TypeError: EncoderDecoder: SwinTransformer: __init__() got an unexpected keyword argument 'embed_dim'

Checklist

1. I have searched related issues but cannot get the expected help.
2. The bug has not been fixed in the latest version.

Describe the bug There was an error when I ran the code, both in predicting and training, and hopefully someone can help me answer that question. Thanks!

Reproduction

1. What command or script did you run?

   from mmseg.apis import init_segmentor, inference_segmentor, show_result_pyplot
   from mmseg.core.evaluation import get_palette
   config_file = './configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k.py'
   checkpoint_file = './checkpoints/upernet_swin_base_patch4_window7_512x512.pth'
   model = init_segmentor(config_file, checkpoint_file, device='cuda:0')
   

   error:

   TypeError                                 Traceback (most recent call last)
   /usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py in build_from_cfg(cfg, registry, default_args)
    50     try:
    ---> 51         return obj_cls(**args)
    52     except Exception as e:
    TypeError: __init__() got an unexpected keyword argument 'embed_dim'
    During handling of the above exception, another exception occurred
    TypeError                                 Traceback (most recent call last)
    11 frames
    TypeError: SwinTransformer: __init__() got an unexpected keyword argument 'embed_dim'
    During handling of the above exception, another exception occurred:
    TypeError                                 Traceback (most recent call last)
    /usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py in build_from_cfg(cfg, registry, default_args)
    52     except Exception as e:
    53         # Normal TypeError does not print class name.
    ---> 54         raise type(e)(f'{obj_cls.__name__}: {e}')
    55 
    56 
   TypeError: EncoderDecoder: SwinTransformer: __init__() got an unexpected keyword argument 'embed_dim'
   

Environment

   ```shell
   fatal: not a git repository (or any of the parent directories): .git
   2021-08-03 08:12:00,889 - mmseg - INFO - Environment info:
   ------------------------------------------------------------
   sys.platform: linux
   Python: 3.7.11 (default, Jul  3 2021, 18:01:19) [GCC 7.5.0]
   CUDA available: True
   GPU 0: Tesla T4
   CUDA_HOME: /usr/local/cuda
   NVCC: Build cuda_11.0_bu.TC445_37.28845127_0
   GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
   PyTorch: 1.6.0+cu101
   PyTorch compiling details: PyTorch built with:
   - GCC 7.3
   - C++ Version: 201402
   - Intel(R) Math Kernel Library Version 2019.0.5 Product Build 20190808 for Intel(R) 64 architecture applications
   - Intel(R) MKL-DNN v1.5.0 (Git Hash e2ac1fac44c5078ca927cb9b90e1b3066a0b2ed0)
   - OpenMP 201511 (a.k.a. OpenMP 4.5)
   - NNPACK is enabled
   - CPU capability usage: AVX2
   - CUDA Runtime 10.1
   - 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
   - CuDNN 7.6.3
   - Magma 2.5.2
   - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -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 -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF, 
   
   TorchVision: 0.7.0+cu101
   OpenCV: 4.1.2
   MMCV: 1.3.10
   MMCV Compiler: GCC 7.3
   MMCV CUDA Compiler: 10.1
   MMSegmentation: 0.15.0+
   ------------------------------------------------------------
   ```

Error traceback

• the traceback when i run python './tools/train.py' './configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k.py'.

Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py", line 51, in build_from_cfg
    return obj_cls(**args)
TypeError: __init__() got an unexpected keyword argument 'embed_dim'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py", line 51, in build_from_cfg
    return obj_cls(**args)
  File "/usr/local/lib/python3.7/dist-packages/mmseg/models/segmentors/encoder_decoder.py", line 35, in __init__
    self.backbone = builder.build_backbone(backbone)
  File "/usr/local/lib/python3.7/dist-packages/mmseg/models/builder.py", line 17, in build_backbone
    return BACKBONES.build(cfg)
  File "/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py", line 210, in build
    return self.build_func(*args, **kwargs, registry=self)
  File "/usr/local/lib/python3.7/dist-packages/mmcv/cnn/builder.py", line 26, in build_model_from_cfg
    return build_from_cfg(cfg, registry, default_args)
  File "/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py", line 54, in build_from_cfg
    raise type(e)(f'{obj_cls.__name__}: {e}')
TypeError: SwinTransformer: __init__() got an unexpected keyword argument 'embed_dim'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/content/drive/MyDrive/Colab Notebooks/Swin-Transformer-Semantic-Segmentation/tools/train.py", line 163, in <module>
    main()
  File "/content/drive/MyDrive/Colab Notebooks/Swin-Transformer-Semantic-Segmentation/tools/train.py", line 133, in main
    test_cfg=cfg.get('test_cfg'))
  File "/usr/local/lib/python3.7/dist-packages/mmseg/models/builder.py", line 46, in build_segmentor
    cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg))
  File "/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py", line 210, in build
    return self.build_func(*args, **kwargs, registry=self)
  File "/usr/local/lib/python3.7/dist-packages/mmcv/cnn/builder.py", line 26, in build_model_from_cfg
    return build_from_cfg(cfg, registry, default_args)
  File "/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py", line 54, in build_from_cfg
    raise type(e)(f'{obj_cls.__name__}: {e}')
TypeError: EncoderDecoder: SwinTransformer: __init__() got an unexpected keyword argument 'embed_dim'

