Adapt to new unittest.

python/paddle/v2/framework/tests/test_modified_huber_loss_op.py

 import unittest
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
-from paddle.v2.framework.op import Operator
 import numpy as np
+from op_test import OpTest
 
 
 def modified_huber_loss_forward(val):
@@ -14,11 +12,9 @@ def modified_huber_loss_forward(val):
         return 0
 
 
-class TestModifiedHuberLossOp_f0(unittest.TestCase):
-    __metaclass__ = OpTestMeta
-
+class TestModifiedHuberLossOp(OpTest):
     def setUp(self):
-        self.type = 'modified_huber_loss'
+        self.op_type = 'modified_huber_loss'
         samples_num = 32
         self.inputs = {
             'X': np.random.uniform(-1, 1., (samples_num, 1)).astype('float32'),
@@ -32,22 +28,11 @@ class TestModifiedHuberLossOp_f0(unittest.TestCase):
             'Out': loss.reshape((samples_num, 1))
         }
 
+    def test_check_output(self):
+        self.check_output()
 
-class TestModifiedHuberLossGradOp(GradientChecker):
-    def test_modified_huber_loss_b0(self):
-        samples_num = 10
-        inputs = {
-            'X': np.random.uniform(-1, 1, (samples_num, 1)).astype('float32'),
-            'Y': np.random.choice([0, 1], samples_num).reshape((samples_num, 1))
-        }
-        op = Operator(
-            "modified_huber_loss",
-            X='X',
-            Y='Y',
-            IntermediateVal='IntermediateVal',
-            Out='Out')
-        self.compare_grad(op, inputs, no_grad_set=set(['IntermediateVal', 'Y']))
-        self.check_grad(op, inputs, set(["X"]), "Out")
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out', max_relative_error=0.005)
 
 
 if __name__ == '__main__':