# 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. """ Before this new package paddle.v2.layer, users would need to use functions in paddle.trainer_config_helpers.layers to configure networks. The Old Way: ========= This old way requires that the creation of a network be defined in a Python function, say network_config, and that this Python function being passed to paddle.trainer_config_helpers.parse_network_config for the creation of protobuf message description of this network. ```python def network_config(): img = paddle.trainer_config_helpers.data_layer(name="pixel", size=784) inference = paddle.trainer_config_helpers.fc_layer( input=img, size=10, act=paddle.trainer_config_helpers.SoftmaxActivation()) cost = paddle.trainer_config_helpers.classification_cost( input=inference, label=paddle.trainer_config_helpers.data_layer(name="label", size=10)) proto_desc = parse_network_config(network_config) ``` When parse_network_config executes network_config, those layer definition functions like data_layer and fc_layer would change some Python global variables, so that after the execution, parse_network_config could collect information from these global variables and generates the protobuf message. The New Way: ========= In this PR, we define a function in paddle.v2.layer which creates a Python class for each layer creation function in paddle.trainer_config_helpers.layers. Users can use create a network as follows: ```python img = paddle.v2.layer.data(name="pixel", size=784) inference = paddle.v2.layer.fc(input=img, size=10, act=paddle.v2.layer.Softmax()) cost = paddle.v2.layer.classification( input=inference, label=paddle.v2.layer.data(name="label", size=10)) parameters = paddle.v2.parameters.create(cost) ``` This new way doesn't require those invocations to layer definition functions 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 paddle.trainer_config_helpers as conf_helps from paddle.trainer_config_helpers.config_parser_utils import \ parse_network_config as __parse__ from paddle.trainer_config_helpers.default_decorators import wrap_act_default from paddle.trainer_config_helpers.default_decorators import wrap_bias_attr_default from paddle.trainer_config_helpers.default_decorators import wrap_name_default from paddle.trainer_config_helpers.layers import layer_support import activation import data_type __all__ = [ 'parse_network', 'data', 'fc', 'conv_shift', 'img_conv', 'img_pool', 'spp', 'maxout', 'img_cmrnorm', 'batch_norm', 'sum_to_one_norm', 'recurrent', 'lstmemory', 'grumemory', 'pool', 'last_seq', 'first_seq', 'concat', 'seq_concat', 'block_expand', 'expand', 'repeat', 'seq_reshape', 'addto', 'linear_comb', 'interpolation', 'bilinear_interp', 'power', 'scaling', 'slope_intercept', 'tensor', 'cos_sim', 'trans', 'max_id', 'sampling_id', 'pad', 'classification_cost', 'cross_entropy_cost', 'cross_entropy_with_selfnorm_cost', 'regression_cost', 'multi_binary_label_cross_entropy_cost', 'rank_cost', 'lambda_cost', 'sum_cost', 'huber_cost', 'crf', 'crf_decoding', 'ctc', 'warp_ctc', 'nce', 'hsigmoid', 'eos', 'memory', 'embedding', 'recurrent_group' ] __projection_names__ = filter(lambda x: x.endswith('_projection'), dir(conf_helps)) __all__ += __projection_names__ __operator_names__ = filter(lambda x: x.endswith('_operator'), dir(conf_helps)) __all__ += __operator_names__ def parse_network(*outputs): """ parse all output layers and then generate a model config proto. :param outputs: :return: """ def __real_func__(): context = dict() real_output = [each.to_proto(context=context) for each in outputs] conf_helps.outputs(real_output) 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. """ class DataLayerV2(Layer): def __init__(self, name, type, **kwargs): assert isinstance(type, data_type.InputType) self.type = type self.