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TypeError: RotatedFasterRCNN: __init__() got an unexpected keyword argument 'bbox_head' #607

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zhu011 opened this issue Nov 10, 2022 · 5 comments
Closed

TypeError: RotatedFasterRCNN: __init__() got an unexpected keyword argument 'bbox_head' #607

zhu011 opened this issue Nov 10, 2022 · 5 comments

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@zhu011
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zhu011 commented Nov 10, 2022 •
edited by yangxue0827
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Branch

1.x branch https://mmrotate.readthedocs.io/en/1.x/

📚 The doc issue

sys.platform: linux
Python: 3.7.13 (default, Oct 18 2022, 18:57:03) [GCC 11.2.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 2080 Ti
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.1, V11.1.105
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.7.0
PyTorch compiling details: PyTorch built with:

  • GCC 7.3
  • C++ Version: 201402
  • Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
  • Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
  • 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_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
  • 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-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, 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,

TorchVision: 0.8.1
OpenCV: 4.6.0
MMCV: 1.7.0
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.1
MMRotate: 0.3.3+4bbef14

2022-11-10 09:56:49,571 - mmrotate - INFO - Distributed training: False
2022-11-10 09:56:49,861 - mmrotate - INFO - Config:

dataset_type = 'SARDataset'
data_root = 'data/ssdd/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='RResize', img_scale=(1024, 1024)),
    dict(
        type='RRandomFlip',
        flip_ratio=[0.25, 0.25, 0.25],
        direction=['horizontal', 'vertical', 'diagonal'],
        version='le90'),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(608, 608),
        flip=False,
        transforms=[
            dict(type='RResize'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type='SARDataset',
        ann_file='data/ssdd/train/labelTxt/',
        img_prefix='data/ssdd/train/images/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(type='RResize', img_scale=(1024, 1024)),
            dict(
                type='RRandomFlip',
                flip_ratio=[0.25, 0.25, 0.25],
                direction=['horizontal', 'vertical', 'diagonal'],
                version='le90'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ],
        version='le90'),
    val=dict(
        type='SARDataset',
        ann_file='data/ssdd/test/inshore/labelTxt/',
        img_prefix='data/ssdd/test/inshore/images/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(608, 608),
                flip=False,
                transforms=[
                    dict(type='RResize'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='DefaultFormatBundle'),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        version='le90'),
    test=dict(
        type='SARDataset',
        ann_file='data/ssdd/test/offshore/labelTxt/',
        img_prefix='data/ssdd/test/offshore/images/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(608, 608),
                flip=False,
                transforms=[
                    dict(type='RResize'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='DefaultFormatBundle'),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        version='le90'))
evaluation = dict(interval=1, metric='mAP')
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.3333333333333333,
    step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=100)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
angle_version = 'le90'
model = dict(
    type='RotatedFasterRCNN',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RotatedRPNHead',
        in_channels=256,
        feat_channels=256,
        version='le90',
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(
            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),
    roi_head=dict(
        type='RotatedStandardRoIHead',
        version='le90',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='RotatedShared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=15,
            bbox_coder=dict(
                type='DeltaXYWHAHBBoxCoder',
                angle_range='le90',
                norm_factor=2,
                edge_swap=True,
                target_means=(0.0, 0.0, 0.0, 0.0, 0.0),
                target_stds=(0.1, 0.1, 0.2, 0.2, 0.1)),
            reg_class_agnostic=True,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=0,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=2000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=False,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        rpn=dict(
            nms_pre=2000,
            max_per_img=2000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            nms_pre=2000,
            min_bbox_size=0,
            score_thr=0.05,
            nms=dict(iou_thr=0.1),
            max_per_img=2000)),
    bbox_head=dict(
        reg_decoded_bbox=True,
        loss_bbox=dict(type='GDLoss', loss_type='gwd', loss_weight=5.0)))
work_dir = './work_dirs/rotated_retinanet_obb_gwd_r50_fpn_1x_ssdd_le90'
auto_resume = False
gpu_ids = range(0, 1)

