572 lines
22 KiB
Python
572 lines
22 KiB
Python
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# Copyright 2016 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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"""Tools to work with name-based checkpoints.
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While some of these symbols also work with the TF2 object-based checkpoints,
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they are not recommended for TF2. Please check `tensorflow/python/checkpoint`
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for newer utilities built to work with TF2 checkpoints.
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"""
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from collections import abc
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import os
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import time
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from tensorflow.python.checkpoint import checkpoint_management
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from tensorflow.python.distribute import distribute_lib
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import io_ops
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.ops import variable_scope as vs
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import gfile
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from tensorflow.python.platform import tf_logging as logging
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from tensorflow.python.training import py_checkpoint_reader
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from tensorflow.python.training.saving import saveable_object_util
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from tensorflow.python.util.tf_export import tf_export
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__all__ = [
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"load_checkpoint", "load_variable", "list_variables",
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"checkpoints_iterator", "init_from_checkpoint"
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]
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@tf_export("train.load_checkpoint")
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def load_checkpoint(ckpt_dir_or_file):
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"""Returns `CheckpointReader` for checkpoint found in `ckpt_dir_or_file`.
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If `ckpt_dir_or_file` resolves to a directory with multiple checkpoints,
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reader for the latest checkpoint is returned.
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Example usage:
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```python
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import tensorflow as tf
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a = tf.Variable(1.0)
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b = tf.Variable(2.0)
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ckpt = tf.train.Checkpoint(var_list={'a': a, 'b': b})
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ckpt_path = ckpt.save('tmp-ckpt')
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reader= tf.train.load_checkpoint(ckpt_path)
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print(reader.get_tensor('var_list/a/.ATTRIBUTES/VARIABLE_VALUE')) # 1.0
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```
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Args:
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ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint
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file.
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Returns:
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`CheckpointReader` object.
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Raises:
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ValueError: If `ckpt_dir_or_file` resolves to a directory with no
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checkpoints.
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"""
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filename = _get_checkpoint_filename(ckpt_dir_or_file)
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if filename is None:
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raise ValueError("Couldn't find 'checkpoint' file or checkpoints in "
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"given directory %s" % ckpt_dir_or_file)
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return py_checkpoint_reader.NewCheckpointReader(filename)
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@tf_export("train.load_variable")
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def load_variable(ckpt_dir_or_file, name):
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"""Returns the tensor value of the given variable in the checkpoint.
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When the variable name is unknown, you can use `tf.train.list_variables` to
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inspect all the variable names.
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Example usage:
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```python
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import tensorflow as tf
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a = tf.Variable(1.0)
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b = tf.Variable(2.0)
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ckpt = tf.train.Checkpoint(var_list={'a': a, 'b': b})
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ckpt_path = ckpt.save('tmp-ckpt')
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var= tf.train.load_variable(
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ckpt_path, 'var_list/a/.ATTRIBUTES/VARIABLE_VALUE')
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print(var) # 1.0
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```
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Args:
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ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.
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name: Name of the variable to return.
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Returns:
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A numpy `ndarray` with a copy of the value of this variable.
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"""
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# TODO(b/29227106): Fix this in the right place and remove this.
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if name.endswith(":0"):
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name = name[:-2]
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reader = load_checkpoint(ckpt_dir_or_file)
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return reader.get_tensor(name)
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@tf_export("train.list_variables")
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def list_variables(ckpt_dir_or_file):
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"""Lists the checkpoint keys and shapes of variables in a checkpoint.
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Checkpoint keys are paths in a checkpoint graph.
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Example usage:
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```python
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import tensorflow as tf
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import os
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ckpt_directory = "/tmp/training_checkpoints/ckpt"
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ckpt = tf.train.Checkpoint(optimizer=optimizer, model=model)
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manager = tf.train.CheckpointManager(ckpt, ckpt_directory, max_to_keep=3)
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train_and_checkpoint(model, manager)
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tf.train.list_variables(manager.latest_checkpoint)
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```
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Args:
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ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.
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Returns:
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List of tuples `(key, shape)`.
