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| # Lint as python3 | |
| # 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 abc import abstractmethod | |
| from contextlib import closing | |
| import functools | |
| import hashlib | |
| import multiprocessing | |
| import multiprocessing.dummy | |
| import os | |
| import queue | |
| import random | |
| import shutil | |
| import sys # pylint: disable=unused-import | |
| import tarfile | |
| import threading | |
| import time | |
| import typing | |
| import urllib | |
| import weakref | |
| import zipfile | |
| import numpy as np | |
| from tensorflow.python.framework import ops | |
| from six.moves.urllib.request import urlopen | |
| from tensorflow.python.keras.utils import tf_inspect | |
| from tensorflow.python.keras.utils.generic_utils import Progbar | |
| from tensorflow.python.keras.utils.io_utils import path_to_string | |
| from tensorflow.python.util.tf_export import keras_export | |
| # Required to support google internal urlretrieve | |
| if sys.version_info[0] == 2: | |
| def urlretrieve(url, filename, reporthook=None, data=None): | |
| """Replacement for `urlretrieve` for Python 2. | |
| Under Python 2, `urlretrieve` relies on `FancyURLopener` from legacy | |
| `urllib` module, known to have issues with proxy management. | |
| Args: | |
| 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 urllib.request import urlretrieve # pylint: disable=g-importing-member | |
| def is_generator_or_sequence(x): | |
| """Check if `x` is a Keras generator type.""" | |
| builtin_iterators = (str, list, tuple, dict, set, frozenset) | |
| if isinstance(x, (ops.Tensor, np.ndarray) + builtin_iterators): | |
| return False | |
| return (tf_inspect.isgenerator(x) or | |
| isinstance(x, Sequence) or | |
| isinstance(x, typing.Iterator)) | |
| def _extract_archive(file_path, path='.', archive_format='auto'): | |
| """Extracts an archive if it matches tar, tar.gz, tar.bz, or zip formats. | |
| Args: | |
| 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 == 'auto': | |
| archive_format = ['tar', 'zip'] | |
| if isinstance(archive_format, str): | |
| archive_format = [archive_format] | |
| file_path = path_to_string(file_path) | |
| path = path_to_string(path) | |
| for archive_type in archive_format: | |
| if archive_type == 'tar': | |
| open_fn = tarfile.open | |
| is_match_fn = tarfile.is_tarfile | |
| if archive_type == '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 | |
| @keras_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. | |
| Example: | |
| ```python | |
| path_to_downloaded_file = tf.keras.utils.get_file( | |
| "flower_photos", | |
| "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz", | |
| untar=True) | |
| ``` | |
| Args: | |
| 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` argument. | |
| boolean, whether the file should be decompressed | |
| md5_hash: Deprecated in favor of `file_hash` argument. | |
| 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'` corresponds to `['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 default directory `~/.keras/`. | |
| 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) | |
| _makedirs_exist_ok(datadir) | |
| fname = path_to_string(fname) | |
| 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 == -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 urllib.error.HTTPError as e: | |
| raise Exception(error_msg.format(origin, e.code, e.msg)) | |
| except urllib.error.URLError as e: | |
| raise Exception(error_msg.format(origin, e.errno, e.reason)) | |
| 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 _makedirs_exist_ok(datadir): | |
| os.makedirs(datadir, exist_ok=True) # pylint: disable=unexpected-keyword-arg | |
| def _resolve_hasher(algorithm, file_hash=None): | |
| """Returns hash algorithm as hashlib function.""" | |
| if algorithm == 'sha256': | |
| return hashlib.sha256() | |
| if algorithm == 'auto' and file_hash is not None and len(file_hash) == 64: | |
| return hashlib.sha256() | |
| # This is used only for legacy purposes. | |
| return hashlib.md5() | |
| def _hash_file(fpath, algorithm='sha256', chunk_size=65535): | |
| """Calculates a file sha256 or md5 hash. | |
| Example: | |
| ```python | |
| _hash_file('/path/to/file.zip') | |
| 'e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855' | |
| ``` | |
| Args: | |
| 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 isinstance(algorithm, str): | |
| hasher = _resolve_hasher(algorithm) | |
| else: | |
| hasher = algorithm | |
| 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. | |
| Args: | |
| 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 | |
| """ | |
| hasher = _resolve_hasher(algorithm, file_hash) | |
| if str(_hash_file(fpath, hasher, chunk_size)) == str(file_hash): | |
| return True | |
| else: | |
| return False | |
| class ThreadsafeIter(object): | |
| """Wrap an iterator with a lock and propagate exceptions to all threads.""" | |
