Python torch.utils.data.dataloader.default_collate() Examples

def collate_minibatch(list_of_blobs):
    """Stack samples seperately and return a list of minibatches
    A batch contains NUM_GPUS minibatches and image size in different minibatch may be different.
    Hence, we need to stack smaples from each minibatch seperately.
    """
    # print(list_of_blobs[0])
    Batch = {key: [] for key in list_of_blobs[0]}
    # Because roidb consists of entries of variable length, it can't be batch into a tensor.
    # So we keep roidb in the type of "list of ndarray".
    list_of_roidb = [blobs.pop('roidb') for blobs in list_of_blobs]
    for i in range(0, len(list_of_blobs), cfg.TRAIN.IMS_PER_BATCH):
        mini_list = list_of_blobs[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        # Pad image data
        mini_list = pad_image_data(mini_list)
        minibatch = default_collate(mini_list)
        minibatch['roidb'] = list_of_roidb[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        for key in minibatch:
            Batch[key].append(minibatch[key])

    return Batch 

def collater(self, samples):
        """Merge a list of samples to form a mini-batch.

        Args:
            samples (List[dict]): samples to collate

        Returns:
            dict: a mini-batch suitable for forwarding with a Model
        """
        if len(samples) == 0:
            return {}
        sample = OrderedDict()
        for k, ds in self.defn.items():
            try:
                sample[k] = ds.collater([s[k] for s in samples])
            except NotImplementedError:
                sample[k] = default_collate([s[k] for s in samples])
        return _unflatten(sample) 

def __init__(self, dataset, batch_size, shuffle, validation_split, num_workers, collate_fn=default_collate):
        self.validation_split = validation_split
        self.shuffle = shuffle

        self.batch_idx = 0
        self.n_samples = len(dataset)

        self.sampler, self.valid_sampler = self._split_sampler(self.validation_split)

        self.init_kwargs = {
            'dataset': dataset,
            'batch_size': batch_size,
            'shuffle': self.shuffle,
            'collate_fn': collate_fn,
            'num_workers': num_workers
        }
        super().__init__(sampler=self.sampler, **self.init_kwargs) 

def mt_collate_fn(batch):
    "Pads data and puts it into a tensor of same dimensions"
    max_len = 0
    for b in batch:
        if b[0].shape[0] > max_len:
            max_len = b[0].shape[0]

    new_batch = []
    for b in batch:
        f = np.zeros((max_len, b[0].shape[1], b[0].shape[2], b[0].shape[3]), np.float32)
        m = np.zeros((max_len), np.float32)
        l = np.zeros((max_len, b[1].shape[1]), np.float32)
        f[:b[0].shape[0]] = b[0]
        m[:b[0].shape[0]] = 1
        l[:b[0].shape[0], :] = b[1]
        new_batch.append([video_to_tensor(f), torch.from_numpy(m), torch.from_numpy(l), b[2]])

    return default_collate(new_batch) 

def ava_collate_fn(batch):
    "Pads data and puts it into a tensor of same dimensions"
    max_len = 0
    for b in batch:
        if b[0].shape[0] > max_len:
            max_len = b[0].shape[0]

    new_batch = []
    for b in batch:
        f = np.zeros((max_len, b[0].shape[1], b[0].shape[2], b[0].shape[3]), np.float32)
        m = np.zeros((max_len), np.float32)
        l = np.zeros((max_len, b[1].shape[1]), np.float32)
        f[:b[0].shape[0]] = b[0]
        m[:b[0].shape[0]] = 1
        l[:b[0].shape[0], :] = b[1]
        new_batch.append([video_to_tensor(f), torch.from_numpy(m), torch.from_numpy(l), b[2]])

    return default_collate(new_batch) 

