Python model.getModel() Examples

The following are 2 code examples of model.getModel(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module model , or try the search function .
Example #1
Source File: main.py    From StarMap with GNU General Public License v3.0 4 votes vote down vote up 
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'))
Example #2
Source File: main.py    From pytorch-PyraNet with MIT License 4 votes vote down vote up 
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'))