Upload pretrain.py

pretrain.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.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)