def run_epoch()

in utils_cv/tracking/references/fairmot/trains/base_trainer.py [0:0]

• 53 lines of code
• 16 McCabe index (conditional complexity)

  def run_epoch(self, phase, epoch, data_loader):
    model_with_loss = self.model_with_loss
    if phase == 'train':
      model_with_loss.train()
    else:
      if len(self.opt.gpus) > 1:
        model_with_loss = self.model_with_loss.module
      model_with_loss.eval()
      torch.cuda.empty_cache()

    opt = self.opt
    results = {}
    data_time, batch_time = AverageMeter(), AverageMeter()
    avg_loss_stats = {l: AverageMeter() for l in self.loss_stats}
    num_iters = len(data_loader) if opt.num_iters < 0 else opt.num_iters
    bar = Bar('{}/{}'.format(opt.task, opt.exp_id), max=num_iters)
    end = time.time()
    for iter_id, batch in enumerate(data_loader):
      if iter_id >= num_iters:
        break
      data_time.update(time.time() - end)

      for k in batch:
        if k != 'meta':
          batch[k] = batch[k].to(device=opt.device, non_blocking=True)

      output, loss, loss_stats = model_with_loss(batch)
      loss = loss.mean()
      if phase == 'train':
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
      batch_time.update(time.time() - end)
      end = time.time()

      Bar.suffix = '{phase}: [{0}][{1}/{2}]|Tot: {total:} |ETA: {eta:} '.format(
        epoch, iter_id, num_iters, phase=phase,
        total=bar.elapsed_td, eta=bar.eta_td)
      for l in avg_loss_stats:
        avg_loss_stats[l].update(
          loss_stats[l].mean().item(), batch['input'].size(0))
        Bar.suffix = Bar.suffix + '|{} {:.4f} '.format(l, avg_loss_stats[l].avg)
      if not opt.hide_data_time:
        Bar.suffix = Bar.suffix + '|Data {dt.val:.3f}s({dt.avg:.3f}s) ' \
          '|Net {bt.avg:.3f}s'.format(dt=data_time, bt=batch_time)
      if opt.print_iter > 0:
        if iter_id % opt.print_iter == 0:
          print('{}/{}| {}'.format(opt.task, opt.exp_id, Bar.suffix)) 
      else:
        bar.next()
      
      if opt.test:
        self.save_result(output, batch, results)
      del output, loss, loss_stats, batch
    
    bar.finish()
    ret = {k: v.avg for k, v in avg_loss_stats.items()}
    ret['time'] = bar.elapsed_td.total_seconds() / 60.
    return ret, results