用这句代码 python tools/train.py configs/rotated_rtmdet/rotated_rtmdet_s-3x-dota.py 开始开始训练，但只跑了一次rotated_rtmdet/rotated_rtmdet_s-3x-dota.py的模型代码，就一直卡在代码结尾，无法真正开始训练。anaconda中状态如下，
(mmrotate) D:\Suanfa\MMrotate\mmrotate-dev-1.x>python tools/train.py configs/rotated_rtmdet/rotated_rtmdet_s-3x-dota.py
D:\anaconda\envs\mmrotate\lib\site-packages\torch\cuda\__init__.py:104: UserWarning:
NVIDIA GeForce RTX 4080 with CUDA capability sm_89 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_61 sm_70 sm_75 compute_37.
If you want to use the NVIDIA GeForce RTX 4080 GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/
warnings.warn(incompatible_device_warn.format(device_name, capability, " ".join(arch_list), device_name))
06/14 18:10:23 - mmengine - INFO -
------------------------------------------------------------
System environment:
sys.platform: win32
Python: 3.8.16 (default, Mar 2 2023, 03:18:16) [MSC v.1916 64 bit (AMD64)]
CUDA available: True
numpy_random_seed: 213198004
GPU 0: NVIDIA GeForce RTX 4080
CUDA_HOME: D:\CUDA\ProgramFiles\NVIDIA GPU computing toolkit\CUDA\v11.3
NVCC: Cuda compilation tools, release 11.3, V11.3.58
MSVC: 用于 x64 的 Microsoft (R) C/C++ 优化编译器 19.29.30148 版
GCC: n/a
PyTorch: 1.8.2+cu102
PyTorch compiling details: PyTorch built with:
- C++ Version: 199711
- MSVC 192930040
- Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)
- OpenMP 2019
- 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_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 7.6.5
- Magma 2.5.4
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=C:/w/b/windows/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /w /bigobj -DUSE_PTHREADPOOL -openmp:experimental -DNDEBUG -DUSE_FBGEMM -DUSE_XNNPACK, 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=OFF, USE_NNPACK=OFF, USE_OPENMP=ON,
TorchVision: 0.9.2+cu102
OpenCV: 4.7.0
MMEngine: 0.7.3
Runtime environment:
cudnn_benchmark: False
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: None
Distributed launcher: none
Distributed training: False
GPU number: 1
------------------------------------------------------------
06/14 18:10:24 - mmengine - INFO - Config:
default_scope = 'mmrotate'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=12, max_keep_ckpts=3),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='mmdet.DetVisualizationHook'))
env_cfg = dict(
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='RotLocalVisualizer',
vis_backends=[dict(type='LocalVisBackend')],
name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = None
resume = False
custom_hooks = [
dict(type='mmdet.NumClassCheckHook'),
dict(
type='EMAHook',
ema_type='mmdet.ExpMomentumEMA',
momentum=0.0002,
update_buffers=True,
priority=49)
]
max_epochs = 36
base_lr = 0.00025
interval = 12
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=36, val_interval=12)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0,
end=1000),
dict(
type='CosineAnnealingLR',
eta_min=1.25e-05,
begin=18,
end=36,
T_max=18,
by_epoch=True,
convert_to_iter_based=True)
]
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.00025, weight_decay=0.05),
paramwise_cfg=dict(
norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
dataset_type = 'DOTADataset'
data_root = 'data/split_ss_dota/'
backend_args = None
train_pipeline = [
dict(type='mmdet.LoadImageFromFile', backend_args=None),
dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')),
dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True),
dict(
type='mmdet.RandomFlip',
prob=0.75,
direction=['horizontal', 'vertical', 'diagonal']),
dict(
type='RandomRotate',
prob=0.5,
angle_range=180,
rect_obj_labels=[9, 11]),
dict(
type='mmdet.Pad', size=(1024, 1024),
pad_val=dict(img=(114, 114, 114))),
dict(type='mmdet.PackDetInputs')
]
val_pipeline = [
dict(type='mmdet.LoadImageFromFile', backend_args=None),
dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True),
dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')),
dict(
type='mmdet.Pad', size=(1024, 1024),
pad_val=dict(img=(114, 114, 114))),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
test_pipeline = [
dict(type='mmdet.LoadImageFromFile', backend_args=None),
dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True),
dict(
type='mmdet.Pad', size=(1024, 1024),
pad_val=dict(img=(114, 114, 114))),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=8,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=None,
pin_memory=False,
dataset=dict(
type='DOTADataset',
data_root='data/split_ss_dota/',
ann_file='trainval/annfiles/',
data_prefix=dict(img_path='trainval/images/'),
filter_cfg=dict(filter_empty_gt=True),
pipeline=[
dict(type='mmdet.LoadImageFromFile', backend_args=None),
dict(
type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
dict(
type='ConvertBoxType',
box_type_mapping=dict(gt_bboxes='rbox')),
dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True),
dict(
type='mmdet.RandomFlip',
prob=0.75,
direction=['horizontal', 'vertical', 'diagonal']),
dict(
type='RandomRotate',
prob=0.5,
angle_range=180,
rect_obj_labels=[9, 11]),
dict(
type='mmdet.Pad',
size=(1024, 1024),
pad_val=dict(img=(114, 114, 114))),
dict(type='mmdet.PackDetInputs')
]))
val_dataloader = dict(
batch_size=8,
num_workers=4,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='DOTADataset',
data_root='data/split_ss_dota/',
ann_file='trainval/annfiles/',
data_prefix=dict(img_path='trainval/images/'),
test_mode=True,
pipeline=[
dict(type='mmdet.LoadImageFromFile', backend_args=None),
dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True),
dict(
type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
dict(
type='ConvertBoxType',
box_type_mapping=dict(gt_bboxes='rbox')),
dict(
type='mmdet.Pad',
size=(1024, 1024),
pad_val=dict(img=(114, 114, 114))),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]))
test_dataloader = dict(
batch_size=8,
num_workers=4,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='DOTADataset',
data_root='data/split_ss_dota/',
ann_file='trainval/annfiles/',
data_prefix=dict(img_path='trainval/images/'),
test_mode=True,
pipeline=[
dict(type='mmdet.LoadImageFromFile', backend_args=None),
dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True),
dict(
type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
dict(
type='ConvertBoxType',
box_type_mapping=dict(gt_bboxes='rbox')),
dict(
type='mmdet.Pad',
size=(1024, 1024),
pad_val=dict(img=(114, 114, 114))),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]))
val_evaluator = dict(type='DOTAMetric', metric='mAP')
test_evaluator = dict(type='DOTAMetric', metric='mAP')
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth'
angle_version = 'le90'
model = dict(
type='mmdet.RTMDet',
data_preprocessor=dict(
type='mmdet.DetDataPreprocessor',
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False,
boxtype2tensor=False,
batch_augments=None),
backbone=dict(
type='mmdet.CSPNeXt',
arch='P5',
expand_ratio=0.5,
deepen_factor=0.33,
widen_factor=0.5,
channel_attention=True,
norm_cfg=dict(type='SyncBN'),
act_cfg=dict(type='SiLU'),
init_cfg=dict(
type='Pretrained',
prefix='backbone.',
checkpoint=
'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth'
)),
neck=dict(
type='mmdet.CSPNeXtPAFPN',
in_channels=[128, 256, 512],
out_channels=128,
num_csp_blocks=1,
expand_ratio=0.5,
norm_cfg=dict(type='SyncBN'),
act_cfg=dict(type='SiLU')),
bbox_head=dict(
type='RotatedRTMDetSepBNHead',
num_classes=1,
in_channels=128,
stacked_convs=2,
feat_channels=128,
angle_version='le90',
anchor_generator=dict(
type='mmdet.MlvlPointGenerator', offset=0, strides=[8, 16, 32]),
bbox_coder=dict(type='DistanceAnglePointCoder', angle_version='le90'),
loss_cls=dict(
type='mmdet.QualityFocalLoss',
use_sigmoid=True,
beta=2.0,
loss_weight=1.0),
loss_bbox=dict(type='RotatedIoULoss', mode='linear', loss_weight=2.0),
with_objectness=False,
exp_on_reg=False,
share_conv=True,
pred_kernel_size=1,
use_hbbox_loss=False,
scale_angle=False,
loss_angle=None,
norm_cfg=dict(type='SyncBN'),
act_cfg=dict(type='SiLU')),
train_cfg=dict(
assigner=dict(
type='mmdet.DynamicSoftLabelAssigner',
iou_calculator=dict(type='RBboxOverlaps2D'),
topk=13),
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=2000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms_rotated', iou_threshold=0.1),
max_per_img=2000))
launcher = 'none'
work_dir = './work_dirs\\rotated_rtmdet_s-3x-dota'
