2023/05/31 18:32:47 - mmengine - INFO - 
------------------------------------------------------------
System environment:
    sys.platform: linux
    Python: 3.10.9 (main, Mar  8 2023, 10:47:38) [GCC 11.2.0]
    CUDA available: True
    numpy_random_seed: 530487989
    GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB
    CUDA_HOME: /mnt/petrelfs/share/cuda-11.6
    NVCC: Cuda compilation tools, release 11.6, V11.6.124
    GCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44)
    PyTorch: 1.13.1
    PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201402
  - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 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 11.6
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
  - CuDNN 8.3.2  (built against CUDA 11.5)
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/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 -Werror=cast-function-type -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
    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
    deterministic: False
    Distributed launcher: slurm
    Distributed training: True
    GPU number: 8
------------------------------------------------------------

2023/05/31 18:32:48 - mmengine - INFO - Config:
optim_wrapper = dict(
    optimizer=dict(
        type='AdamW',
        lr=0.001,
        weight_decay=0.05,
        eps=1e-08,
        betas=(0.9, 0.999),
        _scope_='mmpretrain'),
    paramwise_cfg=dict(
        norm_decay_mult=0.0,
        bias_decay_mult=0.0,
        flat_decay_mult=0.0,
        custom_keys=dict({
            '.absolute_pos_embed': dict(decay_mult=0.0),
            '.relative_position_bias_table': dict(decay_mult=0.0)
        })),
    type='AmpOptimWrapper',
    dtype='bfloat16',
    clip_grad=dict(max_norm=5.0))
param_scheduler = [
    dict(type='CosineAnnealingLR', eta_min=1e-05, by_epoch=True, begin=0)
]
train_cfg = dict(by_epoch=True, max_epochs=20, val_interval=1)
val_cfg = dict()
test_cfg = dict()
auto_scale_lr = dict(base_batch_size=1024)
model = dict(
    type='ImageClassifier',
    backbone=dict(
        type='SwinTransformer', arch='base', img_size=224, drop_path_rate=0.5),
    neck=dict(type='GlobalAveragePooling'),
    head=dict(
        type='LinearClsHead',
        num_classes=2,
        in_channels=1024,
        init_cfg=None,
        loss=dict(
            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
        cal_acc=False),
    init_cfg=[
        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.0),
        dict(type='Constant', layer='LayerNorm', val=1.0, bias=0.0)
    ],
    train_cfg=None)
dataset_type = 'CustomDataset'
data_preprocessor = dict(
    num_classes=2,
    mean=[123.675, 116.28, 103.53],
    std=[58.395, 57.12, 57.375],
    to_rgb=True)
bgr_mean = [103.53, 116.28, 123.675]
bgr_std = [57.375, 57.12, 58.395]
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='RandomResizedCrop',
        scale=224,
        backend='pillow',
        interpolation='bicubic'),
    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
    dict(type='PackInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='ResizeEdge',
        scale=256,
        edge='short',
        backend='pillow',
        interpolation='bicubic'),
    dict(type='CenterCrop', crop_size=224),
    dict(type='PackInputs')
]
train_dataloader = dict(
    pin_memory=True,
    persistent_workers=True,
    collate_fn=dict(type='default_collate'),
    batch_size=128,
    num_workers=10,
    dataset=dict(
        type='CustomDataset',
        data_root='',
        ann_file=
        '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/stablediffusionV1-5R2-dpmsolver-25-5m.csv',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='RandomResizedCrop',
                scale=224,
                backend='pillow',
                interpolation='bicubic'),
            dict(type='RandomFlip', prob=0.5, direction='horizontal'),
            dict(type='PackInputs')
        ]),
    sampler=dict(type='DefaultSampler', shuffle=True))
val_dataloader = dict(
    pin_memory=True,
    persistent_workers=True,
    collate_fn=dict(type='default_collate'),
    batch_size=256,
    num_workers=10,
    dataset=dict(
        type='CustomDataset',
        data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset',
        ann_file=
        '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV1-5R2-dpmsolver-25-1w.tsv',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='ResizeEdge',
                scale=256,
                edge='short',
                backend='pillow',
                interpolation='bicubic'),
            dict(type='CenterCrop', crop_size=224),
            dict(type='PackInputs')
        ]),
    sampler=dict(type='DefaultSampler', shuffle=False))
val_evaluator = [
    dict(type='Accuracy', topk=1),
    dict(type='SingleLabelMetric', average=None)
]
test_dataloader = dict(
    pin_memory=True,
    persistent_workers=True,
    collate_fn=dict(type='default_collate'),
    batch_size=256,
    num_workers=10,
    dataset=dict(
        type='CustomDataset',
        data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset',
        ann_file=
        '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV1-5R2-dpmsolver-25-1w.tsv',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='ResizeEdge',
                scale=256,
                edge='short',
                backend='pillow',
                interpolation='bicubic'),
            dict(type='CenterCrop', crop_size=224),
            dict(type='PackInputs')
        ]),
    sampler=dict(type='DefaultSampler', shuffle=False))
test_evaluator = [
    dict(type='Accuracy', topk=1),
    dict(type='SingleLabelMetric', average=None)
]
default_scope = 'mmpretrain'
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=100),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=1),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='VisualizationHook', enable=True))
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='UniversalVisualizer',
    vis_backends=[
        dict(type='LocalVisBackend'),
        dict(type='TensorboardVisBackend')
    ])
log_level = 'INFO'
load_from = None
resume = False
randomness = dict(seed=None, deterministic=False)
launcher = 'slurm'
work_dir = 'workdir/swin_base_8xb128_1e-3lr_5m'

2023/05/31 18:32:52 - 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_epoch:
(NORMAL      ) IterTimerHook                      
 -------------------- 
before_val_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_val_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) VisualizationHook                  
(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:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) VisualizationHook                  
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_run:
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.patch_embed.projection.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.patch_embed.norm.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.patch_embed.norm.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.0.norm1.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.0.norm1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.0.attn.w_msa.relative_position_bias_table:lr=0.001
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.0.attn.w_msa.relative_position_bias_table:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.0.attn.w_msa.relative_position_bias_table:decay_mult=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.0.attn.w_msa.qkv.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.0.attn.w_msa.proj.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.0.norm2.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.0.norm2.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.0.ffn.layers.0.0.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.0.ffn.layers.1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.1.norm1.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.1.norm1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.1.attn.w_msa.relative_position_bias_table:lr=0.001
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.1.attn.w_msa.relative_position_bias_table:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.1.attn.w_msa.relative_position_bias_table:decay_mult=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.1.attn.w_msa.qkv.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.1.attn.w_msa.proj.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.1.norm2.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.1.norm2.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.1.ffn.layers.0.0.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.blocks.1.ffn.layers.1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.downsample.norm.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.0.downsample.norm.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.0.norm1.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.0.norm1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.0.attn.w_msa.relative_position_bias_table:lr=0.001
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.0.attn.w_msa.relative_position_bias_table:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.0.attn.w_msa.relative_position_bias_table:decay_mult=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.0.attn.w_msa.qkv.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.0.attn.w_msa.proj.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.0.norm2.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.0.norm2.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.0.ffn.layers.0.0.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.0.ffn.layers.1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.1.norm1.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.1.norm1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.1.attn.w_msa.relative_position_bias_table:lr=0.001
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.1.attn.w_msa.relative_position_bias_table:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.1.attn.w_msa.relative_position_bias_table:decay_mult=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.1.attn.w_msa.qkv.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.1.attn.w_msa.proj.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.1.norm2.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.1.norm2.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.1.ffn.layers.0.0.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.blocks.1.ffn.layers.1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.downsample.norm.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.1.downsample.norm.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.0.norm1.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.0.norm1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.0.attn.w_msa.relative_position_bias_table:lr=0.001
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.0.attn.w_msa.relative_position_bias_table:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.0.attn.w_msa.relative_position_bias_table:decay_mult=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.0.attn.w_msa.qkv.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.0.attn.w_msa.proj.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.0.norm2.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.0.norm2.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.0.ffn.layers.0.0.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.0.ffn.layers.1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.1.norm1.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.1.norm1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.1.attn.w_msa.relative_position_bias_table:lr=0.001
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.1.attn.w_msa.relative_position_bias_table:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.1.attn.w_msa.relative_position_bias_table:decay_mult=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.1.attn.w_msa.qkv.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.1.attn.w_msa.proj.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.1.norm2.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.1.norm2.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.1.ffn.layers.0.0.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.1.ffn.layers.1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.2.norm1.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.2.norm1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.2.attn.w_msa.relative_position_bias_table:lr=0.001
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.2.attn.w_msa.relative_position_bias_table:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.2.attn.w_msa.relative_position_bias_table:decay_mult=0.0
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2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.16.norm1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.16.attn.w_msa.relative_position_bias_table:lr=0.001
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.16.attn.w_msa.relative_position_bias_table:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.16.attn.w_msa.relative_position_bias_table:decay_mult=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.16.attn.w_msa.qkv.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.16.attn.w_msa.proj.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.16.norm2.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.16.norm2.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.16.ffn.layers.0.0.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.16.ffn.layers.1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.17.norm1.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.17.norm1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.17.attn.w_msa.relative_position_bias_table:lr=0.001
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.17.attn.w_msa.relative_position_bias_table:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.17.attn.w_msa.relative_position_bias_table:decay_mult=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.17.attn.w_msa.qkv.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.17.attn.w_msa.proj.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.17.norm2.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.17.norm2.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.17.ffn.layers.0.0.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.blocks.17.ffn.layers.1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.downsample.norm.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.2.downsample.norm.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.0.norm1.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.0.norm1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.0.attn.w_msa.relative_position_bias_table:lr=0.001
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.0.attn.w_msa.relative_position_bias_table:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.0.attn.w_msa.relative_position_bias_table:decay_mult=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.0.attn.w_msa.qkv.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.0.attn.w_msa.proj.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.0.norm2.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.0.norm2.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.0.ffn.layers.0.0.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.0.ffn.layers.1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.1.norm1.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.1.norm1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.1.attn.w_msa.relative_position_bias_table:lr=0.001
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.1.attn.w_msa.relative_position_bias_table:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.1.attn.w_msa.relative_position_bias_table:decay_mult=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.1.attn.w_msa.qkv.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.1.attn.w_msa.proj.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.1.norm2.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.1.norm2.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.1.ffn.layers.0.0.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.stages.3.blocks.1.ffn.layers.1.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.norm3.weight:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- backbone.norm3.bias:weight_decay=0.0
2023/05/31 18:33:19 - mmengine - INFO - paramwise_options -- head.fc.bias:weight_decay=0.0
Name of parameter - Initialization information

backbone.patch_embed.projection.weight - torch.Size([128, 3, 4, 4]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.patch_embed.projection.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.patch_embed.norm.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.patch_embed.norm.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.0.blocks.0.norm1.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.0.blocks.0.norm1.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.0.blocks.0.attn.w_msa.relative_position_bias_table - torch.Size([169, 4]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.0.blocks.0.attn.w_msa.qkv.weight - torch.Size([384, 128]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.blocks.0.attn.w_msa.qkv.bias - torch.Size([384]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.blocks.0.attn.w_msa.proj.weight - torch.Size([128, 128]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.blocks.0.attn.w_msa.proj.bias - torch.Size([128]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.blocks.0.norm2.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.0.blocks.0.norm2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.0.blocks.0.ffn.layers.0.0.weight - torch.Size([512, 128]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.blocks.0.ffn.layers.0.0.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.blocks.0.ffn.layers.1.weight - torch.Size([128, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.blocks.0.ffn.layers.1.bias - torch.Size([128]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.blocks.1.norm1.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.0.blocks.1.norm1.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.0.blocks.1.attn.w_msa.relative_position_bias_table - torch.Size([169, 4]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.0.blocks.1.attn.w_msa.qkv.weight - torch.Size([384, 128]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.blocks.1.attn.w_msa.qkv.bias - torch.Size([384]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.blocks.1.attn.w_msa.proj.weight - torch.Size([128, 128]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.blocks.1.attn.w_msa.proj.bias - torch.Size([128]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.blocks.1.norm2.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.0.blocks.1.norm2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.0.blocks.1.ffn.layers.0.0.weight - torch.Size([512, 128]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.blocks.1.ffn.layers.0.0.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.blocks.1.ffn.layers.1.weight - torch.Size([128, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.blocks.1.ffn.layers.1.bias - torch.Size([128]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.0.downsample.norm.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.0.downsample.norm.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.0.downsample.reduction.weight - torch.Size([256, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.0.norm1.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.1.blocks.0.norm1.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.1.blocks.0.attn.w_msa.relative_position_bias_table - torch.Size([169, 8]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.1.blocks.0.attn.w_msa.qkv.weight - torch.Size([768, 256]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.0.attn.w_msa.qkv.bias - torch.Size([768]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.0.attn.w_msa.proj.weight - torch.Size([256, 256]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.0.attn.w_msa.proj.bias - torch.Size([256]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.0.norm2.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.1.blocks.0.norm2.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.1.blocks.0.ffn.layers.0.0.weight - torch.Size([1024, 256]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.0.ffn.layers.0.0.bias - torch.Size([1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.0.ffn.layers.1.weight - torch.Size([256, 1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.0.ffn.layers.1.bias - torch.Size([256]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.1.norm1.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.1.blocks.1.norm1.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.1.blocks.1.attn.w_msa.relative_position_bias_table - torch.Size([169, 8]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.1.blocks.1.attn.w_msa.qkv.weight - torch.Size([768, 256]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.1.attn.w_msa.qkv.bias - torch.Size([768]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.1.attn.w_msa.proj.weight - torch.Size([256, 256]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.1.attn.w_msa.proj.bias - torch.Size([256]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.1.norm2.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.1.blocks.1.norm2.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.1.blocks.1.ffn.layers.0.0.weight - torch.Size([1024, 256]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.1.ffn.layers.0.0.bias - torch.Size([1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.1.ffn.layers.1.weight - torch.Size([256, 1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.blocks.1.ffn.layers.1.bias - torch.Size([256]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.1.downsample.norm.weight - torch.Size([1024]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.1.downsample.norm.bias - torch.Size([1024]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.1.downsample.reduction.weight - torch.Size([512, 1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.0.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.0.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.0.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.0.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.0.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.0.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.0.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.0.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.0.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.0.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.0.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.0.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.0.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.1.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.1.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.1.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.1.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.1.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.1.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.1.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.1.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.1.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.1.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.1.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.1.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.1.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.2.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.2.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.2.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.2.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.2.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.2.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.2.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.2.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.2.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.2.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.2.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.2.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.2.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.3.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.3.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.3.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.3.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.3.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.3.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.3.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.3.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.3.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.3.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.3.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.3.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.3.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.4.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.4.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.4.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.4.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.4.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.4.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.4.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.4.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.4.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.4.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.4.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.4.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.4.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.5.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.5.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.5.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.5.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.5.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.5.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.5.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.5.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.5.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.5.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.5.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.5.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.5.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.6.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.6.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.6.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.6.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.6.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.6.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.6.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.6.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.6.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.6.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.6.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.6.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.6.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.7.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.7.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.7.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.7.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.7.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.7.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.7.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.7.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.7.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.7.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.7.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.7.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.7.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.8.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.8.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.8.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.8.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.8.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.8.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.8.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.8.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.8.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.8.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.8.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.8.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.8.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.9.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.9.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.9.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.9.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.9.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.9.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.9.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.9.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.9.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.9.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.9.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.9.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.9.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.10.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.10.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.10.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.10.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.10.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.10.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.10.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.10.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.10.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.10.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.10.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.10.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.10.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.11.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.11.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.11.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.11.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.11.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.11.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.11.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.11.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.11.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.11.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.11.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.11.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.11.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.12.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.12.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.12.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.12.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.12.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.12.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.12.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.12.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.12.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.12.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.12.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.12.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.12.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.13.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.13.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.13.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.13.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.13.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.13.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.13.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.13.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.13.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.13.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.13.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.13.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.13.