eval.py [138:163]:
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    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) 
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train.py [259:284]:
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    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) 
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