Hello,
I think you could try it on model.named_modules(), it will get the sub-module in the whole network recursively including the sub-module in sequential.
import torch.nn as nn

class MyModule(nn.Module):
    def __init__(self):
        super(MyModule, self).__init__()

        dilation1 = [nn.Conv2d(3,3,3, padding=1, bias=False),
                     nn.Conv2d(3,3,3, padding=1, bias=False)]
        self.conv1 = nn.Sequential(*dilation1)

    def forward(self, input):

        output = self.conv1(input)
        return output

model = MyModule()
for module in model.named_modules():
    print(module)
# output
('', MyModule(
  (conv1): Sequential(
    (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (1): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
  )
))
('conv1', Sequential(
  (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
  (1): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
))
('conv1.0', Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))
('conv1.1', Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))