Upload train_nlvr.py

train_nlvr.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 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()}    
+
+
+@torch.no_grad()
+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)