
final_ops={'value': final_ops},
hooks=[
evaluation.StopAfterNEvalsHook(num_evals),
])
self.assertEqual(final_ops_values['value'], num_evals + final_increment)
def testEvaluatePerfectModel(self):
checkpoint_dir = os.path.join(self.get_temp_dir(),
'evaluate_perfect_model_once')
# Train a Model to completion:
self._train_model(checkpoint_dir, num_steps=300)
# Run
inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
labels = constant_op.constant(self._labels, dtype=dtypes.float32)
logits = logistic_classifier(inputs)
predictions = math_ops.round(logits)
accuracy, update_op = metric_ops.streaming_accuracy(predictions, labels)
checkpoint_path = evaluation.wait_for_new_checkpoint(checkpoint_dir)
final_ops_values = evaluation.evaluate_once(
checkpoint_path=checkpoint_path,
eval_ops=update_op,
final_ops={'accuracy': accuracy},
hooks=[
evaluation.StopAfterNEvalsHook(1),
])
self.assertTrue(final_ops_values['accuracy'] > .99)
def testOnlyFinalOp(self):
checkpoint_dir = os.path.join(self.get_temp_dir(), 'only_final_ops')
# Train a model for a single step to get a checkpoint.
self._train_model(checkpoint_dir, num_steps=1)
checkpoint_path = evaluation.wait_for_new_checkpoint(checkpoint_dir)
# Create the model so we have something to restore.
inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
logistic_classifier(inputs)
final_increment = 9.0
my_var = variables.local_variable(0.0, name='MyVar')
final_ops = array_ops.identity(my_var) + final_increment
final_ops_values = evaluation.evaluate_once(
checkpoint_path=checkpoint_path, final_ops={'value': final_ops})
self.assertEqual(final_ops_values['value'], final_increment)
def testOnlyFinalOp(self):
checkpoint_dir = os.path.join(self.get_temp_dir(), 'only_final_ops')
# Train a model for a single step to get a checkpoint.
self._train_model(checkpoint_dir, num_steps=1)
checkpoint_path = evaluation.wait_for_new_checkpoint(checkpoint_dir)
# Create the model so we have something to restore.
inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
logistic_classifier(inputs)
final_increment = 9.0
my_var = variables.local_variable(0.0, name='MyVar')
final_ops = array_ops.identity(my_var) + final_increment
final_ops_values = evaluation.evaluate_once(
checkpoint_path=checkpoint_path, final_ops={'value': final_ops})
self.assertEqual(final_ops_values['value'], final_increment)
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 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):
"""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.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.ConfigProto` that will be used to
configure the `Session`. If left as `None`, the default will be used.
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))
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=hooks,
config=session_config)
