diff --git a/python/paddle/v2/framework/tests/test_conv2d_op.py b/python/paddle/v2/framework/tests/test_conv2d_op.py
index 01513be66e0657cbea8c14b9c0392ff7d65d3f77..29a637a38227deaad99588e2d29e9e706ec1f787 100644
--- a/python/paddle/v2/framework/tests/test_conv2d_op.py
+++ b/python/paddle/v2/framework/tests/test_conv2d_op.py
@@ -26,6 +26,9 @@ class TestConv2dOp(OpTest):
         output = np.ndarray(
             (batch_size, output_channels, output_height, output_width))
 
+        self.inputs = {'Input': input, 'Filter': filter}
+        self.attrs = {'strides': [1, 1], 'paddings': [0, 0]}
+
         for batchid in xrange(batch_size):
             for channelid in xrange(output_channels):
                 for rowid in xrange(output_height):
@@ -50,44 +53,11 @@ class TestConv2dOp(OpTest):
                                     output_value += input_value * filter_value
                         output[batchid][channelid][rowid][colid] = output_value
 
-        self.inputs = {'Input': input, 'Filter': filter}
         self.outputs = {'Output': output}
-        self.attrs = {'strides': [1, 1], 'paddings': [0, 0]}
 
     def test_check_output(self):
         self.check_output()
 
-
-class TestConv2dGradOp(OpTest):
-    def setUp(self):
-        batch_size = 2
-        input_channels = 3
-        input_height = 5
-        input_width = 5
-        output_channels = 6
-        filter_height = 3
-        filter_width = 3
-        stride = 1
-        padding = 0
-        output_height = (input_height - filter_height + 2 * padding
-                         ) / stride + 1
-        output_width = (input_width - filter_width + 2 * padding) / stride + 1
-        input = np.random.random((batch_size, input_channels, input_height,
-                                  input_width)).astype("float32")
-        filter = np.random.random(
-            (output_channels, input_channels, filter_height,
-             filter_width)).astype("float32")
-
-        self.op_type = 'conv2d'
-        self.inputs = {'Input': input, 'Filter': filter}
-        output = np.ndarray(
-            (batch_size, output_channels, output_height, output_width))
-        self.outputs = {'Output': output}
-        self.attrs = {'strides': [1, 1], 'paddings': [0, 0]}
-
-    #def test_compare_grad(self):
-    #    self.compare_grad(self.op, self.inputs)
-
     def test_check_grad(self):
         self.check_grad(set(['Input', 'Filter']), 'Output')