| ''' |
| * 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, coco_caption_eval |
|
|
| def train(model, data_loader, optimizer, epoch, device): |
| |
| 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 = 50 |
|
|
| 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"]) |
|
|
| |
| 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): |
| |
| 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): |
| result.append({"image_id": img_id.item(), "caption": caption}) |
| |
| return result |
|
|
|
|
| def main(args, config): |
| utils.init_distributed_mode(args) |
| |
| device = torch.device(args.device) |
|
|
| |
| seed = args.seed + utils.get_rank() |
| torch.manual_seed(seed) |
| np.random.seed(seed) |
| random.seed(seed) |
| cudnn.benchmark = True |
|
|
| |
| print("Creating captioning dataset") |
| train_dataset, val_dataset, test_dataset = create_dataset('caption_coco', 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]) |
|
|
| |
| 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']) |
|
|
| 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) |
| |
| 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') |
| |
| 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') |
|
|
| if utils.is_main_process(): |
| coco_val = coco_caption_eval(config['coco_gt_root'],val_result_file,'val') |
| coco_test = coco_caption_eval(config['coco_gt_root'],test_result_file,'test') |
| |
| if args.evaluate: |
| log_stats = {**{f'val_{k}': v for k, v in coco_val.eval.items()}, |
| **{f'test_{k}': v for k, v in coco_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 coco_val.eval['CIDEr'] + coco_val.eval['Bleu_4'] > best: |
| best = coco_val.eval['CIDEr'] + coco_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 coco_val.eval.items()}, |
| **{f'test_{k}': v for k, v in coco_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_coco.yaml') |
| parser.add_argument('--output_dir', default='output/Caption_coco') |
| 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) |
