text: import _init_paths
  
  import os
  
  import torch
  import torch.utils.data
  from opts import opts
  from models.model import create_model, load_model, save_model
  from logger import Logger
  from datasets.dataset_factory import get_dataset
  from trains.train_factory import train_factory
  
  
  def main(opt):
      torch.manual_seed(opt.seed)
      torch.backends.cudnn.benchmark = not opt.not_cuda_benchmark and not opt.test
      Dataset = get_dataset(opt.dataset, opt.task)
      opt = opts().update_dataset_info_and_set_heads(opt, Dataset)
      print(opt)
  
      logger = Logger(opt)
  
      os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str
      opt.device = torch.device('cuda' if opt.gpus[0] >= 0 else 'cpu')
  
      print('Creating model...')
      model = create_model(opt.arch, opt.heads, opt.head_conv)
      optimizer = torch.optim.Adam(model.parameters(), opt.lr)
      start_epoch = 0
      if opt.load_model != '':
          model, optimizer, start_epoch = load_model(
              model, opt.load_model, optimizer, opt.resume, opt.lr, opt.lr_step)
  
      Trainer = train_factory[opt.task]
      trainer = Trainer(opt, model, optimizer)
      trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)
  
      print('Setting up data...')
      val_loader = torch.utils.data.DataLoader(
          Dataset(opt, 'val'),
          batch_size=1, shuffle=False,
          num_workers=1, pin_memory=True
      )
  
      if opt.test:
          _, preds = trainer.val(0, val_loader)
          val_loader.dataset.run_eval(preds, opt.save_dir)
          return
  
      train_loader = torch.utils.data.DataLoader(
          Dataset(opt, 'train'),
          batch_size=opt.batch_size, shuffle=True,
          num_workers=opt.num_workers, pin_memory=True, drop_last=True
      )
  
      print('Starting training...')
      best = 1e10
      for epoch in range(start_epoch + 1, opt.num_epochs + 1):
          mark = epoch if opt.save_all else 'last'
          log_dict_train, _ = trainer.train(epoch, train_loader)
          logger.write('epoch: {} |'.format(epoch))
          for k, v in log_dict_train.items():
              logger.scalar_summary('train_{}'.format(k), v, epoch)
              logger.write('{} {:8f} | '.format(k, v))
          if opt.val_intervals > 0 and epoch % opt.val_intervals == 0:
              save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(mark)),
                                   epoch, model, optimizer)
              with torch.no_grad():
                  log_dict_val, preds = trainer.val(epoch, val_loader)
              for k, v in log_dict_val.items():
                  logger.scalar_summary('val_{}'.format(k), v, epoch)
                  logger.write('{} {:8f} | '.format(k, v))
              if log_dict_val[opt.metric] < best:
                  best = log_dict_val[opt.metric]
                  save_model(os.path.join(opt.save_dir, 'model_best.pth'),
                                       epoch, model)
          else:
              save_model(os.path.join(opt.save_dir, 'model_last.pth'),
                                   epoch, model, optimizer)
          logger.write('\n')
          if epoch in opt.lr_step:
              save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(epoch)),
                                   epoch, model, optimizer)
              lr = opt.lr * (0.1 ** (opt.lr_step.index(epoch) + 1))
              print('Drop LR to', lr)
              for param_group in optimizer.param_groups:
                      param_group['lr'] = lr
      logger.close()
  
  if __name__ == '__main__':
      opt = opts().parse()
      main(opt)
  