''' * 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 from torch.utils.data import DataLoader import torch.backends.cudnn as cudnn import torch.distributed as dist from models.blip_vqa import blip_vqa import utils from utils import cosine_lr_schedule from data import create_dataset, create_sampler, create_loader from data.vqa_dataset import vqa_collate_fn from data.utils import save_result 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 Epoch: [{}]'.format(epoch) print_freq = 50 for i,(image, question, answer, weights, n) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): image, weights = image.to(device,non_blocking=True), weights.to(device,non_blocking=True) loss = model(image, question, answer, train=True, n=n, weights=weights) 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 evaluation(model, data_loader, device, config) : # test model.eval() metric_logger = utils.MetricLogger(delimiter=" ") header = 'Generate VQA test result:' print_freq = 50 result = [] if config['inference']=='rank': answer_list = data_loader.dataset.answer_list answer_candidates = model.tokenizer(answer_list, padding='longest', return_tensors='pt').to(device) answer_candidates.input_ids[:,0] = model.tokenizer.bos_token_id for n, (image, question, question_id) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): image = image.to(device,non_blocking=True) if config['inference']=='generate': answers = model(image, question, train=False, inference='generate') for answer, ques_id in zip(answers, question_id): ques_id = int(ques_id.item()) result.append({"question_id":ques_id, "answer":answer}) elif config['inference']=='rank': answer_ids = model(image, question, answer_candidates, train=False, inference='rank', k_test=config['k_test']) for ques_id, answer_id in zip(question_id, answer_ids): result.append({"question_id":int(ques_id.item()), "answer":answer_list[answer_id]}) 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 vqa datasets") datasets = create_dataset('vqa', config) if args.distributed: num_tasks = utils.get_world_size() global_rank = utils.get_rank() samplers = create_sampler(datasets, [True, False], num_tasks, global_rank) else: samplers = [None, None] train_loader, test_loader = create_loader(datasets,samplers, batch_size=[config['batch_size_train'],config['batch_size_test']], num_workers=[4,4],is_trains=[True, False], collate_fns=[vqa_collate_fn,None]) #### Model #### print("Creating model") model = blip_vqa(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']) 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) cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr']) train_stats = train(model, train_loader, optimizer, epoch, device) else: break if utils.is_main_process(): log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, 'epoch': epoch, } with open(os.path.join(args.output_dir, "log.txt"),"a") as f: f.write(json.dumps(log_stats) + "\n") 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)) dist.barrier() vqa_result = evaluation(model_without_ddp, test_loader, device, config) result_file = save_result(vqa_result, args.result_dir, 'vqa_result') 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/vqa.yaml') parser.add_argument('--output_dir', default='output/VQA') 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) 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)