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deeplabv3.log
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2024/10/24 21:20:09 - 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: 629861339
GPU 0,1,2,3: Tesla V100-SXM2-32GB
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: AVX512
- 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: True
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: 629861339
Distributed launcher: pytorch
Distributed training: True
GPU number: 4
------------------------------------------------------------
2024/10/24 21:20:10 - mmengine - INFO - Config:
bs_ratio = 4
crop_size = (
512,
512,
)
data_preprocessor = dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
512,
512,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor')
data_root = 'data/ade/ADEChallengeData2016'
dataset_type = 'ADE20KDataset'
default_hooks = dict(
checkpoint=dict(by_epoch=False, interval=8000, type='CheckpointHook'),
logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
timer=dict(type='IterTimerHook'),
visualization=dict(type='SegVisualizationHook'))
default_scope = 'mmseg'
env_cfg = dict(
cudnn_benchmark=True,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
img_ratios = [
0.5,
0.75,
1.0,
1.25,
1.5,
1.75,
]
launcher = 'pytorch'
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=False)
max_iters = 80000
model = dict(
auxiliary_head=dict(
align_corners=False,
channels=256,
concat_input=False,
dropout_ratio=0.1,
in_channels=376,
in_index=1,
loss_decode=dict(
loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False),
norm_cfg=dict(requires_grad=True, type='SyncBN'),
num_classes=150,
num_convs=1,
type='FCNHead'),
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=False,
num_classes=80,
num_heads=[
4,
4,
4,
],
out_indices=(
1,
2,
3,
),
patch_size=16,
pretrained=
'../../weights/MobileMamba_B4/mobilemamba_b4.pth',
ssm_ratio=2,
stages=[
's',
's',
's',
],
sync_bn=False,
type='MobileMamba',
window_size=[
7,
7,
7,
]),
data_preprocessor=dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
512,
512,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor'),
decode_head=dict(
align_corners=False,
channels=256,
dilations=(
1,
12,
24,
36,
),
dropout_ratio=0.1,
in_channels=448,
in_index=2,
loss_decode=dict(
loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
norm_cfg=dict(requires_grad=True, type='SyncBN'),
num_classes=150,
type='ASPPHead'),
pretrained=None,
test_cfg=dict(mode='whole'),
train_cfg=dict(),
type='EncoderDecoder')
norm_cfg = dict(requires_grad=True, type='SyncBN')
optim_wrapper = dict(
clip_grad=dict(max_norm=0.1, norm_type=2),
optimizer=dict(
betas=(
0.9,
0.999,
), lr=0.00012, 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')
optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005)
param_scheduler = [
dict(
begin=0, by_epoch=False, end=500, start_factor=1e-05, type='LinearLR'),
dict(
T_max=40000,
begin=40000,
by_epoch=False,
end=80000,
eta_min=0,
type='CosineAnnealingLR'),
]
ratio = 1
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
batch_size=1,
dataset=dict(
data_prefix=dict(
img_path='images/validation',
seg_map_path='annotations/validation'),
data_root='data/ade/ADEChallengeData2016',
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
2048,
512,
), type='Resize'),
dict(reduce_zero_label=True, type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='ADE20KDataset'),
num_workers=2,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
iou_metrics=[
'mIoU',
], type='IoUMetric')
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
2048,
512,
), type='Resize'),
dict(reduce_zero_label=True, type='LoadAnnotations'),
dict(type='PackSegInputs'),
]
train_cfg = dict(max_iters=80000, type='IterBasedTrainLoop', val_interval=8000)
train_dataloader = dict(
batch_size=8,
dataset=dict(
data_prefix=dict(
img_path='images/training', seg_map_path='annotations/training'),
data_root='data/ade/ADEChallengeData2016',
pipeline=[
dict(type='LoadImageFromFile'),
dict(reduce_zero_label=True, type='LoadAnnotations'),
dict(
keep_ratio=True,
ratio_range=(
0.5,
2.0,
),
scale=(
2048,
512,
),
type='RandomResize'),
dict(
cat_max_ratio=0.75, crop_size=(
512,
512,
), type='RandomCrop'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs'),
],
type='ADE20KDataset'),
num_workers=8,
persistent_workers=True,
sampler=dict(shuffle=True, type='InfiniteSampler'))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(reduce_zero_label=True, type='LoadAnnotations'),
dict(
keep_ratio=True,
ratio_range=(
0.5,
2.0,
),
scale=(
2048,
512,
),
type='RandomResize'),
dict(cat_max_ratio=0.75, crop_size=(
512,
512,
), type='RandomCrop'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs'),
]
tta_model = dict(type='SegTTAModel')
tta_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(
transforms=[
[
dict(keep_ratio=True, scale_factor=0.5, type='Resize'),
dict(keep_ratio=True, scale_factor=0.75, type='Resize'),
dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
dict(keep_ratio=True, scale_factor=1.25, type='Resize'),
dict(keep_ratio=True, scale_factor=1.5, type='Resize'),
dict(keep_ratio=True, scale_factor=1.75, type='Resize'),
],
[
dict(direction='horizontal', prob=0.0, type='RandomFlip'),
dict(direction='horizontal', prob=1.0, type='RandomFlip'),
],
[
dict(type='LoadAnnotations'),
],
[
dict(type='PackSegInputs'),
],
],
type='TestTimeAug'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
batch_size=1,
dataset=dict(
data_prefix=dict(
img_path='images/validation',
seg_map_path='annotations/validation'),
data_root='data/ade/ADEChallengeData2016',
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
2048,
512,
), type='Resize'),
dict(reduce_zero_label=True, type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='ADE20KDataset'),
num_workers=2,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
iou_metrics=[
'mIoU',
], type='IoUMetric')
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='SegLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
work_dir = './work_dirs/deeplabv3_mobilemamba_B4-80k_ade20k-512x512'
2024/10/24 21:20:12 - 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
(NORMAL ) SegVisualizationHook
(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 ) SegVisualizationHook
(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 ) SegVisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test:
(VERY_HIGH ) RuntimeInfoHook
--------------------
after_run:
(BELOW_NORMAL) LoggerHook
--------------------
2024/10/24 21:20:14 - mmengine - WARNING - backbone.blocks1.0.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False
2024/10/24 21:20:14 - mmengine - WARNING - backbone.blocks1.0.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.00012
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.00012
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0
2024/10/24 21:20:14 - mmengine - WARNING - backbone.blocks1.1.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False
2024/10/24 21:20:14 - mmengine - WARNING - backbone.blocks1.1.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.00012
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.00012
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0
2024/10/24 21:20:14 - mmengine - WARNING - backbone.blocks2.3.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False
2024/10/24 21:20:14 - mmengine - WARNING - backbone.blocks2.3.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.00012
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.00012
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0
2024/10/24 21:20:14 - mmengine - WARNING - backbone.blocks2.4.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False
2024/10/24 21:20:14 - mmengine - WARNING - backbone.blocks2.4.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.00012
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.00012
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0
2024/10/24 21:20:14 - mmengine - WARNING - backbone.blocks2.5.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False
2024/10/24 21:20:14 - mmengine - WARNING - backbone.blocks2.5.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.00012
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.00012
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0
2024/10/24 21:20:14 - mmengine - WARNING - backbone.blocks3.3.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False
2024/10/24 21:20:14 - mmengine - WARNING - backbone.blocks3.3.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.00012
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.00012
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0
2024/10/24 21:20:14 - mmengine - WARNING - backbone.blocks3.4.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False
2024/10/24 21:20:14 - mmengine - WARNING - backbone.blocks3.4.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.00012
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.00012
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0
2024/10/24 21:20:14 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0
2024/10/24 21:20:14 - mmengine - WARNING - The prefix is not set in metric class IoUMetric.
