''' * 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)