data_utils.py

# Copyright 2018 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.
# ==============================================================================
# pylint: disable=g-import-not-at-top
"""Utilities for file download and caching."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from abc import abstractmethod
from contextlib import closing
import hashlib
import multiprocessing
from multiprocessing.pool import ThreadPool
import os
import random
import shutil
import sys
import tarfile
import threading
import time
import traceback
import zipfile

import numpy as np
import six
from six.moves.urllib.error import HTTPError
from six.moves.urllib.error import URLError
from six.moves.urllib.request import urlopen

from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar
from tensorflow.python.util.tf_export import tf_export


try:
  import queue
except ImportError:
  import Queue as queue


if sys.version_info[0] == 2:

  def urlretrieve(url, filename, reporthook=None, data=None):
    """Replacement for `urlretrive` for Python 2.

    Under Python 2, `urlretrieve` relies on `FancyURLopener` from legacy
    `urllib` module, known to have issues with proxy management.

    Arguments:
        url: url to retrieve.
        filename: where to store the retrieved data locally.
        reporthook: a hook function that will be called once
            on establishment of the network connection and once
            after each block read thereafter.
            The hook will be passed three arguments;
            a count of blocks transferred so far,
            a block size in bytes, and the total size of the file.
        data: `data` argument passed to `urlopen`.
    """

    def chunk_read(response, chunk_size=8192, reporthook=None):
      content_type = response.info().get('Content-Length')
      total_size = -1
      if content_type is not None:
        total_size = int(content_type.strip())
      count = 0
      while True:
        chunk = response.read(chunk_size)
        count += 1
        if reporthook is not None:
          reporthook(count, chunk_size, total_size)
        if chunk:
          yield chunk
        else:
          break

    response = urlopen(url, data)
    with open(filename, 'wb') as fd:
      for chunk in chunk_read(response, reporthook=reporthook):
        fd.write(chunk)
else:
  from six.moves.urllib.request import urlretrieve


def _extract_archive(file_path, path='.', archive_format='auto'):
  """Extracts an archive if it matches tar, tar.gz, tar.bz, or zip formats.

  Arguments:
      file_path: path to the archive file
      path: path to extract the archive file
      archive_format: Archive format to try for extracting the file.
          Options are 'auto', 'tar', 'zip', and None.
          'tar' includes tar, tar.gz, and tar.bz files.
          The default 'auto' is ['tar', 'zip'].
          None or an empty list will return no matches found.

  Returns:
      True if a match was found and an archive extraction was completed,
      False otherwise.
  """
  if archive_format is None:
    return False
  if archive_format is 'auto':
    archive_format = ['tar', 'zip']
  if isinstance(archive_format, six.string_types):
    archive_format = [archive_format]

  for archive_type in archive_format:
    if archive_type is 'tar':
      open_fn = tarfile.open
      is_match_fn = tarfile.is_tarfile
    if archive_type is 'zip':
      open_fn = zipfile.ZipFile
      is_match_fn = zipfile.is_zipfile

    if is_match_fn(file_path):
      with open_fn(file_path) as archive:
        try:
          archive.extractall(path)
        except (tarfile.TarError, RuntimeError, KeyboardInterrupt):
          if os.path.exists(path):
            if os.path.isfile(path):
              os.remove(path)
            else:
              shutil.rmtree(path)
          raise
      return True
  return False


@tf_export('keras.utils.get_file')
def get_file(fname,
             origin,
             untar=False,
             md5_hash=None,
             file_hash=None,
             cache_subdir='datasets',
             hash_algorithm='auto',
             extract=False,
             archive_format='auto',
             cache_dir=None):
  """Downloads a file from a URL if it not already in the cache.

  By default the file at the url `origin` is downloaded to the
  cache_dir `~/.keras`, placed in the cache_subdir `datasets`,
  and given the filename `fname`. The final location of a file
  `example.txt` would therefore be `~/.keras/datasets/example.txt`.