2022-11-10 09:56:49,861 - mmrotate - INFO - Set random seed to 1427045959, deterministic: False
Traceback (most recent call last):
File "/root/miniconda3/envs/zyf/lib/python3.7/site-packages/mmcv/utils/registry.py", line 69, in build_from_cfg
return obj_cls(**args)
TypeError: init() got an unexpected keyword argument 'bbox_head'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "tools/train.py", line 192, in
main()
File "tools/train.py", line 165, in main
test_cfg=cfg.get('test_cfg'))
File "/root/autodl-tmp/mmrotate/mmrotate/models/builder.py", line 56, in build_detector
cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg))
File "/root/miniconda3/envs/zyf/lib/python3.7/site-packages/mmcv/utils/registry.py", line 237, in build
return self.build_func(*args, **kwargs, registry=self)
File "/root/miniconda3/envs/zyf/lib/python3.7/site-packages/mmcv/cnn/builder.py", line 27, in build_model_from_cfg
return build_from_cfg(cfg, registry, default_args)
File "/root/miniconda3/envs/zyf/lib/python3.7/site-packages/mmcv/utils/registry.py", line 72, in build_from_cfg
raise type(e)(f'{obj_cls.name}: {e}')
TypeError: RotatedFasterRCNN: init() got an unexpected keyword argument 'bbox_head'

Suggest a potential alternative/fix

TypeError: RotatedFasterRCNN: init() got an unexpected keyword argument 'bbox_head'
@yangxue0827
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bbox_head=dict(
        reg_decoded_bbox=True,
        loss_bbox=dict(type='GDLoss', loss_type='gwd', loss_weight=5.0))

should be put in
roi_head=dict()

@zytx121 zytx121 closed this as completed Nov 13, 2022
@zhu011
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zhu011 commented Nov 13, 2022

好的，谢谢您的解答

@zhu011
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zhu011 commented Nov 13, 2022

base = ['../rotated_faster_rcnn/rotated_faster_rcnn_r50_fpn_1x_dota_le90.py']

model = dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='GDLoss', loss_type='gwd', loss_weight=5.0)))
我在configs中的gwd改成rotated_faster_rcnn_r50_fpn_1x_dota_le90.py
然后在test_cfg=dict(
rpn=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
nms_pre=2000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(iou_thr=0.1),
max_per_img=2000)),
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='GDLoss', loss_type='gwd', loss_weight=5.0)))
出现这个 bbox_head，应如何改才能使在roi_head里

@zhu011
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zhu011 commented Nov 13, 2022 via email

@zhu011
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zhu011 commented Nov 13, 2022

sys.platform: linux
Python: 3.7.13 (default, Oct 18 2022, 18:57:03) [GCC 11.2.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 2080 Ti
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.1, V11.1.105
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.7.0
PyTorch compiling details: PyTorch built with:

  • GCC 7.3
  • C++ Version: 201402
  • Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
  • Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
  • 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_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
  • 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-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, 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,

TorchVision: 0.8.1
OpenCV: 4.6.0
MMCV: 1.7.0
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.1
MMRotate: 0.3.3+4bbef14

