3RNN/Lib/site-packages/tensorflow/python/autograph/utils/misc.py
2024-05-26 19:49:15 +02:00

60 lines
2.1 KiB
Python

# Copyright 2017 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.
# ==============================================================================
"""Miscellaneous utilities that don't fit anywhere else."""
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_math_ops
from tensorflow.python.ops import math_ops
def alias_tensors(*args):
"""Wraps any Tensor arguments with an identity op.
Any other argument, including Variables, is returned unchanged.
Args:
*args: Any arguments. Must contain at least one element.
Returns:
Same as *args, with Tensor instances replaced as described.
Raises:
ValueError: If args doesn't meet the requirements.
"""
def alias_if_tensor(a):
return array_ops.identity(a) if isinstance(a, tensor.Tensor) else a
# TODO(mdan): Recurse into containers?
# TODO(mdan): Anything we can do about variables? Fake a scope reuse?
if len(args) > 1:
return (alias_if_tensor(a) for a in args)
elif len(args) == 1:
return alias_if_tensor(args[0])
raise ValueError('at least one argument required')
def get_range_len(start, limit, delta):
dist = ops.convert_to_tensor(limit - start)
unadjusted_len = dist // delta
adjustment = math_ops.cast(
gen_math_ops.not_equal(dist % delta,
array_ops.zeros_like(unadjusted_len)), dist.dtype)
final_len = unadjusted_len + adjustment
return gen_math_ops.maximum(final_len, array_ops.zeros_like(final_len))