我有一个 3d 张量,并希望确保所有值都落在给定范围内(在本例中为 0-1)。为了做到这一点,我已经编写了以下代码:
function capTo1or0 (Tensor3d)
tensor_width=Tensor3d:size()[2]
tensor_height=Tensor3d:size()[3]
tensor_depth=Tensor3d:size()[1]
for i=1,tensor_width,1 do
for j=1,tensor_height,1 do
for k=1,tensor_depth,1 do
if(Tensor3d[k][i][j])>1 then
Tensor3d[k][i][j]=1
end
if(Tensor3d[k][i][j]<0.0) then
Tensor3d[k][i][j]=0.0
end
end
end
end
return Tensor3d
end
它有效,只有一个问题:性能很糟糕,我知道必须有一些更好的方法来做到这一点,然后循环整个数组,因为大多数张量操作不涉及手动循环数组速度更快。有人知道如何使其更快吗?
An example in this is say that I have a `2-3-3` array with the values
[1, 2, 0.5][0.5,0.2,-0.2]
[0.1,0.2,0.3][1, 1, 1 ]
[-2, -1, 2 ][0.2,-5,-1 ]
then I expect an outcome of
[1, 1, 0.5][0.5,0.2,0]
[0.1,0.2,0.3][1, 1, 1 ]
[0, 0, 1 ] [0.2,0,-1 ]
将 0 下限以下的每个值替换为 0,将 1 上限以上的每个值替换为 1。
有人知道如何快速做到这一点吗?
最佳答案
我从未使用过 Torch,但它的文档说: http://torch7.readthedocs.io/en/rtd/maths/#torch.clamp
[res] torch.clamp([res,] tensor1, min_value, max_value)
Clamp all elements in the tensor into the range [min_value, max_value]. ie:
y_i = x_i, if x_i >= min_value or x_i <= max_value = min_value, if x_i < min_value = max_value, if x_i > max_value
z=torch.clamp(x,0,1)
will return a new tensor with the result of x bounded between 0 and 1.
torch.clamp(z,x,0,1)
will put the result in z.
x:clamp(0,1)
will perform the clamp operation in place (putting the result in x).
z:clamp(x,0,1)
will put the result in z.
我猜这就是您要找的东西?
关于performance - 使张量中的值符合给定范围,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/44417227/