# 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

from tensorflow.python.eager import context

from tensorflow.python.framework import dtypes

from tensorflow.python.framework import ops

from tensorflow.python.keras.engine import base_layer

from tensorflow.python.keras.engine import base_layer_utils

from tensorflow.python.ops import variable_scope as vs

from tensorflow.python.ops import variables as tf_variables

from tensorflow.python.training.tracking import base as trackable

from tensorflow.python.util import function_utils

from tensorflow.python.util import nest

from tensorflow.python.util import tf_contextlib

from tensorflow.python.util.tf_export import tf_export

# Avoid breaking users who directly import this symbol from this file.

# TODO(fchollet): remove this.

InputSpec = base_layer.InputSpec # pylint: disable=invalid-name

_KERAS_STYLE_SCOPE = False

@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.nn.rnn_cell.MultiRNNCell(

[tf.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

@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

@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.

Arguments:

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

super(Layer, self).__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._graph = None

self._call_has_scope_arg = 'scope' in self._call_fn_args

if scope:

with vs.variable_scope(scope) as captured_scope:

self._scope = captured_scope

else:

self._scope = None

self._current_scope = None

@property

def graph(self):

if context.executing_eagerly():

raise RuntimeError('Layer.graph not supported when executing eagerly.')

return self._graph

def _init_set_name(self, name):

# Determine layer name (non-unique).

if isinstance(name, vs.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 = base_layer_utils.unique_layer_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(Layer, self).add_loss(losses, inputs=inputs)

if not context.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,

ops.GraphKeys.REGULARIZATION_LOSSES)

def _name_scope(self):

"""Determines op naming for the Layer."""

if self._keras_style:

return super(Layer, self)._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 vs.variable_scope(

scope if scope is not None else self._base_name) as captured_scope:

self._scope = captured_scope

else:

with vs.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=vs.VariableSynchronization.AUTO,

aggregation=vs.VariableAggregation.NONE,

partitioner=None,

**kwargs):

"""Adds a new variable to the layer, or gets an existing one; returns it.

Arguments:

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.fixed_size_partitioner` and

`tf.variable_axis_size_partitioner`. For more details, see the

documentation of `tf.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 partioned 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(Layer, self).add_weight(

name=name,

shape=shape,

dtype=dtype,

initializer=initializer,

regularizer=regularizer,

trainable=trainable,

constraint=constraint,

use_resource=use_resource,

synchronization=vs.VariableSynchronization.AUTO,

aggregation=vs.VariableAggregation.NONE,

partitioner=partitioner,

**kwargs)

if synchronization == vs.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 isinstance(variable, tf_variables.PartitionedVariable):

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 context.executing_eagerly():

default_graph = ops.get_default_graph()

if default_graph.building_function:

with ops.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 context.executing_eagerly():

init_graph = ops.get_default_graph()

existing_variables = set(tf_variables.global_variables())

else:

# Initialization ops will not be lifted out of the default graph.

init_graph = default_graph

existing_variables = set(tf_variables.global_variables())

if dtype is None:

dtype = self.dtype or dtypes.float32

self._set_scope(None)

reuse = self.built or self._reuse

prev_len_trainable = len(self._trainable_weights)

with vs.variable_scope(

self._scope, reuse=reuse, auxiliary_name_scope=False) as scope:

self._current_scope = scope

with ops.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(Layer, self).add_weight(

name,

shape,

dtype=dtypes.as_dtype(dtype),

initializer=initializer,

trainable=trainable,

constraint=constraint,

partitioner=partitioner,

use_resource=use_resource,

synchronization=synchronization,

aggregation=aggregation,

getter=vs.get_variable,

**kwargs)

if regularizer:

if (ops.executing_eagerly_outside_functions()

or _should_add_regularizer(variable, existing_variables)):

self._handle_weight_regularization(name, 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_variables.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.

Arguments:

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(Layer, self).__call__(inputs, *args, **kwargs)

self._set_scope(scope)

if not context.executing_eagerly():

try:

# Set layer's "graph" at build time

self._graph = ops._get_graph_from_inputs(nest.flatten(inputs), # pylint: disable=protected-access

graph=self._graph)

except ValueError as e:

raise ValueError('Input graph and Layer graph are not the same: %s' % e)

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:

# 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.

self._always_reuse_variable_scope = vs.variable_scope(

self._scope, reuse=True, auxiliary_name_scope=False)

scope_context_manager = self._always_reuse_variable_scope

else:

scope_context_manager = vs.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_fn_args = function_utils.fn_args(self.call)

self._call_has_scope_arg = 'scope' in self._call_fn_args

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 __setattr__(self, value, name):

# By-pass the automatic dependency tracking performed by the parent Layer.

super(trackable.Trackable, self).__setattr__(value, name)

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)