# 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. # ============================================================================= """Contains the base Layer class, from which all layers inherit.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import copy import warnings import tensorflow.compat.v2 as tf from keras import backend from keras.engine import base_layer_utils from keras.engine import base_layer_v1 as base_layer from keras.legacy_tf_layers import variable_scope_shim from keras.mixed_precision import policy from keras.utils import tf_contextlib # isort: off from tensorflow.python.ops import variable_scope as vs from tensorflow.python.util.tf_export import keras_export from tensorflow.python.util.tf_export import tf_export _KERAS_STYLE_SCOPE = False @keras_export( v1=["keras.__internal__.legacy.layers.experimental.keras_style_scope"] ) @tf_export(v1=["layers.experimental.keras_style_scope"]) @tf_contextlib.contextmanager def keras_style_scope(): """Use Keras-style variable management. All tf.layers and tf RNN cells created in this scope use Keras-style variable management. Creating such layers with a scope= argument is disallowed, and reuse=True is disallowed. The purpose of this scope is to allow users of existing layers to slowly transition to a Keras layers API without breaking existing functionality. One example of this is when using TensorFlow's RNN classes with Keras Models or Networks. Because Keras models do not properly set variable scopes, users of RNNs may either accidentally share scopes between two different models, or get errors about variables that already exist. Example: ```python class RNNModel(tf.keras.Model): def __init__(self, name): super(RNNModel, self).__init__(name=name) self.rnn = tf.compat.v1.nn.rnn_cell.MultiRNNCell( [tf.compat.v1.nn.rnn_cell.LSTMCell(64) for _ in range(2)]) def call(self, input, state): return self.rnn(input, state) model_1 = RNNModel("model_1") model_2 = RNNModel("model_2") # OK output_1, next_state_1 = model_1(input, state) # Raises an error about trying to create an already existing variable. output_2, next_state_2 = model_2(input, state) ``` The solution is to wrap the model construction and execution in a keras-style scope: ```python with keras_style_scope(): model_1 = RNNModel("model_1") model_2 = RNNModel("model_2") # model_1 and model_2 are guaranteed to create their own variables. output_1, next_state_1 = model_1(input, state) output_2, next_state_2 = model_2(input, state) assert len(model_1.weights) > 0 assert len(model_2.weights) > 0 assert(model_1.weights != model_2.weights) ``` Yields: A keras layer style scope. """ global _KERAS_STYLE_SCOPE stack = _KERAS_STYLE_SCOPE _KERAS_STYLE_SCOPE = True try: yield finally: _KERAS_STYLE_SCOPE = stack @keras_export( v1=["keras.__internal__.legacy.layers.experimental.set_keras_style"] ) @tf_export(v1=["layers.experimental.set_keras_style"]) def set_keras_style(): """Use Keras-style variable management. All tf.layers and tf RNN cells created after keras style ha been enabled use Keras-style variable management. Creating such layers with a scope= argument is disallowed, and reuse=True is disallowed. The purpose of this function is to allow users of existing layers to slowly transition to Keras layers API without breaking existing functionality. For more details, see the documentation for `keras_style_scope`. Note, once keras style has been set, it is set globally for the entire program and cannot be unset. Example: ```python set_keras_style() model_1 = RNNModel(name="model_1") model_2 = RNNModel(name="model_2") # model_1 and model_2 are guaranteed to create their own variables. output_1, next_state_1 = model_1(input, state) output_2, next_state_2 = model_2(input, state) assert len(model_1.weights) > 0 assert len(model_2.weights) > 0 assert(model_1.weights != model_2.weights) ``` """ global _KERAS_STYLE_SCOPE _KERAS_STYLE_SCOPE = True def _is_in_keras_style_scope(): global _KERAS_STYLE_SCOPE return _KERAS_STYLE_SCOPE @keras_export(v1=["keras.__internal__.legacy.layers.Layer"]) @tf_export(v1=["layers.Layer"]) class Layer(base_layer.Layer): """Base layer class. It is considered legacy, and we recommend the use of `tf.keras.layers.Layer` instead. Args: trainable: Boolean, whether the layer's variables should be trainable. name: String name of the layer. dtype: Default dtype of the layer's weights (default of `None` means use the type of the first input). Read-only properties: name: The name of the layer (string). dtype: Default dtype of the layer's weights (default of `None` means use the type of the first input). trainable_variables: List of trainable variables. non_trainable_variables: List of non-trainable variables. variables: List of all variables of this layer, trainable and non-trainable. updates: List of update ops of this layer. losses: List of losses added by this layer. trainable_weights: List of