add type to datalayer

demo/mnist/api_train.py

@@ -71,8 +71,8 @@ def main():
     assert isinstance(updater, api.ParameterUpdater)
 
     # define network
-    images = paddle_v2.layer.data(name='pixel', size=784)
-    label = paddle_v2.layer.data(name='label', size=10)
+    images = paddle_v2.layer.data(name='pixel', type=dp.dense_vector(784))
+    label = paddle_v2.layer.data(name='label', type=dp.integer_value(10))
     hidden1 = paddle_v2.layer.fc(input=images, size=200)
     hidden2 = paddle_v2.layer.fc(input=hidden1, size=200)
     inference = paddle_v2.layer.fc(input=hidden2,

python/paddle/v2/layer.py

@@ -67,6 +67,7 @@ paddle.v2.parameters.create, no longer exposed to users.
 """
 
 import paddle.trainer_config_helpers as conf_helps
+import paddle.trainer.PyDataProvider2 as dp
 from paddle.trainer_config_helpers.config_parser_utils import \
     parse_network_config as __parse__
 from paddle.trainer_config_helpers.default_decorators import wrap_name_default
@@ -157,7 +158,37 @@ def __convert_to_v2__(method_name, name_prefix, parent_names):
     return V2LayerImpl
 
 
-data = __convert_to_v2__('data_layer', None, [])
+"""
+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.
+"""
+
+
+class DataLayerV2(Layer):
+    def __init__(self, name, type, **kwargs):
+        self.__method_name__ = 'data_layer'
+
+        assert isinstance(type, dp.InputType)
+
+        # get data_size from type.dim
+        args = dict()
+        for key in kwargs:
+            args[key] = kwargs[key]
+        args['size'] = type.dim
+        self.__args__ = args
+
+        super(DataLayerV2, self).__init__(name=name, parent_layers=dict())
+
+    def to_proto_impl(self, **kwargs):
+        args = dict()
+        for each in kwargs:
+            args[each] = kwargs[each]
+        for each in self.__args__:
+            args[each] = self.__args__[each]
+        return getattr(conf_helps, self.__method_name__)(name=self.name, **args)
+
+
+data = DataLayerV2
 fc = __convert_to_v2__('fc_layer', name_prefix='fc', parent_names=['input'])
 max_id = __convert_to_v2__(
     'maxid_layer', name_prefix='maxid_layer', parent_names=['input'])
@@ -171,8 +202,8 @@ cross_entropy_cost = __convert_to_v2__(
     parent_names=['input', 'label'])
 
 if __name__ == '__main__':
-    pixel = data(name='pixel', size=784)
-    label = data(name='label', size=10)
+    pixel = data(name='pixel', type=dp.dense_vector(784))
+    label = data(name='label', type=dp.integer_value(10))
     hidden = fc(input=pixel, size=100, act=conf_helps.SigmoidActivation())
     inference = fc(input=hidden, size=10, act=conf_helps.SoftmaxActivation())
     maxid = max_id(input=inference)