--- a
+++ b/src/train/subregions.py
@@ -0,0 +1,53 @@
+import os
+import sys
+import torch
+from src.dataset.utils import nifi_volume
+from tqdm import tqdm
+import numpy as np
+
+from src.dataset import brats_labels
+from src.compute_metric_results import compute_wt_tc_et
+from src.config import BratsConfiguration
+from src.dataset.utils import dataset, visualization
+from src.models.io_model import load_model
+from src.models.vnet import vnet
+from src.test import predict
+from src.uncertainty.uncertainty import get_variation_uncertainty
+
+
+if __name__ == "__main__":
+
+    config = BratsConfiguration(sys.argv[1])
+    model_config = config.get_model_config()
+    dataset_config = config.get_dataset_config()
+    basic_config = config.get_basic_config()
+    unc_config = config.get_uncertainty_config()
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+    add_padding = True if dataset_config.get("sampling_method").split(".")[-1] == "no_patch" else False
+
+    network = vnet.VNet(elu=model_config.getboolean("use_elu"), in_channels=4, classes=4)
+    network.to(device)
+
+
+    checkpoint_path = os.path.join(model_config.get("model_path"), model_config.get("checkpoint"))
+    model_path = os.path.dirname(checkpoint_path)
+
+    model, _, _, _ = load_model(network, checkpoint_path, device, None, False)
+
+    _, data_test = dataset.read_brats(dataset_config.get("train_csv"))
+
+    patient = data_test[0]
+    patch_size = patient.size
+
+    images = patient.load_mri_volumes(normalize=True)
+
+    prediction_four_channels, vector_prediction_scores = predict.predict(model, images, False, device, monte_carlo=False)
+    pred = prediction_four_channels.max(1)[1]
+
+    et = brats_labels.get_et(pred)
+    tc = brats_labels.get_tc(pred)
+    wt = brats_labels.get_wt(pred)
+
+
+    print()
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