303 lines
10 KiB
Python
303 lines
10 KiB
Python
# --------------------------------------------------------
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# DCNv4
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# Copyright (c) 2024 OpenGVLab
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# Licensed under The MIT License [see LICENSE for details]
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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 = 'imagenet'
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# Input image size
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_C.DATA.IMG_SIZE = 224
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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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# Load data to memory
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_C.DATA.IMG_ON_MEMORY = False
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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 = 'INTERN_IMAGE'
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# Model name
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_C.MODEL.NAME = 'intern_image'
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# Pretrained weight from checkpoint, could be imagenet22k pretrained weight
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# could be overwritten by command line argument
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_C.MODEL.PRETRAINED = ''
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# Checkpoint to resume, could be overwritten by command line argument
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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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# Drop path type
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_C.MODEL.DROP_PATH_TYPE = 'linear' # linear, uniform
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# Label Smoothing
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_C.MODEL.LABEL_SMOOTHING = 0.1
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# INTERN_IMAGE parameters
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_C.MODEL.INTERN_IMAGE = CN()
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_C.MODEL.INTERN_IMAGE.DEPTHS = [4, 4, 18, 4]
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_C.MODEL.INTERN_IMAGE.GROUPS = [4, 8, 16, 32]
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_C.MODEL.INTERN_IMAGE.CHANNELS = 64
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_C.MODEL.INTERN_IMAGE.LAYER_SCALE = None
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_C.MODEL.INTERN_IMAGE.OFFSET_SCALE = 1.0
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_C.MODEL.INTERN_IMAGE.MLP_RATIO = 4.0
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_C.MODEL.INTERN_IMAGE.CORE_OP = 'DCNv3'
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_C.MODEL.INTERN_IMAGE.POST_NORM = False
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_C.MODEL.INTERN_IMAGE.RES_POST_NORM = False
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_C.MODEL.INTERN_IMAGE.DW_KERNEL_SIZE = None
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_C.MODEL.INTERN_IMAGE.USE_CLIP_PROJECTOR = False
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_C.MODEL.INTERN_IMAGE.LEVEL2_POST_NORM = False
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_C.MODEL.INTERN_IMAGE.LEVEL2_POST_NORM_BLOCK_IDS = None
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_C.MODEL.INTERN_IMAGE.CENTER_FEATURE_SCALE = False
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# FLASH_INTERN_IMAGE parameters
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_C.MODEL.FLASH_INTERN_IMAGE = CN()
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_C.MODEL.FLASH_INTERN_IMAGE.DEPTHS = [4, 4, 18, 4]
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_C.MODEL.FLASH_INTERN_IMAGE.GROUPS = [4, 8, 16, 32]
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_C.MODEL.FLASH_INTERN_IMAGE.CHANNELS = 64
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_C.MODEL.FLASH_INTERN_IMAGE.LAYER_SCALE = None
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_C.MODEL.FLASH_INTERN_IMAGE.OFFSET_SCALE = 1.0
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_C.MODEL.FLASH_INTERN_IMAGE.MLP_RATIO = 4.0
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_C.MODEL.FLASH_INTERN_IMAGE.CORE_OP = 'DCNv4'
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_C.MODEL.FLASH_INTERN_IMAGE.POST_NORM = False
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_C.MODEL.FLASH_INTERN_IMAGE.RES_POST_NORM = False
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_C.MODEL.FLASH_INTERN_IMAGE.DW_KERNEL_SIZE = None
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_C.MODEL.FLASH_INTERN_IMAGE.USE_CLIP_PROJECTOR = False
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_C.MODEL.FLASH_INTERN_IMAGE.LEVEL2_POST_NORM = False
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_C.MODEL.FLASH_INTERN_IMAGE.LEVEL2_POST_NORM_BLOCK_IDS = None
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_C.MODEL.FLASH_INTERN_IMAGE.CENTER_FEATURE_SCALE = False
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_C.MODEL.FLASH_INTERN_IMAGE.MLP_FC2_BIAS = False
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_C.MODEL.FLASH_INTERN_IMAGE.DCN_OUTPUT_BIAS = False
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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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# 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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# 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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# ZeRO
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_C.TRAIN.OPTIMIZER.USE_ZERO = False
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# freeze backbone
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_C.TRAIN.OPTIMIZER.FREEZE_BACKBONE = None
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# dcn lr
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_C.TRAIN.OPTIMIZER.DCN_LR_MUL = None
