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# 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.
# ==============================================================================
"""Utility functions for training."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import os.path
from tensorflow.python.framework import ops
from tensorflow.python.lib.io import file_io
from tensorflow.python.ops import variables
from tensorflow.python.platform import tf_logging as logging
def global_step(sess, global_step_tensor):
"""Small helper to get the global step.
```python
# Creates a variable to hold the global_step.
global_step_tensor = tf.Variable(10, trainable=False, name='global_step')
# Creates a session.
sess = tf.Session()
# Initializes the variable.
print('global_step: %s' % tf.train.global_step(sess, global_step_tensor))
global_step: 10
```
Args:
sess: A TensorFlow `Session` object.
global_step_tensor: `Tensor` or the `name` of the operation that contains
the global step.
Returns:
The global step value.
"""
return int(sess.run(global_step_tensor))
def get_global_step(graph=None):
"""Get the global step tensor.
The global step tensor must be an integer variable. We first try to find it
in the collection `GLOBAL_STEP`, or by name `global_step:0`.
Args:
graph: The graph to find the global step in. If missing, use default graph.
Returns:
The global step variable, or `None` if none was found.
Raises:
TypeError: If the global step tensor has a non-integer type, or if it is not
a `Variable`.
"""
graph = ops.get_default_graph() if graph is None else graph
global_step_tensor = None
global_step_tensors = graph.get_collection(ops.GraphKeys.GLOBAL_STEP)
if len(global_step_tensors) == 1:
global_step_tensor = global_step_tensors[0]
elif not global_step_tensors:
try:
global_step_tensor = graph.get_tensor_by_name('global_step:0')
except KeyError:
return None
else:
logging.error('Multiple tensors in global_step collection.')
return None
assert_global_step(global_step_tensor)
return global_step_tensor
def assert_global_step(global_step_tensor):
"""Asserts `global_step_tensor` is a scalar int `Variable` or `Tensor`.
Args:
global_step_tensor: `Tensor` to test.
"""
if not (isinstance(global_step_tensor, variables.Variable) or
isinstance(global_step_tensor, ops.Tensor)):
raise TypeError(
'Existing "global_step" must be a Variable or Tensor: %s.' %
global_step_tensor)
if not global_step_tensor.dtype.base_dtype.is_integer:
raise TypeError('Existing "global_step" does not have integer type: %s' %
global_step_tensor.dtype)
if global_step_tensor.get_shape().ndims != 0:
raise TypeError('Existing "global_step" is not scalar: %s' %
global_step_tensor.get_shape())
def write_graph(graph_or_graph_def, logdir, name, as_text=True):
"""Writes a graph proto to a file.
The graph is written as a binary proto unless `as_text` is `True`.
```python
v = tf.Variable(0, name='my_variable')
sess = tf.Session()
tf.train.write_graph(sess.graph_def, '/tmp/my-model', 'train.pbtxt')
```
or
```python
v = tf.Variable(0, name='my_variable')
sess = tf.Session()
tf.train.write_graph(sess.graph, '/tmp/my-model', 'train.pbtxt')
```
Args:
graph_or_graph_def: A `Graph` or a `GraphDef` protocol buffer.
logdir: Directory where to write the graph. This can refer to remote
filesystems, such as Google Cloud Storage (GCS).
name: Filename for the graph.
as_text: If `True`, writes the graph as an ASCII proto.
"""
if isinstance(graph_or_graph_def, ops.Graph):
graph_def = graph_or_graph_def.as_graph_def()
else:
graph_def = graph_or_graph_def
# gcs does not have the concept of directory at the moment.
if not file_io.file_exists(logdir) and not logdir.startswith('gs:'):
file_io.recursive_create_dir(logdir)
path = os.path.join(logdir, name)
if as_text:
file_io.atomic_write_string_to_file(path, str(graph_def))
else:
file_io.atomic_write_string_to_file(path, graph_def.SerializeToString())