search

chenller / mmseg-extension

mmsegmentation extension library containing the latest paper code.
Apache License 2.0
9 stars 1 forks source link

AssertionError: mmengine does not support to load mmsegextension config #4

Closed antopost closed 1 month ago

antopost commented 1 month ago

I am trying to load just the model for training to incorporate it into my custom training loop. I tried this:

from mmengine.config import Config
from mmseg.registry import MODELS
from mmseg.utils import register_all_modules
import mmsegext

register_all_modules()

config_file = 'mmseg-extension/configs/vit_adapter/mask2former_beitv2_adapter_large_896_80k_ade20k_ss.py'
cfg = Config.fromfile(config_file)

But got he following error: AssertionError: mmengine does not support to load mmsegextension config.

Is there a recommended way how to load just the model?

Edit: I also get the same error when performing: python tools/train.py mmseg-extension/configs/vit_adapter/mask2former_beit_adapter_large_640_160k_ade20k_ss.py

chenller commented 1 month ago

Modify the second line of the the CONFIG file to " 'mmsegext::base/datasets/ade20k_512_tta_without_ratio.py', ".

chenller commented 1 month ago

'mmsegext::_base_/datasets/ade20k_512_tta_without_ratio.py',

antopost commented 1 month ago

I modified the line as described. Now I get a different error. I will post the full output here:

python3 mmseg-extension/tools/train.py /home/anba/catkin_ws/src/tas_dev/dev/anba/Mask2Former/mmseg-extension/configs/vit_adapter/mask2former_beit_adapter_large_896_80k_ade20k_ss.py

/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmengine/optim/optimizer/zero_optimizer.py:11: DeprecationWarning: `TorchScript` support for functional optimizers is deprecated and will be removed in a future PyTorch release. Consider using the `torch.compile` optimizer instead.
  from torch.distributed.optim import \
/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmsegextlib_msda-1.0-py3.9-linux-x86_64.egg/mmsegextlib_msda/functions/ms_deform_attn_func.py:22: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.
  @custom_fwd(cast_inputs=torch.float32)
/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmsegextlib_msda-1.0-py3.9-linux-x86_64.egg/mmsegextlib_msda/functions/ms_deform_attn_func.py:39: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.
  def backward(ctx, grad_output):
/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmsegextlib_dcnv3-1.0-py3.9-linux-x86_64.egg/mmsegextlib_dcnv3/functions/dcnv3_func.py:22: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.
  def forward(
/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmsegextlib_dcnv3-1.0-py3.9-linux-x86_64.egg/mmsegextlib_dcnv3/functions/dcnv3_func.py:51: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.
  def backward(ctx, grad_output):
/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmsegextlib_dcnv3-1.0-py3.9-linux-x86_64.egg/mmsegextlib_dcnv3/modules/dcnv3.py:20: UserWarning: Now, we support DCNv4 in InternImage.
  warnings.warn('Now, we support DCNv4 in InternImage.')
/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/torch/cuda/__init__.py:654: UserWarning: Can't initialize NVML
  warnings.warn("Can't initialize NVML")
/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmsegextlib_dcnv4/functions/dcnv4_func.py:65: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.
  def forward(
/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmsegextlib_dcnv4/functions/dcnv4_func.py:111: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.
  def backward(ctx, grad_output):
09/24 12:37:04 - mmengine - INFO - 
------------------------------------------------------------
System environment:
    sys.platform: linux
    Python: 3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) [GCC 12.3.0]
    CUDA available: True
    MUSA available: False
    numpy_random_seed: 1611889984
    GPU 0: NVIDIA RTX A4000
    CUDA_HOME: /usr/local/cuda
    NVCC: Cuda compilation tools, release 12.1, V12.1.105
    GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
    PyTorch: 2.4.1+cu121
    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 v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX512
  - CUDA Runtime 12.1
  - NVCC architecture flags: -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;-gencode;arch=compute_90,code=sm_90
  - CuDNN 90.1  (built against CUDA 12.4)
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -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 -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.4.1, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 

    TorchVision: 0.19.1+cu121
    OpenCV: 4.8.1
    MMEngine: 0.10.5

Runtime environment:
    cudnn_benchmark: True
    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
    dist_cfg: {'backend': 'nccl'}
    seed: 1611889984
    Distributed launcher: none
    Distributed training: False
    GPU number: 1
------------------------------------------------------------

