# 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. # ============================================================================== """Utilities for file download and caching.""" import functools import hashlib import multiprocessing.dummy import os import pathlib import queue import random import shutil import tarfile import threading import time import typing import urllib import weakref import zipfile from abc import abstractmethod from contextlib import closing import numpy as np import tensorflow.compat.v2 as tf from six.moves.urllib.parse import urlsplit from keras.utils import io_utils from keras.utils import tf_inspect from keras.utils.generic_utils import Progbar # isort: off from tensorflow.python.util.tf_export import keras_export from six.moves.urllib.request import urlopen # Required to support google internal urlretrieve if True: # This gets transformed to `if sys.version_info[0] == 2:` in OSS. 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 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, (tf.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 = io_utils.path_to_string(file_path) path = io_utils.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=None, origin=None, 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. If `None`, the name of the file at `origin` will be used. 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 origin is None: raise ValueError( 'Please specify the "origin" argument (URL of the file ' "to download)." ) 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 = io_utils.path_to_string(fname) if not fname: fname = os.path.basename(urlsplit(origin).path) if not fname: raise ValueError( "Can't parse the file name from the origin provided: " f"'{origin}'." "Please specify the `fname` as the input param." ) if untar: if fname.endswith(".tar.gz"): fname = pathlib.Path(fname) # The 2 `.with_suffix()` are because of `.tar.gz` as pathlib # considers it as 2 suffixes. fname = fname.with_suffix("").with_suffix("") fname = str(fname) 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): io_utils.print_msg( "A local file was found, but it seems to be " f"incomplete or outdated because the {hash_algorithm} " f"file hash does not match the original value of " f"{file_hash} " "so we will re-download the data." ) download = True else: download = True if download: io_utils.print_msg(f"Downloading data from {origin}") class DLProgbar: """Manage progress bar state for use in urlretrieve.""" def __init__(self): self.progbar = None self.finished = False def __call__(self, block_num, block_size, total_size): if not self.progbar: if total_size == -1: total_size = None self.progbar = Progbar(total_size) current = block_num * block_size if current < total_size: self.progbar.update(current) elif not self.finished: self.progbar.update(self.progbar.target) self.finished = True error_msg = "URL fetch failure on {}: {} -- {}" try: try: urlretrieve(origin, fpath, DLProgbar()) 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): if os.path.exists(fpath): os.remove(fpath) raise # Validate download if succeeded and user provided an expected hash # Security conscious users would get the hash of the file from a # separate channel and pass it to this API to prevent MITM / corruption: if os.path.exists(fpath) and file_hash is not None: if not validate_file(fpath, file_hash, algorithm=hash_algorithm): raise ValueError( f"Incomplete or corrupted file detected. " f"The {hash_algorithm} " f"file hash does not match the provided value " f"of {file_hash}." ) 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) 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: """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 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: """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(tf.keras.utils.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: """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 data. """ 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().__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: 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().__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: 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 @keras_export( "keras.utils.pad_sequences", "keras.preprocessing.sequence.pad_sequences" ) def pad_sequences( sequences, maxlen=None, dtype="int32", padding="pre", truncating="pre", value=0.0, ): """Pads sequences to the same length. This function transforms a list (of length `num_samples`) of sequences (lists of integers) into a 2D Numpy array of shape `(num_samples, num_timesteps)`. `num_timesteps` is either the `maxlen` argument if provided, or the length of the longest sequence in the list. Sequences that are shorter than `num_timesteps` are padded with `value` until they are `num_timesteps` long. Sequences longer than `num_timesteps` are truncated so that they fit the desired length. The position where padding or truncation happens is determined by the arguments `padding` and `truncating`, respectively. Pre-padding or removing values from the beginning of the sequence is the default. >>> sequence = [[1], [2, 3], [4, 5, 6]] >>> tf.keras.preprocessing.sequence.pad_sequences(sequence) array([[0, 0, 1], [0, 2, 3], [4, 5, 6]], dtype=int32) >>> tf.keras.preprocessing.sequence.pad_sequences(sequence, value=-1) array([[-1, -1, 1], [-1, 2, 3], [ 4, 5, 6]], dtype=int32) >>> tf.keras.preprocessing.sequence.pad_sequences(sequence, padding='post') array([[1, 0, 0], [2, 3, 0], [4, 5, 6]], dtype=int32) >>> tf.keras.preprocessing.sequence.pad_sequences(sequence, maxlen=2) array([[0, 1], [2, 3], [5, 6]], dtype=int32) Args: sequences: List of sequences (each sequence is a list of integers). maxlen: Optional Int, maximum length of all sequences. If not provided, sequences will be padded to the length of the longest individual sequence. dtype: (Optional, defaults to `"int32"`). Type of the output sequences. To pad sequences with variable length strings, you can use `object`. padding: String, "pre" or "post" (optional, defaults to `"pre"`): pad either before or after each sequence. truncating: String, "pre" or "post" (optional, defaults to `"pre"`): remove values from sequences larger than `maxlen`, either at the beginning or at the end of the sequences. value: Float or String, padding value. (Optional, defaults to 0.) Returns: Numpy array with shape `(len(sequences), maxlen)` Raises: ValueError: In case of invalid values for `truncating` or `padding`, or in case of invalid shape for a `sequences` entry. """ if not hasattr(sequences, "__len__"): raise ValueError("`sequences` must be iterable.") num_samples = len(sequences) lengths = [] sample_shape = () flag = True # take the sample shape from the first non empty sequence # checking for consistency in the main loop below. for x in sequences: try: lengths.append(len(x)) if flag and len(x): sample_shape = np.asarray(x).shape[1:] flag = False except TypeError as e: raise ValueError( "`sequences` must be a list of iterables. " f"Found non-iterable: {str(x)}" ) from e if maxlen is None: maxlen = np.max(lengths) is_dtype_str = np.issubdtype(dtype, np.str_) or np.issubdtype( dtype, np.unicode_ ) if isinstance(value, str) and dtype != object and not is_dtype_str: raise ValueError( f"`dtype` {dtype} is not compatible with `value`'s type: " f"{type(value)}\nYou should set `dtype=object` for variable length " "strings." ) x = np.full((num_samples, maxlen) + sample_shape, value, dtype=dtype) for idx, s in enumerate(sequences): if not len(s): continue # empty list/array was found if truncating == "pre": trunc = s[-maxlen:] elif truncating == "post": trunc = s[:maxlen] else: raise ValueError(f'Truncating type "{truncating}" not understood') # check `trunc` has expected shape trunc = np.asarray(trunc, dtype=dtype) if trunc.shape[1:] != sample_shape: raise ValueError( f"Shape of sample {trunc.shape[1:]} of sequence at " f"position {idx} is different from expected shape " f"{sample_shape}" ) if padding == "post": x[idx, : len(trunc)] = trunc elif padding == "pre": x[idx, -len(trunc) :] = trunc else: raise ValueError(f'Padding type "{padding}" not understood') return x