diff --git a/fluid/PaddleCV/icnet/cityscape.py b/fluid/PaddleCV/icnet/cityscape.py
index c5c08afcf3a3c85b9f43c9110e8a8dedc5900d5b..281658fa1e00bf38a9e7f0fa1a7ed2e9b7559539 100644
--- a/fluid/PaddleCV/icnet/cityscape.py
+++ b/fluid/PaddleCV/icnet/cityscape.py
@@ -155,6 +155,17 @@ class DataGenerater:
         else:
             return np.pad(image, ((0, pad_h), (0, pad_w)), 'constant')
 
+    def random_crop(self, im, out_shape, is_color=True):
+        h, w = im.shape[:2]
+        h_start = np.random.randint(0, h - out_shape[0] + 1)
+        w_start = np.random.randint(0, w - out_shape[1] + 1)
+        h_end, w_end = h_start + out_shape[0], w_start + out_shape[1]
+        if is_color:
+            im = im[h_start:h_end, w_start:w_end, :]
+        else:
+            im = im[h_start:h_end, w_start:w_end]
+        return im
+
     def resize(self, image, label, out_size):
         """
         Resize image and label by padding or cropping.
@@ -166,8 +177,7 @@ class DataGenerater:
         combined = np.concatenate((image, label), axis=2)
         combined = self.padding_as(
             combined, out_size[0], out_size[1], is_color=True)
-        combined = dataset.image.random_crop(
-            combined, out_size[0], is_color=True)
+        combined = self.random_crop(combined, out_size, is_color=True)
         image = combined[:, :, 0:3]
         label = combined[:, :, 3:4] + ignore_label
         return image, label
diff --git a/fluid/PaddleCV/icnet/icnet.py b/fluid/PaddleCV/icnet/icnet.py
index d640621eb9def4bfb1411667ea68f5384fbd5489..3286ce74072f0fde2b215763d50156dcd152a99c 100644
--- a/fluid/PaddleCV/icnet/icnet.py
+++ b/fluid/PaddleCV/icnet/icnet.py
@@ -235,12 +235,12 @@ def proj_block(input, filter_num, padding=0, dilation=None, stride=1,
 
 
 def sub_net_4(input, input_shape):
-    tmp = interp(input, out_shape=np.ceil(input_shape // 32))
+    tmp = interp(input, out_shape=(input_shape // 32))
     tmp = dilation_convs(tmp)
     tmp = pyramis_pooling(tmp, input_shape)
     tmp = conv(tmp, 1, 1, 256, 1, 1, name="conv5_4_k1")
     tmp = bn(tmp, relu=True)
-    tmp = interp(tmp, input_shape // 16)
+    tmp = interp(tmp, out_shape=np.ceil(input_shape / 16))
     return tmp