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HRSC2016_log.log
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2024/04/18 22:20:01 - mmengine - INFO -
------------------------------------------------------------
System 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: 1777183911
GPU 0: NVIDIA GeForce RTX 3090
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.7, V11.7.64
GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
PyTorch: 2.1.0+cu118
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- C++ Version: 201703
- Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.8
- NVCC architecture flags: -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;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_90,code=sm_90
- CuDNN 8.7
- Magma 2.6.1
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.0, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.16.0+cu118
OpenCV: 4.9.0
MMEngine: 0.10.2
Runtime environment:
cudnn_benchmark: False
dist_cfg: {'backend': 'nccl'}
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
seed: 1777183911
Distributed launcher: none
Distributed training: False
GPU number: 1
------------------------------------------------------------
2024/04/18 22:20:01 - mmengine - INFO - Config:
angle_version = 'le90'
backend_args = None
base_lr = 0.000125
checkpoint = '/media/ubuntu/nvidia/wlq/part2/mmrotate/tools/data/weight/cspnext-m_8xb256-rsb-a1-600e_in1k-ecb3bbd9.pth'
custom_hooks = [
dict(type='mmdet.NumClassCheckHook'),
dict(
ema_type='mmdet.ExpMomentumEMA',
momentum=0.0002,
priority=49,
type='EMAHook',
update_buffers=True),
]
data_root = '/media/ubuntu/CE425F4D425F3983/datasets/HRSC2016/'
dataset_type = 'HRSCDataset'
default_hooks = dict(
checkpoint=dict(
interval=4,
max_keep_ckpts=6,
rule='greater',
save_best='auto',
type='CheckpointHook'),
logger=dict(interval=50, type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
timer=dict(type='IterTimerHook'),
visualization=dict(
draw=True,
test_out_dir=
'/media/ubuntu/nvidia/wlq/part2/mmrotate/tools/work_dirs/hrsc/show_result/t2det',
type='mmdet.DetVisualizationHook'))
default_scope = 'mmrotate'
env_cfg = dict(
cudnn_benchmark=False,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
interval = 4
launcher = 'none'
load_from = '/media/ubuntu/nvidia/wlq/part2/mmrotate/tools/work_dirs/hrsc/t2det/t2det-le90_r50_fpn_rr-12x_hrsc_lr-divided-32_bs8/best_dota_mAP_epoch_64.pth'
log_level = 'INFO'
log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
max_epochs = 72
model = dict(
backbone=dict(
act_cfg=dict(type='SiLU'),
arch='P5',
channel_attention=True,
deepen_factor=0.67,
expand_ratio=0.5,
init_cfg=dict(
checkpoint=
'/media/ubuntu/nvidia/wlq/part2/mmrotate/tools/data/weight/cspnext-m_8xb256-rsb-a1-600e_in1k-ecb3bbd9.pth',
prefix='backbone.',
type='Pretrained'),
norm_cfg=dict(type='SyncBN'),
type='mmdet.CSPNeXt',
widen_factor=0.75),
bbox_head=dict(
act_cfg=dict(type='SiLU'),
anchor_generator=dict(
offset=0, strides=[
8,
16,
32,
], type='mmdet.MlvlPointGenerator'),
angle_version='le90',
bbox_coder=dict(angle_version='le90', type='DistanceAnglePointCoder'),
exp_on_reg=True,
feat_channels=192,
in_channels=192,
loss_angle=None,
loss_bbox=dict(loss_weight=2.0, mode='linear', type='RotatedIoULoss'),
loss_cls=dict(
beta=2.0,
loss_weight=1.0,
type='mmdet.QualityFocalLoss',
use_sigmoid=True),
loss_scale_ss=dict(
beta=0.1, loss_weight=0.1, type='mmdet.SmoothL1Loss'),
loss_symmetry_ss=dict(
loss_flp=dict(
beta=0.1, loss_weight=0.1, type='mmdet.SmoothL1Loss'),
loss_rot=dict(
beta=0.1, loss_weight=0.1, type='mmdet.SmoothL1Loss'),
type='T2DetConsistencyLoss',
use_snap_loss=True),
norm_cfg=dict(type='SyncBN'),
num_classes=1,
pred_kernel_size=1,
scale_angle=False,
share_conv=True,
ss_loss_start=0.4,
stacked_convs=2,
type='T2DetHead',
use_hbbox_loss=False,
use_reweighted_loss=False,
use_ss_branch=True,
with_objectness=False),
crop_size=(
800,
800,
),
data_preprocessor=dict(
batch_augments=None,
bgr_to_rgb=False,
boxtype2tensor=False,
mean=[
103.53,
116.28,
123.675,
],
std=[
57.375,
57.12,
58.395,
],
type='mmdet.DetDataPreprocessor'),
