Python PyTorch Tensor.is_leaf用法及代碼示例

本文簡要介紹python語言中 torch.Tensor.is_leaf 的用法。

用法:

Tensor.is_leaf

按照慣例，所有具有 requires_grad 即 False 的張量都將是葉張量。

對於具有 requires_grad 即 True 的張量，如果它們是由用戶創建的，它們將是葉張量。這意味著它們不是操作的結果，因此grad_fn 為無。

在調用 backward() 期間，隻有葉張量的 grad 才會被填充。要為非葉張量填充 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