400 lines
16 KiB
Python
400 lines
16 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =============================================================================
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"""Contains the base Layer class, from which all layers inherit."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import copy
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from tensorflow.python.eager import context
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.keras.engine import base_layer
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from tensorflow.python.ops import variable_scope as vs
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from tensorflow.python.ops import variables as tf_variables
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from tensorflow.python.util import function_utils
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from tensorflow.python.util import nest
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from tensorflow.python.util.tf_export import tf_export
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InputSpec = base_layer.InputSpec # pylint: disable=invalid-name
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@tf_export('layers.Layer')
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class Layer(base_layer.Layer):
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"""Base layer class.
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It is considered legacy, and we recommend the use of `tf.keras.layers.Layer`
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instead.
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Arguments:
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trainable: Boolean, whether the layer's variables should be trainable.
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name: String name of the layer.
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dtype: Default dtype of the layer's weights (default of `None` means use the
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type of the first input).
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Read-only properties:
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name: The name of the layer (string).
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dtype: Default dtype of the layer's weights (default of `None` means use the
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type of the first input).
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trainable_variables: List of trainable variables.
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non_trainable_variables: List of non-trainable variables.
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variables: List of all variables of this layer, trainable and
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non-trainable.
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updates: List of update ops of this layer.
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losses: List of losses added by this layer.
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trainable_weights: List of variables to be included in backprop.
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non_trainable_weights: List of variables that should not be
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included in backprop.
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weights: The concatenation of the lists trainable_weights and
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non_trainable_weights (in this order).
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Mutable properties:
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trainable: Whether the layer should be trained (boolean).
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input_spec: Optional (list of) `InputSpec` object(s) specifying the
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constraints on inputs that can be accepted by the layer.
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"""
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def __init__(self, trainable=True, name=None, dtype=None,
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**kwargs):
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# For backwards compatibility, legacy layers do not use `ResourceVariable`
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# by default.
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self._use_resource_variables = False
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scope = kwargs.pop('_scope', None)
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self._reuse = kwargs.pop('_reuse', None)
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# Avoid an incorrect lint error
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self._trainable_weights = []
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self.built = False
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super(Layer, self).__init__(trainable=trainable, name=name, dtype=dtype,
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**kwargs)
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self._graph = None
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self._call_has_scope_arg = 'scope' in self._call_fn_args
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if scope:
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with vs.variable_scope(scope) as captured_scope:
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self._scope = captured_scope
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else:
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self._scope = None
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self._current_scope = None
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@property
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def graph(self):
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if context.executing_eagerly():
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raise RuntimeError('Layer.graph not supported when executing eagerly.')
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return self._graph
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def _init_set_name(self, name):
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# Determine layer name (non-unique).
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if isinstance(name, vs.VariableScope):
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base_name = name.name
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else:
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base_name = name
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self._name = name
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if not name:
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self._name, base_name = self._make_unique_name()
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self._base_name = base_name
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def _make_unique_name(self, name_uid_map=None, avoid_names=None,
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namespace='', zero_based=False):
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base_name = base_layer.to_snake_case(self.__class__.__name__)
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name = base_layer.unique_layer_name(base_name,
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name_uid_map=name_uid_map,
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avoid_names=avoid_names,
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namespace=namespace,
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zero_based=zero_based)
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return (name, base_name)
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@property
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def scope_name(self):
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if not self._scope:
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raise ValueError('No name available for layer scope because the layer "' +
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self._name + '" has not been used yet. The scope name ' +
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' is determined the first time the layer instance is ' +
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'called. You must therefore call the layer before ' +
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'querying `scope_name`.')
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return self._scope.name
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def add_loss(self, losses, inputs=None):
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previous_losses_length = len(self._losses)
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super(Layer, self).add_loss(losses, inputs=inputs)
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# TODO(fchollet): deprecate collection below.
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new_losses = self._losses[previous_losses_length:]
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_add_elements_to_collection(new_losses, ops.GraphKeys.REGULARIZATION_LOSSES)
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def _name_scope(self):
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"""Determines op naming for the Layer."""
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return self._current_scope.original_name_scope
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def _set_scope(self, scope=None):
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if self._scope is None:
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# If constructed with _scope=None, lazy setting of scope.
