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101 lines
4.0 KiB
101 lines
4.0 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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from py_paddle import swig_paddle
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from py_paddle import DataProviderConverter
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import data_type
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__all__ = ['DataFeeder']
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class DataFeeder(DataProviderConverter):
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"""
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DataFeeder converts the data returned by paddle.reader into a data structure
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of Arguments which is defined in the API. The paddle.reader usually returns
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a list of mini-batch data entries. Each data entry in the list is one sample.
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Each sample is a list or a tuple with one feature or multiple features.
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DataFeeder converts this mini-batch data entries into Arguments in order
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to feed it to C++ interface.
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The example usage:
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data_types = [('image', paddle.data_type.dense_vector(784)),
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('label', paddle.data_type.integer_value(10))]
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reader_dict = {'image':0, 'label':1}
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feeder = DataFeeder(data_types=data_types, reader_dict=reader_dict)
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minibatch_data = [
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( [1.0,2.0,3.0,4.0], 5, [6,7,8] ), # first sample
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( [1.0,2.0,3.0,4.0], 5, [6,7,8] ) # second sample
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]
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# or minibatch_data = [
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# [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ], # first sample
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# [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ] # second sample
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# ]
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arg = feeder(minibatch_data)
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"""
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def __init__(self, data_types, reader_dict):
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"""
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:param data_types: A list to specify data name and type. Each item is
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a tuple of (data_name, data_type). For example:
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[('image', paddle.data_type.dense_vector(784)),
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('label', paddle.data_type.integer_value(10))]
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:type data_types: A list of tuple
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:param reader_dict: A dictionary to specify the position of each data
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in the input data.
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:type reader_dict: dict()
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"""
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self.input_names = []
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input_types = []
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self.reader_dict = reader_dict
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for each in data_types:
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self.input_names.append(each[0])
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assert isinstance(each[1], data_type.InputType)
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input_types.append(each[1])
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DataProviderConverter.__init__(self, input_types)
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def convert(self, dat, argument=None):
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"""
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:param dat: A list of mini-batch data. Each sample is a list or tuple
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one feature or multiple features.
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for example:
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[
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([0.2, 0.2], ), # first sample
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([0.8, 0.3], ), # second sample
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]
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or,
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[
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[[0.2, 0.2], ], # first sample
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[[0.8, 0.3], ], # second sample
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]
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:type dat: List
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:param argument: An Arguments object contains this mini-batch data with
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one or multiple features. The Arguments definition is
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in the API.
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:type argument: swig_paddle.Arguments
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"""
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def reorder_data(data):
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retv = []
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for each in data:
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reorder = []
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for name in self.input_names:
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reorder.append(each[self.reader_dict[name]])
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retv.append(reorder)
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return retv
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return DataProviderConverter.convert(self, reorder_data(dat), argument)
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