Python multiprocessing.Pool方法代码示例

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import Pool [as 别名]
def _download_multiprocess_map_chunk(pool_tup):
    """
    Helper function for Pool imap_unordered.

    Apparently function must be pickable (which apparently means must be
    defined at the top level of a module and can't be a lamdba) to be used in
    imap_unordered. Has to do with how it's passed to the subprocess.

    :param pool_tup: is a tuple where first arg is an array of tuples of url
    and dest file name for the current chunk and second arg is function to be
    called.
    :return: an array of tuples
    """
    items = pool_tup[0]
    path = pool_tup[1]
    fn = pool_tup[2]
    return [fn(it[0], path, it[1]) for it in items] 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import Pool [as 别名]
def load_async(pool: Pool, fn: Callable, *args, callback: Callable = None, **kwargs) -> Any:
    """
    Load data asynchronously and serialize data via dill

    Args:
        pool: multiprocessing pool to use for :func:`apply_async`
        fn: function to load a single sample
        *args: positional arguments to dump with dill
        callback: optional callback. defaults to None.
        **kwargs: keyword arguments to dump with dill

    Returns:
        Any: reference to obtain data with :func:`get`

    """

    if not DILL_AVAILABLE:
        raise RuntimeError('dill is not installed. For async loading '
                           'please install it')

    payload = dill.dumps((fn, args, kwargs))
    return pool.apply_async(dill_helper, (payload,), callback=callback) 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import Pool [as 别名]
def async_mol2graph(q: Queue, 
                    data: MoleculeDataset, 
                    args: Namespace,
                    num_iters: int,
                    iter_size: int,
                    exit_q: Queue,
                    last_batch: bool=False):
    batches = []
    for i in range(0, num_iters, iter_size):  # will only go up to max size of queue, then yield
        if not last_batch and i + args.batch_size > len(data):
            break
        batch = MoleculeDataset(data[i:i + args.batch_size])
        batches.append(batch)
        if len(batches) == args.batches_per_queue_group:  # many at a time, since synchronization is expensive
            with Pool() as pool:
                processed_batches = pool.map(mol2graph_helper, [(batch, args) for batch in batches])
            q.put(processed_batches)
            batches = []
    if len(batches) > 0:
        with Pool() as pool:
            processed_batches = pool.map(mol2graph_helper, [(batch, args) for batch in batches])
        q.put(processed_batches)
    exit_q.get()  # prevent from exiting until main process tells it to; otherwise we apparently can't read the end of the queue and crash 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import Pool [as 别名]
def parallelize(fn, args, num_cpus=NUM_CPUS):
    '''
    Parallelize a method fn, args and return results with order preserved per args.
    args should be a list of tuples.
    @returns {list} results Order preserved output from fn.
    '''
    pool = mp.Pool(num_cpus, maxtasksperchild=1)
    results = pool.starmap(fn, args)
    pool.close()
    pool.join()
    return results 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import Pool [as 别名]
def rerank_mp(all_beams, ext_inds):
    beam_lists = [all_beams[i: i+n] for i, n in ext_inds if n > 0]
    with mp.Pool(8) as pool:
        reranked = pool.map(rerank_one, beam_lists)
    return list(concat(reranked)) 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import Pool [as 别名]
def load_multi_process(self, load_fn: Callable, path: Sequence) -> List:
        """
        Helper function to load dataset with multiple processes

        Args:
            load_fn:  function to load a single sample
            path:  a sequence of paths which should be loaded

        Returns:
            list: loaded data

        """

        _processes = cpu_count() if self._num_workers is None else self._num_workers

        if self._verbosity:
            pbar = tqdm(total=len(path), unit='samples', desc="Loading Samples")

            def update(*a):
                pbar.update(1)

            callback = update
        else:
            callback = None

        with Pool(processes=_processes) as pool:
            jobs = [load_async(pool, load_fn, p, callback=callback) for p in path]
            _data = [j.get() for j in jobs]
        return _data 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import Pool [as 别名]
def test_load_async(self):
        callback = Mock()

        with Pool(processes=1) as p:
            ref = load_async(p, lambda x: x, 0, callback=callback)
            self.assertEqual(ref.get(), 0)

        callback.assert_called_once() 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import Pool [as 别名]
def __init__(self, simulator, workers=2):
        super(ParallelSimulator, self).__init__()
        self.pool = Pool(processes=workers)
        self.simulator = simulator
        self.workers = workers 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import Pool [as 别名]
def sample(self, observations, num_samples, thetas=None):
        assert(thetas is None or len(thetas) is self.chains)
        self.sampler.reset()
        if thetas is None:
            thetas = self._prepare_thetas()
        pool = Pool(processes=self.workers)
        arguments = self._prepare_arguments(observations, thetas, num_samples)
        chains = pool.map(self.sample_chain, arguments)
        del pool

        return chains 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import Pool [as 别名]
def __init__(self, abc, workers=2):
        super(ParallelApproximateBayesianComputation, self).__init__()
        self.abc = abc
        self.pool = Pool(processes=workers)
        self.workers = workers 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import Pool [as 别名]
def propagate(nnf, feat_A, feat_AP, feat_B, feat_BP, patch_size, iters=2, rand_search_radius=200):
    print("\tpatch_size:{}; num_iters:{}; rand_search_radius:{}".format(patch_size, iters, rand_search_radius))