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if self._reuse:
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with vs.variable_scope(
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scope if scope is not None else self._base_name) as captured_scope:
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self._scope = captured_scope
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else:
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with vs.variable_scope(
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scope, default_name=self._base_name) as captured_scope:
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self._scope = captured_scope
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def add_weight(self,
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name,
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shape,
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dtype=None,
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initializer=None,
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regularizer=None,
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trainable=None,
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constraint=None,
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use_resource=None,
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synchronization=vs.VariableSynchronization.AUTO,
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aggregation=vs.VariableAggregation.NONE,
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partitioner=None):
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"""Adds a new variable to the layer, or gets an existing one; returns it.
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Arguments:
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name: variable name.
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shape: variable shape.
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dtype: The type of the variable. Defaults to `self.dtype` or `float32`.
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initializer: initializer instance (callable).
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regularizer: regularizer instance (callable).
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trainable: whether the variable should be part of the layer's
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"trainable_variables" (e.g. variables, biases)
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or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
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Note, if the current variable scope is marked as non-trainable
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then this parameter is ignored and any added variables are also
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marked as non-trainable. `trainable` defaults to `True` unless
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`synchronization` is set to `ON_READ`.
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constraint: constraint instance (callable).
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use_resource: Whether to use `ResourceVariable`.
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synchronization: Indicates when a distributed a variable will be
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aggregated. Accepted values are constants defined in the class
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@{tf.VariableSynchronization}. By default the synchronization is set to
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`AUTO` and the current `DistributionStrategy` chooses
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when to synchronize. If `synchronization` is set to `ON_READ`,
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`trainable` must not be set to `True`.
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aggregation: Indicates how a distributed variable will be aggregated.
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Accepted values are constants defined in the class
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@{tf.VariableAggregation}.
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partitioner: (optional) partitioner instance (callable). If
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provided, when the requested variable is created it will be split
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into multiple partitions according to `partitioner`. In this case,
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an instance of `PartitionedVariable` is returned. Available
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partitioners include `tf.fixed_size_partitioner` and
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`tf.variable_axis_size_partitioner`. For more details, see the
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documentation of `tf.get_variable` and the "Variable Partitioners
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and Sharding" section of the API guide.
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Returns:
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The created variable. Usually either a `Variable` or `ResourceVariable`
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instance. If `partitioner` is not `None`, a `PartitionedVariable`
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instance is returned.
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Raises:
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RuntimeError: If called with partioned variable regularization and
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eager execution is enabled.
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ValueError: When trainable has been set to True with synchronization
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set as `ON_READ`.
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"""
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if synchronization == vs.VariableSynchronization.ON_READ:
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if trainable:
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raise ValueError(
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'Synchronization value can be set to '
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'VariableSynchronization.ON_READ only for non-trainable variables. '
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'You have specified trainable=True and '
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'synchronization=VariableSynchronization.ON_READ.')
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else:
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# Set trainable to be false when variable is to be synced on read.
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trainable = False
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elif trainable is None:
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trainable = True
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def _should_add_regularizer(variable, existing_variable_set):
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if isinstance(variable, tf_variables.PartitionedVariable):
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for var in variable:
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if var in existing_variable_set:
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return False
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return True
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else:
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return variable not in existing_variable_set
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init_graph = None
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if not context.executing_eagerly():
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default_graph = ops.get_default_graph()
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if default_graph.building_function:
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with ops.init_scope():
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# Retrieve the variables from the graph into which variables
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# will be lifted; if initialization ops will be lifted into
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# the eager context, then there is nothing to retrieve, since variable
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# collections are not supported when eager execution is enabled.
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if not context.executing_eagerly():
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init_graph = ops.get_default_graph()
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existing_variables = set(tf_variables.global_variables())
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else:
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# Initialization ops will not be lifted out of the default graph.
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init_graph = default_graph
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existing_variables = set(tf_variables.global_variables())
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if dtype is None:
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dtype = self.dtype or dtypes.float32
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self._set_scope(None)
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reuse = self.built or self._reuse
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prev_len_trainable = len(self._trainable_weights)
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with vs.variable_scope(
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self._scope, reuse=reuse, auxiliary_name_scope=False) as scope:
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self._current_scope = scope
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with ops.name_scope(self._name_scope()):
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use_resource = (use_resource or
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self._use_resource_variables or
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scope.use_resource)
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variable = super(Layer, self).add_weight(
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name,
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shape,
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dtype=dtypes.as_dtype(dtype),
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initializer=initializer or scope.initializer,
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trainable=trainable,
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constraint=constraint,
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partitioner=partitioner,
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use_resource=use_resource,
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synchronization=synchronization,
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aggregation=aggregation,
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getter=vs.get_variable)
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if regularizer:
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if context.executing_eagerly() or _should_add_regularizer(
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variable, existing_variables):
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self._handle_weight_regularization(name, variable, regularizer)
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if init_graph is not None:
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# Handle edge case where a custom getter has overridden `trainable`.
