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config.py
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# -*- coding: utf-8 -*-
# ------------------------------------------------------------------------
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
from detectron2.config import CfgNode as CN
def add_config(cfg):
"""
Add config for MaskDINO.
"""
# NOTE: configs from original mask2former
# data config
# select the dataset mapper
cfg.INPUT.DATASET_MAPPER_NAME = "MaskDINO_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
# MaskDINO model config
cfg.MODEL.MaskDINO = CN()
cfg.MODEL.MaskDINO.LEARN_TGT = False
# loss
cfg.MODEL.MaskDINO.PANO_BOX_LOSS = False
cfg.MODEL.MaskDINO.SEMANTIC_CE_LOSS = False
cfg.MODEL.MaskDINO.DEEP_SUPERVISION = True
cfg.MODEL.MaskDINO.NO_OBJECT_WEIGHT = 0.1
cfg.MODEL.MaskDINO.CLASS_WEIGHT = 4.0
cfg.MODEL.MaskDINO.DICE_WEIGHT = 5.0
cfg.MODEL.MaskDINO.MASK_WEIGHT = 5.0
cfg.MODEL.MaskDINO.BOX_WEIGHT = 5.
cfg.MODEL.MaskDINO.GIOU_WEIGHT = 2.
# cost weight
cfg.MODEL.MaskDINO.COST_CLASS_WEIGHT = 4.0
cfg.MODEL.MaskDINO.COST_DICE_WEIGHT = 5.0
cfg.MODEL.MaskDINO.COST_MASK_WEIGHT = 5.0
cfg.MODEL.MaskDINO.COST_BOX_WEIGHT = 5.
cfg.MODEL.MaskDINO.COST_GIOU_WEIGHT = 2.
# transformer config
cfg.MODEL.MaskDINO.NHEADS = 8
cfg.MODEL.MaskDINO.DROPOUT = 0.1
cfg.MODEL.MaskDINO.DIM_FEEDFORWARD = 2048
cfg.MODEL.MaskDINO.ENC_LAYERS = 0
cfg.MODEL.MaskDINO.DEC_LAYERS = 6
cfg.MODEL.MaskDINO.INITIAL_PRED = True
cfg.MODEL.MaskDINO.PRE_NORM = False
cfg.MODEL.MaskDINO.BOX_LOSS = True
cfg.MODEL.MaskDINO.HIDDEN_DIM = 256
cfg.MODEL.MaskDINO.NUM_OBJECT_QUERIES = 100
cfg.MODEL.MaskDINO.ENFORCE_INPUT_PROJ = False
cfg.MODEL.MaskDINO.TWO_STAGE = True
cfg.MODEL.MaskDINO.INITIALIZE_BOX_TYPE = 'no' # ['no', 'bitmask', 'mask2box']
cfg.MODEL.MaskDINO.DN="seg"
cfg.MODEL.MaskDINO.DN_NOISE_SCALE=0.4
cfg.MODEL.MaskDINO.DN_NUM=100
cfg.MODEL.MaskDINO.PRED_CONV=False
cfg.MODEL.MaskDINO.EVAL_FLAG = 1
# 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
cfg.MODEL.SEM_SEG_HEAD.DIM_FEEDFORWARD = 1024
cfg.MODEL.SEM_SEG_HEAD.NUM_FEATURE_LEVELS = 3
cfg.MODEL.SEM_SEG_HEAD.TOTAL_NUM_FEATURE_LEVELS = 4
cfg.MODEL.SEM_SEG_HEAD.FEATURE_ORDER = 'high2low' # ['low2high', 'high2low'] high2low: from high level to low level
#####################
# MaskDINO inference config
cfg.MODEL.MaskDINO.TEST = CN()
cfg.MODEL.MaskDINO.TEST.TEST_FOUCUS_ON_BOX = False
cfg.MODEL.MaskDINO.TEST.SEMANTIC_ON = True
cfg.MODEL.MaskDINO.TEST.INSTANCE_ON = False
cfg.MODEL.MaskDINO.TEST.PANOPTIC_ON = False
cfg.MODEL.MaskDINO.TEST.OBJECT_MASK_THRESHOLD = 0.0
cfg.MODEL.MaskDINO.TEST.OVERLAP_THRESHOLD = 0.0
cfg.MODEL.MaskDINO.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False
cfg.MODEL.MaskDINO.TEST.PANO_TRANSFORM_EVAL = True
cfg.MODEL.MaskDINO.TEST.PANO_TEMPERATURE = 0.06
# cfg.MODEL.MaskDINO.TEST.EVAL_FLAG = 1
# Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)
# you can use this config to override
cfg.MODEL.MaskDINO.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 = "MaskDINOEncoder"
# transformer module
cfg.MODEL.MaskDINO.TRANSFORMER_DECODER_NAME = "MaskDINODecoder"
# LSJ aug
cfg.INPUT.IMAGE_SIZE = [1024, 1024]
cfg.INPUT.MIN_SCALE = 0.1
cfg.INPUT.MAX_SCALE = 2.0
# point loss configs
# Number of points sampled during training for a mask point head.
cfg.MODEL.MaskDINO.TRAIN_NUM_POINTS = 112 * 112
# Oversampling parameter for PointRend point sampling during training. Parameter `k` in the
# original paper.
cfg.MODEL.MaskDINO.OVERSAMPLE_RATIO = 3.0
# Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in
# the original paper.
cfg.MODEL.MaskDINO.IMPORTANCE_SAMPLE_RATIO = 0.75
# 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
cfg.Default_loading=True # a bug in my d2. resume use this; if first time ResNet load, set it false