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debug_utils.py
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191 lines (158 loc) · 6.83 KB
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# Copyright 2016 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.
# ==============================================================================
"""TensorFlow Debugger (tfdbg) Utilities."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
from six.moves import xrange # pylint: disable=redefined-builtin
def add_debug_tensor_watch(run_options,
node_name,
output_slot=0,
debug_ops="DebugIdentity",
debug_urls=None):
"""Add debug tensor watch option to RunOptions.
Args:
run_options: An instance of tensorflow.core.protobuf.config_pb2.RunOptions
node_name: Name of the node to watch.
output_slot: Output slot index of the tensor from the watched node.
debug_ops: Name(s) of the debug op(s). Default: "DebugIdentity".
Can be a list of strings or a single string. The latter case is
equivalent to a list of string with only one element.
debug_urls: URLs to send debug signals to: a non-empty list of strings or
a string, or None. The case of a string is equivalent to a list of
string with only one element.
"""
watch_opts = run_options.debug_tensor_watch_opts
watch = watch_opts.add()
watch.node_name = node_name
watch.output_slot = output_slot
if isinstance(debug_ops, str):
debug_ops = [debug_ops]
watch.debug_ops.extend(debug_ops)
if debug_urls:
if isinstance(debug_urls, str):
debug_urls = [debug_urls]
watch.debug_urls.extend(debug_urls)
def watch_graph(run_options,
graph,
debug_ops="DebugIdentity",
debug_urls=None,
node_name_regex_whitelist=None,
op_type_regex_whitelist=None):
"""Add debug tensor watch options to RunOptions based on a TensorFlow graph.
To watch all tensors on the graph, set both node_name_regex_whitelist
and op_type_regex_whitelist to None.
Args:
run_options: An instance of tensorflow.core.protobuf.config_pb2.RunOptions
graph: An instance of tensorflow.python.framework.ops.Graph
debug_ops: Name of the debug op to use. Default: "DebugIdentity".
Can be a list of strings of a single string. The latter case is
equivalent to a list of a single string.
debug_urls: Debug urls. Can be a list of strings, a single string, or
None. The case of a single string is equivalen to a list consisting
of a single string.
node_name_regex_whitelist: Regular-expression whitelist for node_name.
This should be a string, e.g., "(weight_[0-9]+|bias_.*)"
op_type_regex_whitelist: Regular-expression whitelist for the op type of
nodes. If both node_name_regex_whitelist and op_type_regex_whitelist
are none, the two filtering operations will occur in an "AND"
relation. In other words, a node will be included if and only if it
hits both whitelists. This should be a string, e.g., "(Variable|Add)".
"""
if isinstance(debug_ops, str):
debug_ops = [debug_ops]
if node_name_regex_whitelist:
node_name_pattern = re.compile(node_name_regex_whitelist)
else:
node_name_pattern = None
if op_type_regex_whitelist:
op_type_pattern = re.compile(op_type_regex_whitelist)
else:
op_type_pattern = None
ops = graph.get_operations()
for op in ops:
# Skip nodes without any output tensors.
if not op.outputs:
continue
node_name = op.name
op_type = op.type
if node_name_pattern and not node_name_pattern.match(node_name):
continue
if op_type_pattern and not op_type_pattern.match(op_type):
continue
for slot in xrange(len(op.outputs)):
add_debug_tensor_watch(
run_options,
node_name,
output_slot=slot,
debug_ops=debug_ops,
debug_urls=debug_urls)
def watch_graph_with_blacklists(run_options,
graph,
debug_ops="DebugIdentity",
debug_urls=None,
node_name_regex_blacklist=None,
op_type_regex_blacklist=None):
"""Add debug tensor watch options, blacklisting nodes and op types.
This is similar to watch_graph(), but the node names and op types can be
blacklisted, instead of whitelisted.
Args:
run_options: An instance of tensorflow.core.protobuf.config_pb2.RunOptions
graph: An instance of tensorflow.python.framework.ops.Graph
debug_ops: Name of the debug op to use. Default: "DebugIdentity".
Can be a list of strings of a single string. The latter case is
equivalent to a list of a single string.
debug_urls: Debug urls. Can be a list of strings, a single string, or
None. The case of a single string is equivalen to a list consisting
of a single string.
node_name_regex_blacklist: Regular-expression blacklist for node_name.
This should be a string, e.g., "(weight_[0-9]+|bias_.*)"
op_type_regex_blacklist: Regular-expression blacklist for the op type of
nodes. If both node_name_regex_blacklist and op_type_regex_blacklist
are none, the two filtering operations will occur in an "OR"
relation. In other words, a node will be excluded if it hits either of
the two blacklists; a node will be included if and only if it hits
none of the blacklists. This should be a string, e.g.,
"(Variable|Add)".
"""
if isinstance(debug_ops, str):
debug_ops = [debug_ops]
if node_name_regex_blacklist:
node_name_pattern = re.compile(node_name_regex_blacklist)
else:
node_name_pattern = None
if op_type_regex_blacklist:
op_type_pattern = re.compile(op_type_regex_blacklist)
else:
op_type_pattern = None
ops = graph.get_operations()
for op in ops:
# Skip nodes without any output tensors.
if not op.outputs:
continue
node_name = op.name
op_type = op.type
if node_name_pattern and node_name_pattern.match(node_name):
continue
if op_type_pattern and op_type_pattern.match(op_type):
continue
for slot in xrange(len(op.outputs)):
add_debug_tensor_watch(
run_options,
node_name,
output_slot=slot,
debug_ops=debug_ops,
debug_urls=debug_urls)