Spaces:
Runtime error
Runtime error
File size: 4,249 Bytes
049c619 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
'''
* 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
@torch.no_grad()
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) |