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