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"""
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reader = load_checkpoint(ckpt_dir_or_file)
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variable_map = reader.get_variable_to_shape_map()
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names = sorted(variable_map.keys())
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result = []
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for name in names:
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result.append((name, variable_map[name]))
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return result
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def wait_for_new_checkpoint(checkpoint_dir,
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last_checkpoint=None,
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seconds_to_sleep=1,
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timeout=None):
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"""Waits until a new checkpoint file is found.
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Args:
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checkpoint_dir: The directory in which checkpoints are saved.
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last_checkpoint: The last checkpoint path used or `None` if we're expecting
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a checkpoint for the first time.
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seconds_to_sleep: The number of seconds to sleep for before looking for a
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new checkpoint.
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timeout: The maximum number of seconds to wait. If left as `None`, then the
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process will wait indefinitely.
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Returns:
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a new checkpoint path, or None if the timeout was reached.
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"""
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logging.info("Waiting for new checkpoint at %s", checkpoint_dir)
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stop_time = time.time() + timeout if timeout is not None else None
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while True:
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checkpoint_path = checkpoint_management.latest_checkpoint(checkpoint_dir)
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if checkpoint_path is None or checkpoint_path == last_checkpoint:
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if stop_time is not None and time.time() + seconds_to_sleep > stop_time:
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return None
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time.sleep(seconds_to_sleep)
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else:
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logging.info("Found new checkpoint at %s", checkpoint_path)
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return checkpoint_path
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@tf_export("train.checkpoints_iterator")
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def checkpoints_iterator(checkpoint_dir,
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min_interval_secs=0,
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timeout=None,
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timeout_fn=None):
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"""Continuously yield new checkpoint files as they appear.
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The iterator only checks for new checkpoints when control flow has been
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reverted to it. This means it can miss checkpoints if your code takes longer
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to run between iterations than `min_interval_secs` or the interval at which
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new checkpoints are written.
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The `timeout` argument is the maximum number of seconds to block waiting for
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a new checkpoint. It is used in combination with the `timeout_fn` as
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follows:
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* If the timeout expires and no `timeout_fn` was specified, the iterator
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stops yielding.
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* If a `timeout_fn` was specified, that function is called and if it returns
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a true boolean value the iterator stops yielding.
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* If the function returns a false boolean value then the iterator resumes the
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wait for new checkpoints. At this point the timeout logic applies again.
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This behavior gives control to callers on what to do if checkpoints do not
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come fast enough or stop being generated. For example, if callers have a way
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to detect that the training has stopped and know that no new checkpoints
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will be generated, they can provide a `timeout_fn` that returns `True` when
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the training has stopped. If they know that the training is still going on
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they return `False` instead.
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Args:
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checkpoint_dir: The directory in which checkpoints are saved.
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min_interval_secs: The minimum number of seconds between yielding
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checkpoints.
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timeout: The maximum number of seconds to wait between checkpoints. If left
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as `None`, then the process will wait indefinitely.
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timeout_fn: Optional function to call after a timeout. If the function
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returns True, then it means that no new checkpoints will be generated and
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the iterator will exit. The function is called with no arguments.
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Yields:
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String paths to latest checkpoint files as they arrive.
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"""
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checkpoint_path = None
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while True:
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new_checkpoint_path = wait_for_new_checkpoint(
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checkpoint_dir, checkpoint_path, timeout=timeout)
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if new_checkpoint_path is None:
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if not timeout_fn:
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# timed out
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logging.info("Timed-out waiting for a checkpoint.")
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return
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if timeout_fn():
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# The timeout_fn indicated that we are truly done.
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return
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else:
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# The timeout_fn indicated that more checkpoints may come.
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continue
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start = time.time()
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checkpoint_path = new_checkpoint_path
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yield checkpoint_path
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time_to_next_eval = start + min_interval_secs - time.time()
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if time_to_next_eval > 0:
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time.sleep(time_to_next_eval)
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@tf_export(v1=["train.init_from_checkpoint"])
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def init_from_checkpoint(ckpt_dir_or_file, assignment_map):
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"""Replaces `tf.Variable` initializers so they load from a checkpoint file.
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@compatibility(TF2)
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`tf.compat.v1.train.init_from_checkpoint` is not recommended for restoring
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variable values in TF2.
