dynamically generate the remapped class

tensorpack/train/input_source.py

@@ -20,7 +20,6 @@ from ..tfutils.summary import add_moving_summary
 from ..tfutils import get_op_tensor_name
 from ..tfutils.tower import get_current_tower_context
 from ..utils import logger
-from ..utils.argtools import memoized
 from ..utils.concurrency import ShareSessionThread
 from ..callbacks.concurrency import StartProcOrThread
 from ..callbacks.base import Callback
@@ -62,6 +61,9 @@ class InputSource(object):
 
     @abstractmethod
     def reset_state(self):
+        """
+        Semantics of this method has not been well defined.
+        """
         pass
 
     @abstractmethod
@@ -80,6 +82,33 @@ class InputSource(object):
         return NotImplementedError()
 
 
+class ProxyInputSource(InputSource):
+    """
+    An InputSource which proxy every method to ``self._input``.
+    """
+    def __init__(self, input):
+        assert isinstance(input, InputSource), input
+        self._input = input
+
+    def get_input_tensors(self):
+        return self._input.get_input_tensors()
+
+    def setup(self, inputs_desc):
+        self._input.setup(inputs_desc)
+
+    def get_callbacks(self):
+        return self._input.get_callbacks()
+
+    def size(self):
+        return self._input.size()
+
+    def next_feed(self):
+        return self._input.next_feed()
+
+    def reset_state(self):
+        self._input.reset_state()
+
+
 class FeedInput(InputSource):
     """ Input by iterating over a DataFlow and feed datapoints. """
     def __init__(self, ds):
@@ -465,57 +494,47 @@ class StagingInputWrapper(FeedfreeInput):
     def get_staging_name(idx):
         return 'StagingArea{}'.format(idx)
 
-    @memoized
     def get_stage_op(self):
         return tf.group(*self._stage_ops)
 
-    @memoized
     def get_unstage_op(self):
         all_outputs = list(chain.from_iterable(self._unstage_ops))
         return tf.group(*all_outputs)
 
 
-# TODO dynamically generate inheritance
-# TODO make it a function, not a class
-class remap_input_source(FeedInput, FeedfreeInput):
+def remap_input_source(input, names):
     """
     When you have some :class:`InputSource` which doesn't match the inputs in
     your :class:`ModelDesc`, use `RemapInputSource`.
     It produces placeholders for all the inputs in your model,
     except that the corresponding ones are replaced with the tensor produced
     by the given :class:`InputSource`.
+
+    Args:
+        input(InputSource): a :class:`InputSource`, whose tensors will get mapped.
+        names(list[str]): list of input names corresponding to the tensors
+            produced by ``input``.
     """
     def __init__(self, input, names):
-        """
-        Args:
-            input(InputSource): a :class:`InputSource`, whose tensors will get mapped.
-            names(list[str]): list of input names corresponding to the tensors
-                produced by ``input``.
-        """
-        assert isinstance(input, InputSource), input
-        self._input = input
+        ProxyInputSource.__init__(self, input)
         assert isinstance(names, (list, tuple)), names
         self._names = tuple(names)
 
-    def size(self):
-        return self._input.size()
-
     def setup(self, inputs):
         self._all_placehdrs = [v.build_placeholder_reuse() for v in inputs]
         inputs_subset = get_sublist_by_names(inputs, self._names)
         self._input.setup(inputs_subset)
 
-    def get_callbacks(self):
-        return self._input.get_callbacks()
-
-    def reset_state(self):
-        self._input.reset_state()
-
-    def next_feed(self):
-        return self._input.next_feed()
-
     def get_input_tensors(self):
         ret = self._input.get_input_tensors()
         assert len(ret) == len(self._names)
         return get_tensors_inputs(
             self._all_placehdrs, ret, self._names)
+
+    oldcls = type(input)
+    # inherit oldcls so that type check in various places would work
+    cls = type('Remapped' + oldcls.__name__, (ProxyInputSource, oldcls), {
+        '__init__': __init__,
+        'setup': setup,
+        'get_input_tensors': get_input_tensors})
+    return cls(input, names)