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.14.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.14.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.14.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.14.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.14.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.14.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.14.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.14.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.14.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.14.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.14.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.14.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.14.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.15.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.15.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.15.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.15.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.15.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.15.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.15.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.15.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.15.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.15.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.15.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.15.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.15.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.16.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.16.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.16.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.16.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.16.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.16.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.16.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.16.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.16.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.16.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.16.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.16.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.16.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.17.norm1.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.17.norm1.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.17.attn.w_msa.relative_position_bias_table - torch.Size([169, 16]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.2.blocks.17.attn.w_msa.qkv.weight - torch.Size([1536, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.17.attn.w_msa.qkv.bias - torch.Size([1536]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.17.attn.w_msa.proj.weight - torch.Size([512, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.17.attn.w_msa.proj.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.17.norm2.weight - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.17.norm2.bias - torch.Size([512]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.blocks.17.ffn.layers.0.0.weight - torch.Size([2048, 512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.17.ffn.layers.0.0.bias - torch.Size([2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.17.ffn.layers.1.weight - torch.Size([512, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.blocks.17.ffn.layers.1.bias - torch.Size([512]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.2.downsample.norm.weight - torch.Size([2048]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.downsample.norm.bias - torch.Size([2048]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.2.downsample.reduction.weight - torch.Size([1024, 2048]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.0.norm1.weight - torch.Size([1024]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.3.blocks.0.norm1.bias - torch.Size([1024]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.3.blocks.0.attn.w_msa.relative_position_bias_table - torch.Size([169, 32]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.3.blocks.0.attn.w_msa.qkv.weight - torch.Size([3072, 1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.0.attn.w_msa.qkv.bias - torch.Size([3072]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.0.attn.w_msa.proj.weight - torch.Size([1024, 1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.0.attn.w_msa.proj.bias - torch.Size([1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.0.norm2.weight - torch.Size([1024]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.3.blocks.0.norm2.bias - torch.Size([1024]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.3.blocks.0.ffn.layers.0.0.weight - torch.Size([4096, 1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.0.ffn.layers.0.0.bias - torch.Size([4096]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.0.ffn.layers.1.weight - torch.Size([1024, 4096]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.0.ffn.layers.1.bias - torch.Size([1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.1.norm1.weight - torch.Size([1024]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.3.blocks.1.norm1.bias - torch.Size([1024]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.3.blocks.1.attn.w_msa.relative_position_bias_table - torch.Size([169, 32]): 
Initialized by user-defined `init_weights` in WindowMSA  

backbone.stages.3.blocks.1.attn.w_msa.qkv.weight - torch.Size([3072, 1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.1.attn.w_msa.qkv.bias - torch.Size([3072]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.1.attn.w_msa.proj.weight - torch.Size([1024, 1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.1.attn.w_msa.proj.bias - torch.Size([1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.1.norm2.weight - torch.Size([1024]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.3.blocks.1.norm2.bias - torch.Size([1024]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.stages.3.blocks.1.ffn.layers.0.0.weight - torch.Size([4096, 1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.1.ffn.layers.0.0.bias - torch.Size([4096]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.1.ffn.layers.1.weight - torch.Size([1024, 4096]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.stages.3.blocks.1.ffn.layers.1.bias - torch.Size([1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

backbone.norm3.weight - torch.Size([1024]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

backbone.norm3.bias - torch.Size([1024]): 
The value is the same before and after calling `init_weights` of ImageClassifier  

head.fc.weight - torch.Size([2, 1024]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 

head.fc.bias - torch.Size([2]): 
TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 
2023/05/31 18:33:19 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
2023/05/31 18:33:19 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
2023/05/31 18:33:19 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/luzeyu/workspace/fakebench/mmpretrain/workdir/swin_base_8xb128_1e-3lr_5m.
2023/05/31 18:34:13 - mmengine - INFO - Epoch(train)  [1][ 100/5758]  lr: 1.0000e-03  eta: 17:01:09  time: 0.4505  data_time: 0.0015  memory: 20328  grad_norm: 1.2535  loss: 0.6617
2023/05/31 18:34:59 - mmengine - INFO - Epoch(train)  [1][ 200/5758]  lr: 1.0000e-03  eta: 15:50:29  time: 0.4186  data_time: 0.0021  memory: 20327  grad_norm: 1.3020  loss: 0.6787
2023/05/31 18:35:45 - mmengine - INFO - Epoch(train)  [1][ 300/5758]  lr: 1.0000e-03  eta: 15:31:14  time: 0.4118  data_time: 0.0016  memory: 20327  grad_norm: 1.6164  loss: 0.6962
2023/05/31 18:36:32 - mmengine - INFO - Epoch(train)  [1][ 400/5758]  lr: 1.0000e-03  eta: 15:21:04  time: 0.4402  data_time: 0.0017  memory: 20327  grad_norm: 0.9162  loss: 0.6910
2023/05/31 18:37:18 - mmengine - INFO - Epoch(train)  [1][ 500/5758]  lr: 1.0000e-03  eta: 15:10:26  time: 0.4888  data_time: 0.0025  memory: 20327  grad_norm: 0.7051  loss: 0.6896
2023/05/31 18:38:04 - mmengine - INFO - Epoch(train)  [1][ 600/5758]  lr: 1.0000e-03  eta: 15:04:36  time: 0.4830  data_time: 0.0025  memory: 20327  grad_norm: 1.0663  loss: 0.6936
2023/05/31 18:38:50 - mmengine - INFO - Epoch(train)  [1][ 700/5758]  lr: 1.0000e-03  eta: 15:00:17  time: 0.4459  data_time: 0.0024  memory: 20327  grad_norm: 0.6849  loss: 0.6929
2023/05/31 18:39:35 - mmengine - INFO - Epoch(train)  [1][ 800/5758]  lr: 1.0000e-03  eta: 14:55:29  time: 0.4738  data_time: 0.0020  memory: 20327  grad_norm: 0.4387  loss: 0.6870
2023/05/31 18:40:20 - mmengine - INFO - Epoch(train)  [1][ 900/5758]  lr: 1.0000e-03  eta: 14:49:50  time: 0.4850  data_time: 0.0022  memory: 20327  grad_norm: 0.7461  loss: 0.6842
2023/05/31 18:41:05 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 18:41:05 - mmengine - INFO - Epoch(train)  [1][1000/5758]  lr: 1.0000e-03  eta: 14:46:26  time: 0.5281  data_time: 0.0018  memory: 20327  grad_norm: 0.5114  loss: 0.6821
2023/05/31 18:41:50 - mmengine - INFO - Epoch(train)  [1][1100/5758]  lr: 1.0000e-03  eta: 14:42:37  time: 0.4209  data_time: 0.0019  memory: 20327  grad_norm: 0.4384  loss: 0.6854
2023/05/31 18:42:36 - mmengine - INFO - Epoch(train)  [1][1200/5758]  lr: 1.0000e-03  eta: 14:40:45  time: 0.4580  data_time: 0.0018  memory: 20327  grad_norm: 0.5817  loss: 0.6899
2023/05/31 18:43:21 - mmengine - INFO - Epoch(train)  [1][1300/5758]  lr: 1.0000e-03  eta: 14:38:28  time: 0.4686  data_time: 0.0014  memory: 20327  grad_norm: 0.4683  loss: 0.6840
2023/05/31 18:44:07 - mmengine - INFO - Epoch(train)  [1][1400/5758]  lr: 1.0000e-03  eta: 14:36:32  time: 0.4447  data_time: 0.0020  memory: 20327  grad_norm: 0.4274  loss: 0.6886
2023/05/31 18:44:51 - mmengine - INFO - Epoch(train)  [1][1500/5758]  lr: 1.0000e-03  eta: 14:32:48  time: 0.4260  data_time: 0.0025  memory: 20327  grad_norm: 0.3759  loss: 0.6896
2023/05/31 18:45:35 - mmengine - INFO - Epoch(train)  [1][1600/5758]  lr: 1.0000e-03  eta: 14:30:42  time: 0.4184  data_time: 0.0017  memory: 20327  grad_norm: 0.4764  loss: 0.6906
2023/05/31 18:46:21 - mmengine - INFO - Epoch(train)  [1][1700/5758]  lr: 1.0000e-03  eta: 14:29:02  time: 0.4273  data_time: 0.0023  memory: 20327  grad_norm: 0.3174  loss: 0.6887
2023/05/31 18:47:08 - mmengine - INFO - Epoch(train)  [1][1800/5758]  lr: 1.0000e-03  eta: 14:29:39  time: 0.4459  data_time: 0.0526  memory: 20327  grad_norm: 0.2392  loss: 0.6858
2023/05/31 18:47:56 - mmengine - INFO - Epoch(train)  [1][1900/5758]  lr: 1.0000e-03  eta: 14:31:11  time: 0.5703  data_time: 0.0018  memory: 20327  grad_norm: 0.3035  loss: 0.6881
2023/05/31 18:48:46 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 18:48:46 - mmengine - INFO - Epoch(train)  [1][2000/5758]  lr: 1.0000e-03  eta: 14:33:21  time: 0.5233  data_time: 0.0017  memory: 20327  grad_norm: 0.2468  loss: 0.6844
2023/05/31 18:49:38 - mmengine - INFO - Epoch(train)  [1][2100/5758]  lr: 1.0000e-03  eta: 14:38:07  time: 0.4493  data_time: 0.0016  memory: 20327  grad_norm: 0.2431  loss: 0.6869
2023/05/31 18:50:28 - mmengine - INFO - Epoch(train)  [1][2200/5758]  lr: 1.0000e-03  eta: 14:40:04  time: 0.4997  data_time: 0.1156  memory: 20327  grad_norm: 0.2144  loss: 0.6881
2023/05/31 18:51:19 - mmengine - INFO - Epoch(train)  [1][2300/5758]  lr: 1.0000e-03  eta: 14:42:47  time: 0.4611  data_time: 0.0017  memory: 20327  grad_norm: 0.3295  loss: 0.6890
2023/05/31 18:52:16 - mmengine - INFO - Epoch(train)  [1][2400/5758]  lr: 1.0000e-03  eta: 14:50:09  time: 0.4768  data_time: 0.0015  memory: 20327  grad_norm: 0.3206  loss: 0.6834
2023/05/31 18:53:12 - mmengine - INFO - Epoch(train)  [1][2500/5758]  lr: 1.0000e-03  eta: 14:55:23  time: 0.4431  data_time: 0.0020  memory: 20327  grad_norm: 0.1802  loss: 0.6857
2023/05/31 18:54:03 - mmengine - INFO - Epoch(train)  [1][2600/5758]  lr: 1.0000e-03  eta: 14:57:08  time: 0.5280  data_time: 0.0015  memory: 20327  grad_norm: 0.2828  loss: 0.6845
2023/05/31 18:54:50 - mmengine - INFO - Epoch(train)  [1][2700/5758]  lr: 1.0000e-03  eta: 14:55:37  time: 0.4529  data_time: 0.0015  memory: 20327  grad_norm: 0.2345  loss: 0.6897
2023/05/31 18:55:38 - mmengine - INFO - Epoch(train)  [1][2800/5758]  lr: 1.0000e-03  eta: 14:55:23  time: 0.5384  data_time: 0.0018  memory: 20327  grad_norm: 0.1539  loss: 0.6886
2023/05/31 18:56:27 - mmengine - INFO - Epoch(train)  [1][2900/5758]  lr: 1.0000e-03  eta: 14:55:04  time: 0.4375  data_time: 0.0015  memory: 20327  grad_norm: 0.2427  loss: 0.6880
2023/05/31 18:57:15 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 18:57:15 - mmengine - INFO - Epoch(train)  [1][3000/5758]  lr: 1.0000e-03  eta: 14:54:23  time: 0.4969  data_time: 0.0014  memory: 20327  grad_norm: 0.2302  loss: 0.6911
2023/05/31 18:58:05 - mmengine - INFO - Epoch(train)  [1][3100/5758]  lr: 1.0000e-03  eta: 14:55:14  time: 0.5301  data_time: 0.0023  memory: 20327  grad_norm: 0.2076  loss: 0.6825
2023/05/31 18:58:55 - mmengine - INFO - Epoch(train)  [1][3200/5758]  lr: 1.0000e-03  eta: 14:55:35  time: 0.5860  data_time: 0.0015  memory: 20327  grad_norm: 0.2537  loss: 0.6854
2023/05/31 18:59:46 - mmengine - INFO - Epoch(train)  [1][3300/5758]  lr: 1.0000e-03  eta: 14:56:15  time: 0.4651  data_time: 0.0025  memory: 20327  grad_norm: 0.1809  loss: 0.6864
2023/05/31 19:00:35 - mmengine - INFO - Epoch(train)  [1][3400/5758]  lr: 1.0000e-03  eta: 14:55:55  time: 0.4548  data_time: 0.0018  memory: 20327  grad_norm: 0.2350  loss: 0.6890
2023/05/31 19:01:20 - mmengine - INFO - Epoch(train)  [1][3500/5758]  lr: 1.0000e-03  eta: 14:53:40  time: 0.4564  data_time: 0.0017  memory: 20327  grad_norm: 0.1906  loss: 0.6882
2023/05/31 19:02:07 - mmengine - INFO - Epoch(train)  [1][3600/5758]  lr: 1.0000e-03  eta: 14:52:00  time: 0.4628  data_time: 0.0016  memory: 20327  grad_norm: 0.2133  loss: 0.6878
2023/05/31 19:02:52 - mmengine - INFO - Epoch(train)  [1][3700/5758]  lr: 1.0000e-03  eta: 14:50:04  time: 0.4189  data_time: 0.0022  memory: 20327  grad_norm: 0.2188  loss: 0.6868
2023/05/31 19:03:38 - mmengine - INFO - Epoch(train)  [1][3800/5758]  lr: 1.0000e-03  eta: 14:48:01  time: 0.4756  data_time: 0.0015  memory: 20327  grad_norm: 0.1568  loss: 0.6829
2023/05/31 19:04:23 - mmengine - INFO - Epoch(train)  [1][3900/5758]  lr: 1.0000e-03  eta: 14:46:13  time: 0.5234  data_time: 0.0014  memory: 20327  grad_norm: 0.2446  loss: 0.6878
2023/05/31 19:05:10 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 19:05:10 - mmengine - INFO - Epoch(train)  [1][4000/5758]  lr: 1.0000e-03  eta: 14:44:57  time: 0.5318  data_time: 0.0016  memory: 20327  grad_norm: 0.2752  loss: 0.6842
2023/05/31 19:05:57 - mmengine - INFO - Epoch(train)  [1][4100/5758]  lr: 1.0000e-03  eta: 14:43:44  time: 0.4641  data_time: 0.0014  memory: 20327  grad_norm: 0.1678  loss: 0.6814
2023/05/31 19:06:48 - mmengine - INFO - Epoch(train)  [1][4200/5758]  lr: 1.0000e-03  eta: 14:44:16  time: 0.6016  data_time: 0.0015  memory: 20327  grad_norm: 0.2206  loss: 0.6909
2023/05/31 19:07:39 - mmengine - INFO - Epoch(train)  [1][4300/5758]  lr: 1.0000e-03  eta: 14:44:53  time: 0.4643  data_time: 0.0018  memory: 20327  grad_norm: 0.1305  loss: 0.6935
2023/05/31 19:08:32 - mmengine - INFO - Epoch(train)  [1][4400/5758]  lr: 1.0000e-03  eta: 14:46:22  time: 0.5172  data_time: 0.0018  memory: 20327  grad_norm: 0.1892  loss: 0.6804
2023/05/31 19:09:26 - mmengine - INFO - Epoch(train)  [1][4500/5758]  lr: 1.0000e-03  eta: 14:47:47  time: 0.5839  data_time: 0.0019  memory: 20327  grad_norm: 4.1434  loss: 0.6887
2023/05/31 19:10:18 - mmengine - INFO - Epoch(train)  [1][4600/5758]  lr: 1.0000e-03  eta: 14:48:38  time: 0.4826  data_time: 0.0444  memory: 20327  grad_norm: 0.2898  loss: 0.6888
2023/05/31 19:11:11 - mmengine - INFO - Epoch(train)  [1][4700/5758]  lr: 1.0000e-03  eta: 14:49:48  time: 0.5790  data_time: 0.0118  memory: 20327  grad_norm: 0.2279  loss: 0.6894
2023/05/31 19:11:59 - mmengine - INFO - Epoch(train)  [1][4800/5758]  lr: 1.0000e-03  eta: 14:48:54  time: 0.4071  data_time: 0.0430  memory: 20327  grad_norm: 0.1455  loss: 0.6874
2023/05/31 19:12:45 - mmengine - INFO - Epoch(train)  [1][4900/5758]  lr: 1.0000e-03  eta: 14:47:08  time: 0.4641  data_time: 0.0017  memory: 20327  grad_norm: 0.2417  loss: 0.6905
2023/05/31 19:13:31 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 19:13:31 - mmengine - INFO - Epoch(train)  [1][5000/5758]  lr: 1.0000e-03  eta: 14:45:29  time: 0.4728  data_time: 0.0017  memory: 20327  grad_norm: 0.1863  loss: 0.6864
2023/05/31 19:14:18 - mmengine - INFO - Epoch(train)  [1][5100/5758]  lr: 1.0000e-03  eta: 14:44:15  time: 0.4919  data_time: 0.0020  memory: 20327  grad_norm: 0.1691  loss: 0.6881
2023/05/31 19:15:03 - mmengine - INFO - Epoch(train)  [1][5200/5758]  lr: 1.0000e-03  eta: 14:42:21  time: 0.4347  data_time: 0.0016  memory: 20327  grad_norm: 0.2515  loss: 0.6828
2023/05/31 19:15:50 - mmengine - INFO - Epoch(train)  [1][5300/5758]  lr: 1.0000e-03  eta: 14:41:10  time: 0.4721  data_time: 0.0015  memory: 20327  grad_norm: 0.1911  loss: 0.6846
2023/05/31 19:16:37 - mmengine - INFO - Epoch(train)  [1][5400/5758]  lr: 1.0000e-03  eta: 14:39:55  time: 0.4641  data_time: 0.0014  memory: 20327  grad_norm: 0.1328  loss: 0.6849
2023/05/31 19:17:24 - mmengine - INFO - Epoch(train)  [1][5500/5758]  lr: 1.0000e-03  eta: 14:38:56  time: 0.5697  data_time: 0.0017  memory: 20327  grad_norm: 0.1780  loss: 0.6843
2023/05/31 19:18:10 - mmengine - INFO - Epoch(train)  [1][5600/5758]  lr: 1.0000e-03  eta: 14:37:20  time: 0.3915  data_time: 0.0016  memory: 20327  grad_norm: 0.1942  loss: 0.6819
2023/05/31 19:18:58 - mmengine - INFO - Epoch(train)  [1][5700/5758]  lr: 1.0000e-03  eta: 14:36:35  time: 0.5306  data_time: 0.0016  memory: 20327  grad_norm: 0.2729  loss: 0.6899
2023/05/31 19:19:24 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 19:19:24 - mmengine - INFO - Saving checkpoint at 1 epochs
2023/05/31 19:19:43 - mmengine - INFO - Epoch(val) [1][8/8]    accuracy/top1: 100.0000  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [100.0, 0.0]  single-label/f1-score_classwise: [100.0, 0.0]  data_time: 0.5274  time: 1.4208
2023/05/31 19:20:35 - mmengine - INFO - Epoch(train)  [2][ 100/5758]  lr: 9.9391e-04  eta: 14:35:41  time: 0.4719  data_time: 0.0019  memory: 20338  grad_norm: 0.1390  loss: 0.6857
2023/05/31 19:21:21 - mmengine - INFO - Epoch(train)  [2][ 200/5758]  lr: 9.9391e-04  eta: 14:34:21  time: 0.3786  data_time: 0.0015  memory: 20334  grad_norm: 0.2039  loss: 0.6875
2023/05/31 19:21:39 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 19:22:05 - mmengine - INFO - Epoch(train)  [2][ 300/5758]  lr: 9.9391e-04  eta: 14:32:13  time: 0.4775  data_time: 0.0023  memory: 20334  grad_norm: 0.1824  loss: 0.6888
2023/05/31 19:22:48 - mmengine - INFO - Epoch(train)  [2][ 400/5758]  lr: 9.9391e-04  eta: 14:30:01  time: 0.4025  data_time: 0.0020  memory: 20334  grad_norm: 0.1098  loss: 0.6871
2023/05/31 19:23:34 - mmengine - INFO - Epoch(train)  [2][ 500/5758]  lr: 9.9391e-04  eta: 14:28:41  time: 0.4650  data_time: 0.0020  memory: 20334  grad_norm: 0.1699  loss: 0.6880
2023/05/31 19:24:20 - mmengine - INFO - Epoch(train)  [2][ 600/5758]  lr: 9.9391e-04  eta: 14:27:18  time: 0.4857  data_time: 0.0022  memory: 20334  grad_norm: 0.2250  loss: 0.6882
2023/05/31 19:25:07 - mmengine - INFO - Epoch(train)  [2][ 700/5758]  lr: 9.9391e-04  eta: 14:26:28  time: 0.5223  data_time: 0.0028  memory: 20334  grad_norm: 0.0980  loss: 0.6891
2023/05/31 19:25:55 - mmengine - INFO - Epoch(train)  [2][ 800/5758]  lr: 9.9391e-04  eta: 14:25:44  time: 0.4998  data_time: 0.0017  memory: 20334  grad_norm: 0.2047  loss: 0.6881
2023/05/31 19:26:43 - mmengine - INFO - Epoch(train)  [2][ 900/5758]  lr: 9.9391e-04  eta: 14:24:48  time: 0.4293  data_time: 0.0015  memory: 20334  grad_norm: 0.1334  loss: 0.6904
2023/05/31 19:27:30 - mmengine - INFO - Epoch(train)  [2][1000/5758]  lr: 9.9391e-04  eta: 14:23:42  time: 0.5042  data_time: 0.0023  memory: 20334  grad_norm: 0.1210  loss: 0.6860
2023/05/31 19:28:16 - mmengine - INFO - Epoch(train)  [2][1100/5758]  lr: 9.9391e-04  eta: 14:22:41  time: 0.4424  data_time: 0.0016  memory: 20334  grad_norm: 0.1209  loss: 0.6867
2023/05/31 19:29:04 - mmengine - INFO - Epoch(train)  [2][1200/5758]  lr: 9.9391e-04  eta: 14:21:42  time: 0.4532  data_time: 0.0015  memory: 20334  grad_norm: 0.1007  loss: 0.6839
2023/05/31 19:29:23 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 19:29:51 - mmengine - INFO - Epoch(train)  [2][1300/5758]  lr: 9.9391e-04  eta: 14:20:46  time: 0.4828  data_time: 0.0014  memory: 20334  grad_norm: 0.2354  loss: 0.6881
2023/05/31 19:30:35 - mmengine - INFO - Epoch(train)  [2][1400/5758]  lr: 9.9391e-04  eta: 14:19:09  time: 0.4426  data_time: 0.0014  memory: 20334  grad_norm: 0.1225  loss: 0.6852
2023/05/31 19:31:21 - mmengine - INFO - Epoch(train)  [2][1500/5758]  lr: 9.9391e-04  eta: 14:17:49  time: 0.4048  data_time: 0.0016  memory: 20334  grad_norm: 0.1605  loss: 0.6865
2023/05/31 19:32:06 - mmengine - INFO - Epoch(train)  [2][1600/5758]  lr: 9.9391e-04  eta: 14:16:20  time: 0.3803  data_time: 0.0014  memory: 20334  grad_norm: 0.0794  loss: 0.6857
2023/05/31 19:32:50 - mmengine - INFO - Epoch(train)  [2][1700/5758]  lr: 9.9391e-04  eta: 14:14:44  time: 0.4127  data_time: 0.0015  memory: 20334  grad_norm: 0.1306  loss: 0.6866
2023/05/31 19:33:35 - mmengine - INFO - Epoch(train)  [2][1800/5758]  lr: 9.9391e-04  eta: 14:13:21  time: 0.4324  data_time: 0.0015  memory: 20334  grad_norm: 0.1867  loss: 0.6870
2023/05/31 19:34:20 - mmengine - INFO - Epoch(train)  [2][1900/5758]  lr: 9.9391e-04  eta: 14:11:48  time: 0.4226  data_time: 0.0015  memory: 20334  grad_norm: 0.1899  loss: 0.6858
2023/05/31 19:35:05 - mmengine - INFO - Epoch(train)  [2][2000/5758]  lr: 9.9391e-04  eta: 14:10:28  time: 0.4057  data_time: 0.0016  memory: 20334  grad_norm: 0.1243  loss: 0.6885
2023/05/31 19:35:50 - mmengine - INFO - Epoch(train)  [2][2100/5758]  lr: 9.9391e-04  eta: 14:09:09  time: 0.4477  data_time: 0.0015  memory: 20334  grad_norm: 0.0797  loss: 0.6879
2023/05/31 19:36:32 - mmengine - INFO - Epoch(train)  [2][2200/5758]  lr: 9.9391e-04  eta: 14:07:08  time: 0.5015  data_time: 0.0018  memory: 20334  grad_norm: 0.1389  loss: 0.6910
2023/05/31 19:36:51 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 19:37:15 - mmengine - INFO - Epoch(train)  [2][2300/5758]  lr: 9.9391e-04  eta: 14:05:21  time: 0.3787  data_time: 0.0016  memory: 20334  grad_norm: 0.0999  loss: 0.6889
2023/05/31 19:37:58 - mmengine - INFO - Epoch(train)  [2][2400/5758]  lr: 9.9391e-04  eta: 14:03:31  time: 0.4515  data_time: 0.0015  memory: 20334  grad_norm: 0.0948  loss: 0.6863
2023/05/31 19:38:40 - mmengine - INFO - Epoch(train)  [2][2500/5758]  lr: 9.9391e-04  eta: 14:01:44  time: 0.4425  data_time: 0.0016  memory: 20334  grad_norm: 0.1123  loss: 0.6916
2023/05/31 19:39:22 - mmengine - INFO - Epoch(train)  [2][2600/5758]  lr: 9.9391e-04  eta: 13:59:44  time: 0.4332  data_time: 0.0016  memory: 20334  grad_norm: 0.1154  loss: 0.6823
2023/05/31 19:40:04 - mmengine - INFO - Epoch(train)  [2][2700/5758]  lr: 9.9391e-04  eta: 13:57:53  time: 0.4113  data_time: 0.0015  memory: 20334  grad_norm: 0.1471  loss: 0.6900
2023/05/31 19:40:46 - mmengine - INFO - Epoch(train)  [2][2800/5758]  lr: 9.9391e-04  eta: 13:56:04  time: 0.3838  data_time: 0.0015  memory: 20334  grad_norm: 0.1072  loss: 0.6860
2023/05/31 19:41:29 - mmengine - INFO - Epoch(train)  [2][2900/5758]  lr: 9.9391e-04  eta: 13:54:28  time: 0.4390  data_time: 0.0016  memory: 20334  grad_norm: 0.1075  loss: 0.6898
2023/05/31 19:42:10 - mmengine - INFO - Epoch(train)  [2][3000/5758]  lr: 9.9391e-04  eta: 13:52:26  time: 0.3877  data_time: 0.0016  memory: 20334  grad_norm: 0.1138  loss: 0.6888
2023/05/31 19:42:52 - mmengine - INFO - Epoch(train)  [2][3100/5758]  lr: 9.9391e-04  eta: 13:50:39  time: 0.3935  data_time: 0.0017  memory: 20334  grad_norm: 0.0867  loss: 0.6898
2023/05/31 19:43:34 - mmengine - INFO - Epoch(train)  [2][3200/5758]  lr: 9.9391e-04  eta: 13:48:50  time: 0.4575  data_time: 0.0016  memory: 20334  grad_norm: 0.1330  loss: 0.6889
2023/05/31 19:43:53 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 19:44:17 - mmengine - INFO - Epoch(train)  [2][3300/5758]  lr: 9.9391e-04  eta: 13:47:20  time: 0.4360  data_time: 0.0018  memory: 20334  grad_norm: 0.0948  loss: 0.6842
2023/05/31 19:44:59 - mmengine - INFO - Epoch(train)  [2][3400/5758]  lr: 9.9391e-04  eta: 13:45:46  time: 0.4034  data_time: 0.0017  memory: 20334  grad_norm: 0.1201  loss: 0.6888
2023/05/31 19:45:39 - mmengine - INFO - Epoch(train)  [2][3500/5758]  lr: 9.9391e-04  eta: 13:43:43  time: 0.3864  data_time: 0.0017  memory: 20334  grad_norm: 0.1380  loss: 0.6826
2023/05/31 19:46:19 - mmengine - INFO - Epoch(train)  [2][3600/5758]  lr: 9.9391e-04  eta: 13:41:41  time: 0.4217  data_time: 0.0016  memory: 20334  grad_norm: 0.0715  loss: 0.6875
2023/05/31 19:46:58 - mmengine - INFO - Epoch(train)  [2][3700/5758]  lr: 9.9391e-04  eta: 13:39:27  time: 0.4025  data_time: 0.0016  memory: 20334  grad_norm: 0.0821  loss: 0.6869
2023/05/31 19:47:38 - mmengine - INFO - Epoch(train)  [2][3800/5758]  lr: 9.9391e-04  eta: 13:37:21  time: 0.3974  data_time: 0.0014  memory: 20334  grad_norm: 0.0815  loss: 0.6842
2023/05/31 19:48:17 - mmengine - INFO - Epoch(train)  [2][3900/5758]  lr: 9.9391e-04  eta: 13:35:14  time: 0.4269  data_time: 0.0016  memory: 20334  grad_norm: 0.0614  loss: 0.6825
2023/05/31 19:48:58 - mmengine - INFO - Epoch(train)  [2][4000/5758]  lr: 9.9391e-04  eta: 13:33:29  time: 0.4035  data_time: 0.0018  memory: 20334  grad_norm: 0.0564  loss: 0.6930
2023/05/31 19:49:36 - mmengine - INFO - Epoch(train)  [2][4100/5758]  lr: 9.9391e-04  eta: 13:31:19  time: 0.4068  data_time: 0.0016  memory: 20334  grad_norm: 0.0691  loss: 0.6821
2023/05/31 19:50:15 - mmengine - INFO - Epoch(train)  [2][4200/5758]  lr: 9.9391e-04  eta: 13:29:18  time: 0.4096  data_time: 0.0016  memory: 20334  grad_norm: 0.0648  loss: 0.6846
2023/05/31 19:50:32 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 19:50:55 - mmengine - INFO - Epoch(train)  [2][4300/5758]  lr: 9.9391e-04  eta: 13:27:23  time: 0.4189  data_time: 0.0018  memory: 20334  grad_norm: 0.0824  loss: 0.6868
2023/05/31 19:51:35 - mmengine - INFO - Epoch(train)  [2][4400/5758]  lr: 9.9391e-04  eta: 13:25:34  time: 0.4179  data_time: 0.0019  memory: 20334  grad_norm: 0.0776  loss: 0.6857
2023/05/31 19:52:15 - mmengine - INFO - Epoch(train)  [2][4500/5758]  lr: 9.9391e-04  eta: 13:23:51  time: 0.4282  data_time: 0.0015  memory: 20334  grad_norm: 0.0703  loss: 0.6881
2023/05/31 19:52:56 - mmengine - INFO - Epoch(train)  [2][4600/5758]  lr: 9.9391e-04  eta: 13:22:08  time: 0.3999  data_time: 0.0016  memory: 20334  grad_norm: 0.0567  loss: 0.6862
2023/05/31 19:53:35 - mmengine - INFO - Epoch(train)  [2][4700/5758]  lr: 9.9391e-04  eta: 13:20:16  time: 0.3663  data_time: 0.0019  memory: 20334  grad_norm: 0.0345  loss: 0.6859
2023/05/31 19:54:14 - mmengine - INFO - Epoch(train)  [2][4800/5758]  lr: 9.9391e-04  eta: 13:18:27  time: 0.3887  data_time: 0.0015  memory: 20334  grad_norm: 0.0623  loss: 0.6874
2023/05/31 19:54:56 - mmengine - INFO - Epoch(train)  [2][4900/5758]  lr: 9.9391e-04  eta: 13:17:01  time: 0.4403  data_time: 0.0027  memory: 20334  grad_norm: 0.0594  loss: 0.6838
2023/05/31 19:55:36 - mmengine - INFO - Epoch(train)  [2][5000/5758]  lr: 9.9391e-04  eta: 13:15:20  time: 0.3890  data_time: 0.0016  memory: 20334  grad_norm: 0.0604  loss: 0.6927
2023/05/31 19:56:18 - mmengine - INFO - Epoch(train)  [2][5100/5758]  lr: 9.9391e-04  eta: 13:13:55  time: 0.4493  data_time: 0.0017  memory: 20334  grad_norm: 0.0538  loss: 0.6867
2023/05/31 19:56:59 - mmengine - INFO - Epoch(train)  [2][5200/5758]  lr: 9.9391e-04  eta: 13:12:23  time: 0.4407  data_time: 0.0017  memory: 20334  grad_norm: 0.0643  loss: 0.6885
2023/05/31 19:57:16 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 19:57:39 - mmengine - INFO - Epoch(train)  [2][5300/5758]  lr: 9.9391e-04  eta: 13:10:52  time: 0.3635  data_time: 0.0022  memory: 20334  grad_norm: 0.0312  loss: 0.6879
2023/05/31 19:58:21 - mmengine - INFO - Epoch(train)  [2][5400/5758]  lr: 9.9391e-04  eta: 13:09:27  time: 0.4347  data_time: 0.0023  memory: 20334  grad_norm: 0.0441  loss: 0.6896
2023/05/31 19:59:03 - mmengine - INFO - Epoch(train)  [2][5500/5758]  lr: 9.9391e-04  eta: 13:08:13  time: 0.4383  data_time: 0.0019  memory: 20334  grad_norm: 0.0362  loss: 0.6920
2023/05/31 19:59:45 - mmengine - INFO - Epoch(train)  [2][5600/5758]  lr: 9.9391e-04  eta: 13:06:57  time: 0.3910  data_time: 0.0016  memory: 20334  grad_norm: 0.0308  loss: 0.6834
2023/05/31 20:00:26 - mmengine - INFO - Epoch(train)  [2][5700/5758]  lr: 9.9391e-04  eta: 13:05:30  time: 0.3994  data_time: 0.0016  memory: 20334  grad_norm: 0.0362  loss: 0.6887
2023/05/31 20:00:51 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 20:00:51 - mmengine - INFO - Saving checkpoint at 2 epochs
2023/05/31 20:01:08 - mmengine - INFO - Epoch(val) [2][8/8]    accuracy/top1: 100.0000  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [100.0, 0.0]  single-label/f1-score_classwise: [100.0, 0.0]  data_time: 0.3736  time: 1.0013
2023/05/31 20:01:54 - mmengine - INFO - Epoch(train)  [3][ 100/5758]  lr: 9.7577e-04  eta: 13:04:09  time: 0.4018  data_time: 0.0015  memory: 20334  grad_norm: 0.0310  loss: 0.6865
2023/05/31 20:02:36 - mmengine - INFO - Epoch(train)  [3][ 200/5758]  lr: 9.7577e-04  eta: 13:02:53  time: 0.3977  data_time: 0.0015  memory: 20334  grad_norm: 0.0339  loss: 0.6903
2023/05/31 20:03:18 - mmengine - INFO - Epoch(train)  [3][ 300/5758]  lr: 9.7577e-04  eta: 13:01:41  time: 0.3921  data_time: 0.0015  memory: 20334  grad_norm: 0.0301  loss: 0.6797
2023/05/31 20:03:59 - mmengine - INFO - Epoch(train)  [3][ 400/5758]  lr: 9.7577e-04  eta: 13:00:18  time: 0.4322  data_time: 0.0016  memory: 20334  grad_norm: 0.0222  loss: 0.6907
2023/05/31 20:04:34 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 20:04:40 - mmengine - INFO - Epoch(train)  [3][ 500/5758]  lr: 9.7577e-04  eta: 12:58:54  time: 0.3707  data_time: 0.0014  memory: 20334  grad_norm: 0.0252  loss: 0.6878
2023/05/31 20:05:21 - mmengine - INFO - Epoch(train)  [3][ 600/5758]  lr: 9.7577e-04  eta: 12:57:29  time: 0.4078  data_time: 0.0014  memory: 20334  grad_norm: 0.0266  loss: 0.6892
2023/05/31 20:06:03 - mmengine - INFO - Epoch(train)  [3][ 700/5758]  lr: 9.7577e-04  eta: 12:56:16  time: 0.4164  data_time: 0.0014  memory: 20334  grad_norm: 0.0289  loss: 0.6871
2023/05/31 20:06:46 - mmengine - INFO - Epoch(train)  [3][ 800/5758]  lr: 9.7577e-04  eta: 12:55:19  time: 0.4127  data_time: 0.0015  memory: 20334  grad_norm: 0.0276  loss: 0.6887
2023/05/31 20:07:31 - mmengine - INFO - Epoch(train)  [3][ 900/5758]  lr: 9.7577e-04  eta: 12:54:27  time: 0.4864  data_time: 0.0014  memory: 20334  grad_norm: 0.0165  loss: 0.6819
2023/05/31 20:08:17 - mmengine - INFO - Epoch(train)  [3][1000/5758]  lr: 9.7577e-04  eta: 12:53:46  time: 0.4091  data_time: 0.0016  memory: 20334  grad_norm: 0.0219  loss: 0.6840
2023/05/31 20:09:03 - mmengine - INFO - Epoch(train)  [3][1100/5758]  lr: 9.7577e-04  eta: 12:53:09  time: 0.4353  data_time: 0.0014  memory: 20334  grad_norm: 0.0235  loss: 0.6887
2023/05/31 20:09:47 - mmengine - INFO - Epoch(train)  [3][1200/5758]  lr: 9.7577e-04  eta: 12:52:14  time: 0.4666  data_time: 0.0014  memory: 20334  grad_norm: 0.0147  loss: 0.6851
2023/05/31 20:10:34 - mmengine - INFO - Epoch(train)  [3][1300/5758]  lr: 9.7577e-04  eta: 12:51:40  time: 0.4219  data_time: 0.0014  memory: 20334  grad_norm: 0.0252  loss: 0.6859
2023/05/31 20:11:17 - mmengine - INFO - Epoch(train)  [3][1400/5758]  lr: 9.7577e-04  eta: 12:50:37  time: 0.4625  data_time: 0.0015  memory: 20334  grad_norm: 0.0157  loss: 0.6885
2023/05/31 20:11:54 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 20:12:01 - mmengine - INFO - Epoch(train)  [3][1500/5758]  lr: 9.7577e-04  eta: 12:49:44  time: 0.4381  data_time: 0.0014  memory: 20334  grad_norm: 0.0259  loss: 0.6954
2023/05/31 20:12:45 - mmengine - INFO - Epoch(train)  [3][1600/5758]  lr: 9.7577e-04  eta: 12:48:48  time: 0.4287  data_time: 0.0023  memory: 20334  grad_norm: 0.0237  loss: 0.6878
2023/05/31 20:13:30 - mmengine - INFO - Epoch(train)  [3][1700/5758]  lr: 9.7577e-04  eta: 12:48:03  time: 0.4157  data_time: 0.0018  memory: 20334  grad_norm: 0.0196  loss: 0.6908
2023/05/31 20:14:14 - mmengine - INFO - Epoch(train)  [3][1800/5758]  lr: 9.7577e-04  eta: 12:47:07  time: 0.4315  data_time: 0.0019  memory: 20334  grad_norm: 0.0223  loss: 0.6813
