def SinGAN_SR(opt, Gs, Zs, reals, NoiseAmp):
mode = opt.mode
in_scale, iter_num = functions.calc_init_scale(opt)
opt.scale_factor = 1 / in_scale
opt.scale_factor_init = 1 / in_scale
opt.mode = 'SR_train'
#opt.alpha = 100
opt.stop_scale = 0
dir2trained_model = functions.generate_dir2save(opt)
if (os.path.exists(dir2trained_model)):
#print('Trained model does not exist, training SinGAN for SR')
Gs, Zs, reals, NoiseAmp = functions.load_trained_pyramid(opt)
opt.mode = mode
else:
SR_train(opt, Gs, Zs, reals, NoiseAmp)
opt.mode = mode
print('%f' % pow(in_scale, iter_num))
Zs_sr = []
reals_sr = []
NoiseAmp_sr = []
Gs_sr = []
real = reals[-1] #read_image(opt)
for j in range(1, iter_num + 1, 1):
real_ = imresize(real, pow(1 / opt.scale_factor, j), opt)
real_ = real_[:, :,
0:int(pow(1 / opt.scale_factor, j) * real.shape[2]),
0:int(pow(1 / opt.scale_factor, j) * real.shape[3])]
reals_sr.append(real_)
Gs_sr.append(Gs[-1])
NoiseAmp_sr.append(NoiseAmp[-1])
z_opt = torch.full(real_.shape, 0, device=opt.device)
m = nn.ZeroPad2d(5)
z_opt = m(z_opt)
Zs_sr.append(z_opt)
out = SinGAN_generate(Gs_sr,
Zs_sr,
reals_sr,
NoiseAmp_sr,
opt,
in_s=reals_sr[0],
num_samples=1)
dir2save = functions.generate_dir2save(opt)
plt.imsave('%s.png' % (dir2save),
functions.convert_image_np(out.detach()),
vmin=0,
vmax=1)
return
Zs = []
reals = []
NoiseAmp = []
dir2save = functions.generate_dir2save(opt)
if dir2save is None:
print('task does not exist')
#elif (os.path.exists(dir2save)):
# print("output already exist")
else:
try:
os.makedirs(dir2save)
except OSError:
pass
mode = opt.mode
in_scale, iter_num = functions.calc_init_scale(opt)
opt.scale_factor = 1 / in_scale
opt.scale_factor_init = 1 / in_scale
opt.mode = 'train'
dir2trained_model = functions.generate_dir2save(opt)
if (os.path.exists(dir2trained_model)):
Gs, Zs, reals, NoiseAmp = functions.load_trained_pyramid(opt)
opt.mode = mode
else:
print('*** Train SinGAN for SR ***')
real = functions.read_image(opt)
opt.min_size = 18
real = functions.adjust_scales2image_SR(real, opt)
train(opt, Gs, Zs, reals, NoiseAmp)
opt.mode = mode
print('%f' % pow(in_scale, iter_num))
