d_optimizer.zero_grad()
disc_d_loss_dict = disc_d_loss(discriminator,
(real_data.detach(), ),
(fake_data.detach(), ),
backward=True,
opts=dis_opts)
d_optimizer.step()
# discriminator.eval()
e_loss = 0
# add l1/l2 regularization
if args.l1_alpha > 0:
l1_penalty = l1_regularization(enhancer, "weight")
e_loss = e_loss + args.l1_alpha * l1_penalty
if args.l2_alpha > 0:
l2_penalty = l2_regularization(enhancer, "weight")
e_loss = e_loss + args.l2_alpha * l2_penalty
if args.dist_alpha > 0:
# label_slices, real_data, fake_data = get_speech_slices(label, results, frame_number)
# get discriminative loss for generator
enhanced_logits, enhanced_feat = discriminator(
(enhanced_data, ))
real_logits, real_feat = discriminator(
(real_data.detach(), ))
rand_logits, rand_feat = discriminator(
(torch.rand_like(real_data) * 2. - 1., ))
# dist_loss = (enhanced_logits - real_logits).pow(2.).mean()
dist_loss = (enhanced_logits - 1).pow(2.).mean()
e_loss = e_loss + dist_loss * args.dist_alpha
if args.feat_alpha > 0:
(fake_data.detach(),), backward=True, opts=dis_opts)
d_optimizer.step()
# get discriminative loss for generator
disc_g_loss_dict = disc_g_loss(discriminator, (fake_data,), backward=False, opts=dis_opts)
g_loss = g_loss + disc_g_loss_dict["g_loss"] * dist_alpha
recon_loss = reconstruction_loss(predict, target, batch_mask, backward=False, opts=None)["loss"] / BATCH_SIZE
g_loss = g_loss + recon_loss
# add l1/l2 regularization
if args.l1_alpha > 0:
l1_penalty = l1_regularization(generator, "weight")
g_loss = g_loss + args.l1_alpha * l1_penalty
if args.l2_alpha > 0:
l2_penalty = l2_regularization(generator, "weight")
g_loss = g_loss + args.l2_alpha * l2_penalty
g_optimizer.zero_grad()
g_loss.backward()
if args.glc_alpha > 0:
if args.target_type.lower() == "mapping_mag":
batch = feat.sqrt()
else:
batch = log_feat
gp = calc_gradient_penalty(generator, batch, batch_mask)
(gp * args.glc_alpha).backward()
g_optimizer.step()
# log and summary
if global_step % args.log_interval == 0:
