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config.py
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config.py
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# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------'
import os
import yaml
from yacs.config import CfgNode as CN
_C = CN()
# Base config files
_C.BASE = ['']
# -----------------------------------------------------------------------------
# Train settings
# -----------------------------------------------------------------------------\
_C.TOOLBOX_MODE = ""
_C.TRAIN = CN()
_C.TRAIN.EPOCHS = 50
_C.TRAIN.BATCH_SIZE = 4
_C.TRAIN.LR = 1e-4
# Optimizer
_C.TRAIN.OPTIMIZER = CN()
# Optimizer Epsilon
_C.TRAIN.OPTIMIZER.EPS = 1e-4
# Optimizer Betas
_C.TRAIN.OPTIMIZER.BETAS = (0.9, 0.999)
# SGD momentum
_C.TRAIN.OPTIMIZER.MOMENTUM = 0.9
_C.TRAIN.MODEL_FILE_NAME = ''
# Train.Data settings
_C.TRAIN.DATA = CN()
_C.TRAIN.DATA.FS = 0
_C.TRAIN.DATA.DATA_PATH = ''
_C.TRAIN.DATA.EXP_DATA_NAME = ''
_C.TRAIN.DATA.CACHED_PATH = 'PreprocessedData'
_C.TRAIN.DATA.FILE_LIST_PATH = os.path.join(_C.TRAIN.DATA.CACHED_PATH, 'DataFileLists')
_C.TRAIN.DATA.DATASET = ''
_C.TRAIN.DATA.DO_PREPROCESS = False
_C.TRAIN.DATA.DATA_FORMAT = 'NDCHW'
_C.TRAIN.DATA.BEGIN = 0.0
_C.TRAIN.DATA.END = 1.0
# Train Data preprocessing
_C.TRAIN.DATA.PREPROCESS = CN()
_C.TRAIN.DATA.PREPROCESS.DO_CHUNK = True
_C.TRAIN.DATA.PREPROCESS.CHUNK_LENGTH = 180
_C.TRAIN.DATA.PREPROCESS.DYNAMIC_DETECTION = True
_C.TRAIN.DATA.PREPROCESS.DYNAMIC_DETECTION_FREQUENCY = 180
_C.TRAIN.DATA.PREPROCESS.CROP_FACE = True
_C.TRAIN.DATA.PREPROCESS.LARGE_FACE_BOX = True
_C.TRAIN.DATA.PREPROCESS.LARGE_BOX_COEF = 1.5
_C.TRAIN.DATA.PREPROCESS.W = 128
_C.TRAIN.DATA.PREPROCESS.H = 128
_C.TRAIN.DATA.PREPROCESS.DATA_TYPE = ['']
_C.TRAIN.DATA.PREPROCESS.LABEL_TYPE = ''
# -----------------------------------------------------------------------------
# Valid settings
# -----------------------------------------------------------------------------\
_C.VALID = CN()
# Valid.Data settings
_C.VALID.DATA = CN()
_C.VALID.DATA.FS = 0
_C.VALID.DATA.DATA_PATH = ''
_C.VALID.DATA.EXP_DATA_NAME = ''
_C.VALID.DATA.CACHED_PATH = 'PreprocessedData'
_C.VALID.DATA.FILE_LIST_PATH = os.path.join(_C.VALID.DATA.CACHED_PATH, 'DataFileLists')
_C.VALID.DATA.DATASET = ''
_C.VALID.DATA.DO_PREPROCESS = False
_C.VALID.DATA.DATA_FORMAT = 'NDCHW'
_C.VALID.DATA.BEGIN = 0.0
_C.VALID.DATA.END = 1.0
# Valid Data preprocessing
_C.VALID.DATA.PREPROCESS = CN()
_C.VALID.DATA.PREPROCESS.DO_CHUNK = True
_C.VALID.DATA.PREPROCESS.CHUNK_LENGTH = 180
_C.VALID.DATA.PREPROCESS.DYNAMIC_DETECTION = True
_C.VALID.DATA.PREPROCESS.DYNAMIC_DETECTION_FREQUENCY = 180
_C.VALID.DATA.PREPROCESS.CROP_FACE = True
_C.VALID.DATA.PREPROCESS.LARGE_FACE_BOX = True
_C.VALID.DATA.PREPROCESS.LARGE_BOX_COEF = 1.5
_C.VALID.DATA.PREPROCESS.W = 128
_C.VALID.DATA.PREPROCESS.H = 128
_C.VALID.DATA.PREPROCESS.DATA_TYPE = ['']
_C.VALID.DATA.PREPROCESS.LABEL_TYPE = ''
# -----------------------------------------------------------------------------
# Test settings
# -----------------------------------------------------------------------------\
_C.TEST = CN()
_C.TEST.METRICS = []
_C.TEST.USE_LAST_EPOCH = True
