def sample(self, loaders): args = self.args nets_ema = self.nets_ema os.makedirs(args.result_dir, exist_ok=True) self._load_checkpoint(args.resume_iter) src = next(InputFetcher(loaders.src, None, args.latent_dim, 'test')) ref = next(InputFetcher(loaders.ref, None, args.latent_dim, 'test')) fname = ospj(args.result_dir, 'reference.jpg') print('Working on {}...'.format(fname)) utils.translate_using_reference(nets_ema, args, src.x, ref.x, ref.y, fname) fname = ospj(args.result_dir, 'video_ref.mp4') print('Working on {}...'.format(fname)) utils.video_ref(nets_ema, args, src.x, ref.x, ref.y, fname) N = src.x.size(0) y_trg_list = [ torch.tensor(y).repeat(N).to(device) for y in range(min(args.num_domains, 5)) ] z_trg_list = torch.randn(args.num_outs_per_domain, 1, args.latent_dim).repeat(1, N, 1).to(device) for psi in [0.5, 0.7, 1.0]: filename = ospj(args.sample_dir, '%06d_latent_psi_%.1f.jpg' % (step, psi)) translate_using_latent(nets, args, src.x, y_trg_list, z_trg_list, psi, fname) fname = ospj(args.result_dir, 'latent.jpg') print('Working on {}...'.format(fname)) utils.video_ref(nets_ema, args, src.x, ref.x, ref.y, fname)
def sample(self, loaders): args = self.args nets_ema = self.nets_ema os.makedirs(args.result_dir, exist_ok=True) self._load_checkpoint(args.resume_iter) src = next(InputFetcher(loaders.src, None, args.latent_dim, 'test')) ref = next(InputFetcher(loaders.ref, None, args.latent_dim, 'test')) fname = ospj(args.result_dir, 'reference.jpg') print('Working on {}...'.format(fname)) utils.translate_using_reference(nets_ema, args, src.x, ref.x, ref.y, fname)
def custom(self, loaders): args = self.args nets_ema = self.nets_ema self._load_checkpoint(100000) src = next(InputFetcher(loaders.src, None, args.latent_dim, 'test')) ref = next(InputFetcher(loaders.ref, None, args.latent_dim, 'test')) fname = args.custom_out_img print('Working on {}...'.format(fname)) utils.translate_using_reference(nets_ema, args, src.x, ref.x, ref.y, fname)
def sample(self, loaders): args = self.args nets_ema = self.nets_ema os.makedirs(args.result_dir, exist_ok=True) self._load_checkpoint(args.resume_iter) fetch_src = InputFetcher(loaders.src, None, args.latent_dim, 'test') fetch_ref = InputFetcher(loaders.ref, None, args.latent_dim, 'test') for i in range(10000): src = next(fetch_src) ref = next(fetch_ref) fname = ospj( args.result_dir, str(i).zfill(5) + "from" + str(src.y.item()) + "to" + str(ref.y.item()) + '.jpg') print('Working on {}...'.format(fname)) utils.translate_using_reference(nets_ema, args, src.x, src.y, ref.x, ref.y, fname)
def sample(self,src,ref): args = self.args nets_ema = self.nets_ema os.makedirs(args.result_dir, exist_ok=True) self._load_checkpoint(args.resume_iter) src = self.convert_to_tensor(src) ref = self.convert_to_tensor(ref) fname = ospj(args.result_dir, 'reference.jpg') print('Working on {}...'.format(fname)) images_all = utils.translate_using_reference(nets_ema, args, src, ref, torch.tensor([1]), fname) image = images_all[0,...] image = image.permute(1,2,0) image = image.cpu().numpy()*255 image = cv2.cvtColor(image,cv2.COLOR_RGB2BGR) return image