def collate(self, batch):
        elem = batch[0]
        if isinstance(elem, Data):
            return Batch.from_data_list(batch, self.follow_batch)
        elif isinstance(elem, torch.Tensor):
            return default_collate(batch)
        elif isinstance(elem, float):
            return torch.tensor(batch, dtype=torch.float)
        elif isinstance(elem, int_classes):
            return torch.tensor(batch)
        elif isinstance(elem, string_classes):
            return batch
        elif isinstance(elem, container_abcs.Mapping):
            return {key: self.collate([d[key] for d in batch]) for key in elem}
        elif isinstance(elem, tuple) and hasattr(elem, '_fields'):
            return type(elem)(*(self.collate(s) for s in zip(*batch)))
        elif isinstance(elem, container_abcs.Sequence):
            return [self.collate(s) for s in zip(*batch)]

        raise TypeError('DataLoader found invalid type: {}'.format(type(elem))) 

def __call__(self, batch):
        """
        Args:
            batch: current batch

        Returns:
            batch values filtered by `keys`
        """
        if isinstance(batch[0], collections.Mapping):
            result = {}
            for key in batch[0]:
                items = [d[key] for d in batch]
                if key not in self.keys:
                    items = default_collate(items)
                result[key] = items
            return result
        else:
            return default_collate(batch) 

def collater(self, samples):
        """Merge a list of samples to form a mini-batch.

        Args:
            samples (List[dict]): samples to collate

        Returns:
            dict: a mini-batch suitable for forwarding with a Model
        """
        if len(samples) == 0:
            return {}
        sample = OrderedDict()
        for k, ds in self.defn.items():
            try:
                sample[k] = ds.collater([s[k] for s in samples])
            except NotImplementedError:
                sample[k] = default_collate([s[k] for s in samples])
        return _unflatten(sample) 

def make_collate_fn(padding_values):

    def _collate_fn(batch):

        for name, padding_value in padding_values.items():

            lengths = [len(sample[name]) for sample in batch]
            max_length = max(lengths)

            for n, size in enumerate(lengths):
                p = max_length - size
                if p:
                    pad_width = [(0, p)] + [(0, 0)] * (batch[n][name].ndim - 1)
                    if padding_value == "edge":
                        batch[n][name] = np.pad(
                            batch[n][name], pad_width,
                            mode="edge")
                    else:
                        batch[n][name] = np.pad(
                            batch[n][name], pad_width,
                            mode="constant", constant_values=padding_value)

        return default_collate(batch)

    return _collate_fn 

def detection_collate(batch):
    """Custom collate fn for dealing with batches of images that have a different
    number of associated object annotations (bounding boxes).

    Arguments:
        batch: (tuple) A tuple of tensor images and lists of annotations

    Return:
        A tuple containing:
            1) (tensor) batch of images stacked on their 0 dim
            2) (list of tensors) annotations for a given image are stacked on 0 dim
    """
    custom = defaultdict(list)
    custom_keys = ['target_size', ]
    for sample in batch:
        for k in custom_keys:
            custom[k] += [sample[k]]
    other = {k: default_collate([b[k] for b in batch]) for k in
             filter(lambda x: x not in custom, batch[0].keys())}
    return {**other, **custom} 

def custom_collate(batch, del_orig_labels=False):
    keys = ['target_size', 'target_size_feats', 'alphas', 'target_level']
    values = {}
    for k in keys:
        if k in batch[0]:
            values[k] = batch[0][k]
    for b in batch:
        if del_orig_labels: del b['original_labels']
        for k in values.keys():
            del b[k]
        if 'mux_indices' in b:
            b['mux_indices'] = b['mux_indices'].view(-1)
    batch = default_collate(batch)
    # if 'image_next' in batch:
    #     batch['image'] = torch.cat([batch['image'], batch['image_next']], dim=0).contiguous()
    #     del batch['image_next']
    for k, v in values.items():
        batch[k] = v
    return batch 

def collate_minibatch(list_of_blobs):
    """Stack samples seperately and return a list of minibatches
    A batch contains NUM_GPUS minibatches and image size in different minibatch may be different.
    Hence, we need to stack smaples from each minibatch seperately.
    """
    Batch = {key: [] for key in list_of_blobs[0]}
    # Because roidb consists of entries of variable length, it can't be batch into a tensor.
    # So we keep roidb in the type of "list of ndarray".
    list_of_roidb = [blobs.pop('roidb') for blobs in list_of_blobs]
    for i in range(0, len(list_of_blobs), cfg.TRAIN.IMS_PER_BATCH):
        mini_list = list_of_blobs[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        # Pad image data
        mini_list = pad_image_data(mini_list)
        minibatch = default_collate(mini_list)
        minibatch['roidb'] = list_of_roidb[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        for key in minibatch:
            Batch[key].append(minibatch[key])