2023/05/31 20:14:59 - mmengine - INFO - Epoch(train)  [3][1900/5758]  lr: 9.7577e-04  eta: 12:46:20  time: 0.4805  data_time: 0.0016  memory: 20334  grad_norm: 0.0273  loss: 0.6873
2023/05/31 20:15:43 - mmengine - INFO - Epoch(train)  [3][2000/5758]  lr: 9.7577e-04  eta: 12:45:30  time: 0.4310  data_time: 0.0015  memory: 20334  grad_norm: 0.0265  loss: 0.6899
2023/05/31 20:16:26 - mmengine - INFO - Epoch(train)  [3][2100/5758]  lr: 9.7577e-04  eta: 12:44:28  time: 0.4255  data_time: 0.0015  memory: 20334  grad_norm: 0.0124  loss: 0.6818
2023/05/31 20:17:10 - mmengine - INFO - Epoch(train)  [3][2200/5758]  lr: 9.7577e-04  eta: 12:43:30  time: 0.4454  data_time: 0.0020  memory: 20334  grad_norm: 0.0120  loss: 0.6839
2023/05/31 20:17:54 - mmengine - INFO - Epoch(train)  [3][2300/5758]  lr: 9.7577e-04  eta: 12:42:41  time: 0.4625  data_time: 0.0014  memory: 20334  grad_norm: 0.0236  loss: 0.6908
2023/05/31 20:18:37 - mmengine - INFO - Epoch(train)  [3][2400/5758]  lr: 9.7577e-04  eta: 12:41:41  time: 0.4348  data_time: 0.0016  memory: 20334  grad_norm: 0.0181  loss: 0.6885
2023/05/31 20:19:16 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 20:19:22 - mmengine - INFO - Epoch(train)  [3][2500/5758]  lr: 9.7577e-04  eta: 12:40:54  time: 0.4458  data_time: 0.0022  memory: 20334  grad_norm: 0.0273  loss: 0.6876
2023/05/31 20:20:06 - mmengine - INFO - Epoch(train)  [3][2600/5758]  lr: 9.7577e-04  eta: 12:40:01  time: 0.3965  data_time: 0.0015  memory: 20334  grad_norm: 0.0133  loss: 0.6822
2023/05/31 20:20:50 - mmengine - INFO - Epoch(train)  [3][2700/5758]  lr: 9.7577e-04  eta: 12:39:03  time: 0.4620  data_time: 0.0013  memory: 20334  grad_norm: 0.0275  loss: 0.6798
2023/05/31 20:21:36 - mmengine - INFO - Epoch(train)  [3][2800/5758]  lr: 9.7577e-04  eta: 12:38:26  time: 0.4705  data_time: 0.0019  memory: 20334  grad_norm: 0.0217  loss: 0.6842
2023/05/31 20:22:20 - mmengine - INFO - Epoch(train)  [3][2900/5758]  lr: 9.7577e-04  eta: 12:37:34  time: 0.4299  data_time: 0.0017  memory: 20334  grad_norm: 0.0169  loss: 0.6871
2023/05/31 20:23:03 - mmengine - INFO - Epoch(train)  [3][3000/5758]  lr: 9.7577e-04  eta: 12:36:34  time: 0.4040  data_time: 0.0015  memory: 20334  grad_norm: 0.0239  loss: 0.6868
2023/05/31 20:23:46 - mmengine - INFO - Epoch(train)  [3][3100/5758]  lr: 9.7577e-04  eta: 12:35:34  time: 0.3936  data_time: 0.0017  memory: 20334  grad_norm: 0.0167  loss: 0.6868
2023/05/31 20:24:31 - mmengine - INFO - Epoch(train)  [3][3200/5758]  lr: 9.7577e-04  eta: 12:34:47  time: 0.4512  data_time: 0.0015  memory: 20334  grad_norm: 0.0154  loss: 0.6861
2023/05/31 20:25:15 - mmengine - INFO - Epoch(train)  [3][3300/5758]  lr: 9.7577e-04  eta: 12:33:56  time: 0.4681  data_time: 0.0016  memory: 20334  grad_norm: 0.0145  loss: 0.6834
2023/05/31 20:26:01 - mmengine - INFO - Epoch(train)  [3][3400/5758]  lr: 9.7577e-04  eta: 12:33:19  time: 0.4666  data_time: 0.0016  memory: 20334  grad_norm: 0.0221  loss: 0.6830
2023/05/31 20:26:38 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 20:26:44 - mmengine - INFO - Epoch(train)  [3][3500/5758]  lr: 9.7577e-04  eta: 12:32:20  time: 0.4276  data_time: 0.0025  memory: 20334  grad_norm: 0.0118  loss: 0.6831
2023/05/31 20:27:27 - mmengine - INFO - Epoch(train)  [3][3600/5758]  lr: 9.7577e-04  eta: 12:31:23  time: 0.3959  data_time: 0.0022  memory: 20334  grad_norm: 0.0161  loss: 0.6844
2023/05/31 20:28:12 - mmengine - INFO - Epoch(train)  [3][3700/5758]  lr: 9.7577e-04  eta: 12:30:34  time: 0.3996  data_time: 0.0014  memory: 20334  grad_norm: 0.0217  loss: 0.6889
2023/05/31 20:28:57 - mmengine - INFO - Epoch(train)  [3][3800/5758]  lr: 9.7577e-04  eta: 12:29:49  time: 0.4285  data_time: 0.0013  memory: 20334  grad_norm: 0.0112  loss: 0.6836
2023/05/31 20:29:41 - mmengine - INFO - Epoch(train)  [3][3900/5758]  lr: 9.7577e-04  eta: 12:29:00  time: 0.4196  data_time: 0.0020  memory: 20334  grad_norm: 0.0170  loss: 0.6858
2023/05/31 20:30:26 - mmengine - INFO - Epoch(train)  [3][4000/5758]  lr: 9.7577e-04  eta: 12:28:09  time: 0.4298  data_time: 0.0015  memory: 20334  grad_norm: 0.0314  loss: 0.6842
2023/05/31 20:31:09 - mmengine - INFO - Epoch(train)  [3][4100/5758]  lr: 9.7577e-04  eta: 12:27:10  time: 0.4046  data_time: 0.0021  memory: 20334  grad_norm: 0.0170  loss: 0.6856
2023/05/31 20:31:53 - mmengine - INFO - Epoch(train)  [3][4200/5758]  lr: 9.7577e-04  eta: 12:26:19  time: 0.4321  data_time: 0.0015  memory: 20334  grad_norm: 0.0161  loss: 0.6863
2023/05/31 20:32:35 - mmengine - INFO - Epoch(train)  [3][4300/5758]  lr: 9.7577e-04  eta: 12:25:20  time: 0.4370  data_time: 0.0015  memory: 20334  grad_norm: 0.0134  loss: 0.6926
2023/05/31 20:33:21 - mmengine - INFO - Epoch(train)  [3][4400/5758]  lr: 9.7577e-04  eta: 12:24:38  time: 0.4092  data_time: 0.0459  memory: 20334  grad_norm: 0.0179  loss: 0.6789
2023/05/31 20:33:57 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 20:34:05 - mmengine - INFO - Epoch(train)  [3][4500/5758]  lr: 9.7577e-04  eta: 12:23:46  time: 0.4261  data_time: 0.0647  memory: 20334  grad_norm: 0.0180  loss: 0.6856
2023/05/31 20:34:49 - mmengine - INFO - Epoch(train)  [3][4600/5758]  lr: 9.7577e-04  eta: 12:22:55  time: 0.4407  data_time: 0.0775  memory: 20334  grad_norm: 0.0139  loss: 0.6850
2023/05/31 20:35:33 - mmengine - INFO - Epoch(train)  [3][4700/5758]  lr: 9.7577e-04  eta: 12:22:03  time: 0.4256  data_time: 0.0435  memory: 20334  grad_norm: 0.0240  loss: 0.6887
2023/05/31 20:36:16 - mmengine - INFO - Epoch(train)  [3][4800/5758]  lr: 9.7577e-04  eta: 12:21:10  time: 0.4609  data_time: 0.0015  memory: 20334  grad_norm: 0.0186  loss: 0.6877
2023/05/31 20:37:01 - mmengine - INFO - Epoch(train)  [3][4900/5758]  lr: 9.7577e-04  eta: 12:20:20  time: 0.4639  data_time: 0.0017  memory: 20334  grad_norm: 0.0175  loss: 0.6846
2023/05/31 20:37:44 - mmengine - INFO - Epoch(train)  [3][5000/5758]  lr: 9.7577e-04  eta: 12:19:28  time: 0.4122  data_time: 0.0012  memory: 20334  grad_norm: 0.0167  loss: 0.6921
2023/05/31 20:38:27 - mmengine - INFO - Epoch(train)  [3][5100/5758]  lr: 9.7577e-04  eta: 12:18:29  time: 0.4096  data_time: 0.0013  memory: 20334  grad_norm: 0.0165  loss: 0.6891
2023/05/31 20:39:11 - mmengine - INFO - Epoch(train)  [3][5200/5758]  lr: 9.7577e-04  eta: 12:17:38  time: 0.4865  data_time: 0.0020  memory: 20334  grad_norm: 0.0147  loss: 0.6927
2023/05/31 20:39:53 - mmengine - INFO - Epoch(train)  [3][5300/5758]  lr: 9.7577e-04  eta: 12:16:38  time: 0.4180  data_time: 0.0017  memory: 20334  grad_norm: 0.0146  loss: 0.6871
2023/05/31 20:40:37 - mmengine - INFO - Epoch(train)  [3][5400/5758]  lr: 9.7577e-04  eta: 12:15:43  time: 0.4236  data_time: 0.0023  memory: 20334  grad_norm: 0.0146  loss: 0.6873
2023/05/31 20:41:15 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 20:41:22 - mmengine - INFO - Epoch(train)  [3][5500/5758]  lr: 9.7577e-04  eta: 12:14:59  time: 0.4132  data_time: 0.0020  memory: 20334  grad_norm: 0.0168  loss: 0.6844
2023/05/31 20:42:06 - mmengine - INFO - Epoch(train)  [3][5600/5758]  lr: 9.7577e-04  eta: 12:14:13  time: 0.4342  data_time: 0.0019  memory: 20334  grad_norm: 0.0145  loss: 0.6877
2023/05/31 20:42:49 - mmengine - INFO - Epoch(train)  [3][5700/5758]  lr: 9.7577e-04  eta: 12:13:17  time: 0.4276  data_time: 0.0016  memory: 20334  grad_norm: 0.0230  loss: 0.6922
2023/05/31 20:43:14 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 20:43:14 - mmengine - INFO - Saving checkpoint at 3 epochs
2023/05/31 20:43:31 - mmengine - INFO - Epoch(val) [3][8/8]    accuracy/top1: 100.0000  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [100.0, 0.0]  single-label/f1-score_classwise: [100.0, 0.0]  data_time: 0.3651  time: 0.9904
2023/05/31 20:44:19 - mmengine - INFO - Epoch(train)  [4][ 100/5758]  lr: 9.4605e-04  eta: 12:12:19  time: 0.3723  data_time: 0.0018  memory: 20334  grad_norm: 0.0164  loss: 0.6823
2023/05/31 20:45:04 - mmengine - INFO - Epoch(train)  [4][ 200/5758]  lr: 9.4605e-04  eta: 12:11:34  time: 0.4839  data_time: 0.0016  memory: 20334  grad_norm: 0.0149  loss: 0.6836
2023/05/31 20:45:46 - mmengine - INFO - Epoch(train)  [4][ 300/5758]  lr: 9.4605e-04  eta: 12:10:31  time: 0.4000  data_time: 0.0018  memory: 20334  grad_norm: 0.0210  loss: 0.6859
2023/05/31 20:46:30 - mmengine - INFO - Epoch(train)  [4][ 400/5758]  lr: 9.4605e-04  eta: 12:09:42  time: 0.4330  data_time: 0.0017  memory: 20334  grad_norm: 0.0166  loss: 0.6917
2023/05/31 20:47:13 - mmengine - INFO - Epoch(train)  [4][ 500/5758]  lr: 9.4605e-04  eta: 12:08:48  time: 0.4329  data_time: 0.0018  memory: 20334  grad_norm: 0.0118  loss: 0.6859
2023/05/31 20:47:58 - mmengine - INFO - Epoch(train)  [4][ 600/5758]  lr: 9.4605e-04  eta: 12:08:00  time: 0.4122  data_time: 0.0017  memory: 20334  grad_norm: 0.0178  loss: 0.6884
2023/05/31 20:48:41 - mmengine - INFO - Epoch(train)  [4][ 700/5758]  lr: 9.4605e-04  eta: 12:07:05  time: 0.4330  data_time: 0.0016  memory: 20334  grad_norm: 0.0183  loss: 0.6913
2023/05/31 20:48:53 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 20:49:24 - mmengine - INFO - Epoch(train)  [4][ 800/5758]  lr: 9.4605e-04  eta: 12:06:13  time: 0.4226  data_time: 0.0017  memory: 20334  grad_norm: 0.0258  loss: 0.6846
2023/05/31 20:50:07 - mmengine - INFO - Epoch(train)  [4][ 900/5758]  lr: 9.4605e-04  eta: 12:05:18  time: 0.4490  data_time: 0.0016  memory: 20334  grad_norm: 0.0205  loss: 0.6850
2023/05/31 20:50:51 - mmengine - INFO - Epoch(train)  [4][1000/5758]  lr: 9.4605e-04  eta: 12:04:25  time: 0.3926  data_time: 0.0018  memory: 20334  grad_norm: 0.0270  loss: 0.6846
2023/05/31 20:51:35 - mmengine - INFO - Epoch(train)  [4][1100/5758]  lr: 9.4605e-04  eta: 12:03:36  time: 0.4475  data_time: 0.0016  memory: 20334  grad_norm: 0.0234  loss: 0.6881
2023/05/31 20:52:18 - mmengine - INFO - Epoch(train)  [4][1200/5758]  lr: 9.4605e-04  eta: 12:02:40  time: 0.4378  data_time: 0.0019  memory: 20334  grad_norm: 0.0179  loss: 0.6856
2023/05/31 20:53:01 - mmengine - INFO - Epoch(train)  [4][1300/5758]  lr: 9.4605e-04  eta: 12:01:46  time: 0.4295  data_time: 0.0015  memory: 20334  grad_norm: 0.0126  loss: 0.6849
2023/05/31 20:53:45 - mmengine - INFO - Epoch(train)  [4][1400/5758]  lr: 9.4605e-04  eta: 12:00:57  time: 0.4935  data_time: 0.0018  memory: 20334  grad_norm: 0.0144  loss: 0.6878
2023/05/31 20:54:27 - mmengine - INFO - Epoch(train)  [4][1500/5758]  lr: 9.4605e-04  eta: 12:00:00  time: 0.4450  data_time: 0.0019  memory: 20334  grad_norm: 0.0175  loss: 0.6796
2023/05/31 20:55:11 - mmengine - INFO - Epoch(train)  [4][1600/5758]  lr: 9.4605e-04  eta: 11:59:09  time: 0.3642  data_time: 0.0019  memory: 20334  grad_norm: 0.0237  loss: 0.6832
2023/05/31 20:55:54 - mmengine - INFO - Epoch(train)  [4][1700/5758]  lr: 9.4605e-04  eta: 11:58:16  time: 0.4355  data_time: 0.0015  memory: 20334  grad_norm: 0.0191  loss: 0.6884
2023/05/31 20:56:05 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 20:56:37 - mmengine - INFO - Epoch(train)  [4][1800/5758]  lr: 9.4605e-04  eta: 11:57:21  time: 0.3870  data_time: 0.0015  memory: 20334  grad_norm: 0.0135  loss: 0.6882
2023/05/31 20:57:19 - mmengine - INFO - Epoch(train)  [4][1900/5758]  lr: 9.4605e-04  eta: 11:56:26  time: 0.3704  data_time: 0.0014  memory: 20334  grad_norm: 0.0217  loss: 0.6863
2023/05/31 20:58:02 - mmengine - INFO - Epoch(train)  [4][2000/5758]  lr: 9.4605e-04  eta: 11:55:33  time: 0.4416  data_time: 0.0015  memory: 20334  grad_norm: 0.0081  loss: 0.6860
2023/05/31 20:58:45 - mmengine - INFO - Epoch(train)  [4][2100/5758]  lr: 9.4605e-04  eta: 11:54:36  time: 0.4142  data_time: 0.0017  memory: 20334  grad_norm: 0.0168  loss: 0.6825
2023/05/31 20:59:28 - mmengine - INFO - Epoch(train)  [4][2200/5758]  lr: 9.4605e-04  eta: 11:53:45  time: 0.4347  data_time: 0.0018  memory: 20334  grad_norm: 0.0182  loss: 0.6867
2023/05/31 21:00:13 - mmengine - INFO - Epoch(train)  [4][2300/5758]  lr: 9.4605e-04  eta: 11:52:59  time: 0.4444  data_time: 0.0015  memory: 20334  grad_norm: 0.0137  loss: 0.6875
2023/05/31 21:00:55 - mmengine - INFO - Epoch(train)  [4][2400/5758]  lr: 9.4605e-04  eta: 11:52:01  time: 0.4392  data_time: 0.0014  memory: 20334  grad_norm: 0.0134  loss: 0.6847
2023/05/31 21:01:37 - mmengine - INFO - Epoch(train)  [4][2500/5758]  lr: 9.4605e-04  eta: 11:51:04  time: 0.4118  data_time: 0.0015  memory: 20334  grad_norm: 0.0210  loss: 0.6840
2023/05/31 21:02:21 - mmengine - INFO - Epoch(train)  [4][2600/5758]  lr: 9.4605e-04  eta: 11:50:16  time: 0.4177  data_time: 0.0023  memory: 20334  grad_norm: 0.0131  loss: 0.6845
2023/05/31 21:03:02 - mmengine - INFO - Epoch(train)  [4][2700/5758]  lr: 9.4605e-04  eta: 11:49:13  time: 0.4065  data_time: 0.0017  memory: 20334  grad_norm: 0.0162  loss: 0.6870
2023/05/31 21:03:13 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 21:03:45 - mmengine - INFO - Epoch(train)  [4][2800/5758]  lr: 9.4605e-04  eta: 11:48:19  time: 0.4555  data_time: 0.0017  memory: 20334  grad_norm: 0.0206  loss: 0.6928
2023/05/31 21:04:28 - mmengine - INFO - Epoch(train)  [4][2900/5758]  lr: 9.4605e-04  eta: 11:47:27  time: 0.4271  data_time: 0.0016  memory: 20334  grad_norm: 0.0206  loss: 0.6828
2023/05/31 21:05:11 - mmengine - INFO - Epoch(train)  [4][3000/5758]  lr: 9.4605e-04  eta: 11:46:35  time: 0.4085  data_time: 0.0021  memory: 20334  grad_norm: 0.0146  loss: 0.6862
2023/05/31 21:05:54 - mmengine - INFO - Epoch(train)  [4][3100/5758]  lr: 9.4605e-04  eta: 11:45:41  time: 0.4297  data_time: 0.0016  memory: 20334  grad_norm: 0.0215  loss: 0.6816
2023/05/31 21:06:36 - mmengine - INFO - Epoch(train)  [4][3200/5758]  lr: 9.4605e-04  eta: 11:44:44  time: 0.4052  data_time: 0.0016  memory: 20334  grad_norm: 0.0207  loss: 0.6868
2023/05/31 21:07:20 - mmengine - INFO - Epoch(train)  [4][3300/5758]  lr: 9.4605e-04  eta: 11:44:00  time: 0.4284  data_time: 0.0016  memory: 20334  grad_norm: 0.0207  loss: 0.6845
2023/05/31 21:08:05 - mmengine - INFO - Epoch(train)  [4][3400/5758]  lr: 9.4605e-04  eta: 11:43:13  time: 0.5038  data_time: 0.0015  memory: 20334  grad_norm: 0.0181  loss: 0.6863
2023/05/31 21:08:48 - mmengine - INFO - Epoch(train)  [4][3500/5758]  lr: 9.4605e-04  eta: 11:42:20  time: 0.4160  data_time: 0.0016  memory: 20334  grad_norm: 0.0179  loss: 0.6884
2023/05/31 21:09:29 - mmengine - INFO - Epoch(train)  [4][3600/5758]  lr: 9.4605e-04  eta: 11:41:22  time: 0.4228  data_time: 0.0023  memory: 20334  grad_norm: 0.0198  loss: 0.6881
2023/05/31 21:10:11 - mmengine - INFO - Epoch(train)  [4][3700/5758]  lr: 9.4605e-04  eta: 11:40:26  time: 0.4068  data_time: 0.0012  memory: 20334  grad_norm: 0.0236  loss: 0.6876
2023/05/31 21:10:23 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 21:10:55 - mmengine - INFO - Epoch(train)  [4][3800/5758]  lr: 9.4605e-04  eta: 11:39:37  time: 0.4778  data_time: 0.0018  memory: 20334  grad_norm: 0.0132  loss: 0.6891
2023/05/31 21:11:39 - mmengine - INFO - Epoch(train)  [4][3900/5758]  lr: 9.4605e-04  eta: 11:38:50  time: 0.4253  data_time: 0.0021  memory: 20334  grad_norm: 0.0147  loss: 0.6857
2023/05/31 21:12:23 - mmengine - INFO - Epoch(train)  [4][4000/5758]  lr: 9.4605e-04  eta: 11:38:04  time: 0.4027  data_time: 0.0020  memory: 20334  grad_norm: 0.0128  loss: 0.6858
2023/05/31 21:13:06 - mmengine - INFO - Epoch(train)  [4][4100/5758]  lr: 9.4605e-04  eta: 11:37:09  time: 0.4351  data_time: 0.0017  memory: 20334  grad_norm: 0.0162  loss: 0.6872
2023/05/31 21:13:48 - mmengine - INFO - Epoch(train)  [4][4200/5758]  lr: 9.4605e-04  eta: 11:36:14  time: 0.3973  data_time: 0.0018  memory: 20334  grad_norm: 0.0114  loss: 0.6922
2023/05/31 21:14:30 - mmengine - INFO - Epoch(train)  [4][4300/5758]  lr: 9.4605e-04  eta: 11:35:18  time: 0.4285  data_time: 0.0018  memory: 20334  grad_norm: 0.0213  loss: 0.6886
2023/05/31 21:15:12 - mmengine - INFO - Epoch(train)  [4][4400/5758]  lr: 9.4605e-04  eta: 11:34:25  time: 0.4498  data_time: 0.0017  memory: 20334  grad_norm: 0.0167  loss: 0.6875
2023/05/31 21:15:55 - mmengine - INFO - Epoch(train)  [4][4500/5758]  lr: 9.4605e-04  eta: 11:33:30  time: 0.4027  data_time: 0.0018  memory: 20334  grad_norm: 0.0458  loss: 0.6877
2023/05/31 21:16:38 - mmengine - INFO - Epoch(train)  [4][4600/5758]  lr: 9.4605e-04  eta: 11:32:41  time: 0.4618  data_time: 0.0017  memory: 20334  grad_norm: 0.0162  loss: 0.6881
2023/05/31 21:17:19 - mmengine - INFO - Epoch(train)  [4][4700/5758]  lr: 9.4605e-04  eta: 11:31:43  time: 0.4060  data_time: 0.0017  memory: 20334  grad_norm: 0.0261  loss: 0.6868
2023/05/31 21:17:31 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 21:18:03 - mmengine - INFO - Epoch(train)  [4][4800/5758]  lr: 9.4605e-04  eta: 11:30:52  time: 0.4904  data_time: 0.0020  memory: 20334  grad_norm: 0.0265  loss: 0.6937
2023/05/31 21:18:44 - mmengine - INFO - Epoch(train)  [4][4900/5758]  lr: 9.4605e-04  eta: 11:29:53  time: 0.3855  data_time: 0.0020  memory: 20334  grad_norm: 0.0134  loss: 0.6906
2023/05/31 21:19:27 - mmengine - INFO - Epoch(train)  [4][5000/5758]  lr: 9.4605e-04  eta: 11:29:05  time: 0.4084  data_time: 0.0027  memory: 20334  grad_norm: 0.0191  loss: 0.6885
2023/05/31 21:20:11 - mmengine - INFO - Epoch(train)  [4][5100/5758]  lr: 9.4605e-04  eta: 11:28:15  time: 0.4244  data_time: 0.0017  memory: 20334  grad_norm: 0.0107  loss: 0.6882
2023/05/31 21:20:54 - mmengine - INFO - Epoch(train)  [4][5200/5758]  lr: 9.4605e-04  eta: 11:27:28  time: 0.4063  data_time: 0.0021  memory: 20334  grad_norm: 0.0165  loss: 0.6871
2023/05/31 21:21:38 - mmengine - INFO - Epoch(train)  [4][5300/5758]  lr: 9.4605e-04  eta: 11:26:38  time: 0.4249  data_time: 0.0021  memory: 20334  grad_norm: 0.0172  loss: 0.6871
2023/05/31 21:22:20 - mmengine - INFO - Epoch(train)  [4][5400/5758]  lr: 9.4605e-04  eta: 11:25:46  time: 0.4152  data_time: 0.0017  memory: 20334  grad_norm: 0.0170  loss: 0.6895
2023/05/31 21:23:03 - mmengine - INFO - Epoch(train)  [4][5500/5758]  lr: 9.4605e-04  eta: 11:24:54  time: 0.4291  data_time: 0.0018  memory: 20334  grad_norm: 0.0172  loss: 0.6827
2023/05/31 21:23:45 - mmengine - INFO - Epoch(train)  [4][5600/5758]  lr: 9.4605e-04  eta: 11:24:02  time: 0.4585  data_time: 0.0016  memory: 20334  grad_norm: 0.0167  loss: 0.6921
2023/05/31 21:24:29 - mmengine - INFO - Epoch(train)  [4][5700/5758]  lr: 9.4605e-04  eta: 11:23:15  time: 0.4407  data_time: 0.0019  memory: 20334  grad_norm: 0.0169  loss: 0.6857
2023/05/31 21:24:40 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 21:24:53 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 21:24:53 - mmengine - INFO - Saving checkpoint at 4 epochs
2023/05/31 21:25:11 - mmengine - INFO - Epoch(val) [4][8/8]    accuracy/top1: 100.0000  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [100.0, 0.0]  single-label/f1-score_classwise: [100.0, 0.0]  data_time: 0.3748  time: 1.0005
2023/05/31 21:26:00 - mmengine - INFO - Epoch(train)  [5][ 100/5758]  lr: 9.0546e-04  eta: 11:22:13  time: 0.4626  data_time: 0.0017  memory: 20334  grad_norm: 0.0135  loss: 0.6892
2023/05/31 21:26:46 - mmengine - INFO - Epoch(train)  [5][ 200/5758]  lr: 9.0546e-04  eta: 11:21:33  time: 0.5576  data_time: 0.0018  memory: 20334  grad_norm: 0.0138  loss: 0.6833
2023/05/31 21:27:29 - mmengine - INFO - Epoch(train)  [5][ 300/5758]  lr: 9.0546e-04  eta: 11:20:46  time: 0.4598  data_time: 0.0018  memory: 20334  grad_norm: 0.0153  loss: 0.6862
2023/05/31 21:28:12 - mmengine - INFO - Epoch(train)  [5][ 400/5758]  lr: 9.0546e-04  eta: 11:19:56  time: 0.4185  data_time: 0.0015  memory: 20334  grad_norm: 0.0148  loss: 0.6823
2023/05/31 21:28:56 - mmengine - INFO - Epoch(train)  [5][ 500/5758]  lr: 9.0546e-04  eta: 11:19:08  time: 0.4824  data_time: 0.0016  memory: 20334  grad_norm: 0.0160  loss: 0.6872
2023/05/31 21:29:39 - mmengine - INFO - Epoch(train)  [5][ 600/5758]  lr: 9.0546e-04  eta: 11:18:19  time: 0.4359  data_time: 0.0017  memory: 20334  grad_norm: 0.0169  loss: 0.6870
2023/05/31 21:30:24 - mmengine - INFO - Epoch(train)  [5][ 700/5758]  lr: 9.0546e-04  eta: 11:17:35  time: 0.4727  data_time: 0.0016  memory: 20334  grad_norm: 0.0149  loss: 0.6889
2023/05/31 21:31:08 - mmengine - INFO - Epoch(train)  [5][ 800/5758]  lr: 9.0546e-04  eta: 11:16:48  time: 0.3930  data_time: 0.0017  memory: 20334  grad_norm: 0.0179  loss: 0.6852
2023/05/31 21:31:51 - mmengine - INFO - Epoch(train)  [5][ 900/5758]  lr: 9.0546e-04  eta: 11:15:59  time: 0.4594  data_time: 0.0018  memory: 20334  grad_norm: 0.0157  loss: 0.6877
2023/05/31 21:32:21 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 21:32:35 - mmengine - INFO - Epoch(train)  [5][1000/5758]  lr: 9.0546e-04  eta: 11:15:14  time: 0.4944  data_time: 0.0013  memory: 20334  grad_norm: 0.0222  loss: 0.6885
2023/05/31 21:33:18 - mmengine - INFO - Epoch(train)  [5][1100/5758]  lr: 9.0546e-04  eta: 11:14:22  time: 0.4181  data_time: 0.0017  memory: 20334  grad_norm: 0.0180  loss: 0.6851
2023/05/31 21:34:03 - mmengine - INFO - Epoch(train)  [5][1200/5758]  lr: 9.0546e-04  eta: 11:13:41  time: 0.4728  data_time: 0.0016  memory: 20334  grad_norm: 0.0201  loss: 0.6893
2023/05/31 21:34:47 - mmengine - INFO - Epoch(train)  [5][1300/5758]  lr: 9.0546e-04  eta: 11:12:54  time: 0.4514  data_time: 0.0024  memory: 20334  grad_norm: 0.0127  loss: 0.6831
2023/05/31 21:35:31 - mmengine - INFO - Epoch(train)  [5][1400/5758]  lr: 9.0546e-04  eta: 11:12:08  time: 0.4225  data_time: 0.0016  memory: 20334  grad_norm: 0.0154  loss: 0.6874
2023/05/31 21:36:14 - mmengine - INFO - Epoch(train)  [5][1500/5758]  lr: 9.0546e-04  eta: 11:11:16  time: 0.4359  data_time: 0.0013  memory: 20334  grad_norm: 0.0270  loss: 0.6900
2023/05/31 21:36:58 - mmengine - INFO - Epoch(train)  [5][1600/5758]  lr: 9.0546e-04  eta: 11:10:34  time: 0.4071  data_time: 0.0019  memory: 20334  grad_norm: 0.0152  loss: 0.6914
2023/05/31 21:37:39 - mmengine - INFO - Epoch(train)  [5][1700/5758]  lr: 9.0546e-04  eta: 11:09:34  time: 0.4208  data_time: 0.0020  memory: 20334  grad_norm: 0.0142  loss: 0.6832
2023/05/31 21:38:21 - mmengine - INFO - Epoch(train)  [5][1800/5758]  lr: 9.0546e-04  eta: 11:08:40  time: 0.4055  data_time: 0.0018  memory: 20334  grad_norm: 0.0170  loss: 0.6816
2023/05/31 21:39:04 - mmengine - INFO - Epoch(train)  [5][1900/5758]  lr: 9.0546e-04  eta: 11:07:54  time: 0.4407  data_time: 0.0018  memory: 20334  grad_norm: 0.0153  loss: 0.6831
2023/05/31 21:39:33 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 21:39:47 - mmengine - INFO - Epoch(train)  [5][2000/5758]  lr: 9.0546e-04  eta: 11:07:02  time: 0.4170  data_time: 0.0020  memory: 20334  grad_norm: 0.0161  loss: 0.6893
2023/05/31 21:40:29 - mmengine - INFO - Epoch(train)  [5][2100/5758]  lr: 9.0546e-04  eta: 11:06:11  time: 0.4147  data_time: 0.0024  memory: 20334  grad_norm: 0.0106  loss: 0.6865
2023/05/31 21:41:11 - mmengine - INFO - Epoch(train)  [5][2200/5758]  lr: 9.0546e-04  eta: 11:05:18  time: 0.3871  data_time: 0.0017  memory: 20334  grad_norm: 0.0140  loss: 0.6822
2023/05/31 21:41:56 - mmengine - INFO - Epoch(train)  [5][2300/5758]  lr: 9.0546e-04  eta: 11:04:34  time: 0.4810  data_time: 0.0025  memory: 20334  grad_norm: 0.0115  loss: 0.6844
2023/05/31 21:42:40 - mmengine - INFO - Epoch(train)  [5][2400/5758]  lr: 9.0546e-04  eta: 11:03:47  time: 0.4656  data_time: 0.0023  memory: 20334  grad_norm: 0.0259  loss: 0.6852
2023/05/31 21:43:24 - mmengine - INFO - Epoch(train)  [5][2500/5758]  lr: 9.0546e-04  eta: 11:03:02  time: 0.4012  data_time: 0.0022  memory: 20334  grad_norm: 0.0133  loss: 0.6867
2023/05/31 21:44:07 - mmengine - INFO - Epoch(train)  [5][2600/5758]  lr: 9.0546e-04  eta: 11:02:13  time: 0.3986  data_time: 0.0025  memory: 20334  grad_norm: 0.0169  loss: 0.6808
2023/05/31 21:44:50 - mmengine - INFO - Epoch(train)  [5][2700/5758]  lr: 9.0546e-04  eta: 11:01:25  time: 0.4566  data_time: 0.0017  memory: 20334  grad_norm: 0.0106  loss: 0.6883
2023/05/31 21:45:32 - mmengine - INFO - Epoch(train)  [5][2800/5758]  lr: 9.0546e-04  eta: 11:00:30  time: 0.3927  data_time: 0.0016  memory: 20334  grad_norm: 0.0138  loss: 0.6906
2023/05/31 21:46:13 - mmengine - INFO - Epoch(train)  [5][2900/5758]  lr: 9.0546e-04  eta: 10:59:36  time: 0.4240  data_time: 0.0020  memory: 20334  grad_norm: 0.0143  loss: 0.6879
2023/05/31 21:46:43 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 21:46:56 - mmengine - INFO - Epoch(train)  [5][3000/5758]  lr: 9.0546e-04  eta: 10:58:48  time: 0.4904  data_time: 0.0023  memory: 20334  grad_norm: 0.0263  loss: 0.6910
2023/05/31 21:47:38 - mmengine - INFO - Epoch(train)  [5][3100/5758]  lr: 9.0546e-04  eta: 10:57:56  time: 0.4185  data_time: 0.0018  memory: 20334  grad_norm: 0.0194  loss: 0.6845
2023/05/31 21:48:21 - mmengine - INFO - Epoch(train)  [5][3200/5758]  lr: 9.0546e-04  eta: 10:57:05  time: 0.4449  data_time: 0.0017  memory: 20334  grad_norm: 0.0220  loss: 0.6875
2023/05/31 21:49:05 - mmengine - INFO - Epoch(train)  [5][3300/5758]  lr: 9.0546e-04  eta: 10:56:20  time: 0.3759  data_time: 0.0014  memory: 20334  grad_norm: 0.0263  loss: 0.6842
2023/05/31 21:49:49 - mmengine - INFO - Epoch(train)  [5][3400/5758]  lr: 9.0546e-04  eta: 10:55:33  time: 0.5078  data_time: 0.0015  memory: 20334  grad_norm: 0.0196  loss: 0.6860
2023/05/31 21:50:32 - mmengine - INFO - Epoch(train)  [5][3500/5758]  lr: 9.0546e-04  eta: 10:54:44  time: 0.5131  data_time: 0.0022  memory: 20334  grad_norm: 0.0172  loss: 0.6866
2023/05/31 21:51:15 - mmengine - INFO - Epoch(train)  [5][3600/5758]  lr: 9.0546e-04  eta: 10:53:59  time: 0.5038  data_time: 0.0016  memory: 20334  grad_norm: 0.0099  loss: 0.6881
2023/05/31 21:51:59 - mmengine - INFO - Epoch(train)  [5][3700/5758]  lr: 9.0546e-04  eta: 10:53:12  time: 0.4277  data_time: 0.0022  memory: 20334  grad_norm: 0.0164  loss: 0.6874
2023/05/31 21:52:41 - mmengine - INFO - Epoch(train)  [5][3800/5758]  lr: 9.0546e-04  eta: 10:52:20  time: 0.4236  data_time: 0.0025  memory: 20334  grad_norm: 0.0197  loss: 0.6891
2023/05/31 21:53:26 - mmengine - INFO - Epoch(train)  [5][3900/5758]  lr: 9.0546e-04  eta: 10:51:37  time: 0.4664  data_time: 0.0020  memory: 20334  grad_norm: 0.0167  loss: 0.6835
2023/05/31 21:53:59 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 21:54:12 - mmengine - INFO - Epoch(train)  [5][4000/5758]  lr: 9.0546e-04  eta: 10:50:58  time: 0.3931  data_time: 0.0024  memory: 20334  grad_norm: 0.0188  loss: 0.6852
2023/05/31 21:54:53 - mmengine - INFO - Epoch(train)  [5][4100/5758]  lr: 9.0546e-04  eta: 10:50:04  time: 0.3859  data_time: 0.0015  memory: 20334  grad_norm: 0.0138  loss: 0.6881
2023/05/31 21:55:37 - mmengine - INFO - Epoch(train)  [5][4200/5758]  lr: 9.0546e-04  eta: 10:49:17  time: 0.4127  data_time: 0.0015  memory: 20334  grad_norm: 0.0129  loss: 0.6901
2023/05/31 21:56:22 - mmengine - INFO - Epoch(train)  [5][4300/5758]  lr: 9.0546e-04  eta: 10:48:37  time: 0.4177  data_time: 0.0017  memory: 20334  grad_norm: 0.0163  loss: 0.6870
2023/05/31 21:57:05 - mmengine - INFO - Epoch(train)  [5][4400/5758]  lr: 9.0546e-04  eta: 10:47:47  time: 0.4095  data_time: 0.0016  memory: 20334  grad_norm: 0.0224  loss: 0.6888
2023/05/31 21:57:47 - mmengine - INFO - Epoch(train)  [5][4500/5758]  lr: 9.0546e-04  eta: 10:46:57  time: 0.4457  data_time: 0.0017  memory: 20334  grad_norm: 0.0121  loss: 0.6857
2023/05/31 21:58:33 - mmengine - INFO - Epoch(train)  [5][4600/5758]  lr: 9.0546e-04  eta: 10:46:16  time: 0.5404  data_time: 0.0016  memory: 20334  grad_norm: 0.0148  loss: 0.6922
2023/05/31 21:59:16 - mmengine - INFO - Epoch(train)  [5][4700/5758]  lr: 9.0546e-04  eta: 10:45:27  time: 0.4003  data_time: 0.0026  memory: 20334  grad_norm: 0.0248  loss: 0.6810
2023/05/31 22:00:00 - mmengine - INFO - Epoch(train)  [5][4800/5758]  lr: 9.0546e-04  eta: 10:44:43  time: 0.4443  data_time: 0.0026  memory: 20334  grad_norm: 0.0229  loss: 0.6823
2023/05/31 22:00:44 - mmengine - INFO - Epoch(train)  [5][4900/5758]  lr: 9.0546e-04  eta: 10:43:57  time: 0.4572  data_time: 0.0023  memory: 20334  grad_norm: 0.0174  loss: 0.6865
2023/05/31 22:01:14 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 22:01:29 - mmengine - INFO - Epoch(train)  [5][5000/5758]  lr: 9.0546e-04  eta: 10:43:15  time: 0.4226  data_time: 0.0025  memory: 20334  grad_norm: 0.0185  loss: 0.6900
2023/05/31 22:02:13 - mmengine - INFO - Epoch(train)  [5][5100/5758]  lr: 9.0546e-04  eta: 10:42:31  time: 0.4421  data_time: 0.0024  memory: 20334  grad_norm: 0.0099  loss: 0.6917
2023/05/31 22:02:59 - mmengine - INFO - Epoch(train)  [5][5200/5758]  lr: 9.0546e-04  eta: 10:41:52  time: 0.4703  data_time: 0.0024  memory: 20334  grad_norm: 0.0149  loss: 0.6890
2023/05/31 22:03:43 - mmengine - INFO - Epoch(train)  [5][5300/5758]  lr: 9.0546e-04  eta: 10:41:06  time: 0.4200  data_time: 0.0026  memory: 20334  grad_norm: 0.0187  loss: 0.6927
2023/05/31 22:04:27 - mmengine - INFO - Epoch(train)  [5][5400/5758]  lr: 9.0546e-04  eta: 10:40:21  time: 0.4258  data_time: 0.0025  memory: 20334  grad_norm: 0.0158  loss: 0.6880
2023/05/31 22:05:11 - mmengine - INFO - Epoch(train)  [5][5500/5758]  lr: 9.0546e-04  eta: 10:39:37  time: 0.4851  data_time: 0.0023  memory: 20334  grad_norm: 0.0193  loss: 0.6876
2023/05/31 22:05:56 - mmengine - INFO - Epoch(train)  [5][5600/5758]  lr: 9.0546e-04  eta: 10:38:54  time: 0.4599  data_time: 0.0018  memory: 20334  grad_norm: 0.0201  loss: 0.6833
2023/05/31 22:06:41 - mmengine - INFO - Epoch(train)  [5][5700/5758]  lr: 9.0546e-04  eta: 10:38:10  time: 0.4305  data_time: 0.0022  memory: 20334  grad_norm: 0.0176  loss: 0.6856
2023/05/31 22:07:05 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 22:07:05 - mmengine - INFO - Saving checkpoint at 5 epochs
2023/05/31 22:07:22 - mmengine - INFO - Epoch(val) [5][8/8]    accuracy/top1: 100.0000  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [100.0, 0.0]  single-label/f1-score_classwise: [100.0, 0.0]  data_time: 0.3672  time: 0.9941
2023/05/31 22:08:08 - mmengine - INFO - Epoch(train)  [6][ 100/5758]  lr: 8.5502e-04  eta: 10:37:02  time: 0.4215  data_time: 0.0017  memory: 20334  grad_norm: 0.0107  loss: 0.6819
2023/05/31 22:08:50 - mmengine - INFO - Epoch(train)  [6][ 200/5758]  lr: 8.5502e-04  eta: 10:36:11  time: 0.4055  data_time: 0.0017  memory: 20334  grad_norm: 0.0142  loss: 0.6858
2023/05/31 22:08:55 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 22:09:31 - mmengine - INFO - Epoch(train)  [6][ 300/5758]  lr: 8.5502e-04  eta: 10:35:16  time: 0.4040  data_time: 0.0022  memory: 20334  grad_norm: 0.0184  loss: 0.6853
2023/05/31 22:10:10 - mmengine - INFO - Epoch(train)  [6][ 400/5758]  lr: 8.5502e-04  eta: 10:34:17  time: 0.3814  data_time: 0.0017  memory: 20334  grad_norm: 0.0178  loss: 0.6879
2023/05/31 22:10:52 - mmengine - INFO - Epoch(train)  [6][ 500/5758]  lr: 8.5502e-04  eta: 10:33:25  time: 0.4531  data_time: 0.0023  memory: 20334  grad_norm: 0.0173  loss: 0.6870
2023/05/31 22:11:31 - mmengine - INFO - Epoch(train)  [6][ 600/5758]  lr: 8.5502e-04  eta: 10:32:27  time: 0.3860  data_time: 0.0020  memory: 20334  grad_norm: 0.0177  loss: 0.6863
2023/05/31 22:12:13 - mmengine - INFO - Epoch(train)  [6][ 700/5758]  lr: 8.5502e-04  eta: 10:31:36  time: 0.4217  data_time: 0.0020  memory: 20334  grad_norm: 0.0179  loss: 0.6891
2023/05/31 22:12:53 - mmengine - INFO - Epoch(train)  [6][ 800/5758]  lr: 8.5502e-04  eta: 10:30:40  time: 0.3811  data_time: 0.0017  memory: 20334  grad_norm: 0.0186  loss: 0.6859
2023/05/31 22:13:35 - mmengine - INFO - Epoch(train)  [6][ 900/5758]  lr: 8.5502e-04  eta: 10:29:47  time: 0.4309  data_time: 0.0031  memory: 20334  grad_norm: 0.0150  loss: 0.6860
2023/05/31 22:14:16 - mmengine - INFO - Epoch(train)  [6][1000/5758]  lr: 8.5502e-04  eta: 10:28:54  time: 0.4587  data_time: 0.0024  memory: 20334  grad_norm: 0.0151  loss: 0.6870
2023/05/31 22:14:58 - mmengine - INFO - Epoch(train)  [6][1100/5758]  lr: 8.5502e-04  eta: 10:28:04  time: 0.4721  data_time: 0.0026  memory: 20334  grad_norm: 0.0219  loss: 0.6868
2023/05/31 22:15:39 - mmengine - INFO - Epoch(train)  [6][1200/5758]  lr: 8.5502e-04  eta: 10:27:10  time: 0.4290  data_time: 0.0030  memory: 20334  grad_norm: 0.0190  loss: 0.6873
2023/05/31 22:15:43 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 22:16:19 - mmengine - INFO - Epoch(train)  [6][1300/5758]  lr: 8.5502e-04  eta: 10:26:16  time: 0.4148  data_time: 0.0020  memory: 20334  grad_norm: 0.0140  loss: 0.6891
2023/05/31 22:17:00 - mmengine - INFO - Epoch(train)  [6][1400/5758]  lr: 8.5502e-04  eta: 10:25:22  time: 0.3663  data_time: 0.0024  memory: 20334  grad_norm: 0.0121  loss: 0.6891
2023/05/31 22:17:41 - mmengine - INFO - Epoch(train)  [6][1500/5758]  lr: 8.5502e-04  eta: 10:24:29  time: 0.4630  data_time: 0.0023  memory: 20334  grad_norm: 0.0190  loss: 0.6904
