sxjscience commented on a change in pull request #15851: [Numpy] Numpy copysign
URL: https://github.com/apache/incubator-mxnet/pull/15851#discussion_r323348447
##########
File path: tests/python/unittest/test_numpy_op.py
##########
@@ -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:
We need to explicitly set the dtype: `a1 = mx.nd.array(a1_np,
dtype=dtype).as_np_ndarray()
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org
With regards,
Apache Git Services
