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maskrcnn.log
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2024/10/27 21:57:32 - mmengine - INFO -
------------------------------------------------------------
System environment:
sys.platform: linux
Python: 3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 4189642
GPU 0,1,2,3: A100-SXM4-40GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.8, V11.8.89
GCC: gcc (GCC) 7.3.1 20180303 (Red Hat 7.3.1-5)
PyTorch: 2.1.2+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=2.1.2, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.16.2+cu118
OpenCV: 4.9.0
MMEngine: 0.10.5
Runtime environment:
cudnn_benchmark: False
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: 4189642
Distributed launcher: pytorch
Distributed training: True
GPU number: 4
------------------------------------------------------------
2024/10/27 21:57:33 - mmengine - INFO - Config:
auto_scale_lr = dict(base_batch_size=16, enable=False)
backend_args = None
bs_ratio = 2
data_root = 'data/coco/'
dataset_type = 'CocoDataset'
default_hooks = dict(
checkpoint=dict(interval=1, type='CheckpointHook'),
logger=dict(interval=50, type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
timer=dict(type='IterTimerHook'),
visualization=dict(type='DetVisualizationHook'))
default_scope = 'mmdet'
env_cfg = dict(
cudnn_benchmark=False,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
launcher = 'pytorch'
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
max_epochs = 12
model = dict(
backbone=dict(
depth=[
2,
3,
2,
],
distillation=False,
down_ops=[
[
'subsample',
2,
],
[
'subsample',
2,
],
[
'',
],
],
drop_path=0.03,
embed_dim=[
200,
376,
448,
],
forward_type='v052d',
frozen_stages=-1,
global_ratio=[
0.8,
0.7,
0.6,
],
img_size=224,
in_chans=3,
kernels=[
7,
5,
3,
],
local_ratio=[
0.2,
0.2,
0.3,
],
norm_eval=True,
num_classes=80,
num_heads=[
4,
4,
4,
],
out_indices=(
1,
2,
3,
),
patch_size=16,
pretrained=
'../../weights/MobileMamba_B1/mobilemamba_b1.pth',
ssm_ratio=2,
stages=[
's',
's',
's',
],
sync_bn=False,
type='MobileMambaF',
window_size=[
7,
7,
7,
]),
data_preprocessor=dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_mask=True,
pad_size_divisor=32,
std=[
58.395,
57.12,
57.375,
],
type='DetDataPreprocessor'),
neck=dict(
in_channels=[
200,
376,
448,
],
num_extra_trans_convs=2,
num_outs=5,
out_channels=256,
start_level=0,
type='EfficientViTFPN'),
roi_head=dict(
bbox_head=dict(
bbox_coder=dict(
target_means=[
0.0,
0.0,
0.0,
0.0,
],
target_stds=[
0.1,
0.1,
0.2,
0.2,
],
type='DeltaXYWHBBoxCoder'),
fc_out_channels=1024,
in_channels=256,
loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
loss_cls=dict(
loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
num_classes=80,
reg_class_agnostic=False,
roi_feat_size=7,
type='Shared2FCBBoxHead'),
bbox_roi_extractor=dict(
featmap_strides=[
4,
8,
16,
32,
],
out_channels=256,
roi_layer=dict(output_size=7, sampling_ratio=0, type='RoIAlign'),
type='SingleRoIExtractor'),
mask_head=dict(
conv_out_channels=256,
in_channels=256,
loss_mask=dict(
loss_weight=1.0, type='CrossEntropyLoss', use_mask=True),
num_classes=80,
num_convs=4,
type='FCNMaskHead'),
mask_roi_extractor=dict(
featmap_strides=[
4,
8,
16,
32,
],
out_channels=256,
roi_layer=dict(output_size=14, sampling_ratio=0, type='RoIAlign'),
type='SingleRoIExtractor'),
type='StandardRoIHead'),
rpn_head=dict(
anchor_generator=dict(
ratios=[
0.5,
1.0,
2.0,
],
scales=[
8,
],
strides=[
4,
8,
16,
32,
64,
],
type='AnchorGenerator'),
bbox_coder=dict(
target_means=[
0.0,
0.0,
0.0,
0.0,
],
target_stds=[
1.0,
1.0,
1.0,
1.0,
],
type='DeltaXYWHBBoxCoder'),
feat_channels=256,
in_channels=256,
loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
loss_cls=dict(
loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
type='RPNHead'),
test_cfg=dict(
rcnn=dict(
mask_thr_binary=0.5,
max_per_img=100,
nms=dict(iou_threshold=0.5, type='nms'),
score_thr=0.05),
rpn=dict(
max_per_img=1000,
min_bbox_size=0,
nms=dict(iou_threshold=0.7, type='nms'),
nms_pre=1000)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
ignore_iof_thr=-1,
match_low_quality=True,
min_pos_iou=0.5,
neg_iou_thr=0.5,
pos_iou_thr=0.5,
type='MaxIoUAssigner'),
debug=False,
mask_size=28,
pos_weight=-1,
sampler=dict(
add_gt_as_proposals=True,
neg_pos_ub=-1,
num=512,
pos_fraction=0.25,
type='RandomSampler')),
rpn=dict(
allowed_border=-1,
assigner=dict(
ignore_iof_thr=-1,
match_low_quality=True,
min_pos_iou=0.3,
neg_iou_thr=0.3,
pos_iou_thr=0.7,
type='MaxIoUAssigner'),
debug=False,
pos_weight=-1,
sampler=dict(
add_gt_as_proposals=False,
neg_pos_ub=-1,
num=256,
pos_fraction=0.5,
type='RandomSampler')),
rpn_proposal=dict(
max_per_img=1000,
min_bbox_size=0,
nms=dict(iou_threshold=0.7, type='nms'),
nms_pre=2000)),
type='MaskRCNN')
optim_wrapper = dict(
clip_grad=dict(max_norm=0.1, norm_type=2),
optimizer=dict(
betas=(
0.9,
0.999,
), lr=0.0002, type='AdamW', weight_decay=0.05),
paramwise_cfg=dict(
custom_keys=dict(
absolute_pos_embed=dict(decay_mult=0.0),
norm=dict(decay_mult=0.0),
relative_position_bias_table=dict(decay_mult=0.0))),
type='OptimWrapper')
param_scheduler = [
dict(
begin=0, by_epoch=False, end=500, start_factor=1e-05, type='LinearLR'),
dict(
T_max=6,
begin=6,
by_epoch=True,
end=12,
eta_min=0,
type='CosineAnnealingLR'),
]
ratio = 1
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file='annotations/instances_val2017.json',