    nnd = np.zeros(nnf.shape[:2])
    A_size = feat_A.shape[:2]
    B_size = feat_B.shape[:2]

    for ay in range(A_size[0]):
        for ax in range(A_size[1]):
            by, bx = nnf[ay, ax]
            nnd[ay, ax] = cal_dist(ay, ax, by, bx, feat_A, feat_AP, feat_B, feat_BP, A_size, B_size, patch_size)

    manager = mp.Manager()
    q = manager.Queue(A_size[1] * A_size[0])
    cpus = min(mp.cpu_count(), A_size[0] // 20 + 1)
    for i in range(iters):

        p = Pool(cpus)

        ay_start = 0

        while ay_start < A_size[0]:
            ax_start = 0
            while ax_start < A_size[1]:
                p.apply_async(pixelmatch, args=(q, ax_start, ay_start,
                                                cpus,
                                                nnf, nnd,
                                                A_size, B_size,
                                                feat_A, feat_AP,
                                                feat_B, feat_BP,
                                                patch_size,
                                                rand_search_radius,))

                ax_start += A_size[1] // cpus + 1
            ay_start += A_size[0] // cpus + 1

        p.close()
        p.join()

        while not q.empty():
            ax, ay, xbest, ybest, dbest = q.get()

            nnf[ay, ax] = np.array([ybest, xbest])
            nnd[ay, ax] = dbest

    return nnf, nnd 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import Pool [as 别名]
def _get_dataset(self, filename, dicts=None):
        if not filename and not dicts:
            raise ValueError("You must either supply `filename` or `dicts`")

        # loading dicts from file (default)
        if dicts is None:
            dicts = list(self.processor.file_to_dicts(filename))
            #shuffle list of dicts here if we later want to have a random dev set splitted from train set
            if str(self.processor.train_filename) in str(filename):
                if not self.processor.dev_filename:
                    if self.processor.dev_split > 0.0:
                        random.shuffle(dicts)

        num_dicts = len(dicts)
        multiprocessing_chunk_size, num_cpus_used = calc_chunksize(
            num_dicts=num_dicts,
            max_processes=self.max_processes,
            max_chunksize=self.max_multiprocessing_chunksize,
        )

        with ExitStack() as stack:
            if self.max_processes > 1:  # use multiprocessing only when max_processes > 1
                p = stack.enter_context(mp.Pool(processes=num_cpus_used))

                logger.info(
                    f"Got ya {num_cpus_used} parallel workers to convert {num_dicts} dictionaries "
                    f"to pytorch datasets (chunksize = {multiprocessing_chunk_size})..."
                )
                log_ascii_workers(num_cpus_used, logger)

                results = p.imap(
                    partial(self._dataset_from_chunk, processor=self.processor),
                    grouper(dicts, multiprocessing_chunk_size),
                    chunksize=1,
                )
            else:
                logger.info(
                    f"Multiprocessing disabled, using a single worker to convert {num_dicts}"
                    f"dictionaries to pytorch datasets."
                )

                results = map(partial(self._dataset_from_chunk, processor=self.processor), grouper(dicts, num_dicts))

            datasets = []

            desc = f"Preprocessing Dataset"
            if filename:
                desc += f" {filename}"
            with tqdm(total=len(dicts), unit=' Dicts', desc=desc) as pbar:
                for dataset, tensor_names in results:
                    datasets.append(dataset)
                    # update progress bar (last step can have less dicts than actual chunk_size)
                    pbar.update(min(multiprocessing_chunk_size, pbar.total-pbar.n))
            # _dataset_from_chunk can return a None in cases where downsampling has occurred
            datasets = [d for d in datasets if d]
            concat_datasets = ConcatDataset(datasets)
            return concat_datasets, tensor_names 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import Pool [as 别名]
def loadSeqs(self):

        # Labels
        self.seqOffset = [0]
        self.phoneLabels = []
        self.phoneOffsets = [0]
        self.data = []
        self.maxSize = 0
        self.maxSizePhone = 0

        # Data

        nprocess = min(30, len(self.seqNames))

        start_time = time.time()
        to_load = [Path(self.pathDB) / x for _, x in self.seqNames]

        with Pool(nprocess) as p:
            poolData = p.map(load, to_load)

        tmpData = []
        poolData.sort()

        totSize = 0
        minSizePhone = 1000000
        for seqName, seq in poolData:
            self.phoneLabels += self.phoneLabelsDict[seqName]
            self.phoneOffsets.append(len(self.phoneLabels))
            self.maxSizePhone = max(self.maxSizePhone,
                                    len(self.phoneLabelsDict[seqName]))
            minSizePhone = min(minSizePhone, len(
                self.phoneLabelsDict[seqName]))
            sizeSeq = seq.size(1)
            self.maxSize = max(self.maxSize, sizeSeq)
            totSize += sizeSeq
            tmpData.append(seq)
            self.seqOffset.append(self.seqOffset[-1] + sizeSeq)
            del seq
        self.data = torch.cat(tmpData, dim=1)
        self.phoneLabels = torch.tensor(self.phoneLabels, dtype=torch.long)
        print(f'Loaded {len(self.phoneOffsets)} sequences '
              f'in {time.time() - start_time:.2f} seconds')
        print(f'maxSizeSeq : {self.maxSize}')
        print(f'maxSizePhone : {self.maxSizePhone}')
        print(f"minSizePhone : {minSizePhone}")
        print(f'Total size dataset {totSize / (16000 * 3600)} hours')