• the config

2021-08-03 08:12:00,890 - mmseg - INFO - Distributed training: False
2021-08-03 08:12:01,217 - mmseg - INFO - Config:
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='EncoderDecoder',
    pretrained=None,
    backbone=dict(
        type='SwinTransformer',
        embed_dim=128,
        depths=[2, 2, 18, 2],
        num_heads=[4, 8, 16, 32],
        window_size=7,
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.3,
        ape=False,
        patch_norm=True,
        out_indices=(0, 1, 2, 3),
        use_checkpoint=False),
    decode_head=dict(
        type='UPerHead',
        in_channels=[128, 256, 512, 1024],
        in_index=[0, 1, 2, 3],
        pool_scales=(1, 2, 3, 6),
        channels=512,
        dropout_ratio=0.1,
        num_classes=150,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
    auxiliary_head=dict(
        type='FCNHead',
        in_channels=512,
        in_index=2,
        channels=256,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=150,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))
dataset_type = 'ADE20KDataset'
data_root = 'data/ade/ADEChallengeData2016'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', reduce_zero_label=True),
    dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
    dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(2048, 512),
        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='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=4,
    train=dict(
        type='ADE20KDataset',
        data_root='data/ade/ADEChallengeData2016',
        img_dir='images/training',
        ann_dir='annotations/training',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', reduce_zero_label=True),
            dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
            dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
            dict(type='RandomFlip', prob=0.5),
            dict(type='PhotoMetricDistortion'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_semantic_seg'])
        ]),
    val=dict(
        type='ADE20KDataset',
        data_root='data/ade/ADEChallengeData2016',
        img_dir='images/validation',
        ann_dir='annotations/validation',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(2048, 512),
                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='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]),
    test=dict(
        type='ADE20KDataset',
        data_root='data/ade/ADEChallengeData2016',
        img_dir='images/validation',
        ann_dir='annotations/validation',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(2048, 512),
                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='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]))
log_config = dict(
    interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True
optimizer = dict(
    type='AdamW',
    lr=6e-05,
    betas=(0.9, 0.999),
    weight_decay=0.01,
    paramwise_cfg=dict(
        custom_keys=dict(
            absolute_pos_embed=dict(decay_mult=0.0),
            relative_position_bias_table=dict(decay_mult=0.0),
            norm=dict(decay_mult=0.0))))
optimizer_config = dict()
lr_config = dict(
    policy='poly',
    warmup='linear',
    warmup_iters=1500,
    warmup_ratio=1e-06,
    power=1.0,
    min_lr=0.0,
    by_epoch=False)
runner = dict(type='IterBasedRunner', max_iters=160000)
checkpoint_config = dict(by_epoch=False, interval=16000)
evaluation = dict(interval=16000, metric='mIoU')
work_dir = './work_dirs/upernet_swin_base_patch4_window7_512x512_160k_ade20k'
gpu_ids = range(0, 1)

[Junjun2016]

Hi @durianer-D https://github.com/open-mmlab/mmsegmentation/blob/58f5dbce7defe0a5421a30c312f6dd81b5db27c9/configs/_base_/models/upernet_swin.py#L10

The arg is embed_dims.

[kelisiya]

Hi, @Junjun2016 and @nocur Thank you for your help! I have solved this problem. Previously, I used the code released by swin, which uses “enbed_ dim”. Now it can be used normally by changing to mmsegmentation. Thank you very much for your patience！

how to use mmsegmentation code ? when i changed embed_dims , thers was a new error: TypeError: EncoderDecoder: SwinTransformer: __init__() got an unexpected keyword argument 'ape'