train with cityscapes datasets

hi, would you like to show me the configs you use to train with cityscapes datasets? or, can you tell me the problems with my config file? thanks a lot.

yaml file MODEL: META_ARCHITECTURE: "PanopticFCN" WEIGHTS: "/mnt/lj/detectron2/projects/PanopticFCN/model_final.pth" MASK_ON: True PIXEL_MEAN: [123.675, 116.28, 103.53] PIXEL_STD: [1.0, 1.0, 1.0] RESNETS: DEPTH: 50 OUT_FEATURES: ["res2", "res3", "res4", "res5"] FPN: IN_FEATURES: ["res2", "res3", "res4", "res5"] FEATURE_ENCODER: DEFORM: True CONVS_DIM: 256 DATASETS: TRAIN: ("cityscapes_fine_panoptic_train",) TEST: ("cityscapes_fine_panoptic_val",) DATALOADER: FILTER_EMPTY_ANNOTATIONS: True SOLVER: BASE_LR: 0.02 WEIGHT_DECAY: 1e-4 LR_SCHEDULER_NAME: "WarmupPolyLR" POLY_LR_POWER: 0.9 WARMUP_ITERS: 1000 WARMUP_FACTOR: 0.001 WARMUP_METHOD: "linear" CLIP_GRADIENTS: ENABLED: True CLIP_VALUE: 35.0 IMS_PER_BATCH: 2 MAX_ITER: 270000 CHECKPOINT_PERIOD: 10000 INPUT: MIN_SIZE_TRAIN: (512, 768, 1024, 1280, 1536, 1792, 2048) MIN_SIZE_TRAIN_SAMPLING: "choice" MIN_SIZE_TEST: 1024 MAX_SIZE_TRAIN: 4096 MAX_SIZE_TEST: 2048 CROP: ENABLED: True TYPE: "absolute" SIZE: (512, 1024) VERSION: 2 OUTPUT_DIR: ./output/panoptic_fn

THING.NUM_CLASSES=8、STUFF.NUM_CLASSES=11、SEM_SEG_HEAD.NUM_CLASSES=11 , is it right?

def add_panopticfcn_config(cfg):
    """
    Add config for Panoptic FCN.
    """
    cfg.MODEL.TENSOR_DIM                 = 100
    cfg.MODEL.IGNORE_VALUE               = 255
    cfg.SOLVER.POLY_LR_POWER             = 0.9
    cfg.SOLVER.POLY_LR_CONSTANT_ENDING   = 0.0

    cfg.MODEL.SEMANTIC_FPN   = CN()
    cfg.MODEL.SEMANTIC_FPN.IN_FEATURES   = ["p2", "p3", "p4", "p5"]
    cfg.MODEL.SEMANTIC_FPN.CONVS_DIM     = 256
    cfg.MODEL.SEMANTIC_FPN.COMMON_STRIDE = 4
    cfg.MODEL.SEMANTIC_FPN.NORM          = "GN"

    cfg.MODEL.POSITION_HEAD   = CN()
    cfg.MODEL.POSITION_HEAD.NUM_CONVS       = 3
    cfg.MODEL.POSITION_HEAD.COORD           = False
    cfg.MODEL.POSITION_HEAD.CONVS_DIM       = 256
    cfg.MODEL.POSITION_HEAD.NORM            = "GN"
    cfg.MODEL.POSITION_HEAD.DEFORM          = True
    cfg.MODEL.POSITION_HEAD.THING = CN()
    cfg.MODEL.POSITION_HEAD.THING.CENTER_TYPE    = "mass"
    cfg.MODEL.POSITION_HEAD.THING.POS_NUM        = 7
    cfg.MODEL.POSITION_HEAD.THING.NUM_CLASSES    = 8
    cfg.MODEL.POSITION_HEAD.THING.BIAS_VALUE     = -2.19
    cfg.MODEL.POSITION_HEAD.THING.MIN_OVERLAP    = 0.7
    cfg.MODEL.POSITION_HEAD.THING.GAUSSIAN_SIGMA = 3
    cfg.MODEL.POSITION_HEAD.THING.THRES          = 0.05
    cfg.MODEL.POSITION_HEAD.THING.TOP_NUM        = 100

    cfg.MODEL.POSITION_HEAD.STUFF = CN()
    cfg.MODEL.POSITION_HEAD.STUFF.NUM_CLASSES  = 11
    cfg.MODEL.POSITION_HEAD.STUFF.WITH_THING   = True
    cfg.MODEL.POSITION_HEAD.STUFF.THRES        = 0.05

    cfg.MODEL.SEM_SEG_HEAD   = CN()
    cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES  = 11
    cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE = 255

    cfg.MODEL.KERNEL_HEAD    = CN()
    cfg.MODEL.KERNEL_HEAD.INSTANCE_SCALES = ((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048),)
    cfg.MODEL.KERNEL_HEAD.TEST_SCALES     = ((1, 64), (64, 128), (128, 256), (256, 512), (512, 2048),)
    cfg.MODEL.KERNEL_HEAD.NUM_CONVS       = 3
    cfg.MODEL.KERNEL_HEAD.DEFORM          = False
    cfg.MODEL.KERNEL_HEAD.COORD           = True
    cfg.MODEL.KERNEL_HEAD.CONVS_DIM       = 256
    cfg.MODEL.KERNEL_HEAD.NORM            = "GN"

    cfg.MODEL.FEATURE_ENCODER    = CN()
    cfg.MODEL.FEATURE_ENCODER.IN_FEATURES     = ["p3", "p4", "p5", "p6", "p7"]
    cfg.MODEL.FEATURE_ENCODER.NUM_CONVS       = 3
    cfg.MODEL.FEATURE_ENCODER.CONVS_DIM       = 64
    cfg.MODEL.FEATURE_ENCODER.DEFORM          = False
    cfg.MODEL.FEATURE_ENCODER.COORD           = True
    cfg.MODEL.FEATURE_ENCODER.NORM            = ""

    cfg.MODEL.LOSS_WEIGHT    = CN()
    cfg.MODEL.LOSS_WEIGHT.POSITION          = 1.0
    cfg.MODEL.LOSS_WEIGHT.SEGMENT           = 3.0
    cfg.MODEL.LOSS_WEIGHT.FOCAL_LOSS_ALPHA  = 0.25
    cfg.MODEL.LOSS_WEIGHT.FOCAL_LOSS_GAMMA  = 2.0

    cfg.MODEL.INFERENCE      = CN()
    cfg.MODEL.INFERENCE.INST_THRES        = 0.4
    cfg.MODEL.INFERENCE.SIMILAR_THRES     = 0.9
    cfg.MODEL.INFERENCE.SIMILAR_TYPE      = "cosine"
    cfg.MODEL.INFERENCE.CLASS_SPECIFIC    = True

    cfg.MODEL.INFERENCE.COMBINE  = CN()
    cfg.MODEL.INFERENCE.COMBINE.ENABLE           = True
    cfg.MODEL.INFERENCE.COMBINE.NO_OVERLAP       = False
    cfg.MODEL.INFERENCE.COMBINE.OVERLAP_THRESH   = 0.5
    cfg.MODEL.INFERENCE.COMBINE.STUFF_AREA_LIMIT = 4096
    cfg.MODEL.INFERENCE.COMBINE.INST_THRESH      = 0.2

    cfg.MODEL.EXPORT_ONNX = False

dvlab-research / PanopticFCN

train with cityscapes datasets #36