def __init__(self,
dataset,
batch_size=None,
shuffle=False,
sampler=None,
last_batch=None,
batch_sampler=None,
batchify_fn=None,
num_workers=0,
pin_memory=False,
prefetch=None,
thread_pool=False):
self._dataset = dataset
self._pin_memory = pin_memory
self._thread_pool = thread_pool
if batch_sampler is None:
if batch_size is None:
raise ValueError('batch_size must be specified unless ' \
'batch_sampler is specified')
if sampler is None:
if shuffle:
sampler = _sampler.RandomSampler(len(dataset))
else:
sampler = _sampler.SequentialSampler(len(dataset))
elif shuffle:
raise ValueError(
'shuffle must not be specified if sampler is specified')
batch_sampler = _sampler.BatchSampler(
sampler, batch_size, last_batch if last_batch else 'keep')
elif batch_size is not None or shuffle or sampler is not None or \
last_batch is not None:
raise ValueError('batch_size, shuffle, sampler and last_batch must ' \
'not be specified if batch_sampler is specified.')
self._batch_sampler = batch_sampler
self._num_workers = num_workers if num_workers >= 0 else 0
self._worker_pool = None
self._prefetch = max(
0,
int(prefetch) if prefetch is not None else 2 * self._num_workers)
if self._num_workers > 0:
if self._thread_pool:
self._worker_pool = ThreadPool(self._num_workers)
else:
self._worker_pool = multiprocessing.Pool(
self._num_workers,
initializer=_worker_initializer,
initargs=[self._dataset])
if batchify_fn is None:
if num_workers > 0:
self._batchify_fn = default_mp_batchify_fn
else:
self._batchify_fn = default_batchify_fn
else:
self._batchify_fn = batchify_fn
def __init__(self, dataset, batch_size=None, shuffle=False, sampler=None,
last_batch=None, batch_sampler=None, batchify_fn=None,
num_workers=0, pin_memory=False, pin_device_id=0,
prefetch=None, thread_pool=False, timeout=120,
sample_times=None):
self._dataset = dataset
self._pin_memory = pin_memory
self._pin_device_id = pin_device_id
self._thread_pool = thread_pool
self._timeout = timeout
self._sample_times = sample_times
assert timeout > 0, "timeout must be positive, given {}".format(timeout)
if batch_sampler is None:
if batch_size is None:
raise ValueError("batch_size must be specified unless " \
"batch_sampler is specified")
if sampler is None:
if shuffle:
sampler = _sampler.RandomSampler(len(dataset))
else:
sampler = _sampler.SequentialSampler(len(dataset))
elif shuffle:
raise ValueError("shuffle must not be specified if sampler is specified")
batch_sampler = _sampler.BatchSampler(
sampler, batch_size, last_batch if last_batch else 'keep')
elif batch_size is not None or shuffle or sampler is not None or \
last_batch is not None:
raise ValueError("batch_size, shuffle, sampler and last_batch must " \
"not be specified if batch_sampler is specified.")
