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))
def _worker_loop(dataset, index_queue, data_queue, collate_fn, seed, init_fn, worker_id):
global _use_shared_memory
_use_shared_memory = True
# Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
# module's handlers are executed after Python returns from C low-level
# handlers, likely when the same fatal signal happened again already.
# https://docs.python.org/3/library/signal.html Sec. 18.8.1.1
_set_worker_signal_handlers()
torch.set_num_threads(1)
torch.manual_seed(seed)
if init_fn is not None:
init_fn(worker_id)
while True:
r = index_queue.get()
if r is None:
break
idx, batch_indices = r
try:
samples = collate_fn([dataset[i] for i in batch_indices])
except Exception:
data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
else:
data_queue.put((idx, samples))
def _worker_loop(dataset, index_queue, data_queue, collate_fn, seed, init_fn,
worker_id):
global _use_shared_memory
_use_shared_memory = True
_set_worker_signal_handlers()
torch.set_num_threads(1)
random.seed(seed)
torch.manual_seed(seed)
if init_fn is not None:
init_fn(worker_id)
watchdog = ManagerWatchdog()
while True:
try:
r = index_queue.get(timeout=MANAGER_STATUS_CHECK_INTERVAL)
except queue.Empty:
if watchdog.is_alive():
continue
else:
break
if r is None:
break
idx, batch_indices = r
try:
samples = collate_fn([dataset[i] for i in batch_indices])
except Exception:
data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
else:
data_queue.put((idx, samples))
del samples
def _worker_loop(dataset, index_queue, data_queue, collate_fn, seed, init_fn, worker_id):
global _use_shared_memory
_use_shared_memory = True
# Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
# module's handlers are executed after Python returns from C low-level
# handlers, likely when the same fatal signal happened again already.
# https://docs.python.org/3/library/signal.html Sec. 18.8.1.1
_set_worker_signal_handlers()
torch.set_num_threads(1)
torch.manual_seed(seed)
np.random.seed(seed)
if init_fn is not None:
init_fn(worker_id)
while True:
r = index_queue.get()
if r is None:
break
idx, batch_indices = r
try:
samples = collate_fn([dataset[i] for i in batch_indices])
except Exception:
data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
else:
data_queue.put((idx, samples))
def _worker_loop(dataset, index_queue, data_queue, done_event, collate_fn,
seed, init_fn, worker_id):
# See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on the
# logic of this function.
try:
global _use_shared_memory
_use_shared_memory = True
# Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
# module's handlers are executed after Python returns from C low-level
# handlers, likely when the same fatal signal happened again already.
# https://docs.python.org/3/library/signal.html Sec. 18.8.1.1
_set_worker_signal_handlers()
torch.set_num_threads(1)
# 设置随机发生器的起始种子点
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:
# 'class' dataset 的调用： dataset.__getitem__(i) ==> dataset[i]
# 可以在函数__getitem__(i)中进行更多的图像操作(如，crop和resize等)
samples = collate_fn([dataset[i] for i in batch_indices])
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
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
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()
watchdog = _utils.worker.ManagerWatchdog()
while watchdog.is_alive():
# try:
# r = index_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
try:
r = index_queue.get(timeout=_utils.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())))
except Exception:
data_queue.put((idx, _utils.ExceptionWrapper(sys.exc_info())))
else:
data_queue.put((idx, samples))
except KeyboardInterrupt:
pass
def _worker_loop(dataset, index_queue, data_queue, done_event, collate_fn,
seed, init_fn, worker_id):
try:
global _use_shared_memory
_use_shared_memory = True
# Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
# module's handlers are executed after Python returns from C low-level
# handlers, likely when the same fatal signal happened again already.
# https://docs.python.org/3/library/signal.html Sec. 18.8.1.1
_set_worker_signal_handlers()
torch.set_num_threads(1)
random.seed(seed)
torch.manual_seed(seed)
