def test_constraints(shape, constraint_):
module = PyroModule()
module.x = PyroParam(torch.full(shape, 1e-4), constraint_)
assert isinstance(module.x, torch.Tensor)
assert isinstance(module.x_unconstrained, nn.Parameter)
assert module.x.shape == shape
assert constraint_.check(module.x).all()
module.x = torch.randn(shape).exp() * 1e-6
assert isinstance(module.x_unconstrained, nn.Parameter)
assert isinstance(module.x, torch.Tensor)
assert module.x.shape == shape
assert constraint_.check(module.x).all()
assert isinstance(module.x_unconstrained, torch.Tensor)
y = module.x_unconstrained.data.normal_()
assert_equal(module.x.data, transform_to(constraint_)(y))
assert constraint_.check(module.x).all()
del module.x
assert 'x' not in module._pyro_params
assert not hasattr(module, 'x')
assert not hasattr(module, 'x_unconstrained')
def test_torch_serialize():
module = PyroModule()
module.x = PyroParam(torch.tensor(1.234), constraints.positive)
module.y = nn.Parameter(torch.randn(3))
assert isinstance(module.x, torch.Tensor)
# Work around https://github.com/pytorch/pytorch/issues/27972
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
f = io.BytesIO()
torch.save(module, f)
pyro.clear_param_store()
f.seek(0)
actual = torch.load(f)
assert_equal(actual.x, module.x)
actual_names = {name for name, _ in actual.named_parameters()}
expected_names = {name for name, _ in module.named_parameters()}
assert actual_names == expected_names