[Bug] - ImportError: /opt/conda/envs/mmagic/lib/python3.9/site-packages/mmcv/_ext.cpython-39-x86_64-linux-gnu.so: undefined symbol: _ZN3c105ErrorC2ENS_14SourceLocationESs

[Joish]

Prerequisite

• I have searched Issues and Discussions but cannot get the expected help.
• I have read the FAQ documentation but cannot get the expected help.
• The bug has not been fixed in the latest version (main) or latest version (0.x).

Task

I'm using the official example scripts/configs for the officially supported tasks/models/datasets.

Branch

main branch https://github.com/open-mmlab/mmagic

Environment

when I ran python mmagic/utils/collect_env.py

/opt/conda/envs/mmagic/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: 'libc10_cuda.so: cannot open shared object file: No such file or directory'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?
  warn(
Traceback (most recent call last):
  File "/mnt/data/mmagic/mmagic/utils/collect_env.py", line 17, in <module>
    for name, val in collect_env().items():
  File "/mnt/data/mmagic/mmagic/utils/collect_env.py", line 10, in collect_env
    env_info = collect_base_env()
  File "/opt/conda/envs/mmagic/lib/python3.9/site-packages/mmcv/utils/env.py", line 72, in collect_env
    from mmcv.ops import get_compiler_version, get_compiling_cuda_version
  File "/opt/conda/envs/mmagic/lib/python3.9/site-packages/mmcv/ops/__init__.py", line 3, in <module>
    from .active_rotated_filter import active_rotated_filter
  File "/opt/conda/envs/mmagic/lib/python3.9/site-packages/mmcv/ops/active_rotated_filter.py", line 10, in <module>
    ext_module = ext_loader.load_ext(
  File "/opt/conda/envs/mmagic/lib/python3.9/site-packages/mmcv/utils/ext_loader.py", line 13, in load_ext
    ext = importlib.import_module('mmcv.' + name)
  File "/opt/conda/envs/mmagic/lib/python3.9/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
ImportError: /opt/conda/envs/mmagic/lib/python3.9/site-packages/mmcv/_ext.cpython-39-x86_64-linux-gnu.so: undefined symbol: _ZN3c105ErrorC2ENS_14SourceLocationESs

How you installed?
I followed instruction from here

Other Info:
using diffusers==0.24.0 as diffusers==0.25.0 was throwing No module named 'diffusers.pipelines.controlnet_xs'
using mmcv==2.1.0

Reproduces the problem - code sample

na

Reproduces the problem - command or script

python tools/test.py configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth

Reproduces the problem - error message

/opt/conda/envs/mmagic/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: 'libc10_cuda.so: cannot open shared object file: No such file or directory'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?
  warn(
01/17 21:18:45 - mmengine - INFO - 
------------------------------------------------------------
System environment:
    sys.platform: linux
    Python: 3.9.18 | packaged by conda-forge | (main, Dec 23 2023, 16:33:10) [GCC 12.3.0]
    CUDA available: False
    numpy_random_seed: 1820081447
    GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
    PyTorch: 2.1.0
    PyTorch compiling details: PyTorch built with:
  - GCC 12.3
  - C++ Version: 201703
  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - Build settings: BLAS_INFO=open, BUILD_TYPE=Release, CXX_COMPILER=/home/conda/feedstock_root/build_artifacts/pytorch-recipe_1699230989218/_build_env/bin/x86_64-conda-linux-gnu-c++, CXX_FLAGS=-fmessage-length=0 -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /home/conda/feedstock_root/build_artifacts/pytorch-recipe_1699230989218/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placeh/include -fdebug-prefix-map=/home/conda/feedstock_root/build_artifacts/pytorch-recipe_1699230989218/work=/usr/local/src/conda/pytorch-2.1.0 -fdebug-prefix-map=/home/conda/feedstock_root/build_artifacts/pytorch-recipe_1699230989218/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placeh=/usr/local/src/conda-prefix -Wno-deprecated-declarations -Wno-error=maybe-uninitialized -D_GLIBCXX_USE_CXX11_ABI=1 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -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=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -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=open, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.0, USE_CUDA=0, USE_CUDNN=OFF, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKLDNN=1, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 

    TorchVision: 0.16.0
    OpenCV: 4.9.0
    MMEngine: 0.10.2

Runtime environment:
    cudnn_benchmark: False
    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 4}
    dist_cfg: {'backend': 'nccl'}
    seed: 1820081447
    Distributed launcher: none
    Distributed training: False
    GPU number: 1
------------------------------------------------------------

