def evaluate_one_epoch()

in train.py [0:0]

• 35 lines of code
• 19 McCabe index (conditional complexity)

def evaluate_one_epoch():
    stat_dict = {} # collect statistics
    ap_calculator = APCalculator(ap_iou_thresh=FLAGS.ap_iou_thresh,
        class2type_map=DATASET_CONFIG.class2type)
    net.eval() # set model to eval mode (for bn and dp)
    for batch_idx, batch_data_label in enumerate(TEST_DATALOADER):
        if batch_idx % 10 == 0:
            print('Eval batch: %d'%(batch_idx))
        for key in batch_data_label:
            batch_data_label[key] = batch_data_label[key].to(device)
        
        # Forward pass
        inputs = {'point_clouds': batch_data_label['point_clouds']}
        with torch.no_grad():
            end_points = net(inputs)

        # Compute loss
        for key in batch_data_label:
            assert(key not in end_points)
            end_points[key] = batch_data_label[key]
        loss, end_points = criterion(end_points, DATASET_CONFIG)

        # Accumulate statistics and print out
        for key in end_points:
            if 'loss' in key or 'acc' in key or 'ratio' in key:
                if key not in stat_dict: stat_dict[key] = 0
                stat_dict[key] += end_points[key].item()

        batch_pred_map_cls = parse_predictions(end_points, CONFIG_DICT) 
        batch_gt_map_cls = parse_groundtruths(end_points, CONFIG_DICT) 
        ap_calculator.step(batch_pred_map_cls, batch_gt_map_cls)

        # Dump evaluation results for visualization
        if FLAGS.dump_results and batch_idx == 0 and EPOCH_CNT %10 == 0:
            MODEL.dump_results(end_points, DUMP_DIR, DATASET_CONFIG) 

    # Log statistics
    TEST_VISUALIZER.log_scalars({key:stat_dict[key]/float(batch_idx+1) for key in stat_dict},
        (EPOCH_CNT+1)*len(TRAIN_DATALOADER)*BATCH_SIZE)
    for key in sorted(stat_dict.keys()):
        log_string('eval mean %s: %f'%(key, stat_dict[key]/(float(batch_idx+1))))

    # Evaluate average precision
    metrics_dict = ap_calculator.compute_metrics()
    for key in metrics_dict:
        log_string('eval %s: %f'%(key, metrics_dict[key]))

    mean_loss = stat_dict['loss']/float(batch_idx+1)
    return mean_loss