Make FeedInput & QueueInput support dict-based dataflow (#768)

tensorpack/input_source/input_source.py

@@ -36,6 +36,18 @@ def _get_reset_callback(df):
     return CallbackFactory(setup_graph=lambda _: df.reset_state())
 
 
+def _make_feeds(placeholders, datapoint):
+    assert len(datapoint) == len(placeholders), \
+        "Size of datapoint and placeholders are different: {} != {}".format(
+            len(datapoint), len(placeholders))
+
+    if isinstance(datapoint, (list, tuple)):
+        return dict(zip(placeholders, datapoint))
+    elif isinstance(datapoint, dict):
+        ret = {p: datapoint[p.op.name] for p in placeholders}
+        return ret
+
+
 class PlaceholderInput(InputSource):
     """
     Just produce placeholders as input tensors.
@@ -69,7 +81,7 @@ class FeedInput(InputSource):
         def _before_run(self, _):
             dp = next(self._itr)
             assert len(dp) == len(self._placeholders), "[FeedInput] datapoints and inputs are of different length!"
-            feed = dict(zip(self._placeholders, dp))
+            feed = _make_feeds(self._placeholders, dp)
             return tf.train.SessionRunArgs(fetches=[], feed_dict=feed)
 
         def _reset(self):
@@ -142,7 +154,7 @@ class EnqueueThread(ShareSessionThread):
                         self._running.wait()
 
                     dp = next(self._itr)
-                    feed = dict(zip(self.placehdrs, dp))
+                    feed = _make_feeds(self.placehdrs, dp)
                     # _, sz = sess.run([self.op, self._sz], feed_dict=feed)
                     self.op.run(feed_dict=feed)
             except (tf.errors.CancelledError, tf.errors.OutOfRangeError, DataFlowTerminated):
@@ -473,12 +485,13 @@ class TFDatasetInput(FeedfreeInput):
         dataset, if the dataflow iterator can terminate.
 
         Args:
-            df (DataFlow)
+            df (DataFlow): a dataflow which produces lists
             types([tf.DType])
 
         Returns:
             (tf.data.Dataset)
         """
+        # TODO theoretically it can support dict
         assert isinstance(df, DataFlow), df
         assert isinstance(types, (list, tuple)), types
         df = MapData(df, lambda dp: tuple(dp))