fix prelu(add filter_num output_x output_y) and add channel_shared param

python/paddle/trainer/config_parser.py

@@ -2052,9 +2052,15 @@ class ParameterReluLayer(LayerBase):
         config_assert(len(self.inputs) == 1, "prelu layer has only one input.")
         config_assert(input_layer.size % partial_sum == 0,
                       "a wrong setting for partial_sum")
+
+        dims = [1, input_layer.size / partial_sum]
         self.set_layer_size(input_layer.size)
         self.config.partial_sum = partial_sum
-        self.create_input_parameter(0, input_layer.size / partial_sum)
+        self.create_input_parameter(0, input_layer.size / partial_sum, dims)
+
+        self.set_layer_height_width(self.get_input_layer(0).height, \
+                                        self.get_input_layer(0).width)
+        self.set_layer_depth(self.get_input_layer(0).depth)
 
 
 @config_layer('conv')

python/paddle/trainer_config_helpers/layers.py

@@ -6393,10 +6393,11 @@ def row_conv_layer(input,
 
 @layer_support()
 @wrap_name_default()
-@wrap_param_attr_default()
 def prelu_layer(input,
                 name=None,
                 partial_sum=1,
+                channel_shared=None,
+                num_channels=None,
                 param_attr=None,
                 layer_attr=None):
     """
@@ -6427,6 +6428,10 @@ def prelu_layer(input,
         - partial_sum = number of outputs, indicates all elements share the same weight.
 
     :type partial_sum: int
+    :param channel_shared: whether or not the parameter are shared across channels.
+        - channel_shared = True, we set the partial_sum to the number of outputs.
+        - channel_shared = False, we set the partial_sum to the number of elements in one channel.
+    :type channel_shared: bool
     :param param_attr: The parameter attribute. See ParameterAttribute for details.
     :type param_attr: ParameterAttribute
     :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
@@ -6437,8 +6442,23 @@ def prelu_layer(input,
     """
 
     assert isinstance(input, LayerOutput), 'prelu_layer accepts only one input.'
+    if not param_attr:
+        param_attr = ParamAttr(initial_mean=0.25,
+                               initial_std=0.0)
+    else:
         assert isinstance(param_attr, ParameterAttribute)
 
+    if num_channels is None:
+        assert input.num_filters is not None
+        num_channels = input.num_filters
+
+    if channel_shared is not None:
+        assert isinstance(channel_shared, bool)
+        if channel_shared:
+            partial_sum = input.height * input.width * num_channels
+        else:
+            partial_sum = input.height * input.width
+
     l = Layer(
         name=name,
         type=LayerType.PRELU,
@@ -6449,6 +6469,7 @@ def prelu_layer(input,
         name=name,
         layer_type=LayerType.PRELU,
         parents=input,
+        num_filters = num_channels,
         size=l.config.size)