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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598 ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18598 Turn on F401: Unused import warning.** This was requested by someone at Facebook; this lint is turned on for Facebook by default. "Sure, why not." I had to noqa a number of imports in __init__. Hypothetically we're supposed to use __all__ in this case, but I was too lazy to fix it. Left for future work. Be careful! flake8-2 and flake8-3 behave differently with respect to import resolution for # type: comments. flake8-3 will report an import unused; flake8-2 will not. For now, I just noqa'd all these sites. All the changes were done by hand. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: D14687478 fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
import math
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from torch._six import inf
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from torch.distributions import constraints
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from torch.distributions.transforms import AbsTransform
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from torch.distributions.normal import Normal
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from torch.distributions.transformed_distribution import TransformedDistribution
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class HalfNormal(TransformedDistribution):
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r"""
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Creates a half-normal distribution parameterized by `scale` where::
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X ~ Normal(0, scale)
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Y = |X| ~ HalfNormal(scale)
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Example::
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>>> m = HalfNormal(torch.tensor([1.0]))
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>>> m.sample() # half-normal distributed with scale=1
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tensor([ 0.1046])
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Args:
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scale (float or Tensor): scale of the full Normal distribution
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"""
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arg_constraints = {'scale': constraints.positive}
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support = constraints.positive
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has_rsample = True
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def __init__(self, scale, validate_args=None):
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base_dist = Normal(0, scale)
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super(HalfNormal, self).__init__(base_dist, AbsTransform(),
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validate_args=validate_args)
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def expand(self, batch_shape, _instance=None):
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new = self._get_checked_instance(HalfNormal, _instance)
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return super(HalfNormal, self).expand(batch_shape, _instance=new)
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@property
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def scale(self):
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return self.base_dist.scale
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@property
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def mean(self):
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return self.scale * math.sqrt(2 / math.pi)
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@property
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def variance(self):
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return self.scale.pow(2) * (1 - 2 / math.pi)
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def log_prob(self, value):
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log_prob = self.base_dist.log_prob(value) + math.log(2)
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log_prob[value.expand(log_prob.shape) < 0] = -inf
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return log_prob
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def cdf(self, value):
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return 2 * self.base_dist.cdf(value) - 1
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def icdf(self, prob):
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return self.base_dist.icdf((prob + 1) / 2)
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def entropy(self):
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return self.base_dist.entropy() - math.log(2)
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