  Files in tar, tar.gz, tar.bz, and zip formats can also be extracted.
  Passing a hash will verify the file after download. The command line
  programs `shasum` and `sha256sum` can compute the hash.

  Arguments:
      fname: Name of the file. If an absolute path `/path/to/file.txt` is
          specified the file will be saved at that location.
      origin: Original URL of the file.
      untar: Deprecated in favor of 'extract'.
          boolean, whether the file should be decompressed
      md5_hash: Deprecated in favor of 'file_hash'.
          md5 hash of the file for verification
      file_hash: The expected hash string of the file after download.
          The sha256 and md5 hash algorithms are both supported.
      cache_subdir: Subdirectory under the Keras cache dir where the file is
          saved. If an absolute path `/path/to/folder` is
          specified the file will be saved at that location.
      hash_algorithm: Select the hash algorithm to verify the file.
          options are 'md5', 'sha256', and 'auto'.
          The default 'auto' detects the hash algorithm in use.
      extract: True tries extracting the file as an Archive, like tar or zip.
      archive_format: Archive format to try for extracting the file.
          Options are 'auto', 'tar', 'zip', and None.
          'tar' includes tar, tar.gz, and tar.bz files.
          The default 'auto' is ['tar', 'zip'].
          None or an empty list will return no matches found.
      cache_dir: Location to store cached files, when None it
          defaults to the [Keras
            Directory](/faq/#where-is-the-keras-configuration-filed-stored).

  Returns:
      Path to the downloaded file
  """
  if cache_dir is None:
    cache_dir = os.path.join(os.path.expanduser('~'), '.keras')
  if md5_hash is not None and file_hash is None:
    file_hash = md5_hash
    hash_algorithm = 'md5'
  datadir_base = os.path.expanduser(cache_dir)
  if not os.access(datadir_base, os.W_OK):
    datadir_base = os.path.join('/tmp', '.keras')
  datadir = os.path.join(datadir_base, cache_subdir)
  if not os.path.exists(datadir):
    os.makedirs(datadir)

  if untar:
    untar_fpath = os.path.join(datadir, fname)
    fpath = untar_fpath + '.tar.gz'
  else:
    fpath = os.path.join(datadir, fname)

  download = False
  if os.path.exists(fpath):
    # File found; verify integrity if a hash was provided.
    if file_hash is not None:
      if not validate_file(fpath, file_hash, algorithm=hash_algorithm):
        print('A local file was found, but it seems to be '
              'incomplete or outdated because the ' + hash_algorithm +
              ' file hash does not match the original value of ' + file_hash +
              ' so we will re-download the data.')
        download = True
  else:
    download = True

  if download:
    print('Downloading data from', origin)

    class ProgressTracker(object):
      # Maintain progbar for the lifetime of download.
      # This design was chosen for Python 2.7 compatibility.
      progbar = None

    def dl_progress(count, block_size, total_size):
      if ProgressTracker.progbar is None:
        if total_size is -1:
          total_size = None
        ProgressTracker.progbar = Progbar(total_size)
      else:
        ProgressTracker.progbar.update(count * block_size)

    error_msg = 'URL fetch failure on {}: {} -- {}'
    try:
      try:
        urlretrieve(origin, fpath, dl_progress)
      except URLError as e:
        raise Exception(error_msg.format(origin, e.errno, e.reason))
      except HTTPError as e:
        raise Exception(error_msg.format(origin, e.code, e.msg))
    except (Exception, KeyboardInterrupt) as e:
      if os.path.exists(fpath):
        os.remove(fpath)
      raise
    ProgressTracker.progbar = None

  if untar:
    if not os.path.exists(untar_fpath):
      _extract_archive(fpath, datadir, archive_format='tar')
    return untar_fpath

  if extract:
    _extract_archive(fpath, datadir, archive_format)

  return fpath


def _hash_file(fpath, algorithm='sha256', chunk_size=65535):
  """Calculates a file sha256 or md5 hash.

