[Trainerv2] use v2 inference interface in v1 trainer

tensorpack/callbacks/inference_runner.py

@@ -108,7 +108,7 @@ class InferenceRunner(InferenceRunnerBase):
             infs (list): a list of :class:`Inferencer` instances.
             tower_name (str): the name scope of the tower to build. Need to set a
                 different one if multiple InferenceRunner are used.
-            gpu (int): the device to use
+            device (int): the device to use
         """
         if isinstance(input, DataFlow):
             input = FeedInput(input, infinite=False)
@@ -124,32 +124,17 @@ class InferenceRunner(InferenceRunnerBase):
         return InferencerToHook(inf, fetches)
 
     def _setup_graph(self):
-        if self.trainer._API_VERSION == 1:
-            # old Trainer API
-            assert self.trainer.model is not None
-            # Use predict_tower in train config. either gpuid or -1
-            if self.trainer._config.predict_tower is not None:
+        if self.trainer._API_VERSION == 1 and self.trainer._config.predict_tower is not None:
             device = self.trainer._config.predict_tower[0]
         else:
             device = self._device
-            device = '/gpu:{}'.format(device) if device >= 0 else '/cpu:0'
-
-            input_callbacks = self._input_source.setup(self.trainer.model.get_inputs_desc())
-
-            with tf.variable_scope(tf.get_variable_scope(), reuse=True):
-                self._tower_handle = self.trainer.predictor_factory.build(
-                    self._tower_name, device, self._input_source)
-        else:
-            # new Trainer API
-            from ..trainv2 import TowerTrainer
-            assert isinstance(self.trainer, TowerTrainer), self.trainer
         assert self.trainer.tower_func is not None, "You must set tower_func of the trainer to use InferenceRunner!"
         input_callbacks = self._input_source.setup(self.trainer.inputs_desc)
 
         with tf.variable_scope(tf.get_variable_scope(), reuse=True):
             SimplePredictBuilder(
                 ns_name=self._tower_name,
-                    vs_name=self.trainer._main_tower_vs_name, device=0).build(
+                vs_name=self.trainer._main_tower_vs_name, device=device).build(
                     self._input_source, self.trainer.tower_func)
             self._tower_handle = self.trainer.tower_func.towers[-1]
 
@@ -202,21 +187,7 @@ class DataParallelInferenceRunner(InferenceRunnerBase):
 
     def _setup_graph(self):
         self._handles = []
-        if self.trainer._API_VERSION == 1:
-            # old Trainer API
-            input_callbacks = self._input_source.setup(self.trainer.model.get_inputs_desc())
-            # build each predict tower
-            with tf.variable_scope(tf.get_variable_scope(), reuse=True):
-                for idx, t in enumerate(self._gpus):
-                    tower_name = self._tower_names[idx]
-                    device = '/gpu:{}'.format(t)
-                    self._handles.append(
-                        self.trainer.predictor_factory.build(
-                            tower_name, device, self._input_source))
-        else:
-            # new Trainer API
-            from ..trainv2 import TowerTrainer
-            assert isinstance(self.trainer, TowerTrainer), self.trainer
+
         assert self.trainer.tower_func is not None, "You must set tower_func of the trainer to use InferenceRunner!"
         input_callbacks = self._input_source.setup(self.trainer.inputs_desc)
         with tf.variable_scope(tf.get_variable_scope(), reuse=True):

tensorpack/tfutils/tower.py

@@ -60,9 +60,6 @@ class TowerContext(object):
             (self.is_training and len(self._vs_name) > 0) or \
             (not self.is_training and len(self._vs_name) > 0 and not self._initial_vs_reuse)
 
-    # TODO clarify the interface on name/vs_name/ns_name.
-    # TODO in inference, vs_name may need to be different from ns_name.i
-    # How to deal with this?
     @property
     def name(self):
         return self._name
@@ -151,7 +148,6 @@ class TowerContext(object):
 
 
 def get_current_tower_context():
-    global _CurrentTowerContext
     return _CurrentTowerContext
 
