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| from tqdm import trange | |
| import torch | |
| from torch.utils.data import DataLoader | |
| from logger import Logger | |
| from modules.model import GeneratorFullModel | |
| from torch.optim.lr_scheduler import MultiStepLR | |
| from torch.nn.utils import clip_grad_norm_ | |
| from frames_dataset import DatasetRepeater | |
| import math | |
| def train(config, inpainting_network, kp_detector, bg_predictor, dense_motion_network, checkpoint, log_dir, dataset): | |
| train_params = config['train_params'] | |
| optimizer = torch.optim.Adam( | |
| [{'params': list(inpainting_network.parameters()) + | |
| list(dense_motion_network.parameters()) + | |
| list(kp_detector.parameters()), 'initial_lr': train_params['lr_generator']}],lr=train_params['lr_generator'], betas=(0.5, 0.999), weight_decay = 1e-4) | |
| optimizer_bg_predictor = None | |
| if bg_predictor: | |
| optimizer_bg_predictor = torch.optim.Adam( | |
| [{'params':bg_predictor.parameters(),'initial_lr': train_params['lr_generator']}], | |
| lr=train_params['lr_generator'], betas=(0.5, 0.999), weight_decay = 1e-4) | |
| if checkpoint is not None: | |
| start_epoch = Logger.load_cpk( | |
| checkpoint, inpainting_network = inpainting_network, dense_motion_network = dense_motion_network, | |
| kp_detector = kp_detector, bg_predictor = bg_predictor, | |
| optimizer = optimizer, optimizer_bg_predictor = optimizer_bg_predictor) | |
| print('load success:', start_epoch) | |
| start_epoch += 1 | |
| else: | |
| start_epoch = 0 | |
| scheduler_optimizer = MultiStepLR(optimizer, train_params['epoch_milestones'], gamma=0.1, | |
| last_epoch=start_epoch - 1) | |
| if bg_predictor: | |
| scheduler_bg_predictor = MultiStepLR(optimizer_bg_predictor, train_params['epoch_milestones'], | |
| gamma=0.1, last_epoch=start_epoch - 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) | |
| generator_full = GeneratorFullModel(kp_detector, bg_predictor, dense_motion_network, inpainting_network, train_params) | |
| if torch.cuda.is_available(): | |
| generator_full = torch.nn.DataParallel(generator_full).cuda() | |
| bg_start = train_params['bg_start'] | |
| 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']): | |
| for x in dataloader: | |
| if(torch.cuda.is_available()): | |
| x['driving'] = x['driving'].cuda() | |
| x['source'] = x['source'].cuda() | |
| losses_generator, generated = generator_full(x, epoch) | |
| loss_values = [val.mean() for val in losses_generator.values()] | |
| loss = sum(loss_values) | |
| loss.backward() | |
| clip_grad_norm_(kp_detector.parameters(), max_norm=10, norm_type = math.inf) | |
| clip_grad_norm_(dense_motion_network.parameters(), max_norm=10, norm_type = math.inf) | |
| if bg_predictor and epoch>=bg_start: | |
| clip_grad_norm_(bg_predictor.parameters(), max_norm=10, norm_type = math.inf) | |
| optimizer.step() | |
| optimizer.zero_grad() | |
| if bg_predictor and epoch>=bg_start: | |
| optimizer_bg_predictor.step() | |
| optimizer_bg_predictor.zero_grad() | |
| losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()} | |
| logger.log_iter(losses=losses) | |
| scheduler_optimizer.step() | |
| if bg_predictor: | |
| scheduler_bg_predictor.step() | |
| model_save = { | |
| 'inpainting_network': inpainting_network, | |
| 'dense_motion_network': dense_motion_network, | |
| 'kp_detector': kp_detector, | |
| 'optimizer': optimizer, | |
| } | |
| if bg_predictor and epoch>=bg_start: | |
| model_save['bg_predictor'] = bg_predictor | |
| model_save['optimizer_bg_predictor'] = optimizer_bg_predictor | |
| logger.log_epoch(epoch, model_save, inp=x, out=generated) | |