# 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. __all__ = [ 'map_readers', 'buffered', 'compose', 'chain', 'shuffle', 'ComposeNotAligned', 'batched' ] from Queue import Queue from threading import Thread import itertools import random def map_readers(func, *readers): """ Creates a data reader that outputs return value of function using output of each data readers as arguments. :param func: function to use. :param *readers: readers whose outputs will be used as arguments of func. :returns: the created data reader. """ def reader(): rs = [] for r in readers: rs.append(r()) for e in itertools.imap(func, *rs): yield e return reader def shuffle(reader, buf_size): """ Creates a data reader whose data output is suffled. Output from the iterator that created by original reader will be buffered into shuffle buffer, and then shuffled. The size of shuffle buffer is determined by argument buf_size. :param reader: the original reader whose output will be shuffled. :param buf_size: shuffle buffer size. :returns:the new reader whose output is shuffled. """ def data_reader(): buf = [] for e in reader(): buf.append(e) if len(buf) >= buf_size: random.shuffle(buf) for b in buf: yield b buf = [] if len(buf) > 0: random.shuffle(buf) for b in buf: yield b return data_reader def chain(*readers): """ Creates a data reader whose output is the outputs of input data readers chained together. If input readers output following data entries: [0, 0, 0] [1, 1, 1] [2, 2, 2] The chained reader will output: [0, 0, 0, 1, 1, 1, 2, 2, 2] :param readers: input readers. :returns: the new data reader. """ def reader(): rs = [] for r in readers: rs.append(r()) for e in itertools.chain(*rs): yield e return reader class ComposeNotAligned(ValueError): pass def compose(*readers, **kwargs): """ Creates a data reader whose output is the combination of input readers. If input readers output following data entries: (1, 2) 3 (4, 5) The composed reader will output: (1, 2, 3, 4, 5) :*readers: readers that will be composed together. :check_alignment: if True, will check if input readers are aligned correctly. If False, will not check alignment and trailing outputs will be discarded. Defaults to True. :returns: the new data reader. :raises ComposeNotAligned: outputs of readers are not aligned. Will not raise when check_alignment is set to False. """ check_alignment = kwargs.pop('check_alignment', True) def make_tuple(x): if isinstance(x, tuple): return x else: return (x, ) def reader(): rs = [] for r in readers: rs.append(r()) if not check_alignment: for outputs in itertools.izip(*rs): yield sum(map(make_tuple, outputs), ()) else: for outputs in itertools.izip_longest(*rs): for o in outputs: if o is None: # None will be not be present if compose is aligned raise ComposeNotAligned( "outputs of readers are not aligned.") yield sum(map(make_tuple, outputs), ()) return reader def buffered(reader, size): """ Creates a buffered data reader. The buffered data reader will read and save data entries into a buffer. Reading from the buffered data reader will proceed as long as the buffer is not empty. :param reader: the data reader to read from. :param size: max buffer size. :returns: the buffered data reader. """ class EndSignal(): pass end = EndSignal() def read_worker(r, q): for d in r: q.put(d) q.put(end) def data_reader(): r = reader() q = Queue(maxsize=size) t = Thread( target=read_worker, args=( r, q, )) t.daemon = True t.start() e = q.get() while e != end: yield e e = q.get() return data_reader def batched(reader, batch_size): """ Create a batched reader. :param reader: the data reader to read from. :param batch_size: batch_size :return: the batched reader. """ def __impl__(): r = reader() batch = [] for instance in r: batch.append(instance) if len(batch) == batch_size: yield batch batch = [] if batch: yield batch return __impl__