if i % 100 == 0:
a=np.array(buf)
plotStats(a,opt.outputFolder+desc)
vutils.save_image(text, '%s/real_textures.jpg' % opt.outputFolder, normalize=True)
IG=invblend(fake,mixedI,alpha[:,:1],alpha[:,1:2])
vutils.save_image(torch.cat([content[:,:3], mixedI, IG,fake, alpha, rgb_channels(A)], 2), '%s/mosaic_epoch_%03d_%s.jpg' % (opt.outputFolder, epoch, desc), normalize=True)
fixnoise=setNoise(fixnoise)
for net in Gnets:
net.eval()
with torch.no_grad():
if False:##do whole mosaic in 1 pass -- warning, takes a lot of memory, do not use unless you have a good reason
templates=templates.to(device)
fakebig, alpha, A, mixedbig = famosGeneration(targetMosaic, fixnoise, templates.unsqueeze(0), True)
else:
fakebig, alpha, A, mixedbig = splitW(targetMosaic, fixnoise, templates.unsqueeze(0), famosGeneration)
vutils.save_image(mixedbig,'%s/mixed_epoch_%03d_%s.jpg' % (opt.outputFolder, epoch,desc), normalize =True)
if True:#
vutils.save_image(alpha,'%s/alpha_epoch_%03d_%s.jpg' % (opt.outputFolder, epoch,desc), normalize=False)
vutils.save_image(rgb_channels(A), '%s/blenda_epoch_%03d_%s.jpg' % (opt.outputFolder, epoch, desc), normalize=False)##already 01
v=nn.functional.avg_pool2d(A.view(-1, 1, A.shape[2], A.shape[3]), int(16))
vutils.save_image(v,'%s/blendaBW_epoch_%03d_%s.jpg' % (opt.outputFolder, epoch,desc), normalize=False)
vutils.save_image(fakebig,'%s/mosaicBig_epoch_%03d_%s.jpg' % (opt.outputFolder, epoch,desc),normalize=True)
##RUN OUT-OF-SAMPLE
with torch.no_grad():
try:
im=getImage(opt.testImage, bDel=True)
if im.shape[2]>targetMosaic.shape[2] or im.shape[3]>targetMosaic.shape[3]:
print ("cropping to original mosaic size")
normalize=True)
fixnoise = setNoise(fixnoise)
vutils.save_image(fixnoise.view(-1, 1, fixnoise.shape[2],
fixnoise.shape[3]),
'%s/noiseBig_epoch_%03d_%s.jpg' %
(opt.outputFolder, epoch, desc),
normalize=True)
netMix.eval()
with torch.no_grad():
if False: ##if desired use this to make prediction in 1 pass, may be good for real-time video
fakebig = ganGeneration(targetMosaic, fixnoise)
else:
fakebig, _, _, _ = splitW(targetMosaic, fixnoise, None,
ganGeneration)
vutils.save_image(fakebig,
'%s/mosaicBig_epoch_%03d_%s.jpg' %
(opt.outputFolder, epoch, desc),
normalize=True)
##RUN OUT-OF-SAMPLE
VIDEO_SAVE_FREQ = 10
with torch.no_grad():
import os
t0 = time.time()
try:
try:
files = os.listdir(opt.testImage)
os.makedirs(opt.outputFolder + "video/")