KeyError: 'dataset_type is not in the mmseg::dataset registry. Please check whether the value of `dataset_type` is correct or it was registered as expected.

import mmengine.fileio as fileio
from mmseg.registry import DATASETS
from .basesegdataset import BaseSegDataset

@DATASETS.register_module()
class LawnSegmentationDataset(BaseSegDataset):
    METAINFO = dict(
        classes=('background', 'red', 'green', 'white', 'seed-black', 'seed-white'),
        palette=[[127, 127, 127], [200, 0, 0], [0, 200, 0], [144, 238, 144], [30, 30, 30], [251, 189, 8]])

    def __init__(self,
                 img_suffix='.jpg',
                 seg_map_suffix='.png',
                 reduce_zero_label=False,
                 **kwargs) -> None:
        super().__init__(
            img_suffix=img_suffix,
            seg_map_suffix=seg_map_suffix,
            reduce_zero_label=reduce_zero_label,
            **kwargs)
        assert fileio.exists(
            self.data_prefix['img_path'], backend_args=self.backend_args)

# Copyright (c) OpenMMLab. All rights reserved.
# yapf: disable
from .ade import ADE20KDataset
from .basesegdataset import BaseCDDataset, BaseSegDataset
from .bdd100k import BDD100KDataset
from .chase_db1 import ChaseDB1Dataset
from .cityscapes import CityscapesDataset
from .coco_stuff import COCOStuffDataset
from .dark_zurich import DarkZurichDataset
from .dataset_wrappers import MultiImageMixDataset
from .decathlon import DecathlonDataset
from .drive import DRIVEDataset
from .dsdl import DSDLSegDataset
from .hrf import HRFDataset
from .hsi_drive import HSIDrive20Dataset
from .isaid import iSAIDDataset
from .isprs import ISPRSDataset
from .levir import LEVIRCDDataset
from .lip import LIPDataset
from .loveda import LoveDADataset
from .mapillary import MapillaryDataset_v1, MapillaryDataset_v2
from .night_driving import NightDrivingDataset
from .nyu import NYUDataset
from .pascal_context import PascalContextDataset, PascalContextDataset59
from .potsdam import PotsdamDataset
from .refuge import REFUGEDataset
from .stare import STAREDataset
from .synapse import SynapseDataset
# yapf: disable
from .transforms import (CLAHE, AdjustGamma, Albu, BioMedical3DPad,
                         BioMedical3DRandomCrop, BioMedical3DRandomFlip,
                         BioMedicalGaussianBlur, BioMedicalGaussianNoise,
                         BioMedicalRandomGamma, ConcatCDInput, GenerateEdge,
                         LoadAnnotations, LoadBiomedicalAnnotation,
                         LoadBiomedicalData, LoadBiomedicalImageFromFile,
                         LoadImageFromNDArray, LoadMultipleRSImageFromFile,
                         LoadSingleRSImageFromFile, PackSegInputs,
                         PhotoMetricDistortion, RandomCrop, RandomCutOut,
                         RandomMosaic, RandomRotate, RandomRotFlip, Rerange,
                         ResizeShortestEdge, ResizeToMultiple, RGB2Gray,
                         SegRescale)
from .voc import PascalVOCDataset
from .lawn_dataset import LawnSegmentationDataset

# yapf: enable
__all__ = [
    'BaseSegDataset', 'BioMedical3DRandomCrop', 'BioMedical3DRandomFlip',
    'CityscapesDataset', 'PascalVOCDataset', 'ADE20KDataset',
    'PascalContextDataset', 'PascalContextDataset59', 'ChaseDB1Dataset',
    'DRIVEDataset', 'HRFDataset', 'STAREDataset', 'DarkZurichDataset',
    'NightDrivingDataset', 'COCOStuffDataset', 'LoveDADataset',
    'MultiImageMixDataset', 'iSAIDDataset', 'ISPRSDataset', 'PotsdamDataset',
    'LoadAnnotations', 'RandomCrop', 'SegRescale', 'PhotoMetricDistortion',
    'RandomRotate', 'AdjustGamma', 'CLAHE', 'Rerange', 'RGB2Gray',
    'RandomCutOut', 'RandomMosaic', 'PackSegInputs', 'ResizeToMultiple',
    'LoadImageFromNDArray', 'LoadBiomedicalImageFromFile',
    'LoadBiomedicalAnnotation', 'LoadBiomedicalData', 'GenerateEdge',
    'DecathlonDataset', 'LIPDataset', 'ResizeShortestEdge',
    'BioMedicalGaussianNoise', 'BioMedicalGaussianBlur',
    'BioMedicalRandomGamma', 'BioMedical3DPad', 'RandomRotFlip',
    'SynapseDataset', 'REFUGEDataset', 'MapillaryDataset_v1',
    'MapillaryDataset_v2', 'Albu', 'LEVIRCDDataset',
    'LoadMultipleRSImageFromFile', 'LoadSingleRSImageFromFile',
    'ConcatCDInput', 'BaseCDDataset', 'DSDLSegDataset', 'BDD100KDataset',
    'NYUDataset', 'HSIDrive20Dataset', 'LawnSegmentationDataset'
]

