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403 lines
15 KiB
403 lines
15 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 data_type
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import activation
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import attr
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import pooling
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__all__ = [
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'parse_network', 'data', 'fc', 'conv_shift', 'img_conv', 'img_pool', 'spp',
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'maxout', 'img_cmrnorm', 'batch_norm', 'sum_to_one_norm', 'recurrent',
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'lstmemory', 'grumemory', 'pool', 'last_seq', 'first_seq', 'concat',
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'seq_concat', 'block_expand', 'expand', 'repeat', 'seq_reshape', 'addto',
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'linear_comb', 'interpolation', 'bilinear_interp', 'power', 'scaling',
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'slope_intercept', 'tensor', 'cos_sim', 'trans', 'max_id', 'sampling_id',
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'pad', 'classification_cost', 'cross_entropy_cost',
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'cross_entropy_with_selfnorm_cost', 'regression_cost',
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'multi_binary_label_cross_entropy_cost', 'rank_cost', 'lambda_cost',
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'sum_cost', 'huber_cost', 'crf', 'crf_decoding', 'ctc', 'warp_ctc', 'nce',
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'hsigmoid', 'eos'
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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):
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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.__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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kwargs[layer_name] = v1_layer
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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, parent_names):
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wrapper = wrap_name_default(name_prefix=method_name)
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class V2LayerImpl(Layer):
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def __init__(self, name=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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if kwargs.has_key(pname):
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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)
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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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data = DataLayerV2
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AggregateLevel = conf_helps.layers.AggregateLevel
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ExpandLevel = conf_helps.layers.ExpandLevel
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layer_list = [
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# [V2LayerImpl, V1_method_name, parent_names]
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# fully connected layers
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['fc', 'fc_layer', ['input']],
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# conv layers
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['conv_shift', 'conv_shift_layer', ['a', 'b']],
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['img_conv', 'img_conv_layer', ['input']],
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# image pooling layers
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['img_pool', 'img_pool_layer', ['input']],
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['spp', 'spp_layer', ['input']],
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['maxout', 'maxout_layer', ['input']],
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# norm layers
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['img_cmrnorm', 'img_cmrnorm_layer', ['input']],
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['batch_norm', 'batch_norm_layer', ['input']],
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['sum_to_one_norm', 'sum_to_one_norm_layer', ['input']],
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# recurrent layers
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['recurrent', 'recurrent_layer', ['input']],
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['lstmemory', 'lstmemory', ['input']],
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['grumemory', 'grumemory', ['input']],
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# aggregate layers
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['pool', 'pooling_layer', ['input']],
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['last_seq', 'last_seq', ['input']],
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['first_seq', 'first_seq', ['input']],
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['concat', 'concat_layer', ['input']],
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['seq_concat', 'seq_concat_layer', ['a', 'b']],
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# reshaping layers
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['block_expand', 'block_expand_layer', ['input']],
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['expand', 'expand_layer', ['input', 'expand_as']],
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['repeat', 'repeat_layer', ['input']],
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['rotate', 'rotate_layer', ['input']],
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['seq_reshape', 'seq_reshape_layer', ['input']],
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# math layers
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['addto', 'addto_layer', ['input']],
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['linear_comb', 'linear_comb_layer', ['weights', 'vectors']],
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['interpolation', 'interpolation_layer', ['input', 'weight']],
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['bilinear_interp', 'bilinear_interp_layer', ['input']],
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['power', 'power_layer', ['input', 'weight']],
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['scaling', 'scaling_layer', ['input', 'weight']],
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['slope_intercept', 'slope_intercept_layer', ['input']],
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['tensor', 'tensor_layer', ['a', 'b']],
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['cos_sim', 'cos_sim', ['a', 'b']],
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['trans', 'trans_layer', ['input']],
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# sampling layers
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['max_id', 'maxid_layer', ['input']],
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['sampling_id', 'sampling_id_layer', ['input']],
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# slicing and joining layers
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['pad', 'pad_layer', ['input']],
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# cost layers
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[
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'classification_cost', 'classification_cost',
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['input', 'label', 'weight']
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],
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['regression_cost', 'regression_cost', ['input', 'label', 'weight']],
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['cross_entropy_cost', 'cross_entropy', ['input', 'label']],
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[
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'cross_entropy_with_selfnorm_cost', 'cross_entropy_with_selfnorm',
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['input', 'label']
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],
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[
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'multi_binary_label_cross_entropy_cost',
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'multi_binary_label_cross_entropy', ['input', 'label']
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],
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['rank_cost', 'rank_cost', ['left', 'right', 'label', 'weight']],
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['lambda_cost', 'lambda_cost', ['input', 'score']],
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['sum_cost', 'sum_cost', ['input']],
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['huber_cost', 'huber_cost', ['input', 'label']],
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['crf', 'crf_layer', ['input', 'label']],
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['crf_decoding', 'crf_decoding_layer', ['input']],
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['ctc', 'ctc_layer', ['input', 'label']],
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['warp_ctc', 'warp_ctc_layer', ['input', 'label']],
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['nce', 'nce_layer', ['input', 'label']],
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['hsigmoid', 'hsigmoid', ['input', 'label']],
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# check layers
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['eos', 'eos_layer', ['input']]
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]
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for l in layer_list:
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globals()[l[0]] = __convert_to_v2__(l[1], l[2])
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if __name__ == '__main__':
