train.py

"""
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
import os

import warnings
warnings.filterwarnings('ignore')

def main(args):
    args.masks = True
    args.dataset_file = 'joint' # joint training of ytvos and refcoco
    args.binary = 1             # only run on binary referred

    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], find_unused_parameters=True)
        model_without_ddp = model.module

    # for n, p in model_without_ddp.named_parameters():
    #     print(n)
    # ls = [name for name,para in model_without_ddp.named_parameters() if para.grad==None]
    # print(ls)

    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)

    # auto resume
    args.output_dir = os.path.join('results', args.backbone, 'ckpt')
    os.makedirs(args.output_dir, exist_ok=True)
    ckpts = os.listdir(args.output_dir)
    
    if len(ckpts) > 0:
        resume_path = os.path.join(args.output_dir, 'checkpoint.pth')
        print("Resume from {}".format(resume_path))
        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(resume_path, 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


    output_dir = Path(args.output_dir)
    if args.resume:
        print("Resume from {}".format(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


    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, args.use_fp16)
        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.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('MUTR 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)