Add config_base.py for Layer

python/paddle/v2/config_base.py

@@ -0,0 +1,72 @@
+import collections
+
+from paddle.trainer_config_helpers.default_decorators import wrap_name_default
+import paddle.trainer_config_helpers as conf_helps
+
+
+class Layer(object):
+    def __init__(self, name=None, parent_layers=None):
+        assert isinstance(parent_layers, dict)
+        self.name = name
+        self.__parent_layers__ = parent_layers
+
+    def to_proto(self, context):
+        """
+        function to set proto attribute
+        """
+        kwargs = dict()
+        for layer_name in self.__parent_layers__:
+            if not isinstance(self.__parent_layers__[layer_name],
+                              collections.Sequence):
+                v1_layer = self.__parent_layers__[layer_name].to_proto(
+                    context=context)
+            else:
+                v1_layer = map(lambda x: x.to_proto(context=context),
+                               self.__parent_layers__[layer_name])
+            kwargs[layer_name] = v1_layer
+
+        if self.name is None:
+            return self.to_proto_impl(**kwargs)
+        elif self.name not in context:
+            context[self.name] = self.to_proto_impl(**kwargs)
+
+        return context[self.name]
+
+    def to_proto_impl(self, **kwargs):
+        raise NotImplementedError()
+
+
+def __convert_to_v2__(method_name, parent_names, is_default_name=True):
+    if is_default_name:
+        wrapper = wrap_name_default(name_prefix=method_name)
+    else:
+        wrapper = None
+
+    class V2LayerImpl(Layer):
+        def __init__(self, **kwargs):
+            parent_layers = dict()
+            other_kwargs = dict()
+            for pname in parent_names:
+                if kwargs.has_key(pname):
+                    parent_layers[pname] = kwargs[pname]
+
+            for key in kwargs.keys():
+                if key not in parent_names:
+                    other_kwargs[key] = kwargs[key]
+
+            name = kwargs.get('name', None)
+            super(V2LayerImpl, self).__init__(name, parent_layers)
+            self.__other_kwargs__ = other_kwargs
+
+        if wrapper is not None:
+            __init__ = wrapper(__init__)
+
+        def to_proto_impl(self, **kwargs):
+            args = dict()
+            for each in kwargs:
+                args[each] = kwargs[each]
+            for each in self.__other_kwargs__:
+                args[each] = self.__other_kwargs__[each]
+            return getattr(conf_helps, method_name)(**args)
+
+    return V2LayerImpl

python/paddle/v2/layer.py

@@ -65,10 +65,7 @@ to be in a Python function but could be anywhere.
 Also, the creation of a protobuf message is hidden in the invocation of
 paddle.v2.parameters.create, no longer exposed to users.
 """
-
-import collections
-import inspect
-
+from config_base import Layer, __convert_to_v2__
 import paddle.trainer_config_helpers as conf_helps
 from paddle.trainer_config_helpers.config_parser_utils import \
     parse_network_config as __parse__
@@ -107,74 +104,6 @@ def parse_network(*outputs):
     return __parse__(__real_func__)
 
 
-class Layer(object):
-    def __init__(self, name=None, parent_layers=None):
-        assert isinstance(parent_layers, dict)
-        self.name = name
-        self.__parent_layers__ = parent_layers
-
-    def to_proto(self, context):
-        """
-        function to set proto attribute
-        """
-        kwargs = dict()
-        for layer_name in self.__parent_layers__:
-            if not isinstance(self.__parent_layers__[layer_name],
-                              collections.Sequence):
-                v1_layer = self.__parent_layers__[layer_name].to_proto(
-                    context=context)
-            else:
-                v1_layer = map(lambda x: x.to_proto(context=context),
-                               self.__parent_layers__[layer_name])
-            kwargs[layer_name] = v1_layer
-
-        if self.name is None:
-            return self.to_proto_impl(**kwargs)
-        elif self.name not in context:
-            context[self.name] = self.to_proto_impl(**kwargs)
-
-        return context[self.name]
-
-    def to_proto_impl(self, **kwargs):
-        raise NotImplementedError()
-
-
-def __convert_to_v2__(method_name, parent_names, is_default_name=True):
-    if is_default_name:
-        wrapper = wrap_name_default(name_prefix=method_name)
-    else:
-        wrapper = None
-
-    class V2LayerImpl(Layer):
-        def __init__(self, **kwargs):
-            parent_layers = dict()
-            other_kwargs = dict()
-            for pname in parent_names:
-                if kwargs.has_key(pname):
-                    parent_layers[pname] = kwargs[pname]
-
-            for key in kwargs.keys():
-                if key not in parent_names:
-                    other_kwargs[key] = kwargs[key]
-
-            name = kwargs.get('name', None)
-            super(V2LayerImpl, self).__init__(name, parent_layers)
-            self.__other_kwargs__ = other_kwargs
-
-        if wrapper is not None:
-            __init__ = wrapper(__init__)
-
-        def to_proto_impl(self, **kwargs):
-            args = dict()
-            for each in kwargs:
-                args[each] = kwargs[each]
-            for each in self.__other_kwargs__:
-                args[each] = self.__other_kwargs__[each]
-            return getattr(conf_helps, method_name)(**args)
-
-    return V2LayerImpl
-
-
 """
 Some layer may need some special config, and can not use __convert_to_v2__ to convert.
 So we also need to implement some special LayerV2.