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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 json | |
| import pickle | |
| 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_nlvr import blip_nlvr | |
| import utils | |
| from utils import cosine_lr_schedule, warmup_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', utils.SmoothedValue(window_size=50, fmt='{value:.4f}')) | |
| header = 'Train Epoch: [{}]'.format(epoch) | |
| print_freq = 50 | |
| step_size = 10 | |
| for i,(image0, image1, text, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): | |
| images = torch.cat([image0, image1], dim=0) | |
| images, targets = images.to(device), targets.to(device) | |
| loss = model(images, text, targets=targets, train=True) | |
| optimizer.zero_grad() | |
| loss.backward() | |
| optimizer.step() | |
| metric_logger.update(lr=optimizer.param_groups[0]["lr"]) | |
| metric_logger.update(loss=loss.item()) | |
| # gather the stats from all processes | |
| metric_logger.synchronize_between_processes() | |
| print("Averaged stats:", metric_logger.global_avg()) | |
| return {k: "{:.4f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()} | |
| def evaluate(model, data_loader, device, config): | |
| # test | |
| model.eval() | |
| metric_logger = utils.MetricLogger(delimiter=" ") | |
| header = 'Evaluation:' | |
| print_freq = 50 | |
| for image0, image1, text, targets in metric_logger.log_every(data_loader, print_freq, header): | |
| images = torch.cat([image0, image1], dim=0) | |
| images, targets = images.to(device), targets.to(device) | |
| prediction = model(images, text, targets=targets, train=False) | |
| _, pred_class = prediction.max(1) | |
| accuracy = (targets==pred_class).sum() / targets.size(0) | |
| metric_logger.meters['acc'].update(accuracy.item(), n=image0.size(0)) | |
| # gather the stats from all processes | |
| metric_logger.synchronize_between_processes() | |
| print("Averaged stats:", metric_logger.global_avg()) | |
| return {k: "{:.4f}".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('nlvr', config) | |
| if args.distributed: | |
| num_tasks = utils.get_world_size() | |
| global_rank = utils.get_rank() | |
| samplers = create_sampler(datasets, [True,False,False], num_tasks, global_rank) | |
| else: | |
| samplers = [None, None, None] | |
| batch_size=[config['batch_size_train'],config['batch_size_test'],config['batch_size_test']] | |
| train_loader, val_loader, test_loader = create_loader(datasets,samplers,batch_size=batch_size, | |
| num_workers=[4,4,4],is_trains=[True,False,False], | |
| collate_fns=[None,None,None]) | |
| #### Model #### | |
| print("Creating model") | |
| model = blip_nlvr(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']) | |
| print("Start training") | |
| start_time = time.time() | |
| best = 0 | |
| best_epoch = 0 | |
| 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, config) | |
| val_stats = evaluate(model, val_loader, device, config) | |
| test_stats = evaluate(model, test_loader, device, config) | |
| if utils.is_main_process(): | |
| if args.evaluate: | |
| log_stats = {**{f'val_{k}': v for k, v in val_stats.items()}, | |
| **{f'test_{k}': v for k, v in test_stats.items()}, | |
| } | |
| with open(os.path.join(args.output_dir, "log.txt"),"a") as f: | |
| f.write(json.dumps(log_stats) + "\n") | |
| else: | |
| log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, | |
| **{f'val_{k}': v for k, v in val_stats.items()}, | |
| **{f'test_{k}': v for k, v in test_stats.items()}, | |
| 'epoch': epoch, | |
| } | |
| if float(val_stats['acc'])>best: | |
| 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_best.pth')) | |
| best = float(val_stats['acc']) | |
| best_epoch = epoch | |
| with open(os.path.join(args.output_dir, "log.txt"),"a") as f: | |
| f.write(json.dumps(log_stats) + "\n") | |
| if args.evaluate: | |
| break | |
| dist.barrier() | |
| if utils.is_main_process(): | |
| with open(os.path.join(args.output_dir, "log.txt"),"a") as f: | |
| f.write("best epoch: %d"%best_epoch) | |
| 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.yaml') | |
| parser.add_argument('--output_dir', default='output/NLVR') | |
| 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) |