##################################
if opt.multiScale:
netMix = NetU_MultiScale(ngf, nDep, nz, bSkip=opt.skipConnections, nc=N + 5, ncIn=5, bTanh=False, bCopyIn=opt.coordCopy, Ubottleneck=opt.Ubottleneck)
else:
netMix =NetUskip(ngf, nDep, nz, bSkip=opt.skipConnections, nc=N + 5, ncIn=5, bTanh=False, bCopyIn=opt.coordCopy, Ubottleneck=opt.Ubottleneck)##copy coords more often
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print ("device",device)
Gnets=[netMix]
if opt.refine:
netRefine=NetUskip(ngf, nDep, nz, bSkip=True, nc=5, ncIn=4 * 3 + 2 + 2, bTanh=False)
Gnets +=[netRefine]
if opt.cLoss>=100:
from network import ColorReconstruction
netR = ColorReconstruction(50, 1)#
Gnets+=[netR]
elif opt.cLoss==10:
from network import PerceptualF
netR=PerceptualF()
else:
netR = None
if opt.zPeriodic:
Gnets += [learnedWN]
for net in [netD] + Gnets:
try:
net.apply(weights_init)
except Exception as e:
print (e,"weightinit")
pass
##################################
netMix = NetUskip(ngf,
nDep,
nz,
bSkip=opt.skipConnections,
nc=3,
ncIn=3,
bTanh=True,
Ubottleneck=opt.Ubottleneck)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("device", device)
Gnets = [netMix]
if opt.cLoss >= 100:
from network import ColorReconstruction
netR = ColorReconstruction(50, 1) #
Gnets += [netR]
elif opt.cLoss == 10:
from network import PerceptualF
netR = PerceptualF()
else:
netR = None
if opt.zPeriodic:
Gnets += [learnedWN]
for net in [netD] + Gnets:
try:
net.apply(weights_init)
except Exception as e:
print(e, "weightinit")
pass