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