train_caption.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.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 import blip_decoder
import utils
from utils import cosine_lr_schedule
from data import create_dataset, create_sampler, create_loader
from data.utils import save_result
from eval.eval_caption import coco_caption_eval, uit_viic_caption_eval

# import googletrans

def train(model, data_loader, optimizer, epoch, device):
    # train
    model.train()  
    
    metric_logger = utils.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
    metric_logger.add_meter('loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
    header = 'Train Caption Epoch: [{}]'.format(epoch)
    print_freq = 500

    for i, (image, caption, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
        image = image.to(device)       
        
        loss = model(image, caption)      
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()    
        
        metric_logger.update(loss=loss.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()}  


@torch.no_grad()
def evaluate(model, data_loader, device, config):
    # evaluate
    model.eval() 
    
    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Caption generation:'
    print_freq = 10

    result = []
    for image, image_id in metric_logger.log_every(data_loader, print_freq, header): 
        image = image.to(device)       
        
        captions = model.generate(image, sample=False, num_beams=config['num_beams'], max_length=config['max_length'], 
                                  min_length=config['min_length'])
        
        for caption, img_id in zip(captions, image_id):
            # caption = googletrans.Translator().translate(caption, src='en', dest='vi').text
            result.append({"image_id": img_id.item(), "caption": caption})

        # for img_id in image_id:
        #     result.append({"image_id": img_id.item(), "caption": ""})
  
    return result


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 captioning dataset")
    train_dataset, val_dataset, test_dataset = create_dataset(config)  

    if args.distributed:
        num_tasks = utils.get_world_size()
        global_rank = utils.get_rank()            
        samplers = create_sampler([train_dataset,val_dataset,test_dataset], [True,False,False], num_tasks, global_rank)         
    else:
        samplers = [None, None, None]
    
    train_loader, val_loader, test_loader = create_loader([train_dataset, val_dataset, test_dataset],samplers,
                                                          batch_size=[config['batch_size']]*3,num_workers=[4,4,4],
                                                          is_trains=[True, False, False], collate_fns=[None,None,None])         

    #### Model #### 
    print("Creating model")
    model = blip_decoder(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'], 
                           vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'], 
                           prompt=config['prompt'], med_config = config['med_config'])

    model = model.to(device)   
    
    model_without_ddp = model
    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        model_without_ddp = model.module    
    
    optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])
            
    best = 0
    best_epoch = 0

    print("Start training")
    start_time = time.time()    
    for epoch in range(0, config['max_epoch']):
        if not args.evaluate:        
            if args.distributed:
                train_loader.sampler.set_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.pth'))
                
            cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])
                
            train_stats = train(model, train_loader, optimizer, epoch, device)
        else:
            config['max_epoch'] = 1 

        if (epoch + 1) % config['val_interval'] == 0 or epoch == config['max_epoch'] - 1 or epoch == 0:
        
            val_result = evaluate(model_without_ddp, val_loader, device, config)  
            val_result_file = save_result(val_result, args.result_dir, 'val_epoch%d'%epoch, remove_duplicate='image_id', distributed=args.distributed)        
    
            test_result = evaluate(model_without_ddp, test_loader, device, config)  
            test_result_file = save_result(test_result, args.result_dir, 'test_epoch%d'%epoch, remove_duplicate='image_id', distributed=args.distributed)  

            # val_result_file = os.path.join(args.result_dir, 'val_epoch%d.json'%epoch)
            # test_result_file = os.path.join(args.result_dir, 'test_epoch%d.json'%epoch)

            if utils.is_main_process():   
                if config['dataset'] == 'caption_coco':
                    cap_val = coco_caption_eval(config['coco_gt_root'],val_result_file,'val')
                    cap_test = coco_caption_eval(config['coco_gt_root'],test_result_file,'test')
                elif config['dataset'] == 'uit_viic':
                    cap_val = uit_viic_caption_eval(config['caption_gt_root'],val_result_file,'val')
                    cap_test = uit_viic_caption_eval(config['caption_gt_root'],test_result_file,'test')
                
                if args.evaluate:            
                    log_stats = {**{f'val_{k}': v for k, v in cap_val.eval.items()},
                                **{f'test_{k}': v for k, v in cap_test.eval.items()},                       
                                }
                    with open(os.path.join(args.output_dir, "evaluate.txt"),"a") as f:
                        f.write(json.dumps(log_stats) + "\n")                   
                else:             
                    save_obj = {
                        'model': model_without_ddp.state_dict(),
                        'optimizer': optimizer.state_dict(),
                        'config': config,
                        'epoch': epoch,
                    }

                    if cap_val.eval['CIDEr'] + cap_val.eval['Bleu_4'] > best:
                        best = cap_val.eval['CIDEr'] + cap_val.eval['Bleu_4']
                        best_epoch = epoch                
                        torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_best.pth')) 
                        
                    log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                                **{f'val_{k}': v for k, v in cap_val.eval.items()},
                                **{f'test_{k}': v for k, v in cap_test.eval.items()},                       
                                'epoch': epoch,
                                'best_epoch': best_epoch,
                                }
                    with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
                        f.write(json.dumps(log_stats) + "\n")     
                    
        if args.evaluate: 
            break
        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/caption_uit_viic.yaml', help='path to config file')
    parser.add_argument('--output_dir', default='output/UIT_ViIC', help='path to output directory')       
    parser.add_argument('--evaluate', action='store_true')    
    parser.add_argument('--device', default='cpu')
    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)

    args.result_dir = os.path.join(args.output_dir, 'result')

    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    Path(args.result_dir).mkdir(parents=True, exist_ok=True)
        
    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
    
    main(args, config)