230 lines
7.4 KiB
Python
230 lines
7.4 KiB
Python
# --------------------------------------------------------
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# Swin Transformer
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# Copyright (c) 2021 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Ze Liu
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# --------------------------------------------------------'
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import os
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import yaml
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from yacs.config import CfgNode as CN
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_C = CN()
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# Base config files
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_C.BASE = ['']
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# # -----------------------------------------------------------------------------
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# # Data settings
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# # -----------------------------------------------------------------------------
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_C.DATA = CN()
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# # Batch size for a single GPU, could be overwritten by command line argument
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# _C.DATA.BATCH_SIZE = 128
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# # Path to dataset, could be overwritten by command line argument
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# _C.DATA.DATA_PATH = ''
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# # Dataset name
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_C.DATA.DATASET = 'mypv'
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# Input image size
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_C.DATA.IMG_SIZE = 512
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# # Interpolation to resize image (random, bilinear, bicubic)
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# _C.DATA.INTERPOLATION = 'bicubic'
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# # Use zipped dataset instead of folder dataset
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# # could be overwritten by command line argument
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# _C.DATA.ZIP_MODE = False
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# # Cache Data in Memory, could be overwritten by command line argument
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# _C.DATA.CACHE_MODE = 'part'
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# # Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.
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# _C.DATA.PIN_MEMORY = True
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# # Number of data loading threads
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# _C.DATA.NUM_WORKERS = 8
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# -----------------------------------------------------------------------------
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# Model settings
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# -----------------------------------------------------------------------------
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_C.MODEL = CN()
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# Model type
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_C.MODEL.TYPE = 'swin'
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# Model name
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_C.MODEL.NAME = 'swin_tiny_patch4_window7_224'
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# Checkpoint to resume, could be overwritten by command line argument
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_C.MODEL.PRETRAIN_CKPT = './pretrained_ckpt/swin_tiny_patch4_window7_224.pth'
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_C.MODEL.RESUME = ''
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# Number of classes, overwritten in data preparation
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_C.MODEL.NUM_CLASSES = 1000
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# Dropout rate
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_C.MODEL.DROP_RATE = 0.0
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# Drop path rate
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_C.MODEL.DROP_PATH_RATE = 0.1
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# Label Smoothing
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_C.MODEL.LABEL_SMOOTHING = 0.1
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# Swin Transformer parameters
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_C.MODEL.SWIN = CN()
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_C.MODEL.SWIN.PATCH_SIZE = 4
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_C.MODEL.SWIN.IN_CHANS = 3
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_C.MODEL.SWIN.EMBED_DIM = 96
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_C.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]
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_C.MODEL.SWIN.DECODER_DEPTHS = [2, 2, 6, 2]
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_C.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]
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_C.MODEL.SWIN.WINDOW_SIZE = 7
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_C.MODEL.SWIN.MLP_RATIO = 4.
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_C.MODEL.SWIN.QKV_BIAS = True
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_C.MODEL.SWIN.QK_SCALE = None
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_C.MODEL.SWIN.APE = False
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_C.MODEL.SWIN.PATCH_NORM = True
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_C.MODEL.SWIN.FINAL_UPSAMPLE= "expand_first"
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# # -----------------------------------------------------------------------------
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# # Training settings
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# # -----------------------------------------------------------------------------
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_C.TRAIN = CN()
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# _C.TRAIN.START_EPOCH = 0
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# _C.TRAIN.EPOCHS = 300
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# _C.TRAIN.WARMUP_EPOCHS = 20
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# _C.TRAIN.WEIGHT_DECAY = 0.05
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# _C.TRAIN.BASE_LR = 5e-4
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# _C.TRAIN.WARMUP_LR = 5e-7
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# _C.TRAIN.MIN_LR = 5e-6
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# # Clip gradient norm
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# _C.TRAIN.CLIP_GRAD = 5.0
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# # Auto resume from latest checkpoint
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# _C.TRAIN.AUTO_RESUME = True
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# # Gradient accumulation steps
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# # could be overwritten by command line argument
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# _C.TRAIN.ACCUMULATION_STEPS = 0
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# # Whether to use gradient checkpointing to save memory
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# # could be overwritten by command line argument
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_C.TRAIN.USE_CHECKPOINT = False
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#
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# # LR scheduler
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# _C.TRAIN.LR_SCHEDULER = CN()
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# _C.TRAIN.LR_SCHEDULER.NAME = 'cosine'
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# # Epoch interval to decay LR, used in StepLRScheduler
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# _C.TRAIN.LR_SCHEDULER.DECAY_EPOCHS = 30
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# # LR decay rate, used in StepLRScheduler
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# _C.TRAIN.LR_SCHEDULER.DECAY_RATE = 0.1
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#
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# # Optimizer
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# _C.TRAIN.OPTIMIZER = CN()
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# _C.TRAIN.OPTIMIZER.NAME = 'adamw'
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# # Optimizer Epsilon
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# _C.TRAIN.OPTIMIZER.EPS = 1e-8
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# # Optimizer Betas
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# _C.TRAIN.OPTIMIZER.BETAS = (0.9, 0.999)
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# # SGD momentum
