Spaces:
Build error
Build error
| ''' | |
| * 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_pretrain import blip_pretrain | |
| import utils | |
| from utils import warmup_lr_schedule, step_lr_schedule | |
| from data import create_dataset, create_sampler, create_loader | |
| def train(model, data_loader, optimizer, epoch, device, 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_ita', utils.SmoothedValue(window_size=50, fmt='{value:.4f}')) | |
| metric_logger.add_meter('loss_itm', utils.SmoothedValue(window_size=50, fmt='{value:.4f}')) | |
| metric_logger.add_meter('loss_lm', utils.SmoothedValue(window_size=50, fmt='{value:.4f}')) | |
| header = 'Train Epoch: [{}]'.format(epoch) | |
| print_freq = 50 | |
| if config['laion_path']: | |
| data_loader.dataset.reload_laion(epoch) | |
| data_loader.sampler.set_epoch(epoch) | |
| for i, (image, caption) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): | |
| if epoch==0: | |
| warmup_lr_schedule(optimizer, i, config['warmup_steps'], config['warmup_lr'], config['init_lr']) | |
| optimizer.zero_grad() | |
| image = image.to(device,non_blocking=True) | |
| # ramp up alpha in the first 2 epochs | |
| alpha = config['alpha']*min(1,(epoch*len(data_loader)+i)/(2*len(data_loader))) | |
| loss_ita, loss_itm, loss_lm = model(image, caption, alpha = alpha) | |
| loss = loss_ita + loss_itm + loss_lm | |
| loss.backward() | |
| optimizer.step() | |
| metric_logger.update(loss_ita=loss_ita.item()) | |
| metric_logger.update(loss_itm=loss_itm.item()) | |
| metric_logger.update(loss_lm=loss_lm.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()} | |
| 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 dataset") | |
| datasets = [create_dataset('pretrain', config, min_scale=0.2)] | |
| print('number of training samples: %d'%len(datasets[0])) | |
| num_tasks = utils.get_world_size() | |
| global_rank = utils.get_rank() | |
| samplers = create_sampler(datasets, [True], num_tasks, global_rank) | |
| data_loader = create_loader(datasets,samplers,batch_size=[config['batch_size']], num_workers=[4], is_trains=[True], collate_fns=[None])[0] | |
| #### Model #### | |
| print("Creating model") | |
| model = blip_pretrain(image_size=config['image_size'], vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], | |
| vit_ckpt_layer=config['vit_ckpt_layer'], queue_size=config['queue_size']) | |
| model = model.to(device) | |
| optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay']) | |
| start_epoch = 0 | |
| if args.checkpoint: | |
| checkpoint = torch.load(args.checkpoint, map_location='cpu') | |
| state_dict = checkpoint['model'] | |
| model.load_state_dict(state_dict) | |
| optimizer.load_state_dict(checkpoint['optimizer']) | |
| start_epoch = checkpoint['epoch']+1 | |
| print('resume checkpoint from %s'%args.checkpoint) | |
| model_without_ddp = model | |
| if args.distributed: | |
| model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) | |
| model_without_ddp = model.module | |
| print("Start training") | |
| start_time = time.time() | |
| for epoch in range(start_epoch, config['max_epoch']): | |
| step_lr_schedule(optimizer, epoch, config['init_lr'], config['min_lr'], config['lr_decay_rate']) | |
| train_stats = train(model, data_loader, optimizer, epoch, device, 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(), | |
| '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/pretrain.yaml') | |
| parser.add_argument('--output_dir', default='output/Pretrain') | |
| parser.add_argument('--checkpoint', default='') | |
| 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) | |
| 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) |