variables to be included in backprop. non_trainable_weights: List of variables that should not be included in backprop. weights: The concatenation of the lists trainable_weights and non_trainable_weights (in this order). Mutable properties: trainable: Whether the layer should be trained (boolean). input_spec: Optional (list of) `InputSpec` object(s) specifying the constraints on inputs that can be accepted by the layer. """ def __init__(self, trainable=True, name=None, dtype=None, **kwargs): # For backwards compatibility, legacy layers do not use # `ResourceVariable` by default. self._use_resource_variables = False scope = kwargs.pop("_scope", None) self._reuse = kwargs.pop("_reuse", None) # Avoid an incorrect lint error self._trainable_weights = [] self.built = False if dtype is None: # Indicates to infer dtype from inputs. When the V2 dtype behavior # is enabled, Keras layers default their dtype to floatx instead, so # we pass an "_infer" policy to keep the old V1 behavior. dtype = policy.Policy("_infer") if "autocast" not in kwargs: kwargs["autocast"] = False # Mark that legacy layers should not be instrumented as Keras usage self._disable_keras_instrumentation = True super().__init__(trainable=trainable, name=name, dtype=dtype, **kwargs) if _is_in_keras_style_scope(): if scope is not None: raise ValueError( "scope argument not allowed when keras style layers are " "enabled, but saw: {}".format(scope) ) if self._reuse is not None: raise ValueError( "reuse argument not allowed when keras style layers are " "enabled, but saw: {}".format(self._reuse) ) self._keras_style = True else: self._keras_style = False self._call_has_scope_arg = "scope" in self._call_spec.arg_names if scope: with tf.compat.v1.variable_scope(scope) as captured_scope: self._scope = captured_scope else: self._scope = None self._current_scope = None def apply(self, *args, **kwargs): return self(*args, **kwargs) # We no longer track graph in tf.layers layers. This property is only kept # to maintain API backward compatibility. @property def graph(self): warnings.warn( "`Layer.graph` is deprecated and " "will be removed in a future version. " "Please stop using this property because tf.layers layers no " "longer track their graph.", stacklevel=2, ) if tf.executing_eagerly(): raise RuntimeError( "Layer.graph not supported when executing eagerly." ) return None def _init_set_name(self, name): # Determine layer name (non-unique). if isinstance(name, tf.compat.v1.VariableScope): base_name = name.name self._name, _ = self._make_unique_name() else: base_name = name self._name = name if not name: self._name, base_name = self._make_unique_name() self._base_name = base_name def _make_unique_name( self, name_uid_map=None, avoid_names=None, namespace="", zero_based=False, ): base_name = base_layer.to_snake_case(self.__class__.__name__) name = backend.unique_object_name( base_name, name_uid_map=name_uid_map, avoid_names=avoid_names, namespace=namespace, zero_based=zero_based, ) return (name, base_name) @property def scope_name(self): if not self._scope: raise ValueError( 'No name available for layer scope because the layer "' + self._name + '" has not been used yet. The scope name ' + " is determined the first time the layer instance is " + "called. You must therefore call the layer before " + "querying `scope_name`." ) return self._scope.name def add_loss(self, losses, inputs=None): previous_losses_length = len(self._losses) previous_callable_losses_length = len(self._callable_losses) super().add_loss(losses, inputs=inputs) if not tf.executing_eagerly(): # TODO(fchollet): deprecate collection below. new_losses = self._losses[previous_losses_length:] new_callable_losses = self._callable_losses[ previous_callable_losses_length: ] for regularizer in new_callable_losses: loss_tensor = regularizer() if loss_tensor is not None: new_losses.append(loss_tensor) _add_elements_to_collection( new_losses, tf.compat.v1.GraphKeys.REGULARIZATION_LOSSES ) def _name_scope(self): """Determines op naming for the Layer.""" if self._keras_style: return super()._name_scope() return self._current_scope.original_name_scope def _set_scope(self, scope=None): if self._scope is None: # If constructed with _scope=None, lazy setting of scope. if self._reuse: with tf.compat.v1.variable_scope( scope if scope is not None else self._base_name ) as captured_scope: self._scope = captured_scope else: with tf.compat.v1.variable_scope( scope, default_name=self._base_name ) as captured_scope: self._scope = captured_scope def add_weight( self, name, shape, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, use_resource=None, synchronization=tf.VariableSynchronization.AUTO, aggregation=tf.compat.v1.VariableAggregation.NONE, partitioner=None, **kwargs ): """Adds a new variable to the layer, or gets an existing one; returns it Args: name: variable name. shape: variable shape. dtype: The type of the