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config.py
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# --------------------------------------------------------
# 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'
# 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.DROP_HEAD = True
_C.MODEL.DROP_META = 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 = 2023
# 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 = int(os.environ["LOCAL_RANK"]) if "LOCAL_RANK" in os.environ else 0
# Balanced Softmax Loss
_C.USE_BALANCED_SOFTMAX_LOSS = False
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("-= Merging 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
config.PRINT_FREQ = args.accumulation_steps # to show grad_norm correctly on logs
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 Balanced Softmax Loss
if args.use_balanced_softmax_loss:
config.USE_BALANCED_SOFTMAX_LOSS = True
# 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 ####################