  Example:

  ```python
      >>> from keras.data_utils import _hash_file
      >>> _hash_file('/path/to/file.zip')
      'e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855'
  ```

  Arguments:
      fpath: path to the file being validated
      algorithm: hash algorithm, one of 'auto', 'sha256', or 'md5'.
          The default 'auto' detects the hash algorithm in use.
      chunk_size: Bytes to read at a time, important for large files.

  Returns:
      The file hash
  """
  if (algorithm is 'sha256') or (algorithm is 'auto' and len(hash) is 64):
    hasher = hashlib.sha256()
  else:
    hasher = hashlib.md5()

  with open(fpath, 'rb') as fpath_file:
    for chunk in iter(lambda: fpath_file.read(chunk_size), b''):
      hasher.update(chunk)

  return hasher.hexdigest()


def validate_file(fpath, file_hash, algorithm='auto', chunk_size=65535):
  """Validates a file against a sha256 or md5 hash.

  Arguments:
      fpath: path to the file being validated
      file_hash:  The expected hash string of the file.
          The sha256 and md5 hash algorithms are both supported.
      algorithm: Hash algorithm, one of 'auto', 'sha256', or 'md5'.
          The default 'auto' detects the hash algorithm in use.
      chunk_size: Bytes to read at a time, important for large files.

  Returns:
      Whether the file is valid
  """
  if ((algorithm is 'sha256') or
      (algorithm is 'auto' and len(file_hash) is 64)):
    hasher = 'sha256'
  else:
    hasher = 'md5'

  if str(_hash_file(fpath, hasher, chunk_size)) == str(file_hash):
    return True
  else:
    return False


@tf_export('keras.utils.Sequence')
class Sequence(object):
  """Base object for fitting to a sequence of data, such as a dataset.

  Every `Sequence` must implements the `__getitem__` and the `__len__` methods.
  If you want to modify your dataset between epochs you may implement
  `on_epoch_end`.
  The method `__getitem__` should return a complete batch.

  # Notes

  `Sequence` are a safer way to do multiprocessing. This structure guarantees
  that the network will only train once
   on each sample per epoch which is not the case with generators.

  Examples:

  ```python
      from skimage.io import imread
      from skimage.transform import resize
      import numpy as np
      import math

      # Here, `x_set` is list of path to the images
      # and `y_set` are the associated classes.

      class CIFAR10Sequence(Sequence):

          def __init__(self, x_set, y_set, batch_size):
              self.x, self.y = x_set, y_set
              self.batch_size = batch_size

          def __len__(self):
              return math.ceil(len(self.x) / self.batch_size)

          def __getitem__(self, idx):
              batch_x = self.x[idx * self.batch_size:(idx + 1) *
              self.batch_size]
              batch_y = self.y[idx * self.batch_size:(idx + 1) *
              self.batch_size]

              return np.array([
                  resize(imread(file_name), (200, 200))
                     for file_name in batch_x]), np.array(batch_y)
  ```
  """

  @abstractmethod
  def __getitem__(self, index):
    """Gets batch at position `index`.

    Arguments:
        index: position of the batch in the Sequence.

    Returns:
        A batch
    """
    raise NotImplementedError

  @abstractmethod
  def __len__(self):
    """Number of batch in the Sequence.

    Returns:
        The number of batches in the Sequence.
    """
    raise NotImplementedError

  def on_epoch_end(self):
    """Method called at the end of every epoch.
    """
    pass

  def __iter__(self):
    """Creates an infinite generator that iterate over the Sequence.

    Yields:
      Sequence items.
    """
    while True:
      for item in (self[i] for i in range(len(self))):
        yield item


# Global variables to be shared across processes
_SHARED_SEQUENCES = {}
# We use a Value to provide unique id to different processes.
_SEQUENCE_COUNTER = None


def init_pool(seqs):
  global _SHARED_SEQUENCES
  _SHARED_SEQUENCES = seqs


def get_index(uid, i):
  """Get the value from the Sequence `uid` at index `i`.

  To allow multiple Sequences to be used at the same time, we use `uid` to
  get a specific one. A single Sequence would cause the validation to
  overwrite the training Sequence.