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 EncoderDecoder
backbone.patch_embed.0.bn.weight - torch.Size([25]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.patch_embed.0.bn.bias - torch.Size([25]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.patch_embed.2.c.weight - torch.Size([50, 25, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.patch_embed.2.bn.weight - torch.Size([50]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.patch_embed.2.bn.bias - torch.Size([50]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.patch_embed.4.c.weight - torch.Size([100, 50, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.patch_embed.4.bn.weight - torch.Size([100]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.patch_embed.4.bn.bias - torch.Size([100]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.patch_embed.6.c.weight - torch.Size([200, 100, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.patch_embed.6.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.patch_embed.6.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
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 EncoderDecoder
backbone.blocks1.0.dw0.m.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks1.0.dw0.m.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
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 EncoderDecoder
backbone.blocks1.0.ffn0.m.pw1.bn.weight - torch.Size([400]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks1.0.ffn0.m.pw1.bn.bias - torch.Size([400]):
The value is the same before and after calling `init_weights` of EncoderDecoder
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 EncoderDecoder
backbone.blocks1.0.ffn0.m.pw2.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks1.0.ffn0.m.pw2.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
backbone.blocks1.0.dw1.m.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks1.0.dw1.m.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
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 EncoderDecoder
backbone.blocks1.0.ffn1.m.pw1.bn.weight - torch.Size([400]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks1.0.ffn1.m.pw1.bn.bias - torch.Size([400]):
The value is the same before and after calling `init_weights` of EncoderDecoder
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 EncoderDecoder
backbone.blocks1.0.ffn1.m.pw2.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks1.0.ffn1.m.pw2.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
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 EncoderDecoder
backbone.blocks1.1.dw0.m.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks1.1.dw0.m.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
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 EncoderDecoder
backbone.blocks1.1.ffn0.m.pw1.bn.weight - torch.Size([400]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks1.1.ffn0.m.pw1.bn.bias - torch.Size([400]):
The value is the same before and after calling `init_weights` of EncoderDecoder
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 EncoderDecoder
backbone.blocks1.1.ffn0.m.pw2.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks1.1.ffn0.m.pw2.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
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 EncoderDecoder
backbone.blocks1.1.dw1.m.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks1.1.dw1.m.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
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 EncoderDecoder
backbone.blocks1.1.ffn1.m.pw1.bn.weight - torch.Size([400]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks1.1.ffn1.m.pw1.bn.bias - torch.Size([400]):
The value is the same before and after calling `init_weights` of EncoderDecoder
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 EncoderDecoder
backbone.blocks1.1.ffn1.m.pw2.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks1.1.ffn1.m.pw2.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
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 EncoderDecoder
backbone.blocks2.0.0.m.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.0.0.m.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
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 EncoderDecoder
backbone.blocks2.0.1.m.pw1.bn.weight - torch.Size([400]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.0.1.m.pw1.bn.bias - torch.Size([400]):
The value is the same before and after calling `init_weights` of EncoderDecoder
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 EncoderDecoder
backbone.blocks2.0.1.m.pw2.bn.weight - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.0.1.m.pw2.bn.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.1.conv1.c.weight - torch.Size([800, 200, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.1.conv1.bn.weight - torch.Size([800]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.1.conv1.bn.bias - torch.Size([800]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.1.conv2.c.weight - torch.Size([800, 1, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.1.conv2.bn.weight - torch.Size([800]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.1.conv2.bn.bias - torch.Size([800]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.1.se.fc1.weight - torch.Size([200, 800, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.1.se.fc1.bias - torch.Size([200]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.1.se.fc2.weight - torch.Size([800, 200, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.1.se.fc2.bias - torch.Size([800]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.1.conv3.c.weight - torch.Size([376, 800, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.1.conv3.bn.weight - torch.Size([376]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.1.conv3.bn.bias - torch.Size([376]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.2.0.m.c.weight - torch.Size([376, 1, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.2.0.m.bn.weight - torch.Size([376]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.2.0.m.bn.bias - torch.Size([376]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.2.1.m.pw1.c.weight - torch.Size([752, 376, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.2.1.m.pw1.bn.weight - torch.Size([752]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.2.1.m.pw1.bn.bias - torch.Size([752]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.2.1.m.pw2.c.weight - torch.Size([376, 752, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.2.1.m.pw2.bn.weight - torch.Size([376]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.2.1.m.pw2.bn.bias - torch.Size([376]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.3.dw0.m.c.weight - torch.Size([376, 1, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.3.dw0.m.bn.weight - torch.Size([376]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.3.dw0.m.bn.bias - torch.Size([376]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.3.ffn0.m.pw1.c.weight - torch.Size([752, 376, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.3.ffn0.m.pw1.bn.weight - torch.Size([752]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.3.ffn0.m.pw1.bn.bias - torch.Size([752]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.3.ffn0.m.pw2.c.weight - torch.Size([376, 752, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.3.ffn0.m.pw2.bn.weight - torch.Size([376]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.3.ffn0.m.pw2.bn.bias - torch.Size([376]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.3.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([75, 1, 5, 5]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.3.mixer.m.attn.local_op.bn1.weight - torch.Size([75]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.3.mixer.m.attn.local_op.bn1.bias - torch.Size([75]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.3.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([75, 1, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.3.mixer.m.attn.local_op.bn2.weight - torch.Size([75]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.blocks2.3.mixer.m.attn.local_op.bn2.bias - torch.Size([75]):
The value is the same before and after calling `init_weights` of EncoderDecoder