reader.py

# Copyright 2015 The TensorFlow 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.
# ==============================================================================


"""Utilities for parsing PTB text files."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import os
import sys
import numpy as np

import tensorflow as tf

Py3 = sys.version_info[0] == 3


def _read_words(filename):
    with tf.gfile.GFile(filename, "r") as f:
        if Py3:
            return f.read().replace("\n", "<eos>").split()
        else:
            return f.read().decode("utf-8").replace("\n", "<eos>").split()


def _build_vocab(filename):
    data = _read_words(filename)

    counter = collections.Counter(data)
    count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))

    words, _ = list(zip(*count_pairs))
    word_to_id = dict(zip(words, range(len(words))))

    return word_to_id


def _file_to_word_ids(filename, word_to_id):
    data = _read_words(filename)
    return [word_to_id[word] for word in data if word in word_to_id]


def ptb_raw_data(data_path=None):
    """Load PTB raw data from data directory "data_path".
    Reads PTB text files, converts strings to integer ids,
    and performs mini-batching of the inputs.
    The PTB dataset comes from Tomas Mikolov's webpage:
    http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz
    Args:
      data_path: string path to the directory where simple-examples.tgz has
        been extracted.
    Returns:
      tuple (train_data, valid_data, test_data, vocabulary)
      where each of the data objects can be passed to PTBIterator.
    """

    train_path = os.path.join(data_path, "ptb.train.txt")
    valid_path = os.path.join(data_path, "ptb.valid.txt")
    test_path = os.path.join(data_path, "ptb.test.txt")

    word_to_id = _build_vocab(train_path)
    train_data = _file_to_word_ids(train_path, word_to_id)
    valid_data = _file_to_word_ids(valid_path, word_to_id)
    test_data = _file_to_word_ids(test_path, word_to_id)
    vocabulary = len(word_to_id)
    return train_data, valid_data, test_data, vocabulary


def ptb_producer(raw_data, batch_size, num_steps, name=None):
    """Iterate on the raw PTB data.
    This chunks up raw_data into batches of examples and returns Tensors that
    are drawn from these batches.
    Args:
      raw_data: one of the raw data outputs from ptb_raw_data.
      batch_size: int, the batch size.
      num_steps: int, the number of unrolls.
      name: the name of this operation (optional).
    Returns:
      A pair of Tensors, each shaped [batch_size, num_steps]. The second element
      of the tuple is the same data time-shifted to the right by one.
    Raises:
      tf.errors.InvalidArgumentError: if batch_size or num_steps are too high.
    """
    with tf.name_scope(name, "PTBProducer", [raw_data, batch_size, num_steps]):
        raw_data = tf.convert_to_tensor(
            raw_data, name="raw_data", dtype=tf.int32)

        data_len = tf.size(raw_data)
        batch_len = data_len // batch_size
        data = tf.reshape(raw_data[0: batch_size * batch_len],
                          [batch_size, batch_len])

        epoch_size = (batch_len - 1) // num_steps
        assertion = tf.assert_positive(
            epoch_size,
            message="epoch_size == 0, decrease batch_size or num_steps")
        with tf.control_dependencies([assertion]):
            epoch_size = tf.identity(epoch_size, name="epoch_size")

        i = tf.train.range_input_producer(epoch_size, shuffle=False).dequeue()
        x = tf.strided_slice(data, [0, i * num_steps],
                             [batch_size, (i + 1) * num_steps])
        x.set_shape([batch_size, num_steps])
        y = tf.strided_slice(data, [0, i * num_steps + 1],
                             [batch_size, (i + 1) * num_steps + 1])
        y.set_shape([batch_size, num_steps])
        return x, y


def ptb_iterator(raw_data, batch_size, num_steps):
    """Iterate on the raw PTB data.
    This generates batch_size pointers into the raw PTB data, and allows
    minibatch iteration along these pointers.
    Args:
      raw_data: one of the raw data outputs from ptb_raw_data.
      batch_size: int, the batch size.
      num_steps: int, the number of unrolls.
    Yields:
      Pairs of the batched data, each a matrix of shape [batch_size, num_steps].
      The second element of the tuple is the same data time-shifted to the
      right by one.
    Raises:
      ValueError: if batch_size or num_steps are too high.
    """
    raw_data = np.array(raw_data, dtype=np.int32)

    data_len = len(raw_data)
    batch_len = data_len // batch_size
    data = np.zeros([batch_size, batch_len], dtype=np.int32)
    for i in range(batch_size):
        data[i] = raw_data[batch_len * i:batch_len * (i + 1)]

    epoch_size = (batch_len - 1) // num_steps

    if epoch_size == 0:
        raise ValueError("epoch_size == 0, decrease batch_size or num_steps")

    for i in range(epoch_size):
        x = data[:, i * num_steps:(i + 1) * num_steps]
        y = data[:, i * num_steps + 1:(i + 1) * num_steps + 1]
        yield (x, y)