Dose pytorch support 2-D (nested) Modulelist?

In the __init__() of a module class, I want to pack and register some conv layers like this:

self.outs = nn.ModuleList(
            [nn.ModuleList([Conv(inp_dim + j * increase, oup_dim, 1, relu=False, bn=False) for j in range(5)]) for i in

then in the forward() method, I want to use the conv layers in the nested module list.

Am I right? I’m debugging my module which claimed:

RuntimeError: Trying to backward through the graph a second time, but the buffers have already been freed. Specify retain_graph=True when calling backward the first time.

Now I am trying to locate the error.

It looks like you are using all conv layers separately on some slices of your features. Is that correct?
Could you post a bit more code, since the current ModuleList seems to work using this small example:

outs = nn.ModuleList([nn.ModuleList([nn.Conv2d(3, 3, 3, 1, 1) for j in range(5)]) for i in range(3)])

x = torch.randn(5, 3, 24, 24)
outputs = []
for i in range(3):
    for j in range(5):

outputs = torch.stack(outputs)

Let me know, if I’m using it in a wrong way.

Hi ptrblck, grateful for your reply and help! Now I’m sure I can use 2-D ModuleList.