ClassBalancedDataset conflict with YOLOXModeSwitchHook. TypeError: class `ClassBalancedDataset` in mmengine/dataset/dataset_wrapper.py: __init__() got an unexpected keyword argument 'pipeline' in <mmyolo.engine.hooks.yolox_mode_switch_hook.YOLOXModeSwitchHook object at 0x7f6dc78ddc10>

[jamiechoi1995]

Prerequisite

• [X] I have searched the existing and past issues but cannot get the expected help.
• [X] I have read the FAQ documentation but cannot get the expected help.
• [X] The bug has not been fixed in the latest version.

🐞 Describe the bug

Here is my config:

_base_ = ['../_base_/default_runtime.py', '1280_tta.py']

# dataset settings
data_root = ''
dataset_type = 'YOLOv5CocoDataset'
classes = ('knife', 'electronicequipment', 'lighter', 'OCbottle',
           'metalbottle', 'battery', 'pressure', 'glassbottle', 'umbrella')
metainfo = dict(classes=classes, 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)])

# parameters that often need to be modified
img_scale = (1280, 1280)  # width, height
deepen_factor = 0.33
widen_factor = 0.5
max_epochs = 110
stage2_num_epochs = 30
num_classes = 9
save_epoch_intervals = 1
train_batch_size_per_gpu = 8
train_num_workers = 8
val_batch_size_per_gpu = 1
val_num_workers = 2
random_affine_scaling_ratio_range = (0.1, 2)
mixup_ratio_range = (0.8, 1.6)

# The pretrained model is geted and converted from official PPYOLOE.
# https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/configs/ppyoloe/README.md
load_from = 'https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_pretrain/ppyoloe_plus_s_obj365_pretrained-bcfe8478.pth'  # noqa

# persistent_workers must be False if num_workers is 0.
persistent_workers = True

# Base learning rate for optim_wrapper
base_lr = 0.001

strides = [8, 16, 32]

model = dict(
    type='YOLODetector',
    data_preprocessor=dict(
        # use this to support multi_scale training
        type='PPYOLOEDetDataPreprocessor',
        pad_size_divisor=32,
        batch_augments=[
            dict(
                type='PPYOLOEBatchRandomResize',
                random_size_range=(320, 1280),
                interval=1,
                size_divisor=32,
                random_interp=True,
                keep_ratio=False)
        ],
        mean=[0., 0., 0.],
        std=[255., 255., 255.],
        bgr_to_rgb=True),
    backbone=dict(
        type='PPYOLOECSPResNet',
        deepen_factor=deepen_factor,
        widen_factor=widen_factor,
        block_cfg=dict(
            type='PPYOLOEBasicBlock', shortcut=True, use_alpha=True),
        norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
        act_cfg=dict(type='SiLU', inplace=True),
        attention_cfg=dict(
            type='EffectiveSELayer', act_cfg=dict(type='HSigmoid')),
        use_large_stem=True),
    neck=dict(
        type='PPYOLOECSPPAFPN',
        in_channels=[256, 512, 1024],
        out_channels=[192, 384, 768],
        deepen_factor=deepen_factor,
        widen_factor=widen_factor,
        num_csplayer=1,
        num_blocks_per_layer=3,
        block_cfg=dict(
            type='PPYOLOEBasicBlock', shortcut=False, use_alpha=False),
        norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
        act_cfg=dict(type='SiLU', inplace=True),
        drop_block_cfg=None,
        use_spp=True),
    bbox_head=dict(
        type='PPYOLOEHead',
        head_module=dict(
            type='PPYOLOEHeadModule',
            num_classes=num_classes,
            in_channels=[192, 384, 768],
            widen_factor=widen_factor,
            featmap_strides=strides,
            reg_max=16,
            norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
            act_cfg=dict(type='SiLU', inplace=True),
            num_base_priors=1),
        prior_generator=dict(
            type='mmdet.MlvlPointGenerator', offset=0.5, strides=strides),
        bbox_coder=dict(type='DistancePointBBoxCoder'),
        loss_cls=dict(
            type='mmdet.VarifocalLoss',
            use_sigmoid=True,
            alpha=0.75,
            gamma=2.0,
            iou_weighted=True,
            reduction='sum',
            loss_weight=1.0),
        loss_bbox=dict(
            type='IoULoss',
            iou_mode='giou',
            bbox_format='xyxy',
            reduction='mean',
            loss_weight=2.5,
            return_iou=False),
        # Since the dflloss is implemented differently in the official
        # and mmdet, we're going to divide loss_weight by 4.
        loss_dfl=dict(
            type='mmdet.DistributionFocalLoss',
            reduction='mean',
            loss_weight=0.5 / 4)),
    train_cfg=dict(
        initial_epoch=30,
        initial_assigner=dict(
            type='BatchATSSAssigner',
            num_classes=num_classes,
            topk=9,
            iou_calculator=dict(type='mmdet.BboxOverlaps2D')),
        assigner=dict(
            type='BatchTaskAlignedAssigner',
            num_classes=num_classes,
            topk=13,
            alpha=1,
            beta=6,
            eps=1e-9)),
    test_cfg=dict(
        multi_label=True,
        nms_pre=1000,
        score_thr=0.01,
        nms=dict(type='nms', iou_threshold=0.7),
        max_per_img=300))

