| '''
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| * Copyright (c) 2022, salesforce.com, inc.
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| * All rights reserved.
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| * SPDX-License-Identifier: BSD-3-Clause
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| * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
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| * By Junnan Li
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| '''
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| import argparse
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| import os
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| import ruamel_yaml as yaml
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| import numpy as np
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| import random
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| import time
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| import datetime
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| import json
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| from pathlib import Path
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|
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| import torch
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| import torch.nn as nn
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| import torch.nn.functional as F
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| from torch.utils.data import DataLoader
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| import torch.backends.cudnn as cudnn
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| import torch.distributed as dist
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|
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| from models.blip_vqa import blip_vqa
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| import utils
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| from utils import cosine_lr_schedule
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| from data import create_dataset, create_sampler, create_loader
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| from data.vqa_dataset import vqa_collate_fn
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| from data.utils import save_result
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| def train(model, data_loader, optimizer, epoch, device):
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|
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| model.train()
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|
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| metric_logger = utils.MetricLogger(delimiter=" ")
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| metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
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| metric_logger.add_meter('loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
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|
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| header = 'Train Epoch: [{}]'.format(epoch)
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| print_freq = 50
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|
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| for i,(image, question, answer, weights, n) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
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| image, weights = image.to(device,non_blocking=True), weights.to(device,non_blocking=True)
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|
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| loss = model(image, question, answer, train=True, n=n, weights=weights)
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|
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| optimizer.zero_grad()
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| loss.backward()
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| optimizer.step()
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|
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| metric_logger.update(loss=loss.item())
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| metric_logger.update(lr=optimizer.param_groups[0]["lr"])
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| metric_logger.synchronize_between_processes()
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| print("Averaged stats:", metric_logger.global_avg())
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| return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}
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|
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| @torch.no_grad()
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| def evaluation(model, data_loader, device, config) :
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| model.eval()
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| metric_logger = utils.MetricLogger(delimiter=" ")
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| header = 'Generate VQA test result:'
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| print_freq = 50
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|
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| result = []
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|
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| if config['inference']=='rank':
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| answer_list = data_loader.dataset.answer_list
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| answer_candidates = model.tokenizer(answer_list, padding='longest', return_tensors='pt').to(device)
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| answer_candidates.input_ids[:,0] = model.tokenizer.bos_token_id
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|
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| for n, (image, question, question_id) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
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| image = image.to(device,non_blocking=True)
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|
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| if config['inference']=='generate':
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| answers = model(image, question, train=False, inference='generate')
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|
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| for answer, ques_id in zip(answers, question_id):
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| ques_id = int(ques_id.item())
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| result.append({"question_id":ques_id, "answer":answer})
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|
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| elif config['inference']=='rank':
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| answer_ids = model(image, question, answer_candidates, train=False, inference='rank', k_test=config['k_test'])
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| for ques_id, answer_id in zip(question_id, answer_ids):
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| result.append({"question_id":int(ques_id.item()), "answer":answer_list[answer_id]})
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| return result
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| def main(args, config):
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| utils.init_distributed_mode(args)
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| device = torch.device(args.device)
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| seed = args.seed + utils.get_rank()
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| torch.manual_seed(seed)
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| np.random.seed(seed)
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| random.seed(seed)
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| cudnn.benchmark = True
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| print("Creating vqa datasets")
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| datasets = create_dataset('vqa', config)
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|
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| if args.distributed:
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| num_tasks = utils.get_world_size()
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| global_rank = utils.get_rank()
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| samplers = create_sampler(datasets, [True, False], num_tasks, global_rank)
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| else:
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| samplers = [None, None]
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|
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| train_loader, test_loader = create_loader(datasets,samplers,
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| batch_size=[config['batch_size_train'],config['batch_size_test']],
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| num_workers=[4,4],is_trains=[True, False],
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| collate_fns=[vqa_collate_fn,None])
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| print("Creating model")
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| model = blip_vqa(pretrained=config['pretrained'], image_size=config['image_size'],
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| vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'])
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|
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| model = model.to(device)
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|
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| model_without_ddp = model
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| if args.distributed:
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| model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
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| model_without_ddp = model.module
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|
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| optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])
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| best = 0
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| best_epoch = 0
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|
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| print("Start training")
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| start_time = time.time()
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| for epoch in range(0, config['max_epoch']):
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| if not args.evaluate:
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| if args.distributed:
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| train_loader.sampler.set_epoch(epoch)
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|
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| cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])
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|
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| train_stats = train(model, train_loader, optimizer, epoch, device)
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|
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| else:
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| break
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|
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| if utils.is_main_process():
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| log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
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| 'epoch': epoch,
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| }
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| with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
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| f.write(json.dumps(log_stats) + "\n")
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|
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| save_obj = {
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| 'model': model_without_ddp.state_dict(),
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| 'optimizer': optimizer.state_dict(),
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| 'config': config,
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| 'epoch': epoch,
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| }
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| torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_%02d.pth'%epoch))
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|
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| dist.barrier()
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| vqa_result = evaluation(model_without_ddp, test_loader, device, config)
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| result_file = save_result(vqa_result, args.result_dir, 'vqa_result')
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|
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| total_time = time.time() - start_time
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| total_time_str = str(datetime.timedelta(seconds=int(total_time)))
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| print('Training time {}'.format(total_time_str))
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|
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|
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| if __name__ == '__main__':
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| parser = argparse.ArgumentParser()
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| parser.add_argument('--config', default='./configs/vqa.yaml')
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| parser.add_argument('--output_dir', default='output/VQA')
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| parser.add_argument('--evaluate', action='store_true')
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| parser.add_argument('--device', default='cuda')
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| parser.add_argument('--seed', default=42, type=int)
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| parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
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| parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
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| parser.add_argument('--distributed', default=True, type=bool)
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| args = parser.parse_args()
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|
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| config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
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|
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| args.result_dir = os.path.join(args.output_dir, 'result')
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|
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| Path(args.output_dir).mkdir(parents=True, exist_ok=True)
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| Path(args.result_dir).mkdir(parents=True, exist_ok=True)
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|
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| yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))
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|
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| main(args, config) |