09/24 12:37:05 - mmengine - INFO - Config:
crop_size = (
    896,
    896,
)
data_preprocessor = dict(
    bgr_to_rgb=True,
    mean=[
        123.675,
        116.28,
        103.53,
    ],
    pad_val=0,
    seg_pad_val=255,
    size=(
        896,
        896,
    ),
    std=[
        58.395,
        57.12,
        57.375,
    ],
    type='SegDataPreProcessor')
data_root = '/home/yansu/dataset/mmseg/ADEChallengeData2016/'
dataset_type = 'ADE20KDataset'
default_hooks = dict(
    checkpoint=dict(
        _scope_='mmseg', by_epoch=False, interval=8000, type='CheckpointHook'),
    logger=dict(
        _scope_='mmseg',
        interval=50,
        log_metric_by_epoch=False,
        type='LoggerHook'),
    param_scheduler=dict(_scope_='mmseg', type='ParamSchedulerHook'),
    sampler_seed=dict(_scope_='mmseg', type='DistSamplerSeedHook'),
    timer=dict(_scope_='mmseg', type='IterTimerHook'),
    visualization=dict(_scope_='mmseg', type='SegVisualizationHook'))
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))
launcher = 'none'
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=False)
model = dict(
    backbone=dict(
        _scope_='mmsegextension',
        cffn_ratio=0.25,
        conv_inplane=64,
        deform_num_heads=16,
        deform_ratio=0.5,
        depth=24,
        drop_path_rate=0.3,
        embed_dim=1024,
        img_size=896,
        init_values=1e-06,
        interaction_indexes=[
            [
                0,
                5,
            ],
            [
                6,
                11,
            ],
            [
                12,
                17,
            ],
            [
                18,
                23,
            ],
        ],
        mlp_ratio=4,
        n_points=4,
        num_heads=16,
        patch_size=16,
        qkv_bias=True,
        type='BEiTAdapter',
        use_abs_pos_emb=False,
        use_rel_pos_bias=True,
        with_cp=True),
    decode_head=dict(
        _scope_='mmsegextension',
        enforce_decoder_input_project=False,
        feat_channels=1024,
        in_channels=[
            1024,
            1024,
            1024,
            1024,
        ],
        in_index=[
            0,
            1,
            2,
            3,
        ],
        loss_cls=dict(
            class_weight=[
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
                0.1,
            ],
            loss_weight=2.0,
            reduction='mean',
            type='CrossEntropyLoss',
            use_sigmoid=False),
        loss_dice=dict(
            activate=True,
            eps=1.0,
            loss_weight=5.0,
            naive_dice=True,
            reduction='mean',
            type='DiceLoss',
            use_sigmoid=True),
        loss_mask=dict(
            loss_weight=5.0,
            reduction='mean',
            type='CrossEntropyLoss',
            use_sigmoid=True),
        num_classes=150,
        num_queries=200,
        num_stuff_classes=50,
        num_things_classes=100,
        num_transformer_feat_level=3,
        out_channels=1024,
        pixel_decoder=dict(
            act_cfg=dict(type='ReLU'),
            encoder=dict(
                init_cfg=None,
                num_layers=6,
                transformerlayers=dict(
                    attn_cfgs=dict(
                        batch_first=False,
                        dropout=0.0,
                        embed_dims=1024,
                        im2col_step=64,
                        init_cfg=None,
                        norm_cfg=None,
                        num_heads=32,
                        num_levels=3,
                        num_points=4,
                        type='MultiScaleDeformableAttention'),
                    ffn_cfgs=dict(
                        act_cfg=dict(inplace=True, type='ReLU'),
                        embed_dims=1024,
                        feedforward_channels=4096,
                        ffn_drop=0.0,
                        num_fcs=2,
                        type='FFN'),
                    operation_order=(
                        'self_attn',
                        'norm',
                        'ffn',
                        'norm',
                    ),
                    type='BaseTransformerLayer'),
                type='DetrTransformerEncoder'),
            init_cfg=None,
            norm_cfg=dict(num_groups=32, type='GN'),
            num_outs=3,
            positional_encoding=dict(
                _scope_='mmdet',
                normalize=True,
                num_feats=512,
                type='SinePositionalEncoding'),
            type='MSDeformAttnPixelDecoder'),
        positional_encoding=dict(
            _scope_='mmdet',
            normalize=True,
            num_feats=512,
            type='SinePositionalEncoding'),
        test_cfg=dict(
            crop_size=(
                896,
                896,
            ),
            filter_low_score=True,
            instance_on=True,
            iou_thr=0.8,
            max_per_image=100,
            mode='slide',
            panoptic_on=True,
            semantic_on=False,
            stride=(
                512,
                512,
            )),
        train_cfg=dict(
            assigner=dict(
                _scope_='mmdet',
                cls_cost=dict(type='ClassificationCost', weight=2.0),
                dice_cost=dict(
                    eps=1.0, pred_act=True, type='DiceCost', weight=5.0),
                mask_cost=dict(
                    type='CrossEntropyLossCost', use_sigmoid=True, weight=5.0),
                type='MaskHungarianAssigner'),
            importance_sample_ratio=0.75,
            num_points=12544,
            oversample_ratio=3.0,
            sampler=dict(_scope_='mmdet', type='MaskPseudoSampler')),
        transformer_decoder=dict(
            init_cfg=None,
            num_layers=9,
            return_intermediate=True,
            transformerlayers=dict(
                attn_cfgs=dict(
                    attn_drop=0.0,
                    batch_first=False,
                    dropout_layer=None,
                    embed_dims=1024,
                    num_heads=32,
                    proj_drop=0.0,
                    type='MultiheadAttention'),
                feedforward_channels=4096,
                ffn_cfgs=dict(
                    act_cfg=dict(inplace=True, type='ReLU'),
                    add_identity=True,
                    dropout_layer=None,
                    embed_dims=1024,
                    feedforward_channels=4096,
                    ffn_drop=0.0,
                    num_fcs=2),
                operation_order=(
                    'cross_attn',
                    'norm',
                    'self_attn',
                    'norm',
                    'ffn',
                    'norm',
                ),
                type='DetrTransformerDecoderLayer'),
            type='DetrTransformerDecoder'),
        type='AdapterMask2FormerHead'),
    test_cfg=dict(crop_size=(
        896,
        896,
    ), mode='slide', stride=(
        512,
        512,
    )),
    train_cfg=dict(),
    type='EncoderDecoder')
norm_cfg = dict(requires_grad=True, type='SyncBN')
num_classes = 150
optim_wrapper = dict(
    _scope_='mmseg',
    clip_grad=None,
    optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005),
    type='OptimWrapper')
optimizer = dict(
    _scope_='mmseg', lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005)
param_scheduler = [
    dict(
        _scope_='mmseg',
        begin=0,
        by_epoch=False,
        end=80000,
        eta_min=0.0001,
        power=0.9,
        type='PolyLR'),
]
resume = False
test_cfg = dict(_scope_='mmseg', type='TestLoop')
test_dataloader = dict(
    batch_size=1,
    dataset=dict(
        _scope_='mmseg',
        data_prefix=dict(
            img_path='images/validation',
            seg_map_path='annotations/validation'),
        data_root='/home/yansu/dataset/mmseg/ADEChallengeData2016/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                3584,
                896,
            ), type='Resize'),
            dict(reduce_zero_label=True, type='LoadAnnotations'),
            dict(type='PackSegInputs'),
        ],
        type='ADE20KDataset'),
    num_workers=4,
    persistent_workers=True,
    sampler=dict(_scope_='mmsegext', shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
    _scope_='mmseg', iou_metrics=[
        'mIoU',
    ], type='IoUMetric')
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(keep_ratio=True, scale=(
        3584,
        896,
    ), type='Resize'),
    dict(reduce_zero_label=True, type='LoadAnnotations'),
    dict(type='PackSegInputs'),
]
train_cfg = dict(
    _scope_='mmseg',
    max_iters=80000,
    type='IterBasedTrainLoop',
    val_interval=8000)
train_dataloader = dict(
    batch_size=2,
    dataset=dict(
        _scope_='mmseg',
        data_prefix=dict(
            img_path='images/training', seg_map_path='annotations/training'),
        data_root='/home/yansu/dataset/mmseg/ADEChallengeData2016/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(reduce_zero_label=True, type='LoadAnnotations'),
            dict(
                keep_ratio=True,
                ratio_range=(
                    0.5,
                    2.0,
                ),
                scale=(
                    3584,
                    896,
                ),
                type='RandomResize'),
            dict(
                cat_max_ratio=0.75, crop_size=(
                    896,
                    896,
                ), type='RandomCrop'),
            dict(prob=0.5, type='RandomFlip'),
            dict(type='PhotoMetricDistortion'),
            dict(type='PackSegInputs'),
        ],
        type='ADE20KDataset'),
    num_workers=4,
    persistent_workers=True,
    sampler=dict(_scope_='mmsegext', shuffle=True, type='InfiniteSampler'))
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(reduce_zero_label=True, type='LoadAnnotations'),
    dict(
        keep_ratio=True,
        ratio_range=(
            0.5,
            2.0,
        ),
        scale=(
            3584,
            896,
        ),
        type='RandomResize'),
    dict(cat_max_ratio=0.75, crop_size=(
        896,
        896,
    ), type='RandomCrop'),
    dict(prob=0.5, type='RandomFlip'),
    dict(type='PhotoMetricDistortion'),
    dict(type='PackSegInputs'),
]
tta_model = dict(_scope_='mmseg', type='SegTTAModel')
tta_pipeline = [
    dict(backend_args=None, type='LoadImageFromFile'),
    dict(
        _scope_='mmseg',
        transforms=[
            [
                dict(keep_ratio=True, scale=(
                    3584,
                    896,
                ), 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(_scope_='mmseg', type='ValLoop')
val_dataloader = dict(
    batch_size=1,
    dataset=dict(
        _scope_='mmseg',
        data_prefix=dict(
            img_path='images/validation',
            seg_map_path='annotations/validation'),
        data_root='/home/yansu/dataset/mmseg/ADEChallengeData2016/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                3584,
                896,
            ), type='Resize'),
            dict(reduce_zero_label=True, type='LoadAnnotations'),
            dict(type='PackSegInputs'),
        ],
        type='ADE20KDataset'),
    num_workers=4,
    persistent_workers=True,
    sampler=dict(_scope_='mmsegext', shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
    _scope_='mmseg', iou_metrics=[
        'mIoU',
    ], type='IoUMetric')
vis_backends = [
    dict(_scope_='mmseg', type='LocalVisBackend'),
]
visualizer = dict(
    _scope_='mmseg',
    name='visualizer',
    type='SegLocalVisualizer',
    vis_backends=[
        dict(type='LocalVisBackend'),
    ])
work_dir = './work_dirs/mask2former_beit_adapter_large_896_80k_ade20k_ss'

