noise=setNoise(noise)
fake, alpha, A, mixedI = famosGeneration(content, noise, templatePatch, True)
output = netD(fake)
loss_adv = criterion(output, output.detach()*0+real_label)
D_G_z2 = output.mean()
if opt.fAdvM>0:
outputM = netD(mixedI)
loss_adv_mixed = criterion(outputM, outputM.detach() * 0 + real_label)
D_G_z2m = outputM.mean()
else:
D_G_z2m =D_G_z2*0
loss_adv_mixed = loss_adv*0
cLoss= contentLoss(fake,content[:,:3],netR,opt)
cLoss2= contentLoss(mixedI,content[:,:3],netR,opt)
entropy = (-A * (1e-8 + A).log()).mean()##entropy
tv= total_variation(A)
alpha_loss = alpha.mean()##large means more conv content; small is focus on mixedtemplate result
atv= total_variation(alpha)
diversity= gramMatrix(A.mean(3).mean(2).unsqueeze(2).unsqueeze(1)).mean() #.std(1).mean()*-1 #force various templates -- across batch variance
errG = loss_adv + opt.fAdvM * loss_adv_mixed + opt.fContent * cLoss + opt.fContentM * cLoss2 + opt.fAlpha * alpha_loss + opt.fEntropy * entropy + opt.fTV * tv + \
+0.02 * atv + opt.fDiversity * diversity
errG.backward()
optimizerU.step()
print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f mixP %.4f content %4f template %.4f alphareg %.4f entropy %.4f tv %.4f atv %.4f diversity %.4f time %.4f'
% (epoch, opt.niter, i, len(dataloader),
optimizerD.step()
if i > 0 and opt.WGAN and i % opt.dIter != 0:
continue ##critic steps to 1 GEN steps
for net in Gnets:
net.zero_grad()
content = next(iter(cdataloader))[0]
content = content.to(device)
# train with fake -- create again
noise = setNoise(noise)
fake = ganGeneration(content, noise)
output = netD(fake)
loss_adv = criterion(output, output.detach() * 0 + real_label)
D_G_z2 = output.mean()
cLoss = contentLoss(fake, content[:, :3], netR, opt)
errG = loss_adv + opt.fContent * cLoss
errG.backward()
optimizerU.step()
print(
'[%d/%d][%d/%d] D(x): %.4f D(G(z)): %.4f / %.4f content %4f time %.4f'
% (epoch, opt.niter, i, len(dataloader), D_x, D_G_z1, D_G_z2,
cLoss.item(), time.time() - t0))
buf += [[D_x.item(), D_G_z1.item(), D_G_z2.item(), cLoss.item()]]
### RUN INFERENCE AND SAVE LARGE OUTPUT MOSAICS
if i == 1:
a = np.array(buf)