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# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
from detectron2.config import CfgNode as CN
def add_tensormask_config(cfg):
"""
Add config for TensorMask.
"""
cfg.MODEL.TENSOR_MASK = CN()
# Anchor parameters
cfg.MODEL.TENSOR_MASK.IN_FEATURES = ["p2", "p3", "p4", "p5", "p6", "p7"]
# Convolutions to use in the towers
cfg.MODEL.TENSOR_MASK.NUM_CONVS = 4
# Number of foreground classes.
cfg.MODEL.TENSOR_MASK.NUM_CLASSES = 80
# Channel size for the classification tower
cfg.MODEL.TENSOR_MASK.CLS_CHANNELS = 256
cfg.MODEL.TENSOR_MASK.SCORE_THRESH_TEST = 0.05
# Only the top (1000 * #levels) candidate boxes across all levels are
# considered jointly during test (to improve speed)
cfg.MODEL.TENSOR_MASK.TOPK_CANDIDATES_TEST = 6000
cfg.MODEL.TENSOR_MASK.NMS_THRESH_TEST = 0.5
# Box parameters
# Channel size for the box tower
cfg.MODEL.TENSOR_MASK.BBOX_CHANNELS = 128
# Weights on (dx, dy, dw, dh)
cfg.MODEL.TENSOR_MASK.BBOX_REG_WEIGHTS = (1.5, 1.5, 0.75, 0.75)
# Loss parameters
cfg.MODEL.TENSOR_MASK.FOCAL_LOSS_GAMMA = 3.0
cfg.MODEL.TENSOR_MASK.FOCAL_LOSS_ALPHA = 0.3
# Mask parameters
# Channel size for the mask tower
cfg.MODEL.TENSOR_MASK.MASK_CHANNELS = 128
# Mask loss weight
cfg.MODEL.TENSOR_MASK.MASK_LOSS_WEIGHT = 2.0
# weight on positive pixels within the mask
cfg.MODEL.TENSOR_MASK.POSITIVE_WEIGHT = 1.5
# Whether to predict in the aligned representation
cfg.MODEL.TENSOR_MASK.ALIGNED_ON = False
# Whether to use the bipyramid architecture
cfg.MODEL.TENSOR_MASK.BIPYRAMID_ON = False
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