2022-11-13 12:56:10,838 - mmrotate - INFO - Distributed training: False
2022-11-13 12:56:11,123 - mmrotate - INFO - Config:
dataset_type = 'SARDataset'
data_root = 'data/ssdd/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RResize', img_scale=(1024, 1024)),
dict(
type='RRandomFlip',
flip_ratio=[0.25, 0.25, 0.25],
direction=['horizontal', 'vertical', 'diagonal'],
version='le90'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(608, 608),
flip=False,
transforms=[
dict(type='RResize'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='SARDataset',
ann_file='data/ssdd/train/labelTxt/',
img_prefix='data/ssdd/train/images/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RResize', img_scale=(1024, 1024)),
dict(
type='RRandomFlip',
flip_ratio=[0.25, 0.25, 0.25],
direction=['horizontal', 'vertical', 'diagonal'],
version='le90'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
],
version='le90'),
val=dict(
type='SARDataset',
ann_file='data/ssdd/test/inshore/labelTxt/',
img_prefix='data/ssdd/test/inshore/images/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(608, 608),
flip=False,
transforms=[
dict(type='RResize'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
],
version='le90'),
test=dict(
type='SARDataset',
ann_file='data/ssdd/test/offshore/labelTxt/',
img_prefix='data/ssdd/test/offshore/images/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(608, 608),
flip=False,
transforms=[
dict(type='RResize'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
],
version='le90'))
evaluation = dict(interval=1, metric='mAP')
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.3333333333333333,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=100)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
angle_version = 'le90'
model = dict(
type='RotatedFasterRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RotatedRPNHead',
in_channels=256,
feat_channels=256,
version='le90',
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),
roi_head=dict(
type='RotatedStandardRoIHead',
version='le90',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='RotatedShared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=15,
bbox_coder=dict(
type='DeltaXYWHAHBBoxCoder',
angle_range='le90',
norm_factor=2,
edge_swap=True,
target_means=(0.0, 0.0, 0.0, 0.0, 0.0),
target_stds=(0.1, 0.1, 0.2, 0.2, 0.1)),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(
type='GDLoss', beta=1.0, loss_weight=5.0, loss_type='gwd'),
reg_decoded_bbox=True)),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
nms_pre=2000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(iou_thr=0.1),
max_per_img=2000)))
work_dir = './work_dirs/rotated_faster_rcnn_obb_gwd_r50_fpn_1x_ssdd_le90'
auto_resume = False
gpu_ids = range(0, 1)

2022-11-13 12:56:11,124 - mmrotate - INFO - Set random seed to 2073921763, deterministic: False
/root/miniconda3/envs/zyf/lib/python3.7/site-packages/mmdet/models/dense_heads/anchor_head.py:116: UserWarning: DeprecationWarning: num_anchors is deprecated, for consistency or also use num_base_priors instead
warnings.warn('DeprecationWarning: num_anchors is deprecated, '
2022-11-13 12:56:11,686 - mmrotate - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}
2022-11-13 12:56:11,687 - mmcv - INFO - load model from: torchvision://resnet50
2022-11-13 12:56:11,688 - mmcv - INFO - load checkpoint from torchvision path: torchvision://resnet50
2022-11-13 12:56:12,113 - mmcv - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: fc.weight, fc.bias

2022-11-13 12:56:12,280 - mmrotate - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2022-11-13 12:56:12,475 - mmrotate - INFO - initialize RotatedRPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01}
2022-11-13 12:56:12,486 - mmrotate - INFO - initialize RotatedShared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'layer': 'Linear', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]
2022-11-13 12:56:15,645 - mmrotate - INFO - Start running, host: root@container-b8cd11b052-650e1e62, work_dir: /root/autodl-tmp/mmrotate/work_dirs/rotated_faster_rcnn_obb_gwd_r50_fpn_1x_ssdd_le90
2022-11-13 12:56:15,645 - mmrotate - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) CheckpointHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook

before_train_epoch:
(VERY_HIGH ) StepLrUpdaterHook
(LOW ) IterTimerHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook

before_train_iter:
(VERY_HIGH ) StepLrUpdaterHook
(LOW ) IterTimerHook
(LOW ) EvalHook

after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(NORMAL ) CheckpointHook
(LOW ) IterTimerHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook

after_train_epoch:
(NORMAL ) CheckpointHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook

before_val_epoch:
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook

before_val_iter:
(LOW ) IterTimerHook

after_val_iter:
(LOW ) IterTimerHook

after_val_epoch:
(VERY_LOW ) TextLoggerHook

after_run:
(VERY_LOW ) TextLoggerHook

2022-11-13 12:56:15,645 - mmrotate - INFO - workflow: [('train', 1)], max: 100 epochs
2022-11-13 12:56:15,646 - mmrotate - INFO - Checkpoints will be saved to /root/autodl-tmp/mmrotate/work_dirs/rotated_faster_rcnn_obb_gwd_r50_fpn_1x_ssdd_le90 by HardDiskBackend.
/root/miniconda3/envs/zyf/lib/python3.7/site-packages/mmdet/models/dense_heads/anchor_head.py:123: UserWarning: DeprecationWarning: anchor_generator is deprecated, please use "prior_generator" instead
warnings.warn('DeprecationWarning: anchor_generator is deprecated, '
Traceback (most recent call last):
File "tools/train.py", line 192, in
main()
File "tools/train.py", line 188, in main
meta=meta)
File "/root/autodl-tmp/mmrotate/mmrotate/apis/train.py", line 141, in train_detector
runner.run(data_loaders, cfg.workflow)
File "/root/miniconda3/envs/zyf/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 136, in run
epoch_runner(data_loaders[i], **kwargs)
File "/root/miniconda3/envs/zyf/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 53, in train
self.run_iter(data_batch, train_mode=True, **kwargs)
File "/root/miniconda3/envs/zyf/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 32, in run_iter
**kwargs)
File "/root/miniconda3/envs/zyf/lib/python3.7/site-packages/mmcv/parallel/data_parallel.py", line 77, in train_step
return self.module.train_step(*inputs[0], **kwargs[0])
File "/root/miniconda3/envs/zyf/lib/python3.7/site-packages/mmdet/models/detectors/base.py", line 248, in train_step
losses = self(**data)
File "/root/miniconda3/envs/zyf/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/root/miniconda3/envs/zyf/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 119, in new_func
return old_func(*args, **kwargs)
File "/root/miniconda3/envs/zyf/lib/python3.7/site-packages/mmdet/models/detectors/base.py", line 172, in forward
return self.forward_train(img, img_metas, **kwargs)
File "/root/autodl-tmp/mmrotate/mmrotate/models/detectors/two_stage.py", line 150, in forward_train
**kwargs)
File "/root/autodl-tmp/mmrotate/mmrotate/models/roi_heads/rotate_standard_roi_head.py", line 154, in forward_train
img_metas)
File "/root/autodl-tmp/mmrotate/mmrotate/models/roi_heads/rotate_standard_roi_head.py", line 203, in _bbox_forward_train
*bbox_targets)
File "/root/miniconda3/envs/zyf/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 208, in new_func
return old_func(*args, **kwargs)
File "/root/autodl-tmp/mmrotate/mmrotate/models/roi_heads/bbox_heads/rotated_bbox_head.py", line 352, in loss
reduction_override=reduction_override)
File "/root/miniconda3/envs/zyf/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/root/autodl-tmp/mmrotate/mmrotate/models/losses/gaussian_dist_loss.py", line 408, in forward
**_kwargs) * self.loss_weight
File "/root/miniconda3/envs/zyf/lib/python3.7/site-packages/mmdet/models/losses/utils.py", line 101, in wrapper
loss = loss_func(pred, target, **kwargs)
TypeError: gwd_loss() got an unexpected keyword argument 'beta'
谢谢您，上个问题改好了，但又出现这类问题，还想麻烦您解惑一下

jistiak added a commit to jistiak/mmrotate that referenced this issue Dec 2, 2022
@jistiak
Update 'bbox_head' to align with new config files
e5ff994 
In the new version of the config files, the 'bbox_head' dict is inside 'roi_head'. If one follows the old documentation, it results in this following issue - 

open-mmlab#607

This way 'bbox_head' is not repeated and the error is solved.
@jistiak jistiak mentioned this issue Dec 2, 2022
Open
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@zytx121 @yangxue0827 @zhu011