allow empty model & data in config

tensorpack/train/config.py

@@ -3,7 +3,7 @@
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 from ..callbacks import (
-    Callbacks, MovingAverageSummary,
+    MovingAverageSummary,
     ProgressBar, MergeAllSummaries,
     TFEventWriter, JSONWriter, ScalarPrinter, RunUpdateOps)
 from ..dataflow.base import DataFlow
@@ -24,20 +24,23 @@ class TrainConfig(object):
     """
 
     def __init__(self,
-                 dataflow=None, data=None,
-                 model=None,
-                 callbacks=None, extra_callbacks=None,
-                 monitors=None,
+                 dataflow=None, data=None, model=None,
+                 callbacks=None, extra_callbacks=None, monitors=None,
                  session_creator=None, session_config=None, session_init=None,
                  starting_epoch=1, steps_per_epoch=None, max_epoch=99999,
-                 nr_tower=1, tower=None, predict_tower=[0],
+                 nr_tower=1, tower=None, predict_tower=None,
                  **kwargs):
         """
+        Note:
+            It depends on the specific trainer what fields are necessary.
+            Most existing trainers in tensorpack requires one of `dataflow` or `data`,
+            and `model` to be present in the config.
+
         Args:
-            dataflow (DataFlow): the dataflow to train.
-            data (InputSource): an `InputSource` instance. Only one of ``dataflow``
-                or ``data`` has to be present.
-            model (ModelDesc): the model to train.
+            dataflow (DataFlow):
+            data (InputSource):
+            model (ModelDesc):
+
             callbacks (list): a list of :class:`Callback` to perform during training.
             extra_callbacks (list): the same as ``callbacks``. This argument
                 is only used to provide the defaults. The defaults are
@@ -45,14 +48,17 @@ class TrainConfig(object):
                 callbacks that will be used in the end are ``callbacks + extra_callbacks``.
             monitors (list): a list of :class:`TrainingMonitor`.
                 Defaults to ``[TFEventWriter(), JSONWriter(), ScalarPrinter()]``.
+
             session_creator (tf.train.SessionCreator): Defaults to :class:`sesscreate.NewSessionCreator()`
                 with the config returned by :func:`tfutils.get_default_sess_config()`.
             session_config (tf.ConfigProto): when session_creator is None, use this to create the session.
             session_init (SessionInit): how to initialize variables of a session. Defaults to do nothing.
+
             starting_epoch (int): The index of the first epoch.
             steps_per_epoch (int): the number of steps (defined by :meth:`Trainer.run_step`) to run in each epoch.
                 Defaults to the input data size.
             max_epoch (int): maximum number of epoch to run training.
+
             nr_tower (int): number of training towers.
             tower (list of int): list of training towers in relative id.
             predict_tower (list of int): list of prediction towers in their relative gpu id. Use -1 for cpu.
@@ -62,7 +68,7 @@ class TrainConfig(object):
         def assert_type(v, tp):
             assert isinstance(v, tp), v.__class__
 
-        # process data
+        # process data & model
         if 'dataset' in kwargs:
             dataflow = kwargs.pop('dataset')
             log_deprecated("TrainConfig.dataset", "Use TrainConfig.dataflow instead.", "2017-09-11")
@@ -71,16 +77,16 @@ class TrainConfig(object):
             self.dataflow = dataflow
             assert_type(self.dataflow, DataFlow)
             self.data = None
-        else:
+        if data is not None:
             self.data = data
             assert_type(self.data, InputSource)
             self.dataflow = None
+        if model is not None:
+            assert_type(model, ModelDesc)
+        self.model = model
 
         if callbacks is None:
             callbacks = []
-        assert not isinstance(callbacks, Callbacks), \
-            "TrainConfig(callbacks=Callbacks([...]))" \
-            "Change the argument 'callbacks=' to a *list* of callbacks without StatPrinter()."
         assert_type(callbacks, list)
         if extra_callbacks is None:
             extra_callbacks = [
@@ -89,16 +95,11 @@ class TrainConfig(object):
                 MergeAllSummaries(),
                 RunUpdateOps()]
         self._callbacks = callbacks + extra_callbacks
-        assert_type(self._callbacks, list)
 
         if monitors is None:
             monitors = [TFEventWriter(), JSONWriter(), ScalarPrinter()]
         self.monitors = monitors
 
-        if model is not None:
-            assert_type(model, ModelDesc)
-        self.model = model
-
         if session_init is None:
             session_init = JustCurrentSession()
         self.session_init = session_init
@@ -128,22 +129,19 @@ class TrainConfig(object):
 
         self.starting_epoch = int(starting_epoch)
         self.max_epoch = int(max_epoch)
-        assert self.steps_per_epoch >= 0 and self.max_epoch > 0
+        assert self.steps_per_epoch > 0 and self.max_epoch > 0
 
         self.nr_tower = nr_tower
         if tower is not None:
             assert self.nr_tower == 1, "Cannot set both nr_tower and tower in TrainConfig!"
             self.tower = tower
 
+        if predict_tower is None:
+            predict_tower = [0]
         self.predict_tower = predict_tower
         if isinstance(self.predict_tower, int):
             self.predict_tower = [self.predict_tower]
-        assert len(set(self.predict_tower)) == len(self.predict_tower), \
-            "Cannot have duplicated predict_tower!"
 
-        assert 'optimizer' not in kwargs, \
-            "TrainConfig(optimizer=...) was already deprecated! " \
-            "Use ModelDesc._get_optimizer() instead."
         assert len(kwargs) == 0, 'Unknown arguments: {}'.format(str(kwargs.keys()))
 
     @property