be clear about "reinitialize" iterator and "reset" dataflow.

docs/tutorial/extend/dataflow.md

 
 ### Write a DataFlow
 
-There are several existing DataFlow, e.g. ImageFromFile, DataFromList, which you can
-use if your data format is simple.
-However in general, you will probably need to write a new DataFlow to produce data for your task.
+There are several existing DataFlow, e.g. [ImageFromFile](../../modules/dataflow.html#tensorpack.dataflow.ImageFromFile),
+[DataFromList](../../http://tensorpack.readthedocs.io/en/latest/modules/dataflow.html#tensorpack.dataflow.DataFromList),
+which you can use if your data format is simple.
+In general, you probably need to write a source DataFlow to produce data for your task,
+and then compose it with existing modules (e.g. mapping, batching, prefetching, ...).
 
 Usually, you just need to implement the `get_data()` method which yields a datapoint every time.
 ```python
@@ -17,7 +19,7 @@ class MyDataFlow(DataFlow):
 
 Optionally, you can implement the following two methods:
 
-+ `size()`. Return the number of elements the generator can produce. Certain tensorpack features might require this.
++ `size()`. Return the number of elements the generator can produce. Certain tensorpack features might use it.
 
 + `reset_state()`. It is guaranteed that the actual process which runs a DataFlow will invoke this method before using it.
 	So if this DataFlow needs to do something after a `fork()`, you should put it here.
@@ -26,9 +28,9 @@ Optionally, you can implement the following two methods:
 	Otherwise, child processes will have the same random seed. The `RNGDataFlow` base class does this for you.
 	You can subclass `RNGDataFlow` to access `self.rng` whose seed has been taken care of.
 
-With a "low-level" DataFlow defined like above, you can then compose it with existing modules (e.g. batching, prefetching, ...).
+The convention is that, `reset_state()` must be called once and usually only once for each DataFlow instance.
+To reinitialize the dataflow (i.e. get a new iterator from the beginning), simply call `get_data()` again.
 
 DataFlow implementations for several well-known datasets are provided in the
 [dataflow.dataset](../../modules/dataflow.dataset.html)
 module, you can take them as a reference.
-

tensorpack/input_source/input_source.py

@@ -71,7 +71,6 @@ class FeedInput(InputSource):
             return tf.train.SessionRunArgs(fetches=[], feed_dict=feed)
 
         def _reset(self):
-            self._ds.reset_state()
             self._itr = self._ds.get_data()
 
     def __init__(self, ds, infinite=True):
@@ -132,7 +131,7 @@ class EnqueueThread(ShareSessionThread):
     def run(self):
         with self.default_sess():
             try:
-                self._itr = self.dataflow.get_data()
+                self.reinitialize_dataflow()
                 while True:
                     # pausable loop
                     self._lock.acquire()
@@ -155,8 +154,7 @@ class EnqueueThread(ShareSessionThread):
                     pass
                 logger.info("{} Exited.".format(self.name))
 
-    def reset_dataflow(self):
-        self.dataflow.reset_state()
+    def reinitialize_dataflow(self):
         self._itr = self.dataflow.get_data()
 
     def pause(self):
@@ -217,7 +215,7 @@ class QueueInput(FeedfreeInput):
             pass
 
         # reset dataflow, start thread
-        self.thread.reset_dataflow()
+        self.thread.reinitialize_dataflow()
         self.thread.resume()
 
     def _create_ema_callback(self):