fix test file

tests/python/unittest/test_numpy_op.py

@@ -1522,69 +1522,37 @@ def hybrid_forward(self, F, a):
 @with_seed()
 @use_np
 def test_np_windows():
-    class TestBlackman(HybridBlock):
-        def __init__(self, M, dtype):
-            super(TestBlackman, self).__init__()
+    class TestWindows(HybridBlock):
+        def __init__(self, func, M, dtype):
+            super(TestWindows, self).__init__()
+            self._func = func
             self._M = M
             self._dtype = dtype
 
         def hybrid_forward(self, F, x, *args, **kwargs):
-            return x + F.np.blackman(M=self._M, dtype=self._dtype)
-
-    @use_np
-    class TestHamming(HybridBlock):
-        def __init__(self, M, dtype):
-            super(TestHamming, self).__init__()
-            self._M = M
-            self._dtype = dtype
-
-        def hybrid_forward(self, F, x, *args, **kwargs):
-            return x + F.np.hamming(M=self._M, dtype=self._dtype)
-
-    @use_np
-    class TestHanning(HybridBlock):
-        def __init__(self, M, dtype):
-            super(TestHanning, self).__init__()
-            self._M = M
-            self._dtype = dtype
-
-        def hybrid_forward(self, F, x, *args, **kwargs):
-            return x + F.np.hanning(M=self._M, dtype=self._dtype)
+            op = getattr(F.np, self._func)
+            assert op is not None
+            return x + op(M=self._M, dtype=self._dtype)
 
     configs = [-10, -3, -1, 0, 1, 6, 10, 20]
     dtypes = ['float32', 'float64']
-
+    funcs = ['hanning', 'hamming', 'blackman']
     for config in configs:
         for dtype in dtypes:
-            x = np.zeros(shape=(), dtype=dtype)
-            for hybridize in [False, True]:
-                net_hanning = TestHanning(M=config, dtype=dtype)
-                net_hamming = TestHamming(M=config, dtype=dtype)
-                net_blackman = TestBlackman(M=config, dtype=dtype)
-                np_out_hanning = _np.hanning(M=config)
-                np_out_hamming = _np.hamming(M=config)
-                np_out_blackman = _np.blackman(M=config)
-                if hybridize:
-                    net_hanning.hybridize()
-                    net_hamming.hybridize()
-                    net_blackman.hybridize()
-
-                mx_out_hanning = net_hanning(x)
-                mx_out_hamming = net_hamming(x)
-                mx_out_blackman = net_blackman(x)
-                assert_almost_equal(mx_out_hanning.asnumpy(), np_out_hanning, rtol=1e-3, atol=1e-5)
-                assert_almost_equal(mx_out_hamming.asnumpy(), np_out_hamming, rtol=1e-3, atol=1e-5)
-                assert_almost_equal(mx_out_blackman.asnumpy(), np_out_blackman, rtol=1e-3, atol=1e-5)
-                # test imperative
-                mx_out_hanning = np.hanning(M=config, dtype=dtype)
-                mx_out_hamming = np.hamming(M=config, dtype=dtype)
-                mx_out_blackman = np.blackman(M=config, dtype=dtype)
-                np_out_hanning = _np.hanning(M=config)
-                np_out_hamming = _np.hamming(M=config)
-                np_out_blackman = _np.blackman(M=config)
-                assert_almost_equal(mx_out_hanning.asnumpy(), np_out_hanning, rtol=1e-3, atol=1e-5)
-                assert_almost_equal(mx_out_hamming.asnumpy(), np_out_hamming, rtol=1e-3, atol=1e-5)
-                assert_almost_equal(mx_out_blackman.asnumpy(), np_out_blackman, rtol=1e-3, atol=1e-5)
+            for func in funcs:
+                x = np.zeros(shape=(), dtype=dtype)
+                for hybridize in [False, True]:
+                    np_func = getattr(_np, func)
+                    mx_func = TestWindows(func, M=config, dtype=dtype)
+                    np_out = np_func(M=config).astype(dtype)
+                    if hybridize:
+                        mx_func.hybridize()
+                    mx_out = mx_func(x)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+                    # test imperative
+                    mx_out = getattr(np, func)(M=config, dtype=dtype)
+                    np_out = np_func(M=config).astype(dtype)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
 if __name__ == '__main__':