如何判断ResNet-18中的层数？

Question

以下是RestNet-18的Pytorch实现： https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py 以下是ResNet-18的结构，任何人都知道为什么这个网络有18层？

ResNet (
    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) 
    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True) 
    (relu): ReLU (inplace) 
    (maxpool): MaxPool2d (size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1)) 
    (layer1): Sequential (
    (0): BasicBlock (
     (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 
     (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True) 
     (relu): ReLU (inplace) 
     (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 
     (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True) 
    ) 
    (1): BasicBlock (
     (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 
     (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True) 
     (relu): ReLU (inplace) 
     (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 
     (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True) 
    ) 
) 
    (layer2): Sequential (
    (0): BasicBlock (
     (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) 
     (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True) 
     (relu): ReLU (inplace) 
     (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 
     (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True) 
     (downsample): Sequential (
     (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False) 
     (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True) 
    ) 
    ) 
    (1): BasicBlock (
     (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 
     (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True) 
     (relu): ReLU (inplace) 
     (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 
     (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True) 
    ) 
) 
    (layer3): Sequential (
    (0): BasicBlock (
     (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) 
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True) 
     (relu): ReLU (inplace) 
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True) 
     (downsample): Sequential (
     (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False) 
     (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True) 
    ) 
    ) 
    (1): BasicBlock (
     (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True) 
     (relu): ReLU (inplace) 
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True) 
    ) 
) 
    (layer4): Sequential (
    (0): BasicBlock (
     (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) 
     (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True) 
     (relu): ReLU (inplace) 
     (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 
     (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True) 
     (downsample): Sequential (
     (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False) 
     (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True) 
    ) 
    ) 
    (1): BasicBlock (
     (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 
     (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True) 
     (relu): ReLU (inplace) 
     (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) 
     (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True) 
    ) 
) 
    (avgpool): AvgPool2d (
) 
    (fc): Linear (512 -> 1000) 
) 

Answer 1

从你的输出，我们可以知道，有20个卷积层（一个7x7的转换次数，16 3x3的转换次数，并加上下采样3的1x1 CONV）。基本上，如果你忽略1x1转换，并计算fc（线性）层，层数为18.

而且我也做了一个示例https://gist.github.com/wangg12/f11258583ffcc4728eb71adc0f38e832如何通过graphviz在pytorch中可视化您的体系结构，希望它会帮助你理解你的架构。