update: code 240822

Change-Id: I979120eecab1b45527452bec8a31817b8d170c8b

Author: zengyan-97

Grounding_bbox.py

@@ -0,0 +1,283 @@
+import argparse
+import datetime
+import json
+import math
+import os
+import random
+import time
+from pathlib import Path
+
+import numpy as np
+import ruamel.yaml as yaml
+import torch
+import torch.backends.cudnn as cudnn
+import torch.distributed as dist
+
+import utils
+from utils.checkpointer import Checkpointer
+
+from dataset import create_dataset, create_sampler, create_loader, build_tokenizer
+from dataset.utils import collect_tensor_result, grounding_eval_bbox, grounding_eval_bbox_vlue
+
+from models.model_grounding import XVLMForGrounding
+
+from optim import create_optimizer
+from refTools.refer_python3 import REFER
+from scheduler import create_scheduler
+from utils.hdfs_io import hmkdir, hcopy, hexists
+
+
+def train(model, data_loader, optimizer, tokenizer, epoch, device, scheduler, config):
+    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_bbox', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
+    metric_logger.add_meter('loss_giou', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
+    header = 'Train Epoch: [{}]'.format(epoch)
+    print_freq = 50
+    step_size = 100
+
+    accumulate_steps = int(config.get('accumulate_steps', 1))
+    for i, (image, text, target_bbox) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
+        image = image.to(device, non_blocking=True)
+        text_input = tokenizer(text, padding='longest', truncation=True, max_length=config['max_tokens'], return_tensors="pt").to(device)
+        target_bbox = target_bbox.to(device)
+
+        _, loss_bbox, loss_giou = model(image, text_input.input_ids, text_input.attention_mask, target_bbox=target_bbox)
+        loss = loss_bbox + loss_giou
+
+        if accumulate_steps > 1:
+            loss = loss / accumulate_steps
+        
+        # backward
+        loss.backward()
+
+        if (i+1) % accumulate_steps == 0:
+            # update
+            optimizer.step()
+            scheduler.step()
+            optimizer.zero_grad()
+        
+        metric_logger.update(loss_bbox=loss_bbox.item())
+        metric_logger.update(loss_giou=loss_giou.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: "{:.5f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}
+
+
+def val(model, data_loader, tokenizer, device):
+    model.eval()
+            
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    header = 'Evaluation:'
+    print_freq = 50
+
+    result = []
+    for image, text, ref_ids in metric_logger.log_every(data_loader, print_freq, header):
+        image = image.to(device)
+        text_input = tokenizer(text, padding='longest', return_tensors="pt").to(device)
+
+        with torch.no_grad():
+            outputs_coord = model(image, text_input.input_ids, text_input.attention_mask, target_bbox=None)
+
+        assert len(ref_ids) == outputs_coord.shape[0]
+
+        for r_id, coord in zip(ref_ids, outputs_coord):
+            result.append({'ref_id': r_id.item(), 'pred': coord})
+
+    return result
+
+
+def main(args, config):
+    utils.init_distributed_mode(args)
+    device = torch.device(args.device)
+
+    world_size = utils.get_world_size()
+
+    if world_size > 8:
+        assert hexists(args.output_hdfs) and args.output_hdfs.startswith('hdfs'), "for collect_result among nodes"
+
+    if args.bs > 0:
+        config['batch_size'] = args.bs // world_size
+
+    seed = args.seed + utils.get_rank()
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    cudnn.benchmark = True
+
+    print("Creating dataset")
+    grd_train_dataset, grd_test_dataset = create_dataset('grounding_bbox', config, args.evaluate)
+
+
+    print("Creating model")
+    model = XVLMForGrounding(config=config)
+    model.load_pretrained(args.checkpoint, config, load_bbox_pretrain=args.load_bbox_pretrain, is_eval=args.evaluate)
+    model = model.to(device)
+    print("### Total Params: ", sum(p.numel() for p in model.parameters() if p.requires_grad))
+
+    model_without_ddp = model
+    if args.distributed:
+        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
+        model_without_ddp = model.module   
+
+    tokenizer = build_tokenizer(config['text_encoder'])
+
+    print("### output_dir, ", args.output_dir, flush=True)
+    print("### output_hdfs, ", args.output_hdfs, flush=True)
+    start_time = time.time()
+
+    if args.evaluate:
+        print("Start evaluating")
+
+        if args.distributed:
+            num_tasks = utils.get_world_size()
+            global_rank = utils.get_rank()
+            samplers = create_sampler([grd_test_dataset], [False], num_tasks, global_rank)
+        else:
+            samplers = [None]
+
+        test_loader = create_loader([grd_test_dataset], samplers,
+                                    batch_size=[config['batch_size']],
+                                    num_workers=[4], is_trains=[False], collate_fns=[None])[0]
+
+        result = val(model_without_ddp, test_loader, tokenizer, device)
+        results = collect_tensor_result(result, filename='grounding_bbox_eval', local_wdir=args.result_dir,
+                                        hdfs_wdir=args.output_hdfs,
+                                        write_to_hdfs=world_size > 8)
+
