mae_pretrain.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# DeiT: https://github.com/facebookresearch/deit
# BEiT: https://github.com/microsoft/unilm/tree/master/beit
# --------------------------------------------------------
import argparse
import datetime
import json
import os
import time
from pathlib import Path

import numpy as np
import timm
import torch
import torch.backends.cudnn as cudnn
import torch.utils.data
from torch.utils.tensorboard import SummaryWriter

from MAE.dataset import ImgMaskDataset

assert timm.__version__ == "0.3.2"  # version check

from MAE.util import misc
from MAE.util.misc import NativeScalerWithGradNormCount as NativeScaler
from MAE.util.misc import NativeScalerWithGradNormCount_finetune
from MAE.engine_pretrain import train_one_epoch, train_one_epoch_finetune


def get_args_parser():
    parser = argparse.ArgumentParser('MAE pre-training', add_help=False)
    parser.add_argument("--ngpus", default=2, type=int, help="Number of gpus to request on each node")
    parser.add_argument('--batch_size', default=256, type=int,
                        help='Batch size per GPU (effective batch size is batch_size * accum_iter * # gpus')
    parser.add_argument('--epochs', default=200, type=int)
    parser.add_argument('--accum_iter', default=1, type=int,
                        help='Accumulate gradient iterations (for increasing the effective batch size under memory constraints)')

    # Model parameters
    parser.add_argument('--model', default='mae_vit_base_patch16', type=str, metavar='MODEL',
                        help='Name of model to train')

    parser.add_argument('--input_size', default=256, type=int,
                        help='images input size')

    parser.add_argument('--mask_ratio', default=0.75, type=float,
                        help='Masking ratio (percentage of removed patches).')

    parser.add_argument('--norm_pix_loss', action='store_true',
                        help='Use (per-patch) normalized pixels as targets for computing loss')
    parser.set_defaults(norm_pix_loss=False)

    # Optimizer parameters
    parser.add_argument('--weight_decay', type=float, default=0.05,
                        help='weight decay (default: 0.05)')

    parser.add_argument('--lr', type=float, default=None, metavar='LR',
                        help='learning rate (absolute lr)')
    parser.add_argument('--blr', type=float, default=1e-3, metavar='LR',
                        help='base learning rate: absolute_lr = base_lr * total_batch_size / 256')
    parser.add_argument('--min_lr', type=float, default=0., metavar='LR',
                        help='lower lr bound for cyclic schedulers that hit 0')

    parser.add_argument('--warmup_epochs', type=int, default=5, metavar='N',
                        help='epochs to warmup LR')

    # Dataset parameters
    parser.add_argument('--data_path', type=str, default='train_list.txt', help='Indicate where is the training set')
    parser.add_argument('--mask_path', nargs='+',
                        default=['/mnt/storage/dongqiaole/irregular_mask/irregular_mask_list.txt',
                                 '/mnt/storage/dongqiaole/coco_mask/coco_mask_list.txt'])

    parser.add_argument('--output_dir', default='./output_dir',
                        help='path where to save, empty for no saving')
    parser.add_argument('--log_dir', default='./output_dir',
                        help='path where to tensorboard log')
    parser.add_argument('--device', default='cuda',
                        help='device to use for training / testing')
    parser.add_argument('--seed', default=0, type=int)
    parser.add_argument('--resume', default='',
                        help='resume from checkpoint')

    parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
                        help='start epoch')
    parser.add_argument('--num_workers', default=10, type=int)
    parser.add_argument('--pin_mem', action='store_true',
                        help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
    parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem')
    parser.set_defaults(pin_mem=True)

    # distributed training parameters
    parser.add_argument('--world_size', default=1, type=int,
                        help='number of distributed processes')
    parser.add_argument('--local_rank', default=-1, type=int)
    parser.add_argument('--dist_on_itp', action='store_true')
    parser.add_argument('--dist_url', default='env://',
                        help='url used to set up distributed training')
    parser.add_argument('--distributed', default=True)
    parser.add_argument('--aug', default=False, action='store_true')

