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