09/24 12:37:05 - mmengine - WARNING - Failed to import `mmsegextension.registry` make sure the registry.py exists in `mmsegextension` package.
09/24 12:37:05 - mmengine - WARNING - Failed to search registry with scope "mmsegextension" in the "model" registry tree. As a workaround, the current "model" registry in "mmseg" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmsegextension" is a correct scope, or whether the registry is initialized.
Traceback (most recent call last):
  File "/home/anba/catkin_ws/src/tas_dev/dev/anba/Mask2Former/mmseg-extension/tools/train.py", line 106, in <module>
    main()
  File "/home/anba/catkin_ws/src/tas_dev/dev/anba/Mask2Former/mmseg-extension/tools/train.py", line 95, in main
    runner = Runner.from_cfg(cfg)
  File "/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmengine/runner/runner.py", line 462, in from_cfg
    runner = cls(
  File "/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmengine/runner/runner.py", line 429, in __init__
    self.model = self.build_model(model)
  File "/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmengine/runner/runner.py", line 836, in build_model
    model = MODELS.build(model)
  File "/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmengine/registry/registry.py", line 570, in build
    return self.build_func(cfg, *args, **kwargs, registry=self)
  File "/home/anba/anaconda3/envs/mmsegext/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 "/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg
    obj = obj_cls(**args)  # type: ignore
  File "/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmseg/models/segmentors/encoder_decoder.py", line 89, in __init__
    self.backbone = MODELS.build(backbone)
  File "/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmengine/registry/registry.py", line 570, in build
    return self.build_func(cfg, *args, **kwargs, registry=self)
  File "/home/anba/anaconda3/envs/mmsegext/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 "/home/anba/anaconda3/envs/mmsegext/lib/python3.9/site-packages/mmengine/registry/build_functions.py", line 100, in build_from_cfg
    raise KeyError(
KeyError: 'BEiTAdapter is not in the mmseg::model registry. Please check whether the value of `BEiTAdapter` is correct or it was registered as expected. More details can be found at https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#import-the-custom-module'
chenller commented 1 month ago

