Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/utils/version_utils.py

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2023-06-19 00:49:18 +02:00
# Copyright 2019 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.
# ==============================================================================
"""Utilities for Keras classes with v1 and v2 versions."""
import tensorflow.compat.v2 as tf
from keras.utils.generic_utils import LazyLoader
# TODO(b/134426265): Switch back to single-quotes once the issue
# with copybara is fixed.
training = LazyLoader("training", globals(), "keras.engine.training")
training_v1 = LazyLoader("training_v1", globals(), "keras.engine.training_v1")
base_layer = LazyLoader("base_layer", globals(), "keras.engine.base_layer")
base_layer_v1 = LazyLoader(
"base_layer_v1", globals(), "keras.engine.base_layer_v1"
)
callbacks = LazyLoader("callbacks", globals(), "keras.callbacks")
callbacks_v1 = LazyLoader("callbacks_v1", globals(), "keras.callbacks_v1")
class ModelVersionSelector:
"""Chooses between Keras v1 and v2 Model class."""
def __new__(cls, *args, **kwargs):
use_v2 = should_use_v2()
cls = swap_class(cls, training.Model, training_v1.Model, use_v2)
return super(ModelVersionSelector, cls).__new__(cls)
class LayerVersionSelector:
"""Chooses between Keras v1 and v2 Layer class."""
def __new__(cls, *args, **kwargs):
use_v2 = should_use_v2()
cls = swap_class(cls, base_layer.Layer, base_layer_v1.Layer, use_v2)
return super(LayerVersionSelector, cls).__new__(cls)
class TensorBoardVersionSelector:
"""Chooses between Keras v1 and v2 TensorBoard callback class."""
def __new__(cls, *args, **kwargs):
use_v2 = should_use_v2()
start_cls = cls
cls = swap_class(
start_cls, callbacks.TensorBoard, callbacks_v1.TensorBoard, use_v2
)
if (
start_cls == callbacks_v1.TensorBoard
and cls == callbacks.TensorBoard
):
# Since the v2 class is not a subclass of the v1 class, __init__ has
# to be called manually.
return cls(*args, **kwargs)
return super(TensorBoardVersionSelector, cls).__new__(cls)
def should_use_v2():
"""Determine if v1 or v2 version should be used."""
if tf.executing_eagerly():
return True
elif tf.compat.v1.executing_eagerly_outside_functions():
# Check for a v1 `wrap_function` FuncGraph.
# Code inside a `wrap_function` is treated like v1 code.
graph = tf.compat.v1.get_default_graph()
if getattr(graph, "name", False) and graph.name.startswith(
"wrapped_function"
):
return False
return True
else:
return False
def swap_class(cls, v2_cls, v1_cls, use_v2):
"""Swaps in v2_cls or v1_cls depending on graph mode."""
if cls == object:
return cls
if cls in (v2_cls, v1_cls):
return v2_cls if use_v2 else v1_cls
# Recursively search superclasses to swap in the right Keras class.
new_bases = []
for base in cls.__bases__:
if (
use_v2
and issubclass(base, v1_cls)
# `v1_cls` often extends `v2_cls`, so it may still call `swap_class`
# even if it doesn't need to. That being said, it may be the safest
# not to over optimize this logic for the sake of correctness,
# especially if we swap v1 & v2 classes that don't extend each
# other, or when the inheritance order is different.
or (not use_v2 and issubclass(base, v2_cls))
):
new_base = swap_class(base, v2_cls, v1_cls, use_v2)
else:
new_base = base
new_bases.append(new_base)
cls.__bases__ = tuple(new_bases)
return cls
def disallow_legacy_graph(cls_name, method_name):
if not tf.compat.v1.executing_eagerly_outside_functions():
error_msg = (
f"Calling `{cls_name}.{method_name}` in graph mode is not "
f"supported when the `{cls_name}` instance was constructed with "
f"eager mode enabled. Please construct your `{cls_name}` instance "
f"in graph mode or call `{cls_name}.{method_name}` with "
"eager mode enabled."
)
raise ValueError(error_msg)
def is_v1_layer_or_model(obj):
return isinstance(obj, (base_layer_v1.Layer, training_v1.Model))