2023/05/31 22:18:23 - mmengine - INFO - Epoch(train)  [6][1600/5758]  lr: 8.5502e-04  eta: 10:23:39  time: 0.4389  data_time: 0.0030  memory: 20334  grad_norm: 0.0143  loss: 0.6874
2023/05/31 22:19:03 - mmengine - INFO - Epoch(train)  [6][1700/5758]  lr: 8.5502e-04  eta: 10:22:44  time: 0.3718  data_time: 0.0019  memory: 20334  grad_norm: 0.0181  loss: 0.6867
2023/05/31 22:19:45 - mmengine - INFO - Epoch(train)  [6][1800/5758]  lr: 8.5502e-04  eta: 10:21:53  time: 0.3911  data_time: 0.0017  memory: 20334  grad_norm: 0.0164  loss: 0.6881
2023/05/31 22:20:26 - mmengine - INFO - Epoch(train)  [6][1900/5758]  lr: 8.5502e-04  eta: 10:21:01  time: 0.4190  data_time: 0.0026  memory: 20334  grad_norm: 0.0180  loss: 0.6916
2023/05/31 22:21:06 - mmengine - INFO - Epoch(train)  [6][2000/5758]  lr: 8.5502e-04  eta: 10:20:05  time: 0.3862  data_time: 0.0022  memory: 20334  grad_norm: 0.0198  loss: 0.6882
2023/05/31 22:21:47 - mmengine - INFO - Epoch(train)  [6][2100/5758]  lr: 8.5502e-04  eta: 10:19:12  time: 0.4260  data_time: 0.0032  memory: 20334  grad_norm: 0.0146  loss: 0.6861
2023/05/31 22:22:29 - mmengine - INFO - Epoch(train)  [6][2200/5758]  lr: 8.5502e-04  eta: 10:18:23  time: 0.4317  data_time: 0.0025  memory: 20334  grad_norm: 0.0162  loss: 0.6831
2023/05/31 22:22:33 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 22:23:08 - mmengine - INFO - Epoch(train)  [6][2300/5758]  lr: 8.5502e-04  eta: 10:17:26  time: 0.3788  data_time: 0.0024  memory: 20334  grad_norm: 0.0135  loss: 0.6834
2023/05/31 22:23:50 - mmengine - INFO - Epoch(train)  [6][2400/5758]  lr: 8.5502e-04  eta: 10:16:34  time: 0.4986  data_time: 0.0027  memory: 20334  grad_norm: 0.0187  loss: 0.6832
2023/05/31 22:24:31 - mmengine - INFO - Epoch(train)  [6][2500/5758]  lr: 8.5502e-04  eta: 10:15:43  time: 0.4379  data_time: 0.0024  memory: 20334  grad_norm: 0.0160  loss: 0.6868
2023/05/31 22:25:12 - mmengine - INFO - Epoch(train)  [6][2600/5758]  lr: 8.5502e-04  eta: 10:14:51  time: 0.4220  data_time: 0.0025  memory: 20334  grad_norm: 0.0152  loss: 0.6876
2023/05/31 22:25:53 - mmengine - INFO - Epoch(train)  [6][2700/5758]  lr: 8.5502e-04  eta: 10:14:00  time: 0.3924  data_time: 0.0022  memory: 20334  grad_norm: 0.0156  loss: 0.6884
2023/05/31 22:26:33 - mmengine - INFO - Epoch(train)  [6][2800/5758]  lr: 8.5502e-04  eta: 10:13:04  time: 0.3723  data_time: 0.0024  memory: 20334  grad_norm: 0.0140  loss: 0.6841
2023/05/31 22:27:16 - mmengine - INFO - Epoch(train)  [6][2900/5758]  lr: 8.5502e-04  eta: 10:12:16  time: 0.4717  data_time: 0.0028  memory: 20334  grad_norm: 0.0248  loss: 0.6864
2023/05/31 22:27:56 - mmengine - INFO - Epoch(train)  [6][3000/5758]  lr: 8.5502e-04  eta: 10:11:23  time: 0.4016  data_time: 0.0021  memory: 20334  grad_norm: 0.0141  loss: 0.6857
2023/05/31 22:28:38 - mmengine - INFO - Epoch(train)  [6][3100/5758]  lr: 8.5502e-04  eta: 10:10:33  time: 0.4071  data_time: 0.0019  memory: 20334  grad_norm: 0.0214  loss: 0.6921
2023/05/31 22:29:19 - mmengine - INFO - Epoch(train)  [6][3200/5758]  lr: 8.5502e-04  eta: 10:09:41  time: 0.4218  data_time: 0.0025  memory: 20334  grad_norm: 0.0125  loss: 0.6927
2023/05/31 22:29:23 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 22:30:00 - mmengine - INFO - Epoch(train)  [6][3300/5758]  lr: 8.5502e-04  eta: 10:08:51  time: 0.3823  data_time: 0.0025  memory: 20334  grad_norm: 0.0199  loss: 0.6829
2023/05/31 22:30:41 - mmengine - INFO - Epoch(train)  [6][3400/5758]  lr: 8.5502e-04  eta: 10:07:57  time: 0.4505  data_time: 0.0018  memory: 20334  grad_norm: 0.0151  loss: 0.6896
2023/05/31 22:31:21 - mmengine - INFO - Epoch(train)  [6][3500/5758]  lr: 8.5502e-04  eta: 10:07:05  time: 0.4410  data_time: 0.0022  memory: 20334  grad_norm: 0.0186  loss: 0.6846
2023/05/31 22:32:02 - mmengine - INFO - Epoch(train)  [6][3600/5758]  lr: 8.5502e-04  eta: 10:06:13  time: 0.3710  data_time: 0.0017  memory: 20334  grad_norm: 0.0135  loss: 0.6896
2023/05/31 22:32:43 - mmengine - INFO - Epoch(train)  [6][3700/5758]  lr: 8.5502e-04  eta: 10:05:21  time: 0.4106  data_time: 0.0021  memory: 20334  grad_norm: 0.0225  loss: 0.6931
2023/05/31 22:33:23 - mmengine - INFO - Epoch(train)  [6][3800/5758]  lr: 8.5502e-04  eta: 10:04:27  time: 0.4088  data_time: 0.0025  memory: 20334  grad_norm: 0.0137  loss: 0.6867
2023/05/31 22:34:04 - mmengine - INFO - Epoch(train)  [6][3900/5758]  lr: 8.5502e-04  eta: 10:03:35  time: 0.4119  data_time: 0.0017  memory: 20334  grad_norm: 0.0203  loss: 0.6895
2023/05/31 22:34:44 - mmengine - INFO - Epoch(train)  [6][4000/5758]  lr: 8.5502e-04  eta: 10:02:41  time: 0.3971  data_time: 0.0028  memory: 20334  grad_norm: 0.0173  loss: 0.6862
2023/05/31 22:35:25 - mmengine - INFO - Epoch(train)  [6][4100/5758]  lr: 8.5502e-04  eta: 10:01:51  time: 0.3677  data_time: 0.0017  memory: 20334  grad_norm: 0.0246  loss: 0.6876
2023/05/31 22:36:06 - mmengine - INFO - Epoch(train)  [6][4200/5758]  lr: 8.5502e-04  eta: 10:01:00  time: 0.4329  data_time: 0.0016  memory: 20334  grad_norm: 0.0176  loss: 0.6919
2023/05/31 22:36:10 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 22:36:47 - mmengine - INFO - Epoch(train)  [6][4300/5758]  lr: 8.5502e-04  eta: 10:00:09  time: 0.4061  data_time: 0.0029  memory: 20334  grad_norm: 0.0139  loss: 0.6934
2023/05/31 22:37:27 - mmengine - INFO - Epoch(train)  [6][4400/5758]  lr: 8.5502e-04  eta: 9:59:16  time: 0.4042  data_time: 0.0019  memory: 20334  grad_norm: 0.0120  loss: 0.6842
2023/05/31 22:38:08 - mmengine - INFO - Epoch(train)  [6][4500/5758]  lr: 8.5502e-04  eta: 9:58:25  time: 0.3866  data_time: 0.0019  memory: 20334  grad_norm: 0.0176  loss: 0.6855
2023/05/31 22:38:50 - mmengine - INFO - Epoch(train)  [6][4600/5758]  lr: 8.5502e-04  eta: 9:57:36  time: 0.3988  data_time: 0.0020  memory: 20334  grad_norm: 0.0179  loss: 0.6893
2023/05/31 22:39:30 - mmengine - INFO - Epoch(train)  [6][4700/5758]  lr: 8.5502e-04  eta: 9:56:43  time: 0.4070  data_time: 0.0023  memory: 20334  grad_norm: 0.0114  loss: 0.6911
2023/05/31 22:40:10 - mmengine - INFO - Epoch(train)  [6][4800/5758]  lr: 8.5502e-04  eta: 9:55:50  time: 0.3735  data_time: 0.0022  memory: 20334  grad_norm: 0.0144  loss: 0.6865
2023/05/31 22:40:50 - mmengine - INFO - Epoch(train)  [6][4900/5758]  lr: 8.5502e-04  eta: 9:54:57  time: 0.3939  data_time: 0.0024  memory: 20334  grad_norm: 0.0183  loss: 0.6883
2023/05/31 22:41:30 - mmengine - INFO - Epoch(train)  [6][5000/5758]  lr: 8.5502e-04  eta: 9:54:04  time: 0.4602  data_time: 0.0022  memory: 20334  grad_norm: 0.0245  loss: 0.6903
2023/05/31 22:42:11 - mmengine - INFO - Epoch(train)  [6][5100/5758]  lr: 8.5502e-04  eta: 9:53:13  time: 0.4275  data_time: 0.0022  memory: 20334  grad_norm: 0.0258  loss: 0.6845
2023/05/31 22:42:52 - mmengine - INFO - Epoch(train)  [6][5200/5758]  lr: 8.5502e-04  eta: 9:52:21  time: 0.4513  data_time: 0.0023  memory: 20334  grad_norm: 0.0158  loss: 0.6855
2023/05/31 22:42:56 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 22:43:34 - mmengine - INFO - Epoch(train)  [6][5300/5758]  lr: 8.5502e-04  eta: 9:51:34  time: 0.4971  data_time: 0.0019  memory: 20334  grad_norm: 0.0185  loss: 0.6863
2023/05/31 22:44:14 - mmengine - INFO - Epoch(train)  [6][5400/5758]  lr: 8.5502e-04  eta: 9:50:42  time: 0.4245  data_time: 0.0024  memory: 20334  grad_norm: 0.0111  loss: 0.6906
2023/05/31 22:44:55 - mmengine - INFO - Epoch(train)  [6][5500/5758]  lr: 8.5502e-04  eta: 9:49:51  time: 0.4005  data_time: 0.0028  memory: 20334  grad_norm: 0.0163  loss: 0.6868
2023/05/31 22:45:42 - mmengine - INFO - Epoch(train)  [6][5600/5758]  lr: 8.5502e-04  eta: 9:49:15  time: 0.5972  data_time: 0.0018  memory: 20334  grad_norm: 0.0120  loss: 0.6849
2023/05/31 22:46:22 - mmengine - INFO - Epoch(train)  [6][5700/5758]  lr: 8.5502e-04  eta: 9:48:22  time: 0.3656  data_time: 0.0018  memory: 20334  grad_norm: 0.0100  loss: 0.6855
2023/05/31 22:46:44 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 22:46:44 - mmengine - INFO - Saving checkpoint at 6 epochs
2023/05/31 22:47:03 - mmengine - INFO - Epoch(val) [6][8/8]    accuracy/top1: 100.0000  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [100.0, 0.0]  single-label/f1-score_classwise: [100.0, 0.0]  data_time: 0.3767  time: 1.0327
2023/05/31 22:47:51 - mmengine - INFO - Epoch(train)  [7][ 100/5758]  lr: 7.9595e-04  eta: 9:47:15  time: 0.3915  data_time: 0.0027  memory: 20334  grad_norm: 0.0088  loss: 0.6865
2023/05/31 22:48:32 - mmengine - INFO - Epoch(train)  [7][ 200/5758]  lr: 7.9595e-04  eta: 9:46:25  time: 0.3915  data_time: 0.0023  memory: 20334  grad_norm: 0.0178  loss: 0.6882
2023/05/31 22:49:13 - mmengine - INFO - Epoch(train)  [7][ 300/5758]  lr: 7.9595e-04  eta: 9:45:36  time: 0.4006  data_time: 0.0028  memory: 20334  grad_norm: 0.0171  loss: 0.6869
2023/05/31 22:49:55 - mmengine - INFO - Epoch(train)  [7][ 400/5758]  lr: 7.9595e-04  eta: 9:44:48  time: 0.4888  data_time: 0.0027  memory: 20334  grad_norm: 0.0208  loss: 0.6847
2023/05/31 22:50:17 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 22:50:38 - mmengine - INFO - Epoch(train)  [7][ 500/5758]  lr: 7.9595e-04  eta: 9:44:01  time: 0.4256  data_time: 0.0025  memory: 20334  grad_norm: 0.0154  loss: 0.6861
2023/05/31 22:51:18 - mmengine - INFO - Epoch(train)  [7][ 600/5758]  lr: 7.9595e-04  eta: 9:43:11  time: 0.4233  data_time: 0.0031  memory: 20334  grad_norm: 0.0174  loss: 0.6872
2023/05/31 22:52:00 - mmengine - INFO - Epoch(train)  [7][ 700/5758]  lr: 7.9595e-04  eta: 9:42:22  time: 0.4026  data_time: 0.0020  memory: 20334  grad_norm: 0.0144  loss: 0.6850
2023/05/31 22:53:08 - mmengine - INFO - Epoch(train)  [7][ 800/5758]  lr: 7.9595e-04  eta: 9:42:33  time: 1.0925  data_time: 0.0024  memory: 20334  grad_norm: 0.0130  loss: 0.6876
2023/05/31 22:53:45 - mmengine - INFO - Epoch(train)  [7][ 900/5758]  lr: 7.9595e-04  eta: 9:41:35  time: 0.3737  data_time: 0.0025  memory: 20334  grad_norm: 0.0184  loss: 0.6845
2023/05/31 22:54:24 - mmengine - INFO - Epoch(train)  [7][1000/5758]  lr: 7.9595e-04  eta: 9:40:41  time: 0.3978  data_time: 0.0023  memory: 20334  grad_norm: 0.0258  loss: 0.6840
2023/05/31 22:55:04 - mmengine - INFO - Epoch(train)  [7][1100/5758]  lr: 7.9595e-04  eta: 9:39:48  time: 0.3873  data_time: 0.0025  memory: 20334  grad_norm: 0.0184  loss: 0.6846
2023/05/31 22:55:45 - mmengine - INFO - Epoch(train)  [7][1200/5758]  lr: 7.9595e-04  eta: 9:38:58  time: 0.4038  data_time: 0.0028  memory: 20334  grad_norm: 0.0246  loss: 0.6853
2023/05/31 22:56:26 - mmengine - INFO - Epoch(train)  [7][1300/5758]  lr: 7.9595e-04  eta: 9:38:07  time: 0.4370  data_time: 0.0019  memory: 20334  grad_norm: 0.0178  loss: 0.6870
2023/05/31 22:57:05 - mmengine - INFO - Epoch(train)  [7][1400/5758]  lr: 7.9595e-04  eta: 9:37:14  time: 0.3937  data_time: 0.0030  memory: 20334  grad_norm: 0.0242  loss: 0.6844
2023/05/31 22:57:25 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 22:57:45 - mmengine - INFO - Epoch(train)  [7][1500/5758]  lr: 7.9595e-04  eta: 9:36:21  time: 0.4178  data_time: 0.0019  memory: 20334  grad_norm: 0.0087  loss: 0.6863
2023/05/31 22:58:24 - mmengine - INFO - Epoch(train)  [7][1600/5758]  lr: 7.9595e-04  eta: 9:35:28  time: 0.4099  data_time: 0.0021  memory: 20334  grad_norm: 0.0165  loss: 0.6887
2023/05/31 22:59:05 - mmengine - INFO - Epoch(train)  [7][1700/5758]  lr: 7.9595e-04  eta: 9:34:39  time: 0.4031  data_time: 0.0026  memory: 20334  grad_norm: 0.0192  loss: 0.6890
2023/05/31 22:59:46 - mmengine - INFO - Epoch(train)  [7][1800/5758]  lr: 7.9595e-04  eta: 9:33:48  time: 0.3943  data_time: 0.0024  memory: 20334  grad_norm: 0.0139  loss: 0.6847
2023/05/31 23:00:27 - mmengine - INFO - Epoch(train)  [7][1900/5758]  lr: 7.9595e-04  eta: 9:32:59  time: 0.4035  data_time: 0.0023  memory: 20334  grad_norm: 0.0245  loss: 0.6876
2023/05/31 23:01:09 - mmengine - INFO - Epoch(train)  [7][2000/5758]  lr: 7.9595e-04  eta: 9:32:11  time: 0.4083  data_time: 0.0030  memory: 20334  grad_norm: 0.0135  loss: 0.6861
2023/05/31 23:01:49 - mmengine - INFO - Epoch(train)  [7][2100/5758]  lr: 7.9595e-04  eta: 9:31:20  time: 0.3640  data_time: 0.0024  memory: 20334  grad_norm: 0.0168  loss: 0.6863
2023/05/31 23:02:29 - mmengine - INFO - Epoch(train)  [7][2200/5758]  lr: 7.9595e-04  eta: 9:30:29  time: 0.4015  data_time: 0.0023  memory: 20334  grad_norm: 0.0144  loss: 0.6854
2023/05/31 23:03:09 - mmengine - INFO - Epoch(train)  [7][2300/5758]  lr: 7.9595e-04  eta: 9:29:38  time: 0.3630  data_time: 0.0021  memory: 20334  grad_norm: 0.0163  loss: 0.6862
2023/05/31 23:03:50 - mmengine - INFO - Epoch(train)  [7][2400/5758]  lr: 7.9595e-04  eta: 9:28:47  time: 0.3996  data_time: 0.0017  memory: 20334  grad_norm: 0.0198  loss: 0.6823
2023/05/31 23:04:10 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 23:04:30 - mmengine - INFO - Epoch(train)  [7][2500/5758]  lr: 7.9595e-04  eta: 9:27:56  time: 0.4455  data_time: 0.0023  memory: 20334  grad_norm: 0.0218  loss: 0.6903
2023/05/31 23:05:10 - mmengine - INFO - Epoch(train)  [7][2600/5758]  lr: 7.9595e-04  eta: 9:27:06  time: 0.4206  data_time: 0.0026  memory: 20334  grad_norm: 0.0181  loss: 0.6891
2023/05/31 23:05:51 - mmengine - INFO - Epoch(train)  [7][2700/5758]  lr: 7.9595e-04  eta: 9:26:16  time: 0.3833  data_time: 0.0017  memory: 20334  grad_norm: 0.0279  loss: 0.6872
2023/05/31 23:06:32 - mmengine - INFO - Epoch(train)  [7][2800/5758]  lr: 7.9595e-04  eta: 9:25:27  time: 0.4038  data_time: 0.0017  memory: 20334  grad_norm: 0.0139  loss: 0.6853
2023/05/31 23:07:12 - mmengine - INFO - Epoch(train)  [7][2900/5758]  lr: 7.9595e-04  eta: 9:24:36  time: 0.3934  data_time: 0.0025  memory: 20334  grad_norm: 0.0113  loss: 0.6854
2023/05/31 23:07:52 - mmengine - INFO - Epoch(train)  [7][3000/5758]  lr: 7.9595e-04  eta: 9:23:45  time: 0.4036  data_time: 0.0023  memory: 20334  grad_norm: 0.0171  loss: 0.6871
2023/05/31 23:08:32 - mmengine - INFO - Epoch(train)  [7][3100/5758]  lr: 7.9595e-04  eta: 9:22:55  time: 0.4592  data_time: 0.0017  memory: 20334  grad_norm: 0.0211  loss: 0.6862
2023/05/31 23:09:13 - mmengine - INFO - Epoch(train)  [7][3200/5758]  lr: 7.9595e-04  eta: 9:22:05  time: 0.3824  data_time: 0.0029  memory: 20334  grad_norm: 0.0181  loss: 0.6914
2023/05/31 23:09:53 - mmengine - INFO - Epoch(train)  [7][3300/5758]  lr: 7.9595e-04  eta: 9:21:15  time: 0.4048  data_time: 0.0025  memory: 20334  grad_norm: 0.0138  loss: 0.6873
2023/05/31 23:10:33 - mmengine - INFO - Epoch(train)  [7][3400/5758]  lr: 7.9595e-04  eta: 9:20:23  time: 0.3673  data_time: 0.0021  memory: 20334  grad_norm: 0.0177  loss: 0.6882
2023/05/31 23:10:54 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 23:11:13 - mmengine - INFO - Epoch(train)  [7][3500/5758]  lr: 7.9595e-04  eta: 9:19:32  time: 0.4064  data_time: 0.0025  memory: 20334  grad_norm: 0.0139  loss: 0.6901
2023/05/31 23:11:54 - mmengine - INFO - Epoch(train)  [7][3600/5758]  lr: 7.9595e-04  eta: 9:18:43  time: 0.4259  data_time: 0.0017  memory: 20334  grad_norm: 0.0126  loss: 0.6852
2023/05/31 23:12:34 - mmengine - INFO - Epoch(train)  [7][3700/5758]  lr: 7.9595e-04  eta: 9:17:53  time: 0.4189  data_time: 0.0026  memory: 20334  grad_norm: 0.0186  loss: 0.6862
2023/05/31 23:13:15 - mmengine - INFO - Epoch(train)  [7][3800/5758]  lr: 7.9595e-04  eta: 9:17:05  time: 0.3871  data_time: 0.0015  memory: 20334  grad_norm: 0.0215  loss: 0.6917
2023/05/31 23:13:55 - mmengine - INFO - Epoch(train)  [7][3900/5758]  lr: 7.9595e-04  eta: 9:16:15  time: 0.4560  data_time: 0.0015  memory: 20334  grad_norm: 0.0234  loss: 0.6864
2023/05/31 23:14:35 - mmengine - INFO - Epoch(train)  [7][4000/5758]  lr: 7.9595e-04  eta: 9:15:24  time: 0.3990  data_time: 0.0019  memory: 20334  grad_norm: 0.0159  loss: 0.6854
2023/05/31 23:15:16 - mmengine - INFO - Epoch(train)  [7][4100/5758]  lr: 7.9595e-04  eta: 9:14:35  time: 0.3968  data_time: 0.0016  memory: 20334  grad_norm: 0.0163  loss: 0.6881
2023/05/31 23:15:55 - mmengine - INFO - Epoch(train)  [7][4200/5758]  lr: 7.9595e-04  eta: 9:13:43  time: 0.4082  data_time: 0.0016  memory: 20334  grad_norm: 0.0161  loss: 0.6845
2023/05/31 23:16:37 - mmengine - INFO - Epoch(train)  [7][4300/5758]  lr: 7.9595e-04  eta: 9:12:57  time: 0.3830  data_time: 0.0019  memory: 20334  grad_norm: 0.0181  loss: 0.6873
2023/05/31 23:17:18 - mmengine - INFO - Epoch(train)  [7][4400/5758]  lr: 7.9595e-04  eta: 9:12:08  time: 0.4112  data_time: 0.0026  memory: 20334  grad_norm: 0.0142  loss: 0.6873
2023/05/31 23:17:38 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 23:17:58 - mmengine - INFO - Epoch(train)  [7][4500/5758]  lr: 7.9595e-04  eta: 9:11:19  time: 0.3989  data_time: 0.0018  memory: 20334  grad_norm: 0.0202  loss: 0.6883
2023/05/31 23:18:40 - mmengine - INFO - Epoch(train)  [7][4600/5758]  lr: 7.9595e-04  eta: 9:10:31  time: 0.4141  data_time: 0.0017  memory: 20334  grad_norm: 0.0102  loss: 0.6852
2023/05/31 23:19:20 - mmengine - INFO - Epoch(train)  [7][4700/5758]  lr: 7.9595e-04  eta: 9:09:41  time: 0.5179  data_time: 0.0019  memory: 20334  grad_norm: 0.0136  loss: 0.6882
2023/05/31 23:20:01 - mmengine - INFO - Epoch(train)  [7][4800/5758]  lr: 7.9595e-04  eta: 9:08:53  time: 0.4319  data_time: 0.0017  memory: 20334  grad_norm: 0.0147  loss: 0.6873
2023/05/31 23:20:42 - mmengine - INFO - Epoch(train)  [7][4900/5758]  lr: 7.9595e-04  eta: 9:08:05  time: 0.3954  data_time: 0.0017  memory: 20334  grad_norm: 0.0126  loss: 0.6876
2023/05/31 23:21:22 - mmengine - INFO - Epoch(train)  [7][5000/5758]  lr: 7.9595e-04  eta: 9:07:16  time: 0.3781  data_time: 0.0016  memory: 20334  grad_norm: 0.0188  loss: 0.6844
2023/05/31 23:22:03 - mmengine - INFO - Epoch(train)  [7][5100/5758]  lr: 7.9595e-04  eta: 9:06:26  time: 0.4298  data_time: 0.0018  memory: 20334  grad_norm: 0.0119  loss: 0.6844
2023/05/31 23:22:44 - mmengine - INFO - Epoch(train)  [7][5200/5758]  lr: 7.9595e-04  eta: 9:05:39  time: 0.4400  data_time: 0.0017  memory: 20334  grad_norm: 0.0133  loss: 0.6878
2023/05/31 23:23:26 - mmengine - INFO - Epoch(train)  [7][5300/5758]  lr: 7.9595e-04  eta: 9:04:52  time: 0.4161  data_time: 0.0020  memory: 20334  grad_norm: 0.0077  loss: 0.6867
2023/05/31 23:24:08 - mmengine - INFO - Epoch(train)  [7][5400/5758]  lr: 7.9595e-04  eta: 9:04:08  time: 0.4377  data_time: 0.0017  memory: 20334  grad_norm: 0.0167  loss: 0.6833
2023/05/31 23:24:31 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 23:24:51 - mmengine - INFO - Epoch(train)  [7][5500/5758]  lr: 7.9595e-04  eta: 9:03:22  time: 0.3777  data_time: 0.0021  memory: 20334  grad_norm: 0.0198  loss: 0.6925
2023/05/31 23:25:29 - mmengine - INFO - Epoch(train)  [7][5600/5758]  lr: 7.9595e-04  eta: 9:02:30  time: 0.4018  data_time: 0.0017  memory: 20334  grad_norm: 0.0204  loss: 0.6840
2023/05/31 23:26:09 - mmengine - INFO - Epoch(train)  [7][5700/5758]  lr: 7.9595e-04  eta: 9:01:40  time: 0.3764  data_time: 0.0021  memory: 20334  grad_norm: 0.0142  loss: 0.6851
2023/05/31 23:26:34 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 23:26:34 - mmengine - INFO - Saving checkpoint at 7 epochs
2023/05/31 23:26:51 - mmengine - INFO - Epoch(val) [7][8/8]    accuracy/top1: 100.0000  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [100.0, 0.0]  single-label/f1-score_classwise: [100.0, 0.0]  data_time: 0.3704  time: 1.0113
2023/05/31 23:27:36 - mmengine - INFO - Epoch(train)  [8][ 100/5758]  lr: 7.2973e-04  eta: 9:00:33  time: 0.4223  data_time: 0.0014  memory: 20334  grad_norm: 0.0150  loss: 0.6873
2023/05/31 23:28:16 - mmengine - INFO - Epoch(train)  [8][ 200/5758]  lr: 7.2973e-04  eta: 8:59:43  time: 0.3914  data_time: 0.0015  memory: 20334  grad_norm: 0.0126  loss: 0.6861
2023/05/31 23:28:56 - mmengine - INFO - Epoch(train)  [8][ 300/5758]  lr: 7.2973e-04  eta: 8:58:55  time: 0.4302  data_time: 0.0018  memory: 20334  grad_norm: 0.0213  loss: 0.6850
2023/05/31 23:29:37 - mmengine - INFO - Epoch(train)  [8][ 400/5758]  lr: 7.2973e-04  eta: 8:58:06  time: 0.4036  data_time: 0.0015  memory: 20334  grad_norm: 0.0211  loss: 0.6903
2023/05/31 23:30:18 - mmengine - INFO - Epoch(train)  [8][ 500/5758]  lr: 7.2973e-04  eta: 8:57:20  time: 0.4276  data_time: 0.0016  memory: 20334  grad_norm: 0.0141  loss: 0.6874
2023/05/31 23:30:59 - mmengine - INFO - Epoch(train)  [8][ 600/5758]  lr: 7.2973e-04  eta: 8:56:31  time: 0.3991  data_time: 0.0015  memory: 20334  grad_norm: 0.0171  loss: 0.6926
2023/05/31 23:31:38 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 23:31:41 - mmengine - INFO - Epoch(train)  [8][ 700/5758]  lr: 7.2973e-04  eta: 8:55:45  time: 0.4612  data_time: 0.0016  memory: 20334  grad_norm: 0.0161  loss: 0.6854
2023/05/31 23:32:22 - mmengine - INFO - Epoch(train)  [8][ 800/5758]  lr: 7.2973e-04  eta: 8:54:59  time: 0.3963  data_time: 0.0015  memory: 20334  grad_norm: 0.0195  loss: 0.6854
2023/05/31 23:33:03 - mmengine - INFO - Epoch(train)  [8][ 900/5758]  lr: 7.2973e-04  eta: 8:54:10  time: 0.4091  data_time: 0.0015  memory: 20334  grad_norm: 0.0093  loss: 0.6858
2023/05/31 23:33:43 - mmengine - INFO - Epoch(train)  [8][1000/5758]  lr: 7.2973e-04  eta: 8:53:21  time: 0.3946  data_time: 0.0014  memory: 20334  grad_norm: 0.0173  loss: 0.6882
2023/05/31 23:34:22 - mmengine - INFO - Epoch(train)  [8][1100/5758]  lr: 7.2973e-04  eta: 8:52:31  time: 0.3819  data_time: 0.0015  memory: 20334  grad_norm: 0.0206  loss: 0.6901
2023/05/31 23:35:03 - mmengine - INFO - Epoch(train)  [8][1200/5758]  lr: 7.2973e-04  eta: 8:51:42  time: 0.3753  data_time: 0.0015  memory: 20334  grad_norm: 0.0139  loss: 0.6842
2023/05/31 23:35:43 - mmengine - INFO - Epoch(train)  [8][1300/5758]  lr: 7.2973e-04  eta: 8:50:53  time: 0.3834  data_time: 0.0014  memory: 20334  grad_norm: 0.0207  loss: 0.6848
2023/05/31 23:36:23 - mmengine - INFO - Epoch(train)  [8][1400/5758]  lr: 7.2973e-04  eta: 8:50:05  time: 0.4026  data_time: 0.0015  memory: 20334  grad_norm: 0.0194  loss: 0.6852
2023/05/31 23:37:05 - mmengine - INFO - Epoch(train)  [8][1500/5758]  lr: 7.2973e-04  eta: 8:49:19  time: 0.4379  data_time: 0.0015  memory: 20334  grad_norm: 0.0174  loss: 0.6905
2023/05/31 23:37:46 - mmengine - INFO - Epoch(train)  [8][1600/5758]  lr: 7.2973e-04  eta: 8:48:32  time: 0.4068  data_time: 0.0015  memory: 20334  grad_norm: 0.0078  loss: 0.6846
2023/05/31 23:38:25 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 23:38:27 - mmengine - INFO - Epoch(train)  [8][1700/5758]  lr: 7.2973e-04  eta: 8:47:45  time: 0.3627  data_time: 0.0016  memory: 20334  grad_norm: 0.0138  loss: 0.6831
2023/05/31 23:39:08 - mmengine - INFO - Epoch(train)  [8][1800/5758]  lr: 7.2973e-04  eta: 8:46:57  time: 0.4403  data_time: 0.0015  memory: 20334  grad_norm: 0.0195  loss: 0.6897
2023/05/31 23:39:49 - mmengine - INFO - Epoch(train)  [8][1900/5758]  lr: 7.2973e-04  eta: 8:46:09  time: 0.4031  data_time: 0.0015  memory: 20334  grad_norm: 0.0151  loss: 0.6837
2023/05/31 23:40:28 - mmengine - INFO - Epoch(train)  [8][2000/5758]  lr: 7.2973e-04  eta: 8:45:20  time: 0.4056  data_time: 0.0015  memory: 20334  grad_norm: 0.0154  loss: 0.6881
2023/05/31 23:41:10 - mmengine - INFO - Epoch(train)  [8][2100/5758]  lr: 7.2973e-04  eta: 8:44:33  time: 0.4114  data_time: 0.0015  memory: 20334  grad_norm: 0.0176  loss: 0.6852
2023/05/31 23:41:49 - mmengine - INFO - Epoch(train)  [8][2200/5758]  lr: 7.2973e-04  eta: 8:43:43  time: 0.4120  data_time: 0.0012  memory: 20334  grad_norm: 0.0169  loss: 0.6877
2023/05/31 23:42:30 - mmengine - INFO - Epoch(train)  [8][2300/5758]  lr: 7.2973e-04  eta: 8:42:56  time: 0.4703  data_time: 0.0013  memory: 20334  grad_norm: 0.0109  loss: 0.6843
2023/05/31 23:43:10 - mmengine - INFO - Epoch(train)  [8][2400/5758]  lr: 7.2973e-04  eta: 8:42:08  time: 0.4309  data_time: 0.0013  memory: 20334  grad_norm: 0.0137  loss: 0.6947
2023/05/31 23:43:50 - mmengine - INFO - Epoch(train)  [8][2500/5758]  lr: 7.2973e-04  eta: 8:41:19  time: 0.4007  data_time: 0.0012  memory: 20334  grad_norm: 0.0156  loss: 0.6836
2023/05/31 23:44:32 - mmengine - INFO - Epoch(train)  [8][2600/5758]  lr: 7.2973e-04  eta: 8:40:34  time: 0.4241  data_time: 0.0017  memory: 20334  grad_norm: 0.0155  loss: 0.6884
2023/05/31 23:45:12 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 23:45:14 - mmengine - INFO - Epoch(train)  [8][2700/5758]  lr: 7.2973e-04  eta: 8:39:48  time: 0.4685  data_time: 0.0017  memory: 20334  grad_norm: 0.0120  loss: 0.6840
2023/05/31 23:45:54 - mmengine - INFO - Epoch(train)  [8][2800/5758]  lr: 7.2973e-04  eta: 8:38:59  time: 0.4118  data_time: 0.0014  memory: 20334  grad_norm: 0.0096  loss: 0.6828
2023/05/31 23:46:35 - mmengine - INFO - Epoch(train)  [8][2900/5758]  lr: 7.2973e-04  eta: 8:38:12  time: 0.4031  data_time: 0.0019  memory: 20334  grad_norm: 0.0122  loss: 0.6836
2023/05/31 23:47:14 - mmengine - INFO - Epoch(train)  [8][3000/5758]  lr: 7.2973e-04  eta: 8:37:23  time: 0.3638  data_time: 0.0018  memory: 20334  grad_norm: 0.0161  loss: 0.6854
2023/05/31 23:47:54 - mmengine - INFO - Epoch(train)  [8][3100/5758]  lr: 7.2973e-04  eta: 8:36:33  time: 0.4331  data_time: 0.0020  memory: 20334  grad_norm: 0.0202  loss: 0.6902
2023/05/31 23:48:33 - mmengine - INFO - Epoch(train)  [8][3200/5758]  lr: 7.2973e-04  eta: 8:35:44  time: 0.3863  data_time: 0.0021  memory: 20334  grad_norm: 0.0199  loss: 0.6832
2023/05/31 23:49:14 - mmengine - INFO - Epoch(train)  [8][3300/5758]  lr: 7.2973e-04  eta: 8:34:57  time: 0.4603  data_time: 0.0018  memory: 20334  grad_norm: 0.0144  loss: 0.6889
2023/05/31 23:49:53 - mmengine - INFO - Epoch(train)  [8][3400/5758]  lr: 7.2973e-04  eta: 8:34:07  time: 0.3926  data_time: 0.0016  memory: 20334  grad_norm: 0.0170  loss: 0.6835
2023/05/31 23:50:34 - mmengine - INFO - Epoch(train)  [8][3500/5758]  lr: 7.2973e-04  eta: 8:33:19  time: 0.4093  data_time: 0.0018  memory: 20334  grad_norm: 0.0176  loss: 0.6804
2023/05/31 23:51:14 - mmengine - INFO - Epoch(train)  [8][3600/5758]  lr: 7.2973e-04  eta: 8:32:32  time: 0.4041  data_time: 0.0016  memory: 20334  grad_norm: 0.0166  loss: 0.6864
2023/05/31 23:51:51 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 23:51:53 - mmengine - INFO - Epoch(train)  [8][3700/5758]  lr: 7.2973e-04  eta: 8:31:42  time: 0.3867  data_time: 0.0019  memory: 20334  grad_norm: 0.0128  loss: 0.6867
2023/05/31 23:52:33 - mmengine - INFO - Epoch(train)  [8][3800/5758]  lr: 7.2973e-04  eta: 8:30:53  time: 0.4175  data_time: 0.0023  memory: 20334  grad_norm: 0.0122  loss: 0.6845
2023/05/31 23:53:12 - mmengine - INFO - Epoch(train)  [8][3900/5758]  lr: 7.2973e-04  eta: 8:30:04  time: 0.3992  data_time: 0.0015  memory: 20334  grad_norm: 0.0148  loss: 0.6846
2023/05/31 23:53:52 - mmengine - INFO - Epoch(train)  [8][4000/5758]  lr: 7.2973e-04  eta: 8:29:15  time: 0.4085  data_time: 0.0017  memory: 20334  grad_norm: 0.0194  loss: 0.6884
2023/05/31 23:54:32 - mmengine - INFO - Epoch(train)  [8][4100/5758]  lr: 7.2973e-04  eta: 8:28:27  time: 0.4341  data_time: 0.0019  memory: 20334  grad_norm: 0.0218  loss: 0.6902
2023/05/31 23:55:12 - mmengine - INFO - Epoch(train)  [8][4200/5758]  lr: 7.2973e-04  eta: 8:27:39  time: 0.4425  data_time: 0.0026  memory: 20334  grad_norm: 0.0092  loss: 0.6831
2023/05/31 23:55:53 - mmengine - INFO - Epoch(train)  [8][4300/5758]  lr: 7.2973e-04  eta: 8:26:52  time: 0.3826  data_time: 0.0031  memory: 20334  grad_norm: 0.0142  loss: 0.6839
2023/05/31 23:56:32 - mmengine - INFO - Epoch(train)  [8][4400/5758]  lr: 7.2973e-04  eta: 8:26:03  time: 0.3903  data_time: 0.0020  memory: 20334  grad_norm: 0.0141  loss: 0.6895
2023/05/31 23:57:11 - mmengine - INFO - Epoch(train)  [8][4500/5758]  lr: 7.2973e-04  eta: 8:25:14  time: 0.4005  data_time: 0.0021  memory: 20334  grad_norm: 0.0095  loss: 0.6898
2023/05/31 23:57:51 - mmengine - INFO - Epoch(train)  [8][4600/5758]  lr: 7.2973e-04  eta: 8:24:25  time: 0.3952  data_time: 0.0024  memory: 20334  grad_norm: 0.0182  loss: 0.6843
2023/05/31 23:58:29 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/05/31 23:58:31 - mmengine - INFO - Epoch(train)  [8][4700/5758]  lr: 7.2973e-04  eta: 8:23:39  time: 0.3863  data_time: 0.0017  memory: 20334  grad_norm: 0.0133  loss: 0.6857
2023/05/31 23:59:12 - mmengine - INFO - Epoch(train)  [8][4800/5758]  lr: 7.2973e-04  eta: 8:22:52  time: 0.4085  data_time: 0.0020  memory: 20334  grad_norm: 0.0106  loss: 0.6831
2023/05/31 23:59:52 - mmengine - INFO - Epoch(train)  [8][4900/5758]  lr: 7.2973e-04  eta: 8:22:04  time: 0.3887  data_time: 0.0021  memory: 20334  grad_norm: 0.0196  loss: 0.6865
2023/06/01 00:00:34 - mmengine - INFO - Epoch(train)  [8][5000/5758]  lr: 7.2973e-04  eta: 8:21:19  time: 0.4114  data_time: 0.0019  memory: 20334  grad_norm: 0.0168  loss: 0.6831
2023/06/01 00:01:13 - mmengine - INFO - Epoch(train)  [8][5100/5758]  lr: 7.2973e-04  eta: 8:20:30  time: 0.4419  data_time: 0.0017  memory: 20334  grad_norm: 0.0174  loss: 0.6849
2023/06/01 00:01:53 - mmengine - INFO - Epoch(train)  [8][5200/5758]  lr: 7.2973e-04  eta: 8:19:42  time: 0.4285  data_time: 0.0015  memory: 20334  grad_norm: 0.0248  loss: 0.6894
2023/06/01 00:02:33 - mmengine - INFO - Epoch(train)  [8][5300/5758]  lr: 7.2973e-04  eta: 8:18:55  time: 0.3812  data_time: 0.0017  memory: 20334  grad_norm: 0.0194  loss: 0.6841
2023/06/01 00:03:13 - mmengine - INFO - Epoch(train)  [8][5400/5758]  lr: 7.2973e-04  eta: 8:18:07  time: 0.4205  data_time: 0.0017  memory: 20334  grad_norm: 0.0108  loss: 0.6873
2023/06/01 00:03:53 - mmengine - INFO - Epoch(train)  [8][5500/5758]  lr: 7.2973e-04  eta: 8:17:19  time: 0.4115  data_time: 0.0016  memory: 20334  grad_norm: 0.0099  loss: 0.6845
2023/06/01 00:04:33 - mmengine - INFO - Epoch(train)  [8][5600/5758]  lr: 7.2973e-04  eta: 8:16:31  time: 0.3997  data_time: 0.0018  memory: 20334  grad_norm: 0.0236  loss: 0.6868
2023/06/01 00:05:10 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 00:05:12 - mmengine - INFO - Epoch(train)  [8][5700/5758]  lr: 7.2973e-04  eta: 8:15:43  time: 0.3803  data_time: 0.0017  memory: 20334  grad_norm: 0.0141  loss: 0.6926
2023/06/01 00:05:35 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 00:05:35 - mmengine - INFO - Saving checkpoint at 8 epochs
2023/06/01 00:05:52 - mmengine - INFO - Epoch(val) [8][8/8]    accuracy/top1: 100.0000  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [100.0, 0.0]  single-label/f1-score_classwise: [100.0, 0.0]  data_time: 0.3851  time: 1.0094
2023/06/01 00:06:37 - mmengine - INFO - Epoch(train)  [9][ 100/5758]  lr: 6.5796e-04  eta: 8:14:34  time: 0.3657  data_time: 0.0016  memory: 20334  grad_norm: 0.0142  loss: 0.6866
2023/06/01 00:07:16 - mmengine - INFO - Epoch(train)  [9][ 200/5758]  lr: 6.5796e-04  eta: 8:13:46  time: 0.4105  data_time: 0.0016  memory: 20334  grad_norm: 0.0199  loss: 0.6880
2023/06/01 00:07:57 - mmengine - INFO - Epoch(train)  [9][ 300/5758]  lr: 6.5796e-04  eta: 8:12:59  time: 0.3799  data_time: 0.0017  memory: 20334  grad_norm: 0.0085  loss: 0.6842
2023/06/01 00:08:36 - mmengine - INFO - Epoch(train)  [9][ 400/5758]  lr: 6.5796e-04  eta: 8:12:11  time: 0.3870  data_time: 0.0016  memory: 20334  grad_norm: 0.0180  loss: 0.6870
2023/06/01 00:09:17 - mmengine - INFO - Epoch(train)  [9][ 500/5758]  lr: 6.5796e-04  eta: 8:11:24  time: 0.4097  data_time: 0.0016  memory: 20334  grad_norm: 0.0165  loss: 0.6872
2023/06/01 00:09:56 - mmengine - INFO - Epoch(train)  [9][ 600/5758]  lr: 6.5796e-04  eta: 8:10:36  time: 0.4078  data_time: 0.0015  memory: 20334  grad_norm: 0.0171  loss: 0.6877
2023/06/01 00:10:37 - mmengine - INFO - Epoch(train)  [9][ 700/5758]  lr: 6.5796e-04  eta: 8:09:50  time: 0.4025  data_time: 0.0015  memory: 20334  grad_norm: 0.0262  loss: 0.6899
2023/06/01 00:11:17 - mmengine - INFO - Epoch(train)  [9][ 800/5758]  lr: 6.5796e-04  eta: 8:09:03  time: 0.4089  data_time: 0.0016  memory: 20334  grad_norm: 0.0134  loss: 0.6825
2023/06/01 00:11:59 - mmengine - INFO - Epoch(train)  [9][ 900/5758]  lr: 6.5796e-04  eta: 8:08:18  time: 0.4349  data_time: 0.0017  memory: 20334  grad_norm: 0.0166  loss: 0.6853
2023/06/01 00:12:14 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 00:12:39 - mmengine - INFO - Epoch(train)  [9][1000/5758]  lr: 6.5796e-04  eta: 8:07:31  time: 0.4156  data_time: 0.0016  memory: 20334  grad_norm: 0.0297  loss: 0.6826
2023/06/01 00:13:18 - mmengine - INFO - Epoch(train)  [9][1100/5758]  lr: 6.5796e-04  eta: 8:06:43  time: 0.4004  data_time: 0.0017  memory: 20334  grad_norm: 0.0241  loss: 0.6842
2023/06/01 00:13:58 - mmengine - INFO - Epoch(train)  [9][1200/5758]  lr: 6.5796e-04  eta: 8:05:55  time: 0.3890  data_time: 0.0015  memory: 20334  grad_norm: 0.0166  loss: 0.6880