01/17 21:18:45 - mmengine - INFO - Config:
custom_hooks = [
    dict(interval=1, type='BasicVisualizationHook'),
]
data_root = 'data/REDS'
default_hooks = dict(
    checkpoint=dict(
        by_epoch=False,
        interval=5000,
        max_keep_ckpts=10,
        out_dir='./work_dirs',
        rule='greater',
        save_best='PSNR',
        type='CheckpointHook'),
    logger=dict(interval=100, type='LoggerHook'),
    param_scheduler=dict(type='ParamSchedulerHook'),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    timer=dict(type='IterTimerHook'))
default_scope = 'mmagic'
demo_pipeline = [
    dict(interval_list=[
        1,
    ], type='GenerateSegmentIndices'),
    dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
    dict(type='PackInputs'),
]
env_cfg = dict(
    cudnn_benchmark=False,
    dist_cfg=dict(backend='nccl'),
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4))
experiment_name = 'basicvsr-pp_c64n7_8xb1-600k_reds4'
find_unused_parameters = True
launcher = 'none'
load_from = 'https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth'
log_level = 'INFO'
log_processor = dict(by_epoch=False, type='LogProcessor', window_size=100)
model = dict(
    data_preprocessor=dict(
        mean=[
            0.0,
            0.0,
            0.0,
        ],
        std=[
            255.0,
            255.0,
            255.0,
        ],
        type='DataPreprocessor'),
    generator=dict(
        is_low_res_input=True,
        mid_channels=64,
        num_blocks=7,
        spynet_pretrained=
        'https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth',
        type='BasicVSRPlusPlusNet'),
    pixel_loss=dict(loss_weight=1.0, reduction='mean', type='CharbonnierLoss'),
    train_cfg=dict(fix_iter=5000),
    type='BasicVSR')
optim_wrapper = dict(
    constructor='DefaultOptimWrapperConstructor',
    optimizer=dict(betas=(
        0.9,
        0.99,
    ), lr=0.0001, type='Adam'),
    paramwise_cfg=dict(custom_keys=dict(spynet=dict(lr_mult=0.25))),
    type='OptimWrapper')
param_scheduler = dict(
    by_epoch=False,
    eta_min=1e-07,
    periods=[
        600000,
    ],
    restart_weights=[
        1,
    ],
    type='CosineRestartLR')
reds_data_root = 'data/REDS'
reds_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='meta_info_reds4_val.txt',
        data_prefix=dict(gt='train_sharp', img='train_sharp_bicubic/X4'),
        data_root='data/REDS',
        depth=1,
        fixed_seq_len=100,
        metainfo=dict(dataset_type='reds_reds4', task_name='vsr'),
        num_input_frames=100,
        pipeline=[
            dict(interval_list=[
                1,
            ], type='GenerateSegmentIndices'),
            dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
            dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
            dict(type='PackInputs'),
        ],
        type='BasicFramesDataset'),
    num_workers=1,
    persistent_workers=False,
    sampler=dict(shuffle=False, type='DefaultSampler'))
reds_evaluator = [
    dict(prefix='REDS4-BIx4-RGB', type='PSNR'),
    dict(prefix='REDS4-BIx4-RGB', type='SSIM'),
]
reds_pipeline = [
    dict(interval_list=[
        1,
    ], type='GenerateSegmentIndices'),
    dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
    dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
    dict(type='PackInputs'),
]
resume = False
save_dir = './work_dirs'
scale = 4
test_cfg = dict(type='MultiTestLoop')
test_dataloader = [
    dict(
        batch_size=1,
        dataset=dict(
            ann_file='meta_info_reds4_val.txt',
            data_prefix=dict(gt='train_sharp', img='train_sharp_bicubic/X4'),
            data_root='data/REDS',
            depth=1,
            fixed_seq_len=100,
            metainfo=dict(dataset_type='reds_reds4', task_name='vsr'),
            num_input_frames=100,
            pipeline=[
                dict(interval_list=[
                    1,
                ], type='GenerateSegmentIndices'),
                dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
                dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
                dict(type='PackInputs'),
            ],
            type='BasicFramesDataset'),
        num_workers=1,
        persistent_workers=False,
        sampler=dict(shuffle=False, type='DefaultSampler')),
    dict(
        batch_size=1,
        dataset=dict(
            ann_file='meta_info_Vimeo90K_test_GT.txt',
            data_prefix=dict(gt='GT', img='BDx4'),
            data_root='data/vimeo90k',
            depth=2,
            fixed_seq_len=7,
            load_frames_list=dict(
                gt=[
                    'im4.png',
                ],
                img=[
                    'im1.png',
                    'im2.png',
                    'im3.png',
                    'im4.png',
                    'im5.png',
                    'im6.png',
                    'im7.png',
                ]),
