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About abs_pos_embed in swin #8187

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Money-HY opened this issue Jun 15, 2022 · 1 comment
Closed

About abs_pos_embed in swin #8187

Money-HY opened this issue Jun 15, 2022 · 1 comment
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@jbwang1997

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@Money-HY
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When I use Swin as a backbone for my model, I try to use the use_abs_pos_embed in config file but get an error. How to solve this problem.

Detailed information:
sys.platform: linux
Python: 3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]
CUDA available: True
GPU 0,1,2,3: Tesla V100-PCIE-32GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 10.2, V10.2.89
GCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-36)
PyTorch: 1.8.2
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 v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)
  • OpenMP 201511 (a.k.a. OpenMP 4.5)
  • NNPACK is enabled
  • CPU capability usage: AVX2
  • CUDA Runtime 11.1
  • 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_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
  • CuDNN 8.0.5
  • Magma 2.5.2
  • Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -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, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.8.2, USE_CUDA=ON, USE_CUDNN=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: 0.9.2
OpenCV: 4.5.5
MMCV: 1.4.8
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 11.1
MMRotate: 0.3.0+HEAD

fatal: ambiguous argument 'HEAD': unknown revision or path not in the working tree.
Use '--' to separate paths from revisions, like this:
'git [...] -- [...]'
fatal: ambiguous argument 'HEAD': unknown revision or path not in the working tree.
Use '--' to separate paths from revisions, like this:
'git [...] -- [...]'
2022-06-15 10:57:41,178 - mmrotate - INFO - Distributed training: True
2022-06-15 10:57:41,601 - mmrotate - INFO - Config:
dataset_type = 'DOTADataset'
data_root = '../Dataset/DOTA/DOTA1.0/split_ss_dota1_0/'
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='oc'),
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=(1024, 1024),
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='DOTADataset',
ann_file='../Dataset/DOTA/DOTA1.0/split_ss_dota1_0/trainval/annfiles/',
img_prefix='../Dataset/DOTA/DOTA1.0/split_ss_dota1_0/trainval/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='oc'),
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='oc'),
val=dict(
type='DOTADataset',
ann_file='../Dataset/DOTA/DOTA1.0/split_ss_dota1_0/val/annfiles/',
img_prefix='../Dataset/DOTA/DOTA1.0/split_ss_dota1_0/val/images/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1024, 1024),
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='oc'),
test=dict(
type='DOTADataset',
ann_file='../Dataset/DOTA/DOTA1.0/split_ss_dota1_0/test/images/',
img_prefix='../Dataset/DOTA/DOTA1.0/split_ss_dota1_0/test/images/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1024, 1024),
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='oc'))
evaluation = dict(interval=12, metric='mAP')
optimizer = dict(
type='AdamW',
lr=0.0001,
betas=(0.9, 0.999),
weight_decay=0.05,
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(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=12)
checkpoint_config = dict(interval=12)
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'
pretrained = '../pretrained/swin/swin_tiny.pth'
angle_version = 'oc'
model = dict(
type='R3Det',
backbone=dict(
type='SwinTransformer',
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.2,
patch_norm=True,
use_abs_pos_embed=True,
out_indices=(0, 1, 2, 3),
with_cp=False,
convert_weights=True,
init_cfg=dict(
type='Pretrained', checkpoint='../pretrained/swin/swin_tiny.pth')),
neck=dict(
type='FPN',
in_channels=[96, 192, 384, 768],
out_channels=256,
start_level=1,
add_extra_convs='on_input',
num_outs=5),
bbox_head=dict(
type='RotatedRetinaHead',
num_classes=15,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type='RotatedAnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[1.0, 0.5, 2.0],
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='DeltaXYWHAOBBoxCoder',
angle_range='oc',
norm_factor=None,
edge_swap=False,
proj_xy=False,
target_means=(0.0, 0.0, 0.0, 0.0, 0.0),
target_stds=(1.0, 1.0, 1.0, 1.0, 1.0)),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
frm_cfgs=[dict(in_channels=256, featmap_strides=[8, 16, 32, 64, 128])],
num_refine_stages=1,
refine_heads=[
dict(
type='RotatedRetinaRefineHead',
num_classes=15,
in_channels=256,
stacked_convs=4,
feat_channels=256,
assign_by_circumhbbox=None,
anchor_generator=dict(
type='PseudoAnchorGenerator', strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='DeltaXYWHAOBBoxCoder',
angle_range='oc',
norm_factor=None,
edge_swap=False,
proj_xy=False,
target_means=(0.0, 0.0, 0.0, 0.0, 0.0),
target_stds=(1.0, 1.0, 1.0, 1.0, 1.0)),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0))
],
train_cfg=dict(
s0=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1,
iou_calculator=dict(type='RBboxOverlaps2D')),
allowed_border=-1,
pos_weight=-1,
debug=False),
sr=[
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.6,
neg_iou_thr=0.5,
min_pos_iou=0,
ignore_iof_thr=-1,
iou_calculator=dict(type='RBboxOverlaps2D')),
allowed_border=-1,
pos_weight=-1,
debug=False)
],
stage_loss_weights=[1.0]),
test_cfg=dict(
nms_pre=2000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(iou_thr=0.1),
max_per_img=2000))
find_unused_parameters = True
work_dir = './work_dirs/r3det_swin_abs_fpn_1x_dota_oc'
auto_resume = False
gpu_ids = range(0, 4)

