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| from tqdm import trange | |
| import torch | |
| from torch.utils.data import DataLoader | |
| from logger import Logger | |
| from torch.optim.lr_scheduler import MultiStepLR | |
| from frames_dataset import DatasetRepeater | |
| def random_scale(kp_params, scale): | |
| theta = torch.rand(kp_params['fg_kp'].shape[0], 2) * (2 * scale) + (1 - scale) | |
| theta = torch.diag_embed(theta).unsqueeze(1).type(kp_params['fg_kp'].type()) | |
| new_kp_params = {'fg_kp': torch.matmul(theta, kp_params['fg_kp'].unsqueeze(-1)).squeeze(-1)} | |
| return new_kp_params | |
| def train_avd(config, inpainting_network, kp_detector, bg_predictor, dense_motion_network, | |
| avd_network, checkpoint, log_dir, dataset): | |
| train_params = config['train_avd_params'] | |
| optimizer = torch.optim.Adam(avd_network.parameters(), lr=train_params['lr'], betas=(0.5, 0.999)) | |
| if checkpoint is not None: | |
| Logger.load_cpk(checkpoint, inpainting_network=inpainting_network, kp_detector=kp_detector, | |
| bg_predictor=bg_predictor, avd_network=avd_network, | |
| dense_motion_network= dense_motion_network,optimizer_avd=optimizer) | |
| start_epoch = 0 | |
| else: | |
| raise AttributeError("Checkpoint should be specified for mode='train_avd'.") | |
| scheduler = MultiStepLR(optimizer, train_params['epoch_milestones'], gamma=0.1) | |
| if 'num_repeats' in train_params or train_params['num_repeats'] != 1: | |
| dataset = DatasetRepeater(dataset, train_params['num_repeats']) | |
| dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True, | |
| num_workers=train_params['dataloader_workers'], drop_last=True) | |
| with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], | |
| checkpoint_freq=train_params['checkpoint_freq']) as logger: | |
| for epoch in trange(start_epoch, train_params['num_epochs']): | |
| avd_network.train() | |
| for x in dataloader: | |
| with torch.no_grad(): | |
| kp_source = kp_detector(x['source'].cuda()) | |
| kp_driving_gt = kp_detector(x['driving'].cuda()) | |
| kp_driving_random = random_scale(kp_driving_gt, scale=train_params['random_scale']) | |
| rec = avd_network(kp_source, kp_driving_random) | |
| reconstruction_kp = train_params['lambda_shift'] * \ | |
| torch.abs(kp_driving_gt['fg_kp'] - rec['fg_kp']).mean() | |
| loss_dict = {'rec_kp': reconstruction_kp} | |
| loss = reconstruction_kp | |
| loss.backward() | |
| optimizer.step() | |
| optimizer.zero_grad() | |
| losses = {key: value.mean().detach().data.cpu().numpy() for key, value in loss_dict.items()} | |
| logger.log_iter(losses=losses) | |
| # Visualization | |
| avd_network.eval() | |
| with torch.no_grad(): | |
| source = x['source'][:6].cuda() | |
| driving = torch.cat([x['driving'][[0, 1]].cuda(), source[[2, 3, 2, 1]]], dim=0) | |
| kp_source = kp_detector(source) | |
| kp_driving = kp_detector(driving) | |
| out = avd_network(kp_source, kp_driving) | |
| kp_driving = out | |
| dense_motion = dense_motion_network(source_image=source, kp_driving=kp_driving, | |
| kp_source=kp_source) | |
| generated = inpainting_network(source, dense_motion) | |
| generated.update({'kp_source': kp_source, 'kp_driving': kp_driving}) | |
| scheduler.step(epoch) | |
| model_save = { | |
| 'inpainting_network': inpainting_network, | |
| 'dense_motion_network': dense_motion_network, | |
| 'kp_detector': kp_detector, | |
| 'avd_network': avd_network, | |
| 'optimizer_avd': optimizer | |
| } | |
| if bg_predictor : | |
| model_save['bg_predictor'] = bg_predictor | |
| logger.log_epoch(epoch, model_save, | |
| inp={'source': source, 'driving': driving}, | |
| out=generated) | |