code: def validate(args, model, val_loader, epoch=0, threshold=0.5):
      model.eval()
      outputs = []
      focal_loss, lovaz_loss, salt_loss, w_loss = 0, 0, 0, 0
      with torch.no_grad():
          for img, target, salt_target in val_loader:
              if args.depths:
                  add_depth_channel(img, args.pad_mode)
              img, target, salt_target = (
                  img.cuda(),
                  target.cuda(),
                  salt_target.cuda(),
              )
              output, salt_out = model(img)
  
              _, floss, lovaz, _salt_loss, _w_loss = weighted_loss(
                  args,
                  (output, salt_out),
                  (target, salt_target),
                  epoch=epoch,
              )
              focal_loss += floss
              lovaz_loss += lovaz
              salt_loss += _salt_loss
              w_loss += _w_loss
              output = torch.sigmoid(output)
  
              for o in output.cpu():
                  outputs.append(o.squeeze().numpy())
  
      n_batches = (
          val_loader.num // args.batch_size
          if val_loader.num % args.batch_size == 0
          else val_loader.num // args.batch_size + 1
      )
  
      # y_pred, list of np array, each np array's shape is 101,101
      y_pred = generate_preds(
          args, outputs, (settings.ORIG_H, settings.ORIG_W), threshold
      )
  
      iou_score = intersection_over_union(val_loader.y_true, y_pred)
      iout_score = intersection_over_union_thresholds(
          val_loader.y_true, y_pred
      )
  
      return (
          iout_score,
          iou_score,
          focal_loss / n_batches,
          lovaz_loss / n_batches,
          salt_loss / n_batches,
          iout_score * 4 - w_loss,
      )
  
imports: import os
  import argparse
  import time
  import torch
  import torch.nn
  import torch.optim
  import torch.nn.functional
  from torch.optim.lr_scheduler import CosineAnnealingLR, ReduceLROnPlateau
  from loader import get_train_loaders, add_depth_channel
  from models import UNetResNetV4, UNetResNetV5, UNetResNetV6
  from lovasz_losses import lovasz_hinge
  from focal_loss import FocalLoss2d
  from postprocessing import binarize, crop_image, resize_image
  from metrics import intersection_over_union, intersection_over_union_thresholds
  import settings
label_desc: Use image node_id 4 for calling a global function with example usage: validate(args, model, val_loader, epoch, threshold) and returns: iout_score,  iou_score,  unknown,  unknown,  unknown,  unknown