BLIP / train_vqa.py
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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.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)