            metainfo=dict(dataset_type='vimeo90k_seq', task_name='vsr'),
            num_input_frames=7,
            pipeline=[
                dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
                dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
                dict(keys=[
                    'img',
                ], type='MirrorSequence'),
                dict(type='PackInputs'),
            ],
            type='BasicFramesDataset'),
        num_workers=1,
        persistent_workers=False,
        sampler=dict(shuffle=False, type='DefaultSampler')),
    dict(
        batch_size=1,
        dataset=dict(
            ann_file='meta_info_Vimeo90K_test_GT.txt',
            data_prefix=dict(gt='GT', img='BIx4'),
            data_root='data/vimeo90k',
            depth=2,
            fixed_seq_len=7,
            load_frames_list=dict(
                gt=[
                    'im4.png',
                ],
                img=[
                    'im1.png',
                    'im2.png',
                    'im3.png',
                    'im4.png',
                    'im5.png',
                    'im6.png',
                    'im7.png',
                ]),
            metainfo=dict(dataset_type='vimeo90k_seq', task_name='vsr'),
            num_input_frames=7,
            pipeline=[
                dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
                dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
                dict(keys=[
                    'img',
                ], type='MirrorSequence'),
                dict(type='PackInputs'),
            ],
            type='BasicFramesDataset'),
        num_workers=1,
        persistent_workers=False,
        sampler=dict(shuffle=False, type='DefaultSampler')),
    dict(
        batch_size=1,
        dataset=dict(
            data_prefix=dict(gt='GT', img='BDx4'),
            data_root='data/UDM10',
            metainfo=dict(dataset_type='udm10', task_name='vsr'),
            pipeline=[
                dict(
                    filename_tmpl='{:04d}.png',
                    interval_list=[
                        1,
                    ],
                    type='GenerateSegmentIndices'),
                dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
                dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
                dict(type='PackInputs'),
            ],
            type='BasicFramesDataset'),
        num_workers=1,
        persistent_workers=False,
        sampler=dict(shuffle=False, type='DefaultSampler')),
    dict(
        batch_size=1,
        dataset=dict(
            ann_file='meta_info_Vid4_GT.txt',
            data_prefix=dict(gt='GT', img='BDx4'),
            data_root='data/Vid4',
            depth=1,
            metainfo=dict(dataset_type='vid4', task_name='vsr'),
            pipeline=[
                dict(interval_list=[
                    1,
                ], type='GenerateSegmentIndices'),
                dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
                dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
                dict(type='PackInputs'),
            ],
            type='BasicFramesDataset'),
        num_workers=1,
        persistent_workers=False,
        sampler=dict(shuffle=False, type='DefaultSampler')),
    dict(
        batch_size=1,
        dataset=dict(
            ann_file='meta_info_Vid4_GT.txt',
            data_prefix=dict(gt='GT', img='BIx4'),
            data_root='data/Vid4',
            depth=1,
            metainfo=dict(dataset_type='vid4', task_name='vsr'),
            pipeline=[
                dict(interval_list=[
                    1,
                ], type='GenerateSegmentIndices'),
                dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
                dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
                dict(type='PackInputs'),
            ],
            type='BasicFramesDataset'),
        num_workers=1,
        persistent_workers=False,
        sampler=dict(shuffle=False, type='DefaultSampler')),
]
test_evaluator = [
    [
        dict(prefix='REDS4-BIx4-RGB', type='PSNR'),
        dict(prefix='REDS4-BIx4-RGB', type='SSIM'),
    ],
    [
        dict(convert_to='Y', prefix='Vimeo-90K-T-BDx4-Y', type='PSNR'),
        dict(convert_to='Y', prefix='Vimeo-90K-T-BDx4-Y', type='SSIM'),
    ],
    [
        dict(convert_to='Y', prefix='Vimeo-90K-T-BIx4-Y', type='PSNR'),
        dict(convert_to='Y', prefix='Vimeo-90K-T-BIx4-Y', type='SSIM'),
    ],
    [
        dict(convert_to='Y', prefix='UDM10-BDx4-Y', type='PSNR'),
        dict(convert_to='Y', prefix='UDM10-BDx4-Y', type='SSIM'),
    ],
    [
        dict(convert_to='Y', prefix='VID4-BDx4-Y', type='PSNR'),
        dict(convert_to='Y', prefix='VID4-BDx4-Y', type='SSIM'),
    ],
    [
        dict(convert_to='Y', prefix='VID4-BIx4-Y', type='PSNR'),
