run_pti.py

# Copyright (c) SenseTime Research. All rights reserved.

from random import choice
from string import ascii_uppercase
from torch.utils.data import DataLoader
from torchvision.transforms import transforms
import os
from pti.pti_configs import global_config, paths_config
import wandb

from pti.training.coaches.multi_id_coach import MultiIDCoach
from pti.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}'
    # print('embedding_dir_path: ', embedding_dir_path) #./embeddings/barcelona/PTI 
    os.makedirs(embedding_dir_path, exist_ok=True)

    dataset = ImagesDataset(paths_config.input_data_path, transforms.Compose([
        transforms.Resize((1024, 512)),
        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)