+        if utils.is_main_process():
+            if 'vlue_test' in config.keys() and config['vlue_test']:
+                grounding_acc = grounding_eval_bbox_vlue(results, config['test_file'][0])
+            else:
+                # refcoco evaluation tools
+                refer = REFER(config['refcoco_data'], 'refcoco+', 'unc')
+                grounding_acc = grounding_eval_bbox(results, refer)
+
+            log_stats = {**{f'{k}': v for k, v in grounding_acc.items()}}
+            print(log_stats)
+
+        dist.barrier()
+
+    else:
+        print("Start training")
+
+        datasets = [grd_train_dataset, grd_test_dataset]
+
+        train_dataset_size = len(grd_train_dataset)
+        train_batch_size = config['batch_size']
+
+        if utils.is_main_process():
+            print(f"### data {train_dataset_size}, batch size, {train_batch_size} x {world_size}")
+
+        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'], config['batch_size']],
+                                                  num_workers=[4, 4], is_trains=[True, False], collate_fns=[None, None])
+
+        arg_opt = utils.AttrDict(config['optimizer'])
+        optimizer = create_optimizer(arg_opt, model)
+        arg_sche = utils.AttrDict(config['schedular'])
+        accumulate_steps = int(config.get('accumulate_steps', 1))
+        arg_sche['step_per_epoch'] = math.ceil(train_dataset_size/(train_batch_size*world_size) / accumulate_steps)
+        arg_sche['min_rate'] = config['min_lr'] / arg_opt['lr'] if 'min_lr' in config else 0
+        lr_scheduler = create_scheduler(arg_sche, optimizer)
+
+        checkpointer = Checkpointer(args.output_dir)
+
+        max_epoch = config['schedular']['epochs']
+        best = 0
+        best_epoch = 0
+        for epoch in range(0, max_epoch):
+            if args.distributed:
+                train_loader.sampler.set_epoch(epoch)
+            train_stats = train(model, train_loader, optimizer, tokenizer, epoch, device, lr_scheduler, config)
+
+            result = val(model_without_ddp, test_loader, tokenizer, device)
+            results = collect_tensor_result(result, filename='epoch%d' % epoch, local_wdir=args.result_dir, hdfs_wdir=args.output_hdfs,
+                                     write_to_hdfs=world_size > 8)
+
+            if utils.is_main_process():
+                # refcoco evaluation tools
+                refer = REFER(config['refcoco_data'], 'refcoco+', 'unc')
+                grounding_acc = grounding_eval_bbox(results, refer)
+                log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
+                             **{f'{k}': v for k, v in grounding_acc.items()},
+                             'epoch': epoch}
+
+                if grounding_acc['val_d'] > best:
+                    save_obj = {
+                        'model': model_without_ddp.state_dict(),
+                        # 'optimizer': optimizer.state_dict(),
+                        # 'lr_scheduler': lr_scheduler.state_dict(),
+                        'config': config,
+                        # 'epoch': epoch,
+                    }
+
+                    # torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_best.pth'))
+                    checkpointer.save_checkpoint(model_state=save_obj,
+                                                 epoch='best', training_states=optimizer.state_dict())
+
+                    best = grounding_acc['val_d']
+                    best_epoch = epoch
+
+                with open(os.path.join(args.output_dir, "log.txt"), "a") as f:
+                    f.write(json.dumps(log_stats) + "\n")
+
+            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)
+
+            os.system(f"cat {args.output_dir}/log.txt")
+
+    total_time = time.time() - start_time
+    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+    print('### Time {}'.format(total_time_str))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--checkpoint', type=str, required=True)
+    parser.add_argument('--config', type=str, default='configs/Grounding_bbox.yaml')
+    parser.add_argument('--output_dir', type=str, default='output/refcoco_bbox')
+    parser.add_argument('--output_hdfs', type=str, default='', help="to collect eval results among nodes")
+
+    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('--distributed', action='store_false')
+    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
+
+    parser.add_argument('--evaluate', action='store_true')
+    parser.add_argument('--override_cfg', default="", type=str, help="Use ; to separate keys")
+    parser.add_argument('--load_bbox_pretrain', action='store_true')
+    parser.add_argument('--bs', default=-1, type=int, help="for each gpu, batch_size = bs // num_gpus")
+
+    args = parser.parse_args()
+
+    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
+    utils.update_config(config, args.override_cfg)
+    if utils.is_main_process():
+        print('config:', json.dumps(config))
+    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'))    
+
+    if len(args.output_hdfs):
+        hmkdir(args.output_hdfs)
+
+    main(args, config)

LICENSE

@@ -0,0 +1,12 @@
+Copyright (c) 2023, ByteDance Inc.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+* Neither the name of Salesforce.com nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.