''' * Copyright (c) 2022, salesforce.com, inc. * All rights reserved. * SPDX-License-Identifier: BSD-3-Clause * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause * By Junnan Li ''' import argparse import os import ruamel_yaml as yaml import numpy as np import random import time import datetime import json from pathlib import Path import torch import torch.nn as nn import torch.nn.functional as F import torch.backends.cudnn as cudnn import torch.distributed as dist from torch.utils.data import DataLoader from models.blip_pretrain import blip_pretrain import utils from utils import warmup_lr_schedule, step_lr_schedule from data import create_dataset, create_sampler, create_loader def train(model, data_loader, optimizer, epoch, device, config): # train model.train() metric_logger = utils.MetricLogger(delimiter=" ") metric_logger.add_meter('lr', utils.SmoothedValue(window_size=50, fmt='{value:.6f}')) metric_logger.add_meter('loss_ita', utils.SmoothedValue(window_size=50, fmt='{value:.4f}')) metric_logger.add_meter('loss_itm', utils.SmoothedValue(window_size=50, fmt='{value:.4f}')) metric_logger.add_meter('loss_lm', utils.SmoothedValue(window_size=50, fmt='{value:.4f}')) header = 'Train Epoch: [{}]'.format(epoch) print_freq = 50 if config['laion_path']: data_loader.dataset.reload_laion(epoch) data_loader.sampler.set_epoch(epoch) for i, (image, caption) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): if epoch==0: warmup_lr_schedule(optimizer, i, config['warmup_steps'], config['warmup_lr'], config['init_lr']) optimizer.zero_grad() image = image.to(device,non_blocking=True) # ramp up alpha in the first 2 epochs alpha = config['alpha']*min(1,(epoch*len(data_loader)+i)/(2*len(data_loader))) loss_ita, loss_itm, loss_lm = model(image, caption, alpha = alpha) loss = loss_ita + loss_itm + loss_lm loss.backward() optimizer.step() metric_logger.update(loss_ita=loss_ita.item()) metric_logger.update(loss_itm=loss_itm.item()) metric_logger.update(loss_lm=loss_lm.item()) metric_logger.update(lr=optimizer.param_groups[0]["lr"]) # gather the stats from all processes metric_logger.synchronize_between_processes() print("Averaged stats:", metric_logger.global_avg()) return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()} def main(args, config): utils.init_distributed_mode(args) device = torch.device(args.device) # fix the seed for reproducibility seed = args.seed + utils.get_rank() torch.manual_seed(seed) np.random.seed(seed) random.seed(seed) cudnn.benchmark = True #### Dataset #### print("Creating dataset") datasets = [create_dataset('pretrain', config, min_scale=0.2)] print('number of training samples: %d'%len(datasets[0])) num_tasks = utils.get_world_size() global_rank = utils.get_rank() samplers = create_sampler(datasets, [True], num_tasks, global_rank) data_loader = create_loader(datasets,samplers,batch_size=[config['batch_size']], num_workers=[4], is_trains=[True], collate_fns=[None])[0] #### Model #### print("Creating model") model = blip_pretrain(image_size=config['image_size'], vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'], queue_size=config['queue_size']) model = model.to(device) optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay']) start_epoch = 0 if args.checkpoint: checkpoint = torch.load(args.checkpoint, map_location='cpu') state_dict = checkpoint['model'] model.load_state_dict(state_dict) optimizer.load_state_dict(checkpoint['optimizer']) start_epoch = checkpoint['epoch']+1 print('resume checkpoint from %s'%args.checkpoint) model_without_ddp = model if args.distributed: model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) model_without_ddp = model.module print("Start training") start_time = time.time() for epoch in range(start_epoch, config['max_epoch']): step_lr_schedule(optimizer, epoch, config['init_lr'], config['min_lr'], config['lr_decay_rate']) train_stats = train(model, data_loader, optimizer, epoch, device, config) if utils.is_main_process(): log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, 'epoch': epoch, } save_obj = { 'model': model_without_ddp.state_dict(), 'optimizer': optimizer.state_dict(), 'config': config, 'epoch': epoch, } torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_%02d.pth'%epoch)) with open(os.path.join(args.output_dir, "log.txt"),"a") as f: f.write(json.dumps(log_stats) + "\n") dist.barrier() total_time = time.time() - start_time total_time_str = str(datetime.timedelta(seconds=int(total_time))) print('Training time {}'.format(total_time_str)) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--config', default='./configs/pretrain.yaml') parser.add_argument('--output_dir', default='output/Pretrain') parser.add_argument('--checkpoint', default='') parser.add_argument('--evaluate', action='store_true') parser.add_argument('--device', default='cuda') parser.add_argument('--seed', default=42, type=int) parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') parser.add_argument('--distributed', default=True, type=bool) args = parser.parse_args() config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader) Path(args.output_dir).mkdir(parents=True, exist_ok=True) yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w')) main(args, config)