def in_place_subclassed_model_state_restoration(model):
"""Restores the original state of a model after it was "reset".
This undoes this action of `_in_place_subclassed_model_reset`, which is called
in `clone_and_build_model` if `in_place_reset` is set to True.
Args:
model: Instance of a Keras model created via subclassing, on which
`_in_place_subclassed_model_reset` was previously called.
"""
assert not model._is_graph_network
# Restore layers and build attributes
if (hasattr(model, '_original_attributes_cache')
and model._original_attributes_cache is not None):
# Models have sticky attribute assignment, so we want to be careful to add
# back the previous attributes and track Layers by their original names
# without adding dependencies on "utility" attributes which Models exempt
# when they're constructed.
model._layers = data_structures.NoDependency([])
for name, value in model._original_attributes_cache.items():
if not isinstance(value, checkpointable.CheckpointableBase):
# If this value is not already checkpointable, it's probably that way
# for a reason; we don't want to start tracking data structures that the
# original Model didn't.
value = data_structures.NoDependency(value)
setattr(model, name, value)
model._original_attributes_cache = None
else:
# Restore to the state of a never-called model.
model.built = False
model.inputs = None
model.outputs = None
def __init__(self):
super(Foo, self).__init__()
self.isdep = keras.layers.Dense(1)
self.notdep = data_structures.NoDependency(
keras.layers.Dense(2))
self.notdep_var = data_structures.NoDependency(
resource_variable_ops.ResourceVariable(1.,
name='notdep_var'))
def testNestedLists(self):
a = tracking.Checkpointable()
a.l = []
b = tracking.Checkpointable()
a.l.append([b])
c = tracking.Checkpointable()
a.l[0].append(c)
a_deps = util.list_objects(a)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
a.l[0].append(1)
d = tracking.Checkpointable()
a.l[0].append(d)
a_deps = util.list_objects(a)
self.assertIn(d, a_deps)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
self.assertNotIn(1, a_deps)
e = tracking.Checkpointable()
f = tracking.Checkpointable()
a.l1 = [[], [e]]
a.l1[0].append(f)
a_deps = util.list_objects(a)
self.assertIn(e, a_deps)
self.assertIn(f, a_deps)
checkpoint = util.Checkpoint(a=a)
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
a.l[0].append(data_structures.NoDependency([]))
a.l[0][-1].append(5)
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
# Dirtying the inner list means the root object is unsaveable.
a.l[0][1] = 2
with self.assertRaisesRegexp(ValueError,
"A list element was replaced"):
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
def testDictWrapperNoDependency(self):
a = tracking.Checkpointable()
a.d = data_structures.NoDependency({})
a.d[1] = [3]
self.assertEqual([a], util.list_objects(a))
model = training.Model()
model.sub = a
save_path = os.path.join(self.get_temp_dir(), "ckpt")
model.save_weights(save_path)
model.load_weights(save_path)
def testNonAppendNotCheckpointable(self):
# Non-append mutations (deleting or overwriting values) are OK when the
# values aren't tracked.
a = tracking.Checkpointable()
a.d = {}
a.d["a"] = [3]
a.d[1] = 3
a.d[1] = 2
self.assertEqual(2, a.d[1])
del a.d[1]
a.d[2] = data_structures.NoDependency(tracking.Checkpointable())
second = tracking.Checkpointable()
a.d[2] = data_structures.NoDependency(second)
self.assertIs(second, a.d[2])
self.assertEqual([a, a.d, a.d["a"]], util.list_objects(a))
model = training.Model()
model.sub = a
save_path = os.path.join(self.get_temp_dir(), "ckpt")
model.save_weights(save_path)
model.load_weights(save_path)
def testNonStringKeyNotCheckpointableValue(self):
a = tracking.Checkpointable()
a.d = {}
a.d["a"] = [3]
a.d[1] = data_structures.NoDependency([3])
self.assertEqual([a, a.d, a.d["a"]], util.list_objects(a))
model = training.Model()
model.sub = a
save_path = os.path.join(self.get_temp_dir(), "ckpt")
model.save_weights(save_path)
model.load_weights(save_path)
def testNoDepList(self):
a = training.Model()
a.l1 = data_structures.NoDependency([])
a.l1.insert(1, 0)
self.assertTrue(isinstance(a.l1, list))
checkpoint = util.Checkpoint(a=a)
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
a.l2 = []
a.l2.insert(1, 0)
with self.assertRaisesRegexp(ValueError, "A list element was replaced"):
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
def __init__(self):
self.slotdeps = containers.UniqueNameTracker()
slotdeps = self.slotdeps
slots = []
slots.append(slotdeps.track(
resource_variable_ops.ResourceVariable(3.), "x"))
slots.append(slotdeps.track(
resource_variable_ops.ResourceVariable(4.), "y"))
slots.append(slotdeps.track(
resource_variable_ops.ResourceVariable(5.), "x"))
self.slots = data_structures.NoDependency(slots)
def testNoDependency(self):
root = tracking.Checkpointable()
hasdep = tracking.Checkpointable()
root.hasdep = hasdep
nodep = tracking.Checkpointable()
root.nodep = data_structures.NoDependency(nodep)
self.assertEqual(1, len(root._checkpoint_dependencies))
self.assertIs(root._checkpoint_dependencies[0].ref, root.hasdep)
self.assertIs(root.hasdep, hasdep)
self.assertIs(root.nodep, nodep)
class NoDependencyModel(training.Model):
@base.no_automatic_dependency_tracking
def __init__(self):
super(NoDependencyModel, self).__init__()
self.a = []
self.b = tracking.Checkpointable()
nodeps = NoDependencyModel()
self.assertEqual([nodeps], util.list_objects(nodeps))
def commit(self, prefix, session):
"""
Commit the latest checkpoint.
