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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)
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