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pixel = data(name='pixel', type=data_type.dense_vector(128))
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label = data(name='label', type=data_type.integer_value(10))
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weight = data(name='weight', type=data_type.dense_vector(10))
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word = data(name='word', type=data_type.integer_value(12))
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score = data(name='score', type=data_type.dense_vector(1))
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hidden = fc(input=pixel,
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size=100,
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act=activation.Sigmoid(),
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param_attr=attr.Param(name='hidden'))
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inference = fc(input=hidden, size=10, act=activation.Softmax())
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print parse_network(inference)
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# test conv layers
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conv1 = conv_shift(a=pixel, b=score)
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conv2 = img_conv(
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input=pixel,
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filter_size=1,
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filter_size_y=1,
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num_channels=8,
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num_filters=16,
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act=activation.Linear())
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print parse_network(conv1, conv2)
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# test image pooling layers
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maxpool = img_pool(
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input=conv2,
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pool_size=2,
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num_channels=16,
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padding=1,
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pool_type=pooling.Max())
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spp = spp(input=conv2,
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pyramid_height=2,
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num_channels=16,
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pool_type=pooling.Max())
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maxout = maxout(input=conv2, num_channels=16, groups=4)
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print parse_network(maxpool, spp, maxout)
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# test norm layers
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norm1 = img_cmrnorm(input=maxpool, size=5)
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norm2 = batch_norm(input=maxpool)
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norm3 = sum_to_one_norm(input=maxpool)
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print parse_network(norm1, norm2, norm3)
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# test recurrent layers
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recurrent = recurrent(input=word)
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lstm = lstmemory(input=word)
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gru = grumemory(input=word)
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print parse_network(recurrent, lstm, gru)
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# test aggregate layers
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pool = pool(
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input=pixel,
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pooling_type=pooling.Avg(),
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agg_level=AggregateLevel.EACH_SEQUENCE)
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last_seq = last_seq(input=pixel)
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first_seq = first_seq(input=pixel)
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concat = concat(input=[last_seq, first_seq])
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seq_concat = seq_concat(a=last_seq, b=first_seq)
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print parse_network(pool, last_seq, first_seq, concat, seq_concat)
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# test reshaping layers
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block_expand = block_expand(
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input=maxout, num_channels=4, stride_x=1, block_x=1)
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expand = expand(
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input=last_seq, expand_as=pixel, expand_level=ExpandLevel.FROM_TIMESTEP)
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repeat = repeat(input=last_seq, num_repeats=4)
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reshape = seq_reshape(input=last_seq, reshape_size=4)
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rotate = rotate(input=pixel, height=16, width=49)
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print parse_network(block_expand, expand, repeat, reshape, rotate)
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# test math layers
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addto = addto(input=[last_seq, first_seq])
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linear_comb = linear_comb(weights=weight, vectors=hidden, size=10)
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interpolation = interpolation(input=[hidden, hidden], weight=score)
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bilinear = bilinear_interp(input=conv2, out_size_x=4, out_size_y=4)
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power = power(input=conv1, weight=score)
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scaling = scaling(input=conv1, weight=score)
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slope = slope_intercept(input=conv1)
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tensor = tensor(a=last_seq, b=first_seq, size=1000)
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cos_sim = cos_sim(a=last_seq, b=first_seq)
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trans = trans(input=tensor)
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print parse_network(addto, linear_comb, interpolation, bilinear, power,
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scaling, slope, tensor, cos_sim, trans)
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# test sampling layers
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maxid = max_id(input=inference)
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sampling_id = sampling_id(input=inference)
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print parse_network(maxid, sampling_id)
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# test slicing and joining layers
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pad = pad(input=maxpool, pad_c=[2, 3], pad_h=[1, 2], pad_w=[3, 1])
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print parse_network(pad)
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# test cost layers
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cost1 = classification_cost(input=inference, label=label)
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cost2 = classification_cost(input=inference, label=label, weight=weight)
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cost3 = cross_entropy_cost(input=inference, label=label)
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cost4 = cross_entropy_with_selfnorm_cost(input=inference, label=label)
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cost5 = regression_cost(input=inference, label=label)
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cost6 = regression_cost(input=inference, label=label, weight=weight)
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cost7 = multi_binary_label_cross_entropy_cost(input=inference, label=label)
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cost8 = rank_cost(left=score, right=score, label=score)
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cost9 = lambda_cost(input=inference, score=score)
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cost10 = sum_cost(input=inference)
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cost11 = huber_cost(input=score, label=label)
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print parse_network(cost3, cost4)
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print parse_network(cost5, cost6)
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print parse_network(cost7, cost8, cost9, cost10, cost11)
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crf = crf(input=inference, label=label)
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crf_decoding = crf_decoding(input=inference, size=3)
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ctc = ctc(input=inference, label=label)
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warp_ctc = warp_ctc(input=pixel, label=label)
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nce = nce(input=inference, label=label, num_classes=3)
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hsigmoid = hsigmoid(input=inference, label=label, num_classes=3)
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print parse_network(crf, crf_decoding, ctc, warp_ctc, nce, hsigmoid)
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# test check layers
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eos = eos(input=maxid, eos_id=5)
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print parse_network(eos)
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