variable. Defaults to `self.dtype` or `float32`. initializer: initializer instance (callable). regularizer: regularizer instance (callable). trainable: whether the variable should be part of the layer's "trainable_variables" (e.g. variables, biases) or "non_trainable_variables" (e.g. BatchNorm mean, stddev). Note, if the current variable scope is marked as non-trainable then this parameter is ignored and any added variables are also marked as non-trainable. `trainable` defaults to `True` unless `synchronization` is set to `ON_READ`. constraint: constraint instance (callable). use_resource: Whether to use `ResourceVariable`. synchronization: Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class `tf.VariableSynchronization`. By default the synchronization is set to `AUTO` and the current `DistributionStrategy` chooses when to synchronize. If `synchronization` is set to `ON_READ`, `trainable` must not be set to `True`. aggregation: Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class `tf.VariableAggregation`. partitioner: (optional) partitioner instance (callable). If provided, when the requested variable is created it will be split into multiple partitions according to `partitioner`. In this case, an instance of `PartitionedVariable` is returned. Available partitioners include `tf.compat.v1.fixed_size_partitioner` and `tf.compat.v1.variable_axis_size_partitioner`. For more details, see the documentation of `tf.compat.v1.get_variable` and the "Variable Partitioners and Sharding" section of the API guide. **kwargs: Additional keyword arguments. Returns: The created variable. Usually either a `Variable` or `ResourceVariable` instance. If `partitioner` is not `None`, a `PartitionedVariable` instance is returned. Raises: RuntimeError: If called with partitioned variable regularization and eager execution is enabled. ValueError: When trainable has been set to True with synchronization set as `ON_READ`. """ for kwarg in kwargs: if kwarg != "experimental_autocast": raise TypeError("Unknown keyword argument:", kwarg) if self._keras_style: return super().add_weight( name=name, shape=shape, dtype=dtype, initializer=initializer, regularizer=regularizer, trainable=trainable and self.trainable, constraint=constraint, use_resource=use_resource, synchronization=tf.VariableSynchronization.AUTO, aggregation=tf.compat.v1.VariableAggregation.NONE, partitioner=partitioner, **kwargs ) if synchronization == tf.VariableSynchronization.ON_READ: if trainable: raise ValueError( "Synchronization value can be set to " "VariableSynchronization.ON_READ only for non-trainable " "variables. You have specified trainable=True and " "synchronization=VariableSynchronization.ON_READ." ) else: # Set trainable to be false when variable is to be synced on # read. trainable = False elif trainable is None: trainable = True def _should_add_regularizer(variable, existing_variable_set): if base_layer_utils.is_split_variable(variable): for var in variable: if var in existing_variable_set: return False return True else: return variable not in existing_variable_set init_graph = None if not tf.executing_eagerly(): default_graph = tf.compat.v1.get_default_graph() if default_graph.building_function: with tf.init_scope(): # Retrieve the variables from the graph into which variables # will be lifted; if initialization ops will be lifted into # the eager context, then there is nothing to retrieve, # since variable collections are not supported when eager # execution is enabled. if not tf.executing_eagerly(): init_graph = tf.compat.v1.get_default_graph() existing_variables = set( tf.compat.v1.global_variables() ) else: # Initialization ops will not be lifted out of the default # graph. init_graph = default_graph existing_variables = set(tf.compat.v1.global_variables()) if dtype is None: dtype = self.dtype or tf.float32 self._set_scope(None) reuse = self.built or self._reuse prev_len_trainable = len(self._trainable_weights) with tf.compat.v1.variable_scope( self._scope, reuse=reuse, auxiliary_name_scope=False ) as scope: self._current_scope = scope with backend.name_scope(self._name_scope()): use_resource = ( use_resource or self._use_resource_variables or scope.use_resource ) if initializer is None: initializer = scope.initializer variable = super().add_weight( name, shape, dtype=tf.as_dtype(dtype), initializer=initializer, trainable=trainable and self.trainable, constraint=constraint, partitioner=partitioner, use_resource=use_resource, synchronization=synchronization, aggregation=aggregation, getter=tf.compat.v1.get_variable, **kwargs ) if regularizer: if ( tf.compat.v1.executing_eagerly_outside_functions() or _should_add_regularizer(variable, existing_variables) ): self._handle_weight_regularization( name, variable, regularizer ) var_store = vs._get_default_variable_store() # When the shim to get variable scope working in TF2 is # used, We need to explicitly make the