xidulu commented on a change in pull request #17302: [numpy]add op
random.logistic, random.gumbel
URL: https://github.com/apache/incubator-mxnet/pull/17302#discussion_r369386680
##########
File path: tests/python/unittest/test_numpy_op.py
##########
@@ -3323,21 +3322,96 @@ def hybrid_forward(self, F, loc, scale):
(3, 4, 5)
]
for hybridize in [False, True]:
- for dtype in dtypes:
- for ((shape1, shape2), out_shape) in zip(param_shape,
output_shapes):
- test_normal_grad = TestNormalGrad(out_shape)
- if hybridize:
- test_normal_grad.hybridize()
- loc = np.zeros(shape1)
- loc.attach_grad()
- scale = np.ones(shape2)
- scale.attach_grad()
- with mx.autograd.record():
- samples = test_normal_grad(loc, scale)
- samples.backward()
- assert loc.grad.shape == shape1
- assert scale.grad.shape == shape2
- assert_almost_equal(loc.grad.asnumpy().sum(),
_np.ones(out_shape).sum(), rtol=1e-3, atol=1e-5)
+ for ((shape1, shape2), out_shape) in zip(param_shape, output_shapes):
+ test_normal_grad = TestNormalGrad(out_shape)
+ if hybridize:
+ test_normal_grad.hybridize()
+ loc = np.zeros(shape1)
+ loc.attach_grad()
+ scale = np.ones(shape2)
+ scale.attach_grad()
+ with mx.autograd.record():
+ samples = test_normal_grad(loc, scale)
+ samples.backward()
+ assert loc.grad.shape == shape1
+ assert scale.grad.shape == shape2
+ 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_logistic_grad():
+ class TestLogisticGrad(HybridBlock):
+ def __init__(self, shape):
+ super(TestLogisticGrad, self).__init__()
+ self._shape = shape
+
+ def hybrid_forward(self, F, loc, scale):
+ return F.np.random.logistic(loc, scale, 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_logistic_grad = TestLogisticGrad(out_shape)
+ if hybridize:
+ test_logistic_grad.hybridize()
+ loc = np.zeros(shape1)
+ loc.attach_grad()
+ scale = np.ones(shape2)
+ scale.attach_grad()
+ with mx.autograd.record():
+ samples = test_logistic_grad(loc, scale)
+ samples.backward()
+ assert loc.grad.shape == shape1
+ assert scale.grad.shape == shape2
+ 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_gumbel_grad():
Review comment:
You could merge `test_np_normal_grad`,`test_np_gumbel_grad` and
`test_np_logistic_grad` into one test case, and iterate the corresponding
methods through a list and `getattr` like
https://github.com/apache/incubator-mxnet/blob/master/tests/python/unittest/test_numpy_op.py#L3411
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