1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
|
# Copyright 2017 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.
# ==============================================================================
r"""Convert checkpoints using RNNCells to new name convention.
Usage:
python checkpoint_convert.py [--write_v1_checkpoint] \
'/path/to/checkpoint' '/path/to/new_checkpoint'
For example, if there is a V2 checkpoint to be converted and the files include:
/tmp/my_checkpoint/model.ckpt.data-00000-of-00001
/tmp/my_checkpoint/model.ckpt.index
/tmp/my_checkpoint/model.ckpt.meta
use the following command:
mkdir /tmp/my_converted_checkpoint &&
python checkpoint_convert.py \
/tmp/my_checkpoint/model.ckpt /tmp/my_converted_checkpoint/model.ckpt
This will generate three converted checkpoint files corresponding to the three
old ones in the new directory:
/tmp/my_converted_checkpoint/model.ckpt.data-00000-of-00001
/tmp/my_converted_checkpoint/model.ckpt.index
/tmp/my_converted_checkpoint/model.ckpt.meta
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import re
import sys
from tensorflow.core.protobuf import saver_pb2
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.client import session
from tensorflow.python.framework import ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import app
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.training import saver as saver_lib
_RNN_NAME_REPLACEMENTS = collections.OrderedDict([
############################################################################
# contrib/rnn/python/ops/core_rnn_cell_impl.py
# BasicRNNCell
('basic_rnn_cell/weights', 'basic_rnn_cell/kernel'),
('basic_rnn_cell/biases', 'basic_rnn_cell/bias'),
# GRUCell
('gru_cell/weights', 'gru_cell/kernel'),
('gru_cell/biases', 'gru_cell/bias'),
('gru_cell/gates/weights', 'gru_cell/gates/kernel'),
('gru_cell/gates/biases', 'gru_cell/gates/bias'),
('gru_cell/candidate/weights', 'gru_cell/candidate/kernel'),
('gru_cell/candidate/biases', 'gru_cell/candidate/bias'),
# BasicLSTMCell
('basic_lstm_cell/weights', 'basic_lstm_cell/kernel'),
('basic_lstm_cell/biases', 'basic_lstm_cell/bias'),
# LSTMCell
('lstm_cell/weights', 'lstm_cell/kernel'),
('lstm_cell/biases', 'lstm_cell/bias'),
('lstm_cell/projection/weights', 'lstm_cell/projection/kernel'),
('lstm_cell/projection/biases', 'lstm_cell/projection/bias'),
# OutputProjectionWrapper
('output_projection_wrapper/weights', 'output_projection_wrapper/kernel'),
('output_projection_wrapper/biases', 'output_projection_wrapper/bias'),
# InputProjectionWrapper
('input_projection_wrapper/weights', 'input_projection_wrapper/kernel'),
('input_projection_wrapper/biases', 'input_projection_wrapper/bias'),
############################################################################
# contrib/rnn/python/ops/lstm_ops.py
# LSTMBlockFusedCell ??
('lstm_block_wrapper/weights', 'lstm_block_wrapper/kernel'),
('lstm_block_wrapper/biases', 'lstm_block_wrapper/bias'),
############################################################################
# contrib/rnn/python/ops/rnn_cell.py
# LayerNormBasicLSTMCell
('layer_norm_basic_lstm_cell/weights', 'layer_norm_basic_lstm_cell/kernel'),
('layer_norm_basic_lstm_cell/biases', 'layer_norm_basic_lstm_cell/bias'),
# UGRNNCell, not found in g3, but still need it?
('ugrnn_cell/weights', 'ugrnn_cell/kernel'),
('ugrnn_cell/biases', 'ugrnn_cell/bias'),
# NASCell
('nas_rnn/weights', 'nas_rnn/kernel'),
('nas_rnn/recurrent_weights', 'nas_rnn/recurrent_kernel'),
# IntersectionRNNCell
('intersection_rnn_cell/weights', 'intersection_rnn_cell/kernel'),
('intersection_rnn_cell/biases', 'intersection_rnn_cell/bias'),
('intersection_rnn_cell/in_projection/weights',
'intersection_rnn_cell/in_projection/kernel'),
('intersection_rnn_cell/in_projection/biases',
'intersection_rnn_cell/in_projection/bias'),
# PhasedLSTMCell
('phased_lstm_cell/mask_gates/weights',
'phased_lstm_cell/mask_gates/kernel'),
('phased_lstm_cell/mask_gates/biases', 'phased_lstm_cell/mask_gates/bias'),
('phased_lstm_cell/new_input/weights', 'phased_lstm_cell/new_input/kernel'),
('phased_lstm_cell/new_input/biases', 'phased_lstm_cell/new_input/bias'),
('phased_lstm_cell/output_gate/weights',
'phased_lstm_cell/output_gate/kernel'),
('phased_lstm_cell/output_gate/biases',
'phased_lstm_cell/output_gate/bias'),