# dataset settings
dataset_type = 'LawnSegmentationDataset'
# 数据集路径（相对于mmsegmentation主目录）
data_root = '/home/kevin/deep_learning_collection/mmsegmentation/data/lawn/'
# data_root = 'lawn'
crop_size = (512, 512)  # 输入模型的图像裁剪尺寸，一般是128的倍数，越小显存开销越少

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(
        type='Resize',
        # scale=(720, 1280),
        scale=(2048, 1024),
        ratio_range=(0.5, 2.0),
        keep_ratio=True),
    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(type='PackSegInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    # dict(type='Resize', scale=(720, 1280), keep_ratio=True),
    dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
    # add loading annotation after ``Resize`` because ground truth
    # does not need to do resize data transform
    dict(type='LoadAnnotations'),
    dict(type='PackSegInputs')
]

img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
tta_pipeline = [
    dict(type='LoadImageFromFile', backend_args=None),
    dict(
        type='TestTimeAug',
        transforms=[[
            dict(type='Resize', scale_factor=r, keep_ratio=True)
            for r in img_ratios
        ],
            [
                dict(type='RandomFlip', prob=0., direction='horizontal'),
                dict(type='RandomFlip', prob=1., direction='horizontal')
            ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')]])
]

train_dataloader = dict(
    batch_size=4,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='InfiniteSampler', shuffle=True),
    dataset=dict(
        type='dataset_type',
        data_root='data_root',
        data_prefix=dict(
            img_path='img_dir/train', seg_map_path='ann_dir/train'),
        pipeline='train_pipeline'))

val_dataloader = dict(
    batch_size=1,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='dataset_type',
        data_root='data_root',
        data_prefix=dict(
            img_path='img_dir/val', seg_map_path='ann_dir/val'),
        pipeline='test_pipeline'))

test_dataloader = 'val_dataloader'

# val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'], ignore_index=2)
val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mDice', 'mFscore'])

test_evaluator = val_evaluator

# _base_ = [
#     '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/lawn_segmentation_pipeline.py',
#     '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
# ]

_base_ = [
    '/home/kevin/deep_learning_collection/mmsegmentation/configs/_base_/models/pspnet_r50-d8.py',
    '/home/kevin/deep_learning_collection/mmsegmentation/configs/_base_/datasets/lawn_segmentation_pipeline.py',
    '/home/kevin/deep_learning_collection/mmsegmentation/configs/_base_/default_runtime.py',
    '/home/kevin/deep_learning_collection/mmsegmentation/configs/_base_/schedules/schedule_40k.py'
]

crop_size = (512, 1024)
data_preprocessor = dict(size=crop_size)
model = dict(data_preprocessor=data_preprocessor)

