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config.py
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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 torch
import yaml
from yacs.config import CfgNode as CN
# pytorch major version (1.x or 2.x)
PYTORCH_MAJOR_VERSION = int(torch.__version__.split('.')[0])
_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 = 128
# 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'
# 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 = 8
# [SimMIM] Mask patch size for MaskGenerator
_C.DATA.MASK_PATCH_SIZE = 32
# [SimMIM] Mask ratio for MaskGenerator
_C.DATA.MASK_RATIO = 0.6
# -----------------------------------------------------------------------------
# Model settings
# -----------------------------------------------------------------------------
_C.MODEL = CN()
# Model type
_C.MODEL.TYPE = 'swin'
# Model name
_C.MODEL.NAME = 'swin_tiny_patch4_window7_224'
# Pretrained weight from checkpoint, could be imagenet22k pretrained weight
# could be overwritten by command line argument
_C.MODEL.PRETRAINED = ''
# 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
# Swin Transformer parameters
_C.MODEL.SWIN = CN()
_C.MODEL.SWIN.PATCH_SIZE = 4
_C.MODEL.SWIN.IN_CHANS = 3
_C.MODEL.SWIN.EMBED_DIM = 96
_C.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]
_C.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]
_C.MODEL.SWIN.WINDOW_SIZE = 7
_C.MODEL.SWIN.MLP_RATIO = 4.
_C.MODEL.SWIN.QKV_BIAS = True
_C.MODEL.SWIN.QK_SCALE = None
_C.MODEL.SWIN.APE = False
_C.MODEL.SWIN.PATCH_NORM = True
# Swin Transformer V2 parameters
_C.MODEL.SWINV2 = CN()
_C.MODEL.SWINV2.PATCH_SIZE = 4
_C.MODEL.SWINV2.IN_CHANS = 3
_C.MODEL.SWINV2.EMBED_DIM = 96
_C.MODEL.SWINV2.DEPTHS = [2, 2, 6, 2]
_C.MODEL.SWINV2.NUM_HEADS = [3, 6, 12, 24]
_C.MODEL.SWINV2.WINDOW_SIZE = 7
_C.MODEL.SWINV2.MLP_RATIO = 4.
_C.MODEL.SWINV2.QKV_BIAS = True
_C.MODEL.SWINV2.APE = False
_C.MODEL.SWINV2.PATCH_NORM = True
_C.MODEL.SWINV2.PRETRAINED_WINDOW_SIZES = [0, 0, 0, 0]
# Swin Transformer MoE parameters
_C.MODEL.SWIN_MOE = CN()
_C.MODEL.SWIN_MOE.PATCH_SIZE = 4
_C.MODEL.SWIN_MOE.IN_CHANS = 3
_C.MODEL.SWIN_MOE.EMBED_DIM = 96
_C.MODEL.SWIN_MOE.DEPTHS = [2, 2, 6, 2]
_C.MODEL.SWIN_MOE.NUM_HEADS = [3, 6, 12, 24]
_C.MODEL.SWIN_MOE.WINDOW_SIZE = 7
_C.MODEL.SWIN_MOE.MLP_RATIO = 4.
_C.MODEL.SWIN_MOE.QKV_BIAS = True
_C.MODEL.SWIN_MOE.QK_SCALE = None
_C.MODEL.SWIN_MOE.APE = False
_C.MODEL.SWIN_MOE.PATCH_NORM = True
_C.MODEL.SWIN_MOE.MLP_FC2_BIAS = True
_C.MODEL.SWIN_MOE.INIT_STD = 0.02
_C.MODEL.SWIN_MOE.PRETRAINED_WINDOW_SIZES = [0, 0, 0, 0]
_C.MODEL.SWIN_MOE.MOE_BLOCKS = [[-1], [-1], [-1], [-1]]
_C.MODEL.SWIN_MOE.NUM_LOCAL_EXPERTS = 1
_C.MODEL.SWIN_MOE.TOP_VALUE = 1
_C.MODEL.SWIN_MOE.CAPACITY_FACTOR = 1.25
_C.MODEL.SWIN_MOE.COSINE_ROUTER = False
_C.MODEL.SWIN_MOE.NORMALIZE_GATE = False
_C.MODEL.SWIN_MOE.USE_BPR = True
_C.MODEL.SWIN_MOE.IS_GSHARD_LOSS = False
_C.MODEL.SWIN_MOE.GATE_NOISE = 1.0
_C.MODEL.SWIN_MOE.COSINE_ROUTER_DIM = 256
_C.MODEL.SWIN_MOE.COSINE_ROUTER_INIT_T = 0.5
_C.MODEL.SWIN_MOE.MOE_DROP = 0.0
_C.MODEL.SWIN_MOE.AUX_LOSS_WEIGHT = 0.01
# Swin MLP parameters
_C.MODEL.SWIN_MLP = CN()
_C.MODEL.SWIN_MLP.PATCH_SIZE = 4
_C.MODEL.SWIN_MLP.IN_CHANS = 3
_C.MODEL.SWIN_MLP.EMBED_DIM = 96
_C.MODEL.SWIN_MLP.DEPTHS = [2, 2, 6, 2]
_C.MODEL.SWIN_MLP.NUM_HEADS = [3, 6, 12, 24]
_C.MODEL.SWIN_MLP.WINDOW_SIZE = 7
_C.MODEL.SWIN_MLP.MLP_RATIO = 4.
