Pretrain_nlvr.py

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.model_pretrain_nlvr import ALBEF
from models.vit import interpolate_pos_embed
from models.tokenization_bert import BertTokenizer

import  utils
from dataset.handle_data import create_dataset, create_sampler, create_loader
from scheduler import create_scheduler
from optim import create_optimizer


def train(model, data_loader, optimizer, tokenizer, epoch, warmup_steps, device, scheduler, config):
    # train
    model.train()  
    
    metric_logger = utils.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=50, fmt='{value:.6f}'))
    metric_logger.add_meter('loss', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
    
    header = 'Train Epoch: [{}]'.format(epoch)
    print_freq = 50   
    step_size = 100
    warmup_iterations = warmup_steps*step_size  
    
    if args.distributed:
        data_loader.sampler.set_epoch(epoch)

    for i, (image, text) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
        
        optimizer.zero_grad()
  
        image = image.to(device,non_blocking=True) 
        text_input = tokenizer(text, padding='longest', truncation=True, max_length=25, return_tensors="pt").to(device)  

        loss = model(image, text_input)   
        loss.backward()

        optimizer.step()    
        
        metric_logger.update(loss=loss.item())
        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
        
        if epoch==0 and i%step_size==0 and i<=warmup_iterations: 
            scheduler.step(i//step_size)         
        
    # 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()}    
    
    
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
    
    start_epoch = 0
    max_epoch = config['schedular']['epochs']
    warmup_steps = config['schedular']['warmup_epochs']    

    #### Dataset #### 
    print("Creating dataset")
    datasets = [create_dataset('nlvr_pretrain', config)]
    
    if args.distributed:
        num_tasks = utils.get_world_size()
        global_rank = utils.get_rank()            
        samplers = create_sampler(datasets, [True], num_tasks, global_rank)         
    else:
        samplers = [None]

    data_loader = create_loader(datasets,samplers,batch_size=[config['batch_size']], num_workers=[4], is_trains=[True], collate_fns=[None])[0]

    tokenizer = BertTokenizer.from_pretrained(args.text_encoder)

    #### Model #### 
    print("Creating model")
    model = ALBEF(config=config, text_encoder=args.text_encoder, tokenizer=tokenizer)
    
    model = model.to(device)   
        
    arg_opt = utils.AttrDict(config['optimizer'])
    optimizer = create_optimizer(arg_opt, model)
    arg_sche = utils.AttrDict(config['schedular'])
    lr_scheduler, _ = create_scheduler(arg_sche, optimizer)      
    
    if args.checkpoint:    
        checkpoint = torch.load(args.checkpoint, map_location='cpu') 
        state_dict = checkpoint['model']
        pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)         
        state_dict['visual_encoder.pos_embed'] = pos_embed_reshaped
            
        for key in list(state_dict.keys()):
            if 'bert' in key:
                new_key = key.replace('bert.','')

                if 'layer' in new_key:
                    keys = new_key.split('.')
                    layer_num = int(keys[3])
                    # replicate the multimodal encoder's blocks for two images
                    if layer_num>=6:
                        new_layer_num = (layer_num-6)*2+6
                        keys[3] = str(new_layer_num)
                        new_key_0 = '.'.join(keys)                       
                        state_dict[new_key_0] = state_dict[key] 
                        keys[3] = str(new_layer_num+1)
                        new_key_1 = '.'.join(keys)                       
                        state_dict[new_key_1] = state_dict[key]                         
                    else:
                        state_dict[new_key] = state_dict[key] 
                else:
                    state_dict[new_key] = state_dict[key] 
                del state_dict[key]

        msg = model.load_state_dict(state_dict,strict=False)
        print('load checkpoint from %s'%args.checkpoint)
        print(msg)
    
    model_without_ddp = model
    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
        model_without_ddp = model.module    
    
    print("Start training")
    start_time = time.time()

    for epoch in range(start_epoch, max_epoch):
        
        if epoch>0:
            lr_scheduler.step(epoch+warmup_steps)  
            
        train_stats = train(model, data_loader, optimizer, tokenizer, epoch, warmup_steps, device, lr_scheduler, config) 
        
        if utils.is_main_process():  
            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                         'epoch': epoch,
                        }                     
            save_obj = {
                'model': model_without_ddp.state_dict(),
                'optimizer': optimizer.state_dict(),
                'lr_scheduler': lr_scheduler.state_dict(),
                'config': config,
                'epoch': epoch,
            }
            torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_%02d.pth'%epoch))  
            
            with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
                f.write(json.dumps(log_stats) + "\n")

        dist.barrier()   
                
    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/NLVR_pretrain.yaml')
    parser.add_argument('--checkpoint', default='') 
    parser.add_argument('--output_dir', default='output/NLVR_pretrain')
    parser.add_argument('--text_encoder', default='bert-base-uncased')
    parser.add_argument('--device', default='cuda')
    parser.add_argument('--seed', default=0, 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)

    Path(args.output_dir).mkdir(parents=True, exist_ok=True)

    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
    
    main(args, config)