neck=dict(
act_cfg=dict(type='SiLU'),
expand_ratio=0.5,
in_channels=[
192,
384,
768,
],
norm_cfg=dict(type='SyncBN'),
num_csp_blocks=2,
out_channels=192,
type='mmdet.CSPNeXtPAFPN'),
prob_flp=0.034999999999999996,
prob_rot=0.6649999999999999,
random_transform=True,
test_cfg=dict(
max_per_img=2000,
min_bbox_size=0,
nms=dict(iou_threshold=0.1, type='nms_rotated'),
nms_pre=2000,
score_thr=0.05),
train_cfg=dict(
allowed_border=-1,
assigner=dict(
iou_calculator=dict(type='RBboxOverlaps2D'),
topk=13,
type='mmdet.DynamicSoftLabelAssignerSaveMemory'),
debug=False,
pos_weight=-1),
type='T2Detector',
view_range=(
0.25,
0.75,
))
optim_wrapper = dict(
optimizer=dict(lr=0.000125, type='AdamW', weight_decay=0.05),
paramwise_cfg=dict(
bias_decay_mult=0, bypass_duplicate=True, norm_decay_mult=0),
type='OptimWrapper')
param_scheduler = [
dict(
begin=0, by_epoch=False, end=1000, start_factor=1e-05,
type='LinearLR'),
dict(
T_max=72,
begin=72,
by_epoch=True,
convert_to_iter_based=True,
end=144,
eta_min=1.6666666666666667e-05,
type='CosineAnnealingLR'),
]
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
batch_size=8,
dataset=dict(
ann_file='Test/test.txt',
backend_args=None,
data_prefix=dict(sub_data_root='Test/'),
data_root='/media/ubuntu/CE425F4D425F3983/datasets/HRSC2016/',
pipeline=[
dict(backend_args=None, type='mmdet.LoadImageFromFile'),
dict(keep_ratio=True, scale=(
800,
800,
), type='mmdet.Resize'),
dict(
box_type='qbox', type='mmdet.LoadAnnotations', with_bbox=True),
dict(
box_type_mapping=dict(gt_bboxes='rbox'),
type='ConvertBoxType'),
dict(
pad_val=dict(img=(
114,
114,
114,
)),
size=(
800,
800,
),
type='mmdet.Pad'),
dict(
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
),
type='mmdet.PackDetInputs'),
],
test_mode=True,
type='HRSCDataset'),
drop_last=False,
num_workers=8,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(metric='mAP', type='DOTAMetric')
test_pipeline = [
dict(backend_args=None, type='mmdet.LoadImageFromFile'),
dict(keep_ratio=True, scale=(
800,
800,
), type='mmdet.Resize'),
dict(
pad_val=dict(img=(
114,
114,
114,
)),
size=(
800,
800,
),
type='mmdet.Pad'),
dict(
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
),
type='mmdet.PackDetInputs'),
]
train_cfg = dict(max_epochs=144, type='EpochBasedTrainLoop', val_interval=4)
train_dataloader = dict(
batch_sampler=None,
batch_size=8,
dataset=dict(
dataset=dict(
ann_file='Train/train.txt',
backend_args=None,
data_prefix=dict(sub_data_root='Train/'),
data_root='/media/ubuntu/CE425F4D425F3983/datasets/HRSC2016/',
filter_cfg=dict(filter_empty_gt=True),
pipeline=[
dict(backend_args=None, type='mmdet.LoadImageFromFile'),
dict(
box_type='qbox',
type='mmdet.LoadAnnotations',
with_bbox=True),
dict(
box_type_mapping=dict(gt_bboxes='rbox'),
type='ConvertBoxType'),
dict(keep_ratio=True, scale=(
800,
800,
), type='mmdet.Resize'),
dict(
direction=[
'horizontal',
'vertical',
'diagonal',
],
prob=0.75,
type='mmdet.RandomFlip'),
dict(angle_range=180, prob=0.5, type='RandomRotate'),
dict(
pad_val=dict(img=(
114,
114,
114,
)),
size=(
800,
800,
),
type='mmdet.Pad'),
dict(type='mmdet.PackDetInputs'),
],
type='HRSCDataset'),
times=3,
type='RepeatDataset'),
num_workers=8,
persistent_workers=True,
sampler=dict(shuffle=True, type='DefaultSampler'))
train_pipeline = [
dict(backend_args=None, type='mmdet.LoadImageFromFile'),
dict(box_type='qbox', type='mmdet.LoadAnnotations', with_bbox=True),
dict(box_type_mapping=dict(gt_bboxes='rbox'), type='ConvertBoxType'),
dict(keep_ratio=True, scale=(
800,
800,
), type='mmdet.Resize'),
dict(
direction=[
'horizontal',
'vertical',
'diagonal',
],
prob=0.75,
type='mmdet.RandomFlip'),
dict(angle_range=180, prob=0.5, type='RandomRotate'),
dict(
pad_val=dict(img=(
114,
114,
114,
)),
size=(
800,
800,
),
type='mmdet.Pad'),
dict(type='mmdet.PackDetInputs'),
]
use_ss_branch = True
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file='Test/test.txt',
backend_args=None,
data_prefix=dict(sub_data_root='Test/'),
data_root='/media/ubuntu/CE425F4D425F3983/datasets/HRSC2016/',
pipeline=[
dict(backend_args=None, type='mmdet.LoadImageFromFile'),