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# _C.TRAIN.OPTIMIZER.MOMENTUM = 0.9
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#
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# # -----------------------------------------------------------------------------
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# # Augmentation settings
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# # -----------------------------------------------------------------------------
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# _C.AUG = CN()
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# # Color jitter factor
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# _C.AUG.COLOR_JITTER = 0.4
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# # Use AutoAugment policy. "v0" or "original"
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# _C.AUG.AUTO_AUGMENT = 'rand-m9-mstd0.5-inc1'
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# # Random erase prob
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# _C.AUG.REPROB = 0.25
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# # Random erase mode
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# _C.AUG.REMODE = 'pixel'
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# # Random erase count
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# _C.AUG.RECOUNT = 1
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# # Mixup alpha, mixup enabled if > 0
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# _C.AUG.MIXUP = 0.8
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# # Cutmix alpha, cutmix enabled if > 0
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# _C.AUG.CUTMIX = 1.0
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# # Cutmix min/max ratio, overrides alpha and enables cutmix if set
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# _C.AUG.CUTMIX_MINMAX = None
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# # Probability of performing mixup or cutmix when either/both is enabled
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# _C.AUG.MIXUP_PROB = 1.0
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# # Probability of switching to cutmix when both mixup and cutmix enabled
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# _C.AUG.MIXUP_SWITCH_PROB = 0.5
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# # How to apply mixup/cutmix params. Per "batch", "pair", or "elem"
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# _C.AUG.MIXUP_MODE = 'batch'
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#
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# # -----------------------------------------------------------------------------
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# # Testing settings
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# # -----------------------------------------------------------------------------
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# _C.TEST = CN()
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# # Whether to use center crop when testing
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# _C.TEST.CROP = True
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#
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# # -----------------------------------------------------------------------------
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# # Misc
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# # -----------------------------------------------------------------------------
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# # Mixed precision opt level, if O0, no amp is used ('O0', 'O1', 'O2')
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# # overwritten by command line argument
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# _C.AMP_OPT_LEVEL = ''
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# # Path to output folder, overwritten by command line argument
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# _C.OUTPUT = ''
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# # Tag of experiment, overwritten by command line argument
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# _C.TAG = 'default'
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# # Frequency to save checkpoint
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# _C.SAVE_FREQ = 1
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# # Frequency to logging info
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# _C.PRINT_FREQ = 10
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# # Fixed random seed
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# _C.SEED = 0
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# # Perform evaluation only, overwritten by command line argument
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# _C.EVAL_MODE = False
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# # Test throughput only, overwritten by command line argument
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# _C.THROUGHPUT_MODE = False
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# # local rank for DistributedDataParallel, given by command line argument
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# _C.LOCAL_RANK = 0
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#
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def _update_config_from_file(config, cfg_file):
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config.defrost()
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with open(cfg_file, 'r') as f:
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yaml_cfg = yaml.load(f, Loader=yaml.FullLoader)
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for cfg in yaml_cfg.setdefault('BASE', ['']):
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if cfg:
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_update_config_from_file(
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config, os.path.join(os.path.dirname(cfg_file), cfg)
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)
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print('=> merge config from {}'.format(cfg_file))
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config.merge_from_file(cfg_file)
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config.freeze()
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def update_config(config, args):
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_update_config_from_file(config, args.cfg)
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config.defrost()
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if args.opts:
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config.merge_from_list(args.opts)
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# merge from specific arguments
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# if args.batch_size:
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# config.DATA.BATCH_SIZE = args.batch_size
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# if args.zip:
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# config.DATA.ZIP_MODE = True
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# if args.cache_mode:
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# config.DATA.CACHE_MODE = args.cache_mode
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# if args.resume:
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# config.MODEL.RESUME = args.resume
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# if args.accumulation_steps:
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# config.TRAIN.ACCUMULATION_STEPS = args.accumulation_steps
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# if args.use_checkpoint:
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# config.TRAIN.USE_CHECKPOINT = True
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# if args.amp_opt_level:
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# config.AMP_OPT_LEVEL = args.amp_opt_level
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# if args.tag:
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# config.TAG = args.tag
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# if args.eval:
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# config.EVAL_MODE = True
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# if args.throughput:
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# config.THROUGHPUT_MODE = True
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config.freeze()
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def get_config(args):
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"""Get a yacs CfgNode object with default values."""
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# Return a clone so that the defaults will not be altered
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# This is for the "local variable" use pattern
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config = _C.clone()
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update_config(config, args)
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return config
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