Add train_with_defaults

examples/GAN/ConditionalGAN-mnist.py

@@ -134,11 +134,9 @@ if __name__ == '__main__':
         sample(args.load)
     else:
         logger.auto_set_dir()
-        config = TrainConfig(
+        GANTrainer(QueueInput(get_data()), Model()).train_with_defaults(
             callbacks=[ModelSaver()],
             steps_per_epoch=500,
             max_epoch=100,
+            session_init=SaverRestore(args.load) if args.load else None
         )
-        if args.load:
-            config.session_init = SaverRestore(args.load)
-        GANTrainer(QueueInput(get_data()), Model()).train_with_config(config)

examples/GAN/CycleGAN.py

@@ -218,7 +218,7 @@ if __name__ == '__main__':
     data = get_data(args.data)
     data = PrintData(data)
 
-    config = TrainConfig(
+    GANTrainer(QueueInput(data), Model()).train_with_defaults(
         callbacks=[
             ModelSaver(),
             ScheduledHyperParamSetter(
@@ -230,5 +230,3 @@ if __name__ == '__main__':
         steps_per_epoch=data.size(),
         session_init=SaverRestore(args.load) if args.load else None
     )
-
-    GANTrainer(QueueInput(data), Model()).train_with_config(config)

examples/GAN/DCGAN.py

@@ -156,12 +156,11 @@ if __name__ == '__main__':
     else:
         assert args.data
         logger.auto_set_dir()
-        config = TrainConfig(
+        GANTrainer(
+            input=QueueInput(get_data(args.data)),
+            model=Model()).train_with_defaults(
             callbacks=[ModelSaver()],
             steps_per_epoch=300,
             max_epoch=200,
             session_init=SaverRestore(args.load) if args.load else None
         )
-        GANTrainer(
-            input=QueueInput(get_data(args.data)),
-            model=Model()).train_with_config(config)

examples/GAN/DiscoGAN-CelebA.py

@@ -217,13 +217,11 @@ if __name__ == '__main__':
 
     data = get_celebA_data(args.data, args.style_A, args.style_B)
 
-    config = TrainConfig(
+    # train 1 D after 2 G
+    SeparateGANTrainer(
+        QueueInput(data), Model(), d_period=3).train_with_defaults(
         callbacks=[ModelSaver()],
         steps_per_epoch=300,
         max_epoch=250,
         session_init=SaverRestore(args.load) if args.load else None
     )
-
-    # train 1 D after 2 G
-    SeparateGANTrainer(
-        QueueInput(data), Model(), d_period=3).train_with_config(config)

examples/GAN/Image2Image.py

@@ -210,15 +210,13 @@ if __name__ == '__main__':
         logger.auto_set_dir()
 
         data = QueueInput(get_data())
-        config = TrainConfig(
+
+        GANTrainer(data, Model()).train_with_defaults(
             callbacks=[
                 PeriodicTrigger(ModelSaver(), every_k_epochs=3),
                 ScheduledHyperParamSetter('learning_rate', [(200, 1e-4)])
             ],
             steps_per_epoch=data.size(),
             max_epoch=300,
+            session_init=SaverRestore(args.load) if args.load else None
         )
-        if args.load:
-            config.session_init = SaverRestore(args.load)
-
-        GANTrainer(data, Model()).train_with_config(config)

examples/GAN/Improved-WGAN.py

@@ -95,12 +95,11 @@ if __name__ == '__main__':
     else:
         assert args.data
         logger.auto_set_dir()
-        config = TrainConfig(
+        SeparateGANTrainer(
+            QueueInput(DCGAN.get_data(args.data)),
+            Model(), g_period=6).train_with_defaults(
             callbacks=[ModelSaver()],
             steps_per_epoch=300,
             max_epoch=200,
             session_init=SaverRestore(args.load) if args.load else None
         )
-        SeparateGANTrainer(
-            QueueInput(DCGAN.get_data(args.data)),
-            Model(), g_period=6).train_with_config(config)

examples/GAN/InfoGAN-mnist.py

@@ -245,11 +245,10 @@ if __name__ == '__main__':
         sample(args.load)
     else:
         logger.auto_set_dir()
-        cfg = TrainConfig(
+        GANTrainer(QueueInput(get_data()),
+                   Model()).train_with_defaults(
             callbacks=[ModelSaver(keep_freq=0.1)],
             steps_per_epoch=500,
             max_epoch=100,
             session_init=SaverRestore(args.load) if args.load else None
         )
-        GANTrainer(QueueInput(get_data()),
-                   Model()).train_with_config(cfg)

examples/GAN/WGAN.py

@@ -76,14 +76,15 @@ if __name__ == '__main__':
     else:
         assert args.data
         logger.auto_set_dir()
-        config = TrainConfig(
+
+        # The original code uses a different schedule, but this seems to work well.
+        # Train 1 D after 2 G
+        SeparateGANTrainer(
+            input=QueueInput(DCGAN.get_data(args.data)),
+            model=Model(),
+            d_period=3).train_with_defaults(
             callbacks=[ModelSaver(), ClipCallback()],
             steps_per_epoch=500,
             max_epoch=200,
             session_init=SaverRestore(args.load) if args.load else None
         )
-        # The original code uses a different schedule, but this seems to work well.
-        # Train 1 D after 2 G
-        SeparateGANTrainer(
-            input=QueueInput(DCGAN.get_data(args.data)),
-            model=Model(), d_period=3).train_with_config(config)

tensorpack/train/base.py

@@ -9,13 +9,16 @@ from six.moves import range
 import six
 from abc import abstractmethod, ABCMeta
 
