Doubt about dis_opt and gen_opt

[songbo0925]

In trainer.py, why only update the parameters of dis_a and gen_a and ignore the parameters of dis_b and gen_b?

DG-Net/trainer.py

Lines 242 to 248 in a067be1

	dis_params = list(self.dis_a.parameters()) #+ list(self.dis_b.parameters())
	gen_params = list(self.gen_a.parameters()) #+ list(self.gen_b.parameters())
	self.dis_opt = torch.optim.Adam([p for p in dis_params if p.requires_grad],
	lr=lr_d, betas=(beta1, beta2), weight_decay=hyperparameters['weight_decay'])
	self.gen_opt = torch.optim.Adam([p for p in gen_params if p.requires_grad],
	lr=lr_g, betas=(beta1, beta2), weight_decay=hyperparameters['weight_decay'])

[layumi]

Hi @songbo0925
Thanks for your attention on our paper. The weight is shared between dis_a and dis_b, so as gen_a and gen_b.
Therefore, only one optimizer is needed.

[songbo0925]

Hi @layumi
Thanks for your wonderful work and reply.
In line 180-181 of trainer.py, gen_b is set to be the same as gen_a just this once. But in each iterations, if just the parameters gen_a is updated, then in next forward gen_b and gen_a may have different parameters. So I want to know how you achieve weight sharing？So as dis_a and dis_b. Maybe I ignored some code, please advise.

DG-Net/trainer.py

Lines 180 to 181 in a067be1

	self.gen_a = AdaINGen(hyperparameters['input_dim_a'], hyperparameters['gen'], fp16 = False) # auto-encoder for domain a
	self.gen_b = self.gen_a # auto-encoder for domain b