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open-mmlab / mmsegmentation

OpenMMLab Semantic Segmentation Toolbox and Benchmark.
https://mmsegmentation.readthedocs.io/en/main/
Apache License 2.0
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TypeError: __init__() got an unexpected keyword argument 'style' #3746

Open WHUThcz opened 1 month ago

WHUThcz commented 1 month ago

I change the backbone of PVT config

  07/23 15:14:37 - mmengine - INFO - Config:
checkpoint = 'work_dirs/fpn/max_acc.pth'
crop_size = (
    512,
    512,
)
data_preprocessor = dict(
    bgr_to_rgb=True,
    mean=[
        123.675,
        116.28,
        103.53,
    ],
    pad_val=0,
    seg_pad_val=255,
    size=(
        512,
        512,
    ),
    std=[
        58.395,
        57.12,
        57.375,
    ],
    type='SegDataPreProcessor')
data_root = 'data/VOCdevkit/VOC2012'
dataset_type = 'PascalVOCDataset'
default_hooks = dict(
    checkpoint=dict(by_epoch=False, interval=2000, type='CheckpointHook'),
    logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'),
    param_scheduler=dict(type='ParamSchedulerHook'),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    timer=dict(type='IterTimerHook'),
    visualization=dict(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))
img_ratios = [
    0.5,
    0.75,
    1.0,
    1.25,
    1.5,
    1.75,
]
launcher = 'pytorch'
load_from = 'work_dirs/fpn/max_acc.pth'
log_level = 'INFO'
log_processor = dict(by_epoch=False)
model = dict(
    backbone=dict(
        init_cfg=dict(
            checkpoint='work_dirs/fpn/max_acc.pth', type='Pretrained'),
        style='pytorch',
        type='fpvt_tiny'),
    data_preprocessor=dict(
        bgr_to_rgb=True,
        mean=[
            123.675,
            116.28,
            103.53,
        ],
        pad_val=0,
        seg_pad_val=255,
        size=(
            512,
            512,
        ),
        std=[
            58.395,
            57.12,
            57.375,
        ],
        type='SegDataPreProcessor'),
    decode_head=dict(
        align_corners=False,
        channels=128,
        dropout_ratio=0.1,
        feature_strides=[
            4,
            8,
            16,
            32,
        ],
        in_channels=[
            256,
            256,
            256,
            256,
        ],
        in_index=[
            0,
            1,
            2,
            3,
        ],
        loss_decode=dict(
            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
        norm_cfg=dict(requires_grad=True, type='SyncBN'),
        num_classes=21,
        type='FPNHead'),
    neck=dict(
        in_channels=[
            64,
            128,
            320,
            512,
        ],
        num_outs=4,
        out_channels=256,
        type='FPN'),
    pretrained=True,
    test_cfg=dict(mode='whole'),
    train_cfg=dict(),
    type='EncoderDecoder')
norm_cfg = dict(requires_grad=True, type='SyncBN')
optim_wrapper = dict(
    optimizer=dict(
        betas=(
            0.9,
            0.999,
        ), lr=0.004, type='AdamW', weight_decay=0.01),
    paramwise_cfg=dict(
        custom_keys=dict(
            head=dict(lr_mult=10.0),
            norm=dict(decay_mult=0.0),
            pos_block=dict(decay_mult=0.0))),
    type='OptimWrapper')
optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005)
param_scheduler = [
    dict(
        begin=0, by_epoch=False, end=1500, start_factor=1e-06,
        type='LinearLR'),
    dict(
        begin=1500,
        by_epoch=False,
        end=320000,
        eta_min=0.0,
        power=0.9,
        type='PolyLR'),
]
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='ImageSets/Segmentation/val.txt',
        data_prefix=dict(
            img_path='JPEGImages', seg_map_path='SegmentationClass'),
