本文整理汇总了Python中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的用法示例。
在下文中一共展示了config.num_workers方法的1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: train
# 需要导入模块: import config [as 别名]
# 或者: from config import num_workers [as 别名]
def train(config):
# prepare
if not os.path.exists(config.save_dir):
os.mkdir(config.save_dir)
use_cuda = torch.cuda.is_available()
# define the model
model = NetsTorch(net_name=config.net_name, pretrained=config.load_pretrained, num_classes=config.num_classes)
if use_cuda:
os.environ['CUDA_VISIBLE_DEVICES'] = config.gpus
if config.ngpus > 1:
model = nn.DataParallel(model).cuda()
else:
model = model.cuda()
model.train()
# dataset
dataset_train = ImageFolder(data_dir=config.traindata_dir, image_size=config.image_size, is_train=True)
saveClasses(dataset_train.classes, config.clsnamespath)
dataset_test = ImageFolder(data_dir=config.testdata_dir, image_size=config.image_size, is_train=False)
dataloader_train = torch.utils.data.DataLoader(dataset_train, batch_size=config.batch_size, shuffle=False, num_workers=config.num_workers)
dataloader_test = torch.utils.data.DataLoader(dataset_test, batch_size=config.batch_size, shuffle=False, num_workers=config.num_workers)
Logging('Train dataset size: %d...' % len(dataset_train), config.logfile)
Logging('Test dataset size: %d...' % len(dataset_test), config.logfile)
# optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
criterion = nn.CrossEntropyLoss()
# train
FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
for epoch in range(1, config.num_epochs+1):
Logging('[INFO]: epoch now is %d...' % epoch, config.logfile)
for batch_i, (imgs, labels) in enumerate(dataloader_train):
imgs = imgs.type(FloatTensor)
labels = labels.type(FloatTensor)
optimizer.zero_grad()
preds = model(imgs)
loss = criterion(preds, labels.long())
if config.ngpus > 1:
loss = loss.mean()
Logging('[INFO]: batch%d of epoch%d, loss is %.2f...' % (batch_i, epoch, loss.item()), config.logfile)
loss.backward()
optimizer.step()
if ((epoch % config.save_interval == 0) and (epoch > 0)) or (epoch == config.num_epochs):
pklpath = os.path.join(config.save_dir, 'epoch_%s.pkl' % str(epoch))
if config.ngpus > 1:
cur_model = model.module
else:
cur_model = model
torch.save(cur_model.state_dict(), pklpath)
acc = test(model, dataloader_test)
Logging('[INFO]: Accuracy of epoch %d is %.2f...' % (epoch, acc), config.logfile)