| from random import choice | |
| from string import ascii_uppercase | |
| from torch.utils.data import DataLoader | |
| from torchvision.transforms import transforms | |
| import os | |
| from configs import global_config, paths_config | |
| from training.coaches.multi_id_coach import MultiIDCoach | |
| from training.coaches.single_id_coach import SingleIDCoach | |
| from utils.ImagesDataset import ImagesDataset | |
| def run_PTI(run_name='', use_wandb=False, use_multi_id_training=False): | |
| os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' | |
| os.environ['CUDA_VISIBLE_DEVICES'] = global_config.cuda_visible_devices | |
| if run_name == '': | |
| global_config.run_name = ''.join(choice(ascii_uppercase) for i in range(12)) | |
| else: | |
| global_config.run_name = run_name | |
| if use_wandb: | |
| run = wandb.init(project=paths_config.pti_results_keyword, reinit=True, name=global_config.run_name) | |
| global_config.pivotal_training_steps = 1 | |
| global_config.training_step = 1 | |
| embedding_dir_path = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}/{paths_config.pti_results_keyword}' | |
| os.makedirs(embedding_dir_path, exist_ok=True) | |
| dataset = ImagesDataset(paths_config.input_data_path, transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])) | |
| dataloader = DataLoader(dataset, batch_size=1, shuffle=False) | |
| if use_multi_id_training: | |
| coach = MultiIDCoach(dataloader, use_wandb) | |
| else: | |
| coach = SingleIDCoach(dataloader, use_wandb) | |
| coach.train() | |
| return global_config.run_name | |
| if __name__ == '__main__': | |
| run_PTI(run_name='', use_wandb=False, use_multi_id_training=False) | |