Merge branch 'feature/expose_networks' into feature/recommendation_v2_api

python/paddle/v2/__init__.py

@@ -25,12 +25,13 @@ from . import reader
 import attr
 import pooling
 import inferencer
+import networks
 import py_paddle.swig_paddle as api
 
 __all__ = [
     'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer',
     'event', 'data_type', 'attr', 'pooling', 'data_feeder', 'dataset', 'reader',
-    'topology', 'inferencer', 'infer'
+    'topology', 'networks', 'inferencer', 'infer'
 ]
 
 

python/paddle/v2/config_base.py

@@ -0,0 +1,86 @@
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+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.
 """
-
+from config_base import Layer, __convert_to_v2__
-import collections
-import inspect
-
 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.

python/paddle/v2/networks.py

@@ -0,0 +1,45 @@
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.trainer_config_helpers.networks as conf_nw
+import inspect
+from config_base import __convert_to_v2__
+
+__all__ = []
+
+
+def __initialize__():
+    for each_subnetwork in conf_nw.__all__:
+        if each_subnetwork in ['inputs', 'outputs']:
+            continue
+        func = getattr(conf_nw, each_subnetwork)
+        if hasattr(func, 'argspec'):
+            argspec = func.argspec
+        else:
+            argspec = inspect.getargspec(func)
+        if each_subnetwork == 'simple_attention':
+            parents = ['encoded_sequence', 'encoded_proj', 'decoder_state']
+        else:
+            parents = filter(lambda x: x.startswith('input'), argspec.args)
+        assert len(parents) != 0, each_subnetwork
+        v2_subnet = __convert_to_v2__(
+            each_subnetwork,
+            parent_names=parents,
+            is_default_name='name' in argspec.args)
+        globals()[each_subnetwork] = v2_subnet
+        global __all__
+        __all__.append(each_subnetwork)
+
+
+__initialize__()

python/paddle/v2/tests/test_layer.py

@@ -18,6 +18,7 @@ import paddle.v2.attr as attr
 import paddle.v2.data_type as data_type
 import paddle.v2.layer as layer
 import paddle.v2.pooling as pooling
+import paddle.v2.networks as networks
 
 pixel = layer.data(name='pixel', type=data_type.dense_vector(128))
 label = layer.data(name='label', type=data_type.integer_value(10))
@@ -251,5 +252,13 @@ class ProjOpTest(unittest.TestCase):
         print layer.parse_network(conv1)
 
 
+class NetworkTests(unittest.TestCase):
+    def test_vgg(self):
+        img = layer.data(name='pixel', type=data_type.dense_vector(784))
+        vgg_out = networks.small_vgg(
+            input_image=img, num_channels=1, num_classes=2)
+        print layer.parse_network(vgg_out)
+
+
 if __name__ == '__main__':
     unittest.main()