Format the python file.

proto/ModelConfig.proto

@@ -253,8 +253,6 @@ message PriorBoxConfig {
   repeated uint32 max_size = 2;
   repeated float aspect_ratio = 3;
   repeated float variance = 4;
-  optional bool flip = 5 [default = true];
-  optional bool clip = 6 [default = true];
 }
 
 message LayerInputConfig {

python/paddle/trainer/config_parser.py

@@ -1577,9 +1577,11 @@ class PrintLayer(LayerBase):
     def __init__(self, name, inputs):
         super(PrintLayer, self).__init__(name, 'print', 0, inputs)
 
+
 @config_layer('priorbox')
 class PriorBoxLayer(LayerBase):
-    def __init__(self, name, inputs, size, min_size, max_size, aspect_ratio, variance):
+    def __init__(self, name, inputs, size, min_size, max_size, aspect_ratio,
+                 variance):
         super(PriorBoxLayer, self).__init__(name, 'priorbox', 0, inputs)
         config_assert(len(inputs) == 2, 'PriorBoxLayer must have 2 input')
         self.config.inputs[0].priorbox_conf.min_size.extend(min_size)

python/paddle/trainer_config_helpers/layers.py

@@ -935,8 +935,15 @@ def print_layer(input, name=None):
         inputs=[l.name for l in input], )
     # this layer don't return anything, can not be input of other layer.
 
+
 @wrap_name_default("priorbox")
-def priorbox_layer(input, img_shape, aspect_ratio, variance, min_size, max_size=[], name=None):
+def priorbox_layer(input,
+                   img_shape,
+                   aspect_ratio,
+                   variance,
+                   min_size,
+                   max_size=[],
+                   name=None):
     """
     Compute the priorbox and set the variance. This layer is necessary for ssd.
 
@@ -957,7 +964,7 @@ def priorbox_layer(input, img_shape, aspect_ratio, variance, min_size, max_size=
     """
     # plus one for ratio 1.
     num_filters = (len(aspect_ratio) * 2 + 1 + len(max_size)) * 4
-    size=(input.size / input.num_filters) * num_filters * 2
+    size = (input.size / input.num_filters) * num_filters * 2
     Layer(
         name=name,
         type=LayerType.PRIORBOX_LAYER,
@@ -968,7 +975,12 @@ def priorbox_layer(input, img_shape, aspect_ratio, variance, min_size, max_size=
         aspect_ratio=aspect_ratio,
         variance=variance)
     return LayerOutput(
-        name, LayerType.PRIORBOX_LAYER, parents=[input, img_shape], num_filters=num_filters, size=size)
+        name,
+        LayerType.PRIORBOX_LAYER,
+        parents=[input, img_shape],
+        num_filters=num_filters,
+        size=size)
+
 
 @wrap_name_default("seq_pooling")
 @wrap_bias_attr_default(has_bias=False)