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coco_segformer_mit-b2_mIoU-44.2.log
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coco_segformer_mit-b2_mIoU-44.2.log
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2023/05/24 01:23:18 - mmengine - INFO -
------------------------------------------------------------
System environment:
sys.platform: linux
Python: 3.9.2 (default, Feb 28 2021, 17:03:44) [GCC 10.2.1 20210110]
CUDA available: True
numpy_random_seed: 681425373
GPU 0,1,2,3,4,5,6,7: Tesla V100-SXM2-32GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.7, V11.7.99
GCC: x86_64-linux-gnu-gcc (Debian 10.2.1-6) 10.2.1 20210110
PyTorch: 1.13.1
PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.7
- NVCC architecture flags: -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
- CuDNN 8.5
- Magma 2.6.1
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.14.1+cu117
OpenCV: 4.7.0
MMEngine: 0.7.3
Runtime environment:
cudnn_benchmark: True
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: None
Distributed launcher: pytorch
Distributed training: True
GPU number: 8
------------------------------------------------------------
2023/05/24 01:23:19 - mmengine - INFO - Config:
norm_cfg = dict(type='SyncBN', requires_grad=True)
data_preprocessor = dict(
type='SegDataPreProcessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_val=0,
seg_pad_val=255,
size=(512, 512))
model = dict(
type='EncoderDecoder',
data_preprocessor=dict(
type='SegDataPreProcessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_val=0,
seg_pad_val=255,
size=(512, 512)),
pretrained='pretrain/mit_b2.pth',
backbone=dict(
type='MixVisionTransformer',
in_channels=3,
embed_dims=64,
num_stages=4,
num_layers=[3, 4, 6, 3],
num_heads=[1, 2, 5, 8],
patch_sizes=[7, 3, 3, 3],
sr_ratios=[8, 4, 2, 1],
out_indices=(0, 1, 2, 3),
mlp_ratio=4,
qkv_bias=True,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.1),
decode_head=dict(
type='SegformerHead',
in_channels=[64, 128, 320, 512],
in_index=[0, 1, 2, 3],
channels=256,
dropout_ratio=0.1,
num_classes=171,
norm_cfg=dict(type='SyncBN', requires_grad=True),
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
train_cfg=dict(),
test_cfg=dict(mode='whole'))
dataset_type = 'COCOStuffDataset'
data_root = '/home/tiger/COCO'
crop_size = (512, 512)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
type='RandomResize',
scale=(2048, 512),
ratio_range=(0.5, 2.0),
keep_ratio=True),
dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=(2048, 512), keep_ratio=True),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs')
]
img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[[{
'type': 'Resize',
'scale_factor': 0.5,
'keep_ratio': True
}, {
'type': 'Resize',
'scale_factor': 0.75,
'keep_ratio': True
}, {
'type': 'Resize',
'scale_factor': 1.0,
'keep_ratio': True
}, {
'type': 'Resize',
'scale_factor': 1.25,
'keep_ratio': True
}, {
'type': 'Resize',
'scale_factor': 1.5,
'keep_ratio': True
}, {
'type': 'Resize',
'scale_factor': 1.75,
'keep_ratio': True
}],
[{
'type': 'RandomFlip',
'prob': 0.0,
'direction': 'horizontal'
}, {
'type': 'RandomFlip',
'prob': 1.0,
'direction': 'horizontal'
}], [{
'type': 'LoadAnnotations'
}], [{
'type': 'PackSegInputs'
}]])
]
val_dataloader = dict(
batch_size=1,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='COCOStuffDataset',
data_root='/home/tiger/COCO',
data_prefix=dict(
img_path='images/val2017', seg_map_path='annotations/val2017'),
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=(2048, 512), keep_ratio=True),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs')
]))
test_dataloader = dict(
batch_size=1,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='COCOStuffDataset',
data_root='/home/tiger/COCO',
data_prefix=dict(
img_path='images/val2017', seg_map_path='annotations/val2017'),
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=(2048, 512), keep_ratio=True),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs')
]))
val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
test_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
default_scope = 'mmseg'
env_cfg = dict(
cudnn_benchmark=True,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='SegLocalVisualizer',
vis_backends=[dict(type='LocalVisBackend')],
name='visualizer')
log_processor = dict(by_epoch=False)
log_level = 'INFO'
load_from = None
resume = False
tta_model = dict(type='SegTTAModel')
