Pytorch 中 torch.flatten() 和 torch.nn.Flatten() 实例详解

torch.flatten()

  torch.flatten(x) 等于 torch.flatten(x,0) 默认将张量拉成一维的向量,也就是说从第一维开始平坦化,torch.flatten(x,1) 代表从第二维开始平坦化。

Example:

import torch
x=torch.randn(2,4,2)
print(x)
  
z=torch.flatten(x)
print(z)
  
w=torch.flatten(x,1)
print(w)
  

输出结果:

输出为:
tensor([[[-0.9814,  0.8251],
         [ 0.8197, -1.0426],
         [-0.8185, -1.3367],
         [-0.6293,  0.6714]],
  
        [[-0.5973, -0.0944],
         [ 0.3720,  0.0672],
         [ 0.2681,  1.8025],
         [-0.0606,  0.4855]]])
  
tensor([-0.9814,  0.8251,  0.8197, -1.0426, -0.8185, -1.3367, -0.6293,  0.6714,
        -0.5973, -0.0944,  0.3720,  0.0672,  0.2681,  1.8025, -0.0606,  0.4855])
  
  
tensor([[-0.9814,  0.8251,  0.8197, -1.0426, -0.8185, -1.3367, -0.6293,  0.6714],
        [-0.5973, -0.0944,  0.3720,  0.0672,  0.2681,  1.8025, -0.0606,  0.4855]])

  torch.flatten(x,0,1) 代表在第一维和第二维之间平坦化 

Example:

import torch
x=torch.randn(2,4,2)
print(x)
  
w=torch.flatten(x,0,1) #第一维长度2,第二维长度为4,平坦化后长度为2*4
print(w.shape)
  
print(w)
  
输出为:
tensor([[[-0.5523, -0.1132],
         [-2.2659, -0.0316],
         [ 0.1372, -0.8486],
         [-0.3593, -0.2622]],
  
        [[-0.9130,  1.0038],
         [-0.3996,  0.4934],
         [ 1.7269,  0.8215],
         [ 0.1207, -0.9590]]])
  
torch.Size([8, 2])
  
tensor([[-0.5523, -0.1132],
        [-2.2659, -0.0316],
        [ 0.1372, -0.8486],
        [-0.3593, -0.2622],
        [-0.9130,  1.0038],
        [-0.3996,  0.4934],
        [ 1.7269,  0.8215],
        [ 0.1207, -0.9590]])

torch.nn.Flatten()

  对于 torch.nn.Flatten(),因为其被用在神经网络中,输入为一批数据,第一维为batch,通常要把一个数据拉成一维,而不是将一批数据拉为一维。所以torch.nn.Flatten()默认从第二维开始平坦化。

Example:

import torch
#随机32个通道为1的5*5的图
x=torch.randn(32,1,5,5)
  
model=torch.nn.Sequential(
    #输入通道为1,输出通道为6,3*3的卷积核,步长为1,padding=1
    torch.nn.Conv2d(1,6,3,1,1),
    torch.nn.Flatten()
)
output=model(x)
print(output.shape)  # 6*(7-3+1)*(7-3+1)
  
输出为:
  
torch.Size([32, 150])

 

posted @ 2022-03-30 00:39  Learner-  阅读(340)  评论(0编辑  收藏  举报