def wrapper(self, *args, **kwargs):
layer = self.call_collection.layer
original_losses = _reset_layer_losses(layer)
with base_layer_utils.call_context().enter(layer, None, True, None):
ret = method(self, *args, **kwargs)
_restore_layer_losses(original_losses)
return ret
def _wrapped_model(*args):
"""A concrete tf.function that wraps the model's call function."""
# When given a single input, Keras models will call the model on the tensor
# rather than a list consisting of the single tensor.
# inputs = args[0] if len(input_signature) == 1 else list(args)
inputs = args[0] if len(args) == 1 else list(args)
with base_layer_utils.call_context().enter(
model, inputs=inputs, build_graph=False, training=False, saving=True):
return model(*args, training=False)
def wrapper(self, *args, **kwargs):
"""Calls method within call context."""
layer = self.call_collection.layer
training = None
# pylint: disable=protected-access
if (args or kwargs) and layer._call_arg_was_passed(
'training', args, kwargs, inputs_in_args=True):
training = layer._get_call_arg_value(
'training', args, kwargs, inputs_in_args=True)
# pylint: enable=protected-access
original_losses = _reset_layer_losses(layer)
with base_layer_utils.call_context().enter(layer, None, True, training):
ret = method(self, *args, **kwargs)
_restore_layer_losses(original_losses)
return ret
def _wrapped_model(*args):
"""A concrete tf.function that wraps the model's call function."""
# When given a single input, Keras models will call the model on the tensor
# rather than a list consisting of the single tensor.
inputs = args[0] if len(input_signature) == 1 else list(args)
with base_layer_utils.call_context().enter(
model, inputs=inputs, build_graph=False, training=False, saving=True):
outputs_list = nest.flatten(model(inputs=inputs, training=False))
try:
output_names = model.output_names
except AttributeError:
from tensorflow.python.keras.engine import training_utils # pylint: disable=g-import-not-at-top
output_names = training_utils.generic_output_names(outputs_list)
return {name: output for name, output in zip(output_names, outputs_list)}
def _wrapped_model(*args):
"""A concrete tf.function that wraps the model's call function."""
# When given a single input, Keras models will call the model on the tensor
# rather than a list consisting of the single tensor.
inputs = args[0] if len(input_signature) == 1 else list(args)
with base_layer_utils.call_context().enter(
model, inputs=inputs, build_graph=False, training=False, saving=True):
outputs = model(inputs, training=False)
# Outputs always has to be a flat dict.
output_names = model.output_names # Functional Model.
if output_names is None: # Subclassed Model.
from tensorflow.python.keras.engine import compile_utils # pylint: disable=g-import-not-at-top
output_names = compile_utils.create_pseudo_output_names(outputs)
outputs = nest.flatten(outputs)
return {name: output for name, output in zip(output_names, outputs)}
def test_Bidirectional_updates(self):
if context.executing_eagerly():
self.skipTest('layer.updates is only available in graph mode.')
with self.cached_session():
x = keras.layers.Input(shape=(3, 2))
x_reachable_update = x * x
layer = keras.layers.Bidirectional(keras.layers.SimpleRNN(3))
_ = layer(x)
assert not layer.updates
# TODO(b/128684069): Remove when Wrapper sublayers are __call__'d.
with base_layer_utils.call_context().enter(layer, x, True, None):
layer.forward_layer.add_update(x_reachable_update, inputs=x)
layer.forward_layer.add_update(1, inputs=None)
layer.backward_layer.add_update(x_reachable_update, inputs=x)
layer.backward_layer.add_update(1, inputs=None)
assert len(layer.updates) == 4
def wrapper(*args, **kwargs):
"""Calls method within call context."""
layer = call_collection.layer
training = None
inputs = call_collection.get_input_arg_value(args, kwargs)
# pylint: disable=protected-access
if (args or kwargs) and call_collection.training_arg_was_passed(
args, kwargs):
training = call_collection.get_training_arg_value(args, kwargs)
# pylint: enable=protected-access
original_losses = _reset_layer_losses(layer)
with base_layer_utils.call_context().enter(
layer, inputs=inputs, build_graph=False, training=training,
saving=True):
with base_layer_utils.autocast_context_manager(layer._compute_dtype): # pylint: disable=protected-access
ret = method(*args, **kwargs)
_restore_layer_losses(original_losses)
return ret
def wrapper(*args, **kwargs):
"""Calls method within call context."""
layer = call_collection.layer
training = None
inputs = None
# pylint: disable=protected-access
if (args or kwargs) and call_collection.training_arg_was_passed(
args, kwargs):
inputs = args[0]
training = call_collection.get_training_arg_value(args, kwargs)
# pylint: enable=protected-access
original_losses = _reset_layer_losses(layer)
with base_layer_utils.call_context().enter(layer,
inputs=inputs,
build_graph=False,
training=training):
ret = method(*args, **kwargs)
_restore_layer_losses(original_losses)
return ret
def wrap_layer_functions(layer, serialization_cache):
"""Returns dict of wrapped layer call function and losses in tf.functions.
