264 lines
10 KiB
Python
264 lines
10 KiB
Python
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
||
|
|
#
|
||
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
|
|
# you may not use this file except in compliance with the License.
|
||
|
|
# You may obtain a copy of the License at
|
||
|
|
#
|
||
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||
|
|
#
|
||
|
|
# Unless required by applicable law or agreed to in writing, software
|
||
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
|
|
# See the License for the specific language governing permissions and
|
||
|
|
# limitations under the License.
|
||
|
|
# ==============================================================================
|
||
|
|
# pylint: disable=protected-access
|
||
|
|
"""Code for model cloning, plus model-related API entries.
|
||
|
|
"""
|
||
|
|
from __future__ import absolute_import
|
||
|
|
from __future__ import division
|
||
|
|
from __future__ import print_function
|
||
|
|
|
||
|
|
from tensorflow.python.keras import backend as K
|
||
|
|
from tensorflow.python.keras.engine import saving
|
||
|
|
from tensorflow.python.keras.engine import sequential
|
||
|
|
from tensorflow.python.keras.engine import training
|
||
|
|
from tensorflow.python.keras.engine.input_layer import Input
|
||
|
|
from tensorflow.python.keras.engine.input_layer import InputLayer
|
||
|
|
from tensorflow.python.keras.utils import generic_utils
|
||
|
|
from tensorflow.python.keras.utils.generic_utils import has_arg
|
||
|
|
|
||
|
|
|
||
|
|
# API entries importable from `keras.models`:
|
||
|
|
Model = training.Model # pylint: disable=invalid-name
|
||
|
|
Sequential = sequential.Sequential # pylint: disable=invalid-name
|
||
|
|
save_model = saving.save_model
|
||
|
|
load_model = saving.load_model
|
||
|
|
model_from_config = saving.model_from_config
|
||
|
|
model_from_yaml = saving.model_from_yaml
|
||
|
|
model_from_json = saving.model_from_json
|
||
|
|
|
||
|
|
|
||
|
|
def _clone_functional_model(model, input_tensors=None):
|
||
|
|
"""Clone a functional `Model` instance.
|
||
|
|
|
||
|
|
Model cloning is similar to calling a model on new inputs,
|
||
|
|
except that it creates new layers (and thus new weights) instead
|
||
|
|
of sharing the weights of the existing layers.
|
||
|
|
|
||
|
|
Arguments:
|
||
|
|
model: Instance of `Model`.
|
||
|
|
input_tensors: optional list of input tensors
|
||
|
|
to build the model upon. If not provided,
|
||
|
|
placeholders will be created.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
An instance of `Model` reproducing the behavior
|
||
|
|
of the original model, on top of new inputs tensors,
|
||
|
|
using newly instantiated weights.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: in case of invalid `model` argument value.
|
||
|
|
"""
|
||
|
|
if not isinstance(model, Model):
|
||
|
|
raise ValueError('Expected `model` argument '
|
||
|
|
'to be a `Model` instance, got ', model)
|
||
|
|
if isinstance(model, Sequential):
|
||
|
|
raise ValueError('Expected `model` argument '
|
||
|
|
'to be a functional `Model` instance, '
|
||
|
|
'got a `Sequential` instance instead:', model)
|
||
|
|
|
||
|
|
layer_map = {} # Cache for created layers.
|
||
|
|
tensor_map = {} # Map {reference_tensor: (corresponding_tensor, mask)}
|
||
|
|
if input_tensors is None:
|
||
|
|
# Create placeholders to build the model on top of.
|
||
|
|
input_layers = []
|
||
|
|
input_tensors = []
|
||
|
|
for layer in model._input_layers:
|
||
|
|
input_tensor = Input(
|
||
|
|
batch_shape=layer._batch_input_shape,
|
||
|
|
dtype=layer.dtype,
|
||
|
|
sparse=layer.sparse,
|
||
|
|
name=layer.name)
|
||
|
|
input_tensors.append(input_tensor)
|
||
|
|
# Cache newly created input layer.
|
||
|
|
newly_created_input_layer = input_tensor._keras_history[0]
|
||
|
|
layer_map[layer] = newly_created_input_layer
|
||
|
|
for original_input_layer, cloned_input_layer in zip(model._input_layers,
|
||
|
|
input_layers):
|
||
|
|
layer_map[original_input_layer] = cloned_input_layer
|
||
|
|
else:
|
||
|
|
# Make sure that all input tensors come from a Keras layer.
|
||
|
|
# If tensor comes from an input layer: cache the input layer.
|
||
|
|
input_tensors = generic_utils.to_list(input_tensors)
|
||
|
|
input_tensors_ = []
|
||
|
|
for i, x in enumerate(input_tensors):
|
||
|
|
if not K.is_keras_tensor(x):
|
||
|
|
name = model._input_layers[i].name
|
||
|
|
input_tensor = Input(tensor=x, name='input_wrapper_for_' + name)
|
||
|
|
input_tensors_.append(input_tensor)
|
||
|
|
# Cache newly created input layer.
|
||
|
|
original_input_layer = x._keras_history[0]
|
||
|
|
newly_created_input_layer = input_tensor._keras_history[0]
|
||
|
|
layer_map[original_input_layer] = newly_created_input_layer
|
||
|
|
else:
|
||
|
|
input_tensors_.append(x)
|
||
|
|
input_tensors = input_tensors_
|
||
|
|
|
||
|
|
for x, y in zip(model.inputs, input_tensors):
|
||
|
|
tensor_map[x] = (y, None) # tensor, mask
|
||
|
|
|
||
|
|
# Iterated over every node in the reference model, in depth order.
|
||
|
|
depth_keys = list(model._nodes_by_depth.keys())
|
||
|
|
depth_keys.sort(reverse=True)
|
||
|
|
for depth in depth_keys:
|
||
|
|
nodes = model._nodes_by_depth[depth]
|
||
|
|
for node in nodes:
|
||
|
|
# Recover the corresponding layer.
|
||
|
|
layer = node.outbound_layer
|
||
|
|
|
||
|
|
# Get or create layer.
|
||
|
|
if layer not in layer_map:
|
||
|
|
# Clone layer.
|
||
|
|
new_layer = layer.__class__.from_config(layer.get_config())
|
||
|
|
layer_map[layer] = new_layer
|
||
|
|
layer = new_layer
|
||
|
|
else:
|
||
|
|
# Reuse previously cloned layer.
|
||
|
|
layer = layer_map[layer]
|
||
|
|
# Don't call InputLayer multiple times.
|
||
|
|
if isinstance(layer, InputLayer):
|
||
|
|
continue
|
||
|
|
|
||
|
|
# Gather inputs to call the new layer.
|
||
|
|
referenceinput_tensors_ = node.input_tensors
|
||
|
|
reference_output_tensors = node.output_tensors
|
||
|
|
|
||
|
|
# If all previous input tensors are available in tensor_map,
|
||
|
|
# then call node.inbound_layer on them.
|
||
|
|
computed_data = [] # List of tuples (input, mask).
|
||
|
|
for x in referenceinput_tensors_:
|
||
|
|
if x in tensor_map:
|
||
|
|
computed_data.append(tensor_map[x])
|
||
|
|
|
||
|
|
if len(computed_data) == len(referenceinput_tensors_):
|
||
|
|
# Call layer.
|
||
|
|
if node.arguments:
|
||
|
|
kwargs = node.arguments
|
||
|
|
else:
|
||
|
|
kwargs = {}
|
||
|
|
if len(computed_data) == 1:
|
||
|
|
computed_tensor, computed_mask = computed_data[0]
|
||
|
|
if has_arg(layer.call, 'mask'):
|
||
|
|
if 'mask' not in kwargs:
|
||
|
|
kwargs['mask'] = computed_mask
|
||
|
|
output_tensors = generic_utils.to_list(layer(computed_tensor,
|
||
|
|
**kwargs))
|
||
|
|
output_masks = generic_utils.to_list(
|
||
|
|
layer.compute_mask(computed_tensor, computed_mask))
|
||
|
|
computed_tensors = [computed_tensor]
|
||
|
|
computed_masks = [computed_mask]
|
||
|
|
else:
|
||
|
|
computed_tensors = [x[0] for x in computed_data]
|
||
|
|
computed_masks = [x[1] for x in computed_data]
|
||
|
|
if has_arg(layer.call, 'mask'):
|
||
|
|
if 'mask' not in kwargs:
|
||
|
|
kwargs['mask'] = computed_masks
|
||
|
|
output_tensors = generic_utils.to_list(layer(computed_tensors,
|
||
|
|
**kwargs))
|
||
|
|
output_masks = generic_utils.to_list(
|
||
|
|
layer.compute_mask(computed_tensors, computed_masks))