2022-06-15 10:57:41,604 - mmrotate - INFO - Set random seed to 0, deterministic: False
2022-06-15 10:57:42,102 - mmdet - INFO - load checkpoint from local path: ../pretrained/swin/swin_tiny.pth
2022-06-15 10:57:43,727 - mmrotate - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2022-06-15 10:57:43,760 - mmrotate - INFO - initialize RotatedRetinaHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01, 'override': {'type': 'Normal', 'name': 'retina_cls', 'std': 0.01, 'bias_prob': 0.01}}
2022-06-15 10:57:43,824 - mmrotate - INFO - initialize RotatedRetinaRefineHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01, 'override': {'type': 'Normal', 'name': 'retina_cls', 'std': 0.01, 'bias_prob': 0.01}}
fatal: ambiguous argument 'HEAD': unknown revision or path not in the working tree.
Use '--' to separate paths from revisions, like this:
'git [...] -- [...]'
fatal: ambiguous argument 'HEAD': unknown revision or path not in the working tree.
Use '--' to separate paths from revisions, like this:
'git [...] -- [...]'
fatal: ambiguous argument 'HEAD': unknown revision or path not in the working tree.
Use '--' to separate paths from revisions, like this:
'git [...] -- [...]'
fatal: ambiguous argument 'HEAD': unknown revision or path not in the working tree.
Use '--' to separate paths from revisions, like this:
'git [...] -- [...]'
2022-06-15 10:58:16,067 - mmrotate - INFO - Start running, host: sx1904098@gpu03, work_dir: /fs0/home/sx1904098/qhy/mmrotate-main/work_dirs/r3det_swin_abs_fpn_1x_dota_oc
2022-06-15 10:58:16,068 - mmrotate - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) CheckpointHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook

before_train_epoch:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) DistSamplerSeedHook
(LOW ) IterTimerHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook

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

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

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

before_val_epoch:
(NORMAL ) DistSamplerSeedHook
(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

