本文整理汇总了Python中torch.backends方法的典型用法代码示例。如果您正苦于以下问题：Python torch.backends方法的具体用法？Python torch.backends怎么用？Python torch.backends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的用法示例。
在下文中一共展示了torch.backends方法的2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: main
# 需要导入模块: import torch [as 别名]
# 或者: from torch import backends [as 别名]
def main(config):
from torch.backends import cudnn
# For fast training
cudnn.benchmark = True
data_loader = get_loader(
config.mode_data,
config.image_size,
config.batch_size,
config.dataset_fake,
config.mode,
num_workers=config.num_workers,
all_attr=config.ALL_ATTR,
c_dim=config.c_dim)
from misc.scores import set_score
if set_score(config):
return
if config.mode == 'train':
from train import Train
Train(config, data_loader)
from test import Test
test = Test(config, data_loader)
test(dataset=config.dataset_real)
elif config.mode == 'test':
from test import Test
test = Test(config, data_loader)
if config.DEMO_PATH:
test.DEMO(config.DEMO_PATH)
else:
test(dataset=config.dataset_real) 示例2: main
# 需要导入模块: import torch [as 别名]
# 或者: from torch import backends [as 别名]
def main(args):
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
args.cuda = args.cuda and torch.cuda.is_available()
if args.cuda:
print('using cuda.')
torch.set_default_tensor_type('torch.cuda.FloatTensor')
else:
torch.set_default_tensor_type('torch.FloatTensor')
# Create data loaders
if args.height is None or args.width is None:
args.height, args.width = (32, 100)
dataset_info = DataInfo(args.voc_type)
# Create model
model = ModelBuilder(arch=args.arch, rec_num_classes=dataset_info.rec_num_classes,
sDim=args.decoder_sdim, attDim=args.attDim, max_len_labels=args.max_len,
eos=dataset_info.char2id[dataset_info.EOS], STN_ON=args.STN_ON)
# Load from checkpoint
if args.resume:
checkpoint = load_checkpoint(args.resume)
model.load_state_dict(checkpoint['state_dict'])
if args.cuda:
device = torch.device("cuda")
model = model.to(device)
model = nn.DataParallel(model)
# Evaluation
model.eval()
img = image_process(args.image_path)
with torch.no_grad():
img = img.to(device)
input_dict = {}
input_dict['images'] = img.unsqueeze(0)
# TODO: testing should be more clean.
# to be compatible with the lmdb-based testing, need to construct some meaningless variables.
rec_targets = torch.IntTensor(1, args.max_len).fill_(1)
rec_targets[:,args.max_len-1] = dataset_info.char2id[dataset_info.EOS]
input_dict['rec_targets'] = rec_targets
input_dict['rec_lengths'] = [args.max_len]
output_dict = model(input_dict)
pred_rec = output_dict['output']['pred_rec']
pred_str, _ = get_str_list(pred_rec, input_dict['rec_targets'], dataset=dataset_info)
print('Recognition result: {0}'.format(pred_str[0]))