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(
type='AdamW', lr=6e-05, betas=(0.9, 0.999), weight_decay=0.01),
paramwise_cfg=dict(
custom_keys=dict(
pos_block=dict(decay_mult=0.0),
norm=dict(decay_mult=0.0),
head=dict(lr_mult=10.0))))
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-06, by_epoch=False, begin=0,
end=1500),
dict(
type='PolyLR',
eta_min=0.0,
power=1.0,
begin=1500,
end=160000,
by_epoch=False)
]
train_cfg = dict(
type='IterBasedTrainLoop', max_iters=160000, val_interval=8000)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(
type='CheckpointHook', by_epoch=False, interval=1000,
max_keep_ckpts=1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='SegVisualizationHook'))
train_dataloader = dict(
batch_size=2,
num_workers=4,
persistent_workers=True,
sampler=dict(type='InfiniteSampler', shuffle=True),
dataset=dict(
type='ConcatDataset',
datasets=[
dict(
type='RepeatDataset',
times=4,
dataset=dict(
type='COCOStuffDataset',
data_root='/home/tiger/COCO',
data_prefix=dict(
img_path='images/train2017',
seg_map_path='annotations/train2017'),
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
type='RandomResize',
scale=(2048, 512),
ratio_range=(0.5, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_size=(512, 512),
cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs')
])),
dict(
type='RepeatDataset',
times=1,
dataset=dict(
type='COCOStuffDataset',
data_root='/home/tiger/COCO_train_6x',
data_prefix=dict(
img_path='images_resampled',
seg_map_path='annotations_ignored_1.25_resampled'),
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
type='RandomResize',
scale=(2048, 512),
ratio_range=(0.5, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_size=(512, 512),
cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs')
]))
]))
data_root_syn = '/home/tiger/COCO_train_6x'
dataset_real_train = dict(
type='RepeatDataset',
times=4,
dataset=dict(
type='COCOStuffDataset',
data_root='/home/tiger/COCO',
data_prefix=dict(
img_path='images/train2017', seg_map_path='annotations/train2017'),
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
type='RandomResize',
scale=(2048, 512),
ratio_range=(0.5, 2.0),
keep_ratio=True),
dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs')
]))
dataset_syn_train = dict(
type='RepeatDataset',
times=1,
dataset=dict(
type='COCOStuffDataset',
data_root='/home/tiger/COCO_train_6x',
data_prefix=dict(
img_path='images_resampled',
seg_map_path='annotations_ignored_1.25_resampled'),
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
type='RandomResize',
scale=(2048, 512),
ratio_range=(0.5, 2.0),
keep_ratio=True),
dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs')
]))
work_dir = './work_dirs/segformer_mit-b2_8xb2-160k_cocostuff164k-512x512-joint-train-bs16-160k-lr1x-6x-filter1.25-resampled'
launcher = 'pytorch'
2023/05/24 01:23:25 - 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_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_train:
(VERY_LOW ) CheckpointHook
--------------------
before_test_epoch:
(NORMAL ) IterTimerHook
--------------------
before_test_iter:
(NORMAL ) IterTimerHook
--------------------
after_test_iter:
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(NORMAL ) SegVisualizationHook
(BELOW_NORMAL) LoggerHook
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after_test_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
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after_run:
(BELOW_NORMAL) LoggerHook
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2023/05/24 01:23:40 - mmengine - INFO - paramwise_options -- backbone.layers.0.0.norm.weight:lr=6e-05
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2023/05/24 01:23:40 - mmengine - INFO - paramwise_options -- decode_head.convs.3.conv.weight:lr=0.0006000000000000001
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2023/05/24 01:23:40 - mmengine - INFO - paramwise_options -- decode_head.fusion_conv.conv.weight:lr=0.0006000000000000001
2023/05/24 01:23:40 - mmengine - INFO - paramwise_options -- decode_head.fusion_conv.conv.weight:weight_decay=0.01
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2023/05/24 01:23:40 - mmengine - INFO - paramwise_options -- decode_head.fusion_conv.bn.bias:weight_decay=0.01
2023/05/24 01:23:40 - mmengine - INFO - paramwise_options -- decode_head.fusion_conv.bn.bias:lr_mult=10.0
2023/05/24 01:23:40 - mmengine - WARNING - The prefix is not set in metric class IoUMetric.