self._batch_sampler = batch_sampler
self._num_workers = num_workers if num_workers >= 0 else 0
self._worker_pool = None
self._prefetch = max(0, int(prefetch) if prefetch is not None else 2 * self._num_workers)
if self._num_workers > 0:
if self._thread_pool:
self._worker_pool = ThreadPool(self._num_workers,
initializer=_thread_worker_initializer,
initargs=(False, False))
else:
# set ignore keyboard interupt signal before forking processes
original_sigint_handler = signal.signal(signal.SIGINT, signal.SIG_IGN)
self._worker_pool = multiprocessing.Pool(
self._num_workers, initializer=_worker_initializer,
initargs=[self._dataset, False, False])
# resume keyboard interupt signal in main process
signal.signal(signal.SIGINT, original_sigint_handler)
if batchify_fn is None:
if num_workers > 0:
self._batchify_fn = default_mp_batchify_fn
else:
self._batchify_fn = default_batchify_fn
else:
self._batchify_fn = batchify_fn
def __init__(self,
dataset,
batch_size=None,
shuffle=False,
sampler=None,
last_batch=None,
batch_sampler=None,
batchify_fn=None,
num_workers=0,
pin_memory=False,
pin_device_id=0):
self._dataset = dataset
self._pin_memory = pin_memory
self._pin_device_id = pin_device_id
if batch_sampler is None:
if batch_size is None:
raise ValueError("batch_size must be specified unless " \
"batch_sampler is specified")
if sampler is None:
if shuffle:
sampler = _sampler.RandomSampler(len(dataset))
else:
sampler = _sampler.SequentialSampler(len(dataset))
elif shuffle:
raise ValueError(
"shuffle must not be specified if sampler is specified")
batch_sampler = _sampler.BatchSampler(
sampler, batch_size, last_batch if last_batch else 'keep')
elif batch_size is not None or shuffle or sampler is not None or \
last_batch is not None:
raise ValueError("batch_size, shuffle, sampler and last_batch must " \
"not be specified if batch_sampler is specified.")
self._batch_sampler = batch_sampler
self._num_workers = num_workers if num_workers >= 0 else 0
if batchify_fn is None:
if num_workers > 0:
self._batchify_fn = default_mp_batchify_fn
else:
self._batchify_fn = default_batchify_fn
else:
self._batchify_fn = batchify_fn
def __init__(self,
dataset,
batch_size=None,
shuffle=False,
sampler=None,
last_batch=None,
batch_sampler=None,
batchify_fn=None,
num_workers=0,
pin_memory=False,
pin_device_id=0,
prefetch=None,
thread_pool=False,
timeout=120,
ctx_list=[cpu()]):
self._dataset = dataset
self._pin_memory = pin_memory
self._pin_device_id = pin_device_id
self._thread_pool = thread_pool
self._timeout = timeout
self._ctx_list = ctx_list
assert timeout > 0, "timeout must be positive, given {}".format(
timeout)
# try to split the batch into shards for contexts
num_ctx = len(ctx_list)
bs_per_ctx = math.ceil(batch_size / float(num_ctx))
bs_list = [bs_per_ctx] * (num_ctx - 1)
bs_list += [batch_size - sum(bs_list)]
assert bs_list[-1] <= bs_per_ctx
self._bs_list = np.cumsum([0] + bs_list)
if pin_memory:
warnings.warn('pin_memory not supported.')
pin_memory = False
if batch_sampler is None:
if batch_size is None:
raise ValueError("batch_size must be specified unless " \
"batch_sampler is specified")
if sampler is None:
if shuffle:
sampler = _sampler.RandomSampler(len(dataset))
else:
sampler = _sampler.SequentialSampler(len(dataset))
elif shuffle:
raise ValueError(
"shuffle must not be specified if sampler is specified")
batch_sampler = _sampler.BatchSampler(
sampler, batch_size, last_batch if last_batch else 'keep')
elif batch_size is not None or shuffle or sampler is not None or \
last_batch is not None:
raise ValueError("batch_size, shuffle, sampler and last_batch must " \
"not be specified if batch_sampler is specified.")
self._batch_sampler = batch_sampler
self._num_workers = num_workers if num_workers >= 0 else 0
if batchify_fn is None:
self._batchify_fn = default_batchify_fn
else:
self._batchify_fn = batchify_fn
self._iter = None
self._pool = None
if len(ctx_list) == 1 and ctx_list[0] == cpu() and num_workers > 0:
self._iter = DataLoader(dataset,
batch_size=batch_size,
shuffle=shuffle,
batch_sampler=batch_sampler,
last_batch=last_batch,
batchify_fn=batchify_fn,
num_workers=num_workers,
pin_memory=pin_memory,
pin_device_id=pin_device_id,
prefetch=prefetch,
thread_pool=thread_pool,
timeout=timeout)
elif num_workers > 0:
self._pool = ThreadPool(self._num_workers,
initializer=_thread_worker_initializer,
initargs=(is_np_shape(), is_np_array()))
