sxjscience commented on a change in pull request #15851: [Numpy] Numpy copysign
URL: https://github.com/apache/incubator-mxnet/pull/15851#discussion_r324474508
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File path: tests/python/unittest/test_numpy_op.py
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@@ -1853,6 +1853,111 @@ def hybrid_forward(self, F, x):
assert_almost_equal(mx_ret.asnumpy(), np_ret, atol=1e-5,
rtol=1e-4)
+@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)
+
+ def get_grad_left(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(a1.shape)
+ return sign
+
+ def get_grad_right(a1, a2):
+ return _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 = np.array(a1_np, dtype=dtype)
+ a2 = np.array(a2_np, dtype=dtype)
+ a1.attach_grad()
+ a2.attach_grad()
+ expected_np = _np.copysign(a1_np, a2_np)
+ with mx.autograd.record():
+ mx_out = test_copysign(a1, a2)
+ assert mx_out.shape == expected_np.shape
Review comment:
Test if mx_out.dtype matches expected_np.dtype. I think we can later add a
utility to match the shape + dtype.
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