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hackassin
GitHub Repository: hackassin/learnopencv
 Path: blob/master/FaceMaskOverlay/lib/config/defaults.py
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# ------------------------------------------------------------------------------

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# Copyright (c) Microsoft

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# Licensed under the MIT License.

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# Written by Bin Xiao ([email protected])

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# Modified by Ke Sun ([email protected]), Tianheng Cheng([email protected])

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# ------------------------------------------------------------------------------

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from __future__ import absolute_import

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from __future__ import division

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from __future__ import print_function

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from yacs.config import CfgNode as CN

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_C = CN()

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_C.OUTPUT_DIR = 'output'

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_C.LOG_DIR = 'log'

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_C.GPUS = (0, 1, 2, 4)

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_C.WORKERS = 16

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_C.PRINT_FREQ = 20

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_C.AUTO_RESUME = False

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_C.PIN_MEMORY = True

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# Cudnn related params

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_C.CUDNN = CN()

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_C.CUDNN.BENCHMARK = True

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_C.CUDNN.DETERMINISTIC = False

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_C.CUDNN.ENABLED = True

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# common params for NETWORK

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_C.MODEL = CN()

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_C.MODEL.NAME = 'hrnet'

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_C.MODEL.INIT_WEIGHTS = True

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_C.MODEL.PRETRAINED = ''

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_C.MODEL.NUM_JOINTS = 17

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_C.MODEL.TARGET_TYPE = 'Gaussian'

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_C.MODEL.IMAGE_SIZE = [256, 256]  # width * height

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_C.MODEL.HEATMAP_SIZE = [64, 64]  # width * height

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_C.MODEL.MEAN = [0.485, 0.456, 0.406]

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_C.MODEL.STD = [0.229, 0.224, 0.225]

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_C.MODEL.SIGMA = 1.5

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_C.MODEL.EXTRA = CN()

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# High-Resoluion Net

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_C.MODEL.EXTRA.PRETRAINED_LAYERS = ['*']

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_C.MODEL.EXTRA.STEM_INPLANES = 64

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_C.MODEL.EXTRA.FINAL_CONV_KERNEL = 1

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_C.MODEL.EXTRA.WITH_HEAD = True

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_C.MODEL.EXTRA.STAGE2 = CN()

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_C.MODEL.EXTRA.STAGE2.NUM_MODULES = 1

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_C.MODEL.EXTRA.STAGE2.NUM_BRANCHES = 2

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_C.MODEL.EXTRA.STAGE2.NUM_BLOCKS = [4, 4]

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_C.MODEL.EXTRA.STAGE2.NUM_CHANNELS = [18, 36]

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_C.MODEL.EXTRA.STAGE2.BLOCK = 'BASIC'

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_C.MODEL.EXTRA.STAGE2.FUSE_METHOD = 'SUM'

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_C.MODEL.EXTRA.STAGE3 = CN()

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_C.MODEL.EXTRA.STAGE3.NUM_MODULES = 1

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_C.MODEL.EXTRA.STAGE3.NUM_BRANCHES = 3

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_C.MODEL.EXTRA.STAGE3.NUM_BLOCKS = [4, 4, 4]

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_C.MODEL.EXTRA.STAGE3.NUM_CHANNELS = [18, 36, 72]

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_C.MODEL.EXTRA.STAGE3.BLOCK = 'BASIC'

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_C.MODEL.EXTRA.STAGE3.FUSE_METHOD = 'SUM'

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_C.MODEL.EXTRA.STAGE4 = CN()

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_C.MODEL.EXTRA.STAGE4.NUM_MODULES = 1

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_C.MODEL.EXTRA.STAGE4.NUM_BRANCHES = 4

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_C.MODEL.EXTRA.STAGE4.NUM_BLOCKS = [4, 4, 4, 4]

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_C.MODEL.EXTRA.STAGE4.NUM_CHANNELS = [18, 32, 72, 144]

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_C.MODEL.EXTRA.STAGE4.BLOCK = 'BASIC'

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_C.MODEL.EXTRA.STAGE4.FUSE_METHOD = 'SUM'

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# DATASET related params

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_C.DATASET = CN()

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_C.DATASET.ROOT = ''

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_C.DATASET.DATASET = 'AFLW'

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_C.DATASET.TRAINSET = ''

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_C.DATASET.TESTSET = ''

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# training data augmentation

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_C.DATASET.FLIP = True

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_C.DATASET.SCALE_FACTOR = 0.25

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_C.DATASET.ROT_FACTOR = 30

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# train

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_C.TRAIN = CN()

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_C.TRAIN.LR_FACTOR = 0.1

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_C.TRAIN.LR_STEP = [30, 50]

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_C.TRAIN.LR = 0.0001

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_C.TRAIN.OPTIMIZER = 'adam'

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_C.TRAIN.MOMENTUM = 0.0

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_C.TRAIN.WD = 0.0

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_C.TRAIN.NESTEROV = False

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_C.TRAIN.BEGIN_EPOCH = 0

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_C.TRAIN.END_EPOCH = 60

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_C.TRAIN.RESUME = True

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_C.TRAIN.CHECKPOINT = ''

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_C.TRAIN.BATCH_SIZE_PER_GPU = 16

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_C.TRAIN.SHUFFLE = True

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# testing

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_C.TEST = CN()

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# size of images for each device

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_C.TEST.BATCH_SIZE_PER_GPU = 32

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def update_config(cfg, args):

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    cfg.defrost()

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    cfg.merge_from_file(args.cfg)

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    cfg.freeze()

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if __name__ == '__main__':

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    import sys

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    with open(sys.argv[1], 'w') as f:

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        print(_C, file=f)

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