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287 lines
9.6 KiB
287 lines
9.6 KiB
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Before this new package paddle.v2.layer, users would need to use functions
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in paddle.trainer_config_helpers.layers to configure networks.
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The Old Way:
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=========
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This old way requires that the creation of a network be defined in a Python
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function, say network_config, and that this Python function being passed to
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paddle.trainer_config_helpers.parse_network_config for the creation of
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protobuf message description of this network.
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```python
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def network_config():
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img = paddle.trainer_config_helpers.data_layer(name="pixel", size=784)
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inference = paddle.trainer_config_helpers.fc_layer(
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input=img,
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size=10,
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act=paddle.trainer_config_helpers.SoftmaxActivation())
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cost = paddle.trainer_config_helpers.classification_cost(
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input=inference,
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label=paddle.trainer_config_helpers.data_layer(name="label", size=10))
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proto_desc = parse_network_config(network_config)
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```
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When parse_network_config executes network_config, those layer definition
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functions like data_layer and fc_layer would change some Python global variables,
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so that after the execution, parse_network_config could collect information from
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these global variables and generates the protobuf message.
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The New Way:
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=========
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In this PR, we define a function in paddle.v2.layer which creates a Python
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class for each layer creation function in paddle.trainer_config_helpers.layers.
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Users can use create a network as follows:
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```python
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img = paddle.v2.layer.data(name="pixel", size=784)
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inference = paddle.v2.layer.fc(input=img, size=10, act=paddle.v2.layer.Softmax())
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cost = paddle.v2.layer.classification(
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input=inference,
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label=paddle.v2.layer.data(name="label", size=10))
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parameters = paddle.v2.parameters.create(cost)
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```
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This new way doesn't require those invocations to layer definition functions
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to be in a Python function but could be anywhere.
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Also, the creation of a protobuf message is hidden in the invocation of
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paddle.v2.parameters.create, no longer exposed to users.
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"""
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import collections
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import paddle.trainer_config_helpers as conf_helps
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from paddle.trainer_config_helpers.config_parser_utils import \
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parse_network_config as __parse__
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from paddle.trainer_config_helpers.default_decorators import wrap_name_default
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import activation
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import data_type
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__all__ = [
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'parse_network', 'data', 'fc', 'max_id', 'classification_cost',
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'cross_entropy_cost'
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]
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def parse_network(*outputs):
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"""
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parse all output layers and then generate a model config proto.
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:param outputs:
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:return:
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"""
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def __real_func__():
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context = dict()
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real_output = [each.to_proto(context=context) for each in outputs]
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conf_helps.outputs(real_output)
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return __parse__(__real_func__)
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class Layer(object):
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def __init__(self, name, parent_layers, step_input=None):
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assert isinstance(parent_layers, dict)
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assert isinstance(name, basestring)
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self.name = name
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self.step_input = step_input
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self.__parent_layers__ = parent_layers
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def to_proto(self, context):
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"""
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function to set proto attribute
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"""
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kwargs = dict()
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for layer_name in self.__parent_layers__:
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if not isinstance(self.__parent_layers__[layer_name],
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collections.Sequence):
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v1_layer = self.__parent_layers__[layer_name].to_proto(
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context=context)
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else:
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v1_layer = map(lambda x: x.to_proto(context=context),
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self.__parent_layers__[layer_name])
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if layer_name == "input" and self.step_input is not None:
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v1_layer.insert(0, self.step_input)
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kwargs[layer_name] = v1_layer
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# memory may have the same name with some layer
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if isinstance(self, MemoryV2):
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return self.to_proto_impl(**kwargs)
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if self.name not in context:
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context[self.name] = self.to_proto_impl(**kwargs)
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return context[self.name]
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def to_proto_impl(self, **kwargs):
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raise NotImplementedError()
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def __convert_to_v2__(method_name, name_prefix, parent_names):
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if name_prefix is not None:
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wrapper = wrap_name_default(name_prefix=name_prefix)
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else:
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wrapper = None
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class V2LayerImpl(Layer):
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def __init__(self, name=None, step_input=None, **kwargs):
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parent_layers = dict()
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other_kwargs = dict()
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for pname in parent_names:
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parent_layers[pname] = kwargs[pname]
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for key in kwargs.keys():
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if key not in parent_names:
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other_kwargs[key] = kwargs[key]
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super(V2LayerImpl, self).__init__(name, parent_layers, step_input)
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self.__other_kwargs__ = other_kwargs
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if wrapper is not None:
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__init__ = wrapper(__init__)
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def to_proto_impl(self, **kwargs):
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args = dict()
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for each in kwargs:
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args[each] = kwargs[each]
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for each in self.__other_kwargs__:
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args[each] = self.__other_kwargs__[each]
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return getattr(conf_helps, method_name)(name=self.name, **args)
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return V2LayerImpl
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"""
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Some layer may need some special config, and can not use __convert_to_v2__ to convert.