    # some other settings
    parser.add_argument('--finetune', default=False, action='store_true',
                        help='finetune for partially mask (may cause overfitting for inpainting)')
    parser.add_argument('--random_mask', default=False, action='store_true',
                        help='Using random mask likes original MAE')

    return parser


def main(args):
    misc.init_distributed_mode(args)

    print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))
    print("{}".format(args).replace(', ', ',\n'))

    device = torch.device(args.device)

    # fix the seed for reproducibility
    seed = args.seed + misc.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)

    cudnn.benchmark = True

    # dataloader simple augmentation
    dataset_train = ImgMaskDataset(args.data_path, mask_path=args.mask_path, is_train=True, mask_rates=[0.4, 0.8, 1.0],
                                   image_size=args.input_size)
    print(dataset_train)

    if True:  # args.distributed:
        num_tasks = misc.get_world_size()
        global_rank = misc.get_rank()
        sampler_train = torch.utils.data.DistributedSampler(
            dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
        )
        print("Sampler_train = %s" % str(sampler_train))
    else:
        sampler_train = torch.utils.data.RandomSampler(dataset_train)

    if global_rank == 0 and args.log_dir is not None:
        os.makedirs(args.log_dir, exist_ok=True)
        log_writer = SummaryWriter(log_dir=args.log_dir)
    else:
        log_writer = None

    data_loader_train = torch.utils.data.DataLoader(
        dataset_train, sampler=sampler_train,
        batch_size=args.batch_size,
        num_workers=args.num_workers,
        pin_memory=args.pin_mem,
        drop_last=True,
    )

    # define the model
    model = misc.get_mae_model(args.model, finetune=args.finetune, random_mask=args.random_mask, norm_pix_loss=args.norm_pix_loss)

    model.to(device)

    model_without_ddp = model
    print("Model = %s" % str(model_without_ddp))

    eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size()

    if args.lr is None:  # only base_lr is specified
        args.lr = args.blr * eff_batch_size / 256

    print("base lr: %.2e" % (args.lr * 256 / eff_batch_size))
    print("actual lr: %.2e" % args.lr)

    print("accumulate grad iterations: %d" % args.accum_iter)
    print("effective batch size: %d" % eff_batch_size)

    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
        model_without_ddp = model.module

    if args.finetune:
        # following timm: set wd as 0 for bias and norm layers
        param_groups, param_groups2 = misc.add_weight_decay_finetune(model_without_ddp, args.weight_decay)
        optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95))
        # define another optimizer to finetune partially masked MAE,
        # because the original optimizer must reload state without any new params.
        optimizer_new = torch.optim.AdamW(param_groups2, lr=args.lr, betas=(0.9, 0.95))
        print(optimizer)
        print(optimizer_new)
        # loss_scaler = NativeScaler()
        loss_scaler = NativeScalerWithGradNormCount_finetune()  # only useful during finetuning, or works as NativeScaler
    else:
        # following timm: set wd as 0 for bias and norm layers
        param_groups = misc.add_weight_decay(model_without_ddp, args.weight_decay)
        optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95))
        print(optimizer)
        loss_scaler = NativeScaler()

    misc.load_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler)

    print(f"Start training for {args.start_epoch} epochs")
    start_time = time.time()
    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            data_loader_train.sampler.set_epoch(epoch)
        if args.finetune:
            train_stats = train_one_epoch_finetune(
                model, data_loader_train,
                optimizer, optimizer_new, device, epoch, loss_scaler,
                log_writer=log_writer,
                args=args,
                start_epoch=args.start_epoch
            )
        else:
            train_stats = train_one_epoch(
                model, data_loader_train,
                optimizer, device, epoch, loss_scaler,
                log_writer=log_writer,
                args=args
            )
        if args.output_dir and (epoch % 10 == 0 or epoch + 1 == args.epochs):
            misc.save_model(
                args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                loss_scaler=loss_scaler, epoch=epoch)
        misc.save_model(
            args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
            loss_scaler=loss_scaler, epoch=epoch, last=True)

        log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, 'epoch': epoch, }

        if args.output_dir and misc.is_main_process():
            if log_writer is not None:
                log_writer.flush()
            with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") 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__':
    args = get_args_parser()
    args = args.parse_args()
    if args.output_dir:
        Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    main(args)