| | '''
|
| | * 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
|
| | * By Junnan Li
|
| | '''
|
| | 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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| | import torch.backends.cudnn as cudnn
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| | import torch.distributed as dist
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| | from torch.utils.data import DataLoader
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| |
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| | from models.blip import blip_decoder
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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.utils import save_result, coco_caption_eval
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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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| | header = 'Train Caption Epoch: [{}]'.format(epoch)
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| | print_freq = 50
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| |
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| | for i, (image, caption, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
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| | image = image.to(device)
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| |
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| | loss = model(image, caption)
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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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| |
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| |
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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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| |
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| | @torch.no_grad()
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| | def evaluate(model, data_loader, device, config):
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| |
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| | model.eval()
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| |
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| | metric_logger = utils.MetricLogger(delimiter=" ")
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| | header = 'Caption generation:'
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| | print_freq = 10
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| |
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| | result = []
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| | for image, image_id in metric_logger.log_every(data_loader, print_freq, header):
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| |
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| | image = image.to(device)
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| |
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| | captions = model.generate(image, sample=False, num_beams=config['num_beams'], max_length=config['max_length'],
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| | min_length=config['min_length'])
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| |
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| | for caption, img_id in zip(captions, image_id):
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| | result.append({"image_id": img_id.item(), "caption": caption})
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| |
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| | return result
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| |
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| |
|
| | def main(args, config):
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| | utils.init_distributed_mode(args)
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| |
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| | device = torch.device(args.device)
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| |
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| |
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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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| |
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| |
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| | print("Creating captioning dataset")
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| | train_dataset, val_dataset, test_dataset = create_dataset('caption_coco', 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([train_dataset,val_dataset,test_dataset], [True,False,False], num_tasks, global_rank)
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| | else:
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| | samplers = [None, None, None]
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| |
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| | train_loader, val_loader, test_loader = create_loader([train_dataset, val_dataset, test_dataset],samplers,
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| | batch_size=[config['batch_size']]*3,num_workers=[4,4,4],
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| | is_trains=[True, False, False], collate_fns=[None,None,None])
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| |
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| |
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| | print("Creating model")
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| | model = blip_decoder(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'],
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| | vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'],
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| | prompt=config['prompt'])
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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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| |
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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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| | val_result = evaluate(model_without_ddp, val_loader, device, config)
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| | val_result_file = save_result(val_result, args.result_dir, 'val_epoch%d'%epoch, remove_duplicate='image_id')
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| |
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| | test_result = evaluate(model_without_ddp, test_loader, device, config)
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| | test_result_file = save_result(test_result, args.result_dir, 'test_epoch%d'%epoch, remove_duplicate='image_id')
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| |
|
| | if utils.is_main_process():
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| | coco_val = coco_caption_eval(config['coco_gt_root'],val_result_file,'val')
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| | coco_test = coco_caption_eval(config['coco_gt_root'],test_result_file,'test')
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| |
|
| | if args.evaluate:
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| | log_stats = {**{f'val_{k}': v for k, v in coco_val.eval.items()},
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| | **{f'test_{k}': v for k, v in coco_test.eval.items()},
|
| | }
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| | with open(os.path.join(args.output_dir, "evaluate.txt"),"a") as f:
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| | f.write(json.dumps(log_stats) + "\n")
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| | else:
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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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| |
|
| | if coco_val.eval['CIDEr'] + coco_val.eval['Bleu_4'] > best:
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| | best = coco_val.eval['CIDEr'] + coco_val.eval['Bleu_4']
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| | best_epoch = epoch
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| | torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_best.pth'))
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| |
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| | log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
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| | **{f'val_{k}': v for k, v in coco_val.eval.items()},
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| | **{f'test_{k}': v for k, v in coco_test.eval.items()},
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| | 'epoch': epoch,
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| | 'best_epoch': best_epoch,
|
| | }
|
| | 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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| |
|
| | if args.evaluate:
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| | break
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| | dist.barrier()
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| |
|
| | total_time = time.time() - start_time
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| | total_time_str = str(datetime.timedelta(seconds=int(total_time)))
|
| | print('Training time {}'.format(total_time_str))
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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/caption_coco.yaml')
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| | parser.add_argument('--output_dir', default='output/Caption_coco')
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| | parser.add_argument('--evaluate', action='store_true')
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| | parser.add_argument('--device', default='cuda')
|
| | 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')
|
| | 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) |
