laywerrobot/lib/python3.6/site-packages/tensorflow/contrib/checkpoint/python/visualize.py
2020-08-27 21:55:39 +02:00

99 lines
3.8 KiB
Python

"""Utilities for visualizing dependency graphs."""
# Copyright 2018 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.training.checkpointable import base as checkpointable
from tensorflow.python.training.checkpointable import util as checkpointable_utils
def dot_graph_from_checkpoint(save_path):
r"""Visualizes an object-based checkpoint (from `tf.train.Checkpoint`).
Useful for inspecting checkpoints and debugging loading issues.
Example usage from Python (requires pydot):
```python
import tensorflow as tf
import pydot
dot_string = tf.contrib.checkpoint.dot_graph_from_checkpoint('/path/to/ckpt')
parsed, = pydot.graph_from_dot_data(dot_string)
parsed.write_svg('/tmp/tensorflow/visualized_checkpoint.svg')
```
Example command line usage:
```sh
python -c "import tensorflow as tf;\
print(tf.contrib.checkpoint.dot_graph_from_checkpoint('/path/to/ckpt'))"\
| dot -Tsvg > /tmp/tensorflow/checkpoint_viz.svg
```
Args:
save_path: The checkpoint prefix, as returned by `tf.train.Checkpoint.save`
or `tf.train.latest_checkpoint`.
Returns:
A graph in DOT format as a string.
"""
reader = pywrap_tensorflow.NewCheckpointReader(save_path)
object_graph = checkpointable_utils.object_metadata(save_path)
shape_map = reader.get_variable_to_shape_map()
dtype_map = reader.get_variable_to_dtype_map()
graph = 'digraph {\n'
def _escape(name):
return name.replace('"', '\\"')
slot_ids = set()
for node in object_graph.nodes:
for slot_reference in node.slot_variables:
slot_ids.add(slot_reference.slot_variable_node_id)
for node_id, node in enumerate(object_graph.nodes):
if (len(node.attributes) == 1
and node.attributes[0].name == checkpointable.VARIABLE_VALUE_KEY):
if node_id in slot_ids:
color = 'orange'
tooltip_prefix = 'Slot variable'
else:
color = 'blue'
tooltip_prefix = 'Variable'
attribute = node.attributes[0]
graph += ('N_%d [shape=point label="" color=%s width=.25'
' tooltip="%s %s shape=%s %s"]\n') % (
node_id,
color,
tooltip_prefix,
_escape(attribute.full_name),
shape_map[attribute.checkpoint_key],
dtype_map[attribute.checkpoint_key].name)
elif node.slot_variables:
graph += ('N_%d [shape=point label="" width=.25 color=red,'
'tooltip="Optimizer"]\n') % node_id
else:
graph += 'N_%d [shape=point label="" width=.25]\n' % node_id
for reference in node.children:
graph += 'N_%d -> N_%d [label="%s"]\n' % (
node_id, reference.node_id, _escape(reference.local_name))
for slot_reference in node.slot_variables:
graph += 'N_%d -> N_%d [label="%s" style=dotted]\n' % (
node_id,
slot_reference.slot_variable_node_id,
_escape(slot_reference.slot_name))
graph += 'N_%d -> N_%d [style=dotted]\n' % (
slot_reference.original_variable_node_id,
slot_reference.slot_variable_node_id)
graph += '}\n'
return graph