2023/05/24 01:23:41 - mmengine - INFO - load model from: pretrain/mit_b2.pth
2023/05/24 01:23:41 - mmengine - INFO - Loads checkpoint by local backend from path: pretrain/mit_b2.pth
Name of parameter - Initialization information
backbone.layers.0.0.projection.weight - torch.Size([64, 3, 7, 7]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.0.projection.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.0.norm.weight - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.0.norm.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.norm1.weight - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.norm1.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.attn.attn.in_proj_weight - torch.Size([192, 64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.attn.attn.in_proj_bias - torch.Size([192]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.attn.attn.out_proj.weight - torch.Size([64, 64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.attn.attn.out_proj.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.attn.sr.weight - torch.Size([64, 64, 8, 8]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.attn.sr.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.attn.norm.weight - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.attn.norm.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.norm2.weight - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.norm2.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.ffn.layers.0.weight - torch.Size([256, 64, 1, 1]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.ffn.layers.0.bias - torch.Size([256]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.ffn.layers.1.weight - torch.Size([256, 1, 3, 3]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.ffn.layers.1.bias - torch.Size([256]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.ffn.layers.4.weight - torch.Size([64, 256, 1, 1]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.0.ffn.layers.4.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.norm1.weight - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.norm1.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.attn.attn.in_proj_weight - torch.Size([192, 64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.attn.attn.in_proj_bias - torch.Size([192]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.attn.attn.out_proj.weight - torch.Size([64, 64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.attn.attn.out_proj.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.attn.sr.weight - torch.Size([64, 64, 8, 8]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.attn.sr.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.attn.norm.weight - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.attn.norm.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.norm2.weight - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.norm2.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.ffn.layers.0.weight - torch.Size([256, 64, 1, 1]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.ffn.layers.0.bias - torch.Size([256]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.ffn.layers.1.weight - torch.Size([256, 1, 3, 3]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.ffn.layers.1.bias - torch.Size([256]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.ffn.layers.4.weight - torch.Size([64, 256, 1, 1]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.1.ffn.layers.4.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.norm1.weight - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.norm1.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.attn.attn.in_proj_weight - torch.Size([192, 64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.attn.attn.in_proj_bias - torch.Size([192]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.attn.attn.out_proj.weight - torch.Size([64, 64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.attn.attn.out_proj.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.attn.sr.weight - torch.Size([64, 64, 8, 8]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.attn.sr.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.attn.norm.weight - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.attn.norm.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.norm2.weight - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.norm2.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.ffn.layers.0.weight - torch.Size([256, 64, 1, 1]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.ffn.layers.0.bias - torch.Size([256]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.ffn.layers.1.weight - torch.Size([256, 1, 3, 3]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.ffn.layers.1.bias - torch.Size([256]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.ffn.layers.4.weight - torch.Size([64, 256, 1, 1]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.1.2.ffn.layers.4.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.2.weight - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.0.2.bias - torch.Size([64]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.0.projection.weight - torch.Size([128, 64, 3, 3]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.0.projection.bias - torch.Size([128]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.0.norm.weight - torch.Size([128]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.0.norm.bias - torch.Size([128]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.1.0.norm1.weight - torch.Size([128]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.1.0.norm1.bias - torch.Size([128]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.1.0.attn.attn.in_proj_weight - torch.Size([384, 128]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.1.0.attn.attn.in_proj_bias - torch.Size([384]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.1.0.attn.attn.out_proj.weight - torch.Size([128, 128]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.1.0.attn.attn.out_proj.bias - torch.Size([128]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.1.0.attn.sr.weight - torch.Size([128, 128, 4, 4]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.1.0.attn.sr.bias - torch.Size([128]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.1.0.attn.norm.weight - torch.Size([128]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.1.0.attn.norm.bias - torch.Size([128]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.1.0.norm2.weight - torch.Size([128]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.1.0.norm2.bias - torch.Size([128]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.1.0.ffn.layers.0.weight - torch.Size([512, 128, 1, 1]):
PretrainedInit: load from pretrain/mit_b2.pth
backbone.layers.1.1.0.ffn.layers.0.bias - torch.Size([512]):