2022-06-15 10:58:16,068 - mmrotate - INFO - workflow: [('train', 1)], max: 12 epochs
2022-06-15 10:58:16,069 - mmrotate - INFO - Checkpoints will be saved to /fs0/home/sx1904098/qhy/mmrotate-main/work_dirs/r3det_swin_abs_fpn_1x_dota_oc by HardDiskBackend.
Traceback (most recent call last):
File "/fs0/home/sx1904098/qhy/mmrotate-main/tools/train.py", line 192, in
main()
File "/fs0/home/sx1904098/qhy/mmrotate-main/tools/train.py", line 181, in main
train_detector(
File "/fs0/home/sx1904098/qhy/mmrotate-main/mmrotate/apis/train.py", line 141, in train_detector
Traceback (most recent call last):
File "/fs0/home/sx1904098/qhy/mmrotate-main/tools/train.py", line 192, in
main()
File "/fs0/home/sx1904098/qhy/mmrotate-main/tools/train.py", line 181, in main
train_detector(
File "/fs0/home/sx1904098/qhy/mmrotate-main/mmrotate/apis/train.py", line 141, in train_detector
Traceback (most recent call last):
File "/fs0/home/sx1904098/qhy/mmrotate-main/tools/train.py", line 192, in
Traceback (most recent call last):
File "/fs0/home/sx1904098/qhy/mmrotate-main/tools/train.py", line 192, in
main()
File "/fs0/home/sx1904098/qhy/mmrotate-main/tools/train.py", line 181, in main
train_detector(
File "/fs0/home/sx1904098/qhy/mmrotate-main/mmrotate/apis/train.py", line 141, in train_detector
main()
File "/fs0/home/sx1904098/qhy/mmrotate-main/tools/train.py", line 181, in main
train_detector(
File "/fs0/home/sx1904098/qhy/mmrotate-main/mmrotate/apis/train.py", line 141, in train_detector
runner.run(data_loaders, cfg.workflow)
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
runner.run(data_loaders, cfg.workflow)
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
runner.run(data_loaders, cfg.workflow)
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
runner.run(data_loaders, cfg.workflow)
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
epoch_runner(data_loaders[i], **kwargs) epoch_runner(data_loaders[i], **kwargs)
epoch_runner(data_loaders[i], **kwargs)
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train

epoch_runner(data_loaders[i], **kwargs) File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train

File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train
self.run_iter(data_batch, train_mode=True, **kwargs)
self.run_iter(data_batch, train_mode=True, **kwargs) File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/epoch_based_runner.py", line 29, in run_iter

self.run_iter(data_batch, train_mode=True, **kwargs) File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/epoch_based_runner.py", line 29, in run_iter

self.run_iter(data_batch, train_mode=True, **kwargs) File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/epoch_based_runner.py", line 29, in run_iter

File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/epoch_based_runner.py", line 29, in run_iter
outputs = self.model.train_step(data_batch, self.optimizer,
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/parallel/distributed.py", line 59, in train_step
outputs = self.model.train_step(data_batch, self.optimizer,
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/parallel/distributed.py", line 59, in train_step
outputs = self.model.train_step(data_batch, self.optimizer,
outputs = self.model.train_step(data_batch, self.optimizer, File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/parallel/distributed.py", line 59, in train_step

File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/parallel/distributed.py", line 59, in train_step
output = self.module.train_step(*inputs[0], **kwargs[0])
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmdet-2.24.0-py3.9.egg/mmdet/models/detectors/base.py", line 248, in train_step
output = self.module.train_step(*inputs[0], **kwargs[0]) output = self.module.train_step(*inputs[0], **kwargs[0])
output = self.module.train_step(*inputs[0], **kwargs[0])
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmdet-2.24.0-py3.9.egg/mmdet/models/detectors/base.py", line 248, in train_step

File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmdet-2.24.0-py3.9.egg/mmdet/models/detectors/base.py", line 248, in train_step
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmdet-2.24.0-py3.9.egg/mmdet/models/detectors/base.py", line 248, in train_step
losses = self(**data) losses = self(**data)
losses = self(**data)
losses = self(**data) File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl

File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl

File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/fp16_utils.py", line 109, in new_func
result = self.forward(*input, **kwargs)
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/fp16_utils.py", line 109, in new_func
result = self.forward(*input, **kwargs)
result = self.forward(*input, **kwargs)
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/fp16_utils.py", line 109, in new_func
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmcv/runner/fp16_utils.py", line 109, in new_func
return old_func(*args, **kwargs) return old_func(*args, **kwargs)return old_func(*args, **kwargs)
return old_func(*args, **kwargs)

