Merge pull request #1766 from reyoung/feature/add_list_type_of_feeding

Add list type of feeding

Author: reyoung

python/paddle/v2/data_feeder.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from py_paddle import DataProviderConverter
-
+import collections
 import paddle.trainer.PyDataProvider2 as pydp2
 
 __all__ = ['DataFeeder']
@@ -35,15 +35,30 @@ class DataFeeder(DataProviderConverter):
  DataFeeder converts this mini-batch data entries into Arguments in order
  to feed it to C++ interface.
  
- The example usage:
+ The simple usage shows below
+
+ .. code-block:: python
+
+ feeding = ['image', 'label']
+ data_types = enumerate_data_types_of_data_layers(topology)
+ feeder = DataFeeder(data_types=data_types, feeding=feeding)
+
+ minibatch_data = [([1.0, 2.0, 3.0, ...], 5)]
+
+ arg = feeder(minibatch_data)
+
+
+ If mini-batch data and data layers are not one to one mapping, we
+ could pass a dictionary to feeding parameter to represent the mapping
+ relationship.
 
 
  .. code-block:: python
 
  data_types = [('image', paddle.data_type.dense_vector(784)),
  ('label', paddle.data_type.integer_value(10))]
- reader_dict = {'image':0, 'label':1}
- feeder = DataFeeder(data_types=data_types, reader_dict=reader_dict)
+ feeding = {'image':0, 'label':1}
+ feeder = DataFeeder(data_types=data_types, feeding=feeding)
  minibatch_data = [
  ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ), # first sample
  ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ) # second sample
@@ -65,16 +80,23 @@ class DataFeeder(DataProviderConverter):
  a tuple of (data_name, data_type).
 
  :type data_types: list
- :param reader_dict: A dictionary to specify the position of each data
-   in the input data.
- :type feeding: dict
+ :param feeding: A dictionary or a sequence to specify the position of each
+ data in the input data.
+ :type feeding: dict|collections.Sequence|None
  """
 
  def __init__(self, data_types, feeding=None):
  self.input_names = []
  input_types = []
  if feeding is None:
  feeding = default_feeding_map(data_types)
+ elif isinstance(feeding, collections.Sequence):
+ feed_list = feeding
+ feeding = dict()
+ for i, name in enumerate(feed_list):
+ feeding[name] = i
+ elif not isinstance(feeding, dict):
+ raise TypeError("Feeding should be dict or sequence or None.")
 
  self.feeding = feeding
  for each in data_types:

python/paddle/v2/trainer.py

@@ -81,7 +81,7 @@ def train(self, reader, num_passes=1, event_handler=None, feeding=None):
  :type event_handler: (BaseEvent) => None
  :param feeding: Feeding is a map of neural network input name and array
  index that reader returns.
- :type feeding: dict
+ :type feeding: dict|list
  :return:
  """
  if event_handler is None: