def eval_epoch()

in tools/train_net.py [0:0]

• 24 lines of code
• 5 McCabe index (conditional complexity)

def eval_epoch(test_loader, model, test_meter, cur_epoch, writer_eval=None, params=0, flops=0, is_master=False):
    """Evaluates the model on the test set."""

    # Enable eval mode
    model.eval()
    test_meter.iter_tic()

    for cur_iter, (inputs, labels) in enumerate(test_loader):
        # Transfer the data to the current GPU device
        inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True)
        # Compute the predictions
        preds = model(inputs)
        # Compute the errors
        top1_err, top5_err = mu.topk_errors(preds, labels, [1, 5])
        # Combine the errors across the GPUs
        if cfg.NUM_GPUS > 1:
            top1_err, top5_err = du.scaled_all_reduce([top1_err, top5_err])
        # Copy the errors from GPU to CPU (sync point)
        top1_err, top5_err = top1_err.item(), top5_err.item()
        test_meter.iter_toc()
        # Update and log stats
        test_meter.update_stats(
            top1_err, top5_err, inputs.size(0) * cfg.NUM_GPUS
        )
        test_meter.log_iter_stats(cur_epoch, cur_iter)
        test_meter.iter_tic()
    # Log epoch stats
    # test_meter.log_epoch_stats(cur_epoch,writer_eval,params,flops)
    test_meter.log_epoch_stats(cur_epoch, writer_eval, params, flops, model, is_master=is_master)
    stats = test_meter.get_epoch_stats(cur_epoch)
    test_meter.reset()
    if cfg.RGRAPH.SAVE_GRAPH:
        adj_dict = nu.model2adj(model)
        adj_dict = {**adj_dict, 'top1_err': stats['top1_err']}
        os.makedirs('{}/graphs/{}'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN), exist_ok=True)
        np.savez('{}/graphs/{}/{}.npz'.format(cfg.OUT_DIR, cfg.RGRAPH.SEED_TRAIN, cur_epoch), **adj_dict)