xidulu commented on a change in pull request #17415: [numpy]add op
random.lognormal
URL: https://github.com/apache/incubator-mxnet/pull/17415#discussion_r369928236
##########
File path: tests/python/unittest/test_numpy_op.py
##########
@@ -3340,6 +3340,56 @@ def hybrid_forward(self, F, loc, scale):
assert_almost_equal(loc.grad.asnumpy().sum(),
_np.ones(out_shape).sum(), rtol=1e-3, atol=1e-5)
+@with_seed()
+@use_np
+def test_np_lognormal_grad():
+ class TestLognormalGrad(HybridBlock):
+ def __init__(self, shape):
+ super(TestLognormalGrad, self).__init__()
+ self._shape = shape
+
+ def hybrid_forward(self, F, mean, sigma):
+ return F.np.random.lognormal(mean, sigma, self._shape)
+
+ param_shape = [
+ [(3, 2), (3, 2)],
+ [(3, 2, 2), (3, 2, 2)],
+ [(3, 4, 5), (4, 1)],
+ ]
+ output_shapes = [
+ (3, 2),
+ (4, 3, 2, 2),
+ (3, 4, 5)
+ ]
+ for hybridize in [False, True]:
+ for ((shape1, shape2), out_shape) in zip(param_shape, output_shapes):
+ test_lognormal_grad = TestLognormalGrad(out_shape)
+ if hybridize:
+ test_lognormal_grad.hybridize()
+ mean = np.zeros(shape1)
+ mean.attach_grad()
+ sigma = np.ones(shape2)
+ sigma.attach_grad()
+ with mx.autograd.record():
+ mx_out = test_lognormal_grad(mean, sigma)
+ np_out = _np.random.lognormal(mean = mean.asnumpy(),
+ sigma = sigma.asnumpy(), size =
out_shape)
+ assert_almost_equal(np_out.shape, mx_out.shape)
Review comment:
You'd better use `assert np_out.shape == mx_out.shape`
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