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To restore checkpoints in TF2, please use
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`tf.keras.Model.load_weights` or `tf.train.Checkpoint.restore`. These APIs use
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use an [object-based method of checkpointing]
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(https://www.tensorflow.org/guide/checkpoint#loading_mechanics), while
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`tf.compat.v1.init_from_checkpoint` relies on a more-fragile variable-name
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based method of checkpointing. There is no object-based equivalent of
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`init_from_checkpoint` in TF2.
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Please re-write your checkpoints immediately using the object-based APIs,
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see [migration guide]
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(https://www.tensorflow.org/guide/migrate#checkpoint_compatibility) for more
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details.
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You can load a name-based checkpoint written by `tf.compat.v1.train.Saver`
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using `tf.train.Checkpoint.restore` or `tf.keras.Model.load_weights`. However,
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you may have to change the names of the variables in your model to match the
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variable names in the name-based checkpoint, which can be viewed with
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`tf.train.list_variables(path)`.
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Another option is to create an `assignment_map` that maps the name of the
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variables in the name-based checkpoint to the variables in your model, eg:
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```
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{
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'sequential/dense/bias': model.variables[0],
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'sequential/dense/kernel': model.variables[1]
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}
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```
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and use `tf.compat.v1.train.init_from_checkpoint(path, assignment_map)` to
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restore the name-based checkpoint.
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After restoring, re-encode your checkpoint using `tf.train.Checkpoint.save`
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or `tf.keras.Model.save_weights`.
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@end_compatibility
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Values are not loaded immediately, but when the initializer is run
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(typically by running a `tf.compat.v1.global_variables_initializer` op).
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Note: This overrides default initialization ops of specified variables and
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redefines dtype.
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Assignment map supports following syntax:
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* `'checkpoint_scope_name/': 'scope_name/'` - will load all variables in
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current `scope_name` from `checkpoint_scope_name` with matching tensor
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names.
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* `'checkpoint_scope_name/some_other_variable': 'scope_name/variable_name'` -
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will initialize `scope_name/variable_name` variable
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from `checkpoint_scope_name/some_other_variable`.
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* `'scope_variable_name': variable` - will initialize given `tf.Variable`
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object with tensor 'scope_variable_name' from the checkpoint.
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* `'scope_variable_name': list(variable)` - will initialize list of
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partitioned variables with tensor 'scope_variable_name' from the checkpoint.
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* `'/': 'scope_name/'` - will load all variables in current `scope_name` from
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checkpoint's root (e.g. no scope).
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Supports loading into partitioned variables, which are represented as
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`'<variable>/part_<part #>'`.
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Assignment map can be a dict, or a list of pairs. The latter is
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necessary to initialize multiple variables in the current graph from
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the same variable in the checkpoint.
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Example:
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```python
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# Say, '/tmp/model.ckpt' has the following tensors:
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# -- name='old_scope_1/var1', shape=[20, 2]
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# -- name='old_scope_1/var2', shape=[50, 4]
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# -- name='old_scope_2/var3', shape=[100, 100]
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# Create new model's variables
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with tf.compat.v1.variable_scope('new_scope_1'):
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var1 = tf.compat.v1.get_variable('var1', shape=[20, 2],
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initializer=tf.compat.v1.zeros_initializer())
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with tf.compat.v1.variable_scope('new_scope_2'):
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var2 = tf.compat.v1.get_variable('var2', shape=[50, 4],
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initializer=tf.compat.v1.zeros_initializer())
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# Partition into 5 variables along the first axis.
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var3 = tf.compat.v1.get_variable(name='var3', shape=[100, 100],
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initializer=tf.compat.v1.zeros_initializer(),
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partitioner=lambda shape, dtype: [5, 1])
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# Initialize all variables in `new_scope_1` from `old_scope_1`.
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init_from_checkpoint('/tmp/model.ckpt', {'old_scope_1/': 'new_scope_1/'})
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# Use names to specify which variables to initialize from checkpoint.
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init_from_checkpoint('/tmp/model.ckpt',
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{'old_scope_1/var1': 'new_scope_1/var1',
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'old_scope_1/var2': 'new_scope_2/var2'})
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# Or use tf.Variable objects to identify what to initialize.