# Test.Data settings
_C.TEST.DATA = CN()
_C.TEST.DATA.FS = 0
_C.TEST.DATA.DATA_PATH = ''
_C.TEST.DATA.EXP_DATA_NAME = ''
_C.TEST.DATA.CACHED_PATH = 'PreprocessedData'
_C.TEST.DATA.FILE_LIST_PATH = os.path.join(_C.TEST.DATA.CACHED_PATH, 'DataFileLists')
_C.TEST.DATA.DATASET = ''
_C.TEST.DATA.DO_PREPROCESS = False
_C.TEST.DATA.DATA_FORMAT = 'NDCHW'
_C.TEST.DATA.BEGIN = 0.0
_C.TEST.DATA.END = 1.0
# Test Data preprocessing
_C.TEST.DATA.PREPROCESS = CN()
_C.TEST.DATA.PREPROCESS.DO_CHUNK = True
_C.TEST.DATA.PREPROCESS.CHUNK_LENGTH = 180
_C.TEST.DATA.PREPROCESS.DYNAMIC_DETECTION = True
_C.TEST.DATA.PREPROCESS.DYNAMIC_DETECTION_FREQUENCY = 180
_C.TEST.DATA.PREPROCESS.CROP_FACE = True
_C.TEST.DATA.PREPROCESS.LARGE_FACE_BOX = True
_C.TEST.DATA.PREPROCESS.LARGE_BOX_COEF = 1.5
_C.TEST.DATA.PREPROCESS.W = 128
_C.TEST.DATA.PREPROCESS.H = 128
_C.TEST.DATA.PREPROCESS.DATA_TYPE = ['']
_C.TEST.DATA.PREPROCESS.LABEL_TYPE = ''
# -----------------------------------------------------------------------------
# Unsupervised method settings
# -----------------------------------------------------------------------------\
_C.UNSUPERVISED = CN()
_C.UNSUPERVISED.METHOD = []
_C.UNSUPERVISED.METRICS = []
# Unsupervised.Data settings
_C.UNSUPERVISED.DATA = CN()
_C.UNSUPERVISED.DATA.FS = 0
_C.UNSUPERVISED.DATA.DATA_PATH = ''
_C.UNSUPERVISED.DATA.EXP_DATA_NAME = ''
_C.UNSUPERVISED.DATA.CACHED_PATH = 'PreprocessedData'
_C.UNSUPERVISED.DATA.FILE_LIST_PATH = os.path.join(_C.UNSUPERVISED.DATA.CACHED_PATH, 'DataFileLists')
_C.UNSUPERVISED.DATA.DATASET = ''
_C.UNSUPERVISED.DATA.DO_PREPROCESS = False
_C.UNSUPERVISED.DATA.DATA_FORMAT = 'NDCHW'
_C.UNSUPERVISED.DATA.BEGIN = 0.0
_C.UNSUPERVISED.DATA.END = 1.0
# Unsupervised Data preprocessing
_C.UNSUPERVISED.DATA.PREPROCESS = CN()
_C.UNSUPERVISED.DATA.PREPROCESS.DO_CHUNK = True
_C.UNSUPERVISED.DATA.PREPROCESS.CHUNK_LENGTH = 180
_C.UNSUPERVISED.DATA.PREPROCESS.DYNAMIC_DETECTION = True
_C.UNSUPERVISED.DATA.PREPROCESS.DYNAMIC_DETECTION_FREQUENCY = 180
_C.UNSUPERVISED.DATA.PREPROCESS.CROP_FACE = True
_C.UNSUPERVISED.DATA.PREPROCESS.LARGE_FACE_BOX = True
_C.UNSUPERVISED.DATA.PREPROCESS.LARGE_BOX_COEF = 1.5
_C.UNSUPERVISED.DATA.PREPROCESS.W = 128
_C.UNSUPERVISED.DATA.PREPROCESS.H = 128
_C.UNSUPERVISED.DATA.PREPROCESS.DATA_TYPE = ['']
_C.UNSUPERVISED.DATA.PREPROCESS.LABEL_TYPE = ''
### -----------------------------------------------------------------------------
# Model settings
# -----------------------------------------------------------------------------
_C.MODEL = CN()
# Model name
_C.MODEL.NAME = ''
# Checkpoint to resume, could be overwritten by command line argument
_C.MODEL.RESUME = ''
# Dropout rate
_C.MODEL.DROP_RATE = 0.0
_C.MODEL.MODEL_DIR = 'PreTrainedModels'
# Specific parameters for physnet parameters
_C.MODEL.PHYSNET = CN()
_C.MODEL.PHYSNET.FRAME_NUM = 64
# -----------------------------------------------------------------------------
# Model Settings for TS-CAN
# -----------------------------------------------------------------------------
_C.MODEL.TSCAN = CN()
_C.MODEL.TSCAN.FRAME_DEPTH = 10
# -----------------------------------------------------------------------------
# Model Settings for EfficientPhys
# -----------------------------------------------------------------------------
_C.MODEL.EFFICIENTPHYS = CN()
