问题
stage1 = nn.Sequential( nn.Sequential( nn.Conv2d(16, 32, 3, 1, 1), nn.ReLU(), nn.Conv2d(32, 64, 3, 1, 1), nn.ReLU(), ) ) stage3 = nn.Sequential( nn.Conv2d(3, 16, 3, 1, 1), list(stage1.children())[0], #! 但是可以把list中的元素加进来 ) stage4 = nn.Sequential( nn.Conv2d(3, 16, 3, 1, 1), *list(stage1.children())[0], #! 解包序列后再将每个层加入进来 )
stage3和stage4都可以添加到nn.Sequential()中，二者的区别是什么？
方法
import torch from torch import nn stage1 = nn.Sequential( nn.Sequential( nn.Conv2d(16, 32, 3, 1, 1), nn.ReLU(), nn.Conv2d(32, 64, 3, 1, 1), nn.ReLU(), ) ) # stage2 = nn.Sequential( # nn.Conv2d(3, 16, 3, 1, 1), # list(stage1.children()), #! 不能把一个list加进来，因为list is not a Module subclass # ) # print(stage2) stage3 = nn.Sequential( nn.Conv2d(3, 16, 3, 1, 1), list(stage1.children())[0], #! 但是可以把list中的元素加进来 ) print(stage3) '''stage3输出: Sequential( (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Sequential( (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): ReLU() (2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): ReLU() ) ) ''' '''stage3与stage4的主要区别是: - stage3将整个Sequential加入进来, - 而stage4首先将Sequential解包而后再加入进来; ''' stage4 = nn.Sequential( nn.Conv2d(3, 16, 3, 1, 1), *list(stage1.children())[0], #! 解包序列后再将每个层加入进来 ) print(stage4) ''' stage4输出: Sequential( (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): ReLU() (3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): ReLU() ) '''
