英文:
Can you make assignments between PyTorch tensors using ragged indices without a for loop?
问题
假设我有两个形状相同的PyTorch Tensor对象：
x = torch.randn(2, 10)y = torch.randn(2, 10)
现在，我有一个索引列表（与第一个Tensor轴的长度相同），该列表提供第二个Tensor轴上不同起始位置的索引，我想要从y赋值到x，即
idxs = [2, 6]for i, idx in enumerate(idxs):x[i, idx:] = y[i, idx:]
如上所示，我可以使用for循环做到这一点，但我的问题是是否有一种更高效的方法来实现这一点，而无需显式使用for循环？
英文:
Suppose I have two PyTorch Tensor objects of equal shape:
import torchx = torch.randn(2, 10)y = torch.randn(2, 10)
Now, I have a list of indices (of the same length as the first Tensor axis) which give different starting positions in the second Tensor axis from which I want to assign values from y into x, i.e.,
idxs = [2, 6]for i, idx in enumerate(idxs):x[i, idx:] = y[i, idx:]
As above, I can do this with a for loop, but my question is whether there is a more efficient way of doing this without an explicit for loop?
答案1
得分: 2
首先，在你的张量的第二维上创建一个索引张量：
second_dim_indices = torch.arange(x.shape[1])
然后，将idxs转换为张量：
idxs = torch.LongTensor(idxs)
接着，可以计算一个掩码，当张量索引需要修改时为真：
mask = second_dim_indices.unsqueeze(0) >= idxs.unsqueeze(1)# 在你的情况下 =# tensor([[False, False, True, True, True, True, True, True, True, True],# [False, False, False, False, False, False, True, True, True, True]])
注意，我们必须对索引和idxs进行unsqueeze以进行>=运算。
最后，使用掩码来更新x：
x = y * mask + x * ~mask
英文:
First, create a index tensor on the second dimension of your tensor with
second_dim_indices = torch.arange(x.shape[1])
and turn idxs into a tensor:
idxs = torch.LongTensor(idxs)
Then, it is possible to compute a mask that's true when tensor index must be modified with :
mask = second_dim_indices.unsqueeze(0) >= idxs.unsqueeze(1)# in your case =# tensor([[False, False, True, True, True, True, True, True, True, True],# [False, False, False, False, False, False, True, True, True, True]])
Note that we must unsqueeze indices and idxs to broadcast the >= operation.
Finally, use the mask to update x:
x = y * mask + x * ~mask
通过集体智慧和协作来改善编程学习和解决问题的方式。致力于成为全球开发者共同参与的知识库，让每个人都能够通过互相帮助和分享经验来进步。
评论