radames's picture
first
bb0f5a9
# 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)