_C.MODEL.EFFICIENTPHYS.FRAME_DEPTH = 10
# -----------------------------------------------------------------------------
# Inference settings
# -----------------------------------------------------------------------------
_C.INFERENCE = CN()
_C.INFERENCE.BATCH_SIZE = 4
_C.INFERENCE.EVALUATION_METHOD = 'FFT'
_C.INFERENCE.MODEL_PATH = ''
# -----------------------------------------------------------------------------
# Device settings
# -----------------------------------------------------------------------------
_C.DEVICE = "cuda:0"
_C.NUM_OF_GPU_TRAIN = 1
# -----------------------------------------------------------------------------
# Log settings
# -----------------------------------------------------------------------------
_C.LOG = CN()
_C.LOG.PATH = "runs/exp"
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, os.path.join(os.path.dirname(cfg_file), cfg)
)
print('=> Merging a config file from {}'.format(cfg_file))
config.merge_from_file(cfg_file)
config.freeze()
def update_config(config, args):
# store default file list path for checking against later
default_TRAIN_FILE_LIST_PATH = config.TRAIN.DATA.FILE_LIST_PATH
default_VALID_FILE_LIST_PATH = config.VALID.DATA.FILE_LIST_PATH
default_TEST_FILE_LIST_PATH = config.TEST.DATA.FILE_LIST_PATH
default_UNSUPERVISED_FILE_LIST_PATH = config.UNSUPERVISED.DATA.FILE_LIST_PATH
# update flag from config file
_update_config_from_file(config, args.config_file)
config.defrost()
# UPDATE TRAIN PATHS
if config.TRAIN.DATA.FILE_LIST_PATH == default_TRAIN_FILE_LIST_PATH:
config.TRAIN.DATA.FILE_LIST_PATH = os.path.join(config.TRAIN.DATA.CACHED_PATH, 'DataFileLists')
if config.TRAIN.DATA.EXP_DATA_NAME == '':
config.TRAIN.DATA.EXP_DATA_NAME = "_".join([config.TRAIN.DATA.DATASET, "SizeW{0}".format(
str(config.TRAIN.DATA.PREPROCESS.W)), "SizeH{0}".format(str(config.TRAIN.DATA.PREPROCESS.W)), "ClipLength{0}".format(
str(config.TRAIN.DATA.PREPROCESS.CHUNK_LENGTH)), "DataType{0}".format("_".join(config.TRAIN.DATA.PREPROCESS.DATA_TYPE)),
"LabelType{0}".format(config.TRAIN.DATA.PREPROCESS.LABEL_TYPE),
"Large_box{0}".format(config.TRAIN.DATA.PREPROCESS.LARGE_FACE_BOX),
"Large_size{0}".format(config.TRAIN.DATA.PREPROCESS.LARGE_BOX_COEF),
"Dyamic_Det{0}".format(config.TRAIN.DATA.PREPROCESS.DYNAMIC_DETECTION),
"det_len{0}".format(config.TRAIN.DATA.PREPROCESS.DYNAMIC_DETECTION_FREQUENCY )
])
config.TRAIN.DATA.CACHED_PATH = os.path.join(config.TRAIN.DATA.CACHED_PATH, config.TRAIN.DATA.EXP_DATA_NAME)
name, ext = os.path.splitext(config.TRAIN.DATA.FILE_LIST_PATH)
if not ext: # no file extension
config.TRAIN.DATA.FILE_LIST_PATH = os.path.join(config.TRAIN.DATA.FILE_LIST_PATH, \
config.TRAIN.DATA.EXP_DATA_NAME + '_' + \
str(config.TRAIN.DATA.BEGIN) + '_' + \
str(config.TRAIN.DATA.END) + '.csv')
elif ext != '.csv':
raise ValueError('TRAIN dataset FILE_LIST_PATH must either be a directory path or a .csv file name')
if ext == '.csv' and config.TRAIN.DATA.DO_PREPROCESS:
raise ValueError('User specified TRAIN dataset FILE_LIST_PATH .csv file already exists. \
Please turn DO_PREPROCESS to False or delete existing TRAIN dataset FILE_LIST_PATH .csv file.')