  Arguments:
      uid: int, Sequence identifier
      i: index

  Returns:
      The value at index `i`.
  """
  return _SHARED_SEQUENCES[uid][i]


@tf_export('keras.utils.SequenceEnqueuer')
class SequenceEnqueuer(object):
  """Base class to enqueue inputs.

  The task of an Enqueuer is to use parallelism to speed up preprocessing.
  This is done with processes or threads.

  Examples:

  ```python
      enqueuer = SequenceEnqueuer(...)
      enqueuer.start()
      datas = enqueuer.get()
      for data in datas:
          # Use the inputs; training, evaluating, predicting.
          # ... stop sometime.
      enqueuer.close()
  ```

  The `enqueuer.get()` should be an infinite stream of datas.

  """

  @abstractmethod
  def is_running(self):
    raise NotImplementedError

  @abstractmethod
  def start(self, workers=1, max_queue_size=10):
    """Starts the handler's workers.

    Arguments:
        workers: number of worker threads
        max_queue_size: queue size
            (when full, threads could block on `put()`).
    """
    raise NotImplementedError

  @abstractmethod
  def stop(self, timeout=None):
    """Stop running threads and wait for them to exit, if necessary.

    Should be called by the same thread which called start().

    Arguments:
        timeout: maximum time to wait on thread.join()
    """
    raise NotImplementedError

  @abstractmethod
  def get(self):
    """Creates a generator to extract data from the queue.

    Skip the data if it is `None`.

    Returns:
        Generator yielding tuples `(inputs, targets)`
            or `(inputs, targets, sample_weights)`.
    """
    raise NotImplementedError


class OrderedEnqueuer(SequenceEnqueuer):
  """Builds a Enqueuer from a Sequence.

  Used in `fit_generator`, `evaluate_generator`, `predict_generator`.

  Arguments:
      sequence: A `keras.utils.data_utils.Sequence` object.
      use_multiprocessing: use multiprocessing if True, otherwise threading
      shuffle: whether to shuffle the data at the beginning of each epoch
  """

  def __init__(self, sequence, use_multiprocessing=False, shuffle=False):
    self.sequence = sequence
    self.use_multiprocessing = use_multiprocessing

    global _SEQUENCE_COUNTER
    if _SEQUENCE_COUNTER is None:
      try:
        _SEQUENCE_COUNTER = multiprocessing.Value('i', 0)
      except OSError:
        # In this case the OS does not allow us to use
        # multiprocessing. We resort to an int
        # for enqueuer indexing.
        _SEQUENCE_COUNTER = 0

    if isinstance(_SEQUENCE_COUNTER, int):
      self.uid = _SEQUENCE_COUNTER
      _SEQUENCE_COUNTER += 1
    else:
      # Doing Multiprocessing.Value += x is not process-safe.
      with _SEQUENCE_COUNTER.get_lock():
        self.uid = _SEQUENCE_COUNTER.value
        _SEQUENCE_COUNTER.value += 1

    self.shuffle = shuffle
    self.workers = 0
    self.executor_fn = None
    self.queue = None
    self.run_thread = None
    self.stop_signal = None

  def is_running(self):
    return self.stop_signal is not None and not self.stop_signal.is_set()

  def start(self, workers=1, max_queue_size=10):
    """Start the handler's workers.

    Arguments:
        workers: number of worker threads
        max_queue_size: queue size
            (when full, workers could block on `put()`)
    """
    if self.use_multiprocessing:
      self.executor_fn = lambda seqs: multiprocessing.Pool(  # pylint: disable=g-long-lambda
          workers, initializer=init_pool, initargs=(seqs,))
    else:
       # We do not need the init since it's threads.
      self.executor_fn = lambda _: ThreadPool(workers)
    self.workers = workers
    self.queue = queue.Queue(max_queue_size)
    self.stop_signal = threading.Event()
    self.run_thread = threading.Thread(target=self._run)
    self.run_thread.daemon = True
    self.run_thread.start()

