def transform(self):
""" Applies transformations to the Keras model.
:return: The transformed Keras model
"""
layer_weights_map = {}
for layer in self._model.layers:
original_layer = layer.layer if isinstance(layer,
NNCFWrapper) else layer
layer_weights_map[original_layer.name] = self._get_layer_weights(
layer)
for transform in self._transformations:
self._apply_transformation(transform)
if is_functional_model(self._model):
transformed_model = tf.keras.Model.from_config(
self._model_config, self._custom_objects)
else:
transformed_model = tf.keras.Sequential.from_config(
self._model_config, self._custom_objects)
for layer in transformed_model.layers:
original_layer = layer.layer if isinstance(layer,
NNCFWrapper) else layer
weights = layer_weights_map.get(original_layer.name)
if weights:
self._set_layer_weights(layer, weights)
return transformed_model
def _find_layer_config(self, layer_name):
for idx, layer in enumerate(self._model_config['layers']):
layer_name_ = layer['name'] if is_functional_model(self._model) \
else layer['config']['name']
if layer_name_ == layer_name:
return idx, layer
return None, None
def _replace_config(self, layer_name, replace_layer_config):
replace_layer_name = replace_layer_config['config']['name']
if is_functional_model(self._model):
if 'name' not in replace_layer_config:
replace_layer_config['name'] = replace_layer_name
self._replace_functional(layer_name, replace_layer_config)
else:
self._replace_sequential(layer_name, replace_layer_config)
self._update_layer_mapping(layer_name, replace_layer_name)
def _insert_layers_after(self, layer_name, instance_index, out_port, layers):
functional_model = is_functional_model(self._model)
layer_configs = []
for layer in layers:
config = tf.keras.utils.serialize_keras_object(layer)
if functional_model:
config['name'] = config['config']['name']
config['inbound_nodes'] = [[[layer_name, instance_index, out_port, {}]]]
layer_configs.append(config)
for config in layer_configs:
if functional_model:
self._insert_layer_after_functional(layer_name, instance_index, config)
else:
self._insert_layer_after_sequential(layer_name, config)
def convert_keras_model_to_nncf_graph(model: tf.keras.Model) -> NNCFGraph:
"""
Convert Keras model graph to the NNCFGraph
:param model: Keras model
:return: NNCFGraph
"""
func_model = is_functional_model(model)
seq_model = is_sequential_model(model)
if not func_model and not seq_model:
RuntimeError('convert_keras_model_to_nxmodel function supports '
'only sequential or functional models')
if func_model:
nncf_graph = _get_nncf_graph_from_functional(model)
else:
nncf_graph = _get_nncf_graph_from_sequential(model)
return nncf_graph
def convert_keras_model_to_nxmodel(model):
"""
Convert Keras model graph to the NetworkX directed graph
:param model: Keras model
:return: NetworkX directed graph
"""
func_model = is_functional_model(model)
seq_model = is_sequential_model(model)
if not func_model and not seq_model:
RuntimeError('convert_keras_model_to_nxmodel function supports '
'only sequential or functional models')
if func_model:
nxmodel = _get_nxmodel_from_functional(model)
else:
nxmodel = _get_nxmodel_from_sequential(model)
#nx.drawing.nx_pydot.write_dot(nxmodel, str("nxmodel_graph.dot"))
return nxmodel