Model Analysis - 1.3 English

To run model analysis, you need to define a function that can be used to evaluate the model. The first argument of this function must be the model to be evaluated.

def evaluate(val_loader, model, criterion):
  batch_time = AverageMeter('Time', ':6.3f')
  losses = AverageMeter('Loss', ':.4e')
  top1 = AverageMeter('Acc@1', ':6.2f')
  top5 = AverageMeter('Acc@5', ':6.2f')
  progress = ProgressMeter(
      len(val_loader), [batch_time, losses, top1, top5], prefix='Test: ')

  # switch to evaluate mode
  model.eval()

  with torch.no_grad():
    end = time.time()
    for i, (images, target) in enumerate(val_loader):
      model = model.cuda()
      images = images.cuda(non_blocking=True)
      target = target.cuda(non_blocking=True)

      # compute output
      output = model(images)
      loss = criterion(output, target)

      # measure accuracy and record loss
      acc1, acc5 = accuracy(output, target, topk=(1, 5))
      losses.update(loss.item(), images.size(0))
      top1.update(acc1[0], images.size(0))
      top5.update(acc5[0], images.size(0))

      # measure elapsed time
      batch_time.update(time.time() - end)
      end = time.time()

      if i % 50 == 0:
        progress.display(i)

    # TODO: this should also be done with the ProgressMeter
    print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'.format(
        top1=top1, top5=top5))

  return top1.avg, top5.avg

def ana_eval_fn(model, val_loader, loss_fn):
  return evaluate(val_loader, model, loss_fn)[1]

Then, call ana() method with the function defined above as the first argument.

pruner.ana(ana_eval_fn, args=(val_loader, criterion))

Here, the ‘args’ is the tuple of arguments starting from the second argument required by ‘ana_eval_fn’.