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So we also need to implement some special LayerV2.
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"""
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class DataLayerV2(Layer):
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def __init__(self, name, type, **kwargs):
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assert isinstance(type, data_type.InputType)
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self.type = type
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self.__method_name__ = 'data_layer'
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self.__kwargs__ = kwargs
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super(DataLayerV2, self).__init__(name=name, parent_layers=dict())
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def to_proto_impl(self, **kwargs):
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args = dict()
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args['size'] = self.type.dim
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for each in kwargs:
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args[each] = kwargs[each]
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for each in self.__kwargs__:
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args[each] = self.__kwargs__[each]
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return getattr(conf_helps, self.__method_name__)(name=self.name, **args)
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class MemoryV2(Layer):
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def __init__(self, name, size, **kwargs):
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self.name = name
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self.size = size
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self.__kwargs__ = kwargs
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super(MemoryV2, self).__init__(name=name, parent_layers=dict())
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def to_proto_impl(self, **kwargs):
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args = dict()
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for each in kwargs:
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args[each] = kwargs[each]
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for each in self.__kwargs__:
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args[each] = self.__kwargs__[each]
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return conf_helps.memory(name=self.name, size=self.size, **args)
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data = DataLayerV2
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fc = __convert_to_v2__('fc_layer', name_prefix='fc', parent_names=['input'])
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max_id = __convert_to_v2__(
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'maxid_layer', name_prefix='maxid_layer', parent_names=['input'])
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classification_cost = __convert_to_v2__(
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'classification_cost',
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name_prefix='classification_cost',
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parent_names=['input', 'label'])
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cross_entropy_cost = __convert_to_v2__(
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'cross_entropy',
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name_prefix='cross_entropy',
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parent_names=['input', 'label'])
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embedding = __convert_to_v2__(
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'embedding_layer', name_prefix='embedding', parent_names=['input'])
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last_seq = __convert_to_v2__(
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'last_seq', name_prefix='last_seq', parent_names=['input'])
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recurrent_group = __convert_to_v2__(
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'recurrent_group', name_prefix='recurrent_layer', parent_names=['input'])
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memory = MemoryV2
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if __name__ == '__main__':
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pixel = data(name='pixel', type=data_type.dense_vector(784))
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label = data(name='label', type=data_type.integer_value(10))
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hidden = fc(input=pixel, size=100, act=conf_helps.SigmoidActivation())
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inference = fc(input=hidden, size=10, act=conf_helps.SoftmaxActivation())
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maxid = max_id(input=inference)
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cost1 = classification_cost(input=inference, label=label)
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cost2 = cross_entropy_cost(input=inference, label=label)
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mem = memory(name="rnn_state", size=10)
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# print parse_network(cost1)
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# print parse_network(cost2)
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# print parse_network(cost1, cost2)
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# print parse_network(cost2)
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# print parse_network(inference, maxid)
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dict_dim = 10
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word_dim = 8
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hidden_dim = 8
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label_dim = 3
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def step(y):
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mem = conf_helps.memory(name="rnn_state", size=hidden_dim)
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out = conf_helps.fc_layer(
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input=[y, mem],
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size=hidden_dim,
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act=activation.Tanh(),
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bias_attr=True,
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name="rnn_state")
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return out
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def test():
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data1 = conf_helps.data_layer(name="word", size=dict_dim)
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embd = conf_helps.embedding_layer(input=data1, size=word_dim)
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conf_helps.recurrent_group(name="rnn", step=step, input=embd)
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# print __parse__(test)
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# yyyyyyyy
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def new_step(y):
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mem = memory(name="rnn_state", size=hidden_dim)
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out = fc(input=[mem],
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step_input=y,
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size=hidden_dim,
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act=activation.Tanh(),
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bias_attr=True,
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name="rnn_state")
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return out.to_proto(dict())
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data1 = data(name="word", type=data_type.integer_value(dict_dim))
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embd = embedding(input=data1, size=word_dim)
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aaa = recurrent_group(name="rnn", step=new_step, input=embd)
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print parse_network(aaa)
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