diff --git a/model_zoo/official/cv/unet/postprocess.py b/model_zoo/official/cv/unet/postprocess.py
index ce6485be436..67def617c84 100644
--- a/model_zoo/official/cv/unet/postprocess.py
+++ b/model_zoo/official/cv/unet/postprocess.py
@@ -18,75 +18,24 @@ import cv2
 import numpy as np
 
 from src.model_utils.config import config
-
-class dice_coeff():
-    def __init__(self):
-        self.clear()
-    def clear(self):
-        self._dice_coeff_sum = 0
-        self._iou_sum = 0
-        self._samples_num = 0
-
-    def update(self, *inputs):
-        if len(inputs) != 2:
-            raise ValueError('Need 2 inputs ((y_softmax, y_argmax), y), but got {}'.format(len(inputs)))
-        y = np.array(inputs[1])
-        self._samples_num += y.shape[0]
-        y = y.transpose(0, 2, 3, 1)
-        b, h, w, c = y.shape
-        if b != 1:
-            raise ValueError('Batch size should be 1 when in evaluation.')
-        y = y.reshape((h, w, c))
-        if config.eval_activate.lower() == "softmax":
-            y_softmax = np.squeeze(inputs[0][0], axis=0)
-            if config.eval_resize:
-                y_pred = []
-                for m in range(config.num_classes):
-                    y_pred.append(cv2.resize(np.uint8(y_softmax[:, :, m] * 255), (w, h)) / 255)
-                y_pred = np.stack(y_pred, axis=-1)
-            else:
-                y_pred = y_softmax
-        elif config.eval_activate.lower() == "argmax":
-            y_argmax = np.squeeze(inputs[0][1], axis=0)
-            y_pred = []
-            for n in range(config.num_classes):
-                if config.eval_resize:
-                    y_pred.append(cv2.resize(np.uint8(y_argmax == n), (w, h), interpolation=cv2.INTER_NEAREST))
-                else:
-                    y_pred.append(np.float32(y_argmax == n))
-            y_pred = np.stack(y_pred, axis=-1)
-        else:
-            raise ValueError('config eval_activate should be softmax or argmax.')
-        y_pred = y_pred.astype(np.float32)
-        inter = np.dot(y_pred.flatten(), y.flatten())
-        union = np.dot(y_pred.flatten(), y_pred.flatten()) + np.dot(y.flatten(), y.flatten())
-
-        single_dice_coeff = 2*float(inter)/float(union+1e-6)
-        single_iou = single_dice_coeff / (2 - single_dice_coeff)
-        print("single dice coeff is: {}, IOU is: {}".format(single_dice_coeff, single_iou))
-        self._dice_coeff_sum += single_dice_coeff
-        self._iou_sum += single_iou
-
-    def eval(self):
-        if self._samples_num == 0:
-            raise RuntimeError('Total samples num must not be 0.')
-        return (self._dice_coeff_sum / float(self._samples_num), self._iou_sum / float(self._samples_num))
-
+from src.utils import dice_coeff
 
 if __name__ == '__main__':
     rst_path = config.rst_path
     metrics = dice_coeff()
+    eval_activate = config.eval_activate.lower()
+    if eval_activate not in ("softmax", "argmax"):
+        raise ValueError("eval_activate only support 'softmax' or 'argmax'")
 
     if hasattr(config, "dataset") and config.dataset == "Cell_nuclei":
         img_size = tuple(config.image_size)
         for i, bin_name in enumerate(os.listdir('./preprocess_Result/')):
             f = bin_name.replace(".png", "")
-            bin_name_softmax = f + "_0.bin"
-            bin_name_argmax = f + "_1.bin"
-            file_name_sof = rst_path + bin_name_softmax
-            file_name_arg = rst_path + bin_name_argmax
-            softmax_out = np.fromfile(file_name_sof, np.float32).reshape(1, 96, 96, 2)
-            argmax_out = np.fromfile(file_name_arg, np.float32).reshape(1, 96, 96)
+            file_name = rst_path + f + "_0.bin"
+            if eval_activate == "softmax":
+                rst_out = np.fromfile(file_name, np.float32).reshape(1, 96, 96, 2)
+            else:
+                rst_out = np.fromfile(file_name, np.int32).reshape(1, 96, 96)
             mask = cv2.imread(os.path.join(config.data_path, f, "mask.png"), cv2.IMREAD_GRAYSCALE)
             mask = cv2.resize(mask, img_size)
             mask = mask.astype(np.float32) / 255
@@ -94,16 +43,17 @@ if __name__ == '__main__':
             mask = (np.arange(2) == mask[..., None]).astype(int)
             mask = mask.transpose(2, 0, 1).astype(np.float32)
             label = mask.reshape(1, 2, 96, 96)
-            metrics.update((softmax_out, argmax_out), label)
+            metrics.update(rst_out, label)
     else:
         label_list = np.load('label.npy')
         for j in range(len(os.listdir('./preprocess_Result/'))):
-            file_name_sof = rst_path + "ISBI_test_bs_1_" + str(j) + "_0" + ".bin"
-            file_name_arg = rst_path + "ISBI_test_bs_1_" + str(j) + "_1" + ".bin"
-            softmax_out = np.fromfile(file_name_sof, np.float32).reshape(1, 576, 576, 2)
-            argmax_out = np.fromfile(file_name_arg, np.float32).reshape(1, 576, 576)
+            file_name = rst_path + "ISBI_test_bs_1_" + str(j) + "_0" + ".bin"
+            if eval_activate == "softmax":
+                rst_out = np.fromfile(file_name, np.float32).reshape(1, 576, 576, 2)
+            else:
+                rst_out = np.fromfile(file_name, np.int32).reshape(1, 576, 576)
             label = label_list[j]
-            metrics.update((softmax_out, argmax_out), label)
+            metrics.update(rst_out, label)
 
     eval_score = metrics.eval()
     print("============== Cross valid dice coeff is:", eval_score[0])
diff --git a/model_zoo/official/cv/unet/preprocess.py b/model_zoo/official/cv/unet/preprocess.py
index 77fec76db3c..90c81e26715 100644
--- a/model_zoo/official/cv/unet/preprocess.py
+++ b/model_zoo/official/cv/unet/preprocess.py
@@ -44,7 +44,6 @@ class CellNucleiDataset:
         self.data_dir = data_dir
         self.img_ids = sorted(next(os.walk(self.data_dir))[1])
         self.train_ids = self.img_ids[:int(len(self.img_ids) * split)] * repeat
-        np.random.shuffle(self.train_ids)
         self.val_ids = self.img_ids[int(len(self.img_ids) * split):]
         self.is_train = is_train
         self.result_path = result_path