Modify the line 22 of the the CONFIG file to " type='ext-BEiTAdapter', _scope_='mmsegextension', ".

chenller commented 1 month ago

Set the dataset path on line 198

antopost commented 1 month ago

I am still getting this error:

KeyError: 'AdapterMask2FormerHead is not in the mmseg::model registry. Please check whether the value of AdapterMask2FormerHead is correct or it was registered as expected. More details can be found at https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#import-the-custom-module'

chenller commented 1 month ago

Thank you for using this library. I regret to inform you that Mask2FormerHead has been removed in previous versions. The main reasons are as follows. It is difficult to migrate Mask2FormerHead from the old version of mmseg (version<1.0) to the new version (version>=1.0). Mainly because the old version of mmseg uses the old version of mmdet library, while the new version of mmseg uses the new version of mmdet library. The new version of mmdet library has undergone significant changes compared to the old version. In the previous version, I copied the old mmdet library functions and class code used to the new file and re registered them in the new version of mmdet. But this will conflict with functions and classes with the same name in the new version of mmdet. For stability reasons, I have to remove support for Mask2FormerHead. I apologize for my negligence in not being able to update the document in a timely manner. If there are any other options, please let me know and I will update the relevant code to support the model.

antopost commented 1 month ago

I see. I would still like to train and test some state-of-the-art semantic segmentation models. As far as I know ViT-Adapter with Mask2Former head is one of the best models on current benchmarks which is why I find it a bit strange that version>=1.0 no longer supports it.

But maybe you could give me some suggestions for other SOTA models that perform comparably to ViT-Adapter+M2F head that I could easily train and test?

chenller commented 1 month ago

To my knowledge, there is none. You can track papers after April 2024. Thank you.