Question

我想知道如何通过Pytorch中的遮罩选择通道。 [channel1 channel2 channel3 channel4] x [1,0,0,1]-> [channel1，channel4] 我尝试了torch.masked_select（），但没有用。

如果输入的形状类似于[B,C,H,W]，则输出的形状应为[B,masked_C,H,W]，

import torch
from torch import nn
input = torch.randn((1,5,3,3))
pool = nn.AdaptiveAvgPool2d(1)
w = torch.sigmoid(pool(input)).view(1,-1)
mask = torch.gt(w,0.5)
print(input)
print(w)
print(mask)

输出如下：

tensor([[[[ 0.9129, -0.9763,  1.4460],
          [ 0.3608,  0.5561, -1.4612],
          [ 1.4953, -1.2474,  0.4069]],

         [[-0.9121,  0.1261,  0.4661],
          [-1.1624, -1.0266, -1.5419],
          [ 1.0644,  1.0039, -0.4022]],

         [[-1.8454, -0.2150,  2.3703],
          [ 0.5224,  0.3366,  1.7545],
          [-0.4624,  1.2639,  1.8032]],

         [[-1.1558, -1.9985, -1.1336],
          [-0.4400, -0.2092,  0.0677],
          [-0.4172, -0.3614, -1.3193]],

         [[-0.9441, -0.2944,  0.3381],
          [ 1.6562, -0.5623,  0.0599],
          [ 0.7229,  0.0472, -0.5122]]]])
tensor([[0.5414, 0.4341, 0.6489, 0.3156, 0.5142]])
tensor([[1, 0, 1, 0, 1]], dtype=torch.uint8)

我想要的结果是这样的：

tensor([[[[ 0.9129, -0.9763,  1.4460],
          [ 0.3608,  0.5561, -1.4612],
          [ 1.4953, -1.2474,  0.4069]],

         [[-1.8454, -0.2150,  2.3703],
          [ 0.5224,  0.3366,  1.7545],
          [-0.4624,  1.2639,  1.8032]],

         [[-0.9441, -0.2944,  0.3381],
          [ 1.6562, -0.5623,  0.0599],
          [ 0.7229,  0.0472, -0.5122]]]])

Answer 1

我相信您可以做到：

import org.springframework.data.jpa.repository.Query;

顺便说一句。您无需先致电//Controller @yourMapping public returnType method1() { myService.performBusinessLoginonMethod1(...) //if you are using services myReposiroty.DBOpertaion(...) //as per above case } //Service: myService returnType performBusinessLoginonMethod1(...) { myRepository.DBOpertaion(...) } //Repository: myRepository @Modifying // import org.springframework.data.jpa.repository.Modifying; @Query(...) returnType DBOpertaion(...)，然后再致电input[mask]。您可以直接执行sigmoid，而无需调用Sigmoid。

pytorch如何通过遮罩选择通道？

1 个答案: