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# Copyright 2023 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.
"""Util functions for loading checkpoints.
Especially for loading Tensorflow 1.x
checkpoint to Tensorflow 2.x (keras) model.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
from absl import logging
import tensorflow as tf, tf_keras
def _build_assignment_map(keras_model,
prefix='',
skip_variables_regex=None,
var_to_shape_map=None):
"""Builds the variable assignment map.
Compute an assignment mapping for loading older checkpoints into a Keras
model. Variable names are remapped from the original TPUEstimator model to
the new Keras name.
Args:
keras_model: tf_keras.Model object to provide variables to assign.
prefix: prefix in the variable name to be remove for alignment with names in
the checkpoint.
skip_variables_regex: regular expression to math the names of variables that
do not need to be assign.
var_to_shape_map: variable name to shape mapping from the checkpoint.
Returns:
The variable assignment map.
"""
assignment_map = {}
checkpoint_names = []
if var_to_shape_map:
# pylint: disable=g-long-lambda
checkpoint_names = list(
filter(
lambda x: not x.endswith('Momentum') and not x.endswith(
'global_step'), var_to_shape_map.keys()))
# pylint: enable=g-long-lambda
logging.info('Number of variables in the checkpoint %d',
len(checkpoint_names))
for var in keras_model.variables:
var_name = var.name
if skip_variables_regex and re.match(skip_variables_regex, var_name):
continue
# Trim the index of the variable.
if ':' in var_name:
var_name = var_name[:var_name.rindex(':')]
if var_name.startswith(prefix):
var_name = var_name[len(prefix):]
if not var_to_shape_map:
assignment_map[var_name] = var
continue
# Match name with variables in the checkpoint.
# pylint: disable=cell-var-from-loop
match_names = list(filter(lambda x: x.endswith(var_name), checkpoint_names))
# pylint: enable=cell-var-from-loop
try:
if match_names:
assert len(match_names) == 1, 'more then on matches for {}: {}'.format(
var_name, match_names)
checkpoint_names.remove(match_names[0])
assignment_map[match_names[0]] = var
else:
logging.info('Error not found var name: %s', var_name)
except Exception as e:
logging.info('Error removing the match_name: %s', match_names)
logging.info('Exception: %s', e)
raise
logging.info('Found matching variable in checkpoint: %d', len(assignment_map))
return assignment_map
def _get_checkpoint_map(checkpoint_path):
reader = tf.train.load_checkpoint(checkpoint_path)
return reader.get_variable_to_shape_map()
def make_restore_checkpoint_fn(checkpoint_path, prefix='', skip_regex=None):
"""Returns scaffold function to restore parameters from v1 checkpoint.
Args:
checkpoint_path: path of the checkpoint folder or file.
Example 1: '/path/to/model_dir/'
Example 2: '/path/to/model.ckpt-22500'
prefix: prefix in the variable name to be remove for alignment with names in
the checkpoint.
skip_regex: regular expression to math the names of variables that do not
need to be assign.
Returns:
Callable[tf.kears.Model] -> void. Fn to load v1 checkpoint to keras model.
"""
def _restore_checkpoint_fn(keras_model):
"""Loads pretrained model through scaffold function."""
if not checkpoint_path:
logging.info('checkpoint_path is empty')
return
var_prefix = prefix
if prefix and not prefix.endswith('/'):
var_prefix += '/'
var_to_shape_map = _get_checkpoint_map(checkpoint_path)
assert var_to_shape_map, 'var_to_shape_map should not be empty'
vars_to_load = _build_assignment_map(
keras_model,
prefix=var_prefix,
skip_variables_regex=skip_regex,
var_to_shape_map=var_to_shape_map)
if not vars_to_load:
raise ValueError('Variables to load is empty.')
tf.compat.v1.train.init_from_checkpoint(checkpoint_path, vars_to_load)
return _restore_checkpoint_fn
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