如何有效地检索 Torch 张量中最大值的索引? [英] How to efficiently retrieve the indices of maximum values in a Torch tensor?
问题描述
假设有一个火炬张量,例如以下形状:
Assume to have a torch tensor, for example of the following shape:
x = torch.rand(20, 1, 120, 120)
我现在想要的是获取每个 120x120 矩阵的最大值的索引.为了简化问题,我首先将 x.squeeze()
与形状 [20, 120, 120]
一起使用.然后我想得到火炬张量,它是一个形状为 [20, 2]
的索引列表.
What I would like now, is to get the indices of the maximum values of each 120x120 matrix. To simplify the problem I would first x.squeeze()
to work with shape [20, 120, 120]
. I would then like to get torch tensor which is a list of indices with shape [20, 2]
.
我怎样才能快速做到这一点?
How can I do this fast?
推荐答案
如果我理解正确,您不需要值,而是索引.不幸的是,没有现成的解决方案.存在一个 argmax()
函数,但我不知道如何让它完全按照你的意愿去做.
If I get you correctly you don't want the values, but the indices. Unfortunately there is no out of the box solution. There exists an argmax()
function, but I cannot see how to get it to do exactly what you want.
所以这里有一个小的解决方法,效率应该还可以,因为我们只是对张量进行除法:
So here is a small workaround, the efficiency should also be okay since we're just dividing tensors:
n = torch.tensor(4)
d = torch.tensor(4)
x = torch.rand(n, 1, d, d)
m = x.view(n, -1).argmax(1)
# since argmax() does only return the index of the flattened
# matrix block we have to calculate the indices by ourself
# by using / and % (// would also work, but as we are dealing with
# type torch.long / works as well
indices = torch.cat(((m / d).view(-1, 1), (m % d).view(-1, 1)), dim=1)
print(x)
print(indices)
n
代表你的第一个维度,d
代表最后两个维度.我在这里取较小的数字来显示结果.但当然这也适用于 n=20
和 d=120
:
n
represents your first dimension, and d
the last two dimensions. I take smaller numbers here to show the result. But of course this will also work for n=20
and d=120
:
n = torch.tensor(20)
d = torch.tensor(120)
x = torch.rand(n, 1, d, d)
m = x.view(n, -1).argmax(1)
indices = torch.cat(((m / d).view(-1, 1), (m % d).view(-1, 1)), dim=1)
#print(x)
print(indices)
这是 n=4
和 d=4
的输出:
Here is the output for n=4
and d=4
:
tensor([[[[0.3699, 0.3584, 0.4940, 0.8618],
[0.6767, 0.7439, 0.5984, 0.5499],
[0.8465, 0.7276, 0.3078, 0.3882],
[0.1001, 0.0705, 0.2007, 0.4051]]],
[[[0.7520, 0.4528, 0.0525, 0.9253],
[0.6946, 0.0318, 0.5650, 0.7385],
[0.0671, 0.6493, 0.3243, 0.2383],
[0.6119, 0.7762, 0.9687, 0.0896]]],
[[[0.3504, 0.7431, 0.8336, 0.0336],
[0.8208, 0.9051, 0.1681, 0.8722],
[0.5751, 0.7903, 0.0046, 0.1471],
[0.4875, 0.1592, 0.2783, 0.6338]]],
[[[0.9398, 0.7589, 0.6645, 0.8017],
[0.9469, 0.2822, 0.9042, 0.2516],
[0.2576, 0.3852, 0.7349, 0.2806],
[0.7062, 0.1214, 0.0922, 0.1385]]]])
tensor([[0, 3],
[3, 2],
[1, 1],
[1, 0]])
我希望这是你想要的!:)
I hope this is what you wanted to get! :)
这里有一个稍微修改过的,它可能会稍微快一点(我猜不是很多:),但它更简单和更漂亮:
Here is a slightly modified which might be minimally faster (not much I guess :), but it is a bit simpler and prettier:
而不是像以前那样:
m = x.view(n, -1).argmax(1)
indices = torch.cat(((m // d).view(-1, 1), (m % d).view(-1, 1)), dim=1)
已经对 argmax
值进行了必要的整形:
The necessary reshaping already done on the argmax
values:
m = x.view(n, -1).argmax(1).view(-1, 1)
indices = torch.cat((m // d, m % d), dim=1)
但正如评论中提到的那样.我认为不可能从中得到更多.
But as mentioned in the comments. I don't think it is possible to get much more out of it.
您可以做的一件事是,如果真的对您来说获得最后可能的性能改进很重要,则将上述功能作为低级扩展实现(例如C++) 用于 pytorch.
One thing you could do, if it is really important for you to get the last possible bit of performance improvement out of it, is implementing this above function as a low-level extension (like in C++) for pytorch.
这只会给你一个你可以调用它的函数,并且会避免运行缓慢的 Python 代码.
This would give you just one function you can call for it and would avoid slow python code.
https://pytorch.org/tutorials/advanced/cpp_extension.html
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