| from tqdm import trange | |
| import torch | |
| from torch.utils.data import DataLoader | |
| from logger import Logger | |
| from modules.model import GeneratorFullModel, DiscriminatorFullModel | |
| from torch.optim.lr_scheduler import MultiStepLR | |
| from sync_batchnorm import DataParallelWithCallback | |
| from frames_dataset import DatasetRepeater | |
| def train(config, generator, discriminator, kp_detector, checkpoint, log_dir, dataset, device_ids): | |
| train_params = config['train_params'] | |
| optimizer_generator = torch.optim.Adam(generator.parameters(), lr=train_params['lr_generator'], betas=(0.5, 0.999)) | |
| optimizer_discriminator = torch.optim.Adam(discriminator.parameters(), lr=train_params['lr_discriminator'], betas=(0.5, 0.999)) | |
| optimizer_kp_detector = torch.optim.Adam(kp_detector.parameters(), lr=train_params['lr_kp_detector'], betas=(0.5, 0.999)) | |
| if checkpoint is not None: | |
| start_epoch = Logger.load_cpk(checkpoint, generator, discriminator, kp_detector, | |
| optimizer_generator, optimizer_discriminator, | |
| None if train_params['lr_kp_detector'] == 0 else optimizer_kp_detector) | |
| else: | |
| start_epoch = 0 | |
| scheduler_generator = MultiStepLR(optimizer_generator, train_params['epoch_milestones'], gamma=0.1, | |
| last_epoch=start_epoch - 1) | |
| scheduler_discriminator = MultiStepLR(optimizer_discriminator, train_params['epoch_milestones'], gamma=0.1, | |
| last_epoch=start_epoch - 1) | |
| scheduler_kp_detector = MultiStepLR(optimizer_kp_detector, train_params['epoch_milestones'], gamma=0.1, | |
| last_epoch=-1 + start_epoch * (train_params['lr_kp_detector'] != 0)) | |
| 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=6, drop_last=True) | |
| generator_full = GeneratorFullModel(kp_detector, generator, discriminator, train_params) | |
| discriminator_full = DiscriminatorFullModel(kp_detector, generator, discriminator, train_params) | |
| if torch.cuda.is_available(): | |
| generator_full = DataParallelWithCallback(generator_full, device_ids=device_ids) | |
| discriminator_full = DataParallelWithCallback(discriminator_full, device_ids=device_ids) | |
| 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: | |
| losses_generator, generated = generator_full(x) | |
| loss_values = [val.mean() for val in losses_generator.values()] | |
| loss = sum(loss_values) | |
| loss.backward() | |
| optimizer_generator.step() | |
| optimizer_generator.zero_grad() | |
| optimizer_kp_detector.step() | |
| optimizer_kp_detector.zero_grad() | |
| if train_params['loss_weights']['generator_gan'] != 0: | |
| optimizer_discriminator.zero_grad() | |
| losses_discriminator = discriminator_full(x, generated) | |
| loss_values = [val.mean() for val in losses_discriminator.values()] | |
| loss = sum(loss_values) | |
| loss.backward() | |
| optimizer_discriminator.step() | |
| optimizer_discriminator.zero_grad() | |
| else: | |
| losses_discriminator = {} | |
| losses_generator.update(losses_discriminator) | |
| losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()} | |
| logger.log_iter(losses=losses) | |
| scheduler_generator.step() | |
| scheduler_discriminator.step() | |
| scheduler_kp_detector.step() | |
| logger.log_epoch(epoch, {'generator': generator, | |
| 'discriminator': discriminator, | |
| 'kp_detector': kp_detector, | |
| 'optimizer_generator': optimizer_generator, | |
| 'optimizer_discriminator': optimizer_discriminator, | |
| 'optimizer_kp_detector': optimizer_kp_detector}, inp=x, out=generated) | |