[GitHub] [incubator-mxnet] zoeygxy commented on a change in pull request #15306: Numpy bitwise_and operation added

[GitBox <gi...@apache.org>]

zoeygxy commented on a change in pull request #15306: Numpy bitwise_and operation added
URL: https://github.com/apache/incubator-mxnet/pull/15306#discussion_r296142502
 
 

 ##########
 File path: tests/python/unittest/test_numpy_op.py
 ##########
 @@ -1018,6 +1018,56 @@ def hybrid_forward(self, F, x):
             assert same(ret_mx.asnumpy(), ret_np)
 
 
+@with_seed()
+@npx.use_np_shape
+def test_np_bitwise_and():
+    @npx.use_np_shape
+    class TestBitwiseAnd(HybridBlock):
+        def __init__(self):
+            super(TestBitwiseAnd, self).__init__()
+
+        def hybrid_forward(self, F, x1, x2):
+            return F.np.bitwise_and(x1, x2)
+
+    shapes = [
+        ((3, 1), (3, 1)),
+        ((3, 1, 2), (3, 1, 2)),
+        ((1, ),(1, )),
+        ((3, 0), (3, 0)),  # zero-size shape
+        ((0, 1), (0, 1)),  # zero-size shape
+        ((2, 0, 2), (2, 0, 2)),  # zero-size shape
+        ((1, ), (3, )),  # broadcast
+        ((2, 3), (2, 1)),  # broadcast
+        ((1, 3), (2, 3)),  # broadcast
+        ((1, 3), (2, 0, 3)),  # broadcast to zero-dim shape
+        ((1, 0, 1), (3, 0, 1)), # broadcast of zero-dim shape
+        ((), ()),  # zero-dim shape
+    ]
+
+    for hybridize in [True, False]:
+        for shape in shapes:
+            x1_shape, x2_shape = shape
+
+            test_bitwise_and = TestBitwiseAnd()
+            if hybridize:
+                test_bitwise_and.hybridize()
+
+            x1 = rand_ndarray(x1_shape, dtype=_np.dtype(int)).as_np_ndarray()
+            x1.attach_grad()
 
 Review comment:
   Fixed.

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