| def __init__(self, it): | |
| self.it = it | |
| self.lock = threading.Lock() | |
| # After a generator throws an exception all subsequent next() calls raise a | |
| # StopIteration Exception. This, however, presents an issue when mixing | |
| # generators and threading because it means the order of retrieval need not | |
| # match the order in which the generator was called. This can make it appear | |
| # that a generator exited normally when in fact the terminating exception is | |
| # just in a different thread. In order to provide thread safety, once | |
| # self.it has thrown an exception we continue to throw the same exception. | |
| self._exception = None | |
| def __iter__(self): | |
| return self | |
| def next(self): | |
| return self.__next__() | |
| def __next__(self): | |
| with self.lock: | |
| if self._exception: | |
| raise self._exception # pylint: disable=raising-bad-type | |
| try: | |
| return next(self.it) | |
| except Exception as e: | |
| self._exception = e | |
| raise | |
| def threadsafe_generator(f): | |
| @functools.wraps(f) | |
| def g(*a, **kw): | |
| return ThreadsafeIter(f(*a, **kw)) | |
| return g | |
| @keras_export('keras.utils.Sequence') | |
| class Sequence(object): | |
| """Base object for fitting to a sequence of data, such as a dataset. | |
| Every `Sequence` must implement 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`. | |
| Args: | |
| 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): | |
| """Create a generator that iterate over the Sequence.""" | |
| for item in (self[i] for i in range(len(self))): | |
| yield item | |
| def iter_sequence_infinite(seq): | |
| """Iterates indefinitely over a Sequence. | |
| Args: | |
| seq: `Sequence` instance. | |
| Yields: | |
| Batches of data from the `Sequence`. | |
| """ | |
| while True: | |
| for item in seq: | |
| 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 | |
| # Because multiprocessing pools are inherently unsafe, starting from a clean | |
| # state can be essential to avoiding deadlocks. In order to accomplish this, we | |
| # need to be able to check on the status of Pools that we create. | |
| _DATA_POOLS = weakref.WeakSet() | |
| _WORKER_ID_QUEUE = None # Only created if needed. | |
| _WORKER_IDS = set() | |
| _FORCE_THREADPOOL = False | |
| _FORCE_THREADPOOL_LOCK = threading.RLock() | |
| def dont_use_multiprocessing_pool(f): | |
| @functools.wraps(f) | |
| def wrapped(*args, **kwargs): | |
| with _FORCE_THREADPOOL_LOCK: | |
| global _FORCE_THREADPOOL | |
| old_force_threadpool, _FORCE_THREADPOOL = _FORCE_THREADPOOL, True | |
| out = f(*args, **kwargs) | |
| _FORCE_THREADPOOL = old_force_threadpool | |
| return out | |
| return wrapped | |
| def get_pool_class(use_multiprocessing): | |
| global _FORCE_THREADPOOL | |
| if not use_multiprocessing or _FORCE_THREADPOOL: | |
| return multiprocessing.dummy.Pool # ThreadPool | |
| return multiprocessing.Pool | |
| def get_worker_id_queue(): | |
| """Lazily create the queue to track worker ids.""" | |
| global _WORKER_ID_QUEUE | |
| if _WORKER_ID_QUEUE is None: | |
| _WORKER_ID_QUEUE = multiprocessing.Queue() | |
| return _WORKER_ID_QUEUE | |
| 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. | |
| Args: | |
| uid: int, Sequence identifier | |
| i: index | |
| Returns: | |
| The value at index `i`. | |
| """ | |
| return _SHARED_SEQUENCES[uid][i] | |
| @keras_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. | |
| Example: | |
| ```python | |
| enqueuer = SequenceEnqueuer(...) | |
| enqueuer.start() | |
| datas = enqueuer.get() | |
| for data in datas: | |
| # Use the inputs; training, evaluating, predicting. | |
| # ... stop sometime. | |
| enqueuer.stop() | |
| ``` | |
| The `enqueuer.get()` should be an infinite stream of datas. | |
| """ | |
| def __init__(self, sequence, | |
| use_multiprocessing=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.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): | |
| """Starts the handler's workers. | |
| Args: | |
| workers: Number of workers. | |
| max_queue_size: queue size | |
| (when full, workers could block on `put()`) | |
| """ | |
| if self.use_multiprocessing: | |
| self.executor_fn = self._get_executor_init(workers) | |
| else: | |
| # We do not need the init since it's threads. | |
| self.executor_fn = lambda _: get_pool_class(False)(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 _send_sequence(self): | |
| """Sends current Iterable 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()`. | |
| Args: | |
| 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 | |
| def __del__(self): | |
| if self.is_running(): | |
| self.stop() | |
| @abstractmethod | |
| def _run(self): | |
| """Submits request to the executor and queue the `Future` objects.""" | |
| raise NotImplementedError | |
| @abstractmethod | |
| def _get_executor_init(self, workers): | |
| """Gets the Pool initializer for multiprocessing. | |
| Args: | |
| workers: Number of workers. | |
