mae-cnn/downstream_imagenet/arg.py

# Copyright (c) ByteDance, Inc. and its 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.

import json
import os
import sys

from tap import Tap

HP_DEFAULT_NAMES = ['bs', 'ep', 'wp_ep', 'opt', 'base_lr', 'lr_scale', 'wd', 'mixup', 'rep_aug', 'drop_path', 'ema']
HP_DEFAULT_VALUES = {
    'convnext_small':     (4096, 400, 20, 'adam', 0.0002,  0.7, 0.01, 0.8, 3, 0.3,  0.9999),
    'convnext_base':      (4096, 400, 20, 'adam', 0.0001,  0.7, 0.01, 0.8, 3, 0.4,  0.9999),
    'convnext_large':     (4096, 200, 10, 'adam', 0.0001,  0.7, 0.02, 0.8, 3, 0.5,  0.9999),
    'convnext_large_384': (1024, 200, 20, 'adam', 0.00006, 0.7, 0.01, 0.8, 3, 0.5,  0.99995),

    'resnet50':           (2048, 300, 5,  'lamb', 0.002,   0.7, 0.02, 0.1, 0, 0.05, 0.9999),
    'resnet101':          (2048, 300, 5,  'lamb', 0.001,   0.8, 0.02, 0.1, 0, 0.2,  0.9999),
    'resnet152':          (2048, 300, 5,  'lamb', 0.001,   0.8, 0.02, 0.1, 0, 0.2,  0.9999),
    'resnet200':          (2048, 300, 5,  'lamb', 0.001,   0.8, 0.02, 0.1, 0, 0.2,  0.9999),
}


class FineTuneArgs(Tap):
    # environment
    exp_name: str
    exp_dir: str
    data_path: str
    model: str
    resume_from: str = ''   # resume from some checkpoint.pth

    img_size: int = 224
    dataloader_workers: int = 8

    # ImageNet classification fine-tuning hyperparameters; see `HP_DEFAULT_VALUES` above for detailed default values
    # - batch size, epoch
    bs: int = 0             # global batch size (== batch_size_per_gpu * num_gpus)
    ep: int = 0             # number of epochs
    wp_ep: int = 0          # epochs for warmup

    # - optimization
    opt: str = ''           # optimizer; 'adam' or 'lamb'
    base_lr: float = 0.     # lr == base_lr * (bs)
    lr_scale: float = 0.    # see file `lr_decay.py` for more details
    clip: int = -1          # use gradient clipping if clip > 0

    # - regularization tricks
    wd: float = 0.          # weight decay
    mixup: float = 0.       # use mixup if mixup > 0
    rep_aug: int = 0        # use repeated augmentation if rep_aug > 0
    drop_path: float = 0.   # drop_path ratio

    # - other tricks
    ema: float = 0.         # use EMA if ema > 0
    sbn: bool = True        # use SyncBatchNorm

    # NO NEED TO SPECIFIED; each of these args would be updated in runtime automatically
    lr: float = None
    batch_size_per_gpu: int = 0
    glb_batch_size: int = 0
    device: str = 'cpu'
    world_size: int = 1
    global_rank: int = 0
    local_rank: int = 0     # we DO USE this arg
    is_master: bool = False
    is_local_master: bool = False
    cmd: str = ' '.join(sys.argv[1:])
    commit_id: str = os.popen(f'git rev-parse HEAD').read().strip()
    commit_msg: str = os.popen(f'git log -1').read().strip().splitlines()[-1].strip()
    log_txt_name: str = '{args.exp_dir}/pretrain_log.txt'
    tb_lg_dir: str = ''     # tensorboard log directory

    train_loss: float = 0.
    train_acc: float = 0.
    best_val_acc: float = 0.
    cur_ep: str = ''
    remain_time: str = ''
    finish_time: str = ''
    first_logging: bool = True

    def log_epoch(self):
        if not self.is_local_master:
            return

        if self.first_logging:
            self.first_logging = False
            with open(self.log_txt_name, 'w') as fp:
                json.dump({
                    'name': self.exp_name, 'cmd': self.cmd, 'git_commit_id': self.commit_id, 'git_commit_msg': self.commit_msg,
                    'model': self.model,
                }, fp)
                fp.write('\n\n')

        with open(self.log_txt_name, 'a') as fp:
            json.dump({
                'cur_ep': self.cur_ep,
                'train_L': self.train_loss, 'train_acc': self.train_acc,
                'best_val_acc': self.best_val_acc,
                'rema': self.remain_time, 'fini': self.finish_time,
            }, fp)
            fp.write('\n')


def get_args(world_size, global_rank, local_rank, device) -> FineTuneArgs:
    # parse args and prepare directories
    args = FineTuneArgs(explicit_bool=True).parse_args()
    d_name, b_name = os.path.dirname(os.path.abspath(args.exp_dir)), os.path.basename(os.path.abspath(args.exp_dir))
    b_name = ''.join(ch if (ch.isalnum() or ch == '-') else '_' for ch in b_name)
    args.exp_dir = os.path.join(d_name, b_name)
    os.makedirs(args.exp_dir, exist_ok=True)
    args.log_txt_name = os.path.join(args.exp_dir, 'finetune_log.txt')

    args.tb_lg_dir = args.tb_lg_dir or os.path.join(args.exp_dir, 'tensorboard_log')
    try: os.makedirs(args.tb_lg_dir, exist_ok=True)
    except: pass

    # fill in args.bs, args.ep, etc. with their default values (if their values are not explicitly specified, i.e., if bool(they) == False)
    if args.model == 'convnext_large' and args.img_size == 384:
        default_values = HP_DEFAULT_VALUES['convnext_large_384']
    else:
        default_values = HP_DEFAULT_VALUES[args.model]
    for k, v in zip(HP_DEFAULT_NAMES, default_values):
        if bool(getattr(args, k)) == False:
            setattr(args, k, v)

    # update other runtime args
    args.world_size, args.global_rank, args.local_rank, args.device = world_size, global_rank, local_rank, device
    args.is_master = global_rank == 0
    args.is_local_master = local_rank == 0
    args.batch_size_per_gpu = args.bs // world_size
    args.glb_batch_size = args.batch_size_per_gpu * world_size
    args.lr = args.base_lr * args.glb_batch_size / 256

    return args