fix import

tensorpack/__init__.py

@@ -21,6 +21,6 @@ if _HAS_TF:
         from tensorpack.trainv2 import *
     else:
         from tensorpack.train import *
-    from tensorpack.graph_builder import *
+    from tensorpack.graph_builder import InputDesc, ModelDesc, ModelDescBase
     from tensorpack.input_source import *
     from tensorpack.predict import *

tensorpack/graph_builder/__init__.py

@@ -20,7 +20,7 @@ def global_import(name):
 
 
 _CURR_DIR = os.path.dirname(__file__)
-_SKIP = ['utils']
+_SKIP = []
 for _, module_name, _ in iter_modules(
         [_CURR_DIR]):
     srcpath = os.path.join(_CURR_DIR, module_name + '.py')

tensorpack/train/base.py

@@ -319,58 +319,3 @@ def _get_property(name):
 for name in ['global_step', 'local_step', 'steps_per_epoch',
              'epoch_num', 'starting_epoch', 'max_epoch']:
     setattr(Trainer, name, _get_property(name))
-
-
-def launch_train(
-        run_step, 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):
-    """
-    ** Work In Progress! Don't use**
-
-    This is another trainer 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 with callbacks & monitors.
-    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 not known
-    only until 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.
-        model (None or ModelDesc): Certain callbacks (e.g. InferenceRunner) depends on
-            the existence of :class:`ModelDesc`. If you use a :class:`ModelDesc` to
-            build the graph, add it here to to allow those callbacks to work.
-            If you didn't use :class:`ModelDesc`, leave it empty.
-        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, model=model, 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(
-        model=model,
-        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()