|
||
|
|
# Update tensor_map.
|
||
|
|
for x, y, mask in zip(reference_output_tensors, output_tensors,
|
||
|
|
output_masks):
|
||
|
|
tensor_map[x] = (y, mask)
|
||
|
|
|
||
|
|
# Check that we did compute the model outputs,
|
||
|
|
# then instantiate a new model from inputs and outputs.
|
||
|
|
output_tensors = []
|
||
|
|
for x in model.outputs:
|
||
|
|
assert x in tensor_map, 'Could not compute output ' + str(x)
|
||
|
|
tensor, _ = tensor_map[x]
|
||
|
|
output_tensors.append(tensor)
|
||
|
|
return Model(input_tensors, output_tensors, name=model.name)
|
||
|
|
|
||
|
|
|
||
|
|
def _clone_sequential_model(model, input_tensors=None):
|
||
|
|
"""Clone a `Sequential` model instance.
|
||
|
|
|
||
|
|
Model cloning is similar to calling a model on new inputs,
|
||
|
|
except that it creates new layers (and thus new weights) instead
|
||
|
|
of sharing the weights of the existing layers.
|
||
|
|
|
||
|
|
Arguments:
|
||
|
|
model: Instance of `Sequential`.
|
||
|
|
input_tensors: optional list of input tensors
|
||
|
|
to build the model upon. If not provided,
|
||
|
|
placeholders will be created.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
An instance of `Sequential` reproducing the behavior
|
||
|
|
of the original model, on top of new inputs tensors,
|
||
|
|
using newly instantiated weights.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: in case of invalid `model` argument value.
|
||
|
|
"""
|
||
|
|
if not isinstance(model, Sequential):
|
||
|
|
raise ValueError('Expected `model` argument '
|
||
|
|
'to be a `Sequential` model instance, '
|
||
|
|
'but got:', model)
|
||
|
|
|
||
|
|
def clone(layer):
|
||
|
|
return layer.__class__.from_config(layer.get_config())
|
||
|
|
|
||
|
|
layers = [clone(layer) for layer in model.layers]
|
||
|
|
if input_tensors is None:
|
||
|
|
return Sequential(layers=layers, name=model.name)
|
||
|
|
else:
|
||
|
|
if len(generic_utils.to_list(input_tensors)) != 1:
|
||
|
|
raise ValueError('To clone a `Sequential` model, we expect '
|
||
|
|
' at most one tensor '
|
||
|
|
'as part of `input_tensors`.')
|
||
|
|
x = generic_utils.to_list(input_tensors)[0]
|
||
|
|
if K.is_keras_tensor(x):
|
||
|
|
origin_layer = x._keras_history[0]
|
||
|
|
if isinstance(origin_layer, InputLayer):
|
||
|
|
return Sequential(layers=[origin_layer] + layers, name=model.name)
|
||
|
|
else:
|
||
|
|
raise ValueError('Cannot clone a `Sequential` model on top '
|
||
|
|
'of a tensor that comes from a Keras layer '
|
||
|
|
'other than an `InputLayer`. '
|
||
|
|
'Use the functional API instead.')
|
||
|
|
input_tensor = Input(tensor=x, name='input_wrapper_for_' + str(x.name))
|
||
|
|
input_layer = input_tensor._keras_history[0]
|
||
|
|
return Sequential(layers=[input_layer] + layers, name=model.name)
|
||
|
|
|
||
|
|
|
||
|
|
def clone_model(model, input_tensors=None):
|
||
|
|
"""Clone any `Model` instance.
|
||
|
|
|
||
|
|
Model cloning is similar to calling a model on new inputs,
|
||
|
|
except that it creates new layers (and thus new weights) instead
|
||
|
|
of sharing the weights of the existing layers.
|
||
|
|
|
||
|
|
Arguments:
|
||
|
|
model: Instance of `Model`
|
||
|
|
(could be a functional model or a Sequential model).
|
||
|
|
input_tensors: optional list of input tensors
|
||
|
|
to build the model upon. If not provided,
|
||
|
|
placeholders will be created.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
An instance of `Model` reproducing the behavior
|
||
|
|
of the original model, on top of new inputs tensors,
|
||
|
|
using newly instantiated weights.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: in case of invalid `model` argument value.
|
||
|
|
"""
|
||
|
|
if isinstance(model, Sequential):
|
||
|
|
return _clone_sequential_model(model, input_tensors=input_tensors)
|
||
|
|
else:
|
||
|
|
return _clone_functional_model(model, input_tensors=input_tensors)
|