Class MovingAverageSummary

tensorpack/callbacks/summary.py:17–63

Maintain the moving average of summarized tensors in every step, by ops added to the collection. Note that it only **maintains** the moving averages by updating the relevant variables in the graph, the actual summary should be done in other callbacks. This callback is one o

class MovingAverageSummary(Callback):
    """
    Maintain the moving average of summarized tensors in every step,
    by ops added to the collection.
    Note that it only **maintains** the moving averages by updating
    the relevant variables in the graph,
    the actual summary should be done in other callbacks.

    This callback is one of the :func:`DEFAULT_CALLBACKS()`.
    """
    def __init__(self, collection=MOVING_SUMMARY_OPS_KEY, train_op=None):
        """
        Args:
            collection(str): the collection of EMA-maintaining ops.
                The default value would work with
                the tensors you added by :func:`tfutils.summary.add_moving_summary()`,
                but you can use other collections as well.
            train_op (tf.Operation or str): the (name of) training op to associate the maintaing ops with.
                If not provided, the EMA-maintaining ops will be hooked to
                `trainer.hooked_session` and be executed in every iteration.
                Otherwise, the EMA-maintaining ops will be executed whenever
                the training op is executed.
        """
        self._collection = collection
        self._train_op = train_op

    def _setup_graph(self):
        ops = [k.op for k in tf.get_collection(self._collection)]
        if self._train_op is None:
            logger.info("[MovingAverageSummary] {} operations in collection '{}' "
                        "will be run with session hooks.".format(len(ops), self._collection))

            self.ema_op = tf.group(*ops, name='maintain_moving_average_summary')
            self._fetch = tf.train.SessionRunArgs(fetches=self.ema_op)
        else:
            if isinstance(self._train_op, tf.Tensor):
                self._train_op = self._train_op.op
            if not isinstance(self._train_op, tf.Operation):
                self._train_op = self.graph.get_operation_by_name(self._train_op)
            self._train_op._add_control_inputs(ops)
            logger.info("[MovingAverageSummary] {} operations in collection '{}'"
                        " will be run together with operation '{}'.".format(
                            len(ops), self._collection, self._train_op.name))

    def _before_run(self, _):
        if self._train_op is None:
            return self._fetch


class MergeAllSummaries_RunAlone(Callback):