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init_from_checkpoint('/tmp/model.ckpt',
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{'old_scope_1/var1': var1,
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'old_scope_1/var2': var2})
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# Initialize partitioned variables using variable's name
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|
init_from_checkpoint('/tmp/model.ckpt',
|
||
|
|
{'old_scope_2/var3': 'new_scope_2/var3'})
|
||
|
|
|
||
|
|
# Or specify the list of tf.Variable objects.
|
||
|
|
init_from_checkpoint('/tmp/model.ckpt',
|
||
|
|
{'old_scope_2/var3': var3._get_variable_list()})
|
||
|
|
|
||
|
|
```
|
||
|
|
|
||
|
|
Args:
|
||
|
|
ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.
|
||
|
|
assignment_map: Dict, or a list of key-value pairs, where keys are names
|
||
|
|
of the variables in the checkpoint and values are current variables or
|
||
|
|
names of current variables (in default graph).
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: If missing variables in current graph, or if missing
|
||
|
|
checkpoints or tensors in checkpoints.
|
||
|
|
|
||
|
|
"""
|
||
|
|
init_from_checkpoint_fn = lambda _: _init_from_checkpoint(
|
||
|
|
ckpt_dir_or_file, assignment_map)
|
||
|
|
if distribute_lib.get_cross_replica_context():
|
||
|
|
init_from_checkpoint_fn(None)
|
||
|
|
else:
|
||
|
|
distribute_lib.get_replica_context().merge_call(
|
||
|
|
init_from_checkpoint_fn)
|
||
|
|
|
||
|
|
|
||
|
|
def _init_from_checkpoint(ckpt_dir_or_file, assignment_map):
|
||
|
|
"""See `init_from_checkpoint` for documentation."""
|
||
|
|
ckpt_file = _get_checkpoint_filename(ckpt_dir_or_file)
|
||
|
|
reader = load_checkpoint(ckpt_dir_or_file)
|
||
|
|
variable_map = reader.get_variable_to_shape_map()
|
||
|
|
if isinstance(assignment_map, abc.Mapping):
|
||
|
|
assignment_map = assignment_map.items()
|
||
|
|
|
||
|
|
# We only want to sort by tensor names.
|
||
|
|
sort_key = lambda pair: pair[0]
|
||
|
|
|
||
|
|
for tensor_name_in_ckpt, current_var_or_name in sorted(
|
||
|
|
assignment_map, key=sort_key):
|
||
|
|
var = None
|
||
|
|
# Check if this is Variable object or list of Variable objects (in case of
|
||
|
|
# partitioned variables).
|
||
|
|
if _is_variable(current_var_or_name) or (
|
||
|
|
isinstance(current_var_or_name, list)
|
||
|
|
and all(_is_variable(v) for v in current_var_or_name)):
|
||
|
|
var = current_var_or_name
|
||
|
|
else:
|
||
|
|
store_vars = vs._get_default_variable_store()._vars # pylint:disable=protected-access
|
||
|
|
# Check if this variable is in var_store.
|
||
|
|
var = store_vars.get(current_var_or_name, None)
|
||
|
|
# Also check if variable is partitioned as list.
|
||
|
|
if var is None:
|
||
|
|
var = _collect_partitioned_variable(current_var_or_name, store_vars)
|
||
|
|
if var is not None:
|
||
|
|
# If 1 to 1 mapping was provided, find variable in the checkpoint.
|
||
|
|
if tensor_name_in_ckpt not in variable_map:
|
||
|
|
raise ValueError("Tensor %s is not found in %s checkpoint %s" % (
|
||
|
|
tensor_name_in_ckpt, ckpt_dir_or_file, variable_map
|
||
|
|
))
|
||
|
|
if _is_variable(var):
|
||
|
|
# Additional at-call-time checks.
|
||
|
|
if not var.get_shape().is_compatible_with(
|
||
|
|
variable_map[tensor_name_in_ckpt]):
|
||
|
|
raise ValueError(
|
||
|
|
"Shape of variable %s (%s) doesn't match with shape of "
|
||
|
|
"tensor %s (%s) from checkpoint reader." % (
|
||
|
|
var.name, str(var.get_shape()),
|
||
|
|
tensor_name_in_ckpt, str(variable_map[tensor_name_in_ckpt])
|
||
|
|
))
|
||
|
|
var_name = var.name
|
||
|
|
else:
|
||
|
|
var_name = ",".join(v.name for v in var)
|
||
|
|
_set_variable_or_list_initializer(var, ckpt_file, tensor_name_in_ckpt)
|
||
|
|
logging.debug("Initialize variable %s from checkpoint %s with %s",
|
||
|
|
var_name, ckpt_dir_or_file, tensor_name_in_ckpt)
|
||
|
|
else:
|
||
|
|
scopes = ""
