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| ''' | |
| * 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 data import create_dataset, create_sampler, create_loader | |
| from data.utils import save_result | |
| def evaluate(model, data_loader, device, config): | |
| # evaluate | |
| model.eval() | |
| metric_logger = utils.MetricLogger(delimiter=" ") | |
| header = 'Evaluation:' | |
| 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'], repetition_penalty=1.1) | |
| 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) | |
| # 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 captioning dataset") | |
| val_dataset, test_dataset = create_dataset('nocaps', config) | |
| if args.distributed: | |
| num_tasks = utils.get_world_size() | |
| global_rank = utils.get_rank() | |
| samplers = create_sampler([val_dataset,test_dataset], [False,False], num_tasks, global_rank) | |
| else: | |
| samplers = [None,None] | |
| val_loader, test_loader = create_loader([val_dataset, test_dataset],samplers, | |
| batch_size=[config['batch_size']]*2,num_workers=[4,4], | |
| is_trains=[False, False], collate_fns=[None,None]) | |
| #### Model #### | |
| print("Creating model") | |
| model = blip_decoder(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'], | |
| 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 | |
| val_result = evaluate(model_without_ddp, val_loader, device, config) | |
| val_result_file = save_result(val_result, args.result_dir, 'val', 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', remove_duplicate='image_id') | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--config', default='./configs/nocaps.yaml') | |
| parser.add_argument('--output_dir', default='output/NoCaps') | |
| 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) |