2023/06/01 00:14:39 - mmengine - INFO - Epoch(train)  [9][1300/5758]  lr: 6.5796e-04  eta: 8:05:09  time: 0.4182  data_time: 0.0019  memory: 20334  grad_norm: 0.0172  loss: 0.6901
2023/06/01 00:15:19 - mmengine - INFO - Epoch(train)  [9][1400/5758]  lr: 6.5796e-04  eta: 8:04:22  time: 0.3768  data_time: 0.0018  memory: 20334  grad_norm: 0.0236  loss: 0.6926
2023/06/01 00:16:00 - mmengine - INFO - Epoch(train)  [9][1500/5758]  lr: 6.5796e-04  eta: 8:03:36  time: 0.3780  data_time: 0.0016  memory: 20334  grad_norm: 0.0203  loss: 0.6883
2023/06/01 00:16:40 - mmengine - INFO - Epoch(train)  [9][1600/5758]  lr: 6.5796e-04  eta: 8:02:49  time: 0.4377  data_time: 0.0018  memory: 20334  grad_norm: 0.0207  loss: 0.6895
2023/06/01 00:17:19 - mmengine - INFO - Epoch(train)  [9][1700/5758]  lr: 6.5796e-04  eta: 8:02:01  time: 0.4236  data_time: 0.0015  memory: 20334  grad_norm: 0.0263  loss: 0.6902
2023/06/01 00:17:58 - mmengine - INFO - Epoch(train)  [9][1800/5758]  lr: 6.5796e-04  eta: 8:01:12  time: 0.3900  data_time: 0.0015  memory: 20334  grad_norm: 0.0167  loss: 0.6927
2023/06/01 00:18:38 - mmengine - INFO - Epoch(train)  [9][1900/5758]  lr: 6.5796e-04  eta: 8:00:25  time: 0.4081  data_time: 0.0014  memory: 20334  grad_norm: 0.0168  loss: 0.6866
2023/06/01 00:18:53 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 00:19:18 - mmengine - INFO - Epoch(train)  [9][2000/5758]  lr: 6.5796e-04  eta: 7:59:39  time: 0.4291  data_time: 0.0015  memory: 20334  grad_norm: 0.0119  loss: 0.6865
2023/06/01 00:19:57 - mmengine - INFO - Epoch(train)  [9][2100/5758]  lr: 6.5796e-04  eta: 7:58:50  time: 0.3696  data_time: 0.0015  memory: 20334  grad_norm: 0.0185  loss: 0.6884
2023/06/01 00:20:38 - mmengine - INFO - Epoch(train)  [9][2200/5758]  lr: 6.5796e-04  eta: 7:58:04  time: 0.4319  data_time: 0.0014  memory: 20334  grad_norm: 0.0166  loss: 0.6853
2023/06/01 00:21:18 - mmengine - INFO - Epoch(train)  [9][2300/5758]  lr: 6.5796e-04  eta: 7:57:17  time: 0.4174  data_time: 0.0015  memory: 20334  grad_norm: 0.0148  loss: 0.6844
2023/06/01 00:21:58 - mmengine - INFO - Epoch(train)  [9][2400/5758]  lr: 6.5796e-04  eta: 7:56:30  time: 0.4347  data_time: 0.0015  memory: 20334  grad_norm: 0.0178  loss: 0.6882
2023/06/01 00:22:38 - mmengine - INFO - Epoch(train)  [9][2500/5758]  lr: 6.5796e-04  eta: 7:55:44  time: 0.3834  data_time: 0.0017  memory: 20334  grad_norm: 0.0168  loss: 0.6823
2023/06/01 00:23:18 - mmengine - INFO - Epoch(train)  [9][2600/5758]  lr: 6.5796e-04  eta: 7:54:57  time: 0.3810  data_time: 0.0017  memory: 20334  grad_norm: 0.0126  loss: 0.6860
2023/06/01 00:23:58 - mmengine - INFO - Epoch(train)  [9][2700/5758]  lr: 6.5796e-04  eta: 7:54:10  time: 0.3961  data_time: 0.0017  memory: 20334  grad_norm: 0.0197  loss: 0.6837
2023/06/01 00:24:39 - mmengine - INFO - Epoch(train)  [9][2800/5758]  lr: 6.5796e-04  eta: 7:53:26  time: 0.4435  data_time: 0.0018  memory: 20334  grad_norm: 0.0157  loss: 0.6875
2023/06/01 00:25:19 - mmengine - INFO - Epoch(train)  [9][2900/5758]  lr: 6.5796e-04  eta: 7:52:38  time: 0.3820  data_time: 0.0018  memory: 20334  grad_norm: 0.0180  loss: 0.6824
2023/06/01 00:25:33 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 00:25:59 - mmengine - INFO - Epoch(train)  [9][3000/5758]  lr: 6.5796e-04  eta: 7:51:51  time: 0.4053  data_time: 0.0016  memory: 20334  grad_norm: 0.0163  loss: 0.6910
2023/06/01 00:26:38 - mmengine - INFO - Epoch(train)  [9][3100/5758]  lr: 6.5796e-04  eta: 7:51:04  time: 0.3820  data_time: 0.0016  memory: 20334  grad_norm: 0.0174  loss: 0.6902
2023/06/01 00:27:18 - mmengine - INFO - Epoch(train)  [9][3200/5758]  lr: 6.5796e-04  eta: 7:50:18  time: 0.4061  data_time: 0.0018  memory: 20334  grad_norm: 0.0130  loss: 0.6888
2023/06/01 00:27:58 - mmengine - INFO - Epoch(train)  [9][3300/5758]  lr: 6.5796e-04  eta: 7:49:31  time: 0.3685  data_time: 0.0017  memory: 20334  grad_norm: 0.0157  loss: 0.6879
2023/06/01 00:28:39 - mmengine - INFO - Epoch(train)  [9][3400/5758]  lr: 6.5796e-04  eta: 7:48:45  time: 0.4373  data_time: 0.0017  memory: 20334  grad_norm: 0.0146  loss: 0.6809
2023/06/01 00:29:19 - mmengine - INFO - Epoch(train)  [9][3500/5758]  lr: 6.5796e-04  eta: 7:47:59  time: 0.3634  data_time: 0.0019  memory: 20334  grad_norm: 0.0162  loss: 0.6866
2023/06/01 00:29:59 - mmengine - INFO - Epoch(train)  [9][3600/5758]  lr: 6.5796e-04  eta: 7:47:13  time: 0.3802  data_time: 0.0017  memory: 20334  grad_norm: 0.0205  loss: 0.6849
2023/06/01 00:30:39 - mmengine - INFO - Epoch(train)  [9][3700/5758]  lr: 6.5796e-04  eta: 7:46:25  time: 0.3969  data_time: 0.0016  memory: 20334  grad_norm: 0.0171  loss: 0.6884
2023/06/01 00:31:19 - mmengine - INFO - Epoch(train)  [9][3800/5758]  lr: 6.5796e-04  eta: 7:45:40  time: 0.4089  data_time: 0.0017  memory: 20334  grad_norm: 0.0101  loss: 0.6838
2023/06/01 00:31:59 - mmengine - INFO - Epoch(train)  [9][3900/5758]  lr: 6.5796e-04  eta: 7:44:53  time: 0.3888  data_time: 0.0018  memory: 20334  grad_norm: 0.0148  loss: 0.6896
2023/06/01 00:32:14 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 00:32:40 - mmengine - INFO - Epoch(train)  [9][4000/5758]  lr: 6.5796e-04  eta: 7:44:08  time: 0.4657  data_time: 0.0015  memory: 20334  grad_norm: 0.0133  loss: 0.6859
2023/06/01 00:33:21 - mmengine - INFO - Epoch(train)  [9][4100/5758]  lr: 6.5796e-04  eta: 7:43:22  time: 0.4193  data_time: 0.0016  memory: 20334  grad_norm: 0.0126  loss: 0.6846
2023/06/01 00:34:01 - mmengine - INFO - Epoch(train)  [9][4200/5758]  lr: 6.5796e-04  eta: 7:42:36  time: 0.4574  data_time: 0.0016  memory: 20334  grad_norm: 0.0164  loss: 0.6868
2023/06/01 00:34:40 - mmengine - INFO - Epoch(train)  [9][4300/5758]  lr: 6.5796e-04  eta: 7:41:49  time: 0.4001  data_time: 0.0016  memory: 20334  grad_norm: 0.0199  loss: 0.6844
2023/06/01 00:35:20 - mmengine - INFO - Epoch(train)  [9][4400/5758]  lr: 6.5796e-04  eta: 7:41:03  time: 0.4158  data_time: 0.0014  memory: 20334  grad_norm: 0.0163  loss: 0.6905
2023/06/01 00:36:00 - mmengine - INFO - Epoch(train)  [9][4500/5758]  lr: 6.5796e-04  eta: 7:40:16  time: 0.4090  data_time: 0.0013  memory: 20334  grad_norm: 0.0154  loss: 0.6873
2023/06/01 00:36:40 - mmengine - INFO - Epoch(train)  [9][4600/5758]  lr: 6.5796e-04  eta: 7:39:30  time: 0.4068  data_time: 0.0014  memory: 20334  grad_norm: 0.0163  loss: 0.6849
2023/06/01 00:37:20 - mmengine - INFO - Epoch(train)  [9][4700/5758]  lr: 6.5796e-04  eta: 7:38:43  time: 0.3742  data_time: 0.0013  memory: 20334  grad_norm: 0.0120  loss: 0.6895
2023/06/01 00:38:00 - mmengine - INFO - Epoch(train)  [9][4800/5758]  lr: 6.5796e-04  eta: 7:37:57  time: 0.3626  data_time: 0.0013  memory: 20334  grad_norm: 0.0179  loss: 0.6838
2023/06/01 00:38:40 - mmengine - INFO - Epoch(train)  [9][4900/5758]  lr: 6.5796e-04  eta: 7:37:11  time: 0.4155  data_time: 0.0016  memory: 20334  grad_norm: 0.0104  loss: 0.6882
2023/06/01 00:38:55 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 00:39:21 - mmengine - INFO - Epoch(train)  [9][5000/5758]  lr: 6.5796e-04  eta: 7:36:26  time: 0.4163  data_time: 0.0016  memory: 20334  grad_norm: 0.0163  loss: 0.6855
2023/06/01 00:40:00 - mmengine - INFO - Epoch(train)  [9][5100/5758]  lr: 6.5796e-04  eta: 7:35:39  time: 0.3880  data_time: 0.0014  memory: 20334  grad_norm: 0.0136  loss: 0.6881
2023/06/01 00:40:40 - mmengine - INFO - Epoch(train)  [9][5200/5758]  lr: 6.5796e-04  eta: 7:34:53  time: 0.3931  data_time: 0.0015  memory: 20334  grad_norm: 0.0151  loss: 0.6864
2023/06/01 00:41:20 - mmengine - INFO - Epoch(train)  [9][5300/5758]  lr: 6.5796e-04  eta: 7:34:06  time: 0.3680  data_time: 0.0015  memory: 20334  grad_norm: 0.0214  loss: 0.6849
2023/06/01 00:42:00 - mmengine - INFO - Epoch(train)  [9][5400/5758]  lr: 6.5796e-04  eta: 7:33:20  time: 0.4489  data_time: 0.0017  memory: 20334  grad_norm: 0.0175  loss: 0.6825
2023/06/01 00:42:39 - mmengine - INFO - Epoch(train)  [9][5500/5758]  lr: 6.5796e-04  eta: 7:32:33  time: 0.4165  data_time: 0.0017  memory: 20334  grad_norm: 0.0185  loss: 0.6859
2023/06/01 00:43:18 - mmengine - INFO - Epoch(train)  [9][5600/5758]  lr: 6.5796e-04  eta: 7:31:46  time: 0.3864  data_time: 0.0018  memory: 20334  grad_norm: 0.0111  loss: 0.6867
2023/06/01 00:43:58 - mmengine - INFO - Epoch(train)  [9][5700/5758]  lr: 6.5796e-04  eta: 7:31:00  time: 0.3709  data_time: 0.0015  memory: 20334  grad_norm: 0.0168  loss: 0.6819
2023/06/01 00:44:21 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 00:44:21 - mmengine - INFO - Saving checkpoint at 9 epochs
2023/06/01 00:44:38 - mmengine - INFO - Epoch(val) [9][8/8]    accuracy/top1: 100.0000  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [100.0, 0.0]  single-label/f1-score_classwise: [100.0, 0.0]  data_time: 0.3715  time: 0.9948
2023/06/01 00:45:22 - mmengine - INFO - Epoch(train) [10][ 100/5758]  lr: 5.8244e-04  eta: 7:29:53  time: 0.4262  data_time: 0.0016  memory: 20334  grad_norm: 0.0136  loss: 0.6862
2023/06/01 00:45:54 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 00:46:03 - mmengine - INFO - Epoch(train) [10][ 200/5758]  lr: 5.8244e-04  eta: 7:29:07  time: 0.3890  data_time: 0.0016  memory: 20334  grad_norm: 0.0158  loss: 0.6849
2023/06/01 00:46:41 - mmengine - INFO - Epoch(train) [10][ 300/5758]  lr: 5.8244e-04  eta: 7:28:19  time: 0.4332  data_time: 0.0018  memory: 20334  grad_norm: 0.0155  loss: 0.6848
2023/06/01 00:47:20 - mmengine - INFO - Epoch(train) [10][ 400/5758]  lr: 5.8244e-04  eta: 7:27:32  time: 0.3700  data_time: 0.0018  memory: 20334  grad_norm: 0.0152  loss: 0.6876
2023/06/01 00:48:00 - mmengine - INFO - Epoch(train) [10][ 500/5758]  lr: 5.8244e-04  eta: 7:26:47  time: 0.4105  data_time: 0.0016  memory: 20334  grad_norm: 0.0105  loss: 0.6874
2023/06/01 00:48:41 - mmengine - INFO - Epoch(train) [10][ 600/5758]  lr: 5.8244e-04  eta: 7:26:01  time: 0.4195  data_time: 0.0016  memory: 20334  grad_norm: 0.0116  loss: 0.6903
2023/06/01 00:49:20 - mmengine - INFO - Epoch(train) [10][ 700/5758]  lr: 5.8244e-04  eta: 7:25:15  time: 0.3901  data_time: 0.0018  memory: 20334  grad_norm: 0.0214  loss: 0.6843
2023/06/01 00:50:00 - mmengine - INFO - Epoch(train) [10][ 800/5758]  lr: 5.8244e-04  eta: 7:24:29  time: 0.4323  data_time: 0.0014  memory: 20334  grad_norm: 0.0161  loss: 0.6893
2023/06/01 00:50:39 - mmengine - INFO - Epoch(train) [10][ 900/5758]  lr: 5.8244e-04  eta: 7:23:42  time: 0.3841  data_time: 0.0017  memory: 20334  grad_norm: 0.0155  loss: 0.6848
2023/06/01 00:51:19 - mmengine - INFO - Epoch(train) [10][1000/5758]  lr: 5.8244e-04  eta: 7:22:56  time: 0.3833  data_time: 0.0019  memory: 20334  grad_norm: 0.0183  loss: 0.6794
2023/06/01 00:51:59 - mmengine - INFO - Epoch(train) [10][1100/5758]  lr: 5.8244e-04  eta: 7:22:10  time: 0.3641  data_time: 0.0018  memory: 20334  grad_norm: 0.0127  loss: 0.6832
2023/06/01 00:52:31 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 00:52:39 - mmengine - INFO - Epoch(train) [10][1200/5758]  lr: 5.8244e-04  eta: 7:21:25  time: 0.3908  data_time: 0.0017  memory: 20334  grad_norm: 0.0186  loss: 0.6863
2023/06/01 00:53:20 - mmengine - INFO - Epoch(train) [10][1300/5758]  lr: 5.8244e-04  eta: 7:20:40  time: 0.4295  data_time: 0.0015  memory: 20334  grad_norm: 0.0171  loss: 0.6921
2023/06/01 00:53:58 - mmengine - INFO - Epoch(train) [10][1400/5758]  lr: 5.8244e-04  eta: 7:19:52  time: 0.3710  data_time: 0.0017  memory: 20334  grad_norm: 0.0203  loss: 0.6852
2023/06/01 00:54:38 - mmengine - INFO - Epoch(train) [10][1500/5758]  lr: 5.8244e-04  eta: 7:19:06  time: 0.3865  data_time: 0.0018  memory: 20334  grad_norm: 0.0182  loss: 0.6863
2023/06/01 00:55:18 - mmengine - INFO - Epoch(train) [10][1600/5758]  lr: 5.8244e-04  eta: 7:18:21  time: 0.4194  data_time: 0.0015  memory: 20334  grad_norm: 0.0177  loss: 0.6906
2023/06/01 00:55:58 - mmengine - INFO - Epoch(train) [10][1700/5758]  lr: 5.8244e-04  eta: 7:17:35  time: 0.4043  data_time: 0.0015  memory: 20334  grad_norm: 0.0141  loss: 0.6836
2023/06/01 00:56:38 - mmengine - INFO - Epoch(train) [10][1800/5758]  lr: 5.8244e-04  eta: 7:16:50  time: 0.3795  data_time: 0.0016  memory: 20334  grad_norm: 0.0189  loss: 0.6863
2023/06/01 00:57:18 - mmengine - INFO - Epoch(train) [10][1900/5758]  lr: 5.8244e-04  eta: 7:16:04  time: 0.4205  data_time: 0.0014  memory: 20334  grad_norm: 0.0117  loss: 0.6885
2023/06/01 00:57:59 - mmengine - INFO - Epoch(train) [10][2000/5758]  lr: 5.8244e-04  eta: 7:15:19  time: 0.4040  data_time: 0.0016  memory: 20334  grad_norm: 0.0186  loss: 0.6876
2023/06/01 00:58:38 - mmengine - INFO - Epoch(train) [10][2100/5758]  lr: 5.8244e-04  eta: 7:14:33  time: 0.3708  data_time: 0.0015  memory: 20334  grad_norm: 0.0202  loss: 0.6900
2023/06/01 00:59:09 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 00:59:17 - mmengine - INFO - Epoch(train) [10][2200/5758]  lr: 5.8244e-04  eta: 7:13:46  time: 0.4004  data_time: 0.0016  memory: 20334  grad_norm: 0.0131  loss: 0.6886
2023/06/01 00:59:56 - mmengine - INFO - Epoch(train) [10][2300/5758]  lr: 5.8244e-04  eta: 7:13:00  time: 0.3760  data_time: 0.0019  memory: 20334  grad_norm: 0.0103  loss: 0.6827
2023/06/01 01:00:37 - mmengine - INFO - Epoch(train) [10][2400/5758]  lr: 5.8244e-04  eta: 7:12:15  time: 0.4136  data_time: 0.0017  memory: 20334  grad_norm: 0.0192  loss: 0.6844
2023/06/01 01:01:17 - mmengine - INFO - Epoch(train) [10][2500/5758]  lr: 5.8244e-04  eta: 7:11:30  time: 0.4294  data_time: 0.0020  memory: 20334  grad_norm: 0.0161  loss: 0.6878
2023/06/01 01:01:57 - mmengine - INFO - Epoch(train) [10][2600/5758]  lr: 5.8244e-04  eta: 7:10:44  time: 0.4233  data_time: 0.0013  memory: 20334  grad_norm: 0.0128  loss: 0.6899
2023/06/01 01:02:36 - mmengine - INFO - Epoch(train) [10][2700/5758]  lr: 5.8244e-04  eta: 7:09:58  time: 0.4120  data_time: 0.0015  memory: 20334  grad_norm: 0.0128  loss: 0.6877
2023/06/01 01:03:15 - mmengine - INFO - Epoch(train) [10][2800/5758]  lr: 5.8244e-04  eta: 7:09:12  time: 0.4175  data_time: 0.0019  memory: 20334  grad_norm: 0.0157  loss: 0.6882
2023/06/01 01:03:55 - mmengine - INFO - Epoch(train) [10][2900/5758]  lr: 5.8244e-04  eta: 7:08:26  time: 0.4090  data_time: 0.0015  memory: 20334  grad_norm: 0.0175  loss: 0.6817
2023/06/01 01:04:34 - mmengine - INFO - Epoch(train) [10][3000/5758]  lr: 5.8244e-04  eta: 7:07:40  time: 0.3908  data_time: 0.0016  memory: 20334  grad_norm: 0.0069  loss: 0.6842
2023/06/01 01:05:13 - mmengine - INFO - Epoch(train) [10][3100/5758]  lr: 5.8244e-04  eta: 7:06:54  time: 0.4035  data_time: 0.0017  memory: 20334  grad_norm: 0.0181  loss: 0.6884
2023/06/01 01:05:46 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 01:05:54 - mmengine - INFO - Epoch(train) [10][3200/5758]  lr: 5.8244e-04  eta: 7:06:09  time: 0.3929  data_time: 0.0016  memory: 20334  grad_norm: 0.0088  loss: 0.6831
2023/06/01 01:06:34 - mmengine - INFO - Epoch(train) [10][3300/5758]  lr: 5.8244e-04  eta: 7:05:23  time: 0.4198  data_time: 0.0016  memory: 20334  grad_norm: 0.0117  loss: 0.6844
2023/06/01 01:08:24 - mmengine - INFO - Epoch(train) [10][3400/5758]  lr: 5.8244e-04  eta: 7:05:54  time: 0.4055  data_time: 0.0017  memory: 20334  grad_norm: 0.0115  loss: 0.6901
2023/06/01 01:09:02 - mmengine - INFO - Epoch(train) [10][3500/5758]  lr: 5.8244e-04  eta: 7:05:07  time: 0.3676  data_time: 0.0017  memory: 20334  grad_norm: 0.0141  loss: 0.6913
2023/06/01 01:09:41 - mmengine - INFO - Epoch(train) [10][3600/5758]  lr: 5.8244e-04  eta: 7:04:20  time: 0.3884  data_time: 0.0018  memory: 20334  grad_norm: 0.0159  loss: 0.6866
2023/06/01 01:10:21 - mmengine - INFO - Epoch(train) [10][3700/5758]  lr: 5.8244e-04  eta: 7:03:35  time: 0.4087  data_time: 0.0018  memory: 20334  grad_norm: 0.0216  loss: 0.6877
2023/06/01 01:11:01 - mmengine - INFO - Epoch(train) [10][3800/5758]  lr: 5.8244e-04  eta: 7:02:49  time: 0.4177  data_time: 0.0016  memory: 20334  grad_norm: 0.0146  loss: 0.6817
2023/06/01 01:11:41 - mmengine - INFO - Epoch(train) [10][3900/5758]  lr: 5.8244e-04  eta: 7:02:04  time: 0.3804  data_time: 0.0019  memory: 20334  grad_norm: 0.0110  loss: 0.6878
2023/06/01 01:12:21 - mmengine - INFO - Epoch(train) [10][4000/5758]  lr: 5.8244e-04  eta: 7:01:18  time: 0.3712  data_time: 0.0016  memory: 20334  grad_norm: 0.0101  loss: 0.6867
2023/06/01 01:13:02 - mmengine - INFO - Epoch(train) [10][4100/5758]  lr: 5.8244e-04  eta: 7:00:34  time: 0.3736  data_time: 0.0019  memory: 20334  grad_norm: 0.0115  loss: 0.6877
2023/06/01 01:13:32 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 01:13:41 - mmengine - INFO - Epoch(train) [10][4200/5758]  lr: 5.8244e-04  eta: 6:59:48  time: 0.3945  data_time: 0.0018  memory: 20334  grad_norm: 0.0124  loss: 0.6830
2023/06/01 01:14:21 - mmengine - INFO - Epoch(train) [10][4300/5758]  lr: 5.8244e-04  eta: 6:59:02  time: 0.3841  data_time: 0.0020  memory: 20334  grad_norm: 0.0208  loss: 0.6918
2023/06/01 01:15:00 - mmengine - INFO - Epoch(train) [10][4400/5758]  lr: 5.8244e-04  eta: 6:58:16  time: 0.4067  data_time: 0.0017  memory: 20334  grad_norm: 0.0135  loss: 0.6818
2023/06/01 01:15:39 - mmengine - INFO - Epoch(train) [10][4500/5758]  lr: 5.8244e-04  eta: 6:57:30  time: 0.3725  data_time: 0.0019  memory: 20334  grad_norm: 0.0127  loss: 0.6808
2023/06/01 01:16:20 - mmengine - INFO - Epoch(train) [10][4600/5758]  lr: 5.8244e-04  eta: 6:56:45  time: 0.3779  data_time: 0.0015  memory: 20334  grad_norm: 0.0210  loss: 0.6907
2023/06/01 01:17:01 - mmengine - INFO - Epoch(train) [10][4700/5758]  lr: 5.8244e-04  eta: 6:56:02  time: 0.4157  data_time: 0.0017  memory: 20334  grad_norm: 0.0121  loss: 0.6793
2023/06/01 01:17:40 - mmengine - INFO - Epoch(train) [10][4800/5758]  lr: 5.8244e-04  eta: 6:55:15  time: 0.3864  data_time: 0.0017  memory: 20334  grad_norm: 0.0156  loss: 0.6881
2023/06/01 01:18:20 - mmengine - INFO - Epoch(train) [10][4900/5758]  lr: 5.8244e-04  eta: 6:54:30  time: 0.4160  data_time: 0.0017  memory: 20334  grad_norm: 0.0154  loss: 0.6878
2023/06/01 01:19:00 - mmengine - INFO - Epoch(train) [10][5000/5758]  lr: 5.8244e-04  eta: 6:53:44  time: 0.4001  data_time: 0.0017  memory: 20334  grad_norm: 0.0165  loss: 0.6827
2023/06/01 01:19:40 - mmengine - INFO - Epoch(train) [10][5100/5758]  lr: 5.8244e-04  eta: 6:52:59  time: 0.3830  data_time: 0.0016  memory: 20334  grad_norm: 0.0135  loss: 0.6873
2023/06/01 01:20:12 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 01:20:21 - mmengine - INFO - Epoch(train) [10][5200/5758]  lr: 5.8244e-04  eta: 6:52:16  time: 0.4249  data_time: 0.0016  memory: 20334  grad_norm: 0.0196  loss: 0.6893
2023/06/01 01:21:01 - mmengine - INFO - Epoch(train) [10][5300/5758]  lr: 5.8244e-04  eta: 6:51:31  time: 0.4225  data_time: 0.0016  memory: 20334  grad_norm: 0.0183  loss: 0.6847
2023/06/01 01:21:42 - mmengine - INFO - Epoch(train) [10][5400/5758]  lr: 5.8244e-04  eta: 6:50:46  time: 0.3960  data_time: 0.0016  memory: 20334  grad_norm: 0.0171  loss: 0.6869
2023/06/01 01:22:22 - mmengine - INFO - Epoch(train) [10][5500/5758]  lr: 5.8244e-04  eta: 6:50:01  time: 0.4215  data_time: 0.0019  memory: 20334  grad_norm: 0.0119  loss: 0.6865
2023/06/01 01:23:01 - mmengine - INFO - Epoch(train) [10][5600/5758]  lr: 5.8244e-04  eta: 6:49:15  time: 0.3706  data_time: 0.0018  memory: 20334  grad_norm: 0.0249  loss: 0.6873
2023/06/01 01:23:41 - mmengine - INFO - Epoch(train) [10][5700/5758]  lr: 5.8244e-04  eta: 6:48:30  time: 0.4217  data_time: 0.0019  memory: 20334  grad_norm: 0.0146  loss: 0.6857
2023/06/01 01:24:04 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 01:24:04 - mmengine - INFO - Saving checkpoint at 10 epochs
2023/06/01 01:24:21 - mmengine - INFO - Epoch(val) [10][8/8]    accuracy/top1: 100.0000  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [100.0, 0.0]  single-label/f1-score_classwise: [100.0, 0.0]  data_time: 0.3693  time: 1.0086
2023/06/01 01:25:05 - mmengine - INFO - Epoch(train) [11][ 100/5758]  lr: 5.0500e-04  eta: 6:47:23  time: 0.4090  data_time: 0.0017  memory: 20334  grad_norm: 0.0156  loss: 0.6873
2023/06/01 01:25:45 - mmengine - INFO - Epoch(train) [11][ 200/5758]  lr: 5.0500e-04  eta: 6:46:38  time: 0.3714  data_time: 0.0017  memory: 20334  grad_norm: 0.0134  loss: 0.6865
2023/06/01 01:26:25 - mmengine - INFO - Epoch(train) [11][ 300/5758]  lr: 5.0500e-04  eta: 6:45:53  time: 0.3854  data_time: 0.0017  memory: 20334  grad_norm: 0.0179  loss: 0.6903
2023/06/01 01:27:06 - mmengine - INFO - Epoch(train) [11][ 400/5758]  lr: 5.0500e-04  eta: 6:45:09  time: 0.4526  data_time: 0.0016  memory: 20334  grad_norm: 0.0126  loss: 0.6907
2023/06/01 01:27:14 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 01:27:46 - mmengine - INFO - Epoch(train) [11][ 500/5758]  lr: 5.0500e-04  eta: 6:44:24  time: 0.4103  data_time: 0.0018  memory: 20334  grad_norm: 0.0132  loss: 0.6839
2023/06/01 01:28:25 - mmengine - INFO - Epoch(train) [11][ 600/5758]  lr: 5.0500e-04  eta: 6:43:38  time: 0.3825  data_time: 0.0016  memory: 20334  grad_norm: 0.0110  loss: 0.6890
2023/06/01 01:29:05 - mmengine - INFO - Epoch(train) [11][ 700/5758]  lr: 5.0500e-04  eta: 6:42:53  time: 0.4215  data_time: 0.0017  memory: 20334  grad_norm: 0.0168  loss: 0.6823
2023/06/01 01:29:45 - mmengine - INFO - Epoch(train) [11][ 800/5758]  lr: 5.0500e-04  eta: 6:42:08  time: 0.3628  data_time: 0.0017  memory: 20334  grad_norm: 0.0158  loss: 0.6853
2023/06/01 01:30:24 - mmengine - INFO - Epoch(train) [11][ 900/5758]  lr: 5.0500e-04  eta: 6:41:23  time: 0.3953  data_time: 0.0017  memory: 20334  grad_norm: 0.0144  loss: 0.6872
2023/06/01 01:31:04 - mmengine - INFO - Epoch(train) [11][1000/5758]  lr: 5.0500e-04  eta: 6:40:37  time: 0.3968  data_time: 0.0016  memory: 20334  grad_norm: 0.0158  loss: 0.6875
2023/06/01 01:31:43 - mmengine - INFO - Epoch(train) [11][1100/5758]  lr: 5.0500e-04  eta: 6:39:51  time: 0.3885  data_time: 0.0017  memory: 20334  grad_norm: 0.0135  loss: 0.6835
2023/06/01 01:32:23 - mmengine - INFO - Epoch(train) [11][1200/5758]  lr: 5.0500e-04  eta: 6:39:07  time: 0.4766  data_time: 0.0016  memory: 20334  grad_norm: 0.0178  loss: 0.6883
2023/06/01 01:33:04 - mmengine - INFO - Epoch(train) [11][1300/5758]  lr: 5.0500e-04  eta: 6:38:22  time: 0.3716  data_time: 0.0018  memory: 20334  grad_norm: 0.0212  loss: 0.6876
2023/06/01 01:33:45 - mmengine - INFO - Epoch(train) [11][1400/5758]  lr: 5.0500e-04  eta: 6:37:39  time: 0.4112  data_time: 0.0020  memory: 20334  grad_norm: 0.0115  loss: 0.6881
2023/06/01 01:33:53 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 01:34:26 - mmengine - INFO - Epoch(train) [11][1500/5758]  lr: 5.0500e-04  eta: 6:36:55  time: 0.4026  data_time: 0.0020  memory: 20334  grad_norm: 0.0114  loss: 0.6862
2023/06/01 01:35:05 - mmengine - INFO - Epoch(train) [11][1600/5758]  lr: 5.0500e-04  eta: 6:36:09  time: 0.4091  data_time: 0.0018  memory: 20334  grad_norm: 0.0144  loss: 0.6901
2023/06/01 01:35:45 - mmengine - INFO - Epoch(train) [11][1700/5758]  lr: 5.0500e-04  eta: 6:35:25  time: 0.4363  data_time: 0.0016  memory: 20334  grad_norm: 0.0201  loss: 0.6836
2023/06/01 01:36:26 - mmengine - INFO - Epoch(train) [11][1800/5758]  lr: 5.0500e-04  eta: 6:34:41  time: 0.3936  data_time: 0.0017  memory: 20334  grad_norm: 0.0148  loss: 0.6905
2023/06/01 01:37:07 - mmengine - INFO - Epoch(train) [11][1900/5758]  lr: 5.0500e-04  eta: 6:33:56  time: 0.4215  data_time: 0.0016  memory: 20334  grad_norm: 0.0212  loss: 0.6817
2023/06/01 01:37:46 - mmengine - INFO - Epoch(train) [11][2000/5758]  lr: 5.0500e-04  eta: 6:33:11  time: 0.3910  data_time: 0.0015  memory: 20334  grad_norm: 0.0194  loss: 0.6845
2023/06/01 01:38:26 - mmengine - INFO - Epoch(train) [11][2100/5758]  lr: 5.0500e-04  eta: 6:32:27  time: 0.4174  data_time: 0.0015  memory: 20334  grad_norm: 0.0126  loss: 0.6814
2023/06/01 01:39:06 - mmengine - INFO - Epoch(train) [11][2200/5758]  lr: 5.0500e-04  eta: 6:31:42  time: 0.3951  data_time: 0.0015  memory: 20334  grad_norm: 0.0177  loss: 0.6864
2023/06/01 01:39:46 - mmengine - INFO - Epoch(train) [11][2300/5758]  lr: 5.0500e-04  eta: 6:30:57  time: 0.4045  data_time: 0.0015  memory: 20334  grad_norm: 0.0183  loss: 0.6857
2023/06/01 01:40:27 - mmengine - INFO - Epoch(train) [11][2400/5758]  lr: 5.0500e-04  eta: 6:30:13  time: 0.4253  data_time: 0.0013  memory: 20334  grad_norm: 0.0133  loss: 0.6857
2023/06/01 01:40:36 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 01:41:08 - mmengine - INFO - Epoch(train) [11][2500/5758]  lr: 5.0500e-04  eta: 6:29:29  time: 0.3719  data_time: 0.0014  memory: 20334  grad_norm: 0.0134  loss: 0.6874
2023/06/01 01:41:48 - mmengine - INFO - Epoch(train) [11][2600/5758]  lr: 5.0500e-04  eta: 6:28:45  time: 0.4071  data_time: 0.0013  memory: 20334  grad_norm: 0.0163  loss: 0.6870
2023/06/01 01:42:29 - mmengine - INFO - Epoch(train) [11][2700/5758]  lr: 5.0500e-04  eta: 6:28:01  time: 0.4196  data_time: 0.0014  memory: 20334  grad_norm: 0.0130  loss: 0.6926
2023/06/01 01:43:09 - mmengine - INFO - Epoch(train) [11][2800/5758]  lr: 5.0500e-04  eta: 6:27:16  time: 0.4231  data_time: 0.0015  memory: 20334  grad_norm: 0.0163  loss: 0.6841
2023/06/01 01:43:48 - mmengine - INFO - Epoch(train) [11][2900/5758]  lr: 5.0500e-04  eta: 6:26:31  time: 0.4027  data_time: 0.0013  memory: 20334  grad_norm: 0.0160  loss: 0.6814
2023/06/01 01:44:28 - mmengine - INFO - Epoch(train) [11][3000/5758]  lr: 5.0500e-04  eta: 6:25:47  time: 0.3883  data_time: 0.0014  memory: 20334  grad_norm: 0.0186  loss: 0.6886
2023/06/01 01:45:09 - mmengine - INFO - Epoch(train) [11][3100/5758]  lr: 5.0500e-04  eta: 6:25:02  time: 0.4226  data_time: 0.0019  memory: 20334  grad_norm: 0.0186  loss: 0.6887
2023/06/01 01:45:49 - mmengine - INFO - Epoch(train) [11][3200/5758]  lr: 5.0500e-04  eta: 6:24:18  time: 0.3969  data_time: 0.0016  memory: 20334  grad_norm: 0.0239  loss: 0.6888
2023/06/01 01:46:29 - mmengine - INFO - Epoch(train) [11][3300/5758]  lr: 5.0500e-04  eta: 6:23:33  time: 0.3683  data_time: 0.0017  memory: 20334  grad_norm: 0.0195  loss: 0.6818
2023/06/01 01:47:10 - mmengine - INFO - Epoch(train) [11][3400/5758]  lr: 5.0500e-04  eta: 6:22:49  time: 0.4013  data_time: 0.0015  memory: 20334  grad_norm: 0.0218  loss: 0.6876
2023/06/01 01:47:18 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 01:47:49 - mmengine - INFO - Epoch(train) [11][3500/5758]  lr: 5.0500e-04  eta: 6:22:05  time: 0.3984  data_time: 0.0017  memory: 20334  grad_norm: 0.0136  loss: 0.6868
2023/06/01 01:48:32 - mmengine - INFO - Epoch(train) [11][3600/5758]  lr: 5.0500e-04  eta: 6:21:22  time: 0.4510  data_time: 0.0019  memory: 20334  grad_norm: 0.0144  loss: 0.6916
2023/06/01 01:49:13 - mmengine - INFO - Epoch(train) [11][3700/5758]  lr: 5.0500e-04  eta: 6:20:39  time: 0.4095  data_time: 0.0019  memory: 20334  grad_norm: 0.0127  loss: 0.6799
2023/06/01 01:49:53 - mmengine - INFO - Epoch(train) [11][3800/5758]  lr: 5.0500e-04  eta: 6:19:55  time: 0.4010  data_time: 0.0017  memory: 20334  grad_norm: 0.0263  loss: 0.6835
2023/06/01 01:50:34 - mmengine - INFO - Epoch(train) [11][3900/5758]  lr: 5.0500e-04  eta: 6:19:11  time: 0.4133  data_time: 0.0018  memory: 20334  grad_norm: 0.0257  loss: 0.6871
2023/06/01 01:51:14 - mmengine - INFO - Epoch(train) [11][4000/5758]  lr: 5.0500e-04  eta: 6:18:27  time: 0.3668  data_time: 0.0019  memory: 20334  grad_norm: 0.0124  loss: 0.6869
2023/06/01 01:51:56 - mmengine - INFO - Epoch(train) [11][4100/5758]  lr: 5.0500e-04  eta: 6:17:44  time: 0.4262  data_time: 0.0015  memory: 20334  grad_norm: 0.0131  loss: 0.6843
2023/06/01 01:52:36 - mmengine - INFO - Epoch(train) [11][4200/5758]  lr: 5.0500e-04  eta: 6:16:59  time: 0.4180  data_time: 0.0018  memory: 20334  grad_norm: 0.0095  loss: 0.6876
2023/06/01 01:53:17 - mmengine - INFO - Epoch(train) [11][4300/5758]  lr: 5.0500e-04  eta: 6:16:16  time: 0.4135  data_time: 0.0015  memory: 20334  grad_norm: 0.0148  loss: 0.6903
2023/06/01 01:53:58 - mmengine - INFO - Epoch(train) [11][4400/5758]  lr: 5.0500e-04  eta: 6:15:32  time: 0.4193  data_time: 0.0018  memory: 20334  grad_norm: 0.0247  loss: 0.6850
2023/06/01 01:54:06 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 01:54:39 - mmengine - INFO - Epoch(train) [11][4500/5758]  lr: 5.0500e-04  eta: 6:14:48  time: 0.3970  data_time: 0.0018  memory: 20334  grad_norm: 0.0213  loss: 0.6857
2023/06/01 01:55:19 - mmengine - INFO - Epoch(train) [11][4600/5758]  lr: 5.0500e-04  eta: 6:14:04  time: 0.4347  data_time: 0.0017  memory: 20334  grad_norm: 0.0156  loss: 0.6863
2023/06/01 01:55:58 - mmengine - INFO - Epoch(train) [11][4700/5758]  lr: 5.0500e-04  eta: 6:13:19  time: 0.3953  data_time: 0.0018  memory: 20334  grad_norm: 0.0190  loss: 0.6859
2023/06/01 01:56:39 - mmengine - INFO - Epoch(train) [11][4800/5758]  lr: 5.0500e-04  eta: 6:12:35  time: 0.4231  data_time: 0.0019  memory: 20334  grad_norm: 0.0218  loss: 0.6859
2023/06/01 01:57:20 - mmengine - INFO - Epoch(train) [11][4900/5758]  lr: 5.0500e-04  eta: 6:11:52  time: 0.3895  data_time: 0.0018  memory: 20334  grad_norm: 0.0144  loss: 0.6904
2023/06/01 01:58:00 - mmengine - INFO - Epoch(train) [11][5000/5758]  lr: 5.0500e-04  eta: 6:11:07  time: 0.3818  data_time: 0.0027  memory: 20334  grad_norm: 0.0133  loss: 0.6810
2023/06/01 01:58:40 - mmengine - INFO - Epoch(train) [11][5100/5758]  lr: 5.0500e-04  eta: 6:10:24  time: 0.3831  data_time: 0.0017  memory: 20334  grad_norm: 0.0147  loss: 0.6874
2023/06/01 01:59:22 - mmengine - INFO - Epoch(train) [11][5200/5758]  lr: 5.0500e-04  eta: 6:09:41  time: 0.3923  data_time: 0.0023  memory: 20334  grad_norm: 0.0130  loss: 0.6864
2023/06/01 02:00:03 - mmengine - INFO - Epoch(train) [11][5300/5758]  lr: 5.0500e-04  eta: 6:08:57  time: 0.4071  data_time: 0.0016  memory: 20334  grad_norm: 0.0164  loss: 0.6879
2023/06/01 02:00:43 - mmengine - INFO - Epoch(train) [11][5400/5758]  lr: 5.0500e-04  eta: 6:08:13  time: 0.3822  data_time: 0.0017  memory: 20334  grad_norm: 0.0173  loss: 0.6896
2023/06/01 02:00:51 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 02:01:23 - mmengine - INFO - Epoch(train) [11][5500/5758]  lr: 5.0500e-04  eta: 6:07:29  time: 0.4003  data_time: 0.0018  memory: 20334  grad_norm: 0.0177  loss: 0.6919
2023/06/01 02:02:04 - mmengine - INFO - Epoch(train) [11][5600/5758]  lr: 5.0500e-04  eta: 6:06:45  time: 0.3750  data_time: 0.0019  memory: 20334  grad_norm: 0.0145  loss: 0.6873
2023/06/01 02:02:45 - mmengine - INFO - Epoch(train) [11][5700/5758]  lr: 5.0500e-04  eta: 6:06:01  time: 0.4209  data_time: 0.0016  memory: 20334  grad_norm: 0.0182  loss: 0.6873
2023/06/01 02:03:08 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 02:03:08 - mmengine - INFO - Saving checkpoint at 11 epochs
2023/06/01 02:03:25 - mmengine - INFO - Epoch(val) [11][8/8]    accuracy/top1: 100.0000  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [100.0, 0.0]  single-label/f1-score_classwise: [100.0, 0.0]  data_time: 0.3622  time: 0.9878
2023/06/01 02:04:11 - mmengine - INFO - Epoch(train) [12][ 100/5758]  lr: 4.2756e-04  eta: 6:04:56  time: 0.4174  data_time: 0.0016  memory: 20334  grad_norm: 0.0167  loss: 0.6899
2023/06/01 02:04:52 - mmengine - INFO - Epoch(train) [12][ 200/5758]  lr: 4.2756e-04  eta: 6:04:13  time: 0.3964  data_time: 0.0016  memory: 20334  grad_norm: 0.0208  loss: 0.6836
2023/06/01 02:05:32 - mmengine - INFO - Epoch(train) [12][ 300/5758]  lr: 4.2756e-04  eta: 6:03:28  time: 0.3718  data_time: 0.0018  memory: 20334  grad_norm: 0.0154  loss: 0.6921
2023/06/01 02:06:12 - mmengine - INFO - Epoch(train) [12][ 400/5758]  lr: 4.2756e-04  eta: 6:02:44  time: 0.3979  data_time: 0.0017  memory: 20334  grad_norm: 0.0131  loss: 0.6874
2023/06/01 02:06:53 - mmengine - INFO - Epoch(train) [12][ 500/5758]  lr: 4.2756e-04  eta: 6:02:01  time: 0.3903  data_time: 0.0015  memory: 20334  grad_norm: 0.0158  loss: 0.6895
2023/06/01 02:07:34 - mmengine - INFO - Epoch(train) [12][ 600/5758]  lr: 4.2756e-04  eta: 6:01:18  time: 0.3752  data_time: 0.0017  memory: 20334  grad_norm: 0.0149  loss: 0.6878
2023/06/01 02:08:00 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 02:08:15 - mmengine - INFO - Epoch(train) [12][ 700/5758]  lr: 4.2756e-04  eta: 6:00:34  time: 0.4368  data_time: 0.0018  memory: 20334  grad_norm: 0.0146  loss: 0.6890
2023/06/01 02:08:56 - mmengine - INFO - Epoch(train) [12][ 800/5758]  lr: 4.2756e-04  eta: 5:59:51  time: 0.4049  data_time: 0.0017  memory: 20334  grad_norm: 0.0140  loss: 0.6842
2023/06/01 02:09:37 - mmengine - INFO - Epoch(train) [12][ 900/5758]  lr: 4.2756e-04  eta: 5:59:07  time: 0.3810  data_time: 0.0029  memory: 20334  grad_norm: 0.0170  loss: 0.6824
2023/06/01 02:10:17 - mmengine - INFO - Epoch(train) [12][1000/5758]  lr: 4.2756e-04  eta: 5:58:23  time: 0.3715  data_time: 0.0018  memory: 20334  grad_norm: 0.0112  loss: 0.6865