pre_transform = [
    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
    dict(type='LoadAnnotations', with_bbox=True)
]

train_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='PPYOLOERandomDistort'),
    dict(type='mmdet.Expand', mean=(103.53, 116.28, 123.675)),
    dict(type='PPYOLOERandomCrop'),
    dict(
        type='Mosaic',
        img_scale=img_scale,
        pad_val=114.0,
        pre_transform=pre_transform),
    dict(
        type='mmdet.RandomAffine',
        scaling_ratio_range=random_affine_scaling_ratio_range,
        # img_scale is (width, height)
        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
    dict(
        type='YOLOXMixUp',
        img_scale=img_scale,
        ratio_range=mixup_ratio_range,
        pad_val=114.0,
        pre_transform=pre_transform),
    dict(type='mmdet.YOLOXHSVRandomAug'),
    dict(type='mmdet.RandomFlip', prob=0.75, direction=['horizontal',
         'vertical', 'diagonal']),
    dict(
        type='mmdet.FilterAnnotations',
        min_gt_bbox_wh=(1, 1),
        keep_empty=False),    
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
                   'flip_direction'))
]

train_pipeline_stage2 = [
    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='PPYOLOERandomDistort'),
    dict(type='mmdet.Expand', mean=(103.53, 116.28, 123.675)),
    dict(type='PPYOLOERandomCrop'),
    dict(type='mmdet.RandomFlip', prob=0.75, direction=['horizontal',
         'vertical', 'diagonal']),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
                   'flip_direction'))
]

train_dataloader = dict(
    batch_size=train_batch_size_per_gpu,
    num_workers=train_num_workers,
    persistent_workers=persistent_workers,
    pin_memory=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    collate_fn=dict(type='yolov5_collate', use_ms_training=True),
    dataset=dict(
        type='ClassBalancedDataset',
        oversample_thr=1e-3,
        dataset=dict(
        type=dataset_type,
        data_root=data_root,
        metainfo=metainfo,
        ann_file='/home/jovyan/work/me/images/data/images_train_0516.json',
        data_prefix=dict(img=''),
        filter_cfg=dict(filter_empty_gt=True, min_size=0),
        pipeline=train_pipeline)))

test_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
    dict(
        type='mmdet.FixShapeResize',
        width=img_scale[0],
        height=img_scale[1],
        keep_ratio=False,
        interpolation='bicubic'),
    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]

