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# 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.

# ==============================================================================

"""Common values and methods for TensorFlow Debugger."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import collections

import json

GRPC_URL_PREFIX = "grpc://"

# A key for a Session.run() call.

RunKey = collections.namedtuple("RunKey", ["feed_names", "fetch_names"])

def get_graph_element_name(elem):

"""Obtain the name or string representation of a graph element.

If the graph element has the attribute "name", return name. Otherwise, return

a __str__ representation of the graph element. Certain graph elements, such as

`SparseTensor`s, do not have the attribute "name".

Args:

elem: The graph element in question.

Returns:

If the attribute 'name' is available, return the name. Otherwise, return

str(fetch).

"""

return elem.name if hasattr(elem, "name") else str(elem)

def get_flattened_names(feeds_or_fetches):

"""Get a flattened list of the names in run() call feeds or fetches.

Args:

feeds_or_fetches: Feeds or fetches of the `Session.run()` call. It maybe

a Tensor, an Operation or a Variable. It may also be nested lists, tuples

or dicts. See doc of `Session.run()` for more details.

Returns:

(list of str) A flattened list of fetch names from `feeds_or_fetches`.

"""

lines = []

if isinstance(feeds_or_fetches, (list, tuple)):

for item in feeds_or_fetches:

lines.extend(get_flattened_names(item))

elif isinstance(feeds_or_fetches, dict):

for key in feeds_or_fetches:

lines.extend(get_flattened_names(feeds_or_fetches[key]))

else:

# This ought to be a Tensor, an Operation or a Variable, for which the name

# attribute should be available. (Bottom-out condition of the recursion.)

lines.append(get_graph_element_name(feeds_or_fetches))

return lines

def get_run_key(feed_dict, fetches):

"""Summarize the names of feeds and fetches as a RunKey JSON string.

Args:

feed_dict: The feed_dict given to the `Session.run()` call.

fetches: The fetches from the `Session.run()` call.

Returns:

A JSON Array consisting of two items. They first items is a flattened

Array of the names of the feeds. The second item is a flattened Array of

the names of the fetches.

"""

return json.dumps(RunKey(get_flattened_names(feed_dict),

get_flattened_names(fetches)))

tensorflow/tensorflow/python/debug/lib/common.py at master · codeplaysoftware/tensorflow · GitHub

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