Args:
layer: Keras Layer object.
serialization_cache: Dictionary shared between all objects during
serialization.
Returns:
A dictionary containing all keras tf.functions to serialize. See
LayerAttributes and ModelAttributes for the list of all attributes.
"""
# Since Sequential models may be modified in place using model.add() or
# model.pop(), don't use saved functions.
if (isinstance(layer, keras_load.RevivedLayer)
and not isinstance(layer, keras_load.RevivedSequential)):
return {
fn_name: getattr(layer.keras_api, fn_name, None)
for fn_name in serialized_attributes.LayerAttributes.all_functions
}
# Reset the losses of the layer and its children. The call function in each
# child layer is replaced with tf.functions.
original_fns = _replace_child_layer_functions(layer, serialization_cache)
original_losses = _reset_layer_losses(layer)
# Wrap all the layer call and activity regularizer functions.
# Use LayerCallCollection to ensure that all layer call functions (__call__,
# call with losses) are traced with the same inputs.
call_collection = LayerCallCollection(layer)
call_fn_with_losses = call_collection.add_function(
_wrap_call_and_conditional_losses(layer),
'{}_layer_call_and_return_conditional_losses'.format(layer.name))
call_fn = call_collection.add_function(
_extract_outputs_from_fn(layer, call_fn_with_losses),
'{}_layer_call_fn'.format(layer.name))
fns = {
'call_and_return_conditional_losses': call_fn_with_losses,
'__call__': call_fn
}
if layer.activity_regularizer is not None:
fns['activity_regularizer_fn'] = _wrap_activity_regularizer(layer)
fns['call_and_return_all_conditional_losses'] = (
call_collection.add_function(
_append_activity_regularizer_loss(
layer, call_fn_with_losses,
fns['activity_regularizer_fn']),
'{}_layer_call_and_return_all_conditional_losses'.format(
layer.name)))
else:
fns['activity_regularizer_fn'] = None
fns['call_and_return_all_conditional_losses'] = call_fn_with_losses
# Manually trigger traces before restoring the overwritten functions. The
# functions are traced within the layer call context to ensure that layer
# functions (e.g. add_loss) behave as though running in graph mode.
with base_layer_utils.call_context().enter(layer,
inputs=None,
build_graph=True,
training=None,
saving=True):
for fn in fns.values():
if fn is not None and fn.input_signature is not None:
fn.get_concrete_function()
# Restore overwritten functions and losses
_restore_child_layer_functions(original_fns)
_restore_layer_losses(original_losses)
return fns
def _is_building_keras_layer():
return base_layer_utils.call_context().layer is not None
def inference(self, inputs, *args, **kwargs):
call_context = base_layer_utils.call_context()
input_list = nest.flatten(inputs)
# We will attempt to build a TF graph if & only if all inputs are symbolic.
# This is always the case in graph mode. It can also be the case in eager
# mode when all inputs can be traced back to `keras.Input()` (when building
# models using the functional API).
build_graph = tf_utils.are_all_symbolic_tensors(input_list)
# Accept NumPy and scalar inputs by converting to Tensors.
if any(isinstance(x, (np.ndarray, float, int)) for x in input_list):
def _convert_non_tensor(x):
# Don't call `ops.convert_to_tensor` on all `inputs` because
# `SparseTensors` can't be converted to `Tensor`.
if isinstance(x, (np.ndarray, float, int)):
return ops.convert_to_tensor(x)
return x
inputs = nest.map_structure(_convert_non_tensor, inputs)
input_list = nest.flatten(inputs)
# Handle `mask` propagation from previous layer to current layer. Masks can
# be propagated explicitly via the `mask` argument, or implicitly via
# setting the `_keras_mask` attribute on the inputs to a Layer. Masks passed
# explicitly take priority.
mask_arg_passed_by_framework = False
input_masks = self._collect_input_masks(inputs, args, kwargs)
if (self._expects_mask_arg and input_masks is not None and
not self._call_arg_was_passed('mask', args, kwargs)):
mask_arg_passed_by_framework = True
kwargs['mask'] = input_masks
# If `training` argument was not explicitly passed, propagate `training`
# value from this layer's calling layer.
training_arg_passed_by_framework = False
# Priority 1: `training` was explicitly passed.
if self._call_arg_was_passed('training', args, kwargs):
training_value = self._get_call_arg_value('training', args, kwargs)
if not self._expects_training_arg:
kwargs.pop('training')
else:
training_value = None
# Priority 2: `training` was passed to a parent layer.
if call_context.training is not None:
training_value = call_context.training
# Priority 3a: `learning_phase()` has been set.
elif backend.global_learning_phase_is_set():
training_value = backend.learning_phase()
# Priority 3b: Pass the `learning_phase()` if in the Keras FuncGraph.
elif build_graph:
with backend.get_graph().as_default():
if base_layer_utils.is_in_keras_graph():
training_value = backend.learning_phase()
if self._expects_training_arg and training_value is not None:
# Force the training_value to be bool type which matches to the contract
# for layer/model call args.
if tensor_util.is_tensor(training_value):
training_value = math_ops.cast(training_value, dtypes.bool)
else:
training_value = bool(training_value)
kwargs['training'] = training_value
training_arg_passed_by_framework = True
# Only create Keras history if at least one tensor originates from a
# `keras.Input`. Otherwise this Layer may be being used outside the Keras
# framework.
if build_graph and base_layer_utils.needs_keras_history(inputs):
base_layer_utils.create_keras_history(inputs)
