diff --git a/ppocr/modeling/architectures/det_model.py b/ppocr/modeling/architectures/det_model.py
index d86454be5b4af26e5fb999fe7ea21a7d7bbbab65..7ea0949bc80c55724182704616a93b879be92314 100755
--- a/ppocr/modeling/architectures/det_model.py
+++ b/ppocr/modeling/architectures/det_model.py
@@ -59,28 +59,23 @@ class DetModel(object):
         return: (image, corresponding label, dataloader)
         """
         image_shape = deepcopy(self.image_shape)
+        if image_shape[1] % 4 != 0 or image_shape[2] % 4 != 0:
+            raise Exception("The size of the image must be divisible by 4, "
+                            "received image shape is {}, please reset the "
+                            "Global.image_shape in the yml file".format(
+                                image_shape))
+
         image = fluid.layers.data(
             name='image', shape=image_shape, dtype='float32')
         if mode == "train":
             if self.algorithm == "EAST":
+                h, w = int(image_shape[1] // 4), int(image_shape[2] // 4)
                 score = fluid.layers.data(
-                    name='score',
-                    shape=[
-                        1, int(image_shape[1] // 4), int(image_shape[2] // 4)
-                    ],
-                    dtype='float32')
+                    name='score', shape=[1, h, w], dtype='float32')
                 geo = fluid.layers.data(
-                    name='geo',
-                    shape=[
-                        9, int(image_shape[1] // 4), int(image_shape[2] // 4)
-                    ],
-                    dtype='float32')
+                    name='geo', shape=[9, h, w], dtype='float32')
                 mask = fluid.layers.data(
-                    name='mask',
-                    shape=[
-                        1, int(image_shape[1] // 4), int(image_shape[2] // 4)
-                    ],
-                    dtype='float32')
+                    name='mask', shape=[1, h, w], dtype='float32')
                 feed_list = [image, score, geo, mask]
                 labels = {'score': score, 'geo': geo, 'mask': mask}
             elif self.algorithm == "DB":