Hi,
ResNet model definitions are here:

  
      github.com
  
  
    pytorch/vision/blob/master/torchvision/models/resnet.py#L106 40

class ResNet(nn.Module):


def __init__(self, block, layers, num_classes=1000):
    self.inplanes = 64
    super(ResNet, self).__init__()
    self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                           bias=False)
    self.bn1 = nn.BatchNorm2d(64)
    self.relu = nn.ReLU(inplace=True)
    self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
    self.layer1 = self._make_layer(block, 64, layers[0])
    self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
    self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
    self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
    self.avgpool = nn.AvgPool2d(7, stride=1)
    self.fc = nn.Linear(512 * block.expansion, num_classes)


    for m in self.modules():
        if isinstance(m, nn.Conv2d):
            n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            m.weight.data.normal_(0, math.sqrt(2. / n))



  
  
    
    
  
  


Look at _make_layer function and how the residual blocks were coded:

  
      github.com
  
  
    pytorch/vision/blob/master/torchvision/models/resnet.py#L57 14



    if self.downsample is not None:
        residual = self.downsample(x)


    out += residual
    out = self.relu(out)


    return out




class Bottleneck(nn.Module):
expansion = 4


def __init__(self, inplanes, planes, stride=1, downsample=None):
    super(Bottleneck, self).__init__()
    self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
    self.bn1 = nn.BatchNorm2d(planes)
    self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                           padding=1, bias=False)
    self.bn2 = nn.BatchNorm2d(planes)
    self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)



  
  
    
    
  
  


So you could adapt these however you want.