        dict(convert_to='Y', prefix='VID4-BIx4-Y', type='SSIM'),
    ],
]
train_cfg = dict(
    max_iters=600000, type='IterBasedTrainLoop', val_interval=5000)
train_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='meta_info_reds4_train.txt',
        data_prefix=dict(gt='train_sharp', img='train_sharp_bicubic/X4'),
        data_root='data/REDS',
        depth=1,
        metainfo=dict(dataset_type='reds_reds4', task_name='vsr'),
        num_input_frames=30,
        pipeline=[
            dict(interval_list=[
                1,
            ], type='GenerateSegmentIndices'),
            dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
            dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
            dict(dictionary=dict(scale=4), type='SetValues'),
            dict(gt_patch_size=256, type='PairedRandomCrop'),
            dict(
                direction='horizontal',
                flip_ratio=0.5,
                keys=[
                    'img',
                    'gt',
                ],
                type='Flip'),
            dict(
                direction='vertical',
                flip_ratio=0.5,
                keys=[
                    'img',
                    'gt',
                ],
                type='Flip'),
            dict(
                keys=[
                    'img',
                    'gt',
                ],
                transpose_ratio=0.5,
                type='RandomTransposeHW'),
            dict(type='PackInputs'),
        ],
        type='BasicFramesDataset'),
    num_workers=6,
    persistent_workers=False,
    sampler=dict(shuffle=True, type='InfiniteSampler'))
train_pipeline = [
    dict(interval_list=[
        1,
    ], type='GenerateSegmentIndices'),
    dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
    dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
    dict(dictionary=dict(scale=4), type='SetValues'),
    dict(gt_patch_size=256, type='PairedRandomCrop'),
    dict(
        direction='horizontal',
        flip_ratio=0.5,
        keys=[
            'img',
            'gt',
        ],
        type='Flip'),
    dict(
        direction='vertical',
        flip_ratio=0.5,
        keys=[
            'img',
            'gt',
        ],
        type='Flip'),
    dict(keys=[
        'img',
        'gt',
    ], transpose_ratio=0.5, type='RandomTransposeHW'),
    dict(type='PackInputs'),
]
udm10_data_root = 'data/UDM10'
udm10_dataloader = dict(
    batch_size=1,
    dataset=dict(
        data_prefix=dict(gt='GT', img='BDx4'),
        data_root='data/UDM10',
        metainfo=dict(dataset_type='udm10', task_name='vsr'),
        pipeline=[
            dict(
                filename_tmpl='{:04d}.png',
                interval_list=[
                    1,
                ],
                type='GenerateSegmentIndices'),
            dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
            dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
            dict(type='PackInputs'),
        ],
        type='BasicFramesDataset'),
    num_workers=1,
    persistent_workers=False,
    sampler=dict(shuffle=False, type='DefaultSampler'))
udm10_evaluator = [
    dict(convert_to='Y', prefix='UDM10-BDx4-Y', type='PSNR'),
    dict(convert_to='Y', prefix='UDM10-BDx4-Y', type='SSIM'),
]
udm10_pipeline = [
    dict(
        filename_tmpl='{:04d}.png',
        interval_list=[
            1,
        ],
        type='GenerateSegmentIndices'),
    dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
    dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
    dict(type='PackInputs'),
]
val_cfg = dict(type='MultiValLoop')
val_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='meta_info_reds4_val.txt',
        data_prefix=dict(gt='train_sharp', img='train_sharp_bicubic/X4'),
        data_root='data/REDS',
        depth=1,
        fixed_seq_len=100,
        metainfo=dict(dataset_type='reds_reds4', task_name='vsr'),
        num_input_frames=100,
        pipeline=[
            dict(interval_list=[
                1,
            ], type='GenerateSegmentIndices'),
            dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
            dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
            dict(type='PackInputs'),
        ],
        type='BasicFramesDataset'),
    num_workers=1,
    persistent_workers=False,
    sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
    metrics=[
        dict(type='PSNR'),
        dict(type='SSIM'),
    ], type='Evaluator')
val_pipeline = [
    dict(interval_list=[
        1,
    ], type='GenerateSegmentIndices'),
    dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
    dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
    dict(type='PackInputs'),
]
vid4_bd_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='meta_info_Vid4_GT.txt',
        data_prefix=dict(gt='GT', img='BDx4'),
        data_root='data/Vid4',
        depth=1,
        metainfo=dict(dataset_type='vid4', task_name='vsr'),