        data_root='data/VOCdevkit/VOC2012',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                2048,
                512,
            ), type='Resize'),
            dict(type='LoadAnnotations'),
            dict(type='PackSegInputs'),
        ],
        reduce_zero_label=True,
        type='PascalVOCDataset'),
    num_workers=4,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
    iou_metrics=[
        'mIoU',
    ], type='IoUMetric')
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(keep_ratio=True, scale=(
        2048,
        512,
    ), type='Resize'),
    dict(type='LoadAnnotations'),
    dict(type='PackSegInputs'),
]
train_cfg = dict(max_iters=20000, type='IterBasedTrainLoop', val_interval=2000)
train_dataloader = dict(
    batch_size=8,
    dataset=dict(
        ann_file='ImageSets/Segmentation/train.txt',
        data_prefix=dict(
            img_path='JPEGImages', seg_map_path='SegmentationClass'),
        data_root='data/VOCdevkit/VOC2012',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations'),
            dict(
                keep_ratio=True,
                ratio_range=(
                    0.5,
                    2.0,
                ),
                scale=(
                    2048,
                    512,
                ),
                type='RandomResize'),
            dict(
                cat_max_ratio=0.75, crop_size=(
                    512,
                    512,
                ), type='RandomCrop'),
            dict(prob=0.5, type='RandomFlip'),
            dict(type='PhotoMetricDistortion'),
            dict(type='PackSegInputs'),
        ],
        reduce_zero_label=True,
        type='PascalVOCDataset'),
    num_workers=2,
    persistent_workers=True,
    sampler=dict(shuffle=True, type='InfiniteSampler'))
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(
        keep_ratio=True,
        ratio_range=(
            0.5,
            2.0,
        ),
        scale=(
            2048,
            512,
        ),
        type='RandomResize'),
    dict(cat_max_ratio=0.75, crop_size=(
        512,
        512,
    ), type='RandomCrop'),
    dict(prob=0.5, type='RandomFlip'),
    dict(type='PhotoMetricDistortion'),
    dict(type='PackSegInputs'),
]
tta_model = dict(type='SegTTAModel')
tta_pipeline = [
    dict(backend_args=None, type='LoadImageFromFile'),
    dict(
        transforms=[
            [
                dict(keep_ratio=True, scale_factor=0.5, type='Resize'),
                dict(keep_ratio=True, scale_factor=0.75, type='Resize'),
                dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
                dict(keep_ratio=True, scale_factor=1.25, type='Resize'),
                dict(keep_ratio=True, scale_factor=1.5, type='Resize'),
                dict(keep_ratio=True, scale_factor=1.75, 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(type='ValLoop')
val_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='ImageSets/Segmentation/val.txt',
        data_prefix=dict(
            img_path='JPEGImages', seg_map_path='SegmentationClass'),
        data_root='data/VOCdevkit/VOC2012',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(keep_ratio=True, scale=(
                2048,
                512,
            ), type='Resize'),
            dict(type='LoadAnnotations'),
            dict(type='PackSegInputs'),
        ],
        reduce_zero_label=True,
        type='PascalVOCDataset'),
    num_workers=4,
    persistent_workers=True,
    sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
    iou_metrics=[
        'mIoU',
    ], type='IoUMetric')
vis_backends = [
    dict(type='LocalVisBackend'),
]
visualizer = dict(
    name='visualizer',
    type='SegLocalVisualizer',
    vis_backends=[
        dict(type='LocalVisBackend'),
    ])
work_dir = './work_dirs/fpn_fpvt_t_pascal_voc12_40k'

