Memory leaks when a model is transferred from gpu to cpu and save

jit
#1 
def save_model:
        net = self.my_net.module
        dummy_input = torch.randn(1,3,112,112).cpu()
        net.eval()
        net.cpu() # switch to cpu to save the model to avoid some issues on Pytorch 1.1.0
        script_module = torch.jit.trace(net,dummy_input)
        script_module.save('my_model.pt')
        net.cuda() # switch to cuda to continue training on GPU
        net.train()

The memory usage keeps increasing after each call to save_model, in other words, memory is being leaked.

May anyone help to me to understand this issue? Thank you.

#2

Memory on the cpu or gpu side?
How do you measure this?
Could you provide a small script to reproduce this?