--- a
+++ b/BraTs18Challege/predict_Brats.py
@@ -0,0 +1,41 @@
+import os
+
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+from tensorflow.python.client import device_lib
+
+print(device_lib.list_local_devices())
+
+from Vnet.model_vnet3d import Vnet3dModule
+from dataprocess.utils import calcu_dice
+import numpy as np
+import pandas as pd
+
+
+def predict():
+    '''
+    Preprocessing for dataset
+    '''
+    # Read  data set (Train data from CSV file)
+    csvdata = pd.read_csv('dataprocess\\data/test.csv')
+    maskdata = csvdata.iloc[:, 1].values
+    imagedata = csvdata.iloc[:, 0].values
+
+    dice_values = []
+    Vnet3d = Vnet3dModule(128, 128, 64, channels=4, numclass=3, costname=("dice coefficient",), inference=True,
+                          model_path="log\segmeation\VNet\model\Vnet3d.pd")
+    for index in range(imagedata.shape[0]):
+        image_gt = np.load(imagedata[index])
+        mask_pd = Vnet3d.prediction(image_gt)
+        mask_gt = np.load(maskdata[index])
+        mask_gt_255 = mask_gt.copy()
+        mask_gt_255[mask_gt == 0] = 0
+        mask_gt_255[mask_gt != 0] = 255
+        dice_value = calcu_dice(mask_pd, mask_gt_255)
+        print("index,dice:", (index, dice_value))
+        dice_values.append(dice_value)
+    average = sum(dice_values) / len(dice_values)
+    print("average dice:", average)
+
+
+predict()