本文整理汇总了Python中model.getModel方法的典型用法代码示例。如果您正苦于以下问题：Python model.getModel方法的具体用法？Python model.getModel怎么用？Python model.getModel使用的例子？那么, 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model的用法示例。
在下文中一共展示了model.getModel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 model [as 别名]
# 或者: from model import getModel [as 别名]
def main():
now = datetime.datetime.now()
logger = Logger(opt.saveDir + '/logs_{}'.format(now.isoformat()))
model, optimizer = getModel(opt)
criterion = torch.nn.MSELoss()
if opt.GPU > -1:
print('Using GPU', opt.GPU)
model = model.cuda(opt.GPU)
criterion = criterion.cuda(opt.GPU)
val_loader = torch.utils.data.DataLoader(
Dataset(opt, 'val'),
batch_size = 1,
shuffle = True if opt.DEBUG > 1 else False,
num_workers = 1
)
if opt.test:
_, preds = val(0, opt, val_loader, model, criterion)
torch.save({'opt': opt, 'preds': preds}, os.path.join(opt.saveDir, 'preds.pth'))
return
train_loader = torch.utils.data.DataLoader(
Dataset(opt, 'train'),
batch_size = opt.trainBatch,
shuffle = True,
num_workers = int(opt.nThreads)
)
for epoch in range(1, opt.nEpochs + 1):
mark = epoch if opt.saveAllModels else 'last'
log_dict_train, _ = train(epoch, opt, train_loader, model, criterion, optimizer)
for k, v in log_dict_train.items():
logger.scalar_summary('train_{}'.format(k), v, epoch)
logger.write('{} {:8f} | '.format(k, v))
if epoch % opt.valIntervals == 0:
log_dict_val, preds = val(epoch, opt, val_loader, model, criterion)
for k, v in log_dict_val.items():
logger.scalar_summary('val_{}'.format(k), v, epoch)
logger.write('{} {:8f} | '.format(k, v))
saveModel(os.path.join(opt.saveDir, 'model_{}.checkpoint'.format(mark)), model) # optimizer
logger.write('\n')
if epoch % opt.dropLR == 0:
lr = opt.LR * (0.1 ** (epoch // opt.dropLR))
print('Drop LR to', lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
logger.close()
torch.save(model.cpu(), os.path.join(opt.saveDir, 'model_cpu.pth')) 示例2: main
# 需要导入模块: import model [as 别名]
# 或者: from model import getModel [as 别名]
def main():
opt = opts().parse()
now = datetime.datetime.now()
logger = Logger(opt.saveDir, now.isoformat())
model, optimizer = getModel(opt)
criterion = torch.nn.MSELoss().cuda()
# if opt.GPU > -1:
# print('Using GPU {}',format(opt.GPU))
# model = model.cuda(opt.GPU)
# criterion = criterion.cuda(opt.GPU)
# dev = opt.device
model = model.cuda()
val_loader = torch.utils.data.DataLoader(
MPII(opt, 'val'),
batch_size = 1,
shuffle = False,
num_workers = int(ref.nThreads)
)
if opt.test:
log_dict_train, preds = val(0, opt, val_loader, model, criterion)
sio.savemat(os.path.join(opt.saveDir, 'preds.mat'), mdict = {'preds': preds})
return
# pyramidnet pretrain一次，先定义gen的训练数据loader
train_loader = torch.utils.data.DataLoader(
MPII(opt, 'train'),
batch_size = opt.trainBatch,
shuffle = True if opt.DEBUG == 0 else False,
num_workers = int(ref.nThreads)
)
# 调用train方法
for epoch in range(1, opt.nEpochs + 1):
log_dict_train, _ = train(epoch, opt, train_loader, model, criterion, optimizer)
for k, v in log_dict_train.items():
logger.scalar_summary('train_{}'.format(k), v, epoch)
logger.write('{} {:8f} | '.format(k, v))
if epoch % opt.valIntervals == 0:
log_dict_val, preds = val(epoch, opt, val_loader, model, criterion)
for k, v in log_dict_val.items():
logger.scalar_summary('val_{}'.format(k), v, epoch)
logger.write('{} {:8f} | '.format(k, v))
#saveModel(model, optimizer, os.path.join(opt.saveDir, 'model_{}.checkpoint'.format(epoch)))
torch.save(model, os.path.join(opt.saveDir, 'model_{}.pth'.format(epoch)))
sio.savemat(os.path.join(opt.saveDir, 'preds_{}.mat'.format(epoch)), mdict = {'preds': preds})
logger.write('\n')
if epoch % opt.dropLR == 0:
lr = opt.LR * (0.1 ** (epoch // opt.dropLR))
print('Drop LR to {}'.format(lr))
adjust_learning_rate(optimizer, lr)
logger.close()
torch.save(model.cpu(), os.path.join(opt.saveDir, 'model_cpu.pth'))