docs update

docs/tutorial/callback.md

@@ -77,4 +77,4 @@ These features may not be always useful, but think about how messy the main loop
 were to write the logic together with the loops.
 
 See [Write a callback](http://tensorpack.readthedocs.io/en/latest/tutorial/extend/callback.html)
-on how to implement a callback.
+for details on how callbacks work, what they can do, and how to write them.

docs/tutorial/extend/callback.md

 
 ## Write a Callback
 
-The places where each callback method gets called is demonstrated in this snippet:
+In the main loop of the trainer,
+the callbacks will be called in the order they are given in `TrainConfig`.
+The time where each callback method gets called is demonstrated in this snippet:
 
 ```python
-def main_loop():
-  # ... create graph for the model ...
+def train(self):
+  # ... a predefined trainer may create graph for the model here ...
   callbacks.setup_graph()
   # ... create session, initialize session, finalize graph ...
   # start training:
@@ -13,7 +15,7 @@ def main_loop():
   for epoch in range(epoch_start, epoch_end):
     callbacks.before_epoch()
     for step in range(steps_per_epoch):
-      run_one_step()  # callbacks.{before,after}_run are hooked with session
+      self.run_step()  # callbacks.{before,after}_run are hooked with session
       callbacks.trigger_step()
     callbacks.after_epoch()
     callbacks.trigger_epoch()
@@ -87,6 +89,7 @@ to let this method run every k steps or every k epochs.
 ### What you can do in the callback
 
 * Access tensors / ops in either training / inference mode (need to create them in `_setup_graph`).
+	To create a callable function under inference mode, use `self.trainer.get_predictor`.
 * Write stuff to the monitor backend, by `self.trainer.monitors.put_xxx`.
 	The monitors might direct your events to TensorFlow events file, JSON file, stdout, etc.
 	You can get history monitor data as well. See the docs for [Monitors](http://tensorpack.readthedocs.io/en/latest/modules/callbacks.html#tensorpack.callbacks.Monitors)

tensorpack/train/base.py

@@ -261,6 +261,9 @@ class Trainer(object):
 
     def get_predictor(self, input_names, output_names, tower=0):
         """
+        Returns a callable predictor built under ``is_training=False`` tower context.
+        Note that this method is only valid when this trainer has a ``ModelDesc``.
+
         Args:
             input_names (list), output_names(list): list of names
             tower (int): build the predictor on device '/gpu:{tower}' or use -1 for '/cpu:0'.
@@ -273,6 +276,8 @@ class Trainer(object):
 
     @property
     def predictor_factory(self):
+        assert self.model is not None, \
+            "Predictor can only be built one Trainer has ModelDesc!"
         if not hasattr(self, '_predictor_factory'):
             self._predictor_factory = PredictorFactory(
                 self.model, self.vs_name_for_predictor)

tensorpack/utils/logger.py

@@ -42,7 +42,7 @@ def _getlogger():
 
 
 _logger = _getlogger()
-_LOGGING_METHOD = ['info', 'warning', 'error', 'critical', 'warn', 'exception', 'debug']
+_LOGGING_METHOD = ['info', 'warning', 'error', 'critical', 'warn', 'exception', 'debug', 'setLevel']
 # export logger functions
 for func in _LOGGING_METHOD:
     locals()[func] = getattr(_logger, func)