Python PyTorch Tensor.is_leaf用法及代码示例

本文简要介绍python语言中 torch.Tensor.is_leaf 的用法。

用法:

Tensor.is_leaf

按照惯例，所有具有 requires_grad 即 False 的张量都将是叶张量。

对于具有 requires_grad 即 True 的张量，如果它们是由用户创建的，它们将是叶张量。这意味着它们不是操作的结果，因此grad_fn 为无。

在调用 backward() 期间，只有叶张量的 grad 才会被填充。要为非叶张量填充 grad ，您可以使用 retain_grad() 。

例子：

>>> a = torch.rand(10, requires_grad=True)
>>> a.is_leaf
True
>>> b = torch.rand(10, requires_grad=True).cuda()
>>> b.is_leaf
False
# b was created by the operation that cast a cpu Tensor into a cuda Tensor
>>> c = torch.rand(10, requires_grad=True) + 2
>>> c.is_leaf
False
# c was created by the addition operation
>>> d = torch.rand(10).cuda()
>>> d.is_leaf
True
# d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)
>>> e = torch.rand(10).cuda().requires_grad_()
>>> e.is_leaf
True
# e requires gradients and has no operations creating it
>>> f = torch.rand(10, requires_grad=True, device="cuda")
>>> f.is_leaf
True
# f requires grad, has no operation creating it