split the input list of conv_operator into two inputs: image and filter (#104)

python/paddle/trainer_config_helpers/layers.py

@@ -2667,7 +2667,7 @@ def classification_cost(input, label, name=None,
 
     return LayerOutput(name, LayerType.COST, parents=[input, label])
 
-def conv_operator(input, filter_size, num_filters,
+def conv_operator(img, filter, filter_size, num_filters,
                   num_channel=None, stride=1, padding=0, groups=1,
                   filter_size_y=None, stride_y=None, padding_y=None):
     """
@@ -2680,13 +2680,16 @@ def conv_operator(input, filter_size, num_filters,
 
     .. code-block:: python
 
-       op = conv_operator(input=[layer1, layer2],
+       op = conv_operator(img=input1,
+                          filter=input2,
                           filter_size=3.0,
                           num_filters=64,
                           num_channels=64)
 
-    :param input: Input layer.
-    :type input: LayerOutput|list|tuple
+    :param img: input image
+    :type img: LayerOutput
+    :param filter: input filter
+    :type filter: LayerOutput
     :param filter_size: The x dimension of a filter kernel.
     :type filter_size: int
     :param filter_size_y: The y dimension of a filter kernel. Since
@@ -2708,14 +2711,13 @@ def conv_operator(input, filter_size, num_filters,
     :return: A ConvOperator Object.
     :rtype: ConvOperator
     """
-    assert isinstance(input, list) or isinstance(input, tuple)
     if filter_size_y is None:
         filter_size_y = filter_size
     if stride_y is None:
         stride_y = stride
     if padding_y is None:
         padding_y = padding
-    op = ConvOperator(input_layer_names=[x.name for x in input],
+    op = ConvOperator(input_layer_names=[img.name, filter.name],
                       num_filters = num_filter,
                       conv_conf=Conv(filter_size=filter_size,
                                      padding=padding,
@@ -2725,7 +2727,7 @@ def conv_operator(input, filter_size, num_filters,
                                      padding_y=padding_y,
                                      stride_y=stride_y,
                                      groups=groups))
-    op.origin = input
+    op.origin = [img, filter]
     op.origin.operator = "conv_op"
     return op