# Do not wait for putting thread to join when this worker exits.
# Otherwise, this worker may always be waiting to put and doesn't check
# index_queue and done_event for termination signal.
data_queue.cancel_join_thread()
if init_fn is not None:
init_fn(worker_id)
watchdog = ManagerWatchdog()
while True:
try:
r = index_queue.get(timeout=MANAGER_STATUS_CHECK_INTERVAL)
except queue.Empty:
if watchdog.is_alive() and not done_event.is_set():
continue
else:
break
# use done_event so that we can get faster exiting signal even if there
# are still indices in index_queue
if r is None or done_event.is_set():
break
idx, batch_indices = r
try:
samples = collate_fn([dataset[i] for i in batch_indices])
except Exception:
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
def _worker_loop(dataset, index_queue, data_queue, collate_fn, seed, init_fn,
worker_id):
global _use_shared_memory
_use_shared_memory = True
# Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
# module's handlers are executed after Python returns from C low-level
# handlers, likely when the same fatal signal happened again already.
# https://docs.python.org/3/library/signal.html Sec. 18.8.1.1
_set_worker_signal_handlers()
torch.set_num_threads(1)
random.seed(seed)
torch.manual_seed(seed)
if init_fn is not None:
init_fn(worker_id)
watchdog = ManagerWatchdog()
while True:
try:
r = index_queue.get(timeout=MANAGER_STATUS_CHECK_INTERVAL)
except queue.Empty:
if watchdog.is_alive():
continue
else:
break
if r is None:
break
idx, batch_indices = r
try:
start_time = time.time()
batch_examples = [dataset[i] for i in batch_indices]
end_time = time.time()
print('Obtain a batch: %f seconds' % (end_time - start_time))
start_time = time.time()
samples = collate_fn(batch_examples)
end_time = time.time()
print('Process a batch: %f seconds' % (end_time - start_time))
except Exception:
data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
else:
data_queue.put((idx, samples))
del samples
print('current len(queue)=%d' % data_queue.qsize())
def _worker_loop(gen, data_queue, collate_fn, batch_size, seed, worker_id):
global _use_shared_memory
_use_shared_memory = True
# Initialize C side signal handlers for SIGBUS and SIGSEGV
_set_worker_signal_handlers()
torch.set_num_threads(1)
random.seed(seed)
torch.manual_seed(seed)
while True:
try:
samples = collate_fn([gen.get() for _ in range(batch_size)])
except Exception:
data_queue.put(ExceptionWrapper(sys.exc_info()))
else:
data_queue.put(samples, True)
def _worker_loop(
data_reader,
batch_queue,
data_queue,
global_done_event,
worker_done_event,
seed,
init_fn,
worker_id,
):
# Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
# module's handlers are executed after Python returns from C low-level
# handlers, likely when the same fatal signal happened again already.
# https://docs.python.org/3/library/signal.html Sec. 18.8.1.1
_set_worker_signal_handlers()
torch.set_num_threads(1)
random.seed(seed)
# TODO: numpy doesn't take seed bigger than INT32
# np.random.seed(seed)
torch.manual_seed(seed)
# Do not wait for putting thread to join when this worker exits. Otherwise,
# this worker may always be waiting to put and doesn't check batch_queue
# and global_done_event for termination signal.
data_queue.cancel_join_thread()
if init_fn is not None:
init_fn(worker_id)
watchdog = ManagerWatchdog()
shard = data_reader.get_shard(worker_id)
shard_itr = iter(shard)
shard_done = False
while True:
if shard_done:
# Wait until the main thread acknowledge the WorkerDone message or
# it signals shutdown.
if (
not watchdog.is_alive()
or global_done_event.is_set()
or worker_done_event.wait(0.1)
):
break
continue
try:
idx = batch_queue.get(timeout=MANAGER_STATUS_CHECK_INTERVAL)
except queue.Empty:
if watchdog.is_alive() and not global_done_event.is_set():
continue
else:
break
# use global_done_event so that we can get faster exiting signal even if there
# are still batches in batch_queue
if idx is None or global_done_event.is_set():
break
try:
samples = next(shard_itr)
except StopIteration:
# Signal to the main thread that this worker has run out of data.
# The worker cannot exit immediately because the queue might not be
# flushed immediately.
data_queue.put((idx, WorkerDone(worker_id)))
shard_done = True
except Exception:
data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
else:
data_queue.put((idx, samples))
del samples