|
||
|
|
# TODO(vihanjain): Support list of 'current_var_or_name' here.
|
||
|
|
if "/" in current_var_or_name:
|
||
|
|
scopes = current_var_or_name[:current_var_or_name.rindex("/")]
|
||
|
|
if not tensor_name_in_ckpt.endswith("/"):
|
||
|
|
raise ValueError(
|
||
|
|
"Assignment map with scope only name {} should map to scope only "
|
||
|
|
"{}. Should be 'scope/': 'other_scope/'.".format(
|
||
|
|
scopes, tensor_name_in_ckpt))
|
||
|
|
# If scope to scope mapping was provided, find all variables in the scope
|
||
|
|
# and create variable to variable mapping.
|
||
|
|
scope_variables = set()
|
||
|
|
for var_name in store_vars:
|
||
|
|
if not scopes or var_name.startswith(scopes + "/"):
|
||
|
|
# Consume /part_ if partitioned variable.
|
||
|
|
if "/part_" in var_name:
|
||
|
|
var_name = var_name[:var_name.index("/part_")]
|
||
|
|
scope_variables.add(var_name)
|
||
|
|
for var_name in sorted(scope_variables):
|
||
|
|
# Lookup name with specified prefix and suffix from current variable.
|
||
|
|
# If tensor_name given is '/' (root), don't use it for full name.
|
||
|
|
full_tensor_name = var_name[len(scopes):]
|
||
|
|
if current_var_or_name != "/":
|
||
|
|
full_tensor_name = full_tensor_name[1:]
|
||
|
|
if tensor_name_in_ckpt != "/":
|
||
|
|
full_tensor_name = tensor_name_in_ckpt + full_tensor_name
|
||
|
|
# Remove trailing '/', if any, in the full_tensor_name
|
||
|
|
if full_tensor_name.endswith("/"):
|
||
|
|
full_tensor_name = full_tensor_name[:-1]
|
||
|
|
if full_tensor_name not in variable_map:
|
||
|
|
raise ValueError(
|
||
|
|
"Tensor %s (%s in %s) is not found in %s checkpoint" % (
|
||
|
|
full_tensor_name, var_name[len(scopes) + 1:],
|
||
|
|
tensor_name_in_ckpt, ckpt_dir_or_file
|
||
|
|
))
|
||
|
|
var = store_vars.get(var_name, None)
|
||
|
|
if var is None:
|
||
|
|
var = _collect_partitioned_variable(var_name, store_vars)
|
||
|
|
_set_variable_or_list_initializer(var, ckpt_file, full_tensor_name)
|
||
|
|
logging.debug("Initialize variable %s from checkpoint %s with %s",
|
||
|
|
var_name, ckpt_dir_or_file, full_tensor_name)
|
||
|
|
|
||
|
|
|
||
|
|
def _get_checkpoint_filename(ckpt_dir_or_file):
|
||
|
|
"""Returns checkpoint filename given directory or specific checkpoint file."""
|
||
|
|
if isinstance(ckpt_dir_or_file, os.PathLike):
|
||
|
|
ckpt_dir_or_file = os.fspath(ckpt_dir_or_file)
|
||
|
|
if gfile.IsDirectory(ckpt_dir_or_file):
|
||
|
|
return checkpoint_management.latest_checkpoint(ckpt_dir_or_file)
|
||
|
|
return ckpt_dir_or_file
|
||
|
|
|
||
|
|
|
||
|
|
def _set_checkpoint_initializer(variable,
|
||
|
|
ckpt_file,
|
||
|
|
tensor_name,
|
||
|
|
slice_spec,
|
||
|
|
name="checkpoint_initializer"):
|
||
|
|
"""Overrides given variable's initialization op.