"""
if self._cached_checkpoint == None:
if len(self._checkpoints) > 0:
return self._checkpoints[-1]
else:
return ""
if len(self._checkpoints) == self._max_to_keep:
for filename in self._get_checkpoint_filenames(
self._checkpoints.pop(0)):
os.remove(filename)
# Replication from checkpoint.save
if self._checkpoint._save_counter is None:
session.run(self._checkpoint.save_counter.initializer)
if self._checkpoint._save_assign_op is None:
self._checkpoint._save_assign_op = data_structures.NoDependency(
self._checkpoint.save_counter.assign_add(1, read_value=True))
checkpoint_count = session.run(self._checkpoint._save_assign_op)
filename_prefix = "%s-%d" % (prefix, checkpoint_count)
for filename in self._get_checkpoint_filenames(
self._cached_checkpoint):
# Change prefix
os.rename(
filename,
filename.replace(self._cached_checkpoint, filename_prefix))
self._checkpoints.append(filename_prefix)
self._cached_checkpoint = None
# Update checkpoint state file (@tf.train.latest_checkpoint)
checkpoint_management.update_checkpoint_state_internal(
self._directory, self._checkpoints[-1], self._checkpoints)
return filename_prefix
def _in_place_subclassed_model_reset(model):
"""Substitute for model cloning that works for subclassed models.
Subclassed models cannot be cloned because their topology is not serializable.
To "instantiate" an identical model in a new TF graph, we reuse the original
model object, but we clear its state.
After calling this function on a model instance, you can use the model
instance as if it were a model clone (in particular you can use it in a new
graph).
This method clears the state of the input model. It is thus destructive.
However the original state can be restored fully by calling
`_in_place_subclassed_model_state_restoration`.
Args:
model: Instance of a Keras model created via subclassing.
Raises:
ValueError: In case the model uses a subclassed model as inner layer.
"""
assert not model._is_graph_network # Only makes sense for subclassed networks
# Retrieve all layers tracked by the model as well as their attribute names
attributes_cache = {}
for name in dir(model):
try:
value = getattr(model, name)
except (AttributeError, ValueError, TypeError):
continue
if isinstance(value, Layer):
attributes_cache[name] = value
assert value in model._layers
elif isinstance(value, (list, tuple)) and name not in (
'layers', '_layers', 'metrics',
'_compile_stateful_metric_functions'):
# Handle case: list/tuple of layers (also tracked by the Network API).
if value and all(isinstance(val, Layer) for val in value):
raise ValueError(
'We do not support the use of list-of-layers '
'attributes in subclassed models used with '
'`model_to_estimator` at this time. Found list '
'model: %s' % name)
# Replace layers on the model with fresh layers
layers_to_names = {value: key for key, value in attributes_cache.items()}
original_layers = model._layers[:]
model._layers = data_structures.NoDependency([])
for layer in original_layers: # We preserve layer order.
config = layer.get_config()
# This will not work for nested subclassed models used as layers.
# This would be theoretically possible to support, but would add complexity.
# Only do it if users complain.
if isinstance(layer, Network) and not layer._is_graph_network:
raise ValueError(
'We do not support the use of nested subclassed models '
'in `model_to_estimator` at this time. Found nested '
'model: %s' % layer)
fresh_layer = layer.__class__.from_config(config)
name = layers_to_names[layer]
setattr(model, name, fresh_layer)
# Cache original model build attributes (in addition to layers)
if (not hasattr(model, '_original_attributes_cache')
or model._original_attributes_cache is None):
if model.built:
attributes_to_cache = [
'inputs',
'outputs',
'_feed_outputs',
'_feed_output_names',
'_feed_output_shapes',
'_feed_loss_fns',
'loss_weights_list',
'targets',
'_feed_targets',
'sample_weight_modes',
'total_loss',
'sample_weights',
'_feed_sample_weights',
'_fit_function',
'_eval_function',
'train_function',
'test_function',
'predict_function',
'_collected_trainable_weights',
'_feed_inputs',
'_feed_input_names',
'_feed_input_shapes',
'optimizer',
]
for name in attributes_to_cache:
attributes_cache[name] = getattr(model, name)
model._original_attributes_cache = data_structures.NoDependency(
attributes_cache)
# Reset built state
model.built = False
model.inputs = None
model.outputs = None
def _no_dependency(self, value):
"""Override to allow CheckpointableBase to disable dependency tracking."""
return data_structures.NoDependency(value)