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# EMA
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_C.TRAIN.EMA = CN()
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_C.TRAIN.EMA.ENABLE = False
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_C.TRAIN.EMA.DECAY = 0.9998
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# LR_LAYER_DECAY
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_C.TRAIN.LR_LAYER_DECAY = False
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_C.TRAIN.LR_LAYER_DECAY_RATIO = 0.875
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# FT head init weights
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_C.TRAIN.RAND_INIT_FT_HEAD = False
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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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# RandomResizedCrop
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_C.AUG.RANDOM_RESIZED_CROP = False
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_C.AUG.MEAN = (0.485, 0.456, 0.406)
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_C.AUG.STD = (0.229, 0.224, 0.225)
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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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# Whether to use SequentialSampler as validation sampler
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_C.TEST.SEQUENTIAL = False
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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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# eval freq
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_C.EVAL_FREQ = 1
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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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_C.EVAL_22K_TO_1K = False
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_C.AMP_TYPE = 'float16'
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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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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 hasattr(args, 'opts') and 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 hasattr(args, 'batch_size') and args.batch_size:
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config.DATA.BATCH_SIZE = args.batch_size
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if hasattr(args, 'dataset') and args.dataset:
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config.DATA.DATASET = args.dataset
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if hasattr(args, 'data_path') and args.data_path:
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config.DATA.DATA_PATH = args.data_path
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if hasattr(args, 'zip') and args.zip:
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config.DATA.ZIP_MODE = True
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if hasattr(args, 'cache_mode') and args.cache_mode:
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config.DATA.CACHE_MODE = args.cache_mode
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if hasattr(args, 'pretrained') and args.pretrained:
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config.MODEL.PRETRAINED = args.pretrained
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if hasattr(args, 'resume') and args.resume:
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config.MODEL.RESUME = args.resume
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if hasattr(args, 'accumulation_steps') and args.accumulation_steps:
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config.TRAIN.ACCUMULATION_STEPS = args.accumulation_steps
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if hasattr(args, 'use_checkpoint') and args.use_checkpoint:
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config.TRAIN.USE_CHECKPOINT = True
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if hasattr(args, 'amp_opt_level') and args.amp_opt_level:
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config.AMP_OPT_LEVEL = args.amp_opt_level
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if hasattr(args, 'output') and args.output:
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config.OUTPUT = args.output
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if hasattr(args, 'tag') and args.tag:
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config.TAG = args.tag
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if hasattr(args, 'eval') and args.eval:
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config.EVAL_MODE = True
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if hasattr(args, 'throughput') and args.throughput:
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config.THROUGHPUT_MODE = True
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if hasattr(args, 'save_ckpt_num') and args.save_ckpt_num:
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config.SAVE_CKPT_NUM = args.save_ckpt_num
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if hasattr(args, 'use_zero') and args.use_zero:
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config.TRAIN.OPTIMIZER.USE_ZERO = True
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# set local rank for distributed training
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if hasattr(args, 'local_rank') and args.local_rank:
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config.LOCAL_RANK = args.local_rank
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# output folder
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config.MODEL.NAME = args.cfg.split('/')[-1].replace('.yaml', '')
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config.OUTPUT = os.path.join(config.OUTPUT, config.MODEL.NAME)
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# config.OUTPUT = os.path.join(config.OUTPUT, config.MODEL.NAME, config.TAG)
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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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