【pytorch】1x1卷积

对image进行1x1卷积，输入为 ( b a t c h , C , H , W ) (batch, C, H, W) (batch,C,H,W)，如下例子，输入为 ( 4 , 3 , 225 , 225 ) (4, 3, 225, 225) (4,3,225,225)的图片，输出为 ( 4 , 256 , 225 , 225 ) (4, 256, 225, 225) (4,256,225,225)的特征图

import torch
import torch.nn as nn

batch = 4
C = 3
H = W = 225
x = torch.rand((batch, C, H, W))
conv = nn.Conv2d(in_channels=C, out_channels=256, kernel_size=1, stride=1, padding=0)
out = conv(x)
print(out.shape)  # (4, 256, 225, 225)

对3维张量进行1x1卷积，由于pytorch进行2D卷积要求输入是4维张量，因此要对形如 ( b a t c h , C , N ) (batch,C,N) (batch,C,N)的张量进行1x1卷积时要先将其扩展一个维度，可以扩展为 ( b a t c h , C , 1 , N ) (batch,C,1,N) (batch,C,1,N)或 ( b a t c h , C , N , 1 ) (batch,C,N,1) (batch,C,N,1)再进行1x1卷积，结果都是一样的，例如

import torch
import torch.nn as nn

torch.manual_seed(28)
x = torch.rand((4, 128, 2048))
conv = nn.Conv2d(128, 256, kernel_size=1, stride=1)

# 扩展最后一个维度和倒数第二个维度结果都是一样的
out1 = conv(x.unsqueeze(-1))
res1 = out1.squeeze(-1)
print(res1.shape)  # (4,256,2048)
print(torch.sum(torch.sum(torch.sum(res1, dim=-1), dim=-1), dim=-1))  # 17060.7676

out2 = conv(x.unsqueeze(-2))
res2 = out2.squeeze(-2)
print(res2.shape)  # (4,256,2048)
print(torch.sum(torch.sum(torch.sum(res2, dim=-1), dim=-1), dim=-1))  # 17060.7676