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 = 16
# Path to dataset, could be overwritten by command line argument
_C.DATA.DATA_PATH = ''
# Dataset name
_C.DATA.DATASET = 'toyce'
# Interpolation to resize image (random, bilinear, bicubic)
_C.DATA.INTERPOLATION = 'bicubic'
# Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.
_C.DATA.PIN_MEMORY = False
# Number of data loading threads
_C.DATA.NUM_WORKERS = 8

# -----------------------------------------------------------------------------
# Model settings
# -----------------------------------------------------------------------------
_C.MODEL = CN()
# Model type
_C.MODEL.ENCODER = 'VGG'
# Model name
_C.MODEL.DECODER = 'PSCC'
_C.MODEL.NAME = f'{_C.MODEL.ENCODER}_{_C.MODEL.DECODER}'
# Checkpoint to resume, could be overwritten by command line argument
_C.MODEL.RESUME = ''
_C.MODEL.FACTOR = 1.

# -----------------------------------------------------------------------------
# Training settings
# -----------------------------------------------------------------------------
_C.TRAIN = CN()
_C.TRAIN.START_EPOCH = 0
_C.TRAIN.EPOCHS = 200
_C.TRAIN.WARMUP_EPOCHS = int(_C.TRAIN.EPOCHS * 0.1)
_C.TRAIN.WEIGHT_DECAY = 0.95

scale = 1
_C.TRAIN.BASE_LR = 2e-5 * scale
_C.TRAIN.WARMUP_LR = 5e-7 * scale
_C.TRAIN.MIN_LR = 5e-7 * scale
# 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 = 0.5
# 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
# -----------------------------------------------------------------------------

# -----------------------------------------------------------------------------
# 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 = 'O1'
# 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 = 5
# Frequency to logging info
_C.PRINT_FREQ =50
# 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


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):

    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.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.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

    # output folder
    config.OUTPUT = os.path.join('exp', config.TAG, 'output')

    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