Source code for tensorpack.callbacks.summary

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

import numpy as np
from collections import deque
import tensorflow as tf

from ..tfutils.common import get_op_tensor_name
from ..utils import logger
from ..utils.naming import MOVING_SUMMARY_OPS_KEY
from .base import Callback

__all__ = ['MovingAverageSummary', 'MergeAllSummaries', 'SimpleMovingAverage']

[docs]class MovingAverageSummary(Callback): """ This callback is enabled by default. 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. """
[docs] 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:"[MovingAverageSummary] {} operations in collection '{}' " "will be run with session hooks.".format(len(ops), self._collection)) self.ema_op =*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)"[MovingAverageSummary] {} operations in collection '{}'" " will be run together with operation '{}'.".format( len(ops), self._collection, def _before_run(self, _): if self._train_op is None: return self._fetch
class MergeAllSummaries_RunAlone(Callback): def __init__(self, period, key): self._period = period self._key = key def _setup_graph(self): size = len(tf.get_collection(self._key))"Summarizing collection '{}' of size {}.".format(self._key, size)) self.summary_op = tf.summary.merge_all(self._key) def _trigger_step(self): if self._period: if (self.local_step + 1) % self._period == 0: self._trigger() def _trigger(self): if self.summary_op: summary = self.summary_op.eval() self.trainer.monitors.put_summary(summary) class MergeAllSummaries_RunWithOp(Callback): def __init__(self, period, key): self._period = period self._key = key def _setup_graph(self): size = len(tf.get_collection(self._key))"Summarizing collection '{}' of size {}.".format(self._key, size)) self.summary_op = tf.summary.merge_all(self._key) if self.summary_op is not None: self._fetches = tf.train.SessionRunArgs(self.summary_op) else: self._fetches = None def _need_run(self): if self.local_step == self.trainer.steps_per_epoch - 1: return True if self._period > 0 and (self.local_step + 1) % self._period == 0: return True return False def _before_run(self, ctx): if self._need_run(): return self._fetches return None def _after_run(self, _, run_values): summary = run_values.results if summary is None: return self.trainer.monitors.put_summary(summary)
[docs]def MergeAllSummaries(period=0, run_alone=False, key=None): """ This callback is enabled by default. Evaluate all summaries by `tf.summary.merge_all`, and write them to logs. Args: period (int): by default the callback summarizes once every epoch. This option (if not set to 0) makes it additionally summarize every ``period`` steps. run_alone (bool): whether to evaluate the summaries alone. If True, summaries will be evaluated after each epoch alone. If False, summaries will be evaluated together with the `` calls, in the last step of each epoch. For :class:`SimpleTrainer`, it needs to be False because summary may depend on inputs. key (str): the collection of summary tensors. Same as in `tf.summary.merge_all`. Default is ``tf.GraphKeys.SUMMARIES``. """ if key is None: key = tf.GraphKeys.SUMMARIES period = int(period) if run_alone: return MergeAllSummaries_RunAlone(period, key) else: return MergeAllSummaries_RunWithOp(period, key)
[docs]class SimpleMovingAverage(Callback): """ Monitor Simple Moving Average (SMA), i.e. an average within a sliding window, of some tensors. """
[docs] def __init__(self, tensors, window_size): """ Args: tensors (str or [str]): names of tensors window_size (int): size of the moving window """ self._tensor_names = [get_op_tensor_name(x)[1] for x in tensors] self._display_names = [get_op_tensor_name(x)[0] for x in tensors] self._window = int(window_size) self._queue = deque(maxlen=window_size)
def _setup_graph(self): tensors = self.get_tensors_maybe_in_tower(self._tensor_names) for t in tensors: assert t.get_shape().ndims == 0, \ "SimpleMovingAverage only accepts scalar tensor! Got one with {}".format(t.get_shape()) self._fetch = tf.train.SessionRunArgs(fetches=tensors) def _before_run(self, _): return self._fetch def _after_run(self, _, rv): results = rv.results self._queue.append(results) def _trigger_step(self): if self.global_step % self._window == 0: averages = np.asarray(self._queue).mean(axis=0) for name, avg in zip(self._display_names, averages): self.trainer.monitors.put_scalar(name + '/SMA', avg)