sxjscience commented on a change in pull request #16009: [Numpy] Numpy
compatible bitwise_and operator
URL: https://github.com/apache/incubator-mxnet/pull/16009#discussion_r324475447
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File path: tests/python/unittest/test_numpy_op.py
##########
@@ -1080,6 +1080,53 @@ def hybrid_forward(self, F, a, *args):
assert same(mx_out.asnumpy(), np_out)
+@with_seed()
+@use_np
+def test_np_bitwise_and():
+ 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-size shape
+ ((1, 0, 1), (3, 0, 1)), # broadcast of zero-size 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()
+ x2 = rand_ndarray(x2_shape, dtype=_np.dtype(int)).as_np_ndarray()
Review comment:
Should we test for all the supported dtypes, I think we support uint8, int8,
int32, int64.
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