How to create a copy of nn.Sequential in torch?

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I am trying to create a copy of a nn.Sequential network. For example, the following is the easiest way to do the same-

net = nn.Sequential(
        nn.Conv2d(16, 32, 3, stride=2),
        nn.ReLU(),
        nn.Conv2d(32, 64, 3, stride=2),
        nn.ReLU(),
    )

net_copy = nn.Sequential(
        nn.Conv2d(16, 32, 3, stride=2),
        nn.ReLU(),
        nn.Conv2d(32, 64, 3, stride=2),
        nn.ReLU(),
    )

However, it is not so great to define the network again. I tried the following ways but it didn't work-

  1. net_copy = nn.Sequential(net): In this approach, it seems that net_copy is just a shared pointer of net
  2. net_copy = nn.Sequential(*net.modules()): In this approach, net_copy contains many more layers.

Finally, I tired deepcopy in the following way which worked fine-

net_copy = deepcopy(net)

However, I am wondering if it is the proper way. I assume it is fine because it works.

pytorch neural-network python
1 Answers

Well, I just use torch.load and torch.save with io.BytesIO

import io, torch


# write to a buffer
buffer = io.BytesIO()
torch.save(model, buffer) #<--- model is some nn.module
print(buffer.tell()) #<---- no of bytes written 

del model

# read from buffer
buffer.seek(0) #<--- must see to origin every time before reading
model = torch.load(buffer)
del buffer
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