本文整理汇总了Python中torch.autograd.Variable.ceil_方法的典型用法代码示例。如果您正苦于以下问题：Python Variable.ceil_方法的具体用法？Python Variable.ceil_怎么用？Python Variable.ceil_使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torch.autograd.Variable的用法示例。
在下文中一共展示了Variable.ceil_方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_local_var_unary_methods
# 需要导入模块: from torch.autograd import Variable [as 别名]
# 或者: from torch.autograd.Variable import ceil_ [as 别名]
def test_local_var_unary_methods(self):
''' Unit tests for methods mentioned on issue 1385
https://github.com/OpenMined/PySyft/issues/1385'''
x = Var(torch.FloatTensor([1, 2, -3, 4, 5]))
assert torch.equal(x.abs(), Var(torch.FloatTensor([1, 2, 3, 4, 5])))
assert torch.equal(x.abs_(), Var(torch.FloatTensor([1, 2, 3, 4, 5])))
x = Var(torch.FloatTensor([1, 2, -3, 4, 5]))
assert torch.equal(x.cos().int(), Var(torch.IntTensor(
[0, 0, 0, 0, 0])))
x = Var(torch.FloatTensor([1, 2, -3, 4, 5]))
assert torch.equal(x.cos_().int(), Var(torch.IntTensor(
[0, 0, 0, 0, 0])))
x = Var(torch.FloatTensor([1, 2, -3, 4, 5]))
assert torch.equal(x.ceil(), x)
assert torch.equal(x.ceil_(), x)
assert torch.equal(x.cpu(), x)示例2: test_remote_var_unary_methods
# 需要导入模块: from torch.autograd import Variable [as 别名]
# 或者: from torch.autograd.Variable import ceil_ [as 别名]
def test_remote_var_unary_methods(self):
''' Unit tests for methods mentioned on issue 1385
https://github.com/OpenMined/PySyft/issues/1385'''
hook = TorchHook(verbose=False)
local = hook.local_worker
remote = VirtualWorker(id=2,hook=hook)
local.add_worker(remote)
x = Var(torch.FloatTensor([1, 2, -3, 4, 5])).send(remote)
assert torch.equal(x.abs().get(), Var(torch.FloatTensor([1, 2, 3, 4, 5])))
assert torch.equal(x.abs_().get(), Var(torch.FloatTensor([1, 2, 3, 4, 5])))
assert torch.equal(x.cos().int().get(), Var(torch.IntTensor(
[0, 0, 0, 0, 0])))
assert torch.equal(x.cos_().int().get(), Var(torch.IntTensor(
[0, 0, 0, 0, 0])))
x = Var(torch.FloatTensor([1, 2, -3, 4, 5])).send(remote)
assert torch.equal(x.ceil().get(), Var(torch.FloatTensor([1, 2, -3, 4, 5])))
assert torch.equal(x.ceil_().get(), Var(torch.FloatTensor([1, 2, -3, 4, 5])))
assert torch.equal(x.cpu().get(), Var(torch.FloatTensor([1, 2, -3, 4, 5])))