""" Training script of ReferFormer Modified from DETR (https://github.com/facebookresearch/detr) """ import argparse import datetime import json import random import time from pathlib import Path import numpy as np import torch from torch.utils.data import DataLoader, DistributedSampler import util.misc as utils import datasets.samplers as samplers from datasets import build_dataset, get_coco_api_from_dataset from engine import train_one_epoch, evaluate, evaluate_a2d from models import build_model from tools.load_pretrained_weights import pre_trained_model_to_finetune import opts def main(args): args.masks = True utils.init_distributed_mode(args) print("git:\n {}\n".format(utils.get_sha())) print(args) print(f'\n Run on {args.dataset_file} dataset.') print('\n') 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) model, criterion, postprocessor = build_model(args) 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 # for n, p in model_without_ddp.named_parameters(): # print(n) n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) print('number of params:', n_parameters) def match_name_keywords(n, name_keywords): out = False for b in name_keywords: if b in n: out = True break return out param_dicts = [ { "params": [p for n, p in model_without_ddp.named_parameters() if not match_name_keywords(n, args.lr_backbone_names) and not match_name_keywords(n, args.lr_text_encoder_names) and not match_name_keywords(n, args.lr_linear_proj_names) and p.requires_grad], "lr": args.lr, }, { "params": [p for n, p in model_without_ddp.named_parameters() if match_name_keywords(n, args.lr_backbone_names) and p.requires_grad], "lr": args.lr_backbone, }, { "params": [p for n, p in model_without_ddp.named_parameters() if match_name_keywords(n, args.lr_text_encoder_names) and p.requires_grad], "lr": args.lr_text_encoder, }, { "params": [p for n, p in model_without_ddp.named_parameters() if match_name_keywords(n, args.lr_linear_proj_names) and p.requires_grad], "lr": args.lr * args.lr_linear_proj_mult, } ] optimizer = torch.optim.AdamW(param_dicts, lr=args.lr, weight_decay=args.weight_decay) lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, args.lr_drop) # no validation ground truth for ytvos dataset dataset_train = build_dataset(args.dataset_file, image_set='train', args=args) if args.distributed: if args.cache_mode: sampler_train = samplers.NodeDistributedSampler(dataset_train) else: sampler_train = samplers.DistributedSampler(dataset_train) else: sampler_train = torch.utils.data.RandomSampler(dataset_train) batch_sampler_train = torch.utils.data.BatchSampler( sampler_train, args.batch_size, drop_last=True) data_loader_train = DataLoader(dataset_train, batch_sampler=batch_sampler_train, collate_fn=utils.collate_fn, num_workers=args.num_workers) # A2D-Sentences if args.dataset_file == 'a2d' or args.dataset_file == 'jhmdb': dataset_val = build_dataset(args.dataset_file, image_set='val', args=args) if args.distributed: if args.cache_mode: sampler_val = samplers.NodeDistributedSampler(dataset_val, shuffle=False) else: sampler_val = samplers.DistributedSampler(dataset_val, shuffle=False) else: sampler_val = torch.utils.data.SequentialSampler(dataset_val) data_loader_val = DataLoader(dataset_val, args.batch_size, sampler=sampler_val, drop_last=False, collate_fn=utils.collate_fn, num_workers=args.num_workers, pin_memory=True) if args.dataset_file == "davis": assert args.pretrained_weights is not None, "Please provide the pretrained weight to finetune for Ref-DAVIS17" print("============================================>") print("Ref-DAVIS17 are finetuned using the checkpoint trained on Ref-Youtube-VOS") print("Load checkpoint weights from {} ...".format(args.pretrained_weights)) checkpoint = torch.load(args.pretrained_weights, map_location="cpu") checkpoint_dict = pre_trained_model_to_finetune(checkpoint, args) model_without_ddp.load_state_dict(checkpoint_dict, strict=False) print("============================================>") if args.dataset_file == "jhmdb": assert args.resume is not None, "Please provide the checkpoint to resume for