def translate_using_latent(nets, args, x_src, y_trg_list, z_trg_list, psi,
filename):
n_images = 100
x_src.stop_gradient = True
N, C, H, W = x_src.shape
latent_dim = z_trg_list[0].shape[1]
x_concat = [x_src]
masks = nets.fan.get_heatmap(x_src) if args.w_hpf > 0 else None
for i, y_trg in enumerate(y_trg_list):
z_many = porch.randn(n_images, latent_dim)
# y_many = porch.LongTensor(10000).fill_(y_trg[0])
y_many = np.empty([n_images])
y_many.fill(y_trg[0].numpy()[0])
y_many = to_variable(y_many)
s_many = nets.mapping_network(z_many, y_many)
s_avg = porch.mean(s_many, dim=0, keepdim=True)
s_avg = s_avg.repeat(N, 1)
for z_trg in z_trg_list:
s_trg = nets.mapping_network(z_trg, y_trg)
s_trg = porch.lerp(s_avg, s_trg, psi)
x_fake = nets.generator(x_src, s_trg, masks=masks)
x_concat += [x_fake]
x_concat = porch.cat(x_concat, dim=0)
save_image(x_concat, N, filename)
def video_latent(nets, args, x_src, y_list, z_list, psi, fname):
x_src.stop_gradient = True
latent_dim = z_list[0].size(1)
s_list = []
for i, y_trg in enumerate(y_list):
z_many = porch.randn(10000, latent_dim)
y_many = porch.LongTensor(10000).fill_(y_trg[0])
s_many = nets.mapping_network(z_many, y_many)
s_avg = porch.mean(s_many, dim=0, keepdim=True)
s_avg = s_avg.repeat(x_src.size(0), 1)
for z_trg in z_list:
s_trg = nets.mapping_network(z_trg, y_trg)
s_trg = porch.lerp(s_avg, s_trg, psi)
s_list.append(s_trg)
s_prev = None
video = []
# fetch reference images
for idx_ref, s_next in enumerate(tqdm(s_list, 'video_latent',
len(s_list))):
if s_prev is None:
s_prev = s_next
continue
if idx_ref % len(z_list) == 0:
s_prev = s_next
continue
frames = interpolate(nets, args, x_src, s_prev, s_next).cpu()
video.append(frames)
s_prev = s_next
for _ in range(10):
video.append(frames[-1:])
video = tensor2ndarray255(porch.cat(video))
save_video(fname, video)
def interpolate(nets, args, x_src, s_prev, s_next):
''' returns T x C x H x W '''
B = x_src.shape[0]
frames = []
masks = nets.fan.get_heatmap(x_src) if args.w_hpf > 0 else None
alphas = get_alphas()
for alpha in alphas:
s_ref = porch.lerp(s_prev, s_next, alpha)
x_fake = nets.generator(x_src, s_ref, masks=masks)
entries = porch.cat([x_src, x_fake], dim=2)
frame = porchvision.utils.make_grid(entries,
nrow=B,
padding=0,
pad_value=-1).unsqueeze(0)
frames.append(frame)
frames = porch.cat(frames)
return frames
def moving_average(model, model_test, beta=0.999):
for param, param_test in zip(model.parameters(), model_test.parameters()):
porch.copy(porch.lerp(param, param_test, beta), param_test)