| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
|
|
| import os.path as op |
| import yaml |
| from yacs.config import CfgNode as CN |
|
|
| |
| import comm |
|
|
| _C = CN() |
|
|
| _C.BASE = [''] |
| _C.NAME = '' |
| _C.DATA_DIR = '' |
| _C.DIST_BACKEND = 'nccl' |
| _C.GPUS = (0,) |
| |
| _C.MULTIPROCESSING_DISTRIBUTED = True |
| _C.OUTPUT_DIR = '' |
| _C.PIN_MEMORY = True |
| _C.PRINT_FREQ = 20 |
| _C.RANK = 0 |
| _C.VERBOSE = True |
| _C.WORKERS = 4 |
| _C.MODEL_SUMMARY = False |
|
|
| _C.AMP = CN() |
| _C.AMP.ENABLED = False |
| _C.AMP.MEMORY_FORMAT = 'nchw' |
|
|
| |
| _C.CUDNN = CN() |
| _C.CUDNN.BENCHMARK = True |
| _C.CUDNN.DETERMINISTIC = False |
| _C.CUDNN.ENABLED = True |
|
|
| |
| _C.MODEL = CN() |
| _C.MODEL.NAME = 'cls_hrnet' |
| _C.MODEL.INIT_WEIGHTS = True |
| _C.MODEL.PRETRAINED = '' |
| _C.MODEL.PRETRAINED_LAYERS = ['*'] |
| _C.MODEL.NUM_CLASSES = 1000 |
| _C.MODEL.SPEC = CN(new_allowed=True) |
|
|
| _C.LOSS = CN(new_allowed=True) |
| _C.LOSS.LABEL_SMOOTHING = 0.0 |
| _C.LOSS.LOSS = 'softmax' |
|
|
| |
| _C.DATASET = CN() |
| _C.DATASET.ROOT = '' |
| _C.DATASET.DATASET = 'imagenet' |
| _C.DATASET.TRAIN_SET = 'train' |
| _C.DATASET.TEST_SET = 'val' |
| _C.DATASET.DATA_FORMAT = 'jpg' |
| _C.DATASET.LABELMAP = '' |
| _C.DATASET.TRAIN_TSV_LIST = [] |
| _C.DATASET.TEST_TSV_LIST = [] |
| _C.DATASET.SAMPLER = 'default' |
|
|
| _C.DATASET.TARGET_SIZE = -1 |
|
|
| |
| _C.INPUT = CN() |
| _C.INPUT.MEAN = [0.485, 0.456, 0.406] |
| _C.INPUT.STD = [0.229, 0.224, 0.225] |
|
|
| |
| _C.AUG = CN() |
| _C.AUG.SCALE = (0.08, 1.0) |
| _C.AUG.RATIO = (3.0/4.0, 4.0/3.0) |
| _C.AUG.COLOR_JITTER = [0.4, 0.4, 0.4, 0.1, 0.0] |
| _C.AUG.GRAY_SCALE = 0.0 |
| _C.AUG.GAUSSIAN_BLUR = 0.0 |
| _C.AUG.DROPBLOCK_LAYERS = [3, 4] |
| _C.AUG.DROPBLOCK_KEEP_PROB = 1.0 |
| _C.AUG.DROPBLOCK_BLOCK_SIZE = 7 |
| _C.AUG.MIXUP_PROB = 0.0 |
| _C.AUG.MIXUP = 0.0 |
| _C.AUG.MIXCUT = 0.0 |
| _C.AUG.MIXCUT_MINMAX = [] |
| _C.AUG.MIXUP_SWITCH_PROB = 0.5 |
| _C.AUG.MIXUP_MODE = 'batch' |
| _C.AUG.MIXCUT_AND_MIXUP = False |
| _C.AUG.INTERPOLATION = 2 |
| _C.AUG.TIMM_AUG = CN(new_allowed=True) |
| _C.AUG.TIMM_AUG.USE_LOADER = False |
| _C.AUG.TIMM_AUG.USE_TRANSFORM = False |
|
|
| |
| _C.TRAIN = CN() |
|
|
| _C.TRAIN.AUTO_RESUME = True |
| _C.TRAIN.CHECKPOINT = '' |