JHMDB-Sentences" print("============================================>") print("JHMDB-Sentences are directly evaluated using the checkpoint trained on A2D-Sentences") print("Load checkpoint weights from {} ...".format(args.pretrained_weights)) # load checkpoint in the args.resume print("============================================>") # for Ref-Youtube-VOS and A2D-Sentences # finetune using the pretrained weights on Ref-COCO if args.dataset_file != "davis" and args.dataset_file != "jhmdb" and args.pretrained_weights is not None: print("============================================>") print("Load pretrained weights from {} ...".format(args.pretrained_weights)) checkpoint = torch.load(args.pretrained_weights, map_location="cpu") checkpoint_dict = pre_trained_model_to_finetune(checkpoint, args) model_without_ddp.load_state_dict(checkpoint_dict, strict=False) print("============================================>") output_dir = Path(args.output_dir) if args.resume: if args.resume.startswith('https'): checkpoint = torch.hub.load_state_dict_from_url( args.resume, map_location='cpu', check_hash=True) else: checkpoint = torch.load(args.resume, map_location='cpu') missing_keys, unexpected_keys = model_without_ddp.load_state_dict(checkpoint['model'], strict=False) unexpected_keys = [k for k in unexpected_keys if not (k.endswith('total_params') or k.endswith('total_ops'))] if len(missing_keys) > 0: print('Missing Keys: {}'.format(missing_keys)) if len(unexpected_keys) > 0: print('Unexpected Keys: {}'.format(unexpected_keys)) if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint: import copy p_groups = copy.deepcopy(optimizer.param_groups) optimizer.load_state_dict(checkpoint['optimizer']) for pg, pg_old in zip(optimizer.param_groups, p_groups): pg['lr'] = pg_old['lr'] pg['initial_lr'] = pg_old['initial_lr'] print(optimizer.param_groups) lr_scheduler.load_state_dict(checkpoint['lr_scheduler']) # todo: this is a hack for doing experiment that resume from checkpoint and also modify lr scheduler (e.g., decrease lr in advance). args.override_resumed_lr_drop = True if args.override_resumed_lr_drop: print('Warning: (hack) args.override_resumed_lr_drop is set to True, so args.lr_drop would override lr_drop in resumed lr_scheduler.') lr_scheduler.step_size = args.lr_drop lr_scheduler.base_lrs = list(map(lambda group: group['initial_lr'], optimizer.param_groups)) lr_scheduler.step(lr_scheduler.last_epoch) args.start_epoch = checkpoint['epoch'] + 1 if args.eval: assert args.dataset_file == 'a2d' or args.dataset_file == 'jhmdb', \ 'Only A2D-Sentences and JHMDB-Sentences datasets support evaluation' test_stats = evaluate_a2d(model, data_loader_val, postprocessor, device, args) return print("Start training") start_time = time.time() for epoch in range(args.start_epoch, args.epochs): if args.distributed: sampler_train.set_epoch(epoch) train_stats = train_one_epoch( model, criterion, data_loader_train, optimizer, device, epoch, args.clip_max_norm) lr_scheduler.step() if args.output_dir: checkpoint_paths = [output_dir / 'checkpoint.pth'] # extra checkpoint before LR drop and every epochs # if (epoch + 1) % args.lr_drop == 0 or (epoch + 1) % 1 == 0: if (epoch + 1) % 1 == 0: checkpoint_paths.append(output_dir / f'checkpoint{epoch:04}.pth') for checkpoint_path in checkpoint_paths: utils.save_on_master({ 'model': model_without_ddp.state_dict(), 'optimizer': optimizer.state_dict(), 'lr_scheduler': lr_scheduler.state_dict(), 'epoch': epoch, 'args': args, }, checkpoint_path) log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, 'epoch': epoch, 'n_parameters': n_parameters} if args.dataset_file == 'a2d': test_stats = evaluate_a2d(model, data_loader_val, postprocessor, device, args) log_stats.update({**{f'{k}': v for k, v in test_stats.items()}}) if args.output_dir and utils.is_main_process(): with (output_dir / "log.txt").open("a") as f: f.write(json.dumps(log_stats) + "\n") 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('ReferFormer training and evaluation script', parents=[opts.get_args_parser()]) args = parser.parse_args() if args.output_dir: Path(args.output_dir).mkdir(parents=True, exist_ok=True) main(args)