def _export_mode(mode, has_saved_vars, builder, model, custom_objects,
checkpoint_path, input_signature):
"""Exports a model, and optionally saves new vars from the clone model.
Args:
mode: A `tf.estimator.ModeKeys` string.
has_saved_vars: A `boolean` indicating whether the SavedModel has already
exported variables.
builder: A `SavedModelBuilder` object.
model: A `tf.keras.Model` object.
custom_objects: A dictionary mapping string names to custom classes
or functions.
checkpoint_path: String path to checkpoint.
input_signature: Nested TensorSpec containing the expected inputs. Can be
`None`, in which case the signature will be inferred from the model.
Raises:
ValueError: If the train/eval mode is being exported, but the model does
not have an optimizer.
"""
from tensorflow.python.keras import models as models_lib # pylint: disable=g-import-not-at-top
compile_clone = (mode != mode_keys.ModeKeys.PREDICT)
if compile_clone and not model.optimizer:
raise ValueError(
'Model does not have an optimizer. Cannot export mode %s' % mode)
model_graph = ops.get_default_graph()
with ops.Graph().as_default() as g, K.learning_phase_scope(
mode == mode_keys.ModeKeys.TRAIN):
if input_signature is None:
input_tensors = None
else:
input_tensors = nest.map_structure(create_placeholder,
input_signature)
# Clone the model into blank graph. This will create placeholders for inputs
# and targets.
clone = models_lib.clone_and_build_model(model,
input_tensors=input_tensors,
custom_objects=custom_objects,
compile_clone=compile_clone)
# Make sure that iterations variable is added to the global step collection,
# to ensure that, when the SavedModel graph is loaded, the iterations
# variable is returned by `tf.train.get_global_step()`. This is required for
# compatibility with the SavedModelEstimator.
if compile_clone:
g.add_to_collection(ops.GraphKeys.GLOBAL_STEP,
clone.optimizer.iterations)
# Extract update and train ops from train/test/predict functions.
train_op = None
if mode == mode_keys.ModeKeys.TRAIN:
clone._make_train_function()
train_op = clone.train_function.updates_op
elif mode == mode_keys.ModeKeys.TEST:
clone._make_test_function()
else:
clone._make_predict_function()
g.get_collection_ref(ops.GraphKeys.UPDATE_OPS).extend(
clone.state_updates)
clone_var_list = checkpointable_utils.named_saveables(clone)
with session.Session().as_default():
if has_saved_vars:
# Confirm all variables in the clone have an entry in the checkpoint.
status = clone.load_weights(checkpoint_path)
status.assert_existing_objects_matched()
else:
# Confirm that variables between the clone and model match up exactly,
# not counting optimizer objects. Optimizer objects are ignored because
# if the model has not trained, the slot variables will not have been
# created yet.
# TODO(b/113179535): Replace with checkpointable equivalence.
_assert_same_non_optimizer_objects(model, model_graph, clone,
g)
# TODO(b/113178242): Use value transfer for checkpointable objects.
clone.load_weights(checkpoint_path)
# Add graph and variables to SavedModel.
# TODO(b/113134168): Switch to add_meta_graph_and_variables.
clone.save_weights(checkpoint_path,
save_format='tf',
overwrite=True)
builder._has_saved_variables = True
# Add graph to the SavedModel builder.
builder.add_meta_graph(model_utils.EXPORT_TAG_MAP[mode],
signature_def_map=_create_signature_def_map(
clone, mode),
saver=saver_lib.Saver(clone_var_list),
init_op=variables.local_variables_initializer(),
train_op=train_op)
return None
def _export_mode(
mode, has_saved_vars, builder, model, custom_objects, checkpoint_path):
"""Export a model, and optionally save new vars from the clone model.
Args:
mode: A `tf.estimator.ModeKeys` string.
has_saved_vars: A `boolean` indicating whether the SavedModel has already
exported variables.
builder: A `SavedModelBuilder` object.
model: A `tf.keras.Model` object.
custom_objects: A dictionary mapping string names to custom classes
or functions.
checkpoint_path: String path to checkpoint.
Raises:
ValueError: If the train/eval mode is being exported, but the model does
not have an optimizer.
"""
compile_clone = (mode != model_fn_lib.ModeKeys.PREDICT)
if compile_clone and not model.optimizer:
raise ValueError(
'Model does not have an optimizer. Cannot export mode %s' % mode)
model_graph = ops.get_default_graph()
with ops.Graph().as_default() as g:
K.set_learning_phase(mode == model_fn_lib.ModeKeys.TRAIN)
# Clone the model into blank graph. This will create placeholders for inputs
# and targets.
clone = models_lib.clone_and_build_model(
model, custom_objects=custom_objects, compile_clone=compile_clone)
# Make sure that iterations variable is added to the global step collection,
# to ensure that, when the SavedModel graph is loaded, the iterations
# variable is returned by `tf.train.get_global_step()`. This is required for
# compatibility with the SavedModelEstimator.
if compile_clone:
g.add_to_collection(ops.GraphKeys.GLOBAL_STEP, clone.optimizer.iterations)
# Extract update and train ops from train/test/predict functions.
train_op = None
if mode == model_fn_lib.ModeKeys.TRAIN:
clone._make_train_function()
train_op = clone.train_function.updates_op
elif mode == model_fn_lib.ModeKeys.EVAL:
clone._make_test_function()
else:
clone._make_predict_function()
g.get_collection_ref(ops.GraphKeys.UPDATE_OPS).extend(clone.state_updates)
clone_var_list = checkpointable_utils.named_saveables(clone)
with session.Session().as_default():
if has_saved_vars:
# Confirm all variables in the clone have an entry in the checkpoint.
status = clone.load_weights(checkpoint_path)
status.assert_existing_objects_matched()
else:
# Confirm that variables between the clone and model match up exactly,
# not counting optimizer objects. Optimizer objects are ignored because
# if the model has not trained, the slot variables will not have been
# created yet.
# TODO(b/113179535): Replace with checkpointable equivalence.
_assert_same_non_optimizer_objects(model, model_graph, clone, g)
# TODO(b/113178242): Use value transfer for checkpointable objects.
clone.load_weights(checkpoint_path)
# Add graph and variables to SavedModel.
# TODO(b/113134168): Switch to add_meta_graph_and_variables.
clone.save_weights(checkpoint_path, save_format='tf', overwrite=True)
builder._has_saved_variables = True
# Add graph to the SavedModel builder.
builder.add_meta_graph(
model_fn_lib.EXPORT_TAG_MAP[mode],
signature_def_map=_create_signature_def_map(clone, mode),
saver=saver_lib.Saver(clone_var_list),
init_op=variables.local_variables_initializer(),
train_op=train_op)
return None
def _get_var_list(model):
"""Returns list of all checkpointed saveable objects in the model."""
return checkpointable_utils.named_saveables(model)
def _get_var_list(model): """Return list of all checkpointed saveable objects in the model.""" return checkpointable_utils.named_saveables(model)
