text: import torch
  import torch.nn.functional as F
  
  from dice_loss import dice_coeff
  
  
  def eval_net(net, dataset, gpu=False):
      """Evaluation without the densecrf with the dice coefficient"""
      net.eval()
      tot = 0
      for i, b in enumerate(dataset):
          img = b[0]
          true_mask = b[1]
  
          img = torch.from_numpy(img).unsqueeze(0)
          true_mask = torch.from_numpy(true_mask).unsqueeze(0)
  
          if gpu:
              img = img.cuda()
              true_mask = true_mask.cuda()
  
          mask_pred = net(img)[0]
          mask_pred = (F.sigmoid(mask_pred) > 0.5).float()
  
          tot += dice_coeff(mask_pred, true_mask).item()
      return tot / i
  