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''' |
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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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import torch |
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import torch.backends.cudnn as cudnn |
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import torch.distributed as dist |
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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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model.train() |
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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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header = 'Train Epoch: [{}]'.format(epoch) |
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print_freq = 50 |
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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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loss = model(image, question, answer, train=True, n=n, weights=weights) |
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optimizer.zero_grad() |
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loss.backward() |
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optimizer.step() |
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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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@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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result = [] |
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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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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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if config['inference']=='generate': |
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answers = model(image, question, train=False, inference='generate') |
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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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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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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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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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model = model.to(device) |
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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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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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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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cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr']) |
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train_stats = train(model, train_loader, optimizer, epoch, device) |
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else: |
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break |
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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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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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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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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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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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config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader) |
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args.result_dir = os.path.join(args.output_dir, 'result') |
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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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yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w')) |
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main(args, config) |