Add NoOpTrainer

examples/FasterRCNN/README.md

@@ -36,7 +36,7 @@ download the annotation files `instances_minival2014.json`,
 [here](https://github.com/rbgirshick/py-faster-rcnn/blob/master/data/README.md)
 to `annotations/` as well.
 
-<sub><sup>Note that train2017==trainval35k==train2014+val2014-minival2014, and val2017==minival2014</sup></sub>
+<sup>Note that train2017==trainval35k==train2014+val2014-minival2014, and val2017==minival2014</sup>
 
 
 ## Usage

tensorpack/callbacks/inference.py

@@ -19,7 +19,16 @@ __all__ = ['ScalarStats', 'Inferencer',
 @six.add_metaclass(ABCMeta)
 class Inferencer(Callback):
     """ Base class of Inferencer.
-    Inferencer is a special kind of callback that should be called by :class:`InferenceRunner`. """
+    Inferencer is a special kind of callback that should be called by :class:`InferenceRunner`.
+    It has the methods `_get_fetches` and `_on_fetches` which are like
+    :class:`SessionRunHooks`, except that they will be used only by :class:`InferenceRunner`.
+
+    .. document private functions
+    .. automethod:: _before_inference
+    .. automethod:: _after_inference
+    .. automethod:: _get_fetches
+    .. automethod:: _on_fetches
+    """
 
     def _before_epoch(self):
         self._before_inference()
@@ -58,6 +67,9 @@ class Inferencer(Callback):
         return [get_op_tensor_name(n)[1] for n in ret]
 
     def _get_fetches(self):
+        """
+        To be implemented by subclasses
+        """
         raise NotImplementedError()
 
     def on_fetches(self, results):
@@ -71,6 +83,9 @@ class Inferencer(Callback):
         self._on_fetches(results)
 
     def _on_fetches(self, results):
+        """
+        To be implemented by subclasses
+        """
         raise NotImplementedError()
 
 

tensorpack/callbacks/monitor.py

@@ -200,8 +200,11 @@ class Monitors(Callback):
 
         If you run multiprocess training, keep in mind that
         the data is perhaps only available on chief process.
+
+        Returns:
+            scalar
         """
-        return self._scalar_history.get_latest(name)
+        return self._scalar_history.get_latest(name)[1]
 
     def get_history(self, name):
         """

tensorpack/dataflow/common.py

@@ -318,6 +318,8 @@ class MapDataComponent(MapData):
         if r is None:
             return None
         dp = copy(dp)   # shallow copy to avoid modifying the datapoint
+        if isinstance(dp, tuple):
+            dp = list(dp)  # to be able to modify it in the next line
         dp[self._index] = r
         return dp
 

tensorpack/predict/config.py

@@ -17,8 +17,8 @@ __all__ = ['PredictConfig']
 class PredictConfig(object):
     def __init__(self,
                  model=None,
-                 inputs_desc=None,
                  tower_func=None,
+                 inputs_desc=None,
 
                  input_names=None,
                  output_names=None,
@@ -35,8 +35,10 @@ class PredictConfig(object):
 
         Args:
             model (ModelDescBase): to be used to obtain inputs_desc and tower_func.
-            inputs_desc ([InputDesc]):
             tower_func: a callable which takes input tensors (by positional args) and construct a tower.
+                or a :class:`tfutils.TowerFuncWrapper` instance, which packs both `inputs_desc` and function together.
+            inputs_desc ([InputDesc]): if tower_func is a plain function (instead of a TowerFuncWrapper), this describes
+                the list of inputs it takes.
 
             input_names (list): a list of input tensor names. Defaults to match inputs_desc.
             output_names (list): a list of names of the output tensors to predict, the
@@ -59,6 +61,8 @@ class PredictConfig(object):
             self.inputs_desc = model.get_inputs_desc()
             self.tower_func = TowerFuncWrapper(model.build_graph, self.inputs_desc)
         else:
+            if isinstance(tower_func, TowerFuncWrapper):
+                inputs_desc = tower_func.inputs_desc
             assert inputs_desc is not None and tower_func is not None
             self.inputs_desc = inputs_desc
             self.tower_func = TowerFuncWrapper(tower_func, inputs_desc)

tensorpack/train/trainers.py

@@ -25,7 +25,7 @@ from ..graph_builder.utils import override_to_local_variable
 
 from .tower import SingleCostTrainer
 
-__all__ = ['SimpleTrainer',
+__all__ = ['NoOpTrainer', 'SimpleTrainer',
            'QueueInputTrainer',
            'SyncMultiGPUTrainer',
            'SyncMultiGPUTrainerReplicated',
@@ -56,6 +56,18 @@ class SimpleTrainer(SingleCostTrainer):
         return []
 
 
+class NoOpTrainer(SimpleTrainer):
+    """
+    A special trainer that builds the graph (if given a tower function)
+    and does nothing in each step.
+    It is used to only run the callbacks.
+
+    Note that `steps_per_epoch` and `max_epochs` are still valid options.
+    """
+    def run_step(self):
+        pass
+
+
 # Only exists for type check & back-compatibility
 class QueueInputTrainer(SimpleTrainer):
     def _setup_graph(self, input, get_cost_fn, get_opt_fn):