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Upload eval_nocaps.py

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  1. eval_nocaps.py +118 -0
eval_nocaps.py ADDED
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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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+
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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 data import create_dataset, create_sampler, create_loader
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+ from data.utils import save_result
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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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+ # evaluate
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+ model.eval()
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+
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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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+
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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'], repetition_penalty=1.1)
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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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+
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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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+ # fix the seed for reproducibility
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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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+ #### Dataset ####
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+ print("Creating captioning dataset")
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+ val_dataset, test_dataset = create_dataset('nocaps', 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([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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+
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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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+
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+ #### Model ####
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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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+
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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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+ 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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+
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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/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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+
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+ config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
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+
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+ args.result_dir = os.path.join(args.output_dir, 'result')
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+
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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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+
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+ yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))
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+
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+ main(args, config)