2023/06/01 02:10:58 - mmengine - INFO - Epoch(train) [12][1100/5758]  lr: 4.2756e-04  eta: 5:57:40  time: 0.4021  data_time: 0.0017  memory: 20334  grad_norm: 0.0156  loss: 0.6854
2023/06/01 02:11:41 - mmengine - INFO - Epoch(train) [12][1200/5758]  lr: 4.2756e-04  eta: 5:56:58  time: 0.4122  data_time: 0.0023  memory: 20334  grad_norm: 0.0186  loss: 0.6856
2023/06/01 02:12:23 - mmengine - INFO - Epoch(train) [12][1300/5758]  lr: 4.2756e-04  eta: 5:56:15  time: 0.4182  data_time: 0.0014  memory: 20334  grad_norm: 0.0197  loss: 0.6861
2023/06/01 02:13:04 - mmengine - INFO - Epoch(train) [12][1400/5758]  lr: 4.2756e-04  eta: 5:55:32  time: 0.3827  data_time: 0.0015  memory: 20334  grad_norm: 0.0152  loss: 0.6856
2023/06/01 02:13:45 - mmengine - INFO - Epoch(train) [12][1500/5758]  lr: 4.2756e-04  eta: 5:54:49  time: 0.4138  data_time: 0.0015  memory: 20334  grad_norm: 0.0193  loss: 0.6876
2023/06/01 02:14:25 - mmengine - INFO - Epoch(train) [12][1600/5758]  lr: 4.2756e-04  eta: 5:54:05  time: 0.3940  data_time: 0.0020  memory: 20334  grad_norm: 0.0134  loss: 0.6887
2023/06/01 02:14:51 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 02:15:07 - mmengine - INFO - Epoch(train) [12][1700/5758]  lr: 4.2756e-04  eta: 5:53:22  time: 0.4305  data_time: 0.0019  memory: 20334  grad_norm: 0.0235  loss: 0.6858
2023/06/01 02:15:47 - mmengine - INFO - Epoch(train) [12][1800/5758]  lr: 4.2756e-04  eta: 5:52:38  time: 0.4081  data_time: 0.0018  memory: 20334  grad_norm: 0.0164  loss: 0.6866
2023/06/01 02:16:28 - mmengine - INFO - Epoch(train) [12][1900/5758]  lr: 4.2756e-04  eta: 5:51:55  time: 0.4181  data_time: 0.0020  memory: 20334  grad_norm: 0.0173  loss: 0.6868
2023/06/01 02:17:10 - mmengine - INFO - Epoch(train) [12][2000/5758]  lr: 4.2756e-04  eta: 5:51:12  time: 0.4066  data_time: 0.0016  memory: 20334  grad_norm: 0.0169  loss: 0.6861
2023/06/01 02:17:51 - mmengine - INFO - Epoch(train) [12][2100/5758]  lr: 4.2756e-04  eta: 5:50:29  time: 0.3917  data_time: 0.0016  memory: 20334  grad_norm: 0.0211  loss: 0.6913
2023/06/01 02:18:32 - mmengine - INFO - Epoch(train) [12][2200/5758]  lr: 4.2756e-04  eta: 5:49:45  time: 0.4172  data_time: 0.0017  memory: 20334  grad_norm: 0.0162  loss: 0.6850
2023/06/01 02:19:12 - mmengine - INFO - Epoch(train) [12][2300/5758]  lr: 4.2756e-04  eta: 5:49:02  time: 0.3820  data_time: 0.0019  memory: 20334  grad_norm: 0.0124  loss: 0.6903
2023/06/01 02:19:53 - mmengine - INFO - Epoch(train) [12][2400/5758]  lr: 4.2756e-04  eta: 5:48:19  time: 0.3993  data_time: 0.0019  memory: 20334  grad_norm: 0.0221  loss: 0.6847
2023/06/01 02:20:34 - mmengine - INFO - Epoch(train) [12][2500/5758]  lr: 4.2756e-04  eta: 5:47:35  time: 0.4088  data_time: 0.0020  memory: 20334  grad_norm: 0.0126  loss: 0.6928
2023/06/01 02:21:14 - mmengine - INFO - Epoch(train) [12][2600/5758]  lr: 4.2756e-04  eta: 5:46:51  time: 0.3915  data_time: 0.0018  memory: 20334  grad_norm: 0.0179  loss: 0.6878
2023/06/01 02:21:39 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 02:21:55 - mmengine - INFO - Epoch(train) [12][2700/5758]  lr: 4.2756e-04  eta: 5:46:08  time: 0.3912  data_time: 0.0021  memory: 20334  grad_norm: 0.0172  loss: 0.6884
2023/06/01 02:22:37 - mmengine - INFO - Epoch(train) [12][2800/5758]  lr: 4.2756e-04  eta: 5:45:25  time: 0.4016  data_time: 0.0018  memory: 20334  grad_norm: 0.0187  loss: 0.6887
2023/06/01 02:23:18 - mmengine - INFO - Epoch(train) [12][2900/5758]  lr: 4.2756e-04  eta: 5:44:42  time: 0.3667  data_time: 0.0017  memory: 20334  grad_norm: 0.0153  loss: 0.6836
2023/06/01 02:23:59 - mmengine - INFO - Epoch(train) [12][3000/5758]  lr: 4.2756e-04  eta: 5:43:58  time: 0.4098  data_time: 0.0017  memory: 20334  grad_norm: 0.0124  loss: 0.6891
2023/06/01 02:24:38 - mmengine - INFO - Epoch(train) [12][3100/5758]  lr: 4.2756e-04  eta: 5:43:14  time: 0.4134  data_time: 0.0026  memory: 20334  grad_norm: 0.0183  loss: 0.6834
2023/06/01 02:25:19 - mmengine - INFO - Epoch(train) [12][3200/5758]  lr: 4.2756e-04  eta: 5:42:31  time: 0.4088  data_time: 0.0018  memory: 20334  grad_norm: 0.0214  loss: 0.6866
2023/06/01 02:26:00 - mmengine - INFO - Epoch(train) [12][3300/5758]  lr: 4.2756e-04  eta: 5:41:48  time: 0.4331  data_time: 0.0018  memory: 20334  grad_norm: 0.0172  loss: 0.6874
2023/06/01 02:26:41 - mmengine - INFO - Epoch(train) [12][3400/5758]  lr: 4.2756e-04  eta: 5:41:05  time: 0.4540  data_time: 0.0016  memory: 20334  grad_norm: 0.0108  loss: 0.6891
2023/06/01 02:27:23 - mmengine - INFO - Epoch(train) [12][3500/5758]  lr: 4.2756e-04  eta: 5:40:22  time: 0.4149  data_time: 0.0018  memory: 20334  grad_norm: 0.0150  loss: 0.6788
2023/06/01 02:28:03 - mmengine - INFO - Epoch(train) [12][3600/5758]  lr: 4.2756e-04  eta: 5:39:38  time: 0.4236  data_time: 0.0017  memory: 20334  grad_norm: 0.0179  loss: 0.6834
2023/06/01 02:28:30 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 02:28:46 - mmengine - INFO - Epoch(train) [12][3700/5758]  lr: 4.2756e-04  eta: 5:38:56  time: 0.4062  data_time: 0.0017  memory: 20334  grad_norm: 0.0107  loss: 0.6892
2023/06/01 02:29:26 - mmengine - INFO - Epoch(train) [12][3800/5758]  lr: 4.2756e-04  eta: 5:38:13  time: 0.4004  data_time: 0.0018  memory: 20334  grad_norm: 0.0169  loss: 0.6833
2023/06/01 02:30:07 - mmengine - INFO - Epoch(train) [12][3900/5758]  lr: 4.2756e-04  eta: 5:37:29  time: 0.4242  data_time: 0.0019  memory: 20334  grad_norm: 0.0192  loss: 0.6863
2023/06/01 02:30:48 - mmengine - INFO - Epoch(train) [12][4000/5758]  lr: 4.2756e-04  eta: 5:36:46  time: 0.4238  data_time: 0.0017  memory: 20334  grad_norm: 0.0180  loss: 0.6898
2023/06/01 02:31:30 - mmengine - INFO - Epoch(train) [12][4100/5758]  lr: 4.2756e-04  eta: 5:36:04  time: 0.3922  data_time: 0.0018  memory: 20334  grad_norm: 0.0174  loss: 0.6843
2023/06/01 02:32:11 - mmengine - INFO - Epoch(train) [12][4200/5758]  lr: 4.2756e-04  eta: 5:35:21  time: 0.4629  data_time: 0.0018  memory: 20334  grad_norm: 0.0163  loss: 0.6855
2023/06/01 02:32:52 - mmengine - INFO - Epoch(train) [12][4300/5758]  lr: 4.2756e-04  eta: 5:34:38  time: 0.4016  data_time: 0.0020  memory: 20334  grad_norm: 0.0091  loss: 0.6874
2023/06/01 02:33:34 - mmengine - INFO - Epoch(train) [12][4400/5758]  lr: 4.2756e-04  eta: 5:33:55  time: 0.3769  data_time: 0.0018  memory: 20334  grad_norm: 0.0186  loss: 0.6868
2023/06/01 02:34:14 - mmengine - INFO - Epoch(train) [12][4500/5758]  lr: 4.2756e-04  eta: 5:33:11  time: 0.4596  data_time: 0.0017  memory: 20334  grad_norm: 0.0145  loss: 0.6851
2023/06/01 02:34:54 - mmengine - INFO - Epoch(train) [12][4600/5758]  lr: 4.2756e-04  eta: 5:32:27  time: 0.3866  data_time: 0.0019  memory: 20334  grad_norm: 0.0203  loss: 0.6899
2023/06/01 02:35:19 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 02:35:34 - mmengine - INFO - Epoch(train) [12][4700/5758]  lr: 4.2756e-04  eta: 5:31:44  time: 0.3940  data_time: 0.0017  memory: 20334  grad_norm: 0.0242  loss: 0.6864
2023/06/01 02:36:15 - mmengine - INFO - Epoch(train) [12][4800/5758]  lr: 4.2756e-04  eta: 5:31:01  time: 0.4072  data_time: 0.0018  memory: 20334  grad_norm: 0.0172  loss: 0.6876
2023/06/01 02:36:57 - mmengine - INFO - Epoch(train) [12][4900/5758]  lr: 4.2756e-04  eta: 5:30:18  time: 0.4369  data_time: 0.0021  memory: 20334  grad_norm: 0.0201  loss: 0.6837
2023/06/01 02:37:37 - mmengine - INFO - Epoch(train) [12][5000/5758]  lr: 4.2756e-04  eta: 5:29:35  time: 0.4183  data_time: 0.0017  memory: 20334  grad_norm: 0.0112  loss: 0.6864
2023/06/01 02:38:19 - mmengine - INFO - Epoch(train) [12][5100/5758]  lr: 4.2756e-04  eta: 5:28:52  time: 0.4049  data_time: 0.0019  memory: 20334  grad_norm: 0.0135  loss: 0.6894
2023/06/01 02:38:59 - mmengine - INFO - Epoch(train) [12][5200/5758]  lr: 4.2756e-04  eta: 5:28:09  time: 0.3898  data_time: 0.0018  memory: 20334  grad_norm: 0.0130  loss: 0.6878
2023/06/01 02:39:39 - mmengine - INFO - Epoch(train) [12][5300/5758]  lr: 4.2756e-04  eta: 5:27:25  time: 0.3955  data_time: 0.0018  memory: 20334  grad_norm: 0.0145  loss: 0.6886
2023/06/01 02:40:20 - mmengine - INFO - Epoch(train) [12][5400/5758]  lr: 4.2756e-04  eta: 5:26:41  time: 0.3866  data_time: 0.0016  memory: 20334  grad_norm: 0.0206  loss: 0.6910
2023/06/01 02:41:00 - mmengine - INFO - Epoch(train) [12][5500/5758]  lr: 4.2756e-04  eta: 5:25:58  time: 0.4252  data_time: 0.0020  memory: 20334  grad_norm: 0.0150  loss: 0.6893
2023/06/01 02:41:41 - mmengine - INFO - Epoch(train) [12][5600/5758]  lr: 4.2756e-04  eta: 5:25:15  time: 0.4106  data_time: 0.0020  memory: 20334  grad_norm: 0.0154  loss: 0.6877
2023/06/01 02:42:07 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 02:42:21 - mmengine - INFO - Epoch(train) [12][5700/5758]  lr: 4.2756e-04  eta: 5:24:31  time: 0.3690  data_time: 0.0016  memory: 20334  grad_norm: 0.0150  loss: 0.6824
2023/06/01 02:42:45 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 02:42:45 - mmengine - INFO - Saving checkpoint at 12 epochs
2023/06/01 02:43:03 - mmengine - INFO - Epoch(val) [12][8/8]    accuracy/top1: 100.0000  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [100.0, 0.0]  single-label/f1-score_classwise: [100.0, 0.0]  data_time: 0.3588  time: 0.9851
2023/06/01 02:43:49 - mmengine - INFO - Epoch(train) [13][ 100/5758]  lr: 3.5204e-04  eta: 5:23:26  time: 0.4269  data_time: 0.0025  memory: 20334  grad_norm: 0.0141  loss: 0.6865
2023/06/01 02:44:30 - mmengine - INFO - Epoch(train) [13][ 200/5758]  lr: 3.5204e-04  eta: 5:22:43  time: 0.4002  data_time: 0.0017  memory: 20334  grad_norm: 0.0101  loss: 0.6878
2023/06/01 02:45:10 - mmengine - INFO - Epoch(train) [13][ 300/5758]  lr: 3.5204e-04  eta: 5:21:59  time: 0.4012  data_time: 0.0014  memory: 20334  grad_norm: 0.0100  loss: 0.6862
2023/06/01 02:45:49 - mmengine - INFO - Epoch(train) [13][ 400/5758]  lr: 3.5204e-04  eta: 5:21:15  time: 0.3791  data_time: 0.0024  memory: 20334  grad_norm: 0.0128  loss: 0.6865
2023/06/01 02:46:30 - mmengine - INFO - Epoch(train) [13][ 500/5758]  lr: 3.5204e-04  eta: 5:20:32  time: 0.4206  data_time: 0.0020  memory: 20334  grad_norm: 0.0171  loss: 0.6865
2023/06/01 02:47:11 - mmengine - INFO - Epoch(train) [13][ 600/5758]  lr: 3.5204e-04  eta: 5:19:49  time: 0.4063  data_time: 0.0023  memory: 20334  grad_norm: 0.0095  loss: 0.6879
2023/06/01 02:47:51 - mmengine - INFO - Epoch(train) [13][ 700/5758]  lr: 3.5204e-04  eta: 5:19:05  time: 0.4161  data_time: 0.0020  memory: 20334  grad_norm: 0.0248  loss: 0.6804
2023/06/01 02:48:32 - mmengine - INFO - Epoch(train) [13][ 800/5758]  lr: 3.5204e-04  eta: 5:18:22  time: 0.3798  data_time: 0.0020  memory: 20334  grad_norm: 0.0139  loss: 0.6857
2023/06/01 02:49:14 - mmengine - INFO - Epoch(train) [13][ 900/5758]  lr: 3.5204e-04  eta: 5:17:40  time: 0.4143  data_time: 0.0019  memory: 20334  grad_norm: 0.0180  loss: 0.6901
2023/06/01 02:49:15 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 02:49:53 - mmengine - INFO - Epoch(train) [13][1000/5758]  lr: 3.5204e-04  eta: 5:16:56  time: 0.3758  data_time: 0.0020  memory: 20334  grad_norm: 0.0149  loss: 0.6854
2023/06/01 02:50:34 - mmengine - INFO - Epoch(train) [13][1100/5758]  lr: 3.5204e-04  eta: 5:16:13  time: 0.4092  data_time: 0.0018  memory: 20334  grad_norm: 0.0154  loss: 0.6901
2023/06/01 02:51:15 - mmengine - INFO - Epoch(train) [13][1200/5758]  lr: 3.5204e-04  eta: 5:15:30  time: 0.4044  data_time: 0.0018  memory: 20334  grad_norm: 0.0099  loss: 0.6862
2023/06/01 02:51:56 - mmengine - INFO - Epoch(train) [13][1300/5758]  lr: 3.5204e-04  eta: 5:14:47  time: 0.3941  data_time: 0.0017  memory: 20334  grad_norm: 0.0154  loss: 0.6870
2023/06/01 02:52:37 - mmengine - INFO - Epoch(train) [13][1400/5758]  lr: 3.5204e-04  eta: 5:14:04  time: 0.3964  data_time: 0.0024  memory: 20334  grad_norm: 0.0230  loss: 0.6842
2023/06/01 02:53:17 - mmengine - INFO - Epoch(train) [13][1500/5758]  lr: 3.5204e-04  eta: 5:13:21  time: 0.3983  data_time: 0.0016  memory: 20334  grad_norm: 0.0152  loss: 0.6912
2023/06/01 02:53:58 - mmengine - INFO - Epoch(train) [13][1600/5758]  lr: 3.5204e-04  eta: 5:12:38  time: 0.3649  data_time: 0.0017  memory: 20334  grad_norm: 0.0111  loss: 0.6871
2023/06/01 02:54:40 - mmengine - INFO - Epoch(train) [13][1700/5758]  lr: 3.5204e-04  eta: 5:11:55  time: 0.3884  data_time: 0.0018  memory: 20334  grad_norm: 0.0183  loss: 0.6873
2023/06/01 02:55:21 - mmengine - INFO - Epoch(train) [13][1800/5758]  lr: 3.5204e-04  eta: 5:11:12  time: 0.4034  data_time: 0.0017  memory: 20334  grad_norm: 0.0086  loss: 0.6863
2023/06/01 02:56:03 - mmengine - INFO - Epoch(train) [13][1900/5758]  lr: 3.5204e-04  eta: 5:10:30  time: 0.3939  data_time: 0.0017  memory: 20334  grad_norm: 0.0165  loss: 0.6863
2023/06/01 02:56:04 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 02:56:44 - mmengine - INFO - Epoch(train) [13][2000/5758]  lr: 3.5204e-04  eta: 5:09:47  time: 0.4203  data_time: 0.0017  memory: 20334  grad_norm: 0.0131  loss: 0.6839
2023/06/01 02:57:25 - mmengine - INFO - Epoch(train) [13][2100/5758]  lr: 3.5204e-04  eta: 5:09:04  time: 0.4262  data_time: 0.0021  memory: 20334  grad_norm: 0.0209  loss: 0.6896
2023/06/01 02:58:06 - mmengine - INFO - Epoch(train) [13][2200/5758]  lr: 3.5204e-04  eta: 5:08:21  time: 0.3909  data_time: 0.0019  memory: 20334  grad_norm: 0.0167  loss: 0.6838
2023/06/01 02:58:46 - mmengine - INFO - Epoch(train) [13][2300/5758]  lr: 3.5204e-04  eta: 5:07:38  time: 0.3971  data_time: 0.0020  memory: 20334  grad_norm: 0.0200  loss: 0.6866
2023/06/01 02:59:27 - mmengine - INFO - Epoch(train) [13][2400/5758]  lr: 3.5204e-04  eta: 5:06:54  time: 0.4094  data_time: 0.0019  memory: 20334  grad_norm: 0.0117  loss: 0.6851
2023/06/01 03:00:08 - mmengine - INFO - Epoch(train) [13][2500/5758]  lr: 3.5204e-04  eta: 5:06:12  time: 0.3848  data_time: 0.0025  memory: 20334  grad_norm: 0.0142  loss: 0.6870
2023/06/01 03:00:48 - mmengine - INFO - Epoch(train) [13][2600/5758]  lr: 3.5204e-04  eta: 5:05:28  time: 0.4538  data_time: 0.0018  memory: 20334  grad_norm: 0.0174  loss: 0.6851
2023/06/01 03:01:28 - mmengine - INFO - Epoch(train) [13][2700/5758]  lr: 3.5204e-04  eta: 5:04:45  time: 0.3970  data_time: 0.0018  memory: 20334  grad_norm: 0.0168  loss: 0.6873
2023/06/01 03:02:08 - mmengine - INFO - Epoch(train) [13][2800/5758]  lr: 3.5204e-04  eta: 5:04:01  time: 0.4020  data_time: 0.0014  memory: 20334  grad_norm: 0.0135  loss: 0.6868
2023/06/01 03:02:49 - mmengine - INFO - Epoch(train) [13][2900/5758]  lr: 3.5204e-04  eta: 5:03:19  time: 0.3962  data_time: 0.0013  memory: 20334  grad_norm: 0.0152  loss: 0.6831
2023/06/01 03:02:51 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 03:03:30 - mmengine - INFO - Epoch(train) [13][3000/5758]  lr: 3.5204e-04  eta: 5:02:36  time: 0.4417  data_time: 0.0021  memory: 20334  grad_norm: 0.0159  loss: 0.6846
2023/06/01 03:04:11 - mmengine - INFO - Epoch(train) [13][3100/5758]  lr: 3.5204e-04  eta: 5:01:53  time: 0.4251  data_time: 0.0020  memory: 20334  grad_norm: 0.0134  loss: 0.6819
2023/06/01 03:04:52 - mmengine - INFO - Epoch(train) [13][3200/5758]  lr: 3.5204e-04  eta: 5:01:10  time: 0.4020  data_time: 0.0019  memory: 20334  grad_norm: 0.0189  loss: 0.6857
2023/06/01 03:05:32 - mmengine - INFO - Epoch(train) [13][3300/5758]  lr: 3.5204e-04  eta: 5:00:27  time: 0.4200  data_time: 0.0016  memory: 20334  grad_norm: 0.0112  loss: 0.6902
2023/06/01 03:06:12 - mmengine - INFO - Epoch(train) [13][3400/5758]  lr: 3.5204e-04  eta: 4:59:43  time: 0.3886  data_time: 0.0016  memory: 20334  grad_norm: 0.0155  loss: 0.6882
2023/06/01 03:06:52 - mmengine - INFO - Epoch(train) [13][3500/5758]  lr: 3.5204e-04  eta: 4:59:00  time: 0.4287  data_time: 0.0019  memory: 20334  grad_norm: 0.0182  loss: 0.6857
2023/06/01 03:07:33 - mmengine - INFO - Epoch(train) [13][3600/5758]  lr: 3.5204e-04  eta: 4:58:17  time: 0.4204  data_time: 0.0018  memory: 20334  grad_norm: 0.0161  loss: 0.6881
2023/06/01 03:08:14 - mmengine - INFO - Epoch(train) [13][3700/5758]  lr: 3.5204e-04  eta: 4:57:34  time: 0.4773  data_time: 0.0016  memory: 20334  grad_norm: 0.0174  loss: 0.6847
2023/06/01 03:08:55 - mmengine - INFO - Epoch(train) [13][3800/5758]  lr: 3.5204e-04  eta: 4:56:51  time: 0.4264  data_time: 0.0019  memory: 20334  grad_norm: 0.0183  loss: 0.6820
2023/06/01 03:09:36 - mmengine - INFO - Epoch(train) [13][3900/5758]  lr: 3.5204e-04  eta: 4:56:08  time: 0.4156  data_time: 0.0018  memory: 20334  grad_norm: 0.0165  loss: 0.6912
2023/06/01 03:09:37 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 03:10:16 - mmengine - INFO - Epoch(train) [13][4000/5758]  lr: 3.5204e-04  eta: 4:55:25  time: 0.4144  data_time: 0.0017  memory: 20334  grad_norm: 0.0192  loss: 0.6915
2023/06/01 03:10:58 - mmengine - INFO - Epoch(train) [13][4100/5758]  lr: 3.5204e-04  eta: 4:54:43  time: 0.4298  data_time: 0.0016  memory: 20334  grad_norm: 0.0150  loss: 0.6840
2023/06/01 03:11:38 - mmengine - INFO - Epoch(train) [13][4200/5758]  lr: 3.5204e-04  eta: 4:54:00  time: 0.4044  data_time: 0.0017  memory: 20334  grad_norm: 0.0147  loss: 0.6888
2023/06/01 03:12:19 - mmengine - INFO - Epoch(train) [13][4300/5758]  lr: 3.5204e-04  eta: 4:53:16  time: 0.4214  data_time: 0.0016  memory: 20334  grad_norm: 0.0135  loss: 0.6862
2023/06/01 03:12:59 - mmengine - INFO - Epoch(train) [13][4400/5758]  lr: 3.5204e-04  eta: 4:52:33  time: 0.4440  data_time: 0.0018  memory: 20334  grad_norm: 0.0154  loss: 0.6865
2023/06/01 03:13:40 - mmengine - INFO - Epoch(train) [13][4500/5758]  lr: 3.5204e-04  eta: 4:51:50  time: 0.4069  data_time: 0.0016  memory: 20334  grad_norm: 0.0135  loss: 0.6880
2023/06/01 03:14:20 - mmengine - INFO - Epoch(train) [13][4600/5758]  lr: 3.5204e-04  eta: 4:51:07  time: 0.3917  data_time: 0.0018  memory: 20334  grad_norm: 0.0148  loss: 0.6874
2023/06/01 03:14:59 - mmengine - INFO - Epoch(train) [13][4700/5758]  lr: 3.5204e-04  eta: 4:50:23  time: 0.3876  data_time: 0.0019  memory: 20334  grad_norm: 0.0158  loss: 0.6909
2023/06/01 03:15:40 - mmengine - INFO - Epoch(train) [13][4800/5758]  lr: 3.5204e-04  eta: 4:49:40  time: 0.3883  data_time: 0.0017  memory: 20334  grad_norm: 0.0157  loss: 0.6887
2023/06/01 03:16:21 - mmengine - INFO - Epoch(train) [13][4900/5758]  lr: 3.5204e-04  eta: 4:48:58  time: 0.3957  data_time: 0.0019  memory: 20334  grad_norm: 0.0104  loss: 0.6860
2023/06/01 03:16:23 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 03:17:03 - mmengine - INFO - Epoch(train) [13][5000/5758]  lr: 3.5204e-04  eta: 4:48:16  time: 0.4279  data_time: 0.0016  memory: 20334  grad_norm: 0.0171  loss: 0.6907
2023/06/01 03:17:44 - mmengine - INFO - Epoch(train) [13][5100/5758]  lr: 3.5204e-04  eta: 4:47:33  time: 0.4068  data_time: 0.0018  memory: 20334  grad_norm: 0.0140  loss: 0.6830
2023/06/01 03:18:25 - mmengine - INFO - Epoch(train) [13][5200/5758]  lr: 3.5204e-04  eta: 4:46:50  time: 0.3933  data_time: 0.0017  memory: 20334  grad_norm: 0.0163  loss: 0.6807
2023/06/01 03:19:07 - mmengine - INFO - Epoch(train) [13][5300/5758]  lr: 3.5204e-04  eta: 4:46:08  time: 0.3786  data_time: 0.0017  memory: 20334  grad_norm: 0.0129  loss: 0.6895
2023/06/01 03:19:48 - mmengine - INFO - Epoch(train) [13][5400/5758]  lr: 3.5204e-04  eta: 4:45:25  time: 0.3915  data_time: 0.0019  memory: 20334  grad_norm: 0.0120  loss: 0.6844
2023/06/01 03:20:28 - mmengine - INFO - Epoch(train) [13][5500/5758]  lr: 3.5204e-04  eta: 4:44:42  time: 0.4118  data_time: 0.0019  memory: 20334  grad_norm: 0.0198  loss: 0.6846
2023/06/01 03:21:10 - mmengine - INFO - Epoch(train) [13][5600/5758]  lr: 3.5204e-04  eta: 4:44:00  time: 0.4156  data_time: 0.0020  memory: 20334  grad_norm: 0.0192  loss: 0.6796
2023/06/01 03:21:50 - mmengine - INFO - Epoch(train) [13][5700/5758]  lr: 3.5204e-04  eta: 4:43:17  time: 0.4015  data_time: 0.0025  memory: 20334  grad_norm: 0.0195  loss: 0.6879
2023/06/01 03:22:13 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 03:22:13 - mmengine - INFO - Saving checkpoint at 13 epochs
2023/06/01 03:22:30 - mmengine - INFO - Epoch(val) [13][8/8]    accuracy/top1: 100.0000  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [100.0, 0.0]  single-label/f1-score_classwise: [100.0, 0.0]  data_time: 0.3619  time: 0.9856
2023/06/01 03:23:15 - mmengine - INFO - Epoch(train) [14][ 100/5758]  lr: 2.8027e-04  eta: 4:42:11  time: 0.4051  data_time: 0.0016  memory: 20334  grad_norm: 0.0117  loss: 0.6840
2023/06/01 03:23:33 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 03:23:56 - mmengine - INFO - Epoch(train) [14][ 200/5758]  lr: 2.8027e-04  eta: 4:41:28  time: 0.4049  data_time: 0.0018  memory: 20334  grad_norm: 0.0189  loss: 0.6888
2023/06/01 03:24:35 - mmengine - INFO - Epoch(train) [14][ 300/5758]  lr: 2.8027e-04  eta: 4:40:45  time: 0.4188  data_time: 0.0016  memory: 20334  grad_norm: 0.0146  loss: 0.6866
2023/06/01 03:25:16 - mmengine - INFO - Epoch(train) [14][ 400/5758]  lr: 2.8027e-04  eta: 4:40:02  time: 0.4036  data_time: 0.0017  memory: 20334  grad_norm: 0.0139  loss: 0.6841
2023/06/01 03:25:57 - mmengine - INFO - Epoch(train) [14][ 500/5758]  lr: 2.8027e-04  eta: 4:39:19  time: 0.4008  data_time: 0.0016  memory: 20334  grad_norm: 0.0176  loss: 0.6901
2023/06/01 03:26:38 - mmengine - INFO - Epoch(train) [14][ 600/5758]  lr: 2.8027e-04  eta: 4:38:37  time: 0.3748  data_time: 0.0018  memory: 20334  grad_norm: 0.0164  loss: 0.6843
2023/06/01 03:27:18 - mmengine - INFO - Epoch(train) [14][ 700/5758]  lr: 2.8027e-04  eta: 4:37:53  time: 0.3973  data_time: 0.0017  memory: 20334  grad_norm: 0.0147  loss: 0.6854
2023/06/01 03:27:59 - mmengine - INFO - Epoch(train) [14][ 800/5758]  lr: 2.8027e-04  eta: 4:37:11  time: 0.3918  data_time: 0.0020  memory: 20334  grad_norm: 0.0120  loss: 0.6881
2023/06/01 03:28:39 - mmengine - INFO - Epoch(train) [14][ 900/5758]  lr: 2.8027e-04  eta: 4:36:27  time: 0.4074  data_time: 0.0015  memory: 20334  grad_norm: 0.0124  loss: 0.6873
2023/06/01 03:29:20 - mmengine - INFO - Epoch(train) [14][1000/5758]  lr: 2.8027e-04  eta: 4:35:45  time: 0.4161  data_time: 0.0017  memory: 20334  grad_norm: 0.0130  loss: 0.6828
2023/06/01 03:30:00 - mmengine - INFO - Epoch(train) [14][1100/5758]  lr: 2.8027e-04  eta: 4:35:02  time: 0.4034  data_time: 0.0017  memory: 20334  grad_norm: 0.0188  loss: 0.6833
2023/06/01 03:30:20 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 03:30:42 - mmengine - INFO - Epoch(train) [14][1200/5758]  lr: 2.8027e-04  eta: 4:34:19  time: 0.4017  data_time: 0.0015  memory: 20334  grad_norm: 0.0133  loss: 0.6858
2023/06/01 03:31:23 - mmengine - INFO - Epoch(train) [14][1300/5758]  lr: 2.8027e-04  eta: 4:33:37  time: 0.3840  data_time: 0.0017  memory: 20334  grad_norm: 0.0218  loss: 0.6842
2023/06/01 03:32:03 - mmengine - INFO - Epoch(train) [14][1400/5758]  lr: 2.8027e-04  eta: 4:32:53  time: 0.3650  data_time: 0.0025  memory: 20334  grad_norm: 0.0217  loss: 0.6864
2023/06/01 03:32:43 - mmengine - INFO - Epoch(train) [14][1500/5758]  lr: 2.8027e-04  eta: 4:32:10  time: 0.3948  data_time: 0.0022  memory: 20334  grad_norm: 0.0172  loss: 0.6841
2023/06/01 03:33:23 - mmengine - INFO - Epoch(train) [14][1600/5758]  lr: 2.8027e-04  eta: 4:31:27  time: 0.4120  data_time: 0.0018  memory: 20334  grad_norm: 0.0167  loss: 0.6881
2023/06/01 03:34:04 - mmengine - INFO - Epoch(train) [14][1700/5758]  lr: 2.8027e-04  eta: 4:30:45  time: 0.4402  data_time: 0.0019  memory: 20334  grad_norm: 0.0161  loss: 0.6885
2023/06/01 03:34:44 - mmengine - INFO - Epoch(train) [14][1800/5758]  lr: 2.8027e-04  eta: 4:30:01  time: 0.3780  data_time: 0.0028  memory: 20334  grad_norm: 0.0162  loss: 0.6849
2023/06/01 03:35:25 - mmengine - INFO - Epoch(train) [14][1900/5758]  lr: 2.8027e-04  eta: 4:29:19  time: 0.4140  data_time: 0.0017  memory: 20334  grad_norm: 0.0153  loss: 0.6846
2023/06/01 03:36:05 - mmengine - INFO - Epoch(train) [14][2000/5758]  lr: 2.8027e-04  eta: 4:28:36  time: 0.3821  data_time: 0.0019  memory: 20334  grad_norm: 0.0220  loss: 0.6852
2023/06/01 03:36:46 - mmengine - INFO - Epoch(train) [14][2100/5758]  lr: 2.8027e-04  eta: 4:27:53  time: 0.4418  data_time: 0.0032  memory: 20334  grad_norm: 0.0167  loss: 0.6878
2023/06/01 03:37:05 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 03:37:26 - mmengine - INFO - Epoch(train) [14][2200/5758]  lr: 2.8027e-04  eta: 4:27:10  time: 0.3728  data_time: 0.0019  memory: 20334  grad_norm: 0.0161  loss: 0.6832
2023/06/01 03:38:07 - mmengine - INFO - Epoch(train) [14][2300/5758]  lr: 2.8027e-04  eta: 4:26:27  time: 0.3868  data_time: 0.0021  memory: 20334  grad_norm: 0.0172  loss: 0.6870
2023/06/01 03:38:47 - mmengine - INFO - Epoch(train) [14][2400/5758]  lr: 2.8027e-04  eta: 4:25:45  time: 0.3791  data_time: 0.0019  memory: 20334  grad_norm: 0.0179  loss: 0.6842
2023/06/01 03:39:28 - mmengine - INFO - Epoch(train) [14][2500/5758]  lr: 2.8027e-04  eta: 4:25:02  time: 0.4111  data_time: 0.0023  memory: 20334  grad_norm: 0.0167  loss: 0.6866
2023/06/01 03:40:09 - mmengine - INFO - Epoch(train) [14][2600/5758]  lr: 2.8027e-04  eta: 4:24:19  time: 0.4093  data_time: 0.0028  memory: 20334  grad_norm: 0.0161  loss: 0.6886
2023/06/01 03:40:49 - mmengine - INFO - Epoch(train) [14][2700/5758]  lr: 2.8027e-04  eta: 4:23:36  time: 0.3964  data_time: 0.0019  memory: 20334  grad_norm: 0.0148  loss: 0.6856
2023/06/01 03:41:30 - mmengine - INFO - Epoch(train) [14][2800/5758]  lr: 2.8027e-04  eta: 4:22:54  time: 0.4002  data_time: 0.0022  memory: 20334  grad_norm: 0.0129  loss: 0.6868
2023/06/01 03:42:10 - mmengine - INFO - Epoch(train) [14][2900/5758]  lr: 2.8027e-04  eta: 4:22:11  time: 0.4013  data_time: 0.0016  memory: 20334  grad_norm: 0.0194  loss: 0.6817
2023/06/01 03:42:51 - mmengine - INFO - Epoch(train) [14][3000/5758]  lr: 2.8027e-04  eta: 4:21:28  time: 0.3891  data_time: 0.0030  memory: 20334  grad_norm: 0.0112  loss: 0.6808
2023/06/01 03:43:30 - mmengine - INFO - Epoch(train) [14][3100/5758]  lr: 2.8027e-04  eta: 4:20:45  time: 0.3894  data_time: 0.0022  memory: 20334  grad_norm: 0.0140  loss: 0.6876
2023/06/01 03:43:50 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 03:44:12 - mmengine - INFO - Epoch(train) [14][3200/5758]  lr: 2.8027e-04  eta: 4:20:02  time: 0.4051  data_time: 0.0018  memory: 20334  grad_norm: 0.0148  loss: 0.6881
2023/06/01 03:44:53 - mmengine - INFO - Epoch(train) [14][3300/5758]  lr: 2.8027e-04  eta: 4:19:20  time: 0.4410  data_time: 0.0026  memory: 20334  grad_norm: 0.0166  loss: 0.6841
2023/06/01 03:45:33 - mmengine - INFO - Epoch(train) [14][3400/5758]  lr: 2.8027e-04  eta: 4:18:37  time: 0.3974  data_time: 0.0017  memory: 20334  grad_norm: 0.0212  loss: 0.6881
2023/06/01 03:46:13 - mmengine - INFO - Epoch(train) [14][3500/5758]  lr: 2.8027e-04  eta: 4:17:54  time: 0.4076  data_time: 0.0025  memory: 20334  grad_norm: 0.0132  loss: 0.6879
2023/06/01 03:46:53 - mmengine - INFO - Epoch(train) [14][3600/5758]  lr: 2.8027e-04  eta: 4:17:11  time: 0.4075  data_time: 0.0023  memory: 20334  grad_norm: 0.0202  loss: 0.6878
2023/06/01 03:47:34 - mmengine - INFO - Epoch(train) [14][3700/5758]  lr: 2.8027e-04  eta: 4:16:28  time: 0.4230  data_time: 0.0018  memory: 20334  grad_norm: 0.0168  loss: 0.6904
2023/06/01 03:48:14 - mmengine - INFO - Epoch(train) [14][3800/5758]  lr: 2.8027e-04  eta: 4:15:45  time: 0.4008  data_time: 0.0020  memory: 20334  grad_norm: 0.0209  loss: 0.6921
2023/06/01 03:48:56 - mmengine - INFO - Epoch(train) [14][3900/5758]  lr: 2.8027e-04  eta: 4:15:03  time: 0.4062  data_time: 0.0026  memory: 20334  grad_norm: 0.0159  loss: 0.6849
2023/06/01 03:49:37 - mmengine - INFO - Epoch(train) [14][4000/5758]  lr: 2.8027e-04  eta: 4:14:20  time: 0.4196  data_time: 0.0024  memory: 20334  grad_norm: 0.0141  loss: 0.6823
2023/06/01 03:50:19 - mmengine - INFO - Epoch(train) [14][4100/5758]  lr: 2.8027e-04  eta: 4:13:38  time: 0.4289  data_time: 0.0028  memory: 20334  grad_norm: 0.0155  loss: 0.6923
2023/06/01 03:50:37 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 03:51:00 - mmengine - INFO - Epoch(train) [14][4200/5758]  lr: 2.8027e-04  eta: 4:12:56  time: 0.4182  data_time: 0.0023  memory: 20334  grad_norm: 0.0169  loss: 0.6882
2023/06/01 03:51:41 - mmengine - INFO - Epoch(train) [14][4300/5758]  lr: 2.8027e-04  eta: 4:12:14  time: 0.4597  data_time: 0.0022  memory: 20334  grad_norm: 0.0163  loss: 0.6863
2023/06/01 03:52:21 - mmengine - INFO - Epoch(train) [14][4400/5758]  lr: 2.8027e-04  eta: 4:11:30  time: 0.4341  data_time: 0.0023  memory: 20334  grad_norm: 0.0165  loss: 0.6871
2023/06/01 03:53:02 - mmengine - INFO - Epoch(train) [14][4500/5758]  lr: 2.8027e-04  eta: 4:10:48  time: 0.3826  data_time: 0.0024  memory: 20334  grad_norm: 0.0174  loss: 0.6883
2023/06/01 03:53:42 - mmengine - INFO - Epoch(train) [14][4600/5758]  lr: 2.8027e-04  eta: 4:10:05  time: 0.4040  data_time: 0.0028  memory: 20334  grad_norm: 0.0177  loss: 0.6873
2023/06/01 03:54:23 - mmengine - INFO - Epoch(train) [14][4700/5758]  lr: 2.8027e-04  eta: 4:09:23  time: 0.4057  data_time: 0.0029  memory: 20334  grad_norm: 0.0138  loss: 0.6849
2023/06/01 03:55:02 - mmengine - INFO - Epoch(train) [14][4800/5758]  lr: 2.8027e-04  eta: 4:08:39  time: 0.3935  data_time: 0.0018  memory: 20334  grad_norm: 0.0143  loss: 0.6844
2023/06/01 03:55:43 - mmengine - INFO - Epoch(train) [14][4900/5758]  lr: 2.8027e-04  eta: 4:07:57  time: 0.3880  data_time: 0.0022  memory: 20334  grad_norm: 0.0140  loss: 0.6846
2023/06/01 03:56:24 - mmengine - INFO - Epoch(train) [14][5000/5758]  lr: 2.8027e-04  eta: 4:07:14  time: 0.4062  data_time: 0.0018  memory: 20334  grad_norm: 0.0130  loss: 0.6860
2023/06/01 03:57:04 - mmengine - INFO - Epoch(train) [14][5100/5758]  lr: 2.8027e-04  eta: 4:06:31  time: 0.4120  data_time: 0.0016  memory: 20334  grad_norm: 0.0183  loss: 0.6916
2023/06/01 03:57:23 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 03:57:44 - mmengine - INFO - Epoch(train) [14][5200/5758]  lr: 2.8027e-04  eta: 4:05:49  time: 0.3808  data_time: 0.0028  memory: 20334  grad_norm: 0.0202  loss: 0.6902
2023/06/01 03:58:25 - mmengine - INFO - Epoch(train) [14][5300/5758]  lr: 2.8027e-04  eta: 4:05:06  time: 0.4348  data_time: 0.0020  memory: 20334  grad_norm: 0.0183  loss: 0.6889
2023/06/01 03:59:05 - mmengine - INFO - Epoch(train) [14][5400/5758]  lr: 2.8027e-04  eta: 4:04:23  time: 0.4004  data_time: 0.0017  memory: 20334  grad_norm: 0.0187  loss: 0.6876
2023/06/01 03:59:47 - mmengine - INFO - Epoch(train) [14][5500/5758]  lr: 2.8027e-04  eta: 4:03:41  time: 0.4484  data_time: 0.0020  memory: 20334  grad_norm: 0.0207  loss: 0.6857
2023/06/01 04:00:27 - mmengine - INFO - Epoch(train) [14][5600/5758]  lr: 2.8027e-04  eta: 4:02:58  time: 0.3978  data_time: 0.0018  memory: 20334  grad_norm: 0.0182  loss: 0.6878
2023/06/01 04:01:06 - mmengine - INFO - Epoch(train) [14][5700/5758]  lr: 2.8027e-04  eta: 4:02:15  time: 0.4089  data_time: 0.0019  memory: 20334  grad_norm: 0.0149  loss: 0.6852
2023/06/01 04:01:30 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 04:01:30 - mmengine - INFO - Saving checkpoint at 14 epochs
2023/06/01 04:01:47 - mmengine - INFO - Epoch(val) [14][8/8]    accuracy/top1: 100.0000  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [100.0, 0.0]  single-label/f1-score_classwise: [100.0, 0.0]  data_time: 0.3668  time: 0.9949
2023/06/01 04:02:32 - mmengine - INFO - Epoch(train) [15][ 100/5758]  lr: 2.1405e-04  eta: 4:01:10  time: 0.3993  data_time: 0.0020  memory: 20334  grad_norm: 0.0204  loss: 0.6880
2023/06/01 04:03:13 - mmengine - INFO - Epoch(train) [15][ 200/5758]  lr: 2.1405e-04  eta: 4:00:27  time: 0.4345  data_time: 0.0018  memory: 20334  grad_norm: 0.0132  loss: 0.6877
2023/06/01 04:03:54 - mmengine - INFO - Epoch(train) [15][ 300/5758]  lr: 2.1405e-04  eta: 3:59:44  time: 0.4268  data_time: 0.0021  memory: 20334  grad_norm: 0.0208  loss: 0.6855
2023/06/01 04:04:30 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 04:04:34 - mmengine - INFO - Epoch(train) [15][ 400/5758]  lr: 2.1405e-04  eta: 3:59:02  time: 0.4004  data_time: 0.0031  memory: 20334  grad_norm: 0.0173  loss: 0.6879
2023/06/01 04:05:16 - mmengine - INFO - Epoch(train) [15][ 500/5758]  lr: 2.1405e-04  eta: 3:58:20  time: 0.3764  data_time: 0.0019  memory: 20334  grad_norm: 0.0120  loss: 0.6882
2023/06/01 04:05:56 - mmengine - INFO - Epoch(train) [15][ 600/5758]  lr: 2.1405e-04  eta: 3:57:37  time: 0.3963  data_time: 0.0019  memory: 20334  grad_norm: 0.0103  loss: 0.6887
2023/06/01 04:06:37 - mmengine - INFO - Epoch(train) [15][ 700/5758]  lr: 2.1405e-04  eta: 3:56:54  time: 0.4071  data_time: 0.0019  memory: 20334  grad_norm: 0.0194  loss: 0.6878