# AttentionCellWrapper
('attention_cell_wrapper/weights', 'attention_cell_wrapper/kernel'),
('attention_cell_wrapper/biases', 'attention_cell_wrapper/bias'),
('attention_cell_wrapper/attn_output_projection/weights',
'attention_cell_wrapper/attn_output_projection/kernel'),
('attention_cell_wrapper/attn_output_projection/biases',
'attention_cell_wrapper/attn_output_projection/bias'),
('attention_cell_wrapper/attention/weights',
'attention_cell_wrapper/attention/kernel'),
('attention_cell_wrapper/attention/biases',
'attention_cell_wrapper/attention/bias'),
])
_RNN_SHARDED_NAME_REPLACEMENTS = collections.OrderedDict([
('LSTMCell/W_', 'lstm_cell/weights/part_'),
('BasicLSTMCell/Linear/Matrix_', 'basic_lstm_cell/weights/part_'),
('GRUCell/W_', 'gru_cell/weights/part_'),
('MultiRNNCell/Cell', 'multi_rnn_cell/cell_'),
])
def _rnn_name_replacement(var_name):
for pattern in _RNN_NAME_REPLACEMENTS:
if pattern in var_name:
old_var_name = var_name
var_name = var_name.replace(pattern, _RNN_NAME_REPLACEMENTS[pattern])
logging.info('Converted: %s --> %s' % (old_var_name, var_name))
break
return var_name
def _rnn_name_replacement_sharded(var_name):
for pattern in _RNN_SHARDED_NAME_REPLACEMENTS:
if pattern in var_name:
old_var_name = var_name
var_name = var_name.replace(pattern,
_RNN_SHARDED_NAME_REPLACEMENTS[pattern])
logging.info('Converted: %s --> %s' % (old_var_name, var_name))
return var_name
def _split_sharded_vars(name_shape_map):
"""Split shareded variables.
Args:
name_shape_map: A dict from variable name to variable shape.
Returns:
not_sharded: Names of the non-sharded variables.
sharded: Names of the sharded varibales.
"""
sharded = []
not_sharded = []
for name in name_shape_map:
if re.match(name, '_[0-9]+$'):
if re.sub('_[0-9]+$', '_1', name) in name_shape_map:
sharded.append(name)
else:
not_sharded.append(name)
else:
not_sharded.append(name)
return not_sharded, sharded
def convert_names(checkpoint_from_path,
checkpoint_to_path,
write_v1_checkpoint=False):
"""Migrates the names of variables within a checkpoint.
Args:
checkpoint_from_path: Path to source checkpoint to be read in.
checkpoint_to_path: Path to checkpoint to be written out.
write_v1_checkpoint: Whether the output checkpoint will be in V1 format.
Returns:
A dictionary that maps the new variable names to the Variable objects.
A dictionary that maps the old variable names to the new variable names.
"""
with ops.Graph().as_default():
logging.info('Reading checkpoint_from_path %s' % checkpoint_from_path)
reader = pywrap_tensorflow.NewCheckpointReader(checkpoint_from_path)
name_shape_map = reader.get_variable_to_shape_map()
not_sharded, sharded = _split_sharded_vars(name_shape_map)
new_variable_map = {}
conversion_map = {}
for var_name in not_sharded:
new_var_name = _rnn_name_replacement(var_name)
tensor = reader.get_tensor(var_name)
var = variables.Variable(tensor, name=var_name)
new_variable_map[new_var_name] = var
if new_var_name != var_name:
conversion_map[var_name] = new_var_name
for var_name in sharded:
new_var_name = _rnn_name_replacement_sharded(var_name)
var = variables.Variable(tensor, name=var_name)
new_variable_map[new_var_name] = var
if new_var_name != var_name:
conversion_map[var_name] = new_var_name
write_version = (saver_pb2.SaverDef.V1
if write_v1_checkpoint else saver_pb2.SaverDef.V2)
saver = saver_lib.Saver(new_variable_map, write_version=write_version)
with session.Session() as sess:
sess.run(variables.global_variables_initializer())
logging.info('Writing checkpoint_to_path %s' % checkpoint_to_path)
saver.save(sess, checkpoint_to_path)
logging.info('Summary:')
logging.info(' Converted %d variable name(s).' % len(new_variable_map))
return new_variable_map, conversion_map
def main(_):
convert_names(
FLAGS.checkpoint_from_path,
FLAGS.checkpoint_to_path,
write_v1_checkpoint=FLAGS.write_v1_checkpoint)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.register('type', 'bool', lambda v: v.lower() == 'true')
parser.add_argument('checkpoint_from_path', type=str,
help='Path to source checkpoint to be read in.')
parser.add_argument('checkpoint_to_path', type=str,
help='Path to checkpoint to be written out.')
parser.add_argument('--write_v1_checkpoint', action='store_true',
help='Write v1 checkpoint')
FLAGS, unparsed = parser.parse_known_args()
app.run(main=main, argv=[sys.argv[0]] + unparsed)
|
