<filename>train_vqa.py
'''
 * 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 <NAME>
'''
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)
<filename>train_vqa.py
'''
 * 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 <NAME>
'''
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)
* 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 <NAME> # train # gather the stats from all processes # test # fix the seed for reproducibility #### Dataset #### #### Model ####