managed to create my own example!
import torch
import torch.nn as nn
import copy
from collections import OrderedDict
model = nn.Sequential( OrderedDict( [ ('fc0', nn.Linear(3,1)) ] ) )
#model.fc0.weight = nn.Parameter( torch.randn(3,1) + 3 )
print(model.fc0.weight)
w = model.fc0.weight
for i in range(5):
w_new = w - 2*(w)
print()
print(w_new.is_leaf)
#model.fc0.weight = nn.Parameter( w_new )
setattr(model,'fc0.weight', w_new )
print(model.fc0.weight.is_leaf)
print(model.fc0.weight)
model_copy = copy.deepcopy(model)
seems that the issue is with this line of code:
def __deepcopy__(self, memo):
if not self.is_leaf:
raise RuntimeError("Only Variables created explicitly by the user "
"(graph leaves) support the deepcopy protocol at the moment")
result = type(self)(self.data.clone())
result.requires_grad = self.requires_grad
result.volatile = self.volatile
memo[id(self)] = result
return result
pytorch determines this by checking if things are leafs. Somehow doing nn.Parameter(w_new) sets things as new leafs.
but the setattr somehow avoids it and introduces the error.