2023/06/01 04:07:18 - mmengine - INFO - Epoch(train) [15][ 800/5758]  lr: 2.1405e-04  eta: 3:56:12  time: 0.4206  data_time: 0.0020  memory: 20334  grad_norm: 0.0173  loss: 0.6901
2023/06/01 04:08:01 - mmengine - INFO - Epoch(train) [15][ 900/5758]  lr: 2.1405e-04  eta: 3:55:30  time: 0.3930  data_time: 0.0017  memory: 20334  grad_norm: 0.0251  loss: 0.6883
2023/06/01 04:08:41 - mmengine - INFO - Epoch(train) [15][1000/5758]  lr: 2.1405e-04  eta: 3:54:48  time: 0.3753  data_time: 0.0016  memory: 20334  grad_norm: 0.0246  loss: 0.6852
2023/06/01 04:09:22 - mmengine - INFO - Epoch(train) [15][1100/5758]  lr: 2.1405e-04  eta: 3:54:05  time: 0.4012  data_time: 0.0021  memory: 20334  grad_norm: 0.0125  loss: 0.6830
2023/06/01 04:10:03 - mmengine - INFO - Epoch(train) [15][1200/5758]  lr: 2.1405e-04  eta: 3:53:23  time: 0.3802  data_time: 0.0017  memory: 20334  grad_norm: 0.0187  loss: 0.6915
2023/06/01 04:10:44 - mmengine - INFO - Epoch(train) [15][1300/5758]  lr: 2.1405e-04  eta: 3:52:41  time: 0.4221  data_time: 0.0018  memory: 20334  grad_norm: 0.0111  loss: 0.6896
2023/06/01 04:11:20 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 04:11:25 - mmengine - INFO - Epoch(train) [15][1400/5758]  lr: 2.1405e-04  eta: 3:51:58  time: 0.4112  data_time: 0.0020  memory: 20334  grad_norm: 0.0220  loss: 0.6879
2023/06/01 04:12:05 - mmengine - INFO - Epoch(train) [15][1500/5758]  lr: 2.1405e-04  eta: 3:51:15  time: 0.4390  data_time: 0.0026  memory: 20334  grad_norm: 0.0219  loss: 0.6829
2023/06/01 04:12:45 - mmengine - INFO - Epoch(train) [15][1600/5758]  lr: 2.1405e-04  eta: 3:50:33  time: 0.3933  data_time: 0.0015  memory: 20334  grad_norm: 0.7235  loss: 0.6865
2023/06/01 04:13:26 - mmengine - INFO - Epoch(train) [15][1700/5758]  lr: 2.1405e-04  eta: 3:49:50  time: 0.3944  data_time: 0.0015  memory: 20334  grad_norm: 0.0194  loss: 0.6884
2023/06/01 04:14:08 - mmengine - INFO - Epoch(train) [15][1800/5758]  lr: 2.1405e-04  eta: 3:49:08  time: 0.4042  data_time: 0.0019  memory: 20334  grad_norm: 0.0194  loss: 0.6874
2023/06/01 04:14:47 - mmengine - INFO - Epoch(train) [15][1900/5758]  lr: 2.1405e-04  eta: 3:48:25  time: 0.4023  data_time: 0.0018  memory: 20334  grad_norm: 0.0235  loss: 0.6824
2023/06/01 04:15:28 - mmengine - INFO - Epoch(train) [15][2000/5758]  lr: 2.1405e-04  eta: 3:47:43  time: 0.4261  data_time: 0.0028  memory: 20334  grad_norm: 0.0168  loss: 0.6863
2023/06/01 04:16:09 - mmengine - INFO - Epoch(train) [15][2100/5758]  lr: 2.1405e-04  eta: 3:47:00  time: 0.4147  data_time: 0.0019  memory: 20334  grad_norm: 0.0195  loss: 0.6848
2023/06/01 04:16:49 - mmengine - INFO - Epoch(train) [15][2200/5758]  lr: 2.1405e-04  eta: 3:46:17  time: 0.4664  data_time: 0.0023  memory: 20334  grad_norm: 0.0115  loss: 0.6897
2023/06/01 04:17:29 - mmengine - INFO - Epoch(train) [15][2300/5758]  lr: 2.1405e-04  eta: 3:45:35  time: 0.3852  data_time: 0.0019  memory: 20334  grad_norm: 0.0186  loss: 0.6862
2023/06/01 04:18:05 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 04:18:10 - mmengine - INFO - Epoch(train) [15][2400/5758]  lr: 2.1405e-04  eta: 3:44:53  time: 0.4318  data_time: 0.0021  memory: 20334  grad_norm: 0.0214  loss: 0.6847
2023/06/01 04:18:50 - mmengine - INFO - Epoch(train) [15][2500/5758]  lr: 2.1405e-04  eta: 3:44:10  time: 0.3894  data_time: 0.0020  memory: 20334  grad_norm: 0.0150  loss: 0.6843
2023/06/01 04:19:31 - mmengine - INFO - Epoch(train) [15][2600/5758]  lr: 2.1405e-04  eta: 3:43:27  time: 0.4034  data_time: 0.0023  memory: 20334  grad_norm: 0.0115  loss: 0.6823
2023/06/01 04:20:11 - mmengine - INFO - Epoch(train) [15][2700/5758]  lr: 2.1405e-04  eta: 3:42:45  time: 0.3971  data_time: 0.0018  memory: 20334  grad_norm: 0.0240  loss: 0.6818
2023/06/01 04:20:52 - mmengine - INFO - Epoch(train) [15][2800/5758]  lr: 2.1405e-04  eta: 3:42:02  time: 0.3992  data_time: 0.0021  memory: 20334  grad_norm: 0.0106  loss: 0.6865
2023/06/01 04:21:32 - mmengine - INFO - Epoch(train) [15][2900/5758]  lr: 2.1405e-04  eta: 3:41:20  time: 0.3959  data_time: 0.0019  memory: 20334  grad_norm: 0.0105  loss: 0.6884
2023/06/01 04:22:14 - mmengine - INFO - Epoch(train) [15][3000/5758]  lr: 2.1405e-04  eta: 3:40:38  time: 0.4292  data_time: 0.0017  memory: 20334  grad_norm: 0.0187  loss: 0.6831
2023/06/01 04:22:55 - mmengine - INFO - Epoch(train) [15][3100/5758]  lr: 2.1405e-04  eta: 3:39:55  time: 0.4178  data_time: 0.0017  memory: 20334  grad_norm: 0.0196  loss: 0.6889
2023/06/01 04:23:35 - mmengine - INFO - Epoch(train) [15][3200/5758]  lr: 2.1405e-04  eta: 3:39:13  time: 0.3990  data_time: 0.0018  memory: 20334  grad_norm: 0.0221  loss: 0.6835
2023/06/01 04:24:17 - mmengine - INFO - Epoch(train) [15][3300/5758]  lr: 2.1405e-04  eta: 3:38:30  time: 0.4686  data_time: 0.0017  memory: 20334  grad_norm: 8.7150  loss: 0.6825
2023/06/01 04:24:53 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 04:24:58 - mmengine - INFO - Epoch(train) [15][3400/5758]  lr: 2.1405e-04  eta: 3:37:48  time: 0.4047  data_time: 0.0016  memory: 20334  grad_norm: 0.0140  loss: 0.6852
2023/06/01 04:25:39 - mmengine - INFO - Epoch(train) [15][3500/5758]  lr: 2.1405e-04  eta: 3:37:06  time: 0.4381  data_time: 0.0018  memory: 20334  grad_norm: 0.0253  loss: 0.6840
2023/06/01 04:26:19 - mmengine - INFO - Epoch(train) [15][3600/5758]  lr: 2.1405e-04  eta: 3:36:23  time: 0.3851  data_time: 0.0017  memory: 20334  grad_norm: 0.0284  loss: 0.6848
2023/06/01 04:27:00 - mmengine - INFO - Epoch(train) [15][3700/5758]  lr: 2.1405e-04  eta: 3:35:41  time: 0.4300  data_time: 0.0018  memory: 20334  grad_norm: 0.0278  loss: 0.6871
2023/06/01 04:27:41 - mmengine - INFO - Epoch(train) [15][3800/5758]  lr: 2.1405e-04  eta: 3:34:59  time: 0.4136  data_time: 0.0021  memory: 20334  grad_norm: 0.0438  loss: 0.6851
2023/06/01 04:28:21 - mmengine - INFO - Epoch(train) [15][3900/5758]  lr: 2.1405e-04  eta: 3:34:16  time: 0.3976  data_time: 0.0018  memory: 20334  grad_norm: 0.0293  loss: 0.6853
2023/06/01 04:29:03 - mmengine - INFO - Epoch(train) [15][4000/5758]  lr: 2.1405e-04  eta: 3:33:34  time: 0.4272  data_time: 0.0030  memory: 20334  grad_norm: 0.0542  loss: 0.6835
2023/06/01 04:29:47 - mmengine - INFO - Epoch(train) [15][4100/5758]  lr: 2.1405e-04  eta: 3:32:53  time: 0.4178  data_time: 0.0025  memory: 20334  grad_norm: 0.1979  loss: 0.6902
2023/06/01 04:30:27 - mmengine - INFO - Epoch(train) [15][4200/5758]  lr: 2.1405e-04  eta: 3:32:10  time: 0.4293  data_time: 0.0020  memory: 20334  grad_norm: 0.0218  loss: 0.6847
2023/06/01 04:31:07 - mmengine - INFO - Epoch(train) [15][4300/5758]  lr: 2.1405e-04  eta: 3:31:28  time: 0.3864  data_time: 0.0019  memory: 20334  grad_norm: 0.1379  loss: 0.6823
2023/06/01 04:31:42 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 04:31:47 - mmengine - INFO - Epoch(train) [15][4400/5758]  lr: 2.1405e-04  eta: 3:30:45  time: 0.3929  data_time: 0.0020  memory: 20334  grad_norm: 0.0296  loss: 0.6803
2023/06/01 04:32:28 - mmengine - INFO - Epoch(train) [15][4500/5758]  lr: 2.1405e-04  eta: 3:30:02  time: 0.4120  data_time: 0.0018  memory: 20334  grad_norm: 0.1280  loss: 0.6833
2023/06/01 04:33:08 - mmengine - INFO - Epoch(train) [15][4600/5758]  lr: 2.1405e-04  eta: 3:29:20  time: 0.4210  data_time: 0.0020  memory: 20334  grad_norm: 0.0831  loss: 0.6848
2023/06/01 04:33:48 - mmengine - INFO - Epoch(train) [15][4700/5758]  lr: 2.1405e-04  eta: 3:28:37  time: 0.4033  data_time: 0.0015  memory: 20334  grad_norm: 0.0285  loss: 0.6901
2023/06/01 04:34:30 - mmengine - INFO - Epoch(train) [15][4800/5758]  lr: 2.1405e-04  eta: 3:27:55  time: 0.4446  data_time: 0.0015  memory: 20334  grad_norm: 0.0201  loss: 0.6823
2023/06/01 04:35:10 - mmengine - INFO - Epoch(train) [15][4900/5758]  lr: 2.1405e-04  eta: 3:27:13  time: 0.4031  data_time: 0.0019  memory: 20334  grad_norm: 0.0154  loss: 0.6880
2023/06/01 04:35:50 - mmengine - INFO - Epoch(train) [15][5000/5758]  lr: 2.1405e-04  eta: 3:26:30  time: 0.4032  data_time: 0.0016  memory: 20334  grad_norm: 8.6955  loss: 0.6833
2023/06/01 04:36:30 - mmengine - INFO - Epoch(train) [15][5100/5758]  lr: 2.1405e-04  eta: 3:25:48  time: 0.3882  data_time: 0.0020  memory: 20334  grad_norm: 0.0649  loss: 0.6860
2023/06/01 04:37:11 - mmengine - INFO - Epoch(train) [15][5200/5758]  lr: 2.1405e-04  eta: 3:25:05  time: 0.3962  data_time: 0.0024  memory: 20334  grad_norm: 0.0877  loss: 0.6858
2023/06/01 04:37:52 - mmengine - INFO - Epoch(train) [15][5300/5758]  lr: 2.1405e-04  eta: 3:24:23  time: 0.4034  data_time: 0.0021  memory: 20334  grad_norm: 3.8150  loss: 0.6732
2023/06/01 04:38:28 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 04:38:32 - mmengine - INFO - Epoch(train) [15][5400/5758]  lr: 2.1405e-04  eta: 3:23:41  time: 0.3718  data_time: 0.0022  memory: 20334  grad_norm: 2.4243  loss: 0.6876
2023/06/01 04:39:15 - mmengine - INFO - Epoch(train) [15][5500/5758]  lr: 2.1405e-04  eta: 3:22:59  time: 0.4638  data_time: 0.0023  memory: 20334  grad_norm: 1.6639  loss: 0.6721
2023/06/01 04:39:57 - mmengine - INFO - Epoch(train) [15][5600/5758]  lr: 2.1405e-04  eta: 3:22:17  time: 0.4014  data_time: 0.0020  memory: 20334  grad_norm: 46.6717  loss: 0.6611
2023/06/01 04:40:36 - mmengine - INFO - Epoch(train) [15][5700/5758]  lr: 2.1405e-04  eta: 3:21:34  time: 0.4040  data_time: 0.0022  memory: 20334  grad_norm: 2.5040  loss: 0.6639
2023/06/01 04:40:59 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 04:40:59 - mmengine - INFO - Saving checkpoint at 15 epochs
2023/06/01 04:41:16 - mmengine - INFO - Epoch(val) [15][8/8]    accuracy/top1: 93.8956  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [93.89559173583984, 0.0]  single-label/f1-score_classwise: [96.8517074584961, 0.0]  data_time: 0.3732  time: 0.9995
2023/06/01 04:42:01 - mmengine - INFO - Epoch(train) [16][ 100/5758]  lr: 1.5498e-04  eta: 3:20:29  time: 0.4125  data_time: 0.0017  memory: 20334  grad_norm: 12.9511  loss: 0.6620
2023/06/01 04:42:42 - mmengine - INFO - Epoch(train) [16][ 200/5758]  lr: 1.5498e-04  eta: 3:19:46  time: 0.4402  data_time: 0.0018  memory: 20334  grad_norm: 20.3987  loss: 0.6426
2023/06/01 04:43:22 - mmengine - INFO - Epoch(train) [16][ 300/5758]  lr: 1.5498e-04  eta: 3:19:04  time: 0.3781  data_time: 0.0025  memory: 20334  grad_norm: 166.1285  loss: 0.6846
2023/06/01 04:44:03 - mmengine - INFO - Epoch(train) [16][ 400/5758]  lr: 1.5498e-04  eta: 3:18:22  time: 0.4268  data_time: 0.0031  memory: 20334  grad_norm: 17.4556  loss: 0.6753
2023/06/01 04:44:43 - mmengine - INFO - Epoch(train) [16][ 500/5758]  lr: 1.5498e-04  eta: 3:17:39  time: 0.4338  data_time: 0.0017  memory: 20334  grad_norm: 153.9520  loss: 0.6711
2023/06/01 04:45:23 - mmengine - INFO - Epoch(train) [16][ 600/5758]  lr: 1.5498e-04  eta: 3:16:56  time: 0.4157  data_time: 0.0019  memory: 20334  grad_norm: 16.2066  loss: 0.6689
2023/06/01 04:45:35 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 04:46:05 - mmengine - INFO - Epoch(train) [16][ 700/5758]  lr: 1.5498e-04  eta: 3:16:14  time: 0.4042  data_time: 0.0025  memory: 20334  grad_norm: 5.0856  loss: 0.6964
2023/06/01 04:46:45 - mmengine - INFO - Epoch(train) [16][ 800/5758]  lr: 1.5498e-04  eta: 3:15:32  time: 0.4223  data_time: 0.0034  memory: 20334  grad_norm: 6.3401  loss: 0.6841
2023/06/01 04:47:27 - mmengine - INFO - Epoch(train) [16][ 900/5758]  lr: 1.5498e-04  eta: 3:14:50  time: 0.3991  data_time: 0.0016  memory: 20334  grad_norm: 0.9693  loss: 0.6880
2023/06/01 04:48:07 - mmengine - INFO - Epoch(train) [16][1000/5758]  lr: 1.5498e-04  eta: 3:14:08  time: 0.4078  data_time: 0.0020  memory: 20334  grad_norm: 1.2147  loss: 0.6861
2023/06/01 04:48:49 - mmengine - INFO - Epoch(train) [16][1100/5758]  lr: 1.5498e-04  eta: 3:13:26  time: 0.4397  data_time: 0.0017  memory: 20334  grad_norm: 0.8336  loss: 0.6850
2023/06/01 04:49:30 - mmengine - INFO - Epoch(train) [16][1200/5758]  lr: 1.5498e-04  eta: 3:12:44  time: 0.4458  data_time: 0.0019  memory: 20334  grad_norm: 1.7692  loss: 0.6863
2023/06/01 04:50:11 - mmengine - INFO - Epoch(train) [16][1300/5758]  lr: 1.5498e-04  eta: 3:12:01  time: 0.4181  data_time: 0.0022  memory: 20334  grad_norm: 1.9187  loss: 0.6828
2023/06/01 04:50:51 - mmengine - INFO - Epoch(train) [16][1400/5758]  lr: 1.5498e-04  eta: 3:11:19  time: 0.3825  data_time: 0.0031  memory: 20334  grad_norm: 10.5395  loss: 0.6742
2023/06/01 04:51:32 - mmengine - INFO - Epoch(train) [16][1500/5758]  lr: 1.5498e-04  eta: 3:10:36  time: 0.4437  data_time: 0.0026  memory: 20334  grad_norm: 0.7334  loss: 0.6746
2023/06/01 04:52:14 - mmengine - INFO - Epoch(train) [16][1600/5758]  lr: 1.5498e-04  eta: 3:09:55  time: 0.4179  data_time: 0.0022  memory: 20334  grad_norm: 0.8798  loss: 0.6776
2023/06/01 04:52:26 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 04:52:55 - mmengine - INFO - Epoch(train) [16][1700/5758]  lr: 1.5498e-04  eta: 3:09:13  time: 0.4212  data_time: 0.0036  memory: 20334  grad_norm: 3.3916  loss: 0.6739
2023/06/01 04:53:34 - mmengine - INFO - Epoch(train) [16][1800/5758]  lr: 1.5498e-04  eta: 3:08:30  time: 0.3852  data_time: 0.0019  memory: 20334  grad_norm: 0.2993  loss: 0.6853
2023/06/01 04:54:14 - mmengine - INFO - Epoch(train) [16][1900/5758]  lr: 1.5498e-04  eta: 3:07:47  time: 0.4051  data_time: 0.0019  memory: 20334  grad_norm: 0.3990  loss: 0.6886
2023/06/01 04:54:55 - mmengine - INFO - Epoch(train) [16][2000/5758]  lr: 1.5498e-04  eta: 3:07:05  time: 0.4105  data_time: 0.0028  memory: 20334  grad_norm: 0.1702  loss: 0.6884
2023/06/01 04:55:36 - mmengine - INFO - Epoch(train) [16][2100/5758]  lr: 1.5498e-04  eta: 3:06:23  time: 0.4038  data_time: 0.0029  memory: 20334  grad_norm: 0.4629  loss: 0.6927
2023/06/01 04:56:17 - mmengine - INFO - Epoch(train) [16][2200/5758]  lr: 1.5498e-04  eta: 3:05:41  time: 0.4490  data_time: 0.0022  memory: 20334  grad_norm: 17.8738  loss: 0.6876
2023/06/01 04:56:59 - mmengine - INFO - Epoch(train) [16][2300/5758]  lr: 1.5498e-04  eta: 3:04:59  time: 0.4195  data_time: 0.0023  memory: 20334  grad_norm: 11.7259  loss: 0.6912
2023/06/01 04:57:40 - mmengine - INFO - Epoch(train) [16][2400/5758]  lr: 1.5498e-04  eta: 3:04:16  time: 0.4291  data_time: 0.0018  memory: 20334  grad_norm: 1.5582  loss: 0.6845
2023/06/01 04:58:20 - mmengine - INFO - Epoch(train) [16][2500/5758]  lr: 1.5498e-04  eta: 3:03:34  time: 0.3938  data_time: 0.0028  memory: 20334  grad_norm: 4.2360  loss: 0.6870
2023/06/01 04:59:01 - mmengine - INFO - Epoch(train) [16][2600/5758]  lr: 1.5498e-04  eta: 3:02:52  time: 0.4125  data_time: 0.0018  memory: 20334  grad_norm: 0.7279  loss: 0.6894
2023/06/01 04:59:12 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 04:59:40 - mmengine - INFO - Epoch(train) [16][2700/5758]  lr: 1.5498e-04  eta: 3:02:09  time: 0.4068  data_time: 0.0019  memory: 20334  grad_norm: 0.4202  loss: 0.6843
2023/06/01 05:00:20 - mmengine - INFO - Epoch(train) [16][2800/5758]  lr: 1.5498e-04  eta: 3:01:27  time: 0.4023  data_time: 0.0018  memory: 20334  grad_norm: 0.1388  loss: 0.6857
2023/06/01 05:01:01 - mmengine - INFO - Epoch(train) [16][2900/5758]  lr: 1.5498e-04  eta: 3:00:45  time: 0.4056  data_time: 0.0019  memory: 20334  grad_norm: 0.0492  loss: 0.6896
2023/06/01 05:01:42 - mmengine - INFO - Epoch(train) [16][3000/5758]  lr: 1.5498e-04  eta: 3:00:02  time: 0.3845  data_time: 0.0018  memory: 20334  grad_norm: 2.3024  loss: 0.6834
2023/06/01 05:02:22 - mmengine - INFO - Epoch(train) [16][3100/5758]  lr: 1.5498e-04  eta: 2:59:20  time: 0.3881  data_time: 0.0021  memory: 20334  grad_norm: 0.8119  loss: 0.6922
2023/06/01 05:03:03 - mmengine - INFO - Epoch(train) [16][3200/5758]  lr: 1.5498e-04  eta: 2:58:38  time: 0.4399  data_time: 0.0018  memory: 20334  grad_norm: 0.2433  loss: 0.6882
2023/06/01 05:03:43 - mmengine - INFO - Epoch(train) [16][3300/5758]  lr: 1.5498e-04  eta: 2:57:56  time: 0.4182  data_time: 0.0018  memory: 20334  grad_norm: 0.3354  loss: 0.6832
2023/06/01 05:04:24 - mmengine - INFO - Epoch(train) [16][3400/5758]  lr: 1.5498e-04  eta: 2:57:13  time: 0.4243  data_time: 0.0017  memory: 20334  grad_norm: 0.2514  loss: 0.6861
2023/06/01 05:05:04 - mmengine - INFO - Epoch(train) [16][3500/5758]  lr: 1.5498e-04  eta: 2:56:31  time: 0.4271  data_time: 0.0015  memory: 20334  grad_norm: 0.6776  loss: 0.6859
2023/06/01 05:05:45 - mmengine - INFO - Epoch(train) [16][3600/5758]  lr: 1.5498e-04  eta: 2:55:49  time: 0.3950  data_time: 0.0016  memory: 20334  grad_norm: 0.6123  loss: 0.6826
2023/06/01 05:05:57 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 05:06:27 - mmengine - INFO - Epoch(train) [16][3700/5758]  lr: 1.5498e-04  eta: 2:55:07  time: 0.4651  data_time: 0.0020  memory: 20334  grad_norm: 0.2018  loss: 0.6864
2023/06/01 05:07:08 - mmengine - INFO - Epoch(train) [16][3800/5758]  lr: 1.5498e-04  eta: 2:54:25  time: 0.4244  data_time: 0.0019  memory: 20334  grad_norm: 0.2864  loss: 0.6877
2023/06/01 05:07:49 - mmengine - INFO - Epoch(train) [16][3900/5758]  lr: 1.5498e-04  eta: 2:53:43  time: 0.4155  data_time: 0.0017  memory: 20334  grad_norm: 0.1539  loss: 0.6884
2023/06/01 05:08:30 - mmengine - INFO - Epoch(train) [16][4000/5758]  lr: 1.5498e-04  eta: 2:53:01  time: 0.4340  data_time: 0.0017  memory: 20334  grad_norm: 0.1950  loss: 0.6865
2023/06/01 05:09:11 - mmengine - INFO - Epoch(train) [16][4100/5758]  lr: 1.5498e-04  eta: 2:52:18  time: 0.4267  data_time: 0.0016  memory: 20334  grad_norm: 0.5413  loss: 0.6927
2023/06/01 05:09:51 - mmengine - INFO - Epoch(train) [16][4200/5758]  lr: 1.5498e-04  eta: 2:51:36  time: 0.3973  data_time: 0.0017  memory: 20334  grad_norm: 0.7295  loss: 0.6888
2023/06/01 05:10:32 - mmengine - INFO - Epoch(train) [16][4300/5758]  lr: 1.5498e-04  eta: 2:50:54  time: 0.3891  data_time: 0.0019  memory: 20334  grad_norm: 1.2996  loss: 0.6843
2023/06/01 05:11:13 - mmengine - INFO - Epoch(train) [16][4400/5758]  lr: 1.5498e-04  eta: 2:50:12  time: 0.3753  data_time: 0.0016  memory: 20334  grad_norm: 0.5797  loss: 0.6847
2023/06/01 05:11:52 - mmengine - INFO - Epoch(train) [16][4500/5758]  lr: 1.5498e-04  eta: 2:49:29  time: 0.3714  data_time: 0.0016  memory: 20334  grad_norm: 4.0275  loss: 0.6887
2023/06/01 05:12:34 - mmengine - INFO - Epoch(train) [16][4600/5758]  lr: 1.5498e-04  eta: 2:48:47  time: 0.4044  data_time: 0.0016  memory: 20334  grad_norm: 2.0650  loss: 0.6807
2023/06/01 05:12:46 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 05:13:14 - mmengine - INFO - Epoch(train) [16][4700/5758]  lr: 1.5498e-04  eta: 2:48:05  time: 0.3974  data_time: 0.0016  memory: 20334  grad_norm: 2.3120  loss: 0.6872
2023/06/01 05:13:56 - mmengine - INFO - Epoch(train) [16][4800/5758]  lr: 1.5498e-04  eta: 2:47:23  time: 0.4134  data_time: 0.0016  memory: 20334  grad_norm: 6.7637  loss: 0.6780
2023/06/01 05:14:38 - mmengine - INFO - Epoch(train) [16][4900/5758]  lr: 1.5498e-04  eta: 2:46:41  time: 0.4374  data_time: 0.0017  memory: 20334  grad_norm: 16.9621  loss: 0.6605
2023/06/01 05:15:18 - mmengine - INFO - Epoch(train) [16][5000/5758]  lr: 1.5498e-04  eta: 2:45:59  time: 0.3812  data_time: 0.0016  memory: 20334  grad_norm: 7.3323  loss: 0.6797
2023/06/01 05:15:58 - mmengine - INFO - Epoch(train) [16][5100/5758]  lr: 1.5498e-04  eta: 2:45:17  time: 0.3892  data_time: 0.0017  memory: 20334  grad_norm: 18.0871  loss: 0.6598
2023/06/01 05:16:39 - mmengine - INFO - Epoch(train) [16][5200/5758]  lr: 1.5498e-04  eta: 2:44:35  time: 0.4083  data_time: 0.0015  memory: 20334  grad_norm: 422.3003  loss: 0.6643
2023/06/01 05:17:20 - mmengine - INFO - Epoch(train) [16][5300/5758]  lr: 1.5498e-04  eta: 2:43:52  time: 0.3825  data_time: 0.0017  memory: 20334  grad_norm: 293.7801  loss: 0.6675
2023/06/01 05:18:00 - mmengine - INFO - Epoch(train) [16][5400/5758]  lr: 1.5498e-04  eta: 2:43:10  time: 0.3898  data_time: 0.0016  memory: 20334  grad_norm: 63.2483  loss: 0.6756
2023/06/01 05:18:40 - mmengine - INFO - Epoch(train) [16][5500/5758]  lr: 1.5498e-04  eta: 2:42:28  time: 0.4151  data_time: 0.0018  memory: 20334  grad_norm: 40.1539  loss: 0.6775
2023/06/01 05:19:21 - mmengine - INFO - Epoch(train) [16][5600/5758]  lr: 1.5498e-04  eta: 2:41:46  time: 0.3952  data_time: 0.0020  memory: 20334  grad_norm: 77.3026  loss: 0.6732
2023/06/01 05:19:33 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 05:20:01 - mmengine - INFO - Epoch(train) [16][5700/5758]  lr: 1.5498e-04  eta: 2:41:03  time: 0.3873  data_time: 0.0017  memory: 20334  grad_norm: 126.4350  loss: 0.6708
2023/06/01 05:20:25 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 05:20:25 - mmengine - INFO - Saving checkpoint at 16 epochs
2023/06/01 05:20:41 - mmengine - INFO - Epoch(val) [16][8/8]    accuracy/top1: 98.0999  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [98.09986877441406, 0.0]  single-label/f1-score_classwise: [99.04082489013672, 0.0]  data_time: 0.3740  time: 1.0006
2023/06/01 05:21:25 - mmengine - INFO - Epoch(train) [17][ 100/5758]  lr: 1.0454e-04  eta: 2:39:58  time: 0.4126  data_time: 0.0017  memory: 20334  grad_norm: 3.2546  loss: 0.6642
2023/06/01 05:22:06 - mmengine - INFO - Epoch(train) [17][ 200/5758]  lr: 1.0454e-04  eta: 2:39:16  time: 0.4131  data_time: 0.0016  memory: 20334  grad_norm: 9.8711  loss: 0.6585
2023/06/01 05:22:48 - mmengine - INFO - Epoch(train) [17][ 300/5758]  lr: 1.0454e-04  eta: 2:38:34  time: 0.4243  data_time: 0.0016  memory: 20334  grad_norm: 16.2878  loss: 0.6528
2023/06/01 05:23:29 - mmengine - INFO - Epoch(train) [17][ 400/5758]  lr: 1.0454e-04  eta: 2:37:52  time: 0.4310  data_time: 0.0016  memory: 20334  grad_norm: 15.2030  loss: 0.6579
2023/06/01 05:24:10 - mmengine - INFO - Epoch(train) [17][ 500/5758]  lr: 1.0454e-04  eta: 2:37:09  time: 0.4290  data_time: 0.0016  memory: 20334  grad_norm: 11.4554  loss: 0.6541
2023/06/01 05:24:50 - mmengine - INFO - Epoch(train) [17][ 600/5758]  lr: 1.0454e-04  eta: 2:36:27  time: 0.4141  data_time: 0.0016  memory: 20334  grad_norm: 12.4674  loss: 0.6616
2023/06/01 05:25:31 - mmengine - INFO - Epoch(train) [17][ 700/5758]  lr: 1.0454e-04  eta: 2:35:45  time: 0.4093  data_time: 0.0015  memory: 20334  grad_norm: 15.2415  loss: 0.6654
2023/06/01 05:26:12 - mmengine - INFO - Epoch(train) [17][ 800/5758]  lr: 1.0454e-04  eta: 2:35:03  time: 0.4104  data_time: 0.0017  memory: 20334  grad_norm: 6.9286  loss: 0.6621
2023/06/01 05:26:41 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 05:26:53 - mmengine - INFO - Epoch(train) [17][ 900/5758]  lr: 1.0454e-04  eta: 2:34:21  time: 0.4183  data_time: 0.0016  memory: 20334  grad_norm: 11.7854  loss: 0.6657
2023/06/01 05:27:33 - mmengine - INFO - Epoch(train) [17][1000/5758]  lr: 1.0454e-04  eta: 2:33:39  time: 0.4516  data_time: 0.0019  memory: 20334  grad_norm: 19.8003  loss: 0.6556
2023/06/01 05:28:13 - mmengine - INFO - Epoch(train) [17][1100/5758]  lr: 1.0454e-04  eta: 2:32:57  time: 0.3782  data_time: 0.0018  memory: 20334  grad_norm: 13.8714  loss: 0.6472
2023/06/01 05:28:54 - mmengine - INFO - Epoch(train) [17][1200/5758]  lr: 1.0454e-04  eta: 2:32:15  time: 0.4360  data_time: 0.0033  memory: 20334  grad_norm: 22.1152  loss: 0.6549
2023/06/01 05:29:35 - mmengine - INFO - Epoch(train) [17][1300/5758]  lr: 1.0454e-04  eta: 2:31:32  time: 0.4103  data_time: 0.0019  memory: 20334  grad_norm: 5.6001  loss: 0.6542
2023/06/01 05:30:16 - mmengine - INFO - Epoch(train) [17][1400/5758]  lr: 1.0454e-04  eta: 2:30:50  time: 0.4596  data_time: 0.0024  memory: 20334  grad_norm: 11.6462  loss: 0.6577
2023/06/01 05:30:56 - mmengine - INFO - Epoch(train) [17][1500/5758]  lr: 1.0454e-04  eta: 2:30:08  time: 0.3715  data_time: 0.0031  memory: 20334  grad_norm: 129.4207  loss: 0.6680
2023/06/01 05:31:36 - mmengine - INFO - Epoch(train) [17][1600/5758]  lr: 1.0454e-04  eta: 2:29:26  time: 0.3841  data_time: 0.0024  memory: 20334  grad_norm: 32.7622  loss: 0.6641
2023/06/01 05:32:16 - mmengine - INFO - Epoch(train) [17][1700/5758]  lr: 1.0454e-04  eta: 2:28:44  time: 0.3952  data_time: 0.0017  memory: 20334  grad_norm: 4.8323  loss: 0.6561
2023/06/01 05:32:56 - mmengine - INFO - Epoch(train) [17][1800/5758]  lr: 1.0454e-04  eta: 2:28:01  time: 0.4079  data_time: 0.0021  memory: 20334  grad_norm: 3108.9795  loss: 0.6573
2023/06/01 05:33:26 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 05:33:38 - mmengine - INFO - Epoch(train) [17][1900/5758]  lr: 1.0454e-04  eta: 2:27:20  time: 0.4417  data_time: 0.0018  memory: 20334  grad_norm: 7.6875  loss: 0.6517
2023/06/01 05:34:18 - mmengine - INFO - Epoch(train) [17][2000/5758]  lr: 1.0454e-04  eta: 2:26:37  time: 0.4202  data_time: 0.0017  memory: 20334  grad_norm: 86.2196  loss: 0.6473
2023/06/01 05:34:59 - mmengine - INFO - Epoch(train) [17][2100/5758]  lr: 1.0454e-04  eta: 2:25:55  time: 0.4056  data_time: 0.0027  memory: 20334  grad_norm: 60.8420  loss: 0.6557
2023/06/01 05:35:40 - mmengine - INFO - Epoch(train) [17][2200/5758]  lr: 1.0454e-04  eta: 2:25:13  time: 0.3857  data_time: 0.0016  memory: 20334  grad_norm: 19.7107  loss: 0.6611
2023/06/01 05:36:20 - mmengine - INFO - Epoch(train) [17][2300/5758]  lr: 1.0454e-04  eta: 2:24:31  time: 0.4219  data_time: 0.0028  memory: 20334  grad_norm: 8.5472  loss: 0.6529
2023/06/01 05:37:01 - mmengine - INFO - Epoch(train) [17][2400/5758]  lr: 1.0454e-04  eta: 2:23:49  time: 0.4285  data_time: 0.0025  memory: 20334  grad_norm: 21.4679  loss: 0.6664
2023/06/01 05:37:41 - mmengine - INFO - Epoch(train) [17][2500/5758]  lr: 1.0454e-04  eta: 2:23:07  time: 0.4013  data_time: 0.0024  memory: 20334  grad_norm: 29.7841  loss: 0.6476
2023/06/01 05:38:23 - mmengine - INFO - Epoch(train) [17][2600/5758]  lr: 1.0454e-04  eta: 2:22:25  time: 0.4078  data_time: 0.0025  memory: 20334  grad_norm: 49.7052  loss: 0.6484
2023/06/01 05:39:05 - mmengine - INFO - Epoch(train) [17][2700/5758]  lr: 1.0454e-04  eta: 2:21:43  time: 0.3936  data_time: 0.0023  memory: 20334  grad_norm: 8.6435  loss: 0.6472
2023/06/01 05:39:45 - mmengine - INFO - Epoch(train) [17][2800/5758]  lr: 1.0454e-04  eta: 2:21:01  time: 0.4298  data_time: 0.0020  memory: 20334  grad_norm: 51.1484  loss: 0.6423
2023/06/01 05:40:14 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 05:40:25 - mmengine - INFO - Epoch(train) [17][2900/5758]  lr: 1.0454e-04  eta: 2:20:19  time: 0.3986  data_time: 0.0033  memory: 20334  grad_norm: 19.7588  loss: 0.6388
2023/06/01 05:41:05 - mmengine - INFO - Epoch(train) [17][3000/5758]  lr: 1.0454e-04  eta: 2:19:37  time: 0.4048  data_time: 0.0017  memory: 20334  grad_norm: 4.2124  loss: 0.6483
2023/06/01 05:41:45 - mmengine - INFO - Epoch(train) [17][3100/5758]  lr: 1.0454e-04  eta: 2:18:54  time: 0.3725  data_time: 0.0019  memory: 20334  grad_norm: 3.8446  loss: 0.6351
2023/06/01 05:42:26 - mmengine - INFO - Epoch(train) [17][3200/5758]  lr: 1.0454e-04  eta: 2:18:12  time: 0.4294  data_time: 0.0017  memory: 20334  grad_norm: 18.1920  loss: 0.6391
2023/06/01 05:43:06 - mmengine - INFO - Epoch(train) [17][3300/5758]  lr: 1.0454e-04  eta: 2:17:30  time: 0.4056  data_time: 0.0030  memory: 20334  grad_norm: 9.1583  loss: 0.6451
2023/06/01 05:43:47 - mmengine - INFO - Epoch(train) [17][3400/5758]  lr: 1.0454e-04  eta: 2:16:48  time: 0.3879  data_time: 0.0024  memory: 20334  grad_norm: 41.2997  loss: 0.6426
2023/06/01 05:44:27 - mmengine - INFO - Epoch(train) [17][3500/5758]  lr: 1.0454e-04  eta: 2:16:06  time: 0.3872  data_time: 0.0018  memory: 20334  grad_norm: 37.7454  loss: 0.6478
2023/06/01 05:45:07 - mmengine - INFO - Epoch(train) [17][3600/5758]  lr: 1.0454e-04  eta: 2:15:24  time: 0.3866  data_time: 0.0028  memory: 20334  grad_norm: 18.1691  loss: 0.6497
2023/06/01 05:45:47 - mmengine - INFO - Epoch(train) [17][3700/5758]  lr: 1.0454e-04  eta: 2:14:42  time: 0.4066  data_time: 0.0017  memory: 20334  grad_norm: 63.3308  loss: 0.6567
2023/06/01 05:46:27 - mmengine - INFO - Epoch(train) [17][3800/5758]  lr: 1.0454e-04  eta: 2:14:00  time: 0.3708  data_time: 0.0019  memory: 20334  grad_norm: 37.7261  loss: 0.6425
2023/06/01 05:46:57 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 05:47:07 - mmengine - INFO - Epoch(train) [17][3900/5758]  lr: 1.0454e-04  eta: 2:13:17  time: 0.3940  data_time: 0.0021  memory: 20334  grad_norm: 221.2627  loss: 0.6479
2023/06/01 05:47:48 - mmengine - INFO - Epoch(train) [17][4000/5758]  lr: 1.0454e-04  eta: 2:12:35  time: 0.3877  data_time: 0.0024  memory: 20334  grad_norm: 16.5339  loss: 0.6504
2023/06/01 05:48:29 - mmengine - INFO - Epoch(train) [17][4100/5758]  lr: 1.0454e-04  eta: 2:11:53  time: 0.3799  data_time: 0.0022  memory: 20334  grad_norm: 46.5200  loss: 0.6470
2023/06/01 05:49:10 - mmengine - INFO - Epoch(train) [17][4200/5758]  lr: 1.0454e-04  eta: 2:11:11  time: 0.4132  data_time: 0.0019  memory: 20334  grad_norm: 45.2016  loss: 0.6488
2023/06/01 05:49:50 - mmengine - INFO - Epoch(train) [17][4300/5758]  lr: 1.0454e-04  eta: 2:10:29  time: 0.4007  data_time: 0.0017  memory: 20334  grad_norm: 82.1602  loss: 0.6456
2023/06/01 05:50:30 - mmengine - INFO - Epoch(train) [17][4400/5758]  lr: 1.0454e-04  eta: 2:09:47  time: 0.3833  data_time: 0.0016  memory: 20334  grad_norm: 13.5351  loss: 0.6291
2023/06/01 05:51:09 - mmengine - INFO - Epoch(train) [17][4500/5758]  lr: 1.0454e-04  eta: 2:09:05  time: 0.3848  data_time: 0.0022  memory: 20334  grad_norm: 8.1859  loss: 0.6446
2023/06/01 05:51:50 - mmengine - INFO - Epoch(train) [17][4600/5758]  lr: 1.0454e-04  eta: 2:08:23  time: 0.3876  data_time: 0.0016  memory: 20334  grad_norm: 12.5262  loss: 0.6456
2023/06/01 05:52:31 - mmengine - INFO - Epoch(train) [17][4700/5758]  lr: 1.0454e-04  eta: 2:07:41  time: 0.4495  data_time: 0.0024  memory: 20334  grad_norm: 54.9705  loss: 0.6526
2023/06/01 05:53:12 - mmengine - INFO - Epoch(train) [17][4800/5758]  lr: 1.0454e-04  eta: 2:06:59  time: 0.4094  data_time: 0.0026  memory: 20334  grad_norm: 27.8509  loss: 0.6469
2023/06/01 05:53:42 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 05:53:52 - mmengine - INFO - Epoch(train) [17][4900/5758]  lr: 1.0454e-04  eta: 2:06:17  time: 0.4024  data_time: 0.0023  memory: 20334  grad_norm: 15.3742  loss: 0.6598
2023/06/01 05:54:33 - mmengine - INFO - Epoch(train) [17][5000/5758]  lr: 1.0454e-04  eta: 2:05:35  time: 0.4276  data_time: 0.0020  memory: 20334  grad_norm: 23.7485  loss: 0.6477
2023/06/01 05:55:13 - mmengine - INFO - Epoch(train) [17][5100/5758]  lr: 1.0454e-04  eta: 2:04:53  time: 0.3852  data_time: 0.0021  memory: 20334  grad_norm: 8.8197  loss: 0.6474
2023/06/01 05:55:53 - mmengine - INFO - Epoch(train) [17][5200/5758]  lr: 1.0454e-04  eta: 2:04:11  time: 0.4023  data_time: 0.0018  memory: 20334  grad_norm: 37.9932  loss: 0.6425
2023/06/01 05:56:33 - mmengine - INFO - Epoch(train) [17][5300/5758]  lr: 1.0454e-04  eta: 2:03:29  time: 0.4000  data_time: 0.0023  memory: 20334  grad_norm: 34.1676  loss: 0.6537
2023/06/01 05:57:14 - mmengine - INFO - Epoch(train) [17][5400/5758]  lr: 1.0454e-04  eta: 2:02:47  time: 0.4286  data_time: 0.0026  memory: 20334  grad_norm: 48.1832  loss: 0.6532
2023/06/01 05:57:54 - mmengine - INFO - Epoch(train) [17][5500/5758]  lr: 1.0454e-04  eta: 2:02:04  time: 0.3951  data_time: 0.0017  memory: 20334  grad_norm: 16.8611  loss: 0.6360
2023/06/01 05:58:34 - mmengine - INFO - Epoch(train) [17][5600/5758]  lr: 1.0454e-04  eta: 2:01:22  time: 0.4180  data_time: 0.0026  memory: 20334  grad_norm: 43.3313  loss: 0.6361
2023/06/01 05:59:15 - mmengine - INFO - Epoch(train) [17][5700/5758]  lr: 1.0454e-04  eta: 2:00:40  time: 0.4013  data_time: 0.0019  memory: 20334  grad_norm: 32.4200  loss: 0.6425
2023/06/01 05:59:38 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 05:59:38 - mmengine - INFO - Saving checkpoint at 17 epochs
2023/06/01 05:59:55 - mmengine - INFO - Epoch(val) [17][8/8]    accuracy/top1: 88.1131  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [88.11312103271484, 0.0]  single-label/f1-score_classwise: [93.68099212646484, 0.0]  data_time: 0.3581  time: 0.9834
2023/06/01 06:00:41 - mmengine - INFO - Epoch(train) [18][ 100/5758]  lr: 6.3952e-05  eta: 1:59:35  time: 0.4257  data_time: 0.0021  memory: 20334  grad_norm: 63.2827  loss: 0.6445
2023/06/01 06:00:47 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 06:01:22 - mmengine - INFO - Epoch(train) [18][ 200/5758]  lr: 6.3952e-05  eta: 1:58:53  time: 0.3952  data_time: 0.0027  memory: 20334  grad_norm: 71.9475  loss: 0.6376
2023/06/01 06:02:03 - mmengine - INFO - Epoch(train) [18][ 300/5758]  lr: 6.3952e-05  eta: 1:58:11  time: 0.4254  data_time: 0.0019  memory: 20334  grad_norm: 27.9691  loss: 0.6340
2023/06/01 06:02:43 - mmengine - INFO - Epoch(train) [18][ 400/5758]  lr: 6.3952e-05  eta: 1:57:29  time: 0.4136  data_time: 0.0029  memory: 20334  grad_norm: 33.1160  loss: 0.6605