    return Batch 

def collate_minibatch(list_of_blobs):
    """Stack samples seperately and return a list of minibatches
    A batch contains NUM_GPUS minibatches and image size in different minibatch may be different.
    Hence, we need to stack smaples from each minibatch seperately.
    """
    Batch = {key: [] for key in list_of_blobs[0]}
    # Because roidb consists of entries of variable length, it can't be batch into a tensor.
    # So we keep roidb in the type of "list of ndarray".
    list_of_roidb = [blobs.pop('roidb') for blobs in list_of_blobs]
    for i in range(0, len(list_of_blobs), cfg.TRAIN.IMS_PER_BATCH):
        mini_list = list_of_blobs[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        # Pad image data
        mini_list = pad_image_data(mini_list)
        minibatch = default_collate(mini_list)
        minibatch['roidb'] = list_of_roidb[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        for key in minibatch:
            Batch[key].append(minibatch[key])

    return Batch 

def decimal_friendly_collate(batch):
    """A wrapper on top of ``default_collate`` function that allows decimal.Decimal types to be collated.

    We use ``decimal.Decimal`` types in petastorm dataset to represent timestamps. PyTorch's ``default_collate``
    implementation does not support collating ``decimal.Decimal`` types. ``decimal_friendly_collate`` collates
    ``decimal.Decimal`` separately and then combines with the rest of the fields collated by a standard
    ``default_collate``.

    :param batch: A list of dictionaries to collate
    :return: A dictionary of lists/pytorch.Tensor types
    """

    if isinstance(batch[0], decimal.Decimal):
        return batch
    elif isinstance(batch[0], collections.Mapping):
        return {key: decimal_friendly_collate([d[key] for d in batch]) for key in batch[0]}
    elif isinstance(batch[0], _string_classes):
        return batch
    elif isinstance(batch[0], collections.Sequence):
        transposed = zip(*batch)
        return [decimal_friendly_collate(samples) for samples in transposed]
    else:
        return default_collate(batch) 

def collate_minibatch(list_of_blobs):
    """Stack samples seperately and return a list of minibatches
    A batch contains NUM_GPUS minibatches and image size in different minibatch may be different.
    Hence, we need to stack smaples from each minibatch seperately.
    """
    Batch = {key: [] for key in list_of_blobs[0]}
    # Because roidb consists of entries of variable length, it can't be batch into a tensor.
    # So we keep roidb in the type of "list of ndarray".
    list_of_roidb = [blobs.pop('roidb') for blobs in list_of_blobs]
    for i in range(0, len(list_of_blobs), cfg.TRAIN.IMS_PER_BATCH):
        mini_list = list_of_blobs[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        # Pad image data
        mini_list = pad_image_data(mini_list)
        minibatch = default_collate(mini_list)
        minibatch['roidb'] = list_of_roidb[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        for key in minibatch:
            Batch[key].append(minibatch[key])

    return Batch 

def __init__(self, dataset, **kwargs):
        # drop_last is handled transparently by _SafeDataLoaderIter (bypassing
        # DataLoader). Since drop_last cannot be changed after initializing the
        # DataLoader instance, it needs to be intercepted here.
        assert isinstance(
            dataset, SafeDataset
        ), "dataset must be an instance of SafeDataset."

