diff --git a/python/paddle/v2/framework/tests/test_pool2d_op.py b/python/paddle/v2/framework/tests/test_pool2d_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..549898ec94e1d7f81928e47ea6f651f1fc778033
--- /dev/null
+++ b/python/paddle/v2/framework/tests/test_pool2d_op.py
@@ -0,0 +1,98 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def max_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0]):
+
+    N, C, H, W = x.shape
+    H_out = (H - ksize[0] + 2 * paddings[0]) / strides[0] + 1
+    W_out = (W - ksize[1] + 2 * paddings[1]) / strides[1] + 1
+    out = np.zeros((N, C, H_out, W_out))
+    for i in xrange(H_out):
+        for j in xrange(W_out):
+            r_start = np.max((i * strides[0] - paddings[0], 0))
+            r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
+            c_start = np.max((j * strides[1] - paddings[1], 0))
+            c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
+            x_masked = x[:, :, r_start:r_end, c_start:c_end]
+
+            out[:, :, i, j] = np.max(x_masked, axis=(2, 3))
+    return out
+
+
+def ave_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0]):
+
+    N, C, H, W = x.shape
+    H_out = (H - ksize[0] + 2 * paddings[0]) / strides[0] + 1
+    W_out = (W - ksize[1] + 2 * paddings[1]) / strides[1] + 1
+    out = np.zeros((N, C, H_out, W_out))
+    for i in xrange(H_out):
+        for j in xrange(W_out):
+            r_start = np.max((i * strides[0] - paddings[0], 0))
+            r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
+            c_start = np.max((j * strides[1] - paddings[1], 0))
+            c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
+            x_masked = x[:, :, r_start:r_end, c_start:c_end]
+
+            out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / (
+                (r_end - r_start) * (c_end - c_start))
+    return out
+
+
+class TestPool2d_Op(OpTest):
+    def setUp(self):
+        self.initTestCase()
+        self.op_type = "pool2d"
+        input = np.random.random(self.shape).astype("float32")
+        output = self.pool2D_forward_naive(input, self.ksize, self.strides,
+                                           self.paddings)
+        self.inputs = {'Input': input}
+
+        self.attrs = {
+            'strides': self.strides,
+            'paddings': self.paddings,
+            'ksize': self.ksize,
+            'pooling_type': self.pool_type,
+        }
+
+        self.outputs = {'Output': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(set(['Input']), 'Output', max_relative_error=0.07)
+
+    def initTestCase(self):
+        self.pool_type = "ave"
+        self.pool2D_forward_naive = ave_pool2D_forward_naive
+        self.shape = [2, 3, 5, 5]
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [0, 0]
+
+
+class TestCase2(TestPool2d_Op):
+    def initTestCase(self):
+        self.op_type = "pool2d"
+        self.pool_type = "ave"
+        self.pool2D_forward_naive = ave_pool2D_forward_naive
+        self.shape = [2, 3, 5, 5]
+        self.ksize = [3, 3]
+        self.strides = [1, 1]
+        self.paddings = [1, 1]
+
+
+# class TestCase1(TestPool2d_Op):
+#     def initTestCase(self):
+#         self.op_type = "pool2d"
+#         self.pool_type = "max"
+#         self.pool2D_forward_naive = max_pool2D_forward_naive
+#         self.shape = [2, 3, 5, 5]
+#         self.ksize = [3, 3]
+#         self.strides = [1, 1]
+#         self.paddings = [1, 1]
+
+if __name__ == '__main__':
+    unittest.main()