__method_name__ = 'data_layer' self.__kwargs__ = kwargs super(DataLayerV2, self).__init__(name=name, parent_layers=dict()) def to_proto_impl(self, **kwargs): args = dict() args['size'] = self.type.dim for each in kwargs: args[each] = kwargs[each] for each in self.__kwargs__: args[each] = self.__kwargs__[each] return getattr(conf_helps, self.__method_name__)(name=self.name, **args) class MemoryV2(Layer): def __init__(self, name, size, **kwargs): self.name = name self.size = size parent_names = ['boot_layer'] 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] super(MemoryV2, self).__init__(name=name, parent_layers=parent_layers) self.__kwargs__ = other_kwargs def to_proto_impl(self, **kwargs): args = dict() for each in kwargs: args[each] = kwargs[each] for each in self.__kwargs__: args[each] = self.__kwargs__[each] return conf_helps.memory(name=self.name, size=self.size, **args) class LayerOutputV2(Layer): """ LayerOutputV2 is used to store the result of LayerOutput in v1 api. It will not store it's parents because layer_output has been parsed already. """ def __init__(self, layer_output): assert isinstance(layer_output, conf_helps.LayerOutput) self.layer_output = layer_output super(LayerOutputV2, self).__init__( name=layer_output.name, parent_layers=dict()) def to_proto_impl(self): return self.layer_output class RecurrentGroupV2(Layer): def __init__(self, name, **kwargs): self.__parent_names__ = ['input'] other_kwargs = dict() parent_layers = dict() for pname in self.__parent_names__: if kwargs.has_key(pname): parent_layers[pname] = kwargs[pname] for key in kwargs.keys(): if key not in self.__parent_names__: other_kwargs[key] = kwargs[key] self.__kwargs__ = other_kwargs super(RecurrentGroupV2, self).__init__( name=name, parent_layers=parent_layers) wrapper = wrap_name_default(name_prefix='recurrent_group') __init__ = wrapper(__init__) def to_proto_impl(self, **kwargs): def in_args_converter(*in_args): if not isinstance(in_args, collections.Sequence): in_args = [in_args] return [LayerOutputV2(input) for input in in_args] args = dict() for each in kwargs: args[each] = kwargs[each] for each in self.__kwargs__: args[each] = self.__kwargs__[each] return conf_helps.recurrent_group( name=self.name, in_args_converter=in_args_converter, **args) class MixedLayerV2(Layer): """ This class is use to support `with` grammar. If not, the following code could convert mixed_layer simply. mixed = __convert_to_v2__( 'mixed_layer', name_prefix='mixed', parent_names=['input']) """ class AddToSealedMixedLayerExceptionV2(Exception): pass def __init__(self, size=0, input=None, name=None, act=None, bias_attr=None, layer_attr=None): self.__method_name__ = 'mixed_layer' self.finalized = False self.__inputs__ = [] if input is not None: self.__inputs__ = input other_kwargs = dict() other_kwargs['name'] = name other_kwargs['size'] = size other_kwargs['act'] = act other_kwargs['bias_attr'] = bias_attr other_kwargs['layer_attr'] = layer_attr parent_layers = {"input": self.__inputs__} super(MixedLayerV2, self).__init__(name, parent_layers) self.__other_kwargs__ = other_kwargs def __iadd__(self, other): if not self.finalized: self.__inputs__.append(other) return self else: raise MixedLayerTypeV2.AddToSealedMixedLayerExceptionV2() def __enter__(self): assert len(self.__inputs__) == 0 return self def __exit__(self, *args, **kwargs): self.finalized = True 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, self.