分类目录：《深入浅出Pytorch函数》总目录
禁用梯度计算的上下文管理器。当我们确信不会调用Tensor.backward()时，禁用梯度计算对推理很有用。它将减少计算的内存消耗，否则我们需要设置requires_grad=True。在这种模式下，即使输入的requires_grad为True，每次计算的结果也将为requires_grad=False。这个上下文管理器是线程本地的，它不会影响其他线程中的计算。同时，这个类也可以起到装饰器的作用。
语法
torch.no_grad()
实例
x = torch.tensor([1.], requires_grad=True)
with torch.no_grad():y = x * 2
y.requires_grad
.no_grad()
def doubler(x):return x * 2
z = doubler(x)
z.requires_grad
函数实现
class no_grad(_DecoratorContextManager):r"""Context-manager that disabled gradient calculation.Disabling gradient calculation is useful for inference, when you are surethat you will not call :meth:`Tensor.backward()`. It will reduce memoryconsumption for computations that would otherwise have `requires_grad=True`.In this mode, the result of every computation will have`requires_grad=False`, even when the inputs have `requires_grad=True`.This context manager is thread local; it will not affect computationin other threads.Also functions as a decorator. (Make sure to instantiate with parenthesis.).. note::No-grad is one of several mechanisms that can enable ordisable gradients locally see :ref:`locally-disable-grad-doc` formore information on how they compare... note::This API does not apply to :ref:`forward-mode AD <forward-mode-ad>`.If you want to disable forward AD for a computation, you can unpackyour dual tensors.Example::>>> # xdoctest: +SKIP>>> x = torch.tensor([1.], requires_grad=True)>>> with torch.no_grad():... y = x * 2>>> y.requires_gradFalse>>> @torch.no_grad()... def doubler(x):... return x * 2>>> z = doubler(x)>>> z.requires_gradFalse"""def __init__(self) -> None:if not torch._jit_internal.is_scripting():super().__init__()self.prev = Falsedef __enter__(self) -> None:self.prev = torch.is_grad_enabled()torch.set_grad_enabled(False)def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:torch.set_grad_enabled(self.prev)