  def _wait_queue(self):
    """Wait for the queue to be empty."""
    while True:
      time.sleep(0.1)
      if self.queue.unfinished_tasks == 0 or self.stop_signal.is_set():
        return

  def _run(self):
    """Submits request to the executor and queue the `Future` objects."""
    sequence = list(range(len(self.sequence)))
    self._send_sequence()  # Share the initial sequence
    while True:
      if self.shuffle:
        random.shuffle(sequence)

      with closing(self.executor_fn(_SHARED_SEQUENCES)) as executor:
        for i in sequence:
          if self.stop_signal.is_set():
            return
          self.queue.put(
              executor.apply_async(get_index, (self.uid, i)), block=True)

        # Done with the current epoch, waiting for the final batches
        self._wait_queue()

        if self.stop_signal.is_set():
          # We're done
          return

      # Call the internal on epoch end.
      self.sequence.on_epoch_end()
      self._send_sequence()  # Update the pool

  def get(self):
    """Creates a generator to extract data from the queue.

    Skip the data if it is `None`.

    Yields:
        The next element in the queue, i.e. a tuple
        `(inputs, targets)` or
        `(inputs, targets, sample_weights)`.
    """
    try:
      while self.is_running():
        inputs = self.queue.get(block=True).get()
        self.queue.task_done()
        if inputs is not None:
          yield inputs
    except Exception as e:  # pylint: disable=broad-except
      self.stop()
      six.raise_from(StopIteration(e), e)

  def _send_sequence(self):
    """Send current Sequence to all workers."""
    # For new processes that may spawn
    _SHARED_SEQUENCES[self.uid] = self.sequence

  def stop(self, timeout=None):
    """Stops running threads and wait for them to exit, if necessary.

    Should be called by the same thread which called `start()`.

    Arguments:
        timeout: maximum time to wait on `thread.join()`
    """
    self.stop_signal.set()
    with self.queue.mutex:
      self.queue.queue.clear()
      self.queue.unfinished_tasks = 0
      self.queue.not_full.notify()
    self.run_thread.join(timeout)
    _SHARED_SEQUENCES[self.uid] = None


@tf_export('keras.utils.GeneratorEnqueuer')
class GeneratorEnqueuer(SequenceEnqueuer):
  """Builds a queue out of a data generator.

  The provided generator can be finite in which case the class will throw
  a `StopIteration` exception.

  Used in `fit_generator`, `evaluate_generator`, `predict_generator`.

  Arguments:
      generator: a generator function which yields data
      use_multiprocessing: use multiprocessing if True, otherwise threading
      wait_time: time to sleep in-between calls to `put()`
      random_seed: Initial seed for workers,
          will be incremented by one for each worker.
  """

  def __init__(self,
               generator,
               use_multiprocessing=False,
               wait_time=0.05,
               seed=None):
    self.wait_time = wait_time
    self._generator = generator
    if os.name is 'nt' and use_multiprocessing is True:
      # On Windows, avoid **SYSTEMATIC** error in `multiprocessing`:
      # `TypeError: can't pickle generator objects`
      # => Suggest multithreading instead of multiprocessing on Windows
      raise ValueError('Using a generator with `use_multiprocessing=True`'
                       ' is not supported on Windows (no marshalling of'
                       ' generators across process boundaries). Instead,'
                       ' use single thread/process or multithreading.')
    else:
      self._use_multiprocessing = use_multiprocessing
    self._threads = []
    self._stop_event = None
    self._manager = None
    self.queue = None
    self.seed = seed

