Spaces:
Runtime error
Runtime error
File size: 3,522 Bytes
f8f62f3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
from detectron2.config import CfgNode as CN
def add_cat_seg_config(cfg):
"""
Add config for MASK_FORMER.
"""
# data config
# select the dataset mapper
cfg.INPUT.DATASET_MAPPER_NAME = "mask_former_semantic"
cfg.DATASETS.VAL_ALL = ("coco_2017_val_all_stuff_sem_seg",)
# 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()
# 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
# 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"]
# zero shot config
cfg.MODEL.SEM_SEG_HEAD.TRAIN_CLASS_JSON = "datasets/ADE20K_2021_17_01/ADE20K_847.json"
cfg.MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON = "datasets/ADE20K_2021_17_01/ADE20K_847.json"
cfg.MODEL.SEM_SEG_HEAD.TRAIN_CLASS_INDEXES = "datasets/coco/coco_stuff/split/seen_indexes.json"
cfg.MODEL.SEM_SEG_HEAD.TEST_CLASS_INDEXES = "datasets/coco/coco_stuff/split/unseen_indexes.json"
cfg.MODEL.SEM_SEG_HEAD.CLIP_PRETRAINED = "ViT-B/16"
cfg.MODEL.PROMPT_ENSEMBLE = False
cfg.MODEL.PROMPT_ENSEMBLE_TYPE = "single"
cfg.MODEL.CLIP_PIXEL_MEAN = [122.7709383, 116.7460125, 104.09373615]
cfg.MODEL.CLIP_PIXEL_STD = [68.5005327, 66.6321579, 70.3231630]
# three styles for clip classification, crop, mask, cropmask
cfg.MODEL.SEM_SEG_HEAD.TEXT_AFFINITY_DIM = 512
cfg.MODEL.SEM_SEG_HEAD.TEXT_AFFINITY_PROJ_DIM = 128
cfg.MODEL.SEM_SEG_HEAD.APPEARANCE_AFFINITY_DIM = 512
cfg.MODEL.SEM_SEG_HEAD.APPEARANCE_AFFINITY_PROJ_DIM = 128
cfg.MODEL.SEM_SEG_HEAD.DECODER_DIMS = [64, 32]
cfg.MODEL.SEM_SEG_HEAD.DECODER_AFFINITY_DIMS = [256, 128]
cfg.MODEL.SEM_SEG_HEAD.DECODER_AFFINITY_PROJ_DIMS = [32, 16]
cfg.MODEL.SEM_SEG_HEAD.NUM_LAYERS = 4
cfg.MODEL.SEM_SEG_HEAD.NUM_HEADS = 4
cfg.MODEL.SEM_SEG_HEAD.HIDDEN_DIMS = 128
cfg.MODEL.SEM_SEG_HEAD.POOLING_SIZES = [6, 6]
cfg.MODEL.SEM_SEG_HEAD.FEATURE_RESOLUTION = [24, 24]
cfg.MODEL.SEM_SEG_HEAD.WINDOW_SIZES = 12
cfg.MODEL.SEM_SEG_HEAD.ATTENTION_TYPE = "linear"
cfg.MODEL.SEM_SEG_HEAD.PROMPT_DEPTH = 0
cfg.MODEL.SEM_SEG_HEAD.PROMPT_LENGTH = 0
cfg.SOLVER.CLIP_MULTIPLIER = 0.01
cfg.MODEL.SEM_SEG_HEAD.CLIP_FINETUNE = "attention"
cfg.TEST.SLIDING_WINDOW = False |