88 lines
3.0 KiB
Python
88 lines
3.0 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Common values and methods for TensorFlow Debugger."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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import json
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GRPC_URL_PREFIX = "grpc://"
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# A key for a Session.run() call.
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RunKey = collections.namedtuple("RunKey", ["feed_names", "fetch_names"])
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def get_graph_element_name(elem):
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"""Obtain the name or string representation of a graph element.
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If the graph element has the attribute "name", return name. Otherwise, return
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a __str__ representation of the graph element. Certain graph elements, such as
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`SparseTensor`s, do not have the attribute "name".
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Args:
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elem: The graph element in question.
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Returns:
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If the attribute 'name' is available, return the name. Otherwise, return
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str(fetch).
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"""
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return elem.name if hasattr(elem, "name") else str(elem)
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def get_flattened_names(feeds_or_fetches):
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"""Get a flattened list of the names in run() call feeds or fetches.
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Args:
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feeds_or_fetches: Feeds or fetches of the `Session.run()` call. It maybe
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a Tensor, an Operation or a Variable. It may also be nested lists, tuples
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or dicts. See doc of `Session.run()` for more details.
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Returns:
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(list of str) A flattened list of fetch names from `feeds_or_fetches`.
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"""
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lines = []
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if isinstance(feeds_or_fetches, (list, tuple)):
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for item in feeds_or_fetches:
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lines.extend(get_flattened_names(item))
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elif isinstance(feeds_or_fetches, dict):
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for key in feeds_or_fetches:
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lines.extend(get_flattened_names(feeds_or_fetches[key]))
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else:
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# This ought to be a Tensor, an Operation or a Variable, for which the name
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# attribute should be available. (Bottom-out condition of the recursion.)
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lines.append(get_graph_element_name(feeds_or_fetches))
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return lines
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def get_run_key(feed_dict, fetches):
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"""Summarize the names of feeds and fetches as a RunKey JSON string.
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Args:
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feed_dict: The feed_dict given to the `Session.run()` call.
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fetches: The fetches from the `Session.run()` call.
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Returns:
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A JSON Array consisting of two items. They first items is a flattened
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Array of the names of the feeds. The second item is a flattened Array of
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the names of the fetches.
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"""
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return json.dumps(RunKey(get_flattened_names(feed_dict),
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get_flattened_names(fetches)))
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