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# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
from detectron2.config import CfgNode as CN
def add_motionnet_config(cfg: CN):
_C = cfg
_C.MODEL.MOTIONNET = CN()
_C.MODEL.MOTIONNET.TYPE = "BMOC_V0"
cfg.MODEL.MASK_FORMER.MTYPE_WEIGHT = 2.0
cfg.MODEL.MASK_FORMER.MORIGIN_WEIGHT = 16.0
cfg.MODEL.MASK_FORMER.MAXIS_WEIGHT = 16.0
cfg.MODEL.MASK_FORMER.MSTATE_WEIGHT = 16.0
cfg.MODEL.MASK_FORMER.MSTATEMAX_WEIGHT = 16.0
cfg.MODEL.MASK_FORMER.EXTRINSIC_WEIGHT = 30.0
def add_maskformer2_config(cfg):
"""
Add config for MASK_FORMER.
"""
# NOTE: configs from original maskformer
# data config
# select the dataset mapper
cfg.INPUT.DATASET_MAPPER_NAME = "mask_former_semantic"
# Color augmentation
cfg.INPUT.COLOR_AUG_SSD = False
# We retry random cropping until no single category in semantic segmentation GT occupies more
# than `SINGLE_CATEGORY_MAX_AREA` part of the crop.
cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0
# Pad image and segmentation GT in dataset mapper.
cfg.INPUT.SIZE_DIVISIBILITY = -1
# solver config
# weight decay on embedding
cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0
# optimizer
cfg.SOLVER.OPTIMIZER = "ADAMW"
cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1
# mask_former model config
cfg.MODEL.MASK_FORMER = CN()
# loss
cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION = True
cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT = 0.1
cfg.MODEL.MASK_FORMER.CLASS_WEIGHT = 1.0
cfg.MODEL.MASK_FORMER.DICE_WEIGHT = 1.0
cfg.MODEL.MASK_FORMER.MASK_WEIGHT = 20.0
# transformer config
cfg.MODEL.MASK_FORMER.NHEADS = 8
cfg.MODEL.MASK_FORMER.DROPOUT = 0.1
cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = 2048
cfg.MODEL.MASK_FORMER.ENC_LAYERS = 0
cfg.MODEL.MASK_FORMER.DEC_LAYERS = 6
cfg.MODEL.MASK_FORMER.PRE_NORM = False
cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256
cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = 100
cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE = "res5"
cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ = False
# mask_former inference config
cfg.MODEL.MASK_FORMER.TEST = CN()
cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True
cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = False
cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = False
cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD = 0.0
cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD = 0.0
cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False
# Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)
# you can use this config to override
cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY = 32
# pixel decoder config
cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256
# adding transformer in pixel decoder
cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0
# pixel decoder
cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = "BasePixelDecoder"
# swin transformer backbone
cfg.MODEL.SWIN = CN()
cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224
cfg.MODEL.SWIN.PATCH_SIZE = 4
cfg.MODEL.SWIN.EMBED_DIM = 96
cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]
cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]
cfg.MODEL.SWIN.WINDOW_SIZE = 7
cfg.MODEL.SWIN.MLP_RATIO = 4.0
cfg.MODEL.SWIN.QKV_BIAS = True
cfg.MODEL.SWIN.QK_SCALE = None
cfg.MODEL.SWIN.DROP_RATE = 0.0
cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0
cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3
cfg.MODEL.SWIN.APE = False
cfg.MODEL.SWIN.PATCH_NORM = True
cfg.MODEL.SWIN.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
cfg.MODEL.SWIN.USE_CHECKPOINT = False
# NOTE: maskformer2 extra configs
# transformer module
cfg.MODEL.MASK_FORMER.TRANSFORMER_DECODER_NAME = "MultiScaleMaskedTransformerDecoder"
# LSJ aug
cfg.INPUT.IMAGE_SIZE = 1024
cfg.INPUT.MIN_SCALE = 0.1
cfg.INPUT.MAX_SCALE = 2.0
# MSDeformAttn encoder configs
cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["res3", "res4", "res5"]
cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4
cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8
# point loss configs
# Number of points sampled during training for a mask point head.
cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS = 112 * 112
# Oversampling parameter for PointRend point sampling during training. Parameter `k` in the
# original paper.
cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO = 3.0
# Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in
# the original paper.
cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75
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