        self.drop_last_original = False
        if "drop_last" in kwargs:
            self.drop_last_original = kwargs["drop_last"]
            kwargs["drop_last"] = False
        super(SafeDataLoader, self).__init__(dataset, **kwargs)

        self.safe_dataset = self.dataset
        self.dataset = _OriginalDataset(self.safe_dataset)

        if self.collate_fn is default_collate:
            self.collate_fn = SafeDataLoader._safe_default_collate 

def collate_minibatch(list_of_blobs):
    """Stack samples seperately and return a list of minibatches
    A batch contains NUM_GPUS minibatches and image size in different minibatch may be different.
    Hence, we need to stack smaples from each minibatch seperately.
    """
    Batch = {key: [] for key in list_of_blobs[0]}
    # Because roidb consists of entries of variable length, it can't be batch into a tensor.
    # So we keep roidb in the type of "list of ndarray".
    list_of_roidb = [blobs.pop('roidb') for blobs in list_of_blobs]
    for i in range(0, len(list_of_blobs), cfg.TRAIN.IMS_PER_BATCH):
        mini_list = list_of_blobs[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        # Pad image data
        mini_list = pad_image_data(mini_list)
        minibatch = default_collate(mini_list)
        minibatch['roidb'] = list_of_roidb[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        for key in minibatch:
            Batch[key].append(minibatch[key])

    return Batch 

def collate_minibatch(list_of_blobs):
    """Stack samples seperately and return a list of minibatches
    A batch contains NUM_GPUS minibatches and image size in different minibatch may be different.
    Hence, we need to stack smaples from each minibatch seperately.
    """
    Batch = {key: [] for key in list_of_blobs[0]}
    # Because roidb consists of entries of variable length, it can't be batch into a tensor.
    # So we keep roidb in the type of "list of ndarray".
    list_of_roidb = [blobs.pop('roidb') for blobs in list_of_blobs]
    for i in range(0, len(list_of_blobs), cfg.TRAIN.IMS_PER_BATCH):
        mini_list = list_of_blobs[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        # Pad image data
        mini_list = pad_image_data(mini_list)
        minibatch = default_collate(mini_list)
        minibatch['roidb'] = list_of_roidb[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        for key in minibatch:
            Batch[key].append(minibatch[key])

    return Batch 

def disable_shared_memory():
  """
  Sometimes a runtime error occurs when the shared memory in a virtual machine
  is too small to hold an object. This function disables using shared memory.

  https://github.com/huaweicloud/dls-example/issues/26
  """
  setattr(dataloader, 'default_collate', default_collate_override)

  for t in torch._storage_classes:
    if sys.version_info[0] == 2:
      if t in ForkingPickler.dispatch:
          del ForkingPickler.dispatch[t]
    else:
      if t in ForkingPickler._extra_reducers:
          del ForkingPickler._extra_reducers[t] 

def __init__(self, dataset, batch_size, shuffle, validation_split, num_workers,
     collate_fn=default_collate):
        self.validation_split = validation_split
        self.shuffle = shuffle
        
        self.batch_idx = 0
        self.n_samples = len(dataset)

        self.sampler, self.valid_sampler = self._split_sampler(self.validation_split)

        self.init_kwargs = {
            'dataset': dataset,
            'batch_size': batch_size,
            'shuffle': self.shuffle,
            'collate_fn': collate_fn,
            'num_workers': num_workers
            }
        super(BaseDataLoader, self).__init__(sampler=self.sampler, **self.init_kwargs) 

def collate_minibatch(list_of_blobs):
    """Stack samples seperately and return a list of minibatches
    A batch contains NUM_GPUS minibatches and image size in different minibatch may be different.
    Hence, we need to stack smaples from each minibatch seperately.
    """
    Batch = {key: [] for key in list_of_blobs[0]}
    # Because roidb consists of entries of variable length, it can't be batch into a tensor.
    # So we keep roidb in the type of "list of ndarray".
    list_of_roidb = [blobs.pop('roidb') for blobs in list_of_blobs]
    for i in range(0, len(list_of_blobs), cfg.TRAIN.IMS_PER_BATCH):
        mini_list = list_of_blobs[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        # Pad image data
        mini_list = pad_image_data(mini_list)
        minibatch = default_collate(mini_list)
        minibatch['roidb'] = list_of_roidb[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        for key in minibatch:
            Batch[key].append(minibatch[key])

