def setup(args, config):
"""
Create configs and perform basic setups.
"""
from detectron2.config import CfgNode
# detectron2 default cfg
# cfg = get_cfg()
cfg = CfgNode()
cfg.OUTPUT_DIR = "./output"
cfg.SEED = -1
cfg.CUDNN_BENCHMARK = False
cfg.DATASETS = CfgNode()
cfg.SOLVER = CfgNode()
cfg.DATALOADER = CfgNode()
cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True
cfg.DATALOADER.SAMPLER_TRAIN = "TrainingSampler"
cfg.MODEL = CfgNode()
cfg.MODEL.KEYPOINT_ON = False
cfg.MODEL.LOAD_PROPOSALS = False
cfg.MODEL.WEIGHTS = ""
# cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg = detection2_utils.D2Utils.cfg_merge_from_easydict(cfg, config)
cfg.freeze()
default_setup(
cfg, args
) # if you don't like any of the default setup, write your own setup code
return cfg
def create_cfg():
"""
Create configs and perform basic setups.
"""
from detectron2.config import CfgNode
# detectron2 default cfg
# cfg = get_cfg()
cfg = CfgNode()
cfg.OUTPUT_DIR = "./output"
cfg.SEED = -1
cfg.CUDNN_BENCHMARK = False
cfg.DATASETS = CfgNode()
cfg.SOLVER = CfgNode()
cfg.DATALOADER = CfgNode()
cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True
cfg.DATALOADER.SAMPLER_TRAIN = "TrainingSampler"
cfg.MODEL = CfgNode()
cfg.MODEL.KEYPOINT_ON = False
cfg.MODEL.LOAD_PROPOSALS = False
cfg.MODEL.WEIGHTS = ""
cfg.freeze()
return cfg
# based on the limit established for the COCO dataset).
_C.TEST.DETECTIONS_PER_IMAGE = 100
_C.TEST.AUG = CN({"ENABLED": False})
_C.TEST.AUG.MIN_SIZES = (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)
_C.TEST.AUG.MAX_SIZE = 4000
_C.TEST.AUG.FLIP = True
_C.TEST.PRECISE_BN = CN({"ENABLED": False})
_C.TEST.PRECISE_BN.NUM_ITER = 200
# ---------------------------------------------------------------------------- #
# Misc options
# ---------------------------------------------------------------------------- #
# Directory where output files are written
_C.OUTPUT_DIR = "./output"
# Set seed to negative to fully randomize everything.
# Set seed to positive to use a fixed seed. Note that a fixed seed does not
# guarantee fully deterministic behavior.
_C.SEED = -1
# Benchmark different cudnn algorithms.
# If input images have very different sizes, this option will have large overhead
# for about 10k iterations. It usually hurts total time, but can benefit for certain models.
# If input images have the same or similar sizes, benchmark is often helpful.
_C.CUDNN_BENCHMARK = False
# The period (in terms of steps) for minibatch visualization at train time.
# Set to 0 to disable.
_C.VIS_PERIOD = 0
# global config is for quick hack purposes.
# You can set them in command line or config files,
