update docs and clean up some legacy

docs/tutorial/callback.md

@@ -44,8 +44,20 @@ TrainConfig(
     MovingAverageSummary(),
     # draw a nice progress bar
     ProgressBar(),
-    # run `tf.summary.merge_all` and save results every epoch
+    # run `tf.summary.merge_all` every epoch and send results to monitors
     MergeAllSummaries(),
-  ]
+  ],
+	monitors=[				# monitors are a special kind of callbacks. these are also enabled by default
+		# write all monitor data to tensorboard
+		TFSummaryWriter(),
+		# write all scalar data to a json file, for easy parsing
+		JSONWriter(),
+		# print all scalar data every epoch (can be configured differently)
+		ScalarPrinter(),
+	]
 )
 ```
+
+Notice that callbacks really cover every detail of training, ranging from graph operations to the progress bar.
+This means you can customize every part of the training to your preference, e.g. display something
+different in the progress bar, evaluating part of the summaries at a different frequency, etc.

tensorpack/callbacks/inference.py

@@ -4,11 +4,9 @@
 
 import numpy as np
 from abc import ABCMeta, abstractmethod
-import sys
 import six
 from six.moves import zip
 
-from ..utils import logger
 from ..utils.stats import RatioCounter, BinaryStatistics
 from ..tfutils import get_op_tensor_name
 
@@ -140,14 +138,10 @@ class ClassificationError(Inferencer):
 
     def _datapoint(self, outputs):
         vec = outputs[0]
-        if vec.ndim == 0:
-            logger.error("[DEPRECATED] use a 'wrong vector' for ClassificationError instead of nr_wrong. Exiting..")
-            sys.exit(1)
-        else:
-            # TODO put shape assertion into inference-runner
-            assert vec.ndim == 1, "{} is not a vector!".format(self.wrong_tensor_name)
-            batch_size = len(vec)
-            wrong = np.sum(vec)
+        # TODO put shape assertion into inference-runner
+        assert vec.ndim == 1, "{} is not a vector!".format(self.wrong_tensor_name)
+        batch_size = len(vec)
+        wrong = np.sum(vec)
         self.err_stat.feed(wrong, batch_size)
 
     def _after_inference(self):

tensorpack/callbacks/steps.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 # File: steps.py
-# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 """ Some common step callbacks. """
 

tensorpack/callbacks/trigger.py

@@ -4,10 +4,8 @@
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 from .base import ProxyCallback, Callback
-from ..utils.develop import log_deprecated
 
-
-__all__ = ['PeriodicTrigger', 'PeriodicCallback']
+__all__ = ['PeriodicTrigger']
 
 
 class PeriodicTrigger(ProxyCallback):
@@ -48,30 +46,3 @@ class PeriodicTrigger(ProxyCallback):
 
     def __str__(self):
         return "PeriodicTrigger-" + str(self.cb)
-
-
-class PeriodicCallback(ProxyCallback):
-    """
-    Wrap a callback so that after every ``period`` epochs, its :meth:`trigger_epoch`
-    method is called.
-
-    This wrapper is legacy. It will only proxy the :meth:`trigger_step` method as-is.
-    To be able to schedule a callback more frequent than once per epoch, use :class:`PeriodicTrigger` instead.
-    """
-
-    def __init__(self, cb, period):
-        """
-        Args:
-            cb(Callback): the callback to be triggered periodically
-            period(int): the period, the number of epochs for a callback to be triggered.
-        """
-        super(PeriodicCallback, self).__init__(cb)
-        self.period = int(period)
-        log_deprecated("PeriodicCallback", "Use the more powerful `PeriodicTrigger`.")
-
-    def _trigger_epoch(self):
-        if self.epoch_num % self.period == 0:
-            self.cb.trigger_epoch()
-
-    def __str__(self):
-        return "Periodic-" + str(self.cb)

tensorpack/predict/concurrency.py

@@ -9,7 +9,6 @@ from six.moves import queue, range
 import tensorflow as tf
 
 from ..utils import logger
-from ..utils.develop import deprecated
 from ..utils.concurrency import DIE, StoppableThread, ShareSessionThread
 from ..tfutils.model_utils import describe_model
 from .base import OnlinePredictor, OfflinePredictor, AsyncPredictorBase
@@ -165,10 +164,6 @@ class MultiThreadAsyncPredictor(AsyncPredictorBase):
         for t in self.threads:
             t.start()
 
-    @deprecated("Use 'start()' instead!", "2017-03-11")
-    def run(self):      # temporarily for back-compatibility
-        self.start()
-
     def put_task(self, dp, callback=None):
         """
         Same as in :meth:`AsyncPredictorBase.put_task`.