Source code for tensorpack.callbacks.saver

# -*- coding: utf-8 -*-
# File:

import os
from datetime import datetime

from ..compat import tfv1 as tf
from ..utils import fs, logger
from .base import Callback

__all__ = ['ModelSaver', 'MinSaver', 'MaxSaver']

[docs]class ModelSaver(Callback): """ Save the model once triggered. """
[docs] def __init__(self, max_to_keep=10, keep_checkpoint_every_n_hours=0.5, checkpoint_dir=None, var_collections=None): """ Args: max_to_keep (int): the same as in ``tf.train.Saver``. keep_checkpoint_every_n_hours (float): the same as in ``tf.train.Saver``. Note that "keep" does not mean "create", but means "don't delete". checkpoint_dir (str): Defaults to ``logger.get_logger_dir()``. var_collections (str or list of str): collection of the variables (or list of collections) to save. """ if var_collections is None: var_collections = [tf.GraphKeys.GLOBAL_VARIABLES] self._max_to_keep = max_to_keep self._keep_every_n_hours = keep_checkpoint_every_n_hours if not isinstance(var_collections, list): var_collections = [var_collections] self.var_collections = var_collections if checkpoint_dir is None: checkpoint_dir = logger.get_logger_dir() if checkpoint_dir is not None: if not tf.gfile.IsDirectory(checkpoint_dir): # v2: tf.gfile.MakeDirs(checkpoint_dir) # v2: # If None, allow it to be init, but fail later if used # For example, if chief_only=True, it can still be safely initialized # in non-chief workers which don't have logger dir self.checkpoint_dir = fs.normpath(checkpoint_dir) if checkpoint_dir is not None else checkpoint_dir
def _setup_graph(self): assert self.checkpoint_dir is not None, \ "Please provide 'checkpoint_dir' for ModelSaver, or use logger.set_logger_dir()" vars = [] for key in self.var_collections: vars.extend(tf.get_collection(key)) vars = list(set(vars)) self.path = os.path.join(self.checkpoint_dir, 'model') self.saver = tf.train.Saver( var_list=vars, max_to_keep=self._max_to_keep, keep_checkpoint_every_n_hours=self._keep_every_n_hours, write_version=tf.train.SaverDef.V2, save_relative_paths=True) # Scaffold will call from this collection tf.add_to_collection(tf.GraphKeys.SAVERS, self.saver) def _before_train(self): # graph is finalized, OK to write it now. time ='%m%d-%H%M%S') self.saver.export_meta_graph( os.path.join(self.checkpoint_dir, 'graph-{}.meta'.format(time)), collection_list=self.graph.get_all_collection_keys()) def _trigger(self): try: tf.get_default_session(), self.path, global_step=tf.train.get_global_step(), write_meta_graph=False)"Model saved to %s." % tf.train.get_checkpoint_state(self.checkpoint_dir).model_checkpoint_path) except (IOError, tf.errors.PermissionDeniedError, tf.errors.ResourceExhaustedError, tf.errors.AlreadyExistsError): # disk error sometimes.. just ignore it logger.exception("Exception in ModelSaver!")
[docs]class MinSaver(Callback): """ Separately save the model with minimum value of some statistics. """
[docs] def __init__(self, monitor_stat, reverse=False, filename=None, checkpoint_dir=None): """ Args: monitor_stat(str): the name of the statistics. reverse (bool): if True, will save the maximum. filename (str): the name for the saved model. Defaults to ``min-{monitor_stat}.tfmodel``. checkpoint_dir (str): the directory containing checkpoints. Example: Save the model with minimum validation error to "min-val-error.tfmodel": .. code-block:: python MinSaver('val-error') Note: 1. It assumes that :class:`ModelSaver` is used with the same ``checkpoint_dir`` and appears earlier in the callback list. The default for both :class:`ModelSaver` and :class:`MinSaver` is ``checkpoint_dir=logger.get_logger_dir()`` 2. Callbacks are executed in the order they are defined. Therefore you'd want to use this callback after the callback (e.g. InferenceRunner) that produces the statistics. """ self.monitor_stat = monitor_stat self.reverse = reverse self.filename = filename = None self.checkpoint_dir = checkpoint_dir if self.checkpoint_dir is None: self.checkpoint_dir = logger.get_logger_dir() self.checkpoint_dir = fs.normpath(self.checkpoint_dir)
def _get_stat(self): try: v = self.trainer.monitors.get_history(self.monitor_stat)[-1] except (KeyError, IndexError): v = None, None return v def _trigger(self): curr_step, curr_val = self._get_stat() if curr_step is None: return if is None or (curr_val >[1] if self.reverse else curr_val <[1]): = (curr_step, curr_val) self._save() def _save(self): ckpt = tf.train.get_checkpoint_state(self.checkpoint_dir) if ckpt is None: raise RuntimeError( "[MinSaver] Cannot find a checkpoint state. Do you forget to use ModelSaver?") path = ckpt.model_checkpoint_path extreme_name = 'maximum' if self.reverse else 'minimum' if not path.endswith(str([0])): logger.warn("[MinSaver] New {} '{}' found at global_step={}, but the latest checkpoint is {}.".format( extreme_name, self.monitor_stat,[0], path )) logger.warn("MinSaver will do nothing this time. " "The callbacks may have inconsistent frequency or wrong order.") return newname = os.path.join(self.checkpoint_dir, self.filename or ('max-' + self.monitor_stat if self.reverse else 'min-' + self.monitor_stat)) files_to_copy = tf.gfile.Glob(path + '*') for file_to_copy in files_to_copy: tf.gfile.Copy(file_to_copy, file_to_copy.replace(path, newname), overwrite=True)"Model at global_step={} with {} {}={:.5g} saved.".format([0], extreme_name, self.monitor_stat,[1]))
[docs]class MaxSaver(MinSaver): """ Separately save the model with maximum value of some statistics. See docs of :class:`MinSaver` for details. """
[docs] def __init__(self, monitor_stat, filename=None, checkpoint_dir=None): """ Args: monitor_stat(str): the name of the statistics. filename (str): the name for the saved model. Defaults to ``max-{monitor_stat}.tfmodel``. """ super(MaxSaver, self).__init__(monitor_stat, True, filename=filename, checkpoint_dir=checkpoint_dir)