the command 

  bash tools/dist_train.sh configs/fpvt/fpn_fpvt_t_pascal_voc12_40k.py 2

I have previously used this model for image classification tasks on ImageNet1k in the same environment. Now, I want to transfer the model weights to mmsegmentation for training on segmentation tasks.

Environment

sys.platform: linux
Python: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0]
CUDA available: True
numpy_random_seed: 2147483648
GPU 0,1: NVIDIA GeForce RTX 2080 Ti
CUDA_HOME: :/home/zhangbo/cuda-10.2
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.12.1+cu102
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.2
  - 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
  - CuDNN 7.6.5
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 

TorchVision: 0.13.1+cu102
OpenCV: 4.9.0
MMEngine: 0.10.2
MMSegmentation: 1.2.2+c685fe6

**Error traceback**

```none
Traceback (most recent call last):
  File "tools/train.py", line 105, in <module>
    main()
  File "tools/train.py", line 94, in main
    runner = Runner.from_cfg(cfg)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/runner/runner.py", line 462, in from_cfg
    runner = cls(
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/runner/runner.py", line 429, in __init__
    self.model = self.build_model(model)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/runner/runner.py", line 836, in build_model
    model = MODELS.build(model)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/registry/registry.py", line 570, in build
    return self.build_func(cfg, *args, **kwargs, registry=self)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 232, in build_model_from_cfg
    return build_from_cfg(cfg, registry, default_args)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg
    obj = obj_cls(**args)  # type: ignore
  File "/data/datas/hcz/mmsegmentation/mmseg/models/segmentors/encoder_decoder.py", line 89, in __init__
    self.backbone = MODELS.build(backbone)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/registry/registry.py", line 570, in build
    return self.build_func(cfg, *args, **kwargs, registry=self)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 232, in build_model_from_cfg
    return build_from_cfg(cfg, registry, default_args)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg
    obj = obj_cls(**args)  # type: ignore
  File "/data/datas/hcz/mmsegmentation/mmseg/models/backbones/fpvt.py", line 662, in fpvt_tiny
    model = PyramidVisionTransformerV2(
TypeError: __init__() got an unexpected keyword argument 'style'
Traceback (most recent call last):
  File "tools/train.py", line 105, in <module>
    main()
  File "tools/train.py", line 94, in main
    runner = Runner.from_cfg(cfg)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/runner/runner.py", line 462, in from_cfg
    runner = cls(
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/runner/runner.py", line 429, in __init__
    self.model = self.build_model(model)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/runner/runner.py", line 836, in build_model
    model = MODELS.build(model)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/registry/registry.py", line 570, in build
    return self.build_func(cfg, *args, **kwargs, registry=self)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 232, in build_model_from_cfg
    return build_from_cfg(cfg, registry, default_args)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg
    obj = obj_cls(**args)  # type: ignore
  File "/data/datas/hcz/mmsegmentation/mmseg/models/segmentors/encoder_decoder.py", line 89, in __init__
    self.backbone = MODELS.build(backbone)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/registry/registry.py", line 570, in build
    return self.build_func(cfg, *args, **kwargs, registry=self)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 232, in build_model_from_cfg
    return build_from_cfg(cfg, registry, default_args)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg
    obj = obj_cls(**args)  # type: ignore
  File "/data/datas/hcz/mmsegmentation/mmseg/models/backbones/fpvt.py", line 662, in fpvt_tiny
    model = PyramidVisionTransformerV2(
TypeError: __init__() got an unexpected keyword argument 'style'
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 5205) of binary: /data/datas/hcz/envs/mmseg/bin/python
Traceback (most recent call last):
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in <module>
    main()
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main
    launch(args)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch
    run(args)
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/torch/distributed/run.py", line 752, in run
    elastic_launch(
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/data/datas/hcz/envs/mmseg/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
tools/train.py FAILED
------------------------------------------------------------
Failures:
[1]:
  time      : 2024-07-23_15:14:42
  host      : ubuntu
  rank      : 1 (local_rank: 1)
  exitcode  : 1 (pid: 5206)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2024-07-23_15:14:42
  host      : ubuntu
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 5205)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
AI-Tianlong commented 1 month ago

Hello, fpvt是你自己实现的嘛，看起来在config里，你给了一个 style='pytorch'的参数，因为我看不到fpvt的模型定义，不知道这个style是干什么，能提供更多地信息嘛？或者你在config里，将 style='pytorch' 注释掉呢？

WHUThcz commented 1 month ago

Hello, fpvt是你自己实现的嘛，看起来在config里，你给了一个 style='pytorch'的参数，因为我看不到fpvt的模型定义，不知道这个style是干什么，能提供更多地信息嘛？或者你在config里，将 style='pytorch' 注释掉呢？

我刚看了一下，我在运行的config文件中没有找到style='pytorch'这一行参数，但是我在执行了之前的command命令之后所产生的work_dir文件中找到了包含所有参数的config文件，其中包含这一行参数，删去之后执行它发现是可以运行的。万分感谢！