Upload train_vqa.py

train_vqa.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
+from torch.utils.data import DataLoader
+import torch.backends.cudnn as cudnn
+import torch.distributed as dist
+
+from models.blip_vqa import blip_vqa
+import utils
+from utils import cosine_lr_schedule
+from data import create_dataset, create_sampler, create_loader
+from data.vqa_dataset import vqa_collate_fn
+from data.utils import save_result
+
+
+def train(model, data_loader, optimizer, epoch, device):
+    # train
+    model.train()  
+    
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
+    metric_logger.add_meter('loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
+
+    header = 'Train Epoch: [{}]'.format(epoch)
+    print_freq = 50    
+    
+    for i,(image, question, answer, weights, n) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
+        image, weights = image.to(device,non_blocking=True), weights.to(device,non_blocking=True)      
+
+        loss = model(image, question, answer, train=True, n=n, weights=weights)        
+        
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()    
+        
+        metric_logger.update(loss=loss.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()} 
+
+
+@torch.no_grad()
+def evaluation(model, data_loader, device, config) :
+    # test
+    model.eval()
+            
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    header = 'Generate VQA test result:'
+    print_freq = 50
+    
+    result = []
+    
+    if config['inference']=='rank':   
+        answer_list = data_loader.dataset.answer_list
+        answer_candidates = model.tokenizer(answer_list, padding='longest', return_tensors='pt').to(device)    
+        answer_candidates.input_ids[:,0] = model.tokenizer.bos_token_id
+        
+    for n, (image, question, question_id) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):        
+        image = image.to(device,non_blocking=True)             
+
+        if config['inference']=='generate':
+            answers = model(image, question, train=False, inference='generate') 
+            
+            for answer, ques_id in zip(answers, question_id):
+                ques_id = int(ques_id.item())       
+                result.append({"question_id":ques_id, "answer":answer})             
+            
+        elif config['inference']=='rank':    
+            answer_ids = model(image, question, answer_candidates, train=False, inference='rank', k_test=config['k_test'])      
+
+            for ques_id, answer_id in zip(question_id, answer_ids):
+                result.append({"question_id":int(ques_id.item()), "answer":answer_list[answer_id]})   
+
+    return result
+
+
+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 vqa datasets")
+    datasets = create_dataset('vqa', config)   
+    
+    if args.distributed:
+        num_tasks = utils.get_world_size()
+        global_rank = utils.get_rank()            
+        samplers = create_sampler(datasets, [True, False], num_tasks, global_rank)         
+    else:
+        samplers = [None, None]
+    
+    train_loader, test_loader = create_loader(datasets,samplers,
+                                              batch_size=[config['batch_size_train'],config['batch_size_test']],
+                                              num_workers=[4,4],is_trains=[True, False], 
+                                              collate_fns=[vqa_collate_fn,None]) 
+    #### Model #### 
+    print("Creating model")
+    model = blip_vqa(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'])
+
+    best = 0
+    best_epoch = 0 
+       
+    print("Start training")
+    start_time = time.time()    
+    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) 
+
+        else:         
+            break        
+        
+        if utils.is_main_process():     
+            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
+                         'epoch': epoch,
+                        }                
+            with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
+                f.write(json.dumps(log_stats) + "\n")                        
+                    
+            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))  
+
+        dist.barrier()         
+
+    vqa_result = evaluation(model_without_ddp, test_loader, device, config)        
+    result_file = save_result(vqa_result, args.result_dir, 'vqa_result')  
+                      
+    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/vqa.yaml') 
+    parser.add_argument('--output_dir', default='output/VQA')
+    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)
+
+    args.result_dir = os.path.join(args.output_dir, 'result')
+
+    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
+    Path(args.result_dir).mkdir(parents=True, exist_ok=True)
+        
+    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
+    
+    main(args, config)