if not config.TEST.USE_LAST_EPOCH and config.VALID.DATA.DATASET is not None:
# UPDATE VALID PATHS
if config.VALID.DATA.FILE_LIST_PATH == default_VALID_FILE_LIST_PATH:
config.VALID.DATA.FILE_LIST_PATH = os.path.join(config.VALID.DATA.CACHED_PATH, 'DataFileLists')
if config.VALID.DATA.EXP_DATA_NAME == '':
config.VALID.DATA.EXP_DATA_NAME = "_".join([config.VALID.DATA.DATASET, "SizeW{0}".format(
str(config.VALID.DATA.PREPROCESS.W)), "SizeH{0}".format(str(config.VALID.DATA.PREPROCESS.W)), "ClipLength{0}".format(
str(config.VALID.DATA.PREPROCESS.CHUNK_LENGTH)), "DataType{0}".format("_".join(config.VALID.DATA.PREPROCESS.DATA_TYPE)),
"LabelType{0}".format(config.VALID.DATA.PREPROCESS.LABEL_TYPE),
"Large_box{0}".format(config.VALID.DATA.PREPROCESS.LARGE_FACE_BOX),
"Large_size{0}".format(config.VALID.DATA.PREPROCESS.LARGE_BOX_COEF),
"Dyamic_Det{0}".format(config.VALID.DATA.PREPROCESS.DYNAMIC_DETECTION),
"det_len{0}".format(config.VALID.DATA.PREPROCESS.DYNAMIC_DETECTION_FREQUENCY )
])
config.VALID.DATA.CACHED_PATH = os.path.join(config.VALID.DATA.CACHED_PATH, config.VALID.DATA.EXP_DATA_NAME)
name, ext = os.path.splitext(config.VALID.DATA.FILE_LIST_PATH)
if not ext: # no file extension
config.VALID.DATA.FILE_LIST_PATH = os.path.join(config.VALID.DATA.FILE_LIST_PATH, \
config.VALID.DATA.EXP_DATA_NAME + '_' + \
str(config.VALID.DATA.BEGIN) + '_' + \
str(config.VALID.DATA.END) + '.csv')
elif ext != '.csv':
raise ValueError('VALIDATION dataset FILE_LIST_PATH must either be a directory path or a .csv file name')
if ext == '.csv' and config.VALID.DATA.DO_PREPROCESS:
raise ValueError('User specified VALIDATION dataset FILE_LIST_PATH .csv file already exists. \
Please turn DO_PREPROCESS to False or delete existing VALIDATION dataset FILE_LIST_PATH .csv file.')
elif not config.TEST.USE_LAST_EPOCH and config.VALID.DATA.DATASET is None:
raise ValueError('VALIDATION dataset is not provided despite USE_LAST_EPOCH being False!')
# UPDATE TEST PATHS
if config.TEST.DATA.FILE_LIST_PATH == default_TEST_FILE_LIST_PATH:
config.TEST.DATA.FILE_LIST_PATH = os.path.join(config.TEST.DATA.CACHED_PATH, 'DataFileLists')
if config.TEST.DATA.EXP_DATA_NAME == '':
config.TEST.DATA.EXP_DATA_NAME = "_".join([config.TEST.DATA.DATASET, "SizeW{0}".format(
str(config.TEST.DATA.PREPROCESS.W)), "SizeH{0}".format(str(config.TEST.DATA.PREPROCESS.W)), "ClipLength{0}".format(
str(config.TEST.DATA.PREPROCESS.CHUNK_LENGTH)), "DataType{0}".format("_".join(config.TEST.DATA.PREPROCESS.DATA_TYPE)),
"LabelType{0}".format(config.TEST.DATA.PREPROCESS.LABEL_TYPE),
"Large_box{0}".format(config.TEST.DATA.PREPROCESS.LARGE_FACE_BOX),
"Large_size{0}".format(config.TEST.DATA.PREPROCESS.LARGE_BOX_COEF),
"Dyamic_Det{0}".format(config.TEST.DATA.PREPROCESS.DYNAMIC_DETECTION),
"det_len{0}".format(config.TEST.DATA.PREPROCESS.DYNAMIC_DETECTION_FREQUENCY )
])
config.TEST.DATA.CACHED_PATH = os.path.join(config.TEST.DATA.CACHED_PATH, config.TEST.DATA.EXP_DATA_NAME)
name, ext = os.path.splitext(config.TEST.DATA.FILE_LIST_PATH)
if not ext: # no file extension
config.TEST.DATA.FILE_LIST_PATH = os.path.join(config.TEST.DATA.FILE_LIST_PATH, \
config.TEST.DATA.EXP_DATA_NAME + '_' + \
str(config.TEST.DATA.BEGIN) + '_' + \
str(config.TEST.DATA.END) + '.csv')
elif ext != '.csv':
raise ValueError('TEST dataset FILE_LIST_PATH must either be a directory path or a .csv file name')
if ext == '.csv' and config.TEST.DATA.DO_PREPROCESS:
raise ValueError('User specified TEST dataset FILE_LIST_PATH .csv file already exists. \
Please turn DO_PREPROCESS to False or delete existing TEST dataset FILE_LIST_PATH .csv file.')
