Custom define Dataset is not in the dataset registry

[Neo-Hn]

I want to train a custom dataset which file type is coco2017, then I copy coco.py and change some content and train, but have a error 'PeachDataset is not in the dataset registry'.

env

PyTorch 1.10.0
Python 3.8(ubuntu20.04)
Cuda 11.3
RTX A5000
torch 1.10.0+cu113
torchvision 0.11.1+cu113
mmcv-full 1.7.1
mmdet 2.27.0

Dataset config

@DATASETS.register_module()
class PeachDataset(CustomDataset):

    CLASSES = ('peach')

    PALETTE = [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230),
               (106, 0, 228), (0, 60, 100), (0, 80, 100), (0, 0, 70),
               (0, 0, 192), (250, 170, 30), (100, 170, 30), (220, 220, 0),
               (175, 116, 175), (250, 0, 30), (165, 42, 42), (255, 77, 255),
               (0, 226, 252), (182, 182, 255), (0, 82, 0), (120, 166, 157),
               (110, 76, 0), (174, 57, 255), (199, 100, 0), (72, 0, 118),
               (255, 179, 240), (0, 125, 92), (209, 0, 151), (188, 208, 182),
               (0, 220, 176), (255, 99, 164), (92, 0, 73), (133, 129, 255),
               (78, 180, 255), (0, 228, 0), (174, 255, 243), (45, 89, 255),
               (134, 134, 103), (145, 148, 174), (255, 208, 186),
               (197, 226, 255), (171, 134, 1), (109, 63, 54), (207, 138, 255),
               (151, 0, 95), (9, 80, 61), (84, 105, 51), (74, 65, 105),
               (166, 196, 102), (208, 195, 210), (255, 109, 65), (0, 143, 149),
               (179, 0, 194), (209, 99, 106), (5, 121, 0), (227, 255, 205),
               (147, 186, 208), (153, 69, 1), (3, 95, 161), (163, 255, 0),
               (119, 0, 170), (0, 182, 199), (0, 165, 120), (183, 130, 88),
               (95, 32, 0), (130, 114, 135), (110, 129, 133), (166, 74, 118),
               (219, 142, 185), (79, 210, 114), (178, 90, 62), (65, 70, 15),
               (127, 167, 115), (59, 105, 106), (142, 108, 45), (196, 172, 0),
               (95, 54, 80), (128, 76, 255), (201, 57, 1), (246, 0, 122),
               (191, 162, 208)]

# Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .peachs import PeachDataset
from .coco_panoptic import CocoPanopticDataset
from .custom import CustomDataset
from .dataset_wrappers import (ClassBalancedDataset, ConcatDataset,
                               MultiImageMixDataset, RepeatDataset)
from .deepfashion import DeepFashionDataset
from .lvis import LVISDataset, LVISV1Dataset, LVISV05Dataset
from .openimages import OpenImagesChallengeDataset, OpenImagesDataset
from .samplers import DistributedGroupSampler, DistributedSampler, GroupSampler
from .utils import (NumClassCheckHook, get_loading_pipeline,
                    replace_ImageToTensor)
from .voc import VOCDataset
from .wider_face import WIDERFaceDataset
from .xml_style import XMLDataset

__all__ = [
    'CustomDataset', 'XMLDataset', 'CocoDataset', 'DeepFashionDataset',
    'VOCDataset', 'CityscapesDataset', 'LVISDataset', 'LVISV05Dataset',
    'LVISV1Dataset', 'GroupSampler', 'DistributedGroupSampler',
    'DistributedSampler', 'build_dataloader', 'ConcatDataset', 'RepeatDataset',
    'ClassBalancedDataset', 'WIDERFaceDataset', 'DATASETS', 'PIPELINES',
    'build_dataset', 'replace_ImageToTensor', 'get_loading_pipeline',
    'NumClassCheckHook', 'CocoPanopticDataset', 'MultiImageMixDataset',
    'OpenImagesDataset', 'OpenImagesChallengeDataset', 'PeachDataset'
]

train config

Here is train config:

model = dict(
    type='FasterRCNN',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    roi_head=dict(
        type='StandardRoIHead',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='Shared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=1,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0.0, 0.0, 0.0, 0.0],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=False,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=False,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        rpn=dict(
            nms_pre=1000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100)))
dataset_type = 'PeachDataset'
data_root = 'data/peach/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=64,
    workers_per_gpu=15,
    train=dict(
        type=dataset_type,
        ann_file='data/peach/annotations/instances_train2017.json',
        img_prefix='data/peach/train2017/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ]),
    val=dict(
        type=dataset_type,
        ann_file='data/peach/annotations/instances_val2017.json',
        img_prefix='data/peach/val2017/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]),
    test=dict(
        type=dataset_type,
        ann_file='data/peach/annotations/instances_test2017.json',
        img_prefix='data/peach/test2017/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]))
evaluation = dict(interval=1, metric='bbox')
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=5,
    warmup_ratio=0.001,
    step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=300)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=16)

error info

Error info is here:

Traceback (most recent call last):
  File "/root/miniconda3/lib/python3.8/site-packages/mmdet/.mim/tools/train.py", line 247, in <module>
    main()
  File "/root/miniconda3/lib/python3.8/site-packages/mmdet/.mim/tools/train.py", line 221, in main
    datasets = [build_dataset(cfg.data.train)]
  File "/root/miniconda3/lib/python3.8/site-packages/mmdet/datasets/builder.py", line 82, in build_dataset
    dataset = build_from_cfg(cfg, DATASETS, default_args)
  File "/root/miniconda3/lib/python3.8/site-packages/mmcv/utils/registry.py", line 61, in build_from_cfg
    raise KeyError(
KeyError: 'PeachDataset is not in the dataset registry'
Traceback (most recent call last):
  File "/root/miniconda3/bin/mim", line 8, in <module>
    sys.exit(cli())
  File "/root/miniconda3/lib/python3.8/site-packages/click/core.py", line 1130, in __call__
    return self.main(*args, **kwargs)
  File "/root/miniconda3/lib/python3.8/site-packages/click/core.py", line 1055, in main
    rv = self.invoke(ctx)
  File "/root/miniconda3/lib/python3.8/site-packages/click/core.py", line 1657, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/root/miniconda3/lib/python3.8/site-packages/click/core.py", line 1404, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/root/miniconda3/lib/python3.8/site-packages/click/core.py", line 760, in invoke
    return __callback(*args, **kwargs)
  File "/root/miniconda3/lib/python3.8/site-packages/mim/commands/train.py", line 100, in cli
    is_success, msg = train(
  File "/root/miniconda3/lib/python3.8/site-packages/mim/commands/train.py", line 261, in train
    ret = subprocess.check_call(
  File "/root/miniconda3/lib/python3.8/subprocess.py", line 364, in check_call
    raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['/root/miniconda3/bin/python', '/root/miniconda3/lib/python3.8/site-packages/mmdet/.mim/tools/train.py', 'faster_rcnn_r50_fpn_1x_coco.py', '--launcher', 'none', '--gpus', '1']' returned non-zero exit status 1.

Addition info

When I replace PeachDataset to CocoDataset, console just tell me num_classes not right but not have Dataset is not in the dataset registry.