should I add set_requires_grad(net_d, True/False) for discriminator during training?

[vince2003]

Hello,
Thank you for your great work. However, I think you should add set_requires_grad(net_d, True/False) for discriminator during training. Is it true?

modified code:

    # (1) Update D network
    ######################
    set_requires_grad(net_d, True) # add it here
    optimizer_d.zero_grad()        
    # train with fake
    fake_ab = torch.cat((real_a, fake_b), 1)
    pdb.set_trace()
    pred_fake = net_d.forward(fake_ab.detach())
    loss_d_fake = criterionGAN(pred_fake, False)

    # train with real
    real_ab = torch.cat((reala, real_b), 1)
    pred_real = net_d.forward(real_ab)
    loss_d_real = criterionGAN(pred_real, True)
    
    # Combined D loss
    loss_d = (loss_d_fake + loss_d_real) * 0.5

    loss_d.backward()
   
    optimizer_d.step()

    set_requires_grad(net_d, False) # add it here

    ######################

I am looking forward to hearing from you. Thank you in advance!