make old GANs work with new trainer

docs/tutorial/extend/trainer.md

@@ -8,22 +8,27 @@ If you want to do something different during training, first consider writing it
 or write an issue to see if there is a better solution than creating new trainers.
 If your task is fundamentally different from single-cost optimization, you may need to write a trainer.
 
-Trainers just run __some__ iterations, so there is no limit in where the data come from or what to do in an iteration.
-The existing common trainers all implement two things:
-1. Setup the graph and input pipeline, using the given `TrainConfig`.
-2. Minimize `model.cost` in each iteration.
-
-But you can customize it by using the base `Trainer` class.
-
-* To customize the graph:
-
-  Add any tensors and ops you like, either before creating the trainer or inside `Trainer.__init__`.
-	In this case you don't need to set model/data in `TrainConfig` any more.
-
-* Two ways to customize the iteration:
-
-	1. Set `Trainer.train_op`. This op will be run by default.
-	2. Subclass `Trainer` and override the `run_step()` method. This way you can do something more than running an op.
-
-There are several different [GAN trainers](../../examples/GAN/GAN.py) for reference.
-The implementation of [SimpleTrainer](../../tensorpack/train/simple.py) may also be helpful.
+Trainers are recently being redesigned, the they best wayt to customize the trainer will likely to change.
+We leave the tutorial empty for now.
+
+<!--
+   -Trainers just run __some__ iterations, so there is no limit in where the data come from or what to do in an iteration.
+   -The existing common trainers all implement two things:
+   -1. Setup the graph and input pipeline, using the given `TrainConfig`.
+   -2. Minimize `model.cost` in each iteration.
+   -
+   -But you can customize it by using the base `Trainer` class.
+   -
+   -* To customize the graph:
+   -
+   -  Add any tensors and ops you like, either before creating the trainer or inside `Trainer.__init__`.
+	 -  In this case you don't need to set model/data in `TrainConfig` any more.
+   -
+   -* Two ways to customize the iteration:
+   -
+	 -  1. Set `Trainer.train_op`. This op will be run by default.
+	 -  2. Subclass `Trainer` and override the `run_step()` method. This way you can do something more than running an op.
+   -
+   -There are several different [GAN trainers](../../examples/GAN/GAN.py) for reference.
+   -The implementation of [SimpleTrainer](../../tensorpack/train/simple.py) may also be helpful.
+	 -->

docs/tutorial/trainer.md

@@ -26,27 +26,30 @@ Most neural network training tasks are single-cost optimization.
 Tensorpack provides some trainer implementations for such tasks.
 These trainers will build the graph based on the given `ModelDesc`, and minimizes `ModelDesc.cost`.
 
-To use trainers, pass a `TrainConfig` to configure them:
-
-```python
-config = TrainConfig(
-           model=MyModel()
-           dataflow=my_dataflow,
-           # data=my_inputsource, # alternatively, use a customized InputSource
-           callbacks=[...]
-         )
-
-# start training:
-SomeTrainer(config, other_arguments).train()
-
-# start multi-GPU training with synchronous update:
-# SyncMultiGPUTrainerParameterServer(config).train()
-```
-
-When you set the DataFlow (rather than the InputSource) in the config,
-tensorpack trainers automatically adopt certain prefetch mechanism, as mentioned
-in the [Input Pipeline](input-source.html) tutorial.
-You can set the InputSource instead, to customize this behavior.
+<!--
+   -To use trainers, pass a `TrainConfig` to configure them:
+   -
+   -```python
+   -config = TrainConfig(
+   -           model=MyModel()
+   -           dataflow=my_dataflow,
+   -           # data=my_inputsource, # alternatively, use a customized InputSource
+   -           callbacks=[...]
+   -         )
+   -
+   -# start training:
+   -SomeTrainer(config, other_arguments).train()
+   -
+   -# start multi-GPU training with synchronous update:
+   -# SyncMultiGPUTrainerParameterServer(config).train()
+   -```
+   -
+   -When you set the DataFlow (rather than the InputSource) in the config,
+   -tensorpack trainers automatically adopt certain prefetch mechanism, as mentioned
+   -in the [Input Pipeline](input-source.html) tutorial.
+   -You can set the InputSource instead, to customize this behavior.
+	 -->
+Trainers are being redesigned, so the recommended API will likely be changed soon.
 
 Existing multi-GPU trainers include the logic of data-parallel training.
 You can enable them by just one line, and all the necessary logic to achieve the best performance was baked into the trainers already.

tensorpack/trainv2/base.py

@@ -38,11 +38,41 @@ class Trainer(object):
 
     is_chief = True
 
-    def __init__(self):
+    def __init__(self, config=None):
+        """
+        config is only for compatibility reasons in case you're
+        using custom trainers with old-style API.
+        You should never use config.
+        """
         self._callbacks = []
         self.loop = TrainLoop()
         self._monitors = []  # Clarify the type. Don't change from list to monitors.
 
+        # Hacks!
+        if config is not None:
+            logger.warn("You're initializing new trainer with old trainer API!")
+            logger.warn("This could happen if you wrote a custom trainer before.")
+            logger.warn("It may work now through some hacks, but please switch to the new API!")
+            self._config = config
+            self.inputs_desc = config.model.get_inputs_desc()
+            self.tower_func = TowerFuncWrapper(
+                lambda *inputs: config.model.build_graph(inputs),
+                self.inputs_desc)
+            self._main_tower_vs_name = ""
+
+            def gp(input_names, output_names, tower=0):
+                return TowerTrainer.get_predictor(self, input_names, output_names, device=tower)
+            self.get_predictor = gp
+
+            old_train = self.train
+
+            def train():
+                return old_train(
+                    config.callbacks, config.monitors,
+                    config.session_creator, config.session_init,
+                    config.steps_per_epoch, config.starting_epoch, config.max_epoch)
+            self.train = train
+
     def _register_callback(self, cb):
         """
         Register a callback to the trainer.
@@ -192,7 +222,16 @@ class Trainer(object):
         if (len(args) > 0 and isinstance(args[0], old_train.TrainConfig)) \
                 or 'config' in kwargs:
             name = cls.__name__
+            try:
                 old_trainer = getattr(old_train, name)
+            except AttributeError:
+                # custom trainer. has to live with it
+                return super(Trainer, cls).__new__(cls)
+            else:
+                logger.warn("You're creating trainers with old trainer API!")
+                logger.warn("Now it returns the old trainer for you, please switch to the new API!")
+                logger.warn("'SomeTrainer(config, ...).train()' should be equivalent to "
+                            "'launch_train_with_config(config, SomeTrainer(...))' in the new API.")
                 return old_trainer(*args, **kwargs)
         else:
             return super(Trainer, cls).__new__(cls)