fix import; introducing new interface to train after graph has been built.

examples/GAN/GAN.py

@@ -8,8 +8,9 @@ import numpy as np
 import time
 from tensorpack import (Trainer, QueueInput,
                         ModelDescBase, DataFlow, StagingInputWrapper,
-                        MultiGPUTrainerBase, LeastLoadedDeviceSetter,
+                        MultiGPUTrainerBase,
                         TowerContext)
+from tensorpack.train.utility import LeastLoadedDeviceSetter
 from tensorpack.tfutils.summary import add_moving_summary
 from tensorpack.utils.argtools import memoized
 

tensorpack/graph_builder/model_desc.py

@@ -32,30 +32,11 @@ class InputDesc(
             shape (tuple):
             name (str):
         """
-        shape = tuple(shape)    # has to be tuple for self to be hashable
+        shape = tuple(shape)    # has to be tuple for "self" to be hashable
         self = super(InputDesc, cls).__new__(cls, type, shape, name)
         self._cached_placeholder = None
         return self
 
-    # TODO in serialization, skip _cached_placeholder
-    # def dumps(self):
-    #     """
-    #     Returns:
-    #         str: serialized string
-    #     """
-    #     return pickle.dumps(self)
-
-    # @staticmethod
-    # def loads(buf):
-    #     """
-    #     Args:
-    #         buf (str): serialized string
-
-    #     Returns:
-    #         InputDesc:
-    #     """
-    #     return pickle.loads(buf)
-
     def build_placeholder(self, prefix=''):
         """
         Build a tf.placeholder from the metadata, with an optional prefix.

tensorpack/train/base.py

@@ -284,3 +284,51 @@ class Trainer(object):
             self._predictor_factory = PredictorFactory(
                 self.model, self.vs_name_for_predictor)
         return self._predictor_factory
+
+
+def launch_train(
+        run_step, 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):
+    """
+    This is a simpler interface to start training after the graph has been built already.
+    You can build the graph however you like
+    (with or without tensorpack), and invoke this function to start training.
+    This provides the flexibility to define the training config after graph has been buit.
+    One typical use is that callbacks often depend on names that are uknown
+    only after the graph has been built.
+
+    Args:
+        run_step (tf.Tensor or function): Define what the training iteration is.
+            If given a Tensor/Operation, will eval it every step.
+            If given a function, will invoke this function under the default session in every step.
+        Other arguments are the same as in :class:`TrainConfig`.
+
+    Examples:
+
+    .. code-block:: python
+
+        model = MyModelDesc()
+        train_op, cbs = SimpleTrainer.setup_graph(model, QueueInput(mydataflow))
+        launch_train(train_op, callbacks=[...] + cbs, steps_per_epoch=mydataflow.size())
+        # the above is equivalent to:
+        config = TrainConfig(model=MyModelDesc(), data=QueueInput(mydataflow) callbacks=[...])
+        SimpleTrainer(config).train()
+    """
+    assert steps_per_epoch is not None, steps_per_epoch
+    trainer = Trainer(TrainConfig(
+        callbacks=callbacks,
+        extra_callbacks=extra_callbacks,
+        monitors=monitors,
+        session_creator=session_creator,
+        session_config=session_config,
+        session_init=session_init,
+        starting_epoch=starting_epoch,
+        steps_per_epoch=steps_per_epoch,
+        max_epoch=max_epoch))
+    if isinstance(run_step, (tf.Tensor, tf.Operation)):
+        trainer.train_op = run_step
+    else:
+        assert callable(run_step), run_step
+        trainer.run_step = lambda self: run_step()
+    trainer.train()

tensorpack/train/config.py

@@ -9,7 +9,6 @@ from ..callbacks import (
 from ..dataflow.base import DataFlow
 from ..graph_builder.model_desc import ModelDescBase
 from ..utils import logger
-from ..utils.develop import log_deprecated
 from ..tfutils import (JustCurrentSession,
                        get_default_sess_config, SessionInit)
 from ..tfutils.sesscreate import NewSessionCreator
@@ -69,9 +68,6 @@ class TrainConfig(object):
             assert isinstance(v, tp), v.__class__
 
         # process data & model
-        if 'dataset' in kwargs:
-            dataflow = kwargs.pop('dataset')
-            log_deprecated("TrainConfig.dataset", "Use TrainConfig.dataflow instead.", "2017-09-11")
         assert data is None or dataflow is None, "dataflow and data cannot be both presented in TrainConfig!"
         if dataflow is not None:
             assert_type(dataflow, DataFlow)