this might be caused by insufficient shared memory (shm), please check whether use_shared_memory is set and storage space in /dev/shm is enough" 时,通常表示 PyTorch 的 DataLoader 在使用多进程加载数据时遇到了共享内存不足的问题。下面是
from torch.utils.data.dataloader import DataLoader from torch.utils.data.dataloader import _use_shared_memory _use_shared_memory(False) # 避免使用共享内存 dataloader = DataLoader(dataset, batch_size=32, shuffle=True, num_workers=4, multiprocessing_context='spawn') 5. 最佳实践 选择合适的num_work...
若 use_buffer_reader 为 True,DataLoader 会异步地预读取 prefetch_factor 个 mini-batch。默认值为 2。 use_shared_memory (bool,可选) - 是否使用共享内存来提升子进程将数据放入进程间队列的速度,该参数尽在多进程模式下有效(即 num_workers > 0 ),请确认机器上有足够的共享内存空间(如 Linux 系统下 ...
def _worker_loop(dataset, index_queue, data_queue, collate_fn): global _use_shared_memory _use_shared_memory = True torch.set_num_threads(1) while True: r = index_queue.get() if r is None: #想 data_queue 中放 None data_queue.put(None) break idx, batch_indices = r try: # 这...
_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. ...
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, like...
self.batch_size =Noneself.drop_last =NoneifsamplerisnotNoneandshuffle:# sampler 和shuffle不能兼容raiseValueError('sampler option is mutually exclusive with ''shuffle')ifself.num_workers <0:raiseValueError('num_workers option cannot be negative; ''use num_workers=0 to disable multiprocessing.')if...
is_tensor(batch[0]): out = None if _use_shared_memory: # If we're in a background process, concatenate directly into a # shared memory tensor to avoid an extra copy # 计算 batch 中所有 元素的个数 numel = sum([x.numel() for x in batch]) # 没有找到对应的 api 。。。 storage ...
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...
_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....