sxjscience commented on a change in pull request #15851: [Numpy] Numpy copysign
URL: https://github.com/apache/incubator-mxnet/pull/15851#discussion_r323830449
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File path: tests/python/unittest/test_numpy_op.py
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@@ -149,6 +149,66 @@ def hybrid_forward(self, F, a):
assert same(a.grad.asnumpy(), expected_grad)
+@with_seed()
+@use_np
+def test_np_copysign():
+ class TestCopysign(HybridBlock):
+ def __init__(self):
+ super(TestCopysign, self).__init__()
+
+ def hybrid_forward(self, F, a1, a2):
+ return F.np.copysign(a1, a2)
+
+ def get_grad(a1, a2):
+ sign = _np.logical_or(_np.logical_and(a1 < 0, a2 < 0),
+ _np.logical_and(a1 >= 0, a2 >= 0))
+ sign = 2 * sign.astype(int) - 1
+ sign = sign.reshape(-1, *a1.shape)
+ sign = _np.sum(sign, axis=0)
+ return sign, _np.zeros_like(a2)
+
+ shapes = [
+ (),
+ (1),
+ (2, 1),
+ (3, 2, 1),
+ (4, 3, 2, 1),
+ (2, 4, 3, 2, 1)
+ ]
+ types = ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']
+ for a1shape in shapes:
+ for a2shape in shapes:
+ for hybridize in [True, False]:
+ for dtype in types:
+ test_copysign = TestCopysign()
+ if hybridize:
+ test_copysign.hybridize()
+ rtol = 1e-3
+ atol = 1e-5
+ a1_np = _np.array(_np.random.uniform(-1.0, 1.0, a1shape),
dtype=dtype)
+ a2_np = _np.array(_np.random.uniform(-1.0, 1.0, a2shape),
dtype=dtype)
+ a1 = mx.nd.array(a1_np).as_np_ndarray()
Review comment:
Currently the dtype of the original numpy array will not be passed to
construct the mxnet numpy array.
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