Training deeper networks using MAML framework

vision
1

Currently the pytorch implementation of networks trained by MAML, are implemented such that weights have to be passed as arguments, an example is given below

Blockquote def functional_forward(self, x, weights):
“”“Applies the same forward pass using PyTorch functional operators using a specified set of weights.”“”

    for block in [1, 2, 3, 4]:
        x = functional_conv_block(x, weights[f'conv{block}.0.weight'], weights[f'conv{block}.0.bias'],
                                  weights.get(f'conv{block}.1.weight'), weights.get(f'conv{block}.1.bias'))

    x = x.view(x.size(0), -1)

    x = F.linear(x, weights['logits.weight'], weights['logits.bias'])

return x

Now this foward implementation is fine, if the network has just 4 layers, but with techniques like meta transfer learning (https://arxiv.org/pdf/1812.02391.pdf) deeper networks are being trained within the MAML framework.

So my question is it possible to train a model using the MAML framework, without having to code up the model in a functional way.

Any help would be appreciated ! Thanks