torch.topk()使用方法及其示例

1.作用取一个tensor的topk元素2.使用方法dim=0表示按照列求topn，dim=1表示按照行求topn，None情况下，dim=1.任务一：取top1：pred = torch.tensor([[-0.5816, -0.3873, -1.0215, -1.0145,0.4053],[ 0.7265,1.4164,1.3443,1.2035,1.8823],[-0.4451,0.1673

凝眸伏笔

13693人浏览 · 2021-01-04 22:23:35

凝眸伏笔 · 2021-01-04 22:23:35 发布

1.作用

取一个tensor的topk元素

2.使用方法

dim=0表示按照列求topn，dim=1表示按照行求topn，None情况下，dim=1.

任务一：

取top1：

pred = torch.tensor([[-0.5816, -0.3873, -1.0215, -1.0145,  0.4053],
        [ 0.7265,  1.4164,  1.3443,  1.2035,  1.8823],
        [-0.4451,  0.1673,  1.2590, -2.0757,  1.7255],
        [ 0.2021,  0.3041,  0.1383,  0.3849, -1.6311]])
print(pred)
values, indices = pred.topk(1, dim=0, largest=True, sorted=True)
print(indices)
print(values)
# 用max得到的结果，设置keepdim为True，避免降维。因为topk函数返回的index不降维，shape和输入一致。
_, indices_max = pred.max(dim=0, keepdim=True)
print(indices_max)
print(indices_max == indices)
输出：
tensor([[-0.5816, -0.3873, -1.0215, -1.0145,  0.4053],
        [ 0.7265,  1.4164,  1.3443,  1.2035,  1.8823],
        [-0.4451,  0.1673,  1.2590, -2.0757,  1.7255],
        [ 0.2021,  0.3041,  0.1383,  0.3849, -1.6311]])
tensor([[1, 1, 1, 1, 1]])
tensor([[0.7265, 1.4164, 1.3443, 1.2035, 1.8823]])
tensor([[1, 1, 1, 1, 1]])
tensor([[True, True, True, True, True]])

任务二：
按行取出topk，将小于topk的置为inf

pred = torch.tensor([[-0.5816, -0.3873, -1.0215, -1.0145,  0.4053],
        [ 0.7265,  1.4164,  1.3443,  1.2035,  1.8823],
        [-0.4451,  0.1673,  1.2590, -2.0757,  1.7255],
        [ 0.2021,  0.3041,  0.1383,  0.3849, -1.6311]])
print(pred)
top_k = 2
filter_value=-float('Inf')
indices_to_remove = pred < torch.topk(pred, top_k)[0][..., -1, None]
print(indices_to_remove)
pred[indices_to_remove] = filter_value  # 对于topk之外的其他元素的logits值设为负无穷
print(pred)

输出：
tensor([[-0.5816, -0.3873, -1.0215, -1.0145,  0.4053],
        [ 0.7265,  1.4164,  1.3443,  1.2035,  1.8823],
        [-0.4451,  0.1673,  1.2590, -2.0757,  1.7255],
        [ 0.2021,  0.3041,  0.1383,  0.3849, -1.6311]])
tensor([[4],
        [4],
        [4],
        [3]])
tensor([[0.4053],
        [1.8823],
        [1.7255],
        [0.3849]])
tensor([[ True, False,  True,  True, False],
        [ True, False,  True,  True, False],
        [ True,  True, False,  True, False],
        [ True, False,  True, False,  True]])
tensor([[   -inf, -0.3873,    -inf,    -inf,  0.4053],
        [   -inf,  1.4164,    -inf,    -inf,  1.8823],
        [   -inf,    -inf,  1.2590,    -inf,  1.7255],
        [   -inf,  0.3041,    -inf,  0.3849,    -inf]])

参考：

https://blog.csdn.net/qq_34914551/article/details/103738160
https://blog.csdn.net/u014264373/article/details/86525621
https://blog.csdn.net/weixin_45062709/article/details/102711885