config.py

# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------'

import os
import yaml
from yacs.config import CfgNode as CN

_C = CN()

# Base config files
_C.BASE = ['']

# -----------------------------------------------------------------------------
# Data settings
# -----------------------------------------------------------------------------
_C.DATA = CN()
# Batch size for a single GPU, could be overwritten by command line argument
_C.DATA.BATCH_SIZE = 32
# Path to dataset, could be overwritten by command line argument
_C.DATA.DATA_PATH = ''
# Dataset name
_C.DATA.DATASET = 'imagenet'
# Dataset root folder
_C.DATA.DATASET_ROOT = None
# Input image size
_C.DATA.IMG_SIZE = 224
# Interpolation to resize image (random, bilinear, bicubic)
_C.DATA.INTERPOLATION = 'bicubic'
_C.DATA.TRAIN_INTERPOLATION = 'bicubic'
# Use zipped dataset instead of folder dataset
# could be overwritten by command line argument
_C.DATA.ZIP_MODE = False
# Cache Data in Memory, could be overwritten by command line argument
_C.DATA.CACHE_MODE = 'part'
# Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.
_C.DATA.PIN_MEMORY = True
# Number of data loading threads
_C.DATA.NUM_WORKERS = 4
# hdfs data dir
_C.DATA.TRAIN_PATH = None
_C.DATA.VAL_PATH = None
# arnold dataset parallel
_C.DATA.NUM_READERS = 4


#meta info
_C.DATA.ADD_META = False
_C.DATA.FUSION = 'early'
_C.DATA.MASK_PROB = 0.0
_C.DATA.MASK_TYPE = 'constant'
_C.DATA.LATE_FUSION_LAYER = -1

# -----------------------------------------------------------------------------
# Model settings
# -----------------------------------------------------------------------------
_C.MODEL = CN()
# Model type
_C.MODEL.TYPE = ''
# Model name
_C.MODEL.NAME = ''
# Checkpoint to resume, could be overwritten by command line argument
_C.MODEL.RESUME = ''
# Number of classes, overwritten in data preparation
_C.MODEL.NUM_CLASSES = 1000
# Dropout rate
_C.MODEL.DROP_RATE = 0.0
# Drop path rate
_C.MODEL.DROP_PATH_RATE = 0.1
# Label Smoothing
_C.MODEL.LABEL_SMOOTHING = 0.1
#pretrain
_C.MODEL.PRETRAINED = None
_C.MODEL.DORP_HEAD = True
_C.MODEL.DORP_META = True
_C.MODEL.FREEZE_BACKBONE = True

_C.MODEL.ONLY_LAST_CLS = False
_C.MODEL.EXTRA_TOKEN_NUM = 1
_C.MODEL.META_DIMS = []



# -----------------------------------------------------------------------------
# Training settings
# -----------------------------------------------------------------------------
_C.TRAIN = CN()
_C.TRAIN.START_EPOCH = 0
_C.TRAIN.EPOCHS = 300
_C.TRAIN.WARMUP_EPOCHS = 20
_C.TRAIN.WEIGHT_DECAY = 0.05
_C.TRAIN.BASE_LR = 1e-4 # 5e-4
_C.TRAIN.WARMUP_LR = 5e-7
_C.TRAIN.MIN_LR = 1e-5 # 5e-6
# Clip gradient norm
_C.TRAIN.CLIP_GRAD = 5.0
# Auto resume from latest checkpoint
_C.TRAIN.AUTO_RESUME = True
# Gradient accumulation steps
# could be overwritten by command line argument
_C.TRAIN.ACCUMULATION_STEPS = 0
# Whether to use gradient checkpointing to save memory
# could be overwritten by command line argument
_C.TRAIN.USE_CHECKPOINT = False

# LR scheduler
_C.TRAIN.LR_SCHEDULER = CN()
_C.TRAIN.LR_SCHEDULER.NAME = 'cosine'
# Epoch interval to decay LR, used in StepLRScheduler
_C.TRAIN.LR_SCHEDULER.DECAY_EPOCHS = 30
# LR decay rate, used in StepLRScheduler
_C.TRAIN.LR_SCHEDULER.DECAY_RATE = 0.1

# Optimizer
_C.TRAIN.OPTIMIZER = CN()
_C.TRAIN.OPTIMIZER.NAME = 'adamw'
# Optimizer Epsilon
_C.TRAIN.OPTIMIZER.EPS = 1e-8
# Optimizer Betas
_C.TRAIN.OPTIMIZER.BETAS = (0.9, 0.999)
# SGD momentum
_C.TRAIN.OPTIMIZER.MOMENTUM = 0.9