# Clear eager losses on top level model call.
# We are clearing the losses only on the top level model call and not on
# every layer/model call because layer/model may be reused.
if (base_layer_utils.is_in_eager_or_tf_function() and
not call_context.in_call):
self._clear_losses()
with call_context.enter(self, inputs, build_graph, training_value):
# Check input assumptions set after layer building, e.g. input shape.
if build_graph:
# Symbolic execution on symbolic tensors. We will attempt to build
# the corresponding TF subgraph inside `backend.get_graph()`
# TODO(reedwm): We should assert input compatibility after the inputs
# are casted, not before.
input_spec.assert_input_compatibility(self.input_spec, inputs,
self.name)
if (any(isinstance(x, ragged_tensor.RaggedTensor) for x in input_list)
and self._supports_ragged_inputs is False): # pylint: disable=g-bool-id-comparison
raise ValueError('Layer %s does not support RaggedTensors as input. '
'Inputs received: %s. You can try converting your '
'input to an uniform tensor.' % (self.name, inputs))
graph = backend.get_graph()
with graph.as_default(), backend.name_scope(self._name_scope()):
# Build layer if applicable (if the `build` method has been
# overridden).
self._maybe_build(inputs)
cast_inputs = self._maybe_cast_inputs(inputs)
# Wrapping `call` function in autograph to allow for dynamic control
# flow and control dependencies in call. We are limiting this to
# subclassed layers as autograph is strictly needed only for
# subclassed layers and models.
# tf_convert will respect the value of autograph setting in the
# enclosing tf.function, if any.
if (base_layer_utils.is_subclassed(self) and
not base_layer_utils.from_saved_model(self)):
call_fn = autograph.tf_convert(
self._inference, ag_ctx.control_status_ctx())
else:
call_fn = self._inference
if not self.dynamic:
try:
with base_layer_utils.autocast_context_manager(
self._compute_dtype):
# Add auto_control_deps in V2 when they are not already added by
# a `tf.function`.
if (ops.executing_eagerly_outside_functions() and
not base_layer_utils.is_in_eager_or_tf_function()):
with auto_control_deps.AutomaticControlDependencies() as acd:
outputs = call_fn(cast_inputs, *args, **kwargs)
# Wrap Tensors in `outputs` in `tf.identity` to avoid
# circular dependencies.
outputs = base_layer_utils.mark_as_return(outputs, acd)
else:
outputs = call_fn(cast_inputs, *args, **kwargs)
except errors.OperatorNotAllowedInGraphError as e:
raise TypeError('You are attempting to use Python control '
'flow in a layer that was not declared to be '
'dynamic. Pass `dynamic=True` to the class '
'constructor.\nEncountered error:\n"""\n' +
str(e) + '\n"""')
else:
# We will use static shape inference to return symbolic tensors
# matching the specifications of the layer outputs.
# Since `self.dynamic` is True, we will never attempt to
# run the underlying TF graph (which is disconnected).
# TODO(fchollet): consider py_func as an alternative, which
# would enable us to run the underlying graph if needed.
outputs = self._symbolic_call(inputs)
if outputs is None:
raise ValueError('A layer\'s `call` method should return a '
'Tensor or a list of Tensors, not None '
'(layer: ' + self.name + ').')
if base_layer_utils.have_all_keras_metadata(inputs):
if training_arg_passed_by_framework:
kwargs.pop('training')
if mask_arg_passed_by_framework:
kwargs.pop('mask')
inputs, outputs = self._set_connectivity_metadata_(
inputs, outputs, args, kwargs)
self._handle_activity_regularization(inputs, outputs)
self._set_mask_metadata(inputs, outputs, input_masks)
if hasattr(self, '_set_inputs') and not self.inputs:
# Subclassed network: explicitly set metadata normally set by
# a call to self._set_inputs().
# TODO(b/120997007): This should be done in Eager as well, but
# causes garbage collection issues because of the placeholders
# created on the default Keras graph.
self._set_inputs(inputs, outputs)
else:
# Eager execution on data tensors.
with backend.name_scope(self._name_scope()):
self._maybe_build(inputs)
cast_inputs = self._maybe_cast_inputs(inputs)
with base_layer_utils.autocast_context_manager(
self._compute_dtype):
outputs = self._inference(cast_inputs, *args, **kwargs)
self._handle_activity_regularization(inputs, outputs)
self._set_mask_metadata(inputs, outputs, input_masks)
return outputs
