本文整理汇总了Python中torch.utils.data.dataloader.ExceptionWrapper方法的典型用法代码示例。如果您正苦于以下问题：Python dataloader.ExceptionWrapper方法的具体用法？Python dataloader.ExceptionWrapper怎么用？Python dataloader.ExceptionWrapper使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torch.utils.data.dataloader的用法示例。
在下文中一共展示了dataloader.ExceptionWrapper方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _ms_loop
# 需要导入模块: from torch.utils.data import dataloader [as 别名]
# 或者: from torch.utils.data.dataloader import ExceptionWrapper [as 别名]
def _ms_loop(dataset, index_queue, data_queue, collate_fn, scale, seed, init_fn, worker_id):
global _use_shared_memory
_use_shared_memory = True
_set_worker_signal_handlers()
torch.set_num_threads(1)
torch.manual_seed(seed)
while True:
r = index_queue.get()
if r is None:
break
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)) 示例2: _ms_loop
# 需要导入模块: from torch.utils.data import dataloader [as 别名]
# 或者: from torch.utils.data.dataloader import ExceptionWrapper [as 别名]
def _ms_loop(dataset, index_queue, data_queue, collate_fn, scale, seed, init_fn, worker_id):
global _use_shared_memory
_use_shared_memory = True
_set_worker_signal_handlers()
torch.set_num_threads(1)
torch.manual_seed(seed)
while True:
r = index_queue.get()
if r is None:
break
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)
#This is why idx_scale appears in the samples of the train loader
except Exception:
data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
else:
data_queue.put((idx, samples)) 示例3: _ms_loop
# 需要导入模块: from torch.utils.data import dataloader [as 别名]
# 或者: from torch.utils.data.dataloader import ExceptionWrapper [as 别名]
def _ms_loop(dataset, index_queue, data_queue, done_event, collate_fn, scale, seed, init_fn, worker_id):
try:
global _use_shared_memory
_use_shared_memory = True
_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:
# Received the final signal
assert done_event.is_set()
return
elif done_event.is_set():
# Done event is set. But I haven't received the final signal
# (None) yet. I will keep continuing until get it, and skip the
# processing steps.
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)
#This is why idx_scale appears in the samples of the train loader
except Exception:
# It is important that we don't store exc_info in a variable,
# see NOTE [ Python Traceback Reference Cycle Problem ]
data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
else:
data_queue.put((idx, samples))
del samples
except KeyboardInterrupt:
# Main process will raise KeyboardInterrupt anyways.
pass