    return Batch 

def collate_minibatch(list_of_blobs):
    """Stack samples seperately and return a list of minibatches
    A batch contains NUM_GPUS minibatches and image size in different minibatch may be different.
    Hence, we need to stack smaples from each minibatch seperately.
    """
    Batch = {key: [] for key in list_of_blobs[0]}
    # Because roidb consists of entries of variable length, it can't be batch into a tensor.
    # So we keep roidb in the type of "list of ndarray".
    lists = []
    for blobs in list_of_blobs:
        lists.append({'data' : blobs.pop('data'),
                      'rois' : blobs.pop('rois'),
                      'labels' : blobs.pop('labels')})
    for i in range(0, len(list_of_blobs), cfg.TRAIN.IMS_PER_BATCH):
        mini_list = lists[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        minibatch = default_collate(mini_list)
        for key in minibatch:
            Batch[key].append(minibatch[key])

    return Batch 

def collate_minibatch(list_of_blobs):
    """Stack samples seperately and return a list of minibatches
    A batch contains NUM_GPUS minibatches and image size in different minibatch may be different.
    Hence, we need to stack smaples from each minibatch seperately.
    """
    Batch = {key: [] for key in list_of_blobs[0]}
    # Because roidb consists of entries of variable length, it can't be batch into a tensor.
    # So we keep roidb in the type of "list of ndarray".
    list_of_roidb = [blobs.pop('roidb') for blobs in list_of_blobs]
    for i in range(0, len(list_of_blobs), cfg.TRAIN.IMS_PER_BATCH):
        mini_list = list_of_blobs[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        # Pad image data
        mini_list = pad_image_data(mini_list)
        minibatch = default_collate(mini_list)
        minibatch['roidb'] = list_of_roidb[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        for key in minibatch:
            Batch[key].append(minibatch[key])

    return Batch 

def collate_minibatch(list_of_blobs):
    """Stack samples seperately and return a list of minibatches
    A batch contains NUM_GPUS minibatches and image size in different minibatch may be different.
    Hence, we need to stack smaples from each minibatch seperately.
    """
    Batch = {key: [] for key in list_of_blobs[0]}
    # Because roidb consists of entries of variable length, it can't be batch into a tensor.
    # So we keep roidb in the type of "list of ndarray".
    list_of_roidb = [blobs.pop('roidb') for blobs in list_of_blobs]
    for i in range(0, len(list_of_blobs), cfg.TRAIN.IMS_PER_BATCH):
        mini_list = list_of_blobs[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        # Pad image data
        mini_list = pad_image_data(mini_list)
        minibatch = default_collate(mini_list)
        minibatch['roidb'] = list_of_roidb[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        for key in minibatch:
            Batch[key].append(minibatch[key])

    return Batch 

def ctc_collate(batch):
    '''
    Stack samples into CTC style inputs.
    Modified based on default_collate() in PyTorch.
    By Yuan-Hang Zhang.
    '''
    xs, ys, lens, indices = zip(*batch)
    max_len = max(lens)
    x = default_collate(xs)
    x.narrow(2, 0, max_len)
    y = []
    for sub in ys: y += sub
    y = torch.IntTensor(y)
    lengths = torch.IntTensor(lens)
    y_lengths = torch.IntTensor([len(label) for label in ys])
    ids = default_collate(indices)

    return x, y, lengths, y_lengths, ids 

def pad_collate(batch):
    max_input_len = float('-inf')
    max_target_len = float('-inf')

    for elem in batch:
        feature, trn = elem
        max_input_len = max_input_len if max_input_len > feature.shape[0] else feature.shape[0]
        max_target_len = max_target_len if max_target_len > len(trn) else len(trn)

    for i, elem in enumerate(batch):
        feature, trn = elem
        input_length = feature.shape[0]
        input_dim = feature.shape[1]
        padded_input = np.zeros((max_input_len, input_dim), dtype=np.float32)
        padded_input[:input_length, :] = feature
        padded_target = np.pad(trn, (0, max_target_len - len(trn)), 'constant', constant_values=IGNORE_ID)
        batch[i] = (padded_input, padded_target, input_length)

