diff --git a/paddle/fluid/operators/random_crop_op.h b/paddle/fluid/operators/random_crop_op.h
index 8764bd0bc7848f682f419d7096a41d8d7d349c64..a34294f5eeb53c19b4089b76ffdf79716d227662 100644
--- a/paddle/fluid/operators/random_crop_op.h
+++ b/paddle/fluid/operators/random_crop_op.h
@@ -129,7 +129,7 @@ struct RandomCropFunctor {
     for (int i = num_batchsize_dims_; i < rank_; ++i) {
       typename Random<DeviceContext>::template UniformIntDist<size_t> dist(
           0, x_dims_[i] - out_dims_[i]);
-      offsets[i] = dist(engine);
+      offsets[i - num_batchsize_dims_] = dist(engine);
     }
 
     const T* x = x_ + ins_idx * prod_x_ins_dims_;
diff --git a/python/paddle/fluid/tests/unittests/op_test.py b/python/paddle/fluid/tests/unittests/op_test.py
index 709b4bf2fcfb180c747ba3539711a58a57e3b77f..9f9ee271f82eb560b2b780f85731d5dec0118aca 100644
--- a/python/paddle/fluid/tests/unittests/op_test.py
+++ b/python/paddle/fluid/tests/unittests/op_test.py
@@ -336,6 +336,8 @@ class OpTest(unittest.TestCase):
                 actual_t = np.array(actual)
                 expect = self.outputs[out_name]
                 expect_t = expect[0] if isinstance(expect, tuple) else expect
+                import pdb
+                pdb.set_trace()
                 self.assertTrue(
                     np.allclose(
                         actual_t, expect_t, atol=atol),
diff --git a/python/paddle/fluid/tests/unittests/test_random_crop_op.py b/python/paddle/fluid/tests/unittests/test_random_crop_op.py
index e609e2c99fba733325b24991a701270170637bda..1c708d0386da4028f1f3d177d0a3fd494c077c6e 100644
--- a/python/paddle/fluid/tests/unittests/test_random_crop_op.py
+++ b/python/paddle/fluid/tests/unittests/test_random_crop_op.py
@@ -20,14 +20,26 @@ from op_test import OpTest
 
 class TestRandomCropOp(OpTest):
     def setUp(self):
-        to_crop = np.random.random((1, 10, 15)).astype("float32")
+        to_crop = np.array([[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]] *
+                           5).astype("float32")
+        self.possible_res = [
+            np.array([[1, 2, 3], [5, 6, 7]]), np.array([[2, 3, 4], [6, 7, 8]]),
+            np.array([[5, 6, 7], [9, 10, 11]]),
+            np.array([[6, 7, 8], [10, 11, 12]])
+        ]
         self.op_type = "random_crop"
         self.inputs = {'X': to_crop, 'Seed': np.array([10])}
-        self.outputs = {'Out': np.array([1, 2, 3]), 'SeedOut': np.array([2])}
-        self.attrs = {'shape': [5, 5]}
+        self.outputs = {'Out': np.array([]), 'SeedOut': np.array([])}
+        self.attrs = {'shape': [2, 3]}
 
     def test_check_output(self):
-        self.check_output()
+        self.check_output_customized(self.verify_output)
+
+    def verify_output(self, outs):
+        out = np.array(outs[1])
+        for ins in out[:]:
+            is_equal = [(ins == res).all() for res in self.possible_res]
+            self.assertIn(True, is_equal)
 
 
 if __name__ == "__main__":