config.py

# Copyright (c) 2021 PPViT Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Configuration
Configurations for (1) data processing, (2) model archtecture, and (3) training settings, etc.
Config can be set by .yaml file or by argparser
"""
import os
from yacs.config import CfgNode as CN
import yaml

_C = CN()
_C.BASE = ['']

# data settings
_C.DATA = CN()
_C.DATA.BATCH_SIZE = 256  # train batch_size on single GPU
_C.DATA.BATCH_SIZE_EVAL = None  # (disabled in update_config) val batch_size on single GPU
_C.DATA.ANNO_FOLDER = './anno'  # path to dataset
_C.DATA.DATA_FOLDER = './data'  # path to dataset
_C.DATA.DATA_LIST_VAL = None  # path to dataset
_C.DATA.DATA_LIST_TRAIN = None  # path to dataset
_C.DATA.DATASET = 'ABAW'  # dataset name, currently only support ABAW
_C.DATA.CLASS_TYPE = 'all'  # [all, corase, negative]
_C.DATA.IMAGE_SIZE = 224  # input image size e.g., 224
_C.DATA.IMAGE_CHANNELS = 3  # input image channels: e.g., 3
_C.DATA.CROP_PCT = 0.9  # input image scale ratio, scale is applied before centercrop in eval mode
_C.DATA.NUM_WORKERS = 2  # number of data loading threads
_C.DATA.IMAGENET_MEAN = [0.485, 0.456, 0.406]  # imagenet mean values
_C.DATA.IMAGENET_STD = [0.229, 0.224, 0.225]  # imagenet std values

# model general settings
_C.MODEL = CN()
_C.MODEL.TYPE = 'swin'
_C.MODEL.NAME = 'swin'
_C.MODEL.RESUME = None  # full model path for resume training
_C.MODEL.PRETRAINED = None  # full model path for finetuning
_C.MODEL.NUM_CLASSES = 8 # num of classes for classifier: 4 for negative, 5 for coarse, 8 for all
_C.MODEL.DROPOUT = 0.0
_C.MODEL.ATTENTION_DROPOUT = 0.0
_C.MODEL.DROPPATH = 0.1 # same as official
# model transformer settings
_C.MODEL.PATCH_SIZE = 16
_C.MODEL.WINDOW_SIZE = 7
_C.MODEL.EMBED_DIM = 96
_C.MODEL.NUM_HEADS = [3, 6, 12, 24]
_C.MODEL.STAGE_DEPTHS =[2, 2, 6, 2] 
_C.MODEL.MLP_RATIO = 4.0
_C.MODEL.QKV_BIAS = True
_C.MODEL.QK_SCALE = None
_C.MODEL.APE = False  # absolute positional embedding
_C.MODEL.PATCH_NORM = True

# training settings (for ViT-B/16 pretrain)
_C.TRAIN = CN()
_C.TRAIN.LAST_EPOCH = 0
_C.TRAIN.NUM_EPOCHS = 80
_C.TRAIN.WARMUP_EPOCHS = 5
_C.TRAIN.WEIGHT_DECAY = 0.05
_C.TRAIN.BASE_LR = 5e-4
_C.TRAIN.WARMUP_START_LR = 5e-5
_C.TRAIN.END_LR = 0.0
_C.TRAIN.GRAD_CLIP = 5.0
_C.TRAIN.ACCUM_ITER = 1
_C.TRAIN.LINEAR_SCALED_LR = 512

# optimizer
_C.TRAIN.OPTIMIZER = CN()
_C.TRAIN.OPTIMIZER.NAME = 'AdamW'
_C.TRAIN.OPTIMIZER.EPS = 1e-8
_C.TRAIN.OPTIMIZER.BETAS = (0.9, 0.999)

