一、pytorch返回最值索引
1 官方文档资料
1.1 torch.argmax()介绍
返回最大值的索引下标
函数：
torch.argmax(input, dim, keepdim=False) → LongTensor
返回值：
Returns the indices of the maximum values of a tensor across a dimension.
参数：
input (Tensor) – the input tensor.
dim (int) – the dimension to reduce. If None, the argmax of the flattened input is returned.
keepdim (bool) – whether the output tensor has dim retained or not. Ignored if dim=None.
1.2 torch.argmin()介绍
返回最小值的索引下标
函数：
torch.argmin(input, dim, keepdim=False) → LongTensor
返回值：
Returns the indices of the mimimum values of a tensor across a dimension.
参数：
input (Tensor) – the input tensor.
dim (int) – the dimension to reduce. If None, the argmax of the flattened input is returned.
keepdim (bool) – whether the output tensor has dim retained or not. Ignored if dim=None.
2 代码示例
2.1 torch.argmax()代码示例
>>> import torch
>>> Matrix = torch.randn(2,2,2)
>>> print(Matrix)
tensor([[[ 0.3772, -0.1143],
[ 0.2217, -0.1897]],
[[ 0.1488, -0.8758],
[ 1.7734, -0.5929]]])
>>> print(Matrix.argmax(dim=0))
tensor([[0, 0],
[1, 0]])
>>> print(Matrix.argmax(dim=1))
tensor([[0, 0],
[1, 1]])
>>> print(Matrix.argmax(dim=2))
tensor([[0, 0],
[0, 0]])
>>> print(Matrix.argmax())
tensor(6)
2.2 torch.argmin()代码示
>>> import torch
>>> Matrix = torch.randn(2,2,2)
>>> print(Matrix)
tensor([[[ 0.5821, 0.2889],
[ 0.4669, -0.3135]],
[[-0.4567, 0.2975],
[-1.5084, 0.7320]]])
>>> print(Matrix.argmin(dim=0))
tensor([[1, 0],
[1, 0]])
>>> print(Matrix.argmin(dim=1))
tensor([[1, 1],
[1, 0]])
>>> print(Matrix.argmin(dim=2))
tensor([[1, 1],
[0, 0]])
>>> print(Matrix.argmin())
tensor(6)
二、pytorch返回任意值索引
tens = tensor([[ 101, 146, 1176, 21806, 1116, 1105, 18621, 119, 102, 0,
0, 0, 0],
[ 101, 1192, 1132, 1136, 1184, 146, 1354, 1128, 1127, 117,
1463, 119, 102],
[ 101, 6816, 1905, 1132, 14918, 119, 102, 0, 0, 0,
0, 0, 0]])
idxs = torch.tensor([(i == 101).nonzero() for i in tens])
from torch import tensor
tens = torch.tensor([[ 101, 146, 1176, 21806, 1116, 1105, 18621, 119, 102, 0,
...: 0, 0, 0],
...: [ 101, 1192, 1132, 1136, 1184, 146, 1354, 1128, 1127, 117,
...: 1463, 119, 102],
...: [ 101, 6816, 1905, 1132, 14918, 119, 102, 0, 0, 0,
...: 0, 0, 0]])
(tens == 101).nonzero()[:, 1]
tensor([0, 0, 0])
三、pytorch 只保留tensor的最大值或最小值，其他位置置零
如下，x是输入张量，dim指定维度，max可以替换成min
import torch
if __name__ == '__main__':
x = torch.randn([1, 3, 4, 4]).cuda()
mask = (x == x.max(dim=1, keepdim=True)[0]).to(dtype=torch.int32)
result = torch.mul(mask, x)
print(x)
print(mask)
print(result)
输出效果：
tensor([[[[-0.8807, 0.1029, 0.0184, 1.2695],
[-0.0934, 1.0650, -0.2927, 0.0049],
[ 0.2338, -1.8663, 1.2763, 0.7248],
[-1.5138, 0.6834, 0.1463, 0.0650]],
[[ 0.5020, 1.6078, -0.0104, 1.2042],
[ 1.8859, -0.4682, -0.1177, 0.5197],
[ 1.7649, 0.4585, 0.6002, 0.3350],
[-1.1384, -0.0325, 0.8490, 0.6080]],
[[-0.5618, 0.5388, -0.0572, -0.7240],
[-0.3458, 1.3494, -0.0603, -1.1562],
[-0.3652, 1.1885, 1.6293, 0.4134],
[ 1.3009, 1.2027, -0.8711, 1.3321]]]], device='cuda:0')
tensor([[[[0, 0, 1, 1],
[0, 0, 0, 0],
[0, 0, 0, 1],
[0, 0, 0, 0]],
[[1, 1, 0, 0],
[1, 0, 0, 1],
[1, 0, 0, 0],
[0, 0, 1, 0]],
[[0, 0, 0, 0],
[0, 1, 1, 0],
[0, 1, 1, 0],
[1, 1, 0, 1]]]], device='cuda:0', dtype=torch.int32)
tensor([[[[-0.0000, 0.0000, 0.0184, 1.2695],
[-0.0000, 0.0000, -0.0000, 0.0000],
[ 0.0000, -0.0000, 0.0000, 0.7248],
[-0.0000, 0.0000, 0.0000, 0.0000]],
[[ 0.5020, 1.6078, -0.0000, 0.0000],
[ 1.8859, -0.0000, -0.0000, 0.5197],
[ 1.7649, 0.0000, 0.0000, 0.0000],
[-0.0000, -0.0000, 0.8490, 0.0000]],
[[-0.0000, 0.0000, -0.0000, -0.0000],
[-0.0000, 1.3494, -0.0603, -0.0000],
[-0.0000, 1.1885, 1.6293, 0.0000],
[ 1.3009, 1.2027, -0.0000, 1.3321]]]], device='cuda:0')
Process finished with exit code 0
四、使用pytorch获取tensor每行中的top k
？？？老铁，这么简单的问题还有问，自己解决去！！！！
参考：