Function evaluate

AutoFormer/supernet_engine.py:115–160

(data_loader, model, device, amp=True, choices=None, mode='super', retrain_config=None)

@torch.no_grad()
def evaluate(data_loader, model, device, amp=True, choices=None, mode='super', retrain_config=None):
    criterion = torch.nn.CrossEntropyLoss()

    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Test:'

    # switch to evaluation mode
    model.eval()
    if mode == 'super':
        config = sample_configs(choices=choices)
        model_module = unwrap_model(model)
        model_module.set_sample_config(config=config)
    else:
        config = retrain_config
        model_module = unwrap_model(model)
        model_module.set_sample_config(config=config)


    print("sampled model config: {}".format(config))
    parameters = model_module.get_sampled_params_numel(config)
    print("sampled model parameters: {}".format(parameters))

    for images, target in metric_logger.log_every(data_loader, 10, header):
        images = images.to(device, non_blocking=True)
        target = target.to(device, non_blocking=True)
        # compute output
        if amp:
            with torch.cuda.amp.autocast():
                output = model(images)
                loss = criterion(output, target)
        else:
            output = model(images)
            loss = criterion(output, target)

        acc1, acc5 = accuracy(output, target, topk=(1, 5))

        batch_size = images.shape[0]
        metric_logger.update(loss=loss.item())
        metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
        metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
          .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))

    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}