|
||
|
|
|
||
|
|
Sets variable initializer to assign op that initializes variable from tensor's
|
||
|
|
value in the checkpoint.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
variable: `tf.Variable` object.
|
||
|
|
ckpt_file: string, full path of the checkpoint.
|
||
|
|
tensor_name: Name of the tensor to load from the checkpoint.
|
||
|
|
slice_spec: Slice specification for loading partitioned tensors.
|
||
|
|
name: Name of the operation.
|
||
|
|
"""
|
||
|
|
base_type = variable.dtype.base_dtype
|
||
|
|
# Do not colocate with variable since RestoreV2 op only runs on CPU and
|
||
|
|
# colocation will force variable (and other ops that colocate with variable)
|
||
|
|
# to be on CPU as well. It is okay to place the variable's initializer op on
|
||
|
|
# CPU since it will only be run once at the start.
|
||
|
|
with ops.device(variable.device), ops.device("/cpu:0"):
|
||
|
|
restore_op = io_ops.restore_v2(
|
||
|
|
ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0]
|
||
|
|
|
||
|
|
names_to_saveables = saveable_object_util.op_list_to_dict([variable])
|
||
|
|
saveable_objects = []
|
||
|
|
for name, op in names_to_saveables.items():
|
||
|
|
for s in saveable_object_util.saveable_objects_for_op(op, name):
|
||
|
|
saveable_objects.append(s)
|
||
|
|
|
||
|
|
assert len(saveable_objects) == 1 # Should be only one variable.
|
||
|
|
init_op = saveable_objects[0].restore([restore_op], restored_shapes=None)
|
||
|
|
|
||
|
|
# pylint:disable=protected-access
|
||
|
|
variable._initializer_op = init_op
|
||
|
|
restore_op.set_shape(variable.shape)
|
||
|
|
variable._initial_value = restore_op
|
||
|
|
# pylint:enable=protected-access
|
||
|
|
|
||
|
|
|
||
|
|
def _set_variable_or_list_initializer(variable_or_list, ckpt_file,
|
||
|
|
tensor_name):
|
||
|
|
"""Overrides initialization op of given variable or list of variables.
|
||
|
|
|
||
|
|
Calls `_set_checkpoint_initializer` for each variable in the given list of
|
||
|
|
variables.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
variable_or_list: `tf.Variable` object or a list of `tf.Variable` objects.
|
||
|
|
ckpt_file: string, full path of the checkpoint.
|
||
|
|
tensor_name: Name of the tensor to load from the checkpoint.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: if all objects in `variable_or_list` are not partitions of the
|
||
|
|
same large variable.
|
||
|
|
"""
|
||
|
|
if isinstance(variable_or_list, (list, tuple)):
|
||
|
|
# A set of slices.
|
||
|
|
slice_name = None
|
||
|
|
for v in variable_or_list:
|
||
|
|
slice_info = v._save_slice_info # pylint:disable=protected-access
|
||
|
|
if slice_name is None:
|
||
|
|
slice_name = slice_info.full_name
|
||
|
|
elif slice_name != slice_info.full_name:
|
||
|
|
raise ValueError("Slices must all be from the same tensor: %s != %s" %
|
||
|
|
(slice_name, slice_info.full_name))
|
||
|
|
_set_checkpoint_initializer(v, ckpt_file, tensor_name, slice_info.spec)
|
||
|
|
else:
|
||
|
|
_set_checkpoint_initializer(variable_or_list, ckpt_file, tensor_name, "")
|
||
|
|
|
||
|
|
|
||
|
|
def _is_variable(x):
|
||
|
|
return (isinstance(x, variables.Variable) or
|
||
|
|
resource_variable_ops.is_resource_variable(x))
|
||
|
|
|
||
|
|
|
||
|
|
def _collect_partitioned_variable(name, all_vars):
|
||
|
|
"""Returns list of `tf.Variable` that comprise the partitioned variable."""
|
||
|
|
if name + "/part_0" in all_vars:
|
||
|
|
var = []
|
||
|
|
i = 0
|
||
|
|
while name + "/part_%d" % i in all_vars:
|
||
|
|
var.append(all_vars[name + "/part_%d" % i])
|
||
|
|
i += 1
|
||
|
|
return var
|
||
|
|
return None
|