本文整理汇总了Python中config.config.num_workers方法的典型用法代码示例。如果您正苦于以下问题：Python config.num_workers方法的具体用法？Python config.num_workers怎么用？Python config.num_workers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config.config的用法示例。
在下文中一共展示了config.num_workers方法的5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: get_train_loader
# 需要导入模块: from config import config [as 别名]
# 或者: from config.config import num_workers [as 别名]
def get_train_loader(engine, dataset):
data_setting = {'img_root': config.img_root_folder,
'gt_root': config.gt_root_folder,
'train_source': config.train_source,
'eval_source': config.eval_source}
train_preprocess = TrainPre(config.image_mean, config.image_std,
config.target_size)
train_dataset = dataset(data_setting, "train", train_preprocess,
config.niters_per_epoch * config.batch_size)
train_sampler = None
is_shuffle = True
batch_size = config.batch_size
if engine.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
batch_size = config.batch_size // engine.world_size
is_shuffle = False
train_loader = data.DataLoader(train_dataset,
batch_size=batch_size,
num_workers=config.num_workers,
drop_last=True,
shuffle=is_shuffle,
pin_memory=True,
sampler=train_sampler)
return train_loader, train_sampler 示例2: get_train_loader
# 需要导入模块: from config import config [as 别名]
# 或者: from config.config import num_workers [as 别名]
def get_train_loader(engine, dataset):
data_setting = {'train_root': config.train_root_folder,
'val_root': config.eval_root_folder,
'train_source': config.train_source,
'eval_source': config.eval_source}
train_preprocess = TrainPre(config.image_mean, config.image_std)
train_dataset = dataset(data_setting, "train", train_preprocess, \
config.batch_size * config.niters_per_epoch)
train_sampler = None
is_shuffle = True
batch_size = config.batch_size
if engine.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
batch_size = config.batch_size // engine.world_size
is_shuffle = False
# import pdb;pdb.set_trace()
train_loader = data.DataLoader(train_dataset,
batch_size=batch_size,
num_workers=config.num_workers,
drop_last=True,
shuffle=is_shuffle,
pin_memory=True,
sampler=train_sampler)
return train_loader, train_sampler 示例3: get_train_loader
# 需要导入模块: from config import config [as 别名]
# 或者: from config.config import num_workers [as 别名]
def get_train_loader(engine, dataset):
data_setting = {'img_root': config.img_root_folder,
'gt_root': config.gt_root_folder,
'train_source': config.train_source,
'eval_source': config.eval_source}
train_preprocess = TrainPre(config.image_mean, config.image_std,
config.target_size)
train_dataset = dataset(data_setting, "train", train_preprocess,
config.niters_per_epoch * config.batch_size)
train_sampler = None
is_shuffle = True
batch_size = config.batch_size
if engine.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
batch_size = config.batch_size // engine.world_size
is_shuffle = False
train_loader = data.DataLoader(train_dataset,
batch_size=batch_size,
num_workers=config.num_workers,
drop_last=False,
shuffle=is_shuffle,
pin_memory=True,
sampler=train_sampler)
return train_loader, train_sampler 示例4: get_train_loader
# 需要导入模块: from config import config [as 别名]
# 或者: from config.config import num_workers [as 别名]
def get_train_loader(engine, dataset):
data_setting = {'img_root': config.img_root_folder,
'gt_root': config.gt_root_folder,
'train_source': config.train_source,
'eval_source': config.eval_source}
train_preprocess = TrainPre(config.image_mean, config.image_std)
train_dataset = dataset(data_setting, "train", train_preprocess,
config.batch_size * config.niters_per_epoch)
train_sampler = None
is_shuffle = True
batch_size = config.batch_size
if engine.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
batch_size = config.batch_size // engine.world_size
is_shuffle = False
train_loader = data.DataLoader(train_dataset,
batch_size=batch_size,
num_workers=config.num_workers,
drop_last=True,
shuffle=is_shuffle,
pin_memory=True,
sampler=train_sampler)
return train_loader, train_sampler 示例5: __init__
# 需要导入模块: from config import config [as 别名]
# 或者: from config.config import num_workers [as 别名]
def __init__(self, symbol, data_names, label_names,
logger=logging, context=ctx.cpu(), work_load_list=None,
asymbol = None,
args = None):
super(ParallModule, self).__init__(logger=logger)
self._symbol = symbol
self._asymbol = asymbol
self._data_names = data_names
self._label_names = label_names
self._context = context
self._work_load_list = work_load_list
self._num_classes = config.num_classes
self._batch_size = args.batch_size
self._verbose = args.verbose
self._emb_size = config.emb_size
self._local_class_start = args.local_class_start
self._iter = 0
self._curr_module = None
self._num_workers = config.num_workers
self._num_ctx = len(self._context)
self._ctx_num_classes = args.ctx_num_classes
self._nd_cache = {}
self._ctx_cpu = mx.cpu()
self._ctx_single_gpu = self._context[-1]
self._fixed_param_names = None
self._curr_module = Module(self._symbol, self._data_names, self._label_names, logger=self.logger,
context=self._context, work_load_list=self._work_load_list,
fixed_param_names=self._fixed_param_names)
self._arcface_modules = []
self._ctx_class_start = []
for i in range(len(self._context)):
args._ctxid = i
_module = Module(self._asymbol(args), self._data_names, self._label_names, logger=self.logger,
context=mx.gpu(i), work_load_list=self._work_load_list,
fixed_param_names=self._fixed_param_names)
self._arcface_modules.append(_module)
_c = args.local_class_start + i*args.ctx_num_classes
self._ctx_class_start.append(_c)
self._usekv = False
if self._usekv:
self._distkv = mx.kvstore.create('dist_sync')
self._kvinit = {}