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# There is one known occurrence of this, in unit test
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# testBasicRNNCellNotTrainable in
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# contrib.rnn.python.kernel_tests.core_rnn_cell_test
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with init_graph.as_default():
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trainable_variables = tf_variables.trainable_variables()
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if (trainable and self.trainable and
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variable not in trainable_variables):
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# A custom getter / variable scope overrode the trainable flag.
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extra_trainable_vars = self._trainable_weights[prev_len_trainable:]
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self._trainable_weights = self._trainable_weights[
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:prev_len_trainable]
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self._non_trainable_weights += extra_trainable_vars
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return variable
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def __call__(self, inputs, *args, **kwargs):
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"""Wraps `call`, applying pre- and post-processing steps.
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Arguments:
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inputs: input tensor(s).
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*args: additional positional arguments to be passed to `self.call`.
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**kwargs: additional keyword arguments to be passed to `self.call`.
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**Note**: kwarg `scope` is reserved for use by the layer.
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Returns:
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Output tensor(s).
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Note:
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- If the layer's `call` method takes a `scope` keyword argument,
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this argument will be automatically set to the current variable scope.
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- If the layer's `call` method takes a `mask` argument (as some Keras
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layers do), its default value will be set to the mask generated
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for `inputs` by the previous layer (if `input` did come from
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a layer that generated a corresponding mask, i.e. if it came from
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a Keras layer with masking support.
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Raises:
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ValueError: if the layer's `call` method returns None (an invalid value).
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"""
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self._set_scope(kwargs.pop('scope', None))
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if not context.executing_eagerly():
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try:
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# Set layer's "graph" at build time
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self._graph = ops._get_graph_from_inputs(nest.flatten(inputs), # pylint: disable=protected-access
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graph=self._graph)
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except ValueError as e:
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raise ValueError('Input graph and Layer graph are not the same: %s' % e)
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if self.built:
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try:
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# Some classes which inherit from Layer do not use its constructor, so
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# rather than initializing to None we check for an AttributeError.
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scope_context_manager = self._always_reuse_variable_scope
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except AttributeError:
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# From this point we will always set reuse=True, so create a "final"
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# variable scope with this setting. We avoid re-creating variable scopes
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# after this point as an optimization.
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self._always_reuse_variable_scope = vs.variable_scope(
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self._scope, reuse=True, auxiliary_name_scope=False)
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scope_context_manager = self._always_reuse_variable_scope
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else:
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scope_context_manager = vs.variable_scope(
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self._scope, reuse=self._reuse, auxiliary_name_scope=False)
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with scope_context_manager as scope:
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self._current_scope = scope
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try:
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call_has_scope_arg = self._call_has_scope_arg
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except AttributeError:
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self._call_fn_args = function_utils.fn_args(self.call)
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self._call_has_scope_arg = 'scope' in self._call_fn_args
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call_has_scope_arg = self._call_has_scope_arg
|
||
|
|
if call_has_scope_arg:
|
||
|
|
kwargs['scope'] = scope
|
||
|
|
|
||
|
|
# Actually call layer
|
||
|
|
outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
|
||
|
|
|
||
|
|
if not context.executing_eagerly():
|
||
|
|
# Update global default collections.
|
||
|
|
_add_elements_to_collection(self.updates, ops.GraphKeys.UPDATE_OPS)
|
||
|
|
return outputs
|
||
|
|
|
||
|
|
def __deepcopy__(self, memo):
|
||
|
|
no_copy = set(['_graph'])
|
||
|
|
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 _add_elements_to_collection(elements, collection_list):
|
||
|
|
if context.executing_eagerly():
|
||
|
|
raise RuntimeError('Using collections from Layers not supported in Eager '
|
||
|
|
'mode. Tried to add %s to %s' % (elements,
|
||
|
|
collection_list))
|
||
|
|
elements = nest.flatten(elements)
|
||
|
|
collection_list = nest.flatten(collection_list)
|
||
|
|
for name in collection_list:
|
||
|
|
collection = ops.get_collection_ref(name)
|
||
|
|
collection_set = set(collection)
|
||
|
|
for element in elements:
|
||
|
|
if element not in collection_set:
|
||
|
|
collection.append(element)
|