# -------------------------------------------------------- # 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 ####################