'''
* 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
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from models.blip_vqa import blip_vqa
import utils
from utils import cosine_lr_schedule
from data import create_dataset, create_sampler, create_loader
from data.vqa_dataset import vqa_collate_fn
from data.utils import save_result
def train(model, data_loader, optimizer, epoch, device):
# train
model.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
metric_logger.add_meter('loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
header = 'Train Epoch: [{}]'.format(epoch)
print_freq = 50
for i,(image, question, answer, weights, n) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
image, weights = image.to(device,non_blocking=True), weights.to(device,non_blocking=True)
loss = model(image, question, answer, train=True, n=n, weights=weights)
optimizer.zero_grad()
loss.backward()
optimizer.step()
metric_logger.update(loss=loss.item())
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger.global_avg())
return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluation(model, data_loader, device, config) :
# test
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Generate VQA test result:'
print_freq = 50
result = []
if config['inference']=='rank':
answer_list = data_loader.dataset.answer_list
answer_candidates = model.tokenizer(answer_list, padding='longest', return_tensors='pt').to(device)
answer_candidates.input_ids[:,0] = model.tokenizer.bos_token_id
for n, (image, question, question_id) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
image = image.to(device,non_blocking=True)
if config['inference']=='generate':
answers = model(image, question, train=False, inference='generate')
for answer, ques_id in zip(answers, question_id):
ques_id = int(ques_id.item())
result.append({"question_id":ques_id, "answer":answer})
elif config['inference']=='rank':
answer_ids = model(image, question, answer_candidates, train=False, inference='rank', k_test=config['k_test'])
for ques_id, answer_id in zip(question_id, answer_ids):
result.append({"question_id":int(ques_id.item()), "answer":answer_list[answer_id]})
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 vqa datasets")
datasets = create_dataset('vqa', config)
if args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
samplers = create_sampler(datasets, [True, False], num_tasks, global_rank)
else:
samplers = [None, None]
train_loader, test_loader = create_loader(datasets,samplers,
batch_size=[config['batch_size_train'],config['batch_size_test']],
num_workers=[4,4],is_trains=[True, False],
collate_fns=[vqa_collate_fn,None])
#### Model ####
print("Creating model")
model = blip_vqa(pretrained=config['pretrained'], image_size=config['image_size'],
vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'])
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
optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])
best = 0
best_epoch = 0
print("Start training")
start_time = time.time()
for epoch in range(0, config['max_epoch']):
if not args.evaluate:
if args.distributed:
train_loader.sampler.set_epoch(epoch)
cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])
train_stats = train(model, train_loader, optimizer, epoch, device)
else:
break
if utils.is_main_process():
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
'epoch': epoch,
}
with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
f.write(json.dumps(log_stats) + "\n")
save_obj = {
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'config': config,
'epoch': epoch,
}
torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_%02d.pth'%epoch))
dist.barrier()
vqa_result = evaluation(model_without_ddp, test_loader, device, config)
result_file = save_result(vqa_result, args.result_dir, 'vqa_result')
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', default='./configs/vqa.yaml')
parser.add_argument('--output_dir', default='output/VQA')
parser.add_argument('--evaluate', action='store_true')
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)