_C.MODEL.SWIN_MLP.APE = False
_C.MODEL.SWIN_MLP.PATCH_NORM = True
# [SimMIM] Norm target during training
_C.MODEL.SIMMIM = CN()
_C.MODEL.SIMMIM.NORM_TARGET = CN()
_C.MODEL.SIMMIM.NORM_TARGET.ENABLE = False
_C.MODEL.SIMMIM.NORM_TARGET.PATCH_SIZE = 47
# -----------------------------------------------------------------------------
# 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 = 5e-4
_C.TRAIN.WARMUP_LR = 5e-7
_C.TRAIN.MIN_LR = 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 = 1
# 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
# warmup_prefix used in CosineLRScheduler
_C.TRAIN.LR_SCHEDULER.WARMUP_PREFIX = True
# [SimMIM] Gamma / Multi steps value, used in MultiStepLRScheduler
_C.TRAIN.LR_SCHEDULER.GAMMA = 0.1
_C.TRAIN.LR_SCHEDULER.MULTISTEPS = []
# 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
# [SimMIM] Layer decay for fine-tuning
_C.TRAIN.LAYER_DECAY = 1.0
# MoE
_C.TRAIN.MOE = CN()
# Only save model on master device
_C.TRAIN.MOE.SAVE_MASTER = False
# -----------------------------------------------------------------------------
# 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
# Whether to use SequentialSampler as validation sampler
_C.TEST.SEQUENTIAL = False
_C.TEST.SHUFFLE = False
# -----------------------------------------------------------------------------
# Misc
# -----------------------------------------------------------------------------
# [SimMIM] Whether to enable pytorch amp, overwritten by command line argument
_C.ENABLE_AMP = False
# Enable Pytorch automatic mixed precision (amp).
_C.AMP_ENABLE = True
# [Deprecated] Mixed precision opt level of apex, if O0, no apex amp is used ('O0', 'O1', 'O2')
_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
# for acceleration
_C.FUSED_WINDOW_PROCESS = False
_C.FUSED_LAYERNORM = 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('=> 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)
def _check_args(name):
if hasattr(args, name) and eval(f'args.{name}'):
return True
return False
# merge from specific arguments
if _check_args('batch_size'):
config.DATA.BATCH_SIZE = args.batch_size
if _check_args('data_path'):
config.DATA.DATA_PATH = args.data_path
if _check_args('zip'):
config.DATA.ZIP_MODE = True
if _check_args('cache_mode'):
config.DATA.CACHE_MODE = args.cache_mode
if _check_args('pretrained'):
config.MODEL.PRETRAINED = args.pretrained
if _check_args('resume'):
config.MODEL.RESUME = args.resume
if _check_args('accumulation_steps'):
config.TRAIN.ACCUMULATION_STEPS = args.accumulation_steps
if _check_args('use_checkpoint'):
config.TRAIN.USE_CHECKPOINT = True
if _check_args('amp_opt_level'):
print("[warning] Apex amp has been deprecated, please use pytorch amp instead!")
if args.amp_opt_level == 'O0':
config.AMP_ENABLE = False
if _check_args('disable_amp'):
config.AMP_ENABLE = False
if _check_args('output'):
config.OUTPUT = args.output
if _check_args('tag'):
config.TAG = args.tag
if _check_args('eval'):
config.EVAL_MODE = True
if _check_args('throughput'):
config.THROUGHPUT_MODE = True
# [SimMIM]
if _check_args('enable_amp'):
config.ENABLE_AMP = args.enable_amp
# for acceleration
if _check_args('fused_window_process'):
config.FUSED_WINDOW_PROCESS = True
if _check_args('fused_layernorm'):
config.FUSED_LAYERNORM = True
## Overwrite optimizer if not None, currently we use it for [fused_adam, fused_lamb]
if _check_args('optim'):
config.TRAIN.OPTIMIZER.NAME = args.optim
# set local rank for distributed training
if PYTORCH_MAJOR_VERSION == 1:
config.LOCAL_RANK = args.local_rank
else:
config.LOCAL_RANK = int(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