__method_name__)(**args) @wrap_name_default("mixed") @wrap_act_default(act=activation.Linear()) @wrap_bias_attr_default(has_bias=False) @layer_support(conf_helps.layers.ERROR_CLIPPING, conf_helps.layers.DROPOUT) def mixed(size=0, name=None, input=None, act=None, bias_attr=False, layer_attr=None): return MixedLayerV2(size, input, name, act, bias_attr, layer_attr) data = DataLayerV2 AggregateLevel = conf_helps.layers.AggregateLevel ExpandLevel = conf_helps.layers.ExpandLevel recurrent_group = RecurrentGroupV2 memory = MemoryV2 layer_list = [ # [V2LayerImpl, V1_method_name, parent_names] # fully connected layers ['fc', 'fc_layer', ['input']], ['embedding', 'embedding_layer', ['input']], # conv layers ['conv_shift', 'conv_shift_layer', ['a', 'b']], ['img_conv', 'img_conv_layer', ['input']], # image pooling layers ['img_pool', 'img_pool_layer', ['input']], ['spp', 'spp_layer', ['input']], ['maxout', 'maxout_layer', ['input']], # norm layers ['img_cmrnorm', 'img_cmrnorm_layer', ['input']], ['batch_norm', 'batch_norm_layer', ['input']], ['sum_to_one_norm', 'sum_to_one_norm_layer', ['input']], # recurrent layers ['recurrent', 'recurrent_layer', ['input']], ['lstmemory', 'lstmemory', ['input']], ['grumemory', 'grumemory', ['input']], # aggregate layers ['pool', 'pooling_layer', ['input']], ['last_seq', 'last_seq', ['input']], ['first_seq', 'first_seq', ['input']], ['concat', 'concat_layer', ['input']], ['seq_concat', 'seq_concat_layer', ['a', 'b']], # reshaping layers ['block_expand', 'block_expand_layer', ['input']], ['expand', 'expand_layer', ['input', 'expand_as']], ['repeat', 'repeat_layer', ['input']], ['rotate', 'rotate_layer', ['input']], ['seq_reshape', 'seq_reshape_layer', ['input']], # math layers ['addto', 'addto_layer', ['input']], ['linear_comb', 'linear_comb_layer', ['weights', 'vectors']], ['interpolation', 'interpolation_layer', ['input', 'weight']], ['bilinear_interp', 'bilinear_interp_layer', ['input']], ['power', 'power_layer', ['input', 'weight']], ['scaling', 'scaling_layer', ['input', 'weight']], ['slope_intercept', 'slope_intercept_layer', ['input']], ['tensor', 'tensor_layer', ['a', 'b']], ['cos_sim', 'cos_sim', ['a', 'b']], ['trans', 'trans_layer', ['input']], # sampling layers ['max_id', 'maxid_layer', ['input']], ['sampling_id', 'sampling_id_layer', ['input']], # slicing and joining layers ['pad', 'pad_layer', ['input']], # cost layers [ 'classification_cost', 'classification_cost', ['input', 'label', 'weight'] ], ['regression_cost', 'regression_cost', ['input', 'label', 'weight']], ['cross_entropy_cost', 'cross_entropy', ['input', 'label']], [ 'cross_entropy_with_selfnorm_cost', 'cross_entropy_with_selfnorm', ['input', 'label'] ], [ 'multi_binary_label_cross_entropy_cost', 'multi_binary_label_cross_entropy', ['input', 'label'] ], ['rank_cost', 'rank_cost', ['left', 'right', 'label', 'weight']], ['lambda_cost', 'lambda_cost', ['input', 'score']], ['sum_cost', 'sum_cost', ['input']], ['huber_cost', 'huber_cost', ['input', 'label']], ['crf', 'crf_layer', ['input', 'label']], ['crf_decoding', 'crf_decoding_layer', ['input']], ['ctc', 'ctc_layer', ['input', 'label']], ['warp_ctc', 'warp_ctc_layer', ['input', 'label']], ['nce', 'nce_layer', ['input', 'label']], ['hsigmoid', 'hsigmoid', ['input', 'label']], # check layers ['eos', 'eos_layer', ['input']] ] for l in layer_list: globals()[l[0]] = __convert_to_v2__(l[1], l[2]) # convert projection for prj in __projection_names__: globals()[prj] = __convert_to_v2__( prj, parent_names=['input'], is_default_name=False) # convert operator operator_list = [ # [V1_method_name, parent_names], ['dotmul_operator', ['a', 'b']], ['conv_operator', ['img', 'filter']] ] for op in operator_list: globals()[op[0]] = __convert_to_v2__( op[0], parent_names=op[1], is_default_name=False)