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import torch
import torch.multiprocessing as multiprocessing
from torch.utils.data import DataLoader
from torch.utils.data import SequentialSampler
from torch.utils.data import RandomSampler
from torch.utils.data import BatchSampler
from torch.utils.data import _utils
from torch.utils.data.dataloader import _DataLoaderIter
from torch.utils.data._utils import collate
from torch.utils.data._utils import signal_handling
from torch.utils.data._utils import MP_STATUS_CHECK_INTERVAL
from torch.utils.data._utils import ExceptionWrapper
from torch.utils.data._utils import IS_WINDOWS
from torch.utils.data._utils.worker import ManagerWatchdog
from torch._six import queue
def _ms_loop(dataset, index_queue, data_queue, done_event, collate_fn, scale, seed, init_fn, worker_id):
try:
collate._use_shared_memory = True
signal_handling._set_worker_signal_handlers()
torch.set_num_threads(1)
random.seed(seed)
torch.manual_seed(seed)
data_queue.cancel_join_thread()
if init_fn is not None:
init_fn(worker_id)
watchdog = ManagerWatchdog()
while watchdog.is_alive():
try:
r = index_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
except queue.Empty:
continue
if r is None:
assert done_event.is_set()
return
elif done_event.is_set():
continue
idx, batch_indices = r
try:
idx_scale = 0
if len(scale) > 1 and dataset.train:
idx_scale = random.randrange(0, len(scale))
dataset.set_scale(idx_scale)
samples = collate_fn([dataset[i] for i in batch_indices])
samples.append(idx_scale)
except Exception:
data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
else:
data_queue.put((idx, samples))
del samples
except KeyboardInterrupt:
pass
class _MSDataLoaderIter(_DataLoaderIter):
def __init__(self, loader):
self.dataset = loader.dataset
self.scale = loader.scale
self.collate_fn = loader.collate_fn
self.batch_sampler = loader.batch_sampler
self.num_workers = loader.num_workers
self.pin_memory = loader.pin_memory and torch.cuda.is_available()
self.timeout = loader.timeout
self.sample_iter = iter(self.batch_sampler)
base_seed = torch.LongTensor(1).random_().item()
if self.num_workers > 0:
self.worker_init_fn = loader.worker_init_fn
self.worker_queue_idx = 0
self.worker_result_queue = multiprocessing.Queue()
self.batches_outstanding = 0
self.worker_pids_set = False
self.shutdown = False
self.send_idx = 0
self.rcvd_idx = 0
self.reorder_dict = {}
self.done_event = multiprocessing.Event()
base_seed = torch.LongTensor(1).random_()[0]
self.index_queues = []
self.workers = []
for i in range(self.num_workers):
index_queue = multiprocessing.Queue()
index_queue.cancel_join_thread()
w = multiprocessing.Process(
target=_ms_loop,
args=(
self.dataset,
index_queue,
self.worker_result_queue,
self.done_event,
self.collate_fn,
self.scale,
base_seed + i,
self.worker_init_fn,
i
)
)
w.daemon = True
w.start()
self.index_queues.append(index_queue)
self.workers.append(w)
if self.pin_memory:
self.data_queue = queue.Queue()
pin_memory_thread = threading.Thread(
target=_utils.pin_memory._pin_memory_loop,
args=(
self.worker_result_queue,
self.data_queue,
torch.cuda.current_device(),
self.done_event
)
)
pin_memory_thread.daemon = True
pin_memory_thread.start()
self.pin_memory_thread = pin_memory_thread
else:
self.data_queue = self.worker_result_queue
_utils.signal_handling._set_worker_pids(
id(self), tuple(w.pid for w in self.workers)
)
_utils.signal_handling._set_SIGCHLD_handler()
self.worker_pids_set = True
for _ in range(2 * self.num_workers):
self._put_indices()
class MSDataLoader(DataLoader):
def __init__(self, cfg, *args, **kwargs):
super(MSDataLoader, self).__init__(
*args, **kwargs, num_workers=cfg.n_threads
)
self.scale = cfg.scale
def __iter__(self):
return _MSDataLoaderIter(self)
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