code: def main(args):
      device = torch.device(
          args.device if torch.cuda.is_available() else "cpu"
      )
      assert os.path.exists(
          args.weights
      ), f"weights {args.weights} not found."
  
      # segmentation nun_classes + background
      num_classes = args.num_classes + 1
  
      # VOCdevkit -> VOC2012 -> ImageSets -> Segmentation -> val.txt
      val_dataset = VOCSegmentation(
          args.data_path,
          year="2012",
          transforms=SegmentationPresetEval(520),
          txt_name="val.txt",
      )
  
      num_workers = 8
      val_loader = torch.utils.data.DataLoader(
          val_dataset,
          batch_size=1,
          num_workers=num_workers,
          pin_memory=True,
          collate_fn=val_dataset.collate_fn,
      )
  
      model = fcn_resnet50(aux=args.aux, num_classes=num_classes)
      model.load_state_dict(
          torch.load(args.weights, map_location=device)["model"]
      )
      model.to(device)
  
      confmat = evaluate(
          model, val_loader, device=device, num_classes=num_classes
      )
      print(confmat)
  
file_path: deep-learning-for-image-processing/pytorch_segmentation/fcn/validation.py
imports: import os
  import torch
  from src import fcn_resnet50
  from train_utils import evaluate
  from my_dataset import VOCSegmentation
  import transforms
label_desc: Use image node_id 1 for calling a global function with example usage: main(args) without return types