# model ema
_C.TRAIN.MODEL_EMA = False
_C.TRAIN.MODEL_EMA_DECAY = 0.99996
_C.TRAIN.MODEL_EMA_FORCE_CPU = False

# data augmentation (optional, check datasets.py)
_C.TRAIN.SMOOTHING = 0.1
_C.TRAIN.COLOR_JITTER = 0.4  # if both auto augment and rand augment are False, use color jitter
_C.TRAIN.AUTO_AUGMENT = False  # rand augment is used if both rand and auto augment are set True
_C.TRAIN.RAND_AUGMENT = True
_C.TRAIN.RAND_AUGMENT_LAYERS = 2
_C.TRAIN.RAND_AUGMENT_MAGNITUDE = 9  # scale from 0 to 9
# mixup params (optional, check datasets.py, to turn off: mixup_prob=0, cutmix_alpha=0, cutmix_minmax=None)
_C.TRAIN.MIXUP_ALPHA = 0.8
_C.TRAIN.MIXUP_PROB = 0.0
_C.TRAIN.MIXUP_SWITCH_PROB = 0.5
_C.TRAIN.MIXUP_MODE = 'batch'
_C.TRAIN.CUTMIX_ALPHA = 0.0 #1.0
_C.TRAIN.CUTMIX_MINMAX = None
# random erase params (optional, check datasets.py)
_C.TRAIN.RANDOM_ERASE_PROB = 0.25
_C.TRAIN.RANDOM_ERASE_MODE = 'pixel'
_C.TRAIN.RANDOM_ERASE_COUNT = 1
_C.TRAIN.RANDOM_ERASE_SPLIT = False

# misc
_C.SAVE = "./output"  # output folder, saves logs and weights
_C.SAVE_FREQ = 10  # freq to save chpt
_C.REPORT_FREQ = 20  # freq to logging info
_C.VALIDATE_FREQ = 1  # freq to do validation
_C.SEED = 0  # random seed
_C.EVAL = False  # run evaluation only
_C.AMP = False  # auto mix precision training


def _update_config_from_file(config, cfg_file):
    """Load cfg file (.yaml) and update config object

    Args:
        config: config object
        cfg_file: config file (.yaml)
    Return:
        None
    """
    config.defrost()
    with open(cfg_file, 'r') as infile:
        yaml_cfg = yaml.load(infile, Loader=yaml.FullLoader)
    for cfg in yaml_cfg.setdefault('BASE', ['']):
        if cfg:
            _update_config_from_file(
                config, os.path.join(os.path.dirname(cfg_file), cfg)
            )
    config.merge_from_file(cfg_file)
    config.freeze()


def update_config(config, args):
    """Update config by ArgumentParser
    Configs that are often used can be updated from arguments
    Args:
        args: ArgumentParser contains options
    Return:
        config: updated config
    """
    if args.cfg:
        _update_config_from_file(config, args.cfg)
    config.defrost()
    if args.dataset:
        config.DATA.DATASET = args.dataset
    if args.batch_size:
        config.DATA.BATCH_SIZE = args.batch_size
        config.DATA.BATCH_SIZE_EVAL = args.batch_size
    if args.batch_size_eval:
        config.DATA.BATCH_SIZE_EVAL = args.batch_size_eval
    if args.image_size:
        config.DATA.IMAGE_SIZE = args.image_size
    if args.accum_iter:
        config.TRAIN.ACCUM_ITER = args.accum_iter
    if args.data_folder:
        config.DATA.DATA_FOLDER = args.data_folder
    if args.anno_folder:
        config.DATA.ANNO_FOLDER = args.anno_folder
    if args.data_list_train:
        config.DATA.DATA_LIST_TRAIN = args.data_list_train
    if args.data_list_val:
        config.DATA.DATA_LIST_VAL = args.data_list_val
    if args.class_type:
        config.DATA.CLASS_TYPE = args.class_type
        if args.class_type == 'all':
            config.MODEL.NUM_CLASSES = 8
        if args.class_type == 'negative':
            config.MODEL.NUM_CLASSES = 4
        if args.class_type == 'coarse':
            config.MODEL.NUM_CLASSES = 5
    if args.eval:
        config.EVAL = True
    if args.pretrained:
        config.MODEL.PRETRAINED = args.pretrained
    if args.resume:
        config.MODEL.RESUME = args.resume
    if args.last_epoch:
        config.TRAIN.LAST_EPOCH = args.last_epoch
    if args.amp:  # only for training
        config.AMP = not config.EVAL
    # config.freeze()
    return config


def get_config(cfg_file=None):
    """Return a clone of config and optionally overwrite it from yaml file"""
    config = _C.clone()
    if cfg_file:
        _update_config_from_file(config, cfg_file)
    return config