 

tensorpack/train/base.py

@@ -18,8 +18,11 @@ from ..tfutils import get_global_step_value
 from ..tfutils.model_utils import describe_trainable_vars
 from ..tfutils.sesscreate import ReuseSessionCreator
 from ..tfutils.sessinit import JustCurrentSession
+from ..tfutils.tower import TowerFuncWrapper
 
-from ..graph_builder.predictor_factory import PredictorFactory
+from ..input_source import PlaceholderInput
+from ..graph_builder.predictor_factory import SimplePredictBuilder
+from ..predict.base import OnlinePredictor
 from ..callbacks.steps import MaintainStepCounter
 
 __all__ = ['Trainer', 'StopTraining']
@@ -117,6 +120,16 @@ class Trainer(object):
         assert isinstance(config, TrainConfig), type(config)
         self._config = config
         self.model = config.model
+        if self.model is not None:
+
+            def f(*inputs):
+                self.model.build_graph(inputs)
+
+            """
+            Only to mimic new trainer interafce on inference.
+            """
+            self.inputs_desc = self.model.get_inputs_desc()
+            self.tower_func = TowerFuncWrapper(f, self.inputs_desc)
 
         self._callbacks = []
         self._monitors = []
@@ -268,8 +281,7 @@ class Trainer(object):
 
     def get_predictor(self, input_names, output_names, tower=0):
         """
-        Returns a callable predictor built under ``is_training=False`` tower context.
-        Note that this method is only valid when this trainer has a ``ModelDesc``.
+        Returns a callable predictor built under ``TowerContext(is_training=False)``.
 
         Args:
             input_names (list), output_names(list): list of names
@@ -278,19 +290,27 @@ class Trainer(object):
         Returns:
             an :class:`OnlinePredictor`.
         """
-        return self.predictor_factory.get_predictor(input_names, output_names, tower)
+        device = tower
+        assert self.tower_func is not None, "Must set tower_func on the trainer to use get_predictor()!"
+        tower_name = 'tower-pred-{}'.format(device) if device >= 0 else 'tower-pred-cpu'
 
-    @property
-    def predictor_factory(self):
-        assert self.model is not None, \
-            "Predictor can only be built one Trainer has ModelDesc!"
-        if not hasattr(self, '_predictor_factory'):
-            self._predictor_factory = PredictorFactory(
-                self.model, self.vs_name_for_predictor)
-        return self._predictor_factory
+        try:
+            tower = self.tower_func.towers[tower_name]
+        except KeyError:
+            input = PlaceholderInput()
+            input.setup(self.inputs_desc)
+
+            with tf.variable_scope(tf.get_variable_scope(), reuse=True):
+                SimplePredictBuilder(
+                    ns_name=tower_name, vs_name=self._main_tower_vs_name,
+                    device=device).build(input, self.tower_func)
+            tower = self.tower_func.towers[tower_name]
+        input_tensors = tower.get_tensors(input_names)
+        output_tensors = tower.get_tensors(output_names)
+        return OnlinePredictor(input_tensors, output_tensors)
 
     @property
-    def vs_name_for_predictor(self):
+    def _main_tower_vs_name(self):
         # The vs name a predictor should be built under.
         # for internal use only. Should let graphbuilder return it.
         return ""

tensorpack/train/distributed.py

@@ -95,5 +95,5 @@ class DistributedTrainerReplicated(Trainer):
         self._config.session_creator = get_distributed_session_creator(self.server)
 
     @property
-    def vs_name_for_predictor(self):
+    def _main_tower_vs_name(self):
         return "tower0"

tensorpack/trainv2/base.py

@@ -268,6 +268,7 @@ class TowerTrainer(Trainer):
             input = PlaceholderInput()
             input.setup(self.inputs_desc)
 
+            with tf.variable_scope(tf.get_variable_scope(), reuse=True):
                 SimplePredictBuilder(
                     ns_name=tower_name, vs_name=self._main_tower_vs_name,
                     device=device).build(input, self.tower_func)