# UPDATE UNSUPERVISED PATHS
if config.UNSUPERVISED.DATA.FILE_LIST_PATH == default_UNSUPERVISED_FILE_LIST_PATH:
config.UNSUPERVISED.DATA.FILE_LIST_PATH = os.path.join(config.UNSUPERVISED.DATA.CACHED_PATH, 'DataFileLists')
if config.UNSUPERVISED.DATA.EXP_DATA_NAME == '':
config.UNSUPERVISED.DATA.EXP_DATA_NAME = "_".join([config.UNSUPERVISED.DATA.DATASET, "SizeW{0}".format(
str(config.UNSUPERVISED.DATA.PREPROCESS.W)), "SizeH{0}".format(str(config.UNSUPERVISED.DATA.PREPROCESS.W)), "ClipLength{0}".format(
str(config.UNSUPERVISED.DATA.PREPROCESS.CHUNK_LENGTH)), "DataType{0}".format("_".join(config.UNSUPERVISED.DATA.PREPROCESS.DATA_TYPE)),
"LabelType{0}".format(config.UNSUPERVISED.DATA.PREPROCESS.LABEL_TYPE),
"Large_box{0}".format(config.UNSUPERVISED.DATA.PREPROCESS.LARGE_FACE_BOX),
"Large_size{0}".format(config.UNSUPERVISED.DATA.PREPROCESS.LARGE_BOX_COEF),
"Dyamic_Det{0}".format(config.UNSUPERVISED.DATA.PREPROCESS.DYNAMIC_DETECTION),
"det_len{0}".format(config.UNSUPERVISED.DATA.PREPROCESS.DYNAMIC_DETECTION_FREQUENCY),
"unsupervised"
])
config.UNSUPERVISED.DATA.CACHED_PATH = os.path.join(config.UNSUPERVISED.DATA.CACHED_PATH, config.UNSUPERVISED.DATA.EXP_DATA_NAME)
name, ext = os.path.splitext(config.UNSUPERVISED.DATA.FILE_LIST_PATH)
if not ext: # no file extension
config.UNSUPERVISED.DATA.FILE_LIST_PATH = os.path.join(config.UNSUPERVISED.DATA.FILE_LIST_PATH, \
config.UNSUPERVISED.DATA.EXP_DATA_NAME + '_' + \
str(config.UNSUPERVISED.DATA.BEGIN) + '_' + \
str(config.UNSUPERVISED.DATA.END) + '.csv')
elif ext != '.csv':
raise ValueError('UNSUPERVISED dataset FILE_LIST_PATH must either be a directory path or a .csv file name')
if ext == '.csv' and config.UNSUPERVISED.DATA.DO_PREPROCESS:
raise ValueError('User specified UNSUPERVISED dataset FILE_LIST_PATH .csv file already exists. \
Please turn DO_PREPROCESS to False or delete existing UNSUPERVISED dataset FILE_LIST_PATH .csv file.')
config.LOG.PATH = os.path.join(
config.LOG.PATH, config.VALID.DATA.EXP_DATA_NAME)
config.MODEL.MODEL_DIR = os.path.join(config.MODEL.MODEL_DIR, config.TRAIN.DATA.EXP_DATA_NAME)
config.freeze()
return
def get_config(args):
# Return a clone so that the defaults will not be altered
# This is for the "local variable" use pattern
config = _C.clone()
update_config(config, args)
return config