def draw_concat(Gs, reals, NoiseAmp, in_s, mode, fixed_noise, opt):
if len(Gs) > 0:
if mode == 'rand':
count = 0
for G, real_curr, real_next, noise_amp in zip(Gs, reals, reals[1:], NoiseAmp):
if not opt.add_inject or count >= opt.inject_level:
if count == 0:
G_z = in_s
z = fixed_noise[count]
if count == opt.inject_level and opt.add_inject:
z_in = noise_amp * z + real_curr.cuda()
G_z = real_curr.cuda()
else:
G_z = G_z[:, :, 0:real_curr.shape[2], 0:real_curr.shape[3]]
z_in = noise_amp * z + G_z
if count > opt.switch_scale:
G_z = G(z_in.detach())
else:
G_z = G(z_in.detach(), G_z)
G_z = imresize2(G_z.detach(), 1 / opt.scale_factor, opt)
G_z = G_z[:, :, 0:real_next.shape[2], 0:real_next.shape[3]]
count += 1
return G_z
def draw_concat(Gs, reals, NoiseAmp, in_s, mode, inject_level, opt):
if len(Gs) > 0:
if mode == 'rand':
count = 0
for G,real_curr,real_next,noise_amp in zip(Gs,reals,reals[1:],NoiseAmp):
print("cnt " + str(count))
if count == 0:
z = generate_noise2([1, 3, real_curr.shape[2], real_curr.shape[3]], device=opt.device)
G_z = in_s
else:
z = generate_noise2([1, opt.nc_z,real_curr.shape[2], real_curr.shape[3]], device=opt.device)
G_z = G_z[:,:,0:real_curr.shape[2],0:real_curr.shape[3]]
if count == inject_level:
print("Inject")
z_in = noise_amp*z + real_curr.cuda()
else:
z_in = noise_amp*z+G_z
if count > opt.switch_scale:
G_z = G(z_in.detach())
else:
G_z = G(z_in.detach(), G_z)
G_z = imresize2(G_z.detach(),1/opt.scale_factor,opt)
G_z = G_z[:,:,0:real_next.shape[2],0:real_next.shape[3]]
count += 1
return G_z
def draw_concat(Gs, reals, NoiseAmp, in_s, mode, opt):
if len(Gs) > 0:
if mode == 'rand':
count = 0
for G, real_curr, real_next, noise_amp in zip(
Gs, reals, reals[1:], NoiseAmp):
G = G.cuda()
if count == 0:
z = generate_noise2(
[1, 3, real_curr.shape[2], real_curr.shape[3]],
device=opt.device)
G_z = in_s
else:
z = generate_noise2(
[1, opt.nc_z, real_curr.shape[2], real_curr.shape[3]],
device=opt.device)
G_z = G_z[:, :, 0:real_curr.shape[2], 0:real_curr.shape[3]]
z_in = noise_amp * z + G_z
if count > opt.switch_scale:
G_z = G(z_in.detach())
else:
G_z = G(z_in.detach(), G_z)
G_z = imresize2(G_z.detach(), 1 / opt.scale_factor, opt)
G_z = G_z[:, :, 0:real_next.shape[2], 0:real_next.shape[3]]
count += 1
if mode == 'rec':
count = 0
for G, real_curr, real_next, noise_amp in zip(
Gs, reals, reals[1:], NoiseAmp):
G = G.cuda()
if count == 0:
size = list(real_curr.size())
#print(size)
G_z = generate_noise2(size, device=opt.device)
G_z = G_z[:, :, 0:real_curr.shape[2], 0:real_curr.shape[3]]
if count > opt.switch_scale:
G_z = G(G_z)
else:
G_z = G(G_z, G_z)
G_z = imresize2(G_z.detach(), 1 / opt.scale_factor, opt)
G_z = G_z[:, :, 0:real_next.shape[2], 0:real_next.shape[3]]
count += 1
return G_z