| _C.TRAIN.LR_SCHEDULER = CN(new_allowed=True) |
| _C.TRAIN.SCALE_LR = True |
| _C.TRAIN.LR = 0.001 |
|
|
| _C.TRAIN.OPTIMIZER = 'sgd' |
| _C.TRAIN.OPTIMIZER_ARGS = CN(new_allowed=True) |
| _C.TRAIN.MOMENTUM = 0.9 |
| _C.TRAIN.WD = 0.0001 |
| _C.TRAIN.WITHOUT_WD_LIST = [] |
| _C.TRAIN.NESTEROV = True |
| |
| _C.TRAIN.GAMMA1 = 0.99 |
| _C.TRAIN.GAMMA2 = 0.0 |
|
|
| _C.TRAIN.BEGIN_EPOCH = 0 |
| _C.TRAIN.END_EPOCH = 100 |
|
|
| _C.TRAIN.IMAGE_SIZE = [224, 224] |
| _C.TRAIN.BATCH_SIZE_PER_GPU = 32 |
| _C.TRAIN.SHUFFLE = True |
|
|
| _C.TRAIN.EVAL_BEGIN_EPOCH = 0 |
|
|
| _C.TRAIN.DETECT_ANOMALY = False |
|
|
| _C.TRAIN.CLIP_GRAD_NORM = 0.0 |
| _C.TRAIN.SAVE_ALL_MODELS = False |
|
|
| |
| _C.TEST = CN() |
|
|
| |
| _C.TEST.BATCH_SIZE_PER_GPU = 32 |
| _C.TEST.CENTER_CROP = True |
| _C.TEST.IMAGE_SIZE = [224, 224] |
| _C.TEST.INTERPOLATION = 2 |
| _C.TEST.MODEL_FILE = '' |
| _C.TEST.REAL_LABELS = False |
| _C.TEST.VALID_LABELS = '' |
|
|
| _C.FINETUNE = CN() |
| _C.FINETUNE.FINETUNE = False |
| _C.FINETUNE.USE_TRAIN_AUG = False |
| _C.FINETUNE.BASE_LR = 0.003 |
| _C.FINETUNE.BATCH_SIZE = 512 |
| _C.FINETUNE.EVAL_EVERY = 3000 |
| _C.FINETUNE.TRAIN_MODE = True |
| |
| _C.FINETUNE.FROZEN_LAYERS = [] |
| _C.FINETUNE.LR_SCHEDULER = CN(new_allowed=True) |
| _C.FINETUNE.LR_SCHEDULER.DECAY_TYPE = 'step' |
|
|
| |
| _C.DEBUG = CN() |
| _C.DEBUG.DEBUG = 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, op.join(op.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() |
| config.merge_from_list(args.opts) |
| if config.TRAIN.SCALE_LR: |
| config.TRAIN.LR *= comm.world_size |
| file_name, _ = op.splitext(op.basename(args.cfg)) |
| config.NAME = file_name + config.NAME |
| config.RANK = comm.rank |
|
|
| if 'timm' == config.TRAIN.LR_SCHEDULER.METHOD: |
| config.TRAIN.LR_SCHEDULER.ARGS.epochs = config.TRAIN.END_EPOCH |
|
|
| if 'timm' == config.TRAIN.OPTIMIZER: |
| config.TRAIN.OPTIMIZER_ARGS.lr = config.TRAIN.LR |
|
|
| aug = config.AUG |
| if aug.MIXUP > 0.0 or aug.MIXCUT > 0.0 or aug.MIXCUT_MINMAX: |
| aug.MIXUP_PROB = 1.0 |
| config.freeze() |
|
|
|
|
| def save_config(cfg, path): |
| if comm.is_main_process(): |
| with open(path, 'w') as f: |
| f.write(cfg.dump()) |
|
|
|
|
| if __name__ == '__main__': |
| import sys |
| with open(sys.argv[1], 'w') as f: |
| print(_C, file=f) |
