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63 lines
2.7 KiB
Python
63 lines
2.7 KiB
Python
from typing import Any, List
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import tensorflow as tf
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from rl_coach.saver import Saver
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class GlobalVariableSaver(Saver):
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def __init__(self, name):
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self._names = [name]
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# if graph is finalized, savers must have already already been added. This happens
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# in the case of a MonitoredSession
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self._variables = tf.global_variables()
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# target network is never saved or restored directly from checkpoint, so we are removing all its variables from the list
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# the target network would be synched back from the online network in graph_manager.improve(...), at the beginning of the run flow.
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self._variables = [v for v in self._variables if '/target' not in v.name]
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self._saver = tf.train.Saver(self._variables)
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@property
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def path(self):
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"""
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Relative path for save/load. If two checkpoint objects return the same path, they must be merge-able.
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"""
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return "" # use empty string for global file
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def save(self, sess: None, save_path: str) -> List[str]:
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"""
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Save to save_path
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:param sess: active session
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:param save_path: full path to save checkpoint (typically directory plus checkpoint prefix plus self.path)
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:return: list of all saved paths
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"""
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save_path = self._saver.save(sess, save_path)
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return [save_path]
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def restore(self, sess: Any, restore_path: str):
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"""
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Restore from restore_path
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:param sess: active session for session-based frameworks (e.g. TF)
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:param restore_path: full path to load checkpoint from.
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"""
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# We don't use saver.restore() because checkpoint is loaded to online network, but if the checkpoint
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# is from the global network, a namespace mismatch exists and variable name must be modified before loading.
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variables = dict()
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reader = tf.contrib.framework.load_checkpoint(restore_path)
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for var_name, _ in reader.get_variable_to_shape_map().items():
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# if variable was saved using global network, re-map it to online network
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# TODO: Can this be more generic so that `global/` and `online/` are not hardcoded here?
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new_name = var_name.replace('global/', 'online/')
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variables[new_name] = reader.get_tensor(var_name)
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# Assign all variables
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sess.run([v.assign(variables[v.name.split(':')[0]]) for v in self._variables])
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def merge(self, other: 'Saver'):
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"""
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Merge other saver into this saver
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:param other: saver to be merged into self
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"""
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assert isinstance(other, GlobalVariableSaver)
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self._names.extend(other._names)
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# There is nothing else to do because variables must already be part of the global collection.
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