pretrain.py

'''
 * 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.backends.cudnn as cudnn
import torch.distributed as dist

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)