code: def evaluate(model, data_loader, device, num_classes):
      model.eval()
      confmat = utils.ConfusionMatrix(num_classes)
      metric_logger = utils.MetricLogger(delimiter="  ")
      header = "Test:"
      num_processed_samples = 0
      with torch.inference_mode():
          for image, target in metric_logger.log_every(
              data_loader, 100, header
          ):
              image, target = image.to(device), target.to(device)
              output = model(image)
              output = output["out"]
  
              confmat.update(
                  target.flatten(), output.argmax(1).flatten()
              )
              # FIXME need to take into account that the datasets
              # could have been padded in distributed setup
              num_processed_samples += image.shape[0]
  
          confmat.reduce_from_all_processes()
  
      num_processed_samples = utils.reduce_across_processes(
          num_processed_samples
      )
      if (
          hasattr(data_loader.dataset, "__len__")
          and len(data_loader.dataset) != num_processed_samples
          and torch.distributed.get_rank() == 0
      ):
          # See FIXME above
          warnings.warn(
              f"It looks like the dataset has {len(data_loader.dataset)} samples, but {num_processed_samples} "
              "samples were used for the validation, which might bias the results. "
              "Try adjusting the batch size and / or the world size. "
              "Setting the world size to 1 is always a safe bet."
          )
  
      return confmat
  
file_path: vision/references/segmentation/train.py
imports: import datetime
  import os
  import time
  import warnings
  import presets
  import torch
  import torch.utils.data
  import torchvision
  import utils
  from coco_utils import get_coco
  from torch import nn
  from torch.optim.lr_scheduler import PolynomialLR
  from torchvision.transforms import functional, InterpolationMode
label_desc: Use image node_id 4 for calling a global function with example usage: evaluate(model, data_loader, device, num_classes) and returns: confmat