  def _data_generator_task(self):
    if self._use_multiprocessing is False:
      while not self._stop_event.is_set():
        with self.genlock:
          try:
            if (self.queue is not None and
                self.queue.qsize() < self.max_queue_size):
              # On all OSes, avoid **SYSTEMATIC** error
              # in multithreading mode:
              # `ValueError: generator already executing`
              # => Serialize calls to
              # infinite iterator/generator's next() function
              generator_output = next(self._generator)
              self.queue.put((True, generator_output))
            else:
              time.sleep(self.wait_time)
          except StopIteration:
            break
          except Exception as e:  # pylint: disable=broad-except
            # Can't pickle tracebacks.
            # As a compromise, print the traceback and pickle None instead.
            if not hasattr(e, '__traceback__'):
              setattr(e, '__traceback__', sys.exc_info()[2])
            self.queue.put((False, e))
            self._stop_event.set()
            break
    else:
      while not self._stop_event.is_set():
        try:
          if (self.queue is not None and
              self.queue.qsize() < self.max_queue_size):
            generator_output = next(self._generator)
            self.queue.put((True, generator_output))
          else:
            time.sleep(self.wait_time)
        except StopIteration:
          break
        except Exception as e:  # pylint: disable=broad-except
          # Can't pickle tracebacks.
          # As a compromise, print the traceback and pickle None instead.
          traceback.print_exc()
          setattr(e, '__traceback__', None)
          self.queue.put((False, e))
          self._stop_event.set()
          break

  def start(self, workers=1, max_queue_size=10):
    """Kicks off threads which add data from the generator into the queue.

    Arguments:
        workers: number of worker threads
        max_queue_size: queue size
            (when full, threads could block on `put()`)
    """
    try:
      self.max_queue_size = max_queue_size
      if self._use_multiprocessing:
        self._manager = multiprocessing.Manager()
        self.queue = self._manager.Queue(maxsize=max_queue_size)
        self._stop_event = multiprocessing.Event()
      else:
        # On all OSes, avoid **SYSTEMATIC** error in multithreading mode:
        # `ValueError: generator already executing`
        # => Serialize calls to infinite iterator/generator's next() function
        self.genlock = threading.Lock()
        self.queue = queue.Queue(maxsize=max_queue_size)
        self._stop_event = threading.Event()

      for _ in range(workers):
        if self._use_multiprocessing:
          # Reset random seed else all children processes
          # share the same seed
          np.random.seed(self.seed)
          thread = multiprocessing.Process(target=self._data_generator_task)
          thread.daemon = True
          if self.seed is not None:
            self.seed += 1
        else:
          thread = threading.Thread(target=self._data_generator_task)
        self._threads.append(thread)
        thread.start()
    except:
      self.stop()
      raise

  def is_running(self):
    return self._stop_event is not None and not self._stop_event.is_set()

  def stop(self, timeout=None):
    """Stops running threads and wait for them to exit, if necessary.

    Should be called by the same thread which called `start()`.

    Arguments:
        timeout: maximum time to wait on `thread.join()`.
    """
    if self.is_running():
      self._stop_event.set()

    for thread in self._threads:
      if self._use_multiprocessing:
        if thread.is_alive():
          thread.terminate()
      else:
        # The thread.is_alive() test is subject to a race condition:
        # the thread could terminate right after the test and before the
        # join, rendering this test meaningless -> Call thread.join()
        # always, which is ok no matter what the status of the thread.
        thread.join(timeout)

    if self._manager:
      self._manager.shutdown()

    self._threads = []
    self._stop_event = None
    self.queue = None

  def get(self):
    """Creates a generator to extract data from the queue.

    Skip the data if it is `None`.

    Yields:
        The next element in the queue, i.e. a tuple
        `(inputs, targets)` or
        `(inputs, targets, sample_weights)`.
    """
    while self.is_running():
      if not self.queue.empty():
        success, value = self.queue.get()
        # Rethrow any exceptions found in the queue
        if not success:
          six.reraise(value.__class__, value, value.__traceback__)
        # Yield regular values
        if value is not None:
          yield value
      else:
        all_finished = all([not thread.is_alive() for thread in self._threads])
        if all_finished and self.queue.empty():
          raise StopIteration()
        else:
          time.sleep(self.wait_time)

    # Make sure to rethrow the first exception in the queue, if any
    while not self.queue.empty():
      success, value = self.queue.get()
      if not success:
        six.reraise(value.__class__, value, value.__traceback__)