diff options
Diffstat (limited to 'tensorflow/python/keras/engine/network.py')
| -rw-r--r-- | tensorflow/python/keras/engine/network.py | 60 |
1 files changed, 47 insertions, 13 deletions
diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index aa84eaa8ab..752e9963ca 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -81,6 +81,20 @@ class Network(base_layer.Layer): # Subclassed network self._init_subclassed_network(**kwargs) + # Several Network methods have "no_automatic_dependency_tracking" + # annotations. Since Network does automatic dependency tracking on attribute + # assignment, including for common data structures such as lists, by default + # we'd have quite a few empty dependencies which users don't care about (or + # would need some way to ignore dependencies automatically, which is confusing + # when applied to user code). Some attributes, such as _layers, would cause + # structural issues (_layers being the place where Layers assigned to tracked + # attributes are stored). + # + # Aside from these aesthetic and structural issues, useless dependencies on + # empty lists shouldn't cause issues; adding or removing them will not break + # checkpoints, but may cause "all Python objects matched" assertions to fail + # (in which case less strict assertions may be substituted if necessary). + @checkpointable.no_automatic_dependency_tracking def _base_init(self, name=None): # The following are implemented as property functions: # self.trainable_weights @@ -135,6 +149,7 @@ class Network(base_layer.Layer): # restore operations when graph building. self._in_progress_restore_finalizer = None + @checkpointable.no_automatic_dependency_tracking def _init_graph_network(self, inputs, outputs, name=None): self._call_convention = base_layer.CallConvention.EXPLICIT_INPUTS_ARGUMENT # Normalize and set self.inputs, self.outputs. @@ -293,6 +308,7 @@ class Network(base_layer.Layer): for layer in self._output_layers: self.output_names.append(layer.name) + @checkpointable.no_automatic_dependency_tracking def _init_subclassed_network(self, name=None): self._base_init(name=name) self._is_graph_network = False @@ -302,8 +318,8 @@ class Network(base_layer.Layer): else: self._expects_training_arg = False self._call_convention = self._determine_call_convention(call_argspec) - self.outputs = None - self.inputs = None + self.outputs = [] + self.inputs = [] self.built = False def _determine_call_convention(self, call_argspec): @@ -362,10 +378,31 @@ class Network(base_layer.Layer): self._track_checkpointable( layer, name='layer-%d' % layer_index, overwrite=True) + def _no_dependency(self, value): + """Override to allow `Layer` to disable dependency tracking. + + `CheckpointableBase` defines this method, whose semantics are "if a subclass + does dependency tracking, this method exempts `value`." Layer uses + `_no_dependency` to exempt some of its attribute assignments (conditional on + attribute assignment causing tracking in the subclass). + + Args: + value: An object which will be assigned to an object attribute, whose + value should not be tracked. + + Returns: + A wrapped object which, when assigned to an attribute, will not be + tracked (`value` will be stored in the attribute). + """ + return data_structures.NoDependency(value) + def __setattr__(self, name, value): - no_dependency = isinstance(value, checkpointable.NoDependency) - if no_dependency: - value = value.value + if not getattr(self, '_setattr_tracking', True): + super(Network, self).__setattr__(name, value) + return + no_dependency = isinstance(value, data_structures.NoDependency) + value = data_structures.sticky_attribute_assignment( + checkpointable=self, value=value, name=name) if isinstance(value, ( base_layer.Layer, Network, @@ -377,7 +414,9 @@ class Network(base_layer.Layer): 'forgot to call `super(YourClass, self).__init__()`.' ' Always start with this line.') if not is_graph_network: - if value not in self._layers: + # We need to check object identity to avoid de-duplicating empty + # container types which compare equal. + if not any((layer is value for layer in self._layers)): self._layers.append(value) if hasattr(value, '_use_resource_variables'): # In subclassed models, legacy layers (tf.layers) must always use @@ -385,12 +424,6 @@ class Network(base_layer.Layer): value._use_resource_variables = True if (not no_dependency and isinstance(value, checkpointable.CheckpointableBase)): - # Layer (and therefore Network/Model) inherit from CheckpointableBase - # rather than Checkpointable, which means there is no Checkpointable - # __setattr__ override (it would be a performance issue for functional - # layers). Therefore Model tracks Checkpointable objects itself. - self._track_checkpointable( - checkpointable=value, name=name, overwrite=True) if ( # For subclassed models only, users may add extra weights/variables # simply by assigning them to attributes. not self._is_graph_network @@ -493,7 +526,8 @@ class Network(base_layer.Layer): @property def layers(self): - return self._layers + return checkpointable_layer_utils.filter_empty_layer_containers( + self._layers) def get_layer(self, name=None, index=None): """Retrieves a layer based on either its name (unique) or index. |