        pipeline=[
            dict(interval_list=[
                1,
            ], type='GenerateSegmentIndices'),
            dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
            dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
            dict(type='PackInputs'),
        ],
        type='BasicFramesDataset'),
    num_workers=1,
    persistent_workers=False,
    sampler=dict(shuffle=False, type='DefaultSampler'))
vid4_bd_evaluator = [
    dict(convert_to='Y', prefix='VID4-BDx4-Y', type='PSNR'),
    dict(convert_to='Y', prefix='VID4-BDx4-Y', type='SSIM'),
]
vid4_bi_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='meta_info_Vid4_GT.txt',
        data_prefix=dict(gt='GT', img='BIx4'),
        data_root='data/Vid4',
        depth=1,
        metainfo=dict(dataset_type='vid4', task_name='vsr'),
        pipeline=[
            dict(interval_list=[
                1,
            ], type='GenerateSegmentIndices'),
            dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
            dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
            dict(type='PackInputs'),
        ],
        type='BasicFramesDataset'),
    num_workers=1,
    persistent_workers=False,
    sampler=dict(shuffle=False, type='DefaultSampler'))
vid4_bi_evaluator = [
    dict(convert_to='Y', prefix='VID4-BIx4-Y', type='PSNR'),
    dict(convert_to='Y', prefix='VID4-BIx4-Y', type='SSIM'),
]
vid4_data_root = 'data/Vid4'
vid4_pipeline = [
    dict(interval_list=[
        1,
    ], type='GenerateSegmentIndices'),
    dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
    dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
    dict(type='PackInputs'),
]
vimeo_90k_bd_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='meta_info_Vimeo90K_test_GT.txt',
        data_prefix=dict(gt='GT', img='BDx4'),
        data_root='data/vimeo90k',
        depth=2,
        fixed_seq_len=7,
        load_frames_list=dict(
            gt=[
                'im4.png',
            ],
            img=[
                'im1.png',
                'im2.png',
                'im3.png',
                'im4.png',
                'im5.png',
                'im6.png',
                'im7.png',
            ]),
        metainfo=dict(dataset_type='vimeo90k_seq', task_name='vsr'),
        num_input_frames=7,
        pipeline=[
            dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
            dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
            dict(keys=[
                'img',
            ], type='MirrorSequence'),
            dict(type='PackInputs'),
        ],
        type='BasicFramesDataset'),
    num_workers=1,
    persistent_workers=False,
    sampler=dict(shuffle=False, type='DefaultSampler'))
vimeo_90k_bd_evaluator = [
    dict(convert_to='Y', prefix='Vimeo-90K-T-BDx4-Y', type='PSNR'),
    dict(convert_to='Y', prefix='Vimeo-90K-T-BDx4-Y', type='SSIM'),
]
vimeo_90k_bi_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='meta_info_Vimeo90K_test_GT.txt',
        data_prefix=dict(gt='GT', img='BIx4'),
        data_root='data/vimeo90k',
        depth=2,
        fixed_seq_len=7,
        load_frames_list=dict(
            gt=[
                'im4.png',
            ],
            img=[
                'im1.png',
                'im2.png',
                'im3.png',
                'im4.png',
                'im5.png',
                'im6.png',
                'im7.png',
            ]),
        metainfo=dict(dataset_type='vimeo90k_seq', task_name='vsr'),
        num_input_frames=7,
        pipeline=[
            dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
            dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
            dict(keys=[
                'img',
            ], type='MirrorSequence'),
            dict(type='PackInputs'),
        ],
        type='BasicFramesDataset'),
    num_workers=1,
    persistent_workers=False,
    sampler=dict(shuffle=False, type='DefaultSampler'))
vimeo_90k_bi_evaluator = [
    dict(convert_to='Y', prefix='Vimeo-90K-T-BIx4-Y', type='PSNR'),
    dict(convert_to='Y', prefix='Vimeo-90K-T-BIx4-Y', type='SSIM'),
]
vimeo_90k_data_root = 'data/vimeo90k'
vimeo_90k_file_list = [
    'im1.png',
    'im2.png',
    'im3.png',
    'im4.png',
    'im5.png',
    'im6.png',
    'im7.png',
]
vimeo_90k_pipeline = [
    dict(channel_order='rgb', key='img', type='LoadImageFromFile'),
    dict(channel_order='rgb', key='gt', type='LoadImageFromFile'),
    dict(keys=[
        'img',
    ], type='MirrorSequence'),
    dict(type='PackInputs'),
]
vis_backends = [
    dict(type='LocalVisBackend'),
]
visualizer = dict(
    bgr2rgb=True,
    fn_key='gt_path',
    img_keys=[
        'gt_img',
        'input',
        'pred_img',
    ],
    type='ConcatImageVisualizer',
    vis_backends=[
        dict(type='LocalVisBackend'),
    ])
work_dir = './work_dirs/basicvsr-pp_c64n7_8xb1-600k_reds4'