    # sort it by input lengths (long to short)
    batch.sort(key=lambda x: x[2], reverse=True)

    return default_collate(batch) 

def collate_minibatch(list_of_blobs):
    """Stack samples seperately and return a list of minibatches
    A batch contains NUM_GPUS minibatches and image size in different minibatch may be different.
    Hence, we need to stack smaples from each minibatch seperately.
    """
    Batch = {key: [] for key in list_of_blobs[0]}
    # Because roidb consists of entries of variable length, it can't be batch into a tensor.
    # So we keep roidb in the type of "list of ndarray".
    list_of_roidb = [blobs.pop('roidb') for blobs in list_of_blobs]
    for i in range(0, len(list_of_blobs), cfg.TRAIN.IMS_PER_BATCH):
        mini_list = list_of_blobs[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        # Pad image data
        mini_list = pad_image_data(mini_list)
        minibatch = default_collate(mini_list)
        minibatch['roidb'] = list_of_roidb[i:(i + cfg.TRAIN.IMS_PER_BATCH)]
        for key in minibatch:
            Batch[key].append(minibatch[key])

    return Batch 

def __init__(self, dataset, batch_size, shuffle, validation_split, num_workers, collate_fn=default_collate):
        self.validation_split = validation_split
        self.shuffle = shuffle

        self.batch_idx = 0
        self.n_samples = len(dataset)

        self.sampler, self.valid_sampler = self._split_sampler(self.validation_split)

        self.init_kwargs = {
            'dataset': dataset,
            'batch_size': batch_size,
            'shuffle': self.shuffle,
            'collate_fn': collate_fn,
            'num_workers': num_workers
        }
        super(BaseDataLoader, self).__init__(sampler=self.sampler, **self.init_kwargs) 

def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None,
                 num_workers=0, collate_fn=default_collate, pin_memory=False, drop_last=False,
                 timeout=0, base_seed=None, worker_init_fn=None, worker_init_args=None, worker_init_kwargs=None,
                 worker_recv_fn=None, **kwargs):

        worker_init_args = worker_init_args if worker_init_args is not None else [tuple() for _ in range(num_workers)]
        worker_init_kwargs = worker_init_kwargs if worker_init_kwargs is not None else [{} for _ in range(num_workers)]

        base_seed = base_seed if base_seed is not None else gen_seed()
        self.worker_recv_fn = worker_recv_fn
        if worker_recv_fn is not None:
            self.pipe_master = DataLoaderPipeMaster(num_workers)
        else:
            self.pipe_master = None

        worker_init_fn = _InitFunctionWrapper(
            base_seed, worker_init_fn, worker_init_args, worker_init_kwargs,
            self.pipe_master, DataLoaderPipeSlave(worker_recv_fn)
        )
        super().__init__(dataset, batch_size=batch_size, shuffle=shuffle, sampler=sampler, batch_sampler=batch_sampler,
                         num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory, drop_last=drop_last,
                         timeout=timeout, worker_init_fn=worker_init_fn, **kwargs) 

def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None,
                 num_workers=0, collate_fn=default_collate, pin_memory=False, drop_last=False, seed=None,
                 worker_init_fn=None, worker_init_args=None, worker_init_kwargs=None):
        super().__init__(dataset, batch_size=batch_size, shuffle=shuffle, sampler=sampler, batch_sampler=batch_sampler,
                         num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory, drop_last=drop_last)
        self.worker_init_fn = worker_init_fn
        self.worker_init_args = worker_init_args
        self.worker_init_kwargs = worker_init_kwargs

        if num_workers > 0:
            self.seed_generator = gen_rng(seed)
            self.worker_init_args = worker_init_args if worker_init_args is not None else [tuple() for _ in range(num_workers)]
            self.worker_init_kwargs = worker_init_kwargs if worker_init_kwargs is not None else [{} for _ in range(num_workers)]
        else:
            self.worker_init_args = worker_init_args if worker_init_args is not None else tuple()
            self.worker_init_kwargs = worker_init_kwargs if worker_init_kwargs is not None else {}