| Returns: | |
| Function, a Function to initialize the pool | |
| """ | |
| 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 | |
| @keras_export('keras.utils.OrderedEnqueuer') | |
| class OrderedEnqueuer(SequenceEnqueuer): | |
| """Builds a Enqueuer from a Sequence. | |
| Args: | |
| sequence: A `tf.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): | |
| super(OrderedEnqueuer, self).__init__(sequence, use_multiprocessing) | |
| self.shuffle = shuffle | |
| def _get_executor_init(self, workers): | |
| """Gets the Pool initializer for multiprocessing. | |
| Args: | |
| workers: Number of workers. | |
| Returns: | |
| Function, a Function to initialize the pool | |
| """ | |
| def pool_fn(seqs): | |
| pool = get_pool_class(True)( | |
| workers, initializer=init_pool_generator, | |
| initargs=(seqs, None, get_worker_id_queue())) | |
| _DATA_POOLS.add(pool) | |
| return pool | |
| return pool_fn | |
| 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)`. | |
| """ | |
| while self.is_running(): | |
| try: | |
| inputs = self.queue.get(block=True, timeout=5).get() | |
| if self.is_running(): | |
| self.queue.task_done() | |
| if inputs is not None: | |
| yield inputs | |
| except queue.Empty: | |
| pass | |
| except Exception as e: # pylint: disable=broad-except | |
| self.stop() | |
| raise e | |
| def init_pool_generator(gens, random_seed=None, id_queue=None): | |
| """Initializer function for pool workers. | |
| Args: | |
| gens: State which should be made available to worker processes. | |
| random_seed: An optional value with which to seed child processes. | |
| id_queue: A multiprocessing Queue of worker ids. This is used to indicate | |
| that a worker process was created by Keras and can be terminated using | |
| the cleanup_all_keras_forkpools utility. | |
| """ | |
| global _SHARED_SEQUENCES | |
| _SHARED_SEQUENCES = gens | |
| worker_proc = multiprocessing.current_process() | |
| # name isn't used for anything, but setting a more descriptive name is helpful | |
| # when diagnosing orphaned processes. | |
| worker_proc.name = 'Keras_worker_{}'.format(worker_proc.name) | |
| if random_seed is not None: | |
| np.random.seed(random_seed + worker_proc.ident) | |
| if id_queue is not None: | |
| # If a worker dies during init, the pool will just create a replacement. | |
| id_queue.put(worker_proc.ident, block=True, timeout=0.1) | |
| def next_sample(uid): | |
| """Gets the next value from the generator `uid`. | |
| To allow multiple generators to be used at the same time, we use `uid` to | |
| get a specific one. A single generator would cause the validation to | |
| overwrite the training generator. | |
| Args: | |
| uid: int, generator identifier | |
| Returns: | |
| The next value of generator `uid`. | |
| """ | |
| return next(_SHARED_SEQUENCES[uid]) | |
| @keras_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. | |
| Args: | |
| generator: a generator function which yields data | |
| use_multiprocessing: use multiprocessing if True, otherwise threading | |
| random_seed: Initial seed for workers, | |
| will be incremented by one for each worker. | |
| """ | |
| def __init__(self, generator, | |
| use_multiprocessing=False, | |
| random_seed=None): | |
| super(GeneratorEnqueuer, self).__init__(generator, use_multiprocessing) | |
| self.random_seed = random_seed | |
| def _get_executor_init(self, workers): | |
| """Gets the Pool initializer for multiprocessing. | |
| Args: | |
| workers: Number of works. | |
| Returns: | |
| A Function to initialize the pool | |
| """ | |
| def pool_fn(seqs): | |
| pool = get_pool_class(True)( | |
| workers, initializer=init_pool_generator, | |
| initargs=(seqs, self.random_seed, get_worker_id_queue())) | |
| _DATA_POOLS.add(pool) | |
| return pool | |
| return pool_fn | |
| def _run(self): | |
| """Submits request to the executor and queue the `Future` objects.""" | |
| self._send_sequence() # Share the initial generator | |
| with closing(self.executor_fn(_SHARED_SEQUENCES)) as executor: | |
| while True: | |
| if self.stop_signal.is_set(): | |
| return | |
| self.queue.put( | |
| executor.apply_async(next_sample, (self.uid,)), block=True) | |
| 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 StopIteration: | |
| # Special case for finite generators | |
| last_ones = [] | |
| while self.queue.qsize() > 0: | |
| last_ones.append(self.queue.get(block=True)) | |
| # Wait for them to complete | |
| for f in last_ones: | |
| f.wait() | |
| # Keep the good ones | |
| last_ones = [future.get() for future in last_ones if future.successful()] | |
| for inputs in last_ones: | |
| if inputs is not None: | |
| yield inputs | |
| except Exception as e: # pylint: disable=broad-except | |
| self.stop() | |
| if 'generator already executing' in str(e): | |
| raise RuntimeError( | |
| 'Your generator is NOT thread-safe. ' | |
| 'Keras requires a thread-safe generator when ' | |
| '`use_multiprocessing=False, workers > 1`. ') | |
| raise e |