2023/06/01 06:03:22 - mmengine - INFO - Epoch(train) [18][ 500/5758]  lr: 6.3952e-05  eta: 1:56:47  time: 0.3749  data_time: 0.0022  memory: 20334  grad_norm: 21.2225  loss: 0.6445
2023/06/01 06:04:03 - mmengine - INFO - Epoch(train) [18][ 600/5758]  lr: 6.3952e-05  eta: 1:56:05  time: 0.4187  data_time: 0.0020  memory: 20334  grad_norm: 75.3499  loss: 0.6529
2023/06/01 06:04:43 - mmengine - INFO - Epoch(train) [18][ 700/5758]  lr: 6.3952e-05  eta: 1:55:23  time: 0.4211  data_time: 0.0018  memory: 20334  grad_norm: 131.6462  loss: 0.6250
2023/06/01 06:05:23 - mmengine - INFO - Epoch(train) [18][ 800/5758]  lr: 6.3952e-05  eta: 1:54:41  time: 0.3788  data_time: 0.0018  memory: 20334  grad_norm: 71.8548  loss: 0.6417
2023/06/01 06:06:04 - mmengine - INFO - Epoch(train) [18][ 900/5758]  lr: 6.3952e-05  eta: 1:53:59  time: 0.3884  data_time: 0.0018  memory: 20334  grad_norm: 14.1204  loss: 0.6533
2023/06/01 06:06:44 - mmengine - INFO - Epoch(train) [18][1000/5758]  lr: 6.3952e-05  eta: 1:53:17  time: 0.4122  data_time: 0.0025  memory: 20334  grad_norm: 82.0788  loss: 0.6442
2023/06/01 06:07:24 - mmengine - INFO - Epoch(train) [18][1100/5758]  lr: 6.3952e-05  eta: 1:52:35  time: 0.4319  data_time: 0.0026  memory: 20334  grad_norm: 17.0746  loss: 0.6485
2023/06/01 06:07:30 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 06:08:05 - mmengine - INFO - Epoch(train) [18][1200/5758]  lr: 6.3952e-05  eta: 1:51:53  time: 0.3998  data_time: 0.0019  memory: 20334  grad_norm: 12.5568  loss: 0.6545
2023/06/01 06:08:45 - mmengine - INFO - Epoch(train) [18][1300/5758]  lr: 6.3952e-05  eta: 1:51:11  time: 0.3827  data_time: 0.0024  memory: 20334  grad_norm: 43.7911  loss: 0.6472
2023/06/01 06:09:26 - mmengine - INFO - Epoch(train) [18][1400/5758]  lr: 6.3952e-05  eta: 1:50:29  time: 0.3796  data_time: 0.0030  memory: 20334  grad_norm: 19.4557  loss: 0.6481
2023/06/01 06:10:07 - mmengine - INFO - Epoch(train) [18][1500/5758]  lr: 6.3952e-05  eta: 1:49:47  time: 0.4128  data_time: 0.0015  memory: 20334  grad_norm: 19.8311  loss: 0.6462
2023/06/01 06:10:49 - mmengine - INFO - Epoch(train) [18][1600/5758]  lr: 6.3952e-05  eta: 1:49:05  time: 0.4163  data_time: 0.0018  memory: 20334  grad_norm: 30.9645  loss: 0.6447
2023/06/01 06:11:30 - mmengine - INFO - Epoch(train) [18][1700/5758]  lr: 6.3952e-05  eta: 1:48:23  time: 0.4512  data_time: 0.0023  memory: 20334  grad_norm: 20.1364  loss: 0.6417
2023/06/01 06:12:10 - mmengine - INFO - Epoch(train) [18][1800/5758]  lr: 6.3952e-05  eta: 1:47:41  time: 0.3844  data_time: 0.0025  memory: 20334  grad_norm: 32.4031  loss: 0.6447
2023/06/01 06:12:50 - mmengine - INFO - Epoch(train) [18][1900/5758]  lr: 6.3952e-05  eta: 1:46:59  time: 0.3970  data_time: 0.0023  memory: 20334  grad_norm: 14.2658  loss: 0.6529
2023/06/01 06:13:31 - mmengine - INFO - Epoch(train) [18][2000/5758]  lr: 6.3952e-05  eta: 1:46:17  time: 0.3929  data_time: 0.0021  memory: 20334  grad_norm: 20.6992  loss: 0.6427
2023/06/01 06:14:12 - mmengine - INFO - Epoch(train) [18][2100/5758]  lr: 6.3952e-05  eta: 1:45:36  time: 0.4156  data_time: 0.0026  memory: 20334  grad_norm: 70.5440  loss: 0.6593
2023/06/01 06:14:18 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 06:14:52 - mmengine - INFO - Epoch(train) [18][2200/5758]  lr: 6.3952e-05  eta: 1:44:54  time: 0.3908  data_time: 0.0024  memory: 20334  grad_norm: 21.3339  loss: 0.6493
2023/06/01 06:15:33 - mmengine - INFO - Epoch(train) [18][2300/5758]  lr: 6.3952e-05  eta: 1:44:12  time: 0.3997  data_time: 0.0027  memory: 20334  grad_norm: 27.2712  loss: 0.6560
2023/06/01 06:16:13 - mmengine - INFO - Epoch(train) [18][2400/5758]  lr: 6.3952e-05  eta: 1:43:30  time: 0.3754  data_time: 0.0022  memory: 20334  grad_norm: 25.0591  loss: 0.6598
2023/06/01 06:16:54 - mmengine - INFO - Epoch(train) [18][2500/5758]  lr: 6.3952e-05  eta: 1:42:48  time: 0.3689  data_time: 0.0018  memory: 20334  grad_norm: 26.6567  loss: 0.6417
2023/06/01 06:17:35 - mmengine - INFO - Epoch(train) [18][2600/5758]  lr: 6.3952e-05  eta: 1:42:06  time: 0.3946  data_time: 0.0029  memory: 20334  grad_norm: 44.6973  loss: 0.6490
2023/06/01 06:18:15 - mmengine - INFO - Epoch(train) [18][2700/5758]  lr: 6.3952e-05  eta: 1:41:24  time: 0.3943  data_time: 0.0018  memory: 20334  grad_norm: 225.2489  loss: 0.6620
2023/06/01 06:18:56 - mmengine - INFO - Epoch(train) [18][2800/5758]  lr: 6.3952e-05  eta: 1:40:42  time: 0.3994  data_time: 0.0018  memory: 20334  grad_norm: 83.4851  loss: 0.6607
2023/06/01 06:19:37 - mmengine - INFO - Epoch(train) [18][2900/5758]  lr: 6.3952e-05  eta: 1:40:00  time: 0.3779  data_time: 0.0022  memory: 20334  grad_norm: 44.0406  loss: 0.6465
2023/06/01 06:20:18 - mmengine - INFO - Epoch(train) [18][3000/5758]  lr: 6.3952e-05  eta: 1:39:18  time: 0.4228  data_time: 0.0032  memory: 20334  grad_norm: 24.5456  loss: 0.6401
2023/06/01 06:20:59 - mmengine - INFO - Epoch(train) [18][3100/5758]  lr: 6.3952e-05  eta: 1:38:37  time: 0.3933  data_time: 0.0018  memory: 20334  grad_norm: 128.9199  loss: 0.6634
2023/06/01 06:21:05 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 06:21:41 - mmengine - INFO - Epoch(train) [18][3200/5758]  lr: 6.3952e-05  eta: 1:37:55  time: 0.4023  data_time: 0.0022  memory: 20334  grad_norm: 152.1927  loss: 0.6575
2023/06/01 06:22:23 - mmengine - INFO - Epoch(train) [18][3300/5758]  lr: 6.3952e-05  eta: 1:37:13  time: 0.3871  data_time: 0.0017  memory: 20334  grad_norm: 34.5417  loss: 0.6583
2023/06/01 06:23:04 - mmengine - INFO - Epoch(train) [18][3400/5758]  lr: 6.3952e-05  eta: 1:36:31  time: 0.3989  data_time: 0.0022  memory: 20334  grad_norm: 10.8451  loss: 0.6594
2023/06/01 06:23:45 - mmengine - INFO - Epoch(train) [18][3500/5758]  lr: 6.3952e-05  eta: 1:35:49  time: 0.4142  data_time: 0.0020  memory: 20334  grad_norm: 136.5663  loss: 0.6515
2023/06/01 06:24:25 - mmengine - INFO - Epoch(train) [18][3600/5758]  lr: 6.3952e-05  eta: 1:35:08  time: 0.3946  data_time: 0.0016  memory: 20334  grad_norm: 26.9938  loss: 0.6600
2023/06/01 06:25:06 - mmengine - INFO - Epoch(train) [18][3700/5758]  lr: 6.3952e-05  eta: 1:34:26  time: 0.3926  data_time: 0.0021  memory: 20334  grad_norm: 32.2152  loss: 0.6732
2023/06/01 06:25:47 - mmengine - INFO - Epoch(train) [18][3800/5758]  lr: 6.3952e-05  eta: 1:33:44  time: 0.4148  data_time: 0.0030  memory: 20334  grad_norm: 63.9369  loss: 0.6618
2023/06/01 06:26:28 - mmengine - INFO - Epoch(train) [18][3900/5758]  lr: 6.3952e-05  eta: 1:33:02  time: 0.4113  data_time: 0.0028  memory: 20334  grad_norm: 25.9544  loss: 0.6741
2023/06/01 06:27:08 - mmengine - INFO - Epoch(train) [18][4000/5758]  lr: 6.3952e-05  eta: 1:32:20  time: 0.4400  data_time: 0.0018  memory: 20334  grad_norm: 71.6971  loss: 0.6596
2023/06/01 06:27:49 - mmengine - INFO - Epoch(train) [18][4100/5758]  lr: 6.3952e-05  eta: 1:31:38  time: 0.3902  data_time: 0.0021  memory: 20334  grad_norm: 46.5575  loss: 0.6668
2023/06/01 06:27:55 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 06:28:31 - mmengine - INFO - Epoch(train) [18][4200/5758]  lr: 6.3952e-05  eta: 1:30:56  time: 0.4282  data_time: 0.0022  memory: 20334  grad_norm: 102.1191  loss: 0.6648
2023/06/01 06:29:12 - mmengine - INFO - Epoch(train) [18][4300/5758]  lr: 6.3952e-05  eta: 1:30:15  time: 0.3854  data_time: 0.0017  memory: 20334  grad_norm: 91.0550  loss: 0.6600
2023/06/01 06:29:53 - mmengine - INFO - Epoch(train) [18][4400/5758]  lr: 6.3952e-05  eta: 1:29:33  time: 0.3878  data_time: 0.0019  memory: 20334  grad_norm: 40.9992  loss: 0.6546
2023/06/01 06:30:34 - mmengine - INFO - Epoch(train) [18][4500/5758]  lr: 6.3952e-05  eta: 1:28:51  time: 0.4111  data_time: 0.0018  memory: 20334  grad_norm: 58.0564  loss: 0.6499
2023/06/01 06:31:15 - mmengine - INFO - Epoch(train) [18][4600/5758]  lr: 6.3952e-05  eta: 1:28:09  time: 0.4121  data_time: 0.0021  memory: 20334  grad_norm: 38.4158  loss: 0.6537
2023/06/01 06:31:56 - mmengine - INFO - Epoch(train) [18][4700/5758]  lr: 6.3952e-05  eta: 1:27:27  time: 0.4433  data_time: 0.0022  memory: 20334  grad_norm: 47.8747  loss: 0.6522
2023/06/01 06:32:37 - mmengine - INFO - Epoch(train) [18][4800/5758]  lr: 6.3952e-05  eta: 1:26:45  time: 0.4109  data_time: 0.0029  memory: 20334  grad_norm: 42.6568  loss: 0.6453
2023/06/01 06:33:18 - mmengine - INFO - Epoch(train) [18][4900/5758]  lr: 6.3952e-05  eta: 1:26:04  time: 0.4342  data_time: 0.0020  memory: 20334  grad_norm: 59.3426  loss: 0.6423
2023/06/01 06:33:58 - mmengine - INFO - Epoch(train) [18][5000/5758]  lr: 6.3952e-05  eta: 1:25:22  time: 0.3906  data_time: 0.0017  memory: 20334  grad_norm: 15.6146  loss: 0.6566
2023/06/01 06:34:37 - mmengine - INFO - Epoch(train) [18][5100/5758]  lr: 6.3952e-05  eta: 1:24:40  time: 0.3962  data_time: 0.0027  memory: 20334  grad_norm: 21.6596  loss: 0.6484
2023/06/01 06:34:44 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 06:35:18 - mmengine - INFO - Epoch(train) [18][5200/5758]  lr: 6.3952e-05  eta: 1:23:58  time: 0.3806  data_time: 0.0023  memory: 20334  grad_norm: 296.3698  loss: 0.6488
2023/06/01 06:35:59 - mmengine - INFO - Epoch(train) [18][5300/5758]  lr: 6.3952e-05  eta: 1:23:16  time: 0.4201  data_time: 0.0024  memory: 20334  grad_norm: 171.4270  loss: 0.6475
2023/06/01 06:36:42 - mmengine - INFO - Epoch(train) [18][5400/5758]  lr: 6.3952e-05  eta: 1:22:34  time: 0.4169  data_time: 0.0020  memory: 20334  grad_norm: 15.7709  loss: 0.6602
2023/06/01 06:37:22 - mmengine - INFO - Epoch(train) [18][5500/5758]  lr: 6.3952e-05  eta: 1:21:52  time: 0.4175  data_time: 0.0030  memory: 20334  grad_norm: 15.7511  loss: 0.6604
2023/06/01 06:38:03 - mmengine - INFO - Epoch(train) [18][5600/5758]  lr: 6.3952e-05  eta: 1:21:11  time: 0.4094  data_time: 0.0024  memory: 20334  grad_norm: 16.8459  loss: 0.6537
2023/06/01 06:38:44 - mmengine - INFO - Epoch(train) [18][5700/5758]  lr: 6.3952e-05  eta: 1:20:29  time: 0.4027  data_time: 0.0019  memory: 20334  grad_norm: 27.6579  loss: 0.6491
2023/06/01 06:39:07 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 06:39:07 - mmengine - INFO - Saving checkpoint at 18 epochs
2023/06/01 06:39:24 - mmengine - INFO - Epoch(val) [18][8/8]    accuracy/top1: 86.9895  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [86.98945617675781, 0.0]  single-label/f1-score_classwise: [93.0420913696289, 0.0]  data_time: 0.3680  time: 0.9957
2023/06/01 06:40:09 - mmengine - INFO - Epoch(train) [19][ 100/5758]  lr: 3.4227e-05  eta: 1:19:23  time: 0.4029  data_time: 0.0020  memory: 20334  grad_norm: 66.6539  loss: 0.6515
2023/06/01 06:40:49 - mmengine - INFO - Epoch(train) [19][ 200/5758]  lr: 3.4227e-05  eta: 1:18:41  time: 0.4083  data_time: 0.0019  memory: 20334  grad_norm: 34.7455  loss: 0.6644
2023/06/01 06:41:31 - mmengine - INFO - Epoch(train) [19][ 300/5758]  lr: 3.4227e-05  eta: 1:18:00  time: 0.3937  data_time: 0.0020  memory: 20334  grad_norm: 213.3387  loss: 0.6749
2023/06/01 06:41:54 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 06:42:12 - mmengine - INFO - Epoch(train) [19][ 400/5758]  lr: 3.4227e-05  eta: 1:17:18  time: 0.4123  data_time: 0.0018  memory: 20334  grad_norm: 74.3776  loss: 0.6606
2023/06/01 06:42:53 - mmengine - INFO - Epoch(train) [19][ 500/5758]  lr: 3.4227e-05  eta: 1:16:36  time: 0.4405  data_time: 0.0019  memory: 20334  grad_norm: 89.7455  loss: 0.6422
2023/06/01 06:43:34 - mmengine - INFO - Epoch(train) [19][ 600/5758]  lr: 3.4227e-05  eta: 1:15:54  time: 0.4268  data_time: 0.0017  memory: 20334  grad_norm: 40.8558  loss: 0.6535
2023/06/01 06:44:14 - mmengine - INFO - Epoch(train) [19][ 700/5758]  lr: 3.4227e-05  eta: 1:15:12  time: 0.3833  data_time: 0.0018  memory: 20334  grad_norm: 15.9707  loss: 0.6586
2023/06/01 06:44:54 - mmengine - INFO - Epoch(train) [19][ 800/5758]  lr: 3.4227e-05  eta: 1:14:30  time: 0.4180  data_time: 0.0018  memory: 20334  grad_norm: 18.2879  loss: 0.6529
2023/06/01 06:45:35 - mmengine - INFO - Epoch(train) [19][ 900/5758]  lr: 3.4227e-05  eta: 1:13:49  time: 0.3997  data_time: 0.0018  memory: 20334  grad_norm: 14.3042  loss: 0.6278
2023/06/01 06:46:16 - mmengine - INFO - Epoch(train) [19][1000/5758]  lr: 3.4227e-05  eta: 1:13:07  time: 0.3913  data_time: 0.0018  memory: 20334  grad_norm: 9.4428  loss: 0.6414
2023/06/01 06:46:58 - mmengine - INFO - Epoch(train) [19][1100/5758]  lr: 3.4227e-05  eta: 1:12:25  time: 0.3959  data_time: 0.0019  memory: 20334  grad_norm: 40.7144  loss: 0.6593
2023/06/01 06:47:38 - mmengine - INFO - Epoch(train) [19][1200/5758]  lr: 3.4227e-05  eta: 1:11:43  time: 0.3841  data_time: 0.0023  memory: 20334  grad_norm: 13.6276  loss: 0.6553
2023/06/01 06:48:19 - mmengine - INFO - Epoch(train) [19][1300/5758]  lr: 3.4227e-05  eta: 1:11:01  time: 0.4204  data_time: 0.0023  memory: 20334  grad_norm: 14.2798  loss: 0.6425
2023/06/01 06:48:43 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 06:49:01 - mmengine - INFO - Epoch(train) [19][1400/5758]  lr: 3.4227e-05  eta: 1:10:20  time: 0.4097  data_time: 0.0019  memory: 20334  grad_norm: 61.6538  loss: 0.6414
2023/06/01 06:49:43 - mmengine - INFO - Epoch(train) [19][1500/5758]  lr: 3.4227e-05  eta: 1:09:38  time: 0.3920  data_time: 0.0017  memory: 20334  grad_norm: 19.8346  loss: 0.6525
2023/06/01 06:50:24 - mmengine - INFO - Epoch(train) [19][1600/5758]  lr: 3.4227e-05  eta: 1:08:56  time: 0.3671  data_time: 0.0021  memory: 20334  grad_norm: 21.9790  loss: 0.6535
2023/06/01 06:51:05 - mmengine - INFO - Epoch(train) [19][1700/5758]  lr: 3.4227e-05  eta: 1:08:14  time: 0.4107  data_time: 0.0027  memory: 20334  grad_norm: 9.2302  loss: 0.6392
2023/06/01 06:51:47 - mmengine - INFO - Epoch(train) [19][1800/5758]  lr: 3.4227e-05  eta: 1:07:33  time: 0.4140  data_time: 0.0027  memory: 20334  grad_norm: 25.4786  loss: 0.6501
2023/06/01 06:52:29 - mmengine - INFO - Epoch(train) [19][1900/5758]  lr: 3.4227e-05  eta: 1:06:51  time: 0.4219  data_time: 0.0018  memory: 20334  grad_norm: 31.8239  loss: 0.6490
2023/06/01 06:53:10 - mmengine - INFO - Epoch(train) [19][2000/5758]  lr: 3.4227e-05  eta: 1:06:09  time: 0.3887  data_time: 0.0018  memory: 20334  grad_norm: 42.6173  loss: 0.6465
2023/06/01 06:53:51 - mmengine - INFO - Epoch(train) [19][2100/5758]  lr: 3.4227e-05  eta: 1:05:27  time: 0.4121  data_time: 0.0020  memory: 20334  grad_norm: 23.1560  loss: 0.6574
2023/06/01 06:54:31 - mmengine - INFO - Epoch(train) [19][2200/5758]  lr: 3.4227e-05  eta: 1:04:46  time: 0.4002  data_time: 0.0023  memory: 20334  grad_norm: 22.8971  loss: 0.6566
2023/06/01 06:55:12 - mmengine - INFO - Epoch(train) [19][2300/5758]  lr: 3.4227e-05  eta: 1:04:04  time: 0.3821  data_time: 0.0017  memory: 20334  grad_norm: 30.9176  loss: 0.6556
2023/06/01 06:55:34 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 06:55:52 - mmengine - INFO - Epoch(train) [19][2400/5758]  lr: 3.4227e-05  eta: 1:03:22  time: 0.3650  data_time: 0.0019  memory: 20334  grad_norm: 41.5924  loss: 0.6587
2023/06/01 06:56:33 - mmengine - INFO - Epoch(train) [19][2500/5758]  lr: 3.4227e-05  eta: 1:02:40  time: 0.4692  data_time: 0.0019  memory: 20334  grad_norm: 33.9543  loss: 0.6456
2023/06/01 06:57:12 - mmengine - INFO - Epoch(train) [19][2600/5758]  lr: 3.4227e-05  eta: 1:01:58  time: 0.3927  data_time: 0.0018  memory: 20334  grad_norm: 40.9530  loss: 0.6533
2023/06/01 06:57:54 - mmengine - INFO - Epoch(train) [19][2700/5758]  lr: 3.4227e-05  eta: 1:01:17  time: 0.4043  data_time: 0.0018  memory: 20334  grad_norm: 14.2156  loss: 0.6593
2023/06/01 06:58:34 - mmengine - INFO - Epoch(train) [19][2800/5758]  lr: 3.4227e-05  eta: 1:00:35  time: 0.4019  data_time: 0.0020  memory: 20334  grad_norm: 143.3173  loss: 0.6481
2023/06/01 06:59:16 - mmengine - INFO - Epoch(train) [19][2900/5758]  lr: 3.4227e-05  eta: 0:59:53  time: 0.4302  data_time: 0.0020  memory: 20334  grad_norm: 40.7847  loss: 0.6658
2023/06/01 06:59:57 - mmengine - INFO - Epoch(train) [19][3000/5758]  lr: 3.4227e-05  eta: 0:59:11  time: 0.4114  data_time: 0.0019  memory: 20334  grad_norm: 19.6428  loss: 0.6577
2023/06/01 07:00:37 - mmengine - INFO - Epoch(train) [19][3100/5758]  lr: 3.4227e-05  eta: 0:58:29  time: 0.4089  data_time: 0.0015  memory: 20334  grad_norm: 75.8778  loss: 0.6442
2023/06/01 07:01:18 - mmengine - INFO - Epoch(train) [19][3200/5758]  lr: 3.4227e-05  eta: 0:57:48  time: 0.4103  data_time: 0.0015  memory: 20334  grad_norm: 12.3155  loss: 0.6522
2023/06/01 07:01:58 - mmengine - INFO - Epoch(train) [19][3300/5758]  lr: 3.4227e-05  eta: 0:57:06  time: 0.4080  data_time: 0.0017  memory: 20334  grad_norm: 14.5197  loss: 0.6573
2023/06/01 07:02:21 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 07:02:38 - mmengine - INFO - Epoch(train) [19][3400/5758]  lr: 3.4227e-05  eta: 0:56:24  time: 0.3837  data_time: 0.0022  memory: 20334  grad_norm: 49.5584  loss: 0.6521
2023/06/01 07:03:19 - mmengine - INFO - Epoch(train) [19][3500/5758]  lr: 3.4227e-05  eta: 0:55:42  time: 0.3867  data_time: 0.0017  memory: 20334  grad_norm: 34.7282  loss: 0.6657
2023/06/01 07:04:00 - mmengine - INFO - Epoch(train) [19][3600/5758]  lr: 3.4227e-05  eta: 0:55:00  time: 0.3831  data_time: 0.0016  memory: 20334  grad_norm: 17.0216  loss: 0.6684
2023/06/01 07:04:41 - mmengine - INFO - Epoch(train) [19][3700/5758]  lr: 3.4227e-05  eta: 0:54:19  time: 0.3945  data_time: 0.0017  memory: 20334  grad_norm: 30.0264  loss: 0.6665
2023/06/01 07:05:22 - mmengine - INFO - Epoch(train) [19][3800/5758]  lr: 3.4227e-05  eta: 0:53:37  time: 0.3869  data_time: 0.0016  memory: 20334  grad_norm: 19.3257  loss: 0.6644
2023/06/01 07:06:03 - mmengine - INFO - Epoch(train) [19][3900/5758]  lr: 3.4227e-05  eta: 0:52:55  time: 0.4218  data_time: 0.0016  memory: 20334  grad_norm: 36.1686  loss: 0.6727
2023/06/01 07:06:43 - mmengine - INFO - Epoch(train) [19][4000/5758]  lr: 3.4227e-05  eta: 0:52:13  time: 0.4407  data_time: 0.0017  memory: 20334  grad_norm: 12.6983  loss: 0.6665
2023/06/01 07:07:23 - mmengine - INFO - Epoch(train) [19][4100/5758]  lr: 3.4227e-05  eta: 0:51:32  time: 0.4180  data_time: 0.0016  memory: 20334  grad_norm: 18.5814  loss: 0.6727
2023/06/01 07:08:05 - mmengine - INFO - Epoch(train) [19][4200/5758]  lr: 3.4227e-05  eta: 0:50:50  time: 0.4268  data_time: 0.0016  memory: 20334  grad_norm: 40.6030  loss: 0.6681
2023/06/01 07:08:44 - mmengine - INFO - Epoch(train) [19][4300/5758]  lr: 3.4227e-05  eta: 0:50:08  time: 0.4036  data_time: 0.0016  memory: 20334  grad_norm: 85.0285  loss: 0.6686
2023/06/01 07:09:08 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 07:09:26 - mmengine - INFO - Epoch(train) [19][4400/5758]  lr: 3.4227e-05  eta: 0:49:26  time: 0.4441  data_time: 0.0016  memory: 20334  grad_norm: 62.7240  loss: 0.6558
2023/06/01 07:10:07 - mmengine - INFO - Epoch(train) [19][4500/5758]  lr: 3.4227e-05  eta: 0:48:45  time: 0.4474  data_time: 0.0018  memory: 20334  grad_norm: 99.8403  loss: 0.6742
2023/06/01 07:10:47 - mmengine - INFO - Epoch(train) [19][4600/5758]  lr: 3.4227e-05  eta: 0:48:03  time: 0.4304  data_time: 0.0016  memory: 20334  grad_norm: 57.7155  loss: 0.6722
2023/06/01 07:11:28 - mmengine - INFO - Epoch(train) [19][4700/5758]  lr: 3.4227e-05  eta: 0:47:21  time: 0.4323  data_time: 0.0017  memory: 20334  grad_norm: 80.3763  loss: 0.6625
2023/06/01 07:12:08 - mmengine - INFO - Epoch(train) [19][4800/5758]  lr: 3.4227e-05  eta: 0:46:39  time: 0.3881  data_time: 0.0020  memory: 20334  grad_norm: 62.8092  loss: 0.6744
2023/06/01 07:12:50 - mmengine - INFO - Epoch(train) [19][4900/5758]  lr: 3.4227e-05  eta: 0:45:58  time: 0.3931  data_time: 0.0020  memory: 20334  grad_norm: 31.4577  loss: 0.6706
2023/06/01 07:13:31 - mmengine - INFO - Epoch(train) [19][5000/5758]  lr: 3.4227e-05  eta: 0:45:16  time: 0.4111  data_time: 0.0020  memory: 20334  grad_norm: 56.2091  loss: 0.6599
2023/06/01 07:14:12 - mmengine - INFO - Epoch(train) [19][5100/5758]  lr: 3.4227e-05  eta: 0:44:34  time: 0.4001  data_time: 0.0014  memory: 20334  grad_norm: 206.1507  loss: 0.6731
2023/06/01 07:14:53 - mmengine - INFO - Epoch(train) [19][5200/5758]  lr: 3.4227e-05  eta: 0:43:52  time: 0.3867  data_time: 0.0036  memory: 20334  grad_norm: 44.8787  loss: 0.6735
2023/06/01 07:15:33 - mmengine - INFO - Epoch(train) [19][5300/5758]  lr: 3.4227e-05  eta: 0:43:11  time: 0.4130  data_time: 0.0024  memory: 20334  grad_norm: 79.7090  loss: 0.6719
2023/06/01 07:15:57 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 07:16:15 - mmengine - INFO - Epoch(train) [19][5400/5758]  lr: 3.4227e-05  eta: 0:42:29  time: 0.4001  data_time: 0.0019  memory: 20334  grad_norm: 58.0159  loss: 0.6580
2023/06/01 07:16:55 - mmengine - INFO - Epoch(train) [19][5500/5758]  lr: 3.4227e-05  eta: 0:41:47  time: 0.3854  data_time: 0.0022  memory: 20334  grad_norm: 86.6426  loss: 0.6738
2023/06/01 07:17:36 - mmengine - INFO - Epoch(train) [19][5600/5758]  lr: 3.4227e-05  eta: 0:41:05  time: 0.3885  data_time: 0.0017  memory: 20334  grad_norm: 38.4154  loss: 0.6705
2023/06/01 07:18:17 - mmengine - INFO - Epoch(train) [19][5700/5758]  lr: 3.4227e-05  eta: 0:40:24  time: 0.3995  data_time: 0.0018  memory: 20334  grad_norm: 203.1712  loss: 0.6734
2023/06/01 07:18:40 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 07:18:40 - mmengine - INFO - Saving checkpoint at 19 epochs
2023/06/01 07:18:56 - mmengine - INFO - Epoch(val) [19][8/8]    accuracy/top1: 85.8342  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [85.834228515625, 0.0]  single-label/f1-score_classwise: [92.377197265625, 0.0]  data_time: 0.3582  time: 0.9835
2023/06/01 07:19:41 - mmengine - INFO - Epoch(train) [20][ 100/5758]  lr: 1.6094e-05  eta: 0:39:18  time: 0.3837  data_time: 0.0018  memory: 20334  grad_norm: 37.8316  loss: 0.6657
2023/06/01 07:20:23 - mmengine - INFO - Epoch(train) [20][ 200/5758]  lr: 1.6094e-05  eta: 0:38:36  time: 0.3624  data_time: 0.0023  memory: 20334  grad_norm: 131.8389  loss: 0.6685
2023/06/01 07:21:04 - mmengine - INFO - Epoch(train) [20][ 300/5758]  lr: 1.6094e-05  eta: 0:37:55  time: 0.4057  data_time: 0.0032  memory: 20334  grad_norm: 73.4937  loss: 0.6725
2023/06/01 07:21:44 - mmengine - INFO - Epoch(train) [20][ 400/5758]  lr: 1.6094e-05  eta: 0:37:13  time: 0.3943  data_time: 0.0029  memory: 20334  grad_norm: 48.6913  loss: 0.6698
2023/06/01 07:22:26 - mmengine - INFO - Epoch(train) [20][ 500/5758]  lr: 1.6094e-05  eta: 0:36:31  time: 0.3894  data_time: 0.0024  memory: 20334  grad_norm: 47.0496  loss: 0.6681
2023/06/01 07:23:05 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 07:23:06 - mmengine - INFO - Epoch(train) [20][ 600/5758]  lr: 1.6094e-05  eta: 0:35:49  time: 0.3859  data_time: 0.0024  memory: 20334  grad_norm: 1127.3899  loss: 0.6733
2023/06/01 07:23:47 - mmengine - INFO - Epoch(train) [20][ 700/5758]  lr: 1.6094e-05  eta: 0:35:08  time: 0.4176  data_time: 0.0024  memory: 20334  grad_norm: 82.1451  loss: 0.6699
2023/06/01 07:24:29 - mmengine - INFO - Epoch(train) [20][ 800/5758]  lr: 1.6094e-05  eta: 0:34:26  time: 0.4404  data_time: 0.0017  memory: 20334  grad_norm: 11.3550  loss: 0.6541
2023/06/01 07:25:09 - mmengine - INFO - Epoch(train) [20][ 900/5758]  lr: 1.6094e-05  eta: 0:33:44  time: 0.3815  data_time: 0.0021  memory: 20334  grad_norm: 44.5827  loss: 0.6699
2023/06/01 07:25:49 - mmengine - INFO - Epoch(train) [20][1000/5758]  lr: 1.6094e-05  eta: 0:33:03  time: 0.3839  data_time: 0.0020  memory: 20334  grad_norm: 181.1916  loss: 0.6704
2023/06/01 07:26:31 - mmengine - INFO - Epoch(train) [20][1100/5758]  lr: 1.6094e-05  eta: 0:32:21  time: 0.4080  data_time: 0.0026  memory: 20334  grad_norm: 29.3792  loss: 0.6695
2023/06/01 07:27:11 - mmengine - INFO - Epoch(train) [20][1200/5758]  lr: 1.6094e-05  eta: 0:31:39  time: 0.3870  data_time: 0.0019  memory: 20334  grad_norm: 112.6470  loss: 0.6738
2023/06/01 07:27:51 - mmengine - INFO - Epoch(train) [20][1300/5758]  lr: 1.6094e-05  eta: 0:30:57  time: 0.3776  data_time: 0.0025  memory: 20334  grad_norm: 97.6848  loss: 0.6756
2023/06/01 07:28:31 - mmengine - INFO - Epoch(train) [20][1400/5758]  lr: 1.6094e-05  eta: 0:30:16  time: 0.3918  data_time: 0.0017  memory: 20334  grad_norm: 71.3823  loss: 0.6758
2023/06/01 07:29:13 - mmengine - INFO - Epoch(train) [20][1500/5758]  lr: 1.6094e-05  eta: 0:29:34  time: 0.4175  data_time: 0.0031  memory: 20334  grad_norm: 12.3704  loss: 0.6698
2023/06/01 07:29:53 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 07:29:54 - mmengine - INFO - Epoch(train) [20][1600/5758]  lr: 1.6094e-05  eta: 0:28:52  time: 0.4094  data_time: 0.0020  memory: 20334  grad_norm: 217.7896  loss: 0.6712
2023/06/01 07:30:35 - mmengine - INFO - Epoch(train) [20][1700/5758]  lr: 1.6094e-05  eta: 0:28:11  time: 0.4169  data_time: 0.0020  memory: 20334  grad_norm: 75.2327  loss: 0.6697
2023/06/01 07:31:16 - mmengine - INFO - Epoch(train) [20][1800/5758]  lr: 1.6094e-05  eta: 0:27:29  time: 0.3831  data_time: 0.0026  memory: 20334  grad_norm: 49.7328  loss: 0.6667
2023/06/01 07:31:56 - mmengine - INFO - Epoch(train) [20][1900/5758]  lr: 1.6094e-05  eta: 0:26:47  time: 0.3818  data_time: 0.0018  memory: 20334  grad_norm: 33.9477  loss: 0.6709
2023/06/01 07:32:37 - mmengine - INFO - Epoch(train) [20][2000/5758]  lr: 1.6094e-05  eta: 0:26:05  time: 0.3901  data_time: 0.0018  memory: 20334  grad_norm: 148.2320  loss: 0.6696
2023/06/01 07:33:17 - mmengine - INFO - Epoch(train) [20][2100/5758]  lr: 1.6094e-05  eta: 0:25:24  time: 0.3885  data_time: 0.0021  memory: 20334  grad_norm: 24.1435  loss: 0.6764
2023/06/01 07:33:57 - mmengine - INFO - Epoch(train) [20][2200/5758]  lr: 1.6094e-05  eta: 0:24:42  time: 0.3702  data_time: 0.0018  memory: 20334  grad_norm: 29.8747  loss: 0.6745
2023/06/01 07:34:39 - mmengine - INFO - Epoch(train) [20][2300/5758]  lr: 1.6094e-05  eta: 0:24:00  time: 0.4365  data_time: 0.0020  memory: 20334  grad_norm: 91.6214  loss: 0.6710
2023/06/01 07:35:20 - mmengine - INFO - Epoch(train) [20][2400/5758]  lr: 1.6094e-05  eta: 0:23:19  time: 0.4153  data_time: 0.0017  memory: 20334  grad_norm: 86.2144  loss: 0.6671
2023/06/01 07:36:01 - mmengine - INFO - Epoch(train) [20][2500/5758]  lr: 1.6094e-05  eta: 0:22:37  time: 0.4084  data_time: 0.0026  memory: 20334  grad_norm: 304.8029  loss: 0.6622
2023/06/01 07:36:41 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 07:36:41 - mmengine - INFO - Epoch(train) [20][2600/5758]  lr: 1.6094e-05  eta: 0:21:55  time: 0.4042  data_time: 0.0022  memory: 20334  grad_norm: 97.6951  loss: 0.6740
2023/06/01 07:37:22 - mmengine - INFO - Epoch(train) [20][2700/5758]  lr: 1.6094e-05  eta: 0:21:14  time: 0.3819  data_time: 0.0019  memory: 20334  grad_norm: 93.4661  loss: 0.6579
2023/06/01 07:38:02 - mmengine - INFO - Epoch(train) [20][2800/5758]  lr: 1.6094e-05  eta: 0:20:32  time: 0.3765  data_time: 0.0019  memory: 20334  grad_norm: 38.1375  loss: 0.6646
2023/06/01 07:38:45 - mmengine - INFO - Epoch(train) [20][2900/5758]  lr: 1.6094e-05  eta: 0:19:50  time: 0.4129  data_time: 0.0021  memory: 20334  grad_norm: 38.6878  loss: 0.6653
2023/06/01 07:39:25 - mmengine - INFO - Epoch(train) [20][3000/5758]  lr: 1.6094e-05  eta: 0:19:09  time: 0.3859  data_time: 0.0021  memory: 20334  grad_norm: 22.5367  loss: 0.6759
2023/06/01 07:40:06 - mmengine - INFO - Epoch(train) [20][3100/5758]  lr: 1.6094e-05  eta: 0:18:27  time: 0.4300  data_time: 0.0021  memory: 20334  grad_norm: 51.0495  loss: 0.6673
2023/06/01 07:40:48 - mmengine - INFO - Epoch(train) [20][3200/5758]  lr: 1.6094e-05  eta: 0:17:45  time: 0.4007  data_time: 0.0033  memory: 20334  grad_norm: 46.9795  loss: 0.6686
2023/06/01 07:41:27 - mmengine - INFO - Epoch(train) [20][3300/5758]  lr: 1.6094e-05  eta: 0:17:03  time: 0.3918  data_time: 0.0025  memory: 20334  grad_norm: 94.8533  loss: 0.6783
2023/06/01 07:42:09 - mmengine - INFO - Epoch(train) [20][3400/5758]  lr: 1.6094e-05  eta: 0:16:22  time: 0.3831  data_time: 0.0033  memory: 20334  grad_norm: 36.0965  loss: 0.6774
2023/06/01 07:42:50 - mmengine - INFO - Epoch(train) [20][3500/5758]  lr: 1.6094e-05  eta: 0:15:40  time: 0.3858  data_time: 0.0018  memory: 20334  grad_norm: 14.7771  loss: 0.6693
2023/06/01 07:43:31 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 07:43:32 - mmengine - INFO - Epoch(train) [20][3600/5758]  lr: 1.6094e-05  eta: 0:14:58  time: 0.4007  data_time: 0.0025  memory: 20334  grad_norm: 48.9178  loss: 0.6821
2023/06/01 07:44:12 - mmengine - INFO - Epoch(train) [20][3700/5758]  lr: 1.6094e-05  eta: 0:14:17  time: 0.4178  data_time: 0.0030  memory: 20334  grad_norm: 41.1830  loss: 0.6624
2023/06/01 07:44:52 - mmengine - INFO - Epoch(train) [20][3800/5758]  lr: 1.6094e-05  eta: 0:13:35  time: 0.3991  data_time: 0.0019  memory: 20334  grad_norm: 49.4230  loss: 0.6683
2023/06/01 07:45:33 - mmengine - INFO - Epoch(train) [20][3900/5758]  lr: 1.6094e-05  eta: 0:12:53  time: 0.3863  data_time: 0.0018  memory: 20334  grad_norm: 61.3406  loss: 0.6697
2023/06/01 07:46:14 - mmengine - INFO - Epoch(train) [20][4000/5758]  lr: 1.6094e-05  eta: 0:12:12  time: 0.4347  data_time: 0.0020  memory: 20334  grad_norm: 20.7913  loss: 0.6625
2023/06/01 07:46:55 - mmengine - INFO - Epoch(train) [20][4100/5758]  lr: 1.6094e-05  eta: 0:11:30  time: 0.4153  data_time: 0.0023  memory: 20334  grad_norm: 58.9290  loss: 0.6697
2023/06/01 07:47:36 - mmengine - INFO - Epoch(train) [20][4200/5758]  lr: 1.6094e-05  eta: 0:10:48  time: 0.3967  data_time: 0.0021  memory: 20334  grad_norm: 18.9529  loss: 0.6848
2023/06/01 07:48:16 - mmengine - INFO - Epoch(train) [20][4300/5758]  lr: 1.6094e-05  eta: 0:10:07  time: 0.3866  data_time: 0.0022  memory: 20334  grad_norm: 20.6123  loss: 0.6800
2023/06/01 07:48:57 - mmengine - INFO - Epoch(train) [20][4400/5758]  lr: 1.6094e-05  eta: 0:09:25  time: 0.4148  data_time: 0.0017  memory: 20334  grad_norm: 46.0141  loss: 0.6571
2023/06/01 07:49:39 - mmengine - INFO - Epoch(train) [20][4500/5758]  lr: 1.6094e-05  eta: 0:08:43  time: 0.4024  data_time: 0.0020  memory: 20334  grad_norm: 135.2398  loss: 0.6718
2023/06/01 07:50:20 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 07:50:20 - mmengine - INFO - Epoch(train) [20][4600/5758]  lr: 1.6094e-05  eta: 0:08:02  time: 0.4077  data_time: 0.0019  memory: 20334  grad_norm: 42.5326  loss: 0.6675
2023/06/01 07:51:00 - mmengine - INFO - Epoch(train) [20][4700/5758]  lr: 1.6094e-05  eta: 0:07:20  time: 0.4156  data_time: 0.0026  memory: 20334  grad_norm: 20.7560  loss: 0.6798
2023/06/01 07:51:41 - mmengine - INFO - Epoch(train) [20][4800/5758]  lr: 1.6094e-05  eta: 0:06:38  time: 0.3626  data_time: 0.0022  memory: 20334  grad_norm: 12.9377  loss: 0.6682
2023/06/01 07:52:24 - mmengine - INFO - Epoch(train) [20][4900/5758]  lr: 1.6094e-05  eta: 0:05:57  time: 0.4467  data_time: 0.0019  memory: 20334  grad_norm: 40.2078  loss: 0.6652
2023/06/01 07:53:05 - mmengine - INFO - Epoch(train) [20][5000/5758]  lr: 1.6094e-05  eta: 0:05:15  time: 0.3934  data_time: 0.0022  memory: 20334  grad_norm: 925.7427  loss: 0.6716
2023/06/01 07:53:45 - mmengine - INFO - Epoch(train) [20][5100/5758]  lr: 1.6094e-05  eta: 0:04:34  time: 0.3918  data_time: 0.0017  memory: 20334  grad_norm: 31.5509  loss: 0.6629
2023/06/01 07:54:25 - mmengine - INFO - Epoch(train) [20][5200/5758]  lr: 1.6094e-05  eta: 0:03:52  time: 0.4472  data_time: 0.0027  memory: 20334  grad_norm: 53.2940  loss: 0.6778
2023/06/01 07:55:06 - mmengine - INFO - Epoch(train) [20][5300/5758]  lr: 1.6094e-05  eta: 0:03:10  time: 0.3977  data_time: 0.0023  memory: 20334  grad_norm: 202.7078  loss: 0.6775
2023/06/01 07:55:47 - mmengine - INFO - Epoch(train) [20][5400/5758]  lr: 1.6094e-05  eta: 0:02:29  time: 0.4490  data_time: 0.0017  memory: 20334  grad_norm: 117.0472  loss: 0.6648
2023/06/01 07:56:28 - mmengine - INFO - Epoch(train) [20][5500/5758]  lr: 1.6094e-05  eta: 0:01:47  time: 0.4497  data_time: 0.0024  memory: 20334  grad_norm: 166.6944  loss: 0.6743
2023/06/01 07:57:08 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 07:57:09 - mmengine - INFO - Epoch(train) [20][5600/5758]  lr: 1.6094e-05  eta: 0:01:05  time: 0.4039  data_time: 0.0032  memory: 20334  grad_norm: 60.1827  loss: 0.6722
2023/06/01 07:57:51 - mmengine - INFO - Epoch(train) [20][5700/5758]  lr: 1.6094e-05  eta: 0:00:24  time: 0.3753  data_time: 0.0018  memory: 20334  grad_norm: 116.3336  loss: 0.6590
2023/06/01 07:58:14 - mmengine - INFO - Exp name: swin_base_8xb128_fake5m_20230531_183241
2023/06/01 07:58:14 - mmengine - INFO - Saving checkpoint at 20 epochs
2023/06/01 07:58:30 - mmengine - INFO - Epoch(val) [20][8/8]    accuracy/top1: 80.5252  single-label/precision_classwise: [100.0, 0.0]  single-label/recall_classwise: [80.52522277832031, 0.0]  single-label/f1-score_classwise: [89.212158203125, 0.0]  data_time: 0.3744  time: 1.0008