91ca6d9c056351ceca85a73bad313fa9fe485da7,deepmedic/routines/training.py,,process_in_batches,#,26

Before Change

            list_RpRnPpPn_per_class = results_of_run[1:-1]  // [-1] is from updates_grouped_op, returns nothing

        else:  // validation
            if batch_i == 0 or ((batch_i + 1) % print_progress_step) == 0 or (batch_i + 1) == n_batches:
                log.print3("[VALIDATION] Validated on " +
                           str(batch_i) + "/" + str(n_batches) + " batches for this subepoch...")

            ops_to_fetch = cnn3d.get_main_ops("val")
            list_of_ops = ops_to_fetch["list_rp_rn_tp_tn"]

            min_idx_batch = batch_i * batchsize

After Change

    //     Real Positives, Real Neg, True Predicted Pos, True Predicted Neg in subepoch, in this order.
    arr_RpRnTpTn_per_class_in_subep = np.zeros([cnn3d.num_classes, 4], dtype="int32")
    
    prefix_progress_str = "[TRAINING]" if train_or_val == "train" else "[VALIDATION]"
    print_progress_step_tr_val(log, n_batches, 0, batchsize, prefix_progress_str)
    for batch_i in range(n_batches):

        if train_or_val == "train":

Instances