Create default.py

default.py

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+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
+
+from config import comm
+
+
+_C = CN()
+
+_C.BASE = ['']
+_C.NAME = ''
+_C.DATA_DIR = ''
+_C.DIST_BACKEND = 'nccl'
+_C.GPUS = (0,)
+# _C.LOG_DIR = ''
+_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'
+
+# Cudnn related params
+_C.CUDNN = CN()
+_C.CUDNN.BENCHMARK = True
+_C.CUDNN.DETERMINISTIC = False
+_C.CUDNN.ENABLED = True
+
+# common params for NETWORK
+_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'
+
+# DATASET related params
+_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
+
+# training data augmentation
+_C.INPUT = CN()
+_C.INPUT.MEAN = [0.485, 0.456, 0.406]
+_C.INPUT.STD = [0.229, 0.224, 0.225]
+
+# data augmentation
+_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
+
+# train
+_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
+# for adam
+_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]  # width * height, ex: 192 * 256
+_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
+
+# testing
+_C.TEST = CN()
+
+# size of images for each device
+_C.TEST.BATCH_SIZE_PER_GPU = 32
+_C.TEST.CENTER_CROP = True
+_C.TEST.IMAGE_SIZE = [224, 224]  # width * height, ex: 192 * 256
+_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.MODEL_FILE = ''
+_C.FINETUNE.FROZEN_LAYERS = []
+_C.FINETUNE.LR_SCHEDULER = CN(new_allowed=True)
+_C.FINETUNE.LR_SCHEDULER.DECAY_TYPE = 'step'
+
+# debug
+_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)