shim track the # regularization losses as the collections will not be # accessible. if hasattr(var_store, "add_regularizer"): var_store.add_regularizer(variable, regularizer) if init_graph is not None: # Handle edge case where a custom getter has overridden # `trainable`. There is one known occurrence of this, in # unit test testBasicRNNCellNotTrainable in # contrib.rnn.python.kernel_tests.core_rnn_cell_test with init_graph.as_default(): trainable_variables = tf.compat.v1.trainable_variables() if ( trainable and self.trainable and variable not in trainable_variables ): # A custom getter / variable scope overrode the # trainable flag. extra_trainable_vars = self._trainable_weights[ prev_len_trainable: ] self._trainable_weights = self._trainable_weights[ :prev_len_trainable ] self._non_trainable_weights += extra_trainable_vars return variable def __call__(self, inputs, *args, **kwargs): """Wraps `call`, applying pre- and post-processing steps. Args: inputs: input tensor(s). *args: additional positional arguments to be passed to `self.call`. **kwargs: additional keyword arguments to be passed to `self.call`. **Note**: kwarg `scope` is reserved for use by the layer. Returns: Output tensor(s). Note: - If the layer's `call` method takes a `scope` keyword argument, this argument will be automatically set to the current variable scope. - If the layer's `call` method takes a `mask` argument (as some Keras layers do), its default value will be set to the mask generated for `inputs` by the previous layer (if `input` did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support. Raises: ValueError: if the layer's `call` method returns None (an invalid value). """ scope = kwargs.pop("scope", None) if self._keras_style: if scope is not None: raise ValueError( "scope argument not allowed when keras style layers are " "enabled, but saw: {}".format(scope) ) return super().__call__(inputs, *args, **kwargs) self._set_scope(scope) if self.built: try: # Some classes which inherit from Layer do not use its # constructor, so rather than initializing to None we check for # an AttributeError. scope_context_manager = self._always_reuse_variable_scope except AttributeError: scope_context_manager = None if scope_context_manager is None: # From this point we will always set reuse=True, so create a # "final" variable scope with this setting. We avoid re-creating # variable scopes after this point as an optimization. scope_context_manager = tf.compat.v1.variable_scope( self._scope, reuse=True, auxiliary_name_scope=False ) # Do not cache variable scopes if Eager mode is enabled. If # Eager mode is enabled then we don't want to reuse scopes # because the cached scope might be from a FuncGraph or Eager # scope we are no longer in. if not tf.compat.v1.executing_eagerly_outside_functions(): self._always_reuse_variable_scope = scope_context_manager else: scope_context_manager = tf.compat.v1.variable_scope( self._scope, reuse=self._reuse, auxiliary_name_scope=False ) with scope_context_manager as scope: self._current_scope = scope try: call_has_scope_arg = self._call_has_scope_arg except AttributeError: self._call_spec.arg_names = variable_scope_shim.fn_args( self.call ) self._call_has_scope_arg = "scope" in self._call_spec.arg_names call_has_scope_arg = self._call_has_scope_arg if call_has_scope_arg: kwargs["scope"] = scope # Actually call layer outputs = super().__call__(inputs, *args, **kwargs) if not tf.executing_eagerly(): # Update global default collections. _add_elements_to_collection( self.updates, tf.compat.v1.GraphKeys.UPDATE_OPS ) return outputs def __deepcopy__(self, memo): no_copy = set(["_graph", "_thread_local", "_metrics_lock"]) shallow_copy = set(["_scope", "_always_reuse_variable_scope"]) cls = self.__class__ result = cls.__new__(cls) memo[id(self)] = result for k, v in self.__dict__.items(): if k in no_copy: setattr(result, k, v) elif k in shallow_copy: setattr(result, k, copy.copy(v)) elif base_layer.is_tensor_or_tensor_list(v): setattr(result, k, v) else: setattr(result, k, copy.deepcopy(v, memo)) return result def __setattr__(self, value, name): # By-pass the automatic dependency tracking performed by the parent # Layer. super(tf.__internal__.tracking.Trackable, self).__setattr__(value, name) @property def _is_legacy_layer(self): """Used by keras to check compatibility. This should not be overridden.""" return True def _add_elements_to_collection(elements, collection_list): if tf.executing_eagerly(): raise RuntimeError( "Using collections from Layers not supported in Eager " "mode. Tried to add %s to %s" % (elements, collection_list) ) elements = tf.nest.flatten(elements) collection_list = tf.nest.flatten(collection_list) for name in collection_list: collection = tf.compat.v1.get_collection_ref(name) collection_set = {id(e) for e in collection} for element in elements: if id(element) not in collection_set: collection.append(element)