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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.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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from models.blip import blip_decoder |
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import utils |
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from data import create_dataset, create_sampler, create_loader |
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from data.utils import save_result |
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@torch.no_grad() |
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def evaluate(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 = 'Evaluation:' |
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print_freq = 10 |
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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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image = image.to(device) |
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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'], repetition_penalty=1.1) |
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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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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 captioning dataset") |
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val_dataset, test_dataset = create_dataset('nocaps', 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([val_dataset,test_dataset], [False,False], num_tasks, global_rank) |
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else: |
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samplers = [None,None] |
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val_loader, test_loader = create_loader([val_dataset, test_dataset],samplers, |
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batch_size=[config['batch_size']]*2,num_workers=[4,4], |
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is_trains=[False, False], collate_fns=[None,None]) |
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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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prompt=config['prompt']) |
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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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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', remove_duplicate='image_id') |
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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', remove_duplicate='image_id') |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--config', default='./configs/nocaps.yaml') |
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parser.add_argument('--output_dir', default='output/NoCaps') |
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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) |