backend_args=None,
data_prefix=dict(img='val2017/'),
data_root='data/coco/',
pipeline=[
dict(backend_args=None, type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
1333,
800,
), type='Resize'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
),
type='PackDetInputs'),
],
test_mode=True,
type='CocoDataset'),
drop_last=False,
num_workers=2,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
ann_file='data/coco/annotations/instances_val2017.json',
backend_args=None,
format_only=False,
metric=[
'bbox',
'segm',
],
type='CocoMetric')
test_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
1333,
800,
), type='Resize'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
),
type='PackDetInputs'),
]
train_cfg = dict(max_epochs=12, type='EpochBasedTrainLoop', val_interval=1)
train_dataloader = dict(
batch_sampler=dict(type='AspectRatioBatchSampler'),
batch_size=4,
dataset=dict(
ann_file='annotations/instances_train2017.json',
backend_args=None,
data_prefix=dict(img='train2017/'),
data_root='data/coco/',
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=[
dict(backend_args=None, type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(keep_ratio=True, scale=(
1333,
800,
), type='Resize'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PackDetInputs'),
],
type='CocoDataset'),
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=True, type='DefaultSampler'))
train_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(keep_ratio=True, scale=(
1333,
800,
), type='Resize'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PackDetInputs'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file='annotations/instances_val2017.json',
backend_args=None,
data_prefix=dict(img='val2017/'),
data_root='data/coco/',
pipeline=[
dict(backend_args=None, type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
1333,
800,
), type='Resize'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
),
type='PackDetInputs'),
],
test_mode=True,
type='CocoDataset'),
drop_last=False,
num_workers=2,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
ann_file='data/coco/annotations/instances_val2017.json',
backend_args=None,
format_only=False,
metric=[
'bbox',
'segm',
],
type='CocoMetric')
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='DetLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
work_dir = './work_dirs/mask-rcnn_mobilemamba_b1_fpn_1x_coco'
2024/10/27 21:59:40 - mmengine - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) RuntimeInfoHook
(BELOW_NORMAL) LoggerHook
--------------------
before_train:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(VERY_LOW ) CheckpointHook
--------------------
before_train_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(NORMAL ) DistSamplerSeedHook
--------------------
before_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
--------------------
after_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(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:
(NORMAL ) IterTimerHook
--------------------
before_val_iter:
(NORMAL ) IterTimerHook
--------------------
after_val_iter:
(NORMAL ) IterTimerHook
(NORMAL ) DetVisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_val_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
after_val:
(VERY_HIGH ) RuntimeInfoHook
--------------------
after_train:
(VERY_HIGH ) RuntimeInfoHook
(VERY_LOW ) CheckpointHook
--------------------
before_test:
(VERY_HIGH ) RuntimeInfoHook
--------------------
before_test_epoch:
(NORMAL ) IterTimerHook
--------------------
before_test_iter:
(NORMAL ) IterTimerHook
--------------------
after_test_iter:
(NORMAL ) IterTimerHook
(NORMAL ) DetVisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test:
(VERY_HIGH ) RuntimeInfoHook
--------------------
after_run:
(BELOW_NORMAL) LoggerHook
--------------------
2024/10/27 22:00:04 - mmengine - WARNING - backbone.blocks1.0.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False
2024/10/27 22:00:04 - mmengine - WARNING - backbone.blocks1.0.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.0002
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.0002
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0
2024/10/27 22:00:04 - mmengine - WARNING - backbone.blocks1.1.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False
2024/10/27 22:00:04 - mmengine - WARNING - backbone.blocks1.1.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.0002
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.0002
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0
2024/10/27 22:00:04 - mmengine - WARNING - backbone.blocks2.3.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False
2024/10/27 22:00:04 - mmengine - WARNING - backbone.blocks2.3.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.0002
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.0002
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0
2024/10/27 22:00:04 - mmengine - WARNING - backbone.blocks2.4.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False
2024/10/27 22:00:04 - mmengine - WARNING - backbone.blocks2.4.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.0002
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.0002
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0
2024/10/27 22:00:04 - mmengine - WARNING - backbone.blocks2.5.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False
