Monkey patching a PyTorch model

Well, ideally you should not do this.
But sometimes you just need to quickly test something and amend your model on the fly.

This helps:

import torch
import functools

def rsetattr(obj, attr, val):
    pre, _, post = attr.rpartition('.')
    return setattr(rgetattr(obj, pre) if pre else obj, post, val)

def rgetattr(obj, attr, *args):
    def _getattr(obj, attr):
        return getattr(obj, attr, *args)
    return functools.reduce(_getattr, [obj] + attr.split('.'))

for module in model.named_modules():
    old_module_path = module[0]
    old_module_object = module[1]
    # replace an old object with the new one
    # copy some settings and its state
    if isinstance(old_module_object,torch.nn.SomeClass):
        new_module = SomeOtherClass(old_module_object.some_settings,
                                    old_module_object.some_other_settings)
        
        new_module.load_state_dict(module_object.state_dict())
        rsetattr(model,old_module_path,new_module)

The above code essentially does the same as:

model

.path.to.some.block = some_other_block
`

#python
#pytorch
#deep_learning
#oop