def tensor_to_np(frame):
"""
turn float-point tensor to uint8 np.array
"""
frame = functions.denorm(frame).detach()
frame = frame[0, :, :, :].cpu().numpy()
frame = frame.transpose(1, 2, 0) * 255
frame = frame.astype(np.uint8)
return frame
def generate_gif(Gs,Zs,reals,NoiseAmp,opt,alpha=0.1,beta=0.9,start_scale=2,fps=10):
in_s = torch.full(Zs[0].shape, 0, device=opt.device)
images_cur = []
count = 0
for G,Z_opt,noise_amp,real in zip(Gs,Zs,NoiseAmp,reals):
pad_image = int(((opt.ker_size - 1) * opt.num_layer) / 2)
nzx = Z_opt.shape[2]
nzy = Z_opt.shape[3]
#pad_noise = 0
#m_noise = nn.ZeroPad2d(int(pad_noise))
m_image = nn.ZeroPad2d(int(pad_image))
images_prev = images_cur
images_cur = []
if count == 0:
z_rand = functions.generate_noise([1,nzx,nzy], device=opt.device)
z_rand = z_rand.expand(1,3,Z_opt.shape[2],Z_opt.shape[3])
z_prev1 = 0.95*Z_opt +0.05*z_rand
z_prev2 = Z_opt
else:
z_prev1 = 0.95*Z_opt +0.05*functions.generate_noise([opt.nc_z,nzx,nzy], device=opt.device)
z_prev2 = Z_opt
for i in range(0,100,1):
if count == 0:
z_rand = functions.generate_noise([1,nzx,nzy], device=opt.device)
z_rand = z_rand.expand(1,3,Z_opt.shape[2],Z_opt.shape[3])
diff_curr = beta*(z_prev1-z_prev2)+(1-beta)*z_rand
else:
diff_curr = beta*(z_prev1-z_prev2)+(1-beta)*(functions.generate_noise([opt.nc_z,nzx,nzy], device=opt.device))
z_curr = alpha*Z_opt+(1-alpha)*(z_prev1+diff_curr)
z_prev2 = z_prev1
z_prev1 = z_curr
if images_prev == []:
I_prev = in_s
else:
I_prev = images_prev[i]
I_prev = imresize(I_prev, 1 / opt.scale_factor, opt)
I_prev = I_prev[:, :, 0:real.shape[2], 0:real.shape[3]]
I_prev = m_image(I_prev)
if count < start_scale:
z_curr = Z_opt
z_in = noise_amp*z_curr+I_prev
I_curr = G(z_in.detach(),I_prev)
if (count == len(Gs)-1):
I_curr = functions.denorm(I_curr).detach()
I_curr = I_curr[0,:,:,:].cpu().numpy()
I_curr = I_curr.transpose(1, 2, 0)*255
I_curr = I_curr.astype(np.uint8)
images_cur.append(I_curr)
count += 1
dir2save = functions.generate_dir2save(opt)
try:
os.makedirs('%s/start_scale=%d' % (dir2save,start_scale) )
except OSError:
pass
imageio.mimsave('%s/start_scale=%d/alpha=%f_beta=%f.gif' % (dir2save,start_scale,alpha,beta),images_cur,fps=fps)
del images_cur
functions.generate_noise([nc_z, nzx, nzy], device=device))
z_curr = alpha * Z_opt + (1 - alpha) * (z_prev1 + diff_curr)
z_prev2 = z_prev1
z_prev1 = z_curr
if count < start_scale:
z_curr = Z_opt
if images_prev == []:
I_prev = in_s
else:
I_prev = images_prev[i]
I_prev = imresize(I_prev, 1 / scale_factor, opt) # edit
I_prev = I_prev[:, :, 0:real.shape[2], 0:real.shape[3]]
I_prev = m_image(I_prev)
z_in = noise_amp * z_curr + I_prev
I_curr = G(z_in.detach(), I_prev)
# convert result from GPU to CPU at last iteration
if (count == len(Gs) - 1):
I_curr = functions.denorm(I_curr).detach()
I_curr = I_curr[0, :, :, :].cpu().numpy()
I_curr = I_curr.transpose(1, 2, 0) * 255
I_curr = I_curr.astype(np.uint8)
images_cur.append(I_curr)
# count = 10 at the end
count += 1
# can be kick out