2024/10/27 22:00:04 - mmengine - WARNING - backbone.blocks2.5.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.0002
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.0002
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0
2024/10/27 22:00:04 - mmengine - WARNING - backbone.blocks3.3.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False
2024/10/27 22:00:04 - mmengine - WARNING - backbone.blocks3.3.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.0002
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.0002
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0
2024/10/27 22:00:04 - mmengine - WARNING - backbone.blocks3.4.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False
2024/10/27 22:00:04 - mmengine - WARNING - backbone.blocks3.4.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.0002
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.0002
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0
2024/10/27 22:00:04 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0
Name of parameter - Initialization information
backbone.patch_embed.0.c.weight - torch.Size([25, 3, 3, 3]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.patch_embed.0.bn.weight - torch.Size([25]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.patch_embed.0.bn.bias - torch.Size([25]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.patch_embed.2.c.weight - torch.Size([50, 25, 3, 3]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.patch_embed.2.bn.weight - torch.Size([50]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.patch_embed.2.bn.bias - torch.Size([50]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.patch_embed.4.c.weight - torch.Size([100, 50, 3, 3]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.patch_embed.4.bn.weight - torch.Size([100]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.patch_embed.4.bn.bias - torch.Size([100]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.patch_embed.6.c.weight - torch.Size([200, 100, 3, 3]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.patch_embed.6.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.patch_embed.6.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.dw0.m.c.weight - torch.Size([200, 1, 3, 3]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.dw0.m.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.dw0.m.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.ffn0.m.pw1.c.weight - torch.Size([400, 200, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.ffn0.m.pw1.bn.weight - torch.Size([400]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.ffn0.m.pw1.bn.bias - torch.Size([400]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.ffn0.m.pw2.c.weight - torch.Size([200, 400, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.ffn0.m.pw2.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.ffn0.m.pw2.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([40, 1, 7, 7]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.local_op.bn1.weight - torch.Size([40]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.local_op.bn1.bias - torch.Size([40]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([40, 1, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.local_op.bn2.weight - torch.Size([40]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.local_op.bn2.bias - torch.Size([40]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.wt_filter - torch.Size([640, 1, 2, 2]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.iwt_filter - torch.Size([640, 1, 2, 2]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 12, 320]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.global_atten.Ds - torch.Size([640]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([640, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 320, 10]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 320]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([320]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([320]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([640, 160, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([640]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([320, 1, 3, 3]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([320]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([160, 320, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([160]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 160, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([640, 1, 7, 7]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 640, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.proj.1.c.weight - torch.Size([200, 200, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.proj.1.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.mixer.m.attn.proj.1.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.dw1.m.c.weight - torch.Size([200, 1, 3, 3]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.dw1.m.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.dw1.m.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.ffn1.m.pw1.c.weight - torch.Size([400, 200, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.ffn1.m.pw1.bn.weight - torch.Size([400]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.ffn1.m.pw1.bn.bias - torch.Size([400]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.ffn1.m.pw2.c.weight - torch.Size([200, 400, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.ffn1.m.pw2.