crop_size = (
    512,
    1024,
)
data_preprocessor = dict(
    bgr_to_rgb=True,
    mean=[
        123.675,
        116.28,
        103.53,
    ],
    pad_val=0,
    seg_pad_val=255,
    size=(
        512,
        1024,
    ),
    std=[
        58.395,
        57.12,
        57.375,
    ],
    type='SegDataPreProcessor')
data_root = '/home/kevin/deep_learning_collection/mmsegmentation/data/lawn/'
dataset_type = 'LawnSegmentationDataset'
default_hooks = dict(
    checkpoint=dict(
        by_epoch=False,
        interval=2500,
        max_keep_ckpt=1,
        save_best='mIoU',
        type='CheckpointHook'),
    logger=dict(interval=100, log_metric_by_epoch=False, type='LoggerHook'),
    param_scheduler=dict(type='ParamSchedulerHook'),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    timer=dict(type='IterTimerHook'))
default_scope = 'mmseg'
env_cfg = dict(
    cudnn_benchmark=True,
    dist_cfg=dict(backend='nccl'),
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
img_ratios = [
    0.5,
    0.75,
    1.0,
    1.25,
    1.5,
    1.75,
]
launcher = 'none'
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=False)
model = dict(
    auxiliary_head=dict(
        align_corners=False,
        channels=256,
        concat_input=False,
        dropout_ratio=0.1,
        in_channels=1024,
        in_index=2,
        loss_decode=dict(
            loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False),
        norm_cfg=dict(requires_grad=True, type='BN'),
        num_classes=6,
        num_convs=1,
        type='FCNHead'),
    backbone=dict(
        contract_dilation=True,
        depth=50,
        dilations=(
            1,
            1,
            2,
            4,
        ),
        norm_cfg=dict(requires_grad=True, type='BN'),
        norm_eval=False,
        num_stages=4,
        out_indices=(
            0,
            1,
            2,
            3,
        ),
        strides=(
            1,
            2,
            1,
            1,
        ),
        style='pytorch',
        type='ResNetV1c'),
    data_preprocessor=dict(
        bgr_to_rgb=True,
        mean=[
            123.675,
            116.28,
            103.53,
        ],
        pad_val=0,
        seg_pad_val=255,
        size=(
            512,
            1024,
        ),
        std=[
            58.395,
            57.12,
            57.375,
        ],
        type='SegDataPreProcessor'),
    decode_head=dict(
        align_corners=False,
        channels=512,
        dropout_ratio=0.1,
        in_channels=2048,
        in_index=3,
        loss_decode=dict(
            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
        norm_cfg=dict(requires_grad=True, type='BN'),
        num_classes=6,
        pool_scales=(
            1,
            2,
            3,
            6,
        ),
        type='PSPHead'),
    pretrained='open-mmlab://resnet50_v1c',
    test_cfg=dict(mode='whole'),
    train_cfg=dict(),
    type='EncoderDecoder')
norm_cfg = dict(requires_grad=True, type='BN')
optim_wrapper = dict(
    clip_grad=None,
    optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005),
    type='OptimWrapper')
optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005)
param_scheduler = [
    dict(
        begin=0,
        by_epoch=False,
        end=40000,
        eta_min=0.0001,
        power=0.9,
        type='PolyLR'),
]
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = 'val_dataloader'
test_evaluator = dict(
    iou_metrics=[
        'mIoU',
        'mDice',
        'mFscore',
    ], type='IoUMetric')
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(keep_ratio=True, scale=(
        2048,
        1024,
    ), type='Resize'),
    dict(type='LoadAnnotations'),
    dict(type='PackSegInputs'),
]
train_cfg = dict(max_iters=40000, type='IterBasedTrainLoop', val_interval=500)
train_dataloader = dict(
    batch_size=4,
    dataset=dict(
        data_prefix=dict(
            img_path='img_dir/train', seg_map_path='ann_dir/train'),
        data_root='data_root',
        pipeline='train_pipeline',
        type='dataset_type'),
    num_workers=4,
    persistent_workers=True,
    sampler=dict(shuffle=True, type='InfiniteSampler'))
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(
        keep_ratio=True,
        ratio_range=(
            0.5,
            2.0,
        ),
        scale=(
            2048,
            1024,
        ),
        type='Resize'),
    dict(cat_max_ratio=0.75, crop_size=(
        512,
        512,
    ), type='RandomCrop'),
    dict(prob=0.5, type='RandomFlip'),
    dict(type='PhotoMetricDistortion'),
    dict(type='PackSegInputs'),
]
tta_model = dict(type='SegTTAModel')
tta_pipeline = [
    dict(backend_args=None, type='LoadImageFromFile'),
    dict(
        transforms=[
            [
                dict(keep_ratio=True, scale_factor=0.5, type='Resize'),
                dict(keep_ratio=True, scale_factor=0.75, type='Resize'),
                dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
                dict(keep_ratio=True, scale_factor=1.25, type='Resize'),
                dict(keep_ratio=True, scale_factor=1.5, type='Resize'),
                dict(keep_ratio=True, scale_factor=1.75, type='Resize'),
            ],
            [
                dict(direction='horizontal', prob=0.0, type='RandomFlip'),
                dict(direction='horizontal', prob=1.0, type='RandomFlip'),
            ],
            [
                dict(type='LoadAnnotations'),
            ],
            [
                dict(type='PackSegInputs'),
            ],
        ],
        type='TestTimeAug'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
    batch_size=1,
    dataset=dict(
        data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'),
        data_root='data_root',
        pipeline='test_pipeline',
        type='dataset_type'),
    num_workers=4,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
    iou_metrics=[
        'mIoU',
        'mDice',
        'mFscore',
    ], type='IoUMetric')
vis_backends = [
    dict(type='LocalVisBackend'),
]
visualizer = dict(
    name='visualizer',
    type='SegLocalVisualizer',
    vis_backends=[
        dict(type='LocalVisBackend'),
    ])
work_dir = '/home/kevin/deep_learning_collection/mmsegmentation/outputs'

/home/kevin/deep_learning_collection/mmsegmentation/mmseg/models/backbones/resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead
  warnings.warn('DeprecationWarning: pretrained is a deprecated, '
/home/kevin/deep_learning_collection/mmsegmentation/mmseg/models/losses/cross_entropy_loss.py:250: UserWarning: Default 'avg_non_ignore' is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set  'avg_non_ignore=True'.
  warnings.warn(
04/25 16:56:35 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm