Hello, new here. I am trying to create a custom optimizer in PyTorch, where the backprop takes place in a meta RL policy. However, I am seeing the above error. My models work fine on Adam and SGD, but not my optimizer.
Code:
class MetaBackProp(torch.optim.Optimizer):
def __init__(self, params):
self.param_shape_list = np.array([])
for param in list(params):
np.append(self.param_shape_list, list(param.size()))
pseudo_lr = 1e-4
pseudo_defaults = dict(lr=pseudo_lr)
length = 100 #TODO: get shape, flatten, multiply...
self.policy = AEPolicy(length)
self.policy_optim = torch.optim.Adam(self.policy.parameters(), lr=pseudo_lr)
super(MetaBackProp, self).__init__(params, pseudo_defaults)
def step(self, closure=None):
params = torch.cat([p.view(-1) for p in self.param_groups])
self.policy_optim.zero_grad()
quit()
Traceback:
Traceback (most recent call last):
File "main.py", line 6, in <module>
gan = CycleGAN()
File "/home/ai/Projects_v2/R/cycle_gan.py", line 32, in __init__
self.discriminator2_optim = MetaBackProp(self.discriminator2.parameters())
File "/home/ai/Projects_v2/R/lr_schedule.py", line 34, in __init__
super(MetaBackProp, self).__init__(params, pseudo_defaults)
File "/home/ai/anaconda3/lib/python3.7/site-packages/torch/optim/optimizer.py", line 46, in __init__
raise ValueError("optimizer got an empty parameter list")
ValueError: optimizer got an empty parameter list