Traceback (most recent call last):
  File "/mnt/data/mmagic/tools/test.py", line 89, in <module>
    main()
  File "/mnt/data/mmagic/tools/test.py", line 69, in main
    runner = Runner.from_cfg(cfg)
  File "/opt/conda/envs/mmagic/lib/python3.9/site-packages/mmengine/runner/runner.py", line 462, in from_cfg
    runner = cls(
  File "/opt/conda/envs/mmagic/lib/python3.9/site-packages/mmengine/runner/runner.py", line 429, in __init__
    self.model = self.build_model(model)
  File "/opt/conda/envs/mmagic/lib/python3.9/site-packages/mmengine/runner/runner.py", line 836, in build_model
    model = MODELS.build(model)
  File "/opt/conda/envs/mmagic/lib/python3.9/site-packages/mmengine/registry/registry.py", line 570, in build
    return self.build_func(cfg, *args, **kwargs, registry=self)
  File "/opt/conda/envs/mmagic/lib/python3.9/site-packages/mmengine/registry/build_functions.py", line 232, in build_model_from_cfg
    return build_from_cfg(cfg, registry, default_args)
  File "/opt/conda/envs/mmagic/lib/python3.9/site-packages/mmengine/registry/build_functions.py", line 98, in build_from_cfg
    obj_cls = registry.get(obj_type)
  File "/opt/conda/envs/mmagic/lib/python3.9/site-packages/mmengine/registry/registry.py", line 451, in get
    self.import_from_location()
  File "/opt/conda/envs/mmagic/lib/python3.9/site-packages/mmengine/registry/registry.py", line 376, in import_from_location
    import_module(loc)
  File "/opt/conda/envs/mmagic/lib/python3.9/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1030, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1007, in _find_and_load
  File "<frozen importlib._bootstrap>", line 986, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 680, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 850, in exec_module
  File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed
  File "/mnt/data/mmagic/mmagic/models/__init__.py", line 6, in <module>
    from .editors import *  # noqa: F401, F403
  File "/mnt/data/mmagic/mmagic/models/editors/__init__.py", line 6, in <module>
    from .basicvsr_plusplus_net import BasicVSRPlusPlusNet
  File "/mnt/data/mmagic/mmagic/models/editors/basicvsr_plusplus_net/__init__.py", line 2, in <module>
    from .basicvsr_plusplus_net import BasicVSRPlusPlusNet
  File "/mnt/data/mmagic/mmagic/models/editors/basicvsr_plusplus_net/basicvsr_plusplus_net.py", line 5, in <module>
    from mmcv.ops import ModulatedDeformConv2d, modulated_deform_conv2d
  File "/opt/conda/envs/mmagic/lib/python3.9/site-packages/mmcv/ops/__init__.py", line 3, in <module>
    from .active_rotated_filter import active_rotated_filter
  File "/opt/conda/envs/mmagic/lib/python3.9/site-packages/mmcv/ops/active_rotated_filter.py", line 10, in <module>
    ext_module = ext_loader.load_ext(
  File "/opt/conda/envs/mmagic/lib/python3.9/site-packages/mmcv/utils/ext_loader.py", line 13, in load_ext
    ext = importlib.import_module('mmcv.' + name)
  File "/opt/conda/envs/mmagic/lib/python3.9/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
ImportError: /opt/conda/envs/mmagic/lib/python3.9/site-packages/mmcv/_ext.cpython-39-x86_64-linux-gnu.so: undefined symbol: _ZN3c105ErrorC2ENS_14SourceLocationESs```

### Additional information

1. Docker build is also failing.
2. Docs are very bad.