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# 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.
# ==============================================================================
"""Graph-only versions of a few op functions, for internal use only."""
# Must be separate from array_ops to avoid a cyclic dependency.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.core.framework import attr_value_pb2
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
def graph_zeros_like(tensor):
"""Graph-only version of tf.zeros_like(), for internal use only."""
g = ops._get_graph_from_inputs([tensor]) # pylint: disable=protected-access
with g.as_default(), ops.name_scope(None, "zeros_like", [tensor]) as name:
tensor = ops.convert_to_tensor(tensor, name="tensor")
dtype = tensor.dtype.base_dtype
dtype_value = attr_value_pb2.AttrValue(type=dtype.as_datatype_enum)
op = g.create_op("ZerosLike", [tensor], [dtype], input_types=[dtype],
attrs={"T": dtype_value}, name=name)
result, = op.outputs
return result
def graph_placeholder(dtype, shape, name=None):
"""Graph-only version of tf.placeholder(), for internal use only."""
dtype = dtype.base_dtype
dtype_value = attr_value_pb2.AttrValue(type=dtype.as_datatype_enum)
if isinstance(shape, (list, tuple)):
shape = tensor_shape.TensorShape(shape)
assert isinstance(shape, tensor_shape.TensorShape)
shape = attr_value_pb2.AttrValue(shape=shape.as_proto())
g = ops.get_default_graph()
with ops.name_scope(name, "placeholder", []) as name:
op = g.create_op("Placeholder", [], [dtype], input_types=[],
attrs={"dtype": dtype_value, "shape": shape}, name=name)
result, = op.outputs
return result
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