class myModel(nn.Module):
        def __init__(self):
        super(myModel, self).__init__()
        # self.keep_prob=0.9
        # self.block_size=7
        self.block1 = nn.Sequential(
            nn.Conv2d(1, 32, kernel_size=3, padding=1, bias=False),
            nn.GroupNorm(4,32,affine=False),
            # nn.BatchNorm2d(32, affine=False),
            nn.ReLU())
         self.bolck2=nn.Sequential(...)
         self.bolck3=nn.Sequential(...)
         self.bolck4=nn.Sequential(...)
         self.bolck5=nn.Sequential(...)
         self.bolck6=nn.Sequential(...)
         self.bolck7=nn.Sequential(...)
       
         self.block1.apply(weights_init)
        self.block2.apply(weights_init)
        self.block3.apply(weights_init)
        self.block4.apply(weights_init)
        self.block5.apply(weights_init)
        self.block6.apply(weights_init)
        self.block7.apply(weights_init)
     return 
      def forward(self, input,keep_prob=1.0):
        x = self.block1(self.input_norm(input))
        # x = DropBlock2D(keep_prob)(x)
        x = self.block2(x)
        # x = DropBlock2D(keep_prob)(x)
        x = self.block3(x)
        # x = DropBlock2D(keep_prob)(x)
        x = self.block4(x)
        x = self.block5(x)
        # x = DropBlock2D(keep_prob)(x)
        x = self.block6(x)
        x = DropBlock2D(keep_prob=keep_prob)(x)
        x = self.block7(x)
        # x_features=self.features(input)
        x = x.view(x.size(0), -1)
        return L2Norm()(x)

writer = SummaryWriter(log_dir=args.tensorboardx_log, comment='Hardnet')
dumpy_input = torch.zeros((1, 1, 32, 32))
temp_input = Variable(dumpy_input)
dumpy_keep_prob=Variable(torch.tensor([1]))
writer.add_graph(model, (temp_input,dumpy_keep_prob))

When I use the tensorboadX to  save the model graph, 
image.png420×877 30.2 KB

But the submodel DropBlock2D does not appear in the graph, where I am wrong.
Thanks in advance.