File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmdet-2.24.0-py3.9.egg/mmdet/models/detectors/base.py", line 172, in forward

File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmdet-2.24.0-py3.9.egg/mmdet/models/detectors/base.py", line 172, in forward
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmdet-2.24.0-py3.9.egg/mmdet/models/detectors/base.py", line 172, in forward
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmdet-2.24.0-py3.9.egg/mmdet/models/detectors/base.py", line 172, in forward
return self.forward_train(img, img_metas, **kwargs)
return self.forward_train(img, img_metas, **kwargs)return self.forward_train(img, img_metas, **kwargs) File "/fs0/home/sx1904098/qhy/mmrotate-main/mmrotate/models/detectors/r3det.py", line 83, in forward_train
return self.forward_train(img, img_metas, **kwargs)

File "/fs0/home/sx1904098/qhy/mmrotate-main/mmrotate/models/detectors/r3det.py", line 83, in forward_train
File "/fs0/home/sx1904098/qhy/mmrotate-main/mmrotate/models/detectors/r3det.py", line 83, in forward_train
File "/fs0/home/sx1904098/qhy/mmrotate-main/mmrotate/models/detectors/r3det.py", line 83, in forward_train
x = self.extract_feat(img)
x = self.extract_feat(img) File "/fs0/home/sx1904098/qhy/mmrotate-main/mmrotate/models/detectors/r3det.py", line 54, in extract_feat

  File "/fs0/home/sx1904098/qhy/mmrotate-main/mmrotate/models/detectors/r3det.py", line 54, in extract_feat
x = self.extract_feat(img)x = self.extract_feat(img)

File "/fs0/home/sx1904098/qhy/mmrotate-main/mmrotate/models/detectors/r3det.py", line 54, in extract_feat
File "/fs0/home/sx1904098/qhy/mmrotate-main/mmrotate/models/detectors/r3det.py", line 54, in extract_feat
x = self.backbone(img)x = self.backbone(img)

File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
x = self.backbone(img)x = self.backbone(img)

File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)result = self.forward(*input, **kwargs)

File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmdet-2.24.0-py3.9.egg/mmdet/models/backbones/swin.py", line 749, in forward
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmdet-2.24.0-py3.9.egg/mmdet/models/backbones/swin.py", line 749, in forward
result = self.forward(*input, **kwargs)result = self.forward(*input, **kwargs)

File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmdet-2.24.0-py3.9.egg/mmdet/models/backbones/swin.py", line 749, in forward
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/mmdet-2.24.0-py3.9.egg/mmdet/models/backbones/swin.py", line 749, in forward
x = x + self.absolute_pos_embed
x = x + self.absolute_pos_embedRuntimeError x = x + self.absolute_pos_embed
: x = x + self.absolute_pos_embed
The size of tensor a (65536) must match the size of tensor b (3136) at non-singleton dimension 1
RuntimeError
RuntimeError: RuntimeError: The size of tensor a (65536) must match the size of tensor b (3136) at non-singleton dimension 1: The size of tensor a (65536) must match the size of tensor b (3136) at non-singleton dimension 1
The size of tensor a (65536) must match the size of tensor b (3136) at non-singleton dimension 1

Traceback (most recent call last):
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/torch/distributed/launch.py", line 340, in
main()
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/torch/distributed/launch.py", line 326, in main
sigkill_handler(signal.SIGTERM, None) # not coming back
File "/fs0/software/anaconda/3-2020.07/envs/sx1904098/lib/python3.9/site-packages/torch/distributed/launch.py", line 301, in sigkill_handler
raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd)
subprocess.CalledProcessError: Command '['/fs0/software/anaconda/3-2020.07/envs/sx1904098/bin/python', '-u', 'tools/train.py', '--local_rank=3', 'configs/qhyuse/r3det_swin_abs_fpn_1x_dota_oc.py', '--seed', '0', '--launcher', 'pytorch']' returned non-zero exit status 1.
@jbwang1997
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It's a bug. The pr is in progress. #8127

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