text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import _init_paths
  
  from comet_ml import Experiment
  import sys
  import os
  
  import torch
  import torch.utils.data
  from opts import opts
  from models.model import create_model, load_model, save_model
  from models.data_parallel import DataParallel
  from logger import Logger
  from datasets.dataset_factory import get_dataset
  from trains.train_factory import train_factory
  
  
  def main(opt):
    # init comet_ml
    project_name = "centernet_ddd" # if not 'dev' in opt.exp_tags else "centernet_ddd_dev"
    experiment = Experiment(project_name=project_name)
    experiment.add_tag("centernet_original")
  
    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
    )
  
    experiment.log_parameter('torch version', torch.__version__)
    experiment.log_parameter('cudnn version', torch.backends.cudnn.version())
    experiment.log_parameter('cmd', str(sys.argv))
  
    opt_dict = dict((name, getattr(opt, name)) for name in dir(opt) if not name.startswith('_'))
    experiment.log_parameters(opt_dict)
    experiment.log_parameter("log_folder", logger.log_folder)
  
    print('Starting training...')
    best = 1e10
    with experiment.train():
      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))
        experiment.log_metrics(log_dict_train, epoch=epoch)
        if opt.val_intervals > 0 and epoch % opt.val_intervals == 0:
          with experiment.validate():
            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))
            experiment.log_metrics(log_dict_val, epoch=epoch)
            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)
  