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.0.ffn1.m.pw2.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.dw0.m.c.weight - torch.Size([200, 1, 3, 3]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.dw0.m.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.dw0.m.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.ffn0.m.pw1.c.weight - torch.Size([400, 200, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.ffn0.m.pw1.bn.weight - torch.Size([400]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.ffn0.m.pw1.bn.bias - torch.Size([400]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.ffn0.m.pw2.c.weight - torch.Size([200, 400, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.ffn0.m.pw2.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.ffn0.m.pw2.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([40, 1, 7, 7]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.local_op.bn1.weight - torch.Size([40]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.local_op.bn1.bias - torch.Size([40]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([40, 1, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.local_op.bn2.weight - torch.Size([40]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.local_op.bn2.bias - torch.Size([40]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.wt_filter - torch.Size([640, 1, 2, 2]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.iwt_filter - torch.Size([640, 1, 2, 2]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 12, 320]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.global_atten.Ds - torch.Size([640]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([640, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 320, 10]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 320]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([320]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([320]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([640, 160, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([640]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([320, 1, 3, 3]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([320]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([160, 320, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([160]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 160, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([640, 1, 7, 7]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 640, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.proj.1.c.weight - torch.Size([200, 200, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.proj.1.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.mixer.m.attn.proj.1.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.dw1.m.c.weight - torch.Size([200, 1, 3, 3]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.dw1.m.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.dw1.m.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.ffn1.m.pw1.c.weight - torch.Size([400, 200, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.ffn1.m.pw1.bn.weight - torch.Size([400]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.ffn1.m.pw1.bn.bias - torch.Size([400]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.ffn1.m.pw2.c.weight - torch.Size([200, 400, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.ffn1.m.pw2.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks1.1.ffn1.m.pw2.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.0.0.m.c.weight - torch.Size([200, 1, 3, 3]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.0.0.m.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.0.0.m.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.0.1.m.pw1.c.weight - torch.Size([400, 200, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.0.1.m.pw1.bn.weight - torch.Size([400]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.0.1.m.pw1.bn.bias - torch.Size([400]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.0.1.m.pw2.c.weight - torch.Size([200, 400, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.0.1.m.pw2.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.0.1.m.pw2.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.1.conv1.c.weight - torch.Size([800, 200, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.1.conv1.bn.weight - torch.Size([800]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.1.conv1.bn.bias - torch.Size([800]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.1.conv2.c.weight - torch.Size([800, 1, 3, 3]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.1.conv2.bn.weight - torch.Size([800]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.1.conv2.bn.bias - torch.Size([800]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.1.se.fc1.weight - torch.Size([200, 800, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.1.se.fc1.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.1.se.fc2.weight - torch.Size([800, 200, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.1.se.fc2.bias - torch.Size([800]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.1.conv3.c.weight - torch.Size([376, 800, 1, 1]):
The value is the same before and after calling `init_weights` of MaskRCNN
backbone.blocks2.1.conv3.bn.weight - torch.Size([376]):
The value is the same before and after calling `init_weights` of MaskRCNN