Forward hooks break when model is deep copied

In order to extract intermediate features from a torchvision model, I implemented a class where the forward return the ouput of the network and the intermediate features.

This is what it looks like:

import torch
import torchvision.models as models
import torch.nn as nn
from copy import deepcopy

class CustomMobilenetV3(nn.Module):
    def __init__(self):
        super().__init__()
        self.model = models.mobilenet_v3_small()
        self.features = []
        self.layers  = [3,8,11]
        for i,ln in enumerate(self.layers):
            self.model.features[ln].register_forward_hook(self.get_features())



    def get_features(self):
        def hook(model, input, output):
            self.features.append(output)
        return hook

    def forward(self, x):

        x = self.model(x)

        return x, self.features

This works fine If I do this:

device = torch.device('cpu')
x = torch.randn(1,3,192,192).to(device)

mymodel = CustomMobilenetV3()
o, features = mymodel(x)

for i in features:
    print(i.shape)

It does not work If I deepcopy the model, then run it:

deep_model = deepcopy(mymodel)

o, features = deep_model(x)
print(features)
for i in features:
    print(i.shape)

in this case the variable features remains empty.

Interestingly, this works fine:

def get_features():
    def hook(model, input, output):
        feats.append(output)
    return hook

model = models.mobilenet_v3_small()
layers  = [3,8,11]
for i,ln in enumerate(layers):
    model.features[ln].register_forward_hook(get_features())

deep_model = deepcopy(model)

feats = []

o = deep_model(x)
for f in feats:
    print(f.shape)

What am I missing?
I need to deepcopy the model to use EMA.

Thanks!