+from ..callbacks import (
+    Callback, Callbacks, Monitors, TrainingMonitor,
+    MovingAverageSummary,
+    ProgressBar, MergeAllSummaries,
+    TFEventWriter, JSONWriter, ScalarPrinter, RunUpdateOps)
 from ..utils import logger
 from ..utils.argtools import call_only_once, memoized
-from ..callbacks import Callback, Callbacks
-from ..callbacks.monitor import Monitors, TrainingMonitor
 from ..tfutils.model_utils import describe_trainable_vars
 from ..tfutils.sessinit import JustCurrentSession
-from ..tfutils.sesscreate import ReuseSessionCreator
+from ..tfutils.sesscreate import ReuseSessionCreator, NewSessionCreator
 from ..tfutils.tower import TowerFuncWrapper, get_current_tower_context
 from ..tfutils.gradproc import FilterNoneGrad
 from ..callbacks.steps import MaintainStepCounter
@@ -31,6 +34,18 @@ from ..trainv1.config import TrainConfig
 __all__ = ['TrainConfig', 'Trainer', 'SingleCostTrainer', 'TowerTrainer']
 
 
+def DEFAULT_CALLBACKS():
+    return [
+        MovingAverageSummary(),
+        ProgressBar(),
+        MergeAllSummaries(),
+        RunUpdateOps()]
+
+
+def DEFAULT_MONITORS():
+    return [TFEventWriter(), JSONWriter(), ScalarPrinter()]
+
+
 class Trainer(object):
     """ Base class for a trainer.
     """
@@ -220,8 +235,8 @@ class Trainer(object):
 
     def train_with_config(self, config):
         """
-        An alias to simplify the use of `TrainConfig`.
-        It is equivalent to the following:
+        An alias to simplify the use of `TrainConfig` with `Trainer`.
+        This method is literally the following:
 
         .. code-block:: python
 
@@ -240,6 +255,28 @@ class Trainer(object):
             config.session_creator, config.session_init,
             config.steps_per_epoch, config.starting_epoch, config.max_epoch)
 
+    def train_with_defaults(
+            self, callbacks=None, monitors=None,
+            session_creator=None, session_init=None,
+            steps_per_epoch=None, starting_epoch=1, max_epoch=9999):
+        """
+        Same as :meth:`train()`, but will:
+
+        1. Append `DEFAULT_CALLBACKS()` to callbacks.
+        2. Append `DEFAULT_MONITORS()` to monitors.
+        3. Provide default values for every option except `steps_per_epoch`.
+        """
+        callbacks = (callbacks or []) + DEFAULT_CALLBACKS()
+        monitors = (monitors or []) + DEFAULT_MONITORS()
+
+        assert steps_per_epoch is not None
+        session_creator = session_creator or NewSessionCreator()
+        session_init = session_init or JustCurrentSession()
+
+        self.train(callbacks, monitors,
+                   session_creator, session_init,
+                   steps_per_epoch, starting_epoch, max_epoch)
+
     # create the old trainer when called with TrainConfig
     def __new__(cls, *args, **kwargs):
         if (len(args) > 0 and isinstance(args[0], TrainConfig)) \

tensorpack/train/interface.py

@@ -86,4 +86,5 @@ def launch_train_with_config(config, trainer):
     trainer.setup_graph(
         inputs_desc, input,
         model._build_graph_get_cost, model.get_optimizer)
+    config.data = config.dataflow = config.model = None
     trainer.train_with_config(config)

tensorpack/trainv1/config.py

@@ -17,6 +17,18 @@ from ..utils.develop import log_deprecated
 __all__ = ['TrainConfig']
 
 
+def DEFAULT_CALLBACKS():
+    return [
+        MovingAverageSummary(),
+        ProgressBar(),
+        MergeAllSummaries(),
+        RunUpdateOps()]
+
+
+def DEFAULT_MONITORS():
+    return [TFEventWriter(), JSONWriter(), ScalarPrinter()]
+
+
 class TrainConfig(object):
     """
     A collection of options to be used for trainers.
@@ -84,9 +96,9 @@ class TrainConfig(object):
             callbacks = []
         assert_type(callbacks, list)
         self._callbacks = callbacks + \
-            (extra_callbacks or TrainConfig.DEFAULT_EXTRA_CALLBACKS())
+            (extra_callbacks or DEFAULT_CALLBACKS())
 
-        self.monitors = monitors or TrainConfig.DEFAULT_MONITORS()
+        self.monitors = monitors or DEFAULT_MONITORS()
 
         if session_init is None:
             session_init = JustCurrentSession()
@@ -148,15 +160,3 @@ class TrainConfig(object):
     @property
     def callbacks(self):        # disable setter
         return self._callbacks
-
-    @staticmethod
-    def DEFAULT_EXTRA_CALLBACKS():
-        return [
-            MovingAverageSummary(),
-            ProgressBar(),
-            MergeAllSummaries(),
-            RunUpdateOps()]
-
-    @staticmethod
-    def DEFAULT_MONITORS():
-        return [TFEventWriter(), JSONWriter(), ScalarPrinter()]