dict(keep_ratio=True, scale=(
800,
800,
), type='mmdet.Resize'),
dict(
box_type='qbox', type='mmdet.LoadAnnotations', with_bbox=True),
dict(
box_type_mapping=dict(gt_bboxes='rbox'),
type='ConvertBoxType'),
dict(
pad_val=dict(img=(
114,
114,
114,
)),
size=(
800,
800,
),
type='mmdet.Pad'),
dict(
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
),
type='mmdet.PackDetInputs'),
],
test_mode=True,
type='HRSCDataset'),
drop_last=False,
num_workers=8,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(metric='mAP', type='DOTAMetric')
val_pipeline = [
dict(backend_args=None, type='mmdet.LoadImageFromFile'),
dict(keep_ratio=True, scale=(
800,
800,
), type='mmdet.Resize'),
dict(box_type='qbox', type='mmdet.LoadAnnotations', with_bbox=True),
dict(box_type_mapping=dict(gt_bboxes='rbox'), type='ConvertBoxType'),
dict(
pad_val=dict(img=(
114,
114,
114,
)),
size=(
800,
800,
),
type='mmdet.Pad'),
dict(
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
),
type='mmdet.PackDetInputs'),
]
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='RotLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
work_dir = './work_dirs/hrsc/t2det/t2det-le90_r50_fpn_rr-12x_hrsc_lr-divided-32_bs8/'
2024/04/18 22:20:03 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
2024/04/18 22:20:03 - mmengine - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) RuntimeInfoHook
(49 ) EMAHook
(BELOW_NORMAL) LoggerHook
--------------------
after_load_checkpoint:
(49 ) EMAHook
--------------------
before_train:
(VERY_HIGH ) RuntimeInfoHook
(49 ) EMAHook
(NORMAL ) IterTimerHook
(VERY_LOW ) CheckpointHook
--------------------
before_train_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(NORMAL ) DistSamplerSeedHook
(NORMAL ) NumClassCheckHook
--------------------
before_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
--------------------
after_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(49 ) EMAHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
after_train_epoch:
(NORMAL ) IterTimerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
before_val:
(VERY_HIGH ) RuntimeInfoHook
--------------------
before_val_epoch:
(49 ) EMAHook
(NORMAL ) IterTimerHook
(NORMAL ) NumClassCheckHook
--------------------
before_val_iter:
(NORMAL ) IterTimerHook
--------------------
after_val_iter:
(NORMAL ) IterTimerHook
(NORMAL ) DetVisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_val_epoch:
(VERY_HIGH ) RuntimeInfoHook
(49 ) EMAHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
after_val:
(VERY_HIGH ) RuntimeInfoHook
--------------------
before_save_checkpoint:
(49 ) EMAHook
--------------------
after_train:
(VERY_HIGH ) RuntimeInfoHook
(VERY_LOW ) CheckpointHook
--------------------
before_test:
(VERY_HIGH ) RuntimeInfoHook
--------------------
before_test_epoch:
(49 ) EMAHook
(NORMAL ) IterTimerHook
--------------------
before_test_iter:
(NORMAL ) IterTimerHook
--------------------
after_test_iter:
(NORMAL ) IterTimerHook
(NORMAL ) DetVisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test_epoch:
(VERY_HIGH ) RuntimeInfoHook
(49 ) EMAHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test:
(VERY_HIGH ) RuntimeInfoHook
--------------------
after_run:
(BELOW_NORMAL) LoggerHook
--------------------
2024/04/18 22:20:04 - mmengine - INFO - Load checkpoint from /media/ubuntu/nvidia/wlq/part2/mmrotate/tools/work_dirs/hrsc/t2det/t2det-le90_r50_fpn_rr-12x_hrsc_lr-divided-32_bs8/best_dota_mAP_epoch_64.pth
2024/04/18 22:20:57 - mmengine - INFO - Epoch(test) [50/56] eta: 0:00:06 time: 1.0287 data_time: 0.9084 memory: 1081
2024/04/18 22:21:02 - mmengine - INFO -
---------------iou_thr: 0.5---------------
2024/04/18 22:21:04 - mmengine - INFO -
+-------+------+------+--------+--------+
| class | gts | dets | recall | ap |
+-------+------+------+--------+--------+
| ship | 1228 | 2377 | 0.9910 | 0.9066 |
+-------+------+------+--------+--------+
| mAP | | | | 0.9066 |
+-------+------+------+--------+--------+
2024/04/18 22:21:04 - mmengine - INFO - Epoch(test) [56/56] dota/mAP: 0.9066 dota/AP50: 0.9070 data_time: 0.9086 time: 1.0267