# -----------------------------------------------------------------------------
# Augmentation settings
# -----------------------------------------------------------------------------
_C.AUG = CN()
# Color jitter factor
_C.AUG.COLOR_JITTER = 0.4
# Use AutoAugment policy. "v0" or "original"
_C.AUG.AUTO_AUGMENT = 'rand-m9-mstd0.5-inc1'
# Random erase prob
_C.AUG.REPROB = 0.25
# Random erase mode
_C.AUG.REMODE = 'pixel'
# Random erase count
_C.AUG.RECOUNT = 1
# Mixup alpha, mixup enabled if > 0
_C.AUG.MIXUP = 0.8
# Cutmix alpha, cutmix enabled if > 0
_C.AUG.CUTMIX = 1.0
# Cutmix min/max ratio, overrides alpha and enables cutmix if set
_C.AUG.CUTMIX_MINMAX = None
# Probability of performing mixup or cutmix when either/both is enabled
_C.AUG.MIXUP_PROB = 1.0
# Probability of switching to cutmix when both mixup and cutmix enabled
_C.AUG.MIXUP_SWITCH_PROB = 0.5
# How to apply mixup/cutmix params. Per "batch", "pair", or "elem"
_C.AUG.MIXUP_MODE = 'batch'

# -----------------------------------------------------------------------------
# Testing settings
# -----------------------------------------------------------------------------
_C.TEST = CN()
# Whether to use center crop when testing
_C.TEST.CROP = True

# -----------------------------------------------------------------------------
# Misc
# -----------------------------------------------------------------------------
# Mixed precision opt level, if O0, no amp is used ('O0', 'O1', 'O2')
# overwritten by command line argument
_C.AMP_OPT_LEVEL = ''
# Path to output folder, overwritten by command line argument
_C.OUTPUT = ''
# Tag of experiment, overwritten by command line argument
_C.TAG = 'default'
# Frequency to save checkpoint
_C.SAVE_FREQ = 1
# Frequency to logging info
_C.PRINT_FREQ = 10
# Fixed random seed
_C.SEED = 0
# Perform evaluation only, overwritten by command line argument
_C.EVAL_MODE = False
# Test throughput only, overwritten by command line argument
_C.THROUGHPUT_MODE = False
# local rank for DistributedDataParallel, given by command line argument
_C.LOCAL_RANK = 0




def _update_config_from_file(config, cfg_file):
    config.defrost()
    with open(cfg_file, 'r') as f:
        yaml_cfg = yaml.load(f, 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)
            )
    print('=> merge config from {}'.format(cfg_file))
    config.merge_from_file(cfg_file)
    config.freeze()


def update_config(config, args):
    _update_config_from_file(config, args.cfg)

    config.defrost()
    if args.opts:
        config.merge_from_list(args.opts)

    # merge from specific arguments
    if args.batch_size:
        config.DATA.BATCH_SIZE = args.batch_size
    if args.data_path:
        config.DATA.DATA_PATH = args.data_path
    if args.zip:
        config.DATA.ZIP_MODE = True
    if args.cache_mode:
        config.DATA.CACHE_MODE = args.cache_mode
    if args.resume:
        config.MODEL.RESUME = args.resume
    if args.accumulation_steps:
        config.TRAIN.ACCUMULATION_STEPS = args.accumulation_steps
    if args.use_checkpoint:
        config.TRAIN.USE_CHECKPOINT = True
    if args.amp_opt_level:
        config.AMP_OPT_LEVEL = args.amp_opt_level
    if args.output:
        config.OUTPUT = args.output
    if args.tag:
        config.TAG = args.tag
    if args.eval:
        config.EVAL_MODE = True
    if args.throughput:
        config.THROUGHPUT_MODE = True

        
    if args.num_workers is not None:
        config.DATA.NUM_WORKERS = args.num_workers
        
    #set lr and weight decay
    if args.lr is not None:
        config.TRAIN.BASE_LR = args.lr
    if args.min_lr is not None:
        config.TRAIN.MIN_LR = args.min_lr
    if args.warmup_lr is not None:
        config.TRAIN.WARMUP_LR = args.warmup_lr
    if args.warmup_epochs is not None:
        config.TRAIN.WARMUP_EPOCHS = args.warmup_epochs
    if args.weight_decay is not None:
        config.TRAIN.WEIGHT_DECAY = args.weight_decay

    if args.epochs is not None:
        config.TRAIN.EPOCHS = args.epochs
    if args.dataset is not None:
        config.DATA.DATASET = args.dataset
    if args.lr_scheduler_name is not None:
        config.TRAIN.LR_SCHEDULER.NAME = args.lr_scheduler_name
    if args.pretrain is not None:
        config.MODEL.PRETRAINED = args.pretrain

    # set local rank for distributed training
    config.LOCAL_RANK = os.environ['LOCAL_RANK']

    # output folder
    config.OUTPUT = os.path.join(config.OUTPUT, config.MODEL.NAME, config.TAG)

    config.freeze()


def get_config(args):
    """Get a yacs CfgNode object with default values."""
    # Return a clone so that the defaults will not be altered
    # This is for the "local variable" use pattern
    config = _C.clone()
    update_config(config, args)

    return config


################### For Inferencing ####################
def update_inference_config(config, args):
    _update_config_from_file(config, args.cfg)

    config.defrost()

    config.freeze()


def get_inference_config(cfg_path):
    """Get a yacs CfgNode object with default values."""
    # Return a clone so that the defaults will not be altered
    # This is for the "local variable" use pattern
    config = _C.clone()
    update_inference_config(config, cfg_path)

    return config

################### For Inferencing ####################