val_dataloader = dict(
    batch_size=val_batch_size_per_gpu,
    num_workers=val_num_workers,
    persistent_workers=persistent_workers,
    pin_memory=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        metainfo=metainfo,
        data_root=data_root,
        test_mode=True,
        data_prefix=dict(img=''),
        filter_cfg=dict(filter_empty_gt=True, min_size=0),
        ann_file='/home/jovyan/work/me/images/data/images_test_0516.json',
        pipeline=test_pipeline))

test_dataloader = val_dataloader

param_scheduler = None
optim_wrapper = dict(
    type='AmpOptimWrapper',
    optimizer=dict(
        type='SGD',
        lr=base_lr,
        momentum=0.9,
        weight_decay=5e-4,
        nesterov=False),
    loss_scale='dynamic',
    paramwise_cfg=dict(norm_decay_mult=0.))

default_hooks = dict(
    param_scheduler=dict(
        type='PPYOLOEParamSchedulerHook',
        warmup_min_iter=1000,
        start_factor=0.,
        warmup_epochs=5,
        min_lr_ratio=0.0,
        total_epochs=int(max_epochs * 1.2)),
    checkpoint=dict(
        type='CheckpointHook',
        interval=save_epoch_intervals,
        save_best='coco/bbox_mAP_50',
        max_keep_ckpts=3))

custom_hooks = [
    dict(
        type='YOLOXModeSwitchHook',
        num_last_epochs=stage2_num_epochs,
        new_train_pipeline=train_pipeline_stage2,
        priority=48),
    dict(type='mmdet.SyncNormHook', priority=48),    
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
        momentum=0.0002,
        update_buffers=True,
        strict_load=False,
        priority=49),
]    

val_evaluator = dict(
    type='mmdet.CocoMetric',
    proposal_nums=(100, 1, 10),
    ann_file=data_root + '/home/jovyan/work/me/images/data/images_test_0516.json',
    metric='bbox')
test_evaluator = val_evaluator

train_cfg = dict(
    type='EpochBasedTrainLoop',
    max_epochs=max_epochs,
    val_interval=save_epoch_intervals)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

Environment

sys.platform: linux Python: 3.8.16 | packaged by conda-forge | (default, Feb 1 2023, 16:01:55) [GCC 11.3.0] CUDA available: True numpy_random_seed: 2147483648 GPU 0,1: NVIDIA A100-PCIE-40GB CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.4, V11.4.152 GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 PyTorch: 2.0.1+cu117 PyTorch compiling details: PyTorch built with:

• GCC 9.3
• C++ Version: 201703
• Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• LAPACK is enabled (usually provided by MKL)
• NNPACK is enabled
• CPU capability usage: AVX2
• CUDA Runtime 11.7
• NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
• CuDNN 8.2.4 (built against CUDA 11.4)
  • Built with CuDNN 8.5
• Magma 2.6.1
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.15.2+cu117 OpenCV: 4.7.0 MMEngine: 0.7.3 MMCV: 2.0.0 MMDetection: 3.0.0 MMYOLO: 0.5.0+

Additional information

No response

[jamiechoi1995]

when running to the switch epoch, I got the following error:

Traceback (most recent call last):
  File "tools/train.py", line 115, in <module>
    main()
  File "tools/train.py", line 111, in main
    runner.train()
  File "/opt/conda/envs/mmdet3/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1721, in train
    model = self.train_loop.run()  # type: ignore
  File "/opt/conda/envs/mmdet3/lib/python3.8/site-packages/mmengine/runner/loops.py", line 96, in run
    self.run_epoch()
  File "/opt/conda/envs/mmdet3/lib/python3.8/site-packages/mmengine/runner/loops.py", line 109, in run_epoch
    self.runner.call_hook('before_train_epoch')
  File "/opt/conda/envs/mmdet3/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1785, in call_hook
    raise TypeError(f'{e} in {hook}') from None
TypeError: class `ClassBalancedDataset` in mmengine/dataset/dataset_wrapper.py: __init__() got an unexpected keyword argument 'pipeline' in <mmyolo.engine.hooks.yolox_mode_switch_hook.YOLOXModeSwitchHook object at 0x7f6dc78ddc10>